Review and Bibliometric Analysis of Published Literature 

Citing Data Produced by the Gap Analysis Program (GAP) 

By Joan M. Ratz and Shannon J. Conk

Open-File Report 2013–1294

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014 

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment—visit 

http://www.usgs.gov or call 1–888–ASK–USGS

For an overview of USGS information products, including maps, imagery, and publications,

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Suggested citation:

Ratz, J.M., and Conk, S.J., 2014, Review and bibliometric analysis of published literature citing data produced by the 

Gap Analysis Program (GAP): U.S. Geological Survey Open-File Report 2013–1294, 117 p., 

http://dx.doi.org/10.3133/ofr20131294. 

ISSN 2331-1258 (online)

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain

copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be 

secured from the copyright owner.
iii

Contents

Executive Summary....................................................................................................................................................... 1

Background ................................................................................................................................................................... 3

Reference Search Methods ........................................................................................................................................... 4

Limitations of Literature Search Methods................................................................................................................... 5

Data Recording Strategy............................................................................................................................................ 6

Summary of Publication Characteristics ........................................................................................................................ 6

Authors ...................................................................................................................................................................... 6

Publication Outlet....................................................................................................................................................... 7

Year ..........................................................................................................................................................................13

Dataset .....................................................................................................................................................................14

Durability of Datasets................................................................................................................................................17

Summary...................................................................................................................................................................19

Qualitative Review of Comments..................................................................................................................................19

Description of Data ...................................................................................................................................................20

How Data Were Used ...............................................................................................................................................21

Critiques....................................................................................................................................................................22

Comparisons.............................................................................................................................................................24

Commentary .............................................................................................................................................................25

Reference to GAP Data ............................................................................................................................................26

Summary...................................................................................................................................................................26

Bibliometric Analysis.....................................................................................................................................................27

Publication Count......................................................................................................................................................28

Journal Impact ..........................................................................................................................................................28

Citation Count ...........................................................................................................................................................35

Summary...................................................................................................................................................................64

Discussion ....................................................................................................................................................................64

Acknowledgments.........................................................................................................................................................66

List of References.........................................................................................................................................................66

Figures

Figure 1. Proportion of authors receiving credit categorized by number of publications ........................................... 7

Figure 2. Percent of publications by outlet type ........................................................................................................ 8

Figure 3. Number of publications citing state, regional, and national GAP data by year......................................... 13

Figure 4. Number and percent of publications using each type of dataset.............................................................. 14

Figure 5. Number of datasets used by year of publication...................................................................................... 15

Figure 6. Frequency of use for each type of dataset, grouped by state, region, and national level......................... 16

Figure 7. Durability of project datasets.................................................................................................................... 18

Figure 8. Number and percent of articles published in journals at each quartile rank............................................. 35

Figure 9. Number of citations associated with publications using each dataset...................................................... 63

Tables

Table 1. Journal title and number of articles from each journal.............................................................................. 9

Table 2. Number of articles appearing in each journal, impact factor, discipline, and journal impact rank........... 28

Table 3. Citation count for publications using GAP data...................................................................................... 37

Table 4. Datasets, number of publications using each dataset, and number of citations..................................... 57
1

Review and Bibliometric Analysis of Published Literature 

Citing Data Produced by the Gap Analysis Program (GAP) 

By Joan M. Ratz and Shannon J. Conk

Executive Summary

The Gap Analysis Program (GAP) of the U.S. Geological Survey (USGS) produces geospatial 

datasets providing information on land cover, predicted species distributions, stewardship (ownership 

and conservation status), and an analysis dataset which synthesizes the other three datasets. The intent in 

providing these datasets is to support the conservation of biodiversity. The datasets are made available

at no cost. The initial datasets were created at the state level. More recent datasets have been assembled 

at regional and national levels.

GAP entered an agreement with the Policy Analysis and Science Assistance branch of the USGS 

to conduct an evaluation to describe the effect that using GAP data has on those who utilize the datasets 

(GAP users). The evaluation project included multiple components: a discussion regarding use of GAP 

data conducted with participants at a GAP conference, a literature review of publications that cited use 

of GAP data, and a survey of GAP users. The findings of the published literature search were used to 

identify topics to include on the survey. 

This report summarizes the literature search, the characteristics of the resulting set of 

publications, the emergent themes from statements made regarding GAP data, and a bibliometric 

analysis of the publications. We cannot claim that this list includes all publications that have used GAP 

data. Given the time lapse that is common in the publishing process, more recent datasets may be cited 

less frequently in this list of publications. Reports or products that used GAP data may be produced but 

never published in print or released online. In that case, our search strategies would not have located 

those reports. Authors may have used GAP data but failed to cite it in such a way that the search 

strategies we used would have located those publications. These are common issues when using a 

literature search as part of an evaluation project. Although the final list of publications we identified is 

not comprehensive, this set of publications can be considered a sufficient sample of those citing GAP 

data and suitable for the descriptive analyses we conducted.

We searched Web of Science©, Scopus®, Google Scholar, CSA Illumina©, and EBSCO© for 

references in text to “gap analysis project,” “gap analysis program,” “‘gap analysis’ and GIS,” and 

“USGS and gap.” This search resulted in 2,058 unique publications after all duplicates were removed 

from the list. We excluded publications if the content focused on the program itself, gap analysis 

methods, or a specific GAP project because we were interested only in use of GAP data. We excluded 

all references that were published in the GAP Bulletin and all GAP Project Reports because our 

intention was to focus on how GAP data were used outside of the program. We included publications if 

the author(s) made a statement indicating they had used a dataset produced by GAP or indicated a use of 

a GAP dataset in a table or figure. We were unable to locate copies of 138 publications and therefore 

could not make a determination regarding the use of GAP data for those publications. The final list of 

references citing GAP data includes 646 publications. 
2

Most authors were listed on only one publication, and only a few authors received authorship 

credit on more than one publication in the set. This set of references is not characterized by a small set 

of authors who publish repeatedly using GAP data. The authorship pool consists of many authors. The 

publication outlet was primarily journal articles, although there were conference presentations, student 

dissertations and theses, book chapters, and reports. We summarized the titles of the journals and the 

number of publications appearing in each journal. Publications using GAP data appeared in 161 

journals. The topics addressed by the journals were quite diverse and included wildlife (Journal of 

Wildlife Management), conservation (Biological Conservation), weather (Weather and Forecasting), 

and infectious diseases (Emerging Infectious Diseases). We categorized the publications based on the 

year of publication with separate counts for use of state, regional, and national data. The year of 

publication ranged from 1994 to 2011, and state datasets were cited more frequently than regional or 

national datasets. This is not surprising given that the state datasets have been available for a longer 

period of time. Among the types of data provided by GAP, the most frequently used in the published 

literature was land-cover data. We analyzed the frequency with which each dataset was used based on 

the project developing the data (state, region, or national) and the type of data. The most frequently used

dataset was land cover from the California Gap Project. We calculated the durability of data based on 

the earliest and most recent publication dates for publications that used GAP data from each project. 

The length of time that a dataset is used after it is released can be used as one indicator of the usefulness 

of the data. The durability of data varied, and some of the datasets had a length of use that exceeded 

10 years.

The next step was to summarize the comments about GAP data from these publications. We 

searched the text of each publication for statements regarding GAP data. We marked the statements that 

referred to data or maps—ignoring those that referred to the program itself or GAP projects and not 

data—and pulled them into a database. We reviewed the statements and grouped them into categories 

based on the theme of the statement. The themes in the main categories were the following: description 

of data, how data were used, critiques, comparisons, commentary, and reference to GAP data.

The statements included in the description of data category were statements that described which 

GAP dataset was used in the publication. The category of how data were used included methodological 

descriptions of GAP data such as transformations made to the dataset, descriptions of how GAP data 

were compared to other datasets, and descriptions of how GAP data fit the purpose of the study. The 

statements identified as critiques addressed different issues including accuracy, error, how GAP data 

were lacking, limits to the data, and utility of GAP data. The statements included in the category of 

comparisons described conclusions drawn as to how GAP data compared to other data. GAP data were 

described as comparing favorably, unfavorably, favorably in some aspects yet unfavorably in others, 

and equivalently to other datasets. Commentary statements regarding GAP data were mostly favorable. 

Some of the commentary statements indicated that potential issues with GAP data were mitigated in 

some situations. For example, the state datasets are older which can pose a problem in some 

applications. However, in cases in which landscape change is the focus of study, the availability of older 

datasets is beneficial. In those cases, the datedness of the data is mitigated by the purpose of the study. 

Finally, the reference to GAP data category includes statements that describe or refer to a dataset but not 

specifically the use of a dataset.

We conducted a bibliometric analysis to gain additional understanding about the publications 

that used GAP data. The results of bibliometric analyses can be used as a component in the performance 

evaluation process for a research program (Campbell and others, 2010; Trochim and others, 2008; 

Verbeek and others, 2002) and in science policy and planning (Irvine and others, 1987). We used the 

journal impact factors for those publications appearing in journals, and citation counts for all 
3

publications. The journals were associated with 34 disciplines as categorized by the Web of Science 

Journal Citations Report®. The journals publishing articles that used GAP data had a variety of journal 

impact factors—which relate to the average frequency articles from that journal are cited by other 

publications. Forty percent of the journals were rated in the top quartile within their respective 

disciplines. This indicates that GAP data are used in publications that appear in journals considered to 

be at the top in their field. 

As part of the bibliometric analysis, we researched how frequently each of the publications using 

GAP data was cited in other publications. We used Web of Science to determine the citation count for 

each publication. Older publications tend to be cited more, however, the most frequently cited 

publication using GAP data was published in 2004. We cross-referenced the publication citation counts 

with the datasets used in the publications. The datasets included in the most publications were not 

necessarily those in the publications with the most citations. The dataset associated with publications 

receiving the highest number of citations was the Utah land-cover dataset. The citation rates may 

indicate the degree of secondary diffusion of information regarding GAP data. The publications that 

used GAP data, identified their use of GAP data, and wrote about use of GAP data increased the 

visibility of these datasets. The citation counts for those publications provided an index of how many 

others were introduced to GAP data based on their citation of the publications using GAP data. 

In summary, the review of published literature that cites a use of GAP data yields several 

conclusions. First, GAP data are being used by a wide variety of researchers. Second, the comments 

made in the text of the publications regarding GAP data included favorable comments as well as 

specific critiques. Finally, GAP data are used in publications that appear in many high-impact journals. 

The results of this published literature search had several implications for the survey of GAP 

users. First, specific issues that emerged from the qualitative analysis of statements regarding GAP data 

in these publications were used to generate questions for the survey. Specifically, the survey included 

questions regarding the issues of accuracy, error, to what extent issues of accuracy and error are 

problematic to the user, what GAP data lack that would be useful, how GAP data compare to other 

datasets, and what transformations to GAP data are required to make the data useful to the user. Second, 

given the wide range of datasets used and great variety in journals and disciplines in which the 

publications appeared, the survey was designed as an adaptive survey so that respondents were asked 

only questions that pertained to the dataset with which they were most familiar. Finally, the authors of 

the publications were clearly individuals who have used GAP data and their names were added to the 

list of potential survey respondents. 

Background

The Gap Analysis Program (GAP) of the U.S. Geological Survey (USGS) has been operational 

since 1989. GAP provides spatial data for use as a framework to make assessments regarding how well 

biodiversity is protected and to evaluate what aspects of biodiversity need greater protection. Among the 

program goals are providing “information to the public and those entities charged with land use 

research, policy, planning, and management,” and “build[ing] institutional cooperation in the 

application of this information to state and regional management activities.” To this end, GAP produces 

and provides the following types of datasets: land cover; predicted distributions of vertebrate species; 

land stewardship (ownership and conservation status); and analysis data synthesizing the three 

aforementioned datasets. The Gap Program does not charge a fee to download the data. The initial

datasets were created at the state level; Puerto Rico was included as a state project. These datasets are 

archived on the GAP website. More recent datasets have been assembled at regional data levels. 

Specifically, regional data are available for three regions: Southwest (Arizona, Colorado, New Mexico, 
4

Nevada, Utah); Southeast (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South 

Carolina, Tennessee, Virginia); and Northwest (Idaho, Montana, Oregon, Washington, Wyoming). 

However, not all types of data are currently available for all regions. National data for land cover and 

stewardship became available in 2009. Some national species distribution models and ranges are 

available and additional species models will be added to the website as they become available. 

In Federal fiscal year 2007, the Policy Analysis and Science Assistance branch of the USGS 

contracted with GAP to conduct a systematic evaluation. GAP has data development programs for both 

terrestrial and aquatic species. At the request of GAP, we focused our evaluation on the data produced 

for terrestrial species. The intent of the evaluation was to assess the impacts that GAP products—the 

datasets it produces and distributes—have on the individuals who use the products (GAP users) and on 

conservation of biodiversity. The main component of such an evaluation is a data collection process 

using a survey (Rossi and others, 2004) designed to measure the extent to which GAP is achieving its 

stated goal of “keeping common species common” through the conservation of biodiversity. The results 

of this survey will be used by GAP administrators and staff to evaluate past performance of the program 

and to strategically plan changes to the current approach. Changes in the existing approach to 

developing and providing data may be necessary to best meet the needs of current GAP users and to 

expand the number of GAP users. The survey is the key piece of the evaluation project so it must be 

carefully designed. To support and inform survey development, we collected information from several 

outlets. We searched the published literature for studies making use of GAP data, reviewed the literature 

on use of geographic information system (GIS) data for decisionmaking and related topics, and 

conducted two discussion groups that included GAP users. In this report, we describe our study of the 

published literature using GAP data. In addition to providing direction for development of survey 

questions, the review of published literature makes a unique contribution by identifying how GAP is 

described in the public realm. We define the public realm as documents and publications that are 

accessible to the public in hardcopy or electronically over the internet. Information regarding the data 

produced by GAP that is publicly available increases awareness of the program, the data it provides, and 

how the data can be used. Our goal was to understand the external characterization of GAP as opposed 

to how it is characterized internally by GAP staff and cooperators. Focusing on the external view of 

GAP is more likely to result in a characterization of GAP that is independent and bias-free.

We describe the search process used to identify publications citing GAP data, the characteristics 

of these publications including which datasets were cited, the results of a qualitative study of comments 

regarding GAP data in the publications, and the results of a bibliometric analysis of these publications. 

Reference Search Methods

In any study of published literature, use of multiple databases maximizes the potential of 

obtaining a representative set of literature (Okubo, 1997). A study of the overlap between journals 

covered by Web of Science©and Scopus® demonstrated that the two databases have some overlap but 

provide substantial unique coverage (Gavel and Iselid, 2008). The two databases overlap in their journal 

coverage but Scopus includes more publication outlets of other types such as conference presentations 

(Moed, 2009). When databases cover different ranges of publication dates, searching in multiple 

databases can result in a set of search results that cover a longer range of time, rather than a set of results 

limited by the beginning and end date of a single database. The databases searched began their 

collection of citations in different years; the earliest year covered was 1971. 

We searched Web of Science, Scopus, Google Scholar, CSA Illumina© and EBSCO©. CSA 

Illumina and EBSCO enable more efficient searching because searches of multiple databases can be 

performed simultaneously. In CSA Illumina, we searched the following databases: Aquatic Sciences and 
5

Fisheries Abstracts, Biological Sciences, Biology Digest, Digests of Environmental Impact Statements, 

Meteorological and Geoastrophysical Abstracts, NTIS, Plant Science, and Zoological Record. In 

EBSCO, we searched the following databases: Agricola, Biological Abstracts, CAB Abstracts, Fish and 

Fisheries Worldwide, GeoRef, and Wildlife Ecology. We used the search terms “gap analysis project,” 

“gap analysis program,” “‘gap analysis’ and GIS,” and “USGS and gap.” We used the first one thousand 

results for each of the searches in Google Scholar. Even when a search returns more than one thousand 

results in Google Scholar, only the first thousand can be viewed. We combined the results from all 

searches into a master list that included 5,507 publications. There were 2,058 unique publications after 

all duplicates were removed from the list.

We reviewed each entry on the list of 2,058 publications and applied a set of inclusion and 

exclusion criteria to create a final list of publications to include in subsequent phases of this review. We 

included publications if the author(s) made a statement indicating they had used a dataset produced by 

GAP or cited GAP data in a table or figure. We excluded publications if they met any of the following 

criteria:

• the content of the publication focused on the GAP program, gap analysis methods, or a specific 

GAP project, 

• the publication outlet was the GAP Bulletin, 

• the publication was a GAP project report or described a project report, 

• the authors noted the GAP program as a data source but did not make use of GAP data,

• the content of the publication described using gap methodology, but not GAP data, or 

• the content addressed aquatic GAP—the focus of this project is the terrestrial GAP data. 

Although we intended to focus on the use of GAP data and not publications by or about the 

program, we did not automatically exclude publications authored by former and current GAP staff. If 

the content of the publication focused on the program, a GAP project, or gap analysis methods, the 

publication was deleted from the list. If the publications appeared to be applications of GAP data, they 

were included on the list. 

If we could not determine if a reference should be included or excluded based on the title and 

abstract, we reviewed the entire text of the publication if it was accessible. We could not access the text 

for 138 publications and were unable to determine if those publications included use of GAP data. The 

final list of references citing GAP data includes 646 publications. These publications are included in the 

list of references and marked with an asterisk. 

Limitations of Literature Search Methods

Because of the time lag in the publishing process, the more recent regional and national datasets 

are less likely to be used and cited in published literature. The state datasets are older and have been 

available for longer periods of time and therefore are more likely to have been used and cited in 

published research. This reality should temper conclusions regarding the quality or usefulness of the 

datasets. A conclusion that the regional datasets are less used and therefore less useful because they are 

cited in fewer publications would not be an appropriate one. 

GAP produced the datasets used by the researchers who wrote these publications. There may 

have been uses of GAP data to create maps or documents that have not been published because they 

were used exclusively for management of natural areas. The use of publications citing GAP data 

provides what is likely to be an incomplete and conservative depiction of the actual range of GAP data

use. However, these publications provide information that cannot be addressed by other methods. 

Publications include public statements about GAP data, and identify uses and users of GAP data that 

might not be identified through other means. 
6

We cannot claim that this list includes all research publications that have cited GAP data. 

Compiling a complete list of publications generated by a program is a common concern when using 

publications as part of a program evaluation (Narin and Hamilton, 1996). It is difficult to know the 

completeness of the compiled list. One concern regarding constructing the bibliography for a 

bibliometric analysis is that the available information about publications depends on the recordkeeping 

practices of others. Even a database that draws from a wide range of sources is likely to omit some 

publication records. If the researchers used different terminology or if the publication outlet was not 

indexed in the databases we searched, our search strategy would not have identified all publications 

using GAP data. We excluded publications that seemed to primarily focus on specific GAP projects. 

Other readers or the publication authors may believe that these publications were examples of use of 

GAP data that should have been included in our final set of publications. An additional concern is the 

amount of time required to construct a thorough bibliography. The practical constraints of time and 

other resources such as funding and access to publication databases will affect the completeness of the 

final bibliography. We used the most thorough search strategy given the realistic constraints of time and 

resources. We applied our inclusion and exclusion criteria as consistently as possible. We have 

identified a set of publications that can be considered an adequate sample of publications citing GAP 

data that is suitable for the summary analyses conducted.

Data Recording Strategy

For each publication we reviewed, we created a data record that included specific information 

regarding the publication. The record included the citation, list of author names, the source of the 

publication (such as journal title, book title, conference title), the type of publication outlet (journal 

article, book, conference presentation, student paper, and report), year of publication, project that 

produced the data (state, regional, or national), type of data used (land cover, predicted species 

distribution, stewardship, analysis), and quotations from the publication in which GAP data are 

mentioned. If there was any question regarding any of what data should be recorded, we entered 

“unknown” into the data field. If the information was unclear, we did not assume and record data based 

on those assumptions. In some cases, authors did not specifically state which type of dataset from GAP 

was used. In other cases, authors indicated a type of data, such as land cover, but did not indicate if it 

came from state or regional projects. 

Summary of Publication Characteristics

The set of publications citing GAP data on which we based our analyses included 646 

publications. We summarized the publications based on authorship, publication outlet, year of 

publication, and dataset characteristics.

Authors

One of the issues addressed in the evaluation of GAP is the influence of the program. One 

method of assessing the breadth of the program’s influence is to determine if use of GAP data can be 

characterized as repeated use by a limited group of users or infrequent use by a broader set of users. We 

used this set of publications to ascertain if evidence existed that would suggest one characterization over 

the other. If GAP data are used repeatedly by a limited group of users, we expect that authors will be 

credited on multiple publications in this sample. If GAP data are used by a broader set of users, we 

expect that most authors will be credited on fewer publications. In order to address this issue, we created 

a list of authors who received any level of authorship credit (first author, second author, etc.) in this set 
7

of publications. We counted the number of different publications on which an author received credit. 

There were 1,599 individuals listed as authors in this set of publications. The number of publications on 

which authors received credit ranged from 1 to 13. The percent of authors receiving credit on each 

number of publications is depicted in figure 1. 

Figure 1. Proportion of authors receiving credit categorized by number of publications. Percent of authors in each 

category is provided in parentheses.

The majority of authors (80 percent) were credited on only one publication, which suggests GAP 

data are used less frequently by a wide range of users. Only 6.5 percent of authors were listed on three 

or more publications. 

Publication Outlet

Identifying the means through which information about GAP is disseminated is another 

approach to understand the influence of GAP. We categorized each publication according to the type of 

outlet based on five categories: book, conference presentation, student paper, report, and journal article. 

The category labeled “book” includes chapters in edited books and conference proceedings. The reason 

for including conference proceedings in this category is that papers published in proceedings often 

undergo a more rigorous editorial process and are available in a more durable format than other types of 

conference presentations. The “book” category contains 117 publications. The category labeled 
8

“conference presentation” includes presentations, posters, papers identified as being presented in some 

way at a conference, and conference presentation abstracts. This category contains 24 publications. The 

category labeled “student paper” includes dissertations, theses, undergraduate honors and capstone 

papers, and papers written as part of required coursework. This category contains 55 publications. The 

category labeled “report” includes publicly available reports by federal, state, or local agencies, and 

nonprofit and for profit organizations. This category contains 108 publications. The majority of

publications, 342, were from journals. The percent of publications from each outlet type is shown in 

figure 2. 

Figure 2. Percent of publications by outlet type. Percent of publications in each category is provided in 

parentheses.

For the publications included in the report category, we identified the type of organization 

producing the report. Government agencies produced 32 (30 percent) of the reports. Educational 

institutions produced 31 (29 percent) of the reports. Nonprofit organizations produced 27 (25 percent) of 

the reports. Organizations that operate for profit produced 13 (12 percent) of the reports. We could not 

identify the type of organization that produced five of the reports because the identifying information 

was limited.

We identified the type of student paper for the 55 publications in that category. The student 

papers included 34 Master’s theses (62 percent), 10 Doctoral dissertations (18 percent), and the 

remaining 11 (20 percent) were other student papers. 

Because so many of the publications were from journals, we examined the publications from 

journals more closely by conducting a bibliometric analysis. The bibliometric analyses are described in 

a subsequent section of this report. We provide a list of the 161 journal titles and number of articles 

from each of those journals as part of the description of this set of publications citing GAP data. This 

information is provided in table 1.
9

Table 1. Journal title and number of articles from each journal. 

Journal Title Number of Articles

Journal of Wildlife Management 20

Conservation Biology 17

Ecological Applications 14

Landscape Ecology 9

Photogrammetric Engineering & Remote Sensing 9

Wildlife Society Bulletin 8

Biological Conservation 7

Environmental Monitoring and Assessment 6

Natural Areas Journal 6

Environmental Management 5

Journal of Mammalogy 5

Madroño—California Botanical Society 5

Molecular Ecology 5

Remote Sensing of Environment 5

Southwestern Naturalist 5

Monthly Weather Review 4

American Journal of Tropical Medicine and Hygiene 3

American Midland Naturalist 3

Animal Conservation 3

Auk 3

Diversity and Distributions 3

Ecography 3

Environmental Modelling & Software 3

Forest Ecology and Management 3

International Journal of Remote Sensing 3

Journal of Arid Environments 3

Journal of Forestry 3

Journal of Medical Entomology 3

Journal of the American Water Resources Association 3

Landscape and Urban Planning 3

North American Journal of Fisheries Management 3

Southeastern Naturalist 3

Wetlands 3

American Fisheries Society Symposium 2

Applied Geography 2

Atmospheric Environment 2

Biodiversity and Conservation 2

Condor 2

Ecohydrology 2

Ecological Indicators 2
10

Table 1. Journal title and number of articles from each journal.—Continued

Journal Title Number of Articles

Ecological Modelling 2

Ecology and Society 2

Ecosystems 2

Emerging Infectious Diseases 2

Forest Policy and Economics 2

Forest Science 2

Global Change Biology 2

Global Ecology and Biogeography 2

Journal of Coastal Research 2

Journal of Soil and Water Conservation 2

Journal of the Arkansas Academy of Science 2

Journal of Vegetation Science 2

Occasional papers, Museum of Texas Tech University 2

Oikos 2

PloS ONE 2

Professional Geographer 2

Quaternary Research 2

Soil Science Society of America Journal 2

Southern Journal of Applied Forestry 2

Studies in Avian Biology 2

Transactions of the Kansas Academy of Science 2

Ursus 2

Western Journal of Applied Forestry 2

Wildlife Biology 2

Wilson Journal of Ornithology 2

Agricultural Water Management 1

Ambio 1

American Journal of Enology and Viticulture 1

Annals of the Association of American Geographers 1

Applied Vegetation Science 1

Biological Invasions 1

Bioresource Technology 1

BioScience 1

California Agriculture 1

Canadian Journal of Fisheries and Aquatic Science 1

Climatic Change 1

Computing in Science and Engineering 1

Conservation and Society 1

Conservation Genetics 1

Digital Soil Mapping 1

Ecological Monographs 1
11

Table 1. Journal title and number of articles from each journal.—Continued

Journal Title Number of Articles

Ecological Research 1

Ecology 1

Ecology Letters 1

Endangered Species Update 1

Energy Policy 1

Environmental Entomology 1

Environmental Geology 1

Environmental Science and Pollution Research 1

Environmetrics 1

Evolution 1

Fire Ecology 1

Forestry Chronicle 1

Freshwater Biology 1

Frontiers in Ecology and the Environment 1

Geoderma 1

Geographical Review 1

Geoscience and Remote Sensing Symposium Proceedings 1

Geospatial Health 1

GIScience and Remote Sensing 1

Great Basin Naturalist 1

Great Plains Research 1

Human and Ecological Risk Assessment 1

Human Dimensions of Wildlife 1

Human Ecology Review 1

Hydrobiologica 1

Hydrological Sciences Journal 1

Insecta Mundi 1

Integrated Environmental Assessment and Management 1

International Journal of Biodiversity and Conservation 1

International Journal of Wildland Fire 1

Invasive Plant Science and Management 1

IUCN Otter Specialist Group Bulletin 1

Journal of Applied Ecology 1

Journal of Applied Remote Sensing 1

Journal of Biogeography 1

Journal of Conservation Planning 1

Journal of Environmental Engineering 1

Journal of Environmental Planning and Management 1

Journal of Environmental Quality 1

Journal of Great Lakes Research 1

Journal of Insect Conservation 1
12

Table 1. Journal title and number of articles from each journal.—Continued

Journal Title Number of Articles

Journal of Land Use Science 1

Journal of Raptor Research 1

Journal of Remote Sensing 1

Journal of the North American Benthological Society 1

Journal of the Torrey Botanical Society 1

Journal of Water Resources Planning and Management 1

Kalmiopsis 1

Lake and Reservoir Management 1

Marine Geodesy 1

Marine Pollution Bulletin 1

McNair Research Journal 1

Northeastern Naturalist 1

Northern Journal of Applied Forestry 1

Northwest Science 1

Northwestern Naturalist 1

Ocean Engineering 1

Oecologica 1

Parks 1

Peromyscus Newsletter 1

Physics and Chemistry of the Earth 1

Proceedings of the National Academy of Sciences 1

Pure and Applied Geophysics 1

Rangeland Ecology & Management 1

Sensors 1

The Bryologist 1

Transactions in GIS 1

Transactions of the American Entomological Society 1

Transactions of the American Fisheries Society 1

Transactions of the ASABE 1

Urban Ecosystems 1

Water Resources Research 1

Water, Air, and Soil Pollution 1

Waterbirds 1

Weather and Forecasting 1

Western North American Naturalist 1

Wildfowl 1

Wildlife Biology in Practice 1

Wyoming Open Spaces Bulletin 1

Yearbook of the Association of Pacific Coast Geographers 1
13

Year

GAP has been producing data for more than a decade. State GAP datasets predate the regional 

and national datasets. One concern regarding GAP data from the state projects is that the datasets are 

out-of-date and may no longer be as useful. Another view is that the historical perspective provided by 

older datasets is invaluable, and these datasets will continue to be used particularly in light of the 

evolution of landscapes and the threat of climate change. We categorized the publications based on the 

year of publication with separate counts for use of state, regional, and national data (figure 3). 

Figure 3. Number of publications citing state, regional, and national GAP data by year. The data for 2011 

includes only the month of January. 
14

Dataset

In addition to the distinction between state, regional, and national datasets, there are multiple 

datasets produced for each project: land cover, predicted species distributions, stewardship, and 

analysis. The analysis dataset synthesizes the land-cover, predicted species distributions, and 

stewardship datasets. We noted which datasets were used in each publication. Some publications used 

multiple datasets. There were publications in which the authors stated that data from GAP were used but 

did not provide sufficient detail for us to confidently determine which dataset was used. 

First, we counted the number of publications using each type of data. If a publication used the 

land-cover datasets from two adjoining states, the publication would count as one publication using 

land-cover data. If a publication used a land-cover dataset and a predicted species dataset, it would 

count as one publication using land cover and one publication using predicted species data. The level of 

analysis was the publication. The number of publications using each type of dataset is depicted in figure 

4. Land-cover data was used in 73 percent of the publications. Because some of the publications used 

more than one type of dataset, the total number of publications depicted in the pie chart in figure 4 

exceeds the number of publications in the dataset. 

Figure 4. Number and percent of publications using each type of dataset. 

Second, we counted the number and type of datasets used in each publication. For example, if a 

publication used the land-cover datasets from two adjoining states, that counted as two uses of landcover datasets. The level of analysis was the dataset. Figure 5 provides the number of each type of 

dataset used by year of publication. 
15

Figure 5. Number of datasets used by year of publication. The data for 2011 includes only the month of January.

Each type of dataset is provided within the state, regional, and national projects. We counted the 

datasets used based on the state, regional, or national project as well as the type of data. To identify the 

project producing the data, we used the two letter abbreviation for states, PR for Puerto Rico, and the 

acronyms SWReGAP (Southwest regional GAP), SEGAP (Southeast GAP), and NWGAP (Northwest 

GAP). In figure 6, we provide the number of times each dataset was used in the group of publications 

used in this study. The datasets are grouped by state, region, or national level, and each type of data is 

depicted separately. The actual counts are provided when the number of times the dataset was used 

exceeds 10. 
16

Figure 6. Frequency of use for each type of dataset, grouped by state, region, and national level.
17

There is evidence of use of multiple types of data from most state and all regional projects. 

There were no citations of use of Alaska GAP data. As seen in figure 6, there were only four state 

projects from which the analysis type of dataset was used: Arizona, California, Oregon, and 

Washington. Land cover was the only type of data used from the Ohio state project. At the national 

level, the stewardship type of data was used. The frequency counts demonstrate how widely the use of 

GAP datasets varies. There are two datasets that have been cited more frequently than many of the other 

datasets: land cover from the state GAP for California—cited 93 times, and the land cover from the 

Southwest Regional GAP—cited 61 times.

Durability of Datasets

One of the goals of the evaluation project is to understand the usefulness of GAP data. One 

approach is to determine how long GAP data continue to be used. The length of time that people use 

GAP data after the data are released can be interpreted as an indicator of the usefulness of the data. We 

determined the durability of the datasets from each project by comparing the early and the most recent 

years of publications that used data from that project. The state, regional, and national projects were 

finished at different times, so the datasets became available during different years. The durability of data 

from each project is illustrated in figure 7. We included the year that the projects were completed with 

the illustration of the data durability. 
18

Figure 7. Durability of project datasets. The line for 

each project indicates the earliest and most 

recent years of publications included in this 

literature review. The official release date for the 

project data is indicated by a yellow point; this 

information was provided by the GAP office in 

Moscow, Idaho.
19

Information for projects was included in this figure if all relevant data (year of earliest 

publication, year of most recent publication, project release year) were available for that project. The 

durability of the datasets varies. The datasets from some projects (for example, Arizona, California, 

Idaho, Louisiana, Maine, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming) 

have been used for more than 10 years. In the case of the New Hampshire state project, the year of the 

earliest publication, the year of the most recent publication, and the year of the project completion are 

the same—2008. 

There are some publications that used GAP data prior to the project completion dates. There are 

two reasons for this. For many GAP projects, land-cover data are completed and released prior to the 

other datasets and prior to project completion (Jocelyn Aycrigg, GAP, oral commun., 2010). Therefore, 

the project completion date is after the actual release of the data for some projects. Second, several 

authors cited the use of “pre-release” GAP data. In those instances, the publication year can be earlier 

than the project completion year. 

Summary

The summary of this set of publications indicates that most authors were credited on one 

publication, which suggests that a broad group of authors have made use of GAP data. In addition to 

this large group of infrequent users, there is a small group of authors who use GAP data repeatedly. 

There may be others—those who use the data for applied purposes—who also use GAP data. The 

publication outlets for this set of publications were primarily journals. The dominance of journals as a 

publication outlet may have resulted from the search methodology—journals tend to be indexed in 

databases more often than book chapters or conference presentations. It may also result from the manner 

in which publications were excluded—applications of data may be more likely to be published in 

journals whereas descriptions of a program or method may be published as part of a book. 

The types of journals that published articles citing GAP data were numerous and varied. The 

topics covered by the journals were quite diverse and included wildlife (Journal of Wildlife 

Management), conservation (Biological Conservation), weather (Weather and Forecasting), and 

infectious diseases (Emerging Infectious Diseases). 

GAP data are time-sensitive in that they represent the land cover, stewardship, and predicted 

species distributions for a given area at a particular point in time. The state GAP datasets are based on 

older data than the regional GAP datasets. However, publications continue to be produced using GAP 

data from the state projects. This may indicate that GAP data have a longer than anticipated scientific 

life and that state datasets are not considered obsolete when regional or national datasets become 

available.

The land-cover type of data was most frequently used in publications. Used less frequently than 

land cover, predicted species distribution and stewardship datasets were utilized in 14 percent and 12 

percent of publications respectively. The analysis data layer was rarely used.

The next step in the review of published literature was to summarize what the authors of these 

publications wrote regarding their use of GAP data. 

Qualitative Review of Comments

We searched the text of each publication for statements regarding GAP data. We included all 

written language as the text of the publication. Figure and table headings and notes, acknowledgments, 

and reference lists were included in our review of a publication’s text. We looked specifically for “gap” 

in the text and then determined if the statement referred to a dataset, a map, the national program, a 
20

project, or gap methodology. We marked the statements that referred to data or maps and pulled those 

statements into a database. When possible, we conducted this text search electronically using the search 

function. In the cases in which we could not obtain an electronic copy of the publication but could 

obtain a hard copy, we conducted the search manually. This search resulted in 1,376 statements. Each 

publication contributed at least one statement. 

We reviewed this set of statements to identify emergent themes. Emergent themes are those that 

are derived from the set of statements; this is a bottom-up process. An alternative top-down approach 

would be to use a set of predefined categories and identify statements that belonged in each of those 

categories. This approach is limiting in that the categories are restricted to the predefined categories—

what we would expect authors to state about GAP data. Because we were interested in comments 

generated by the authors rather than a select group of themes on which we expected authors to 

comment, we chose to use the bottom-up process of identifying emergent themes. Six broad themes 

emerged from these statements. The themes were description of data, how data were used, critiques, 

comparisons, commentary, and reference to GAP data. We describe each of these themes and provide 

examples. We report the number of statements in the theme and the number of publications that 

produced these statements, because the number of publications from which the statements are drawn 

provides context for understanding the number of statements. For example, accuracy of GAP data was 

cited as an issue in 29 statements. The context to interpret that theme is different if those statements 

came from 24 publications as opposed to if all 29 statements came from two publications. The latter 

case indicates that the accuracy issue was raised more frequently by a small number of authors. 

Description of Data

The 559 statements in this category provide a description of the GAP data used by the authors. 

This was the largest category that emerged from the set of statements derived from the publications. All 

of the publications identified as citing GAP data included some statement about the use of GAP data. 

However, not all of them made statements that would have been included in this category. For example, 

if a citation of GAP data was included on the reference list only and made no other mention of GAP 

data in the text of the article, there would be no text that described the GAP data. The statements 

describing the data were from 430 of the 646 publications. In some cases, the description was concise. 

For example, Coops and Waring (2001a, p. 20) stated, “We obtained data on the spatial distribution of 

current vegetation from the Oregon Gap Analysis Program.” Some publications, such as Singleton and 

others (2004), referenced GAP in the body of tables or in the acknowledgments only and not in the 

actual text of the document. 

Other publications included additional information in the statement describing GAP data. For 

example, Boykin, Boykin, Stovall, and Whitaker (2008, p. 213) included a reason why the GAP data 

was used, “Because of the varying land management occurring within the Bootheel region, the only 

available land cover map was from the original New Mexico Gap Analysis Project (Thompson and 

others 1996)." Other statements included information on how the authors obtained GAP data. For 

example, Mawdsley (2001a, p. 434) wrote, “Two ARC/INFO interchange files were obtained by 

anonymous ftp from the Arizona National Biological Information Infrastructure website 

(http://usgsbrd.srnr.arizona.edu/nbii/index.html; ftp site ftp/srnr/arizona/edu): a high-resolution 

vegetation map of Arizona (the Arizona Gap Vegetation map), and a map indicating the boundaries of 

federally designated wilderness areas in Arizona." Authors also provided additional detail about the 

GAP datasets. Karlik and others (2003, p. 316) wrote, “The GAP database developed for California 

(found at the website http://www.biogeog.ucsb.edu/projects//gap/gap_home.html) is an ARC/INFO GIS 
21

database with plant species and vegetation class attributes associated with polygons within a defined 

geographic region."

How Data Were Used

Statements that describe how the GAP data were used were included in this category—525 

statements from 316 different publications, the second largest category of statements. The descriptions 

of how data were used appeared in two forms—either statements regarding how data were used as part 

of the method or statements regarding how GAP data were used within the context of the intent of the 

study. Statements that indicated GAP data were used in combination with other datasets appeared in 184 

statements from 151 publications. For example, Leu and others (2008b, unpaged) wrote, “Furthermore, 

because the Sagestitch map did not cover the entire study area, we appended missing agricultural lands 

using state GAP coverages for Arizona, New Mexico, California, Oregon, and western Washington * * 

*. We developed a grid of forest habitat from the ‘Sagestitch’ map (http://sagemap.wr.usgs.gov), and 

GAP landcover data (New Mexico, Oregon, Arizona, Washington, and California) by using a crosswalk 

among forest types (deciduous forest, mixed deciduous/conifer forest, conifer forest) and the input data 

layers (Table 1).” Webb and others (2010, p. 111) supplemented GAP data with a soils dataset, “We 

analyzed a land cover dataset (1991–1993) provided by the GAP project of Nebraska (Henebry et al. 

2005) and a hydric soils dataset provided by the Natural Resources Conservation Service to describe 

landscape characteristics."

One hundred ten statements from 87 publications indicated that a transformation to GAP data 

was necessary. Transformations were in the form of excluding portions of datasets, reclassifying data, 

and converting data to a different format. Hopton and Mayer (2006, p. 4480) described their 

transformation of GAP data, “We combined the Gap analysis data for 130 polygons in West Virginia 

with other data sources using ArcView 3.3 (ESRI, Redlands CA). Six of the original 136 Gap analysis 

polygons (#2, 14, 30, 48, 120, and 127) were excluded because none of the polygons extended into the 

state (Figure 1).” Hoffman and others (2008, p. 362) described how they condensed GAP data for their 

purposes, “Land cover data were gathered from the gap analysis program (GAP) data layer. GAP 

represents the highest spatial resolution, 30-m, land cover data for Nebraska. It consists of 20 different 

vegetation and land cover types. For this research, we condensed the GAP data into six major categories 

including native woodland, native grassland, wetland, cropland, water, and human inhabitance (i.e., 

towns and cities).” Roberson (2009, p. 21) described multiple transformations to the GAP data, “The 

location of all roads in the National Forest which was provided by the US Forest Service and the newest 

(2008) version of the South Carolina GAP land use classification dataset. First, the GAP land use 

classification was reclassified to a forest or non-forest dataset. Then the GAP land use classification was 

converted from a 30 meter cell size raster to a polygon feature class.” 

GAP data were used as foundational data for some studies. In these cases, GAP data did not 

seem to be a primary focus or tool in the study. Rather, GAP data were used as a basis for implementing 

or evaluating a strategy or method. Seventy-six statements from 68 studies indicated that GAP data were 

used in a foundational manner. For example, GAP data were used to select sampling sites by Martin and 

others (2007, p. 542) who stated, “Therefore, we sorted the 2,566 available grids by the percentage of 

shortgrass prairie contained in the grid based on data from the Colorado Gap Analysis Program and then 

systematically selected every 50th grid for sampling foxes (with a random starting point between 1 and 

51)." Larson and Sengupta (2004) used GAP data to test a spatial decision support system (SDSS). 

Other publications included descriptions of how GAP data were compared to other data. Ninetyone statements from 63 documents were made regarding comparisons to GAP data. These statements 

did not include the outcomes of the comparisons, or in other words, how GAP data fared when 
22

compared to other datasets. These statements described how GAP data were used in the comparison. For 

example, Howell and others (2008, p. 169) stated their objectives clearly, “to build empirical 

hierarchical models to predict the probability of occurrence for 9 avian species throughout the state of 

Georgia, USA, and to compare the performance of our models to the associated Georgia GAP Analysis 

(GAGAP) models." Similarly, Rice and others (2008, p. 49) stated their goal “to compare the GAP 

LCLU layer and a derived LCLU layer developed with landsat imagery specifically for the Trans-Pecos 

region of Texas." Some of these comparisons were specifically conducted to test or evaluate GAP data. 

Karlik and others (2003, p. 317) wrote, “A subsample of polygons was selected as a test for correctness 

of the geographic location of a specific GAP polygon; in other words, a test of the registration of the 

GAP database.” 

Other studies using GAP data framed their use of the data in terms of the intent or purpose of the 

study. Twenty-two statements from 19 publications indicated GAP data were used for studies regarding 

particular species. However, the predicted species distributions were not necessarily the GAP data used 

in these species studies. Adjemian and others (2006, p. 94) described their use of GAP data to study 

potential spread of plague by fleas, “Geographic information system (GIS) coverages of biological and 

environmental features were obtained from the California Gap Analysis Project (Santa Barbara, CA) 

and were used to develop the bioclimatic rules best associated with the observed locations of the fleas." 

GAP data were also used in studies illustrating the use of these data for environmental planning 

and prediction. Eleven statements were made in eight studies that indicated this use of GAP data. For 

example, Crowe (1996, p. 232) wrote, “Another application of GAP and SCAG data was to prioritize 

planning related to at risk communities based on three criteria: amount of Protection Level III coverage, 

planned urban development coverage, and community size.” Additionally, 16 statements from 14 

publications described the use of GAP data to identify management and protection status for land that is 

of potential conservation interest. Hopton and Mayer (2006, p. 4492) describe their use of GAP data as, 

“While Gap analysis uses assembled data to focus mainly on small groups of species with shared habitat 

requirements that have little to no protection, we used the data to assess whether the current patterns of 

protected areas cover the areas with the highest species richness. As this method illustrated, some Gap 

analysis polygons of high species and habitat richness are not well represented by the current network of 

publicly held protected areas.” Beal (2000, p. 121) concluded, “Of potential importance for conservation 

investment consideration, a comparison of each area with the land ownership layer in the Utah GAP 

(Edwards et al. 1995) indicated a large portion of both areas as privately owned.” 

Finally, fifteen statements were made in eleven publications that GAP data were used in studies 

of change. For example, McKerrow (2007, p. 85) stated, “Specifically, we wanted to use the newly 

available 2001 GAP Ecological Systems map for the Onslow Bight as a base and perform backcasting to 

identify the pattern and types of land cover changes that had occurred between 1992 and 2001.” 

Critiques

The statements that were categorized as critiques described limitations and problems with and

cautions regarding the use of GAP data. There were 121 statements from 85 publications included in 

this category. The critiques addressed several different issues. Accuracy of GAP data and of products 

based on GAP data was one issue. Comments about accuracy of GAP data were part of 29 statements 

from 24 publications. For example, Lipow and others (2004, p. 422) stated, “The accuracy of the treedistribution maps was lowest in southwestern Oregon, where they were based on the relatively poorresolution Oregon GAP coverage.” Peterson (2008) suggested that the accuracy issue warranted a 

particular interpretation. Peterson (2008, p. 33) wrote, “Accuracy assessment on REGAP data suggests 
23

that annual grass cover is generally underestimated for sites with high cover, thus the map should be 

interpreted as an index of cover rather than an estimate of actual cover.” 

Other authors addressed errors in GAP data. There were 26 comments about error from 21 

publications. Some statements were quite general. McCarthy (1998, p. 57) stated, “Like most ecological 

models, Arizona GAP models are subject to error for numerous reasons.” Alfieri and others (2007, p. 

1804) stated, “...while the GAP dataset is more accurate than the NLCD 92 data set, it still contains 

significant errors.” Wolter and others (2006, p. 613) made a more specific statement regarding error. 

They wrote, “The water class of the Michigan GAP classification available at the time of this project 

was found to include erroneous lowland conifer classes—specifically black spruce in Landsat path 

22/row 28.” 

Limits in the use of GAP data were noted in some statements included in the critique category. 

Certain information lacking from GAP data was noted 22 times in 19 publications. Other limits were 

noted in 26 statements in 21 publications. Enquist and Gori (2008, p. 415) noted a lack of certain 

information in the classification of GAP data, and stated “The GAP vegetation system, a widelyemployed vegetation classification for Arizona and New Mexico, was also printed on the maps to serve 

as a general reference to vegetation type (Thompson et al. 1996; Halvorson et al. 2001). However, it 

should be noted that the developers of the GAP map did not incorporate information on the amount of 

shrub cover nor presence of non-native grasses as a means of classifying grassland association types.” A 

limit related to classification was also commented on by Hurley (2001, p. 104), who wrote, “Oregon 

GAP neglects to separate out certain types of agriculture, namely orchards. * * * Consequently, the 

current analysis identifies large chunks of agriculture within areas identified by ORGAP as native forest 

or woodlands. The large tracts of orchard evident from air photos do not appear in the Oregon GAP 

map.” Larson and Sengupta (2004, p. 17) describe the limitation to the use of GAP data created by data 

quality issues. They state, “However, the effectiveness of the results generated by the SDSS [spatial 

decision support system] is currently limited by the quality of the GAP data available to perform such 

analyses.” Thompson and others (2001, p. 240) cautioned others to careful use of GAP data when they 

stated, “Results of our research indicate estimates based on breeding areas do not reliably coincide with 

areas used by non-breeding birds. If the goal of the GAP is to broadly represent biodiversity, including 

migrant birds is essential. However, keeping migrant species distributions and richness estimates 

separate from breeding bird distributions and richness estimates (as currently recommended by GAP 

coordinators) is an acceptable way to present this information as long as it is recognized that breeding 

birds are probably not suitable surrogates for all birds when attempting to locate areas of significant use. 

* * * we stress the importance of carefully framing the questions investigated with GAP data sets.”

Finally, 18 critiques addressed the limits to the utility or usefulness of GAP data. These 

statements came from 15 publications. Peterson (2005, p. 232–233) wrote, “The difference seems to be 

a result of uneven methods in the Kansas Gap program for establishing likely presence of a particular

species in a particular land use or land cover type, making for unstable predictions using the Gap 

method.” Weathers and others (2009, p. 147–148) made this comment about the use of GAP data, “GAP 

land cover data were used as the primary source for habitat type. However, because of (1) availability of 

higher resolution data (i.e., DOQQ [digital orthophoto quarter quadrangles] imagery and DEMs [digital 

elevation models), (2) incorrect land classification resulting from the coarse resolution of the data, and 

(3) changes in land cover realized after the GAP baseline data were captured (in 1992), this data set was 

not solely relied on for habitat classification.”
24

Comparisons

As described in a previous section, some use of GAP data was for the purpose of comparing 

GAP data to other data. The statements included in the section on how data were used included 

statements describing the comparisons. We categorized statements regarding the outcomes of 

comparisons between GAP data and other data into a separate category. There were 51 statements from 

34 publications describing how GAP data fared in comparison to other data. 

Nineteen statements from 15 publications indicated that the result of the comparison was 

unfavorable to GAP. All of the 15 publications cited use of data from state GAP projects. Eleven of the 

15 publications cited the use of land-cover data; three publications used predicted species distribution 

data; one publication used land cover and predicted species distribution data. Thorne and others (2004, 

p. 359) compared GAP data to the CalVeg map and to a map created by the authors based on the 

Manual of California Vegetation (MCV); they concluded that “Where equal vegetation type extents 

were measured between GAP and MCV, MCV provides more information about the distribution.” 

Specifically, they stated, “In a comparison with two previous digital vegetation maps for the area, the 

US Forest Service's CalVeg and the Gap Analysis Program's GAP maps, the MCV map had finer spatial 

and floristic resolution. The MCV map has 15 more vegetation types than CalVeg and 22 more 

vegetation types than GAP. The MCV map contains more riparian corridors and isolated wetlands, 

identifying 157 km2 of these types, compared to 7 km2 for CalVeg and a non-spatial result for GAP.” 

(Thorne and others, 2004, p. 343). 

Fifteen statements from 11 publications indicated that the result of the comparison was favorable 

to GAP. The publications providing these favorable statements most frequently used land cover data 

from state GAP projects. Eight of the 11 publications used state project land cover data. State project 

predicted species distribution data, and regional project land cover and predicted species data were each 

cited in one publication that provided favorable statements regarding the comparison of GAP data to 

other data. Mehaffey and others (2009) compared GAP data to other data when deciding which dataset 

to use in their study of air pollution on habitat. They described their conclusion as, “We used final-draftbefore-release SE-GAP data. We believe the data is high quality and accurately maps potential habitat 

for the species groups we examined (SE-GAP 2007). * * * Qualitative comparison of the SE-GAP data 

compared favorably, in respect to overall distribution of habitat, to other ecological model output for the 

region such as the Southeast Ecological Framework (USEPA 2002) and as such we felt it was adequate 

for the purpose of demonstrating the toxic vulnerability methods of this study.” (Mehaffey and others, 

2009, p.154). 

Nine statements from seven publications described mixed results from a comparison of GAP 

data to other data. We classified statements as mixed if the authors described the results of the 

comparison as being favorable to GAP in some way and unfavorable to GAP in another way. For 

example, Peterson and Kluza (2003, p. 49, 51) wrote, “Omission error was lower in gap models (Table 

1). In 24 of 30 species, gap models had better success in predicting the 20 test presence points, whereas 

GARP [Genetic Algorithm for Rule-Set Prediction] models were better in only three species; the two 

methods tied for three species. Hence, on the omission criterion, gap models performed better than 

GARP models. The situation regarding commission error, however, was different. For this error 

component, GARP models outperformed gap models in 28 of 30 species, with substantial differences in 

commission error (Table 1).” In another example, Wardlow and Egbert (2003, p. 1396) stated, “NLCD 

[National Land Cover Data] gives a useful first-order view of general land-cover patterns at the state to 

regional level and GAP provides a more detailed view of localized land-cover patterns within the state.” 

Four of the seven publications that generated these mixed statements cited use of state project land 

cover data. State project predicted species distribution data and regional project land cover data were 
25

each cited in one of the seven publications. One publication used state project land cover and 

stewardship data.

Finally, eight statements from eight publications described GAP data as equivalent to other data.

Five of these eight publications used state project land cover data. One publication used land cover and 

stewardship data from a state project; one publication used regional predicted species distribution data; 

and one publication used stewardship and predicted species distribution data from a regional project. 

Moen, Burdett, and Niemi (2008) used GAP data and compared it to another land-cover dataset. They 

concluded “The LULC [Land Use Land Cover] and GAP coverages were created from different 

LANDSAT scenes, were classified by different organizations, and had different cover type naming 

conventions (MDNR, 2007a, b, p. 1512). Yet both classifications resulted in similar relationships 

between selected cover types and distance from the den site.” 

Commentary

The theme we labeled as commentary includes statements that are favorable to GAP but that do 

not arise from a comparison of GAP data to other data. Thirty-nine statements from 32 publications 

were categorized as commentary. Twenty-six statements from 22 publications were strictly favorable to 

GAP. For example, Chung and Winer (1999, p. 195) stated, “Despite the discrepancies between 

predicted and observed plant species cover, on average the utility of the GAP database for developing 

BHC [biogenic hydrocarbons] emission inventories appears to be adequate.” Clark and Slusher (2000, 

p. 77) described the importance of GAP stewardship data to their study. “Because areas managed by 

ILDNR [Illinois Department of Natural Resources], IDNR [Indiana Department of Natural Resources], 

TNC [The Nature Conservancy], NAWMP [North American Waterfowl Management Plan], counties, 

and the military were important in the analysis, the Gap Analysis Stewardship data layer proved

essential to Phase 1.” 

Other comments indicated that a potential negative aspect of GAP was mitigated in their 

situation and was either not a problem or was favorable. Thirteen statements from 12 publications 

indicated that GAP issues were mitigated. Dumas (2005, p. 75–76) wrote, “Anyone with a working 

knowledge of ArcGISTM can easily work with the GAP datasets. It is standardized and nationally 

accepted, easy to use with standard GIS tools and free to the public. The only drawback experienced 

was the dating of the data. GAP data for this project was based on LandSat imagery from the mid-1990s 

and a lot can change in that amount of time, however because this project was based within the 

Bienville National Forest, most of the landcovers have not changed in that amount of time.” Another 

example that indicated the datedness of GAP data was not an issue was found in Thatcher and others 

(2009, p. 919), who stated, “We chose a subset of the panther telemetry data for analysis that coincided 

with a time period within 5 years (1988–1998) of the Florida Gap Analysis Program (GAP) land-cover 

data (1993, see “Landscape data”). Although more recent landscape data were available, the majority of 

the radiotelemetry data coincided with the 1993 landscape data set.” Not all of the statements were 

about the datedness of data. Comments in the critique category previously described in this report 

address accuracy of GAP data as a concern. McClain and Porter (2000, p. 558) described GAP accuracy 

as less of a concern. They wrote, “However, in both the HSI [habitat suitability index] and PATREC 

[pattern recognition] models, the data obtained from the GAP classification had higher correlations with 

the harvest data (highest GAP r2

 = 0.584, highest NFLI [Northern Forest Lands Inventory] r

2

 = 0.328). 

The better predictability of the GAP data suggests that GAP’s slightly lower accuracy is not as 

detrimental as NFLI’s 10-ha mmu [minimum mapping unit].” 
26

Reference to GAP Data

The final theme included references to GAP data and products that were not specifically about 

use of GAP data. There were 81 statements from 67 publications in this category. Larson and Sengupta 

(2004, p. 11) made a descriptive statement about GAP data and wrote, “Most of the analysis was 

performed in raster mode, which is the native format of the GAP datasets released by the state 

agencies.” Similarly, McCarthy (1998) described the products of Arizona GAP. McCarthy wrote, 

“Arizona's participation in GAP has resulted in a vegetation map with a greater resolution than previous 

editions and over 500 geo-referenced predicted vertebrate distribution maps” (p. 61). 

Summary

One of the unique contributions of this publication search is that it provided a view of what 

statements were made and how GAP data were described in the public realm. Examination of the 

statements made in these publications illustrated how GAP data were depicted by those who have made 

use of these datasets. Authors identified GAP as their source of data and provide descriptions of varying 

detail regarding the datasets they used. The descriptions of how GAP data were used were also varied. 

Some of the statements described the tactical use of GAP data—how the data were used as a foundation 

for another analytical technique or to select study sites. Statements made in these publications also 

indicated that transformations to GAP data were necessary for some applications. These transformations 

could involve combining GAP with other data, excluding some data from the GAP datasets, or altering 

the format of the data. This suggests that a minimum level of expertise may be needed by those who use 

GAP data.

The statements regarding how GAP data were used suggested a range of applications. While 

some of the applications seem to fall in line with the goal of GAP, such as the studies of species, studies 

for prediction and planning, and identification of management and protection status, other publications 

indicated a use of GAP data for other purposes. The emergent category of change studies which 

primarily focused on landscape change demonstrates that GAP data have been used to study issues of 

increasing importance that may not be directly related to conservation of biodiversity.

The evaluative comments that were made regarding GAP data that were included in the critique, 

comparisons, and commentary themes present a mixed picture of GAP data. The issues raised in some 

publications as critiques of GAP data such as accuracy, errors, and limits based on characteristics of the 

data, were described in other publications as issues that did not impact the study or were otherwise 

mitigated. Comparisons of GAP data to other datasets yielded results that were unfavorable, favorable, 

and mixed towards GAP. The diversity of these comments suggests that issues with GAP data may be 

application specific. In other words, an issue in one study may be a nonissue in another. For example, 

the datedness of the state GAP datasets may create an accuracy issue in one study dependent on the goal 

of that study. However, the datedness may be a nonissue if there has not been much change in the 

characteristic mapped by the GAP dataset (such as land cover) or may actually be a benefit such as in 

studies of changes in landscape characteristics. For all categories of comparison outcome, the most 

frequently used datasets in those publications were land cover data from state GAP projects. This may 

reflect the overall higher levels of use of land cover data from state projects in the set of publications we 

identified.

These themes provided us with information to use in developing topics to include on the survey. 

In particular, the survey will include questions addressing the issues raised in the how data were used, 

critiques, commentary, and comparisons themes. Specifically the survey will include questions 

regarding the issues of accuracy, error, to what extent issues of accuracy and error are problematic to the 

user, what GAP data lack that would be useful, what transformations are necessary to make GAP data 
27

useful, and how GAP data compare to other datasets. The survey will also include questions regarding 

the level of GIS expertise needed to make use of GAP data.

Bibliometric Analysis

Bibliometric methods include a set of techniques for the quantitative study of publications 

(Gauthier, 1998; Schubert and others, 1988; Wallin, 2005). The results of bibliometric analyses have 

been used as components in the performance evaluation process for research programs (Campbell and 

others, 2010; Frame, 1983; Gauthier, 1998; Kostoff, 1995; Leydesdorff, 2005; Narin, 1987; Trochim 

and others, 2008; Verbeek and others, 2002) and in science policy and planning (Irvine and others, 

1987). Bibliometric analyses are not recommended as stand alone methods for evaluation but are 

considered a useful component to use in conjunction with other methods (Melkers, 1993; Pendlebury, 

2009).

The most commonly used measures in bibliometric analyses for evaluation (Martin, 1996; Narin 

and others, 1994) are publication and citation counts. These can be used as partial indicators of research 

impact (Campbell and others, 2010; Frame, 1983; Martin and Irvine, 1983; Mullins, 1987; Schubert and 

others, 1988). Number of publications is considered an acceptable measure of research production 

(Martin, 1996; Melkers, 1993; Narin and Hamilton, 1996; Shapira and others, 2003; van Raan, 1989; 

Verbeek and others, 2002) or research activity (Kostoff, 1995; Narin, 1987). A count of the number of 

publications has been used to evaluate the impact of a program providing resources to researchers 

(Campbell and others, 2010). For example, Campbell and others (2010) evaluated the effect of a 

program that provides funding to researchers. GAP is in a similar position in that it provides resources 

in the form of datasets. Bibliometric techniques have been used in program evaluation for federal 

agencies (U.S. Environmental Protection Agency, 2006; Kostoff, 1994; Kostoff, 1995; Porter and 

others, 2003; Porter and others, 2010; Roessner, 2002; Trochim and others, 2008). These techniques 

have been recommended as useful in documentation for the Government Performance and Results Act 

to demonstrate the results of a program (Hicks and others, 2004; Narin and Hamilton, 1996). 

In the case of GAP, the results of a bibliometric analysis are an indirect evaluation of the 

program because GAP itself did not produce the publications in the analysis. GAP produced the datasets 

which were used by the authors who wrote the publications. There may have been uses of GAP data to 

create maps or documents that have not been published because they were used exclusively for 

management of natural areas. The bibliometric analysis indicated the research impact of GAP—an 

added benefit given that the primary objective of GAP is to promote management of land for the 

conservation of biodiversity. Previous evaluations of databases—instead of programs—have included 

bibliometric analyses (Hicks and others, 2004). Bibliometric analyses have been used to investigate the 

use of publications in diffusion of innovation (Genuis, 2005) and evaluation of research and 

development activities (Melkers, 1993). In a sense, the datasets produced by GAP are an innovation and 

identifying how they have been used to generate publications provides evidence regarding the level of 

diffusion achieved. 

Based on prior use of bibliometric analysis for programs and databases and for federal programs, 

we concluded that this analysis was appropriate to include in the evaluation of published literature citing 

GAP data.

The weakness of any given indicator in bibliometric analysis can be offset by combining it with 

other measures (Thelwall, 2004). In the bibliometric analysis for literature citing datasets produced by 

GAP, we used descriptive indicators including journal impact factors, journal impact ranks, and 

citations. These indicators are considered evidence of the volume of scientific output related to a 
28

program and the impact of program output on the development of knowledge in a field (Campbell and 

others, 2010; Gauthier, 1998). 

Publication Count

The set of 646 nonduplicate publications collected for the literature review was included in the 

bibliometric analysis. The publication dates ranged from 1994 to 2011. Fifty-three percent of the 

publications were journal articles. We summarize the journal impact factors and ranks for the journal 

publications only. We included all 646 publications in our search for citations.

Journal Impact

The journal impact factor represents the frequency of citation for an average article from a 

journal within a defined time period (Garfield, 1994). Although the journal impact factor is not a perfect 

measure of the research impact of any article published in the journal, the impact factor of the journal is 

often used as a proxy measure of impact of the articles included in the journal (Davis and Royle, 1996; 

Seglen, 1997). The journal impact factor has been demonstrated to relate to the perceived prestige of the 

journal (Davis and Royle, 1996). Some of the specific criticisms of use of the journal impact factor, for 

example that many databases are less likely to index non-U.S. publications (Rey-Rocha and others, 

2001; Seglen, 1997; Verbeek and others, 2002), are not relevant to our bibliometric analysis for 

publications citing GAP data. The databases produced by GAP are specific to the U.S. and publications 

using GAP data are likely to be published in U.S.-based journals. However, our set of publications also 

includes journal articles published in European-based journals. We found that some of these journals are 

indexed in the databases we used for the bibliometric analysis making less relevant the criticism that 

non-U.S. publications are less likely to be indexed. 

We obtained five-year journal impact factors and ranks from the Web of Science Journal 

Citations Report® (JCR) Science Edition 2010. These were the current journal impact factors and in 

some cases may differ from the journal impact factor at the publication date of individual articles. 

Journal impact factors do not generalize across disciplines (Gauthier, 1998; Narin and Hamilton, 1996; 

Seglen, 1997) and so caution must be exercised in interpreting the results for multiple disciplines.

Journal ranks indicate the relative standing of a journal based on its impact factor compared to other 

journals within the same discipline. We report the number of articles from each journal appearing in the 

list of publications citing GAP data, with the impact factor, the discipline with which the journal is 

affiliated, the journal impact rank, and the corresponding quartile of the rank in table 2.

Table 2. Number of articles appearing in each journal, 2010 journal impact factor, discipline, and journal impact 

rank. 

Journal Title

Number 

of 

Articles

5 yr 

Journal 

Impact

Category Journal 

Rank Quartile

Journal of Wildlife Management 20 1.977 Ecology 73/129 Q3

Conservation Biology 17 5.963 Biodiversity Conservation 2/33 Q1

Ecological Applications 14 5.067 Ecology 23/129 Q1

Landscape Ecology 9 3.648 Ecology 35/129 Q2
29

Table 2. Number of articles appearing in each journal, 2010 journal impact factor, discipline, and journal impact 

rank.—Continued

Journal Title

Number 

of 

Articles

5 yr 

Journal 

Impact

Category Journal 

Rank Quartile

Photogrammetric Engineering & 

Remote Sensing

9 1.566 Imaging Science & Photographic 

Technology

9/19 Q2

Wildlife Society Bulletin 8 N/A

Biological Conservation 7 4.042 Biodiversity Conservation 6/33 Q1

Environmental Monitoring and 

Assessment

6 1.539 Environmental Sciences 106/192 Q3

Natural Areas Journal 6 0.863 Ecology 109/129 Q4

Environmental Management 5 1.895 Environmental Sciences 101/192 Q3

Journal of Mammalogy 5 2.074 Zoology 43/145 Q2

Madroño: California Botanical 

Society

5 N/A

Molecular Ecology 5 6.633 Ecology 5/129 Q1

Remote Sensing of Environment 5 4.605 Environmental Sciences 16/192 Q1

Southwestern Naturalist 5 0.547 Ecology 120/129 Q4

Monthly Weather Review 4 2.649 Meteorology & Atmospheric 

Sciences

18/68 Q2

American Journal of Tropical 

Medicine and Hygiene

3 2.884 Public, Environmental, & 

Occupational Health

38/140 Q2

American Midland Naturalist 3 0.768 Ecology 115/129 Q4

Animal Conservation 3 3.037 Ecology 42/129 Q2

Auk 3 2.276 Ornithology 3/19 Q1

Diversity and Distributions 3 4.550 Ecology 25/129 Q1

Ecography 3 5.325 Ecology 22/129 Q1

Environmental Modelling & Software 3 2.900 Environmental Sciences 38/192 Q1

Forest Ecology and Management 3 2.507 Forestry 6/54 Q1

International Journal of Remote 

Sensing

3 1.551 Remote Sensing 9/23 Q2

Journal of Arid Environments 3 2.064 Environmental Sciences 99/192 Q3

Journal of Forestry 3 1.465 Forestry 23/54 Q2

Journal of Medical Entomology 3 2.257 Veterinary Sciences 17/145 Q1

Journal of the American Water 

Resources Association

3 1.771 Geosciences, Multidisciplinary 77/165 Q2

Landscape and Urban Planning 3 2.789 Ecology 57/129 Q2

North American Journal of Fisheries 

Management

3 1.439 Fisheries 20/46 Q2

Southeastern Naturalist 3 0.501 Ecology 125/129 Q4

Wetlands 3 1.810 Environmental Sciences 119/192 Q3
30

Table 2. Number of articles appearing in each journal, 2010 journal impact factor, discipline, and journal impact 

rank.—Continued

Journal Title

Number 

of 

Articles

5 yr 

Journal 

Impact

Category Journal 

Rank Quartile

American Fisheries Society 

Symposium

2 N/A

Applied Geography* 2 2.320 Geography 10/65 Q1

Atmospheric Environment 2 3.435 Environmental Sciences 24/192 Q1

Biodiversity and Conservation 2 2.336 Biodiversity Conservation 12/33 Q2

Condor 2 1.775 Ornithology 5/19 Q2

Ecohydrology 2 1.873 Ecology 63/129 Q2

Ecological Indicators 2 3.058 Environmental Sciences 36/192 Q1

Ecological Modelling 2 2.438 Ecology 68/129 Q3

Ecology and Society 2 4.644 Ecology 34/129 Q2

Ecosystems 2 4.735 Ecology 26/129 Q1

Emerging Infectious Diseases 2 6.996 Immunology 14/134 Q1

Forest Policy and Economics 2 1.315 Forestry 25/54 Q2

Forest Science 2 1.590 Forestry 18/54 Q2

Global Change Biology 2 7.814 Ecology 6/129 Q1

Global Ecology and Biogeography 2 6.330 Ecology 10/129 Q1

Journal of Coastal Research 2 0.690 Environmental Sciences 168/192 Q4

Journal of Soil and Water 

Conservation

2 1.695 Water Resources 27/76 Q2

Journal of the Arkansas Academy of 

Science

2 N/A

Journal of Vegetation Science 2 3.002 Forestry 2/54 Q1

Occasional papers, Museum of Texas 

Tech University

2 N/A

Oikos 2 3.920 Ecology 33/129 Q2

PloS ONE 2 4.610 Biology 12/85 Q1

Professional Geographer* 2 1.988 Geography 18/65 Q2

Quaternary Research 2 3.310 Geosciences, Multidisciplinary 28/165 Q1

Soil Science Society of America 

Journal

2 2.598 Soil Science 13/32 Q2

Southern Journal of Applied Forestry 2 0.713 Forestry 40/54 Q3

Studies in Avian Biology 2 N/A

Transactions of the Kansas Academy

of Science

2 N/A

Ursus 2 1.090 Zoology 95/145 Q3

Western Journal of Applied Forestry 2 0.660 Forestry 37/54 Q3

Wildlife Biology 2 1.137 Zoology 96/145 Q3

Wilson Journal of Ornithology 2 0.593 Ornithology 15/19 Q4
31

Table 2. Number of articles appearing in each journal, 2010 journal impact factor, discipline, and journal impact 

rank.—Continued

Journal Title

Number 

of 

Articles

5 yr 

Journal 

Impact

Category Journal 

Rank Quartile

Agricultural Water Management 1 2.391 Water Resources 17/76 Q1

Ambio 1 2.847 Environmental Sciences 86/192 Q2

American Journal of Enology and 

Viticulture

1 2.568 Horticulture 6/30 Q1

Annals of the Association of 

American Geographers*

1 2.986 Geography 11/65 Q1

Applied Vegetation Science 1 2.091 Forestry 9/54 Q1

Biological Invasions 1 3.749 Biodiversity Conservation 7/33 Q1

Bioresource Technology 1 4.901 Energy & Fuels 9/78 Q1

BioScience 1 6.335 Biology 7/85 Q1

Bryologist 1 1.039 Plant Sciences 108/187 Q3

California Agriculture** 1 0.918 Agriculture, Multidisciplinary 15/55 Q2

Canadian Journal of Fisheries and 

Aquatic Sciences

1 2.690 Fisheries 6/46 Q1

Climatic Change 1 4.433 Environmental Sciences 35/192 Q1

Computing in Science and 

Engineering

1 0.899 Computer Science, Interdisciplinary 

Applications

66/97 Q3

Conservation and Society 1 N/A

Conservation Genetics 1 1.889 Biodiversity Conservation 16/33 Q2

Digital Soil Mapping 1 N/A

Ecological Monographs 1 8.827 Ecology 8/129 Q1

Ecological Research 1 1.671 Ecology 89/129 Q3

Ecology 1 6.218 Ecology 12/129 Q1

Ecology Letters 1 14.261 Ecology 1/129 Q1

Endangered Species Update 1 N/A

Energy Policy 1 3.020 Environmental Sciences 46/192 Q1

Environmental Entomology 1 1.702 Entomology 21/83 Q2

Environmental Geology 1 1.344 Environmental Sciences 135/192 Q3

Environmental Science and Pollution 

Research

1 2.733 Environmental Sciences 39/192 Q1

Environmetrics 1 0.986 Statistics & Probability 64/110 Q3

Evolution 1 6.041 Ecology 9/129 Q1

Fire Ecology 1 N/A

Forestry Chronicle 1 0.845 Forestry 34/54 Q3

Freshwater Biology 1 3.785 Marine & Freshwater Biology 7/92 Q1

Frontiers in Ecology and the 

Environment

1 7.931 Environmental Sciences 2/192 Q1

Geoderma 1 2.806 Soil Science 5/32 Q1
32

Table 2. Number of articles appearing in each journal, 2010 journal impact factor, discipline, and journal impact 

rank.—Continued

Journal Title

Number 

of 

Articles

5 yr 

Journal 

Impact

Category Journal 

Rank Quartile

Geographical Review 1 0.648 Geography 50/65 Q4

Geoscience and Remote Sensing 

Symposium Proceedings

1 N/A

Geospatial Health** 1 1.705 Public, Environmental, & 

Occupational Health

64/140 Q2

GIScience and Remote Sensing 1 N/A

Great Basin Naturalist 1 N/A

Great Plains Research 1 N/A

Human and Ecological Risk 

Assessment

1 1.382 Environmental Sciences 103/192 Q3

Human Dimensions of Wildlife 1 N/A

Human Ecology Review** 1 1.000 Sociology 43/129 Q2

Hydrobiologia 1 1.997 Marine & Freshwater Biology 25/92 Q2

Hydrological Sciences Journal 1 1.891 Water Resources 26/76 Q2

Insecta Mundi 1 N/A

Integrated Environmental Assessment 

and Management

1 N/A

International Journal of Biodiversity 

and Conservation

1 N/A

International Journal of Wildland Fire 1 2.523 Forestry 5/54 Q1

Invasive Plant Science and 

Management 

1 N/A

IUCN Otter Specialist Group Bulletin 1 N/A

Journal of Applied Ecology 1 5.715 Ecology 14/129 Q1

Journal of Applied Remote Sensing** 1 1.000 Remote Sensing 11/23 Q2

Journal of Biogeography 1 4.716 Ecology 24/129 Q1

Journal of Conservation Planning 1 N/A

Journal of Environmental Engineering 1 1.217 Engineering, Civil 31/115 Q2

Journal of Environmental Planning 

and Management**

1 1.111 Environmental Studies 39/77 Q3

Journal of Environmental Quality 1 2.738 Environmental Sciences 56/192 Q2

Journal of Great Lakes Research 1 1.789 Environmental Sciences 115/192 Q3

Journal of Insect Conservation 1 1.901 Entomology 17/83 Q1

Journal of Land Use Science 1 N/A

Journal of Raptor Research 1 0.568 Ornithology 16/19 Q4

Journal of Remote Sensing 1 N/A
33

Table 2. Number of articles appearing in each journal, 2010 journal impact factor, discipline, and journal impact 

rank.—Continued

Journal Title

Number 

of 

Articles

5 yr 

Journal 

Impact

Category Journal 

Rank Quartile

Journal of the North American 

Benthological Society

1 3.920 Marine & Freshwater Biology 9/92 Q1

Journal of the Torrey Botanical 

Society

1 1.114 Plant Sciences 128/187 Q3

Journal of Water Resources Planning 

and Management

1 1.993 Engineering, Civil 25/115 Q1

Kalmiopsis 1 N/A

Lake and Reservoir Management 1 0.742 Marine & Freshwater Biology 75/92 Q4

Marine Geodesy** 1 0.917 Oceanography 39/59 Q3

Marine Pollution Bulletin 1 2.899 Marine & Freshwater Biology 15/92 Q1

McNair Research Journal 1 N/A

Northeastern Naturalist 1 0.567 Biodiversity Conservation 23/33 Q3

Northern Journal of Applied Forestry 1 0.741 Forestry 35/54 Q3

Northwest Science 1 0.531 Ecology 124/129 Q4

Northwestern Naturalist 1 N/A

Ocean Engineering 1 1.038 Engineering, Civil 38/115 Q2

Oecologica 1 N/A

Parks 1 N/A

Peromyscus Newsletter 1 N/A

Physics and Chemistry of the Earth 1 1.211 Water Resources 44/76 Q3

Proceedings of the National Academy 

of Sciences

1 10.591 Multidisciplinary Sciences 3/57 Q1

Pure and Applied Geophysics 1 1.231 Geochemistry & Geophysics 46/77 Q3

Rangeland Ecology & Management 1 1.579 Ecology 82/129 Q3

Sensors 1 1.917 Instruments & Instrumentation 14/61 Q1

Transactions in GIS 1 N/A

Transactions of the American 

Entomological Society

1 0.414 Entomology 74/83 Q4

Transactions of the American 

Fisheries Society

1 1.959 Fisheries 13/46 Q2

Transactions of the ASABE 1 1.193 Agricultural Engineering 8/12 Q3

Urban Ecosystems 1 N/A

Water Resources Research 1 3.081 Water Resources 2/76 Q1

Water, Air, and Soil Pollution 1 1.929 Water Resources 19/76 Q2

Waterbirds 1 0.746 Ornithology 14/19 Q3

Weather and Forecasting 1 1.980 Meteorology & Atmospheric 

Sciences

38/68 Q3

Western North American Naturalist 1 0.454 Ecology 121/129 Q4
34

Table 2. Number of articles appearing in each journal, 2010 journal impact factor, discipline, and journal impact 

rank.—Continued

Journal Title

Number 

of 

Articles

5 yr 

Journal 

Iimpact

Category Journal 

Rank Quartile

Wildfowl 1 N/A

Wildlife Biology in Practice 1 N/A

Wyoming Open Spaces Bulletin 1 N/A

Yearbook of the Association of 

Pacific Coast Geographers

1 N/A

*This journal did not appear in JCR Science Edition; the journal was included in JCR Social Science Edition 2010. 

**No five-year Journal Impact Factor was available, the one-year Journal Impact Factor is substituted in this table.

N/A indicates the journal was not listed in either edition of the Journal Citation Report.

The impact factor of the journals in which articles citing GAP data were published ranged from 

14.261 (Ecology Letters) to 0.414 (Transactions of the American Entomological Society). The journals 

were affiliated with 34 disciplines. There were 101 articles citing GAP data published in journals 

affiliated with the Ecology discipline that were ranked at all four quartile levels. Journals affiliated with 

the Environmental Sciences discipline included 40 articles citing GAP data. The Environmental 

Sciences journals that cited GAP data included some journals from each quartile level. In the discipline 

of Biodiversity Conservation, 29 articles published appeared in journals ranked at the top three quartile 

levels. There were 20 articles citing GAP data published in journals affiliated with the Forestry 

discipline that were ranked in the top three quartiles. These are the results for the four disciplines with 

the largest numbers of articles within the set of publications citing GAP data. It is apparent that articles 

citing GAP data are published in multiple disciplines in journals of varying degrees of impact as defined 

by the journal impact factor.

The total number of articles and relative percent published in journals in each quartile within 

their respective disciplines is depicted in figure 8. This is based on the 289 publications for which this 

information was available. The purpose of this figure is to summarize to what extent GAP data appeared 

in publications at each level of relative prestige and impact based on quartile ranking. 
35

Figure 8. Number and percent of articles published in journals at each quartile rank. 

Forty percent of journal articles citing GAP data were published in quartile 1 (Q1) level journals. 

These are journals that are considered top level journals within their respective disciplines. Twentyeight percent of the articles were published in quartile 2 (Q2) level journals. A slightly smaller 

proportion, 23 percent, of articles citing GAP data were published in quartile 3 level journals. In 

reviewing the quartile rank information provided previously in table 2, many of the journals that appear 

at the third and fourth quartiles of their respective disciplines are regional in nature. For example, Q3 

level journals include Northeastern Naturalist and the Southern Journal of Applied Forestry. These are 

journals that are likely to be circulated and cited less frequently than journals that have broader appeal 

such as Landscape Ecology, a Q1 level journal. 

The actual citation counts should be considered when interpreting the journal impact factor 

(Wallin, 2005). Because of issues affecting calculation of journal impact factors, such as the limited 

time frame represented, the number of citations to a paper may be a better indicator of the impact of the 

paper than the journal impact factor (Fava and Ottolini, 2000; Retzer and Jurasinski, 2009). 

Citation Count

Citation analysis indicates what is being cited and by whom (Melkers, 1993). Within the context 

of the larger program analysis of GAP, publication counts and citation analysis provided information 

about the use and impact of GAP data. Citation counts for an article can vary from year to year. Because 

of this phenomenon, the recommendation when using citation counts in bibliometric analyses is that the 

time period over which counts are taken should not be too limited (Verbeek and others, 2002). Citations 

are an imperfect but useful measure of research impact (Kostoff, 1995; Narin and Hamilton, 1996; 

Narin and others, 1994; Okubo, 1997; Porter and others, 2003; Verbeek and others 2002) and visibility 

in the research community (Frame, 1983). Citation data should not be interpreted as a measure of 

research quality (Seglen, 1997). Even though citations counts are considered imperfect, they are a 

conventional measure used to measure impact (Herbstein, 1993; Shapira and others, 2003).
36

Research has demonstrated a correlation between citation data and other measures of influence 

or impact such as peer reviews (Garfield and Welljams-Dorof, 1992). The concerns about citation data 

tend to be reduced at higher levels of aggregation (Garfield and Welljams-Dorof, 1992, Okubo, 1997; 

Schubert and others, 1988). In this case we aggregated data at the program level for GAP and we did not 

evaluate publications at the level of individual authors. Bornmann and Daniel (2008) concluded in their 

review of studies of citation behavior that citations are a multidimensional measure. In part, citations do 

measure the impact of a paper and therefore can be used as a partial measure of research impact 

(Bornmann and Daniel, 2008). 

Because of what is commonly known as the “time lag” in publications—the time it takes to 

write, revise, and publish an article—bibliometric analyses are appropriate for publications at least 2-3 

years old (Herbstein, 1993) but not for very recent ones. When citation analyses are included in a 

bibliometric approach, as they are here, the time lag required is even longer (Frame, 1983). The article 

must be published and available for long enough for others to see it, cite it and then have their article 

published. In many circumstances this phenomenon would be considered a drawback. However, in 

program evaluation the effects of interest often do not occur until years after research is conducted or a 

program begins (Garfield and Welljams-Dorof, 1992; Kostoff, 1995). In this respect, bibliometric 

analyses fit well with the objectives of program evaluation. In the case of GAP, the time lag in 

publications means that many of the publications included in the literature review and bibliometric 

analyses cited the use of the state rather than regional datasets. However, when evaluating the impact of 

a scientist, a paper, or a program, more relevant information is provided by citation frequencies than by 

journal impact factors (Garfield, 2001). The citation frequencies reflect the impact over the life of an 

article whereas impact factors are based on a time limited (two or five year) average for the journal in 

which an article appears (Fava and Ottolini, 2000). Therefore, we used citation frequencies as part of 

our bibliometric analysis even though we knew that this approach would be less accurate for the more 

recent regional and national datasets.

We searched Web of Science to obtain citation lists. At the time of our citation search, the 

citation database had been updated on August 10, 2011. When conducting citation searches in Web of 

Science we used author name and year as search terms. One of the drawbacks to using a database such 

as this is the presence of errors, such as misspellings in authors’ names, which are often present in 

source documents (Wallin, 2005). We used only the search terms of author name and year in order to 

minimize the chance that we would miss relevant citations due to reporting errors in source document 

data. We searched for citation information for publications of all types, not just journal articles. The 

publications included in our list of publications using GAP data were cited a total of 5,556 times with an 

average of 8.6 citations per publication. There were 280 publications for which did not yield any 

citations in our search. 

In table 3, we list the publications that have been cited by other publications and the number of 

citations. Publications are identified by the author(s), year of publication, and the title. Full reference 

citations for the publications included in this table are provided in the list of references. In the table, the 

publications are listed in descending order of citation frequency. Only the publications cited at least 

once were included in this table; not all of the publications that we found that used GAP data are 

included in this table. 
37

Table 3. Citation count for publications using GAP data. 

Publication Reference Citation Count

Homer, Collin; Huang, Chengquan; Yang, Limin; Wylie, Bruce; and Coan, Michael, 2004, 

Development of a 2001 National Land Cover Database for the United States. 272

Breshears, D.D., Cobb, N.S., Rich, P.M., Price, K.P., Allen, C.D., Balice, R. G., Romme, W.H., 

Kastens, J.H., Floyd, M.L., Belnap, Jayne, Anderson, J.J., Myers, O.B., and Meyer, C.W., 2005, 

Regional vegetation die-off in response to global-change-type drought.

254

Church, R.L., Stoms, D.M., and Davis, F.W., 1996, Reserve selection as a maximal covering location 

problem. 200

Scott, J.M., Davis, F.W., McGhie, G., Wright, R.G., Groves, C., and Estes, J., 2001, Nature reserves—

Do they capture the full range of America's biological diversity? 179

Vogelmann, J.E., Sohl, T.L., Campbell, P.V., and Shaw, D.M., 1998, Regional land cover 

characterization using Landsat thematic mapper data and ancillary data. 118

Homer, Collin; Dewitz, Jon; Fry, Joyce; Coan, Michael; Hossain, Nazmul; Larson, Charles; Herold, 

Nate; McKerrow, Alexa; VanDriel, J.N.; and Wickham, James, 2007, Completion of the 2001 National 

Land Cover Database for the conterminous United States.

113

Carroll, Carlos, Noss, R.F. and Paquet, P.C., 2001, Carnivores as focal species for conservation planning 

in the Rocky Mountain region. 106

Noss, R.F.; Carroll, Carlos; Vance-Borland, Ken; and Wuerthner, George, 2002, A multicriteria 

assessment of the irreplaceability and vulnerability of sites in the Greater Yellowstone Ecosystem. 106

Polasky, Stephen, Camm, J.D., Solow, A.R., Csuti, Blair, White, Denis, and Ding, Rugang, 2000, 

Choosing reserve networks with incomplete species information. 95

Guerry, A.D., and Hunter, M.L., Jr., 2002, Amphibian distributions in a landscape of forests and 

agriculture—An examination of landscape composition and configuration. 94

Kiester, A.R.; Scott, J.M.; Csuti, Blair; Noss, R.F.; Butterfield, Bart; Sahr, Kevin; and White, Denis, 

1996, Conservation prioritization using GAP data. 93

Theobald, D.M., 2001, Land-use dynamics beyond the American urban fringe. 83

Guerra, Marta; Walker, Edward; Jones, Carl; Paskewitz, Susan; Cortinas, M.R.; Stancil, Ashley; Beck, 

Louisa; Bobo, Matthew; and Kitron, Uriel, 2002, Predicting the risk of Lyme Disease—Habitat 

suitability for Ixodes scapularis in the north central United States.

74

DeAngelis, D.L., Gross, L.J., Huston, M.A., Wolff, W.F., Fleming, D.M., Comiskey, E.J., and 

Sylvester, S.M., 1998, Landscape modeling for Everglades ecosystem restoration. 65

Edwards, T.C., Jr., Deshler, E.T., Foster, Dan, and Moisen, G.G., 1996, Adequacy of wildlife habitat 

relation models for estimating spatial distributions of terrestrial vertebrates. 63

Sponseller, R.A., and Benfield, E.F., 2001, Influences of land use on leaf breakdown in southern 

Appalachian headwater streams—A multiple-scale analysis. 60

Dark, S.J., 2004, The biogeography of invasive alien plants in California—An application of GIS and 

spatial regression analysis. 53

Marshall, C.H., Pielke, R.A., Sr., Steyaert, L.T., and Willard, D.A., 2004, The impact of anthropogenic 

land-cover change on the Florida peninsula sea breezes and warm season sensible weather. 53

Moritz, M.A., Keeley, J.E., Johnson, E.A., and Schaffner, A.A., 2004, Testing a basic assumption of 

shrubland fire management—How important is fuel age? 51
38

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Stoms, D.M., 1994, Scale dependence of species richness maps. 51

Gitlin, A.R., Sthultz, C.M., Bowker, M.A., Stumpf, Stacy, Paxton, K.L., Kennedy, Karla, Munoz, 

Axhel, Bailey, J.K., and Whithan, T.G., 2006, Mortality gradients within and among dominant plant 

populations as barometers of ecosystem change during extreme drought.

49

Rubbo, M.J., and Kiesecker, J.M., 2005, Amphibian breeding distribution in an urbanized landscape. 49

Guo, Qinghua, Kelly, Maggi, and Graham, C.H., 2005, Support vector machines for predicting 

distribution of Sudden Oak Death in California. 47

Naugle, D.E., Higgins, K.F., Nusser, S.M., and Johnson, W.C., 1999, Scale dependent habitat use in 

three species of prairie wetland birds. 46

Huryn, A.D., Butz Huryn, V.M., Arbuckle, C.J., and Tsomides, Leonidas, 2002, Catchment land-use, 

macroinvertebrates and detrius processing in headwater streams—Taxonomic richness versus function. 43

Coops, N.C., and Waring, R.H., 2001b, The use of multiscale remote sensing imagery to derive regional 

estimates of forest growth capacity using 3–PGS. 41

Frescino, T.S., Edwards, T.C., and Moisen, G.G., 2001, Modeling spatially explicit forest structural 

attributes using generalized additive models. 41

Meentemeyer, Ross; Rizzo, David; Mark, Walter; and Lotz, Elizabeth, 2004, Mapping the risk of 

establishment and spread of Sudden Oak Death in California. 41

Hubbard, M.W., Danielson, B.J., and Schmitz, R.A., 2000, Factors influencing the location of deervehicle accidents in Iowa. 39

Carroll, Carlos, Phillips, M.K., Schumaker, N.H., and Smith, D.W., 2003, Impacts of landscape change 

on wolf restoration success—Planning a reintroduction program based on static and dynamic spatial 

models.

38

Naugle, D.E., Johnson, R.R., Estey, M.E., and Higgins, K.F., 2001, A landscape approach to conserving 

wetland bird habitat in the prairie pothole region of eastern South Dakota. 38

Westerink, J.J., Luettich, R.A., Feyen, J.C., Atkinson, J.H., Dawson, Clint, Roberts, H.J., Powell, M.D., 

Dunion, J.P., Kubatko, E.J., and Pourtaheri, Hasan, 2008, A basin- to channel-scale unstructured grid 

hurricane storm surge model applied to southern Louisiana.

37

Cushman, S.A., McGarigal, Kevin, and Neel, M.C., 2008, Parsimony in landscape metrics—Strength, 

universality, and consistency. 36

Wardlow, B.D., and Egbert, S.L., 2008, Large-area crop mapping using time-series MODIS 250 m

NDVI data—An assessment for the U.S. central Great Plains. 34

Borak, J.S., and Strahler, A.H., 1999, Feature selection and land cover classification of a MODIS-like 

data set for a semiarid environment. 33

DellaSala, D.A., Staus, N.L., Strittholt, J.R., Hackman, Arlin, and Iacobelli, Antonio, 2001, An updated 

protected areas database for the United States and Canada. 33

Fraser, R.H., 1998, Vertebrate species richness at the mesoscale—Relative roles of energy and 

heterogeneity. 33

Lomolino, M.V., and Smith, G.A., 2001, Dynamic biogeography of prairie dog (Cynomys ludovicianus) 

towns near the edge of their range. 33
39

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Beard, K.H., Hengartner, Nicolas, and Skelly, D.K., 1999, Effectiveness of predicting breeding bird 

distributions using probabilistic models. 32

Coops, N.C., and Waring, R.H., 2001a, Assessing forest growth across southwestern Oregon under a 

range of current and future global change scenarios using a process model, 3–PG. 32

Root, K.V., Akçakaya, H.R., and Ginzburg, Lev, 2003, A multispecies approach to ecological valuation 

and conservation. 32

Vandergast, A.G., Bohonak, A.J., Weissman, D.B., and Fisher, R.N., 2007, Understanding the genetic 

effects of recent habitat fragmentation in the context of evolutionary history—Phylogeography and 

landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera–Stenopelmatidae–

stenopelmatus).

32

Boone, J.D., McGwire, K.C., Otteson, E.W., Debaca, R.S., Kuhn, E.A., Villard, Pascal, Brussard, P.F., 

and St. Jeor, S.C., 2000, Remote sensing and geographic information systems—Charting sin nombre 

virus infections in deer mice.

31

Cleland, D.T., Crow, T.R., Saunders, S.C., Dickmann, D.I., Maclean, A.L., Jordan, J.K., Watson, R.L., 

Sloan, A.M., and Brosofske, K.D., 2004, Characterizing historical and modern fire regimes in Michigan 

(USA)—A landscape ecosystem approach.

31

Curnutt, J.L., Comiskey, Jane, Nott, M.P., and Gross, L.J., 2000, Landscape-based spatially explicit 

species index models for Everglades restoration. 31

Isaak, D.J., and Hubert, W.A., 2001a, A hypothesis about factors that affect maximum summer stream 

temperatures across montane landscapes. 31

Peterson, A.T., and Kluza, D.A., 2003, New distributional modelling approaches for gap analysis. 31

Theobald, D.M., 2003, Targeting conservation action through assessment of protection and exurban 

threats. 31

Stoms, D.M., 2000, GAP management status and regional indicators of threats to biodiversity. 30

Wolter, P.T., Johnston, C.A., and Niemi, G.J., 2006, Land use land cover change in the U.S. Great 

Lakes Basin 1992 to 2001. 30

Parmenter, A.W.; Hansen, Andrew; Kennedy, R.E.; Cohen, Warren; Langner, Ute; Lawrence, Rick;

Maxwell, Bruce; Gallant, Alisa; and Aspinall, Richard, 2003, Land use and land cover change in the 

Greater Yellowstone ecosystem—1975–1995.

28

Ackerman, Drew, and Schiff, Kenneth, 2003, Modeling storm water mass emissions to the southern 

California bight. 27

Sowa, S.P., Annis, Gust, Morey, M.E., and Diamond, D.D., 2007, A gap analysis and comprehensive 

conservation strategy for riverine ecosystems of Missouri. 27

Fairbanks, D.H.K., and McGwire, K.C., 2004, Patterns of floristic richness in vegetation communities of 

California—Regional scale analysis with multi-temporal NDVI. 26

Franklin, Janet, 2002, Enhancing a regional vegetation map with predictive models of dominant plant 

species in chaparral. 26

Staus, N.L., Strittholt, J.R., DellaSala, D.A., and Robinson, Rob, 2002, Rate and pattern of forest 

disturbance in the Klamath-Siskiyou ecoregion, USA, between 1972 and 1992. 26
40

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Brooks, R.P., Wardrop, D.H., and Bishop, J.A., 2004, Assessing wetland condition on a watershed basis 

in the Mid-Atlantic region using synoptic land-cover maps. 24

Davis, F.W., Stoms, D.M., and Andelman, Sandy, 1999, Systematic reserve selection in the USA—An 

example from the Columbia Plateau ecoregion. 24

Feist, B.E., Steel, E.A., Pess, G.R., and Bilby, R.E., 2003, The influence of scale on salmon habitat 

restoration priorities. 24

Germino, M.J., Reiners, W.A., Blasko, B.J., McLeod, Donald, and Bastian, C.T., 2001, Estimating 

visual properties of Rocky Mountain landscapes using GIS. 24

Groves, C.R., Kutner, L.S., Stoms, D.M., Murray, M.P., Scott, J.M., Schafale, Michael, Weakley, A.S., 

and Pressey, R.L., 2000, Owning up to our responsibilities—Who owns lands important for biodiversity. 24

Leu, Matthias, Hanser, S.E., and Knick, S.T., 2008b, The human footprint in the West—A large-scale 

analysis of anthropogenic impacts. 24

Wright, R.G., MacCracken, J.G., and Hall, Joel, 1994, An ecological evaluation of proposed new 

conservation areas in Idaho—Evaluating proposed Idaho national parks. 24

Crist, P.J., Kohley, T.W., and Oakleaf, John, 2000, Assessing land-use impacts on biodiversity using an 

expert systems tool. 23

Gillespie, T.W., 2005, Predicting woody-plant species richness in tropical dry forests—A case study 

from south Florida, USA. 23

Haight, R.G., Cleland, D.T., Hammer, R.B., Radeloff, V.C., and Rupp, T.S., 2004, Assessing fire risk in 

the wildland-urban interface. 23

Pratt, P.D., Slone, D.H., Rayamajhi, M.B., Van, T.K., and Center, T.D., 2003, Geographic distribution 

and dispersal rate of Oxyops vitiosa (Coleoptera–Curculionidae), a biological control agent of the 

invasive tree Melaleuca quinquenervia in south Florida.

23

Stoms, D.M., and Hargrove, W.W., 2000, Potential NDVI as a baseline for monitoring ecosystem 

functioning. 23

Shriner, S.A., Wilson, K.R., and Flather, C.H., 2006, Reserve networks based on richness hotspots and 

representation vary with scale. 22

Williams, J.W., Seabloom, E.W., Slayback, Daniel, Stoms, D.M., and Viers, J.H., 2005, Anthropogenic 

impacts upon plant species richness and net primary productivity in California. 22

Wright, R.G., Murray, M.P., and Merrill, Troy, 1998, Ecoregions as a level of ecological analysis. 22

Adjemian, J.C.Z., Girvetz, E.H., Beckett, Laurel, and Foley, J.E., 2006, Analysis of genetic algorithm 

for rule-set production (GARP) modeling approach for predicting distributions of fleas implicated as 

vectors of plague, Yersinia pestis, in California.

21

Eisen, R.J., Lane, R.S., Fritz, C.L., Eisen, Lars, 2006, Spatial patterns of Lyme disease risk in California 

based on disease incidence data and modeling of vector-tick exposure. 21

Peterson, E.B., 2005, Estimating cover of an invasive grass (Bromus tectorum) using tobit regression 

and phenology derived from two dates of Landsat ETM+ data. 21

Bajema, R.A., and Lima, S.L., 2001, Landscape-level analyses of Henslow's Sparrow (Ammodramus 

henslowii) abundance in reclaimed coal mine grasslands. 20
41

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Bajema, R.A., DeVault, T.L., Scott, P.E., and Lima, S.L., 2001, Reclaimed coal mine grasslands and 

their significance for Henslow's Sparrows in the American Midwest. 20

Bond, J. E., Beamer, D.A., Lamb, T., and Hedin, M., 2006, Combining genetic and geospatial analyses 

to infer population extinction in mygalomorph spiders endemic to the Los Angeles region. 20

Dietz, R.W., and Czech, Brian, 2005, Conservation deficits for the continental United States—An 

ecosystem gap analysis. 20

Kelley, Chris; Garson, Justin; Aggarwal, Anshu; and Sarkar, Sahotra, 2002, Place prioritization for 

biodiversity reserve network design—A comparison of the SITES and ResNet software packages for 

coverage and efficiency.

20

Driese, K.L., Reiners, W.A., Merrill, E.H., and Gerow, K.G., 1997, A digital land cover map of 

Wyoming, USA—A tool for vegetation analysis. 19

Grand, Joanna, Buonaccorsi, John, Cushman, S.A., Griffin, C.R., and Neel, M.C., 2004, A multiscale 

landscape approach to predicting bird and moth rarity hotspots in a threatened pitch pine-scrub oak 

community.

19

Walker, Richard, and Craighead, Lance, 1997, Analyzing wildlife movement corridors in Montana using 

GIS. 19

Westrick, K.J., Storck, Pascal, and Mass, C.F., 2002, Description and evaluation of a 

hydrometeorological forecast system for mountainous watersheds. 19

Murphy, H.T., and Lovett-Doust, Jon, 2007, Accounting for regional niche variation in habitat 

suitability models. 18

Rodman, L.C., and Meentemeyer, R.K., 2006, A geographic analysis of wind turbine placement in 

northern California. 18

Eisen, R.J.; Enscore, R.E.; Biggerstaff, B.J.; Reynolds, P.J.; Ettestad, Paul; Brown, Ted; Pape, John; 

Tanda, Dale; Levy, C.E.; Engelthaler, D.M.; Cheek, James; Bueno, Rudy, Jr.; Targhetta, Joseph; 

Montenieri, J.A.; and Gage, K.L., 2007, Human plague in the southwestern United States, 1957–2004—

Spatial models of elevated risk of human exposure to Yersinia pestis.

17

Gustafson, E.J., Knutson, M.G., Niemi, G.J., and Friberg, Mary, 2002, Evaluation of spatial models to 

predict vulnerability of forest birds to brood parasitism by cowbirds. 17

Secchi, Silvia; Tyndall, John; Schulte, L.A.; and Asbjornsen, Heidi, 2008, High crop prices and 

conservation. 17

Wells, M.L.; O'Leary, J.F.; Franklin, Janet; Michaelsen, Joel; and McKinsey, D.E., 2004, Variations in a 

regional fire regime related to vegetation type in San Diego County, California (USA). 17

White, Denis, Preston, E.M., Freemark, K.E., and Kiester, A.R., 1999, A hierarchical framework for 

conserving biodiversity. 17

DiMauro, Danielle, and Hunter, M.L., Jr., 2002, Reproduction of amphibians in natural and 

anthropogenic temporary pools in managed forests. 16

Millar, C.I., King, J.C., Westfall, R.D., Alden, H.A., and Delany, D.L., 2006, Late holocene forest 

dynamics, volcanism, and climate change at Whitewing Mountain and San Joaquin Ridge, Mono 

County, Sierra Nevada, CA, USA.

16
42

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Miller, S.J., Wardrop, D.H., Mahaney, W.M., and Brooks, R.P., 2006, A plant-based index of biotic 

integrity (IBI) for headwater wetlands in central Pennsylvania. 16

Naugle, D.E., Higgins, K.F., Estey, M.E., Johnson, R.R., and Nusser, S.M., 2000, Local and landscapelevel factors influencing black tern habitat suitability. 16

Rao, Mahesh; Fan, Guoliang; Thomas, Johnson; Cherian, Ginto; Chudiwale, Varun; and Awawdeh, 

Muheeb, 2007, A web-based GIS decision support system for managing and planning USDA's 

conservation reserve program (CRP).

16

Rumsey, David, and Williams, Meredith, 2002, Historical maps in GIS. 16

Stoms, D.M., Davis, F.W., Driese, K.L., Cassidy, K.M., and Murray, M.P., 1998, Gap analysis of the 

vegetation of the intermountain semi-desert ecoregion. 16

Witty, J.H., Graham, R.C., Hubbert, K.R., Doolittle, J.A., and Wald, J.A., 2003, Contributions of water 

supply from the weathered bedrock zone to forest soil quality. 16

Dees, C.S., Clark, J.D., and Van Manen, F.T., 2001, Florida panther habitat use in response to 

prescribed fire. 15

Janis, M.W., and Clark, J.D., 2002, Responses of Florida panthers to recreational deer and hog hunting. 15

Jovan, Sarah, and McCune, Bruce, 2005, Air-quality bioindication in the greater central valley of 

California, with epiphytic macrolichen communities. 15

Logan, J.A., Regniere, J., Gray, D.R., and Munson, A.S., 2007, Risk assessment in the face of a 

changing environment—Gypsy moth and climate change in Utah. 15

Murray, L.D., and Best, L.B., 2003, Short-term bird response to harvesting switchgrass for biomass in 

Iowa. 15

Sullivan, T.J., Fernandez, I.J., Herlihy, A.T., Driscoll, C.T., McDonnell, T.C., Nowicki, N.A., Snyder, 

K.U., and Sutherland, J.W., 2006, Acid-base characteristics of soils in the Adirondack Mountains, New 

York.

15

Zhan, Xiaoyong, and Huang, M.L., 2004, ArcCN–Runoff—An ArcGIS tool for generating curve 

number and runoff maps. 15

Dauwalter, D.C., and Pert, E.J., 2003, Electrofishing effort and fish species richness and relative 

abundance in Ozark highland streams of Arkansas. 14

Hunter, J.C., Willett, K.B., McCoy, M.C., Quinn, J.F., and Keller, K.E., 1999, Prospects for 

preservation and restoration of riparian forests in the Sacramento Valley, California, USA. 14

Menking, K.M., Anderson, R.Y., Shafike, N.G, Syed, K.H., and Allen, B.D., 2004, Wetter or colder 

during the Last Glacial Maximum?—Revisiting the pluvial lake question in southwestern North

America.

14

Pitman, J.C., Hagen, C.A., Robel, R.J., Loughin, T.M., and Applegate, R.D., 2005, Location and success 

of lesser prairie-chicken nests in relation to vegetation and human disturbance. 14

Eisen, R.J.; Reynolds, P.J.; Ettestad, Paul; Brown, Ted; Enscore, R.E.; Biggerstaff, B.J.; Cheek, James;

Bueno, Rudy; Targhetta, Joseph; Montenieri, J.A.; and Gage, K.L., 2007, Residence-linked human 

plague in New Mexico—A habitat-suitability model.

13
43

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Grivet, Delphine, Sork, V.L., Westfall, R.D., and Davis, F.W., 2008, Conserving the evolutionary 

potential of California valley oak (Quercus lobata Née)—A multivariate genetic approach to 

conservation planning.

13

Merrill, Troy, Wright, R.G., and Scott, J.M., 1995, Using ecological criteria to evaluate wilderness 

planning options in Idaho. 13

Morrison, J.L., and Humphrey, S.R., 2001, Conservation value of private lands for Crested Caracaras in 

Florida. 13

Thogmartin, W.E., 1999, Landscape attributes and nest-site selection in wild turkeys. 13

Triant, D.A., Pace, R.M., and Stine, Michael, 2004, Abundance, genetic diversity, and conservation of 

Louisiana black bears (Ursus americanus luteolus) as detected through noninvasive sampling. 13

Wright, R.G., Scott, J.M., Mann, Shannon, and Murray, Michael, 2001, Identifying unprotected and 

potentially at risk plant communities in the Western USA. 13

Clinton, N.E., Gong, Peng, and Scott, Klaus, 2006, Quantification of pollutants emitted from very large 

wildland fires in southern California, USA. 12

Crist, M.R., Wilmer, Bo, and Aplet, G.H., 2005, Assessing the value of roadless areas in a conservation 

reserve strategy—Biodiversity and landscape connectivity in the northern Rockies. 12

Eisen, R.J.; Glass, G.E.; Eisen, Lars; Cheek, James; Enscore, R.E.; Ettestad, Paul; and Gage, K.L., 2007, 

A spatial model of shared risk for plague and hantavirus pulmonary syndrome in the southwestern 

United States.

12

Frizzelle, B.G., and Moody, Aaron, 2001, Mapping continuous distributions of land cover—A 

comparison of maximum-likelihood estimation and artificial neural networks. 12

Jiang, Hong, Strittholt, J.R., Frost, P.A., and Slosser, N.C., 2004, The classification of late seral forests 

in the Pacific Northwest, USA, using Landsat ETM+ imagery. 12

Sloat, M.R., Shepard, B.B., White, R.G., and Carson, Steve, 2005, Influence of stream temperature on 

the spatial distribution of westslope cutthroat trout growth potential within the Madison River basin, 

Montana.

12

Underwood, E.C., Klaumeyer, K.R., Cox, R.L., Busby, S.M., Morrison, S.A., and Shaw, M.R., 2009, 

Expanding the global network of protected areas to save the imperiled Mediterranean biome. 12

Pu, R., Gong, P., Li, Z., and Scarborough, J., 2004, A dynamic algorithm for wildfire mapping with 

NOAA/AVHRR data. 11

Scott, K.I., and Benjamin, M.T., 2003, Development of a biogenic volatile organic compounds emission 

inventory for the SCOS97–NARSTO domain. 11

Stralberg, Diana; Jongsomjit, Dennis; Howell, C.A.; Snyder, M.A.; Alexander, J.D.; Wiens, J.A.; and 

Root, T.L., 2009, Re-shuffling of species with climate disruption—A no-analog future for California 

birds?

11

Thogmartin, W.E., and Schaeffer, B.A., 2000, Landscape attributes associated with mortality events of 

wild turkeys in Arkansas. 11

Bassett, S.D., and Edwards, T.C., Jr., 2003, Effect of different sampling schemes on the spatial 

placement of conservation reserves in Utah, USA. 10
44

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Bradley, B.A., and Mustard, J.F., 2008, Comparison of phenology trends by land cover class—A case 

study in the Great Basin, USA. 10

Bunya, S., Dietrich, J.C., Westerink, J.J., Ebersole, B.A., Smith, J.M., Atkinson, J.H., Jensen, R., Resio, 

D.T., Luettich, R.A., Dawson, C., Cardone, V.J., Cox, A.T., Powell, M.D., Westerink, H.J., and Roberts, 

H.J., 2010, A high-resolution coupled riverine flow, tide, wind, wind wave, and storm surge model for 

southern Louisiana and Mississippi—Part I–model development and validation.

10

Clark, F.S., and Slusher, R.B., 2000, Using spatial analysis to drive reserve design—A case study of a 

national wildlife refuge in Indiana and Illinois (USA). 10

Didier, K.A., and Porter, W.F., 1999, Large-scale assessment of potential habitat to restore elk to New 

York State. 10

Ford, T.B., Winslow, D.E., Whitehead, D.R., and Koukol, M.A., 2001, Reproductive success of forestdependent songbirds near an agricultural corridor in south-central Indiana. 10

Laurent, E.J.; Shi, Haijin; Gatziolix, Demetrios; LeBouton, J.P.; Walters, M.B.; and Liu, Jianguo, 2005, 

Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns. 10

Mau-Crimmins, Theresa, de Steiguer, J.E., and Dennis, Donald, 2005, AHP as a means for improving 

public participation—A pre-post experiment with university students. 10

McClain, B.J., and Porter, W.F., 2000, Using satellite imagery to assess large-scale habitat

characteristics of Adirondack Park, New York, USA. 10

Peterson, A.T., 2005, Kansas Gap Analysis—The importance of validating distributional models before 

using them. 10

Pyke, C.R., 2005a, Assessing climate change impacts on vernal pool ecosystems and endemic 

branchiopods. 10

Rasmussen, Craig, 2006, Distribution of soil organic and inorganic carbon pools by biome and soil taxa 

in Arizona. 10

Singleton, P.H., Gaines, W.L., and Lehmkuhl, J.F., 2004, Landscape permeability for grizzly bear 

movements in Washington and southwestern British Columbia. 10

Thorne, J.H., Cameron, Dick, and Quinn, J.F., 2006, A conservation design for the central coast of 

California and the evaluation of mountain lion as an umbrella species. 10

Wardlow, B.D., and Egbert, S.L., 2003, A state-level comparative analysis of the GAP and NLCD landcover data sets. 10

Allen, C.R., Johnson, A.R., Parris, Leslie, 2006, A framework for spatial risk assessments—Potential 

impacts of nonindigenous invasive species on native species. 9 

Fuller, M.M., Gross, L.J., Duke-Sylvester, S.M., and Palmer, Mark, 2008, Testing the robustness of 

management decisions to uncertainty—Everglades restoration scenarios. 9 

Gerrard, R.A., Church, R.L., Stoms, D.M., and Davis, F.W., 1997, Selecting conservation reserves using 

species-covering models—Adapting the ARC/INFO GIS. 9 

Hansen, M.H., and Wendt, D.G., 2000, Using classified Landsat thematic mapper data for stratification 

in a statewide forest inventory. 9 

Hudak, P.F., and Sanmanee, Sirichai, 2003, Spatial patterns of nitrate, chloride, sulfate, and fluoride 

concentrations in the Woodbine aquifer of north-central Texas. 9 
45

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Isaak, D.J., and Hubert, W.A., 2001b, Production of stream habitat gradients by montane watersheds—

Hypothesis tests based on spatially explicit path analyses. 9 

Lee, Hyongki; Shum, C.K.; Yi, Yuchan; Ibaraki, Motomu; Kim, Jin-Woo; Braun, Alexander; Kuo, 

Chung-Yen.; and Lu, Zhong, 2009, Louisiana wetland water level monitoring using retracked 

TOPEX/POSEIDON altimetry.

9 

Lipow, S.R., Vance-Borland, Kenneth, St. Clair, J.B., Henderson, Jan, and McCain, Cindy, 2004, Gap 

analysis of conserved genetic resources for forest trees. 9 

Parajuli, P.B., Mankin, K.R., and Barnes, P.L., 2008, Applicability of targeting vegetative filter strips to 

abate fecal bacteria and sediment yield using SWAT. 9 

Parajuli, P.B., Mankin, K.R., and Barnes, P.L., 2009, Source specific fecal bacteria modeling using soil 

and water assessment tool model. 9 

Sarkar, Sahotra, and Garson, Justin, 2004, Multiple criterion synchronisation for conservation area 

network design—The use of non-dominated alternative sets. 9 

Sork, V.L.; Davis F.W.; Westfall, Robert; Flint, Alan; Ikegami, Makihiko; Wang, Hongfang; and Grivet, 

Delphine, 2010, Gene movement and genetic association with regional climate gradients in California 

valley oak (Quercus lobata Née) in the face of climate change.

9 

Woolmer, Gillian, Trombulak, S.C., Ray, J.C., Doran, P.J., Anderson, M.G., Baldwin, R.F., Morgan, 

Alexis, and Sanderson, E.W., 2008, Rescaling the human footprint—A tool for conservation planning at 

an ecoregional scale.

9 

Brenner, John; Paustian, Keith; Bluhm, George; Cipra, Jan; Easter, Mark; Killian, Kendrick; Schuler, 

Jill; Smith, Phil; and Williams, Steve, 2001, Phase I progress report—Quantifying the change in 

greenhouse gas emissions due to natural resource conservation practice application in Nebraska. 

8 

Mitchell, J.E., and Roberts, T.C., Jr., 1999, Distribution of pinyon-juniper in the Western United States. 8

Monahan, W.B., and Koenig, W.D., 2006, Estimating the potential effects of Sudden Oak Death on oakdependent birds. 8 

Norris, J.R., Jackson, S.T., and Betancourt, J.L., 2006, Classification tree and minimum-volume 

ellipsoid analyses of the distribution of ponderosa pine in the Western USA. 8 

Rabe, M.J., Rosenstock, S.S., and Nielsen, D.I., 2005, Feral africanized honey bees (Apis mellifere) in 

Sonoran desert habitats of southwestern Arizona. 8 

Daggett, Steve; Boule', Marc; Bernert, J.A.; Eilers, J.M.; Blok, Elaine; Peters, Dennis; and Morlan, J.C., 

1998, Wetland and land use change in the Willamette Valley, Oregon—1982–1994, Volume 1, Final 

report.

7 

Ekness, Paul, and Randhir, Timothy, 2007, Effects of riparian areas, stream order, and land use 

disturbance on watershed-scale habitat potential—An ecohydrological approach to policy. 7 

Hoffman, J.D., Narumalani, Sunil, Mishra, D.R., Merani, Paul, and Wilson, R.G., 2008, Predicting 

potential occurrence and spread of invasive plant species along the North Platte River, Nebraska. 7 

Kocovsky, P.M., and Carline, R.F., 2006, Influence of landscape-scale factors in limiting brook trout 

populations in Pennsylvania streams. 7 

Larson, B.D., and Sengupta, R.R., 2004, A spatial decision support system to identify species-specific 

critical habitats based on size and accessibility using US GAP data. 7 
46

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Menzel, J.M., Ford, W.M., Edwards, J.W., Ceperley, L.J., 2006, A habitat model for the Virginia 

northern flying squirrel (Glaucomys sabrinus fuscus) in the central Appalachian Mountains. 7 

Moritz, M.A., and Odion, D.C., 2005, Examining the strength and possible causes of the relationship 

between fire history and Sudden Oak Death. 7 

Parés-Ramos, I.K., Gould, W.A., and Aide, T.M., 2008, Agricultural abandonment, suburban growth, 

and forest expansion in Puerto Rico between 1991 and 2000. 7 

Rachlow, J.L., and Svancara, L.K., 2006, Prioritizing habitat for surveys of an uncommon mammal—A 

modeling approach applied to pygmy rabbits. 7 

Salmon, Olivia; Brunson, Mark; and Kuhns, Michael, 2006, Benefit-based audience segmentation—A 

tool for identifying nonindustrial private forest owner education needs. 7 

Snyder, S.A., Whitmore, J.H., Schneider, I.E., and Becker, D.R., 2008, Ecological criteria, participant 

preferences and location models—A GIS approach toward ATV trail planning. 7 

St-Louis, Véronique, Pidgeon, A.M., Clayton, M.K., Locke, B.A., Bash, Dallas, and Radeloff, V.C., 

2009, Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert 

of New Mexico.

7 

Beck, J.L., Reese, K.P., Connelly, J.W., and Lucia, M.B., 2006, Movements and survival of juvenile 

greater sage-grouse in southeastern Idaho. 6 

Dauwalter, D.C., and Jackson, J.R., 2004, A provisional fish index of biotic integrity for assessing 

Ouachita mountains streams in Arkansas, USA. 6 

Doren, R.F., Rutchey, Ken, and Welch, Roy, 1999, The Everglades—A perspective on the requirements 

and applications for vegetation map and database products. 6 

Fecske, D.M., Jenks, J.A., and Smith, V.J., 2002, Field evaluation of a habitat-relation model for the 

American marten. 6 

Huang, Cho-ying, Asner, G.P., Martin, R.E., Barger, N.N., and Neff, J.C., 2009, Multiscale analysis of 

tree cover and aboveground carbon stocks in pinyon-juniper woodland. 6 

King, D.T., and Michot, T.C., 2002, Distribution, abundance and habitat use of American white pelicans 

in the delta region of Mississippi and along the western Gulf of Mexico coast. 6 

Larrucea, E.S., and Brussard, P.F., 2008, Habitat selection and current distribution of the pygmy rabbit 

in Nevada and California, USA. 6 

Liu, Zhongwei, Volin, J.C., Owen, V.D., Pearlstine, L.G., Allen, J.R., Mazzotti, F.J., and Higer, A.L., 

2009, Validation and ecosystem applications of the EDEN water-surface model for the Florida 

Everglades.

6 

Moen, Ron, Burdett, C.L., and Niemi, G.J., 2008, Movement and habitat use of Canada lynx during 

denning in Minnesota. 6 

Pease, K.M.; Freedman, A.H.; Pollinger, J.P.; McCormack, J.E.; Buermann, Wolfgang; Rodzen, Jeff; 

Banks, Jim; Meredith, Erin; Bleich, V.C.; Schaefer, R.J.; Jones, Ken; and Wayne, R.K., 2009, 

Landscape genetics of California mule deer (Odocoileus hemionus)—the roles of ecological and 

historical factors in generating differentiation.

6 

Pyke, C.R., 2005b, Interactions between habitat loss and climate change—Implications for fairy shrimp 

in the central valley ecoregion of California, USA. 6 
47

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Shriver, D.M., and Randhir, T.O., 2006, Integrating stakeholder values with multiple attributes to 

quantify watershed performance. 6 

Strickland, B.K., and Demarais, Stephen, 2008, Influence of landscape composition and structure on 

antler size of white-tailed deer. 6 

Beckage, Brian; Comiskey, Jane; and Duke-Sylvester, Scott, 2005, Response to Maehr and Larkin—

Natural fire regimes in southern Florida. 5 

Bradley, B.A., 2010, Assessing ecosystem threats from global and regional change—Hierarchical 

modeling of risk to sagebrush ecosystems from climate change, land use and invasive species in Nevada, 

USA.

5 

Burns, K.S., Schoettle, A.W., Jacobi, W.R., and Mahalovich, M.F., 2008, Options for the management 

of white pine blister rust in the Rocky Mountain region. 5 

Buyantuyev, A., and Wu, J., 2009, Urbanization alters spatiotemporal patterns of ecosystem primary 

production—A case study of the Phoenix metropolitan region, USA. 5 

Dauwalter, D.C., Pert, E.J., and Keith, W.E., 2003, An index of biotic integrity for fish assemblages in 

Ozark highland streams of Arkansas. 5 

Dean, D.J., Wilson, K.R., and Flather, C.H., 1997, Spatial error analysis of species richness for a Gap 

Analysis map. 5 

Efroymson, R.A.; Dale, V.H.; Baskaran, L.M.; Chang, Michael; Aldridge, Matthew; and Berry, M.W., 

2005, Planning transboundary ecological risk assessments at military installations. 5 

Goheen, E.M., Hansen, E., Kanaskie, A., Osterbauer, N., Parke, J., Pscheidt, J., and Chastagner, G., 

2006, Sudden Oak Death and Phytophthora ramorum—A guide for forest managers, Christmas tree 

growers, and forest-tree nursery operators in Oregon and Washington.

5 

Gori, D.F., and Enquist, C.A.F., 2003, An assessment of the spatial extent and condition of grasslands in 

central and southern Arizona, southwestern New Mexico and northern Mexico. 5 

Heathman, G.C., Larose, M., and Ascough, J.C., 2009, Soil and water assessment tool evaluation of soil 

and land use geographic information system data sets on simulated stream flow. 5 

Hopton, M.E., and Mayer, A.L., 2006, Using self-organizing maps to explore patterns in species 

richness and protection. 5 

Huang, Cho-ying, and Geiger, E.L., 2008, Climate anomalies provide opportunities for large-scale 

mapping of non-native plant abundance in desert grasslands. 5 

Jacobs, B.F., Romme, W.H., and Allen, C.D., 2008, Mapping old versus young piñon–juniper stands 

with a predictive topo-climatic model in north-central New Mexico, USA. 5 

Kanda, L.L., Fuller, T.K., Sievert, P.R., and Kellogg, R.L., 2009, Seasonal source-sink dynamics at the 

edge of a species' range. 5 

Lindell, C.A., McCullough, D.G., Cappaert, David, Apostolou, N.M., and Roth, M.B., 2008, Factors 

influencing woodpecker predation on emerald ash borer. 5 

Love, J.W., and May, E.B., 2007, Relationships between fish assemblage structure and selected 

environmental factors in Maryland's coastal bays. 5 

Martin, August, Gunter, J.T., and Regens, J.L., 2003, Estimating erosion in a riverine watershed—

Bayou Liberty-Tchefuncta river in Louisiana. 5 
48

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Maxwell, S.K., Hoffer, R.M. and Chapman, P.L., 2002, AVHRR composite period selection for land 

cover classification. 5 

Menking, K.M., Syed, K.H., Anderson, R.Y., Shafike, N.G., and Arnold, J.G., 2003, Model estimates of 

runoff in the closed, semiarid Estancia basin, central New Mexico, USA. 5 

Roth, J.E., Kelly, J.P., Sydeman, W.J., and Colwell, M.A., 2004, Sex differences in space use of 

breeding common ravens in western Marin County, California. 5 

Shirk, A.J., Wallin, D.O., Cushman, S.A., Rice, C.G., and Warheit, K.I., 2010, Inferring landscape 

effects on gene flow—A new model selection framework. 5 

Stoms, D.M., Bueno, M.J., Davis, F.W., Cassidy, K.M., Driese, K.L., and Kagan, J.S., 1998, Mapguided classification of regional land cover with multi-temporal AVHRR data. 5 

Teels, B.M., Rewa, C.A., and Myers, John, 2006, Aquatic condition response to riparian buffer 

establishment. 5 

Wamsley, T.V., Cialone, M.A., Smith, J.M., Atkinson, J.H., and Rosati, J.D., 2010, The potential of 

wetlands in reducing storm surge. 5 

Alfieri, J.G.; Niyogi, Dev; LeMone, M.A.; Chen, Fei; and Fall, Souleymane, 2007, A simple 

reclassification method for correcting uncertainty in land use/land cover data sets used with land surface 

models.

4 

Bauer, M.E., Loeffelholz, Brian, and Wilson, Bruce, 2005, Estimation, mapping, and change analysis of 

impervious surface area by Landsat remote sensing. 4 

Bauer, M.E., Loeffelholz, B.C., Wilson, Bruce, 2007, Estimating and mapping impervious surface area 

by regression analysis of Landsat imagery. 4 

Chen, Y.D., McCutcheon, S.C., Carsel, R.F., Donigian, A.S., Jr., Cannell, J.R., and Craig, J.P., 1995, 

Validation of HSPF for the water balance simulation of the Upper Grande Ronde watershed, Oregon, 

USA.

4 

Diamond, D.D., True, C.D., Gordon, T.M., Sowa, S.P., Foster, W.E., and Jones, B.K., 2005, Influence 

of targets and assessment region size on perceived conservation priorities. 4 

Falzarano, S.R., and Thomas, K.A., 2004, Fuzzy set and spatial analysis techniques for evaluating

thematic accuracy of a land-cover map. 4 

Greenwood, D.L., and Weisberg, P.J., 2009, GIS-based modeling of pinyon-juniper woodland structure 

in the Great Basin. 4 

Gregory, C.J., Carthy, R.R., and Pearlstine, L.G., 2006, Survey and monitoring of species at risk at 

Camp Blanding training site, northeastern Florida. 4 

Howell, J.E., Peterson, J.T., and Conroy, M.J., 2008, Building hierarchical models of avian distributions 

for the state of Georgia. 4 

Kim, Jin-Woo; Lu, Zhong; Lee, Hyongki; Shum, C.K.; Swarzenski, C.M.; Doyle, T.W.; and Baek, 

Sang-Ho, 2009, Integrated analysis of PALSAR/Radarsat-1 InSAR and ENVISAT altimeter data for 

mapping of absolute water level changes in Louisiana wetlands.

4 

Mahan, C.G., and O'Connell, T.J., 2005, Small mammal use of suburban and urban parks in central 

Pennsylvania. 4 
49

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Mattice, J.A., Brauning, D.W., and Diefenbach, D.R., 2005, Abundance of grassland sparrows on 

reclaimed surface mines in western Pennsylvania. 4 

Riitters, K.H., Coulston, J.W., and Wickham, J.D., 2003, Localizing national fragmentation statistics 

with forest type maps. 4 

Roth, T.C., II, Vetter, W.E., and Lima, S.L., 2008, Spatial ecology of winting accipiter hawks—Home 

range, habitat use, and the influence of bird feeders. 4 

Sacks, B.N., Chomel, B.B., and Kasten, R.W., 2004, Modeling the distribution and abundance of the 

non-native parasite, canine heartworm, in California coyotes. 4 

Stein, B.A.; Scott, Cameron; and Benton, Nancy, 2008, Federal lands and endangered species—The role 

of military and other federal lands in sustaining biodiversity. 4 

Taft, O.W, Haig, S.M., and Kiilsgaard, Chris, 2004, Use of radar remote sensing (RADARSAT) to map 

winter wetland habitat for shorebirds in an agricultural landscape. 4 

Telesco, R.L., Van Manen, F.T., Clark, J.D., and Cartwright, M.E., 2007, Identifying sites for elk 

restoration in Arkansas. 4 

Thorne, J.H., Morgan, B.J., and Kennedy, J.A., 2008, Vegetation change over sixty years in the central 

Sierra Nevada, California, USA. 4 

Trapp, J.R., 2004, Wolf den site selection and characteristics in the northern Rocky Mountains—A 

multi-scale analysis. 4 

Wade, A.A., and Theobald, D.M., 2010, Residential development encroachment on U.S. protected areas. 4

Wang, Dali, Carr, Eric, Gross, L.J., and Berry, M.W., 2005, Toward ecosystem modeling on computing 

grids. 4 

Yanoff, Steven, and Muldavin, Esteban, 2008, Grassland–shrubland transformation and grazing—A

century-scale view of a northern Chihuahuan Desert grassland. 4 

Benjamin, M.T.; Winer, A.M.; Karlik, John; Campbell, Skip; Jackson, Bruce; and Lashgari, Ash, 1998, 

Assembling a biogenic hydrocarbon emissions inventory for the SCOS97–NARSTO modeling domain.

3 

Caratti, J.F., 2006, The LANDFIRE Prototype Project reference database. 3

Conway, C.J., and Kirkpatrick, Chris, 2007, Effect of forest fire suppression on buff-breasted 

flycatchers. 3 

Crowe, R.E., 1996, Use of gap analysis in regional planning in southern California. 3

Dilts, T.E., Sibold, J.S., and Biondi, Franco, 2009, A weights-of-evidence model for mapping the 

probability of fire occurrence in Lincoln County, Nevada. 3 

Gunter, J.T., Hodges, D.G., and Regens, J.L., 2005, Probability models for predicting local water quality 

regulations in the southern United States. 3 

Jones, J.W., and Price, S.D., 2007, Conceptual design of the Everglades Depth Estimation Network 

(EDEN) grid. 3 

Lennartz, Steven, 2005, Oregon forest land change mapping. 3

Manier, D.J., Hobbs, N.T., Theobald, D.M., Reich, R.M., Kalkhan, M.A., and Campbell, M.R., 2005, 

Canopy dynamics and human caused disturbance on a semi-arid landscape in the Rocky Mountains, 

USA.

3 
50

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Matocq, M.D., and Murphy, P.J., 2007, Fine-scale phenotypic change across a species transition zone in 

the genus Neotoma—Disentangling independent evolution from phylogenetic history. 3 

Nesslage, G.M., Maurer, B.A., and Gage, S.H., 2007, Gypsy moth response to landscape structure 

differs from neutral model predictions—Implications for invasion monitoring. 3 

Oleson, K.W., Driese, K.L., Maslanik, J.A., Emery, W.J., and Reiners, W.A., 1997, The sensitivity of a 

land surface parameterization scheme to the choice of remotely sensed land-cover datasets. 3 

Parajuli, P.B., Douglas-Mankin, K.R., Barnes, P.L., and Rossi, C.G., 2009, Fecal bacteria source 

characterization and sensitivity analysis of SWAT 2005. 3 

Peterson, D.L., Egbert, S.L., Price, K.P., and Martinko, E.A., 2004, Identifying historical and recent 

land-cover changes in Kansas using postclassification change detection techniques. 3 

Peterson, E.B., 2006, A map of invasive annual grasses in Nevada derived from multitemporal Landsat 

5 TM imagery. 3 

Ramsey, Elijah, III; Rangoonwala, Amina; Middleton, Beth; and Lu, Zhong, 2009, Satellite optical and 

radar data used to track wetland forest impact and short-term recovery from Hurricane Katrina. 3 

Rimmer, C.C., McFarland, K. P., Lambert, J.D., and Renfrew, R.B., 2004, Evaluating the use of 

Vermont ski areas by Bicknell's Thrush—Applications for Whiteface Mountain, New York. 3 

Sader, S.A., and Legaard, K.R., 2008, Inclusion of forest harvest legacies, forest type, and regeneration 

spatial patterns in updated forest maps—A comparison of mapping results. 3 

Schumacher, J.V., Redmond, R.L., Hart, M.M., and Jensen, M.E., 2000, Mapping patterns of human use 

and potential resource conflicts on public lands. 3 

Stoner, D.C., Rieth, W.R., Wolfe, M.L., Mecham, M.B., and Neville, Ann, 2008, Long-distance 

dispersal of a female cougar in a basin and range landscape. 3 

Storm, D.E., White, Michael, Smolen, M.D., and Zhang, Hailin, 2001, Modeling phosphorous loading 

for the Lake Eucha Basin—Final report. 3 

Thatcher, C.A., Van Manen, F.T., and Clark, J.D., 2009, A habitat assessment for Florida panther 

population expansion into central Florida. 3 

Theobald, D.M., Norman, J.B., and Sherburne, M.R., 2006, FunConn v1 user's manual—ArcGIS tools 

for functional connectivity modeling. 3 

Thompson, B.C., Hughes, M.A., and Anderson, M.C., 2001, Effects of including non-breeding bird 

species on predicted bird distributions for conservation planning in New Mexico. 3 

Thorne, J.H.; Kennedy, J.A.; Quinn, J.F.; McCoy, Michael; Keeler-Wolf, Todd; and Menke, John, 2004, 

A vegetation map of Napa County using the Manual of California Vegetation Classification and its 

comparison to other digital vegetation maps.

3 

van Leeuwen, W.J.D., 2008, Monitoring the effects of forest restoration treatments on post-fire 

vegetation recovery with MODIS multitemporal data. 3 

Volin, J.C.; Liu, Zhongwei; Higer, Aaron; Mazzotti, Frank; Owen, Dianne; Allen, Jenny; and Pearlstine, 

Leonard, 2008, Validation of a spatially continuous EDEN water-surface model for the Everglades, 

Florida.

3 
51

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Weber, T.C., 2007, Development and application of a statewide conservation network in Delaware, 

U.S.A. 3 

Allen, K.E., Bradley, R.D., Monk, R.R., Knyazhnitskiy, O.V., Parker, N.C., Schmidly, D.J., and Baker, 

R.J., 2001, Employment of geographic information systems for determining the accuracy of museum 

voucher specimen data.

2 

Barrow, Wylie, Jr.; Buler, J.; Couvillion, Brady; Diehl, Robb; Faulkner, Stephen; Moore, F.; and 

Randall, Lori, 2007, Broad-scale response of landbird migration to the immediate effects of Hurricane 

Katrina.

2 

Brady, L.M., Gray, Floyd, Wissler, C.A., and Guertin, D.P., 2001, Spatial variability of sediment 

erosion processes using GIS analysis within watersheds in a historically mined region, Patagonia 

Mountains, Arizona.

2 

Brinda, J.C., Stark, L.R., Shevock, J.R., and Spence, J.R., 2007, An annotated checklist of the 

bryophytes of Nevada, with notes on collecting history in the state. 2 

Cassidy, K.M., and Grue, C.E., 2000, The role of private and public lands in conservation of at-risk 

vertebrates in Washington State. 2 

Chung, Y.J., and Winer, A.M., 1999, Field assessment of the California Gap Analysis program database 

for San Diego county. 2 

Clark, J.D., Dobey, Steven, Masters, D.V., Scheick, B.K., Pelton, M.R., and Sunquist, M.E., 2005, 

American black bears and bee yard depredation at Okefenokee Swamp, Georgia. 2 

Cogan, Christopher, 1997, California biodiversity project—Application of ecological data to 

biodiversity analysis. 2 

Crist, M.R., Wilmer, Bo, 2002, Roadless areas—The missing link in conservation—An analysis of 

biodiversity and landscape connectivity in the northern Rockies. 2 

Diefenbach, D.R., Riegner, C.F., and Hardisky, T.S., 2000, Harvest and reporting rates of game-farm 

ring-necked pheasants. 2 

Doleman, W.H., 2005, Environmental constraints on forager mobility and the use of cultigens in 

southeastern Arizona and southern New Mexico. 2 

Dymond, R.L., Regmi, B., Lohani, V.K., and Dietz, R., 2004, Interdisciplinary web-enabled spatial 

decision support system for watershed management. 2 

Enwright, Nicholas, and Hudak, P.F., 2009, Spatial distribution of nitrate and related factors in the High 

Plains Aquifer, Texas. 2 

Fertig, Walter, and Thurston, Robert, 2003, Modeling the potential distribution of BLM sensitive and 

USFWS threatened and endangered plant species in Wyoming. 2 

Frescino, T.S., 1998, Development and validation of forest habitat models in the Uinta Mountains, Utah. 2

González, Grizelle, Gould, W.A., Hudak, A.T., and Hollingsworth, T.N., 2008, Decay of aspen 

(Populus tremuloides Michx.) wood in moist and dry boreal, temperate, and tropical forest fragments. 2 

Hanser, S.E., and Huntly, N.J., 2006, The biogeography of small mammals of fragmented sagebrushsteppe landscapes. 2 

Hoffman, J.D., and Choate, J.R., 2008, Distribution and status of the yellow-faced pocket gopher in 

Kansas. 2 
52

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Hoffman, J.D., Choate, J.R., and Channell, Rob, 2007, Effects of land use and soil texture on 

distributions of pocket gophers in Kansas. 2 

Huang, Chengquan, Homer, Collin, and Yang, Limin, 2003, Regional forest land cover characterisation 

using medium spatial resolution satellite data. 2 

Hudak, P.F., and Wachal, D.J., 2001, Oil production, agriculture and groundwater quality in the 

southeastern Gulf Coast Aquifer, Texas. 2 

Hunter, L.M., and Brehm, J.M., 2004, A qualitative examination of value orientations toward wildlife 

and biodiversity by rural residents of the intermountain region. 2 

Hychka, K.C., Wardrop, D.H., and Brooks, R.P., 2007, Enhancing a landscape assessment with 

intensive data—A case study in the Upper Juniata watershed. 2 

Jantz, Patrick, and Goetz, Scott, 2008, Using widely available geospatial data sets to assess the influence 

of roads and buffers on habitat core areas and connectivity. 2 

Karlik, J.F., Chung, Y.J., and Winer, A.M., 2003, Biogenic emission inventory development—Field 

assessment of the GAP vegetation database in California. 2 

Kirkpatrick, Chris, Conway, C.J., Hughes, K.M., and Devos, J.C., 2007, Probability of detecting bandtailed pigeons during call-broadcast versus auditory surveys. 2 

Kretser, H.E., Sullivan, P.J., and Knuth, B.A., 2008, Housing density as an indicator of spatial patterns 

of reported human-wildlife interactions in northern NewYork. 2 

Landis, J.D.; Monzon, J.P.; Reilly, Michael; and Cogan, Chris, 1998, Development and pilot application 

of the California Urban and Biodiversity Analysis (CURBA) model. 2 

Munson, A.B., and Delfino, J.J., 2007, Minimum wet-season flows and levels in southwest Florida 

rivers. 2 

Neely, Betsy; Comer, Pat; Moritz, Cherie; Lammert, Mary; Rondeau, Renee; Pague, Chris; Bell, Gary;

Copeland, Holly; Humke, John; Spackman, Susan; Schulz, Terri; Theobald, David; and Valutis, Laura, 

2001, Southern Rocky Mountains—An ecoregional assessment and conservation blueprint.

2 

Radwell, Andrea, 2000, Ecological integrity assessment of Ozark rivers to determine suitability for 

protective status. 2 

Scarborough, James; Clinton, Nicolas; Pu, Ruilang; and Gong, Peng, 2001, Creating a statewide 

spatially and temporally allocated wildfire and prescribed burn emission inventory using consistent 

emission factors.

2 

Theobald, D.M., Peterson, Nathan, and Romme, W.H., 2004, The Colorado vegetation model—Using 

national land cover data and ancillary spatial data to produce a high resolution, fine-classification map 

of Colorado.

2 

Toschik, P.C., Christman, M.C., Rattner, B.A., and Ottinger, M.A., 2006, Evaluation of osprey habitat 

suitability and interaction with contaminant exposure. 2 

Twilley, R.R.; Couvillion, B.R.; Hossain, Imtiaz; Kaiser, Carola; Owens, A.B.; Steyer, G.D.; and Visser, 

J.M., 2008, Coastal Louisiana ecosystem assessment and restoration program—The role of ecosystem 

forecasting in evaluating restoration planning in the Mississippi River Deltaic plain.

2 

Wall, S.S., and Berry, C.R., Jr., 2006, The importance of multiscale habitat relations and biotic 

associations to the conservation of an endangered fish species, the Topeka shiner. 2 
53

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Ward, Kathleen, and Juzwik, Jennifer, 2005, Change in the Minneapolis/St. Paul Metropolitan area oak 

forests from 1991 to 1998. 2 

Ward, Kathleen; Ostry, Michael; Venette, Robert; Palik, Brian; Hansen, Mark; Hatfield, Mark, 2009, 

Assessment of black ash (Fraxinus nigra) decline in Minnesota. 2 

Webb, E.B., Smith, L.M., Vrtiska, M.P., and Lagrange, T.G., 2010, Effects of local and landscape 

variables on wetland bird habitat use during migration through the Rainwater Basin. 2 

Wright, R.G., and Scott, J.M., 1996, Evaluating the ecological suitability of lands for parks and 

protected areas using gap analysis databases. 2 

Barker, C.M., Bolling, B.G., Moore, C.G., and Eisen, Lars, 2009, Relationship between distance from 

major larval habitats and abundance of adult mosquitoes in semiarid plains landscapes in Colorado. 1 

Borak, J.S, and Strahler, A.H., 1996, Feature selection using decision trees—An application for the 

MODIS land cover algorithm. 1 

Boykin, K.G., 2006, Multiscale analysis of habitat, vegetation change, and streamflow as ecological 

factors affecting population dynamics of Rana chiricahuensis. 1 

Brenner, John; Paustian, Keith; Buhm, George; Cipra, Jan; Easter, Mark; Foulk, Robin, Killian, 

Kendrick; Moore, Ron; Schuler, Jill; Smith, Phil; and Williams, Steve, 2002, Quantifying the change in 

greenhouse gas emissions due to natural resource conservation practice application in Nebraska.

1 

Briggs, N.A., and Sader, S.A., 2009, Tracking forest change and development using low-cost remote 

sensing imagery and GIS integration. 1 

Briskey, J.A., Schulz, K.J., Mosesso, J.P., Horwitz, L.R., and Cunningham, C.G., 2007, Environmental 

planning issues and a conceptual global assessment of undiscovered nonfuel mineral resources. 1 

Clinton, Nicholas; Scarborough, James; Tian, Yong; and Gong, Peng, 2003, A GIS based emissions 

estimation system for wildlife and prescribed burning. 1 

Dietz, R.W., 2000, The use of GIS for integrated watershed analysis—Integration of environmental 

models with GIS in the Upper Roanoake River Watershed. 1 

Dzialak, M.R., Carter, K.M., and Lacki, M.J., 2007, Perch site selection by reintroduced peregrine 

falcons Falco peregrinus. 1 

Enquist, C.A.F., and Gori, D.F., 2008, Application of an expert system approach for assessing grassland 

status in the U.S.–Mexico borderlands—Implications for conservation and management. 1 

Estrada-Peña, Agustín, 2009, Diluting the dilution effect—A spatial Lyme model provides evidence for 

the importance of habitat fragmentation with regard to the risk of infection. 1 

Fisher, H., Butcher, J., Creager, C., and Saucerman, S., 2004, Application of a linked water quality 

response model for establishing nutrient criteria for the California oak and chaparral sub-ecoregion. 1 

Fletcher, J.J., Eli, R.N., Strager, M.P., Sun, Qingyun, Churchill, J.B., Lamont, S.J., Galya, T.A., and 

Schaer, A.N., 2004, The watershed characterization and modeling system (WCMS)—Support tools for 

large watershed CHIA and NPDES analyses.

1 

Greenwald, D.N., and Bradley, Curtis, 2008, Assessing protection for imperiled species of Nevada, 

U.S.A.—Are species slipping through the cracks of existing protections? 1 
54

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Greenwood, D.L., 2006, Landscape analysis of tree mortality and pinyon-juniper woodland structure in 

the Great Basin. 1 

Hagen, C.A., Pitman, J.C., Robel, R.J., Loughin, T.M., and Applegate, R.D., 2007, Niche partitioning 

by lesser prairie-chicken Tympanuchus pallidicinctus and ring-necked pheasant Phasianus colchicus in 

southwestern Kansas.

1 

Hamazaki, Toshihide, Thompson, B.C., Locke, B.A., and Boykin, K.G., 2003, Analysis of ecological 

context for identifying vegetation and animal conservation planning foci—An example from the arid 

south-western USA.

1 

Heaton, J.S., Kiester, A.R., and Meyers, S.M., 2006, LizLand—A geomorphic approach to lizard habitat 

modeling in the Mojave Desert. 1 

Heller, J.A., Guertin, D.P., Miller, S.N., and Stone, J.J., 1999, GIS for watershed assessment—

Integrating spatial and tabular data to derive parameters for a hydrologic simulation model (ARDBSN). 1 

Hubbard, Brett, and Serfass, Tom, 2004, Assessing the distribution of reintroduced populations of river 

otters in Pennsylvania (USA)—Development of a landscape-level approach. 1 

Hunter, L.M., Beal, John, and Dickinson, Thomas, 2003, Integrating demographic and GAP analysis 

biodiversity data—Useful insight? 1 

Islas, C.G.R., 1996, A sensitivity test for species distribution models used for gap analysis in New 

Mexico. 1 

Jochimsen, D.M., 2005, Factors influencing the road mortality of snakes on the Upper Snake River 

Plain, Idaho. 1 

Kanda, L.L., Fuller, T.K., Sievert, P.R., and Friedland, K.D., 2005, Variation in winter microclimate and 

its potential influence on Virginia opossum (Didelphis virginiana) survival in Amherst, Massachusetts. 1 

Kocovsky, P.M., Ross, R.M., Dropkin, D.S., and Campbell, J.M., 2008, Linking landscapes and habitat 

suitability scores for diadromous fish restoration in the Susquehanna River Basin. 1 

Kostelnick, J.C., Peterson, D.L., Egbert, S.L., McNyset, K.M., and Cully, J.F., 2007, Ecological niche 

modeling of black-tailed prairie dog habitats in Kansas. 1 

Kunert, Kelly, 2005, A GIS approach to habitat restoration site selection and prioritization in the New 

York–New Jersey Harbor Estuary. 1 

Kunzmann, M.R., Rybak, Alexander, and Bennett, P.S., 2000, Yellow-billed cuckoo (Coccyzus 

americanus occidentalis) habitat identification using GPS and GIS based survey information. 1 

LaBram, J.A., Peck, A.E., and Allen, C.R., 2007, Monitoring-based assessment of Gap-analysis models. 1

Lipow, S.R., Vance-Borland, Kenneth, St. Clair, J.B., Henderson, J.A., and McCain, Cindy, 2007, In 

situ gene conservation of six conifers in western Washington and Oregon. 1 

Lopez, R.D., Nash, M.S., Heggem, D.T., and Ebert, D.W., 2008, Watershed vulnerability predictions for 

the Ozarks using landscape models. 1 

Martin, D.J., White, G.C., and Pusateri, F.M., 2007, Occupancy rates by swift foxes (Vulpes velox) in 

eastern Colorado. 1 

Martinuzzi, Sebastian, Vierling, L.A., Gould, W.A., Vierling, K.T., and Hudak, A.T., 2009, 

Incorporating remotely sensed tree canopy cover data into broad scale assessments of wildlife habitat 

distribution and conservation. 

1 
55

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Mawdsley, J.R., 2001a, Ecology, biogeography, and conservation of checkered beetles (Insecta: 

Coleoptera: Cleridae) in southeastern Arizona—A geographic information system (GIS) study. 1 

Moen, Ronald, Niemi, Gerald, and Burdett, C.L., 2008, Canada lynx in the Great Lakes region. 1

Monahan, W.B. and Koenig, W.D., 2007, Potential effects of Sudden Oak Death on the oak woodland 

bird community of coastal California. 1 

Morrison, P.H, Karl, J.W., Harma, K.J., Swope, Lindsey, Allen, T.K., and Becwar, Pamela, 2000, 

Assessment of summer 2000 wildfires—Landscape history, current condition and ownership. 1 

Murray, Michael, 2005, Our threatened timberlines—The plight of whitebark pine ecosystems. 1

O'Brien, C.S., Ockenfels, R.A., Bristow, K.D., and Boe, S.R., 2006, Habitat models—Desert bighorn 

sheep in the Silver Bell Mountains revisited. 1 

Overton, C.T., Schmitz, R.A., and Casazza, M.L., 2006, Linking landscape characteristics to mineral 

site use by band-tailed pigeons in western Oregon—Coarse-filter conservation with fine-filter tuning. 1 

Park, Sunyurp, and Egbert, S.L., 2008, Remote sensing-measured impacts of the conservation reserve 

program (CRP) on landscape structure in southwestern Kansas. 1 

Potere, David; Woodcock, C.E.; Schneider, Annemarie; Ozdogan, Mutlu; and Baccini, Alessandro, 

2007, Patterns in forest clearing along the Appalachian Trail corridor. 1 

Randhir, Timothy, and Ekness, Paul, 2009, Urbanization effects on watershed habitat potential—A 

multivariate assessment of thresholds and interactions. 1 

Regmi, Binaya, 2002, Web-enabled spatial decision support system for interdisciplinary watershed 

management. 1 

Rice, M.B., Ballard, W.B., Fish, E.B., McIntyre, N.E., and Holdermann, D., 2008, The importance of 

accurate landuse/landcover maps for assessing habitat suitability for black bear (Ursus americanus) in 

the Trans-Pecos region of Texas.

1 

Rink, G.R., and Cully, A.C., 2007, A checklist of the vascular flora of Yucca House National Monument 

and surrounding lands, Montezuma County, Colorado. 1 

Rogers, P.C., Leffler, A.J., and Ryel, R.J., 2010, Landscape assessment of a stable aspen community in 

southern Utah, USA. 1 

Sader, S.A., Jin, Suming, Metzler, J.W., and Hoppus, Michael, 2006, Exploratory analysis of forest 

harvest and regeneration pattern among multiple landowners. 1 

Sherrouse, B.C., Clement, J.M., and Semmens, D.J., 2011, A GIS application for assessing, mapping, 

and quantifying the social values of ecosystem services. 1 

Shilling, Fraser; Girvetz, Evan; Erichsen, Chris; Johnson, Brenda; and Nichols, Pete, 2002, A guide to 

wildlands conservation in the Greater Sierra Nevada bioregion. 1 

Shinneman, D.J., Watson, John, and Martin, W.W., 2000, The state of the southern Rockies ecoregion—

A look at species imperilment, ecosystem protection, and a conservation opportunity. 1 

Sridhar, Venkataramana, 2007, Evapotranspiration estimation and scaling effects over the Nebraska 

Sand Hills. 1 

Steinitz, Carl; Anderson, Robert; Arias, Hector; Bassett, Scott; Flaxman, Michael; Goode, Tomas; 

Maddock, Thomas, III; Mouat, David; Peiser, Richard; Shearer, Allan, 2005, Alternative futures for 

landscapes in the Upper San Pedro River Basin of Arizona and Sonora.

1 
56

Table 3. Citation count for publications using GAP data.—Continued

Publication Reference Citation Count

Taylor, K.J., 2003, Bayesian belief networks—A conceptual approach to assessing risk to habitat. 1

Theobald, D.M., and Hobbs, N.T., 1999, Calculating landscape fragmentation using a gradient-based 

approach. 1 

Thorne, Jim, Camerson, Dick, and Jigour, Verna, 2002, A guide to wildlands conservation in the central 

coast region of California. 1 

Toney, Chris; Rollins, Matthew; Short, Karen; Frescino, Tracey; Tymcio, Ronald; and Peterson, Birgit, 

2005, Use of FIA plot data in the LANDFIRE project. 1 

Tumbusch, M.L., and Plume, R.W., 2006, Hydrogeologic framework and ground-water levels in basinfill deposits of the Diamond Valley flow system, central Nevada. 1 

Vogelmann, James; Zhu, Zhilang; Kost, Jay; Tolk, Brian; and Ohlen, Donald, 2006, Perspectives on 

LANDFIRE prototype project accuracy assessment. 1 

Wang, Steven; Stiles, Thomas; Flynn, Trevor; Stahl, A.J.; Gutierrez, J.L.; Angelo, R.T.; and Frees, Lyle, 

2009, A modeling approach to water quality management of an agriculturally dominated watershed, 

Kansas, USA.

1 

Warren, D.L., and Seifert, S.N., 2011, Environmental niche modeling in Maxent—The importance of 

model complexity and the performance of model selection criteria. 1 

Webb, W.C., Boarman, W.I., and Rotenberry, J.T., 2009, Movements of juvenile common ravens in an 

arid landscape. 1 

Winer, A.M., and Karlik, J.F., 2001, Development and validation of databases for modeling biogenic 

hydrocarbon emissions in California's airsheds. 1 

Wooten, George, 2002, Shrub-steppe conservation prioritization in Washington state. 1

Zasada, Michal, Cieszewski, C.J., and Lowe, R.C., 2003, Impact of stream management zones and road 

beautifying buffers on long-term fiber supply in Georgia. 1 

Zicus, M.C., Rave, D.P., Fieberg, J.R., Guidice, J.H., and Wright, R.G., 2008, Distribution and 

abundance of Minnesota breeding ring-necked ducks Aythya collaris. 1 

Publications using GAP data have been cited, some of them extensively, in other publications. 

This suggests that when publications cite GAP data there is a potential secondary diffusion effect of 

information regarding GAP data. Those who are not otherwise familiar with GAP data may be

introduced to GAP through these publications. 

We cross-referenced the publications and their respective citation counts with the datasets used

in the publications. We made this comparison to determine if certain datasets were used in publications 

that were cited more frequently. In table 4, we provided a list of the datasets, the number of publications 

using each dataset, and the number of publications that cite the publications using the datasets. For 

example, the Arkansas land-cover dataset was used in 13 publications and those 13 publications were 

cited in 241 other publications. 
57

Table 4. Datasets, number of publications using each dataset, and number of citations. 

Data Set

Number of 

Publications 

Using

Number of 

Publications 

Citing

Alabama

Land Cover 3

Predicted Species Distribution 1 

Stewardship 1

Arizona

Analysis 1 1 

Land Cover 35 442

Predicted Species Distribution 6 33

Stewardship 7 43

Arkansas

Land Cover 13 241

Predicted Species Distribution 1 

Stewardship 4 192

California

Analysis 1 3 

Land Cover 93 927

Predicted Species Distribution 11 223

Stewardship 21 380

Colorado

Land Cover 22 422

Predicted Species Distribution 3 31

Stewardship 5 83

Connecticut

Land Cover 1 

Stewardship 1 2

Delaware

Land Cover 4 10

Predicted Species Distribution 1 3

Stewardship 3 5 

Florida

Land Cover 26 286

Predicted Species Distribution 2 

Stewardship 2

Georgia

Land Cover 12 13

Predicted Species Distribution 3

Stewardship 2

Hawaii

Land Cover 3 

Stewardship 1 4
58

Table 4. Datasets, number of publications using each dataset, and number of citations.—Continued

Data Set

Number of 

Publications 

Using

Number of 

Publications 

Citing

Idaho

Land Cover 32 760

Predicted Species Distribution 7 158

Stewardship 10 326

Illinois

Land Cover 5 37

Predicted Species Distribution 1 10

Stewardship 2 10

Indiana

Land Cover 13 78

Predicted Species Distribution 1 10

Stewardship 1 10

Iowa

Land Cover 6 71

Predicted Species Distribution 1 

Stewardship 3 17

Kansas

Land Cover 21 127

Predicted Species Distribution 2 10

Kentucky

Land Cover 5 19

Stewardship 2 2

Louisiana

Land Cover 19 106

Predicted Species Distribution 1

Stewardship 1

Maine

Land Cover 11 346

Predicted Species Distribution 3 31

Stewardship 2 181

Maryland

Land Cover 4 7

Stewardship 3 2 

Massachusetts

Land Cover 7 13

Predicted Species Distribution 4 14

Stewardship 3 21

Michigan

Land Cover 5 92

Predicted Species Distribution 1 10
59

Table 4. Datasets, number of publications using each dataset, and number of citations.—Continued

Data Set

Number of 

Publications 

Using

Number of 

Publications 

Citing

Minnesota

Land Cover 19 44

Predicted Species Distribution 1 1 

Stewardship 2

Mississippi

Land Cover 7 22

Predicted Species Distribution 2

Stewardship 1

Missouri

Land Cover 3

Predicted Species Distribution 1 4

Stewardship 2 27

Montana

Land Cover 16 376

Predicted Species Distribution 4 109

Stewardship 8 311

Nebraska

Land Cover 8 19

Predicted Species Distribution 1

Stewardship 1

Nevada

Land Cover 16 70

Predicted Species Distribution 1

Stewardship 2 13

New Hampshire

Land Cover 1

Stewardship 2 2 

New Jersey

Land Cover 4 8

Stewardship 4 3 

New Mexico

Land Cover 30 549

Predicted Species Distribution 7 24

Stewardship 10 274

New York

Land Cover 8 43

Predicted Species Distribution 2 1

Stewardship 5 5
60

Table 4. Datasets, number of publications using each dataset, and number of citations.—Continued

Data Set

Number of 

Publications 

Using

Number of 

Publications 

Citing

North Carolina

Land Cover 2

Predicted Species Distribution 2 

Stewardship 3 2

North Dakota

Land Cover 2 

Stewardship 1

Ohio

Land Cover 1 

Oklahoma

Land Cover 9 19

Stewardship 1 33

Oregon

Analysis 1 5

Land Cover 29 250

Predicted Species Distribution 3 95

Stewardship 7 49

Pennsylvania

Land Cover 13 112

Predicted Species Distribution 2 3

Stewardship 3 5 

Puerto Rico

Land Cover 4 9

Predicted Species Distribution 1 

Rhode Island

Land Cover 1

Stewardship 1 2 

South Carolina

Land Cover 7 10

Predicted Species Distribution 3 10

Stewardship 2

South Dakota

Land Cover 7 106

Predicted Species Distribution 1 

Tennessee

Land Cover 2 18

Stewardship 1 2 
61

Table 4. Datasets, number of publications using each dataset, and number of citations.—Continued

Data Set

Number of 

Publications 

Using

Number of 

Publications 

Citing

Texas

Land Cover 13 15

Predicted Species Distribution 8 32

Stewardship 2

Utah

Land Cover 31 970

Predicted Species Distribution 4 3

Stewardship 6 192

Vermont

Land Cover 1

Stewardship 1 2

Virginia

Land Cover 8 69

Predicted Species Distribution 1

Stewardship 3 2 

Washington

Analysis 1

Land Cover 20 150

Predicted Species Distribution 12 2

Stewardship 8 38

West Virginia

Land Cover 10 8

Predicted Species Distribution 1 5

Stewardship 2 7 

Wisconsin

Land Cover 3 91

Stewardship 1 

Wyoming

Land Cover 32 500

Predicted Species Distribution 4 56

Stewardship 9 312

Southeast GAP

Land Cover 4 

Predicted Species Distribution 6

Stewardship 1 

Southwest ReGAP

Land Cover 61 127

Predicted Species Distribution 8

Stewardship 4 1 
62

Table 4. Datasets, number of publications using each dataset, and number of citations.—Continued

Data Set

Number of 

Publications 

Using

Number of 

Publications 

Citing

Northwest GAP

Land Cover 2 5

National

Stewardship 3 5 

There is a strong correlational relationship between the number of publications in which a 

dataset was used and the number of citations: r = 0.75, n = 105. Generally, a higher number of 

publications using a dataset is positively related to the number of citations to the publications using that 

dataset. However, this is not a perfect relationship. Because the California land-cover dataset was used 

most frequently in publications, it is reasonable to expect that this dataset would be associated with the 

most citations. The 93 publications using California land cover were cited in a total of 927 publications. 

Utah land cover was used in fewer publications—this dataset was used in 31 publications—yet 

publications using this data were cited in a total of 970 publications. Utah was one of the first states to 

complete a GAP project which may contribute to the number of citations attributed to publications that 

utilized Utah GAP data (Jocelyn Aycrigg, GAP, oral commun., 2010). 

It is difficult to determine from available evidence why some publications are cited more than 

others. Less cited publications may appear in journal outlets that are of regional interest with limited 

circulation. They may receive less exposure and therefore are cited less frequently. It may be that the 

characteristics of some datasets make them more useful. It is clear that some datasets are not benefitting 

as much as others from a secondary diffusion via use in the published literature. 

Notably, publications using the regional and national GAP datasets lack citations. The 

SWReGAP land-cover dataset is the only one that frequently appeared in publication citations. The lack 

of citations for publications using regional datasets is not surprising given that these are newer datasets. 

It is likely that the publications using these datasets are more recent and therefore have not been 

available long enough to accumulate a citation history.

In a figure paralleling figure 6 which depicts the number of times each dataset was used, figure 9

depicts the number of citations associated with publications using each dataset. Datasets were not 

included in this figure if there were no citations associated with any publication using that dataset. The 

number of citations are provided in the figure when there were more than 100 citations. 
63

Figure 9. Number of citations associated with publications using each dataset. 
64

Summary

The bibliometric analysis of published literature citing use of GAP data was limited in focus. We 

did not compare the research resulting from use of GAP data with research using other similar datasets. 

When bibliometric analysis is used for evaluation purposes, comparisons are often made among

programs (Davis and Royle, 1996), disciplines (Mullins, 1987), or geographic entities such as states 

(Shapira and others, 2003) or countries (Garg, 2003). Our analysis lacked these comparisons. However, 

this bibliometric analysis was only a small part of the overall program evaluation and served the purpose 

of determining an added benefit of GAP—research productivity. The 342 journal articles appeared in 

161 journals associated with 34 disciplines. This suggests that GAP data are used in many disciplines 

for purposes beyond conservation of biodiversity. 

We identified the journal impact factors for the published journal articles using GAP data.

Publications using GAP data appeared in a variety of journals with a wide range of impact factors. In 

bibliometric research, impact factors are used to indicate the impact of the articles included in the 

journal (Davis and Royle, 1996; Seglen, 1997) and indicate the prestige of the journal (Davis and Royle, 

1996). Forty percent of the journal articles included in the publications using GAP data were published 

in journals that are considered top journals within their respective disciplines based on the impact 

factors. Smaller proportions of journal articles were published in the second and third quartiles of 

impact rank, 28 percent and 23 percent respectively. Many of the journals in the third quartile were 

more likely to appeal to a regional rather than national audience which may have affected the impact 

factors. 

Citation rates tend to be skewed (Pendlebury, 2009; Seglen, 1997) with a small number of 

articles being cited frequently and a large number of articles being cited infrequently. This pattern was 

evident in the citations to publications using GAP data. Older publications tend to have higher citation 

counts. Yet, the publication using GAP data that was most frequently cited was published in 2004. The 

publications that are among the most frequently cited are a mix of early (mid-1990s) publications using 

GAP data and those published in the early 2000s. 

The datasets used in the most publications are not necessarily those in the publications with the 

most citations. Citation behavior is complex. Based on the available information, it is difficult to

determine why some datasets were used more frequently in publications or why publications using 

particular datasets were cited more frequently than others. The frequency data does suggest that landcover data is used more frequently than the other types of datasets and that publications using some 

land-cover datasets are cited more frequently than those using other datasets.

The diversity in journals publishing articles using GAP data, the range of journal impact factors 

associated with these publication outlets, and the variation in citation rates for these publications suggest 

variation in exposure to and reaction to the different datasets. This has specific implications for the 

survey of GAP users. The survey must include questions to determine the dataset each survey 

respondent is most familiar with and the questions must be specific to GAP datasets. This requires an 

adaptive survey. For example, we included questions early in the survey that identify which dataset the 

respondent has used and that facilitate directing that respondent to questions particular to that dataset. 

Discussion

We located 646 publications that clearly stated a use of GAP data. One early concern in the 

search for published literature using GAP was the possibility that people were using GAP but not 

correctly identifying GAP as the source of their data. There may be some people who are using the data 

and not attributing it to GAP, but evidence suggests that people do reference the data they use to GAP. 
65

There were a few publications in which the authors noted that they used GAP data in their 

acknowledgments section but did not specifically identify any GAP dataset in the text of the publication. 

Other than correct attribution to a source, the diffusion of information regarding GAP datasets and GAP 

itself is an important reason for GAP data to be identified when it is used in a publication. Publishing 

the use of GAP data is one way to publicize the data as a resource. Impact of a program as determined 

by publication and citation counts can be affected by the visibility of the program (Martin and Irvine, 

1983). A circular pattern exists. The impact of a program is affected by publication based on the 

program’s datasets; more publications are generally interpreted as a greater impact. When more 

publications are available, there is greater likelihood that others will be exposed to information 

regarding the program and the datasets it produces and additional use of the program data will result. 

Increased use of the data, particularly when publications are produced, leads to a higher impact as 

measured by publication and citation counts. 

Many publications used GAP data. These publications were produced by many authors—not a 

limited group of authors. This suggests that knowledge about GAP and the datasets it produces is not 

restricted to a particular group of individuals. The publications were mostly journal articles but also 

included conference presentations, dissertations and theses, reports, and book chapters. The prominence 

of journal articles may be an artifact of the reliance on databases in the search method used to find 

publications using GAP data. The publications in journals were not limited to a select list of titles. The 

journals in which these articles were published represented a broad range of topics and disciplines. Due 

to the nature of the search method, the results may over-represent academic research rather than use of 

GAP data for on-the-ground conservation decisions. 

As anticipated, the datasets cited in the published articles were primarily from state and not 

regional or national projects. This does not mean that the regional and national datasets are less useful or 

that they will have less impact. It simply means that they may be too recent to have accumulated a 

publication and citation history. Continued use of state datasets suggests that, contrary to conventional 

wisdom that older data is not as good, older GAP data is still useful. Even the publications produced in 

the first six months of 2010 used state datasets; this suggests that GAP data has a long “shelf life.” 

Older data is necessary for studies of landscape and climate change. Ramsey and others (2009) used 

GAP data in a study of the recovery after Hurricane Katrina.

Based on the sample of publications using GAP data that were included in this study, land-cover 

data appears to be used more than the other types of data produced by GAP. When we looked at number 

of publications using each type of data, land-cover data was used in 73 percent of the publications. The 

summary of the number of times each dataset was used indicated that the California land-cover dataset 

was used most frequently. In the analysis of citation counts associated with datasets, the Utah landcover dataset was used in the publications that received the most citations in other publications. 

We searched the text of the publications for statements regarding GAP data. The following six 

themes emerged from the statements: description of data, how data were used, critiques, comparisons, 

commentary, and reference to GAP data. We described each of these themes and provided examples.

The theme including statements regarding how GAP data were used yielded two points of particular 

interest. First, some of the statements described how GAP data were transformed by adding data, 

removing data, or otherwise altering existing GAP data. This suggests that some level of GIS expertise 

is necessary to use GAP data. Second, GAP data were used in a wide array of applications. The 

evaluative statements in the critique, comparisons, and commentary themes were a combination of 

favorable and unfavorable statements regarding GAP data. The mixed tenor of these comments suggests 

that the issues raised in some of the statements may be specific to particular uses of GAP data and not 
66

the data. An issue such as the datedness of the data may be problematic in some circumstances but 

beneficial in others.

The bibliometric analysis indicates that journal articles using GAP data appear in journals 

affiliated with multiple disciplines and at all levels of impact as defined by the journal impact factors. 

The set of publications used in this study were cited in a combined number of 2,295 other publications. 

People who read publications in which the authors used GAP data and specifically identified the data as 

from GAP are exposed to GAP data. It may be the reader’s initial introduction to GAP. Other readers 

who are familiar with GAP and the data it provides may develop a greater understanding of GAP data 

and potential uses of these datasets. There is evidence that publications using GAP data have served as a 

means to diffuse this information.

Citation counts indicate that publications using GAP data have been cited in other publications 

at varied rates. Some publications have been cited very frequently. For example, the Homer and others 

(2004) article describing the development of a national land-cover database has—as of the date the 

citation search was conducted in August 2011—been cited in over 250 other publications. 

The results of this published literature study point to diversity in the uses of GAP data. The 

publications that used GAP data appeared in a variety of types of publications although primarily in 

journals. The journals were numerous and from a wide variety of disciplines. Even though most of the 

datasets used were land-cover data, many specific datasets were used in the publications. The text of the 

publications indicated that GAP data were used for different purposes and in different ways. The 

publications appeared in journals at all levels of impact as defined by journal impact factors and withindiscipline quartile ranks. There was diversity in the publications, datasets, comments, and citation 

patterns. The primary implication of this diversity for the development of the survey is that the survey 

questions needed to be tailored to specific datasets. To achieve this tailoring, particular attention is 

necessary in the construction of the survey. The analysis of adaptive survey data is more complicated 

than for a simpler survey. 

In summary, the implications for the survey of GAP users include developing specific questions 

based on issues identified in the qualitative analysis of statements made in the publications, and 

tailoring questions so that they are specific to datasets. The authors who published these articles have 

clearly used GAP data and will be included in the survey sample. 

Acknowledgments

This project was commissioned by the Gap Analysis Program of the U.S. Geological Survey. 

We are indebted to Alison Level, Reference Librarian and Associate Professor of Instruction and 

Research Services at Colorado State University, who assisted us with planning our literature search. 

Two external reviewers provided thoughtful and detailed comments on our report. We express our 

gratitude to Dr. Wayne E. Thogmartin, Quantitative Ecologist at the Upper Midwest Environmental 

Sciences Center, USGS, and Dr. David Alan Weinstein, Senior Research Associate in the Department of 

Natural Resources at Cornell University.

List of References

This list of references includes the references cited in text; the list of publications that cited GAP 

data, some of which were cited in text, marked with an asterisk (*); and the secondary references that 

occurred within the quotations we used from the publications that cited GAP data, marked with two 

asterisks (**). These secondary citations are provided only as a courtesy to the reader.
67

*Ackerman, Drew, and Schiff, Kenneth, 2003, Modeling storm water mass emissions to the southern 

California bight: Journal of Environmental Engineering, v. 129, no. 4., p. 308–317. 

*Acosta, Jessica, Brooks, R.P., and Gould, W.A., 2007, Geographic analysis of habitat change for 

endemic animal species of Puerto Rico [abs.]: Association of American Geographers 2007 Annual 

Meeting Abstracts, San Francisco, Calif., p. 6. 

*Adjemian, J.C.Z., Girvetz, E.H., Beckett, Laurel, and Foley, J.E., 2006, Analysis of genetic algorithm 

for rule-set production (GARP) modeling approach for predicting distributions of fleas implicated as 

vectors of plague, Yersinia pestis, in California: Journal of Medical Entomology, v. 43, no. 1, 

p. 93–103. 

*Alfieri, J.G.; Niyogi, Dev; LeMone, M.A.; Chen, Fei; and Fall, Souleymane, 2007, A simple 

reclassification method for correcting uncertainty in land use/land cover data sets used with land 

surface models: Pure and Applied Geophysics, v. 164, p. 1789–1809. 

*Allen, C.R., Johnson, A.R., and Parris, Leslie, 2006, A framework for spatial risk assessmentsPotential impacts of nonindigenous invasive species on native species: Ecology and Society, v. 11, 

no. 1, art. 39, accessed February 3, 2011, at http://www.ecologyandsociety.org/vol11/iss1/art39/. 

*Allen, K.E., Bradley, R.D., Monk, R.R., Knyazhnitskiy, O.V., Parker, N.C., Schmidly, D.J., and 

Baker, R.J., 2001, Employment of geographic information systems for determining the accuracy of 

museum voucher specimen data: Museum of Texas Tech University Occasional Papers, no. 210, 7 p., 

accessed March 5, 2011, at http://www.nsrl.ttu.edu/publications/opapers/ops/op210.pdf. 

*Ames, Jenna, 2006, Identifying moose habitat in Cache and Rich counties: Logan, Utah, unpublished 

manuscript, 12 p. accessed March 3, 2011, at 

http://www.gis.usu.edu/~doug/RS5750/PastProj/FA2005/JennaAmes_FinalProject.pdf. 

*Arizona Water Atlas, n.d., Southeastern Arizona Planning Area—Section 3.12 San Rafael Basin: 

Phoenix, Arizona Department of Water Resources, v. 3, accessed March 29, 2011, at 

http://www.adwr.state.az.us/AzDWR/StatewidePlanning/WaterAtlas/SEArizona/documents/volume_3_

SRF_final.pdf. 

*Ashley, P.R., and Stovall, S.H., 2004, Draft Methow subbasin wildlife assessment and inventory: 

Portland, Oreg., unpublished manuscript, 82 p., accessed March 8, 2011, at 

http://www.nwcouncil.org/fw/subbasinplanning/methow/Plan/ 

e-Appendix%20L%20Wildlife%20Assessment/MethowSubbasinAssessment.pdf. 

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