Jill Maxwell

US Geological Survey Gap Analysis Program, Moscow, Idaho

Introduction

Gap Analysis is designed to be a proactive approach to conservation. A recent review of applications of GAP data found that the primary uses have included resource management, biodiversity assessment, planning, site prioritization, and as a component of state comprehensive wildlife conservation strategies (Maxwell 2005), now called State Wildlife Action Plans (SWAPs). With the completion of SWAPs and their approval by the U.S. Fish and Wildlife Service, the foundation has been laid for a national strategy for protecting and preserving biological diversity across the U.S. State and regional GAP projects have made significant contributions to the development of this foundation, and have equally significant contributions to make to SWAP implementation and monitoring. I will review the role of GAP data in the SWAP development and discuss the future role of these data in their implementation, monitoring, and revision.

Background

States were mandated by the federal government to submit completed SWAPs by October 2005. Each plan included information on state species of greatest conservation need (SGCN), SGCN habitats, threats to species and habitat, research needs, necessary plan actions, and conservation priorities. States that did not meet the deadline risked losing funds they had received through the State Wildlife Grants program, which has allocated nearly $400 million to states for conservation since 2001(Throckmorton 2005).

The development of state-based conservation strategies provided a well suited opportunity for adoption of GAP’s land cover, stewardship, species richness data, habitat models, and gap analysis results. Through a short e-mail survey of GAP principal investigators and SWAP Coordinators in 2005, I learned that 38 states and Puerto Rico had used GAP data in the development of their SWAP (Maxwell 2005). Based on the information gained from that initial survey, I developed a more detailed survey to assess which GAP data were most helpful, which SWAP issues GAP data were used to address, and changes natural resources professionals would like in the data GAP produces.

Methods

To gain a further understanding of how GAP data were used in SWAP development and to identify possible future uses of GAP, I surveyed 51 plan coordinators in 50 states and Puerto Rico. The survey questionnaire was designed to address four key issues. First, the extents to which different components of GAP data were used in SWAP development. Second, the importance of GAP data in addressing specific SWAP components. Third, the extent to which SWAP coordinators are planning to use GAP data in the future to update and review their plans. And, lastly, to determine meaningful enhancements to GAP data.

A variety of choices were listed for each question. Respondents were asked to indicate their answers along a 5-point scale, which evaluated the importance of the issue or the extent to which GAP data were relied on. The scale ranged from not important/not at all (1) to most important/only used GAP (5). The draft questionnaire was reviewed by GAP staff for content. Once the survey was revised, it was reviewed by the University of Idaho Social Sciences Research Unit. The survey was mailed to 51 SWAP coordinators.

The questionnaire, a cover letter, and a business reply envelope were initially mailed to respondents on February 21, 2006. A reminder postcard was mailed out 2 weeks later. Non-respondents were mailed a second complete survey with cover letter, questionnaire, and business reply envelope on March 21, 2006. Finally, remaining non-respondents were contacted by e-mail on April 10, with the survey included as an e-mail attachment and instructions to return the survey by e-mail, fax, or mail.

Results and Discussion

A total of 44 responses were received, 34 (77%) of whom used GAP data in plan development, and 10 (23%) who did not. This compares favorably with the results of our 2005 survey, in which we learned that out of all states and Puerto Rico, 12 states (24%) did not use GAP data (Maxwell, 2005).

Responses from the initial 2005 survey were used to develop the questions for the more detailed 2006 survey. In 2005, SWAP coordinators indicated using a variety of GAP data. For the 2006 survey, I asked each respondent to what extent they used Gap land cover, habitat models, vegetation classifications, species lists, predicted vertebrate distribution maps, habitat narratives, stewardship data, ownership data, species richness data and aquatic data. Overall 34 states indicated using GAP data elements (i.e. data types), and 27 states used five or more of the 10 data elements specifically asked about (Figure 1).

Figure 1. The number of GAP data elements (i.e. data types) used by states in SWAP development.
One the states that responded to the survey are included (n = 44). Most states used at least five elements.

Use of GAP data?

Table 1 shows the percents and mean responses to question 1 regarding the extent to which each element of GAP data was used. The percent indicates the number of states that used a particular data element, but does not indicate the extent to which the data were used to address specific plan components. For example, if a respondent did not use the aquatic data at all, a score of 1 was given to that item; similarly, if a respondent used only GAP land cover maps and data for their SWAP, that item would have received a 5. Mean scores were obtained by averaging all scores given to a single data item. They indicate the extent to which GAP data was used to address that plan component. For all 44 survey respondents, mean scores ranged from 1.56% to 2.91%. Higher mean scores indicate more states used that data extensively or exclusively.

Table 1. The number of states that used specific GAP data elements in SWAP development. Mean scores were calculated by averaging all scores given to a single data element. The values in the gray columns represent the number of states who gave that score to that Gap data element. A higher mean score indicates that data element was used more extensively. N represents the number of respondents. Not all respondents gave scores to every data element.

GAP data were used in SWAP development. In particular, respondents relied on GAP land cover data far more than they relied on other elements of GAP data (Table 1). Six states (14%) said GAP was their only source for land cover. Another 16 (36%) said they used it extensively. The vegetation classifications and predicted vertebrate distribution maps were also used extensively or exclusively by 56% of respondents. Other components of GAP data were not used as extensively, but still played an important role in SWAP development. Even GAP aquatic data were used by at least 1/3 of respondents, despite the fact that few states included an aquatic component in their GAP project; indicating how important aquatic data is, when available.

GAP land cover data are most likely most commonly used because it is one of the main products of a gap analysis project. GAP was one of the first nation-wide projects to map land cover on a state by state basis. Much of the research conducted by GAP focused on how to map land cover and on the development of meaningful classifications that cross state lines. States projects were allowed some flexibility their mapping efforts (Eve 1997.) In addition, both technology and methodology have improved over the 20 years GAP has been funding projects. The variability in state project products explains the variability in the use of the end product in SWAP development

Plan elements addressed using GAP data

GAP species and habitat distribution models and maps were the most important component of GAP data (Table 2). Of the 34 respondents who used GAP data, more than 75% used the data to address the SWAP requirement to identify and assess the condition of SGCN. For many states, GAP species and habitat distributions provided the data they needed to locate the SGCN on the landscape without recreating the information from scratch. In conjunction with other data, they used GAP to assess the status of particular species in their state. GAP data were also important for mapping species richness, determining habitat associations, and describing habitat, all of which are key elements of identifying high priority conservation areas.

Table 2. The importance of GAP data in addressing specific SWAP plan issues. The values in the gray columns represent the number of states who gave that score to that plan component. Mean scores were calculated by averaging all scores given to a single plan component. A higher mean score means that GAP data was more important in addressing that plan component. Respondents who said they did not use GAP data in Question 1, were not included. N represents the number of respondents. Not all respondents gave scores to every plan component.

Conversely, GAP data did not play an important role in identifying threats, identifying future actions, assessing SGCN status or mapping invasive species. These roles are not central to the GAP concept of “keeping common species common.” GAP projects do not emphasize threatened and rare species. Coarse scale mapping efforts often do not map threatened or rare habitats. Also, few projects mapped invasive species.

GAP data were also not important for mapping land cover change. The data may not have been around long enough to be used to assess changing conditions on the ground. Although some states have used GAP to look at changes in land use (Kramer 2005), this is a relatively new application of the data.

Additional uses of the data mentioned by respondents were using the data to place the state in a regional context and to stress the importance of private land conservation. One state reported using the data to derive data sets of predicted distributions, land cover and known species points,

GAP data used to update and revise SWAPs

An essential element in the initial SWAPs was a description of the procedures to update and review them. The third survey question asked if states would be using GAP data to accomplish this (Table 3). As for previous questions, the list of possible applications of GAP data was compiled from responses to the 2005 survey. Only seven (18%) respondents reported they were not planning to use GAP data in the future, while 33 (82%) said they would address at least one aspect of plan update or review with GAP data. Five states did not respond to the question, although two of those indicated they were waiting to see the data. 16 coordinators (41%) said they would rely heavily or exclusively on GAP to improve the wildlife habitat mapping done for the SWAP. SWAP coordinators also expect GAP data to make a contribution to identifying knowledge gaps and threatened landscapes. One respondent indicated that GAP data would be used to help designate critical habitat.

Table 3. Extent to which SWAP coordinators plan to use GAP data in the future to revise and update their plans. The values in the gray columns represent the number of states who gave that score to that plan component. Mean scores were calculated by averaging all scores given to a single Gap data element. A higher mean score means that GAP data is more likely to be used to address that plan component. N represents the number of respondents. Not all respondents gave scores to every plan component.

To promote the adoption of GAP data, it is important to facilitate contacts between scientists familiar with the projects and other decision-makers. Through a content analysis of the SWAPs, I discovered that GAP-affiliated scientists were involved in the development of SWAPs in 19 states. These scientists were principal investigators, co-principal investigators, researchers, or authors for either on-going or past GAP projects. Their SWAP roles ranged from contributor to technical team member, steering committee member, or contributing author. These scientists were identified by reading the acknowledgement and committee member lists in the SWAP reports. Among survey respondents, 16 state SWAPs were developed with the input of GAP-affiliated scientists. States with GAP-affiliated scientists involved in SWAP development were more likely to use GAP data (Figure 2). These states also placed a higher value on GAP data (as measured by overall mean scores; Figure 3) than did states without the involvement of GAP-affiliated scientists. For example, in response to Question 1 about the extent to which different components of GAP data were relied on in SWAP plan development, 73% of the states with the involvement of GAP affiliated scientists used GAP data land cover data extensively, while only 36% of the remaining projects did so (Figure 2).

Figure 2. Use of GAP data components by SWAPs developed with (yes) and without (no) the involvement of scientists previously affiliated with Gap projects (GAP-affiliated scientists). Through a content analysis of the SWAPs, I discovered that GAP-affiliated scientists were involved in the development of SWAPs in 19 states. These scientists were principal investigators, co-principle investigators, researchers, or authors for either on-going or past GAP projects. Their SWAP roles ranged from contributor to technical team member, steering committee member, or contributing author. These scientists were identified by reading the acknowledgement and committee member lists in the SWAP reports. Among survey respondents, 16 state SWAPs were developed with the input of GAP-affiliated scientists. Plans developed with the involvement of GAP-affiliated scientists were more likely to use the data, than were those without input from GAP-affiliated scientists.

Figure 3. Involvement of GAP-affililiated scientists in SWAP development resulted in a mean score. Through a content analysis of the SWAPs, I discovered that GAP-affiliated scientists were involved in the development of SWAPs in 19 states. These scientists were principal investigators, co-principal investigators, researchers, or authors for either on-going or past GAP projects. Their SWAP roles ranged from contributor to technical team member, steering committee member, or contributing author. These scientists were identified by reading the acknowledgement and committee member lists in the SWAP reports. Among survey respondents, 16 state SWAPs were developed with the input of GAP-affiliated scientists. Mean scores reflect GAP data use and perceived importance among states with GAP-affiliated scientists involved in SWAP development compared to states without input from GAP-affiliated scientists. SWAPs completed with the input of GAP-affiliated scientists were more likely to use the data. Q1 = Use of GAP data, Q2 = SWAP components addressed with Gap data, Q3 = Plans to use Gap data in the future, Q4=Importance of various modifications to GAP data.

GAP data modifications

The fourth question evaluated the perceived importance by SWAP coordinators on modifications to data provided by GAP. A list of possible changes was compiled from responses to our 2005 survey about the use of GAP data in SWAP development. In that survey a review of comments by states that did not use GAP data, reinforced criticisms that have emerged previously (McClafferty, 2002). Eight states did not use the data because they were incomplete, difficult to get, or otherwise unavailable. Other criticisms were that the data were too coarse, inaccurate, outdated, lacked information about habitat quality, lacked accuracy assessment, or lacked information about rare plant species. Two respondents indicated GAP staff had moved on, which made it difficult to work with the data. Despite these criticisms, many of these states are still interested in GAP data. Of those who did not use the data, four indicated that they had tried to use the data before deciding to use other sources; and five indicated that they are willing to use GAP data when the data become available.

Overall, respondents to the current survey felt that each of the identified possible modifications to GAP data would be valuable (Table 4). The modifications deemed most important were: more information on habitat change, finer scale mapping for specific species, more information on habitat condition, and a shorter timeline for project completion. In response to an open-ended question regarding other kinds of information respondents would like to get from GAP data, the following modifications were mentioined: improved delineation of grassland types, successional habitats, and wetlands; accuracy assessment, better resolution to help delineate vegetation cover types; threat assessment, progress assessment, and restoration potential. One respondent indicated that GAP would be the cheapest consistent method of monitoring habitat loss/gain.

Some of the issues identified are being addressed and some continue to pose challenges. Not all GAP projects have modeled invasive vertebrate species nor provided assessments of habitat quality. This is why SWAP coordinators could not find data about invasive species for use in their plans. Similarly, because GAP mapped vegetation from satellite imagery its land cover maps are a representation of what was on the ground in a specific year. This is why there is little information on habitat change. However, as regional mapping projects are completed, at least one more land cover map will be available. Also, in the future, as the Aquatic GAP projects develop, more data about aquatic ecosystems will become available.

Some modifications, such as the need for a shorter timeline, finer scale mapping for select species, and more information on species abundance are more intractable because they depend on data availability, data quality, funding and available technology. These are issues that GAP will continue to be challenged by.

Conclusion

The development of SWAPs has laid a foundation for conservation in the U.S. GAP has played an important role in the development of that foundation and will continue to play a role in ongoing regional mapping efforts to create a unified land cover map of the U.S. The development of the GAPServe data portal (http://gapanalysis.nbii.gov) will make it easier for natural resources planners and other decision makers to access and use GAP data.

Survey results indicate a widespread willingness to use GAP data once they becomes available as well as an ongoing need for new kinds of data, especially on invasive species, habitat change, and habitat condition. There is also a need for finer scale mapping to capture topographic features such as riparian corridors and small habitat patches. However, Gap data has its limitations and will never meet all the needs of natural resources professionals. If used to complement other data sets, GAP data can help natural resources professionals make informed decisions regarding the conservation and management of many vertebrate species.

A key component of GAP projects is the collaborative approach with which they are conducted on a state and regional basis. The extent to which SWAPs completed with the input of GAP-affiliated scientists were able to make greater use of the data indicates the need to continue the development of such relationships. Continued close collaboration between GAP and state and federal natural resource professionals will lead to increased general awareness and use of GAP data. In return, GAP will gain regarding future research and mapping efforts.

Literature Cited

Eve, M. D., J. W. Merchant, and K.C. Kroll, 1997. A Preliminary Analysis of GAP Mapping Procedures. Gap Analysis Bulletin, 6: 14-15.

Kramer, L., and M. Elliott. 2005. Identification of conservation priority areas in Georgia. Gap Analysis Bulletin 13:14-20.

McClafferty, J., 2002. Opportunities and barriers to GAP implementation: a review and analysis: Final Report. Conservation Management Institute, Blacksburg, Virginia.

Maxwell, J., 2005. The integration of GAP data into state comprehensive wildlife conservation strategies. Gap Analysis Bulletin, 13:10-13.

Throckmorton, N. 2005. Norton Announces 56 States and Territories Have Submitted Wildlife Action Plans; Blueprint to Keep Species from Becoming Endangered. Retrieved April 27, 2006, from <http:www.teaming.com/press/11.2.05.htm>.

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