Supporting Community Based Wildlife Conservation in Africa with a GIS Enabled Relational Database

Andrew Lyons
Dale Lewis

Abstract

The ADMADE conservation program in Zambia has over ten years of experience using GIS technology to support rural communities in managing their natural resources. Through this government program, communities living in Game Management Areas become partners with government and private industry to sustainably manage their wildlife resources, striving to achieve both conservation and community development. GIS tools have proven invaluable for analyzing issues and providing feedback to communities on the effectiveness of their resource management activities and developing land use plans. ADMADE recently upgraded its GIS to a truly integrated relational database, building upon years of data collection and experience in analyzing monitoring data. The new database was developed using MS Access in conjunction with Esri MapObjects, and offers exciting new capabilities for multi-scale analyses, improving data quality, automating outputs, and reducing turnaround time. This user-friendly application has also opened new possibilities for sharing monitoring results, and is being used at new locations in the program.

Introduction

A peaceful country landlocked in south-central Africa, Zambia is richly endowed with wildlife, with over 240 species of wild mammals recorded including most of the large game animals associated with classic wild Africa (Aspinwall, Bingham, Chundama, Jeffery, & Sinkamba, 1996). Almost 1/3 of Zambia is under some kind of conservation status either in strictly protected national parks or multiple use zones called Game Management Areas (GMAs) which generally border the national parks (see Figure 1 below). ADMADE (Administrative Management Design) is a program of the Zambia Wildlife Authority (formerly known as the National Parks and Wildlife Services) for wildlife conservation and community development in the GMAs of Zambia. ADMADE is the official management strategy for all GMAs in Zambia, but has an active program in only about half of the 34 GMAs.

Figure 1. Map of Game Management Areas

Operationally, ADMADE achieves its twin goals of wildlife conservation and community development by channeling a portion of the revenue from commercial trophy hunting back to communities for resource management and community development. Although the community-based approach to conservation is today widely embraced by governments and donors alike, when ADMADE was formally established in 1988, it represented a radical paradigm shift from the traditional centralized wildlife management approaches of the 1970s and 80s. These approaches tended to exclude rural communities from any input into planning, managing, or benefit from wildlife resources, which eventually led to an epidemic in poaching in many African countries including Zambia. Although sport trophy hunting may at first seem antithetical to wildlife conservation, when practiced on a controlled basis it can provide a substantial amount of revenue for conservation programs for minimal impact. Trophy hunting has far less impact on habitat and land cover than agriculture, and requires less infrastructure development and client volume than photographic tourism.

Organizationally, the primary unit of management in ADMADE is the GMA. Management of the GMA is implemented through a cooperative effort of government officers and community residents. Representing the Zambia Wildlife Authority (ZWA) in the GMA is a Unit Leader, one or more deputies, and several civil servant scouts. These officers work side-by-side with village scouts, who have comparable responsibilities and training but are local residents and paid from community revenue. The primary role of both civil servant and village scouts is law enforcement, but they also play critical roles in resource monitoring and to a lesser extent public education.

Representing the community in management of the GMA is the Community Resource Board (CRB), an elected body composed of local residents with a mandate to prioritize development needs, manage the revenue from safari hunting, and oversee resource management activities. Traditional chiefs, who remain very influential in the rural areas of Zambia, serve as an honorary patron of the CRBs. Backing up the CRB are three technical committees, all comprised of local residents, which advise the board on issues of resource management, community development, and financial management. To ensure the all areas of the GMA are equally represented on both the technical committees and CRB, GMAs are divided into Village Area Groups (VAGs) which are demarcated based on geography and demography.

Figure 2. ADMADE Unit Organization

Supporting the different community structures at the GMA level is ADMADE's training and research center, the African College of Community Based Resource Management. Formerly known as Nyamaluma Institute, the College is located in a remote area of the Luangwa Valley on the eastern side of the country, and provides support to communities through a variety of educational programs, most of which are delivered as residential courses based at the College itself. The College also has an active extension program and provides in-situ technical support, monitoring, and facilitation services to the ADMADE communities.

Monitoring in ADMADE

Monitoring is an integral component of resource management, and has always been an important focus in ADMADE. Due to a chronically inadequate operational budget for resources such as vehicles and aerial surveys, ADMADE has made a long-term investment in developing a large-scale community-based monitoring system. Although not as rigorous as more traditional methods of wildlife monitoring, the system collects enough data to base management decisions on and in an extremely cost-effective manner. Community-based monitoring has the added bonus of serving as a tool to build the capacity of community organizations for wildlife monitoring (National Parks and Wildlife Services, 1993b).

Datasets

Monitoring has been integrated into the daily operations of village scouts, whose job requirements put them in a good position to collect data on wildlife, habitat, and resource use. The primary datasets which are collected on a regular basis include:

• Field Patrols. Village Scouts collect data while on anti-poaching patrols, which are foot patrols lasting 1-10 days. They record information such as dates and location of the patrol, supplies taken, signs of poaching, habitat disturbances, carcasses of animals, and sightings of live animals. Field Patrol Dataform Side 1, Field Patrol Dataform Side 2

• Safari Hunting Results. Whenever a safari client goes on a hunt, an ADMADE Village scout accompanies the hunting party to record information such as location of the kill, search time, and trophy size. This information is used to help evaluate the sustainability of safari hunting and set quotas. SAFLICE dataform, SAFHUNT dataform, TROPHY dataform

• Population Trends Survey. This is a questionnaire on wildlife population trends administered individually and independently to knowledgeable persons such as wildlife scouts and safari hunting guides. It records qualitative impressions about population trends for individual species, and is analyzed by looking for agreement between the various respondents.

• Hunting License Sales. Each time a safari hunter purchases hunting licenses for a safari hunt, a record is made in the Wildlife Conservation Revolving Fund database. This database is housed at the licensing office in the capital Lusaka, and a portion of the money is returned to ADMADE Units for resource management and community development.

• Poacher Arrest Records. Each time a poacher is arrested, information is collected on their background, methods, and the punishment imposed by the court. This information is used to create a profile of poachers, look for trends in poaching strategies, and evaluate the effectiveness of law enforcement efforts.

• Quotas. Hunting quotas are updated once a year and specify the number of animals which can be legally hunted. Quota recommendations are first developed by CRBs at a stakeholder workshop, and then approved by the ZWA.

• Demography. Household demography data has been collected for about half of the ADMADE GMAs. The demography dataset includes age and gender information, and is used to plan community development activities and evaluate social impact.

Information Flow

The vast majority of monitoring data originates in the GMAs, usually recorded by village scouts. A series of dataforms has been developed and distributed for each of the main datasets. After the data is recorded, it is kept at the GMA headquarters. The Unit Leader and his staff at the GMA headquarters have some capacity to analyze raw data, however data analysis skills are still fairly rudimentary and remain a focus of capacity building efforts supported by the College.

Two to four times a year, monitoring dataforms are sent to the College for processing. Here they are entered into a master database, and summaries are prepared and returned to the GMAs for planning and evaluation exercises. The summaries are provided in tabular format, graphs, and maps created using the College's GIS (National Parks and Wildlife Services, 1995).

Role of GIS

ADMADE was one of the pioneers of applying GIS technology to support community-based wildlife management. Thanks in part to several short term volunteers and generous donations from the Esri conservation program, the research unit at the College has been able to digitize base maps for all the GMAs and set up a centralized monitoring database. All field observations are georeferenced by an overlaid 5 km2 grid. Although this scale compromises precision, it increases accuracy of the locations because scouts can confidently locate themselves on a base map to within 5 km. The scale is also adequate for the management decisions which are the focus of ADMADE's monitoring system, namely annual quota setting, planning law enforcement operations, and land-use planning (Lewis, 1995).

ADMADE uses GIS technology primarily as a means to display monitoring results and land use issues in a format that could be understood by the local residents of GMAs. The use of maps fit well into ADMADE's overall strategy of community empowerment, as they are an effective tool in focusing discussion and visualizing trends not easily communicated through text or numbers . Specifically, ADMADE uses maps to:

• highlight the areas of a GMA which generate the most revenue from safari hunting
• demonstrate the pattern of human encroachment into productive wildlife areas
• illustrate spatial patterns in law enforcement operations
• illustrate patterns in poaching
• illustrate the scale of land use disturbances, such as fire, fishing, and gardens near waterholes (National Parks and Wildlife Services, 1996)

Early GIS Developments

The College's GIS was developed in the early 1990s, and like many low budget GIS's of that era, it was composed of several software applications working together. PC ArcInfo was the primary GIS package used for digitizing base maps and preparing spatial outputs. This was later supplemented with ArcView. Data entry was done primarily through Lotus 123, aided by a series of macros. dBase IV for DOS was used for data conservation and some data entry (National Parks and Wildlife Services, 1993a). Finally, ERDAS Imagine was used to analyze satellite imagery for a few of the GMAs.

As pioneering as the College's GIS was, it was also constrained by the software and hardware of the early 90's. In particular the GIS had the following limitations:

• Difficult to operate. Maintaining the old information system was clunky and required familiarity with multiple software packages. Only a small number of technical staff could use the system, as most of the data processing, file management, and map creation had to be done manually. This also made operating the system time-intensive, taking away research staff from other activities. The orientation time for new staff members was also lengthy.
• Restricted to single-area, single-year summaries. Due to the flat data structure and filing system, the only types of analyses that were feasible were single-GMA single-year summaries. Making maps or tabular summaries which depicted monitoring trends over multiple areas and/or multiple years was tedious almost to the point of being impossible. While this was not a major constraint in providing data summaries for individual GMAs, it made seeing the 'big picture' difficult.
• Little error checking. Due to the limitations of spreadsheets, error checking depended heavily on the skills and experience of the data entry technician. Problems such as inconsistent units of measurement, inconsistent spellings, incorrect dates, and occasional outliers limited the reliability of certain types of analyses.
• Unwieldy file system. The file system of the GIS included literally hundreds of directories, sub-directories, and files, making copying or backing up the database challenging. A more significant problem was the use of multiple computers for entering data, making it incumbent upon the technicians to keep track of which files on which computers are the most up to date. Updating files on other machines carried a small but continuous risk that more recent data could unwittingly be overwritten with older data.
• Difficult to expand. Adding new datasets into the information system was challenging, because new files had to be integrated into both the directory structure, data structure, and in multiple formats. Creating new summaries of data was equally difficult, because summaries had to be either done manually or by programming new Lotus macros. This constraint on the system's expansion and flexibility not only affected staff time, but also the number of datasets that could be entered. Portions of important datasets, including field patrol observations and observations from safari hunts, could not be entered into the database because of the limitations of the software. Other datasets, such as poacher case records, and crop damage, were entered but not integrated with other data, so could not analyzed against other variables.
• Map production was manual. As mentioned previously, one of the most important outputs of the College's information system were the flipchart-sized maps of monitoring data that were returned to communities. Creating these maps in ArcView, although flexible, involved a complicated series of steps that required a significant amount of training and many hours of staff time.
• Difficult to share data. Because it was difficult to extract data except in the limited preset formats, and the clunky file and software system made it impossible to share data electronically, the College's research unit struggled to meet the information needs of its many external stakeholders in Zambia and abroad.

By 1998 the College had acquired newer software, hardware, and technical assistance in the form of a short-term consultant and visiting graduate student. The staff decided it was time to upgrade the GIS into a more flexible and user-friendly system.

Methodology

Objectives

The following objectives were identified for the new information system.

• Integrate datasets. Many of the constraints of the old information system stemmed from the disjointed data and file structure. Hence a key objective of the new system was to integrate the major datasets under one relational structure. In other words, all datasets for all years and all Units had to be combined. This would enable producing summaries and analyses based on data from different years and/or GMAs.
• Make more user-friendly. It was desired that any new database system should be a lot more user-friendly. This would allow a greater number of College staff to input data and generate outputs, and would minimize the amount of disruption in the program resulting from transitions in technical staff. A user-friendly interface would also decrease the likelihood that certain types of errors would occur, and allow the highly capable technical staff to spend less time on repetitive tasks and focus more on the analytical side of monitoring.
• Integrate GIS features. Incorporating GIS capabilities in the same interface as the data entry and tabular summaries was desired as a means to greatly simplify the generation of required outputs.
• Improve error checking. Enforcing data integrity and developing built-in error checking mechanisms were other desired features for the new database system. The practice of identifying GMAs, species, scouts, etc. with ID numbers instead of text is but one example of a strategy to reduce data errors. Other desired error checking features included built in validation for numbers which should fall within a certain range (e.g., trophy sizes, dates), and ensuring that records are not entered more than once.
• Automate standard outputs & analyses. To save staff time and improve reliability, there was a need to automate many of the standard outputs of the database, including tabular summaries, maps, and charts. Automating the standard analyses also improves the consistency of the outputs produced, which can benefit some rural people who are not strongly visually literate.
• Provide multi-user features. More and more of the staff at the College use computers in their work, including some extension teams who carry laptops with them on field visits. One of the hopes for the new information system was that monitoring data could be entered and summarized in the field, eliminating the long delay required by transport to and from the College. Hence the new database had to provide multi-user features such as the ability to synchronize datasets across non-networked computers. A mechanism was needed to also minimize the risks of over-writing new data with old data.
• Facilitate future expansion. An information system for a program as large and dynamic as ADMADE needs to be able to evolve with the times. New datasets, new summaries, new layers of spatial data, new maps, and new users, are all examples of likely changes the database will need to accommodate over its life span.
• Improve documentation. Strong documentation was an important requirement for many of the desired features in the new database, including the high degree of user-friendliness, multi-user features, and flexibility for expansion. Documentation for the new database required not just the standard printed materials, but also context sensitive online help. Furthermore, since the new database was going to have multiple architects and an order of magnitude greater number of ready-made summaries, there was a need to integrate documentation for the individual outputs, including tabular summaries, interactive maps, and graphs.
• Capture qualitative data. GIS and database systems are most adept at processing and analyzing quantitative data, however much of the most interesting data and interpretation of monitoring results in community-based natural resource management projects can only be expressed qualitatively (i.e., through text or pictures). Furthermore, ADMADE's research unit has amassed a significant amount of qualitative data in the form of field reports, correspondence, trip plans, workshop proceedings, and land use resolutions. This qualitative data is critical to interpreting the quantitative results of monitoring data, and also needs to be available to the user through a common interface.
• Make monitoring data more readily accessible. Although it will be some time before rural communities will have the capacity to use computers, the trend is certainly in that direction and there are a host of other potential users for ADMADE's monitoring database. These include other departments in ZWA, the donor USAID, and ADMADE's institutional partners within Zambia and abroad. With a cleaner, more robust information system, ADMADE hoped to improve its technical capacity for sharing all or some of its raw data or summaries with other partners, either electronically or in hard copy.

Software

Once the objectives were identified, the research unit set out to design the data structure for the new information system. A relational data model was chosen, based around a small number of key lookup tables and several details tables. To implement the new information system, MS Access was selected to handle the tabular data processing and analysis. MS Access, which is bundled with the widely used MS Office, was the natural choice as the main development environment for the database, because it is commonly available, relatively cheap, customizable, and quite powerful for small to medium sized databases. MapObjects, an ActiveX component from Esri, was selected as the tool for integrating mapping capabilities into the application. PowerPoint, which speaks the same programming language as both MS Access and MapObjects, was selected to automate the creation of the large format layouts - which mix tables, maps, and charts - needed for community meetings.

Once development objectives and data structure were more or less settled upon, the upgrade process began in earnest in November 1998. All of ADMADE's existing tabular data was first converted from spreadsheets to the relational data structure using a combination of VBA and Access queries, and data entry forms designed. The dozens of GIS coverages for separate GMAs were cleaned, converted into a common projection system, and merged into five nationwide coverages: GMA boundaries, 5 km2 grids, scout camps, roads, and rivers. Lastly, a menu system, filtering system, and mapping capabilities were added to the application. By May 1999, the new database was basically complete and became the working information system for ADMADE.

Results

The new database, dubbed the ADMADE Data Manager (ADM), is now the working information system at the College and is also being used at the ADMADE coordinating office in Lusaka. ADM uses a combination of built-in Access features and customized enhancements, explained below.

Built-In Access Features

• Relational data structure. Like most modern databases, Access supports relational data structures, which simply means that related data is divided into different tables. For example there is a table for staff, a table for species, a table for Units, a table for field patrols, etc. The information in all of these tables are linked together with ID numbers, which computers can process much faster than text. Using a relational data structure saves a significant amount of disk space and improves performance when querying or summarizing data.
• Enforced data integrity. Access is well equipped to ensure that data saved in related tables does not violate referential integrity rules, and that all required fields are filled in. When data integrity is enforced, it becomes impossible to add the same record twice, and impossible to enter incomplete data. For example, you couldn't enter a new field patrol record unless there was a valid value for the date it took place and Unit where it originated from. This feature, along with the relational data structure, eliminates many of the potential errors that can be caused by inconsistent spellings, partial records, etc.
• Replication. Replication is another built-in feature of Access that allows multiple copies of the same database to synchronize data. Replication is a real lifesaver in a non-networked facility like the College, where two or three copies of the database are needed just for data entry workstations, and others may be needed for performing analyses and generating outputs. With replication, it is relatively simple to make certain all copies of the database are using the most up-to-date data, and each copy has the latest collection of preset summaries and outputs.

CUSTOM DESIGNED FEATURES

User-friendly menu system
A database with as many different types of datasets and summaries as the ADM requires a menu system to navigate among the many different choices. ADM features a standard three-tiered point-and-click Main Menu, and a simple single document interface. Choices on the Main Menu can be easily expanded or modified using the Menu Manager, meaning the new summaries, graphs, or slide templates can be added to the menu system. The menu system also features integrated object filtering and documentation, described below.

Decision model menu
In addition to the Main Menu, which is structured around a thematic grouping of the datasets, ADM offers an alternative menu structured around decision making processes. The Decision Model Menu is an effort to merge the conceptual basis of decision making with the wealth of information stored in ADM. Each decision process is represented by a simplified flow chart model, with relevant data objects listed for each factor which affects the ultimate decision. Users can edit decision models or make new ones through a simple easy to use interface.

Object filtering
Because ADM stores monitoring data for all years and GMAs in the same tables, it needs a mechanism to allow the user to specify which year(s), GMA(s), species, village(s), etc. should be presented in the different summaries. This is achieved through a "Filter Manager", which is integrated into the menu system and pops up each time the user opens a new summary, chart, map, etc. The Filter Manager features an easy point-and-click interface, and offers several different ways users can select the data they're interested in. It is easy to build filtering into new queries, reports, graphs, maps, etc. just by including the primarily lookup tables in the record source.

Figure 4 - The ADM Filter Manager

Integrated object documentation
In addition to the users guide, which explains how to use and expand ADM as a whole, the menu system also features documentation for individual objects including data entry forms, tabular queries, interactive graphs, reports, and interactive maps. A user can quickly see in plain English who designed the object, when it was created, what it is trying to represent, and how it is calculated. This information is available both at the Main Menu and after an object has been opened. This feature is critical to enable summaries and analyses to be reused over and over, because often the purpose and methodology behind an analysis may not be readily apparent just by looking at it.

Figure 5 - The ADM About window - part of the integrated object documentation

Data logging
ADM is a true multi-user application, being used by multiple analysts at the College and other locations. ADM's data logging feature keeps track of which records are being added, deleted, or changed. The log saves information about when data was altered, which table and which records were changed, and who made the change. Subsequently, if there are any questions about records getting accidentally altered, duplicate data entry, or synchronization problems, the data log can be referenced and the problem investigated. The data log is primarily a precautionary feature, but has proven useful for problem-solving on several occasions.

Integrated mapping capability
Using MapObjects to serve as the link between the tabular and spatial data, ADM includes user-friendly Interactive Maps for visualizing the spatial element of monitoring data. These maps present spatial summaries of data, such as an annual summary of the grids which are used by safari clients, poaching activity has been observed, generate the most revenue, have been patrolled, etc. Interactive Maps have many of the same toolbar options as ArcView, including the ability to add labels, change display colors or the classification scheme, make additional layers visible, create a legend, pan and zoom, etc. New maps can be easily created and added to the menu system by entering a new map definition, which specifies properties such as the layers which should be added, data the map should be linked to, etc. All interactive maps can be printed, copied to the clipboard, or sent to PowerPoint, and all make use of the standard features of the menu system, including filtering with the Filter Manager and plain-English documentation. ADM's interactive maps use many of the spatial layers that have been digitized at the College, as well as a few others collected from various sources. All layers are national in scope, allowing maps to be produced of multiple GMAs.

Large format layouts
One of the most important outputs of ADMADE's database are the large poster-sized layouts of monitoring data, which are used in community meetings and workshops. ADM uses OLE automation to create new layouts programmatically using PowerPoint based on data and maps from Access. Layouts can contain any combination of maps, charts, text, or summary tables. New layouts can be designed by creating a "Slide Template" definition, which specifies the layout of different elements on the page. Users can use the Filter Manager to select which data should be summarized when creating the layout. PowerPoint contains a number of drawing tools and commands that allows users to edit layout, add other elements such as digital photography, explanatory text, or clipart. When complete the layout can finally be printed on a laser printer or the College's plotter. Although the large-format layouts produced by ADM don't eliminate the need for ArcView, they greatly reduce the workload required to create the most often needed map outputs.

Click the thumbnail to see a larger image

Import-Export Object wizards
ADM is already being used at different sites within Zambia, and may one day be used at sites on different continents. The Import-Export Object wizards make it possible for an ADM user at one site to design new summaries, data tables, maps, slide templates, etc., export those objects to a temporary file, email that temporary file to a different user, and then seamlessly import the objects into the ADM menu system at the other site. These wizards make it feasible to provide long-distance tech support for ADM users who may not have the technical experience or familiarity with Access, an important feature if monitoring data is to become a tool for decision makers some distance from the data entry point.

Documentation
A comprehensive Users Guide has been written describing how to use ADM (National Parks and Wildlife Services, 1999b). The Users Guide has sections both for novice users as well as technical staff who need to know how to maintain and expand the system. The Users Guide comes both in printed format as well as a context-sensitive Windows help file. In addition to the Users Guide, a shorter overview of the technical capabilities of the new database has been written (National Parks and Wildlife Services, 1999a). These documents can be downloaded from http://nersp.nerdc.ufl.edu/~alyons/zm/adm.html.

Discussion

At the time of writing, ADMADE has had approximately one full year to use the new GIS/database on a day-to-day basis, without any external tech support. The following strengths and weaknesses have been noted:

Strengths

• Staff time savings. The new database has reduced or removed many of the data management chores, allowing staff time at the College to be used more productively on other tasks. The turnaround time between data entry and preparing summaries has also been reduced.
• Volume of data. The new database has allowed a greater volume of data to be entered and analyzed. This is because data entry is more user-friendly, allowing it to go quicker and employ a greater number of staff. Also a greater number of datasets can be entered than before, including observations from field patrols and safari hunts, crop damage, and poacher arrest records.
• Data quality. The new database has improved the quality of data used in analyses. Common data-entry errors such as misspellings have been all but eliminated, and duplicated data is much reduced. Furthermore, data quality is able to be assessed through a variety of preset statistical summaries such as histograms which reveal outliers and sampling bias.
• Outputs. The number of preset summary outputs has increased almost exponentially, and can be generated in a short amount of time. Summary maps in particular can be created in a fraction of the time it used to take to create them manually.
• Seeing the big picture. Because it is now possible to present summaries of monitoring data over multiple years and multiple areas, the research unit now has the ability to present results to interpret the 'big picture'. The relational data structure also allows the research unit to look for correlations between datasets never analyzed together before, such as safari revenue and patrolling effort. This capability in part allowed the project to recently publish a series of analytical and lessons learned documents, including a comparison of GMAs to identify the major factors affecting the success of ADMADE.
• Immediate feedback. One of the advantages of housing a research database at a training site is the ability to use monitoring results in course materials. The new database has allowed almost real-time update of monitoring, which has been a great boon to training courses because instructors can interpret results together with participants.

Weaknesses

• Replication problems. The only real technical problem staff have had with the database is using Access' replication feature. Replication is designed to work best on computers connected on a network or through the internet, and is rather clunky when it comes to synchronizing databases on separate machines. College staff therefore were forced to distribute non-replicated versions of the database, so that synchronization had to be done manually.

Future Directions

To date, the ADMADE monitoring database has been centralized at the College. In 2000, several extension outposts are being set up to provide better services to communities around the Kafue National Park, which has been less adequately served by the College's extension program due to the long distances involved. One of the roles of these outposts will to support monitoring in the GMAs buffering Kafue, an activity which will include processing dataforms and creating summaries using the new Data Manager. Computer equipment has already been procured for four of the outposts, and the new staff members have recently completed training. This is the first time ADMADE has experimented with decentralizing data processing, and if successful it will be a major step towards increasing the monitoring capacity of communities.

The development of a powerful, easy to use information system for use in rural communities is timely in ADMADE, as support for the Community Resource Boards is becoming stronger. Students from the University of Zambia have been assisting ADMADE areas and conducting applied research. Several Peace Corps volunteers are also based in ADMADE areas with the mandate to support the community based organizations. ADMADE is planning to pilot test in 2000 the use of computers in one of the Community Resource Boards. The experiment will initially focus on financial record keeping, but will grow to include processing of other monitoring datasets with the Data Manager. As community capacity continues to grow, the demand for information tools such as the Data Manager will only increase as well.

Another option being explored is putting some of the database on the web, either statically or interactively. Although this venture would not serve rural communities, who are the main beneficiaries of the database and the owners of the data, it could benefit other stakeholders including various units in ZWA and ADMADE's partners in the donor community. Putting data online interactively would require a fair amount of technical assistance and system development, however static summaries of monitoring data can be made available fairly easily with the current system.

Conclusion

Information tools including GIS play an important role in community-based resource management. Tools such as the ADMADE Data Manger add value to the much more difficult tasks of data collection for resource monitoring and law enforcement. Ideally information processing and analysis capabilities will one day exist in rural communities themselves, but in the meantime a well-designed information system can enable a small technical team to support the information processing needs of a large project area. GIS outputs help community based organizations plan development activities, evaluate management efforts, and focus interventions and dialog on the key resource issues. In the continuing experiment of community based natural resource management, the ability to use information for strategic planning may just turn out to be the one critical factor that differentiates those areas which are able to maintain vibrant human and wildlife populations, and those areas which follow the unfortunate history of depletion of Africa's unique wildlife.

Acknowledgements

The work described in this paper builds upon ten years of effort developing ADMADE's information system by numerous staff and volunteers. Thanks go first to the many ADMADE staff who have played a formative role in developing the information system, including Bill Muyawa, James Katumbi, Zachs Nyorongo, and Phillip Ngulube. Thanks also go to GIS volunteers Kai Snyder, Jody Hilty.

ADMADE has been a long-time beneficiary of the Esri conservation program, whose support has been critical for developing GIS capability at the College. The work reported on in this paper was also supported by the Wildlife Conservation Society of New York and USAID/Zambia.

References

Aspinwall, D., Bingham, M. G., Chundama, M., Jeffery, R. C. V., & Sinkamba, M. (1996). Zambia a natural resources profile. Lusaka: World Wide Fund for Nature (WWF).

Lewis, D. M. (1995). Importance of GIS to community-based management of wildlife: lessons from Zambia. Ecological-Applications, 5(4), 861-871.

National Parks and Wildlife Services. (1993a). The ADMADE Geographic Information Systems Staff Manual.

National Parks and Wildlife Services. (1993b). ADMADE monitoring designs and methodologies . Mfuwe: National Parks and Wildlife Services, Nyamaluma Community-Based Wildlife Management Centre.

National Parks and Wildlife Services. (1995). Safari Hunting Monitoring Manual . Mfuwe, Zambia: Nyamaluma Institute for Community Based Resource Management.

National Parks and Wildlife Services. (1996). A Portfolio Sample of Monitoring Results from Selected Units in the ADMADE Programme, 1996 . Nyamaluma: Nyamaluma Institute.

National Parks and Wildlife Services. (1999a). ADMADE Data Manager An Introduction. Mfuwe, Zambia: Nyamaluma Institute.

National Parks and Wildlife Services. (1999b). ADMADE Data Manager Users Guide. Mfuwe, Zambia: Nyamaluma Institute.

Author Info:

Dale Lewis
African College for Community Based Natural Resources Management
P.O. Box 82
Mfuwe
Zambia
email: admade@compuserve.com