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Copyright © 2004 by the author(s). Published here under license by The Resilience Alliance.
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Lynam, T., R. Cunliffe, and I. Mapaure. 2004. Assessing the importance of woodland landscape locations for both local communities and conservation in Gorongosa and Muanza Districts, Sofala Province, Mozambique. Ecology and Society 9(4): 1. [online] URL: http://www.ecologyandsociety.org/vol9/iss4/art1/
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Report, part of Special Feature on Strengthening adaptive capacity Assessing the Importance of Woodland Landscape Locations for Both Local Communities and Conservation in Gorongosa and Muanza Districts, Sofala Province, Mozambique Timothy Lynam1, Robert Cunliffe, and Isaac Mapaure
1Tropical Resource Ecology Program, University of Zimbabwe
- Abstract
- Introduction
- Process
- Results
- Basic needs and the natural environment
- The importance of woodland landscape units to local communities
- Biodiversity conservation importance scores and potential conflicts between conservation and livelihood systems uses
- Confronting conservation importance scores with local community importance scores
- Discussion
- Responses to this Article
- Acknowledgments
- Literature Cited
In collaboration with two communities living in, and on the edge of, Gorongosa National Park (GNP), Mozambique, we researched the importance of different landscape units to these communities and used the information to develop a management plan for GNP. We conceived the importance of a landscape to local people as a ratio of the benefits they derive from it and the costs of accessing or using those benefits. To test this expectation, we developed Bayesian belief models, for which the parameters were the relative preference weightings derived from community members (the relative preferences for benefits and relative expectations of costs). We then collected field data to confront the models for each of the two sites.
In a parallel process, we conducted a vegetation survey to generate a map of the vegetation types, as well as an index of biodiversity importance for each vegetation type of the two 20-km2 sites.
For each site, we simplified and converted the benefit:cost model into a local community importance surface, or map, and then overlaid a conservation importance surface on it in order to identify locations that were of high importance to both conservation groups and the local community. Such areas would require careful management attention. This paper discusses the implications of the research for the planning of GNP, as well as the strengths and weaknesses of the approach.
KEY WORDS: Bayesian Belief Network, Mozambique, conservation importance, landscape importance, participatory methods.
Published: April 22, 2004
During the process of developing a management plan for Gorongosa National Park (GNP) in northern Sofala Province, Mozambique, the presence of people living within the Park and its immediate vicinity was identified as a major management problem. The major objective of the Park was to conserve ecosystems and biodiversity. Local people were recognized as users of natural resources, but Park management had set itself the objective of ensuring that the use of resources did not undermine the achievement of conservation, recreation, and knowledge-generation objectives. Little was known about the spatial patterns of resource use by local communities nor what areas were likely to be heavily impacted by community use of resources. Therefore, our research aimed to develop and test an approach for estimating local importance scores for landscape units, and then relating them to formal biodiversity conservation importance scores.
The process we used differed from other assessment procedures in two important ways. First, we sought to identify and assign relative importance scores to elements of a landscape using comparable scoring techniques. We did not attempt to identify the value of goods and services either at the margin or as stocks (e.g., Campbell et al. 1995, Lynam et al. 1994), nor did we try to value land. Second, we did not attempt to assign monetary or quasi-monetary values to the landscape units in the manner of Costanza et al. (1997), Lynam et al. (1994), and Campbell et al. (1995). We do not debate value nor do we use the term at all, as it brings with it a great number of preconceptions that are not useful in this analysis (Farber et al. 2002). Our objective was to identify and then compare the relative importance of landscape units to both local communities and conservation scientists and managers, using a simple approach and neutral units. Our approach is closer to the discourse-based valuation processes that incorporate social and equity issues (Wilson and Howarth 2002). We sought first to develop spatially explicit answers to the following two questions: 1) How important is each landscape unit to the well-being of the people living in the two communities? 2) How important is each landscape unit to the conservation of vegetation diversity in these same areas? Second, we sought to do this in a way that helped us understand which factors contributed meaningfully to determining the importance of each landscape unit to the communities.
We conducted participatory analyses in two village-scale sites (Fig. 1): Muaredzi (Appendix 1), which lies entirely within the boundaries of GNP, and Nhanchururu (Appendix 1) which straddles the boundary of GNP. We used a combination of participatory research methods, Bayesian probability modeling, and spatial data analyses of baseline digital data sets and remotely sensed images to iteratively improve our understanding of the factors determining the importance score that local people assign to specific landscape elements or locations (Appendix 2).
In parallel to this participatory process, we assessed the vegetation diversity of these same areas using standard scientific methods (Appendix 3), interpreting satellite imagery and then field sampling to validate the resultant maps and to fill in the details of species composition in each vegetation type. We scored and ranked vegetation types in order of conservation importance. Conservation importance scores were derived as a function of the relative area of each vegetation type, species diversity of each vegetation type, and the presence of key species of conservation interest. We overlaid the local landscape importance scores with the conservation importance indices to identify areas where conflicts between village use and conservation were likely to be high, i.e., where both conservation and village importance scores were both high.
Community resource use assessment teams (CRUATs) were elected by the people of each village to work with our scientific team. The analysis followed the same pattern in each site. First, for each site. we developed a prior model or hypothesis of the importance of each landscape unit to local villagers. In these models, we defined landscape unit importance as a function of the ratio of benefits derived from the unit to the costs of procuring these benefits. The greater the ratio, the more important the site.
The models were constructed as Bayesian Belief Networks (BBNs). Initial prior models were developed using the weights derived from the CRUAT to define the relative weights of benefits or costs. These models were then updated, using data collected in the field, to yield posterior models.
The CRUAT listed and scored, in terms of relative importance, the basic needs that households require for an adequate quality of life. The CRUAT then mapped the local landscape into locally identified and recognizable units and listed the goods and services that emanated from each unit. Using the scores allocated to basic needs, an index of the gross importance of a landscape unit was estimated as the weighted sum of goods and services derived from the landscape unit or location. The weightings were the local relative importance scores for each good or service. These scores were used as the prior weights in the BBNs. The cost component of the model was estimated as a function of the distance from the village to the location or landscape unit and any institutional or physical barriers that increased the labor costs of procuring or using the resources. Local estimates of the relative contributions of each of these cost components were identified and converted into spatial cost maps using a GIS. Our final estimate of landscape importance was then created as a spatial map of the benefit:cost (B:C) model.
To explore the usefulness of the model, we confronted it with real-world data. Randomly selected locations were visited by members of the CRUAT who scored each location for all model components: benefits, costs, and final importance. We used the resulting data to confront the model and update it.
The livelihood systems of both villages that participated in the local valuation of our landscape functions project are dominated by natural resources-based production with very few external inputs (Tables 1 and 2). Food is derived from local agricultural production based on a tree fallow system of nutrient replenishment, from forest products, from wild foods, and from purchased commodities. The latter contribute only about 20% of the total food input, although this increases in drought or flood years. Most household basic needs are also directly derived from natural resources: houses are constructed from cut trees bound with tree fiber and roofs are thatched using grass; water is drawn from shallow ground wells or rivers. The villagers obtain cash through the sale of grain, livestock, and natural products. Non-agricultural food products become very much more important in drought and flood years, eventually supporting the household.
The Importance of Woodland Landscape Units to Local Communities
A very large number of products were used from the landscape of both village sites. We aggregated many of these into classes of product that satisfied specifically identified needs. For example, there were four different types of honey but we classed them all as “honey”, in the “wild product” category. Thus, the benefit side of the local valuation was based on the supply of between 13 and 25 categories of goods.
The goods that contributed most to the importance of landscape units were water, land for agriculture and housing, construction materials (these included poles, fiber, thatching grass, and reeds), firewood, general household and craft materials (such as wood for tool handles, reeds for mat construction, or materials for constructing pestles and mortars), and various wild foods. This pattern of importance scores associated with the goods derived from natural resources is similar to those observed elsewhere in southern Africa (Cumming and Lynam 1997). Villagers collect or use resources from areas of about 300 km2 for a village of 40 to 100 households. Again, this is a similar area to that observed elsewhere in the region (Cumming and Lynam 1997).
For both sites, the cost factors identified as inhibiting access to natural resources were dominated by a lack of tools, inputs or equipment, and official regulations (Tables 3 and 4). Distance was not seen as a major constraint at either site as the constraints identified were dominated by the unavailability of inputs such as tools or knowledge. As an attribute of a given location, distance from the village area was the most important cost-determining factor.
Important lessons that emerged from the analysis regarding the factors governing local valuation of landscape functions or locations included the following:
- Village landscapes are important for the bundles of ecosystem goods and services that people derive from each location in the landscape (Figs. 2 and 3).
- In terms of predicting the importance of a given location, the preference-weighted sum of stocks of resources on a given site was a good predictor of the importance scores local people assigned to that location (Figs. 4 and 5). Costs, distance, and local (traditional) regulations and institutions did not play much of a role in determining the importance assigned to a location by local users.
- Strictly enforced regulations, such as are prevalent in some areas of GNP and for some resources, did act to exclude users and thus greatly reduce the importance scores assigned to the given location.
Both sites included a range of vegetation types, from open grassland areas through various savanna woodlands to thickets and forests. We identified 13 vegetation types for Muaredzi (Fig. 6, Table 5) and seven for Nhanchururu (Fig. 7, Table 6), although the total number of plant species recorded was similar for both sites (231 for Muaredzi and 246 for Nhanchururu). For both sites, it was the thicket and forest communities that were identified as being of greatest biodiversity conservation importance, both on the basis of their species composition and, particularly, because of their limited occurrence in the overall landscape.
For both village areas, the thicket and forest ecosystem types had both the highest conservation importance and the highest local livelihood importance scores. These landscape units are likely to be under the greatest threat from village-level consumptive use and, thus, are where the greatest conflict is likely to occur in terms of meeting both conservation and livelihoods needs.
Confronting Conservation Importance Scores with Local Community Importance ScoresFor each site, three-dimensional B:C surfaces were generated, based on the logic of the BBN models, where the x- and y- dimensions of the surfaces equated to spatial x- and y- coordinates and the z-dimension represented the B:C ratio (Figs. 8 and 9). The cost components of these surfaces can be visualized as bowls of increasing costs, with the households of the village in the center and valleys of lower cost associated with roads or pathways leaving the village. The benefit component of these surfaces was related to the vegetation maps. These maps provided simple visual representations of the importance of locations in the local landscapes. In Muaredzi, for example, the very steep costs of crossing the Urema River, and thus breeching official regulations, contribute to a steep drop-off in importance across this river boundary. With local confusion as to where the Park boundary lies in Nhanchururu, this steep change in importance is not where the Park administration believes it should be.
In general, the landscape units that had the highest local importance were also those of high conservation importance (Figs. 10 and 11). There were some landscape elements that were of high importance to the community (e.g., termite mounds in Muaredzi) that we were unable to map because the resolution of the data was insufficient in relation to the size of the units. These fine-scale, localized, high importance areas are not captured in the maps we generated.
Community use of resource areas can be divided into two broad classes: land transformation and multiple use. Land transformation includes the conversion of woodland areas into cultivated fields or riverine gardens. This is clearly the most destructive process and directly and negatively impacts biodiversity and thus conservation objectives. Multiple use of given landscape units by the community can, however, under certain management conditions, remain compatible with conservation objectives.
The expansion of human populations in and adjacent to the Park will inevitably result in greater demands from people for agricultural land and for the resources that the Park seeks to conserve. Thus, it seems inevitable that conflict between the Park and the people whose livelihoods depend on Park's resources will intensify. Further conflict is likely to arise through the build-up of wildlife populations, such as elephants and large predators.
One possible solution for the Park management is to identify key ecosystem units, such as forest communities, and put in place fully enforced regulations governing the clearance of these areas for cultivation. Development of land-use zones, in collaboration with the affected local communities, may be one way of achieving this. Once these areas of both high conservation and high local resource importance have been identified, and their use regulated through zoning, co-management structures and institutions could be developed to provide sustainable multiple-use opportunities to those communities with a high dependency and capacity to manage these resource units.
As well, the Park management needs to develop and maintain functional relationships with these communities (i.e., relationships with low levels of conflict and high levels of cooperation), which will require significant management inputs. Maintaining the communities within the Park will incur additional costs, including both direct costs (e.g., the costs of maintaining rangers' posts in the vincinities of the communities), and indirect costs (e.g., increased fire incidence). For some areas or ecosystem units, these costs may be warranted, but for other areas they may not be. In such instances, GNP management may be better off seeking incentives to persuade communities to relocate voluntarily.
The coupling of Park ecosystems to ecosystems outside the Park (particularly hydrological couplings with Gorongosa Mountain), and thus outside the control of GNP management, means that for GNP to survive ecologically, Park management must also seek to develop fully functional co-management relationships with the local communities responsible for managing these external ecosystem elements.
Key Lessons Learned from the ProcessThe project developed and tested a rich and relatively rapid approach for identifying the current importance of landscape units to rural communities in central Mozambique, as well as the factors underpinning their importance. The approach was shown to be capable of using spatial data where available (i.e., through base maps or aerial photography) or site sampling where spatial data were not available.
Confrontation of the prior model with field data made it clear that the costs side of the model, and hence our prior understanding of the effects of costs on local importance scores, was weak. We expect that just as individual goods and services have different benefit values so do each of them have different costs associated with their collection or use. Therefore, future iterations of the approach should seek to improve the development of the cost side of our understanding. One thing that is not clear is whether the current techniques enabled the CRUAT to separate the costs of procuring or using benefits of a landscape unit from its overall importance assignment. For example, do people mentally calculate a net importance estimate for each location (net of the costs of procurement) or do they develop a gross estimate and then evaluate the costs?
The development of the conservation importance scores component of the assessment was, if anything, more difficult than the local community valuations. Mostly, this was because it was much more difficult to identify whose perceptions were of consequence. There was no concentrated community to ask. In contrast, the local community, although diverse, was in one physical location and was able to develop consensus perceptions through the processes used. Equally difficult from the conservation importance scoring perspective was the identification of importance scores for rarity or endemism. How much more important is an endemic species than a rare one?
Complete biodiversity assessments on the ground were not possible given the time and resources available. In retrospect, it would perhaps have been more efficient to use local community knowledge to develop the biodiversity estimates, using morpho-species information, rather than trying to go to species identifications. However, the problem of the importance of what to whom would still remain.
Our method was weakened because of our failure to develop and use a cross-comparative reference point or importance object. We had no absolute zero or reference point to establish the relative importance scores assigned to goods, services, or landscape units across the sites. Thus, we were limited in our ability to compare the effects of such things as tenure on the importance of landscape across the two sites.
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We gratefully acknowledge the key contributions made to the project by a wide variety of people, without whose inputs it would not have been possible to carry out the work. The CRUAT members played an essential role in the collection of field data within the two communities. For Muaredzi, we acknowledge the willing input of the following 26 informants: António Armindo, Fieda Betchane, João Botão, Inês Chuva, Rosário Diogo, Chanaze Fazenda, Mateus Fazenda, Rosa Fazenda, Nina Fernando, Florindo Jambo, Maria Jambo, Pascua Jambo, Anita João, Augusto João, Costamina João, Diogo João, Pedro João, Tima João, Zelinha Luis, Virgínia Macamero, José Mairosse, Celina Manuel, Maria Manuel, Inês Melo, Salita Pita, and Fazenda Sixpence.
For Nhanchururu, we acknowledge the following 20 members: Cassenguere Almeida, Janita Almeida, Ricardo Almeida, Lúcia António, Anita Baptista, Eusébio Fernando, Henriques Fernando, Pureja Fombe, Victória Francisco, José Carlos Fredi, Sarga Joalinho, Laurita João, Melita José, Pureja Matequesse, José Moises, Joalinho Murungo, Rodito Pureja, Fairita Saimone, Manuel Verniz Sandramo, and Melisa Zeca.
Accessing the community data required continual translations from Sena to Portuguese and English, and facilitation. We thank Pedro Dique Camissa, Euzebio Simao Sizinho, Reginaldo Alberto Casse, and Fatima de Jesus Pereira, and Mr. Atanasio Jujuman for capably providing these inputs.
The project would not have been possible without the logistical, technical, and moral support of Roberto Zolho, Administrator of Gorongosa National Park. We are extremely grateful to him. Brit Reichelt has consistently provided us with the most remarkable administrative support in Mozambique. We are equally grateful for the very able field assistance provided by numerous field staff of the Park, particularly those of Muaredzi and Nhanchururu Posts, who facilitated our stays in the field, and played an important role in passing communications to and from Park headquarters and in passing messages to community members.
We thank the District Administrators of Muanza and Gorongosa Districts for giving their permission to implement the project within their respective districts. In Zimbabwe, we thank the administrative staff of TREP for their ongoing support to the project; Ms. Astrid Huelin for assistance with procuring and processing the satellite imagery, and Mr. Isau Bwerinofa for extensive assistance with digital mapping.
We gratefully acknowledge the financial support of CIFOR. We are most grateful to Wil de Jong for his continued encouragement and support, and to Doug Sheil and Miriam Van Heist for sharing their experiences from Indonesia and for contributing toward the initial shaping of the study.
The cost of publishing this article was offset by a grant from the Open Society institute and by the Resilience Alliance.
Two anonymous referees provided helpful comments to improve the paper.
