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Malinga, R., L. J. Gordon, R. Lindborg, and G. Jewitt. 2013. Using participatory scenario planning to identify ecosystem services in changing landscapes. Ecology and Society 18(4): 10.
, part of a special feature on Applied Research for Enhancing Human Well-Being and Environmental Stewardship: Using Complexity Thinking in Southern Africa
Using Participatory Scenario Planning to Identify Ecosystem Services in Changing Landscapes
1Stockholm Resilience Centre, Stockholm University, 2Department of Physical Geography and Quaternary Geology, Stockholm University, 3Centre for Water Resources Research, Umgeni Water Chair of Water
Resources Management, University of KwaZulu-Natal
There is a growing interest in assessing ecosystem services to improve ecosystem management in landscapes containing a mix of different ecosystems. While methodologies for assessing ecosystem services are constantly improving, only little attention has been given to the identification of which ecosystem services to assess. Service selection is mostly based on current state of the landscape although many landscapes are both inherently complex and rapidly changing. In this study we examine whether scenario development, a tool for dealing with uncertainties and complexities of the future, gives important insights into the selection of ecosystem services in changing landscapes. Using an agricultural landscape in South Africa we compared different sets of services selected for an assessment by four different groups: stakeholders making the scenarios, experts who have read the scenarios, experts who had not read the scenarios, and services derived from literature. We found significant differences among the services selected by different groups, especially between the literature services and the other groups. Cultural services were least common in literature and that list was also most dissimilar in terms of identity, ranking, and numbers of services compared to the other three groups. The services selected by experts and the scenario stakeholders were relatively similar indicating that knowledge of a study area gained through the scenario exercise is not very different from that of experts actively working in the area. Although our results show limited value in using scenario development for improved ecosystem service selection per se, the scenario development process triggers important discussions with local and regional stakeholders about key issues of today, helping to more correctly assess changes in the future.
Key words: agriculture; complexity; ecosystem services; future; landscape; scenarios; social-ecological systems; South Africa; uncertainties
There is a growing interest in assessing how complex landscapes generate multiple ecosystem services as a basis for improved ecosystem and land management (Bennett et al. 2009, Parrott and Meyer 2012). While many earlier ecosystem service assessments focus either on a single service in a specific place (Kühn et al. 2006, van Wilgen and De Lange 2011) or on the services generated from a specific ecosystem (Tottrup 2004) or biome (Smit et al. 2008), current research often addresses multiple services studies in real landscapes, containing a mix of social-ecological systems (Reyers et al. 2009, Raudsepp-Hearne et al. 2010, Lavorel et al. 2011). The methodological progress in ecosystem service analysis is developing rapidly (Carpenter et al. 2009, de Groot et al. 2010), but there has been little discussion of how to select the specific services to analyze in an assessment, despite the potential impact the service selection has on the outcome and application of the assessment.
Identifying relevant ecosystem services to assess is a fundamental step (Seppelt et al. 2012), but there is no standardized way of doing this. In the cases when the selection of relevant services is discussed, it is based on one, or a combination of some, of the following approaches: (1) literature reviews (Anderson et al. 2009), (2) data availability (Raudsepp-Hearne et al. 2010, Rogers et al. 2010, Wendland et al. 2010), (3) case-specific needs, issues, and trends (Fisher et al. 2011), (4) local and national policy goals (Wendland et al. 2010, Fisher et al. 2011), (5) representation of ecosystem services categories (Posthumus et al. 2010, Raudsepp-Hearne et al. 2010), and (6) integrated knowledge of stakeholders (Reyers et al. 2009, O'Farrell et al. 2010, Willaarts et al. 2012).
Most assessments focus on ecosystem services that are known to be of current importance, unless the study specifically deals with the outcomes and trade-offs of a specific change process, such as the development of hydropower (Wang et al. 2010), or a water purification plant (Kane and Erickson 2007). However, ecosystem services generated from real landscapes constantly change as a result of complex interactions among several biophysical and social factors. Assessments that do not consider temporal dynamics could hence soon become outdated, if social-ecological dynamics are not taken into account. One way to account for complex and dynamic social-ecological changes is by using scenario planning, a tool for dealing with the uncertainties and complexities of the future (Bennett and Zurek 2006, Kok et al. 2007). Scenario planning has been used to improve ecosystem management, from global (Alcamo et al. 2005, Millennium Assessment 2005) to local scales (Peterson et al. 2003a
, Bohensky et al. 2006, Nelson et al. 2009, Goldstein et al. 2012). By including a wide range of stakeholders, scenario planning can also capture case-specific needs and integrate a variety of perspectives (Reed et al. 2009).
The main objective of this study is to develop a set of qualitative scenarios in the Upper Thukela region in KwaZulu-Natal, South Africa, where a future extended ecosystem services assessment is planned. South Africa is arguably one of the world's most dynamic and changing countries, being a relatively new democracy, with large inequalities in wealth and land ownership, as well as environmental and climatic challenges. The second objective is to analyze if scenario planning is a
useful way of identifying relevant ecosystem services at the initial phase of an ecosystem services assessment. Together with local and regional stakeholders, we develop three scenarios for the future of the region. We use these scenarios to identify relevant ecosystem services for a regional future ecosystem service assessment. The services we identified are compared with an analysis of the services found in the literature on ecosystem service assessments in agricultural landscapes in Southern Africa, and with a selection of services that was carried out by experts not participating in the workshop.
We specifically address the following questions: (1) do scenario exercises envision a different set of ecosystem services to be included in ecosystem services assessments than the most common services found in literature? and (2) is scenario building a useful tool for capturing ecosystem services that are relevant to investigate in changing landscapes? We also discuss the potential usefulness, in general, of including scenario planning at an early stage in an ecosystem services assessment.
Scenario planning is a tool that can be used by various stakeholders for improving ecosystem management in the face of future uncertainties and complexities (Bennett and Zurek 2006, Kok et al. 2007). It can be useful for the management of complex social-ecological systems, since they integrate the effects that development can have on ecosystems and those that ecosystem change can have on development (Cumming et al. 2005). At a local level, they can highlight challenges and enhance opportunities for communities and regions to make decisions that consider changes in ecosystem services and their implications on human well-being (Wollenberg et al. 2000b
, Carpenter et al. 2006). It has been suggested that scenario development should be included as a core part of environmental assessments (Whitfield and Reed 2012), and it is often part of frameworks that analyze social-ecological systems' long-term ability to provide ecosystem services (Walker et al. 2002).
Scenario planning as a tool emerged from military strategy and war planning and has been developed and used across a range of sectors, predominantly in business planning (Wollenberg et al. 2000a
, van der Heijden 2005). Sciences of various disciplines use scenarios for different purposes, including trend analyses, forecasts, modeling and sensitivity analyses (Tress and Tress 2003). The approaches to develop scenarios vary greatly and range from strictly quantitative, using mathematical modeling and statistical forecasting, to qualitative, based on intuitive logic and creativity (Biggs et al. 2007). Scenarios that are presented as a set of different qualitative storylines can be used for exploring outcomes of alternative future developments, all of which are plausible (Bennett et al. 2003). These exploratory story lines are developed to be consistent and plausible narratives about the future that can capture current and future dynamics, integrating both social and ecological aspects, including management choices and their implications for development trajectories (Peterson et al. 2003b
, Bennett and Zurek 2006, Kok et al. 2007, Rounsevell and Metzger 2010). They specifically focus on the analysis of the uncertainties and drivers of change (Wollenberg et al. 2000a
). An analysis of their implications has the potential to capture complex relationships that are otherwise difficult to assess (Bohensky et al. 2006). Despite the complexity, the story lines strive to be comprehensive for scientists of various disciplines, as well as for decision makers and lay people (Enfors et al. 2008). Stakeholder participation in developing story lines varies from being expert driven (Bohensky et al. 2006, Nelson et al. 2009), to being driven mainly by local “grassroot” levels (Enfors et al. 2008).
DEVELOPING SCENARIOS THROUGH SCENARIO PLANNING IN THE UPPER THUKELA
Case study description
To test if scenario planning could be a useful tool to identify important ecosystem services in changing landscapes, we use the Upper Thukela region in South Africa, which is located in the Drakensberg mountain range near the border of Lesotho (Figure 1). The region covers approximately 3000 km2
and consists of 13 quaternary catchments (the fourth-order water resource management unit in South Africa (Department of Water Affairs 2011)) in the upper part of the Thukela River catchment. The mean annual precipitation ranges from around 550 mm/year in the lower valley regions to 2000 mm/year in parts of the Drakensberg Mountains, with an altitude of 3000 m above sea level (Lynch 2004). Approximately 150,000 people live in the region, with an average of 5.2 people per household (estimated from Statistics South Africa 2009). The region has two small towns or commercial centers, Bergville and Winterton, although the majority of the population lives in rural settings. The region hosts large-scale commercial and smallholder farming, as well as a World Heritage site and associated nature reserves adjacent to each other. The landscape hence provides a diversity of land uses and cultural identities. Landholdings by commercial farms are around 200-2000 ha, with sizes of crop fields around 25-200 ha (Figure 1). Smallholder agricultural lands are much more heterogenic, with homesteads and fields mixed in a mosaic landscape. Here, crop fields are 0.5-3 ha, and households, including 5-15 members, seldom have total landholdings larger than 5 ha. The landscape is dominated by grasslands that are either conservation areas (17% of the total area), or managed for commercial or communal grazing (57% of the total area) (Figure 1). Cropland is the second largest land use, and maize is the main crop produced among both commercial and smallholder farmers. Commercial croplands are dominated by large fields and dams. The dams are used for irrigation, which provides an opportunity for double cropping. The smallholder crop fields are rainfed, while vegetable gardens are watered by small-scale rainwater harvesting solutions. Tourism is important for the regional economy and the rural countryside, although most tourists mainly visit the nature reserves and national parks.
The Upper Thukela region, like many other places in South Africa, shows the legacy of the historical distribution of land during apartheid. White farmers took or were given large parts of the land, while the African people were given only small tracts of land for subsistence agriculture. Although some land has been transferred back to Africans, land ownership is still largely unequal. There are structural, biophysical, cultural, and historical constraints to development in the area and many people are trapped in poverty, with low production of their lands (Francis 2006). There are two distinctly different types of farming cultures located next to one another, with similar biophysical preconditions. The different farming practices and methods have evolved in parallel over several decades. The previously oppressed black majority is still, to a large extent, underprivileged and although the new democratic constitution and legislation are striving for equality, the process is very slow (Thornton 2009).
Participatory Scenario Planning Approach
The objective of the scenario planning exercise was to develop a set of alternative scenarios of social-ecological changes in the region, from which ecosystem services, that are prone to change, can be identified. We used a participatory scenario planning method, modified from Bennett and Zurek (2006) and Enfors et al. (2008), in which stakeholders are actively involved in developing the scenarios. The scenarios are developed as story lines of how the area changes until 2030 (i.e., over the next 20 years, from the time when the scenarios were developed). When we developed the scenarios, we also chose to focus on the general social and biophysical changes in the region and their effect on land use, rather than detailed changes in ecosystem services. This was done for three main reasons. First, it can require significant time to sensitize people about the concept of ecosystem services to a satisfying degree (Cowling et al. 2008). The majority of the participating stakeholders had not worked specifically with ecosystem services before. Secondly, this made the scenarios more broadly useful for other change processes in the organizations the stakeholders represented (e.g., local governmental organizations, NGOs and local municipalities). Finally, scenario development is a lengthy activity making it difficult to demand more time from participants.
The scenario development process consisted of four steps (Figure 2): initial interviews with local users (Figure 2, step 1), a workshop with stakeholders defining the main characteristics of the scenarios (Figure 2, step 2), drafting the scenarios (Figure 2, step 3), and then testing them with the local users and with internal, as well as external, stakeholders (Figure 2, step 4) in order to refine them.
First, semi-structured interviews were conducted with individuals or small groups (two to four participants) from the three categories of local stakeholders, i.e., small-scale subsistence farmers, large-scale commercial farmers, and representatives from nature reserves (Figure 2, step 1). We defined local users as people who work at a local scale and who have a broad knowledge of local conditions and prerequisites. The participants were identified by local community facilitators and researchers who have previously been involved in stakeholder analysis in the region and were therefore considered to have sufficient and intimate knowledge of the relevant stakeholders for this purpose (Reed et al. 2009). In total, 22 local users were interviewed. The interviews with subsistence farmers were facilitated by a translator. The main focus of the interviews was to identify drivers (socioeconomic/ cultural or biophysical/ environmental) that were perceived by the local users as possibly causing change in society during the coming 20 years, and that would have a potential influence on land management. The participants then ranked the identified drivers according to which were perceived to be the most important and the most uncertain (see Table A1.1 in Appendix 1).
As a second step, we held a two-day scenario building workshop (Figure 2, step 2) with a wide range of stakeholders (researchers, policy makers, practitioners, resource managers, and resource users), with local, regional, and global knowledge and expertise. The workshop participants were identified through a combination of focus groups and snowball sampling (Reed et al. 2009). We interviewed four key informants, who identified relevant stakeholders for the workshop. These stakeholders were in turn asked to recommend further potential participants. The workshop participants were chosen because they have knowledge of the local area, while working at a regional to global level and thus they have a broad overview of drivers at the regional and global levels that affect local development. They are agricultural extensionists and consultants who work across the three land uses and are influenced by regional and national policies, as well as scientists who have conducted fieldwork in the region, and who have published at a national to international level. A total of 12 out of 25 invited stakeholders participated in the workshop. There was no overlap between the participants in the interviews and the workshop stakeholders.
In this process we integrated the outcomes from the interviews with local users with the experience of the people at the workshop. The drivers that were identified by the local users as the most uncertain and/or important (Table A1.1 in Appendix 1) shaped the basis of the discussions of the regional stakeholders during the workshop. These drivers played a significant role in the identification of the starting points of the scenarios, a process in which the workshop participants integrated the drivers identified by the local users with their own perception of drivers. The drivers were discussed and ranked by the participants. The five drivers that were ranked as most uncertain and/or important (security, enforcement of laws and agreements, investments, entrepreneurship, and climate) formed the starting points of the scenarios. They were combined in alternative ways, driving the future into three different and contrasting development trajectories (Table A1.2 in Appendix 1).
After setting the starting points of the scenarios, the participants developed the initial outlines of the scenarios that describe the year 2030 (Figure 2, step 2). Each scenario was aimed to be an internally consistent story that includes aspects about land use, agricultural production, infrastructure, tourism, equality, rural–urban dynamics, and livelihoods. Other drivers that were identified as important or uncertain by the local resource users, such as land reform, education, and infrastructure, were also used in the scenario development.
The full scenarios were completed and refined by the authors of this study (Figure 2, step 3). The scenarios were tested for consistency and plausibility by all stakeholder groups through an iterative process that included interviews, a survey, and a workshop in a smallholder community (Figure 2, step 4). During this process the local stakeholders confirmed that the views they expressed during the initial interviews had been taken into account in the scenario development. A few external stakeholders were consulted in addition to previous participants.
The scenario exercise resulted in three plausible and contrasting scenarios called “Equal Environment”, “Diverging Climate”, and “Adaptive Collaboration”. They highlight key issues and trends in the Upper Thukela region and describe different development possibilities over the coming 20 years. For key contrasts among the scenarios, see Appendix 2. Summaries of the scenarios are presented in Box 1, but please see Appendix 3 for the full scenarios.
From scenarios to ecosystem services
Box 1: The Upper Thukela scenarios in short
In this scenario, the national government is devoted to sustaining the natural resource base of the country and invests substantially in the rural countryside. Rural communities are blooming and agricultural production among smallholders has increased remarkably. For commercial farmers, however, the situation has become more difficult due to higher input costs, taxes, and restrictions for water and chemical use. Smallholder farmers have, with support from the government, successfully increased their productivity and their shares of the agricultural market, and urbanization has slowed down slightly. Grassland and biodiversity management has improved across the whole region, also involving large- and small-scale farmers through agri-environmental schemes. Tourists are increasingly attracted to this region and improved tourist routes have increased the number of visitors.
In this scenario, the Upper Thukela has benefited from climate change. While the government is criticized for not investing enough in development, private investors take advantage of the improved weather conditions. Commercial farmers are winners, being quite flexible and adaptive to market fluctuations. Some farmers have entered a successful niche market of organic community produce. Many smallholder farmers still struggle with low agricultural productivity due to few rural investments and lack of knowledge and technology. This has contributed to a high level of migration from the region. Carbon offset funds have been developed and they also help secure biodiversity conservation in nature reserves. There are also attempts to improve grazing management among farmers. Increased crime (stock theft and cannabis trade), has, however, pushed grazing into more marginal and vulnerable grassland. Tourism has not developed in the rural areas, due to the reputation for crime, but continues to be substantial in the highly secure nature reserves.
In this scenario, the Upper Thukela has suffered from extreme weather events, but the government does not have the resources to cover the losses. A collaborative spirit has awakened and people realize the benefits of dealing with issues together. Commercial farmers are generally less vulnerable, due to healthier grazing lands and irrigation systems, although their production has been very unreliable over the past two decades. Strong bonds between local farmers and NGOs create a new way of dealing with hardships for smallholder farmers, including the formation of agricultural cooperatives. There is also some collaboration between small- and large-scale farmers. Previously degraded grasslands have improved since the grazing pressure has decreased, due to the weather-induced death of livestock. Collaborative grazing management has also proven to be successful. The agricultural countryside attracts more and more tourists. Some smallholder farmers have opened up traditional African homestead guesthouses.
The three scenarios deal with general social and ecological changes that have implications for ecosystem services, but they do not explicitly describe how ecosystem services will change during the time period. The scenarios were used to identify relevant ecosystem services both by participants in the scenarios workshop and by external experts who had not developed the scenarios themselves.
A third of the stakeholders in the scenario workshop have substantial experience of working within the ecosystem services framework and this smaller group analyzed the scenarios (Appendix 3) in terms of ecosystem services change (Table 1). First, using the participants' previous experience of working in the region in combination with the outcomes of the discussions of the workshop, a list of 16 ecosystem services that are relevant and important today was identified (Table 1). These services were then thoroughly and methodically analyzed in terms of potential changes in each of the three scenarios. A matrix was used in which it was indicated whether a specific ecosystem service in a specific land use was assumed to change substantially (++ or --), slightly (+ or -), not to change much at all (0), or was not relevant (/) (Table 1). The proneness of a service to change was estimated as the sum of all assumed changes across the land uses and scenarios.
External experts reading scenarios
Second, we asked fourteen external experts on ecosystem services and scenarios, who were not involved with this study or with the scenario development, to read the scenarios (Appendix 3), the scenario contrasts (Appendix 2), and a brief description of the case study area. Eight experts agreed to participate, half of which have experience of fieldwork in South Africa. The selected experts are part of our extended research network working with ecosystem services and scenarios from Southern Africa, Europe, and North America. Based on the information given, they were asked to compile a list of five to ten important ecosystem services. We chose the interval five to ten services in order to capture the most common number of services in ecosystem services assessments (five) (Seppelt et al. 2011) and to limit the upper amount to ten to ensure that the experts prioritized the services. The answers from the experts who read the scenarios were gathered into one list that represents their collective view (Table A4.1 in Appendix 4), in total containing 20 different ecosystem services. Please see Appendix 4 for a detailed description of how the list was compiled.
External experts not reading scenarios
To investigate if the scenarios add value to the selection of ecosystem services by experts, we asked an additional expert group to compile a list in a similar way, but without having any knowledge of the scenarios. Fourteen experts were identified through a combination of focus group discussion and snowball sampling (Reed et al. 2009), of which eight participated. These experts have substantial experience of the case study area and are familiar with the concept of ecosystem services. The answers from these experts were gathered into one list that represents their collective view (Table A4.1 in Appendix 4), in total containing 15 different services. Please see Appendix 4 for a detailed description of how the list was compiled.
Finally, we compiled a separate list of the ecosystem services most commonly included in published Southern African ecosystem services assessments carried out in agricultural landscapes. The list was developed using the ISI Web of Knowledge (complete search methods described in Appendix A5.1) and resulted in 10 papers (Appendix A5.2), including a total of 18 different ecosystem services that have been studied in Southern African agricultural areas (Table A5.1 in Appendix 5).
We compared the similarity between all the ranked ecosystem services in the four lists, using the nonmetric Bray-Curtis dissimilarity index (Bray and Curtis 1957). This similarity measure is a modified Manhattan measurement in which the summed differences between the variables are standardized by the summed variables of the objects, i.e., identity, placement, and number of services in each list are taken into account. Low values (%) indicate little similarity and high values indicate that there is a high similarity in ranking among lists.
Multiple ecosystem services assessments most commonly assess up to five services and rarely include much more than ten services (Seppelt et al. 2011). Therefore, we compared the ten highest ranked services in each list, dividing the services into provisioning, regulating, and cultural services, using the nonparametric Kruskal-Wallis ANOVA test. All tests were performed in Primer 6 (statistical software). In order to include the ranking of the service categories in each list as well, the summed score of services belonging to each category was divided with the total scores in each list separately. We considered water quantity and water quality to be both regulating and provisioning services, and biodiversity as both a regulating and a cultural service. Hence, the score in a list, for example, for biodiversity, was equally divided between regulating and cultural services in that list. If the tenth place in a list had more than one service with the same rank all of these were included.
When comparing similarity among the four lists we found that the literature list was the least similar compared to the other three lists (the two lists based on the scenarios and the list from experts not reading the scenarios) (Figure 3). The most similar lists were the two lists based on the scenarios. The list by the experts not reading scenarios only slightly differed from the two lists compiled by using the scenarios.
Figure 4 illustrates the comparison of the ecosystem services present in the four lists, in total containing 23 services. The literature list highlights services that are less prioritized or not present at all in the other three lists, such as other material, biofuel, flood control, disease control, and habitat provision. Some services, such as tourism, cultural identity, and carbon sequestration that have a relatively high ranking in the other lists have a low priority in the literature list. However, the lists also show many similarities. Twelve out of 23 services are present in all four lists. One service, crop production, occurs, not surprisingly, as one of the three highest ranked services in each of the four lists. Two services, biodiversity and water quantity, are ranked among the three highest prioritized services in three of the lists. Water quantity, crop production, and biodiversity, in that order, are the highest ranked when all percentages are added up in all four lists.
There was a statistically significant difference in the distribution of ecosystem service categories among the top-ten lists (df = 2, p = 0.029) (Figure 5). Again, the literature list was most different from the other lists (Tukey post-hoc, P<0.05), showing fewer cultural services in favor of provisioning services. Although no statistical difference is found, the lists developed by the scenario stakeholders and the list of experts not reading scenarios appear to prioritize cultural services higher than what is found in the list by external experts only reading the scenarios and the case study description. Experts not reading the scenarios have the most even distribution among categories in their top-ten list (Figure 5).
When comparing the four lists of suggested ecosystem services to assess in this region, the literature list was most dissimilar in terms of identity, ranking, and number of services compared to the other three lists. It is interesting that the other three lists are so similar. The two things that differ among these groups are the extent to which the people in the groups have experience of working and/or living in the region (both the scenario stakeholders and the experts not reading the scenarios had this experience, while the experts reading the scenarios did not), and the extent to which they were influenced by the scenarios (the scenario stakeholders developed them, and the external experts read them). One could argue that after reading the case study description and the three scenarios, the external group also had a certain knowledge of the site specifics of the region. The fact that all three lists were so similar may therefore mean that site-specific local knowledge, which they all shared to some degree, is more important than understanding how the landscape will change over time, which only two of them shared.
Another reason for the similarity could be that the scenarios were too cautious to generate a large difference between ecosystem services today and in the future. Scenario planning is a tool for exploring uncertainties and complexities in a changing world. The scenarios developed through this research were aimed to be plausible, but none of them deal with extreme change. This may have contributed to the fact that the lists developed by the scenario stakeholders and the experts reading the scenarios were very similar.
Although scenario exercises aim to capture future uncertainties, it must be highlighted that the general imagination of the future is limited and often primarily reflects current worldviews (Carpenter et al. 2006). This could also explain the fact that the three lists compiled based on the scenarios and/or knowledge of the study area were fairly similar.
It is perhaps not surprising that the literature top-ten services are dominated by provisioning services since the literature search specifically focused on agricultural landscapes. The other three lists prioritize regulating, provisioning, and cultural services almost equally. Even though cultural services are often recognized in ecosystem services assessments, they have been given relatively limited attention to this point (Chan et al. 2012, Daniel et al. 2012). This is also reflected in the literature top-ten list in this study, of which cultural services only constitute 6%, compared to the other three lists that contain between 21% and 28% (Figure 5). The scenario stakeholders and the experts thus attribute higher importance to cultural services.
One service that was highly prioritized by scenario stakeholders, experts not reading the scenarios, and the literature search, but had a low ranking on the expert list, was biodiversity. One explanation is that biodiversity is not always considered an ecosystem service in itself (Mace et al. 2012). Some scholars argue that biodiversity should not be treated and viewed as an ecosystem service, because it underpins all ecosystem services generation (Mace et al. 2012). This could be one explanation as to why biodiversity was left out of the list compiled by the group of experts. We argue that biodiversity not only regulates ecosystem services generation, but can also be seen as a cultural ecosystem service because of the intrinsic value many people attribute to it. In addition, biodiversity conservation per se
is a political goal in many international processes (Secretariat of the Convention on Biological Diversity 2005).
Participatory scenario planning definitely takes more time and resources than a “desk top” analysis of important services for a study region. Additionally, there might be a risk that scenario planning raises hopes among
participants if the objectives and planned outcomes are not carefully communicated or completely understood. However, this scenario development process brought substantial additional benefits to our planned future ecosystem services assessment project in the Upper Thukela region. First, it proved to be a useful tool to learn about some of the major fears and expectations about the future among people in the area. Second, it explored key trends and uncertainties and identified major potential changes to people and to ecosystem services in the region over the next 20 years. The current project can now relate to these future uncertainties and changes, to avoid the risk of becoming constrained by a project vision that only applies in a status quo environment (Enfors et al. 2008). Scenario planning has helped us to understand how the project can develop in alternative plausible futures and how that relates to future services. It has also improved the understanding of events and processes that may either challenge the project or provide opportunities for it (cf. Bennett and Zurek 2006). Developing alternative scenarios, parallel to the project vision, encourages project participants to think about factors that might alter the expected development path, and to consider a number of interacting driving forces and how they are linked to ecosystem service generation through time. Finally, scenario planning can, by identifying opportunities and threats that might present themselves in the future, help a community push its development in a more desirable direction (Peterson 2007). While this is a long and challenging process, it certainly helped establish contacts between researchers and local stakeholders and provided a platform for sharing ideas and expectations, which might help future project efforts.
This study examined whether scenario development is a useful tool for selecting ecosystem services that should be included in an ecosystem services assessment of changing landscapes. We found no clear evidence that a different set of services was preferred when using scenario planning compared to when consulting experts with knowledge about the case study area. However, we found a clear difference when comparing scenario planning and expert opinion with what most commonly is found in the literature. In general, the scenario stakeholders and experts prioritize regulating, provisioning, and cultural services almost equally, while literature from agricultural landscapes prioritized provisioning services. We stress that scenario planning could be a useful tool to prioritize among the services to be assessed if you have little or no knowledge of the area, but the additional understanding of the study area gained through the scenario exercise is not very different from the understanding that experts gain from actively working in the area. Scenario development takes a lot of resources, and carrying out scenario planning solely for the purpose of identifying ecosystem services might be cost ineffective. However, through the scenario development process it is possible to establish a platform for discussion with local and regional stakeholders about the future in a region, which is very valuable for an ecosystem service project.
We thank all the farmers of Potshini community, Bergville, and Winterton, workshop participants, and all additional stakeholders and experts for valuable input. We thank three anonymous reviewers for comments that helped improve the manuscript. The work was funded by The Swedish Research Council Formas, and Ebba och Sven Schwartz Stiftelse.
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