Optimizing investments in national-scale forest landscape restoration in Uganda to maximize multiple benefits

Increasing global demand for food, fuel, fiber, and shelter has resulted in expansion of croplands, pastures, and urban areas (Foley et al 2005). Such changes in land use are driving the loss and degradation of natural ecosystems (Gibbs et al 2010) and altering global carbon, hydrologic, and nutrient cycles (MEA 2005). Over the past 300 years, there has been a net loss of ∼700–1100 million hectares of forests globally (Ramankutty and Foley 1999). The loss of natural forest ecosystems has reduced the capacity of these lands to provide valuable ecosystem services and threatens human well-being (Foley et al 2005, Balthazar et al 2015). Confronting the effects of forest loss and degradation will require assessing the tradeoffs between competing land uses and the ecosystem goods and services those lands provide.

In particular, forest landscape restoration (FLR) is gaining prominence as a strategy to mitigate climate change, improve water quality, and increase biodiversity (Lamb et al 2005). This is a strategy that can help countries to meet international targets such as the UNFCCC REDD+ program, Convention of Biological Diversity Aichi Targets, Rio +20 goals, and the newly adopted Sustainable Development Goals. In recognition of these opportunities, the Bonn Challenge was launched in 2011 with the goal of restoring 350 million hectares of degraded lands by 2030. Over 30 countries have already committed over 100 million hectares to FLR under the Bonn Challenge, with more commitments forthcoming (Bonn Challenge 2016).

Achieving the restoration goals set by the Bonn Challenge will require national and sub-national governments and stakeholders to define their objectives for restoration and prioritize where to restore given limited resources. In order to assist national and regional governments and planning authorities in this task, international organizations have developed generalizable methodologies to help decision-makers engage diverse assemblages of stakeholders to identify FLR opportunities, develop local capacity, and identify potential financing mechanisms to support broad-scale restoration (IUCN and WRI 2014). These approaches are currently being applied in over 30 countries at the national and sub-national levels and are generating demand for information on the potential benefits and costs of FLR to more efficiently target investments.

In many countries, ecosystem services information is critical to communicating the potential benefits of restoration commitments and making a business case for restoring degraded lands. Local and regional authorities undertaking restoration planning increasingly require tools that allow them to simultaneously consider a suite of objectives associated with restoration investments, such as carbon sequestration, biodiversity conservation, food security, and water quality. Other countries planning for FLR have used benefits transfer approaches for ecosystem services assessments, such as those broadly applied by The Economics of Ecosystems and Biodiversity (TEEB, TEEB Foundations 2010). These approaches can yield large monetary estimates for the values of restoration, but these methods are aspatial and do not allow users to visualize tradeoffs among competing objectives. Because the potential benefits and costs of restoration are spatially heterogeneous and depend on ecological, socioeconomic, and climatic factors (Chazdon 2008), accurately quantifying and mapping the potential benefits and costs of restoration requires spatially-explicit modeling. Moreover, potential benefits of restoration are often measured using different (i.e. non-monetary) metrics, which can make it challenging for decision-makers to evaluate tradeoffs among competing objectives.

We aimed to add value to the restoration assessment process by combining ecosystem services mapping tools and a multi-objective spatial optimization approach that improves upon benefits transfer approaches. We applied our approach in Uganda in support of their national-level restoration commitments under the Bonn Challenge. Driven by increased demand for forest products and agricultural expansion (Obua et al 2010), Uganda has recently experienced widespread forest loss and degradation. Between 2000 and 2012, forest cover decreased by approximately 300 000 hectares, equal to 1.8% of the country's total land area, excluding water (Hansen et al 2013). In support of transition to a 'Green Economy', a pillar of Uganda's Vision 2040 (Uganda National Planning Authority (NPA) 2007), the Ugandan national government committed to the Bonn Challenge in 2014. The Ugandan government is still in early stages of planning its national restoration strategy and requires information that will help them target investments in restoration.

To identify the objectives for restoration, a stakeholder-engagement process was started in June 2014 by the Ugandan Ministry of Water and Environment. Stakeholder workshops, following the restoration opportunities assessment methodology (IUCN and WRI 2014), were held in seven regions of Uganda. In each region, officials and stakeholders from local government, civil society, and the private sector were invited to workshops to build a common understanding of the restoration planning process, discuss their environmental and social challenges due to land degradation, and to determine what changes communities hope to achieve through restoration. Many of the stakeholders' stated goals were erosion control, climate change mitigation, and increased resilience of ecosystems to disturbances, such as disease and pest outbreaks.

Building on the stakeholder engagement process, we aimed to develop generalizable screening tools that would allow decision-makers to visualize the full range of optimal restoration scenarios and understand tradeoffs among different ecosystem services given resource constraints. Our approach was not intended to prescribe a single 'optimal' solution for restoration prioritization, but instead to deliver spatially-explicit information on multiple ecosystem services provided by restoration, identify districts most likely to deliver the greatest benefits for the lowest costs, and visualize tradeoffs between objectives. Our work contributes to growing demand for and development of multi-objective optimization tools for ecosystem service assessments (Seppelt et al 2013). These approaches are increasingly being applied to conservation planning worldwide (e.g. Nelson et al 2008, Polasky et al 2008, Babbar-Sebens et al 2013, Gaddis et al 2014, Cibin and Chaubey 2015), but have yet to be applied to national-scale FLR. Our work contributes to this growing literature on how to effectively map and prioritize investments in conservation or restoration designed to maximize the delivery of ecosystem services at large spatial scales, with limited data availability and a computationally efficient optimization algorithm. As more countries plan FLR strategies, these approaches are now being scaled worldwide to help decision-makers and stakeholders efficiently identify, target, and allocate national and local resources for restoration. Here we present our methods for ecosystem services assessment and spatial optimization, describe their application in Uganda, and discuss challenges and opportunities associated with broader adoption and use of these methods in other contexts.

2.1. Mapping potential values of restoration

2.2. Identifying opportunities for FLR

2.3. Optimizing restoration for multiple objectives

We used a multi-objective spatial optimization approach to construct efficiency frontiers that illustrate the tradeoffs between different objectives and identify the districts that have the potential to deliver the greatest benefits from restoration. The efficiency frontiers represent a set of spatially-explicit optimal restoration scenarios where returns to one objective cannot be increased without diminishing returns to another objective. We defined the objective function to maximize the value of benefits from carbon storage, water quality, and biodiversity, and minimize opportunity costs by allocating the optimal amount of land for restoration within each district. In the absence of a priori weights for each objective, we generated efficiency frontiers between two objectives by continuously varying weights on each objective from 100% on one objective to 100% on the other objective over 10 000 optimization iterations, thus allowing decision-makers to visualize the tradeoffs associated with a full range of preferences. For a given weight ${w}_{{\rm{o}}}$ on each objective o, the value of restoration on each pixel p on a raster grid is ${v}_{{\rm{\text{wp}}}}=\displaystyle {\sum }_{{\rm{o}}}{{w}}_{{\rm{o}}}{v}_{{\rm{\text{op}}}}.$ We only considered the value of restoration on pixels that were identified as opportunity areas for restoration. To allocate area for restoration within each district, we sorted the ${v}_{{\rm{w}}{\rm{p}}}$ values in descending order to fit a benefit-area curve to the cumulative value with a quadratic function. The fit curves for each district, ${f}_{{\rm{w}}{\rm{d}}}({{\rm{x}}}_{{\rm{d}}}),$ give the weighted value of benefits expected from district d under weights w when ${x}_{{\rm{d}}}$ area is restored. We used a convex optimization algorithm in the CVXOPT package in Python 2.7 to allocate restoration area among decision-units (Andersen et al 2015). The objective function to maximize the total potential benefits across all districts was

$$\begin{eqnarray*}&&\mathop{\max }\limits_{X}\displaystyle \sum _{d}{f}_{{\rm{w}}{\rm{d}}}({x}_{{\rm{d}}})\mathrm{.}\,\end{eqnarray*}$$

Assuming resource constraints limit the total amount of area available for restoration, we set ${x}_{{\rm{d}}}$ equal to 50% of restoration opportunities. We generated frontiers with each possible pair of objectives. While it is possible to optimize for more than two objectives using this approach, it is difficult to visualize tradeoffs using efficiency frontiers with more than two dimensions without a priori weights for each objective (Lautenbach et al 2013). A more detailed explanation of these methods is included in the Supplemental Materials.

2.4. Comparing optimal and non-targeted scenarios

The potential values of restoration, defined by each of the four objectives, varied across the study area (figure 1). Regions of Uganda with more severe degradation, relatively higher mean temperatures and annual precipitation, and less seasonality have greater potential to increase carbon storage if restored to forest. The highest value parcels for improving water quality are those that are close to the stream network, have steep slopes, and have large upslope contributing areas. The combination of these factors results in a relatively small proportion of parcels with high relative value for improving water quality. Restoration is more likely to benefit biodiversity in the western and southeast regions, where there is a high concentration of overlapping species distributions. The opportunity costs of restoration are greatest in the eastern and southern regions, where agricultural production is high. These trends demonstrate that restoration in a particular region of the country may not provide the same relative benefits to each objective.

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We identified the range of optimal restoration scenarios using efficiency frontiers (figure 2). Points on the frontier represent optimal restoration allocation scenarios, whereas points under the frontier indicate the sub-optimal non-targeted scenario, where values for both objectives can be increased with no additional cost. Depending on how the objectives are weighted relative to each other, the optimal amount of land allocated for restoration within each district shifts to different regions of the country. For example, if the goal of restoration is to maximize increases in carbon storage, decision-makers should allocate resources for restoration to the southeastern and southwestern districts (figure 2). However, if water quality were weighed more heavily than carbon storage, the priority districts for restoration would be more evenly distributed. Increasing carbon storage does not come at the cost of diminished water quality. Rather, depending on where restoration occurs, the total potential increases in carbon storage and water quality vary relative to each other. Highly degraded parcels with relatively higher mean temperatures and annual precipitation are most valuable to increasing carbon storage, whereas parcels close to the stream network with steep slopes will be most valuable to improving water quality (Greve et al 2013, Hamel et al 2015).

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Regardless of where restoration occurs, we found that restoration will enhance carbon storage, water quality, and biodiversity. However, different districts provide greater relative benefits to one objective than other objectives, so the exact ratio of benefits relative to each other depends on the location of restoration (figure 3). For example, due to the large number of parcels with relatively high value for both carbon storage and biodiversity, it is possible to simultaneously achieve 83% of the maximum increase in carbon storage and 96% of the maximum increase in biodiversity. The tradeoffs between carbon storage, water quality, and biodiversity are relatively minimal compared with their tradeoffs with the opportunity costs. Under our assumption that restoration and agricultural production are mutually exclusive, the optimal scenario for minimizing opportunity costs will entail forgoing FLR, resulting in very low returns to the other benefits of restoration. Moreover, the shape of the efficiency frontiers (figure 3) depends on the spatial patterns and distribution of restoration values (figure 1). Because carbon storage and biodiversity have a large proportion of high value pixels compared to water quality, the returns to these objectives remain relatively constant as weights are shifted towards another objective. We also show that the optimization approach provides a substantial value over the non-targeted scenario (figure 3).

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Our work added value to the restoration planning processes in Uganda by communicating how the potential benefits and tradeoffs of restoration vary spatially. Our use of efficiency frontiers enabled decision-makers to visualize tradeoffs that may affect where investments in restoration should be targeted. These efficiency frontiers do not prescribe a particular restoration strategy, but rather illustrate the full range of optimal restoration scenarios depending on the relative weights placed on each objective.

Our results were presented to the Ugandan government and other stakeholders as part of their national FLR assessment and planning process. The communication of our analysis, consideration of several ecological and sociopolitical factors (e.g. severity of forest degradation, district-level governance), and the resulting policy discussions have led to a preliminary identification of priority districts for restoration. The final restoration assessment by the Ugandan government is forthcoming, but preliminary results from the government planning process identify districts that overlap with those identified here and those selected by stakeholders. By communicating the potential benefits and tradeoffs of restoration scenarios using accessible visual aides, our analysis underscores the value of salient, credible, and legitimate ecosystem service information in decision-making (Posner et al 2016).

While ecosystem service modeling and spatial optimization provide important information for restoration planning, it is critical that these approaches are embedded within local co-development and participatory processes. The set of objectives that we optimized for were easily quantifiable, however there other considerations for restoration that are more complex and less easily modeled using the tools we presented. Our analysis is therefore simply one of many inputs that are required for developing an efficient and equitable national-scale FLR assessment. There are also several areas of new research that could enhance our approach. These include: (1) collection of more recent, higher resolution, and locally-specific datasets for forest loss and degradation, forest composition, and other model parameters, (2) development and application of models that account for beneficiaries of restoration, temporal variation and spatial dependencies in the potential value of restoration, local biodiversity dynamics, management factors that affect carbon storage, and multifunctional agricultural landscapes, such as agroforestry or silvopasture, and (3) consideration of indirect impacts of restoration, such as displacement of forest loss to other unprotected regions.

As dozens of other countries engage in FLR planning, decision-makers require actionable information to guide investments. Our approach provides a generalizable framework for FLR planning that can be applied to other countries that have committed to the Bonn Challenge and complements existing restoration planning methodologies (IUCN and WRI 2014). For many countries, availability of recent high-resolution data is often sparse and modeling capacity is limited. Although similar approaches have been used in other conservation planning decision-contexts, this framework has relatively low data requirements, is computationally efficient, and is agnostic to the objectives for restoration.

Improving human well-being, mitigating climate change, and protecting biodiversity are all central challenges to both conservation and development. The Bonn Challenge provides a global mechanism to address these challenges. However, there is still the opportunity to better incorporate ecosystem service information into policy and management decisions. Here, we demonstrated a method for integrating ecosystem service information into a national-level FLR assessment. The knowledge gained from implementing these methods is practical and can improve the potential benefits of restoration. In addition to informing FLR planning, this information can also be used to attract private investments in financing restoration, encourage other countries to commit to the Bonn Challenge, and design payments for ecosystem services (PES) schemes (Guerry et al 2015).

This work was funded by UKAid from the UK government through the International Union for Conservation of Nature's KnowFor program. Additional financial and institutional support was provided by the Natural Capital Project, a partnership between the University of Minnesota, Stanford University, the World Wildlife Fund, and the Nature Conservancy. MG was supported by the National Research Foundation of South Africa (Grant Number 98889). The authors declare no conflicts of interest.