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

Abstract

Forest loss and degradation globally has resulted in declines in multiple ecosystem services and reduced habitat for biodiversity. Forest landscape restoration offers an opportunity to mitigate these losses, conserve biodiversity, and improve human well-being. As part of the Bonn Challenge, a global effort to restore 350 million hectares of deforested and degraded land by 2030, over 30 countries have recently made commitments to national forest landscape restoration. In order to achieve these goals, decision-makers require information on the potential benefits and costs of forest landscape restoration to efficiently target investments. In response to this need, we developed an approach using a suite of ecosystem service mapping tools and a multi-objective spatial optimization technique that enables decision-makers to estimate the potential benefits and opportunity costs of restoration, visualize tradeoffs associated with meeting multiple objectives, and prioritize where restoration could deliver the greatest benefits. We demonstrate the potential of this approach in Uganda, one of the nations committed to the Bonn Challenge. Using maps of the potential benefits and costs of restoration and efficiency frontiers for optimal restoration scenarios, we were able to communicate how ecosystem services benefits vary spatially across the country and how different weights on ecosystem services objectives can affect the allocation of restoration across Uganda. This work provides a generalizable approach to improve investments in forest landscape restoration and illuminates the tradeoffs associated with alternative restoration strategies.

Optimizing wetland restoration to improve water quality at a regional scale

“…Outputs from the multi-objective genetic algorithm were used to analyze tradeoffs between P reductions and costs. Such multi-objective spatial optimization approaches are common when solving conservation problems at regional scales (Kennedy et al 2016, Gourevitch et al 2016). We implemented the genetic algorithm using the Distributed Evolutionary Algorithms in Python package in Python version 2.7 (Fortin et al 2012).…”

Decision support tools for forest landscape restoration: Current status and future outlook

“…Efficiency frontiers were modeled in Uganda as part of a multi-criteria spatial optimization study for systematic restoration planning (Gourevitch et al 2016). Different regions of the country vary in their potential for improving the supply of specific ecosystem services and biodiversity.…”

“…This result is explained by the fact that conditions where water quality is improved most differ from those where carbon storage is maximized. Water quality is improved most in areas close to stream networks that have steep slopes and large upslope watershed areas, whereas increase in carbon storage is highest in severely degraded areas with relatively high mean temperatures and annual precipitation and lower seasonality (Gourevitch et al 2016). Efficiency frontiers with other combinations of objectives, such as carbon storage and biodiversity, water quality and biodiversity, or avoided opportunity costs and carbon storage showed that regardless of where restoration is implemented, carbon storage, water quality and biodiversity will be improved.…”

“…Efficiency frontiers with other combinations of objectives, such as carbon storage and biodiversity, water quality and biodiversity, or avoided opportunity costs and carbon storage showed that regardless of where restoration is implemented, carbon storage, water quality and biodiversity will be improved. In particular, restoration of a large area can potentially hold high values for both biodiversity and carbon storage (Gourevitch et al 2016). When these results were presented to the Ugandan government, the use of efficiency frontiers enabled decision makers to clearly visualize trade-offs that may influence target areas for investments in restoration.…”

“…Gourevitch et al (2016) mapped the potential of restoration to increase carbon storage, improve water quality and increase biodiversity across the entire country of Uganda, to determine optimal restoration scenarios based on efficiency frontiers for pairwise combinations of these objectives (Chapter 3). Their approach using ecosystem service mapping tools (Chapter 3) and a multi-objective spatial optimization technique enables decision makers to visualize areas within the country with high potential benefits of restoration, where potential trade-offs among these benefits occur, and how the opportunity costs of restoration may affect feasibility (Figure 11).…”