High variation in biodiversity recovery in restored forests at landscape scale can increase the risk associated with investments in restoration programmes. Crouzeilles  et al. summarise their new approach, which aims to predict and map landscape variation in forest restoration success and thus reduce the unpredictability associated with financial risk.

Investors operating in different businesses usually avoid high-risk transactions, which likely constrains the flow of financial resources to restoration initiatives perceived as ‘financially risky’. Thus, a high level of unpredictability in biodiversity recovery in areas targeted for restoration increases the risks associated with investments in ecological restoration programmes. This high level of unpredictability can constrain both long-term ecological sustainability and functionality, and expected multiple benefits of restoration for biodiversity, ecosystem services, and human wellbeing.    To guide forest restoration policies, plans and implementation, we developed a new approach to spatially-explicit predict and map landscape variation in forest restoration success in tropical and temperate forest biomes. Our map identifies landscapes in previously forested lands where restoration is most likely to foster biodiversity recovery towards levels typical of reference forest ecosystems. Our novel approach helps policymakers, entrepreneurs, practitioners, and researchers to: i) establish forest landscape restoration targets and identify cost-effective priority areas for restoration, ii) improve regulations for biodiversity offsetting, and iii) estimate implementation costs of forest restoration in regions across the globe.

We found low landscape variation in forest restoration success when forest cover is above 30%. Furthermore, the forest biomes with larger potentially restorable areas are those with lower landscape variation in forest restoration success (Temperate Broadleaf & Mixed Forests, Temperate Conifer Forests and Tropical & Subtropical Moist Broadleaf Forests). Despite the large amount of deforested land worldwide, 38% of the 172 countries (238 M ha) that had previously forested areas still have low levels (≤ 10%) of landscape variation in forest restoration success, on average. Countries with marginally higher weighted landscape variation but larger opportunities for restoration also may be considered as no-regret targets for private restoration investments, such as Brazil and Russia (with 324 M ha restorable areas).

Helping to unlock investments in forest landscape restoration

The financial feasibility of restoration is a critical criterion when identifying priority areas for cost-effective restoration. The financial feasibility of restoration is dependent on landscape variation in forest restoration success because risky restoration initiatives (with unpredicted outcomes) are unlikely to attract investors. Identifying landscapes with low risks of restoration success can encourage greater restoration investments from NGOs and the private sector in countries and regions with a lower average of weighted landscape variation, where biodiversity recovery will be favoured. Alternatively, the public sector and government agencies may decide to spatially complement private investments in restoration by focusing more on improving local food security, the supply of ecosystem services and/or supporting local livelihoods.

Supporting biodiversity offsetting with forest landscape restoration

The lack of a robust mechanistic understanding of determinants of forest restoration success has precluded the use of restoration initiatives as a reliable operational approach to compensate for environmental degradation. Our map can be used to support and develop new regulations and policies for biodiversity offsetting, in which the total area to be restored can be weighted by values for landscape variation in forest restoration success. This weighting would require larger areas to be restored where landscape variation is higher, or prohibit compensatory restoration in areas with landscape variation above a given threshold.

Bonn challenge commitments as study cases

The restoration target in the Bonn Challenge is 350 M ha of restored forests by 2030, with 170 M ha within 59 commitments pledged to date. We have shown that the implementation costs of forest restoration could potentially be reduced by more than 80-97% if our approach is adopted (i.e. identifying landscapes with lowest landscape variation) instead of the widely preferred use of full tree planting as a restoration method. Although our approach increases opportunity costs by US$ 12M, 28M, and 282M compared to prioritising restoration in landscapes with lowest opportunity cost, these costs are compensated for by a reduction in implementation costs, which are US$ 121M, 71M, and 1.3B for Brazilian Atlantic Forest, Uganda and USA commitments, respectively.


Our study emphasises the importance of halting deforestation, particularly in areas where forest cover in the landscape declines below 30%. Implementing restoration in landscapes prior to high levels of deforestation improves cost effectiveness by reducing landscape variation in forest restoration success, which can attract the levels of financial investment needed to fund large-scale restoration focused on biodiversity recovery. In areas with high landscape variation (> 50%), forest restoration is more costly and less effective for recovery of native biodiversity. Nevertheless, landscape restoration initiatives in these areas can be vitally important for increasing the supply of a wide range of ecosystem services and improving socioeconomic conditions. These results can help guide restoration efforts towards landscapes where restoration interventions will yield higher cost effectiveness for biodiversity conservation. To implement this approach, a collaboration among International Institute for Sustainability, The Nature Conservancy, Natural Capital Project and the Tropical Forestry Laboratory of University of São Paulo is launching an independent and open source software – Gofor (Restoration Uncertainty Assessment), which will allow users to include data on forest cover to predict, map and quantify landscape variation in targeted areas across tropical and temperate forest biomes.

Read the full article, A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes, in Journal of Applied Ecology.