DOI: 10.5061/dryad.nk98sf7wr
title: A practice-led assessment of landscape restoration potential in a biodiversity hotspot

description: Effective restoration planning tools are needed to mitigate global carbon
 and biodiversity crises. Published spatial assessments of restoration
 potential are often at large scales or coarse resolutions inappropriate
 for local action. Using a Tanzanian case study, we introduce a systematic
 approach to inform landscape restoration planning, estimating spatial
 variation in cost-effectiveness, based on restoration method, logistics,
 biomass modelling and uncertainty mapping. We found potential for biomass
 recovery across 77.7% of a 53,000 km2 region, but with some natural
 spatial discontinuity in moist forest biomass, that was previously
 assigned to human causes. Most areas with biomass deficit (80.5%) were
 restorable through passive or assisted natural regeneration. However,
 cumulative biomass gains from planting outweighed initially high
 implementation costs meaning that, where applicable, this method yielded
 greater long-term returns on investment. Accounting for ecological,
 funding and other uncertainty, the top 25% consistently cost-effective
 sites were within protected areas and/or moderately degraded moist forest
 and savanna. Agro-ecological mosaics had high biomass deficit but little
 cost-effective restoration potential. Socio-economic research will be
 needed to inform action towards environmental and human development goals
 in these areas. Our results highlight value in long-term landscape
 restoration investments and separate treatment of savannas and forests.
 Furthermore, they contradict previously asserted low restoration potential
 in East Africa, emphasising the importance of our regional approach for
 identifying restoration opportunities across the tropics.
description: Here, we develop and apply a
 systematic approach to inform spatially-explicit forest landscape
 restoration planning. Our approach prioritizes cost-effective ecosystem
 recovery for timely achievement of global and regional restoration
 targets, accounting for biomass accumulation (and thus carbon
 sequestration and storage) objectives in a strategic region in Tanzania.
 The approach can be applied to any landscape-scale restoration project,
 using spatial prioritisation methods for more detailed planning than is
 possible with existing restoration decision support tools. It is based on
 direct financial implementation costs of the most appropriate methods for
 restoring native vegetation and associated biomass, biodiversity,
 ecological function and livelihood options under different scenarios and
 investment timeframes. In achieving this, unlike previous studies, we
 account for direct implementation and community engagement costs,
 logistics, expected vegetation growth, and estimated uncertainty resulting
 from incomplete ecological knowledge. The findings are intended to be
 useful for advancing the science of restoration planning and for inspiring
 donors, through development of metrics directly useful for attracting and
 prioritising grant funding. Our approach comprised four steps to
 determine the cost-effectiveness of ecological landscape restoration per
 hectare, using a 5.3 million hectare region in Tanzania as a case study.
 First, we estimated landscape above-ground biomass (AGB) deficit (and thus
 restoration potential) from current and maximum potential AGB, predicted
 by up-scaling AGB measurements in 195 plots using spectral-reflectance and
 climatic predictors, respectively. Secondly, we assigned the most
 appropriate silvicultural method for restoring AGB, i.e. passive
 regeneration, assisted natural regeneration (ANR), or planting native
 vegetation, to each hectare pixel with restoration potential, basing this
 on key landscape variables, including: vegetation type, elevation,
 severity of degradation (using AGB deficit as a proxy), and distance from
 proximal intact habitat. Thirdly, we modelled the expected rate of AGB
 gain through application of these methods and number of years to
 full-recovery of the AGB deficit. This was achieved using a regional
 dataset comprising cumulative modelled annual estimates of above-ground
 carbon (AGC, from zero to maximum) in naturally-regenerating African
 forests, generated based on vegetation plot AGB measurement over time
 (Bernal et al. 2018). Finally, we estimated the financial implementation
 costs of restoration, and therein cost-effectiveness (AGB gain per dollar
 spent), per hectare, accounting for land procurement, labour equipment and
 transport, community engagement, project management and administrative
 costs. We incorporated
 pessimistic, realistic, and optimistic scenarios into all four stages and
 estimated AGB gains, implementation costs and cost-effectiveness over two
 investment timeframes: (1) five years, to represent a typical upper limit
 of donor investment; and (2) expected time to full AGB recovery. A
 combination of expert knowledge, pilot data and literature review were
 used to determine: (a) environmental degradation thresholds for selecting
 methods; and (b) comprehensive implementation costs. Spatial variations in restoration
 potential, AGB gain, cost and cost-effectiveness were evaluated
 retrospectively in terms of technical implementation and landscape
 features of use to practitioners, namely: (a) restoration method; (b)
 landcover class; and (c) governance (protected versus unprotected areas).
 Means with standard deviations were used to summarise estimates of
 landscape AGB, which followed a broadly normal distribution, whereas costs
 and cost-effectiveness were described using medians and inter-quartile
 ranges. Cost-effectiveness of different methods, landcover and governance
 types was compared using Kruskal-Wallis tests with Dunn posthoc tests and
 Holm-adjusted P-values, Padj (Dunn, 1964). We also identified the top 25%
 most cost-effective sites for restoration across all scenarios
 (pessimistic, realistic and optimistic) and investment timeframes (five
 years and to full recovery of AGB deficit) combined. We did this both
 overall within the landscape and specifically outside protected areas, to
 identify locations with potential for community restoration schemes. To
 account for uncertainty in our estimates, we reported restoration
 potential before and after discounting areas with high “ecological
 uncertainty” from climate modelling (defined using envelope uncertainty
 maps, EUMs, following Platts et al. 2008).
description: All statistical and spatial analyses
 were conducted using R version 4.0.1 (R Core Team 2020), besides the
 distance matrices and maps, which were produced in ArcGIS Pro version
 2.7.1 (ESRI Inc. 2020). The <em>caret </em>package was used
 for modelling (Kuhn 2008) and the <em>raster </em>package for
 spatial up-scaling (Hijmans and van Etten 2012). Statistical analyses were
 performed using the R <em>base </em>package. Both our R script
 (https://bit.ly/3KO5Hgz) and all input and output maps (https://bit.ly/3QkIrrI) produced during our stepwise method are available online.