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Mapping Potential Carbon Capture from Global Natural Forest Regrowth

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Authors: Susan C. Cook-Patton1,2*, Sara M. Leavitt1, David Gibbs3, Nancy L. Harris3, Kristine
Lister3, Kristina J. Anderson-Teixeira4,5, Russell D. Briggs6, Robin L. Chazdon3,7,8, Thomas W.
Crowther9, Peter W. Ellis1, Heather P. Griscom10, Valentine Herrmann4, Karen D. Holl11, Richard
A. Houghton12, Cecilia Larrosa13, Guy Lomax14, Richard Lucas15, Palle A. Madsen16, Yadvinder
Malhi17, Alain Paquette18, John D. Parker2, Keryn Paul19, Devin Routh9, Stephen Roxburgh19,
Sassan Saatchi20, Johan van den Hoogen9, Wayne S. Walker12, Charlotte E. Wheeler21, Stephen A.
Wood22, Liang Xu19, Bronson W. Griscom23

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Regrowing natural forests is a prominent natural climate solution, but accurate assessments

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of its potential are limited by uncertainty and variability around carbon accumulation rates. To

41assess why and where rates differ, we compiled 13,112 georeferenced measurements of carbon

1

The Nature Conservancy, Arlington VA, USA
Smithsonian Environmental Research Center, Edgewater MD, USA
3
World Resources Institute, Washington DC, USA
4
Smithsonian Conservation Biology Institute, Front Royal VA, USA
5
Smithsonian Tropical Research Institute, Panama
6
State University of New York, College of Environmental Science and Forestry, Syracuse NY,
USA
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University of Connecticut, Storrs CT, USA
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University of the Sunshine Coast, Queensland, Australia
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ETH Zurich, Switzerland
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James Madison University, Harrisonburg VA, USA
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University of California Santa Cruz, Santa Cruz CA, USA
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Woods Hole Research Center, Falmouth MA, USA
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Department of Zoology, University of Oxford, Oxford, UK
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The Nature Conservancy, London, UK
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Aberystwyth University, Aberystwyth, UK
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InNovaSilva ApS, Vejle, Denmark
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Environmental Change Institute, School of Geography and the Environment, University of
Oxford, Oxford, UK
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Centre for Forest Research, Université du Québec à Montréal, Montréal, Canada
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CSIRO Land and Water, Canberra ACT, Australia
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National Aeronautics and Space Administration, Jet Propulsion Laboratory, Pasadena CA, USA
21
School of Geosciences, University of Edinburgh, Edinburgh, UK
22
Yale University, New Haven CT, USA
23
Conservation International, Arlington VA, USA
2*Corresponding author: susan.cook-patton@tnc.org
Summary42

accumulation. Climate explained variation in rates better than land use history, so we combined

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field data with 66 environmental covariate layers to create a global, 1-km resolution map of44

potential aboveground carbon accumulation rates for the first 30 years of forest regrowth. Our

45results indicate that on average default forest regrowth rates from the Intergovernmental Panel on

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Climate Change are underestimated by 32% and miss 8-fold variation within ecozones.

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Conversely, we conclude that previously reported maximum climate mitigation potential from48

natural forest regrowth is overestimated by 11% due to the use of overly high rates. Our results

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therefore provide a much needed and globally consistent method for assessing natural forest

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regrowth as a climate mitigation strategy.

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Background53

To constrain global warming, we must reduce emissions and capture excess carbon dioxide

54(CO2) in the atmosphere1,2. Restoring forest cover, defined here as the transition from < 25% tree

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cover to > 25% tree cover where forests historically occurred, is a promising option for additional

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carbon capture3 and has been prioritized in many national and international goals4,5. It is57

deployable, scalable, and provides important biodiversity and ecosystem services6. Yet the58

magnitude and distribution of climate mitigation opportunity from restoring forest cover is poorly

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described, with large confidence intervals around estimates2,3. To evaluate the appropriateness of60

forest cover restoration for climate mitigation, compared to the multitude of other potential climate

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mitigation actions, countries, corporations, and multilateral entities need more accurate

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assessments of its potential7.

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Mitigation potential from restoring forest cover (reported here in terms of MgCO2 yr -1) is

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determined by the potential extent and location of new forest (“area of opportunity”) and the rate65

at which those forests remove atmospheric CO2 (reported here in terms of MgC ha-1 yr-1). While66

there are now multiple estimates of area of opportunity based on diverse and often heavily debated67

criteria (e.g., references 3,8–11), we lack spatially explicit and globally comprehensive estimates of68

accumulation rates. This is especially true for natural forest regrowth, defined here as the recovery

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of forest cover on deforested lands through spontaneous regrowth after cessation of prior70

disturbance or land use. Many countries do not have nationally specific forest carbon accumulation71

rates and instead rely on default rates from the Intergovernmental Panel on Climate Change

72(IPCC)12,13. Although these rates were recently updated8,12, they nonetheless represent coarse

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estimates based on continent and ecological zone, and do not account for finer scale variation in74

rates due to more local land use history or environmental conditions.

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We focus here on natural forest regrowth for several reasons, but there are many ways to76

restore forest or tree cover (Table S1) and all have value in specific contexts. Natural forest

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regrowth can cost less than intensive tree planting and also promote re-establishment of local

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biodiversity14,15. Reliance on natural forest regrowth, coupled with maintenance of natural

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disturbance regimes, also avoids perverse tree establishment in native grasslands16. Some reviews

80further suggest that naturally regrowing forests can recover as well as or better than actively

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restored forests17–20. However these reviews are likely biased towards more amenable sites for82

forest establishment and natural forest regrowth can be limited due to severe land degradation

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and/or distant seed sources21. Our comprehensive analysis across a range of starting conditions

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therefore provides a robust baseline for natural forest regrowth, elucidating fundamental

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constraints and drivers of carbon accumulation rates, and serving as a benchmark for alternative

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approaches to restoring forest cover.

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Methods

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To reduce uncertainty and better predict variation in carbon accumulation rates, we

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assembled a global dataset of carbon in naturally regrowing forests. We reviewed 11,360 primarily91

peer-reviewed studies to find those that described carbon or biomass accumulation due to any

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approach for returning forest cover to the landscape (Table S1). From those that described natural93

forest regrowth (N = 256 studies), we compiled 13,033 empirical measurements of carbon storage

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in above and belowground biomass, soil, litter, and coarse woody debris. We further filtered this95

dataset with more stringent criteria (see supplementary methods) to assess potential drivers of96

carbon accumulation rates (N = 5762 carbon measurements; 554 sites; 227 studies; Fig. 1). These

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potential drivers included climate, soil characteristics, and land use history. We next improved the98

geographic and environmental representativeness of our aboveground dataset by including

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available national forest inventory data from three continents (Fig. 1). We combined the100

aboveground point data with 66 global covariate layers that mapped variation in temperature,101

precipitation, seasonality, soil, topographical, and nitrogen deposition variables to develop a102

spatially explicit model of potential carbon accumulation rates across the globe. Throughout, we

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focus on the first thirty years of natural forest regrowth, because 2020 to 2050 represents a104

biophysically critical and policy-relevant window for both reaching net zero emissions and105

limiting the most negative effects of global warming2,22.

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Results

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Potential drivers of carbon accumulation rates

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Biome type, as a proxy for climatic and environmental variation, significantly influenced

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carbon accumulation in total plant pools (e.g., above and belowground biomass combined), but

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not soil, litter or coarse woody debris pools. Total plant carbon accumulated more rapidly in112

warmer and wetter biomes than in cooler and drier ones (F5,2652.2 = 11.8, p < 0.0001; Fig. 2; Table

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S2). In contrast, soil carbon accumulation rates did not vary significantly across biomes (F6,126 =114

1.0, p = 0.393; Fig. S1) or with soil texture (F9,128 = 0.2, p = 0.997), underscoring the known

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challenges of generating default soil carbon accumulation rates12. In litter and coarse woody debris

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pools we did not observe measurable accumulation during the first 30 years of forest regrowth,117

despite differences among biomes in the absolute magnitude of these pools (Fig. S2; Fig. S3).

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Indeed, carbon stocks in these pools often declined with time, presumably due to decomposition

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of residual biomass from prior disturbance. We therefore did not further account for litter or coarse120

woody debris since natural forest regrowth did not directly drive near-term carbon dynamics in121

these pools.

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The type of prior land use/disturbance significantly, but inconsistently, influenced carbon

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accumulation rates in both total plant and soil pools. The literature described seven land

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use/disturbance categories: pasture, long-term cropping, shifting cultivation, clear cut harvest,

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mining, fire, and other natural disturbances (e.g., hurricane windthrow, landslide). In all biomes

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except the Boreal, land use/disturbance type significantly influenced total plant carbon

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accumulation (Boreal: F1,21.1 < 0.1, p = 0.910; Temperate Conifer: F4,32.1 = 31.3, p < 0.0001;

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Temperate Broadleaf: F5,314.7 = 23.6 p < 0.0001; Tropical/Subtropical Dry: F1,539.8 = 13.7, p =1290.0002; Tropical/Subtropical Moist: F5,539.8 =7.7, p < 0.0001; Tropical/Subtropical Savanna: F2,48.0

130= 3.2, p = 0.0495). However, within a biome, rates were often similar across land use/disturbance131

types (inset panels in Fig. 2). Moreover, across biomes, the specific effect of a given land

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use/disturbance type often differed. For example, former cropland showed the highest rates of total

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plant carbon accumulation in the Temperate Broadleaf biome, but only intermediate rates of134

recovery in the Tropical/Subtropical Moist biome. For soil, prior land use/disturbance data were135

limited to Temperate Broadleaf and Tropical/Subtropical Moist forests. Only the former showed a136

significant effect; specifically that disturbance due to cropping or timber harvest led to faster soil

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accumulation than disturbance by pasture (F2,46 = 7.5, p = 0.001). Overall, these results suggest

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that land use/disturbance type cannot be used to definitively predict carbon accumulation rates in139

naturally regrowing forests due to inconsistent effects across biomes for total plant carbon and140

limited data for soil.

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Finally, disturbance intensity influenced carbon accumulation in plant biomass (F2, 992.3 =142

13.7, p < 0.0001), but not soil (F2,78 = 1.4, p = 0.237). The literature-derived data included sites

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that experienced a range of disturbance intensities, from relatively mild (e.g., natural disturbance)

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to very intense (e.g., long term tillage for agriculture), so we categorized sites by low, medium or

145high disturbance intensity (Table S3). In general, total plant carbon accumulation rates were higher

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after the highest intensity of disturbance compared to the lowest intensity of disturbance (Figure

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S4), but this pattern was not consistent within biomes. Instead, within biomes, the highest carbon

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accumulation rates occurred in the category with the lowest starting biomass regardless of149

disturbance intensity (Table S4), reflecting standard sigmoidal growth curves.

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Mapping global, near-term carbon accumulation potential

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Given the significant biome effects and the limited predictive power of land

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use/disturbance history, we used 66 global environmental covariate layers, primarily related to154

climate (Table S5 and supplementary data), to develop a wall-to-wall map of potential

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aboveground carbon accumulation rates at a 1-km scale. We modeled only aboveground carbon

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accumulation, because the aboveground data represented the largest fraction of our literature-157

derived data (N = 2118), showed strong and well-explained variation across the globe, and avoided

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propagating uncertainty from root:shoot ratios. Focusing on aboveground carbon also allowed us

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to improve our geographic and environmental representation with available aboveground carbon

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data from national forest inventories in Australia, Sweden, and the United States (N = 10,994).

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However, to increase the utility of these maps for conservation and policy planning, we estimated162

total plant carbon (i.e., with belowground carbon included) post hoc using IPCC default root:shoot163

ratios12 (see data availability).

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We used an ensemble machine learning model to develop a predictive map of carbon

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accumulation rates in naturally regenerating forests over the next 30 years (Fig. 3a). We found that166

the best fit model included all 66 covariate layers (Table S5) Our ensemble model predicted the167

test data reasonably well (RMSE = 0.80 MgC ha-1 yr-1, R2 = 0.45). We had limited extrapolation,

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with covariate values at the field sites spanning most of the range of covariate values across the169

entire prediction area (Fig. S5). Also, the standard deviation across the ensemble model was ± 13%

170of the predicted value, on average. However, areas of substantial uncertainty remain. We observed171

the highest uncertainty in northern Africa and other savanna biomes, and lowest uncertainty in the172

tropics (Fig. 3b).

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When we examined average carbon accumulation rates using the same spatial boundaries

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underlying the 2019 IPCC defaults (i.e., United Nations Food and Agriculture Organization (FAO)

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ecozones crossed by continent)12, we found that our predicted rates were 32% higher on average176

than IPCC defaults for young forests (Table S6). However, this differed within and across biomes.

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Notably, our predicted rates were consistently higher in the Tropics (53% higher on average)

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compared to 2019 IPCC defaults (Fig. 4), even though some of our data were used to update these179

rates8. Our predicted rates are also on the high end of the range provided by the IPCC for the180

Boreal, though incorporating albedo will limit the climate mitigation potential of natural forest

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regrowth in these locations23.

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Our map of potential carbon accumulation rates also demonstrated the value of improved

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spatial resolution, with over 8-fold variation within an average FAO ecozone and continent

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combination (i.e., the difference between the maximum and minimum predicted value relative to185

the minimum). Variation within countries was also substantial with an average of 1.7-fold186

difference in rates within a country (Table S7) and notable differences in rates at small spatial

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scales (see Colombia as an example, Fig. 5).

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Climate mitigation potential of natural forest regrowth190

Our map of potential near-term carbon accumulation rates also allowed us to refine

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estimates of global mitigation potential from natural forest regrowth. To do so, we combined our

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rate map with two scenarios of forest expansion based on recently published estimates. While there193

are multiple and diverse estimates of area of opportunity3,8–11, we chose two that represented a194

policy-relevant scenario and a maximum biophysical potential. The first “national commitments”

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scenario sums country-level commitments to the Bonn Challenge and nationally determined

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contributions (NDCs) to the Paris Agreement (349 Mha)11. The second “maximum” scenario is a197

spatially-resolved estimate of maximum biophysical area (678 Mha) that excludes grassland

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biomes to avoid negative biodiversity consequences, the Boreal to avoid potentially adverse

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warming effect due to changes in albedo, current croplands to safeguard human needs for food,200

and rural and urban population centers3 (Fig. 3c). Using our maps of potential aboveground carbon

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accumulation, we estimate that natural forest regrowth across 349 and 678 M ha could capture202

between 3.98 and 5.86 PgCO2 yr-1 in aboveground biomass and a further 1.36 and 1.99 PgCO2 yr-

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1

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negative (Fig. S2). However, if we use the global average from our literature-derived data (0.42205

MgC ha-1 yr-1) for the shallower 0-30 cm profile where additional soil accumulation is expected to206

occur24, then these estimates rise to a total of 5.87 and 8.89 PgCO2 yr-1. Under the national

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commitments scenario11, ten countries held 69% of the global mitigation potential, whereas under208

the maximum scenario3, the top ten countries held 61% of the potential (Table S7). However, these209

countries differed between scenarios and in general mitigation potential depended heavily on area

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of opportunity. These two scenarios are illustrative and alternative scenarios would provide

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different results, but regardless the mitigation potential of any scenario can easily be estimated212

using the wall-to-wall map presented here.

in belowground biomass over 30 years. Carbon accumulation in soil may be negligible or

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Discussion

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There is high enthusiasm for natural forest regrowth as a climate mitigation strategy, given216

its potential to capture carbon while also providing additional benefits such as habitat for217

biodiversity6, which is needed to stem the equally urgent biodiversity crisis25. Here we provide a

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consistent method for quantifying potential carbon accumulation in naturally regrowing forests219

over the next 30 years, at global and local scales. We find that current IPCC default rates are on

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average 32% lower than our predicted rates and most notably 53% lower in the tropics, suggesting221

that tropical countries using IPCC default rates may be underestimating the mitigation potential of222

natural forest regrowth. Moreover, the default IPCC rates miss 8-fold variation within ecozones.

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This improved spatial resolution allows us to better match area of opportunity with224

potential carbon accumulation rates and refine prior estimates of climate mitigation potential. We

225find that the maximum biophysical potential for natural forest regrowth to mitigate climate change

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is 8.89 PgCO2 yr-1, which is 11% lower than previously reported due to the overestimation of rates

227(derived from Bonner et al.26). Nevertheless, regrowth of natural forest remains the single largest

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natural climate solution even with our more conservative estimate3.

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Achieving 8.89 Pg CO2 yr-1 under our maximum biophysical scenario is challenging and230

would require dietary shifts towards a plant-based diet, which could release large areas of current

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grazing lands back to forest, as well as croplands that are used to produce fodder for livestock27,28.

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Even 5.87 PgCO2 yr-1 under the more policy-relevant national commitments scenario will be

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difficult to achieve, with some countries committing to restore more forest area than is available10234

and/or relying on approaches other than natural forest regrowth to restore forests11. These

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challenges do not undermine the utility of our map, however, which can be used to estimate

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mitigation potential for any available area of opportunity.

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The urgency of the growing climate crisis means that the global community needs to238

simultaneously deploy multiple climate mitigation strategies to constrain global warming1,2. This239

includes strong reductions in emissions, since natural climate solutions, including the regrowth of240

natural forests, are not a substitute for reducing fossil fuel emissions29, but rather an essential

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complement, especially while carbon capture technologies remain expensive and under242

development30. Regrowing natural forest is also not a substitute for protecting existing forests,

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which store enormous pools of carbon31. In general, there is no “panacea” approach to climate244

mitigation and most, if not all, options (e.g., transformations in our energy sector, carbon taxes)

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will require enormous political will and financial resources to realize. Natural forest regrowth has

246high mitigation potential, but may impose land use trade-offs3,9. Our results can help local

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decisionmakers optimize areas of opportunity for natural forest regrowth by pinpointing areas of248high potential carbon accumulation to consider alongside other important feasibility criteria, such

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as costs, livelihoods, and social suitability. Our analyses of potential carbon accumulation rates

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over the next 30 years also provide an important complement to other global biomass mapping

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efforts which focus on longer term carbon storage. Recent analyses estimate potential carbon

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storage in mature forests10,32,33 or to 210011, but the next thirty years represent an important and253

policy-relevant window for limiting the climate crisis2,22. Our analyses estimate how much carbon

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can be captured during this critical window, enabling comparison of natural forest regrowth to255

other near-term climate mitigation actions.

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There are several sources of uncertainty in our analysis. The first results from limited field

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site coverage, and variation in data quality and methodology. Although our data compilation far

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exceeds prior efforts with an initial consideration of 11360 publications, confidence in our results

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necessarily depend on data availability, which vary considerably across studies and geographies

260(Fig. 1). The dataset employed here spanned 43 countries, but 96% of the data derived from only261

ten countries (United States, Sweden, Mexico, Brazil, Costa Rica, Colombia, China, Indonesia,

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Bolivia and Panama, in descending order). Data may be limited because researchers have not263

collected the data, the data are not publicly available (e.g., many national forest inventories), or

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because some forest types are still fairly intact with limited opportunity to quantify regrowth.

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Despite the patchy plot data, we found that plots covered most of the environmental conditions

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across the prediction area, with the main exceptions being the Sahel and northeast Asia (Fig. S5).

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Increased data collection, ideally in a coordinated fashion to increase comparability across268

sites and using repeated plot measurements to improve robustness, would ameliorate some of these269

issues. To facilitate coordination and enable updates to our analyses as new data becomes

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available, we deliberately merged our efforts with the global Forest Carbon Database (ForC) to271

support the further development of a single, robust, and transparent repository for forest carbon

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data34. Future data collection should not only prioritize aboveground carbon data in northern Africa

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and northeast Asia, but also soil carbon data. Although our review encompasses and expands upon274

all existing reviews of soil carbon accumulation (see supplementary methods), data did not275

substantially elucidate how soil carbon changes with natural forest regrowth. Our global default of

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0.42 MgC ha-1 yr-1 for soil carbon accumulation is similar to that observed by others (e.g.,24,35), but

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further research is clearly merited.

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Another source of uncertainty stems from using historical forest growth to predict future279

carbon accumulation rates. As global warming ramps up, rates in a given location may increase or280

decrease depending on factors such disturbance frequency, CO2 fertilization, or increased

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respiration due to higher temperatures10,36. Moreover, there are other known factors that influence282

natural forest regrowth that we did not capture in our analysis. For example, residual vegetation283

can also accelerate forest regrowth by providing roosting sites for seed-dispersers37 or shade for284

late-successional species38. Others have observed an increased likelihood of regrowth near rivers

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or existing forest fragments, far from roads or on steep (less-accessible) slopes, and in areas286

protected from browsing39–42. Our global map provides a good starting point, but project-level287

planning will require detailed site assessments, as well as additional research to refine how local

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factors and future climate will impact carbon accumulation rates in a given location.

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Further work is also needed to characterize how other approaches to restoring forest cover290

impact carbon accumulation rates and storage. We focused on natural forest regrowth, where

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natural processes rather than management actions predominantly drive carbon accumulation.

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However, the permanence of natural forest regrowth (and the carbon stored therein) cannot be

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assumed43, especially if secondary forests are less valued than plantation forests. Rates from294

naturally regrowing forests also do not capture how silvicultural practices can enhance tree295

establishment and carbon accumulation44 or how harvested wood products from sustainably

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managed forests can provide life cycle benefits through substitution effects and carbon storage in

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long-lived wood products45. While additional work is needed to characterize climate mitigation

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potential of alternative management schemes, we now provide a robust baseline by which to299

characterize any additional benefit of assisted regeneration and/or active planting and300

management17–21.

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As countries, corporations, and multilateral entities develop plans to deploy natural forest

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regrowth as a climate mitigation strategy, our global, 1-km resolution map of potential

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aboveground carbon accumulation rates provides essential information for targeting activities

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towards areas with the highest potential carbon accumulation, for estimating the potential carbon

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return on investment, and for further refining how forests influence terrestrial carbon cycles at306

local, national, and global scales. It will allow governments that have NDCs related to natural307

forest regrowth to quickly estimate potential carbon accumulation and prioritize more detailed308

assessments in regions with higher carbon accumulation rates. We reduce the uncertainty and309

variability around carbon accumulation rates to facilitate comparisons of natural forest regrowth

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with other climate mitigation options and confirm that regrowing natural forests has the potential

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to greatly contribute to stabilizing global warming.

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Acknowledgements:

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We thank the Children’s Investment Fund Foundation, COmON Foundation, the Craig and315

Susan McCaw Foundation, the Doris Duke Charitable Foundation, Good Energies Foundation,

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and Microsoft’s AI for Earth program for financial support. This paper was also developed with

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funding from the Government of Norway, although it does not necessarily reflect their views or318

opinions. We thank Justin Adams, Eriks Brolis, Andy Hector, Jaboury Ghazoul, Marisa Hamsik,

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Simon Lewis, Beatriz Luraschi, Rajesh Thadani, Byford Tsang, and Ana Yang for initial idea

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development at an Oxford University workshop in 2017. We thank Grant Domke and Brian321

Walters (USDA Forest Service) for providing fuzzed FIA plot data, Jonas Fridman (Swedish

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National Forest Inventory) for providing Swedish data, and Han Xu for providing raw biomass

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data from Jainfengling Nature Reserve (Hainan Island, China).

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Author Contributions:

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SCP, BG, NH, DG, KL, SS, and LX designed the study with input from all co-authors.

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SCP contributed to and led all other facets of the study. SML, KJAT, RDB, PWE, HG, KDH, CL,

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RL, KP, SR, SW, CW, WW, and BG contributed to database compilation, analyses, and329

manuscript preparation. NH, KL, DG, TC, DR, SS, LX and JV constructed the global maps and330

contributed to manuscript preparation. GL, RL, VH, KP, and SR contributed to database

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compilation and manuscript preparation. RLC, RAH, YM, PM, AP, and JDP contributed to332

manuscript preparation.

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Data Availability

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The literature-based dataset (both raw and filtered), detailed descriptions of the336

environmental covariates, and code for constructing the global maps and assessing uncertainty are337

all available at https://github.com/forc-db/groa. Spatial data for both aboveground carbon

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accumulation rates and uncertainty (scaled and unscaled by mean pixel value), as well as

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belowground carbon accumulation rates can be downloaded from Global Forest Watch

340(www.globalforestwatch.org) and Microsoft’s Azure platform. While SCP and NH welcome

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discussions around potential collaborations, the data are freely available.

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Figures

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Fig. 1 Distribution of sites after final filtering of the literature-based dataset (blue) and inclusionof the field inventory data (green). We compiled data from forest (dark gray) and savanna
biomes (light gray). We restricted savanna data to portions of these grassland-forest matrices
with forest cover > 25%.

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Figure 2. Total plant carbon (MgC ha-1) through time (scatterplots) and average carbon
accumulation rates as a function of prior land use/disturbance (inset: mean MgC ha-1 yr-1 ± 95%
C.I.). Lines represent overall modeled fit (± 95% C.I., Table S2) regardless of disturbance.
Studies commonly provided information on seven disturbance/land use types: fire (“F”, closed
squares), other natural disturbance (“D”, open squares, e.g., hurricane windthrow), clear cut
harvest of land in forest use (“H”, open circles), shifting cultivation (“SC”, open diamonds),
pasture (“PA”, open triangles), permanent cropland (“C”, closed triangles), and mining (“M”,
closed circles). Small gray points indicate no known disturbance type. Savanna results only
apply to portions of these grassland-forest matrices with forest cover > 25%.

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Fig. 3 (a) Predicted aboveground carbon accumulation rates (MgC ha-1 yr-1) in naturally
regrowing forests in forest (solid colors) and savanna biomes (hatched colors). We denotesavanna biomes differently to note that many of these areas are not appropriate for forest and that
restoration of forest cover should proceed with particular caution in these biomes. Note that themap only predicts accumulation rates if natural forest <30 years were growing there; it does not
exclude currently forested areas or non-forestable parts of these biomes. b) The ratio of model
uncertainty relative to best-fit model value per 1-km pixel. Higher ratios denote greater variation
across random forest decision trees. c) Modeled accumulation rates filtered to the area ofopportunity in Griscom et al.3 to demonstrate where these rates might apply.

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Fig. 4. Average predicted rate of carbon accumulation per ecozone (open circles) compared to 2019 IPCC defaults, which are given asa single number (closed circle) or a range (thick black bars). Colored bars indicate the range between the minimum and maximum
modeled rate per ecozone and continent (Boreal = dark purple, Temperate = light purple, Subtropical = light green, Tropical = darkgreen). Ecozone and continental forest types are listed below the x-axis (NA = North America, NZ = New Zealand, SA = SouthAmerica)

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Fig. 5. (a) Map of predicted carbon accumulation rates in Colombia, as an example of countrylevel variation in rates. (b) Map of predicted rates filtered to the area of opportunity in Griscom
et al.3 to demonstrate where these rates might apply.

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Methods and Supplementary Results

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Assembling a global carbon database385

We systematically reviewed the literature (19 April 2017) with a Web of Science keyword

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search of studies published since 1975: TOPIC: (biomass OR carbon OR agb OR recover* OR

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accumulat*) AND (forest) AND (restorat* OR reforest* OR afforest* OR plantation* OR

388

agroforest* OR secondary*). We included “agb” for aboveground biomass. We included

389“afforest*” because afforestation sometimes describes establishing forest cover in places where390

forests historically occurred, but we eliminated studies that described tree planting in grasslands391(also called “afforestation”), as these efforts are often not successful46, and reduce biodiversity and392

ecosystem integrity47,48.

393

The initial search yield 10,937 peer-reviewed studies, which we augmented to 11,360 with394

additional peer-reviewed studies referenced therein or datasets from distinguished institutions

395(Oak Ridge National Laboratory, International Centre for Research in Agroforestry, and Chinese

396

Academy of Forestry). We reviewed all abstracts to identify accessible studies that quantified

397

forest regrowth after clearing historically forested land (N = 5,464) and fully reviewed these to398find any that quantified carbon or biomass stocks (N ~1400). We categorized the latter by approach399

for restoration of forest or tree cover (Table S1) and focused initially on natural forest regrowth

400

given the need for improved natural forest regrowth data and the immense time required to build

401

this dataset. However, other approaches are currently being reviewed.

402

To be included, studies had to provide (a) empirical measures of carbon (or biomass) in403

above- or belowground plant, litter, coarse woody debris and/or soil pools, (b) stand age with at404

least one stand between 5 and 30 years, and (c) a latitude and longitude, or a discernible

405

geolocation (e.g., an identifiable place name). Papers focusing on soils did not need to include406

other carbon pools but had to include mineral soils deeper than 10 cm, as well as a reference407

measurement (e.g., a younger stand or an adjacent non-forest plot) to assess changes in soil carbon.

408

We included measurements in shallower soils if present in papers with 30 cm or deeper data.

409

deeper data. Similarly, we extracted all available data from stands between 0 and 100 years for

410

studies when included in studies with the correct age range (5 to 30 years), excluding studies with411

only very young forests because of the stochastic nature of early forest establishment, as well as412

papers with only forests greater than 30 years given our 2020 to 2050 focus.

413

To avoid duplicated measurements, we gave priority to primary studies and included the414

earliest instance of repeatedly published data. Our dataset fully encompasses all relevant primary415

studies from many other reviews (e.g.,17,35,49–56) and the Forest Carbon Database (ForC)34. For

416

these, we obtained the original studies to confirm numbers, correct errors, and acquire additional

417

variables. However, we preferentially extracted data from three reviews rather than the primary418

source when authors acquired and reanalyzed original datasets, some of which were previously

419

unpublished (Poorter et al.57) or were published in Russian or Chinese58,59. Guo and Ren58 notably

420

provided 5730 measurements across China that we included in the larger dataset, but ultimately

421

excluded by our more stringent filtering (details below).

422

Beyond geolocation, stand age (years), type of carbon pool, and carbon or biomass estimate423(Mg ha-1), we also extracted any available data on type and intensity of prior land use or

424

disturbance. We used geolocation to extract biome designations from Dinerstein et al.60,61. While425

we acquired data from presumably forested portions of Tropical and Temperate savannas (e.g.,

426

Miombo forests in Africa, Cerrado forests in Brazil, Pinyon-Juniper forests in the United States),427

we note that it is not ecological appropriate to increase forest cover in many areas of savannas and428that we do not advocate expansion of trees on natural low tree cover savannas47,48. We did not429

include mangroves since they are highly dynamic systems that require complex accounting for in

430

situ versus exported soil carbon accumulation62.

431

The resulting dataset includes 13033 carbon or biomass data points. We aggregated data432

by site (N = 2330) and plot (N = 6674), where sites have unique geolocations and plots are spatial

433

units within sites that have unique attributes (e.g., age, prior land use; see metadata for additional434

details). We then further winnowed these data along stricter criteria to exclude (a) locations with435

inappropriate geolocations, such as in the ocean or a non-forest biome according to the biome

436

spatial layer60,61, (b) stands less than one year old because they are not (yet) undergoing natural437

forest regrowth, (c) Mediterranean forests and temperate savanna because sample size was too low

438(N < 10 for any single pool), (d) studies with only shallow soil measurements (30 cm or less)439

because carbon in top soil is highly dynamic and can dramatically underestimate overall soil

440

carbon63, and (e) Guo and Ren58 data because it contained many old stands with little to no plant441

biomass which we could not explain (Fig. S6). The final dataset used in these analyses spanned

442

3058 unique forest plots, 554 sites, 121 ecoregions, and most forest and savanna biomes (Fig. 1).

443
444

Standardizing data across publications

445

For studies that reported biomass only, we converted to carbon (MgC ha-1) using 0.47 as a

446

default conversion factor for above- and belowground pools (combined and described as the “total

447

plant carbon” pool)64, 0.37 for litter biomass65, and 0.50 for coarse woody debris biomass66. If a

448

study used different default conversion factors, we adjusted their carbon numbers to match the449

above defaults for consistency.

450

Most soil organic carbon (SOC) data (72%; N = 1065 of 1485) were already in units of451

MgC ha-1 depth-1 and the remainder we converted from SOC concentration (g 100g-1) or soil

452

organic matter (SOM). For SOM concentration data (N = 38), we estimated SOC concentration as453

SOM/2 based on Pribyl67, which found that the median ratio between SOM and SOC across 481

454

data points from 24 empirical studies was 1.97, with a mean of 2.20. We converted SOC

455

concentration to MgC ha-1 depth-1 with empirical bulk density data where given (N = 355) or depth-456

specific bulk density data from SoilGrids68 (N = 65). SoilGrids provides bulk density modeled at457

15, 30, 60 cm and we used the value nearest in depth to the SOC concentration measure. Modeled

458

bulk density was higher but within the range of empirical estimates (1.29 ± 0.13 versus 0.98 ± 0.31459

Mg m-3, mean ± s.d.). To convert to MgC ha-1 depth-1, we used one bulk density value for each site

460

and reference pairing, using measured bulk density from the pre-forest site if available, measured

461

bulk density from the youngest nearby site as the next option, or SoilGrids bulk density from the462

pre-forest site in the absence of other data.

463

After converting biomass data to carbon, we standardized within pools. Aboveground464

carbon measures typically included foliage, but we retained two measures that excluded foliage,465

since foliage is a small fraction of overall carbon. Studies differed in whether they included

466

understory (e.g., lianas, shrubs). For those without, we added average understory carbon per biome

467

based on our dataset (1.2 to 4.0 MgC ha-1). We did not, however, adjust for differences in diameters468

at breast height (dbh; nominally 1.3 m above ground level). Although studies used different dbh

469

thresholds, ranging from 0 to 10 cm, minimum dbh did not explain variation in aboveground470

biomass (F1,459.2 = 0.5, p = 0.4608) and we assumed that authors used a dbh threshold that captured

471

the majority of biomass at their sites. We summed above- and belowground plant carbon using472

empirically measured belowground carbon when present (N = 444) or standard root-to-shoot ratios

473(R:S)69 when absent (N = 2346). Where it was possible to compare, we found that estimated474

belowground carbon was 1.8 MgC ha-1 higher than measured values, since the field measurements

475

typically only quantified biomass to a specific depth and/or roots greater than a specific diameter.

476

This produced 2790 independent plot measurements of total plant carbon. For dead pools (litter477

and coarse woody debris), measurements often included additional pools, but we did not attempt478

to parse litter and/or coarse woody debris from these combined measurements because these pools479

are highly variable and site-specific65. Thus, we only retained single pool measurements (N = 473

480

litter and 298 coarse woody debris). Finally, for soil, we adjusted data to the nearest of two standard

481

depths (30 and 60 cm). For plots with multiple depth measures, we used the slope from a fitted

482

log-log curve for cumulative SOC stocks as a function of depth to estimate SOC at standard depths,483

but for plots without multiple depth measures, we used a biome-specific slope coefficient70. If

484

standardizing depths resulted in duplicate measures – for example, when a study reported SOC at

485

20 and 40 cm, leading to two predicted values at 30 cm – we calculated the average. Depth-

486

standardized SOC was 1% lower than the empirical measure of SOC and highly correlated (R2 =487

0.84).

488

For plant, litter and coarse woody debris (CWD) pools, we analyzed carbon stocks (MgC489ha-1) as a function of stand age, as these pools can have zero carbon at initiation of regrowth.

490

However, SOC changes are relative to a non-zero baseline so we first converted SOC stock data491

to rates (MgC ha-1 yr-1). For repeated measure designs, we calculated a single rate per plot based492

on SOC change from initial conditions. For the remaining studies, we used linear regression to fit

493

SOC as a function of stand age within each chronosequence, treating any reference plot (e.g., an494

adjacent treeless cropland) as age zero (N = 5 data points on average per regression). We only

495

compared forest and reference plots with the same prior land use35. This produced a single rate496

estimate per chronosequence, and these rates became the foundational data for the soil analyses.

497

We ultimately derived 138 SOC rates from chronosequences (N = 129) and repeated measures (N498= 9). Most rates quantified changes at 0-30 cm (N = 83) and then 0-60 cm (N = 55).

499
500

Potential drivers of carbon accumulation rates

501

To assess fundamental drivers of variation in carbon accumulation rates, we examined502

differences in rates (a) across biomes as a proxy for major climatic differences, (b) across soil

503

texture categories (soil only), and as a function of (c) type of prior disturbance or land use, and (d)504intensity of prior disturbance or land use.

505

First, to examine differences in plant, litter, and coarse woody debris carbon among506

biomes, we used mixed effects models (R v. 3.5.1 packages lme4 and lmertest) to examine carbon

507

stocks as a function of stand age, biome, and stand age × biome with site (or plot nested within

508site) as a random intercept. We were primarily interested in the interaction term here and below,

509

since it describes how the effect of age on carbon stocks (i.e., carbon accumulation rate) is

510

modified by the predictor variable, which in this case is biome. We compared a linear model to

511

one with ln-transformed stand age, selecting the model that minimized the Aikake Information

512

Criterion (AIC). For litter and coarse woody debris, carbon either declined non-linearly from initial

513

starting conditions and/or remained roughly constant with stand age (Fig. S3). We therefore did514

not further examine carbon accumulation in these pools, because residual dead matter from515

previous disturbance obscured any signal of additional accumulation. However, we did examine

516

variation across biomes by removing stand age from the model. We found that litter and CWD

517

carbon stocks were generally higher in Boreal and Temperate biomes compared to other biomes

518(Fig. S2; litter: F5,138.7 = 8.5, p < 0.0001; CWD: F4,125.7 = 5.9, p = 0.0002). For soil, we used linear

519

regression to model carbon accumulation rates as a function of biome identity. We also included

520

depth as a categorical predictor (depth and depth × biome) and found that, although stocks

521

generally declined with depth of measurement as expected, rates of carbon accumulation did not522(F1,126 < 0.1, p = 0.956).

523

Second, we examined how soil carbon accumulation might differ by soil texture. We used

524

SoilGrids data on clay, silt and sand percentages to estimate the soil texture category (e.g., sand,

525

loam, clay, etc.) at each site where texture data were not provided. We used linear regression to526

analyze soil carbon accumulation as a function of texture, and again found that texture was not a527

significant predictor of variation (F9,128 = 0.2, p = 0.9997)

528

Third, we examined how prior land use or disturbance influenced carbon stocks through529

time for disturbance types with > 3 data points per biome. When studies listed multiple disturbance530

or land use types for a single plot, we noted the most recent type where discernable. Otherwise,531

we used the type that was most likely to negatively impact forest regrowth (natural disturbance <532

harvest = shifting cultivation < crop < pasture, based on pers. obs.). We conducted separate

533

analyses per biome, as each biome was associated with different disturbance types. For plant534

biomass (N = 2600), we used mixed effects linear regression, modeling carbon as a function of535

stand age and prior land use, plus their interaction, with site (or plot nested within site) as a random

536

intercept. For soil (N = 132), we used an analysis of variance with prior land use and depth as the537

predictors of SOC.

538

Finally, we examined how the intensity of prior disturbance influences carbon stocks

539

through time. Unfortunately, studies provided fewer details about the intensity of prior land use

540(N = 1567 and 91 for plant biomass and SOC respectively). Three co-authors in this study (HPG,

541

KDH, CL) independently categorized disturbance intensity into low, medium, and high categories

542

using a disturbance rubric (Table S3), assigning the final category based on majority agreement

543

among scorers. Given data scarcity, we only categorized intensity of prior land use for four544

disturbance types: pasture, shifting cultivation, long-term cropland, and clear-cut harvest. We

545

conducted our statistical analysis across disturbance types, using mixed effects to model total plant546

carbon as a function of stand age and disturbance intensity, plus their interaction, with site or plot

547

nested within site and biome as random intercepts. We used a similar model for soil with only

548

disturbance intensity as the predictor and biome as a random intercept. We also ran similar models,549

though without the biome random effect, for each biome with sufficient data.

550
551

Mapping global, near-term forest carbon accumulation potential

552

To develop maps of aboveground carbon accumulation, we extracted the literature-derived

553

data with a separate measurement for aboveground carbon and stand age of 30 years or less (N =5542118). We supplemented these data with three national forest inventories: Australia, Sweden, and555

the United States. The Australia data were collected between 2006 and 2017 from naturally

556

regenerating stands of known age (N = 54)33. These stands were located across contrasting biomes,

557

ranging from relatively productive temperate regions to water-stressed semi-arid regions. Biomass

558

data only include new tree growth and do not include remnant trees. The Swedish National Forest

559

Inventory plot data were collected between 2007 and 2017 (N = 5458)71. The United States data

560

are from the United States Department of Agriculture (USDA) Forest Service’s Forest Inventory

561

and Assessment (FIA) program (N = 5482)33. Due to privacy concerns, FIA data are made562

available only after a fraction of plots are randomly swapped with others’ coordinates. Although563

these security procedures shifted the geolocation of plot data and predictor variables by ~ 1 km,

564

including the FIA data improved the predictive power of the model. We used plots that had (a)565been remeasured at time one (T1) and time two (T2) to estimate a rate of carbon accumulation, (b)566

no treatment at T2 or T1 (TRTCD = 0) to restrict data to natural forest regrowth, (c) no trees

567

recorded as alive in T2 that were recorded as dead in T1 (DEAD_TO_LIVE_COUNT = 0) to

568

remove erroneous measurements, (d) no recorded disturbance in T2 or T1 (DSTRBCD = 0), (e)569

aboveground biomass at T2 (AG_LIVE_BIO_MGHA > 0) to avoid harvested or burned plots, and

570(f) a stand age at T2 between 0 and 30 years (30 > STDAGE > 0). We also only included plots

571

where more than 50% of the area was comprised of the same forest type, owner class, land class,572

and other properties at T1 and T2 to ensure consistency within a site (CONDPROP_UNADJ > 0.5).

573

Combined, all literature-derived and national inventory data represented 13,112 plot

574

measurements. We then calculated carbon accumulation rate by dividing aboveground carbon by575

stand age, providing an average rate over the first 30 years of growth. We removed plots that did576

not fall into forest or savanna biomes or had no recorded biomass to avoid plots that had likely

577

been harvested (N = 685 or 5.2% of data). We also removed any points that had rates greater than578

three standard deviations above the mean (N = 153 or 1.2% of data). Finally, when there were579

multiple point estimates within each of our ~ 1 km pixels, we calculated the average rate to use in

580

model development (N = 10,216). Averaging within pixels improved model performance581

compared to models with no averaging.

582

To create a spatially predictive model of carbon accumulation, we first sampled our

583

prepared stack of 66 environmental covariates at each of the point locations within the literature-584

derived and national inventory datasets. These layers included climate, soil nutrient, soil chemical,

585

soil physical, radiation, topographic, and nitrogen deposition variables (Table S5). We did not use

586

variables that represent current vegetation condition (e.g., leaf area index or percent forest cover)587

or satellite-derived indices such as Normalized Difference Vegetation index (NDVI), as these do

588

not represent fundamental biophysical controls on carbon accumulation rates for the future589

accumulation of plant biomass. We resampled and reprojected these covariate map layers to a590

unified pixel grid in EPSG:4326 (WGS84) at 30 arc-seconds resolution (~1km at the equator),

591

downsampling higher resolution data using mean aggregation method and resampling those with592

a lower original resolution using simple upsampling (i.e., without interpolation). We chose this593

resolution to balance pixel-level uncertainty, which is proportionately larger in smaller pixels, with594

utility for local decision-makers. If multiple resolutions were available for a covariate, we used the595

resolution closest to 30 arc-seconds. Covariates represent different time periods but were all

596

between 1970 and 2017. This time period allows us to capture long-term average conditions under597

current and historical climate.

598

We then split the total number of points into a training set and a test set using an 80/20

599

random split, stratified by data source (i.e., the literature-derived data and each national inventory)

600

and by biome. We used the training set to determine the best machine learning algorithm and set

601

of hyper-parameters, and to train the final model. We used the test set to assess out-of-sample

602

error, as well as model performance with novel data (details below).

603

We compared four machine learning algorithms (random forest (RF)72, a gradient604

boosting decision tree called XGBoost73, support vector machines74, and multi-layer

605

perceptron)75, along with four feature selection methods (support vector machine feature606

selection, RF-based feature selection, principal component analysis, and no feature selection),

607

leading to 16 different combinations of feature selection methods and machine learning608

algorithms (or “model pipelines"). Each model pipeline first applied feature scaling to the data609

(standard scaling for the continuous variables and one-hot encoding of biome as our only

610

categorical variable), then selected features using the feature selection algorithm, and finally611

trained the machine learning model on the transformed data. For each machine learning612

algorithm, we also defined a suite of hyperparameters to test over, often leading to over 1,000

613

tested hyperparameter combinations. We conducted the machine learning steps in Microsoft

614

Azure.

615

We used the Python scikit-learn package and the “gridsearchCV” function to define and616

train model pipelines using three-fold cross-validation and choose the best hyperparameter

617

combination for each model pipeline76. We used the cross-validation root-mean-square error

618(RMSE) to choose the best feature selection method and machine learning algorithm with619

defined hyperparameters. Cross-validation is an important step in training and comparing

620

machine learning algorithms, as it creates pseudo-training sets that can be used to estimate the621

out-of-sample error and reduce over-fitting to the training set, while still keeping the final test set

622

completely independent of the model. In three-fold cross-validation, the training set is randomly

623

split into three equally sized subsets. Two subsets combine to form a new training subset, and the624last subset serves as a validation set to assess the model performance. We trained the model

625

pipeline on the training subset, stored the RMSE of the model predictions over the validation set,626

and then repeated the process twice more with the remaining combinations of training and627

validation subsets. The final cross-validation score is the average of the validation RMSEs across628

each model pipeline, and we used average cross-validation RMSE to compare model pipelines629

and selected the model pipeline with the lowest cross-validation RMSE as our best trained model

630

pipeline. In our case, the best trained model pipeline was the random forest machine learning

631

algorithm with no feature selection.

632
633

After determining the best performing algorithm and set of hyperparameters, we used a
Monte Carlo approach to create an ensemble model for our final predictions and uncertainty

634

analysis. We generated the ensemble model by first drawing 100 independent bootstrapped635

samples with replacement of our training data, stratified on the data source and biome. Next, we636

trained separate random forest models using the best performing set of hyperparameters on each637

of the 100 bootstrapped samples of the training data. Our final model is the ensemble of the 100638

random forest models, where the ensemble model prediction is the average of the predictions of639

the 100 random forest models. To asses our out-of-sample error, we applied this final ensemble

640model to our test set. The ensemble model had an RMSE of 0.798 Mg C ha-1 yr-1 and an R2 of

641

0.445 on our independent test set.

642

To create a final global map of aboveground carbon accumulation and associated643

uncertainty, we sampled all environmental covariate layers over all pixels in forest and savanna

644

biomes and applied the best trained model to each pixel’s covariates. Although the trained model

645

works over any area, we constrained it to forest and savanna biomes. Because our model is an646

ensemble of 100 random forest models with each random forest model trained on an independent647

bootstrapped sample of the training data, we can use the standard deviation of the 100 random

648

forest models’ predictions to estimate model uncertainty in each pixel. Therefore, for each pixel649

we have the model’s prediction and standard deviation across the 100 models. We also tested the650

extent of extrapolation in our models by examining how many of the Earth’s pixels exist outside651

the range of our sampled data for each of the 66 global covariate layers. We first extracted the652minimum and maximum values of each covariate layer across our sampling pixels to determine

653

sample range. We then used the final model to evaluate the number of variables that fell outside654

the sample range, across all terrestrial pixels. Next, we created a per-pixel representation of the655

relative proportion of interpolation and extrapolation (Fig. S5). This revealed that our samples

656

covered most environmental conditions on Earth, with 88% of Earth’s pixels values falling657

within the sampled range of at least 90% of all bands. Across all pixels, the average fraction of658

the pixel values falling within the sampled range of the covariates was 97%.

659

We compared our predicted rates with the latest 2019 IPCC default rates for young forest

660(<20 years)12 by estimating the average pixel value, as well as the minimum and maximum pixel

661

value within each ecozone by continent combination. Whenever a range was provided for IPCC

662

values, we used the average of the lower and upper bound of the range to compare to our663

predicted rates.

664
665

Climate mitigation potential of natural forest regrowth666

To estimate the constrained maximum mitigation potential of natural forest regrowth, we

667

combined the Griscom et al.3 area map with our map of potential aboveground carbon

668

accumulation and a map of potential belowground plant carbon accumulation. We created the latter669

by applying default root:shoot ratios to the aboveground pixels12. This Griscom et al.3 extent raster

670

identifies more area of opportunity than is available, because there are a series of non-spatial

671

deductions that they applied later in their analyses. We therefore proportionally scaled mitigation

672

opportunity within each country so that the final area summed to their reported 678 Mha area of673

opportunity. The Griscom et al.3 analysis assumes that a small fraction of their area of opportunity

674

would have plantations, so we adjusted their mitigation estimate to reflect a scenario of 100%675

natural forest regrowth (10.56 PgCO2 yr-1).

676

Lewis et al.11 compiled national commitments to the Bonn Challenge and from nationally

677

determined contributions to the Paris Agreement. Although that publication focused on tropical678

countries, we acquired the global compilation to use here. Two countries (Niger and Burkina Faso)

679

included commitments that we did not include, because those countries fall outside of our potential

680

rates map. To estimate the mitigation potential of these national commitments, we used the same

681

average predicted rates per country from the overlay of Griscom et al.3 for above- and belowground682

carbon accumulation. Thus, this assumes that the 349 Mha of opportunity under this scenario

683

represents an average subset of the area identified as biophysically possible in Griscom et al. 3.

684

685

Supplementary Figures

686
687
688
689

Figure S1. Observed variation in live plant carbon accumulation rates and soil carbon
accumulation (mean ± 95% confidence intervals) among biomes, from the literature-derived
dataset. We did not have plant biomass data for (sub)-tropical conifer forests.

690

691
692

693
694

Figure S2: Carbon pools (mean ± SE) in coarse woody debris (gray) and litter (black).

695
696
697
698

699
700
701
702

Figure S3: Coarse woody debris (gray) and litter (black) carbon pools over time in each biome.
We did not find studies describing litter or coarse woody debris pools in temperate savannas, orcoarse woody debris in tropical savannas.

703
704
705
706
707
708

709
710
711

Figure S4: Carbon accumulation in plots with high intensity disturbance (black circles, blackline) versus low intensity disturbance (gray circles, gray line). The most disturbed categories had
lower residual biomass at the initiation of regrowth (e.g., 0 MgC ha-1 versus 28 MgC ha-1 in theleast disturbed category; t-value = 5.9, p < 0.0001), suggesting that the higher rate in the most
disturbed category is due to standard sigmoidal growth rates in forests.

712
713
714

715

Fig. S5: Map of extent of extrapolation per pixel across all covariate layers. A value of 1 indicates that 100% of pixels fall within thesample range (i.e., there is no extrapolation).

716
717
718

719
720
721
722
723

Fig. S6: Total plant carbon through time from Guo and Ren58 (black circles) compared to otherstudies (gray circles).

724

Supplementary Tables

725
726
727
728

Table S1: General approaches for restoring forest or tree cover, based on aggregation of existingtaxonomies and expert consultation at a workshop at Oxford University, UK in February 2017.
These approaches will not necessarily reach > 25% forest cover.
Land-use
Semi-naturalforest, protected
or with someselective logging

Type with Definitions
Natural forest regrowth involves allowing forests to spontaneously regrow
without any silvicultural interventions, though may involve removing
disturbance factors (e.g., fire breaks, fencing, control of feral animals such as
camels and goats, reduced grazing pressure)77. This includes both succession
after abandonment and forest recovery following logging, fire or
disturbances.
Assisted natural regeneration aims to accelerate natural forest regrowthand/or guide successional trajectories through activities that enhance tree
growth, such as removing invasive grasses, liana cutting, and/or otherpractices78. We also include enrichment planting in this category.
Active restoration includes smaller tree configurations (e.g., applied
nucleation methods), as well as large scale tree planting endeavors to restore
native forests. Species may be mixed at the stand scale or in patches at thelandscape scale. This strategy may also involve extensive natural forest
regrowth following initial planting.
TimberMixed species plantations include at least two species intermixed on large
plantations
areas in timbers stands and may involve a mix of native and non-native
species.
Monoculture plantations include plantation forests where the same species
is grown on large areas in even-aged stands79. We include estimates forindividual species that are commonly employed, as well as a more generalestimate for species that are more infrequent. This includes both native andnon-native species.
Agroforestry
Intensive tree monocrops include all non-timber monocultures, such as fruitor nut tree monocultures, oil palm plantations, and other commodity crops.
Multistrata systems are those with a mix of under- and overstory species,
and include home gardens and shade-grown cropping systems like cacao(Theobroma cacao L.) and coffee (Coffea sp.) combined with shade-, timberor commercial tree crops80.
Tree intercropping includes agricultural systems where woody species aregrown in crop fields, in scattered or systematic arrangements. These species
may be used for fruit, fodder, fuelwood or timber80.
Silvopastoral systems include grazing under scattered or planted trees, aswell as tree-fodder systems80.
Transitional land The transitional land use strategy involves incorporating a range ofuse
agroforestry and/or plantation approaches in early stages of reforestation, asa transitional phase towards native forest restoration, to overcome
socioeconomic and ecological obstacles to restoring these lands81.
729

730
731
732
733
734
735
736

Table S2: Best fit equations per biome for carbon accumulation in total plant pools (MgC ha-1) asa function of stand age (year) based on the literature-derived data. Parameters are slope ± standard
error (SE) and intercept ± SE. We also provide the number of literature-derived data points per
regression (N). The rate column indicates an average rate for the first 30 years of standdevelopment based on predicted total plant carbon at age 30.

Biome
Boreal Forest
Temperate Broadleaf & Mixed
Forests
Temperate Conifer Forests
Tropical & Subtropical Dry
Broadleaf Forests
Tropical & Subtropical Savannas(forested portions)Tropical & Subtropical Moist
Broadleaf Forests
737

Best fit equation(23.2 ± 3.2) × ln(age) + (-35.7 ± 12.6)

N
45

rate
1.45

(1.7 ± 0.1) × age + (-0.7 ± 6.5)

418

1.63

(1.8 ± 0.1) × age + (-5.5 ± 6.8)

104

1.58

(35.9 ± 1.7) × ln(age) + (-56.6 ± 7.4)

552

2.19

(1.7 ± 0.2) × age + (1.0 ± 7.0)

57

1.70

(2.2 ± 0.1) × age + (28.4 ± 2.8)

1614

3.15

738
739
740

Table S3: Schema for categorizing intensity of land use/disturbance. Other land use/disturbancetypes (mining, fire, and other natural disturbance (e.g., hurricane windthrow, landslide) did nothave sufficient data.

741

742
743

Disturbance/
land use

Low intensity

Medium intensity

High intensity

Shifting
cultivation

PastureNA

HarvestSingle harvest, no fireLong-term shifting
cultivation with ≥ 3 cycles,≥ 15 years of use
Minimal input (e.g.,
herbicides, fertilizers) with< 10 years of usage
Minimal input (e.g.,
herbicides, fertilizers), < 10years of usage
Multiple harvests or harvest
and burn

NALong term crop

Most shifting cultivation
with < 3 cycles and < 15years of use
NA

Most crop
systems and ≥10 years
Most pasturesystems
NA

744
745
746
747
748
749

Table S4: Biome-level effects of disturbance intensity on carbon accumulation in total plantbiomass (MgC ha-1 yr-1) as a function of stand age. Intensity categories are low (L), medium(M), and high (H) based on Table S3. For all biomes, the greatest carbon accumulation rate (e.g.,slope parameter) was observed in the intensity category with the lowest starting biomass (e.g.,intercept parameter).
biomeintensity

Temperate
Broadleaf

L
M
H
L
H
L
M
H
L
M
H
L
H

Temperate
Conifer(Sub)-tropical
Dry(Sub)-tropical
Moist(Sub)-tropical
Savanna
750
751

best fit equation (parameters ± SE)(1.6 ± 0.2) × (age) + (-8.6 ± 28.8)(0.9 ± 0.7) × (age) + (12.7 ± 49.1)(1.2 ± 0.2) × (age) + (17.1 ± 14.6)(-58.5 ± 22.8) × ln(age) + (206.9 ± 63.9)(29.9 ± 5.5) × ln(age) + (-36.8 ± 20.8)(28.1 ± 4.3) × ln(age) + (-42.6 ± 19.3)(17.0 ± 7.0) × ln(age) + (-18.3 ± 24.8)(62.8 ± 3.6) × ln(age) + (-124.2 ± 12.4)(2.5 ± 0.2) × (age) + (35.8 ± 6.1)
(2.6 ± 0.2) × (age) + (19.3 ± 1.9)
(1.9 ± 0.1) × (age) + (23.3 ± 3.6)
(1.4 ± 0.3) × (age) + (-0.1 ± 9.9)
(0.4 ± 0.3) × (age) + (3.0 ± 9.2)

Statistic (age ×Nintensity)
21
F2,62.0 = 1.4,
5
p = 0.248
63
F1,3.3 = 15.0,
3
p = 0.024
6292
F2,71.1 = 37.7,
22
p < 0.0001
126
282
F2,746.7 = 10.3,443
p < 0.0001
255
F1,39.2 = 7.1,
36
p = 0.010
12

752
753
754

Table S5: Environmental covariates used in the machine-learning model. Additional metadata
including the date of the data, original resolution, transformations, and links to data sources areavailable in the supplementary data section.
Covariate
Aridity Index
Probability of occurrence of R horizon
Absolute depth to bedrock (in cm)
Biome
Bulk density (fine earth) (kg/cubic-meter)
Cation Exchange Capacity of the soil (mmol(c)/kg)
Clay content (mass fraction)
Annual mean radiation (W/square-meter)
Highest weekly radiation (W/square-meter)
Lowest weekly radiation (W/square-meter)
Radiation seasonality (C of V)
Radiation of wettest quarter (W/square-meter)
Radiation of driest quarter (W/square-meter)
Radiation of warmest quarter (W/square-meter)
Radiation of coldest quarter (W/square-meter)
Annual mean moisture index
Highest weekly moisture index
Lowest weekly moisture index
Moisture index seasonality (C of V)
Mean moisture index of wettest quarter
Mean moisture index of driest quarter
Mean moisture index of warmest quarter
Mean moisture index of coldest quarter
Coarse fragments volumetric (%)
Annual Evapotranspiration
AspectElevation
Hillshade
Slope (m)NHx Deposition
NOy Deposition
Soil organic carbon density (kg/cubic-meter)
Soil organic carbon stock in (tons/ha)
Soil organic carbon content (g/kg)
Soil pH x 10 in H2O
Soil pH x 10 in KCl
Average Monthly Shortwave Radiation 1982 - 2015
Silt content (mass fraction)
Sand content (mass fraction)
Monthly Average Climate water deficit (mm)Source
82
68
68
60
68
68
68
83
83
83
83
83
83
83
83
83
83
83
83
83
83
83
83
68
82
84
84
84
84
85
85
68
68
68
68
68
86
68
68
87

Covariate
Monthly Average Palmer Drought Severity Index
Monthly Average Runoff (mm)Monthly Average Soil moisture (mm)Monthly Average Vapor pressure (kPa)Monthly Average Vapor pressure deficit (kPa)Monthly Average Wind-speed at 10m (m/s)
Annual mean temperature (°C)
Mean diurnal temperature range (mean(period max-min)) (°C)
Isothermality
Temperature seasonality (C of V)
Max temperature of warmest week (°C)
Min temperature of coldest week (°C)
Temperature annual range (°C)
Mean temperature of wettest quarter (°C)
Mean temperature of driest quarter (°C)
Mean temperature of warmest quarter (°C)
Mean temperature of coldest quarter (°C)
Annual precipitation (mm)Precipitation of wettest week (mm)Precipitation of driest week (mm)Precipitation seasonality (C of V)
Precipitation of wettest quarter (mm)Precipitation of driest quarter (mm)Precipitation of warmest quarter (mm)Precipitation of coldest quarter (mm)Available soil water capacity until wilting point (volumetric fraction)
755

Source
87
87
87
87
87
87
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
68

756
757
758
759
760

Table S6. 2019 IPCC default rates (MgC ha-1 yr-1) for aboveground biomass accumulation inyoung forests12, converted to carbon using 0.4764. We also include predicted average, minimum,and maximum rates (MgC ha-1 yr-1) from our map across the same area. The final column indicatesthe percent difference of the average predicted rate relative to the IPCC rate in each forest ecozone,
where a positive value indicates that the predicted rate is higher than the IPCC rate.
EcozoneBoreal coniferous forest
Boreal coniferous forest
Boreal coniferous forest
Boreal mountain system
Boreal mountain system
Boreal mountain system
Subtropical dry forest
Subtropical dry forest
Subtropical dry forest
Subtropical dry forest
Subtropical humid forest
Subtropical humid forest
Subtropical humid forest
Subtropical humid forest
Subtropical mountain system
Subtropical mountain system
Subtropical mountain system
Subtropical mountain system
Temperate continental forest
Temperate mountain system
Temperate mountain system
Temperate oceanic forest
Temperate oceanic forest
Temperate oceanic forest
Temperate oceanic forest
Tropical dry forest
Tropical dry forest
Tropical dry forest
Tropical dry forest
Tropical moist forest
Tropical moist forest
Tropical moist forest
Tropical moist forest
Tropical mountain system
Tropical mountain system
Tropical mountain system
Tropical mountain system

Continent

IPCC

Asia
EuropeNorth America
Asia
EuropeNorth America
AfricaNorth America
South America
Asia
AfricaAsia
North America
South America
AfricaAsia
North America
South America
North America
North America
South America
EuropeNew Zealand
North America
South America
AfricaAsia
North America
South America
Asia
AfricaNorth America
South America
Asia
North America
South America
Africa0-1
0-1
0-1
0.5 - 0.5
0.5 - 0.5
0.5 - 0.5
1.1 - 1.2
1.9
1.9
2.8
1.2
1.2
1.2
1.2
1.2
1.2
1.2
1.2
1.6
1.5
1.5
1.1
1.5
3
3
1.8
1.8
1.8
1.8
1.1
1.4
2.4
2.4
1.4
2.1
2.1
2.6

Predicted rate
Average (Min - Max)
1.08 (0.71 - 1.43)
0.94 (0.29 - 2.72)
0.89 (0.48 - 2.26)
1.01 (0.52 - 2.16)
1.03 (0.3 - 2.72)
0.87 (0.55 - 2.09)
0.7 (0.22 - 2.49)
0.6 (0.28 - 1.11)
1.54 (0.67 - 2.81)
0.94 (0.25 - 1.37)
1.78 (0.25 - 3.87)
2.08 (0.43 - 4.07)
1.62 (0.55 - 2.74)
1.56 (0.22 - 4.09)
0.91 (0.21 - 2.49)
1.17 (0.23 - 3.58)
1.21 (0.09 - 4.49)
1.01 (0.22 - 1.82)
0.96 (0.61 - 1.92)
0.95 (0.15 - 2.97)
1.53 (0.68 - 2.6)
1.62 (0.84 - 2.95)
1.84 (0.8 - 3.14)
1.57 (1.15 - 2.44)
2.15 (0.76 - 2.82)
1.71 (0.33 - 5.36)
2.4 (0.77 - 5.01)
2 (0.3 - 5.26)
1.88 (0.17 - 4.99)
2.93 (0.95 - 5.13)
2.67 (1.08 - 5.34)
3.4 (0.99 - 5.11)
3.37 (0.59 - 5.61)
2.63 (0.29 - 4.76)
3.08 (1.27 - 5.25)
3.4 (0.63 - 5.36)
2.95 (0.4 - 5.51)

%
Diff
110
81
72
104
108
76
-38
-68
-18
-67
52
77
38
33
-23
0
3
-14
-38
-35
5
50
26
-47
-27
-6
31
9
3
159
96
39
38
93
49
65
14

Ecozone
Tropical rainforest
Tropical rainforest
Tropical rainforest
Tropical rainforest
761

Continent

IPCC

Asia
North America
South America
Africa

1.6
2.8
2.8
3.6

Predicted rate
Average (Min - Max)
3.64 (1.6 - 5.57)
3.8 (1.43 - 5.45)
4.64 (1.55 - 5.89)
4.55 (2.28 - 6.16)

%
Diff
128
37
67
27

762
763
764
765
766

Table S7: Country-level summaries of carbon accumulation rates (MgC ha-1 yr-1) and mitigation
potential from natural forest regrowth (TgCO2 yr-1) under two scenarios for natural forest
regrowth. The first scenario represents a biophysical maximum3 and another based on national
commitments11. The rate column includes rates from pixels that overlap with area of opportunity
pixels in Griscom et al3. We only list countries that are a million hectares or larger.

Geography
Afghanistan
Albania
Algeria
AngolaArgentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
BelarusBelgium
BelizeBenin
BhutanBolivia
Bosnia andHerzegovina
BrazilBulgaria
BurundiCabo VerdeCambodia
Cameroon
CanadaCentral African
Republic
Chad
Chile
China
Colombia
Congo, Rep.
Congo, Dem. Rep.
Costa Rica

Mean (min-max)
aboveground rate,
MgC ha-1 yr-1
0.99 (0.85 - 1.09)
1.26 (0.95 - 1.61)
0.96 (0.36 - 1.52)
2.89 (2.38 - 4.58)
0.93 (0.2 - 2.94)
0.85 (0.61 - 1.04)
1.03 (0.22 - 3.61)
1.2 (0.85 - 1.49)
0.85 (0.59 - 1.33)
3.34 (2.55 - 3.83)
1.03 (0.81 - 1.24)
1.65 (1.29 - 2.13)
4.38 (3.18 - 5.03)
4.94 (3.96 - 5.3)
2.22 (1.46 - 3.81)
2.83 (0.8 - 5.55)

0.33
0.67
0.53
1.34
0.36
0.39
0.39
0.55
0.36
0.92
0.47
0.76
1.04
1.83
0.68
0.98

Mitigation,
maximum
scenario,
TgCO2 yr-1
0.1
9.83
7.78
4.9
79.65
1.62
149.84
7.58
4.42
0.1
30.37
3.47
5.64
0.67
2.32
61.46

1.22 (1.06 - 1.49)
3.95 (1.33 - 5.84)
0.93 (0.7 - 1.24)
4.06 (3.32 - 4.58)
1.32 (1.1 - 1.63)
3.69 (2.54 - 4.98)
5.01 (3.52 - 6.13)
0.96 (0.48 - 2.26)

0.59
1.2
0.43
1.08
0.85
1.58
1.79
0.44

11.51
1830.52
18.02
0.61
1.59
51.94
47.11
38.8

471.7740.76
319.26-

4.77 (3.63 - 5.67)
1.36 (1 - 1.52)
1.73 (0.68 - 2.81)
1.9 (0.57 - 4.93)
4.27 (2.24 - 5.52)
4.9 (3.26 - 5.78)
4.43 (2.03 - 5.78)
3.51 (2.05 - 4.37)

1.68
0.76
0.9
0.48
1.29
1.82
1.63
1.07

10.36
0.32
25.89
1062.15394.38
74
221
28.81

88.23
46.59
6.7
409.7344.24
52.32
403.5522.62

Mean
belowground rate,
MgC ha-1 yr-1

Mitigation,commitment
scenario,
TgCO2 yr-1
6.27
7.28
12.87
13.17
93.08

Geography
Croatia
Cuba
Czech Republic
Cote d'Ivoire
Denmark
Dominican
Republic
EcuadorEl Salvador
Equatorial Guinea
Eritrea
Estonia
Ethiopia
FinlandFranceGabon
Georgia
Germany
Ghana
GreeceGuatemalaGuineaGuinea-Bissau
GuyanaHaiti
Honduras
Hungary
India
Indonesia
Iran
IrelandIsraelItaly
JamaicaJapan
JordanKazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep.

Mean (min-max)
aboveground rate,
MgC ha-1 yr-1
1.19 (0.89 - 1.58)
3.02 (2.19 - 4.48)
1.11 (0.88 - 1.39)
4.86 (3.22 - 5.75)
1.74 (1.36 - 2.24)

0.6
0.71
0.51
1.8
0.8

Mitigation,
maximum
scenario,
TgCO2 yr-1
11.67
72.96
7.09
155.2
2.4

3.2 (1.83 - 4.11)
3.53 (2.15 - 4.87)
2.66 (2.19 - 3.19)
4.77 (4.16 - 5.38)
1.14 (1.03 - 1.24)
1.41 (1.2 - 1.65)
2.62 (0.97 - 4.34)
1.31 (0.7 - 1.59)
1.49 (0.74 - 4.9)
4.72 (3.72 - 5.68)
1.21 (0.67 - 1.63)
1.41 (1.02 - 2.21)
4.87 (3.34 - 5.78)
1.02 (0.58 - 1.44)
3.58 (2.03 - 5.05)
4.6 (2.71 - 5.43)
2.8 (2.55 - 3.03)
4.24 (3.42 - 5.18)
3.34 (1.93 - 4.37)
3.02 (1.97 - 4.4)
0.94 (0.83 - 1.18)
2.12 (0.51 - 4.35)
4.38 (1.92 - 5.17)
0.94 (0.5 - 1.42)
2.33 (1.77 - 2.89)
0.95 (0.44 - 1.07)
1.13 (0.63 - 1.66)
3.47 (2.42 - 4.16)
1.5 (1.16 - 3.18)
0.54 (0.46 - 0.62)
0.8 (0.53 - 0.92)
2.7 (1.42 - 4.28)
1.36 (1.12 - 1.58)
1.55 (1.37 - 1.76)

1.05
1.16
0.93
1.77
0.31
0.65
0.79
0.6
0.68
1.75
0.4
0.65
1.85
0.57
1.13
1.69
0.96
0.91
1.09
1.06
0.43
0.93
1.59
0.2
1.07
0.39
0.6
1.17
0.5
0.17
0.36
0.74
0.62
0.39

36.08
85.05
10.52
0.22
0
7.16
73.4
0.81
120.69
12.61
8.25
27.71
72.74
41.86
55.35
12.8
1.1
4.01
21.49
57.43
7.52
392.2
130.99
6.78
66.11
0.16
53.24
5.27
30.89
0
5.04
10.38
14.75
2.77

Mean
belowground rate,
MgC ha-1 yr-1

Mitigation,commitment
scenario,
TgCO2 yr-1
129.749.38
14.71
210.65
98.46
0.39
52.37
49.18
16.49
267.19686.4572.23
54.1

Geography
Kyrgyzstan
Laos
LatviaLebanon
Liberia
Libya
Lithuania
Madagascar
MalawiMalaysia
MexicoMoldova
Mongolia
Montenegro
MoroccoMozambique
Myanmar
Nepal
Netherlands
New Zealand
Nicaragua
Nigeria
NorwayPakistan
PanamaPapua New Guinea
Paraguay
Peru
Philippines
PolandPortugal
Romania
Russian Federation
RwandaSenegalSerbiaSierra Leone
Slovakia
Slovenia
Solomon Islands

Mean (min-max)
aboveground rate,
MgC ha-1 yr-1
0.71 (0.55 - 0.89)
3.63 (2.72 - 4.34)
1.31 (1.03 - 1.6)
1.04 (0.8 - 1.31)
5.1 (4.29 - 5.66)
0.79 (0.37 - 1.23)
1.2 (0.94 - 1.53)
3.03 (1.65 - 4.09)
2.7 (2.31 - 3.55)
4.59 (3.68 - 5.57)
2.69 (0.28 - 5.23)
0.85 (0.71 - 0.98)
0.89 (0.77 - 1.05)
1.28 (1.05 - 1.66)
0.95 (0.29 - 1.67)
2.97 (1.92 - 4.27)
3.13 (1.41 - 4.9)
2.02 (1.28 - 3.18)
1.69 (1.53 - 1.89)
2.48 (1.01 - 3.08)
3.07 (2.1 - 4.21)
5.28 (4.02 - 5.9)
1.19 (0.93 - 1.63)
1.04 (0.58 - 1.38)
4.03 (3.05 - 5)
3.94 (2.54 - 4.88)
2.14 (0.56 - 4)
3.97 (1.93 - 5.36)
4.29 (2.87 - 5.19)
1.14 (0.86 - 1.87)
1.33 (0.77 - 2.84)
0.95 (0.71 - 1.37)
1.01 (0.59 - 1.56)
4.38 (4.16 - 4.55)
2.65 (1.96 - 2.9)
1.02 (0.82 - 1.32)
4.52 (3.47 - 5.1)
0.99 (0.86 - 1.39)
1.34 (1.03 - 1.6)
3.66 (2.89 - 4.21)

Mean
belowground rate,
MgC ha-1 yr-1
0.33
1.14
0.6
0.56
1.89
0.36
0.55
0.81
0.55
1.7
0.84
0.39
0.41
0.64
0.53
1.12
0.92
0.61
0.78
0.69
0.95
1.95
0.54
0.34
1.16
1.42
0.67
1.26
1.43
0.53
0.72
0.44
0.47
1.18
0.53
0.47
1.61
0.46
0.62
1.35

Mitigation,
maximum
scenario,
TgCO2 yr-1
0.33
45.35
9.2
0.8
5.36
0.16
8.45
17.45
0.6
1.85
450.82
2.32
6.67
4.63
4.71
0.45
226
18.2
6.63
26.58
76.98
112.63
0.03
2.39
48.53
8.53
111.0937.89
153.44
26.12
31.06
24.99
298.94
0
0.02
15.17
1.72
3.98
2.9
0.03

Mitigation,commitment
scenario,
TgCO2 yr-1
144.44
0.59
27.14
62.48
60.57
151.92
0.98
3.78
16.53
7.88
0.85
6.93
43.99
842.32
7.88
11.63
20.58
66.35
43.91
-

Geography
SomaliaSouth Africa
South Sudan
Spain
Sri Lanka
Suriname
SwedenSwitzerland
Syria
Tajikistan
Tanzania
ThailandThe Former
Yugoslav Republicof Macedonia
Timor-Leste
Togo
Tunisia
TurkeyUgandaUkraineUnited Kingdom
United States
Uruguay
Uzbekistan
Vanuatu
Venezuela
Vietnam
ZambiaZimbabwe
767

Mean (min-max)
aboveground rate,
MgC ha-1 yr-1
1.81 (1.21 - 2.49)
1.63 (0.46 - 3.81)
2.07 (1.49 - 2.32)
1.04 (0.4 - 2.94)
3.86 (2.44 - 4.44)
4.22 (3.64 - 4.91)
1.22 (0.67 - 2.3)
1.31 (0.78 - 1.57)
0.98 (0.32 - 1.34)
0.89 (0.72 - 1)
2.11 (1.32 - 4.39)
3.81 (2.45 - 5.53)

0.59
0.56
1.1
0.55
1.34
0.85
0.56
0.6
0.49
0.41
0.82
1.52

Mitigation,
maximum
scenario,
TgCO2 yr-1
4.5
6.97
0.08
79.16
3.17
1.29
0.37
3.2
0.88
0.1
48.73
213.820.97 (0.65 - 1.26)
3.32 (2.24 - 3.82)
4.59 (3.71 - 5.53)
0.91 (0.34 - 1.27)
0.9 (0.42 - 1.62)
3.5 (1.48 - 4.74)
0.96 (0.78 - 1.52)
1.93 (1.34 - 2.78)
1.15 (0.16 - 4.32)
1.55 (0.82 - 2.42)
0.83 (0.65 - 0.93)
3.53 (2.71 - 4.04)
3.6 (1.63 - 5.14)
3.32 (2.39 - 4.71)
2.42 (1.24 - 2.83)
1.4 (1.27 - 1.52)

0.49
1.19
1.6
0.5
0.51
1.19
0.44
0.89
0.43
0.3
0.38
1.3
1.08
0.9
0.5
0.79

6.1
6.54
10.55
1.06
119.384.33
51.16
100.08321.160
0.02
2.1
186.46110.73
2.59
0.06

Mean
belowground rate,
MgC ha-1 yr-1

Mitigation,commitment
scenario,
TgCO2 yr-1
3.24
-

53.97
56.76
18.95
109.86292.96
1.43
-

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966

Metadata

967
968
969

Database structure970

The dataset includes three levels: full citation information (Table M1), variables specific971

to sites (Table M2), and stand (“plot”)-level carbon and biomass data with associated covariates

972(Table M3). Individual measurements are nested within plots, where plots are defined as stands

973

with unique qualities (e.g., a single age, land use or combination) Plots are nested within sites.

974

Sites are defined by having a unique latitude and longitude, though the specificity of geolocation

975

varied across studies with some reporting highly precise locations for each stand and others giving

976

a single geolocation for a larger region.

977

We followed a few general rules for data extraction. If multiple publications described the

978same geolocation, we coded all data with a single site to avoid pseudoreplication. If a range was979

given for a variable, we calculated the average, but excluded data with large ranges, such as a980

forest age that spanned more than 10 years or a geolocation that spanned more than a degree981

latitude or longitude. Finally, for graphical data we used WebPlotDigitizer89 to extract the982

variables.

983

Note that we make available our full dataset, which includes some variables that we did984

not include in our final analysis but may be useful for future work. For some fields, data are

985

missing, because studies did not provide all details (e.g., type of prior disturbance).

986
987

988
989

Table M1: Explanation of variables in literature dataset.
Column namestudy.id
citations.author
citations.year
citations.journal
citations.title

Description
unique numeric identifier for each publication
last name of first author
year of publication
citation information including journal, volume and page number
full title from publication

990
991
992

Table M2: Explanation of variables in site datasheet.
Column namesite.idstudy.idsite.sitenamesite.statesite.country
lat_dec
long_decother reference
elevation
AMT
AMP
soil.classification

993
994

Description
unique numeric identifier for each geolocation
unique numeric identifier for each publicationtext description of site namesub-national jurisdiction such as state, province etc., if given
country namelatitude in decimal degrees
longitude in decimal degrees
other publications or resources used to fill out site information
height above sea level in meters, if given
annual mean temperature in degrees Celsius, if given
annual mean precipitation in millimeters, if given
soil order converted to US system of nomenclature, if given

995
996

Table M3: Explanation of variables in measurement datasheet.
Column namemeasurement.id
plot.idsite.idstudy.idrefor.type

speciesprior

stand.age
date
n
sub_n
plot.size
variables.namemean_hacovar_1
coV1_value
covar_2
coV2_value
covar_3
coV3_value
density

Description
unique identifier for each carbon/biomass measurement
unique identifier for distinct spatial unit(s) within a site, e.g., if a study
reported a single mean aboveground biomass measure for 12 year oldstands, this would receive a single plot.id whereas if separate measures
are given for a 12 year old stand that was previously pasture versus a 12
year old stand that was previously cropped then each of those would adistinct plot.id.
unique numeric identifier for each geolocation
unique numeric identifier for each publication
reforestation type or reference condition; SNR = spontaneous naturalregeneration (or “natural forest regrowth”), TMC = intensive tree
monocrop (reference), C = cropland (reference), PA = pasture (reference)name of dominant species, if given
type of most recent disturbance, if given; C = crop; SC = shifting
cultivation/fallow; H = clearcut harvest of land in forest use; F = fire; D= non-fire disturbance such as landslide or hurricane; PA = pasture; M =mining; TMC = tree monocrop (e.g., banana or rubber plantation)
age of forest stand; crop and pasture = 0, otherwise age is as given instudy; age range is between 0 and 100 years
year data were collected, if given
number of plots (e.g. distinct spatial units) per measurement
number of subplots per plot, e.g., soil samples pooled for a single measurelargest plot dimension in m2 (e.g., plot size used to measure largest
diameter trees)
name of carbon pool; variables include aboveground_biomass/carbon;
understory_biomass/carbon; litter_biomass/carbon;
deadwood_biomass/carbon; belowground_biomass/carbon; soil organiccarbon (SOC)/percent soil organic matter (SOM_per)/percent soilorganic carbon (soil_perC); or combinations of above if study did notparse data by pool, see “Definitions of Pools”
value of biomass or carbon estimate per hectare in Mg/hatype of covariate (see “Definitions of Pools”)
value of covariate 1
type of covariate (see “Definitions of Pools”)
value of covariate 2type of covariate (see “Definitions of Pools”)
value of covariate 3
number of individual trees per hectare, if given

Column namesand.silt.clay
pHallometry

997
998
999

Description
soil texture, if given; sand%:silt%:clay% or text description (e.g., clay,
sandy clay, sandy clay loam, loamy sand, silty clay, silt loam)
pH, if given
direct harvest = direct harvest of all biomass at a site; site-specific harvest= based on trees harvested at the site; species-specific = based on species;
forest-type-specific = based on similar forest in the region; biomespecific = based on general equations for a biome (e.g., 90)

Definitions of Pools

1000

1. Aboveground_biomass/carbon refers to aboveground tree biomass excluding understory

1001

biomass/carbon. If the two pools are combined, we note the presence of the latter by adding1002“+ understory_biomass/carbon” to the variables.name column. A minimum diameter at

1003

breast height (min_dbh, covariate 1) is typically listed with this measurement with a “0”1004

indicating all trees were sampled. Alternatively, studies sometimes measured only trees

1005

above a certain height, in which case we note minimum height (min_height, covariate 1).

1006

Note that aboveground_biomass_woody indicates only stem and branch biomass, not1007

foliage.

1008
1009

2. Understory_biomass/carbon typically refers to herbaceous biomass, shrubs, lianas, and/1010

trees saplings shorter than breast height. Possible covariates (covariate 1) include

1011

maximum height (max_height) or maximum dbh (max_dbh) measured.

1012
1013

3. Belowground_biomass/carbon refers to root biomass. We did not include studies that only

1014

quantified fine root biomass. Possible covariates (covariate 1) include minimum root

1015

diameter measured (root_diameter_min) or maximum depth of sampling (max_depth). If a

1016

study only quantified roots up to a specific size, we noted this in root_diameter_max

1017(covariate 2). We extracted but did not include in our analyses, data quantifying root

1018

biomass where there was no estimate of aboveground biomass.

1019
1020

4. Soil biomass/carbon was reported as soil organic carbon density (SOC), percent soil

1021

organic matter (SOM_per), or soil organic carbon concentration (soil_perC), depending on1022

the study. If a study reported soil organic carbon concentration, we also included

1023

bulk_density (covariate 3) where it was given. For all soil measures, we noted the1024

maximum depth (max_depth, covariate 1) and minimum depth (min_depth, covariate 2) of1025

measurement and analyzed data as the sum of all shallower soil profiles.

1026
1027

5. Litter_biomass/carbon refers to litter and CWD_biomass/carbon refers to coarse woody

1028

debris. We parsed data where possible according to IPCC guidelines91, where coarse

1029

woody debris includes wood lying on the surface, dead roots and stumps larger than or1030

equal to 10cm. Litter includes all non-living biomass that is distinguishable from mineral

1031

soil, typically 2mm or greater and less than 10cm.

1032
1033

1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075

Studies included in database
The references list first author, year, title and citation information for all studies (N = 257) in thelarger database (N = 13033 measurements). We included data from peer-reviewed publications ordatasets from respected institutions with asterisks denoting the latter.
Aide (2000) Forest regeneration in a chronosequence of tropical abandoned pastures: Implications
for restoration ecology. RESTORATION ECOLOGY 8:328-338
Aide (1995) Forest recovery in abandoned tropical pastures in Puerto Rico. FOREST ECOLOGYAND MANAGEMENT 77:77-86
Alberti (2011) Impact of woody encroachment on soil organic carbon and nitrogen in abandoned
agricultural lands along a rainfall gradient in Italy. REGIONAL ENVIRONMENTAL
CHANGE 11:917-924
Ali (2017) Community-weighted mean of leaf traits and divergence of wood traits predict
aboveground biomass in secondary subtropical forests. SCIENCE OF TOTALENVIRONMENT 574: 654-662
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