Global alternatives of natural vegetation cover

Domain: earth_science
Published: 2025-07-16
Editorial outcome: minor_revision

Abstract
Preserving and restoring terrestrial ecosystems is essential to preventing the decline of life on Earth. To guide global conservation efforts, we present a detailed counterfactual map showing Earth’s natural tree, short vegetation, and bare ground cover. This map accounts for environmental filtering along with realistic scenarios of fire frequency and wildlife herbivory. The most likely scenario suggests 43% (5669 ± 74 Mha) of land could support trees, 39% (5183 ± 86 Mha) shrubs and grasses, and 18% (2352 ± 59 Mha) bare ground. Adjustments in fire and herbivory could shift a minimum of 675 Mha of land, stressing the importance of considering alternative outcomes when restoring a landscape. Our findings also suggest that adjustments in fire frequency and wildlife herbivory could have a greater impact on natural vegetation than expected climate changes by 2050, highlighting decision-makers’ responsibility to guide conservation and restoration toward a sustainable and biodiverse future.

Introduction
During the last decades, several planetary boundaries have been crossed, including those related to biosphere integrity and climate change1. These highlight the urgent need to halt and reverse the loss of biodiversity in order to achieve safe living conditions on Earth2–6. International organizations have recognized this urgency, as reflected by the adoption by the General Assembly of the United Nations, on the 1st of March 2019, of a resolution to establish the 2021-2030 period as the “Decade of Ecosystem Restoration”. This resolution is also backed by the latest reports of the 6th Assessment Report of the Intergovernmental Panel on Climate Change2, the adoption of global quantitative restoration targets under the Global Biodiversity Framework by the parties of the Convention on Biological Diversity in Montréal in December 2022, and the adoption of the European Nature Restoration law in June 2024. However, among the key scientific challenges to responsible ecosystem conservation and restoration is understanding the natural baseline conditions of any region, and the natural states that are possible for any plot of land.

While ecosystem restoration is largely supported by governments, NGOs and stakeholders in ecosystems across the globe, restoration actions on the ground have often focused disproportionately on planting trees through reforestation or afforestation, even in areas that were not naturally forested7,8. Such bias toward tree planting could lead to unintended consequences for the mitigation of climate change, the conservation of other life forms (including vegetation, animals, fungi, and microbes), and the rights and livelihoods of local communities9. Examples of negative unwanted impacts include, but are not limited to, warming in boreal regions due to tree cover-induced changes in albedo10, increased evapotranspiration leading to a loss of water availability11, lower carbon storage in soils when replacing old-growth grasslands with tree plantations12, or a collapse in native biodiversity with the replacement of native savannahs by exotic plantations7.

To minimize these risks of damaging land conversion, socially and ecologically responsible land management strategies require a fundamental understanding of the natural state of ecosystems across the globe.

A growing body of research highlights the potential for natural restoration (i.e. ecosystem restoration practices that involve minimal human intervention) to maximize healthy biodiversity outcomes13,14. But successful natural restoration requires a basic understanding of the diversity of natural ecosystem states that can be found within a given landscape, so that different habitat types and maximum biodiversity can be preserved15. In this context, grasslands and shrubland ecosystems must not only be considered as degraded or temporary alternatives to forests (as has occurred in several mass plantation examples), but, where appropriate, as climax ecosystems in their own right16,17, with the necessary consideration of the various mechanisms (including fire and herbivory) shaping the formation of open ecosystems18,19. It is crucial to move beyond a narrow focus on “forest vs. non-forest” possibilities and to explore a wider range of potential natural land cover diversity accounting for the risks and benefits that may arise from various natural restoration targets20. While recognizing the importance of considering diverse ecosystems for restoration is not new in restoration ecology14,21,22, we still lack a comprehensive assessment of the different possible natural states of ecosystems across the globe that would be critical for guiding national and international land management policies, as well as their implementation on the ground7–9.

Results
Here, we generate, to our knowledge, the first map of the potential natural states of broad vegetation types, showing the most probable natural land cover in every pixel across the globe. These broad ecosystem categories include tree cover (tall woody vegetation), short vegetation (shrubs and grasses), and bare ground (areas devoid of perennial vegetation but which might harbor biocrusts and annual plants) at a resolution of 0.25° (Fig. 1). This map, referred to as the global natural vegetation cover, is generated using data collected over 40,000 0.5-hectare plots systematically distributed within all protected areas (Fig. S1; “Methods”). To predict the different natural vegetation cover, we trained models using photo-interpreted vegetation cover data from protected areas and predicted natural variation using climate variables, soil properties, fire frequency, and wildlife herbivory within a neural network modeling framework (“Environmental determinants of land cover” in “Methods”). Using possible scenarios of fire and herbivory at the ecoregion or biome level (“Environmental determinants of land cover” in “Methods”), we aimed to explore the possible mechanisms that might have the greatest potential for establishing natural vegetation states in different regions across the globe. We then generated the final counterfactual map by extrapolating the model beyond protected areas. We refer to this map as “counterfactual” as it illustrates the natural vegetation cover carrying capacity of the planet, i.e. a different situation of current actual vegetation cover that can be mapped directly from satellites23.

The central challenge in our analysis is identifying “natural” reference state of any ecosystem. We chose protected areas as the best representation of the most natural state of our ecosystems. Many protected areas experience some level of human degradation that might ultimately lead our model to the misrepresentation of the reference state. Degradation can relate to historical and current anthropogenic activities like legal and illegal logging, past defaunation and local extinctions, ongoing poaching or excessive hunting, or increased abundance of exotic pests and diseases24–26. To limit the potential resulting bias, we further extrapolate our map considering as natural reference state solely those protected areas under conservative IUCN categories I, II, and III (i.e. over 17,000 plots; “Augmented Visual Interpretation” in “Methods”). The spatial distribution of the remaining protected areas might also lead to the misrepresentation of the global alternatives of natural vegetation cover, due to spatial aggregation and misrepresentation of global environmental conditions. To this end, we control for the spatial structure using spatial blocks for cross validation27 (“model training and control of spatial structure” in “Methods”), and validate their environmental representativity using spatial features approach28,29 (“The representativity of the training dataset” in “Methods”). All the models yielded good results (observed vs. predicted R² ≥ 0.8; average intercept of 0.04 ranging from 0 to 0.07 and average slope = 0.97 ranging from 0.92 to 1), showing a very limited impact (<1%) of the modelling framework and of the choice of climate dataset and a rather limited (~5%) impact of the spatial structure of the training dataset (“The neural network model” in “Methods”). We then generated the final counterfactual map by extrapolating the model beyond protected areas for each climate data source and calculating the mean natural vegetation cover for each pixel, using the most probable (i.e., median) scenario of fire and herbivory (“Environmental determinant of land cover” in “Methods”). We employed various climate databases to account for the model's sensitivity to the choice of climatic data and accounted for potential model overfitting. The control of spatial feature distributions shows that our calibration dataset is well representative of the global dataset (“The representativity of the training dataset” in “Methods”).

Our study reveals that the most probable global natural vegetation cover of the planet (Fig. 1) is comprised of 5669 (±74) Mha of trees (43%), 5183 (±86) Mha of short vegetation (39%), and 2352 (±59) Mha of bare ground (18%). Our counterfactual map reveals two distinct patterns at the global level (Fig. 1). From tropical to desert regions, each biome tends to be dominated by a single type of vegetation cover. Tropical biomes are dominated by trees, subtropical biomes by short vegetation, and desert biomes by bare ground (Table S1). From temperate to boreal regions, each biome tends to present a more balanced distribution of the natural vegetation cover offering more opportunities in term of land management. This finding is in line with a recent assessment of the state of European land cover during the last interglacial period, which showed that Europe was covered by heterogeneous and complex landscapes in the youngest period with a similar climate as today and absence of Homo sapiens30. Tropical and subtropical moist forests have the largest area naturally covered by trees (on average 1616 Mha), but also present 312 Mha of land naturally covered by short vegetation. Similarly, tropical and subtropical grasslands have the largest area naturally covered by short vegetation (1127 Mha), but also present 722 Mha of land naturally covered by trees. Desert biomes, while dominated by bare ground (1300 Mha) might also shelter a significant area of natural short vegetation and tree cover, with 1053 Mha and 419 Mha respectively. The carrying capacity for natural vegetation in desert biomes is expected to decline over the coming decades due to forecasted climate and land use changes4,31. Boreal and temperate biomes exhibit high carrying capacities for both short vegetation and tree cover, with temperate biomes supporting 1095 Mha of short vegetation and 1341 Mha of tree cover, and boreal biomes supporting 656 Mha of short vegetation and 717 Mha of tree cover.

These results highlight that landscape restoration cannot afford to overlook any type of land cover. Whether in arid, boreal, temperate, or tropical biomes, a restoration project should always consider the opportunity, costs, and benefits of restoring different ecosystems, and the possibility of letting restored natural processes determine the outcome. This underscores the remarkable heterogeneity of vegetation on Earth and the intricate challenges associated with their conservation and restoration.

Using multiple realistic fire frequency and wildlife herbivory scenarios, i.e. from percentile 5th to 95th in the observed range of fire frequency and herbivore biomass in each ecoregion of the world, we map all the potential alternatives of natural land cover on Earth (Fig. 2). Each pixel of the map represents the standard deviation of the predictions obtained from the different scenarios for tree, short vegetation, and bare ground cover in green, blue, and red respectively (Fig. 2). The resulting additive color rendering corresponds to landscapes where diverse natural vegetation cover can be found. In total, the map illustrates that at least 675 Mha of the Earth's surface can support alternative vegetation covers, an area equivalent to the size of the Amazonian bassin32 (“Map of the global alternative natural vegetation cover and sensitivity to fire and herbivory scenarios” in “Methods”).

The most substantial hotspots where alternative natural vegetation occur in subtropical and temperate biomes, where fire and herbivory scenarios promote the transition from dense forests to grassy systems and eventually from grassy systems to deserts (Fig. 2). These hotspots result mostly from an asymptotic relationship between land cover and fire regime, as an increase of fire will substantially favor short vegetation over trees beyond a certain threshold of fire frequency (“The neural network model” in “Methods”). The effect of wildlife herbivory appears more complex, as an increase of herbivory biomass is not systematically related to a decrease of tree cover. Indeed, high herbivore densities can be found both within dense forests (e.g., forest elephants, great apes, etc.) and herbaceous savannas33. High herbivore densities can also increase vegetation heterogeneity and consequently their overall resilience vs. external forcing34. Further development of the method at finer scales might benefit from a focus on the potential effect of trait-specific herbivores to better identify the effect of herbivores on the landscape35. Also, wetlands are not included in the modeling process, even though they may contain trees, short vegetation, and barren land. Since conservation or restoration choices could significantly impact wetland ecosystems, we emphasize that they should be carefully considered when assessing alternative options for natural vegetation cover. Here, we estimate that less than 5% of wetlands could be affected by these alternatives, warranting a thorough evaluation of potential outcomes using existing wetland maps for guidance36.

Our counterfactual maps provide valuable insights to help guide ecosystem restoration efforts, accounting for a full range of natural options in terms of vegetation cover that together shape complex and heterogeneous landscapes37. The maps and the model we built can assist decision-makers, policy makers, and local stakeholders in assessing how climate change, prescribed fire intensity, fire exclusion, and wildlife herbivory may affect the tree, short vegetation, and bare ground cover of a given landscape. This could help guiding existing restoration initiatives such as natural grazing projects in Europe's rewilding landscapes, where we can evaluate the scenario needed to promote more heterogeneous landscapes15 and address the effects of, for instance, land abandonment38, trophic rewilding experiments in Siberia (e.g. “the Pleistocene park”) where grassy ecosystems might stabilize soil temperatures and limit permafrost melting39, trophic rewilding experiments in African savannah where elephants help promote semi-open ecosystems40, or reforestation initiatives in Africa aiming to slow down land degradation and desertification (e.g., the Great Green Wall41).

Using our model considering various fire frequency and herbivory scenarios, together with the average expected climate change by 205042, we found that an increase of herbivore biomass up to 30,000 kg per km²—i.e. close to the upper limit observed in Europe latest interglacial age43 or about 30 bisons or six elephants per km2—can decrease the tree cover from 55% to 11% in the Dinaric mountains of Europe, and from 44% to 8% in the Northeast Siberian Taiga (Fig. 3). Fire prescription or exclusion in West Sudanian savannah might change the potential tree cover by 23%, shifting between 33% and 56% when passing from 0.5 to 0 fire yr−1 (Fig. 3). Interestingly, results show that the choice of management action appear to have a much stronger impact on the final land cover than average climate change alone. In this regard, the effects of the average expected climate change by 2050 only changed the resulting land cover of the previous examples by less than 3%. While the maps and model presented here are robust and informative and provide a global picture of the different potential natural alternatives, further refinement and validation of our models would enhance their accuracy and usefulness for specific restoration or rewilding initiatives on the ground. Future refinements should notably consider incorporating additional biogeographical variables, such as more detailed climate-derived indices and improved spatial data on soil properties and hydrology44–46, while also refining disturbance modeling—particularly fire and herbivory—by integrating finer-scale dynamics of herbivore biomass, functional diversity, intake rates, and fire regimes across different ecological contexts24,47,48. The inclusion of punctual but extreme climatic events such as extreme drought49 or tropical cyclones50 might also improve our understanding of the effect of climate change vs. the effect of management action on the resulting land cover.

In conclusion, our model reveals the considerable potential for diverse natural vegetation states across the globe. Given the critical differences in ecosystem states that are possible, our findings highlight that land management actions might outweigh the average effect of climate change on the restoration outcome. As such, we believe that our results can help guide the design of ecologically and socially responsible landscape conservation and restoration initiatives that are required to combat the ongoing climate and biodiversity crises.

Methods
The raw data and the complete annotated code (including data preparation, model training, validation, and evaluation, and mapping) used in the present study are available on GitHub (https://github.com/Nicolas-Latte/GANVC).

Set-up of the study
We set-up a data-driven neural network model to map the Earth´s natural fractional vegetation cover. The model is trained on climatic, topographic, edaphic, herbivory, and fire data in combination with a dataset of land-cover in protected areas of the globe. We used this model to predict the natural fractional vegetation cover beyond protected areas.

Assessment of land cover
The training dataset corresponds to the augmented photo-interpretation of three types of land cover: trees, short vegetation (including grasses and shrubs), and bare ground. We used data from 40,520 0.5-hectare plots distributed across the protected areas of the globe (Fig. S1) and following a systematic sampling grid design (20 by 20 km). This dataset results from previous studies on the global assessment of dryland forests and the global tree restoration potential and corresponds to the land cover of 20154,51.

Augmented visual interpretation of the three land covers
The assessment of land cover in each plot was conducted using the Augmented Visual Interpretation approach with the assistance of Collect Earth52. Collect Earth, an open-access software developed by the Open Foris initiative of the Food and Agriculture Organization of the United Nations (FAO), utilizes Google Earth and Google Earth Engine to provide multi-source and multi-level information to facilitate the photo-interpretation of land cover. This software enables the operator to perform photo-interpretation of a 70×70 m square plot, combining land cover information derived from satellite images with very high spatial (pixel size ≤ 1 m) and temporal (daily data acquisition) resolution.

To interpret the land cover, the operator utilizes freely accessible, very high spatial resolution satellite images which can be visualized on Google Earth. Simultaneously, the operator cross-references the interpretations with spectral information obtained from medium-to-high resolution satellite images, including MODIS, Landsat 7/8 from USGS mission, and Sentinel 2 from Copernicus mission, which have been automatically compiled over the past 20 years. In our case, each plot consists of a systematic grid of 7-by-7 points (49 points), allowing convenient and direct estimations of tree cover, short vegetation cover, and bare ground cover. Each point on the grid represents 2% of the plot area.

The three land covers range from 0 to 100% and sum up to 100% for each plot. Distinguishing trees from short vegetation was based on visual assessment of crown size, where crowns width below 2 meters considered as non-tree woody cover. This dataset and approach have been validated on multiple occasions with ground control points51,53. More details on the photo-interpreted methodology and inventories we use are provided in previous studies4,51,53,54.

Environmental determinants of land cover
To build the global distribution of the three land covers and the subsequent global alternatives of natural vegetation cover map, we further develop an empirical model based on neural network approaches55 to predict the relationship between environmental determinants (climate, topography, soil, fire regime, and wildlife herbivory) and land cover within protected areas. We detail hereafter the preprocessing of each of the datasets used and the selected predictive variables.

Protected areas. We identified regions of the world with limited human activity using the World Database on Protected Areas56 (WDPA), developed by the United Nations Environmental Program (UNEP) and the International Union for Conservation of Nature (IUCN). These regions are nonetheless not entirely exempt from human activity26. Therefore, we distinguish conservative protected areas from the rest. Among the 40,520 plots, two categories were defined. Category 0: 17,638 plots falling under conservative IUCN categories I, II, and III. Category 1: 22,882 plots falling under IUCN categories IV, V, and VI. Model training was done with both categories with a specific predictive variable expressing the category but maps were produced considering only the restrictive IUCN categories I, II, and III (thus category 0) to estimate the natural vegetation cover of the planet with limited human activity.

Climate data. In the present study, we consider that global climate databases, whether derived from meteorological station records or satellite mission predictions, are prone to uncertainties and biases57. To address this potential issue, we have incorporated six global climate reference datasets, namely CHELSA, CRU (TS4), ERA5, MODIS, NEX historical, and Worldclim58–62.

For each climate dataset, we computed four predictive climate variables: the mean and standard deviation of monthly average temperatures, and the mean and standard deviation of monthly total precipitation. Monthly temperatures and precipitations were computed over a significant timespan of 10 to 30 years, between 1980 and 2010, depending on the availability of the data in each specific dataset. These selected climate variables capture both the annual average conditions and the seasonal patterns.

Soil and topography. As in Bastin et al., 20194, we selected five predictive variables derived from soilgrids63 and GMTED201064: soil organic carbon stock from 0- to 15 cm, depth to bedrock, sand content from 0- to 15 cm, elevation, and hillshade.

Fire frequency. We computed fire frequency using the Terra and Aqua combined MCD64A1 Version 6 Burned Area MODIS data product65. It is a monthly, global gridded 500 m product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m MODIS Surface Reflectance imagery coupled with 1 km MODIS active fire observations. Here, we converted the map into binary yearly information, counting whether a fire was detected or not each year of the period 2001–2020. We then computed a single map to assess the yearly fire frequency over the 2000–2020 period.

Herbivory. The herbivory dataset is compiled from the study of Berzaghi et al., of 202466, where the authors studied the global distribution of wild mammal herbivore biomass using dynamic vegetation models calibrated on observed datasets gathered over protected areas. To account for the general trends of herbivore biomass and daily intake in natural systems, we merged the data, that was originally structured in 24 functional groups, into three levels of information, i.e. the estimated dry biomass in kilogram of wet weight per km2 (i.e., the biomass), the estimated total biomass intake in kg of dry mass · per km2 (i.e., the intake) and, finally, the estimated daily litter intake in kg of dry mass per square kilometer. Accounting for the litter intake allows focusing on the impact of herbivores being very dependent on the seasonal source of intake (i.e. the litter).

The herbivore data being the result of a model, it presents some potential shortcomings, in particular regarding the definition of natural vs. domestic mammals that might differ depending on the perspective of the stakeholder67. Therefore, the risk of considering non-native herbivore biomass in protected areas is non-null in our model. Yet, as functional type of herbivore—not nativeness—is what matters in terms of impact on the land cover35, we assume that such bias has little impact on the result of our model which only considers the impact of herbivore intensity on the potential state of vegetation.

List of all the predictive variables. Based on aforementioned steps, we select a total of 14 variables (Fig. S2): protected area category (0: I, II, III and 1: IV, V and VI), mean of precipitation, mean of temperature, standard deviation of precipitation, standard deviation of temperature, bedrock depth, elevation, hillshade, soil organic carbon content, sand content, fire frequency, herbivore total biomass, herbivore total intake, and herbivore litter intake.

The representativity of the training dataset
To assess the representativity of the training dataset, we first analyze the ranges of the environmental variables within and outside protected areas per biomes (Fig. S3). The protected areas cover well the global variation of environmental data. Additionally, to assess statistically the representativity of the training dataset, we performed a nearest neighbor distance analysis in feature spaces following recommendations of Meyer and Pebesma28. The results in Fig. S4 illustrate the good representativity of the training dataset.

As a final test of data representativity, we compare results obtained using the full range of available data in protected areas with those derived from data restricted to Categories I, II, and III protected areas (Supplementary Note 1, Figs. S5, S6).

The neural network model
Neural network architecture. Our model is a simple neural network, i.e., a Multi Layer Perceptron (MLP). The model is composed of a first linear transformation (14 predictive variables -> 64 features) followed by a “tanh” activation function and a dropout of 0.75, then a second linear transformation (64 features -> 3 proportions). At the end of the MLP, we applied a “softmax” activation function to reconstruct proportions of the three land covers: trees, short vegetation, and bare ground (sum of proportions = 1). The total number of parameters is 1155. Input and output data are tabular with one line per photo-interpreted plot for training and one line per pixel of 0.25° (world) for prediction.

Model training and control of spatial structure. As well as the MLP, we configurated the training to avoid model overfitting and ensure prediction robustness. We set the training parameters (number of epochs = 3000, learning rate = 0.001, weight decay = 0.001, etc.) through trial and error. In total, we trained 66 MLPs: 10 (spatial folds) + 1 (all folds) for each of the six climate datasets. We used the weighted mean square error (MSE) as loss function (Fig. S7). We weighted the loss for balancing the number of samples between continents, IUCN categories (0/1), and vegetation cover classes (highest proportion). The 10 folds correspond to spatially and environmentally separated areas (Fig. S8), generated using the blockCV package in R27.

Model evaluation. We validated the model progressively by analyzing the residuals and the variable importance of the predictive variables and their model profiles. The comparison of predicted and observed values (Fig. S9) revealed that the models generally present a good and unbiased prediction (R² ≥ 0.8; average intercept of 0.04 ranging from 0 to 0.07 and average slope = 0.97 ranging from 0.92 to 1), yet with a small systematic underestimation of the prediction of high bare ground values.

The predictive power of the 14 selected variables is high. We performed a first assessment through the averages and ranges of the model profiles (Fig. S10). This figure shows how, at the global level, each land cover responds to the variation of standardized model parameters. Interestingly, and despite being an empirical machine-learning model, the figure presents biologically meaningful relationships between the predictive and the response variables, providing confidence in the quality of the model. We also performed a second assessment through a variable importance analysis (Fig. S11).

Fire regime and herbivory
To model the natural vegetation cover beyond the geographic limits of the world's protected areas, it was necessary to consider potential scenarios of fire regime and herbivory that are currently not present outside protected areas, i.e., counterfactuals. To consider realistic situations, we propose scenarios based on the observed frequency distributions of fire and herbivory within protected areas, calculated for each ecoregion and each biome. For each distribution, we identified 5 intensities: very low (5th percentile), low (25th percentile), median (50th percentile), high (75th percentile), and very high (95th percentile).

The scenarios of fire regime and wildlife herbivory are built from observations within protected areas and are considered per biome and terrestrial ecoregion—as defined by Olson and colleagues68-, as these consist of large units of land containing distinct natural communities and species. We therefore assume that the fire regime and the wildlife herbivory observed within the protected areas of these biomes/ecoregions are realistic scenarios of fire and herbivory that could be implemented for a restoration project at the scale of the biomes/ecoregions.

Maps, uncertainties, and confidence intervals of the global natural vegetation cover
Model uncertainties. To capture four sources of mapping uncertainties, we produced 720 maps by combining the 66 models with different prediction configurations:

• The model uncertainty: we estimated the inherent uncertainty of the models by predicting 10 times each of the 6 climate dataset models (all folds) with the dropout activated. Number of maps: 10 repetitions × 6 climates = 60.
• The spatial uncertainty: we estimated the effect of spatial structure of the training dataset by predicting 10 times each of the 6 climate dataset models (10 different folds, deactivated dropout). Number of maps: 10 folds × 6 climates = 60.
• The climate uncertainty: we estimated the effect of the climate dataset on the land cover proportions by predicting one map per climate dataset model (all folds and deactivated dropout). Number of maps: 6 climates = 6.
• We estimated the overall uncertainty by considering 600 combinations: 10 repetitions (activated dropout) × 10 folds × 6 climates (Fig. S12).

All the 720 maps were produced by considering the median (50th percentile) fire and herbivory intensities. Uncertainty values were finally computed as the standard deviation per source and per land cover (Fig. S13). For model and spatial uncertainties, we computed the mean of climate maps before standard deviation (mean: 60 -> 10, sd: 10 -> 1). All maps produced in this study have 3 bands, one per cover.

The global natural vegetation cover map. We computed the global natural vegetation cover as the mean of 6 maps. One map per climate dataset model (deactivated dropout, all folds, and median fire and herbivory scenarios) (Fig. S14).

This map represents the most probable distribution of the three natural vegetation covers: bare ground (red), tree (green), and short vegetation (blue) (Fig. 1). In this study, we did not consider water bodies as potential land for vegetation cover. Antarctica and permanently ice-covered regions of Greenland were excluded from the natural vegetation cover map.

Confidence Intervals. We first estimate the variance for the area covered by each vegetation type by multiplying the standard deviation value of each pixel by the pixel area and summing all pixels at global level. The confidence interval is then calculated by considering the following equation:

CIi = z * σ / √n

Considering a confidence interval of 95%, z equals 1.96; σ corresponds to the area concerned by the uncertainty of the vegetation cover i, and n (the number of land cover maps) equals 600.

The resulting confidence interval equals 59 Mha for the prediction of the natural bare ground cover, 74 Mha for the prediction of the natural tree cover, and 86 Mha for the prediction of the natural short vegetation.

Map of the global alternative natural vegetation cover and sensitivity to fire and herbivory scenarios
Fire and herbivory scenarios sensitivity. The effects of fire and herbivory scenarios can be tested by predicting maps with different configurations of fire and herbivory intensities (no dropout, all folds). In total we produced 150 maps: 5 fire scenarios x 5 herbivory scenarios x 6 climates.

Global alternatives of natural vegetation cover. We produced the global alternatives of natural vegetation cover map (Fig. 2) from the 150 maps by computing the mean of climate maps (150 -> 25) followed by the standard deviation (25 -> 1) (Fig. S15). To estimate the area of land that can shift from one vegetation cover to another from shifts in herbivory and fire intensity, we sum the pixels area concerned by alternative cover multiplied by the highest standard deviation observed per pixel (one of the three land cover type). This resulted in the estimation of a minimum of 675 Mha of land where vegetation cover can be adjusted from herbivory and fire intensity.

Uncertainty vs. scenarios
To compare the effect of uncertainties (model, spatial and climate) to the effect of fire and herbivory scenarios on the natural vegetation cover, we computed the absolute difference between the global map and the other average maps (10 for model uncertainty, 10 for spatial uncertainty, 6 for climate uncertainty and 25 for scenarios). The boxplot of these differences is available in Fig. S16. The variability of areas affected by the scenarios appear much more important than variations due to uncertainties. Yet, the variability associated with the spatial structure should not be neglected.

Other datasets
Focus on wetlands. We assessed global alternatives of natural vegetation cover in wetland by extracting the results of Fig. 2 over global wetland cover as estimated for 2022 by Zhang and colleagues and scaled at 1 km36.

Climate change. To consider climate change (CC), supplementary predictions were done using “NEX historical” model69 but with “NEX 2050” data. The climate data for 2050 were computed by averaging values of all models in the NEX CMIP6 product (i.e. ACCESS-CM2”, “ACCESS-ESM1-5”, “BCC-CSM2-MR”, “CESM2”, “CESM2-WACCM”, “CMCC-CM2-SR5”, “CMCC-ESM2”, “CNRM-CM6-1”, “CNRM-ESM2-1”, “CanESM5”, “EC-Earth3”, “EC-Earth3-Veg-LR”, “FGOALS-g3”, “GFDL-CM4”, “GFDL-ESM4”, “GISS-E2-1-G”, “HadGEM3-GC31-LL”, “HadGEM3-GC31-MM”, “IITM-ESM”, “INM-CM4-8”, “INM-CM5-0”, “IPSL-CM6A-LR”, “KACE-1-0-G”, “KIOST-ESM”, “MIROC-ES2L”, “MIROC6”, “MPI-ESM1-2-HR”, “MPI-ESM1-2-LR”, “MRI-ESM2-0”, “NESM3”, “NorESM2-LM”, “NorESM2-MM”, “TaiESM1”, “UKESM1-0-LL”) under the scenario ssp5 (Fig. 3).

Statistical analysis synthesis, and reproducibility
We implemented a data-driven modeling approach using a fully connected neural network (multi-layer perceptron, MLP) to predict natural fractional vegetation cover across the globe, divided into three classes: tree cover, short vegetation (shrubs and grasses), and bare ground. The model was trained on 40,520 systematically sampled plots located in protected areas, where vegetation cover was photo-interpreted using high-resolution satellite imagery and the Collect Earth platform. Each plot's cover was annotated as three proportional values summing to 1.

The model utilized 14 predictor variables representing climate, soil, topography, fire frequency, and herbivore biomass and intake. To account for uncertainties in climatic inputs, we trained the model on six global climate datasets (e.g., CHELSA, CRU, ERA5), each with 11 MLP configurations (10 spatial folds and one full training run), totaling 66 models. Spatial structure was addressed using environmentally stratified blocks during training and cross-validation. Training employed a weighted mean squared error loss to correct for imbalances across continents, IUCN categories, and dominant vegetation types. To mitigate overfitting, dropout regularization (rate = 0.75) was applied, and training hyperparameters (e.g., learning rate, epochs) were optimized iteratively.

Model performance was evaluated through predicted–observed comparisons (R² ≥ 0.8) and analyses of variable importance and response profiles. We derived the final global map of natural vegetation cover as the mean prediction across six climate-specific models (all folds, no dropout) under a median scenario of fire and herbivory intensities (i.e., the most probable value of fire intensity), defined per biome and ecoregion from observed percentiles in protected areas.

To quantify prediction uncertainty, we produced 720 model outputs varying climate datasets, dropout settings, and spatial folds, and computed pixel-wise standard deviations per vegetation type. Confidence intervals for area estimates were computed by propagating pixel-level uncertainties and applying a 95% confidence threshold (z = 1.96, n = 600). We further evaluated sensitivity to fire and herbivory intensities by producing 150 additional maps combining five fire and five herbivory scenarios (5th, 25th, 50th, 75th, 95th percentiles) across six climate datasets. A map of alternative vegetation states was generated from these scenarios using the standard deviation across predictions, and the spatial extent of areas with high variability was calculated. Finally, we compared the magnitude of vegetation shifts driven by fire and herbivory to expected changes under 2050 climate scenarios and assessed impacts on global wetlands using spatial overlays with recent wetland distribution datasets.

All the raw data and the statistical processing can be reproduced from the shared GitHub folder70.

All analysis were performed using Google Earth Engine and R version 4.4.3; all maps were produced on R version 4.4.3 or on QGIS version 3.40.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I was attracted to review this paper because years ago i also tried to determine natural, or potential global vegetation\n(without success, i never published my effort). I wanted to see the framing and logic in this paper. I think the authors have\nrightly identified the main drivers, climate and natural disturbance dynamics. However, the neural network methodology i am\npuzzled by, so feel inadequate to evaluate critically. I will offer a few comments i hope are helpful. First i dont see why 6\nclimate datasets are needed for this analyis, they unecessarily complicate the paper. Choose the 1-2 datasets closest in\nspatial/temporal resolution and direct weather observations. Also many previous efforts in global biogeography have used\nderived climate indices, such as water balance, aridity index, growing degree days, frost-free days, daily incoming radiation,\nas climate metrics that are more directly tied to vegetation processes. The authors do not reference or compare their results\nwith any of these previous global biophysically based papers. Second, i am dissappointed in the choice of a simple trees vs\nshort vegetation as the final vegetation predictors. There has been endless efforts to find an optimal landcover definition\nlogic, none being universally satisfactory. Continuous variables like leaf area index, absorbed fraction of PAR, or % tree or\nvegetative cover have seemed more useful for global analysis. For disturbance metrics, i like their use of MODIS time series\nfire datasets to define fire frequency. I am less confident in the natural herbivory analysis. Current global vegetation is hugely\ncontrolled by domestic grazing, i dont know how one could determine natural herbivory with any confidence. I do like the\neffort shown in figure 3 to evaluate the relative importance of climate change versus disturbance dynamics for future global\nvegetation controls.", "reviewer_2": "In this study, Bastin et al. modelled the potential global distribution of forest, short vegetation and bare ground using physical\nenvironmental predictors and natural disturbances. The latter is an important advancement, while the general message is\nnot so ground breaking. Rather, this is an important benchmarking study, a step to rectify the unintended damage Bastin et\nal. (2019) caused in restoration programs (and in the head of laypersons) by neglecting open ecosystems. Previously I\nreviewed this paper for another journal and the authors did not consider my comments, so I am resending the previous\nreport. This submission has no line numbering, so I could not update line numbers in the report; they correspond to lines in\nthe previous submission:\n“Generally I enjoyed reading the text and agree with most of its content. I only have a few moderate concerns and a few\nminor recommendations to improve wording and to remove typos.\nThe choice of training sites is a bit confusing to me. You write in L462-464 that you used only category I-III for the\npredictions, yet in L507 the less strict categories are also mentioned. In L462 categories I-III are merged as category 1, while\nin L507 the merged category 1 corresponds to categories IV-VI. So, which is which and did you include the less strict\ncategories or not? I think you only used categories I-III but am uncertain. And if this was the case, I have a problem.\nGenerally “natural” ecosystems experience a low-herbivory environment as we extirpated most wild ungulates, and replaced\nthem with domesticated ones (see e.g. Greenspoon et al. 2023). If this replacement means low-intensity (silvo-)pastoral\nmanagement, that will help to retain the natural landscape structure, but fall out of your choice of references (category IV and\nabove). With other words, the wild herbivory present in strict nature reserves is often not natural but anthropogenic. I\nunderstand that you may not want to engage into this speculation but in that case you need to drop the 1000 random arid\nsites because most arid regions globally are heavily grazed. Just have a look at the photo in this article to understand the\nmagnitude of this problem: https://theecologist.org/2015/jun/05/over-grazing-and-desertification-syrian-steppe-are-root-causes-war,\nor the masses of scientific papers about overgrazing in the Tibetan Plateau (which you predicted, in my opinion,\nfalsely as completely barren land), central Asia and the Sahel.\nAnother problem is related to the definition of short vegetation. The paper suggests that these are not forested because the\nsites are too dry, burn down too frequently or herbivores eat up everything. What about wetlands? Peatlands? Open water\nsurfaces? Classifying a boreal bog or a reedbed into the same kind of vegetation type (whatever broad it is) as a continental\ndesert-steppe is not just a little strange, particularly if this study is to guide restoration plans. Please explain why you did not\ninclude wetlands as a fourth land cover type and what consequences this may have on your results. (See a potential one in\nthe next paragraph.). Furthermore, what about shrubland? How do they appear in your classification? Is a pure shrubland\nconsidered as 50% forest + 50% short vegetation? Or do you have some other solution?\nI acknowledge that your model has convincing reliability measures but in some cases the generated maps do not seem to\nmatch reality, especially in extra-tropical regions. You failed to predict most of the boreal taiga forest belt (as we now think\nwe know it), the huge steppes of continental Eurasia and the Tibetan Plateau, and I can’t see the expectable cyan of the\nforest-steppe biome in Fig. 2 (cf. Erdős et al. 2018 & 2022). How do you explain these? The effect of herbivores is an\nattractive explanation but can’t it just be the lakes of the boreal zone (particularly in Scandinavia and Canada) that you\nmisinterpret as short vegetation, leading to a lower share of trees? And similar artefacts…\nAre Eurasian steppes (and prairies) properly represented in the training dataset? For prairies there is a huge gap in Fig. S1\nand I feel like the former is also very sparse in points. In your previous study (Bastin et al. 2019) you had 4 times more\ntraining points for modelling a single vegetation type. Are you sure the present training dataset is really representative? I see\nthe boxplots on representativity for climatic variability but what about your gut feeling? Mine says 18K points is not enough or\nmaybe their distribution is unfavourable in temperate-boreal regions. Some sort of stratification (e.g. to have a more or less\nbalanced share of all three land cover types in major zones of the Earth – tropical, subtropical, temperate, boreal-polar) in\nthe choice of training areas would help instead of a systematic selection.\nFor the Eurasian forest-steppe, maybe the less strict reserves are better references as native grazers have been literally\nerased from the zone and they are substituted by domestic livestock, but you avoided pastured regions as references.\nIf you have no clear answers for these inconsistencies, and insist on that increasing the training sites can’t make results\nmore realistic, and do not want to include wet and shrubby habitats, maybe explain that your modelling approach is not that\nreliable in extratropical regions.\nYou make a sharp distinction between physical environmental predictors (especially climate) and natural disturbances and\nhighlight that changes in the disturbance regime have a stronger effect on land cover type than future climate change. This is\nan interesting finding and aligns with the new concepts that question the supremacy of climate in shaping vegetation\nstructure (i.e. the Humboltian view), but one must acknowledge that fire frequency is a function of climate. Changing from a\nclimate with a single moist season to alternating dry and wet seasons (what we see e.g. in some parts of the Amazon) is the\nrecipe for fire: production of a lot of fuel which than gets dry to burn down. So please do not just repeat the idea of anti-\nHumboltians but nuance the interpretation of your results (which are very convincing in this respect).\nMinor issues\nL10: Linden should start with a capital L.\nL12: Life as such is not going to collapse. It survived the snow-ball Earth (twice). Modern civilization and the current\nbiodiversity are the ones collapsing.\nL19: “Management” is misleading here. You do not mean an active modification of fire and wild grazing intensity, as far as I\ncould understand from the main text. Why not simply say Changes fire and grazing intensity\nL20: What strategies do you mean? Diverse outcomes are understandable (not just a single land cover target) but you have\nno recommendation on the strategies, i.e. on how to achieve that target.\nL27: What is biological integrity? Use a more precise term as this includes a lot of unrelated things including physiology,\nhealth, etc.\nL28-34: The recently approved Nature Restoration Law of the EU should also be mentioned (can replace one of the older\nagreements listed here).\nL50: Not at all a must, but I would use natural “recovery” as restoration implies active human intervention.\nL65: This means to me that you think it is only fire and herbivory that can lead to grassland climax, but a large set of\ninteracting physical environmental conditions can lead to that, too (see e.g. Erdős et al. 2022).\nL66: Well, you condemn the narrow focus of forest – non-forest dichotomy but you barely move beyond that, just have bare\nground as a third option, while neglect wetlands, for instance. So please tone this down and do not exaggerate the\nadvancements in the present study.\nL79: referred to as\nL94: missing space in methods).To\nL115: This is I think the first mention that you considered different biomes delineated a priori. Please indicate the source.\nL119-120 tends to be dominated\nL128: Where are the prairies and steppes? See above in the main issues.\nL165: I think your maps, given the inconsistencies described above, are barely usable on the local scale. Rather, your\nfindings are suitable for large-scale planning and could serve as a basis to counterbalance programs like the Great Green\nWall or the Trillion Tree Campaign with similar efforts to restore short vegetation.\nL413-414: Should be placed to the end of the M&M because at this point we do not know what kind of code you prepared.\nL424: How was that systematic sampling design performed? A systematic sampling of a preset area (the globe or the\nprotected areas therein) rarely leads to a round number of samples, so how did you terminate the sampling at exactly 40.000\nplots?\nL478: Why the preset amount of 3 and 2 soil and topographic variables, respectively? Or is it just the wording that is\nmisleading?\nL503: selected. Please use past tense throughout the M&M.\nL509-511: Why not assessing the representativity for topographic and edaphic variables? I do not see why it is not possible\nin a similar way as for climatic variables but maybe you can tell.\nReferences\nBastin, J. F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., ... & Crowther, T. W. (2019). The global tree\nrestoration potential. Science, 365(6448), 76-79.\nErdős, L., Ambarlı, D., Anenkhonov, O. A., Bátori, Z., Cserhalmi, D., Kiss, M., ... & Török, P. (2018). The edge of two worlds:\nA new review and synthesis on Eurasian forest‐steppes. Applied Vegetation Science, 21(3), 345-362.\nErdős, L., Török, P., Veldman, J. W., Bátori, Z., Bede‐Fazekas, Á., Magnes, M., ... & Tölgyesi, C. (2022). How climate,\ntopography, soils, herbivores, and fire control forest–grassland coexistence in the Eurasian forest‐steppe. Biological\nReviews, 97(6), 2195-2208.\nGreenspoon, L., Krieger, E., Sender, R., Rosenberg, Y., Bar-On, Y. M., Moran, U., ... & Milo, R. (2023). The global biomass of\nwild mammals. Proceedings of the National Academy of Sciences, 120(10), e2204892120.”", "reviewer_3": "The study created a training data (40000 0.5 hectare) from protected areas and generate a counterfactual map at 0.25\ndegrees to show the most probable natural land cover globally. They also highlighted that fire frequency and wildlife\nherbivory weighted more than climate change for natural vegetation cover. The idea is not new (get the three-class map), but\nthe interesting part is counterfactual. However, they didn’t address very well in the main text.\n(1) The introduction part is not good enough to support the idea and they didn’t explain why is counterfactual and how this\ntype of map helps to understand ecosystem conservation and restoration. According to the supplementary doc, the\ncounterfactual is from scenarios of fire and herbivory. The figure 1 is median map and figure 2 is standard deviation map and\nboth of them are not counterfactual.\n(2) Why authors choose fire and herbivory?\n(3) With the dataset from protected area, how about some protected area less than designed pixel size? How the authors\ndeal with this condition?\n(4) How to normalize different raster dataset with different spatial resolution? What method they used?\n(5) The training data is from protected area and the map is the entire land. Sometimes the protected areas are different with\nno protected areas. How do authors extrapolate the model to the global?\n(6) Each pixel value based on total 600 maps. Then why they choose median not mean? Considering each climate dataset\nis equal, how they define the most probable situation?\n(7) What’s the year of the map?\n(8) What’s the connection between figure 1 and 2? The features just have one fire related variable and 3 herb related\nfeatures. How about other fire related data?\n(9) I would like to see the alternative natural vegetation cover map for each scenario authors mentioned.\n(10) “We therefore assume that the fire regime and the wildlife herbivory observed within the protected areas of these\nbiomes/ecoregions are realistic scenarios of fire and herbivory that could be implemented for a restoration project at the\nscale of the biomes/ecoregions.” Compared with size of the protected areas, the scale of biomes are much bigger. So this\nassume is too coarse."}, "author_response": "First, we would like to thank the reviewers for the valuable comments they provided. In addition to\naddressing each specific comment (detailed below), we used this opportunity to thoroughly revise our\nmanuscript.\nSpecifically, we conducted a comprehensive review of the methods section and all analyses, seeking\ninput from a third party to ensure clarity and full replicability. To enhance usability, we simplified the\nassociated GitHub repository, allowing end users to work exclusively within R rather than across\nmultiple platforms (e.g., GEE and R).\nDuring this process, we identified and corrected a minor issue related to reprojection in one stage of\nthe workflow. This adjustment resulted in slight changes to numerical values provided in the study\nbut had no impact on the overall results or their interpretation. These revisions improve the\ntransparency and accessibility of our workflow, favoring replicability and ease of use for the broader\nresearch community.\nReviewer 1.\n“First i dont see why 6 climate datasets are needed for this analyis, they unecessarily complicate the\npaper. Choose the 1-2 datasets closest in spatial/temporal resolution and direct weather\nobservations.”\nWe appreciate the reviewer’s suggestion to streamline the analysis by using fewer climate datasets. In\nour 2019 work, we relied solely on WorldClim, which was not questioned at the time but led us to\nreflect on how dataset-specific biases might influence results. Studies, such as Deblauwe et al. (2016),\nhave highlighted the significant impact of climate dataset choice on ecological analyses. These\ninsights motivated us to include multiple datasets in the present study to enhance robustness and\naccount for potential uncertainties inherent in any single dataset.\nWhile the influence of using multiple datasets on this study’s results is limited, our ongoing\nunpublished research suggests that the choice of climate reference data can significantly affect\necosystem-climate relationships in other contexts. Based on this, we now consider it best practice to\ninclude multiple datasets when exploring such relationships globally. To clarify, we have updated the\nMethods section to emphasize the role of uncertainty and cite Deblauwe et al.\nL303: In the present study, we consider that global climate databases, whether derived from\nmeteorological station records or satellite mission predictions, are prone to uncertainties and biases53\n“Also many previous efforts in global biogeography have used derived climate indices, such as water\nbalance, aridity index, growing degree days, frost-free days, daily incoming radiation, as climate\nmetrics that are more directly tied to vegetation processes. The authors do not reference or compare\ntheir results with any of these previous global biophysically based papers.”\nWe thank the reviewer for highlighting the importance of derived climate indices tied to vegetation\nprocesses. While we agree that variables such as water balance, aridity index, and growing degree\ndays are valuable, the datasets used in this study did not consistently allow for their systematic\ncalculation. Despite this, the accuracy of our predictions was satisfactory, as supported by the robust\nresults outlined in the main text and Methods section.\nIn this study, we prioritized simplicity to focus on key drivers and avoid introducing additional\ncomplexity. However, we recognize the importance of these indices and plan to incorporate them in\nfuture research, which will explore refined approaches at local scales and for specific vegetation types.\n“Second, i am dissappointed in the choice of a simple trees vs short vegetation as the final\nvegetation predictors. There has been endless efforts to find an optimal landcover definition\nlogic, none being universally satisfactory. Continuous variables like leaf area index, absorbed fraction of\nPAR, or % tree or vegetative cover have seemed more useful for global analysis”.\nWe appreciate the reviewer’s suggestion to explore more complex vegetation metrics, such as leaf area\nindex or absorbed PAR. Our decision to focus on ‘simple’ vegetation types—trees, short vegetation,\nand bare ground—was driven by two main considerations. First, our goal was to provide a unified\nperspective on potential natural land cover, creating a framework that bridges forest and grassland\necology. Second, modeling more complex vegetation types requires extensive resources, including\ndetailed training datasets, additional variables, and finer spatial scales.\nGiven the current study’s scope and objectives, we believe this approach is a pragmatic choice that\nalready delivers novel insights. We acknowledge the value of more nuanced metrics and view this as\nan exciting avenue for future research.\nFor disturbance metrics, i like their use of MODIS time series fire datasets to define fire frequency. I\nam less confident in the natural herbivory analysis. Current global vegetation is hugely controlled by\ndomestic grazing, i dont know how one could determine natural herbivory with any confidence.\nWe thank the reviewer for raising the important point of characterizing natural herbivory. To estimate\nnatural herbivory, we relied on a published global wildlife herbivory database (Berzaghi et al., 2024).\nBy combining this dataset with a restrictive selection of protected areas for model training, we sought\nto minimize the influence of domestic grazing on our results.\nWe acknowledge that separating natural from domestic herbivory is inherently challenging and may\nintroduce some uncertainty. To address this, we have added text to the Methods section clarifying this\npotential limitation. However, because the impact of herbivory on land cover is linked more to\nfunctional type than nativeness (e.g., grazing vs. browsing pressure), we believe any bias has minimal\nimpact on the model’s results.\nL340 : The herbivore data being the result of a model, it presents some potential shortcomings, in\nparticular regarding the definition of natural vs. domestic mammals that might differ depending on the\nperspective of the stake holder63. Therefore, the risk of considering non-native herbivore biomass in\nprotected areas is non-null in our model. Yet, as functional type of herbivore – not nativeness – is what\nmatters in terms of impact on the land cover64, we assume that such bias as little impact on the result of\nour model which only consider the impact of herbivore intensity on the potential state of vegetation.\nReviewer 2\n“The choice of training sites is a bit confusing to me. You write in L462-464 that you used only\ncategory I-III for the predictions, yet in L507 the less strict categories are also mentioned. In L462\ncategories I-III are merged as category 1, while in L507 the merged category 1 corresponds to\ncategories IV-VI. So, which is which and did you include the less strict categories or not? I think you\nonly used categories I-III but am uncertain. And if this was the case, I have a problem. Generally\n“natural” ecosystems experience a low-herbivory environment as we extirpated most wild\nungulates, and replaced them with domesticated ones (see e.g. Greenspoon et al. 2023). If this\nreplacement means low-intensity (silvo-)pastoral management, that will help to retain the natural\nlandscape structure, but fall out of your choice of references (category IV and above). With other\nwords, the wild herbivory present in strict nature reserves is often not natural but anthropogenic. I\nunderstand that you may not want to engage into this speculation but in that case you need to drop the\n1000 random arid sites because most arid regions globally are heavily grazed. Just have a look at the\nphoto in this article to understand the magnitude of this problem:\nhttps://theecologist.org/2015/jun/05/over-grazing-and-desertification-syrian-steppe-are-root-causes-\nwar, or the masses of scientific papers about overgrazing in the Tibetan Plateau (which you predicted,\nin my opinion, falsely as completely barren land), central Asia and the Sahel.”\nFirst, we thank the reviewer for requesting clarification on this point.\nThe model is calibrated using all I1JCN categories, while considering a factor “category” of “0”\nfor I1JCN categories I,II,III and a factor of “1” for categories IV, V and VI. We make the\ncorrections accordingly in the main text and the method section.\nRegarding the \"naturalness\" of herbivory, we appreciate the reviewer’s perspective and the reference\nto Greenspoon et al. (2023). We acknowledge that wild mammals are sometimes considered pests and\nthat domesticated mammal biomass often surpasses that of wild mammals. However, this dominance\nis not typically observed within strict protected areas (IUCN categories I, II, and III).\nWe utilized a published database that estimates global wildlife herbivory (Berzaghi et al. 2024). By\ncombining this with a highly restrictive selection of protected areas for model training, we believe we\nhave effectively minimized the potential impact of domestic grazing on our results. Also, the\nimportance being the functional trait – not the nativeness - of herbivores (Lundgren et al. 2024), our\nresults should not lead to any misleading information about the impact of grazers on the state of the\necosystems. However, we acknowledge that some of the remaining uncertainty in our model may stem\nfrom this factor. To address this, we have added a few lines in the method section to highlight this\npotential limitation. In the method section on herbivore data :\nL340 : The herbivore data being the result of a model, it presents some potential shortcomings, in\nparticular regarding the definition of natural vs. domestic mammals that might differ depending on\nthe perspective of the stake holder63. Therefore, the risk of considering non-native herbivore biomass\nin protected areas is non-null in our model. Yet, as functional type of herbivore – not nativeness – is\nwhat matters in terms of impact on the land cover (64), we assume that such bias as little impact on\nthe result of our model which only consider the impact of herbivore intensity on the potential state of\nvegetation.\nRegarding the arid regions that we potential falsely assess as barren land, we understand the\nreviewer’s concern regarding the interpretation of barren land on the Tibetan Plateau. Our analysis of\npixel-level data shows that the plateau has actually a potential short vegetation cover of\napproximately 20% in many areas, with the rest dominated by bare ground. Converting the land\ncover in land use category would actually consider these regions as potential grasslands. However,\nthe perceived issue may arise from the color gradient used in the map, which emphasizes bare\nground. This can misleadingly suggest the region is entirely barren, which is not accurate.\nWe understand that this might be the perception of the reader while looking at figures within a .pdf or\nword file. Yet, looking at the pixel values in the region, we can find many places where the grass\ncover rise above 20% of cover at the landscape scale.\nFinally, the 1000 points we randomly distributed in arid regions, are more specifically distributed in\nhyperarid regions which consist almost essentially of deserts, as assessed in our publication of 2017 on\nthe global extent of forest in dryland biomes.\n“Another problem is related to the definition of short vegetation. The paper suggests that these are\nnot forested because the sites are too dry, burn down too frequently or herbivores eat up everything.”\nWe understand the point of the reviewer. Yet, our model considers together the environmental niche\n(climate, soil, topography) and key drivers such as fire and herbivory. Both are defining the state of\nthe natural vegetation cover as illustrated in Fig S8 of the supplementary material. Our map of the\ndistribution of natural vegetation cover (Figure 1) also illustrates it, as only the median case of fire and\nherbivory per ecoregion are considered here yet presenting large variations in vegetation cover across\nthe different biomes of the planet.\nWhat about wetlands? Peatlands? Open water surfaces? Classifying a boreal bog or a reedbed into\nthe same kind of vegetation type (whatever broad it is) as a continental desert-steppe is not just a little\nstrange, particularly if this study is to guide restoration plans. Please explain why you did not\ninclude wetlands as a fourth land cover type and what consequences this may have on your results. (See a\npotential one in the next paragraph.).\nWe appreciate the reviewer’s suggestion to address wetlands as a distinct land cover type. While\nwetlands are not explicitly included in our modeling process, we evaluated their potential alternative\nvegetation cover using the global wetland cover dataset (Zhang et al., 2023). Our analysis indicates\nthat less than 5% of wetlands would be affected by alternative vegetation scenarios.\nTo highlight the importance of wetland and the potential alternative our model finds in wetland we\nnow have added the following sentence in the document:\nAlso, wetlands are not included in the modeling process, even though they may contain trees, short\nvegetation, and barren land. Since conservation or restoration choices could significantly impact\nwetland ecosystems, we emphasize that they should be carefully considered when assessing alternative\noptions for natural vegetation cover. Here, we estimate that less than 5% of wetlands could be affected\nby these alternatives, warranting a thorough evaluation of potential outcomes using existing wetland\nmaps for guidance37.\nWe also added a line in the methods section on the product we used to assess the alternative potentials\nwithin wetlands.\nFurthermore, what about shrubland? How do they appear in your classification? Is a pure shrubland\nconsidered as 50% forest + 50% short vegetation? Or do you have some other solution?”\nShrublands are classified under the \"short vegetation\" category, which also includes grasses and other\nvegetation forms. In the original photo-interpretation, shrub cover was estimated separately but later\nmerged with grasses due to unsatisfactory validation results. The final classification considers shrub\nand grass cover as components of \"short vegetation\" for simplicity and robustness.\nI acknowledge that your model has convincing reliability measures but in some cases the generated\nmaps do not seem to match reality, especially in extra-tropical regions. You failed to predict most of\nthe boreal taiga forest belt (as we now think we know it), the huge steppes of continental Eurasia and\nthe Tibetan Plateau, and I can’t see the expectable cyan of the forest-steppe biome in Fig. 2 (cf. Erdős\net al. 2018 & 2022). How do you explain these? The effect of herbivores is an attractive explanation\nbut can’t it just be the lakes of the boreal zone (particularly in Scandinavia and Canada) that you\nmisinterpret as short vegetation, leading to a lower share of trees? And similar artefacts...\nAre Eurasian steppes (and prairies) properly represented in the training dataset? For prairies there\nis a huge gap in Fig. S1 and I feel like the former is also very sparse in points. In your previous study\n(Bastin et al. 2019) you had 4 times more training points for modelling a single vegetation type. Are\nyou sure the present training dataset is really representative? I see the boxplots on representativity\nfor climatic variability but what about your gut feeling? Mine says 18K points is not enough or\nmaybe their distribution is unfavourable in temperate-boreal regions. Some sort of stratification (e.g.\nto have a more or less balanced share of all three land cover types in major zones of the Earth –\ntropical, subtropical, temperate, boreal-polar) in the choice of training areas would help instead of a\nsystematic selection.\nFor the Eurasian forest-steppe, maybe the less strict reserves are better references as native grazers\nhave been literally erased from the zone and they are substituted by domestic livestock, but you\navoided pastured regions as references.\nIf you have no clear answers for these inconsistencies, and insist on that increasing the training sites\ncan’t make results more realistic, and do not want to include wet and shrubby habitats, maybe\nexplain that your modelling approach is not that reliable in extratropical regions.\nReviewer 2 provided a series of constructive and insightful comments regarding the\nrepresentativeness of our dataset, highlighting examples where our model might misrepresent the\nactual potential vegetation cover of the planet.\nWe took this feedback seriously and conducted additional checks on our dataset to address\nthe concerns raised.\nColor Contrast and Potential Misleading Interpretations\nFirst, we identified that an important factor in the interpretation of the reviewer comes from the color\ncontrast of our maps but not on the result of the map itself.\nIndeed, we investigated the pixel values of the different vegetation cover in several region mentioned\nby the reviewer, specifically the Tibetan Plateau and the Taiga Forest Belt:\n1. Tibetan Plateau: Analyzing pixel-level data reveals that the Tibetan Plateau has a potential\ngrassland cover of approximately 20% in many areas, with the remaining coverage\ndominated by bare ground. Consequently, thematic maps classify much of the plateau as\ngrassland. The perceived issue stems from the contrast in the color gradient, ranging from 0\nto 100, which emphasizes bare ground and can misleadingly suggest the area is entirely red,\nindicating bare ground. However, this is not accurate.\n2. Taiga Forest Belt: Similarly, the Taiga Forest Belt shows a tree cover of 50–70% in\nmany locations.\nTo prevent misinterpretation of our results, we propose adding the following clarifications to the main\ntext:\n• A more explicit explanation of how color gradients reflect vegetation dominance in Figure\nlegends.\n• A link to interactive maps available on a Google app, enabling users to explore the data\nin detail.\no Figure 1 : https://bastinjf-\nclimate.users.earthengine.app/view/gavcglobalfractionalvegetationmedian\no Figure 2: https://bastinjf-\nclimate.users.earthengine.app/view/gavcglobalalternativescenarios\nTest of natural vegetation cover map using less restrictive calibration points\nWe appreciate the reviewer’s concerns regarding the quality of results when using training data from\ncategories IV, V, and VI compared to categories I, II, and III.\nTo address this, we rigorously evaluated the statistical robustness of our model. In addition, we tested\nits performance using data from categories IV, V, and VI of the IUCN within the WDPA database.\nResults are summarized as follows:\n1. Figure Comparison:\no The first figure (Fig Rev1) replicates Figure 1, comparing the original categories\n(top) with the less restrictive categories (bottom). The maps reveal minimal\ndifferences, though the latter shows slightly more bare ground.\n2. Distribution Analysis:\no The second figure (Fig Rev2) compares the maps for each vegetation cover\ncategory, accompanied by a histogram of distribution differences. Results indicate\nthat using less restrictive categories increases bare ground predictions and reduces\ntree cover. For grassland, the model predicts less grassland in arid regions and more\nin subtropical regions.\nWe have produced these figures solely to reply to the reviewer, we do not intend to add these in the\nfinal manuscript.\nKey additional methodological points:\nIn addition to the first two major points we raise, we would like to bring to the attention of the\nreviewers a few methodological points.\n• Our sampling was weighted to ensure representativeness of each continent. This detail\nhas been explicitly added to the main text.\n• Although the number of calibrating points is four times lower, the spatial resolution\nis significantly reduced (750 times lower, 27.5 × 27.5).\n• Statistical robustness was verified using convex hull tests and spatial structure analyses.\nClarifying the Concept of Potential vs. Actual Vegetation\nAs demonstrated in our 2019 paper, the potential niche does not necessarily reflect the actual\ndistribution of vegetation cover. This distinction highlights the pressures currently affecting\necosystems. To address this potential confusion, we propose adding the following clarification to the\nmanuscript:\nL118: We refer to this map as ‘counterfactual’ as it illustrates the natural vegetation cover carrying\ncapacity of the planet, i.e. a different situation of current actual vegetation cover that can be mapped\ndirectly from satellites23.\nConclusion regarding the representativity of our dataset/map\nBased on these points, we believe our approach is well-suited to its intended purpose. However, we\nalso acknowledge the need for further developments for the development of more detailed map at the\nlandscape level to enhance vegetation cover modeling.\nYou make a sharp distinction between physical environmental predictors (especially climate) and\nnatural disturbances and highlight that changes in the disturbance regime have a stronger effect on\nland cover type than future climate change. This is an interesting finding and aligns with the new\nconcepts that question the supremacy of climate in shaping vegetation structure (i.e. the Humboltian\nview), but one must acknowledge that fire frequency is a function of climate. Changing from a climate\nwith a single moist season to alternating dry and wet seasons (what we see e.g. in some parts of the\nAmazon) is the recipe for fire: production of a lot of fuel which than gets dry to burn down. So please\ndo not just repeat the idea of anti-Humboltians but nuance the interpretation of your results (which\nare very convincing in this respect).\nWe thank the reviewer for this comment. Indeed, our point needs to be more nuanced and we adapted\nit accordingly in our abstract and conclusion.\nMinor issues\nL10: Linden should start with a capital L.\nOk\nL12: Life as such is not going to collapse. It survived the snow-ball Earth (twice). Modern civilization\nand the current biodiversity are the ones collapsing.\nOk, we modified the text accordingly\nL19: “Management” is misleading here. You do not mean an active modification of fire and wild\ngrazing intensity, as far as I could understand from the main text. Why not simply say Changes fire\nand grazing intensity\nOk, we modified the text accordingly. Yet, the scenarios we propose can apply various level of fire\nand grazing intensity if wanted. It therefore refer to the idea to manage fire and herbivory on land.\nL20: What strategies do you mean? Diverse outcomes are understandable (not just a single land cover\ntarget) but you have no recommendation on the strategies, i.e. on how to achieve that target.\nBy strategy, we refer to the choice of fire or herbivore intensity.\nL27: What is biological integrity? Use a more precise term as this includes a lot of unrelated things\nincluding physiology, health, etc.\nOk, we now refer to the exact term associated to the planet boundaries, i.e. the biosphere integrity. I\nunderstand this does not reply perfectly to the demand of the reviewer but we are only referring to the\nterms used in the planetary boundaries assessment.\nL28-34: The recently approved Nature Restoration Law of the EU should also be mentioned (can\nreplace one of the older agreements listed here).\nOk\nL50: Not at all a must, but I would use natural “recovery” as restoration implies active human\nintervention.\nI understand the point of the reviewer. Yet, we put great effort in defining what we refer to as\n‘restoration’, which can actually be passive i.e. without human intervention.\nL65: This means to me that you think it is only fire and herbivory that can lead to grassland climax,\nbut a large set of interacting physical environmental conditions can lead to that, too (see e.g. Erdős et\nal. 2022).\nWe use the word “including fire and herbivory” as an addition to the classical environmental niche\nusually considered in ecology. To make sure we do not only refer to fire and herbivory we added the\nreference suggested by the reviewer.\nL66: Well, you condemn the narrow focus of forest – non-forest dichotomy but you barely move\nbeyond that, just have bare ground as a third option, while neglect wetlands, for instance. So please\ntone this down and do not exaggerate the advancements in the present study.\nI understand the point of view of the reviewer. But we were not condemning the forest vs. non-forest\nbut more the fact that the two are rarely considered together in a single modelling framework –\nleading to sterile debate about pro and con forest restoration. In our work we try to model tree cover,\nshort vegetation cover and barren lands. We try to build a bridge between scientists. Yet, as we do not\nprovide the ‘entire’ range of potential alternative but a only ‘wider” one, we adapted the text\naccordingly.\nL79: referred to as ok\nL94: missing space in methods).To ok\nL115: This is I think the first mention that you considered different biomes delineated a priori. Please\nindicate the source. We now refer to ecoregions and biomes earlier and refer to the appropriated\nsection of the Methods.\nL119-120 tends to be dominated ok\nL128: Where are the prairies and steppes? See above in the main issues. We have now replied in\ndetails to this issue.\nL165: I think your maps, given the inconsistencies described above, are barely usable on the local scale.\nRather, your findings are suitable for large-scale planning and could serve as a basis to counterbalance\nprograms like the Great Green Wall or the Trillion Tree Campaign with similar efforts to restore short\nvegetation. We completely agree with the reviewer, and we believe this is a strong point to support a\nmore holistic view about ecosystem restoration that we propose in the present manuscript.\nL413-414: Should be placed to the end of the M&M because at this point we do not know what kind\nof code you prepared.\nL424: How was that systematic sampling design performed? A systematic sampling of a preset area\n(the globe or the protected areas therein) rarely leads to a round number of samples, so how did you\nterminate the sampling at exactly 40.000 plots?\nI believe the point was to highlight the average number of plots, not the exact one. We have reviewed\nthe whole modelling section to make sure everything is clarified now.\nL478: Why the preset amount of 3 and 2 soil and topographic variables, respectively? Or is it just the\nwording that is misleading?\nThis is not a present but the result of a variable selection procedure performed earlier on the same set\nof points.\nL503: selected. Please use past tense throughout the M&M.\nL509-511: Why not assessing the representativity for topographic and edaphic variables? I do not see\nwhy it is not possible in a similar way as for climatic variables but maybe you can tell.\nThis figure illustrates only graphically the representativity of the dataset. The actual validation of the\ndataset relies on (i) the nearest neighbor distance analysis in feature space and the blockCV approache,\nthat both considered the topographic and edaphic variables. We have added a few words to make sure\nthe reader understand these variables were included here.\nGlobal natural vegetation cover (based on category 0 : I,II,II)\nGlobal natural vegetation cover (based on category 1 : IV,V,VI)\nFigure Rev1. Comparison of the global natural vegetation cover considering IUCN categories I,II and\nIII (top) and categories IV, V and VI (bottom).\nGround cover difference Ground cover difference (cat 1 – cat 0)\nTree cover difference Tree cover difference (cat 1 – cat 0)\nShort Vegetation cover difference\nShort Vegetation cover difference (cat 1 – cat 0)\nFigure Rev2. Differences in the natural vegetation cover considering IUCN categories I,II and III and\ncategories IV, V and VI. Positive values illustrates more cover in the map produced from IUCN\ncategories IV, V, VI and negative values more cover in the map produced from IUCN categories I, II\nand III.\nReviewer 3\n(1) The introduction part is not good enough to support the idea and they didn’t explain why is\ncounterfactual and how this type of map helps to understand ecosystem conservation and\nrestoration. According to the supplementary doc, the counterfactual is from scenarios of fire and\nherbivory. The figure 1 is median map and figure 2 is standard deviation map and both of them are\nnot counterfactual.\nWe thank the reviewer for their insightful comment. We agree that the introduction could better\narticulate the rationale behind referring to the maps as counterfactual. To clarify, these maps represent\na hypothetical scenario in which the entire planet is treated as a protected area, with specific\nmodifications to fire and herbivory intensity. By comparing this scenario to the planet's current state,\nstakeholders can identify actionable insights for ecosystem restoration, as demonstrated in our\nprevious work (2019).\nWe have revised the introduction to explicitly define the counterfactual concept and its relevance to\nconservation and restoration strategies. Specifically, we now clarify that the maps represent the\nnatural vegetation cover carrying capacity of the planet under altered fire and herbivory conditions.\nL120 : We refer to this map as ‘counterfactual’ as it illustrates the natural vegetation cover carrying\ncapacity of the planet, i.e. a different situation of current actual vegetation cover that can be mapped\ndirectly from satellites24\n(2) Why authors choose fire and herbivory?\nThank you for raising this important question. Fire and herbivory were chosen because they are two of\nthe most influential determinants of global vegetation cover, capable of driving ecosystems into\ndistinct states such as forests or grasslands. While climate and direct human activities like\ndeforestation are often considered, fire and herbivory have been underexplored in global-scale studies,\nas noted by Bond et al., Erdas et al., and Veldman et al.\nBy incorporating these drivers, our study addresses a critical gap in understanding the interplay\nbetween natural processes and ecosystem resilience.\n(3) With the dataset from protected area, how about some protected area less than designed\npixel size? How the authors deal with this condition?\nWe appreciate the reviewer’s concern about small protected areas relative to pixel size. To address\nthis, we calibrated our model using plot data that are smaller than the protected areas (0.5 ha vs. >1\nha), ensuring representativeness at the local scale. While fire data are slightly coarser (250m), these\nuncertainties are minimized when averaging results at a global scale. Similarly, climate data, while\ncoarser, are considered relatively homogeneous within each pixel. We believe these methodological\nchoices ensure robust extrapolation and accuracy.\n(4) How to normalize different raster dataset with different spatial resolution? What method they\nused?\nWe used the bilinear resampling method within Google Earth Engine.\n(5) The training data is from protected area and the map is the entire land. Sometimes the protected\nareas are different with no protected areas. How do authors extrapolate the model to the global?\nThank you for this observation. The global extrapolation builds on environmental parameters that\nextend beyond the limits of protected areas, allowing us to test various scenarios of fire and herbivory.\nTo ensure robustness, we assessed the representativeness of our training dataset using both\nnearest neighbor feature distance and spatial block cross-validation methods, as detailed in the\nMethods section. These steps were designed to minimize bias when scaling the model globally.\n(6) Each pixel value based on total 600 maps. Then why they choose median not mean? Considering\neach climate dataset is equal, how they define the most probable situation?\nWe thank the reviewer for highlighting this point. To clarify, we used the median fire and herbivory\nintensities (50th percentile) observed per ecoregion to model the most probable natural vegetation\ncover scenario. This choice reflects the central tendency of the data while being less sensitive to\noutliers compared to the mean. When combining the 600 maps, we used the mean to estimate\naggregate values, which we believe provides the most representative outcomes for our analysis.\n(7) What’s the year of the map?\nThe maps refer to the year 2015, based on the photo-interpreted data1. This information is now\nadded in the document.\n(8) What’s the connection between figure 1 and 2? The features just have one fire related variable and\n3 herb related features. How about other fire related data?\nFigure 1 illustrates a single scenario of fire and herbivore intensity (the median per ecoregion), while\nFigure 2 highlights the variation between low and high intensity scenarios for these variables. While\nadditional fire-related variables could be considered, our selection process identified those most\nrelevant for explaining variation in tree, short vegetation, and bare ground cover. We believe this\nsubset is sufficient for the study’s objectives, as supported by our results.\n(9) I would like to see the alternative natural vegetation cover map for each scenario\nauthors mentioned.\nWe appreciate the suggestion to include alternative maps for each scenario. While we have generated\nthese maps (provided as Figure Rev3), we believe the histograms in Figure 3 are more effective in\nsummarizing key information for readers. We chose this format to balance comprehensiveness with\naccessibility.\nFigure\nRev3.\nMaps\nillustrating the Figure 3 with examples of natural vegetation cover distribution variations resulting\nfrom different fire, herbivore and climate change scenarios at the ecoregion level. The effect of fire,\nherbivory, and climate change SSP5 [CC] are illustrated on the potential tree cover of four ecoregions,\ni.e. the Northeast Siberian Taiga, the Dinaric Mountains Mixed Forests, the West Sudanian Savanna\nand the Cerrado. The two panels on the left compare the potential natural tree cover in current situation\nvs. low/high herbivory (30 tons.ha-1) and expected climate change by 2050. The two panels on the\nright compare the current situation vs. fire prescription (+0.25 fire.yr-1)/ exclusion (0 fire.yr-1) and\nexpected climate change by 2050.\n(10) “We therefore assume that the fire regime and the wildlife herbivory observed within the\nprotected areas of these biomes/ecoregions are realistic scenarios of fire and herbivory that could be\nimplemented for a restoration project at the scale of the biomes/ecoregions.” Compared with size of\nthe protected areas, the scale of biomes are much bigger. So this assume is too coarse.\nWe understand the reviewer’s concern regarding the coarseness of the assumption at the biome level.\nWhere detailed ecoregion-level data were unavailable, we used biome-level data to ensure\ncompleteness. While this may introduce some coarseness, we believe the approach remains valid for\nidentifying generalizable patterns relevant to restoration at large scales.\n1. Bastin, J.-F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors addressed most of my concerns by nuancing some concepts and running new predictions,\nso I only have a few last comments.\nI recommend including the new maps and results using all IUCN categories in the supplementary\nmaterial, as it will not only be me, who will find it questionable to narrow down the scope to strict\nprotected areas as training areas. By the way, if the difference is not much, why not use this version in\nthe main text? You have Jens-Chrstian Svenning in the author list. I think he perfectly knows that dark,\nclosed-canopy European forests far from present human influence (which are in top IUCN categories\nnow) but also devoid of large herbivores are less natural than lightly grazed, open woodlands. So,\ne.g.\nthe bias you mention in L344 is actually a strength of your study.\nWhy don’t you share the numbers in L148-172 about temperate and boreal regions? You say that a\ntotal of 5669 Mha of forest is predicted, and the largest extent is in tropical moist forest zones (1616\nMha), but the rest is much more, so temperate and boreal areas are also important. In addition, as\ndiscussed in the previous reviewing round, they are not so easy to see on the maps due to sub100\npercentages, so the numbers would really be useful. Such numbers are also useful, citable items in\nfuture studies.\nWhy not call your counterfactual maps “potential distribution maps”? I do not clearly see the\ndifference, and the latter one has a more benign connotation.\nDespite your explanations it is still not fully clear to me what each land cover category includes.\nParticularly, are water bodies and snow&ice considered as bare ground? 18% seems a bit too little for\nme globally if only rocky, sandy etc. dry surfaces are included. Also asking this because you say in L277\nthat the three percentages sum up to 100% in each pixel.\nDid you include polar region (esp. Greenland and Antarctica) in your predictions or that is out of scope?\nL42: I saw your explanation about why you think it is OK to say that life collapses without restoration,\nbut many readers will not get it. Life as such is not going to collapse, whatever happens.\nL83-85: Grammatically, this sentence is a fragment. Please rephrase.\nL93: range of potential?\nL181: You write here „different vegetation cover” but is this the same as the „alternative natural\nvegetation cover” that you use elsewhere? If so, be consistent.\nMethods section has some errors with the references as all of them appears in my file as „Error!\nReference source not found.” Double check it before resubmission.\nL334-336: Be consistent with the units: „weight per km2” vs. „mass.km-2” vs. mass/square kilometer”.\nL348, 355 and elsewhere: Use past simple tense when you describe what you did.\nL382: correspond to"}, "author_response": "Reviewer #2 (Remarks to the Author):\nThe authors addressed most of my concerns by nuancing some concepts and running new predictions,\nso I only have a few last comments.\nI recommend including the new maps and results using all IUCN categories in the supplementary\nmaterial, as it will not only be me, who will find it questionable to narrow down the scope to strict\nprotected areas as training areas. By the way, if the difference is not much, why not use this version in\nthe main text? You have Jens-Chrstian Svenning in the author list. I think he perfectly knows that dark,\nclosed-canopy European forests far from present human influence (which are in top IUCN categories\nnow) but also devoid of large herbivores are less natural than lightly grazed, open woodlands. So,\ne.g.\nthe bias you mention in L344 is actually a strength of your study.\nWe agree with the reviewer that these additional analysis brings more clarity to our analysis.\nWe\ntherefore added a paragraph about it in the supplementary material and added the figure in\nsupplementary as Figs S5 and S6.\nWhy don’t you share the numbers in L148-172 about temperate and boreal regions? You say that a\ntotal of 5669 Mha of forest is predicted, and the largest extent is in tropical moist forest zones (1616\nMha), but the rest is much more, so temperate and boreal areas are also important. In addition, as\ndiscussed in the previous reviewing round, they are not so easy to see on the maps due to sub100\npercentages, so the numbers would really be useful. Such numbers are also useful, citable items in\nfuture studies.\nWe now added these numbers in the main text (in addition of their presence in Table S1).\nWhy not call your counterfactual maps “potential distribution maps”? I do not clearly see the\ndifference, and the latter one has a more benign connotation.\nBased on previous experience, we prefer to avoid the term “potential” in the main title or the title of\nthe main figure, as some readers might interpret it as a target that must be achieved rather than as a\ntheoretical maximum. This misunderstanding led many people to believe that we were advocating for\ntree planting everywhere in our 2019 paper. We would like to avoid the same issue in this paper.\nDespite your explanations it is still not fully clear to me what each land cover category includes.\nParticularly, are water bodies and snow&ice considered as bare ground? 18% seems a bit too little for\nme globally if only rocky, sandy etc. dry surfaces are included. Also asking this because you say in L277\nthat the three percentages sum up to 100% in each pixel.\nDid you include polar region (esp. Greenland and Antarctica) in your predictions or that is out of scope?\nWe understand the question of the reviewer. We now clarify this point on the methodology section as\nfollows:\n“In this study, we did not consider water bodies as potential land for vegetation cover. Antarctica and\npermanently ice-covered regions of Greenland were excluded from the natural vegetation cover map.”\nL42: I saw your explanation about why you think it is OK to say that life collapses without restoration,\nbut many readers will not get it. Life as such is not going to collapse, whatever happens.\nWe understand the point made by the reviewer. We changed the term “collapse” for the term\n“decline”.\nL83-85: Grammatically, this sentence is a fragment. Please rephrase.\nThe sentence has been corrected.\nL93: range of potential?\nThis has been corrected.\nL181: You write here „different vegetation cover” but is this the same as the „alternative natural\nvegetation cover” that you use elsewhere? If so, be consistent.\nThis has been corrected.\nMethods section has some errors with the references as all of them appears in my file as „Error!\nReference source not found.” Double check it before resubmission.\nThis has been checked.\nL334-336: Be consistent with the units: „weight per km2” vs. „mass.km-2” vs. mass/square kilometer”.\nThis has been corrected.\nL348, 355 and elsewhere: Use past simple tense when you describe what you did.\nThis has been checked.\nL382: correspond to\nThis has been corrected."}]

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