The safety margin of small-scale tree cover loss in global fragmented forests

Domain: neuroscience
Published: 2026-04-08
Editorial outcome: minor_revision

Abstract
Climatic and anthropogenic disturbances have led to intense small-scale tree cover loss in global forests. However, it remains unclear when forest attributes at a large scale (e.g., 0.05° resolution) will decline in response to such sub-grid (e.g., 30-m) tree cover losses within forest ecosystems. Utilizing global maps of forest attribute proxies, we discover that vegetation greenness, canopy structure, composition, and photosynthesis function can all increase under limited tree cover loss, indicating a widely-existed safety margin in global forests that is primarily buffered by a positive edge effect of landscape fragmentation within forest ecosystems. The safety margin varies across biomes (tropical: 7.7%; temperate: 3.7%; boreal: 1.0%) and is often positively correlated with ecosystem resistance. In addition, about 35.7% of the remaining global forests have exceeded the safety margin. Our finding contrasts with the conventional perception that sub-grid tree cover losses are inevitably associated with declines in forest attributes and functions. It provides quantitative information for mitigating forest degradation and has strong implications for sustainable forest management practices.

Introduction
Climate-driven disturbances and anthropogenic activities such as wildfires, urban expansion, agriculture, and forest management cause intense small-scale tree cover loss in global intact forests 1,2, affecting a range of ecosystem attributes that are essential for human society and beneficial to all other organisms on Earth 3–5. Yet, a fundamental and urgent question remains: when does such sub-grid tree cover loss become harmful to forest attributes at the ecosystem level?

At present, the effects of worldwide sub-grid tree cover loss on global forests remain insufficiently explored and are still debated across scientific frameworks6. For instance, disturbance theories suggest that small-scale tree cover loss fragments forest areas 7,8. Landscape fragmentation intensifies the environmental stresses within forests 9,10 and brings negative impacts on forest structure, composition, and function11–13. However, there are still disputes over the theory of the edge effect concept 14. Fragmentation caused by tree cover loss can reduce competition within forests and promote a positive edge effect, enhancing forest structure, composition, and function 15,16. In principle, the balance between the negative effects of tree cover loss and the positive effects from mitigated competition leads to overall negative 12,13, neutral 14, or even positive effects on forest ecosystems 15,17. These highly variable results highlight the complexity of small-scale tree cover loss effects on forest ecosystems, underscoring the need for further investigations.

To address these knowledge gaps, we explore three key research questions: (1) Is there a safety margin (f ) when sub-grid small-scale tree cover fractional loss (f ) within forests leads to an overall degradation of forest ecosystem, i.e., declines in vegetation greenness, ecosystem structure, composition, and function? (2) If so, what is the key driver of the f in fragmented forests and how does this f vary across biomes? (3) Has the tree cover loss in global fragmented forests reached or even exceeded the f ? Answers to these questions will provide important context for sustainable forest management and offer solutions for nature-based measures to mitigate the impacts of climate change.

In this work, we focus on the effects of 30-m resolution tree cover fractional loss on 0.05° resolution vegetation greenness, ecosystem structure, composition, and function in global forests that do not experience land-use change during the period 2000-2012 (Supplementary Fig. 1). Notably, the sub-grid gross tree cover fractional loss is quantified using 30-m resolution tree cover maps from Global Forest Watch (GFW) 1. We use the enhanced vegetation index (EVI) 18, the normalized difference vegetation index (NDVI) 18, and the near-infrared reflectance of vegetation (NIRv) 19 as proxies of vegetation greenness, the leaf area index (LAI) as a proxy of canopy structure 20, the biodiversity index (BDI) as a proxy of ecosystem composition 21, and gross primary production (GPP) 22–24 and solar-induced chlorophyll fluorescence (SIF) 25 as proxies of ecosystem photosynthetic function (Supplementary Table 1). We then quantify anomalies (e.g., denoted as ΔEVI)) of such forest attributes (e.g., EVI) before and after tree cover fractional loss to characterize the response of 0.05° resolution forest attributes to 30-m resolution sub-grid tree cover loss (see Methods). We show that above-mentioned forest attributes all increase under limited f , indicating a widely-existed safety margin of f (denoted as f ) in global forests. Then, we quantify the f across global 0.05° resolution forest grid cells and find that fragmentation-induced edge effect is the key factor in determining f using landscape metrics such as edge density (ED), patch density (PD), and Euclidean nearest neighbor (ENN) index 3. Finally, we show that about 35.7% of the remaining global forests have exceeded the f .

Results
The safety margin of sub-grid tree cover fractional loss
The response of vegetation greenness is negatively modulated by f (Fig. 1) Based on the EVI metric, we find a strong negative correlation between ΔEVI and f (Fig. 1a-d,g). Interestingly, we detected an empirical threshold of f —the safety margin (f ) below which ΔEVI can still increase with limited tree cover losses (Fig. 1e,g). This finding was in contrast to the common understanding that tree cover losses were systematically associated with a decrease in greenness. The positive responses of EVI to tree cover loss were observed across 28.58% of the studied forests. It was most evident in tropical forests (north Amazon: 41.00%; central Africa: 41.51%; tropical Asia: 42.86%), characterized by a higher f (7.76±0.15) (Fig. 1e,h). Temperate forests (central China: 40.00%; eastern USA: 30.09%) had a moderate f (3.69±0.14). In these two biomes, anthropogenic activities such as the commodity-driven deforestation and agricultural expansion were the major drivers of sub-grid tree cover losses (Supplementary Fig. 2a,b). In contrast, forests with positive ΔEVI were sporadically distributed in the boreal region (19.06%) (Fig. 1e,h). Their tree cover losses were mainly driven by wildfires and forestry, showing the lowest f (0.99±0.02) (Supplementary Fig. 2c). Notably, boreal tree cover can take a decade or more to regenerate after wildfire, especially in the northern part of the boreal biome. We thus took into account the factor of tree regeneration (i.e., tree cover gain) and re-quantified the f across those 0.05° resolution boreal forest grid cells where more than 69.97% of 30-m resolution tree cover loss was driven by wildfire (see Methods). The results showed that the f in the boreal region is 1.37 on a regional average, which is 0.68 higher than that without considering the tree regeneration factor (Supplementary Fig. 3).

The EVI-estimated f for vegetation greenness was evaluated using other commonly-used satellite vegetation indices (ΔNDVI and ΔNIRv). All the evaluation results were robust (Supplementary Figs. 4-5). The f presented by canopy structure (ΔLAI), composition (ΔBDI), and photosynthesis function (ΔGPP and ΔSIF) were similar to those of vegetation greenness (Supplementary Figs. 6-9).

The positive edge effect determines the magnitude of f
The edge effect of fragmentation within forest ecosystems was assumed as the key mechanism through which ecosystem attributes might be promoted under limited tree cover loss due to mitigated competition. This deduction was inferred on the grounds of the correlations between changes in ecosystem attributes (i.e., Δ𝐸𝑉𝐼) and changes in forest landscape (e.g., edge density, Δ𝐸𝐷) (Fig. 2). Specifically, Δ𝐸𝑉𝐼 was positively correlated with Δ𝐸𝐷 when f was below f , whereas the correlations between the two variables became negative once f exceeded f (Fig. 2a,c; Supplementary Fig. 12). The contrasting correlations between Δ𝐸𝑉𝐼 and Δ𝐸𝐷 below and beyond the f were also supported by the partial least squares structural equation modeling (PLS-SEM) analyses (inset plots, Fig. 2a,c). In forests with limited tree cover loss (f < f ), two main paths promote EVI (Fig. 3a; Supplementary Fig. 13). Increase in ED significantly (P < 0.001) cools local surface temperature (LST), decreases vapor pressure deficit (VPD), and raises soil moisture (SM), which jointly lead to an increase in EVI. Meanwhile, ED in intact forests may increase sunlight exposure and enhance absorbed photosynthetically active radiation (APAR), benefiting EVI increment. In contrast, when f > f , three paths may result in a negative effect on EVI (Fig. 3b; Supplementary Fig. 14): increases in ED due to intense tree cover loss significantly (P < 0.001) warm LST, increases VPD, and ultimately reduces EVI; ED also lowers SM and decreases APAR, producing a negative impact on EVI.

Overall, the balance between the negative effects of tree cover losses and positive effects from the edge effects collectively determines the direction and magnitude of changes in ecosystem attributes (i.e., Δ𝐸𝑉𝐼 ). This finding was further validated using other alternative edge metrics (Supplementary Figs. 15-16), all of which showed consistent patterns.

The f is strongly associated with forest resistance
The f is closely related to forest resistance (Fig. 4). Globally, f is positively correlated with forest resistance (P < 0.001) across the majority of boreal (70.56%), temperate (66.86%), and tropical forests (66.44%). Temperate forests show the strongest correlation between f and forest resistance (regionally averaged R = 0.29; P < 0.001). Tropical forests exhibit a moderate f -resistance relationship (regionally averaged R = 0.20; P < 0.001). In contrast, boreal forests show the weakest correlation (regionally averaged R = 0.10; P < 0.001). The biome-dependent relationships between f and forest resistance indicate that forests with higher f often exhibit stronger resistance. In comparison, forests with lower f are likely to be more vulnerable to climatic and anthropogenic disturbances.

Global tree cover loss budget
Finally, we evaluated the budget of tree cover fractional loss, defined as the differences (f = f - f ) between actual f and f in global forests (Fig. 5; Supplementary Fig. 20). We found that f had been overspent (f = -0.12±0.001) in approximately 35.7% of the studied forests. In contrast, the remaining 64.3% of the forests still had a f underspending (f = 0.07±0.000). This asymmetry between overspent and underspent f was typically evident in temperate and boreal zones. On a regional average, f is overspent by more than 0.10 in degraded forests, while the remaining undegraded forests had the smallest quota left (f < 0.03). In the tropical region, the situation differed across continents. Amazon retained the world’s largest quota in undegraded forests (82.3%; f = 0.22±0.000), and underwent a moderate degradation in the degraded forests (17.7%; f = -0.08±0.002). In contrast, tropical Asia showed the largest degraded area (43.8%) with the most severe degradation (f = -0.21±0.003). Congo forests experienced the lowest degradation (20.8%; f = -0.04±0.002) and meanwhile maintained a moderate f in the remaining undegraded forests (79.2%; Δf = 0.09±0.000).

Methods
Methods summary
The objective of this study is to evaluate whether and when sub-grid small-scale tree cover fractional loss (f ) within forests leads to overall declines in ecosystem-level vegetation greenness, ecosystem structure, composition, and function (Supplementary Fig. 1). To examine this, we first identified 0.05° forest grid cells that had experienced small-scale (30-m) gross tree cover fraction losses (f > 0.02) within forests (defined as sub-grid tree cover loss) while having not induced a land use change (i.e., deforestation), based on the 30-m resolution tree cover maps from Global Forest Watch (GFW) 1 and 0.05° resolution Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products 34 (Methods: Select target forest pixels). We then used multiple global maps of vegetation greenness, canopy structure, composition, and function metrics (resampled to 0.05° resolution) (Supplementary Table 1) to calculate their anomalies for each 0.05° resolution forest grid cell before and after its sub-grid tree cover loss, with removal of impacts from climate variability, to assess the impacts of sub-grid fractional tree cover losses (f ) on such forest ecosystem attributes (Methods: Quantify impacts of sub-grid tree cover loss on forest attributes). Next, we identified the safety margin (f ) of f beyond which vegetation greenness, canopy structure, composition, and function declined (Methods: Quantify the safety margin of sub-grid tree cover loss). After that, we explored the potential influences of the edge effect of fragmentation within the forest, demoted as the changes in edge density (ED) of the 0.05° forest grid cell associated with sub-grid 30-m resolution tree cover loss (Methods: Calculate sub-grid tree cover losses), on the f in global forest (Methods: Metrics for representing the edge effect of sub-grid fragmentation, Quantify impacts of sub-grid fragmentation on forest attributes). Finally, we calculated the difference between actual f and f to derive the global tree cover loss budget (f = f - f ).

Notably, in the main text, we presented analyses based on the enhanced vegetation index (EVI) from the MOD13Q1 Version 6.1 dataset 18. We also tested the result robustness based on other indicators and presented these analyses in supplementary materials, which include: the normalized difference vegetation index (NDVI) from MOD13Q1 Version 6.1 dataset 18 and near-infrared reflectance of vegetation (NIRv) 19 as proxies of vegetation greenness, the leaf area index (LAI) from MOD15A2H Version 6.1 dataset 20 as a proxy of canopy structure, the biodiversity index (BDI) from projections of biodiversity intactness 21 as a proxy of ecosystem composition, the gross primary production (GPP) from Penman-Monteith-Leuning Evapotranspiration Version 2 dataset 22–24 and solar-induced chlorophyll fluorescence (SIF) retrievals from Orbiting Carbon Observatory-2, MODIS, and reanalysis data 25 as proxies of ecosystem photosynthesis function. Additionally, the edge effect was quantified in the main text using the edge density (ED) metric 3. To further examine the robustness of the results, the supplementary materials also included analyses based on alternative edge effect metrics such as patch density (PD) 35 and Euclidean nearest neighbor (ENN) 36,37.

Select target forest pixels
The target 0.05° resolution forests should have ever experienced small-scale (e.g., 30-m resolution) tree cover loss within forest ecosystems, but should never experience a land use change and consistently remain as forest land during the study period. To ensure this, we first identified grid cells that were consistently classified as ‘forest lands’ from 2000 to 2012 in the MODIS MCD12C1 Version 6.1 land cover images 34. Then, we overlaid the 30-m high-resolution global tree cover change maps from Global Forest Watch (GFW) 38 onto the selected 0.05° resolution MODIS forest grid cells. For each 0.05° forest grid cell, we calculated the 30-m resolution tree cover fraction and selected those with the tree cover fraction consistently greater than 50% from 2000 to 2012 for analysis. To isolate the impact of tree cover fraction loss (f ) on forest attributes while minimizing the influence of tree cover fraction gain (f ), we focused on those 0.05° resolution grid cells that exhibited a considerable tree cover loss (f > 0.02) while maintaining small tree cover gain (f < 0.01) over the same period, based on the 30-m resolution GFW tree cover maps 6.

Calculate sub-grid tree cover losses
The GFW dataset recorded three main maps at a 30-m resolution: (1) a tree cover map in 2000, (2) a tree cover gain map between 2000 and 2012, and (3) a map indicating the year when tree cover loss occurred 1. Using the 30-m resolution tree cover map from 2000 as a baseline, we computed the sub-grid fractions of 30-m resolution tree cover gain (f ) and tree cover loss (f ) during the study period for each 0.05° forest grid cell.

The values of f for each 0.05° forest grid cell were calculated following the method described in Su et al. 6 (Equation 1). To determine the remaining tree cover in 2012 for each 0.05° resolution grid cell, we counted all the 30-m pixels labelled as ‘n’ (i.e., no tree loss during the period 2000-2012) in the GFW dataset for each 0.05° grid cell (Equation 2). Consequently, the sub-grid (30-m resolution) fractional tree cover loss (f ) for each 0.05° resolution grid cell during the study period was computed using Equation 3.

Quantify impacts of sub-grid tree cover loss on forest attributes
In this study, we selected multiple commonly used metrics representing vegetation greenness, canopy structures, composition, and function to evaluate the impacts of sub-grid tree cover losses on forest attributes. The vegetation greenness metrics include the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). The data for these two indicators are sourced from the MOD13Q1 Version 6.1 dataset 18, which has a spatial resolution of 250 meters, a temporal resolution of 16 days, and spans from 2000 to 2012. Additionally, the near-infrared reflectance of vegetation (NIRv) from 2000 to 2012 was included, which was estimated using the method proposed by Badgley et al. 19. The canopy structure is characterized by the leaf area index (LAI) from the MOD15A2H Version 6.1 dataset 20, which provides data with a spatial resolution of 500 meters and a temporal resolution of 8 days from 2000 to 2012. The ecosystem composition is depicted by the biodiversity index (BDI) from projections of biodiversity intactness 21, with a spatial resolution of 100 meters and a time span from 2017 to 2020. The photosynthetic function is quantified by the gross primary production (GPP) from the Penman-Monteith-Leuning Evapotranspiration Version 2 dataset 22–24, which offers data with a spatial resolution of 500 meters and a temporal resolution of 8 days from 2000 to 2012. Meanwhile, the solar-induced chlorophyll fluorescence (SIF) retrieved from Orbiting Carbon Observatory-2, MODIS, and reanalysis data 25 is also adopted as a proxy of photosynthetic function. It has a spatial resolution of 0.05°, a temporal resolution of 8 days, and a time span from 2000 to 2012. Notably, to match the spatial resolution of the target forest grid cells, all these metrics were aggregated annually and resampled to a spatial resolution of 0.05°.

In the main text, we used EVI as a metric for illustrating the effect of sub-grid tree cover losses on vegetation greenness. For each 0.05° resolution grid cell, in a given year with 30-m resolution tree cover loss within forest, we calculated the anomaly in three-year averaged EVI (Δ𝐸𝑉𝐼 ) before and after the year with tree cover loss. Δ𝐸𝑉𝐼 reflects a combination of background climate variability (Δ𝐸𝑉𝐼 ) and changes induced by sub-grid tree cover loss (Δ𝐸𝑉𝐼). To isolate the effects solely attributed to sub-grid tree cover losses, we adjusted Δ𝐸𝑉𝐼 using a time-series analysis approach developed by Alkama and Cescatti 2 (Equation 4). Following the criteria of Su et al. 6, we identified reference grid cells that had experienced minimal sub-grid tree cover change (f < 0.01 & f < 0.01) and maintained a tree cover fraction consistently above 50%. The three-year averaged EVI anomalies before and after the year of tree cover loss for reference grid cells were attributed solely to background climate variability (Δ𝐸𝑉𝐼 ). For the target forest grid cells with substantial tree cover losses (Methods: Select target forest pixels), reference grid cells were selected from neighbouring undisturbed forests within a 50 km radius 2, assuming similar climate conditions within this spatial scale 39. The Δ𝐸𝑉𝐼 within 50 km was averaged using inverse distance weighting to minimize spatial biases 6. To ensure statistical robustness, only target grid cells with at least five reference grid cells within a 50 km radius were included in the analysis.

Quantify the safety margin of sub-grid tree cover loss
In this study, we identified the safety margin of sub-grid tree cover losses (f ) as those where ecosystem attribute anomaly (e.g., Δ𝐸𝑉𝐼) shifts from positive to negative values as f increases. To achieve this, we classified the samples into different f bins and selected those samples with ecosystem attribute anomaly located within the 5th percentiles for each f bin, as these data were more likely to exhibit a strong edge effect. Then, we calculated the average ecosystem attribute anomaly in different f bins. The f was identified as the f when the fitting line of ecosystem attribute anomaly in different f bins crosses the zero line. Notably, we explored a nonlinear fitting approach to generate the fitting curve between f and the ecosystem attribute anomaly. The Δ𝐸𝑉𝐼 -estimated f was shown in Fig. 1g. Using this approach, we mapped global f for each 4°×4° moving window, as shown in Fig. 1e. The regionally averaged f for boreal, temperate, and tropical zones were presented in Fig. 1h. To examine the variations in f due to different drivers of sub-grid tree cover loss, we calculated the average values of f across five key tree cover loss drivers, including commodity-driven deforestation, shifting agriculture, forestry, wildfire, and urbanization 40. These f values were further averaged under different tree cover fraction bins (e.g., 50–60%, 60–70%, 70–80%, and 80–100%) and across various climate zones, as summarized in Supplementary Fig. 2. In addition, post-fire tree regeneration frequently occurs in the boreal forests. Thus, tree cover gain was explicitly considered to re-quantify the f in forest grid cells. To do this, only grid cells, where tree cover loss was attributed to wildfire according to the global tree cover loss driver maps 40, were retained for subsequent analysis. Then, the safety margin (f ) by versus without consideration of post-fire regeneration was quantified for comparison (Supplementary Fig. 3).

We tested the robustness of the results by using other metrics of vegetation greenness (NDVI and NIRv), canopy structure (LAI), ecosystem composition (BDI), and photosynthesis function (GPP or SIF) to estimate the global pattern of f . Results closely aligned with that of EVI-based f (Supplementary Figs. 4-9). Additionally, we also selected those pixels with data within the 4th and 6th percentiles for each f bin and applied the same method to identify f . Spatial patterns of f were shown in Supplementary Figs. 10-11, consistent with those in Fig. 1e.

Metrics for representing the edge effect of sub-grid fragmentation
Edge density (ED), defined as the edge length per unit area 35 (Equation 5), is a key metric for quantifying forest edge intensity 3,41. Higher ED values typically indicate greater fragmentation and higher edge density, implying a stronger edge effect. In this study, we calculated the ED data for each designated 0.05° forest grid cell utilizing 30 m resolution tree cover maps from GFW 38 and the “PyLandStats” package in Python.

We also tested the robustness of ED in representing the edge effect of fragmentation inside each 0.05° forest grid cell by comparing it with two other indicators derived from GFW tree cover data: patch density (PD) and the Euclidean nearest neighbor (ENN). PD was defined as the number of patches per unit area (Equation 6) 35, ENN was defined as the distance (m) to the nearest neighboring patch of the same type based on the shortest edge-to-edge distance averaged over all landscape patches (Equation 7) 36,37.

Quantify impacts of sub-grid fragmentation on forest attributes
To assess the sensitivity of ΔEVI to Δ𝐸𝐷 induced by sub-grid tree cover loss, we categorized the forest grid cells into two groups: those with f below and those beyond f . Within each group, we calculated the slope of the linear correlations (𝑆𝐸𝑉𝐼) and the correlation coefficient (R) between Δ𝐸𝐷 and Δ𝐸𝑉𝐼 within 4°×4° moving windows for different tree cover bins (e.g., 50–60%, 60–70%, 70–80%, and 80–100%), as depicted in Fig. 2a-d. The linear fitting curves between the two variables for each forest grid cell were presented in Supplementary Fig. 12. We also calculated the average 𝑆𝐸𝑉𝐼 for forest grid cells affected by different tree cover loss drivers (commodity-driven deforestation, shifting agriculture, forestry, wildfire, and urbanization) 40 across various tree cover bins (e.g., 50–60%, 60–70%, 70–80%, and 80–100%) in boreal, temperate, and tropical forests, respectively. We further stratified 𝑆𝐸𝑉𝐼 by both tree cover fraction bins and climate zones. These 𝑆𝐸𝑉𝐼 values for grid cells below and beyond f were summarized in Supplementary Figs. 13-14, respectively. The sensitivities of Δ𝐸𝑉𝐼 to fragmentation were also evaluated using other fragmentation metrics like PD and ENN (Supplementary Figs. 15-16).

The partial least squares structural equation modeling (PLS-SEM) is a powerful statistical approach for estimating complex cause-effect relationships in path models involving latent variables 42. Unlike traditional structural equation modeling, which relies on maximum likelihood estimation 43 and assumes large sample sizes with multivariate normality, PLS-SEM imposes fewer restrictions, making it suitable for smaller datasets and non-normally distributed data 44. Additionally, PLS-SEM can effectively handle missing data. The GoF index in PLS-SEM was primarily used to optimize the predictive accuracy of dependent variables 45. In this study, we employed PLS-SEM to compare the effects of Δ𝐸𝐷 versus f on Δ𝐸𝑉𝐼. Specifically, we applied PLS-SEM analysis within each 4°×4° moving window to mitigate the influence of other environmental factors. The PLS-SEM analysis was applied for 0.05° forest grid cells with f below and beyond f separately (inset plots in Fig. 2a,c). Only windows with a goodness of fit (GoF) index greater than 0.5, as determined by PLS-SEM analysis, were included in the evaluation. The PLS-SEM analyses based on other metrics, such as the PD and ENN, (Supplementary Figs. 15-16) were similar to the analysis method for ED.

Depict the paths of sub-grid fragmentation impacts on forest attributes
First, we calculated the differences in land surface temperature (𝐿𝑆𝑇), soil moisture (𝑆𝑀), vapor pressure deficit (𝑉𝑃𝐷), and absorbed photosynthetically active radiation (𝐴𝑃𝐴𝑅) between 2012 and 2000 (denoted as Δ𝐿𝑆𝑇, Δ𝑉𝑃𝐷, Δ𝑆𝑀, and Δ𝐴𝑃𝐴𝑅) to quantify their responses sub-grid tree cover losses. These variables were resampled to a spatial resolution of 0.05° to match the 0.05° resolution forest grid cells. LST was derived from the MOD11C3 Version 6.1 land surface temperature products 46. 𝑉𝑃𝐷 and 𝑆𝑀 were sourced from the TerraClimate dataset 47, and 𝐴𝑃𝐴𝑅 was calculated based on the PAR and FAPAR from the global land surface satellite (GLASS) products 48,49.

Subsequently, to accurately isolate the biophysical consequences of sub-grid tree cover loss, we assumed that adjacent forest and non-forest areas shared similar background climatic conditions. Based on this assumption, we could consider the intrinsic biophysical processes as localized perturbations superimposed on a stable climatic background 50. For each forest grid cell, a space-for-time analogy 39 was employed to estimate the local biophysical effects of sub-grid tree cover on key environmental variables (𝐿𝑆𝑇 , 𝑆𝑀 , 𝑉𝑃𝐷 , 𝐴𝑃𝐴𝑅 ). Taking 𝐿𝑆𝑇 as an example, Δ𝐿𝑆𝑇 was quantified as the temperature difference between forest areas and nearby open areas (grassland and cropland) (Equation 8). Forest and open land pixels were delineated using the MCD12C1 Version 6.1 land cover images 34. These differences were calculated separately for different tree cover bins (e.g., 50–60%, 60–70%, 70–80%, and 80–100%) for the years 2000 and 2012.

Finally, we utilized the PLS-SEM approach to disentangle how changes in Δ𝐸𝐷 altered environmental conditions (Δ𝐿𝑆𝑇 , Δ𝑉𝑃𝐷 , Δ𝑆𝑀 , and Δ𝐴𝑃𝐴𝑅 ) and influenced forest attribute anomalies (e.g., Δ𝐸𝑉𝐼) within each 4°×4° moving window. It is noteworthy that only grid cells with a GoF index greater than 0.5 were included in the analysis. PLS-SEM analyses were conducted separately for forests with f below and beyond f , respectively (Fig. 3a-b). This method was also applied to other landscape metrics (Δ𝑃𝐷 and Δ𝐸𝑁𝑁) to depict the pathways through which the edge effect of landscape fragmentation influenced forest attributes (e.g., Δ𝐸𝑉𝐼) (Supplementary Figs. 17-19).

Calculating ecosystem resistance
Resistance is commonly defined as the capacity of an ecosystem to maintain its normal levels during a climate disturbance event 51. In this study, we quantified forest resistance using EVI as an illustration, calculated as a dimensionless ratio between the baseline vegetation condition and the magnitude of vegetation greenness decline during extreme disturbance events 52,53 (Equation 10). Extreme events were identified by detecting the minimum EVI value in the detrended EVI anomaly time series, which was achieved through linear regression detrending. The EVI value during the disturbance event was denoted as 𝑌 , while 𝑌̅ was derived from the multi-year mean EVI for corresponding calendar months during non-disturbance years.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript titled “The safety margin of small-scale tree cover loss in global forests” describes an approach for characterizing canopy cover loss and associated changes in forest ecosystem “productivity” using indices obtained from Global Forest Watch and MODIS products. Additionally, and importantly, the authors propose a “safety margin” to evaluate forest degradation. First, given contemporary definitions of forest degradation (see Ghazoul et al. 2015, Gao et al. 2020, and Betts et al. 2024), it seems your focus on tree cover loss and vegetation indices alone do not capture important structure, composition, and function attributes used to quantify forest degradation. Further, much of the paper quantifies tree cover loss which may result in a land use change which is often the definition of deforestation, not forest degradation. Being clear in the methods on what you quantified throughout the paper – tree canopy cover loss, changes in vegetation greenness, and changes in forest productivity – and relating the attributes to specific aspects of forest loss, deforestation, and degradation as it has been defined beyond your study would be helpful to avoid misinterpretations. Finally, I would also suggest a thorough proofread to clean up many spelling and grammatical errors throughout.\n\nGhazoul, J., Burivalova, Z., Garcia-Ulloa, J. and King, L.A., 2015. Conceptualizing forest degradation. Trends in ecology & evolution, 30(10), pp.622-632.\nGao, Y., Skutsch, M., Paneque-Gálvez, J. and Ghilardi, A., 2020. Remote sensing of forest degradation: a review. Environmental Research Letters, 15(10), p.103001.\nBetts, M.G., Yang, Z., Hadley, A.S., Hightower, J., Hua, F., Lindenmayer, D., Seo, E. and Healey, S.P., 2024. Quantifying forest degradation requires a long-term, landscape-scale approach. Nature Ecology & Evolution, 8(6), pp.1054-1057.", "reviewer_2": "The global-scale study examined fine-scale losses of forests and their effects on forests using several indexes of forest greenness and productivity (e.g. enhanced vegetation index), as well as how factors such as changes in edge density affected those indexes in 0.5 degree grid squares from 2000-2012. The study has important new findings about how local-scale forest loss and fragmentation affect forest productivity, and also provides interesting comparisons between findings in boreal, temperate and tropical forest biomes. Theory related to changes in edge density is used to show under what circumstances positive, neutral or negative effects on overall forest productivity may occur. The main questions asked are based on that theory, and are answered, and the findings are well supported by the data. I liked the way this theory was tested and shown via PLS-SEM and in Figure 3. Overall this is an excellent paper.\nThe complex methods for which forested grid squares (0.5 degree grid, with changes over 12 years in 30 m pixels within each grid square to determine various levels of change) seem to me to be very nicely done. I read through all of the steps in the analyses and indexes calculated and the logic checks out OK as far as I can tell.\nIn general the manuscript is well written and easy to understand, although I have a few suggestions for revisions below.\nThe Introduction needs a few words added in the paragraph on lines 93-107, to say that that the study examined changes in 30 m pixels nested within 0.5 degree grid squares. It is good that the analyses used these two scales of resolution, and it is so important to the study that the reader should know about it early on.\nFigure 1, I was unable to see the different shading colors for boreal, temperate and tropical in the top panel. Maybe this was just due to the relatively poor resolution of figures in the review copy. I realize that it is complicated to have background shading as well as dots across a color scale that represents results on the same map, but the shading for forested areas looks like the same shade of gray for all three biomes.\nAlso, there is a line running from the Caspian Sea to northern China, that appears to be the southern border of Russia. No other national boundaries that are interior to a continent are shown, and even the eastern border of Russia is not shown--so why show just the southern border of Russia?\nCaution for the boreal biome results: for the boreal biome in general fires are forest regeneration events, and do not represent longer-term loss of forest like urban expansion or conversion to agriculture. Fires there are complex in shape, have unburned islands within the fire perimeter, and generate some increases in edge density, but in many cases those differences will disappear when the forest regenerates. Also the regeneration process is slow compared to temperate forests, so 12 years of observation will not always show the recovery. In addition there is conversion from coniferous to deciduous (birch or aspen) forests in a lot of cases, and those early-successional deciduous boreal forests will have lower values of the indexes calculated here for one to several decades (compared to the pre-fire condition).\nFinally, I saw two very minor editorial issues:\nLine 277, change 'hiders' to 'hinders'\nLine 518: change 'gird' to 'grid'"}, "author_response": "Point-by-point responses to reviewers’ comments\nReviewer #1 (Remarks to the Author):\nGeneral Comment: The manuscript titled “The safety margin of small-scale tree cover loss in global forests” describes an approach for characterizing canopy cover loss and associated changes in forest ecosystem “productivity” using indices obtained from Global Forest Watch and MODIS products. Additionally, and importantly, the authors propose a “safety margin” to evaluate forest degradation.\nResponse: We would like to express our sincere gratitude to Reviewer#1 for your valuable time in reviewing our manuscript and for the insightful comments and valuable suggestions, particularly regarding (1) the definition of degradation versus deforestation, (2) how to relate the selected metrics to forest attributes such as structure, composition, and function, (3) the English language issue (Comment 2). These constructive comments have been extremely helpful in improving our manuscript.\nIn the revised manuscript, we have revised the manuscript by comprehensively addressed your nice suggestions. First, we gave clear definitions of “degradation” [i.e., involves declines in vegetation greenness, structure, composition, and functions] following the references provided by Reviewer#1. Then, we classified our metrics as proxies of forest greenness (vegetation indices such as EVI, NDVI, NIRv), canopy structure (LAI), composition (biodiversity, BDI), and photosynthesis function (GPP and SIF); and used these metrics to evaluate forest ecosystem responses to small-scale tree cover loss. Notably, we explained clearly that we focused on evaluating the safety margin of ecosystem-level forest attributes (0.05-degree resolution) to sub-grid (30-m resolution) tree cover loss (see the newly-added Fig. S1). In other words, such sub-grid tree over losses (30-m resolution) do not lead to land cover change (i.e., deforestation). Finally, we also asked a native speaker to polish the English language thoroughly.\nSpecific revisions in response to these concerns are detailed below:\nComment 1: First, given contemporary definitions of forest degradation (see Ghazoul et al. 2015, Gao et al. 2020, and Betts et al. 2024), it seems your focus on tree cover loss and vegetation indices alone do not capture important structure, composition, and function attributes used to quantify forest degradation.\nGhazoul, J., Burivalova, Z., Garcia-Ulloa, J. and King, L.A., 2015. Conceptualizing forest degradation. Trends in ecology & evolution, 30(10), pp.622-632.\nGao, Y., Skutsch, M., Paneque-Gálvez, J. and Ghilardi, A., 2020. Remote sensing of forest degradation: a review. Environmental Research Letters, 15(10), p.103001.\nBetts, M.G., Yang, Z., Hadley, A.S., Hightower, J., Hua, F., Lindenmayer, D., Seo, E. and Healey, S.P., 2024. Quantifying forest degradation requires a long-term, landscape-scale approach. Nature Ecology & Evolution, 8(6), pp.1054-1057.\nResponse: Thank you for providing the valuable references. We carefully studied these nice references and found that “forest degradation” often involves declines in forest vegetation greenness, structure, composition, and functions. Thus, in the abstract and Introduction sections, we clearly explained that this study mainly focused on evaluating the safety margin of decline in forest greenness, canopy structure, species composition, and photosynthesis function due to sub-grid tree cover loss. See as follows:\n“Utilizing global maps of forest attribute proxies, we discover that vegetation greenness, canopy structure, composition, and photosynthesis function can all increase under limited tree cover loss……” (Abstract, lines 55-58).\n“To address these knowledge gaps, here we explore three key research questions:\n(1) Is there a safety margin (f ) when sub-grid small-scale tree cover fractional loss\nsafety\n(f ) within forests leads to an overall degradation of forest ecosystem, i.e., declines in\nloss\nvegetation greenness, ecosystem structure, composition, and function?……”\n(Introduction, lines 89-92).\nIn addition, we also followed your suggestions to select corresponding metrics to represent forest greenness (EVI, NDVI, NIRv), canopy structure (LAI), composition (biodiversity, BDI), and photosynthesis function (GPP and SIF), respectively. Thus, in the revised manuscript, we captured the changes in vegetation greenness, forest structure, composition, and function to evaluate forest degradation. See as follows:\n(1) We used EVI, NDVI, and NIRv as proxies to evaluate the response of forest greenness to tree cover loss\nFig. 1 The safety margin of sub-grid tree cover losses (f ) using MOD13Q1\nsafety\nenhanced vegetation index (∆𝐸𝑉𝐼). Map boundaries were made with Natural Earth.\nSupplementary Figure 4. The safety margin of sub-grid tree cover losses (f ) using\nsafety\nMOD13Q1 normalized difference vegetation index (NDVI). Map boundaries were\nmade with Natural Earth.\nSupplementary Figure 5. The safety margin of sub-grid tree cover losses (fsafety) using\nnear-infrared reflectance of vegetation (NIRv). Map boundaries were made with\nNatural Earth.\n(2) We used LAI as a proxy to evaluate the response of canopy structure to tree cover loss\nSupplementary Figure 6. The safety margin of sub-grid tree cover losses (f ) using\nsafety\nMOD15A2H leaf area index (LAI). Map boundaries were made with Natural Earth.\n(3) We used biodiversity as a proxy to evaluate the response of forest composition to tree cover loss\nSupplementary Figure 7. The safety margin of sub-grid tree cover losses (f ) using\nsafety\nbiodiversity index (BDI). Map boundaries were made with Natural Earth.\n(4) We used GPP and SIF data as proxies to evaluate the response of forest photosynthesis function to tree cover loss\nSupplementary Figure 8. The safety margin of sub-grid tree cover losses (f ) using\nsafety\nPenman-Monteith-Leuning gross primary production (GPP). Map boundaries were\nmade with Natural Earth.\nSupplementary Figure 9. The safety margin of sub-grid tree cover losses (f ) using\nsafety\nOrbiting Carbon Observatory-2 solar-induced chlorophyll fluorescence (SIF). Map\nboundaries were made with Natural Earth.\nOverall, results collectively showed that there was a safety margin above which additional tree cover loss within forest ecosystems would lead to forest degradation. i.e., declines in forest greenness, canopy structure, composition, and photosynthesis function.\nComment 2: Further, much of the paper quantifies tree cover loss which may result in a land use change which is often the definition of deforestation, not forest degradation. Being clear in the methods on what you quantified throughout the paper – tree canopy cover loss, changes in vegetation greenness, and changes in forest productivity – and relating the attributes to specific aspects of forest loss, and degradation as it has been defined beyond your study would be helpful to avoid misinterpretations.\nResponse: Thank you for this constructive comment as well. We totally agree with you. It is absolutely necessary to explain clearly in the methods whether we focused on deforestation (involves a land use change) or degradation.\nTo avoid misunderstanding, we emphasized that this study selected forest grid cells (0.05-degree resolution) that have consistently remained a forest type throughout the study period. Thus, such small-scale (30-m resolution) tree cover loss within forests has not resulted in a land use change in all the selected 0.05-degree forest grid cells. Instead, we focused on evaluating whether and when sub-grid tree cover loss (30-m resolution) would result in ecosystem-level declines in forest greenness (EVI), canopy structure (LAI), composition (species diversity), and photosynthesis function (GPP), i.e., forest degradation. We added a new figure (Supplementary Fig. 1) to show our objective visually.\nSupplementary Figure 1. Schematic diagram for illustrating the effects of sub-grid small-scale (e.g., 30-m resolution) tree cover loss on ecosystem-level (e.g., 0.05° resolution) vegetation greenness, ecosystem structure, composition, and function. Images include Pinus rigidus adapted from Sidney Anderson, Integration and Application Network (ian.umces.edu/media-library); Fraxinus americana adapted from Joanna Woerner, Integration and Application Network (ian.umces.edu/media-library); Damaged Tree adapted from Jane Thomas, Integration and Application Network (ian.umces.edu/media-library); and Plain 3D adapted from Tracey Saxby, Integration and Application Network (ian.umces.edu/media-library). The license for these images is from https://creativecommons.org/licenses/by-sa/4.0/.\nCorresponding explanations have been thoroughly added in Abstract, Introduction, Discussion, and Method sections. See as follows:\n“Climatic and anthropogenic disturbances have led to intense small-scale tree cover loss in global forests. However, it remains unclear when forest attributes at a large scale (e.g., 0.05° resolution) will decline in response to such sub-grid (e.g., 30-m) tree cover losses within forest ecosystems……” (Abstract, lines 52-55).\n“In this study, we mainly focused on the effects of 30-m resolution tree cover fractional loss on 0.05° resolution vegetation greenness, ecosystem structure, composition, and function in global forests that have not experienced a land use change during the period 2000-2012 (Fig. S1)……” (Introduction, lines 97-100).\n“The objective of this study is to evaluate whether and when sub-grid small-scale tree cover fractional loss (f ) inside forests leads to overall declines in ecosystem-level vegetation greenness, ecosystem structure, composition, and function (Fig. S1). To examine this, we first identified 0.05° forest grid cells that had experienced small-scale (30-m) gross tree cover fraction losses (f > 0.02 and f < 0.01) inside forest (defined as sub-grid tree cover loss), but had not induced a land use change (i.e., deforestation), based on the 30-m resolution tree cover maps from Global Forest Watch (GFW) 1 and 0.05° resolution Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products 34 (sections 2 in Methods). We then used multiple global maps of vegetation greenness, canopy structure, composition, and function metrics (resampled to 0.05° resolution) (Table S1) to calculate their anomalies for each 0.05° resolution forest grid cell before and after its sub-grid tree cover loss, with removal of impacts from climate variability, to assess the impacts of sub-grid fractional tree cover losses (f ) on such forest ecosystem attributes (section 4 in Methods). Next, we identified the safety margin (f ) of f beyond which vegetation greenness, canopy structure, composition, and function declined (section 5 in Methods)……” (Methods, lines 323-338).\n“The target 0.05° resolution forests should have ever experienced small-scale (e.g., 30-m resolution) tree cover loss within forest ecosystems, but should never experience a land use change and consistently remain as forest land during the study period. To ensure this, we first identified grid cells that were consistently classified as ‘forest lands’ from 2000 to 2012 in the MODIS MCD12C1 Version 6.1 land cover images 34. Then, we overlaid the 30-m high-resolution global tree cover change maps from Global Forest Watch (GFW) 38 onto the selected 0.05° resolution MODIS forest grid cells. For each 0.05° forest grid cell, we calculated the 30-m resolution tree cover fraction and selected those with the tree cover fraction consistently greater than 50% from 2000 to 2012 for analysis……” (Methods, lines 362-371).\nComment 3: Finally, I would also suggest a thorough proofread to clean up many spelling and grammatical errors throughout.\nResponse: As suggested, we have asked a native speaker to correct the spelling and grammatical errors and to polish the revised manuscript thoroughly.\nReviewer #2 (Remarks to the Author):\nGeneral Comment: The global-scale study examined fine-scale losses of forests and their effects on forests using several indexes of forest greenness and productivity (e.g. enhanced vegetation index), as well as how factors such as changes in edge density affected those indexes in 0.5 degree grid squares from 2000-2012. The study has important new findings about how local-scale forest loss and fragmentation affect forest productivity, and also provides interesting comparisons between findings in boreal, temperate and tropical forest biomes. Theory related to changes in edge density is used to show under what circumstances positive, neutral or negative effects on overall forest productivity may occur. The main questions asked are based on that theory, and are answered, and the findings are well supported by the data. I liked the way this theory was tested and shown via PLS-SEM and in Figure 3. Overall this is an excellent paper.\nThe complex methods for which forested grid squares (0.5 degree grid, with changes over 12 years in 30 m pixels within each grid square to determine various levels of change) seem to me to be very nicely done. I read through all of the steps in the analyses and indexes calculated and the logic checks out OK as far as I can tell. In general the manuscript is well written and easy to understand, although I have a few suggestions for revisions below.\nResponse: We sincerely thank you for highlighting the importance of our research question and the recognition of our key findings. We are also grateful for the constructive comments on the safety margin of fire-induced tree cover loss in the boreal biome and nice specific suggestions on revisions of texts and figures.\nIn the revised manuscript, we have added new analyses of the safety margin of fire-induced tree cover loss in the boreal biome. We found that post-fire tree regeneration would promote the safety margin in the boreal biome, similar to the perspective of Reviewer#2 (see response to Comment 3). In addition, we also followed all Reviewer#2’s other suggestions to revise the corresponding texts and figures.\nSpecific revisions in response to these concerns are detailed below:\nComment 1: The Introduction needs a few words added in the paragraph on lines 93-107, to say that that the study examined changes in 30 m pixels nested within 0.5 degree grid squares. It is good that the analyses used these two scales of resolution, and it is so important to the study that the reader should know about it early on.\nResponse: Thanks. We totally agree with you that it is necessary to emphasize that we aim to examine changes in 30 m pixels nested within 0.05 degree grid squares. This would be very helpful for avoiding potential misunderstandings. Revisions were shown as follows:\n“In this study, we mainly focused on the effects of 30-m resolution tree cover fractional loss on 0.05° resolution vegetation greenness, ecosystem structure, composition, and function in global forests that have not experienced a land use change during the period 2000-2012 (Fig. S1)……” (lines 97-100).\nSupplementary Figure 1. Schematic diagram for illustrating the effects of sub-grid small-scale (e.g., 30-m resolution) tree cover loss on ecosystem-level (e.g., 0.05° resolution) vegetation greenness, ecosystem structure, composition, and function. Images include Pinus rigidus adapted from Sidney Anderson, Integration and Application Network (ian.umces.edu/media-library); Fraxinus americana adapted from Joanna Woerner, Integration and Application Network (ian.umces.edu/media-library); Damaged Tree adapted from Jane Thomas, Integration and Application Network (ian.umces.edu/media-library); and Plain 3D adapted from Tracey Saxby, Integration and Application Network (ian.umces.edu/media-library). The license for these images is from https://creativecommons.org/licenses/by-sa/4.0/.\nComment 2: Figure 1, I was unable to see the different shading colors for boreal, temperate and tropical in the top panel. Maybe this was just due to the relatively poor resolution of figures in the review copy. I realize that it is complicated to have background shading as well as dots across a color scale that represents results on the same map, but the shading for forested areas looks like the same shade of gray for all three biomes.\nResponse: We have followed your suggestion to redraw the shading colors for boreal, temperate, and tropical regions for all relevant figures (Fig. 1, Fig. 2, Fig. 4, Supplementary Figures 4-11, and Supplementary Figures 15-16). Here we show Fig. 1 as an example.\nFig. 1 The safety margin of sub-grid tree cover losses (f ) using MOD13Q1\nsafety\nenhanced vegetation index (∆𝐸𝑉𝐼). Map boundaries were made with Natural Earth.\nComment 3: Also, there is a line running from the Caspian Sea to northern China, that appears to be the southern border of Russia. No other national boundaries that are interior to a continent are shown, and even the eastern border of Russia is not shown--so why show just the southern border of Russia?\nResponse: This is a typo. In the revised manuscript, we have removed such line on the map in all relevant figures (Fig. 1, Fig. 2, Fig. 4, Supplementary Figures 4-11, and Supplementary Figures 15-16).\nComment 4: Caution for the boreal biome results: for the boreal biome in general fires are forest regeneration events, and do not represent longer-term loss of forest like urban expansion or conversion to agriculture. Fires there are complex in shape, have unburned islands within the fire perimeter, and generate some increases in edge density, but in many cases those differences will disappear when the forest regenerates. Also the regeneration process is slow compared to temperate forests, so 12 years of observation will not always show the recovery. In addition, there is conversion from coniferous to deciduous (birch or aspen) forests in a lot of cases, and those early-successional deciduous boreal forests will have lower values of the indexes calculated here for one to several decades (compared to the pre-fire condition).\nResponse: Thank you so much for the valuable comments. In the revised manuscript, we have followed your suggestions to assess effects of tree regeneration after fires on the safety margin of sub-grid tree cover loss in global forests.\nTo do this, we selected 0.05-degree forest grid cells following two criteria (1) the 0.05-degree forest grid cell was mainly induced by fire; (2) the 0.05-degree forest grid cell should also experience tree cover gain (i.e., tree regeneration) (refer to lines 483-487 in section 5 of the Methods). Then, we quantified the safety margin of sub-grid tree cover loss in those forests. Results showed that tree regeneration after fire significantly increased the safety margin of sub-grid tree cover loss when forest attributes decline (Supplementary Figure 3). This finding is consistent with the perspective of Reviewer #2.\nWe have added corresponding texts in the Results, Discussion, and Methods sections. See as follows:\n“……Notably, boreal tree often regenerates decades after the wildfire. We thus took into account the factor of tree regeneration (i.e., tree cover gain) and re-quantified the f across those 0.05° resolution boreal forest grid cells where more than 69.97% of 30-m resolution tree cover loss was driven by wildfire (see section 5 in Methods). The results showed that the f in the boreal region is 1.37 on a regional average, which is 0.68 higher than that without considering the tree regeneration factor (Fig. S3).” (Results, lines 136-142).\n“……However, once the widespread post-fire tree regeneration is taken into account 29, the f of boreal forests will be largely promoted.” (Discussion, lines 290-292).\n“……In addition, post-fire tree regeneration frequently occurs in the boreal forests. Thus, tree cover gain was explicitly considered to re-quantify the f in forest grid cells. To do this, only grid cells, where tree cover loss was attributed to wildfire according to the global tree cover loss driver maps 40, were retained for subsequent analysis. Then, the safety margin (f ) by versus without consideration of post-fire regeneration was quantified for comparison (Fig. S3).” (Methods, lines 483-488).\nSupplementary Figure 3. Comparison of the safety margin (f ) by and without considering post-fire tree regeneration. a, Global map of the safety margin of sub-grid tree cover losses (f ) attributed to wildfire causing a decline in the EVI, with (upper figure) and without (lower figure) consideration of the tree regeneration factor. Dots are spaced at 1° intervals for both latitude and longitude. Dot size indicates the f magnitude. Map boundaries were made with Natural Earth. b, c, d, e, f, Same with a, but based on NDVI, NIRv, LAI, GPP, and SIF, respectively. g, h, i, Regionally averaged f of these metrics in the boreal, temperate, and tropical zones, respectively. The error bar indicates one standard error.\nComment 5: Finally, I saw two very minor editorial issues:\nLine 277, change 'hiders' to 'hinders'\nLine 518: change 'gird' to 'grid'\nResponse: Thank you. Done as suggested. And we also checked and corrected other spelling and grammatical errors throughout the manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for addressing all comments and suggestions in my initial review. The manuscript is much improved and will make a nice contribution to the topic area.", "reviewer_2": "The revisions in response to my previous comments were very nicely done, and the manuscript is substantially improved.\nThe way boreal forests are handed in the manuscript, taking into account the large area burned by wildfires in recent decades that is likely to regenerate (as opposed to being a forest loss), is much improved compared to the original version of the paper.\nI still think that this is an important and unique contribution to our knowledge about the current state of forests across the globe, with its analysis of the safety margin for the magnitude of small-scale tree cover loss, and all of the reasons I previously stated in the first round of review. I don't think any additional analyses are needed, but I do have suggestions for two very minor editorial items:\n1. Lines 137-138. I suggest better wording for this sentence as follows: ‘Notably, boreal tree cover can take a decade or more to regenerate after wildfire, especially in the northern part of the boreal biome’.\n2. Line 279 add ‘of the’ to the first sentence in the Discussion: ‘One of the most important...'"}, "author_response": ""}]

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