Global loss of mountain vegetated landscapes and its impact on biodiversity conservation

Domain: earth_science
Published: 2025-10-09
Editorial outcome: minor_revision

Abstract
Accelerating human encroachment and natural disasters are causing substantial loss of mountain vegetated landscapes, threatening biodiversity conservation and ecosystem sustainability. The global-scale quantification of the magnitude, variability and drivers of the loss of mountain vegetated landscapes, and its impact on biodiversity conservation, however, has been lacking. Here, we combine global datasets on mountain boundaries, land use, natural disasters, and protected and biodiversity hotspots together with large-scale earth observation data to quantify global mountain vegetated landscape loss, as well as its variation and potential drivers from 2000 to 2020. Overall, we find widespread but uneven mountain vegetated landscape loss across the globe, of which ∼89% can be attributed to human expansion, primarily agriculture, with small contributions of human settlement growth and mining. About ∼11% of mountain vegetated landscape loss can be attributed to natural disasters, primarily through drought. We also observe that ~56% of global mountain vegetated landscape loss occurred within protected areas and in areas with high richness of threatened mountain-occurring species, indicating the urgency of improving protection in these areas of loss. Our results can help formulate conservation strategies and contribute to sustainable development.

Introduction
Mountain ecosystems play a disproportionate role in contributing to global biodiversity, carbon dynamics, water supply and climate stability1–4. At the same time, geographically and biologically diverse mountain ecosystems tend to be ecologically fragile5. Vegetated landscapes within these mountains, including forests, shrublands and grasslands, influence the health and sustainability of the entire mountain ecosystem5–8 by preventing soil and water loss, providing shelter and food for wildlife and supporting the development of local economies (e.g., providing wood, medicinal herbs and food for local residents)9–11. As a result, sustainably managing mountain ecosystems is one of the targets of the 2030 Agenda for Sustainable Development of the United Nations (SDG 15.4)12.

Despite their disproportionate role in supporting biodiversity and ecosystem services, mountain ecosystems continue to be widely developed and degraded13,14, including hillside urbanisation15–18, agriculture expansion19–21 and mining22,23. Although these developments bring economic benefits, they simultaneously lead to the loss of vegetated landscapes within these mountains24–26. At the same time, anthropogenic climate change is increasing the frequency and intensity of natural disasters, including wildfires, floods, landslides and droughts27–31, which can alter vegetation within these landscapes32–36.

Mountain vegetation landscapes (MVL) are being lost across the world due to both human expansions and natural disasters, which can bring serious consequences to biodiversity conservation and sustainable development goals. In 2022, the Convention on Biological Diversity adopted the Kunming-Montreal Global Biodiversity Framework as an urgent response to biodiversity loss37, which will require accurate information of global MVL loss to promote the targets of this framework. Unfortunately, the magnitude of MVL loss and its distribution across the world, as well as the contribution of different sources to this loss (e.g., different kinds of human expansions, natural disasters), are largely incomplete. Such information would provide essential guidance for understanding and potentially mitigating these changes.

Here, we present a global assessment of the magnitude and distribution of MVL loss at 30 m resolution in response to both human expansions and natural disasters from 2000 to 2020 using global datasets on mountain boundaries, human land use, and natural disasters together with Landsat imagery. In particular, we use a Landsat imagery-derived vegetation index to quantify vegetation reduction associated with natural disasters (see details in “Methods”). We estimate MVL loss from changes in human land use, including (i) human settlement growth, (ii) agriculture expansion, and (iii) mining. We estimate MVL loss from natural disasters (a net loss after deducting restored and recovering areas), including (vi) wildfires, (v) floods, (vi) landslides, and (xii) droughts (“Methods”). We separately consider MVL loss across ten global mountain regions. We also consider how MVL loss is aligned with current biodiversity conservation priorities by examining loss within protected areas (PAs) (using the World Database of Protected Areas [WDPA]) and from areas with high richness of threatened mountain-occurring species (AHRTMS) (using the IUCN Red List of threatened species) (Methods). Finally, we validate our results using more than 16,400 high-resolution (≤5 m) random samples to confirm the accuracy of MVL loss (see last part of Methods). We find an overall accuracy of ~88.2% of the global mapping, with a high producer’s accuracy (90.7%) and user’s accuracy (84.0%) of MVL loss (Supplementary Table 1).

Results
Widespread loss of global MVL
Overall, we found widespread global MVL loss of ~356,549.9 km2 from 2000–2020, accounting for ~1.9% of the total initial MVL area, as a result of both human expansion and natural disasters (Fig. 1a and Supplementary Table 2). However, only ~11% of this loss could be attributed to natural disasters, primarily drought, with the remaining 89% of the loss of MVL attributed to human expansion over the 20-year observation period. This human-mediated loss of MVL was mainly due to agriculture expansion (contributing ~83%), with considerably less due to human settlement growth (~5%) and mining (~1%) (Fig. 1a). Of the total observed MVL loss, 57% was in forests, 25% from grasslands, and 18% from shrublands (Fig. 1b). Across these vegetation types, human expansions (i–iii) collectively accounted for over 81% of the MVL loss (Fig. 1c). Among all drivers, agriculture expansion was the primary driver of forest, shrubland, and grassland loss, followed by drought and then human settlement growth (Fig. 1c and Supplementary Table 2).

Spatial unevenness of MVL loss across regions
The global distribution of MVL loss was highly uneven across regions (Table 1). More than 90% of global MVL losses were concentrated among seven regions: East Asia (16.0%), North America (15.6%), Middle East (14.0%), Europe & Russia (13.3%), Sub-Saharan Africa (11.5%), Latin America (11.0%) and Southeast Asia (10.5%) (Table 1). By contrast, Central and South Asia, as well as Oceania, account for only 8.1% of the total global loss (Table 1). However, when we estimated relative loss (% of their initial MVL area), rather than absolute loss, we found lower relative losses in East Asia, Latin America, Europe & Russia and North America (~1.3–1.7%) owing to their large initial MVL area, and higher relative loss in Middle East (6.2%), Sub-Saharan Africa (3.2%) and Central Asia (3.0%), which had a lower initial MVL area (Table 1). This indicates that mountains in these regions are under disproportionate pressure and are in urgent need for protection and restoration.

Most of the MVL loss in the Middle East and Central Asia was from grasslands, while losses in other regions were mostly from forest losses (Supplementary Fig. 3). Notably, North America experienced the greatest loss of forests, and the Middle East experienced the greatest losses of shrublands and grasslands (Table 1). Forest losses from Southeast and East Asia and Europe & Russia (14.3–16.1%), shrubland losses from Latin America, Europe & Russia and North America (14.2–16.7%), and grassland losses from East Asia (24.2%) were also substantial (Table 1). While such forest losses have been documented19,20,24,25,38, losses of grassland and shrubland have not been fully documented on a global scale.

Human expansion was the largest contributor to MVL loss across all regions (ranging from 77.3% to 99.1%; Supplementary Table 3), primarily due to agriculture (more than 69% in all regions) (Fig. 2a). However, there was regional variation in the intensity of MVL loss due to other drivers. For example, human settlement growth was most intense in Latin America, as well as South and East Asia, owing primarily to widespread mountain urbanisation projects in these regions15–17,39. In East Asia, and Europe & Russia, MVL loss due to mining was more intense, likely because of high mineral export23. Drought ranked as the second largest contributor to MVL loss across nine of the ten world regions, with Sub-Saharan Africa being the only exception. (Fig. 2a and Supplementary Table 3). For forest loss, the drivers were similar to the overall MVL loss (Fig. 2b). However, drought contributed disproportionately to shrubland loss in North America (48.9%) and Latin America (41.3%) compared to other regions (Fig. 2c), while human settlement growth contributed disproportionately to grassland loss in Latin America (13.6%) and East Asia (10.9%) compared to other regions (Fig. 2d).

Spatial coincidence of MVL loss and conservation priorities
We examined how MVL loss over the 20-year observation period aligned with current biodiversity conservation priorities by quantifying MVL loss within protected areas (PAs) and areas with high richness of threatened mountain-occurring species (AHRTMS) (Supplementary Figs. 4 and 5). In all, there were ~45,011.6 km2 of MVL loss within PAs (accounting for 1.3% of the initial MVL area of PAs) and ~169,289.9 km2 MVL loss within AHRTMS (accounting for 2.4% of the initial MVL area of AHRTMS) (Supplementary Table 4). After excluding AHRTMS located within PAs, we observed that 55.7% of the total global MVL loss occurred within PAs (12.6%) and in AHRTMS-outside of PAs (43.1%) (Fig. 3a). This emphasises that even since the year 2000, a substantial amount of these mountain habitats that are protected and/or of particular importance for threatened species have lost their natural vegetation, primarily via human activities such as agriculture expansion (Fig. 3b). In fact, nearly three-fifths of the MVL loss within PAs (7.9% of total global MVL loss) occurred after the PAs were officially designated (established before 2000) (Fig. 3a), with almost 77% of this as a result of human expansions, rather than natural disasters. This suggests that the legal effect of mountain PAs has not been fully realised in many parts of the world.

AHRTMS located outside of PAs are not legally protected, but contain high concentrations of mountain-occurring threatened species (Supplementary Fig. 4). As a result, MVL loss within these areas poses a large challenge to biodiversity conservation. We observed that MVL loss within AHRTMS-outside of PAs is mainly concentrated in Southeast Asia, southern East Asia and eastern Sub-Saharan Africa (Supplementary Fig. 5), where attaining protected status would be particularly urgent. Prioritising the increase of PAs in these areas could allow a better response to the United Nations agreement of globally expanding PAs to slow or reverse biodiversity declines40,41.

Methods
Mapping global mountain vegetated landscapes
We first mapped MVL in 2000 as a basemap from which to estimate changes. To do so, we combined a global land-cover dataset in 2000 with a global mountain dataset. For land cover, some moderate spatial resolution products exist, such as the MODIS Land Cover Type from 2001 to 2016 (500 m), the ESA Climate Change Initiative (ESA-CCI) from 1992 to 2015 (300 m), Copernicus Global Land Cover Layers (CGLS-LC100) from 2015 to 2019 (100 m), Finer Resolution Observation and Monitoring of Global Land Cover product (FROM-GLC) in 2010, 2015 and 2017 (30 m/10 m), Globeland30 in 2000, 2010 and 2020 (30 m), global 30 m land cover classification with a fine classification system (GLC_FCS30D) in 2000 and 2020 (30 m) and ESA’s WorldCover in 2020 and 2021 (10 m)78–81. However, we chose GLC_FCS30D in our study mainly due to its high spatial resolution, suitable temporal resolution, fine classification system and more stable accuracy on a global scale compared to other 30-m global land-cover products82. The GLC_FCS30D product was produced by combining time series of Landsat imageries and high-quality training data from the Global Spatial Temporal Spectra Library on the Google Earth Engine cloud computing platform and presented at an approximately 30-m resolution80. The accuracy of GLC_FCS30D has been validated using numerous validation samples, with an overall accuracy of more than 81%83,84 (available from Zenodo: https://doi.org/10.5281/zenodo.8239305), and it has been extensively used for a variety of applications45,85. For mountain delineation, commonly used global datasets include the United Nations Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC) mountain inventory86 and the Global Mountain Biodiversity Assessment (GMBA)87,88 inventories (both standard and broad versions). After checking these mountain datasets, we found that the UNEP-WCMC dataset omits important mountain areas (e.g., southeastern Russia, central New Zealand, northern Norway) and overestimates some mountainous areas (e.g., northwestern China) (Supplementary Table 5). Likewise, the broad GMBA version incorporates not only mountainous terrain but also extends into adjacent landscapes88 (Supplementary Table 5). On the other hand, the standard GMBA version (v2.0 standard) (https://www.earthenv.org/mountains) was better suited and addressed the aforementioned issues, which we thus chose in our study. This dataset delineates mountains covering ~18.2% of the Earth’s land surface (Supplementary Fig. 1).

To combine these datasets, we first converted the mountain boundary polygons into a grid image of 30 m resolution using the polygon to raster tool of ArcGIS. We then reclassified the GLC_FCS30D product into six land cover types following Zhang et al.80: cropland, impervious surface, forest, grassland, shrubland and other land. In this dataset, cropland is defined as land used for crop cultivation, including rain-fed croplands, herbaceous cover, and tree/shrub-covered croplands (e.g., orchards, plantations)80; impervious surface refers to hardened surfaces composed of artificial materials, including constructed materials (e.g., cement, asphalt and bricks) and artificially compacted surfaces (e.g., paved grounds, rocks and building foundations)80; forest, shrubland, and grassland are defined as land cover types dominated by trees, shrubs, and herbaceous plants, respectively. Forests include various types (e.g., evergreen broadleaved forest, deciduous broadleaved forest, and mixed-leaf forest); shrublands include both evergreen and deciduous types80. Following ecological principles, vegetation structure, the Intergovernmental Panel on Climate Change guidelines, and the Food and Agriculture Organisation Global Ecological Zones, these three classes represent the primary vegetated land cover types in global mountain ecosystems89–91. Therefore, we selected forest, shrubland, and grassland to define different vegetated landscapes, with all other classes designated as non-vegetated landscapes. We calculated mountain vegetated landscapes (MVL) by calculating spatial overlays between these vegetated landscapes with standardised mountain boundaries (GMBA v2.0), and defining all overlapping pixels as MVL. All calculations involving the map area were performed in WGS84/Equal Earth Greenwich projection. Overall, we found a total area of approximation 18,681,548.8 km2 of MVL in 2000 (Supplementary Table 6).

Tracking global MVL loss
(1) Human expansions-driven MVL loss. To track MVL loss since 2000 due to the expansion of human activities, we collected multi-source thematic datasets that quantify possible human activities in mountain areas. These included the impervious surface dataset (30 m resolution) and cropland dataset (30 m resolution) from 2000 and 2020, both of which are subsets of the GLC_FCS30D, as well as global mining distribution data (polygons). For global mining distribution data, we combined two recent global mining datasets from Maus et al.22 (total 44,929 polygons, covering 101,583 km²) and Tang et al.23 (total 74,548 polygons, covering 66,000 km2). These two datasets provide spatially explicit estimates of the area directly used for surface mining on a global scale (including large-scale and artisanal and small-scale mining), and the polygons cover all ground features related to mining activities (e.g., open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure). They were produced using remote sensing analysis of high-resolution, publicly available satellite imagery in recent years (e.g., around 2020; images with ≤10 m resolution in Google Earth and Sentinel-2) (details in refs. 22,23). The overall accuracies of these two global mining datasets calculated from the validation points were 88.3% (for Maus et al.22) (version 2, https://doi.org/10.1594/PANGAEA.942325) and ~92% (for Tang et al.23) (version 2, https://zenodo.org/records/7894216).

We merged the two global mining datasets to compensate for the potential omission of global mining areas from the different datasets using union analysis in ArcGIS 10.8. After merging, a combined global mining dataset was obtained (i.e., 81,962 polygons, covering 120,413 km2) (Supplementary Fig. 9). We then converted all polygons into a grid image of 30 m resolution using the polygon to raster tool of ArcGIS. To avoid overestimation of MVL loss caused by pixel overlap between datasets, we used spatial overlay analysis to detect overlapping pixels of impervious surface, cropland and mining areas. If there are overlapping pixels, we assigned their attributes to mining areas, because the mining data holds better spatial resolution and accuracy.

We quantified global MVL loss driven by human expansion according to the following rules:

(i) Human settlement growth. We used the expansion of impervious surface to reflect the growth of human settlements92,93. To do so, we used a pixel-level change detection method to identify the pixels that were impervious surface in 2020, but not in 2000. The MVL loss driven by human settlements denotes the pixels of MVL in 2000 that were transformed into human settlements by 2020 (Fig. 1a).

(ii) Agriculture expansion. We used the expansion of cropland to reflect increases in agriculture. To do so, we also used the change detection method to identify pixels that were defined as cropland pixels in 2020 but not in 2000. The MVL loss driven by agriculture expansion denotes the pixels of MVL in 2000 that were transformed into agriculture by 2020 (Fig. 1a).

(iii) Mining. We used spatial overlay analysis to detect the intersection pixels between mining areas and MVL. The MVL loss driven by mining denotes that the pixels of MVL in 2000 that were transformed into mining surface by 2020 (Fig. 1a).

(2) Natural disasters-driven MVL loss. To track MVL loss due to natural disasters, including wildfires, floods, landslides and drought, we collected multi-source natural disasters datasets that occurred during 2000–2020. For wildfires, we used global annual burned area maps (GABAM) (30-m resolution) (available from https://doi.org/10.7910/DVN/3CTMKP), which were generated via an automated pipeline based on Google Earth Engine using all the available Landsat images (details in ref. 94), and define the spatial extent of wildfires that occur in a given year, but not previous years. We selected GABAM because it covers the appropriate time period and has low commission and omission errors, with high spatial resolution (30 m)94. We collected global flood data (2000–2018) from the Global Flood Database (GFD) (http://global-flood-database.cloudtostreet.info/), it was developed based on flood events reported by the Dartmouth Flood Observatory and 250 m resolution Terra and Aqua MODIS images95. We obtained global landslides data (13,437 points from 2001 to 2020) from the NASA Global Landslide Catalogue (NASA-GLC) (https://catalog.data.gov/dataset/global-landslide-catalog-export), which was developed with the goal of identifying rainfall-triggered landslide events, regardless of size, impacts or location96,97. We converted landslides to polygons using buffer analysis according to their records in GLC (from small to large, attributes 1–596). We set buffer distances to 1 km (small event), 5 km (medium event), 10 km (medium to large event including multiple sliding events over a region), 15 km (large to massive event including many landslides), 20 km (massive event or multiple large events), then all landside polygons were converted into a raster image with 30 m resolution using ArcGIS 10.8. For drought, we obtained global data on the temperature-vegetation dryness index (TVDI) with 1 km resolution (2000–2020) data from the National Earth System Science Data Centre of China (http://www.geodata.cn/). TVDI was produced using 1 km MODIS land surface temperature (i.e., MOD11A2) together with vegetation index products (i.e., MOD13A2) based on the Google Earth Engine platform98. We divided quantitative drought values based on give ranges of TVDI from 0 to 1 at intervals of 0.2 (i.e., extremely wet, wet, moderate, dry, and extremely dry)99. We defined areas with drought as those with dry and extremely dry conditions (i.e., 0.6<TVDI≤1.0), where the probability of vegetation mortality was highest99,100.

For areas affected by floods and drought, we resampled data to 30 m resolution using the nearest neighbour sampling to match other data. To avoid overestimated MVL loss caused by pixel overlap between datasets, we used spatial overlay analysis to detect overlapping pixels from areas burned, flooded, exposed to landslides or experiencing droughts. If there were overlapping pixels, we assigned overlapping pixels according to the priority of original spatial resolution (i.e., landslides > burned areas > floods > drought). If there were overlapping pixels between human expansion and natural disaster-driven MVL loss, we assigned overlapping pixels to the former.

Because some MVL can remain intact (or recovered) after long-term natural disasters, we used a strict Normalised Difference Vegetation Index (NDVI) threshold to obtain a net loss of MVL. NDVI, defined as the relationship operation between near-infrared (NIR) and red (R) bands of satellite imagery (i.e., NDVI=(NIR−R)/(NIR+R)), varies between −1 and 1, where higher values close to 1 indicate dense vegetation coverage, while values close to 0 suggest very little vegetation, and values less than 0 indicate no vegetation101,102. We collected all available global Landsat images (TM/ETM+/OLI, 30-m resolution) in 1999 (the year prior to our focus) and 2021 (the year after our focus) from the United States Geological Survey (USGS) (https://www.usgs.gov/), and used the Google Earth Engine platform and maximum value composites method to generate annual composite NDVI data (before and after our study period). To eliminate spectral response discrepancies among sensors (TM/ETM+/OLI), we applied relative radiometric normalisation to the TM, ETM+ and OLI imagery using the cross-sensor transformation coefficients proposed by Roy et al.103, ensuring the spatiotemporal consistency of NDVI.

We quantified MVL losses driven by four natural disasters: wildfires, floods, landslides, and droughts. To do so, we used a consistent spatial overlay and NDVI thresholding approach. We first delineated MVL in 2000 and intersected them with respective disaster footprints (burned/flooded/landslide/drought areas). If the NDVI at the end of the study period in a given pixel is less than or equal to 0.1, while it was greater than 0.1 at the start of the period, these pixels are labelled as MVL loss104,105 (Fig. 1a).

The MVL loss in separate ten world regions
To examine the spatial pattern of MVL in different geographical zones, we divided the world into ten separate regions by merging national administrative boundaries (i.e, Sub-Saharan Africa, Southeast Asia, Middle East, South Asia, Central Asia, East Asia, North America, Latin America, Europe & Russia, and Oceania) (Supplementary Fig. 2). We then quantified the MVL loss (including total MVL loss and MVL loss types, Fig. 2) and the contribution of drivers (Fig. 2) in these ten regions through spatial analysis (zonal statistics tool of ArcGIS) between global MVL loss layer and the regions (Supplementary Table 2).

The MVL loss within PAs and AHRTMS
To quantify MVL loss within protected areas (PAs) and areas with high richness of threatened mountain-occurring species (AHRTMS), we first mapped mountain PAs and AHRTMS using global PA distributions and the IUCN Red List of threatened species. We collected the global distribution of PAs from the WDPA (version 2023.11, https://www.protectedplanet.net/en) provided by the United Nations Environment Programme’s World Conservation Monitoring Centre. PAs are stratified into pre-2000 and post-2000 cohorts based on establishment year attributes within the geodatabase. We overlapped the distribution of mountains with PA designation to identify the mountain PAs. After excluding the point features and merging overlapped features, we obtained 52,144 global mountain PAs (a total of 57,363 polygon features), comprising an area of approximately 4,799,535.1 km2 (Supplementary Fig. 4a).

The AHRTMS were designated as important biodiversity habitats based on the number of mountain-occurring threatened species (mammals, amphibians, reptiles, birds and plants) they contain. Our AHRTMS was not completely consistent with the global database of IUCN’s key biodiversity areas106 because we only focus on mountain areas that represent areas of the highest richness of threatened mountain-occurring species. To identify AHRTMS, we used the IUCN Red List of threatened species, including distribution information of mammal, amphibian, reptile and plant species (version 6.3, https://www.iucnredlist.org, updated on December 2022), and for birds, the Birdlife International website (version 2022.02, http://datazone.birdlife.org/home). The Red List classifies threatened species as critically endangered (CR), endangered (EN), and vulnerable (VU). We extracted all threatened species (CR+EN+VU) that occurred in mountains, giving a total number of 12,562 species, including mammals (n=2019), amphibians (n=2991), reptiles (n=1576), birds (n=2140) and plants (n=3836). We weighted threatened categories according to ref. 107 (i.e., CR is categorised as 3, EN is categorised as 2, and VU is categorised as 1). We then generated 1×1 km2 grid cells with mountain boundaries, and identified the weighted threatened species in every grid to produce a map of threatened species richness. We normalised the threatened species richness map to the range of 0–100 (%) using the minimum-maximum normalisation method, with 100% having the highest threatened species richness and 0% having the lowest threatened species richness. We defined AHRTMS as those containing the top 20% of the cumulated area of threatened species richness107. By this definition, there was a total about 8,105,865.9 km2 of global mountains contained within AHRTMS (Supplementary Fig. 4b).

We distinguished PAs, AHRTMS-inside PAs and AHRTMS-outside PAs (Fig. 3a). Next, we used spatial overlay analysis of these three layers together with the layer of MVL loss to evaluate the spatial coincidence between MVL loss within PAs, AHRTMS-inside PAs and AHRTMS-outside PAs, including the magnitudes of loss, as well as its drivers (Fig. 3b, Supplementary Fig. 5 and Supplementary Table 4).

Accuracy evaluation and analysis
We adopted two strategies to assess the reliability of our findings. Firstly, although the impervious surface we used is defined as an artificial impervious surface and was used as a proxy for human settlement growth, there may be natural bare rock and soil that were mistakenly classified as artificial surfaces. To incorporate this, we randomly selected a total 5209 samples from impervious surfaces in 2000 and 2020 using a random function to assess the ratio of artificial and natural surfaces (i.e., natural bare rock/soil) in impervious surfaces. Secondly, in order to thoroughly verify the accuracy of our estimates of global MVL loss, we randomly selected a total 16,478 samples using a random function from global mountain areas. These stratified random samples were uniformly and randomly distributed across areas where MVL losses were due to human expansion (4138 samples) and natural disasters (3704 samples), and areas without MVL loss (including non-vegetated mountain areas) (8636 samples) (Supplementary Fig. 10). By selecting samples in non-MVL loss areas, we can ensure that there was no significant omission of MVL loss, and simultaneously assess potential land cover misclassifications.

All samples were tagged geographically with Keyhole Markup Language and placed into Google Earth Pro. We used all cloudless historical high-solution images (e.g., Early Bird-1 (0.8–3.0 m), IKONOS (3.2 m), QuickBird (0.6–2.4 m), GeoEye-1 (0.41–1.65 m), WorldView (0.25–1.84 m), Pleiades-1A (0.5–2.0 m), Pleiades-1B (0.5–2.0 m), SPOT-6 and SPOT-7 (1.5–6.0 m), RapidEye (5 m), Doves (3 m) etc.) stored in Google Earth Pro to assess the result reliability. When high-resolution images were lacking at the sample points, we used ESA Sentinel-2 (10 m) and Landsat TM/ETM+/OLI (30 m) imageries to determine result reliability.

To assess the reliability of impervious surface (year of 2000 and 2020) and the ratio of artificial and natural surface in impervious surface, we labelled samples as correct if they were impervious surface in high-resolution satellite imagery; otherwise, they were labelled as incorrect if they were other land types (Supplementary Data 1). After excluding uncertain samples (only accounting for 2.0% of total samples), we found that the accuracy (correct samples/ (total samples-uncertain samples)) of impervious surfaces reaches about 92.4%, with the vast majority being artificial surfaces (85.9%), while the proportion of natural surface-bare rock/soil is relatively small, indicating that impervious surfaces are reliable as a proxy of human settlement growth.

To assess the reliability of human expansion-driven MVL loss (e.g., human settlement growth, agriculture expansion and mining), we labelled samples as correct if they were MVL surfaces in 2000 (e.g., forest, shrubland or grassland), but not in 2020 (i.e., converted to impervious surface, cropland or mining surface); otherwise, they were labelled as incorrect (Supplementary Data 2). To assess the reliability of natural disaster-driven MVL loss (e.g., wildfires, floods, landslides and droughts), we labelled samples as correct if they were MVL surfaces in 2000, but not in 2020 (i.e., converted to non-artificial surfaces without vegetation or near non-vegetation); otherwise, they were labelled as incorrect (Supplementary Data 3). To assess the reliability of non-MVL loss, we labelled non-MVL loss samples as correct if they were non-MVL surfaces in 2000 and non-MVL surfaces/MVL surfaces in 2020, or if they were MVL surfaces in both 2000 and 2020; otherwise, they were labelled as incorrect results (Supplementary Data 4).

We used a team with specialised knowledge and training in standard procedures and risk assessment to interpret all the above samples. When samples could not be determined during visual interpretation, these samples were labelled as uncertain.

After excluding uncertain samples (only accounting for 1.4% of total samples), we calculated the overall accuracy (OA=correct samples/(total random samples-uncertain samples)) of global mapping, as well as the producer’s accuracy (PA=correct samples of MVL loss/(correct samples of MVL loss+incorrect samples of non-MVL loss)) and user’s accuracy (UA=correct samples of MVL loss/(total random samples of MVL loss-uncertain samples)) of MVL loss108. We estimated these accuracies at different scales, including global scale, continent scale (i.e., separate ten world regions) and biodiversity hotspot scale (i.e., PAs and AHRTMS outside of PAs). We found that the overall accuracy of global mapping was 88.2% (Supplementary Table 1), and the producer’s accuracy and user’s accuracy of MVL loss, respectively, reached 90.7% and 84.0% (Supplementary Table 1).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "My comments for the authors are provided in an uploaded document.", "reviewer_2": "Thank you for putting significant amount of work in producing this manuscript.\nThe manuscript, in general, is interesting, well written and can be a very good reference for readers having an interest in\ndynamic aspects of the mountains in general and mountain ecology and biodiversity conservation in particular. The main\nstrengths of the manuscript are its global scale coverage, interesting research topic, and well written manuscript. There are,\nhowever, some problems, which are also highlighted in the manuscript as its limitations. My main concerns are:\nWhat are the criteria the study took onboard to classify mountain vegetations into forest, shrubland and grassland?\nHow the study considered the seasonal variability of the grasslands?\nIs there any reason to overemphasize human-wildlife conflict in discussion?\nThere are some feedback across the manuscript in sticky notes for authors to consider.", "reviewer_3": "The authors have identified a phenomenon that has yet to be consistently quantified at the global scale, the loss of mountain\nvegetation due to land use and natural disasters, with most loss attributed to agricultural expansion. Particularly interesting\nis that a significant portion of this loss occurred within protected areas. Given how important some mountains are for\nbiodiversity, particularly tropical mountains, as well as the importance of mountains as climate refugia, the results should be\nof interest to a broad audience of ecologists, policy makers, and conservationists. The authors have done considerable work\nfor this analysis and present compelling findings but I do have reservations about some aspects of their approach. (see\nattached doc file for figures associated with my concerns).\nMajor Comments\n• One issue stems from the reliance on one dataset, the UNEP-WCMC mountains database, to delineate mountains. This is\na key decision as it determines the area in which all other datasets are summarized. While the dataset they use is well-cited\nand apparently widely used, it appears to underestimate mountainous areas in key locations that may affect the results of the\nanalysis. Examples of this include tabletop mountains in the Pantepui forests & shrublands, Guianan Highlands moist\nforests, and Guianan piedmont moist forests ecoregions as well as the Borneo montane rain forests ecoregion. Tropical\nmountains can be quite species rich and are frequently under high human pressure. Any omission of them could\nunderestimate MVL loss and its impact. I would appreciate it if the authors could explain this issue and determine whether it\nmay affect their study outcomes. If so, they should consider summarizing land use change within the GMBA database as\nwell (Snethlage et al. 2022). The GMBA has a standard version as well as an extended version that includes lower elevation\nskirts of mountain ranges to better correspond with the UNEP-WCMC database.\n• There are some other issues associated with the UNEP-WCMC database that I should mention. It appears that some areas\nare classified as mountainous even though they are quite flat (e.g. the Qaidam Basin Semi-Desert on the Tibetan Plateau).\nThese are not areas that have mountainous characteristics that are thought to contribute to biodiversity (e.g. substantial\nmicroclimate and habitat heterogeneity over short distances) but are nonetheless included in the analysis. If these places\ntend to be in high elevation, dry, continental locations, they may also contribute to an overestimation of the impacts of\ndrought on mountain vegetation.\n• It appears that canyons are classified as mountains in the UNEP-WCMC database and GMBA database (e.g. the Grand\nCanyon in the southwestern U.S). While canyons certainly can have substantial topographic heterogeneity, they are formed\nby different processes and are usually not, to my knowledge, included in the literature on mountain biodiversity. This may not\nhave any bearing on the conclusions of the study but may be worth mentioning.\n• Another question I have relates to Figure 1, panel a. in the main body of the manuscript. Human settlement growth is\nshown as contributing only 4.8% to the loss of MVL. On the map, however, it appears as abundant, if not more abundant,\nthan agricultural expansion, which is shown as contributing 88.4% to the loss of MVL. Is this a resampling or scale issue? Or\nam I misinterpreting the legend? Please explain.\n• How do the authors classify forestry activities that degrade and remove natural tree cover, sometimes replacing it with\nplantation tree cover? Some of the areas I’m familiar with in Borneo show MVL loss associated with human settlement\ngrowth and agricultural expansion but one of the major drivers of forest loss in the mountains of Borneo is timber production\nand expansion of plantations (e.g. oil palm). Plantation forests could be considered an aspect of agricultural expansion but I\ndo not see them explicitly mentioned in the manuscript. Tree cover associated with orchards is the most relevant mention I\nsee in Supplementary Table 5.\n• Related to this are previous efforts to identify drivers of forest loss globally (Curtis et al. 2018). While the study in Curtis et\nal. (2018) was not specifically focused on mountain vegetation loss, it does, in many cases, overlap in forested mountain\necoregions. In some places where it does overlap it seems to suggest different drivers of loss than the current study. For\nexample, Curtis et al. (2018) indicate forestry as a primary driver of forest loss in the mountains of northern Borneo while this\nstudy suggests that it is human settlement growth or agricultural expansion (the colors are difficult to differentiate in Figure 1\nin the main manuscript). Similar patterns can be seen in the Appalachian Mountains where forest loss is indicated by Curtis\net al. (2018) as driven by forestry while this study indicates a mix of human settlement growth and mining. In western Europe,\nagain Curtis et al. (2018) indicate forest loss is driven by timber while this study attributes it to human settlement growth or\nagricultural expansion. This is a little unexpected in places like northern Spain where, especially rural areas, they are\nexperiencing substantial population declines (González-Leonardo et al. 2023).\nI see that the authors mention logging at the end of the manuscript (line 518) but given the differences with Curtis et al.\n(2018), it would be good to explain in more detail. Potential sources of data include planting year of plantations (Du et al.\n2022) and the global map of oil palm plantations (Descals et al. 2020).\n• After checking the GLC-FCS30 2020 dataset in ArcGIS, it appears common that rocky mountainous areas are classified as\nimpervious surfaces (class number 190). While this may technically be correct, it could be problematic for the current study if\nsuch areas are attributed to human settlement. If, for example, landslides that are not represented in the landslide database\nlead to vegetation loss, such losses may be attributed to human settlement expansion if the newly exposed areas are\nclassified as impervious surface. Floods that wash away vegetation and redistribute rocky floodplain material may be\nanother way this type of error could occur. Please explain whether this is an issue or not for your analysis and whether it\nwould impact the study’s conclusions. The high accuracy of the validation suggests this may not be an issue but reporting on\ncommission errors for this and other classes would alleviate any concerns.\nA couple of examples in northern Spain can be seen at these coordinates 0.7698716°W 42.8643229°N and 0.0171613°W\n42.6763235°N.\n• I suggest that the authors add a figure to the main manuscript that shows zoomed in examples of different types of MVL\nloss in critical areas (e.g. protected areas and/or IBHs). This would help make the authors’ arguments more compelling and\nwould emphasize the value of conducting such a study using relatively high-resolution (30m) land use data.\nMinor Comments (referring to the docx file)\nLine 236 - I think conventionally, 500m would be considered moderate rather than fine scale. In fact, MODIS is known as the\nmoderate resolution imaging spectroradiometer.\nLine 263 - wildly should be widely\nLine 368 – Was any effort made to standardize reflectance across sensors to account for different sensitivities of newer\nsensors?\nReferences\nCurtis, P.G., Slay, C.M., Harris, N.L., Tyukavina, A. and Hansen, M.C., 2018. Classifying drivers of global forest loss.\nScience, 361(6407), pp.1108-1111.\nDescals, A., Wich, S., Meijaard, E., Gaveau, D.L., Peedell, S. and Szantoi, Z., 2020. High-resolution global map of\nsmallholder and industrial closed-canopy oil palm plantations. Earth System Science Data Discussions, 2020, pp.1-22.\nDu, Z., Yu, L., Yang, J., Xu, Y., Chen, B., Peng, S., Zhang, T., Fu, H., Harris, N. and Gong, P., 2022. A global map of planting\nyears of plantations. Scientific data, 9(1), p.141.\nGonzález‐Leonardo, M., Newsham, N. and Rowe, F., 2023. Understanding population decline trajectories in Spain using\nsequence analysis. Geographical Analysis, 55(4), pp.495-516.\nSnethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N.G., Körner, Ch., Jetz, W., Fischer, M. & Urbach, D. 2022.\nA hierarchical inventory of the world’s mountains for global comparative mountain science. Nature Scientific Data.\nhttps://doi.org/10.1038/s41597-022-01256-y."}, "author_response": "Global loss of mountain vegetated landscapes and its impact on\nbiodiversity conservation\n# NCOMMS-25-15367-T Response to Reviewers\nDear reviewers,\nWe greatly appreciate your valuable comments and suggestions, which greatly helped\nus to improve this manuscript.\nThroughout, we have carefully addressed all of the comments we received (see below).\nMost importantly, as requested by Reviewer #3, we have introduced another global\ndatabase (Global Mountain Biodiversity Assessment (GMBA)). We worked extensively\nwith this database over the past months, and reconducted all analyses, including\nsensitivity analyses and accuracy evaluation. Indeed, the standard version of this dataset\nappeared the most accurate, and so we revised the main results from our manuscript\naccordingly. While the overall qualitative nature of our results did not fundamentally\nchange, the use of the new dataset allowed us to more accurately quantitatively estimate\nan number of important analyses.\nPlease see our point-to-point responses below, as well as the tracked changes versions\nof the manuscript and supplementary materials for a full exposition of the changes we\nmade. We hope that this revised version is now suitable for publication consideration,\nbut remain will to make further changes that may be needed.\nOur responses (blue), our revision (purple).\nYour sincerely,\nAll authors\nReviewer #1 (Remarks to the Author):\nHere are my comments for the authors to consider (with line numbers indicated).\n200–208: Near the start of this text, you note that ~35% of MVL loss happened within\nPAs. Then, in the last sentence of this text, you suggest that a focus on unprotected IBHs\nwould be a good idea. But you do not complete the logic of the first part of the paragraph.\nShould PAs also receive better, genuine protection (perhaps the application of\nunenforced existing legal protection)?\nResponse: Thank you very much for your valuable comment. We have revised the\noriginal suggestion to emphasize the importance of strengthening legal enforcement\nwithin existing PAs. Regarding our finding that 12.6% of MVL loss occurred within\nPAs, and the suggestion that this reflects enforcement deficiencies, we have explicitly\naddressed the need to strengthen legal enforcement in PAs in the Discussion.\nAdded some sentences in Discussion part:\nTherefore, it is crucial to strengthen legal enforcement in existing PAs (e.g. through\ncommunity co-management, \"sky-ground\" monitoring systems, judicially enforced\nprotection orders and transboundary governance)74,75. Furthermore, given the high\nbiodiversity and concentration of threatened species within AHRTMS, incorporating\nhigh-risk AHRTMS-outside of PAs into formal PA networks may help to mitigate\nbiodiversity loss in these vulnerable regions.\nAdded References:\n74. Sharkey, W., Milner-Gulland, E. J., Sinovas, P. & Keane, A. A framework for\nunderstanding the contributions of local residents to protected area law\nenforcement. Oryx 58, 746-758 (2024).\n75. Chen, X. et al. Establishing a protected area network in Xinlong with other effective\narea-based conservation measures. Conserv. Biol. 38, e14297 (2024).\n229–231: Again, this ignores your finding that ~35% of MVL loss happened within PAs.\nMerely designating some more IBH areas as PAs may not provide the protection your\nstatement implies here. Consider adding something about this here.\nResponse: Thank you very much for your valuable comment. We found that 12.6% of\nMVL loss occurred PAs, while as much as 43.0% occurred in AHRTMS-outside of\nPAs. This demonstrates that the current PA system has protective efficacy in mitigating\nMVL loss. The disproportionately high MVL loss of AHRTMS-outside of PAs is likely\ndue to the absence of legally binding development prohibitions and insufficient\nsystematic monitoring and rapid-response protocols. A possible solution is to\nincorporate high-risk AHRTMS-outside of PAs into the formal PA network, thereby\ngranting them legal status and protection. This may help curb vegetation loss trends in\nthese vulnerable regions, reduce biodiversity decline in threatened habitats, and fill the\nlegal protection gap in key ecological areas. We have revised the Discussion\naccordingly to reflect these important insights and recommendations.\nAdded sentence in the Discussion:\nFurthermore, given the high biodiversity and concentration of threatened species within\nAHRTMS, incorporating high-risk AHRTMS-outside of PAs into formal PA networks\nmay help to mitigate biodiversity loss in these vulnerable regions.\n261: 30 arc-seconds only represents ~1 km (actually 928 m) at the equator. It can be\nsignificantly less at higher latitudes. You should say this.\nResponse: Thank you for your valuable suggestion. We have replaced the global\nmountain dataset with the standard GMBA inventory. The vector format of the GMBA\neffectively eliminates the problem indicated here that was associated with the previous\nUNEP-WCMC dataset.\n271: I think you need to be clearer what “impervious surfaces” include. Does this\ninclude impermeable rock cover, towns and villages, for example? Later, an expansion\nof this category is interpreted as an expansion of urban settlement, so this needs to be\nclear here.\nResponse: We appreciate the importance of carefully defining terminology. Impervious\nsurface characterized by anthropogenic and hydrological impermeability is closely\nassociated with urban expansion and human settlement activities, serving as a key\nindicator of economic development and human activity intensity. In direct response to\nyour suggestion, we have significantly clarified the definition of impervious surface in\nthe Methods.\nSentence added to the Methods：\nimpervious surface refers to hardened surfaces composed of artificial materials,\nincluding constructed materials (e.g. cement, asphalt and bricks) and artificially\ncompacted surfaces (e.g. paved grounds, rocks and building foundations)80.\nAdded Reference:\n80. Zhang, X. et al. GLC_FCS30: global land-cover product with fine classification\nsystem at 30m using time-series Landsat imagery. Earth Syst. Sci. Data 13, 2753-\n2776 (2021).\n311–326: You do not mention how transitions to these categories from other ones was\nincluded in your calculations. Do you calculate a net loss that you calculate, taking\naccount of both losses and gains?\nResponse: Thank you for your thoughtful and rigorous review of our methodology. Our\nstudy quantified only the net increase in MVL loss driven by human expansion. We did\nnot account for potential reverse transitions (e.g., the restoration of existing human-\nused land to vegetation, the conversion of cropland to forest and grassland.). This aligns\nwith our core objective of identifying and assessing the magnitude and spatial patterns\nof human expansion-driven MVL loss, although we acknowledge the importance of\nconsidering reverse gains to more comprehensively understand the dynamic processes\nshaping these critical landscapes. We have explicitly acknowledged this limitation in\nthe Uncertainties and limitations part of the revised manuscript.\nSentences added in Uncertainties and limitations:\nWe also acknowledge that our study focuses solely on MVL loss driven by human\nexpansions and natural disasters, and neglects potential MVL gains in other areas (e.g.\nthe transition from human-used land to vegetated landscapes). Future work should\nconsider these gains to comprehensively understand dynamic processes of vegetated\nlandscapes.\n382–387: The last sentence is enough on its own. The first sentence is long and more\ncomplex than it needs to be. I recommend removing it. Base your explanation on the\nfinal sentence, but rephrase it. MVL loss was recognized for any area which had\nvegetation at the start but ended the study period with an NDVI of 0.1 or below.\nResponse: Thank you for your valuable suggestions. We have revised the manuscript\naccordingly.\n390–413: This section is very repetitive and should be rewritten in a much more concise\nway.\nResponse: We have made the corresponding revisions to the manuscript as advised.\nImproved sentences in the Methods:\nWe quantified MVL losses driven by four natural disasters: wildfires, floods, landslides,\nand droughts. To do so, we used a consistent spatial overlay and NDVI thresholding\napproach. We first delineated MVL in 2000 and intersected them with respective\ndisaster footprints (burned/flooded/landslide/drought areas). If the NDVI at the end of\nthe study period in a given pixel is less than or equal to 0.1, while it was greater than\n0.1 at the start of the period, these pixels are labelled as MVL loss104,105(Fig.1a).\nAdded Reference:\n104. Yan, J., Zhang, G., Ling, H. & Han, F. Comparison of time-integrated NDVI and\nannual maximum NDVI for assessing grassland dynamics. Ecol. Indic. 136,\n108611 (2022).\n105. Li, L. et al. Increasing sensitivity of alpine grasslands to climate variability along\nan elevational gradient on the Qinghai-Tibet Plateau. Sci. Total Environ. 678, 21-\n29 (2019).\n424–433: Presumably, if a PA partly includes mountains, not all of the PA was counted,\nonly the part which was mountainous. You should make this clear. Somewhere near\nhere, you should also explain that you differentiated between PAs designated before\n2000 (and, therefore, “protected” throughout the study period) and those designated\nlater (which were “unprotected” for some of the study period). Where did you get this\ninformation from?\nResponse: We have explicitly clarified that our statistical analysis is strictly confined\nto PAs located within mountainous areas. We have also now explicitly stated that the\nclassification of PAs before and after 2000 is based on their establishment year. We\nhave added relevant explanations in the Methods.\nImproved sentences in the Methods:\nPAs are stratified into pre-2000 and post-2000 cohorts based on establishment year\nattributes within the geodatabase. We overlapped the distribution of mountains with PA\ndesignations to identify the mountain PAs. After excluding the point features and\nmerging overlapped features, we obtained 52,144 global mountain PAs (a total of\n57,363 polygon features), comprising an area of approximately 4,799,535.1km2\n(Supplementary Fig.4a).\n460–470: I understand from this that you only assessed pixels from the end of your\nstudy period. Did you assess the reliability of pixels at the start of the period? I would\nexpect to see an evaluation of the original classification of MVL at the start. If your\napproach misclassified many pixels at that stage, it would impact the findings. This is\na particularly important comment, and is fundamental to the reliability of your study.\nResponse: Thank you for this important observation regarding the potential impact of\ninitial-year (2000) pixel classification reliability on our conclusions. We replaced the\noriginal land cover dataset with the GLC_FCS30D product, which has been\ndemonstrated to perform better as a classification system and ability to detect change.\nThis dataset is widely used in global land use change, environmental monitoring, and\nsustainable development research, and offers a more reliable land cover identification.\nTo calculate the reliability of impervious surface classification, we conducted a\nstratified random sampling validation to calculate the reliability of impervious surface\nclassification. We also assessed the accuracy of non-MVL loss areas, which we could\nalso use to verify the accuracy and potential misclassification in the original land cover\ndata. Details can be found in the Accuracy evaluation and analysis section. Furthermore,\nwe adopted a stringent logic for identifying MVL loss: only pixels clearly classified as\nMVL in the initial year (2000) were included in the subsequent loss analysis. This\napproach inherently controls for initial-year classification reliability by excluding\nambiguous cases.\n524–525: I am not sure what you mean by non-human expansion and non-natural\ndisasters. You should explain this better using different terms.\nThere are uncertainties and limitations linked to the errors in your classifications. It\nseems you consider 10% as “low” but it is, on a global scale, rather important. You\nshould include some consideration of this in this section of the manuscript. How\nimportant are these errors and how does this level of (un)reliability limit your\ninterpretations of the results you obtained?\nResponse: We sincerely appreciate your valuable suggestions. We have revised the\nmanuscript by replacing \"non-human expansion\" and \"non-natural disasters\"\nexpressions with the more precise term \"other areas\". With regard to classification\nerrors, we fully acknowledge their critical importance in shaping the reliability of our\nfindings. In our revision, we replaced the land cover data with the more detailed\nGLC_FCS30D dataset and adopted the standard version of the GMBA mountain\ninventory, which offers better spatial coverage for delineating mountain boundaries.\nWe employed two strategies to ensure the reliability of our results: (1) We validated the\nclassification of impervious surface to confirm its validity as a proxy of human\nsettlement growth; (2) We comprehensively assessed the accuracy of MVL loss\nincorporating producer's accuracy (PA) and user's accuracy (UA) alongside overall\naccuracy (OA). This assessment indicated that all accuracy metrics fall within a\nreasonable and relatively high range at the global scale. In particular, an overall\naccuracy of ~88.2% of the global mapping, with a high producer's accuracy (90.7%)\nand user's accuracy (84.0%) of MVL loss. Further methodological details can be found\nin the Accuracy evaluation and analysis section.\nI have a list of suggestions for minor edits for the manuscript (with line numbers\nindicated).\n68: I would suggest the more directly accessible “~ 390000 km2” instead of “~3.9 × 105\nkm2”. It would also allow you to choose a potentially more appropriate rounding for\nyour approximate value. I suggest the same approach in other places where you do this\nin the manuscript.\nResponse: done.\n87: “global MVL loss” (to avoid the misinterpretation that these are proportions of all\nMVL.\nResponse: done\n96–97: I would suggest “By contrast” instead of “On the contrary”.\nResponse: done.\n116–117: I am not convinced you need to include this detail of the methods here (they\nare explained clearly in the methods). You do not include equivalent details of methods\nin other captions.\nResponse: Agreed. We have deleted this sentence.\n118–120: You say the same thing twice here. Reconsider this sentence.\nResponse: We have revised the sentence.\nImproved sentences in Table 1:\nMVL/sub-MVL loss (% of total loss from 2000-2020) indicates the proportion of\nMVL/sub-MVL loss in each region.\n136–137: I think you should mention in the caption that the first 50% in the graphics is\nomitted to show better detail of the more minor drivers. Visually, the proportions do not\nmatch the actual values and you highlight this to the reader. Alternatively, you could\ninclude the entire 100% visually, but that would reduce the clarity for the minor drivers.\n167: Again, I think you should mention in the caption that the first 50% in the graphics\nis omitted to show better detail of the more minor drivers.\nResponse: We have re-visualized the figure according to your comment.\n143: replace “was” with “were”\nResponse: done\n143: 40.1 thousand km2 is a strange way to represent this. 40,100 km2 would be better.\nDo the same for the other instance in this paragraph.\nResponse: done.\n163: replace “can” with “could”\nResponse: done.\nFig. 4a: Are the areas in the diagram to the right proportional or illustrative and not to\nscale? Potentially, you could include the values in the diagram with the labels. The text\nin the overlap square should be adjusted to fit inside the square.\nResponse: Our original figure was intended as illustrative example and was not drawn\nto scale. In response to this comment, we have revised and redrawn the figure to\nimprove its clarity and visual quality, and have adjusted the overlapping labels to fit\ninside the square.\n185: You should define “KBAs”.\nResponse: Thank you, we have now defined it.\n186–189: “The mine Mine tailings (the residue remaining after mineral processing) and\ninfrastructures related to the mining sites from these can seriously threaten natural\nenvironments (e.g. polluting downstream water resources) and their biodiversity\nwithin50,51.\nResponse: done\n189: “.Further” needs a space.\nResponse: done\n194: “This is likely because some disaster areas damaged areas in these ways can either\nnaturally recover or…”\nResponse: done\n198: replace “by leading to” with “from”\nResponse: done.\n219: Are omission errors low? They seem to be about 10%. Perhaps “acceptable” would\nbe better than “low”.\nResponse: done.\n223–225: “The spatial pattern… modelling.” I do not understand what this sentence\ntrying to communicate. Reconsider this sentence.\nResponse: We have revised and refined the sentence to improve its clarity and accuracy.\n263–: “WCMC-UNEP” or “UNEP-WCMC”? You should be consistent throughout the\nmanuscript.\nResponse: We have now standardized the term.\n266: replace “wildly” with “widely”\nResponse: done\n241: “NASA-GLC” or “GLC”? You should be consistent.\nResponse: done\n349: “often causing fatalities”. This suggests that fatalities only occur in the largest\nlandslides. I suspect this is not true, so I would recommend removing these words. They\nare not important in this context.\nResponse: done.\n367: replace “assign” with “assigned”\nResponse: done.\n436: I suspect you do not mean “evenly distributed” because you refer to these as\nrandom points elsewhere. Clarify what you did here.\nResponse: We appreciate your insightful comment. We intended to indicate that the\nsamples were uniformly and randomly distributed. This has been clarified in the revised\nmanuscript.\n479 & 488: “assess the reliability of…”\nResponse: done.\n503–504: You should explain what “producer’s accuracy” means.\nResponse: We have added an explanation of producer’s accuracy in the revised\nmanuscript.\nSupplementary materials\nConsider a glossary of abbreviations at the start (or else, define abbreviations in each\ncaption for a figure or table). The reader should not need to find the definition in the\nmain paper.\nResponse: Thank you for this suggestion. We have added the definition of abbreviations\nin each caption for a figure or table.\nFig. 1: Use units in the normal way, such as “m” and “km2”. You are mixing different\nways of referring to units (e.g., “km”, “meters” and “sq.km”).\nResponse: We sincerely appreciate your constructive suggestion. We have redrawn the\nfigure to improve its clarity.\nFig. 8: The text labels should be modified to match the spaces they occupy, for a better\nvisual design.\nResponse: We have revised the figure accordingly based on your comment.\nFig. 10: This does not work very well. The density of the overlapping points means that\nmost of them cannot be seen because only the points on top are shown. Consider a\nsolution to this problem.\nResponse: We have revised the figure accordingly based on your comment.\nTable 1: These are “estimated accuracies”. Again, the term “Producer’s accuracy” needs\nto be explained.\nResponse: We have defined the term it as recommended.\nTable 3: Replace “Drives” with “Drivers”. You should explain that the percentages in\nthe human expansions and natural disasters columns are proportions of the total MVL\nloss for each MVL type in each region.\nResponse: Done, we have now provided this explanation.\nTable 4: The title is on the wrong page. Replace “Drives” with “Drivers”. You should\nexplain that the percentages are proportions of the total MVL loss not total MVL.\nResponse: Done.\nReviewer #2 (Remarks to the Author):\nThank you for putting significant amount of work in producing this manuscript.\nThe manuscript, in general, is interesting, well written and can be a very good reference\nfor readers having an interest in dynamic aspects of the mountains in general and\nmountain ecology and biodiversity conservation in particular. The main strengths of the\nmanuscript are its global scale coverage, interesting research topic, and well written\nmanuscript. There are, however, some problems, which are also highlighted in the\nmanuscript as its limitations. My main concerns are:\nWhat are the criteria the study took onboard to classify mountain vegetations into forest,\nshrubland and grassland?\nResponse: We appreciate the request to provide a clearer description of mountain\nvegetation types. Forests, shrublands, and grasslands are the dominant vegetation types\nwithin mountainous ecosystems, typically showing a characteristic vertical zonation\nthat reflect plant community responses to critical environmental filters (temperature,\nmoisture, soil). According to the Intergovernmental Panel on Climate Change (IPCC),\nthese primary mountain vegetation types are also among the most vulnerable to climate\nchange. These three categories also define mountain vegetation types in the Food and\nAgriculture Organization Global Ecological Zones (FAOGEZ) scheme. In our study,\nwe used the GLC_FCS30D land cover dataset, which offers a 30-m spatial resolution\nand a detailed classification system that accurately captures the majority of vegetation\ntypes in global mountain areas. In this dataset, vegetation communities are defined by\nthe dominance of trees (forests), shrubs (shrublands), or herbaceous vegetation\n(grasslands). This widely used dataset is scientifically rigorous and ecologically\ngrounded, and operationalizes through a high-resolution, rigorously classified dataset.\nThis provides a scientifically defensible basis for analyzing differential drivers of MVL\nloss (e.g. agriculture expansion, wildfires, droughts). We have expanded this\njustification in the manuscript in Methods.\nAdded some sentences in Methods part:\nforest, shrubland, and grassland are defined as land cover types dominated by trees,\nshrubs, and herbaceous plants, respectively. Forests include various types (e.g.\nevergreen broadleaved forest, deciduous broadleaved forest, and mixed-leaf forest);\nshrublands include both evergreen and deciduous types80. Following ecological\nprinciples, vegetation structure, the Intergovernmental Panel on Climate Change\nguidelines, and the Food and Agriculture Organization Global Ecological Zones,\nthese three classes represent the primary vegetated land cover types in global\nmountain ecosystems89-91.\nAdded References:\n80. Zhang, X. et al. GLC_FCS30: global land-cover product with fine classification\nsystem at 30m using time-series Landsat imagery. Earth Syst. Sci. Data 13, 2753-\n2776 (2021).\n89. Hock, R. et al. High Mountain Areas. In: IPCC Special Report on the Ocean and\nCryosphere in a Changing Climate. (eds Pörtner, H.-O. et al.) Ch. 2, 131-202\n(IPCC, 2019).\n90. Helbig, M. et al. Regional atmospheric cooling and wetting effect of permafrost\nthaw-induced boreal forest loss. Glob. Change Biol. 22, 4048-4066 (2016).\n91. Bian, J. H. et al. Global high-resolution mountain green cover index mapping based\non Landsat images and Google Earth Engine. ISPRS J. Photogramm. Remote Sens.\n162, 63-76 (2020).\nHow the study considered the seasonal variability of the grasslands?\nResponse: In this study, we used the GLC_FCS30D dataset, which has been widely\nadopted due to its fine classification system and high accuracy. This dataset uses long-\ntime series of Landsat imagery processed via continuous change detection and\nclassification algorithms to extract training samples from stable regions. A random\nforest classifier integrates multi-temporal spectral, textural, and topographic features,\nalong with annual synthetic indices (e.g. NDVI, NDWI, NBR), to generate an annual\ncomposite product. Moreover, a spatiotemporal consistency optimization algorithm is\nimplemented to minimize inter-annual fluctuations caused by seasonal variability,\nensuring stable year-to-year change detection. Importantly, both the 2000 and 2020 land\ncover layers used in our analysis are derived from the same data system, ensuring\nstrictly consistent classification criteria and technical protocols. This harmonization\nprovides a reliable basis for quantifying land cover change. However, we acknowledge\nthat limitations may exist in regions with ecologically unstable areas (e.g., climatic\ntransition areas, complex landscapes). We addressed this potential constraint in the\nUncertainties and limitations part of the revised manuscript. We agree that future\nstudies could further incorporate seasonal variability of grasslands to enhance the\necological realism and temporal precision.\nSentences added in the Uncertainties and limitations part:\nIn addition, we did not capture seasonal variability of grasslands, which may exist\nuncertainties and limitations in structurally unstable regions (e.g. climate transition\nzones or complex landscapes). Future work should incorporate seasonal grassland\ndynamics to enhance ecological validity and temporal sensitivity.\nIs there any reason to overemphasize human-wildlife conflict in discussion?\nResponse: Thank you for this consideration. Indeed, our original manuscript may have\noveremphasized human-wildlife conflict. We found that 43.0% of MVL loss occurred\nin AHRTMS-outside of PAs, which are core areas for threatened wildlife species, and\nthis loss was predominantly driven by agriculture expansion and human settlement,\nwhich could ultimately pose a threat to biodiversity. We have now emphasized multiple\nmechanisms by which human activity intensity might be mitigated to reduce\nbiodiversity loss in these vulnerable regions.\nRevised sentences in the Discussion:\nMountain PAs and AHRTMS can play important roles in conserving mountain\nbiodiversity when human intrusion is minimized66-68. However, we found that ~ 55.7%\nof global MVL loss is still located within these areas, comprising 12.6% within PAs and\n43.0% in AHRTMS-outside of PAs. This was primarily as a result of human land use\nincreasing the human-threatened species conflict risks and diminishing a number of\nimportant ecosystem functions and services (e.g. landscape connectivity, habitat quality\nand carbon storage)69-73. Therefore, it is crucial to strengthen legal enforcement in\nexisting PAs (e.g. through community co-management, \"sky-ground\" monitoring\nsystems, judicially enforced protection orders and transboundary governance)74,75.\nFurthermore, given the high biodiversity and concentration of threatened species within\nAHRTMS, incorporating high-risk AHRTMS-outside of PAs into formal PA networks\nmay help to mitigate biodiversity loss in these vulnerable regions.\nAdded References:\n66. Barlow, J. et al. Anthropogenic disturbance in tropical forests can double\nbiodiversity loss from deforestation. Nature 535, 144-147 (2016).\n67. Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact\nlandscapes. Nature 547, 441-444 (2017).\n68. Phalan, B., Onial, M., Balmford, A. & Green, R. E. Reconciling Food Production\nand Biodiversity Conservation: Land Sharing and Land Sparing Compared.\nScience 333, 1289-1291 (2011).\n69. Brennan, A. et al. Functional connectivity of the world's protected areas. Science\n376, 1101-1104 (2022).\n70. Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts\non tropical nature. Trends Ecol. Evol. 29, 107-116 (2014).\n71. Ren, Q. et al. Impacts of urban expansion on natural habitats in global drylands.\nNat. Sustain. 5, 869-878 (2022).\n72. Kong, L. Q. et al. Natural capital investments in China undermined by reclamation\nfor cropland. Nat. Ecol. Evol. 7, 1771-1777 (2023).\n73. Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, 8574 (2020).\n74. Sharkey, W., Milner-Gulland, E. J., Sinovas, P. & Keane, A. A framework for\nunderstanding the contributions of local residents to protected area law\nenforcement. Oryx 58, 746-758 (2024).\n75. Chen, X. et al. Establishing a protected area network in Xinlong with other effective\narea-based conservation measures. Conserv. Biol. 38, e14297 (2024).\nThere are some feedback across the manuscript in sticky notes for authors to consider.\nOther comments (marked in PDF)\nResponse: Thank you for your suggestions. We addressed all of these comments\ndirectly in track change version.\nReviewer #3 (Remarks to the Author):\nThe authors have identified a phenomenon that has yet to be consistently quantified at\nthe global scale, the loss of mountain vegetation due to land use and natural disasters,\nwith most loss attributed to agricultural expansion. Particularly interesting is that a\nsignificant portion of this loss occurred within protected areas. Given how important\nsome mountains are for biodiversity, particularly tropical mountains, as well as the\nimportance of mountains as climate refugia, the results should be of interest to a broad\naudience of ecologists, policy makers, and conservationists. The authors have done\nconsiderable work for this analysis and present compelling findings but I do have\nreservations about some aspects of their approach. (see attached doc file for figures\nassociated with my concerns).\nMajor Comments\n• One issue stems from the reliance on one dataset, the UNEP-WCMC mountains\ndatabase, to delineate mountains. This is a key decision as it determines the area in\nwhich all other datasets are summarized. While the dataset they use is well-cited and\napparently widely used, it appears to underestimate mountainous areas in key locations\nthat may affect the results of the analysis. Examples of this include tabletop mountains\nin the Pantepui forests & shrublands, Guianan Highlands moist forests, and Guianan\npiedmont moist forests ecoregions as well as the Borneo montane rain forests ecoregion.\nTropical mountains can be quite species rich and are frequently under high human\npressure. Any omission of them could underestimate MVL loss and its impact. I would\nappreciate it if the authors could explain this issue and determine whether it may affect\ntheir study outcomes. If so, they should consider summarizing land use change within\nthe GMBA database as well (Snethlage et al. 2022). The GMBA has a standard version\nas well as an extended version that includes lower elevation skirts of mountain ranges\nto better correspond with the UNEP-WCMC database.\nResponse: Thank you very much for this valuable suggestion. In response to your\nquestions and suggestions, we have conducted a comparative analysis of the commonly\nused global mountain datasets, including the UNEP-WCMC data we originally used as\nwell as both the standard and extended versions of the GMBA dataset. Indeed, we found\nthat the UNEP-WCMC dataset used in our pervious study omits key mountainous areas\nin certain regions of the world (e.g. southeastern Russia, central New Zealand, northern\nNorway). This omission could have lead to an underestimation of losses in low- and\nmedium-density forested areas and their associated ecological impacts. At the same\ntime, the extended GMBA dataset is constructed by geometrically merging three widely\nused mountain definitions (UNEP-WCMC, GMBA, and USGS), with an additional 5\nkm buffer applied. As a result, its spatial extent includes not only true mountainous\nterrain but also adjacent landscapes, leading to potential overestimation in some areas\n(e.g., Northeastern Sudan, Eastern Iran, Southeastern Brazil). After a thorough\nevaluation, we found that the standard version of the GMBA dataset provides the most\nreasonable spatial coverage. It successfully excludes regions with non-montane\nbiodiversity-poor areas (e.g., the Qaidam Basin) while fully covering globally critical\nmountain areas, including lower-elevation mountainous foothills, without omitting\nimportant mountainous regions. As a consequence, we have followed your\nrecommendation and re-conducted all analyses using the standard GMBA dataset. We\nhave updated results and interpretations accordingly.\nRevisions to the Methods:\nFor mountain delineation, commonly used global datasets include the United Nations\nEnvironment Programme-World Conservation Monitoring Centre (UNEP-WCMC)\nmountain inventory86 and the Global Mountain Biodiversity Assessment (GMBA)87,88\ninventories (both standard and broad versions). After checking these mountain\ndatasets, we found that the UNEP-WCMC dataset omits important mountain areas\n(e.g. southeastern Russia, central New Zealand, northern Norway) and overestimates\nsome mountainous areas (e.g. northwestern China) (Supplementary Table 5). Likewise,\nthe broad GMBA version incorporates not only mountainous terrain but also extends\ninto adjacent landscapes88(Supplementary Table 5). On the other hand, the standard\nGMBA version (v2.0 standard) (https://www.earthenv.org/mountains) was better\nsuited and addressed the aforementioned issues, which we thus chose in our study.\nThis dataset delineates mountains covering ~ 18.2% of the Earth's land surface\n(Supplementary Fig.1).\nAdded References:\n86. UNEP-WCMC. Mountains of the World-2002. (Cambridge UK, 2002).\n87. Snethlage, M. A. et al. GMBA Mountain Inventory v2. GMBA-EarthEnv (2022).\n88. Snethlage, M. A. et al. A hierarchical inventory of the world’s mountains for\nglobal comparative mountain science. Sci. Data 9, 149 (2022).\n• There are some other issues associated with the UNEP-WCMC database that I should\nmention. It appears that some areas are classified as mountainous even though they\nare quite flat (e.g. the Qaidam Basin Semi-Desert on the Tibetan Plateau). These are not\nareas that have mountainous characteristics that are thought to contribute to biodiversity\n(e.g. substantial microclimate and habitat heterogeneity over short distances) but are\nnonetheless included in the analysis. If these places tend to be in high elevation, dry,\ncontinental locations, they may also contribute to an overestimation of the impacts of\ndrought on mountain vegetation.\nResponse: We fully agree with your assessment that regions without mountainous\ncharacteristics should be excluded from the analytical scope. To address this, we\nconducted a comparative analysis of widely used global mountain datasets. Based on\nthis evaluation, we adopted the standard version of the GMBA dataset for its\nscientifically rigorous spatial delineation. As noted in our previous response, we have\nre-conducted the analysis, and all relevant sections of the manuscript have been revised\naccordingly to reflect the new results.\n• It appears that canyons are classified as mountains in the UNEP-WCMC database and\nGMBA database (e.g. the Grand Canyon in the southwestern U.S). While canyons\ncertainly can have substantial topographic heterogeneity, they are formed by different\nprocesses and are usually not, to my knowledge, included in the literature on mountain\nbiodiversity. This may not have any bearing on the conclusions of the study but may be\nworth mentioning.\nResponse: Thank you for bringing up this important point. It is true that canyons have\ndifferent geomorphological formation processes than typical mountains. However, their\nformation is often closely linked to orogenic activity in mountainous regions and\nrepresents a significant component of mountain geomorphic evolution (Perkins et al.,\n2015; Zhang et al., 2020). Researchers in geomorphology and geography commonly\ninclude canyons within mountain areas, and they often have similarly distinct\nbiodiversity and zonation patterns, reflecting their structural and ecological similarity\nto mountainous terrain. At the global scale, clear demarcation between canyons and\nadjacent mountains proves challenging due to inherent spatial overlap in topographic\ndelineation and persistent classification ambiguities in remote sensing. Thus, consistent\nwith prevailing frameworks, we retain canyons as constituent mountainous landscapes.\nNevertheless, we have explicitly addressed this methodological choice in\nthe Uncertainties and Limitations section. We acknowledge that with higher spatial\nresolution or regional scales, it could be possible to delineate canyons from typical\nmountain landforms to investigate their differential roles in ecosystem dynamics and\nbiodiversity patterns. Such efforts would advance understanding of ecological\nfunctions across heterogeneous mountain geomorphic units.\nSentences incorporated into the Uncertainties and Limitations part:\nMeanwhile, we did not distinguish between canyons and typical mountains. Addressing\nthis distinction at higher resolutions or regional scales could offer new insights into the\nroles played by heterogeneous mountain geomorphic units in landscape and\nbiodiversity dynamics.\nAdded References:\nZhang, L. et al. How canyons evolve by incision into bedrock: Rainbow Canyon, Death\nValley National Park, United States. Proc. Natl. Acad. Sci. U.S.A. 117, 14730-\n14737 (2020).\nPerkins, J., Finnegan, N. & de Silva, S. Amplification of bedrock canyon incision by\nwind. Nature Geosci 8, 305–310 (2015).\n• Another question I have relates to Figure 1, panel a. in the main body of the manuscript.\nHuman settlement growth is shown as contributing only 4.8% to the loss of MVL. On\nthe map, however, it appears as abundant, if not more abundant, than agricultural\nexpansion, which is shown as contributing 88.4% to the loss of MVL. Is this a\nresampling or scale issue? Or am I misinterpreting the legend? Please explain.\nResponse: Thank you for your valuable suggestion. YES, it is a resampling and scale\nissue. We have redrawn the figure 1 according to our new data.\nRedrawn the figure 1:\n• How do the authors classify forestry activities that degrade and remove natural tree\ncover, sometimes replacing it with plantation tree cover? Some of the areas I’m familiar\nwith in Borneo show MVL loss associated with human settlement growth and\nagricultural expansion but one of the major drivers of forest loss in the mountains of\nBorneo is timber production and expansion of plantations (e.g. oil palm). Plantation\nforests could be considered an aspect of agricultural expansion but I do not see them\nexplicitly mentioned in the manuscript. Tree cover associated with orchards is the most\nrelevant mention I see in Supplementary Table 5.\nResponse: Thank you for this important question. We defined agriculture expansion as\nthe conversion of original MVL into croplands, which the GLC-FCS30D dataset\nincludes three categories: rain-fed croplands, herbaceous cover, and tree/shrub-covered\ncroplands (i.e, orchards and plantations). Therefore, we categorize orchards and\nplantations as croplands and fall under the umbrella of agriculture expansion. Based on\nthis classification, we have added a clearer explanation of the cropland definition in the\nMethods. Moreover, our findings indicated that the primary driver of MVL loss in\nBorneo is agriculture expansion, which is consistent with Curtis et al. (2018).\nAdded sentences in Methods part:\nIn this dataset, cropland is defined as land used for crop cultivation, including rain-fed\ncroplands, herbaceous cover, and tree/shrub-covered croplands (e.g. orchards,\nplantations)80;\nAdded References:\nCurtis, P. G. et al. Classifying drivers of global forest loss. Science 361,1108-\n1111(2018).\n80. Zhang, X. et al. GLC_FCS30: global land-cover product with fine classification\nsystem at 30m using time-series Landsat imagery. Earth Syst. Sci. Data 13, 2753-\n2776 (2021).\n• Related to this are previous efforts to identify drivers of forest loss globally (Curtis et\nal. 2018). While the study in Curtis et al. (2018) was not specifically focused on\nmountain vegetation loss, it does, in many cases, overlap in forested mountain\necoregions. In some places where it does overlap it seems to suggest different drivers\nof loss than the current study. For example, Curtis et al. (2018) indicate forestry as a\nprimary driver of forest loss in the mountains of northern Borneo while this study\nsuggests that it is human settlement growth or agricultural expansion (the colors are\ndifficult to differentiate in Figure 1 in the main manuscript). Similar patterns can be\nseen in the Appalachian Mountains where forest loss is indicated by Curtis et al. (2018)\nas driven by forestry while this study indicates a mix of human settlement growth and\nmining. In western Europe, again Curtis et al. (2018) indicate forest loss is driven by\ntimber while this study attributes it to human settlement growth or agricultural\nexpansion. This is a little unexpected in places like northern Spain where, especially\nrural areas, they are experiencing substantial population declines (González-Leonardo\net al. 2023).\nResponse: We defined agriculture expansion as the process by which cropland,\nincluding orchards and plantations, encroaches original mountain MVL. As such, we\nclassify conversion to orchards and plantations as cropland under the umbrella of\nagriculture expansion. Our results indicated that forest loss dominates MVL loss in\nnorthern Borneo, the Appalachian Mountains, and western Europe, with agriculture\nexpansion being the primary driver. These findings are consistent with those reported\nby Curtis et al. (2018).\nReferences:\nCurtis, P. G. et al. Classifying drivers of global forest loss. Science 361,1108-\n1111(2018).\nI see that the authors mention logging at the end of the manuscript (line 518) but given\nthe differences with Curtis et al. (2018), it would be good to explain in more detail.\nPotential sources of data include planting year of plantations (Du et al. 2022) and the\nglobal map of oil palm plantations (Descals et al. 2020).\nResponse: The study by Curtis et al. (2018) analyzed the drivers of global forest loss,\nincluding commodity-driven deforestation (e.g., oil palm plantations), shifting\nagriculture (i.e., conversion of small- to medium-scale forests or shrublands to\nagricultural land, often followed by abandonment and vegetation regrowth), forestry\n(large-scale forestry operations occurring within managed forests and tree plantations\nwith evidence of forest regrowth in subsequent years), as well as urbanization and\nwildfires. In contrast, our study addresses the overall loss of MVL, which includes not\nonly forests but also shrublands and grasslands. Accordingly, our classification of\ndrivers differs from that of Curtis et al., as we attribute MVL loss based on the final\nland cover transition rather than the initial intent of disturbance. For example,\ntransitions from natural mountain vegetation to orchards and plantations, are\ncategorized under \"agriculture expansion\" in accordance with our cropland definition,\nwithout further subdividing into subcategories such as horticulture or crop cultivation.\nIt is also important to note that Curtis et al. include forest changes with regrowth\npotential within their analysis of forest loss, while our study focuses exclusively on\npermanent conversions, where MVL has been ultimately transformed into non-\nvegetated landscapes such as agricultural land, human settlements, or mining areas.\nTherefore, the two studies differ significantly in their attribution logic of land cover\nchange drivers. We also acknowledge that the mention of “logging” as a potential driver\nof MVL loss in our original manuscript was not appropriately framed. Since the\nclassification of logging-related MVL loss depends on the resulting land cover (e.g., if\nthe logged area is converted to impervious surface, it is attributed to human settlement\ngrowth; if converted to cropland, it falls under agriculture expansion), listing “logging”\nas an independent driver is not accurate. Therefore, we have discarded the expression\nof “logging”. We have revised the relevant content in the Uncertainties and limitations\nsection to ensure conceptual rigor and logical consistency.\nAdded sentences in Uncertainties and limitations part:\nOur classification of MVL loss drivers is based on final land cover transitions, which\nmay overlook specific land cover processes underlying the loss. Future work could\nrefine this framework by distinguishing key subtypes (e.g. subdividing agriculture\nexpansion into commodity-driven agriculture, horticulture and orchards, and\nplantations, and subdividing human settlement growth into urban sprawl and tourism\ndevelopment) to better capture the diversity and ecological impacts of MVL loss drivers.\nAdded References:\nCurtis, P. G. et al. Classifying drivers of global forest loss. Science 361,1108-\n1111(2018).\n• After checking the GLC-FCS30 2020 dataset in ArcGIS, it appears common that rocky\nmountainous areas are classified as impervious surfaces (class number 190). While this\nmay technically be correct, it could be problematic for the current study if such areas\nare attributed to human settlement. If, for example, landslides that are not represented\nin the landslide database lead to vegetation loss, such losses may be attributed to human\nsettlement expansion if the newly exposed areas are classified as impervious surface.\nFloods that wash away vegetation and redistribute rocky floodplain material may be\nanother way this type of error could occur. Please explain whether this is an issue or not\nfor your analysis and whether it would impact the study’s conclusions. The high\naccuracy of the validation suggests this may not be an issue but reporting on\ncommission errors for this and other classes would alleviate any concerns.\nA couple of examples in northern Spain can be seen at these coordinates 0.7698716°W\n42.8643229°N and 0.0171613°W 42.6763235°N.\nResponse: We appreciate your thorough inquiry regarding the potential impact of\nimpervious surface classification on our results. We replaced original land cover data\nwith the more refined GLC-FCS30D dataset. In this dataset, impervious surface is\nexplicitly defined as artificial surfaces, while naturally exposed rock and soil are\nexplicitly classified as bare areas. We have further clarified this definition in the\nMethods section. To further assess potential misclassification of natural surfaces as\nimpervious surfaces in complex mountain environments (e.g. due to landslides/floods),\nwe conducted a validation of globally mountainous impervious surfaces. Specifically,\nwe randomly and uniformly selected a total 5,209 samples to calculate the ratio of\nartificial and natural surfaces (i.e. natural bare rock/soil) in impervious surface. Each\nsample was visually interpreted using high-resolution imagery in Google Earth Pro.\nThis validation process is now described in the Accuracy evaluation and analysis\nsection of the revised manuscript.\nSentences added in the Methods:\nimpervious surface refers to hardened surfaces composed of artificial materials,\nincluding constructed materials (e.g. cement, asphalt, bricks) and artificially\ncompacted surfaces (e.g. paved grounds, rocks and building foundations)80.\nSentences added in Accuracy evaluation and analysis section:\nFirstly, although the impervious surface we used is defined as an artificial impervious\nsurface and was used as a proxy for human settlement growth, there may be natural\nbare rock and soil that were mistakenly classified as artificial surfaces. To incorporate\nthis, we randomly selected a total 5,209 samples from impervious surfaces in 2000 and\n2020 using a random function to assess the ratio of artificial and natural surfaces（i.e.\nnatural bare rock/soil) in impervious surface.\nTo assess the reliability of impervious surface (year of 2000 and 2020) and the ratio of\nartificial and natural surface in impervious surface, we labelled samples as correct if\nthey were impervious surface in high-resolution satellite imagery; otherwise, they were\nlabelled as incorrect if they were other land (Supplementary Data 1). After excluding\nuncertain samples (only accounting for 2.0% of total samples), we found that the\naccuracy (correct samples/ (total samples- uncertain samples)) of impervious surfaces\nreaches about 92.4%, with the vast majority being artificial surfaces (85.9%), while the\nproportion of natural surface-bare rock/soil is relatively small, indicating that\nimpervious surfaces are reliable as a proxy of human settlement growth.\nAdded References:\n80. Zhang, X. et al. GLC_FCS30: global land-cover product with fine classification\nsystem at 30m using time-series Landsat imagery. Earth Syst. Sci. Data 13, 2753-\n2776 (2021).\n• I suggest that the authors add a figure to the main manuscript that shows zoomed in\nexamples of different types of MVL loss in critical areas (e.g. protected areas and/or\nIBHs). This would help make the authors’ arguments more compelling and would\nemphasize the value of conducting such a study using relatively high-resolution (30m)\nland use data.\nResponse: We have added the MVL loss within both PAs and AHRTMS-outside of\nPAs (see Supplementary Fig. 5), and further emphasized the value of using relatively\nhigh-resolution (30 m) land cover data for conducting such a study in the Discussion.\nImproved sentences in Discussion part:\nThe spatial pattern of MVL loss (especially for 30m resolution of MVL loss within PAs\nand AHRTMS) can identify severe loss areas at national or local scales, which can\nassist their governments to achieve ecological and biodiversity conservation aligned\nwith the Kunming Montreal Global Biodiversity Framework (e.g. establishing new\nprotected areas in severe loss areas). By characterizing global erosion of MVL and its\ndriver attribution, we hope our study will inform and support conservation efforts\naimed at preserving mountain vegetated ecosystems for future generations.\nMinor Comments (referring to the docx file)\nLine 236 - I think conventionally, 500m would be considered moderate rather than fine\nscale. In fact, MODIS is known as the moderate resolution imaging spectroradiometer.\nResponse: We have revised the sentence in the manuscript as recommended.\nLine 263 - wildly should be widely\nResponse: done\nLine 368 – Was any effort made to standardize reflectance across sensors to account for\ndifferent sensitivities of newer sensors?\nResponse: We acknowledge that our original processing of the annual maximum NDVI\ncomposites for 1999 (Landsat 5 TM and Landsat 7 ETM+) and 2021 (Landsat 8 OLI)\nlacked sensor reflectance standardization. To address the spectral response differences\nbetween TM/ETM+ and OLI sensors, we applied relative radiometric normalization to\nthe Landsat 5/7 imagery using the cross-sensor calibration coefficients proposed by\nRoy et al. (2016), aligning their reflectance values with those of Landsat 8. Based on\nthese normalized reflectance values, we regenerated the annual maximum NDVI\ncomposites for global mountain regions in 1999 and 2021. All relevant analyses and\nresults were accordingly revised. The full methodological details have been described\nin the revised Methods section.\nSentences added to the Methods:\nTo eliminate spectral response discrepancies among sensors (TM/ETM+/OLI), we\napplied relative radiometric normalization to the TM, ETM+ and OLI imagery using\nthe cross-sensor transformation coefficients proposed by Roy et al.103, ensuring the\nspatiotemporal consistency of NDVI.\nAdded References：\n103. Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength\nand normalized difference vegetation index continuity. Remote Sens. Environ. 185,\n57-70 (2016)\nReferences\nCurtis, P.G., Slay, C.M., Harris, N.L., Tyukavina, A. and Hansen, M.C., 2018.\nClassifying drivers of global forest loss. Science, 361(6407), pp.1108-1111.\nDescals, A., Wich, S., Meijaard, E., Gaveau, D.L., Peedell, S. and Szantoi, Z., 2020.\nHigh-resolution global map of smallholder and industrial closed-canopy oil palm\nplantations. Earth System Science Data Discussions, 2020, pp.1-22.\nDu, Z., Yu, L., Yang, J., Xu, Y., Chen, B., Peng, S., Zhang, T., Fu, H., Harris, N. and\nGong, P., 2022. A global map of planting years of plantations. Scientific data, 9(1),\np.141.\nGonzález‐Leonardo, M., Newsham, N. and Rowe, F., 2023. Understanding population\ndecline trajectories in Spain using sequence analysis. Geographical Analysis, 55(4),\npp.495-516.\nSnethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N.G., Körner, Ch.,\nJetz, W., Fischer, M. & Urbach, D. 2022. A hierarchical inventory of the world’s\nmountains for global comparative mountain science. Nature Scientific\nData. https://doi.org/10.1038/s41597-022-01256-y."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for the careful consideration of my comments of the previous version of your work. I am satisfied with your\nresponses and I do not have any further comments. The use of the new global database helped to resolve some of my\nprevious concerns too.", "reviewer_2": "Thank you for revising the manuscript and responding my queries.", "reviewer_3": "I appreciate the considerable work the authors have done to improve the manuscript. The evaluation of the mountain\ndatasets was especially informative and will likely be useful to many other researchers going forward. I only have a few\nminor comments and edits on the current version.\nline 45 - Suggest changing \"information\" to \"guidance\" to avoid repetition\nline 50 - Suggest changing \"imageries\" to \"imagery\". This is also a good place to mention that you used a satellite derived\nvegetation index to quantify vegetation reduction associated with natural disasters.\nlines 50-54 - Suggest rewriting to something like the following, as the current formulation is a bit confusing. \"We estimated\nMVL loss from changes in human land use including (i) human settlement growth, (ii) agriculture expansion, and (iii) mining.\nWe estimated MVL loss from natural disasters (a net loss after deducting restored areas) including (vi) wildfires, (v) floods,\n(vi) landslides, and (xii) droughts (Methods).\"\nline 53 - When referring to \"restored\" here, do the authors mean that these are actively restored areas or are that they are\nnaturally recovering some vegetation cover, or some combination of both?\nlines 75-75 - Suggest changing \"Among all drivers, contributing agriculture expansion...\" to \"Among all drivers, agriculture\nexpansion...\"\nline 91 - This line begs the question, disproportionate to what? Just looking at the raw percentages is a little misleading\nwhen reporting unevenness because it doesn't account for the starting extent of MVL in each region. For example, North\nAmerica is called out as having disproportionate loss but is actually under less pressure than expected given its MVL area.\nNorth America accounted for 18% of MVL area in 2000 but 16% of MVL loss occurred there. East Asia accounted for 20% of\nMVL area but 16% of MVL loss occurred there. The difference is largest for Europe and Russia which accounted for 20% of\nMVL area and saw 13% loss. I suggest reworking this paragraph to differentiate between total amounts of loss and the\namount of loss expected given the proportion of MVL area in each region.\nline 93 - Suggest changing \"accounting\" to \"account\""}, "author_response": "Global loss of mountain vegetated landscapes and its impact on\nbiodiversity conservation\n# NCOMMS-25-15367-A Response to Reviewers\nReviewer #1 (Remarks to the Author):\nThank you for the careful consideration of my comments of the previous version\nof your work. I am satisfied with your responses and I do not have any further\ncomments. The use of the new global database helped to resolve some of my\nprevious concerns too.\nResponse: Thank you for your acknowledgement.\nReviewer #2 (Remarks to the Author):\nThank you for revising the manuscript and responding my queries.\nResponse: Thank you for your acknowledgement.\nReviewer #3 (Remarks to the Author):\nI appreciate the considerable work the authors have done to improve the manuscript.\nThe evaluation of the mountain datasets was especially informative and will likely be\nuseful to many other researchers going forward. I only have a few minor comments and\nedits on the current version.\nResponse: Thank you for your acknowledgement.\nline 45 - Suggest changing \"information\" to \"guidance\" to avoid repetition\nResponse: Thank you for your valuable suggestions. We have made the corresponding\nrevisions in the manuscript as advised.\nline 50 - Suggest changing \"imageries\" to \"imagery\". This is also a good place to\nmention that you used a satellite derived vegetation index to quantify vegetation\nreduction associated with natural disasters.\nResponse: Thank you for your valuable suggestions. We have made the corresponding\nrevisions to the manuscript as advised.\nAdded one sentence in Main part:\nIn particular, we use a Landsat imagery-derived vegetation index to quantify vegetation\nreduction associated with natural disasters.\nlines 50-54 - Suggest rewriting to something like the following, as the current\nformulation is a bit confusing. \"We estimated MVL loss from changes in human land\nuse including (i) human settlement growth, (ii) agriculture expansion, and (iii) mining.\nWe estimated MVL loss from natural disasters (a net loss after deducting restored areas)\nincluding (vi) wildfires, (v) floods, (vi) landslides, and (xii) droughts (Methods).\"\nResponse: Thank you for your valuable suggestions. We have made the corresponding\nrevisions to the manuscript as advised.\nline 53 - When referring to \"restored\" here, do the authors mean that these are actively\nrestored areas or are that they are naturally recovering some vegetation cover, or some\ncombination of both?\nResponse: Thank you for your valuable comments. In our study, \"restored\" refers to\nboth actively restored areas and areas that they are naturally recovering some vegetation\ncover.\nlines 75-75 - Suggest changing \"Among all drivers, contributing agriculture\nexpansion...\" to \"Among all drivers, agriculture expansion...\"\nResponse: Thank you for your valuable suggestions. We have made the corresponding\nrevisions to the manuscript as advised.\nline 91 - This line begs the question, disproportionate to what? Just looking at the raw\npercentages is a little misleading when reporting unevenness because it doesn't account\nfor the starting extent of MVL in each region. For example, North America is called out\nas having disproportionate loss but is actually under less pressure than expected given\nits MVL area. North America accounted for 18% of MVL area in 2000 but 16% of MVL\nloss occurred there. East Asia accounted for 20% of MVL area but 16% of MVL loss\noccurred there. The difference is largest for Europe and Russia which accounted for 20%\nof MVL area and saw 13% loss. I suggest reworking this paragraph to differentiate\nbetween total amounts of loss and the amount of loss expected given the proportion of\nMVL area in each region.\nResponse: Thank you for your valuable suggestions. We have carefully taken your\ncomments into account and have revised and refined this paragraph accordingly.\nImproved sentences in Main part:\nThe global distribution of MVL loss was highly uneven across regions (Table 1).\nMore\nthan 90% of global MVL losses were concentrated among seven regions: East Asia\n(16.0%), North America (15.6%), Middle East (14.0%), Europe & Russia (13.3%), Sub-\nSaharan Africa (11.5%), Latin America (11.0%) and Southeast Asia (10.5%) (Table 1).\nBy contrast, Central and South Asia, as well as Oceania, account for only 8.1% of the\ntotal global loss (Table 1). However, when we estimated relative loss (% of their initial\nMVL area), rather than absolute loss, we found lower relative losses in East Asia, Latin\nAmerica, Europe & Russia and North America (~1.3%-1.7%) owing to their large\ninitial MVL area, and higher relative loss in Middle East (6.2%), Sub-Saharan Africa\n(3.2%) and Central Asia (3.0%), which had a lower initial MVL area (Table 1). This\nindicates that mountains in these regions are under disproportionate pressure and are\nin urgent need for protection and restoration.\nline 93 - Suggest changing \"accounting\" to \"account\".\nResponse: Thank you for your valuable suggestions. We have made the corresponding\nrevisions to the manuscript as advised."}]

Decision letter