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Ecological Restoration Zoning Based on the “Importance–Vulnerability” Framework for Ecosystem Services

Sustainability 2026, 18(2), 648; https://doi.org/10.3390/su18020648

by Nan Li¹

, Zezhou Hu¹

, Miao Zhang^1,2,*

, Bei Wang³

and Tian Zhang¹

Reviewer 1:

Ahmed Saqr

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Sustainability 2026, 18(2), 648; https://doi.org/10.3390/su18020648

Submission received: 13 November 2025 / Revised: 16 December 2025 / Accepted: 17 December 2025 / Published: 8 January 2026

(This article belongs to the Special Issue Sustainability of the Water Cycle System and Water Utilization in a Changing Environment)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for inviting me to review the manuscript entitled "Ecological restoration zoning based on the 'importance–vulnerability' framework of ecosystem services," submitted to Sustainability. The study proposes a spatial zoning framework for the Qinling–Bashan region by integrating ecological vulnerability assessment (VSD model) with ecosystem service valuation (InVEST model). The topic is relevant and timely for regional ecological management and sustainable development planning. However, the manuscript requires substantial improvements in methodological transparency, particularly regarding the zoning classification logic, validation of results, and depth of discussion. I recommend major revision with attention to the following comments.

Major Comments
- Abstract: How are vulnerability and ecosystem service importance combined to derive the four zones? Add a brief statement on the classification logic. Clarify that "75% of total value" refers to monetary value and specify the currency.

- Introduction: What specific gap does this study fill compared to previous ecological zoning studies in Qinling–Bashan? Add a focused paragraph comparing your VSD–InVEST approach with earlier work and state the methodological advance clearly.

- Methods – VSD Indicators (Table 1): Why does "Road density" (0.14) have much higher weight than "Agricultural water" (0.02)? Provide ecological justification for indicator selection and weights. Consider adding a sensitivity analysis.

- Methods – InVEST Parameters (Section 2.3.2): Provide a table listing critical input parameters (Z-parameter, R, K, carbon densities) with sources and validation methods. Were parameters calibrated against local data?

- Methods – Zoning Matrix (Figures 5–6): Provide an explicit zoning decision matrix showing how vulnerability levels (low/medium/high) and service importance (low/medium/high) map to each of the four zones. Explain the aggregation method from pixels to townships.

- Results – Validation (Section 3): Compare your vulnerability and zoning results with official ecological red lines or previous assessments to demonstrate credibility.

- Discussion – Zone Definitions (Section 4.2): Clarify the difference between "Ecological Restoration Zone" and "Ecological Repair Zone" with specific management objectives. Provide more concrete, actionable strategies for each zone.

- Conclusion – Uncertainties: Expand discussion of limitations (1 km resolution, 2020 data only, parameter uncertainties). How transferable is this framework to other mountain regions?

Minor Comments
- Abstract: Specify "75%" refers to monetary value in CNY.

- Introduction (Line 69): Clarify "this phenomenon" means ecological importance with increasing vulnerability.

- Equation 8: Define all symbols (θ, λ, m) clearly.

- Table 3: Add "%" to the Proportion column.

- Figure 4: Include full units (e.g., "10⁸ CNY/year") in the legend.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

一、Major Comments

1.Abstract

How are vulnerability and ecosystem service importance combined to derive the four zones? Add a brief statement on the classification logic. Clarify that "75% of total value" refers to monetary value and specify the currency.

Answer:

Thank you very much for your valuable comments and suggestions. We have carefully revised the manuscript accordingly. Below is our response to your comment regarding the zoning-classification logic and the “75% of total value” statement.

(1) Zoning classification logic clarified in Abstract

In the revised manuscript, we added one concise sentence in the Abstract — immediately after the description of the restoration-zoning framework and before the presentation of results — to explicitly summarize the zoning classification logic. The new sentence reads:” ESV and EV raster layers were each classified using the natural-breaks method; these classified layers were overlaid and aggregated at the township level to delineate four functional zones representing different combinations of vulnerability and service value.”

(2) Clarification of “≈ 75% of total value” and currency unit

We clarified that the “≈ 75% of total value” refers to the total monetized ecosystem-service value (ESV). We also added the currency unit used — CNY·km⁻²·yr⁻¹.

2.Introduction

What specific gap does this study fill compared to previous ecological zoning studies in Qinling–Bashan? Add a focused paragraph comparing your VSD–InVEST approach with earlier work and state the methodological advance clearly.

Answer:

Thank you for the reviewer’s helpful suggestion. In the revised manuscript, we have added a dedicated paragraph at the end of the Introduction to clearly compare our work with previous ecological zoning studies in the Qinling–Bashan region. The added content highlights:

(1) A more systematic vulnerability assessment framework: We explain that this study adopts the VSD framework, incorporating exposure, sensitivity, and adaptive capacity, which is more comprehensive than earlier studies that relied mainly on natural factors.

(2) A clearer integration of vulnerability and ecosystem services for zoning: We added a comparison showing that our approach uses natural breaks classification, overlay analysis, and township-level aggregation, resulting in a more explicit and operational vulnerability–ES combination for restoration zoning.

This new paragraph clarifies the research gap addressed and the methodological contributions of our study. “To address the above gap, this study integrates ecological vulnerability and ecosystem services within a unified analytical framework tailored to the ecological structure and functional characteristics of the Qinling–Bashan region. Ecological vulnerability reflects the sensitivity of regional systems to external disturbances and their recovery capacity [18], while ecosystem services represent ecological value and contributions to human well-being [19]. Coupling these two dimensions enables a more comprehensive assessment of both ecological risk and functional benefit. To further highlight the methodological differences from previous studies, earlier ecological vulnerability assessments primarily relied on natural factors—such as slope, terrain relief, vegetation cover, and soil erosion risk—while socioeconomic exposure and adaptive capacity were less frequently considered [27,28]. In contrast, this study adopts the Vulnerability Scoping Diagram (VSD) framework, which decomposes vulnerability into exposure, sensitivity, and adaptive capacify, thereby providing a more systematic socio-ecological perspective than convention-al approaches [29]. Moreover, although some studies have attempted to integrate ecological vulnerability with ecosystem services, most relied on simple overlay analysis or composite indices, lacking standardized combination rules. In this study, natural breaks classification, layer overlay, and township-level aggregation are applied to construct a more transparent and operational “vulnerability–service value” decision matrix. This approach enhances the practicality of ecological restoration zoning and provides a clearer technical pathway for spatial planning and ecological management. Compared with single-process models such as RUSLE (focusing on soil erosion), land-use simulation models such as PLUS, social perception–oriented tools such as SolVES, and data-intensive service-flow platforms such as ARIES, the InVEST model provides a standardized and multi-service biophysical assessment framework that is more suitable for regional-scale ecosystem ser-vice quantification in this study[27,28]. Given the strong coupling between climate change, human disturbance, and ecosystem functions in the Qinling–Bashan region, the joint ap-plication of the VSD framework and the InVEST model is particularly suitable for comprehensively characterizing regional socio-ecological vulnerability and ecosystem service patterns [29].”

3. Methods – VSD Indicators (Table 1):

Why does "Road density" (0.14) have much higher weight than "Agricultural water" (0.02)? Provide ecological justification for indicator selection and weights. Consider adding a sensitivity analysis.

Answer:

Thank you very much for this valuable comment. The weights of the indicators in this study were not subjectively assigned; instead, they were objectively determined using the Entropy Weight Method (EWM). This method assigns weights based on the information content and spatial variability of each indicator: indicators with greater spatial variation and stronger ability to differentiate ecological vulnerability have lower entropy values and thus higher weights, while indicators with limited spatial variability obtain lower weights.

In the Qinling–Bashan region, road density exhibits substantial spatial heterogeneity across townships, particularly along the gradients between urban and mountainous areas and between north- and south-facing slopes. More importantly, from an ecological perspective, road density serves as a key proxy for human disturbance intensity, habitat fragmentation, ecological corridor obstruction, and restrictions on species migration. Its strong spatial contrast reflects uneven human activity patterns, which significantly influence ecological vulnerability. Therefore, road density possesses both high statistical variability and strong ecological relevance, leading to its objectively derived higher weight (0.14). This outcome is ecologically reasonable.

In contrast, agricultural water use shows low density, low intensity, and very limited spatial variability across the study area. Because most of the region is mountainous with limited arable land, irrigation activities are generally weak, resulting in very small spatial gradients. Its higher entropy value and lower weight (0.02) reflect both the mathematical outcome of the entropy method and its limited actual contribution to the spatial differentiation of ecological vulnerability. Thus, the low weight is consistent with both ecological mechanisms and regional characteristics.

Overall, the weights derived from the entropy method are highly consistent with the ecological processes and human–environment interactions in the study area. We have incorporated this explanation into Section 2.2.2 of the revised manuscript to enhance methodological transparency. We sincerely appreciate the reviewer’s helpful suggestion.

4. Methods – InVEST Parameters (Section 2.3.2):

Provide a table listing critical input parameters (Z-parameter, R, K, carbon densities) with sources and validation methods. Were parameters calibrated against local data?

Answer:

We sincerely appreciate the reviewer’s valuable suggestion. In the revised manuscript, we have added Table 3, which systematically summarizes the main input parameters, calculation approaches, and key reference sources for the five ecosystem services, including critical parameters such as the Z parameter, R factor, K factor, and carbon density values for different land-use types.

Regarding parameter calibration, we have clearly stated in the revised manuscript that these parameters were not calibrated using local field measurements. This is primarily because the Qinling–Bashan region is extensive and topographically complex, and consistent field observations at the required spatial scale are not available. Therefore, we adopted empirical models and literature-based parameter values that have been widely validated and commonly applied in regional ecosystem service assessments (e.g., Budyko, RUSLE, CASA), ensuring scientific reliability and comparability.

Table 3. Main parameters, calculation approaches, and key references for the five ecosystem services

Service Type	Main Parameters	Calculation Summary	References
Food Supply	NDVI, land use, crop yield, grain price	Allocate food production by land-use yield weighted by NDVI.	Tsvetkov (2021); Statistical Yearbook
Carbon Storage	Land use, carbon densities of four pools	Assign carbon densities to land types and sum four pools by area.	Turna (2024); regional carbon studies
Water Source Conservation	Precipitation, AET, Z, vegetation coefficient, soil water capacity	Use Budyko model to compute water yield and convert to value via reservoir cost.	Liu & Yu (2023); China Water Conservancy Yearbook
Soil Conservation	RUSLE factors (R,K,L,S,C,P), DEM, land use	Estimate soil conservation using RUSLE difference between potential and actual erosion.	Wischmeier; Williams; RUSLE/CSLE; Lufafa
NPP	APAR, ε, remote sensing	Apply CASA model to estimate NPP and convert to value via energy equivalence.	Potter et al. (CASA)

5. Methods – Zoning Matrix (Figures 5–6):

Provide an explicit zoning decision matrix showing how vulnerability levels (low/medium/high) and service importance (low/medium/high) map to each of the four zones. Explain the aggregation method from pixels to townships.

Answer:

Thank you very much for your constructive suggestions.

(1)In response to your comment, we have added Table 4 (Zoning Decision Matrix) in the revised manuscript. This table explicitly presents how different levels of ecosystem service value (from lowest to highest) and ecological vulnerability (from potential to extreme) correspond to the four ecological restoration zones:

Ecological Development Zone, Ecological Conservation Zone, Ecological Repair Zone, and Ecological Restoration Zone.

The inclusion of this matrix makes the zoning logic transparent and traceable.

(2)Regarding the aggregation method from pixel level to township level

To address your question, we have added a detailed explanation in the Methods section. The aggregation was conducted using the following procedure:

(a)Pixel-level statistics within each township

We calculated the area proportion of the four zoning categories (development / conservation / repair / restoration) based on all pixels contained in each township.

(b)Majority-rule assignment

The township was assigned to the zoning category with the largest area proportion, ensuring a dominant ecological restoration type for each administrative unit.

(c)Minor smoothing for fragmented patches

Small, scattered patches were merged to produce spatially coherent township-level zones, improving practical applicability for regional planning.

This aggregation method balances two needs:

Preserving the spatial precision of pixel-based assessment (Fig. 6a)

Producing township-level zoning results that are feasible for policy implementation (Fig. 6b)

Table 4. Zoning basis for ecological restoration

Ecosystem Service Value	Potential Vulnerability	Slight Vulnerability	Moderate Vulnerability	High Vulnerability	Extreme Vulnerability
Lowest Value	Ecological Development Zone	Ecological Development Zone	Ecological Development Zone	Ecological Restoration Zone	Ecological Restoration Zone
Lower Value	Ecological Development Zone	Ecological Development Zone	Ecological Development Zone	Ecological Restoration Zone	Ecological Restoration Zone
Medium Value	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone
Higher Value	Ecological Conservation Zone	Ecological Conservation Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone
Highest Value	Ecological Conservation Zone	Ecological Conservation Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone

6 Results – Validation (Section 3):

Compare your vulnerability and zoning results with official ecological red lines or previous assessments to demonstrate credibility.

Answer:

Thank you for the reviewer’s comment. To examine the reasonableness of our vulnerability assessment and zoning results, we compared our findings with existing studies and relevant local planning documents. The comparison is briefly summarized as follows:

(1) Repair Zone

The repair zones identified in our study are located mainly along the northern foothills of the Qinling Mountains, the southern slope of the Hanzhong Basin, and around Ankang. Previous studies on land-use change and soil erosion in these areas reported noticeable ecological pressure and frequent LUCC transitions. These reported conditions are generally consistent with the areas classified as repair zones in this study.

(2) Restoration Zone

The restoration zones are mainly concentrated in the urban core of Xi’an and the eastern part of Weinan. LUCC statistics show that these areas are dominated by cropland and built-up land, with relatively weak natural ecological conditions. Local planning documents, such as the Xi’an Territorial Spatial Plan (2021–2035), also mention that these areas require ecological improvement or restoration. Therefore, the areas we identify as restoration zones correspond to some of the locations highlighted in planning documents.

Overall, our zoning results show a certain degree of correspondence with previously reported ecological issues and planning priorities, suggesting that the zoning outcomes reflect the general ecological patterns of the region.

Table Area Distribution of LUCC Types Across Ecological Restoration Zones (km²)

LUCC	ZONE(km²）
LUCC	Development	Conservation	Repair	Restoration
Agriculture	7960	8775	11680	1370
Forest	3850	19905	8295	100
Meadow	8945	16800	10336	503
Unutilized	72	8	29	31
Urban	1080	125	1245	810

7. Discussion – Zone Definitions (Section 4.2):

Clarify the difference between "Ecological Restoration Zone" and "Ecological Repair Zone" with specific management objectives. Provide more concrete, actionable strategies for each zone.

Answer:

Thank you for the reviewer’s valuable suggestion. In the revised manuscript, we have clarified the conceptual difference between the Ecological Restoration Zone and the Ecological Repair Zone and added more specific and operational management strategies for each zone.

The Ecological Restoration Zone refers to areas with severe ecological degradation, where ecological structure and natural recovery capacity have been substantially lost. These areas require engineering-based, systematic, and long-term ecological reconstruction. We now provide concrete measures such as soil remediation, wetland and river restoration, industrial relocation, vegetation reconstruction, and strict urban expansion control.

“Ecological restoration zone:

This zone is primarily located in the core area of the Guanzhong Basin along the Wei River, where transportation networks, urban infrastructure, and urbanization levels are well developed. It is one of the most active regions for human activity. Although some studies [50] suggest that the negative ecological impacts of human activities in the western Baoji and Hanzhong areas have been decreasing, the natural ecological foundation of this zone remains relatively weak. Existing research indicates that rapid urban expansion has intensified human–land conflicts [51]. Industrial pollution and inappropriate water use continue to impose considerable pressure on the already fragile ecosystems in this region.

This area is primarily located at the core of the Guanzhong Basin, distributed along the Wei River, with developed transportation, well-established urban infrastructure, and a high level of urbanization. It is the most active region for human production and living. However, unlike[50] , this study suggests that the negative impact of human activities on the western part of Baoji and the Hanzhong area has been diminishing. Nevertheless, the natural ecological foundation of this region is relatively weak, and existing research indi-cates that rapid urbanization has exacerbated the human-land conflict[51]. Industrial pollution and improper use of water resources have caused significant damage to the al-ready fragile ecological environment of this area.

Management Goal: Rebuild ecological structure and restore ecosystem functioning.

Operational Strategies:

Implement engineering-based restoration such as soil remediation, wetland recon-struction, and river rehabilitation.

Relocate or transform high-pollution industries; promote clean industries.

Reduce excessive land development; strictly control urban expansion boundaries.

Promote large-scale vegetation reconstruction and ecological green space systems.

Improve agricultural water-use efficiency and promote water-saving irrigation.

Strengthen urban ecological land management and conduct targeted soil and green space remediation.”

The Ecological Repair Zone refers to areas where ecosystems are partially degraded but still retain natural recovery potential. These areas mainly require pressure reduction and assisted natural recovery, and we added specific strategies including ecological agriculture, riparian buffer zones, forest and grassland protection, enclosure-based forest restoration, and targeted biodiversity conservation.

“Ecological repair zone:

This area is mainly distributed along the northern and southern edges of the Guan-zhong Basin, functioning as a transitional zone between the basin and the surrounding mountainous regions. Rapid urbanization and intensive agricultural production have placed pressure on the local ecological foundation, resulting in varying degrees of ecolog-ical disturbance. According to previous studies [52], targeted measures are required to al-leviate ecological stress and enhance recovery potential.

Management Goal: Reduce ecological pressure and support natural recovery.

Operational Strategies:

Promote ecological agriculture and reduce the application of chemical fertilizers and pesticides.

Strengthen the protection of existing forests and grasslands, and strictly prevent de-forestation.

Establish riparian buffer zones along rivers and strengthen ecological corridor func-tions.

Implement enclosure-based forest restoration and small-scale afforestation projects.

Strictly enforce biodiversity conservation policies and habitat protection measures.

Convert moderately fragile farmland to forest or grassland where appropriate.

Improve connectivity among ecological patches through corridor planning and veg-etation restoration.”

These additions have been incorporated into Section 4.2 to clearly distinguish the two zones and present actionable management pathways.

8 Conclusion – Uncertainties:

Expand discussion of limitations (1 km resolution, 2020 data only, parameter uncertainties). How transferable is this framework to other mountain regions?

Answer:

Thank you very much for the reviewer’s valuable suggestions. In the revised manuscript, we have expanded the section on uncertainties and limitations, directly addressing all the issues raised by the reviewer. Specifically, we have included the following:

Spatial resolution limitation: We clarified that the 1 km spatial resolution may underestimate fine-scale heterogeneity and local ecological risks in mountainous areas.

Single-year data limitation: We emphasized that relying on data from only the year 2020 makes it difficult to capture interannual variations caused by climate change, land-use transitions, or human activities.

Parameter uncertainty: We added an explanation that key parameters used in the InVEST, RUSLE, CASA, and Budyko models are primarily derived from literature values rather than local field calibration.

Model structural assumptions: We stated that the simplified assumptions underlying these ecological models may not fully represent the complexity of mountain ecosystems.

Framework transferability: We added discussion indicating that the proposed framework has conceptual generality but requires parameter adjustments and contextual adaptation when applied to other mountainous regions.

These additions have been incorporated into Section 4.3 of the revised manuscript, making the conclusions more rigorous and robust.

“4.3. highlights and limitations

This study integrates ecological vulnerability assessment and ecosystem service valuation to provide an essential perspective on understanding the ecological and environ-mental patterns of the Qinling–Bashan region in Shaanxi and its surrounding areas. Based on these assessments, an ecological restoration zoning framework was developed to address region-specific ecological challenges and to explore how differentiated ecological management can be implemented through zoning strategies, thereby linking ecological restoration with socio-economic development goals and promoting sustainable regional development. As the study area serves not only as a critical national ecological barrier and the location of national parks but is also closely connected to regional economic development needs, the proposed framework carries substantial practical relevance.

Nevertheless, several inherent limitations and uncertainties remain. First, the use of 1-km spatial resolution data restricts the ability to capture fine-scale ecological heterogeneity in mountainous environments, potentially underestimating micro-topographic variations and localized fragmented patches. Second, the assessment relies on data from a single year (2020), which cannot reflect interannual dynamics driven by climate change, land-use transitions, or human disturbances. Additionally, key parameters used in the InVEST, RUSLE, CASA, and Budyko-based water yield models are derived primarily from literature sources rather than locally calibrated measurements, which introduces parameter uncertainty. Meanwhile, all models are based on simplified assumptions and may not fully represent the complexity of mountain ecosystems, meaning that the results are more appropriate for relative comparisons than for absolute quantitative interpretation.

Finally, although the ecological restoration zoning framework proposed in this study has conceptual generality and can serve as a methodological reference for other mountainous regions, its practical application requires adaptation to local natural conditions, socio-economic contexts, and data availability. Future research may benefit from using higher-resolution remote sensing data, incorporating long-term time-series observations, conducting local parameter calibration, and integrating machine learning and multi-model ensemble approaches to further enhance the precision and reliability of ecological restoration zoning.”

二、Minor Comments

1. Abstract: Specify "75%" refers to monetary value in CNY.

Answer:

Thank you for the reviewer’s comment. We have clarified this point in the revised Abstract. Specifically, we stated that “≈ 75%” refers to the proportion of the total monetized ecosystem-service value (ESV). In addition, we explicitly added the currency unit “CNY·km⁻²·yr⁻¹” to avoid ambiguity.

2. Introduction (Line 69): Clarify "this phenomenon" means ecological importance with increasing vulnerability.

Answer:

Thank you for the reviewer's careful comment. The reference "this phenomenon" in the original text was indeed ambiguous. Following your suggestion, we have revised the phrasing to clarify its specific reference. The sentence has been modified to: "The Qinling-Bashan region exemplifies this phenomenon," where "this phenomenon" now explicitly refers to the preceding statement: "Mountain ecosystems, in particular, are more vulnerable to the dual threats of climate change and human activity due to their complexity, fragility, and high dependence on agricultural ecosystems and natural resources." This revision enhances logical clarity and coherence. We have updated the manuscript accordingly and appreciate your valuable feedback.

“The Qinling-Bashan region exemplifies this heightened vulnerability of mountain ecosystems to such dual threats.”

3. Equation 8: Define all symbols (θ, λ, m) clearly.

Answer:

Thank you for the reviewer’s comment. We agree that the definitions in Equation 8 could be made clearer. Although the original manuscript briefly explained the meanings of λ (slope length), θ (slope gradient), and m (slope length index), we have now further clarified and explicitly defined all symbols directly below Equation 8 to ensure readability and avoid ambiguity. The revised text now explicitly states:λ: slope length (m), representing the horizontal flow distance of runoff;θ: slope angle (degrees), derived from the DEM; m: slope length index, determined according to the empirical relationship between slope steepness and erosion sensitivity.

This revision provides complete definitions for all symbols used in Equation 8.

4. Table 3: Add "%" to the Proportion column.

Answer:

Thank you for pointing this out. We have added the percentage symbol “(%)” to the header of the “Proportion” column in Table 3 in the revised manuscript to clearly indicate that the values represent percentages.

5. Figure 4: Include full units (e.g., "10⁸ CNY/year") in the legend.

Answer:

Thank you for the reviewer’s valuable suggestion. We agree that the units of ecosystem-service values in the figure should be clearly indicated. We have added the complete unit information in the caption of Figure 4, clarifying that all values are expressed in CNY·km⁻²·yr⁻¹, which is consistent with the 1-km spatial resolution used in this study. This revision ensures that the unit specification is clear and unambiguous.

“Figure 4. Spatial distribution of the value of ecosystem service in 2020 (unit: CNY·km⁻²·yr⁻¹)”

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

Overall, I think the study is a relevant and interesting study, but I feel that the methodological transparency and the depth of the discussion need to be strengthened before the manuscript is ready for publication. Below I outline my main suggestions, section by section.

Abstract

I suggest adding one clear sentence that explicitly states what is new compared with previous ecological zoning studies (for example, what is distinctive about combining VSD, multi-ecosystem-service assessment, and the zoning matrix in this specific ecological barrier region).
I would recommend keeping only the most essential methodological elements in the abstract and avoiding long, formula-like descriptions, which are better placed in the Methods section.
For the results part of the abstract, I suggest highlighting 2–3 concrete findings (e.g. which zones dominate in area, which ecosystem services are most important, and one or two key management implications).

Introduction

I feel that the introduction would benefit from a clearer problem statement. I suggest that the authors explain more directly what is missing in existing studies and how this work aims to fill those gaps.
Some paragraphs are quite long. I recommend splitting longer paragraphs into shorter ones to make the narrative easier to follow.

Materials and Methods

Study area and context

The description of the Qinling–Bashan region is generally clear, but I suggest emphasizing more explicitly why this region is strategically important (for instance, its role as an ecological barrier, a biodiversity hotspot, or a key water source).
I would also encourage the authors to link each key feature (topography, climate, land use, and socio-economic pattern) to its implications for ecological vulnerability and ecosystem services.
I suggest adding one or two sentences that summarize the main environmental problems in the region and explain why ecological restoration zoning is needed.

Data sources

Since many data sources are used (DEM, land use, NDVI, climate, soil, socio-economic data, etc.), I recommend adding a small table listing:

each dataset,
the source institution,
the year(s), and
the spatial resolution.

Ecological vulnerability indicators (VSD framework)

For the VSD-based indicators, I suggest explaining more clearly why each indicator is assigned to exposure, sensitivity, or adaptive capacity (for example, why DEM is treated as “sensitivity” and why food production reflects “adaptive capacity”).
I would also recommend clarifying the expected direction of influence of each indicator (i.e. whether higher values increase or decrease vulnerability).

Ecosystem service modelling (InVEST, RUSLE, CASA)

The descriptions of the ecosystem service models are rather general. I suggest specifying the sources of the carbon density values for each land-use class and indicating whether any local adjustment or validation was done.
For the water yield model, I recommend presenting the key parameters (e.g. Budyko-related parameters) and briefly explaining how they were chosen or calibrated. If no calibration was performed, it would be good to state this explicitly.
For soil conservation (RUSLE), I suggest adding a small table summarizing how the R, K, LS, C and P factors were derived (data sources, equations, and main references).

Zoning framework

The idea of combining “ecosystem service importance” with “ecological vulnerability” into a zoning matrix is central to the paper, but at the moment the rules are a bit hard to follow. I suggest clearly stating how many classes are used for importance and for vulnerability and then explaining which combinations lead to each of the four main zones. A simple matrix or table would help here.

Results

I recommend that the authors focus more on the key spatial patterns rather than listing many similar numbers. When presenting areas or values, I suggest emphasizing the main contrasts and dominant trends.
For each main zone, I think it would be helpful to add a short summary that covers:

the dominant land-use types,
the dominant ecosystem services, and
the main vulnerability characteristics.

Discussion

In the Discussion, I feel that some parts repeat the results. I suggest shifting the emphasis more towards interpretation and comparison. For example, it would be very useful to compare the new zoning with existing ecological red line areas, protected areas, or ecological function zones in the region.
I also recommend adding some reflection on trade-offs and synergies among ecosystem services in the different zones (e.g. production vs. conservation).
It would be valuable to discuss briefly how this framework could be applied or adapted to other mountainous ecological barrier regions, both in China and elsewhere.
I strongly suggest adding a short subsection on limitations (for example, the use of single-year data, model uncertainties, and the absence of future scenarios).

Conclusions

I suggest restructuring the conclusions so that the main findings are summarized in 3–4 clear points, indicating what the study reveals about vulnerability, ecosystem services, and zoning in the Qinling–Bashan region.
I would also recommend adding 2–3 concrete policy and management implications for ecological restoration and protection and briefly indicating possible directions for future work.

Author Response

Abstract

1.I suggest adding one clear sentence that explicitly states what is new compared with previous ecological zoning studies (for example, what is distinctive about combining VSD, multi-ecosystem-service assessment, and the zoning matrix in this specific ecological barrier region).

Answer:

We thank the reviewer for this constructive suggestion. We agree that the unique contribution of this study compared to existing ecological zoning research should be more explicitly stated in the abstract. We have added the following statement: “Unlike previous studies that primarily relied on simple overlay or subjective judgment, this study constructs a standardized 'vulnerability–service value' decision matrix specifically for the Qinling–Bashan Mountains—a nationally important ecological barrier and climatic transition zone—and achieves transparent and operational translation from integrated VSD-InVEST assessment to spatial restoration zoning through natural breaks classification, layer overlay, and township-level aggregation, providing a clear technical pathway for precision restoration in this region."

The core contributions of this study are reflected in: Regional Significance: As a nationally important ecological security barrier, climatic transition zone, and critical water conservation area, ecological restoration zoning in the Qinling–Bashan Mountains holds significant national strategic importance and demonstration value; Technical Standardization: Addressing the lack of standardized combination rules in previous studies integrating vulnerability and service assessments, this study constructs an explicit decision matrix that systematically integrates the three-dimensional vulnerability assessment (exposure–sensitivity–adaptive capacity) with multi-dimensional ecosystem service evaluation, forming a transparent and replicable zoning technical workflow; Operational Applicability: Through township-scale aggregation, the results directly align with practical needs of territorial spatial planning and ecological restoration management, providing differentiated restoration strategies for different vulnerability–service combination types, thereby enhancing the practical application value of the research.

This explicit statement helps readers understand the methodological contributions and practical value of this study in the specific regional context.

2.I would recommend keeping only the most essential methodological elements in the abstract and avoiding long, formula-like descriptions, which are better placed in the Methods section.

Answer:

Thank you very much for your valuable suggestions regarding the Abstract. We have revised the Abstract accordingly. Specifically, we removed lengthy methodological descriptions and formula-like details, and now only retain the essential methodological elements. We believe this makes the Abstract more concise and readable, and helps readers quickly grasp the main contributions and findings. Thank you again for your insightful advice, which has helped us improve the clarity of our manuscript. The following are the revisions made: “The Qinling–Bashan Mountain region in Shaanxi Province and adjacent areas serves as a key ecological barrier and an important socio-economic belt in China. However, the region has experienced increasing ecological pressure due to natural drivers and intensive human activities, highlighting the need for a systematic assessment to guide restoration strategies. This study proposes an ecological restoration zoning framework based on eco-logical vulnerability (EV) and ecosystem service value (ESV). The InVEST model was used to quantify major ecosystem services, while the Vulnerability Scoping Diagram (VSD) model was applied to evaluate ecological vulnerability. Both ESV and EV raster layers were classified using the natural-breaks method, overlaid to determine combined patterns, and aggregated at the township scale to delineate restoration zones. Unlike previous studies that primarily relied on simple overlay or subjective judgment, this study con-structs a standardized 'vulnerability–service value' decision matrix specifically for the Qinling–Bashan Mountains—a nationally important ecological barrier and climatic transition zone—and achieves transparent and operational translation from integrated VSD-InVEST assessment to spatial restoration zoning through natural breaks classification, layer overlay, and township-level aggregation, providing a clear technical pathway for precision restoration in this region. The results reveal three key findings with management implications:

(1) Spatial Vulnerability Pattern—The Qinling and Bashan mountain cores exhibit pre-dominantly low vulnerability (potential and slight levels), whereas severe to extreme vulnerability concentrates in the rapidly urbanizing Guanzhong Plain, highlighting the spatial divergence between protected uplands and stressed lowlands and underscoring the urgent need for urban ecological restoration;(2) Dominant Ecosystem Services—Carbon storage and net primary productivity together account for approximately 93% of the total ecosystem service value, emphasizing the region's critical role in national climate regulation and the priority of forest conservation in restoration planning;(3) Differentiated Zon-ing Strategy—Four functional zones were delineated, with the ecological conservation zone as the largest (44.8%, primarily across the Qinling Mountains) serving as the region-al ecological security backbone, while the smaller ecological restoration zone (2.8%, concentrated in degraded urban peripheries) requires targeted intensive intervention.”

3. For the results part of the abstract, I suggest highlighting 2–3 concrete findings (e.g. which zones dominate in area, which ecosystem services are most important, and one or two key management implications).

Answer:

Thank you for this constructive suggestion. We agree that the original Results section was overly descriptive. We have revised this section to focus on three key findings with clear management implications. Specifically, we now (1) emphasize the contrast between low vulnerability in mountainous core areas and high vulnerability concentrated in urbanized plains, highlighting the need for urban ecological restoration; (2) explicitly link the dominance of carbon storage and net primary productivity (accounting for >75% of total ESV) to climate-regulation functions and forest conservation priorities; and (3) clarify the decision-making relevance of the zoning structure by highlighting the dominant role of the ecological conservation zone and the targeted intervention needs of the ecological restoration zone. These revisions aim to improve clarity and better convey the practical significance of the results. “The results reveal three key findings with management implications: (1) Spatial Vulnerability Pattern—The Qinling and Bashan mountain cores exhibit predominantly low vulnerability (potential and slight levels), whereas severe to extreme vulnerability concentrates in the rapidly urbanizing Guanzhong Plain, highlighting the spatial divergence between protected uplands and stressed lowlands and underscoring the urgent need for urban ecological restoration; (2) Dominant Ecosystem Services—Carbon storage and net primary productivity together account for over 75% of total ecosystem service value, emphasizing the region's critical role in national climate regulation and the priority of forest conservation in restoration planning; (3) Differentiated Zoning Strategy—Four functional zones were delineated, with ecological conservation zone as the largest (44.8%, primarily across Qinling mountains) serving as the regional ecological security backbone, while the smaller ecological restoration zone (2.8%, concentrated in degraded urban peripheries) requires targeted intensive intervention. This zoning framework provides actionable strategies to balance ecological protection with socio-economic development.”

The following are the revisions made:” The results reveal three key findings with management implications: (1) Spatial Vulnerability Pattern—The Qinling and Bashan mountain cores exhibit predominantly low vulnerability (potential and slight levels), whereas severe to extreme vulnerability concentrates in the rapidly urbanizing Guanzhong Plain, highlighting the spatial divergence between protected uplands and stressed lowlands and underscoring the urgent need for urban ecological restoration; (2) Dominant Ecosystem Services—Carbon storage and net primary productivity together account for over 75% of total ecosystem service value, emphasizing the region's critical role in national climate regulation and the priority of forest conservation in restoration planning; (3) Differentiated Zoning Strategy—Four functional zones were delineated, with ecological conservation zone as the largest (44.8%, primarily across Qinling mountains) serving as the regional ecological security backbone, while the smaller ecological restoration zone (2.8%, concentrated in degraded urban peripheries) requires targeted intensive intervention. This zoning framework provides actionable strategies to balance ecological protection with socio-economic development.”

Introduction

4. I feel that the introduction would benefit from a clearer problem statement. I suggest that the authors explain more directly what is missing in existing studies and how this work aims to fill those gaps.

Answer:

This new paragraph clarifies the research gap addressed and the methodological contributions of our study. The following are the revisions made: “The Qinling–Bashan region is an important ecological functional zone and a core area of biodiversity conservation in China. It is characterized by rich biodiversity, high vegetation coverage, and diverse soil types, with complex ecosystems that provide habitats for numerous rare species and supply essential ecological services to human society [27]. Facing the dual challenges of natural resource protection and regional economic development, the area urgently requires integrated governance measures to balance eco-logical protection with socio-economic needs.To further highlight the methodological gap addressed by this study, we added a targeted comparison with previous research in the Qinling–Bashan region. Earlier ecological vulnerability assessments primarily relied on natural factors—such as slope, terrain relief, vegetation cover, and soil erosion risk—while socioeconomic exposure and adaptive capacity were less frequently considered. In contrast, this study adopts the VSD (Vulnerability Scoping Diagram) framework, which decomposes vulnerability into exposure, sensitivity, and adaptive capacity, thereby providing a more systematic socio-ecological perspective than conventional approaches. Moreover, although some studies have attempted to integrate ecological vulnerability with ecosystem services, most relied on simple overlay analysis or composite indices, lacking standardized combination rules. In this study, we apply natural breaks classification, layer overlay, and township-level aggregation to construct a more transparent and operational “vulnerability–service value” decision matrix. This approach enhances the practicality of ecological restoration zoning and offers a clearer technical pathway for spatial planning and ecological management. Based on this methodological foundation, the study aims to: (i) construct a robust framework for ecological restoration zoning; (ii) analyze the spatial distribution of ecological vulnerability and ecosystem service values; and (iii) delineate targeted ecological res-toration zones and propose corresponding protection and restoration strategies.”

5. Some paragraphs are quite long. I recommend splitting longer paragraphs into shorter ones to make the narrative easier to follow.

Answer:

Thank you for this helpful suggestion. We agree that some paragraphs were overly long and may affect readability. Following the reviewer’s advice, we have revised the manuscript by splitting lengthy paragraphs into shorter ones and slightly reorganizing the text where necessary. These changes aim to improve the clarity and flow of the narrative without altering the content or meaning.

Materials and Methods

6.The description of the Qinling–Bashan region is generally clear, but I suggest emphasizing more explicitly why this region is strategically important (for instance, its role as an ecological barrier, a biodiversity hotspot, or a key water source).

Answer:

We thank the reviewer for this valuable suggestion. We agree that the strategic importance of the Qinling-Bashan region should be more explicitly emphasized. We have made two key revisions in the study area description:

Revision Point 1: At the end of the geographic location paragraph (Paragraph 1), we added an explicit statement of strategic importance, articulating the region's national-scale strategic value from three dimensions: As a key ecological barrier serving as the climatic boundary between northern and southern China and the watershed between the Yellow River and Yangtze River; As a nationally recognized biodiversity hotspot harboring over 3,800 plant species and rare fauna including the giant panda; As a strategic water-source region serving the Guanzhong urban cluster and the South-to-North Water Diversion Project, affecting water security for tens of millions of people.

Revision Point 2: We rewrote the opening of the research rationale paragraph (Paragraph 3) with specific quantitative evidence and flagship species examples: Added specific data on endemism rates exceeding 30%;Explicitly listed flagship species including giant panda, golden snub-nosed monkey, and crested ibis; Quantified the region's role in providing freshwater to over 100 million people; Emphasized its official designation as a "national ecological security barrier."

These revisions transform the strategic importance from general description to scientific argumentation supported by concrete data and species examples, more effectively communicating to international readers the unique value of this region in China's and global ecological conservation. The following are the revisions made: “The research area is located within the territory of Shaanxi Province, China, mainly covering the Qinling Mountain Range and the basin areas on both sides of its north and south, covering approximately 108,000 square kilometers (Fig. 1). The southern boundary of the region is adjacent to the provincial boundary of Shaanxi, while the northern boundary extends to the northern boundary of the Guanzhong Plain. To accurately define the research scope, the boundary line is demarcated based on the county-level administrative divisions, which can more finely reflect the geographical and socio-economic characteristics of the region. **This region holds exceptional strategic importance at the national scale for three critical reasons: (1) as a key ecological barrier separating China's northern and southern climatic zones and serving as the watershed between the Yellow River and Yangtze River basins; (2) as a nationally recognized biodiversity hotspot harboring over 3,800 plant species and numerous rare and endemic fauna, including the giant panda; and (3) as a vital water-source region supplying both the Guanzhong urban cluster and the South-to-North Water Diversion Project, directly affecting water security for millions of residents across northern China.”

7.I would also encourage the authors to link each key feature (topography, climate, land use, and socio-economic pattern) to its implications for ecological vulnerability and ecosystem services.

Answer:

We thank the reviewer for this constructive suggestion and agree that clearer and more explicit linkages between regional characteristics and ecological vulnerability and ecosystem services are necessary. Accordingly, we have substantially revised Paragraph 2 of the Physical Geography section in the study area description to move beyond simple feature enumeration toward a causal explanation of underlying mechanisms. Specifically, we clarify that the semi-arid climate on the northern slope limits water yield and soil conservation capacity and increases sensitivity to climate variability, whereas the more humid climate on the southern slope supports higher net primary productivity and carbon storage services. We further explain that the hilly terrain on the southern slope favors water conservation but constrains socio-economic development and adaptive capacity, while the relatively flat northern slope facilitates urbanization but leads to intensified habitat fragmentation. In terms of land use, we emphasize that urbanization and agricultural expansion on the northern slope elevate ecological vulnerability through water stress and landscape fragmentation, whereas higher forest cover on the southern slope supports stronger ecosystem service provision. From a socio-economic perspective, higher population density on the northern slope increases exposure to environmental risks, while lower population density on the southern slope reduces human disturbance but also implies lower adaptive capacity. Finally, we add a synthesizing statement explicitly highlighting that the north–south environmental gradient—characterized by contrasts in climate, topography, land use intensity, and socio-economic development—drives spatially heterogeneous patterns of ecological vulnerability and ecosystem service capacity, providing a clear conceptual foundation for subsequent assessment and differentiated restoration zoning.

8. I suggest adding one or two sentences that summarize the main environmental problems in the region and explain why ecological restoration zoning is needed.

Answer:

We thank the reviewer for this valuable suggestion. In response, we added one to two concise summarizing sentences at the end of the Study Area section to explicitly highlight the major environmental problems and pressures currently facing the region, including increasing water resource stress, habitat fragmentation, and intensified human disturbance. We further clarified that these spatially heterogeneous pressures constitute the direct rationale for conducting ecological restoration and implementing differentiated ecological-restoration zoning in the study area. “As a result, the region is currently facing multiple and spatially differentiated environmental pressures, including increasing water resource stress, habitat fragmentation, and intensified human disturbance driven by rapid urbanization and water demand growth. These compounded pressures highlight the necessity of conducting ecological restoration and implementing differentiated ecological-restoration zoning to mitigate vulnerability and sustain key ecosystem services.”

Data sources

9. Since many data sources are used (DEM, land use, NDVI, climate, soil, socio-economic data, etc.), I recommend adding a small table listing: each dataset, the source institution, the year(s), and the spatial resolution.

Answer:

We thank the reviewer for this helpful suggestion. In response, we added a new table (Table 1) in Section 2.2 (Datasets) that systematically summarizes all datasets used in this study. The table lists each dataset together with its data source institution, year(s) of data acquisition, and spatial resolution. This addition provides a clear overview of the multi-source data employed in the analysis and improves the transparency and reproducibility of the study.

Table 1. Summary of datasets used in this study

Dataset	Data source	Year(s)	Spatial resolution
DEM	Geospatial Data Cloud (http://www.gscloud.cn/)	2020	30 m
Land use / land cover	Resource and Environment Science Data Center, CAS (Landsat 8)	2020	30 m
Annual precipitation	Resource and Environment Science Data Center, CAS	2020	1 km
Mean air temperature	Resource and Environment Science Data Center, CAS	2020	1 km
Solar radiation	National Earth System Science Data Center	2020	1 km
Actual evapotranspiration (ET)	National Earth System Science Data Center	2020	1 km
Potential ET	Global Aridity and PET Database	2020	1 km
NDVI	Resource and Environment Science Data Center, CAS (SPOT/VEGETATION, MVC)	2020	1 km
Population density	Resource and Environment Science Data Center, CAS	2020	1 km
GDP density	Resource and Environment Science Data Center, CAS	2020	1 km
Soil texture / content	Harmonized World Soil Database	—	1 km
Root depth (root limiting layer)	Climate Change Data Center (BNU)	—	1 km

10. For the VSD-based indicators, I suggest explaining more clearly why each indicator is assigned to exposure, sensitivity, or adaptive capacity (for example, why DEM is treated as “sensitivity” and why food production reflects “adaptive capacity”).

Answer:

Thank you for your valuable suggestion. We have added a new column “Rationale (classification rationale)” to Table 2 (“Indicator system and weights of ecological vulnerability assessment in the study area”), clarifying why each indicator is assigned to exposure, sensitivity or adaptive capacity:

For natural-system indicators (e.g. DEM, vegetation cover / NDVI / land-use type, soil / hydrology / soil-conservation related variables), we classify them under sensitivity. The rationale is that these variables represent the intrinsic vulnerability of the ecosystem to disturbances (e.g. rainfall, land-use change, human activities). For example, steep slopes or complex terrain (as shown by DEM) exacerbate risks of soil erosion or slope instability; poor vegetation cover (indicated by NDVI) implies that the ecosystem is more vulnerable to climate variability or anthropogenic disturbance. Given our study area is located in a bioclimatic transition zone (northern–southern climate / vegetation gradient) in the Qin–Ba region, vegetation and terrain conditions strongly determine ecological sensitivity.

For social-system variables (e.g. population density, GDP / economic intensity, land-use intensity / built-up area / road density, water use intensity / pollution discharge, land-use pressure, etc.), we assign them to exposure. These indicators represent pressures or disturbances exerted by human activity on ecosystems — i.e. how much the ecological system is exposed to environmental or anthropogenic stress. High population density, intense urbanization or agriculture, frequent land development or infrastructure expansion all increase stress and risk on ecosystems.

For adaptive capacity indicators (e.g. food production (grain yield), fiscal income / reserves, socio-economic resources, household savings / bank deposit balances, capacity for ecological restoration investment), we regard them as measures of a social system’s or community’s ability to cope with, recover from, or adapt to ecological degradation, resource scarcity, or diminished ecosystem services. Regions or communities with high agricultural yield, stable economy, or substantial reserves have greater livelihood resilience and capacity to invest in ecological restoration or adjust land-use / resource management in response to environmental shocks.

11. I would also recommend clarifying the expected direction of influence of each indicator (i.e. whether higher values increase or decrease vulnerability).

Answer:

Thank you for the helpful suggestion. We have added a column “Direction ( + / – )” in Table 2 (“Indicator system and weights of ecological vulnerability assessment in the study area”) to explicitly indicate whether an increase in each indicator is expected to increase or decrease vulnerability.

12.The descriptions of the ecosystem service models are rather general. I suggest specifying the sources of the carbon density values for each land-use class and indicating whether any local adjustment or validation was done.

Answer:

Table 3. Main parameters, calculation approaches, and key references for the five ecosystems

Service Type	Main Parameters	Calculation Summary	References
Food Supply	NDVI, land use, crop yield, grain price	Allocate food production by land-use yield weighted by NDVI.	Tsvetkov (2021); Statistical Yearbook
Carbon Storage	Land use, carbon densities of four pools	Assign carbon densities to land types and sum four pools by area.	Turna (2024); regional carbon studies
Water Source Conservation	Precipitation, AET, Z, vegetation coefficient, soil water capacity	Use Budyko model to compute water yield and convert to value via reservoir cost.	Liu & Yu (2023); China Water Conservancy Yearbook
Soil Conservation	RUSLE factors (R,K,L,S,C,P), DEM, land use	Estimate soil conservation using RUSLE difference between potential and actual erosion.	Wischmeier; Williams; RUSLE/CSLE; Lufafa
NPP	APAR, ε, remote sensing	Apply CASA model to estimate NPP and convert to value via energy equivalence.	Potter et al. (CASA)

13.For the water yield model, I recommend presenting the key parameters (e.g. Budyko-related parameters) and briefly explaining how they were chosen or calibrated. If no calibration was performed, it would be good to state this explicitly.

Answer:

Thank you for your in-depth attention regarding the parameters of the water yield model. We note that this comment is related to point #12. We hereby confirm that this study did not perform parameter calibration for the InVEST water yield module.

14. For soil conservation (RUSLE), I suggest adding a small table summarizing how the R, K, LS, C and P factors were derived (data sources, equations, and main references).

Answer:

Zoning framework

15.The idea of combining “ecosystem service importance” with “ecological vulnerability” into a zoning matrix is central to the paper, but at the moment the rules are a bit hard to follow. I suggest clearly stating how many classes are used for importance and for vulnerability and then explaining which combinations lead to each of the four main zones. A simple matrix or table would help here.

Answer:

Thank you very much for your constructive suggestions.

(1) In response to your comment, we have added Table 4 (Zoning Decision Matrix) in the revised manuscript. This table explicitly presents how different levels of ecosystem service value (from lowest to highest) and ecological vulnerability (from potential to extreme) correspond to the four ecological restoration zones:

Ecological Development Zone, Ecological Conservation Zone, Ecological Repair Zone, and Ecological Restoration Zone.

The inclusion of this matrix makes the zoning logic transparent and traceable.

(2) Regarding the aggregation method from pixel level to township level

To address your question, we have added a detailed explanation in the Methods section. The aggregation was conducted using the following procedure:

(a)Pixel-level statistics within each township

We calculated the area proportion of the four zoning categories (development / conservation / repair / restoration) based on all pixels contained in each township.

(b)Majority-rule assignment

The township was assigned to the zoning category with the largest area proportion, ensuring a dominant ecological restoration type for each administrative unit.

(c)Minor smoothing for fragmented patches

Small, scattered patches were merged to produce spatially coherent township-level zones, improving practical applicability for regional planning.

This aggregation method balances two needs:

Preserving the spatial precision of pixel-based assessment (Fig. 6a)

Producing township-level zoning results that are feasible for policy implementation (Fig. 6b)

Table 4. Zoning basis for ecological restoration

Ecosystem Service Value	Potential Vulnerability	Slight Vulnerability	Moderate Vulnerability	High Vulnerability	Extreme Vulnerability
Lowest Value	Ecological Development Zone	Ecological Development Zone	Ecological Development Zone	Ecological Restoration Zone	Ecological Restoration Zone
Lower Value	Ecological Development Zone	Ecological Development Zone	Ecological Development Zone	Ecological Restoration Zone	Ecological Restoration Zone
Medium Value	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone
Higher Value	Ecological Conservation Zone	Ecological Conservation Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone
Highest Value	Ecological Conservation Zone	Ecological Conservation Zone	Ecological Repair Zone	Ecological Repair Zone	Ecological Repair Zone

Results

16.I recommend that the authors focus more on the key spatial patterns rather than listing many similar numbers. When presenting areas or values, I suggest emphasizing the main contrasts and dominant trends.

Answer:

We thank the reviewer for this valuable suggestion. We agree that the Results section contained excessive numerical details and place names, which weakened the emphasis on key spatial patterns. We have substantially streamlined and restructured Sections 3.1 (Ecological Vulnerability), 3.2 (Ecosystem Services), and 3.3 (Ecological Restoration Zoning).

Main revisions:

Section 3.1 streamlining: Transformed lengthy city name enumeration into emphasis on the "urban core–transitional zone–mountain" gradient pattern, highlighting the contrast between extreme vulnerability zones (12.7%) concentrated in urban cores and low vulnerability zones (55.8%) dominating mountainous areas.

Section 3.2 restructuring: Removed item-by-item detailed descriptions of 6 service types and numerous specific values; Emphasized carbon storage and NPP as dominant services and their strategic significance; Highlighted the core spatial contrast of "mountain high-value zones vs. plain low-value zones"; Merged water conservation and soil retention into a brief description of supporting services.

Section 3.3 optimization: Emphasized the conservation zone's dominance as the largest zone and its strategic value; Highlighted that the restoration zone, though smallest (2.8%), has the highest management priority; Removed repetitive city name listings, focusing on functional positioning and management strategy differences across zones.

17. For each main zone, I think it would be helpful to add a short summary that covers:the dominant land-use types,the dominant ecosystem services, and the main vulnerability characteristics.

Answer:

We thank the reviewer for the constructive comments. We agree that a more structured characterization for each zone is necessary. Accordingly, substantial revisions have been made to Section 3.3, and an additional land use data table (Table 7) has been introduced.

Major revisions include:

（1）Addition of Table 7: Table 7 provides a detailed breakdown of land use composition for the four zones, including area (km²) and percentage, offering quantitative support for the characterization of each zone.

（2）Structured summaries for each zone: For each zone, a systematic summary has been added, covering: Land use characteristics: Dominant land use types and their proportions are explicitly identified based on Table 7. For example, the ecological conservation zone is dominated by forest (43.6%) and grassland (36.8%), whereas the ecological restoration zone is primarily composed of agricultural land (48.6%) and urban land (28.7%). Dominant ecosystem services: The key ecosystem services provided by each zone are clearly specified. Vulnerability characteristics: The level of ecological vulnerability in each zone and its main driving factors are explicitly described.

Table 7. Area Distribution of LUCC Types Across Ecological Restoration Zones (km²).

LUCC	ZONE(km²）
LUCC	Development	Conservation	Repair	Restoration
Agriculture	7960 (36.3%)	8775(19.2%)	11680(37.0%)	1370(48.6%)
Forest	3850 (17.6%)	19905 (43.6%)	8295(26.3%)	100 (3.5%)
Meadow	8945 (40.8%)	16800(36.8%)	10336(32.7%)	503 (17.8%)
Unutilized	72 (0.3%)	8 (0.02%)	29 (0.1%)	31(1.1%)
Urban	1080 (4.9%)	125 (0.3%)	1245(3.9%)	810 (28.7%)

Discussion

18.In the Discussion, I feel that some parts repeat the results. I suggest shifting the emphasis more towards interpretation and comparison. For example, it would be very useful to compare the new zoning with existing ecological red line areas, protected areas, or ecological function zones in the region.

Answer:

We thank the reviewer for this valuable suggestion. We agree that the Discussion should avoid repeating Results and focus on interpretation and comparison. In response to this comment, we provide the following clarification and revisions:

Regarding comparison with existing conservation systems:

We have added a comparison and validation of our zoning results with existing studies and local planning documents in the Results section (Section 3.3), specifically including: Repair Zones: Spatially consistent with previously reported high-pressure areas of land-use change and soil erosion (northern Qinling foothills, southern Hanzhong Basin slope, Ankang periphery);Restoration Zones: Correspond to areas explicitly identified in local planning documents (e.g., Xi'an Territorial Spatial Plan 2021-2035) as requiring ecological improvement or restoration (Xi'an urban core, eastern Weinan);Overall validation: Our zoning results show reasonable correspondence with reported ecological issues and planning priorities, indicating that the outcomes reflect the general ecological patterns of the region.

Adjustments to Discussion section:

Given that the Results section already contains the above comparison and validation, we have made the following adjustments to the Discussion: Removed spatial distribution descriptions that duplicate Results;

19. I also recommend adding some reflection on trade-offs and synergies among ecosystem services in the different zones (e.g. production vs. conservation).

Answer:

We thank the reviewer for this insightful comment. We fully agree that trade-offs and synergies among ecosystem services, particularly between production-oriented and conservation-oriented services, represent an important topic in ecological research.

However, the primary objective of this study is to develop a decision-oriented ecological restoration zoning framework based on the coupling of ecological vulnerability and ecosystem service values, and to propose differentiated management strategies accordingly. A systematic and quantitative analysis of ecosystem service trade-offs and synergies would typically require additional methodological frameworks, such as long-term time-series analysis, scenario-based simulations, or multi-objective optimization approaches, which are beyond the scope of the present study.

Nevertheless, the results of this study provide a basis for understanding production–conservation relationships. The spatial distribution patterns of ecosystem services (Section 3.2) reveal pronounced spatial differentiation, with production-oriented services dominating in plain areas and regulating services prevailing in mountainous regions, which spatially reflects trade-offs between production and conservation functions. In addition, the proposed ecological restoration zoning and corresponding management strategies (Sections 3.3 and 4.2) adopt differentiated approaches across zones, implicitly accounting for service balance and coordination.

20. It would be valuable to discuss briefly how this framework could be applied or adapted to other mountainous ecological barrier regions, both in China and elsewhere. I strongly suggest adding a short subsection on limitations (for example, the use of single-year data, model uncertainties, and the absence of future scenarios).

Answer:

Spatial resolution limitation: We clarified that the 1 km spatial resolution may underestimate fine-scale heterogeneity and local ecological risks in mountainous areas.

Model structural assumptions: We stated that the simplified assumptions underlying these ecological models may not fully represent the complexity of mountain ecosystems.

These additions have been incorporated into Section 4.3 of the revised manuscript, making the conclusions more rigorous and robust.

“4.3. highlights and limitations

Conclusions

21.I suggest restructuring the conclusions so that the main findings are summarized in 3–4 clear points, indicating what the study reveals about vulnerability, ecosystem services, and zoning in the Qinling–Bashan region.

Answer:

We thank the reviewer for pointing out the insufficient correspondence between conclusions and research objectives. We have carefully reviewed and revised the Conclusions section to ensure comprehensive responses to the three research objectives stated in the Introduction.

Correspondence between research objectives and conclusions:

Research Objective (i): Construct a robust framework for ecological restoration zoning

Corresponding Conclusion: We have added methodological contributions to the conclusions, explicitly stating that this study constructed a "vulnerability–ecosystem services" coupled zoning decision framework based on the VSD framework and InVEST model. This framework employs natural breaks classification, layer overlay, and township-level aggregation to form a transparent and operational decision matrix, providing a standardized technical pathway for ecological restoration zoning.

Research Objective (ii): Analyze the spatial distribution of ecological vulnerability and ecosystem service values

Corresponding Conclusion (1): Detailed elaboration of the spatial distribution characteristics of ecological vulnerability in the study area in 2020, including area proportions of each vulnerability level and spatial differentiation patterns (potentially vulnerable areas account for the highest proportion, while extremely vulnerable areas are mainly concentrated around Xi'an urban district).

Corresponding Conclusion (2): Systematic analysis of the spatial distribution characteristics of ecosystem service values, identifying carbon storage and net primary productivity as dominant services and revealing the spatial differentiation pattern where mountainous areas have significantly higher service values than other regions.

Research Objective (iii): Delineate targeted ecological restoration zones and propose corresponding protection and restoration strategies

Corresponding Conclusion (3): Clear delineation of four ecological restoration zones (Conservation Zone, Development Zone, Repair Zone, and Restoration Zone), elaborating their spatial distribution characteristics, area proportions, and ecological features, with differentiated management measures and restoration strategies proposed for each zone.

Revisions made:

We have enhanced the Conclusions section with the following additions:

Added methodological summary of framework construction: Incorporated a summary of the VSD-InVEST coupled framework and decision matrix construction process at the beginning of the conclusions;

Strengthened the systematic nature of spatial distribution analysis: Presented more detailed spatial differentiation characteristics and driving mechanisms of vulnerability and service values in Conclusions (1) and (2);

Refined the specificity of zoning strategies: Further detailed specific restoration strategies and management recommendations for each zone in Conclusion (3) to ensure complete correspondence with Objective (iii).

The revised Conclusions section now comprehensively covers all three research objectives, with all research findings reflected in the conclusions, ensuring logical consistency and completeness between the conclusions and research objectives.

We thank the reviewer again for the careful review and constructive comments.

“This study constructed an ecological vulnerability assessment index system based on the Vulnerability Scoping Diagram (VSD) framework and evaluated ecosystem service importance and value using the InVEST model in the Qinling–Bashan Mountains of Shaanxi and its adjacent regions. By coupling ecological vulnerability with ecosystem service values and integrating regional development demands and national strategic orientation, a standardized and operational “vulnerability–ecosystem service”–based eco-logical restoration zoning framework was established. On this basis, four ecological restoration zones were delineated: the Ecological Conservation Zone, Ecological Develop-ment Zone, Ecological Repair Zone, and Ecological Reconstruction Zone. The main conclusions are as follows:

(1) Spatial pattern of ecological vulnerability.

In 2020, potentially vulnerable areas accounted for the largest proportion of the study area, while extremely vulnerable areas occupied a relatively smaller share and were mainly concentrated around the urban district of Xi’an. Potentially vulnerable areas were widely distributed in the central and western parts of the Qinling Mountains, indicating significant spatial heterogeneity in ecological vulnerability.

(2) Spatial differentiation of ecosystem service values.

Carbon storage and net primary productivity were identified as the dominant eco-system services, contributing most to the overall ecosystem service value of the region. Mountainous areas exhibited significantly higher ecosystem service values than non-mountainous areas due to favorable natural conditions and relatively low levels of human disturbance, highlighting their role as core ecosystem service supply regions.

(3) Ecological restoration zoning and differentiated management strategies.

Based on the coupling results of ecological vulnerability and ecosystem service val-ues, four ecological restoration zones were clearly defined. The Ecological Conservation Zone occupies the largest proportion and is mainly distributed in the Qinling Mountains and their southern slopes, where ecological protection is prioritized. The Ecological Development Zone and the Ecological Reconstruction Zone are mainly located on the north-ern slopes of the Qinling Mountains, where ecological protection and socio-economic development must be coordinated; the former emphasizes sustainable development models, while the latter focuses on the restoration and reconstruction of severely degraded ecosystems. The Ecological Repair Zone, distributed on both the southern and northern slopes, has experienced varying degrees of ecological degradation due to long-term human disturbances and therefore requires targeted ecological restoration measures.

Overall, this study provides a standardized, decision-oriented ecological restoration zoning framework by integrating vulnerability assessment and ecosystem service evaluation, offering scientific support for spatial planning, ecological restoration, and regional sustainable development in the Qinling–Bashan Mountains.”

22.I would also recommend adding 2–3 concrete policy and management implications for ecological restoration and protection and briefly indicating possible directions for future work.

Answer:

Thank you for this helpful suggestion. We have revised the manuscript by adding a brief discussion of future research directions in the Conclusions section, including multi-temporal vulnerability assessment and the integration of higher-resolution socio-economic and field data.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This study delivers a scientifically grounded ecological restoration zoning framework that integrates ecosystem service importance with ecological vulnerability in the Qinling–Bashan region. However, it has room for improvement and I have listed several comments and suggestions.

The abstract is too long, it can be revised up to 250-260 words.
The introduction emphasizes China-specific ecological challenges but does not sufficiently connect these issues to global ecological restoration discourse. Adding global parallels (e.g., Andes, Alps, Himalayas) would highlight broader relevance.
Although several limitations of previous studies are mentioned, the manuscript does not clearly and explicitly articulate the specific gap this study fills. A 2–3 sentence explicit research gap statement is recommended. Also, explain why you chose this study?
Although significance is scattered across the introduction, a consolidated statement highlighting the ecological, methodological, and policy-level importance is missing.
The introduction should briefly justify why InVEST and VSD are most suitable for this region compared to other models (e.g., RUSLE, PLUS, SolVES, ARIES).
The introduction discusses climate change at length but does not clearly tie it to the justification for the chosen zoning framework. A stronger linkage is recommended.
If the authors add a flowchart to briefly explain the adopted methodology, it would be helpful for the readers and the presentation will be more elaborative.
The results section is well presented.
Figure 5 can be redrawn, as the linkages between its sections are not well presented. it must be revised.
Make sure that the conclusions are coverd with all the objectives initially targeted, and all the results have been achieved?
The Conclusions section seems too lengthy. It should be revised.
Add the limitations of the study, and future research directions also.
There are minor grammatical mistakes throughout the manuscript, which needs to be carefully handled and revised.
Also no need of Hyphenation in the text.

Author Response

1. The abstract is too long, it can be revised up to 250-260 words.

Answer:

Thank you for the reviewer’s valuable suggestion. In accordance with the comment, we have refined and shortened the Abstract to approximately 250 words, while retaining all essential components, including the research objectives, methodological framework, key findings, and practical implications. The revised Abstract is provided below:” The Qinling–Bashan Mountain region in Shaanxi Province and adjacent areas serves as a key ecological barrier and an important socio-economic belt in China. However, the region has experienced increasing ecological pressure due to natural drivers and intensive hu-man activities, highlighting the need for a systematic assessment to guide restoration strategies. This study proposes an ecological restoration zoning framework based on eco-logical vulnerability (EV) and ecosystem service value (ESV). The InVEST model was used to quantify major ecosystem services, while the Vulnerability Scoping Diagram (VSD) model was applied to evaluate ecological vulnerability. Both ESV and EV raster layers were classified using the natural-breaks method, overlaid to determine combined patterns, and aggregated at the township scale to delineate restoration zones. The results show that: (1) Ecological Vulnerability—The Qinling and Bashan ranges are dominated by potential and slight vulnerability, whereas severe and extreme vulnerability occur mainly in densely urbanized areas of the Guanzhong Plain. (2) Ecosystem Services—Mountain are-as exhibit high ESV, especially in carbon storage and net primary productivity, which together account for over 75% of the total ESV (CNY·km⁻²·yr⁻¹). (3) Zoning and Strategies—Four functional zones were identified: ecological development zone (21.5%), ecological restoration zone (2.8%), ecological repair zone (31.0%), and ecological conservation zone (44.8%). Corresponding management strategies are proposed to balance ecological protection with socio-economic development, improve ecosystem functions, and enhance regional ecological security. The Qinling-Bashan Mountain region in Shaanxi Province and adjacent areas is a crucial ecological and industrial belt in China, rich in biodiversity and natural resources that support local socio-economic development. However, natural factors and human activities have led to significant ecological changes, necessitating a systematic assessment to guide targeted ecological restoration measures. In this study, we propose an ecological restoration zoning framework suitable for the region, based on the importance of ecosystem services and ecological vulnerability. We employed the InVEST model to evaluate ecosystem services and the Vulnerability Scoping Diagram (VSD) model to assess ecological vulnerability. Using the natural break method, we classified the eco-logical vulnerability and ecosystem service value into levels and overlaid these layers to identify areas with different combinations, resulting in the delineation of ecological resto-ration zones. The results show that: (1) Ecological Vulnerability: The Qinling and Bashan mountain ranges are mainly characterized by potential and slight vulnerability, while areas of severe and extreme vulnerability are limited and primarily concentrated in urban development zones. This indicates that the overall ecological environment is relatively good, with significant issues mainly in the Guanzhong Plain region;(2) Ecosystem Services: The ecological service value is higher in the Qinling mountainous regions compared to surrounding plains and basins like the Guanzhong Plain and Hanzhong Basin, where rapid urbanization has reduced natural land areas. Carbon storage and net primary productivity contribute the most, accounting for over 75% of the total value. (3) Zoning and Management Strategies: Based on ecological vulnerability and ecosystem services, the study area is divided into four zones: ecological development zone (21.5% of total area), ecological restoration zone (2.8%), ecological repair zone (31.0%), and ecological conservation zone (44.8%). Each zone has specific management strategies aimed at balancing eco-logical protection with economic and social development, enhancing ecosystem functions, promoting ecosystem recovery, and ensuring ecological security. This zoning framework provides a targeted approach to address regional ecological vulnerabilities while promoting sustainable economic and social development.”

2.The introduction emphasizes China-specific ecological challenges but does not sufficiently connect these issues to global ecological restoration discourse. Adding global parallels (e.g., Andes, Alps, Himalayas) would highlight broader relevance.

Answer:

Thank you for the reviewer’s valuable suggestion. We have added a global-scale comparative perspective to the first paragraph of the Introduction, and the newly added paragraph is as follows: “At the global scale, mountain ecosystems are increasingly recognized as critical components of the Earth’s life-support system. In typical mountain regions such as the Andes, the Alps, and the Himalayas, ecosystems are widely subjected to multiple concurrent pressures, including accelerated climate warming, glacier and snowpack retreat, hydro-logical regime alteration, habitat fragmentation, biodiversity loss, and intensified land-use and development activities. Owing to their steep environmental gradients, high habitat heterogeneity, and inherently fragile ecological structure, mountain regions are particularly sensitive to these disturbances. Recent global studies have demonstrated that climate change and unsustainable land-use practices are significantly weakening the capacity of mountain areas to provide key ecosystem services, including water regulation, carbon storage, soil conservation, and biodiversity maintenance. These common global challenges are prominently manifested in the Qinling–Bashan region of China. As a major water conservation area and ecological security barrier in China, the Qinling–Bashan region plays a vital role in water resource protection, sustainable utilization, and the maintenance of ecosystem services, providing irreplaceable support for regional economic development and water security”. This new section systematically introduces typical mountain regions worldwide and describes the common pressures they face, including climate warming, glacier retreat, hydrological regime changes, habitat fragmentation, and biodiversity loss. We further point out that these global challenges are also prominently manifested in the Qinling–Bashan region of China, thereby clearly strengthening the international relevance and general applicability of this study.

3. Although several limitations of previous studies are mentioned, the manuscript does not clearly and explicitly articulate the specific gap this study fills. A 2–3 sentence explicit research gap statement is recommended. Also, explain why you chose this study?

Answer:

We sincerely thank the reviewer for this valuable suggestion. Following this comment, we have substantially revised the relevant paragraph in the Introduction to explicitly clarify the methodological gap addressed by this study. Specifically, we now clearly state that, although previous studies have provided important insights into ecological vulnerability patterns and ecosystem service assessments, there is still a lack of a unified framework that explicitly couples social–ecological vulnerability (including exposure, sensitivity, and adaptive capacity) with ecosystem service values for restoration-oriented spatial zoning. In addition, we further emphasize that few existing studies have transformed the integrated vulnerability–service relationship into an operable zoning scheme that directly supports ecological restoration decision-making under human–nature interactions. These new statements have been added at the end of the revised fourth paragraph of the Introduction, thereby clearly defining the specific research gap that this study aims to fill. The revised content is as follows: “Ecological restoration is an essential means of restoring damaged ecosystem structure and function to meet human needs and is key to improving ecosystem services [11]. Ecological restoration zoning provides a holistic and systematic perspective for implementing restoration and supporting targeted environmental governance [12,13]. In recent years, significant progress has been made in ecological restoration zoning in the Qinling–Bashan region. However, due to the lack of unified technical standards, existing studies employ diverse methods at different spatial scales. Some studies focus on single indicators, such as ecological vulnerability [14] or ecosystem-service supply–demand relationships [15], which may be overly simplified given the region’s complex terrain and climatic conditions. Other studies delineate restoration zones based on ecological source areas, incorporating factors such as ecosystem services, species habitats, natural reserves, and eco-logical red lines [8,16]. In addition, some researchers adopt comprehensive approaches based on ecological risk and vulnerability coupled with land-use analysis [17]. Nevertheless, current restoration-zoning research still faces three major limitations: (1) the lack of a unified zoning framework; (2) inconsistencies in indicator systems and evaluation methodologies; and (3) insufficient consideration of socio-economic disturbance and adaptive capacity. Accordingly, this study integrates ecological vulnerability and ecosystem ser-vices within a unified analytical framework tailored to the ecological structure and functional characteristics of the Qinling–Bashan region. Ecological vulnerability reflects the sensitivity of regional systems to external disturbances and their recovery capacity [18], while ecosystem services represent ecological value and contributions to human well-being [19]. Coupling these two dimensions enables a more comprehensive assessment of both ecological risk and functional benefit. However, although previous studies have provided valuable insights into ecological vulnerability patterns [20–22] and ecosystem service evaluation [23–26], a unified framework that explicitly couples socioecological vulnerability (including exposure, sensitivity, and adaptive capacity) with ecosystem service value for restoration-oriented spatial zoning is still lacking. In particular, few studies have translated the integrated vulnerability–service relationship into an operable zoning scheme that directly supports ecological restoration decision-making under human–nature interaction. This unresolved methodological gap constitutes the direct motivation of the present study, which constructs an integrated VSD–InVEST-based ecological restoration zoning framework for the Qinling–Bashan region.”

We have refined the description of the Qinling–Bashan region in the Introduction by retaining only one integrated paragraph that systematically explains both the ecological pressures and the strategic ecological importance of the region, thereby avoiding redundancy. In the revised version, we clearly state that the Qinling–Bashan region serves as a nationally critical ecological security barrier, a key water conservation area, and a climatic and ecological transition zone in China. Its ecosystems are not only highly important but also highly sensitive to climate change and human disturbance, which makes this region a representative and ideal case for ecological vulnerability assessment and ecological restoration zoning. These revisions directly address the reviewer’s concern regarding the rationale for selecting the Qinling–Bashan region and clarify the uniqueness of this study. The revised content is as follows: “In recent years, the Qinling–Bashan region has been subjected to pronounced ecological pressures driven by the combined effects of climate change and rapid economic development, posing severe challenges to regional ecological health. Uneven spatiotemporal distribution of precipitation has intensified water scarcity in areas such as the Guanzhong Basin and increased the frequency of extreme climatic events. Meanwhile, rapid urbanization and industrial expansion have continuously elevated water demand, while non-point source pollution and infrastructure construction have further aggravated ecological degradation [9,10]. As a climatic and ecological transition zone between northern and south-ern China, as well as a nationally important water conservation area and ecological security barrier, the Qinling–Bashan region occupies an irreplaceable position in the regional and national ecological security pattern. Its ecosystems are characterized by both high ecological importance and strong sensitivity to climate change and human disturbances, making the region a typical and critical testing ground for ecological vulnerability assessment and ecological restoration zoning. Under this background, conducting systematic ecological restoration studies oriented toward regional sustainable development is of great practical significance for improving ecosystem health, enhancing regional resilience, and supporting high-quality development.”

4. Although significance is scattered across the introduction, a consolidated statement highlighting the ecological, methodological, and policy-level importance is missing.

Answer:

We sincerely thank the reviewer for this constructive comment. Following the suggestion, we have added a dedicated paragraph in the Introduction to systematically summarize the ecological, methodological, and policy significance of this study. The newly added content is as follows: “This study has important ecological, methodological, and policy implications. Eco-logically, by integrating ecological vulnerability and ecosystem service value, it provides a scientific basis for identifying priority restoration areas and enhancing ecosystem health, ecological security, and regional resilience in the Qinling–Bashan Mountains. Methodologically, this study advances restoration-oriented spatial zoning by coupling the Vulnerability Scoping Diagram (VSD) with the InVEST model into a unified operational framework. From a policy perspective, the proposed zoning scheme offers direct technical support for territorial spatial planning, ecological conservation, and restoration decision-making. Overall, this study not only deepens the understanding of coupled socio-ecological systems but also provides a transferable reference for ecological restoration and sustainable land management in other vulnerable mountain regions.”

5. The introduction should briefly justify why InVEST and VSD are most suitable for this region compared to other models (e.g., RUSLE, PLUS, SolVES, ARIES).

Answer:

Thank you very much for the reviewer’s valuable suggestion. We have revised the manuscript accordingly: a clear comparison and relevant citations have been added to the Introduction to justify why we adopt the VSD + InVEST framework rather than applying other commonly used models directly.” To address the above gap, this study integrates ecological vulnerability and ecosystem services within a unified analytical framework tailored to the ecological structure and functional characteristics of the Qinling–Bashan region. Ecological vulnerability reflects the sensitivity of regional systems to external disturbances and their recovery capacity [18], while ecosystem services represent ecological value and contributions to human well-being [19]. Coupling these two dimensions enables a more comprehensive assessment of both ecological risk and functional benefit. To further highlight the methodological differences from previous studies, earlier ecological vulnerability assessments primarily relied on natural factors—such as slope, terrain relief, vegetation cover, and soil erosion risk—while socioeconomic exposure and adaptive capacity were less frequently considered [27,28]. In contrast, this study adopts the Vulnerability Scoping Diagram (VSD) framework, which decomposes vulnerability into exposure, sensitivity, and adaptive capacity, thereby providing a more systematic socio-ecological perspective than convention-al approaches [29]. Moreover, although some studies have attempted to integrate ecological vulnerability with ecosystem services, most relied on simple overlay analysis or composite indices, lacking standardized combination rules. In this study, natural breaks classification, layer overlay, and township-level aggregation are applied to construct a more transparent and operational “vulnerability–service value” decision matrix. This approach enhances the practicality of ecological restoration zoning and provides a clearer technical pathway for spatial planning and ecological management. Compared with single-process models such as RUSLE (focusing on soil erosion), land-use simulation models such as PLUS, social perception–oriented tools such as SolVES, and data-intensive service-flow platforms such as ARIES, the InVEST model provides a standardized and multi-service biophysical assessment framework that is more suitable for regional-scale ecosystem ser-vice quantification in this study[27,28]. Given the strong coupling between climate change, human disturbance, and ecosystem functions in the Qinling–Bashan region, the joint ap-plication of the VSD framework and the InVEST model is particularly suitable for comprehensively characterizing regional socio-ecological vulnerability and ecosystem service patterns[29].”

6. The introduction discusses climate change at length but does not clearly tie it to the justification for the chosen zoning framework. A stronger linkage is recommended.

Answer:

Thank you for the valuable comment. In response, we have clarified in the revised manuscript that climate change is one of the key drivers motivating our choice of a coupled vulnerability–ecosystem services (ES) framework. Specifically, climate change — through altered precipitation, temperature and increased frequency of extreme events — can increase pressure on ecosystems (via exposure and sensitivity), affecting vegetation, soil, hydrology and habitat conditions, while simultaneously undermining the supply of ecosystem services such as water regulation, carbon storage, soil conservation and habitat quality. Under these circumstances, traditional static vulnerability assessments based solely on topography or land-use are insufficient to capture the full extent of climate- and human-driven changes. We argue that coupling the InVEST model with the Vulnerability Scoping Diagram (VSD) framework in our study area — which is both climatically sensitive and socio-ecologically complex — is a justified and necessary approach. This enables simultaneous identification of zones demanding protection, maintenance, or restoration under current and future climate stress, thereby offering a more robust technical basis for spatial planning and ecosystem management.

7. If the authors add a flowchart to briefly explain the adopted methodology, it would be helpful for the readers and the presentation will be more elaborative.

Answer:

Thank you very much for your suggestion. We have now added a flowchart illustrating our methodology to the revised manuscript (see Figure 2). We believe this new figure helps to make the workflow clearer and will improve readability by showing how data (e.g., DEM, land-use, vegetation cover, soil, climate, socioeconomic data) feed into ecosystem-services evaluation, ecological vulnerability assessment, overlay analysis, and finally zoning and management recommendations.

We hope this addition meets your expectation and contributes to a more intuitive presentation of our approach.

Figure 2. Methodological Flowchart

8. The results section is well presented.

Answer:

Thank you for your appreciation of the Results section. We are glad that you find the presentation and clarity of our results satisfactory. We will continue to uphold this level of rigor and clarity throughout the manuscript to ensure the overall logical coherence and robustness of our conclusions.

9. Figure 5 can be redrawn, as the linkages between its sections are not well presented. it must be revised.

Answer:

We thank the reviewer for this valuable comment. We agree that the logical connections among different components in the original Figure 5 were not sufficiently clear. In response, we have completely redrawn and optimized Figure 5.

Major improvements include:

(1) Optimized graphical structure: We adopted a matrix-style layout that more intuitively presents the cross-relationship between "ecological vulnerability levels" and "ecosystem service value levels," clearly demonstrating how these two dimensions jointly determine ecological restoration zoning types.

(2) Clarified zoning logic:

Conservation Zone (green): Low vulnerability (Potential/Slight) + High/Higher service value → Priority protection and maintenance

Development Zone (orange-yellow): Medium value areas across all vulnerability gradients + High value areas with high vulnerability → Moderate development and utilization

Repair Zone (blue): Low vulnerability + Lowest/Lower service value → Ecological function enhancement

Restoration Zone (red): High vulnerability (High/Extreme) + Lowest/Lower service value → Priority restoration and rehabilitation

(3) Enhanced visual communication: Color coding (green-yellow-blue-red) intuitively reflects the restoration priority gradient. Left-side label boxes with arrows clearly indicate the vulnerability–service value combinations corresponding to each zone.

(4) Refined decision rules: Each cell explicitly shows the corresponding combination of service value level and vulnerability level, making the zoning decision criteria transparent and facilitating spatial unit classification in practical applications.

The revised Figure 5 more clearly illustrates the core logic of "how to conduct ecological restoration zoning decisions based on the coupled relationship between ecological vulnerability and ecosystem service value," thereby enhancing both the scientific rigor and readability of the figure.

We thank the reviewer again for helping us improve the quality of our figures.

10. Make sure that the conclusions are coverd with all the objectives initially targeted, and all the results have been achieved?

Answer:

Correspondence between research objectives and conclusions:

Research Objective (i): Construct a robust framework for ecological restoration zoning

Research Objective (ii): Analyze the spatial distribution of ecological vulnerability and ecosystem service values

Research Objective (iii): Delineate targeted ecological restoration zones and propose corresponding protection and restoration strategies

Revisions made:

We have enhanced the Conclusions section with the following additions:

Added methodological summary of framework construction: Incorporated a summary of the VSD-InVEST coupled framework and decision matrix construction process at the beginning of the conclusions;

We thank the reviewer again for the careful review and constructive comments.

“This study constructed an ecological vulnerability assessment index system based on the Vulnerability Scoping Diagram (VSD) framework and evaluated ecosystem service importance and value using the InVEST model in the Qinling–Bashan Mountains of Shaanxi and its adjacent regions. By coupling ecological vulnerability with ecosystem service values and integrating regional development demands and national strategic orientation, a standardized and operational “vulnerability–ecosystem service”–based eco-logical restoration zoning framework was established. On this basis, four ecological restoration zones were delineated: the Ecological Conservation Zone, Ecological Development Zone, Ecological Repair Zone, and Ecological Reconstruction Zone. The main conclusions are as follows:

(1) Spatial pattern of ecological vulnerability.

(2) Spatial differentiation of ecosystem service values.

(3) Ecological restoration zoning and differentiated management strategies.

Based on the coupling results of ecological vulnerability and ecosystem service values, four ecological restoration zones were clearly defined. The Ecological Conservation Zone occupies the largest proportion and is mainly distributed in the Qinling Mountains and their southern slopes, where ecological protection is prioritized. The Ecological Development Zone and the Ecological Reconstruction Zone are mainly located on the north-ern slopes of the Qinling Mountains, where ecological protection and socio-economic development must be coordinated; the former emphasizes sustainable development models, while the latter focuses on the restoration and reconstruction of severely degraded ecosystems. The Ecological Repair Zone, distributed on both the southern and northern slopes, has experienced varying degrees of ecological degradation due to long-term human disturbances and therefore requires targeted ecological restoration measures.

11．The Conclusions section seems too lengthy. It should be revised.

Answer:

Thank you for this helpful suggestion. We have carefully condensed and refined the Conclusions section by removing repetitive descriptions, simplifying expressions, and retaining only the most essential findings and implications. As a result, the length of the Conclusions has been substantially reduced, while its clarity, logical structure, and focus on key results have been improved.

12．Add the limitations of the study, and future research directions also.

Answer:

Spatial resolution limitation: We clarified that the 1 km spatial resolution may underestimate fine-scale heterogeneity and local ecological risks in mountainous areas.

Model structural assumptions: We stated that the simplified assumptions underlying these ecological models may not fully represent the complexity of mountain ecosystems.

These additions have been incorporated into Section 4.3 of the revised manuscript, making the conclusions more rigorous and robust.

“4.3. highlights and limitations

This study integrates ecological vulnerability assessment and ecosystem service val-uation to provide an essential perspective on understanding the ecological and environ-mental patterns of the Qinling–Bashan region in Shaanxi and its surrounding areas. Based on these assessments, an ecological restoration zoning framework was developed to address region-specific ecological challenges and to explore how differentiated ecologi-cal management can be implemented through zoning strategies, thereby linking ecologi-cal restoration with socio-economic development goals and promoting sustainable re-gional development. As the study area serves not only as a critical national ecological bar-rier and the location of national parks but is also closely connected to regional economic development needs, the proposed framework carries substantial practical relevance.

Nevertheless, several inherent limitations and uncertainties remain. First, the use of 1-km spatial resolution data restricts the ability to capture fine-scale ecological heteroge-neity in mountainous environments, potentially underestimating micro-topographic var-iations and localized fragmented patches. Second, the assessment relies on data from a single year (2020), which cannot reflect interannual dynamics driven by climate change, land-use transitions, or human disturbances. Additionally, key parameters used in the InVEST, RUSLE, CASA, and Budyko-based water yield models are derived primarily from literature sources rather than locally calibrated measurements, which introduces parame-ter uncertainty. Meanwhile, all models are based on simplified assumptions and may not fully represent the complexity of mountain ecosystems, meaning that the results are more appropriate for relative comparisons than for absolute quantitative interpretation.

Finally, although the ecological restoration zoning framework proposed in this study has conceptual generality and can serve as a methodological reference for other moun-tainous regions, its practical application requires adaptation to local natural conditions, socio-economic contexts, and data availability. Future research may benefit from using higher-resolution remote sensing data, incorporating long-term time-series observations, conducting local parameter calibration, and integrating machine learning and mul-ti-model ensemble approaches to further enhance the precision and reliability of ecologi-cal restoration zoning.”

13．There are minor grammatical mistakes throughout the manuscript, which needs to be carefully handled and revised.

Answer:

Thank you very much for the reviewer’s careful suggestion. We have conducted a comprehensive and detailed language revision of the entire manuscript, with special attention paid to grammar, tense consistency, sentence structure, and academic expression. All identified grammatical issues have been corrected. In addition, the manuscript has been carefully proofread to improve clarity, readability, and overall language quality.

14． Also no need of Hyphenation in the text.

Answer:

Thank you for this helpful suggestion. We have carefully checked the entire manuscript and removed all unnecessary hyphenations, while retaining only those required by standard terminology. The writing style has now been unified and normalized throughout the manuscript.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Accept after reducing the similarity ratio from 23% to less than 15%. According to the iThenticate report, the current percentage is high ~ 23%.

Author Response

Thank you for your feedback. In response to your suggestion, I have revised the manuscript to reduce the similarity ratio, which has now been lowered from 23% to 14%. I believe this addresses your concern, and I appreciate your understanding in considering the revised version.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Authors have clearly made a great effort to revise the manuscript, and many of the points raised in the first round of review have been addressed. However, I would still recommend minor revision. My comments are summarized below, section by section.
Abstract
I would suggest retaining only two or three of the most important quantitative results, for example, the proportions of the major zones and the dominant contribution of carbon storage/NPP to total ESV and using the remaining space to emphasize the central message and key implications of the study.
Introduction
The paragraph in which the authors outline the gaps in existing studies and present the main contributions of this paper is conceptually sound but remains rather long and dense. I recommend condensing it into about two to three precise sentences that explicitly answer the following questions:
1. What, specifically, is missing in previous ecological restoration zoning work?
2. What is genuinely new about your integrated VSD–InVEST–matrix framework in the context of this particular ecological barrier region?
Materials and Methods
The manuscript states that water yield is estimated using a Budyko-based approach. To improve methodological transparency, it would be helpful to specify more clearly:
• Which key parameters were used (for example, the Budyko Z parameter and soil water-holding capacity);
• Whether these values were taken as default values from the literature or adjusted to reflect local conditions in the Qinling–Bashan region; and
• Whether any calibration or simple sensitivity analysis was performed. If no calibration was undertaken, it would be useful to say so explicitly and briefly comment on the implications for the robustness of the water yield estimates.
Discussion
The authors now include some comparison with previous zoning work, for example, noting differences in certain areas such as parts of Baoji and Hanzhong. I think the manuscript would benefit from making this comparative element a little more explicit by clarifying:
• Where the new zoning results broadly coincide with existing ecological function zones or ecological red-line areas; and
• Where they diverge and thereby provide new insights or added value for planning and management.
In addition, the manuscript notes that the proposed framework can serve as a methodological reference for other mountainous ecological barrier regions. I would suggest adding a concrete example of how the approach could be adapted, for instance, to other ecological barrier belts in China or to mountain ranges in different regions of the world.

Author Response

1.Abstract

I would suggest retaining only two or three of the most important quantitative results, for example, the proportions of the major zones and the dominant contribution of carbon storage/NPP to total ESV and using the remaining space to emphasize the central message and key implications of the study.

Answer:

Thank you for the reviewer’s suggestion. We have revised the abstract according to the reviewer's comments. Specifically, we have retained the two most important quantitative results:

Carbon storage and Net Primary Productivity (NPP) together contribute 93% of the total ecosystem service value (ESV).

The proportions of ecological conservation zones (44.8%) and restoration zones (2.8%).

While retaining these key quantitative results, we have simplified other content, particularly by removing some overly detailed technical descriptions (such as the specific applications of InVEST and VSD models), and emphasized the study’s core conclusions and management implications. These changes have made the abstract more concise and better convey the core message and significance of the research.

The following are the revisions made:

“The Qinling–Bashan Mountain region in Shaanxi Province and adjacent areas is a key ecological barrier and an important socio-economic zone in China, facing increasing ecological pressure from both natural drivers and human activities. This study proposes an ecological restoration zoning framework based on ecological vulnerability (EV) and ecosystem service value (ESV). The InVEST model was used to quantify major ecosystem services, while the Vulnerability Scoping Diagram (VSD) model evaluated ecological vulnerability. Both ESV and EV layers were classified using the natural-breaks method and aggregated at the township level to delineate restoration zones. Unlike previous studies relying on subjective judgment, this study constructs a standardized 'vulnerability–service value' decision matrix for the Qinling–Bashan region, providing a clear technical pathway for spatial restoration. Key findings include: (1)Spatial Vulnerability Pattern: The Qinling and Bashan mountain cores exhibit predominantly low vulnerability (potential and slight), while severe vulnerability is concentrated in the urbanizing Guanzhong Plain, emphasizing the need for urban ecological restoration; (2)Dominant Ecosystem Services: Carbon storage and net primary productivity (NPP) together account for 93% of the total ESV, highlighting the importance of forest conservation for national climate regulation; (3)Zoning Strategy: Four functional zones were defined, with the largest being the ecological conservation zone (44.8%), while a smaller ecological restoration zone (2.8%) in urban peripheries requires targeted intervention.”

2.Introduction

The paragraph in which the authors outline the gaps in existing studies and present the main contributions of this paper is conceptually sound but remains rather long and dense. I recommend condensing it into about two to three precise sentences that explicitly answer the following questions:What, specifically, is missing in previous ecological restoration zoning work?What is genuinely new about your integrated VSD–InVEST–matrix framework in the context of this particular ecological barrier region?

Answer:

Thank you for your valuable feedback. We fully agree with your suggestion that the original introduction was lengthy and dense. Therefore, we have revised and condensed this section to clearly address the following points:

What is missing in current ecological restoration zoning work?

Current studies lack a unified framework that couples socio-ecological vulnerability (including exposure, sensitivity, and adaptive capacity) with ecosystem service value and translates this into operable zoning rules. Existing methods often focus on single indicators, such as ecological vulnerability or ecosystem service supply–demand relationships, without fully considering socio-economic factors and adaptive capacity.

What is the true novelty of the integrated VSD–InVEST–matrix framework in the context of this ecological barrier zone?

The novelty of our proposed VSD–InVEST–decision matrix framework lies in its ability to decompose vulnerability into exposure, sensitivity, and adaptive capacity, providing a comprehensive socio-ecological perspective. Additionally, rather than relying on simple overlay analysis, we construct operable zoning rules through an explicit decision matrix, enhancing the transparency, reproducibility, and policy readiness of ecological restoration zoning.

These revisions effectively simplify the introduction and clearly communicate the study's objectives and innovations.

The following are the revisions made:

“Ecosystems provide essential resources for human life and well-being, and their health directly determines the availability and quality of these resources [1], thereby underpinning community livelihoods and economic stability [2]. In recent years, ecological and environmental issues have become a global focus [3,4]. Since the 20th century, rapid urbanization and industrialization have accelerated socio-economic development, yet irrational human activities have severely disturbed natural systems—altering human–land relationships, accelerating habitat loss, reducing biodiversity, and degrading ecosystem services [5]. In parallel, climate change has profoundly affected global ecosystems, further intensifying imbalances in ecosystem structure, function, and service provision, with potential consequences for habitat contraction, ecosystem health deterioration, and regional stability [6].

Mountain ecosystems are particularly vulnerable to the combined pressures of climate change and human activity because of their complex terrain, fragile ecological structure, and strong dependence on agricultural ecosystems and natural resources [7]. Globally, mountain regions such as the Andes, the Alps, and the Himalayas are experiencing concurrent stressors, including accelerated warming, glacier and snowpack retreat, hydrological regime shifts, habitat fragmentation, biodiversity loss, and intensified land-use and development. Due to steep environmental gradients and high habitat heterogeneity, mountain ecosystems respond sensitively to disturbances, and recent studies indicate that climate change and unsustainable land-use practices are weakening key ecosystem services such as water regulation, carbon storage, soil conservation, and biodiversity maintenance. These challenges are also evident in the Qinling–Bashan region of China [8].

In recent years, the Qinling–Bashan region has faced pronounced ecological pressures driven by climate change and rapid economic development, posing serious challenges to regional ecosystem health. The uneven spatiotemporal distribution of precipitation has intensified water scarcity in areas such as the Guanzhong Basin and increased the frequency of extreme climatic events. Meanwhile, rapid urbanization and industrial expansion have continuously elevated water demand, while non-point source pollution and infrastructure construction have further aggravated ecological degradation [9,10]. As a climatic and ecological transition zone between northern and southern China, and a nationally important water conservation area and ecological security barrier, the Qinling–Bashan region plays an irreplaceable role in China’s ecological security pattern. Its ecosystems are characterized by high ecological importance and strong sensitivity to both climate variability and human disturbance, making the region a critical testing ground for ecological vulnerability assessment and restoration-oriented spatial zoning.

Ecological restoration is a key approach for recovering damaged ecosystem structure and function and for improving ecosystem services [11]. Ecological restoration zoning, in particular, provides a holistic and systematic basis for prioritizing interventions and supporting targeted environmental governance [12,13]. Considerable progress has been made in restoration zoning in the Qinling–Bashan region; however, due to the lack of unified technical standards, existing studies employ diverse methods across spatial scales. Some studies rely on single indicators—such as ecological vulnerability [14] or ecosystem-service supply–demand relationships [15]—which may oversimplify the region’s complex terrain and climatic conditions. Other studies delineate zones based on ecological source areas and conservation constraints (e.g., ecosystem services, habitats, protected areas, ecological red lines) [8,16], or integrate ecological risk/vulnerability with land-use analysis [17]. Nevertheless, three limitations remain prominent: (1) the absence of a unified zoning framework; (2) inconsistencies in indicator systems and evaluation methods; and (3) insufficient consideration of socio-economic disturbance and adaptive capacity. Accordingly, although prior work has generated valuable insights into ecological vulnerability patterns [20–22] and ecosystem service evaluation [23–26], an operable framework that explicitly couples socio-ecological vulnerability (including exposure, sensitivity, and adaptive capacity) with ecosystem service value for restoration-oriented zoning is still lacking. In particular, few studies translate the integrated vulnerability–service relationship into transparent zoning rules that directly support restoration decision-making under human–nature interactions.

To address this gap, this study integrates ecological vulnerability and ecosystem services within a unified analytical framework tailored to the ecological structure and functional characteristics of the Qinling–Bashan region. Ecological vulnerability reflects the sensitivity of regional systems to external disturbances and their recovery capacity [18], whereas ecosystem services represent ecological value and contributions to human well-being [19]. Coupling these two dimensions enables a more comprehensive identification of areas facing high ecological risk yet delivering critical functions, thereby improving the relevance of zoning for restoration prioritization. Compared with conventional vulnerability assessments that emphasize natural factors (e.g., slope, terrain relief, vegetation cover, and erosion risk) and often underrepresent socio-economic exposure and adaptive capacity [27,28], we adopt the Vulnerability Scoping Diagram (VSD) framework to decompose vulnerability into exposure, sensitivity, and adaptive capacity, providing a systematic socio-ecological perspective [29].

Moreover, while some studies have attempted to integrate vulnerability with ecosystem services, many have relied on simple overlay analysis or composite indices with limited interpretability and inconsistent combination rules. Here, we employ natural breaks classification, layer overlay, and township-level aggregation to construct an explicit and operational “vulnerability–service value” decision matrix. This matrix-based rule set is designed to enhance transparency, reproducibility, and policy readiness of ecological restoration zoning, thereby strengthening the linkage between scientific assessment and spatial governance.

This study has important ecological, methodological, and policy implications. Ecologically, by jointly considering socio-ecological vulnerability and ecosystem service value, it provides a scientific basis for identifying priority restoration areas and enhancing ecosystem health, ecological security, and regional resilience in the Qinling–Bashan Mountains. Methodologically, it advances restoration-oriented spatial zoning by integrating the VSD framework with ecosystem service assessment and an explicit decision-matrix rule system into a unified operational pathway [29]. From a policy perspective, the proposed zoning scheme offers direct technical support for territorial spatial planning, ecological conservation, and restoration decision-making, and provides a transferable reference for restoration planning in other vulnerable mountain regions.

Based on this foundation, the present study aims to: (i) construct a robust framework for ecological restoration zoning; (ii) analyze the spatial distribution patterns of socio-ecological vulnerability and ecosystem service values; and (iii) delineate targeted ecological restoration zones and propose corresponding protection and restoration strategies.”

3.Materials and Methods

The manuscript states that water yield is estimated using a Budyko-based approach. To improve methodological transparency, it would be helpful to specify more clearly:

Which key parameters were used (for example, the Budyko Z parameter and soil water-holding capacity);Whether these values were taken as default values from the literature or adjusted to reflect local conditions in the Qinling–Bashan region; and Whether any calibration or simple sensitivity analysis was performed. If no calibration was undertaken, it would be useful to say so explicitly and briefly comment on the implications for the robustness of the water yield estimates.

Answer:

Thank you for your valuable suggestions regarding the methodology section. In response to your feedback, we have made further revisions and additions to improve the transparency of the method. The specific changes are as follows:

(1)Detailed explanation of formulas and parameters:

We have added detailed explanations of the formulas and parameters in the manuscript. In particular, we have further clarified the meaning and calculation methods of each parameter in the formulas. Specifically, for Yxj, we now clearly state that it represents the water yield of each grid cell, which is calculated based on the land use type j data to estimate the water source conservation volume of that cell. This ensures that readers can better understand the role of each parameter in the water source conservation estimation.

(2)Detailed explanation of model application:

To more clearly explain how we applied the Budyko model for water source conservation assessment, we have added explanations about how we used annual precipitation data, evapotranspiration (AETxj), and vegetation cover, among other factors. By considering these factors comprehensively, we can more accurately assess the water source conservation service.

“Yxj represents the water yield of grid cell x, which is estimated by the selected land use type j data to calculate the water source conservation volume of that cell, AETxj represents the annual evapotranspiration, and P(x) represents the annual precipitation of grid cell x. is a parameter used to describe climate and soil properties. represents the dryness index of grid cell x for land type j, calculated as the ratio of potential evapotranspiration to precipitation. kxj is the vegetation evapotranspiration coefficient, is the potential evapotranspiration of grid cell x, and panET is the actual evapotranspiration. Z is the Zhang coefficient, which represents the seasonal rainfall distribution and precipitation depth parameter. represents the available water content of grid cell x, and is determined by selecting the minimum value between soil depth (MaxSoilDepthx) and root depth (RootDepthx). sand represents the soil sand content; silt represents the soil silt content; clay represents the soil clay content; and OM represents the soil organic matter content. The proportion of sand, silt, and clay is referenced from the Chinese Soil Database (http://www.soil.csdb.cn/). PAWC represents the plant available water in grid cell x.

The relevant grid factors, including root depth for each land use type (iRoot depth) and crop evapotranspiration coefficient (Kxj), are input into the InVEST model's water yield module along with other related parameters as shown in the table.”

Table 4. Parameters of the InVEST Water Production Module

*Land Use Type*	*Evapotranspiration (mm)*	*iRoot Depth (mm)*
*Dryland*	*750*	*300*
*Paddy Field*	*700*	*300*
*Forest*	*1000*	*5000*
*Grassland*	*600*	*500*
*Water Body*	*1000*	0
*Built-up Area*	1	1
*Unused Land*	1	1

4.Discussion

The authors now include some comparison with previous zoning work, for example, noting differences in certain areas such as parts of Baoji and Hanzhong. I think the manuscript would benefit from making this comparative element a little more explicit by clarifying:

Where the new zoning results broadly coincide with existing ecological function zones or ecological red-line areas; and

Where they diverge and thereby provide new insights or added value for planning and management.

In addition, the manuscript notes that the proposed framework can serve as a methodological reference for other mountainous ecological barrier regions. I would suggest adding a concrete example of how the approach could be adapted, for instance, to other ecological barrier belts in China or to mountain ranges in different regions of the world.

Answer:

We sincerely thank the reviewer for this constructive suggestion.

We have substantially revised the Discussion section to address

both points raised.

(1) Regarding consistency and divergence with existing zoning schemes:

We have added a detailed comparison between our zoning results and the Shaanxi Provincial Territorial Spatial Plan (2021–2035) as well as the Shaanxi Qinling Ecological Environment Protection Regulations.

Areas of consistency:Our ecological conservation zones largely overlap with provincial ecological protection red lines in the Qinba Mountain area and the Ziwuling-Huanglong biodiversity conservation zone. Our restoration zones align with areas designated for urban ecological improvement around the Xi'an metropolitan area.

Areas of divergence providing new insights:Within the "Qinling Northern Foothills Ecological Protection Belt," our analysis identified localized areas with elevated vulnerability scores that require more intensive restoration interventions. The "Guanzhong Northern Mountain Green Reconstruction Belt" similarly exhibited high vulnerability scores in our assessment, validating the "reconstruction" designation in provincial planning.

These discrepancies demonstrate how our integrated VSD-InVEST framework

can complement existing boundary-based schemes by identifying priority

intervention areas within broader protection zones.

(2) Regarding methodological transferability:

We have added a paragraph discussing how the proposed framework can

be adapted to other mountainous ecological barrier regions. We use the Hengduan Mountains in southwestern China as a domestic example, specifying necessary indicator modifications (e.g., permafrost degradation, glacier retreat). We also briefly mention international applicability to mountain ranges such as the European Alps, noting the need for alignment with regional ecosystem service classification standards.

These revisions appear in Section 4.2 (Discussion).

“To examine the validity and practical relevance of our zoning results, we compared them with the ecological conservation scheme established in the Shaanxi Provincial Territorial Spatial Planning (2021–2035) and the Regulations of Shaanxi Province on the Protection of the Ecological Environment of the Qinling Mountains. Overall, our zoning outcomes show strong consistency with the province’s ecological management priorities. The ecological conservation zones identified in this study are mainly located in the core mountainous areas of the Qinling–Daba Mountains, and they broadly overlap with the ecological protection redline delineated in the provincial plan, including the Qinba Mountain region, the Ziwuling–Huanglongshan biodiversity conservation area, and riparian zones within the Weihe River Basin. The provincial plan proposes an ecological security pattern of “one mountain, two rivers, four zones, and six belts,” and our conservation zones align closely with the “Qinba low-mountain and hilly ecological function zone” identified therein. Similarly, the restoration zones in our study are concentrated in the Xi’an metropolitan area and eastern Weinan, which is consistent with the provincial plan’s emphasis on improving urban ecology and controlling the boundary of urban expansion.

However, we also observed some differences that may provide additional insights for planning. First, the provincial plan designates the northern foothills of the Qinling Mountains as the “ecological protection belt of the northern Qinling foothills,” whereas in our analysis, some parts of this area were identified as restoration zones due to relatively high vulnerability scores. This suggests that even within officially designated protection belts, there are localized areas that require more intensive restoration efforts, and that single-indicator or boundary-based approaches may overlook such areas.

The methodological framework proposed in this study is conceptually general and can serve as a reference for other mountainous ecological barrier regions. For example, the Hengduan Mountains in southwestern China—another national-level ecological barrier characterized by high biodiversity value and complex terrain—could benefit from a similar integrated VSD–InVEST approach. Adapting the framework to that region would require incorporating region-specific vulnerability indicators such as permafrost degradation, glacier retreat, and shifts in vertical vegetation belts, as well as calibrating parameters according to local precipitation regimes and vegetation characteristics. Although necessary local adjustments are required, the core logic of the framework—overlaying classified vulnerability and ecosystem service value layers to derive differentiated management zones—has broad applicability in mountainous areas facing the challenge of balancing ecological conservation and socio-economic development.”

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have revised the manuscript as suggested, and now it looks suitable and aligned with the journal's standards, and hence it can be endorsed for acceptance.

Author Response

Thank you very much for your positive assessment and for taking the time to review our manuscript. We appreciate your comment that the revised version now meets the journal’s requirements. We have carefully implemented the suggested revisions throughout the manuscript, and we are grateful for your support for acceptance.

Article Menu

Ecological Restoration Zoning Based on the “Importance–Vulnerability” Framework for Ecosystem Services

一、Major Comments

1.Abstract

2.Introduction

3. Methods – VSD Indicators (Table 1):

4. Methods – InVEST Parameters (Section 2.3.2):

5. Methods – Zoning Matrix (Figures 5–6):

6 Results – Validation (Section 3):

7. Discussion – Zone Definitions (Section 4.2):

8 Conclusion – Uncertainties:

二、Minor Comments

1. Abstract: Specify "75%" refers to monetary value in CNY.

2. Introduction (Line 69): Clarify "this phenomenon" means ecological importance with increasing vulnerability.

3. Equation 8: Define all symbols (θ, λ, m) clearly.

4. Table 3: Add "%" to the Proportion column.

5. Figure 4: Include full units (e.g., "10⁸ CNY/year") in the legend.

Abstract

1.I suggest adding one clear sentence that explicitly states what is new compared with previous ecological zoning studies (for example, what is distinctive about combining VSD, multi-ecosystem-service assessment, and the zoning matrix in this specific ecological barrier region).

2.I would recommend keeping only the most essential methodological elements in the abstract and avoiding long, formula-like descriptions, which are better placed in the Methods section.

3. For the results part of the abstract, I suggest highlighting 2–3 concrete findings (e.g. which zones dominate in area, which ecosystem services are most important, and one or two key management implications).

Introduction

4. I feel that the introduction would benefit from a clearer problem statement. I suggest that the authors explain more directly what is missing in existing studies and how this work aims to fill those gaps.

5. Some paragraphs are quite long. I recommend splitting longer paragraphs into shorter ones to make the narrative easier to follow.

Materials and Methods

6.The description of the Qinling–Bashan region is generally clear, but I suggest emphasizing more explicitly why this region is strategically important (for instance, its role as an ecological barrier, a biodiversity hotspot, or a key water source).

7.I would also encourage the authors to link each key feature (topography, climate, land use, and socio-economic pattern) to its implications for ecological vulnerability and ecosystem services.

8. I suggest adding one or two sentences that summarize the main environmental problems in the region and explain why ecological restoration zoning is needed.

Data sources

9. Since many data sources are used (DEM, land use, NDVI, climate, soil, socio-economic data, etc.), I recommend adding a small table listing: each dataset, the source institution, the year(s), and the spatial resolution.

10. For the VSD-based indicators, I suggest explaining more clearly why each indicator is assigned to exposure, sensitivity, or adaptive capacity (for example, why DEM is treated as “sensitivity” and why food production reflects “adaptive capacity”).

11. I would also recommend clarifying the expected direction of influence of each indicator (i.e. whether higher values increase or decrease vulnerability).

12.The descriptions of the ecosystem service models are rather general. I suggest specifying the sources of the carbon density values for each land-use class and indicating whether any local adjustment or validation was done.

13.For the water yield model, I recommend presenting the key parameters (e.g. Budyko-related parameters) and briefly explaining how they were chosen or calibrated. If no calibration was performed, it would be good to state this explicitly.

14. For soil conservation (RUSLE), I suggest adding a small table summarizing how the R, K, LS, C and P factors were derived (data sources, equations, and main references).

Zoning framework

Results

16.I recommend that the authors focus more on the key spatial patterns rather than listing many similar numbers. When presenting areas or values, I suggest emphasizing the main contrasts and dominant trends.

17. For each main zone, I think it would be helpful to add a short summary that covers:the dominant land-use types,the dominant ecosystem services, and the main vulnerability characteristics.

Discussion

19. I also recommend adding some reflection on trade-offs and synergies among ecosystem services in the different zones (e.g. production vs. conservation).

Conclusions

21.I suggest restructuring the conclusions so that the main findings are summarized in 3–4 clear points, indicating what the study reveals about vulnerability, ecosystem services, and zoning in the Qinling–Bashan region.

22.I would also recommend adding 2–3 concrete policy and management implications for ecological restoration and protection and briefly indicating possible directions for future work.

1. The abstract is too long, it can be revised up to 250-260 words.

2.The introduction emphasizes China-specific ecological challenges but does not sufficiently connect these issues to global ecological restoration discourse. Adding global parallels (e.g., Andes, Alps, Himalayas) would highlight broader relevance.

3. Although several limitations of previous studies are mentioned, the manuscript does not clearly and explicitly articulate the specific gap this study fills. A 2–3 sentence explicit research gap statement is recommended. Also, explain why you chose this study?

4. Although significance is scattered across the introduction, a consolidated statement highlighting the ecological, methodological, and policy-level importance is missing.

5. The introduction should briefly justify why InVEST and VSD are most suitable for this region compared to other models (e.g., RUSLE, PLUS, SolVES, ARIES).

6. The introduction discusses climate change at length but does not clearly tie it to the justification for the chosen zoning framework. A stronger linkage is recommended.

7. If the authors add a flowchart to briefly explain the adopted methodology, it would be helpful for the readers and the presentation will be more elaborative.

8. The results section is well presented.

9. Figure 5 can be redrawn, as the linkages between its sections are not well presented. it must be revised.

10. Make sure that the conclusions are coverd with all the objectives initially targeted, and all the results have been achieved?

11．The Conclusions section seems too lengthy. It should be revised.

12．Add the limitations of the study, and future research directions also.

13．There are minor grammatical mistakes throughout the manuscript, which needs to be carefully handled and revised.

14． Also no need of Hyphenation in the text.

1.Abstract

2.Introduction

3.Materials and Methods

4.Discussion

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