Coupling Land Use with Multiple Geographical Factors Drives the Evolution of Habitat Quality: Case Study from Resource-Exhausted City—Jiawang District, East China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study focuses on resource-exhausted cities, analyzing the changes in ecological quality driven by multiple factors. This is an interesting research perspective. However, the novelty of the work and the writing are not yet sufficient for publication. Below are the specific comments:

1. The English writing needs significant improvement.
2. The title mentions Xuzhou as the research area, but the actual focus is on a lower administrative level within Xuzhou. This creates confusion in the recognition of the study area based on the title, abstract, and introduction.
3. Many studies have already studied the interactions between ecosystem services and multiple factors. This paper lacks innovation in this regard. The research should focus more on the relationship between the characteristics of resource-exhausted cities and changes in ecological quality (HQ), as this might be more meaningful.
4. The introduction should provide a clearer explanation of what constitutes a resource-exhausted city, particularly its relationship with HQ or ecosystems, to highlight the unique aspects of the study and differentiate it from other research. This section is currently underdeveloped. Additionally, a better explanation of the advantages of combining different methods should be included.
5. The description of the study area (JWD) is insufficient. It is recommended to include land use maps rather than just elevation distribution maps. The source of the map data should be provided, and attention should be given to boundary issues. The map legends should show differentiation for provincial, municipal, and county levels. The current map does not provide a good example.
6. The methods section should use flowcharts instead of the current narrative to more clearly demonstrate the combination of methods.
7. Table 1 should provide explanations for the indicators.
8. There is a discrepancy in the spatial resolution of land use and other data; an explanation should be provided.
9. In Table 3, “watershed” might not be an appropriate land use type.
10. In Section 3.1, the place names mentioned may not be the most appropriate, as the authors have not provided a clear context. Furthermore, the land use changes should be explained (including the accuracy of the 2030 land use predictions) and the relationship between land use and HQ should be emphasized to reflect the characteristics of resource-exhausted cities.
11. In Section 3.2, the importance of PA analysis in the results is difficult to justify as a separate section.
12. The results section does not highlight the specific characteristics of resource-exhausted cities, and these aspects are not adequately analyzed or emphasized in the discussion section either.
13. The discussion section repeats the results too much and lacks in-depth analysis, comparison, and extension. For example, the situation before the one-child policy is not analyzed, and related studies are not referenced. The pressure from population growth may stem more from the increasing human capacity to alter the natural environment. In particular, please reconsider the usage of "infamous" in this context.

Comments on the Quality of English Language

English writing needs to be improved

Author Response

Dear reviewers,

Thank you very much for your kind comments on our manuscript. There is no doubt that these comments are valuable and very helpful for revising and improving our manuscript. In what follows, we would like to response the questions you mentioned and give a detailed account of the changes made to the original manuscript.

General overview:

This study focuses on resource-exhausted cities, analyzing the changes in ecological quality driven by multiple factors. This is an interesting research perspective. However, the novelty of the work and the writing are not yet sufficient for publication. Below are the specific comments:

Reply: We gratefully appreciate your valuable suggestions and comments. According to your suggestion, we have revised the problem in our original manuscript concisely and comprehensively. We hope our modifications in the manuscript can meet your expectations.

Specific comments:

1. The English writing needs significant improvement.

Reply: Thank you for your valuable reminder. Following your suggestion, we have sought language editing support from Editage to ensure the accuracy of language, grammar, and logical expression throughout the manuscript. We hope that the revised version will now be clearer and more comprehensible to readers.

2. The title mentions Xuzhou as the research area, but the actual focus is on a lower administrative level within Xuzhou. This creates confusion in the recognition of the study area based on the title, abstract, and introduction.

Reply: Thank you for your significant advice. We acknowledge that the original title, which explicitly mentioned "Xuzhou," might have led to confusion regarding the actual study area, as our research focuses on a lower administrative division within Xuzhou. To address this issue and ensure clarity, we have revised the title by removing "Xuzhou" while retaining the reference to a "resource-exhausted city" in East China. The revised title:

Coupling land use with multiple geographical factors drives the evolution of habitat quality: a case study from resource-exhausted city Jiawang District, East China.

This modification eliminates potential ambiguity while maintaining the relevance of the study's geographical context. We appreciate your insightful feedback, which has helped us improve the clarity and precision of our manuscript.

3. Many studies have already studied the interactions between ecosystem services and multiple factors. This paper lacks innovation in this regard. The research should focus more on the relationship between the characteristics of resource-exhausted cities and changes in ecological quality (HQ), as this might be more meaningful.

Reply: Thank you for your valuable feedback. We recognize that many studies have explored ecosystem services and multiple factors, so we have revised our manuscript to emphasize the unique characteristics of resource-exhausted cities and their impact on habitat quality (HQ). Specifically, we have made the following changes: 1) in the Introduction, we provide a clearer explanation of the composition of resource-exhausted cities, particularly their relationship with HQ; 2) in the Study Area, we enhance the description of the resource-exhausted city under study; 3) in the Results, we focus on the relationship between HQ and land use changes, highlighting the specific characteristics of resource-exhausted cities; and 4) in the Discussion, we conduct an in-depth analysis of the evolutionary mechanisms of HQ at different stages of resource-exhausted city development. These revisions enhance the study’s originality and ensure a clearer research focus. We appreciate your constructive suggestions, which have helped improve the manuscript.

Introduction:

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“The growth of resource-exhausted cities, from the initial stages of resource exploitation to eventual depletion and subsequent economic transformation, is accompanied by intense human activities. Hence, natural habitats for wildlife are often significantly encroached upon or fragmented. This disruption substantially weakens the continuity and integrity of ecosystems, affecting the interactions and migration of species.[1]”

Page 2 line 59 in manuscript

“Prolonged resource extraction not only alters the physical characteristics of the land but also weakens the natural restoration capacity and regulatory functions of ecosystems. For instance, a damaged ecosystem may no longer be able to effectively filter pollutants, regulate moisture, or control temperature, thereby impacting biodiversity and the ability to sustain wildlife habitats. Furthermore, a lack of funding, technology, and policies may delay environmental management and ecological restoration efforts, making it difficult to improve habitat quality. This creates a vicious cycle, exacerbating the vulnerability of ecosystems.”

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“The transportation in JWD is highly convenient, with access to highways, railways, and intercity buses. The Grand Canal and the Bulao River run across the district from east to west.”

Study area:

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 “It is a significant coal industrial base in China, having produced a total of 350 million tons of coal, making substantial contributions to the economic development of the country. However, prolonged coal mining and the growth of related industries led to severe environmental issues, creating sharp contradictions between industrial transformation, ecological restoration, and economic development. In recent years, Jiawang has actively promoted industrial transformation, leveraging ecological restoration to develop eco-tourism, smart manufacturing, modern agriculture, and trade logistics, gradually building a green economy system.”

Results:

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“Based on land use data from 2000, 2010, and 2020, we used the PLUS model to predict the land use in JWD under a natural development scenario for the year 2030. The Kappa coefficient and the overall accuracy were 0.8074 and 0.8989, respectively. This indicates that the prediction results are highly accurate and meet the precision requirements of this study. The results show that from 2000 to 2020, construction land in JWD expanded rapidly, increasing from 13.45% to 28.87%. The fragmentation of land patches intensified, while the area of grassland decreased significantly (from 5.91% to 4.53%). The area of cropland also showed a decreasing trend (from 75.24% to 75.18%, and then to 61.31%), with the rate of decline initially slow and then accelerating. The area of watershed decreased first and then increased (from 3.2% to 1.67%), while woodland followed the opposite trend, first increasing and then decreasing (from 2.13% to 2.38%, and then to 2.27%). Unused land also exhibited a trend of increasing and then decreasing (from 0.06% to 0.07%, and then to 0.03%) (Figure 6). The spatial overlap between change areas of land use and HQ was high, indicating that HQ changed in tandem with land use. Areas of expanded construction land were often those where habitat quality declined, while areas of expanded grassland, woodland, and watershed typically saw improvements in HQ. This vividly illustrates the relationship between land use and HQ changes during different stages of development in resource-depleted cities.”

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“which vividly highlights the typical characteristics of resource-depleted cities: intense human activities are a significant driving force behind the changes in HQ.”

Discussions:

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“Specifically, from 2000 to 2010, JWD was in the expansion phase typical of resource-based cities, with rapid growth in mining areas and industrial enterprises. The resulting mining subsidence, soil pollution, and chaotic land-use structure led to the creation of numerous abandoned lands. In terms of land use, this period was characterized by the expansion of construction land, a reduction in grassland, cropland, and water areas, and an increase in unused land (Figure 5). Correspondingly, the HQ also declined sharply (Figure 4).

Since being designated as a resource-depleted city in 2011, JWD has entered a transition phase. Policy shifts led to the closure of many mining areas and factories, with efforts focused on repairing mining subsidence areas, rehabilitating abandoned mines, and restoring vegetation. This transition is also marked by significant changes in land use, such as a large-scale expansion of construction land, a substantial reduction in cropland, a decrease in unused land, and an increase in woodland and water areas (Figure 5). Although urban greening and the ecological restoration of mining subsidence lands contributed to slowing down the decline in HQ [2], the rapid changes in land use under the influence of intense human activity still led to a marked decrease in HQ. For instance, large-scale tourism infrastructure projects driven by industrial transformation, such as roads and railways, often disrupt habitat integrity, resulting in fragmentation and a consequent decline in HQ [3].”

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“This improvement was likely due to the later stage of resource-depleted city development, where government-led ecological restoration, reduced human activity, enhanced landscape connectivity [4], and the expansion of biological habitats played key roles[5].”

4. The introduction should provide a clearer explanation of what constitutes a resource-exhausted city, particularly its relationship with HQ or ecosystems, to highlight the unique aspects of the study and differentiate it from other research. This section is currently underdeveloped. Additionally, a better explanation of the advantages of combining different methods should be included.

Reply: Thank you for your valuable suggestions. As we have already addressed the clarification of what constitutes a resource-exhausted city and its relationship with HQ in Comment 3, we would like to focus on the second part of your comment. We have added a more detailed explanation of the advantages of combining different methods in the Introduction. We emphasize that the integration of multiple methods allows for a more comprehensive analysis of the complex interactions between land use, population dynamics, and ecological factors, providing a holistic understanding of HQ evolution in resource-exhausted cities.

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“we used a combination of the InVEST and patch-generating land use simulation (PLUS) models to scientifically deduce the spatiotemporal pattern of future HQ. By combining PLS-PM, Pearson correlation analysis (PA), and generalized additive model (GAM), we examine the spatiotemporal evolution of HQ in JWD, investigating the impacts of human activities and the mechanisms behind the multi-factorial interactions affecting HQ. This methodological approach leverages the distinct strengths of each model, allowing for a comprehensive research framework progressing from static evaluation to dynamic forecasting, and from correlation analysis to causal inference. The multi-model integration not only addresses the limitations inherent in individual methods but also enhances both the precision and predictive power of the study.”

5. The description of the study area (JWD) is insufficient. It is recommended to include land use maps rather than just elevation distribution maps. The source of the map data should be provided, and attention should be given to boundary issues. The map legends should show differentiation for provincial, municipal, and county levels. The current map does not provide a good example.

Reply: Thank you for your valuable feedback. As we have addressed the clarification and enhancement of the study area description in Comment 3. For this specific response, we focused on refining the following aspects. We have addressed boundary issues and ensured that the map legends clearly differentiate between provincial, municipal, and county levels. Additionally, we have provided the source of the map data. We believe these changes improve the clarity and accuracy of the study area description.

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Figure 1. Location of Jiawang District. (a) shows the provincial boundaries of China, (b) depicts the municipal boundaries of Jiangsu, and (c) illustrates the county-level boundaries of Xuzhou. Additionally, (d) provides the elevation map of Jiawang District, while (e) represents the land use distribution within the district.

 

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Data	Sources
Annual average temperature	Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences (http://www.resdc.cn) and calculated using ArcGIS, land use resolution is 30 m × 30 m, others are 1 km × 1 km
Annual average precipitation
Normalized difference vegetation index
Administrative boundaries
Population density
Nighttime light index
Land use intensity
Land use for 2000, 2010, 2020
Gross domestic product
Elevation	Geospatial Data Cloud (http://www.gscloud.cn/) and calculated using ArcGIS
Slope
Distance from the road	Open Street Map (http://www.openstreetmap.org/) and calculated in ArcGIS

 

6. The methods section should use flowcharts instead of the current narrative to more clearly demonstrate the combination of methods.

Reply: Your meticulous review and constructive suggestions have been invaluable in addressing the limitations of our methodology. As your suggestions, we have removed the narrative description of the methods and replaced it with a flowchart to more clearly demonstrate the combination of methods. We believe this adjustment improves the clarity and visual presentation of the methodology.

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“We integrated PLS-PM, PLUS models and the PA to scientifically simulate future spatial and temporal patterns of HQ evolution and identify the mechanisms influencing HQ (Figure 2).

Figure 2. Technical roadmap of this study. Pre = average annual precipitation, Tem = average annual temperature, AL = altitude, SL = slope, NDVI = normalized difference vegetation index, PopDen = population density, NL = night light index, DisRoad = distance from the road, LUI = land use intensity index, GDP = gross domestic product.”

7. Table 1 should provide explanations for the indicators.

Reply: We appreciate your insightful feedback. Table 1 has been revised to incorporate detailed explanations for each indicator, which will enhance clarity and facilitate a deeper understanding of the metrics employed in the study.

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“Table 1 Used data and their sources, Annual average temperature and precipitation reflect regional climate, influencing ecosystems and human activities. NDVI measures vegetation coverage, while land use data (2000, 2010, 2020) and intensity track human impact on land. Administrative boundaries define jurisdictional areas, and population density indicates settlement patterns. Nighttime light index serves as a proxy for economic activity, while GDP quantifies economic output. Elevation and slope shape landforms and land use suitability. Distance from the road affects accessibility and development potential.”

8. There is a discrepancy in the spatial resolution of land use and other data; an explanation should be provided.

Reply: Thank you for your valuable advise. Your comprehensive feedback has been carefully considered, and we have revised the manuscript accordingly to enhance its clarity and rigor. In response to your comment, we have added an explanation regarding the discrepancy in the spatial resolution between the land use data (30 m × 30 m) and other data (1 km × 1 km). This difference in resolution has been taken into account during the analysis, and we have clarified how the data were processed to ensure consistency and minimize any potential impact on the results.

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“The land use data is maintained at a finer resolution (30m×30m) to better capture localized land cover changes that may not be well represented at coarser scales. When integrated with other datasets (1 km×1 km), partition-based statistics are applied to calculate the characteristics of each grid cell. This ensures that land use is fully represented in the analysis without compromising spatial accuracy.”

9. In Table 3, “watershed” might not be an appropriate land use type.

Reply: Thank you for your valuable suggestion. In response to your comment, we would like to clarify that "watershed" is included as a land use type in Table 3 because it represents a distinct ecological unit in the study area that significantly influences the hydrological and ecological processes [6]. Watersheds often contain important natural habitats and play a key role in water resource management and biodiversity conservation [7]. Therefore, classifying it as a land use type allows us to capture the ecological and environmental dynamics associated with it, which is essential for understanding the overall habitat quality and its changes in the context of land use.

10. In Section 3.1, the place names mentioned may not be the most appropriate, as the authors have not provided a clear context. Furthermore, the land use changes should be explained (including the accuracy of the 2030 land use predictions) and the relationship between land use and HQ should be emphasized to reflect the characteristics of resource-exhausted cities.

Reply:  Regarding the place names mentioned in Section 3.1, we have now provided a clearer context in the study area description, where we explicitly list all the place names included in the study area. Additionally, the distribution of these places is shown in the updated study area map for better understanding. As for the land use changes, we have already addressed this in Comment 3, where we explained the relationship between land use and habitat quality, including the accuracy of the 2030 land use predictions. We also emphasized how land use changes reflect the specific characteristics of resource-exhausted cities.

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“covers an area of 612.13 km² and includes five towns (Jiangzhuang, Qingshanquan, Zizhuang, Tashan, Biantang), five subdistricts (Daquan, Laokuang, Pananhu, Dawu, Damiao), and one provincial-level industrial park.”

11. In Section 3.2, the importance of PA analysis in the results is difficult to justify as a separate section.

Reply:Thank you for your thoughtful suggestion. In Section 3.2, we performed Pearson correlation analysis to explore the relationships between various factors and habitat quality. While this analysis may not seem as prominent in the results section, it plays an important role in providing a foundation for the subsequent discussion. In the discussion, we aim to thoroughly examine the coupling interactions among the influencing factors, as this deeper analysis is essential for understanding the complex mechanisms driving the changes in habitat quality. By doing so, we believe it will highlight how the different methods used in our study complement each other and help to reveal a more comprehensive understanding of the research area.

12. The results section does not highlight the specific characteristics of resource-exhausted cities, and these aspects are not adequately analyzed or emphasized in the discussion section either.

Reply: We sincerely appreciate your insightful comment. While this issue was previously addressed in our response to Comment 3, we would like to emphasize that a detailed analysis of the relationship between land use dynamics and habitat quality has been systematically incorporated into both the Results and Discussion sections. Our findings underscore the distinct spatiotemporal patterns of habitat quality evolution in resource-exhausted urban areas, where intensive anthropogenic activities and rapid land use transformations serve as primary driving forces. This targeted focus on the unique characteristics of resource-exhausted cities not only strengthens the thematic originality of our study but also enhances its disciplinary relevance within urban ecology and regional sustainability research.

13. The discussion section repeats the results too much and lacks in-depth analysis, comparison, and extension. For example, the situation before the one-child policy is not analyzed, and related studies are not referenced. The pressure from population growth may stem more from the increasing human capacity to alter the natural environment. In particular, please reconsider the usage of "infamous" in this context.

Reply: Thank you for your valuable comment. In the discussion section, we have expanded our analysis to include a more in-depth comparison of the situation before the one-child policy, incorporating relevant references and offering additional insights. Moreover, we have carefully reconsidered the language used and removed the term "infamous," which we acknowledge could be seen as one-sided, replacing it with more neutral and precise wording.

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“Prior to the implementation of the one-child policy in 1980, China experienced rapid population growth, particularly after 1949, when national policies encouraged population increase to support economic and industrial development [8]. However, this rapid population growth, coupled with the expansion of industry and agriculture, exerted immense pressure on the ecological environment [9]. The pressure on ecological space did not solely arise from population growth itself, but rather from the continuous enhancement of the capacity to alter the natural environment by humanity [10].”

 

Thank you again for your positive and constructive comments and suggestions on our manuscript.

 

Reviewer 2 Report

Comments and Suggestions for Authors

The article is devoted to assessing the impact of multiple factors on the quality of the habitat through multifactorial interactions. The methods used in ecology and urban planning to assess such complex interactions are very imperfect. In the best case, it is recommended to use the simplest additive model. In order to improve urban planning and reduce the spatial and temporal heterogeneity of evolution with varying levels of human activity, it is necessary to conduct empirical studies of numerous urban characteristics. In this case, the multifactorial model acts as the basis for environmental quality management in urbanized territories. Therefore, it is very important to develop a flexible and reliable analytical tool, the structure of which would be well suited to the nature of the interaction of any factors, and in any combination of them. Notes to the article.

1. It is clear that in order to adequately quantify the complex (n-factor) impact on the urban ecosystem, it is necessary to jointly take into account as many n factors as possible, even if they have a completely different nature and different dimensions. At the same time, the authors of the article did not substantiate why 5 categories of diverse factors were chosen, according to the totality of which it is difficult to judge various complex impacts on urban ecosystems for comparative study and generalization of knowledge. The choice of groups of factors is rather arbitrary, but I would like to understand whether a certain structure (system of factors) is possible for this study, which would correspond well to the nature of the interaction of any factors for the territory under study, and in an arbitrary combination of them.
2. In the environmental literature, and the authors cite numerous references, there are various ways to approximate particular response functions to individual factors. The most commonly used equations are those whose graphs represent a straight line segment, concave, convex, or S-shaped curve. Instead of various, diverse equations, it is proposed to approximate any response function to any environmental factor. The authors do not explain the mechanism of approximation (Figure 9) of the links between the influencing factors.
3. The proposed approach can be widely used to quantify complex anthropogenic and other anthropogenic impacts on any system. In addition, it becomes possible to manage situations such as land use and environmental protection planning by selecting a combination of limiting factors that will ensure an acceptable level of their resultant effect. However, this approach proposed by the authors does not provide a sound quantitative approach to the normalization of interactions. Do the authors have any explanations on this issue?
4. The nonlinear interactions between many of the variables selected in the study remain poorly understood. In connection with the above, it seems logical to introduce the section "Research perspectives" and describe in more detail the directions of the dynamic evolution of the HQ indicator.
5.  In our opinion, the keywords do not reflect the full content of the article. It is recommended to add.

Comments on the Quality of English Language

The article is devoted to assessing the impact of multiple factors on the quality of the habitat through multifactorial interactions. The methods used in ecology and urban planning to assess such complex interactions are very imperfect. In the best case, it is recommended to use the simplest additive model. In order to improve urban planning and reduce the spatial and temporal heterogeneity of evolution with varying levels of human activity, it is necessary to conduct empirical studies of numerous urban characteristics. In this case, the multifactorial model acts as the basis for environmental quality management in urbanized territories. Therefore, it is very important to develop a flexible and reliable analytical tool, the structure of which would be well suited to the nature of the interaction of any factors, and in any combination of them. Notes to the article. 1. It is clear that in order to adequately quantify the complex (n-factor) impact on the urban ecosystem, it is necessary to jointly take into account as many n factors as possible, even if they have a completely different nature and different dimensions. At the same time, the authors of the article did not substantiate why 5 categories of diverse factors were chosen, according to the totality of which it is difficult to judge various complex impacts on urban ecosystems for comparative study and generalization of knowledge. The choice of groups of factors is rather arbitrary, but I would like to understand whether a certain structure (system of factors) is possible for this study, which would correspond well to the nature of the interaction of any factors for the territory under study, and in an arbitrary combination of them. 2. In the environmental literature, and the authors cite numerous references, there are various ways to approximate particular response functions to individual factors. The most commonly used equations are those whose graphs represent a straight line segment, concave, convex, or S-shaped curve. Instead of various, diverse equations, it is proposed to approximate any response function to any environmental factor. The authors do not explain the mechanism of approximation (Figure 9) of the links between the influencing factors. 3. The proposed approach can be widely used to quantify complex anthropogenic and other anthropogenic impacts on any system. In addition, it becomes possible to manage situations such as land use and environmental protection planning by selecting a combination of limiting factors that will ensure an acceptable level of their resultant effect. However, this approach proposed by the authors does not provide a sound quantitative approach to the normalization of interactions. Do the authors have any explanations on this issue? 4. The nonlinear interactions between many of the variables selected in the study remain poorly understood. In connection with the above, it seems logical to introduce the section "Research perspectives" and describe in more detail the directions of the dynamic evolution of the HQ indicator. 5. In our opinion, the keywords do not reflect the full content of the article. It is recommended to add.

Author Response

Dear reviewers,

Thank you very much for your kind comments on our manuscript. There is no doubt that these comments are valuable and very helpful for revising and improving our manuscript. In what follows, we would like to response the questions you mentioned and give a detailed account of the changes made to the original manuscript.

General overview:

The article is devoted to assessing the impact of multiple factors on the quality of the habitat through multifactorial interactions. The methods used in ecology and urban planning to assess such complex interactions are very imperfect. In the best case, it is recommended to use the simplest additive model. In order to improve urban planning and reduce the spatial and temporal heterogeneity of evolution with varying levels of human activity, it is necessary to conduct empirical studies of numerous urban characteristics. In this case, the multifactorial model acts as the basis for environmental quality management in urbanized territories. Therefore, it is very important to develop a flexible and reliable analytical tool, the structure of which would be well suited to the nature of the interaction of any factors, and in any combination of them. Notes to the article.

Reply: Thank you for your thoughtful feedback and valuable insights. We appreciate your recognition of the complexities involved in assessing habitat quality through multifactorial interactions. We agree that current methods in ecology and urban planning often have limitations, and your suggestion to adopt the simplest additive model where appropriate is noted.

In our study, we sought to bridge these gaps by applying a multifactorial model that considers the complex interactions between various urban and environmental factors. We believe this approach can contribute significantly to improving urban planning, as it accounts for the temporal and spatial heterogeneity of habitat quality in the context of varying human activities. Moreover, we agree that empirical studies are crucial to enhance the reliability of urban characteristics in such models. We appreciate your input, which will guide further improvements in the research methodology and the overall framework.

 

1. It is clear that in order to adequately quantify the complex (n-factor) impact on the urban ecosystem, it is necessary to jointly take into account as many n factors as possible, even if they have a completely different nature and different dimensions. At the same time, the authors of the article did not substantiate why 5 categories of diverse factors were chosen, according to the totality of which it is difficult to judge various complex impacts on urban ecosystems for comparative study and generalization of knowledge. The choice of groups of factors is rather arbitrary, but I would like to understand whether a certain structure (system of factors) is possible for this study, which would correspond well to the nature of the interaction of any factors for the territory under study, and in an arbitrary combination of them.

Reply: Thank you for your insightful comments regarding the selection of factors in our study. We understand that in order to properly quantify the complex impact of multiple factors on urban ecosystems, it is essential to take into account a wide range of factors, even those of different natures and dimensions.

In our study, the selection of the 10 factors was based on an extensive review of the literature and expert opinions. We aimed to choose factors that have been widely recognized as influencing habitat quality in urban environments. While we acknowledge that the choice of factors does involve a degree of subjectivity, we believe that these factors collectively capture the essential aspects of urban ecosystems.

We agree with your suggestion that developing a structured system of factors could be beneficial for understanding the interactions and combinations of factors more comprehensively. In the introduction and outlook sections of the paper, we have addressed this idea, highlighting that future research should explore the development of such a system in order to better understand the causal relationships within complex environmental systems. We hope that this approach will lead to a more thorough and deeper understanding of factor interactions in the context of resource-exhausted cities.

Thank you again for your constructive feedback, which will help enhance the clarity and depth of our work.

 

2. In the environmental literature, and the authors cite numerous references, there are various ways to approximate particular response functions to individual factors. The most commonly used equations are those whose graphs represent a straight line segment, concave, convex, or S-shaped curve. Instead of various, diverse equations, it is proposed to approximate any response function to any environmental factor. The authors do not explain the mechanism of approximation (Figure 9) of the links between the influencing factors.

Reply: Thank you for your valuable suggestion regarding the approximation of response functions for individual factors. We appreciate your point about the various types of equations commonly used in environmental studies, such as straight-line, concave, convex, or S-shaped curves.

In our study, Figure 9 specifically focuses on exploring the nonlinear relationships between the 10 influencing factors and habitat quality. The non-linear interaction between independent and dependent variables plays a crucial role in understanding the underlying mechanisms. To analyze these relationships, we employed Pearson correlation analysis to examine the connections between variables, Generalized Additive Models (GAM) to explore the nonlinear relationships between each factor and habitat quality, and Partial Least Squares Path Modeling (PLS-PM) to establish causal relationships. Combining these three approaches allows us to leverage their strengths and gain a more comprehensive understanding of the habitat quality evolution mechanism.

However, we acknowledge that this approach still has certain limitations. Analyzing the nonlinear relationships for all variables is indeed essential for a deeper understanding of multivariate interactions and their coupling mechanisms. We view this as an important avenue for future research, as it will enable a more thorough exploration of the nonlinear relationships among the various factors. We have also added this point to the "Environment Indication and Limitation" section of the manuscript to emphasize the importance of further developing this aspect.

We appreciate your feedback, which will undoubtedly improve the clarity and depth of our analysis.

 

3. The proposed approach can be widely used to quantify complex anthropogenic and other anthropogenic impacts on any system. In addition, it becomes possible to manage situations such as land use and environmental protection planning by selecting a combination of limiting factors that will ensure an acceptable level of their resultant effect. However, this approach proposed by the authors does not provide a sound quantitative approach to the normalization of interactions. Do the authors have any explanations on this issue?

Reply: Thank you for your insightful comment regarding the proposed approach and the normalization of interactions. We understand that the question pertains to the quantification of interactions and how to properly normalize them within the proposed framework.

In our study, we integrated multiple methods—PLS-PM, Pearson correlation analysis (PA), Generalized Additive Models (GAM), and the PLUS model—each contributing to the analysis of habitat quality (HQ) evolution. These methods, particularly PLS-PM and GAM, provide important insights into the interactions between various factors and their impact on HQ. However, we recognize that normalization of these interactions is a key issue that requires further clarification.

We aim to better explain how these different methods contribute to modeling interactions:

PLS-PM helps us understand the relationships between latent variables and their direct effects on HQ, but it does not directly normalize the interactions in the traditional sense. Instead, it calculates partial correlation coefficients, factor loadings, and goodness of fit (GoF), which are used to measure the strength and significance of the relationships among the factors. Pearson correlation analysis (PA) serves as a preliminary method to identify potential relationships between the influencing factors and HQ. While it provides basic correlation information, it does not directly address normalization of interactions between factors. Generalized Additive Models (GAM) are used to explore the nonlinear relationships between factors and HQ, offering more flexibility in analyzing complex interactions. However, as with PLS-PM, GAM does not involve explicit normalization of interactions across all factors. PLUS models are then used to simulate potential future land use and HQ patterns based on expert evaluations and relevant literature. This model helps identify critical factors influencing HQ but does not directly normalize the interaction effects across all variables.

     We acknowledge that normalization, especially when combining multiple methods and factors, is a challenge that requires a more rigorous approach. While our study does not yet include a direct normalization technique for interactions, this is an area for future research. We plan to explore methods such as standardization or scaling, which could help normalize the interaction effects across the variables in a more robust manner.

     We have included this point in the " Environment Indication and Limitation " section of the manuscript, emphasizing the need for further development of this aspect to better address the normalization of interactions in future studies. We hope this explanation clarifies the approach taken and the limitations regarding the normalization of interactions.

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Additionally, while PLS-PM identifies correlations between variables, its limitations in establishing causality highlight the need for future research to focus on developing standardized techniques, such as normalization or scaling, to better quantify and compare interactions among multiple influencing factors.

4. The nonlinear interactions between many of the variables selected in the study remain poorly understood. In connection with the above, it seems logical to introduce the section "Research perspectives" and describe in more detail the directions of the dynamic evolution of the HQ indicator.

Reply: Thank you for your valuable suggestion. We fully agree that the nonlinear interactions between many of the selected variables require further exploration for a deeper understanding. In response, we have included a dedicated section titled " Environment Indication and Limitation " in the manuscript, where we discuss in more detail the dynamic evolution of the HQ indicator and propose future research directions. This section addresses the need for further investigation into the nonlinear relationships between factors and their impact on HQ, and highlights the potential for developing more robust methods for understanding and quantifying these interactions.

We believe this addition helps to clarify the future research directions in the study of HQ and its influencing factors.

Page 18 line 579 in manuscript

“The construction of a factor system is a crucial part of studying evolutionary mechanisms. In relatively underdeveloped fields, the establishment of a comprehensive factor system is of great significance for understanding the interactions and combinatory properties of various factors. Many of the nonlinear interactions among the variables selected here are still poorly understood. Future research should focus on the complex interactions among multiple variables to provide a deeper and more objective description of the dynamic evolution of ecological indicators.”

 

5. In our opinion, the keywords do not reflect the full content of the article. It is recommended to add.

Reply: Thank you for your insightful comment. We have updated the keywords to better reflect the full content of the article. Specifically, we have added "PLS-PM" to the list of keywords to more accurately represent the key methodologies used in our study.We hope this update addresses your concern and improves the clarity of the article’s focus.

“Keywords: Habitat Quality; Spatiotemporal evolution; Driving mechanisms; integrated valuation of ecosystem services and trade-offs (InVEST) Model; Squares-path modeling; Jiawang District”

 

Thank you very much for your insightful comments and suggestions. They have greatly contributed to improving the quality and clarity of our manuscript. We truly appreciate your time and effort in reviewing our work.

 

Reviewer 3 Report

Comments and Suggestions for Authors

The article significantly contributes to understanding the impact of various factors on the evolution of habitat quality. It provides valuable information for decision-making in biodiversity conservation and sustainable urban development. So, I believe the article is good and ready for publication, but it just needs to be properly referenced before that.

Author Response

General overview:

The article significantly contributes to understanding the impact of various factors on the evolution of habitat quality. It provides valuable information for decision-making in biodiversity conservation and sustainable urban development. So, I believe the article is good and ready for publication, but it just needs to be properly referenced before that.

Reply: Thank you very much for your positive feedback and for recognizing the contribution of our article to understanding the impact of various factors on the evolution of habitat quality. We are glad that you find the study valuable for decision-making in biodiversity conservation and sustainable urban development.

We will ensure that the references are properly formatted and updated before final submission. Thank you again for your support, and we appreciate your time and valuable input.

 

 

Thanks again for your valuable comments, which are very important and instructive for our future research, and hope that we can learn more from you!