Selected Environmental Assessment Model and Spatial Analysis Method to Explain Correlations in Environmental and Socio-Economic Data with Possible Application for Explaining the State of the Ecosystem

Round 1

Reviewer 1 Report

Summary

The authors constructed an index for ecosystem health utilizing the Pressure-State-Response model, developed by the OECD (needs citation). The PSR model is applied at two spatial scales – city & county – to discern changes to ecosystem health, and its indicators, across the Hu Line in China. The authors found that ecosystem health varied across space, but most cities were improving their ecosystem health scores, predominately in the State and Response indicators, spatial variation was increasingly randomly distributed, and that differences across the line were becoming less pronounced. The analysis at two spatial scales also revealed that the counties within each city were variable in ecosystem health.

 

General Comments

Interesting use of PSR model to evaluate differences/similarities and changes in environmental conditions across the Hu Line. Authors state that Hu Line may never be “broken” due to geographical differences between the two areas; however, they also state that the spatial distribution of ecosystem health was becoming more randomly distributed after 2005 (Line 246). This seems to be an indication (from this analysis, at least) that the Hu Line is dissolving, and is further supported by decreasing differences across all indicators, except state scores (Figure 6).

 

A weakness of the methodology is the use of natural breaks to classify the data. This means it is difficult/inappropriate to compare mapped data from 2000 to 2016 (e.g., Figure 5), particularly because the break points are not provided for either map. The authors repeatedly discuss changes in status (moves from one category to another), but these comparisons are not valid unless the same breakpoints are applied across the data. This will change the percentages/number of cities or counties that move from one category to another, and will likely change the interpretation of some of the results.

 

Specific Comments

Introduction:

Line 54 – Missing comma after the word natural.

Lines 61-67 – Vague description and comparison of the VOR and PSR models (e.g., “inherent confines” means what for the reader?).

Line 68 – Reword sentence to discuss the development/economic situation. The use of the term “backward” is found repeatedly in tables and in the discussion. Possible substitution of “lagging” to correspond to use of word “leading” in Table 4.

Line 80 – WWF and IUCN incorrectly used

 

Materials & Methods:

Lines 122-132 - Vague descriptions of most of the variables. The reader doesn’t know why the specific variables were selected and how some of them relate to each other until the discussion. The reader also doesn’t know how some of the data are enumerated (e.g., total output) or standardized.

How do the authors utilize NDVI as an indicator of ecosystem health? High NDVI could also be related to other, human-related vegetation and may not be an indicator of ecosystem health/function.

Lines 150-153 – Missing words and repeated words. Meaning unclear.

Line 192 – Is there a range of values that result from the comprehensive assessment?

Line 202 – Although well documented, a few references are usually given to direct a reader who may not be familiar with the specific methods.

Figure 2 – Statistically significant differences from year to year or overall for Pressure? If Pressure is not changing from year to year, then do the selected Response variables correspond to a reaction to the selected Pressures? Is the takeaway that the policy applied does not impact the underlying pressures?

Figure 4 – Figures Ab and Bb reversed.

Lines 307-308 – “Significant differences in intensity” discussed, but the reader does not know what indicates a significant difference.

 

Discussion:

Lines 332-333, although modified, closely resemble source author's language, "Further, these interactions are highly dynamic in nature; there is generally no instantaneous response of ecosystem conditions to pressures from human
activity (nor the converse), but rather a complex cumulative effect that has complex temporal and spatial manifestations." (Rapport & Singh 2006).

Lines 336-360 - Limitations of PSR model, generically or as applied, are not addressed. The authors don’t discuss the importance of the variables they selected, their contributions to the model, or how they relate to each other, although stating this should be done (Line 355-356). This is important because the PSR model implies a linkage between the pressure, the environmental status, and the response factors, thus the selection and relationships between variables are important to assessing ecosystem health (see Xiaodan, W., Z. Xianghao, and G. Pan. 2000. A GIS-based decision support system for regional eco-security assessment and its application on the Tibetan Plateau. Journal of Environmental Management 91: 1981-1990.).

Lines 356-360 could be simplified to increase clarity.

Lines 382-385 – Regions reversed in discussion of results.

Lines 415-417 – Policy suggestion of population resettlement hints at human rights issues.

Repetition in methodology assessment and Introduction, utilizing some of the same language.

Author Response

Subject: Manuscript revision

Dear Editor and Referees,

First of all, we are very thankful for your constructive comments on our study. Specially, we are heartily grateful to your valuable suggestions.

The manuscript has been revised carefully and strictly according to your letter. We are submitting our revised version entitled “Multi-scale and Integrated Assessment of Ecosystem Health at the Southern End of the Hu Line, China”, Manuscript ID: 564470.

Please find the revised manuscript with track changes. In order to facilitate your review, bold fonts were used to show revision and changes. In the following "Point-to-point response to the editor's letter and the reviewers’ comments".

Please do not hesitate to contact me, if further material or information is needed.

 

Note: in the revised manuscript, all major changes are marked in the revised format (in red)

Thanks again.

Sincerely yours,

Junnan Xiong

Detailed responses to the comments are addressed below. 

Your comments:

Summary

The authors constructed an index for ecosystem health utilizing the Pressure-State-Response model, developed by the OECD (needs citation). The PSR model is applied at two spatial scales – city & county – to discern changes to ecosystem health, and its indicators, across the Hu Line in China. The authors found that ecosystem health varied across space, but most cities were improving their ecosystem health scores, predominately in the State and Response indicators, spatial variation was increasingly randomly distributed, and that differences across the line were becoming less pronounced. The analysis at two spatial scales also revealed that the counties within each city were variable in ecosystem health.

General Comments

Interesting use of PSR model to evaluate differences/similarities and changes in environmental conditions across the Hu Line. Authors state that Hu Line may never be “broken” due to geographical differences between the two areas; however, they also state that the spatial distribution of ecosystem health was becoming more randomly distributed after 2005 (Line 246). This seems to be an indication (from this analysis, at least) that the Hu Line is dissolving, and is further supported by decreasing differences across all indicators, except state scores (Figure 6).

A weakness of the methodology is the use of natural breaks to classify the data. This means it is difficult/inappropriate to compare mapped data from 2000 to 2016 (e.g., Figure 5), particularly because the break points are not provided for either map. The authors repeatedly discuss changes in status (moves from one category to another), but these comparisons are not valid unless the same breakpoints are applied across the data. This will change the percentages/number of cities or counties that move from one category to another, and will likely change the interpretation of some of the results.

Thank you for your valuable comments. We have supplemented some explanation of natural break method.

Line 265-269: Natural breaks method is designed to determine the natural clustering of attribute values through seeking to minimize average deviation within the class while maximizing average deviation between the classes, and the method has good adaptability and high precision in dividing geographical environment units [57, 58]. This division of relative results is still valid for long time series, because all units would not have essential changes or leaps at the same time during the study period.

 

Specific Comments

Introduction:

Line 54 – Missing comma after the word natural.

Yes, we have added the comma.

Lines 61-67 – Vague description and comparison of the VOR and PSR models (e.g., “inherent confines” means what for the reader?).

Thank you for your comments. We have added a detailed description of the two models.

Line 64-73: The vigor was measured in terms of activity, metabolism or productivity; organization can be assessed as the diversity and amount of interactions between system components; resilience was measured in terms of a system’s capacity to maintain structural stability in the presence of stress [25]. Unlike the VOR model, which has the inherent confines due to lack of socio-economic and human health factors, the pressure-state-response (PSR) model emphasizes that human beings are a part of the ecosystem and play a pivotal role in determining the environmental state. Here, pressure indicators describe the pressures on ecosystem health exerted by human activities, including resource pressures and social pressures. State indicators reflect the status quo of the ecosystem health, such as the vigor, organization and resilience of an ecosystem. Response indicators show the response degree to the changes of ecosystem health conditions, including changes from humans and the ecosystem itself [26]. The comprehensive and dynamic features of this model makes it more informatory [27].

 

Line 68 – Reword sentence to discuss the development/economic situation. The use of the term “backward” is found repeatedly in tables and in the discussion. Possible substitution of “lagging” to correspond to use of word “leading” in Table 4.

Yes, done.

 

Line 80 – WWF and IUCN incorrectly used

Yes, we have corrected it.

 

Materials & Methods

Lines 122-132 - Vague descriptions of most of the variables. The reader doesn’t know why the specific variables were selected and how some of them relate to each other until the discussion. The reader also doesn’t know how some of the data are enumerated (e.g., total output) or standardized.

How do the authors utilize NDVI as an indicator of ecosystem health? High NDVI could also be related to other, human-related vegetation and may not be an indicator of ecosystem health/function.

Thank you for your valuable comments. We have supplemented detailed descriptions (including NDVI) of the variables in the discussion section. And the data processing is supplemented.

Line 189-191: Positive indicators denote the ecosystem health scores declining when indicators values decrease; negative indicate the ecosystem health score improving when indicators values decrease. In the study, planting area of crops and all state and response indicators were positive. The extremum difference method was used to normalize each index [50].

Line 353-391: Determining the indicator and its weight are two important steps in the evaluation process [64]. In actual application, it is impossible to incorporate all factors affecting regional ecosystem health in an assessment model. In this study, the indicator system for regional ecosystem health was built based on the PSR framework. Human impacts and natural factors were introduced as indicators into the assessment framework. Overall, this framework consisted of 17 indicators reflecting the pressure, state, and the response of each region to produce ecosystem health scores: (1) Our study area has limited land resources with over 90% mountain cover [32]. Farmland has been shrinking for various reasons since 1957 [65]. In 2013, the per capita cultivated land area of 12 cities, such as Chengdu, Panzhihua, and Luzhou, was lower than the critical level issued by the United Nations (per capita cultivated land should be no less than 0.80 mu). Food supplies require continuous soil fertilization, however, the application of chemical fertilizers can cause environmental pollution and soil nutrient imbalance, such as an increase in heavy metals and toxic elements, decrease in soil microbial activity, and soil acidification. The northwest part of the study area is part of the Tibetan Plateau, which has a fragile environment that is sensitive to climate change [36]. Continuous drought also occurs in many places in Yunnan Province. Precipitation anomalies commonly cause drought events [46]. After 1949, the population of the study area has increased substantially, with the permanent population exceeding 130 million in 2016. These factors of the study area are represented by the pressure indicator of the planting area of crops, fertilizer application amount, percentage of temperature anomaly, percentage of precipitation anomaly, population density, and natural population growth rate. (2) Agriculture production, economic situation and ecological condition were selected as components of the state indicator to reflect ecosystem function and environmental status. Agriculture is a mainstay for human survival and development and, globally, the advances in agriculture is seen as an important means of economic prosperity and human well-being. Land resource vitality serve as a valuable indicator for measuring primary productivity and ecosystem activity [66]. Therefore, agricultural state, such as total output values for agriculture, forestry, animal husbandry, and fisheries and grain yield per unit of cultivated land, were selected as components of the state assessment indicators. Economic state was reflected in the use of rural per capita net income and per capita GDP as state indicators. To a certain extent, social and economic activities can improve people's quality of life, enhance environmental protection awareness, and promote coordinated development of human and land. Healthy ecosystems are more resilient to adverse effects, such as disturbance from excessive human activities or natural disasters. For instance, good forest ecosystem can regulate climate and preserve water and soil, and NDVI has been successfully used to reflect ecosystem vitality and monitor habitat degradation [67]. NDVI is also commonly used as an assessment indicator of ecosystem state [7, 68-70]. Generally, the higher the vegetation coverage (the normalized difference vegetation index; NDVI), the better the quality of the regional environment and the ability of the ecosystem to self-regulate. (3) Ecosystem responses to pressures can adjust a system’s state and agricultural and social countermeasures were used to indicate this response that was transformed into specific outcomes acting on the social and natural environment. For example, local government expenditure is an important measure for governments to coordinate regional economic development. In this study, total power of agricultural machinery, irrigation area, per capita local government budget expenditures, per capita investment in fixed assets of the whole society, tertiary industry proportion and total mileage of highway were selected from social and agricultural responses to assess the responsiveness of the ecosystem.

 

Lines 150-153 – Missing words and repeated words. Meaning unclear.

Yes, we have reworded the sentence.

Line 192 – Is there a range of values that result from the comprehensive assessment?

Yes, the evaluation results at municipal level are shown in the figure of the manuscript. And results at county level are shown in Appendix.

Line 202 – Although well documented, a few references are usually given to direct a reader who may not be familiar with the specific methods.

Yes, done.

 

Figure 2 – Statistically significant differences from year to year or overall for Pressure? If Pressure is not changing from year to year, then do the selected Response variables correspond to a reaction to the selected Pressures? Is the takeaway that the policy applied does not impact the underlying pressures?

Yes, the average pressure scores of the ecosystem showed a slowly increased (Y= 0.0008X – 0.92, R2 = 0.70). Policy applied would impact the underlying pressures, but it is difficult to determine when policies would work and what specific effects they would have. We will study them in the next step.

 

Figure 4 – Figures Ab and Bb reversed.

Yes, done.

Lines 307-308 – “Significant differences in intensity” discussed, but the reader does not know what indicates a significant difference.

Yes, we have reworded the sentence.

 

Discussion:

Lines 332-333, although modified, closely resemble source author's language, "Further, these interactions are highly dynamic in nature; there is generally no instantaneous response of ecosystem conditions to pressures from human activity (nor the converse), but rather a complex cumulative effect that has complex temporal and spatial manifestations." (Rapport & Singh 2006).

Yes, we have reworded the sentence.

Line 345-347: When human activities exert pressure on the ecosystems, the state of the ecosystem do not change immediately. Their interactions, which combines the complex cumulative effects of temporal and spatial patterns, are highly dynamic [38].

 

Lines 336-360 - Limitations of PSR model, generically or as applied, are not addressed. The authors don’t discuss the importance of the variables they selected, their contributions to the model, or how they relate to each other, although stating this should be done (Line 355-356). This is important because the PSR model implies a linkage between the pressure, the environmental status, and the response factors, thus the selection and relationships between variables are important to assessing ecosystem health (see Xiaodan, W., Z. Xianghao, and G. Pan. 2000. A GIS-based decision support system for regional eco-security assessment and its application on the Tibetan Plateau. Journal of Environmental Management 91: 1981-1990.).

Thank you for your valuable comments. We have gotten useful information from the article and quote it. And we have supplemented detailed discussion.

Line 353-391: Determining the indicator and its weight are two important steps in the evaluation process [64]. In actual application, it is impossible to incorporate all factors affecting regional ecosystem health in an assessment model. In this study, the indicator system for regional ecosystem health was built based on the PSR framework. Human impacts and natural factors were introduced as indicators into the assessment framework. Overall, this framework consisted of 17 indicators reflecting the pressure, state, and the response of each region to produce ecosystem health scores: (1) Our study area has limited land resources with over 90% mountain cover [32]. Farmland has been shrinking for various reasons since 1957 [65]. In 2013, the per capita cultivated land area of 12 cities, such as Chengdu, Panzhihua, and Luzhou, was lower than the critical level issued by the United Nations (per capita cultivated land should be no less than 0.80 mu). Food supplies require continuous soil fertilization, however, the application of chemical fertilizers can cause environmental pollution and soil nutrient imbalance, such as an increase in heavy metals and toxic elements, decrease in soil microbial activity, and soil acidification. The northwest part of the study area is part of the Tibetan Plateau, which has a fragile environment that is sensitive to climate change [36]. Continuous drought also occurs in many places in Yunnan Province. Precipitation anomalies commonly cause drought events [46]. After 1949, the population of the study area has increased substantially, with the permanent population exceeding 130 million in 2016. These factors of the study area are represented by the pressure indicator of the planting area of crops, fertilizer application amount, percentage of temperature anomaly, percentage of precipitation anomaly, population density, and natural population growth rate. (2) Agriculture production, economic situation and ecological condition were selected as components of the state indicator to reflect ecosystem function and environmental status. Agriculture is a mainstay for human survival and development and, globally, the advances in agriculture is seen as an important means of economic prosperity and human well-being. Land resource vitality serve as a valuable indicator for measuring primary productivity and ecosystem activity [66]. Therefore, agricultural state, such as total output values for agriculture, forestry, animal husbandry, and fisheries and grain yield per unit of cultivated land, were selected as components of the state assessment indicators. Economic state was reflected in the use of rural per capita net income and per capita GDP as state indicators. To a certain extent, social and economic activities can improve people's quality of life, enhance environmental protection awareness, and promote coordinated development of human and land. Healthy ecosystems are more resilient to adverse effects, such as disturbance from excessive human activities or natural disasters. For instance, good forest ecosystem can regulate climate and preserve water and soil, and NDVI has been successfully used to reflect ecosystem vitality and monitor habitat degradation [67]. NDVI is also commonly used as an assessment indicator of ecosystem state [7, 68-70]. Generally, the higher the vegetation coverage (the normalized difference vegetation index; NDVI), the better the quality of the regional environment and the ability of the ecosystem to self-regulate. (3) Ecosystem responses to pressures can adjust a system’s state and agricultural and social countermeasures were used to indicate this response that was transformed into specific outcomes acting on the social and natural environment. For example, local government expenditure is an important measure for governments to coordinate regional economic development. In this study, total power of agricultural machinery, irrigation area, per capita local government budget expenditures, per capita investment in fixed assets of the whole society, tertiary industry proportion and total mileage of highway were selected from social and agricultural responses to assess the responsiveness of the ecosystem.

 

Lines 356-360 could be simplified to increase clarity.

Yes, done

Lines 382-385 – Regions reversed in discussion of results.

Yes, we have reworded the sentence.

Lines 415-417 – Policy suggestion of population resettlement hints at human rights issues.

Yes, we have reworded the sentence.

Line 454-457: The state should make construction of ecological civilization the goal, and resolve various practical conflicts, especially those associated with multisectoral management. Provinces or regions should guide and coordinate the ecological construction of various cities and counties. For example, areas with better institutional capacity can provide aid to resource-poor areas, and the ecologically sound places should undertake more population residence and economic construction through centralized resettlement.

Repetition in methodology assessment and Introduction, utilizing some of the same language.

Yes, we have reworded the sentence.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

General comments

The study comprehend surely very valuable information, it applied  a quite complex methodics, anyway I feel some deficiencies in the presentations of the results which evoke a motive, whether this is the problem of the presentation and  interpretation only, or the problem of methodics as a whole.

To go to the details would need a considerable time. Therefore 2 basic comments -  which  influenced the methods, results and interpretations, should be more clearly explained:

What consider the authors actually the ecosystem health? The overlaying the three criterion layers (e.g see row 190 „the composite ecosystem health assessment result was obtained by overlaying these three criterion“, or row 348 „Agriculture, economy and ecology were selected as components of the state indicator to reflect the ecosystem health“). Is the index of ecosystem health quality  really only the statistical score and  correlation of input indices? In what way?  

And, even more basically, what consider the authors for „ecology“ ? It is surely not a component of state indicator (probably the ecological conditions). And what consider the authors actually the ecosystem?

Were indices and the methods chosen in proper way? In such a diverse region (the hights range about 7000 m) is critical to use the same indices, the same weight coefficients and same calculations for the whole study area. E.g. the NDVI- what could be really considered as one of the decisive index of environmental health - is very different by nature, as e.g. the nival ecosystems in high mountains are even healthy as the monsun forests in lowlands and basins. Or, the different climatic conditions are rather differences than anomalies, etc.

From these two basic problem circles rose the questions towards the main presented results, that the mountain regions are in worst (morbid and unhealthy) ecosystem health. The fig. 7 and 8 shows that the parts southeast side of the Hu Line are more „healthy“ than the northwest mountain ranges (this is shown also on the Fig. 5., in spite of the statement of the authors on row 383 that „Overall, the ... comprehensive health scores were all poorer on the  southeast side of the Hu Line than the northwest (Figure 4 and 5)“, but the Fig. 5. indicates  more healthy regions exactly on southeast side.

Few other comments just as examples underlining the problems with rigid statistical approach and their (not)interpretation):

It is needed to explain the differences between terms regional – spatial –- county – municipal, and  keep the exact terms for the whole study.. What is the reason to calculate the correlation of lacking data? Has it any „ecosystem“ relevant interpretation? Why calculated only 4 data in the Table 2? The ecosystem related interpretation of autocorrelation rate (Moran Index) is needed! Is it reasonable to calculate the gravity center of ecosystem health? What is the „ecosystem“ relevant interpretation of this calculation? And, if there is really any, it is really for meditation, what is the reason to calculate it up to level of metres on such a huge territory. Interpretations of results are lacking also on municipal levels: e.g. row 318 „The ecosystem of Pidu district was the unhealthiest county in Chengdu ... Why? Just because of score? What is the ecosystem of a district? Is the district ecosystem the same as county, etc.

 

As for conclusion:

I do not disbelieve and dispute  the statistical methods, neither  the proper input of data to the formulas, nor the correctness of calculations. Even I would like to believe the result showing the ecosystem health stage in the region shown on the maps – in spite of my doubts concerning the mountain and other parts of the study area -  but all that need much better explanation  of the relations of rigid statistical formulas and the interpretation of their results to real ecosystem  (better environmental) health.

This is not my role, of course, but for the  presented study would suit much more the title something as "selected correlation of chosen environmental and other data in administrative units in the region of .... " as a study on possibilities of application of statistical methods in different studies.

Author Response

Subject: Manuscript revision

Dear Editor and Referees,

First of all, we are very thankful for your constructive comments on our study. Specially, we are heartily grateful to your valuable suggestions.

The manuscript has been revised carefully and strictly according to your letter. We are submitting our revised version entitled “Multi-scale and Integrated Assessment of Ecosystem Health at the Southern End of the Hu Line, China”, Manuscript ID: 564470.

Please find the revised manuscript with track changes. In order to facilitate your review, bold fonts were used to show revision and changes. In the following "Point-to-point response to the editor's letter and the reviewers’ comments".

Please do not hesitate to contact me, if further material or information is needed.

 

Note: in the revised manuscript, all major changes are marked in the revised format (in red)

Thanks again.

Sincerely yours,

Junnan Xiong

 

Detailed responses to the comments are addressed below. 

Your comments:

General comments

The study comprehend surely very valuable information, it applied a quite complex methodics, anyway I feel some deficiencies in the presentations of the results which evoke a motive, whether this is the problem of the presentation and interpretation only, or the problem of methodics as a whole.

 

To go to the details would need a considerable time. Therefore 2 basic comments - which influenced the methods, results and interpretations, should be more clearly explained:

What consider the authors actually the ecosystem health? The overlaying the three criterion layers (e.g see row 190 „the composite ecosystem health assessment result was obtained by overlaying these three criterion“, or row 348 „Agriculture, economy and ecology were selected as components of the state indicator to reflect the ecosystem health“). Is the index of ecosystem health quality really only the statistical score and correlation of input indices? In what way? 

And, even more basically, what consider the authors for „ecology“? It is surely not a component of state indicator (probably the ecological conditions). And what consider the authors actually the ecosystem?

Thank you for your valuable comments. We used a downscaling model to estimate missing data. And some statements do have minor errors, and we have corrected them.

We have supplemented explanation of assessment framework and Data Acquisition, and discussion of indicator selection was added.

 

Line 121-123: Moreover, health is not the opposite of disability, while ecosystem health is an embodiment of ecological carrying capacity [40]. The deficiency of ecosystem services and management would lead to the decline of ecological carrying capacity, thus reducing the level of ecosystem health [1].

 

Line 168-170: There is usually a demand to translate information from a large spatial scale to finer geographic scales while keeping consistency with the raw dataset [47]. This process is called spatial downscaling, and it is a common method to use existing data with correlation to estimate unknown data [48, 49].

 

Line 189-191: Positive indicators denote the ecosystem health scores declining when indicators values decrease; negative indicate the ecosystem health score improving when indicators values decrease. In the study, planting area of crops and all state and response indicators were positive. The extremum difference method was used to normalize each index [50].

 

Line 353-391: Determining the indicator and its weight are two important steps in the evaluation process [64]. In actual application, it is impossible to incorporate all factors affecting regional ecosystem health in an assessment model. In this study, the indicator system for regional ecosystem health was built based on the PSR framework. Human impacts and natural factors were introduced as indicators into the assessment framework. Overall, this framework consisted of 17 indicators reflecting the pressure, state, and the response of each region to produce ecosystem health scores: (1) Our study area has limited land resources with over 90% mountain cover [32]. Farmland has been shrinking for various reasons since 1957 [65]. In 2013, the per capita cultivated land area of 12 cities, such as Chengdu, Panzhihua, and Luzhou, was lower than the critical level issued by the United Nations (per capita cultivated land should be no less than 0.80 mu). Food supplies require continuous soil fertilization, however, the application of chemical fertilizers can cause environmental pollution and soil nutrient imbalance, such as an increase in heavy metals and toxic elements, decrease in soil microbial activity, and soil acidification. The northwest part of the study area is part of the Tibetan Plateau, which has a fragile environment that is sensitive to climate change [36]. Continuous drought also occurs in many places in Yunnan Province. Precipitation anomalies commonly cause drought events [46]. After 1949, the population of the study area has increased substantially, with the permanent population exceeding 130 million in 2016. These factors of the study area are represented by the pressure indicator of the planting area of crops, fertilizer application amount, percentage of temperature anomaly, percentage of precipitation anomaly, population density, and natural population growth rate. (2) Agriculture production, economic situation and ecological condition were selected as components of the state indicator to reflect ecosystem function and environmental status. Agriculture is a mainstay for human survival and development and, globally, the advances in agriculture is seen as an important means of economic prosperity and human well-being. Land resource vitality serve as a valuable indicator for measuring primary productivity and ecosystem activity [66]. Therefore, agricultural state, such as total output values for agriculture, forestry, animal husbandry, and fisheries and grain yield per unit of cultivated land, were selected as components of the state assessment indicators. Economic state was reflected in the use of rural per capita net income and per capita GDP as state indicators. To a certain extent, social and economic activities can improve people's quality of life, enhance environmental protection awareness, and promote coordinated development of human and land. Healthy ecosystems are more resilient to adverse effects, such as disturbance from excessive human activities or natural disasters. For instance, good forest ecosystem can regulate climate and preserve water and soil, and NDVI has been successfully used to reflect ecosystem vitality and monitor habitat degradation [67]. NDVI is also commonly used as an assessment indicator of ecosystem state [7, 68-70]. Generally, the higher the vegetation coverage (the normalized difference vegetation index; NDVI), the better the quality of the regional environment and the ability of the ecosystem to self-regulate. (3) Ecosystem responses to pressures can adjust a system’s state and agricultural and social countermeasures were used to indicate this response that was transformed into specific outcomes acting on the social and natural environment. For example, local government expenditure is an important measure for governments to coordinate regional economic development. In this study, total power of agricultural machinery, irrigation area, per capita local government budget expenditures, per capita investment in fixed assets of the whole society, tertiary industry proportion and total mileage of highway were selected from social and agricultural responses to assess the responsiveness of the ecosystem.

 

 

Were indices and the methods chosen in proper way? In such a diverse region (the hights range about 7000 m) is critical to use the same indices, the same weight coefficients and same calculations for the whole study area. E.g. the NDVI- what could be really considered as one of the decisive index of environmental health - is very different by nature, as e.g. the nival ecosystems in high mountains are even healthy as the monsun forests in lowlands and basins. Or, the different climatic conditions are rather differences than anomalies, etc.

It is an important question. We thank you for your valuable comment, and detailed discussion was added. The references we cited could be used to discuss the actual situation of the study area, and explain the rationality of index selection (including NDVI) and classification.

Line 353-391: Determining the indicator and its weight are two important steps in the evaluation process [64]. In actual application, it is impossible to incorporate all factors affecting regional ecosystem health in an assessment model. In this study, the indicator system for regional ecosystem health was built based on the PSR framework. Human impacts and natural factors were introduced as indicators into the assessment framework. Overall, this framework consisted of 17 indicators reflecting the pressure, state, and the response of each region to produce ecosystem health scores: (1) Our study area has limited land resources with over 90% mountain cover [32]. Farmland has been shrinking for various reasons since 1957 [65]. In 2013, the per capita cultivated land area of 12 cities, such as Chengdu, Panzhihua, and Luzhou, was lower than the critical level issued by the United Nations (per capita cultivated land should be no less than 0.80 mu). Food supplies require continuous soil fertilization, however, the application of chemical fertilizers can cause environmental pollution and soil nutrient imbalance, such as an increase in heavy metals and toxic elements, decrease in soil microbial activity, and soil acidification. The northwest part of the study area is part of the Tibetan Plateau, which has a fragile environment that is sensitive to climate change [36]. Continuous drought also occurs in many places in Yunnan Province. Precipitation anomalies commonly cause drought events [46]. After 1949, the population of the study area has increased substantially, with the permanent population exceeding 130 million in 2016. These factors of the study area are represented by the pressure indicator of the planting area of crops, fertilizer application amount, percentage of temperature anomaly, percentage of precipitation anomaly, population density, and natural population growth rate. (2) Agriculture production, economic situation and ecological condition were selected as components of the state indicator to reflect ecosystem function and environmental status. Agriculture is a mainstay for human survival and development and, globally, the advances in agriculture is seen as an important means of economic prosperity and human well-being. Land resource vitality serve as a valuable indicator for measuring primary productivity and ecosystem activity [66]. Therefore, agricultural state, such as total output values for agriculture, forestry, animal husbandry, and fisheries and grain yield per unit of cultivated land, were selected as components of the state assessment indicators. Economic state was reflected in the use of rural per capita net income and per capita GDP as state indicators. To a certain extent, social and economic activities can improve people's quality of life, enhance environmental protection awareness, and promote coordinated development of human and land. Healthy ecosystems are more resilient to adverse effects, such as disturbance from excessive human activities or natural disasters. For instance, good forest ecosystem can regulate climate and preserve water and soil, and NDVI has been successfully used to reflect ecosystem vitality and monitor habitat degradation [67]. NDVI is also commonly used as an assessment indicator of ecosystem state [7, 68-70]. Generally, the higher the vegetation coverage (the normalized difference vegetation index; NDVI), the better the quality of the regional environment and the ability of the ecosystem to self-regulate. (3) Ecosystem responses to pressures can adjust a system’s state and agricultural and social countermeasures were used to indicate this response that was transformed into specific outcomes acting on the social and natural environment. For example, local government expenditure is an important measure for governments to coordinate regional economic development. In this study, total power of agricultural machinery, irrigation area, per capita local government budget expenditures, per capita investment in fixed assets of the whole society, tertiary industry proportion and total mileage of highway were selected from social and agricultural responses to assess the responsiveness of the ecosystem.

 

From these two basic problem circles rose the questions towards the main presented results, that the mountain regions are in worst (morbid and unhealthy) ecosystem health. The fig. 7 and 8 shows that the parts southeast side of the Hu Line are more „healthy“ than the northwest mountain ranges (this is shown also on the Fig. 5., in spite of the statement of the authors on row 383 that „Overall, the ... comprehensive health scores were all poorer on the  southeast side of the Hu Line than the northwest (Figure 4 and 5)“, but the Fig. 5. indicates  more healthy regions exactly on southeast side.

Thank you for your comments. Regions reversed in discussion of results and we have corrected the wrong sentence.

 

Few other comments just as examples underlining the problems with rigid statistical approach and their (not) interpretation): It is needed to explain the differences between terms regional – spatial –- county – municipal, and keep the exact terms for the whole study.

Thank you for your comments. We have checked the terms for the whole study and municipal level is unified into prefecture level.

Region: a large area of land, usually without exact limits or borders; a region can be a county, a city or a larger region. Spatial: relating to space and the position, size, shape, etc. of things in it; the temporal and spatial relationship expresses the evolution characteristics of things. Prefecture-level cities and counties are administrative units.

Line 105-106: Our study area include 37 prefecture-level cities and 312 counties (Appendix A and B).

 

What is the reason to calculate the correlation of lacking data? Has it any „ecosystem“relevant interpretation? Why calculated only 4 data in the Table 2? The ecosystem related interpretation of autocorrelation rate (Moran Index) is needed!

Thank you. We have supplemented relevant explanations. We used a downscaling model to estimate missing data and the 4 data are missing.

Line 168-170: There is usually a demand to translate information from a large spatial scale to finer geographic scales while keeping consistency with the raw dataset [47]. This process is called spatial downscaling, and it is a common method to use existing data with correlation to estimate unknown data [48, 49].

 

 

Is it reasonable to calculate the gravity center of ecosystem health? What is the „ecosystem “relevant interpretation of this calculation? And, if there is really any, it is really for meditation, what is the reason to calculate it up to level of metres on such a huge territory.

Thanks a lot. We have supplemented relevant explanations

Line 225-228: The concept of a gravity center is derived from physics, and gravity model has been widely utilized in the fields of economic geography, land use science, urban planning and ecosystem services value etc. [55]. The variation track of gravity center of assessment result value can well reflect the regional difference of the results in changes [56].

 

Interpretations of results are lacking also on municipal levels: e.g. row 318 „The ecosystem of Pidu district was the unhealthiest county in Chengdu ... Why? Just because of score? What is the ecosystem of a district? Is the district ecosystem the same as county, etc.

Yes, done. We have supplemented the interpretations. We may not be able to explain each region one by one, but the accuracy of the model is verified by comparing our results with those of other scholars. So it is reasonable to use the score to judge whether the region is healthy or not.

Line 434-436: The vegetable industry in Pidu District is well developed, and it has suffered from agricultural non-point source pollution for a long time due to excessive application of fertilizer and the ecosystem of Pidu district was the unhealthiest county in Chengdu (Table 4).

 

As for conclusion:

I do not disbelieve and dispute the statistical methods, neither the proper input of data to the formulas, nor the correctness of calculations. Even I would like to believe the result showing the ecosystem health stage in the region shown on the maps – in spite of my doubts concerning the mountain and other parts of the study area - but all that need much better explanation of the relations of rigid statistical formulas and the interpretation of their results to real ecosystem (better environmental) health.

This is not my role, of course, but for the presented study would suit much more the title something as "selected correlation of chosen environmental and other data in administrative units in the region of ....” as a study on possibilities of application of statistical methods in different studies.

 

Thank you for your comments. In this paper, the mature PSR model was used to evaluate the ecosystem health of 37 prefecture-level cities and 312 counties in the study area for nearly 20 years. Although we used downscaling models to estimate missing data, our study focused on ecosystem health and its spatial evolution processes under the influence of natural and human activities. The accuracy of the results has been verified by comparing our results with those of other scholars. For instance, the ecosystem health has improved in most areas, but the situation in Lijiang appears to be very serious (Figure 5). Other studies have also shown that the ecosystem health in much of the region is deteriorating [1]. Jianchuan County, Yunlong County, Yongping County and Yangbi County were found to be the four worst counties in the Dali ecosystem (Figure. 8), which is in accordance with the results of previous studies [41].

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Dear Authors,

very compliments for your paper intetled "Multi-scale and Integrated Assessment of Ecosystem Health at the Southern End of the Hu Line, China". It presents a very interestings results. However, in my opinion, it must be improved using my suggestions presented as notes in the text.

After these changes, the paper could continue in its publication process.

Best Regards.

Comments for author File: Comments.pdf

Author Response

Subject: Manuscript revision

Dear Editor and Referees,

First of all, we are very thankful for your constructive comments on our study. Specially, we are heartily grateful to your valuable suggestions.

The manuscript has been revised carefully and strictly according to your letter. We are submitting our revised version entitled “Multi-scale and Integrated Assessment of Ecosystem Health at the Southern End of the Hu Line, China”, Manuscript ID: 564470.

Please find the revised manuscript with track changes. In order to facilitate your review, bold fonts were used to show revision and changes. In the following "Point-to-point response to the editor's letter and the reviewers’ comments".

Please do not hesitate to contact me, if further material or information is needed.

 

Note: in the revised manuscript, all major changes are marked in the revised format (in red)

Thanks again.

Sincerely yours,

Junnan Xiong

 

Detailed responses to the comments are addressed below. 

Your comments:

General Comments

Dear Authors,

Very compliments for your paper intetled "Multi-scale and Integrated Assessment of Ecosystem Health at the Southern End of the Hu Line, China". It presents a very interestings results. However, in my opinion, it must be improved using my suggestions presented as notes in the text.

After these changes, the paper could continue in its publication process.

Best Regards.

Thank you for your comments

 

Specific Comments

Please, within the keywords, avoid to repeat the same words present in the title. Provide two new keywords.

Yes, done.

 

Two good examples of appreciable changes profoundly affecting regional ecological process are due by Prof. Cano and collaborators, for the Iberian Peninsula:

1) Cano-Ortiz A, Pinto Gomes CJ, Musarella CM, Cano E. 2015. Expansion of the Juniperus genus due to anthropic activity. In: Weber RP, editor. Old-Growth Forest and Coniferous Forests. New York: Nova Science Publishers; p. 55–65.

2) Cano E, Musarella CM, Cano Ortiz A, Piñar Fuentes JC, Rodríguez Torres A, Del Río González S, Pinto Gomes CJ, Quinto-Canas R, Spampinato G. 2019b. Geobotanical study of the microforests of Juniperus oxycedrus subsp. badia in the Central and Southern Iberian Peninsula. Sustainability. 11, 1111; doi:10.3390/su11041111

In entrambi gli articoli, gli autori evidenziano tutto ciò utilizzando un nuovo indice ecologico applicato nello studio della vegetazione.

Thank you for your valuable comments. We have gotten useful information from the articles and quoted it.

 

Please, rearrange this part of the "Introduction" section, simplifying the highlighted text and improving the references.

Thank you for your comments we have rearranged this part and considered that reference.

 

Please, check the correct correspondence of references [26-33] between this section and the References list at the end of the manuscript.

Yes, done.

 

Is it correct?（Line 104）

Yes, we have corrected it

 

Please, rearrange this part of the "Introduction" section, simplifying the highlighted text.

Yes, done.

 

Please, check the space here.

Thanks a lot.

 

Please, provide a better map with a more resolution, especially in the ring box.（Figure.1）

Yes, done.

e.g.

Please, try to put this in the above line. （Table.1）

Yes, done.

 

Please, change "Sichaun" to "Sichuan" in line 258.

Yes, we have changed it.

 

Please, improve the quality of this image.（Figure.6）

Yes, done.

Please, improve the quality of these images in the figure 7.

Yes, we have improved the quality of these images.

Please, change "Both" to "both" in line 367.

Yes, done.

 

Please, extend the "Conclusions" section. It seems a few poor in final considerations, basing in the carried out work.

Thank you, we have enhanced Conclusions.

Line 481-483: Our study focused on the spatial evolution of ecosystem health and contributed to the reasonable planning of ecological and environmental protection with the goal of identifying a path toward breakthrough of the Hu Line. However, this paper has no substantial contribution to the innovation of methods and the improvement of models.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have made significant improvements to the clarity of their paper, particularly in discussion variable selection and relationship to interpreting the results. Minor grammatical errors remain (e.g., changes of verb tense lines 61-62).

Author Response

Subject: Manuscript revision

Dear Editor and Referees,

First of all, we are very thankful for your constructive comments on our study. Specially, we are heartily grateful to your valuable suggestions.

The manuscript has been revised carefully and strictly according to your letter. We are submitting our revised version entitled “Selected environmental assessment model and spatial analysis method to explain correlations on environmental and socio-economic data with possible application for explaining state of the ecosystem”, Manuscript ID: 564470.

Please find the revised manuscript with track changes. In order to facilitate your review, bold fonts were used to show revision and changes. In the following "Point-to-point response to the editor's letter and the reviewers’ comments".

Please do not hesitate to contact me, if further material or information is needed.

 

Note: in the revised manuscript, all major changes are marked in the revised format (in red)

Thanks again.

Sincerely yours,

Junnan Xiong

 

Detailed responses to the comments are addressed below. 

Your comments:

The authors have made significant improvements to the clarity of their paper, particularly in discussion variable selection and relationship to interpreting the results. Minor grammatical errors remain (e.g., changes of verb tense lines 61-62).

Yes, we have corrected it.

Reviewer 2 Report

The authors devoted considerable and creditable work to answer my comments. I appreciate it very much. In spite of this there remained opened several conceptional questions. I would like to underline that my comments were conceptional, not factual, not related to single methods and results.

Might hapen that this misunderstandig has risen from my english.

Again, my basic  concerns:

I did not dispute any of the applied statistical methods, evenly not the input data. I know the PSR, the spatial downscaling, the application of NDVI, the concept of gravity centers, etc., but the goal of the paper is to apply all these methods to explain the state of the environmental health of the region. It is not possible only by calculated figures of statistical indices! All that need proper environmental/ecological interpretations of the whole process and results, explaining the relations of the methods to ecosystems and its health. The ecosystem health should relate to the ecosystem, irrespective to different modified definitions developed for applied studies and methods, including PSR. My most serious comments related this issue.

As for example, triing to use other words, only 2 issues:

You used the same indices for evaluating the ecosystem health for the whole territory from lowland almost up to Himalaya. However, from ecological point of view is obvious, that there are completelly different conditions which can  rank and explain the state of the ecosystem/environment health. E.g. just a single question on NDVI: do you think that the ecosystem health stage in the mountains above 5000-7000 metres (NDVI close to zero) is worse as in rice fields in the lowland, where the NDVI is close to 1? The statistical formulas can not solve this question! Probably similar problems caused that the mountain regions in the study  shows worse results of ecosystem health as the heavily impacted intensivelly cultivated  agricultural lands. Again, irrespective to figures and results risen by formulas! It should be clearly stated, what is the result of formula and what is the relation to the real ecosystem. Just for a comparison: The most healthy person is not that, who is kept in satisfactory state by pharmaceuticals, but that, who is healthy also without them. It is again not the question of formula. The gravity center: the problem is not the concept of gravity center calculation, it is really used in economic geography and has sense. But what ecological issue could be explained by shifting the gravity center of statistically calculated – not really exact ecological/functional - indices of ecosystem health by few km, even displayed by exact figures on two decimal place, on such a huge territory? I do not see any, and, neither the article did not explain any.

As for conclusion:

Since the authors accomplished serious work, I do not want to negate, neither to block the publication of this paper.  Moreover, I am not sure, if the authors are ready, or eager to go to real ecological explanation of the sense of the process and the results, I would propose as a minimum to change the title of the paper as  I proposed in first round, i.e. something as  „selected statistical methods to explain correlations on environmental and socio-economic data with possible application for explaining state of the environment ....“, with clear explanation that their use for the assessment of the ecosystem health need further research and interdisciplinary cooperation.

Author Response

Subject: Manuscript revision

Dear Editor and Referees,

First of all, we are very thankful for your constructive comments on our study. Specially, we are heartily grateful to your valuable suggestions.

The manuscript has been revised carefully and strictly according to your letter. We are submitting our revised version entitled “Selected environmental assessment model and spatial analysis method to explain correlations on environmental and socio-economic data with possible application for explaining state of the ecosystem”, Manuscript ID: 564470.

Please find the revised manuscript with track changes. In order to facilitate your review, bold fonts were used to show revision and changes. In the following "Point-to-point response to the editor's letter and the reviewers’ comments".

Please do not hesitate to contact me, if further material or information is needed.

 

Note: in the revised manuscript, all major changes are marked in the revised format (in red)

Thanks again.

Sincerely yours,

Junnan Xiong

 

Thanks for your valuable comment. We have change the title and added the explanation.

Moreover, maybe we study ecology superficially, or cannot understand it accurately. I would like to say a little about my understanding of ecosystem health. If I misunderstand your meaning, I hope you can respond to me patiently, and I will continue to modify the manuscript according to your comments.

 

As far as I am concerned, health is not the opposite of disability, while ecosystem health is an embodiment of ecological carrying capacity (Costanza, 1992). Although the environment is of good (for example, no haze and few heavy metal pollution) , the ecological carrying capacity is poor in the plateau area, which is often said by scholars to be ecologically fragile. So we think the ecological health here (NDVI close to zero) is worse than that in plain areas (NDVI close to 1). Generally, good forest ecosystem can regulate climate and preserve water and soil, and NDVI has been successfully used to reflect ecosystem vitality and monitor habitat degradation (Pettorelli, 2005). The higher the NDVI, the better the quality of the regional environment and the ability of the ecosystem to self-regulate (sun, 2016). "The most healthy person is not that, who is kept in satisfactory state by pharmaceuticals, but that, who is healthy also without them". I agree with you. However, the real situation: the person who is kept in satisfactory state by pharmaceuticals are not necessarily healthy, but the person still cannot be healthy with the help of pharmaceuticals are definitely unhealthy. This is the case in ecologically fragile areas, where it is difficult to recover once damaged. Consequently, the deficiency of ecosystem services and management would lead to the decline of ecological carrying capacity, thus reducing the level of ecosystem health (Peng, 2017).

 

Title: Selected environmental assessment model and spatial analysis method to explain correlations on environmental and socio-economic data with possible application for explaining state of the ecosystem.

 

Line 488-492: However, this paper has no substantial contribution to the innovation of methods and the improvement of models. Future regional ecosystem health assessment requires a comprehensive system based on different ecological and biological information scales that also takes into account human health and cultural factors. Although NDVI have been selected in many studies, there may be other reasonable results without using NDVI. Many areas, such as plateau areas, have low scores of ecosystem. Perhaps the ecological environment is not unhealthy, it may be fragile or low carrying capacity. It may be unreasonable or inappropriate to apply gravity model to ecosystem health research in this paper, although it is really used in economic geography and has sense. Other suitable indicators need to be explored or the assessment model and analysis method need to be improved.

 

Costanza, R., Norton, B.G. and Haskell, B.D, Ecosystem Health: new goals for environmental management. Ecosystem Health New Goals for Environmental Management 1992.

Peng, J., Y. Liu, T. Li, and J. Wu, Regional ecosystem health response to rural land use change: A case study in Lijiang City, China. Ecological Indicators 2017, 2017, 399-410.

Pettorelli, N., J. Vik, A. Mysterud, J. Gaillard, C. Tucker, and N. Stenseth, Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution 2005, 20, 503-510.

Sun, T., W. Lin, G. Chen, P. Guo, and Z. Ying, Wetland ecosystem health assessment through integrating remote sensing and inventory data with an assessment model for the Hangzhou Bay, China. Science of the Total Environment 2016, 566-567, 627-640.

Reviewer 3 Report

Dear Authors,

very compliments for the quick correction of your very interesting manuscript. I can see that you made all the requested corrections: so, for me, it can continue in its publication process.

Many thanks for trust me.

Best Whishes.

Author Response

Dear Editor and Referees,

We are very thankful for your constructive comments on this manuscript, which are very meaningful and important. Thank you again for your hard work.