The Relationship of Forest Fragmentation to Scots Pine Forest Mortality

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I had the opportunity to read and review the manuscript entitled “The Relationship of Forest Fragmentation to Scots Pine Forest Mortality”.
This manuscript investigated the relationship and effect of forest fragmentation at various spatial scales on the probability of Scots pine forest mortality. The presented study also analyzed the relationship of multi-scale fragmentation index effect on forest dieback. The relationship between multiple stressors emphasizes the distinct role of forest fragmentation in influencing pine forest mortality probability. The study provides substantial policy implications. 
My review below suggests some improvements：
1.At the first occurrence in the main text, abbreviations such as 'FF' and 'FM' should be spelled out in full, followed by the abbreviation in parentheses.

2.The Introduction lacks a clear logical structure. A thorough revision is needed to enhance the argument's clarity and flow. It is recommended to first discuss Forest Mortality and then proceed to Forest Fragmentation, ensuring a logical flow of context. 

3.Figure 1 should include a scale bar and a north arrow to improve spatial reference and interpretation.

4.Line 166 mentions the use of the generalized additive model (GAM), while the abstract describes the analysis as a logistic regression modeling approach. These are not the same model. Please ensure consistency in the methodological description throughout the manuscript. 

5.The results of the GAM model should be comprehensively presented, including variable selection and variable importance, to ensure a complete understanding of the analytical process.

6.The study should further elaborate on the scale effects to clarify their influence on the results. Additionally, the practical applications and policy implications of the research should be explicitly discussed to highlight its broader relevance.

7.Please add a subsection addressing potential limitations, such as sample size biases, unaccounted confounding variables, or spatial/temporal constraints inherent to the dataset or methodology.

8.The citation of references is not standardized enough. The manuscript should incorporate more recent studies to strengthen its literature foundation.

Author Response

Comments 1: At the first occurrence in the main text, abbreviations such as 'FF' and 'FM' should be spelled out in full, followed by the abbreviation in parentheses.

Response 1: Thank you for your careful review and valuable feedback. We appreciate your attention to detail regarding the clarity of the terminology used. To ensure optimal readability for all audiences, we have implemented the following convention throughout the main text. The full term is spelled out at the first occurrence of significant abbreviations (such as forest fragmentation (FF) or forest mortality (FM), followed immediately by the abbreviation in parentheses throughout the manuscript.

Specific modification as following 1: (lines 27 and 28)

Forest fragmentation (FF), forest mortality (FM)

Comments 2: The Introduction lacks a clear logical structure. A thorough revision is needed to enhance the argument's clarity and flow. It is recommended to first discuss Forest Mortality and then proceed to Forest Fragmentation, ensuring a logical flow of context. 

Response 2: We sincerely thank the reviewer for this constructive feedback. We agree that restructuring the introduction improves the logical progression of ideas. As suggested, we thoroughly revised the section. First, we established the context of forest mortality by addressing its drivers, impacts, and global significance. Then, we introduced forest fragmentation as a key exacerbating factor. This reorganization provides a clearer, more cohesive narrative by building foundational context before delving into specific mechanisms.

Specific modification as following 2: (lines 34-41)

Forest mortality (FM) is the death of trees or woody vegetation within a forest ecosystem (Ambrose 2021; Senf et al. 2020). It is natural process that occurs as result of various factors, including biotic and a biotic factors (King et al. 2018; Ma et al. 2023). Moreover, FM occurs when the tree's production of carbohydrates through photosynthesis is exceeded by its respiration. As a result, the tree dries out and ceases to photosynthesize (Filip et al. 2007). FM can occur at the individual tree level, affecting individual trees or small groups of trees, or at large scales, affecting entire stands of forests. It can have significant ecological, environmental, and socio-economic impacts influencing forest structure, composition, biodiversity, carbon cycling and ecosystem services (Harmon and Bell 2020). 

Comments 3: .Figure 1 should include a scale bar and a north arrow to improve spatial reference and interpretation.

Response 3: Thank you for your thoughtful suggestion regarding Figure 1. We agree that clear spatial referencing significantly improves interpretability, and we appreciate your attention to this detail. In accordance with your feedback, we have: added a clearly visible scale bar to provide accurate spatial measurements. Included a north arrow to establish directional orientation. These additions allow readers to precisely interpret spatial relationships, aligning the figure with established standards. The revised Figure 1 now appears in the updated manuscript. We believe these refinements strengthen the figure’s utility, and we thank you for highlighting this opportunity for improvement.

Specific modification as following 3: (lines 126-127)

Comments 4: Line 166 mentions the use of the generalized additive model (GAM), while the abstract describes the analysis as a logistic regression modeling approach. These are not the same model. Please ensure consistency in the methodological description throughout the manuscript.

Response 4: Thank you for your attention to this regard. That was a mistake in the abstract. We've corrected the abstract.

Comments 5: The results of the GAM model should be comprehensively presented, including variable selection and variable importance, to ensure a complete understanding of the analytical process.

Response 5: Thank you. In the modeling process, we selected variables by adding each variable into the model and checking their significance. Variables which have insignificant effect on the modeling were removed by evaluating their p-value and calculating their AIC value. If the resulting p-value was sufficiently low (we used the 0.05 level), we concluded that the more complex model was significantly better than the simpler model and favored the more complex model. Otherwise, we chose the simpler model without the additional variable. The importance of the variable was checked by using VIP library from R program.

Specific modification as following 5: (lines 279-289 and lines 341-346)

The selection process for the variables in the model starts with those that are theoretically relevant to the response variable. We performed analysis, including a correlation check using the variance inflation factor (equation 5) and partial dependence plots to assess the initial relationship. In the model-building approach, we used the select=TRUE function in mgcv to enable double penalty shrinkage. We diagnosed the model summary, watching into p-values, and again checked the term significance evaluation. Effective degrees of freedom (EDF) EDF>1 for non-linear effects, and an EDF near 0 indicates that the term has been effectively removed. P-values <0.05 indicate significance. After completing these steps, the GAM variable selection balances statistical significance, model parsimony, and domain relevance.

Additionally, the significance of the various independent variables included in the model was analyzed. Figure 3 shows the significance of these variables. Variables from the basic model (Figure 3A) and fragmentation indicators (Figure 3B) are presented separately. Note the distinctive impact of the multi-scale forest fragmentation factor, which combines scales ranging from 50 to 3,000 meters. Its impact is more than double that of FF ratios for the other scales.

Comments 6: The study should further elaborate on the scale effects to clarify their influence on the results. Additionally, the practical applications and policy implications of the research should be explicitly discussed to highlight its broader relevance.

Response 6: Thank you again for your valuable insights. We observed scale-dependent effects on the mortality probability of Scots pine forests. We stated its effect on the discussion part in detail. The practical applications and policy implications are separately discussed

Specific modification as following 6: (line 507-531),

Practical applications of multi-scale fragmentation research on Scots pine forests are very important. They identify critical thresholds at which mortality risks point. Using this information, managers can prioritize restoration of landscape connectivity in areas crossing these thresholds. It provides an early warning and helps with climate-resilient reforestation by designing tree-planting schemes using scale-dependent thresholds.

It is critically important to display model results of mortality probability (Figure 8B) and fragmentation effects (Figure 8A) through spatial maps for research, practical applications, and policy development. These maps significantly enhance comprehension by instantly revealing spatial patterns, such as high-risk zones and areas of severe fragmentation. They provide an essential, contextual understanding of the landscape. Furthermore, maps enable targeted action by guiding precise mitigation efforts and allowing policymakers to prioritize funding for high-impact areas. Their visual nature also makes complex data accessible to non-experts, fostering crucial dialogue between researchers, NGOs, and local communities to co-design effective solutions. By identifying regions that require specific resilience strategies, spatial maps transform abstract model outputs into actionable intelligence. This intelligence enables concrete policy outcomes, such as laws mandating ecological connectivity, infrastructure guidelines that avoid biodiversity hotspots, and national conservation agendas.

Comments 7: Please add a subsection addressing potential limitations, such as sample size biases, unaccounted confounding variables, or spatial/temporal constraints inherent to the dataset or methodology.

Response 7: Thank you very much. In this research we conducted study based on forest stands. So, there is no sampling biasness for the research. Unaccounted confounding variables such as wind destruction data, chronological data were not considered which have their own effect on mortality. We put this as research limitation and can be fulfilled in further research.

Specific modification as following 7: (lines 537-543).

While this study provides insights into Scots pine mortality driven by dry periods, several limitations must be acknowledged. First, our focus on dry-period data means the potential influence of wet periods on mortality remains not assessed. Additionally, wind destruction events and the inherent methodological considerations of chronological data analyses may contribute to mortality patterns to some degree. These limitations concerning data scope, disturbance factors, and analytical methods represent important avenues for future research.

Comments 8: The citation of references is not standardized enough. The manuscript should incorporate more recent studies to strengthen its literature foundation.

Response 8: Thank you very much. We checked that and fixed them

Reviewer 2 Report

Comments and Suggestions for Authors

This study integrates a multi-scale forest fragmentation framework with environmental and stand-level data to systematically reveal the spatial effects of fragmentation on Scots pine forest mortality. Leveraging data from remote sensing, national forest inventories, and public ecological databases, the research employs a generalized additive logistic regression model to quantify how fragmentation at multi-scales significantly influences forest dieback. The analysis uncovers critical fragmentation thresholds and demonstrates how edge effects and connectivity loss interact across spatial scales to elevate mortality risks. This study highlights the ecological importance of maintaining forest connectivity and mitigating fragmentation, offering a scientific foundation for targeted conservation zoning and adaptive forest management. The findings provide practical guidance for reducing Scots pine mortality and enhancing forest ecosystem resilience in Central Europe.However, there are still the following shortcomings:
1.The novelty of the manuscript is not sufficiently prominent and needs to be further refined and highlighted.
2.All figures are not properly centered. Moreover, Figure 1 lacks essential map elements such as a scale bar, north arrow, and legend.
3.The parameters listed in Table 1 could be reorganized into clearer categories—such as tree-level parameters and site conditions—to improve clarity and readability.
4.The line charts used in the result section are not sufficiently intuitive and should be improved for better visual interpretation.
5.The discussion section could benefit from deeper ecological interpretation and comparison with similar studies to better contextualize the findings within the broader literature.
6. The conclusion should more clearly differentiate between confirmed findings and inferred implications, and could be strengthened by outlining specific recommendations for forest management or future research directions.

Author Response

Comments 1: The novelty of the manuscript is not sufficiently prominent and needs to be further refined and highlighted.

Response 1: Thank you for your careful review and valuable feedback. We redefined and highlighted in discussion part of revised paper.

Specific modification as following 1: (lines 441-449 )

This framework utilizes both discrete and composite spatial scales. This approach surpasses the limitations of single-scale analyses by explicitly quantifying the synergistic modulation of mortality risk by cross-scale interactions between fine-scale edge effects (50–600 m), landscape-scale fragmentation (800–3,000 m), and integrated all-scale fragmentation (50–3,000 m). These critical mechanistic insights, which are unobtainable via single-scale investigations, elucidate the complex ecological drivers of FM. Furthermore, the framework improves analytical precision by reducing scale-related errors and uncertainties, resulting in optimized model outputs. These robust, scale-integrated findings directly inform and advance evidence-based forest management and conservation planning.

Comments 2: All figures are not properly centered. Moreover, Figure 1 lacks essential map elements such as a scale bar, north arrow, and legend.

Response 2: We sincerely thank the reviewer for this constructive feedback. We solved it in revised manuscript

Specific modification as following 2: (Figure 1)

 

Comments 3: The parameters listed in Table 1 could be reorganized into clearer categories—such as tree-level parameters and site conditions—to improve clarity and readability.

Response 3: Thank you again. We revised and corrected the suggested comments in Table 1

Specific modification as following 3: (Table 1)

Comments 4: The line charts used in the result section are not sufficiently intuitive and should be improved for better visual interpretation.

Response 4: The partial dependency charts presented here are consistent with those found in the literature. The only difference is that several variables, specifically fragmentation indicators, are placed on a single graph. This was done to demonstrate the similarities in the impact of these variables. Thank you for pointing out the graphs' readability issues. We have modified the graphical elements of the charts to improve their readability.

Specific modification as following 4: (Figures 4, 5, 6 and 7)

Comments 5: The discussion section could benefit from deeper ecological interpretation and comparison with similar studies to better contextualize the findings within the broader literature.

Response 5: Thank you very much for your comment. We revised the discussion part of manuscript as much as possible.

Specific modification as following 5: (lines 468-480)

Our study advances the field of forest fragmentation ecology by systematically analyzing fragmentation scale indices at defined spatial extents (50–600 m and 800–3,000 m) and integrating them into composite, multi-scale indices. Multi-scale fragmentation indices are essential for understanding the complex impacts of fragmentation on forest mortality. Different scales reveal different mechanisms: local scales highlight edge effects and microhabitat changes, and landscape scales reveal connectivity and population dynamics. Using multi-scale approaches improves the accuracy of mortality risk assessments and supports better forest management and conservation planning. This approach quantifies the synergistic effects on pine mortality. Recent research shows that the effects of fragmentation on mortality depend strongly on scale. At local scales, fragmentation often increases mortality due to higher exposure to edge effects, and reduced habitat quality. At larger scales, however, the effects can be more complex and sometimes positive for certain species due to geometric effects (Bogaert et al. 2011; Gelber et al. 2024; Netzel et al., 2024).

Comments 6: The conclusion should more clearly differentiate between confirmed findings and inferred implications, and could be strengthened by outlining specific recommendations for forest management or future research directions.

Response 6: Thank you very much for bringing this into our attention. We solve it as commented.

Specific modification as following 6 (lines 537-543)

While this study provides insights into Scots pine mortality driven by dry periods, several limitations must be acknowledged. First, our focus on dry-period data means the potential influence of wet periods on mortality remains not assessed. Additionally, wind destruction events and the inherent methodological considerations of chronological data analyses may contribute to mortality patterns to some degree. These limitations concerning data scope, disturbance factors, and analytical methods represent important avenues for future research.

Reviewer 3 Report

Comments and Suggestions for Authors

This paper assesses the relationship between forest fragmentation and forest mortality across different scales in relation to Scots Pine forests in an area of Poland. The methodology looks sound and the results are interesting, clearly adding new findings to the literature. The paper also critically analyzes the literature and makes a clear case for the need for such research. Some minor comments are provided below:

1. Lines 99 to 109: You can remove this. It is clear what positive and negative relationships mean and do not need to be spelled out like this.
2. Section 2, paragraph 1: The precision of your number is too high and not necessary. For example, 33,754 ha is sufficient. Similarly, most of the other numbers should only be reported to 1 decimal place. 
3. Table 1: State the source of this information in the caption or below the table.
4. Line 280: I think you mean 'choice' instead of 'choose'

Comments on the Quality of English Language

There are a few mistakes in the English throughout the paper (e.g., wrong verb tenses, missing words, spelling mistakes) that should be corrected by a native English speaker or editorial service.

Author Response

Comment 1: Lines 99 to 109: You can remove this. It is clear what positive and negative relationships mean and do not needs to be spelled out like this.

Response 1: Thank you for your thoughtful suggestion to streamline the text. We appreciate your attention to clarity and conciseness. In accordance with your feedback, We deleted the explanation of positive/negative relationships in lines 99–109. We are grateful for your guidance in refining this section, as it strengthens the paper's impact by eliminating redundancy.

Comment 2:  Section 2, paragraph 1: The precision of your number is too high and not necessary. For example, 33,754 ha is sufficient. Similarly, most of the other numbers should only be reported to 1 decimal place.

Response 2: Thank you very much. We have corrected paragraph 1. We also have changed the number of decimal places in the Table 1. Because parts of features were interpolated, we left two decimal places.

Specific modification as following 2: (131-143)

Comment 3: Table 1: State the source of this information in the caption or below the table.

Response 3: Thank you for bringing this into our attention. We have added information about data sources to the caption of the Table 1. We also have added new citation pointing the meteorological data source.

Specific modification as following 3: (Table 1)

Comment 4:  Line 280: I think you mean 'choice' instead of 'choose'

Response 4: Thank you for bringing this to our attention. We have fixed that.

Reviewer 4 Report

Comments and Suggestions for Authors

The keywords in a scientific article are crucial because they facilitate the search and retrieval of information by other researchers and indexing systems, therefore, it is recommended that they be different from those already present in the article title in order to have a greater impact.

 

The location plan lacks attributes for an adequate macrolocation of the study area, as well as the lack of geographic coordinates, compass rose, scale and symbology.

 

It should be described in greater detail if sampling sites were carried out (size, sampling intensity, variables evaluated, etc.)

 

Specify whether the information corresponds to satellite images or processed raster type information, since due to the year of the information (2018) the study area presents changes to the present, for which the use of more recent images or control points with field sampling is recommended.

 

It is important to present some of the plans obtained from the fragmentation indices to observe the dynamics of ecosystems in the processes of ecological deterioration or restoration.

 

It is recommended to consider information from dendrochronological analyses close to the study area to validate the real factors of forest mortality and thus reinforce the claims of this research.

Author Response

Comment 1: Keywords in a scientific article are crucial because they facilitate the search and retrieval of information by other researchers and indexing systems. Therefore, it is recommended that they differ from the keywords already present in the article title to maximize impact.

Response 1: Thank you for bringing this to our attention. We have reviewed and corrected keywords.  Your feedback has been invaluable in improving the quality of our work, and we are grateful for your constructive input.

Specific modification as following 1: (keywords).
Forest dieback; forest pattern effect; mortality probability; spatial scales

Comment 2:  The location plan lacks attributes for an adequate macrolocation of the study area, as well as the lack of geographic coordinates, compass rose, scale and symbology.

Response 2: Thank you for your thoughtful suggestion regarding Figure 1. We agree that clear spatial referencing significantly improves interpretability, and we appreciate your attention to this detail. In accordance with your feedback, we have: added a clearly visible scale bar to provide accurate spatial measurements. Included a north arrow to establish directional orientation. These additions allow readers to precisely interpret spatial relationships, aligning the figure with established cartographic standards. The revised Figure 1 now appears in the updated manuscript (section/page [specify location]). We believe these refinements strengthen the figure’s utility, and we thank you for highlighting this opportunity for improvement. Should further adjustments be needed, we are happy to address them.

Specific modification as following 2: (Figure 1)

Comment 3: It should be described in greater detail if sampling sites were carried out (size, sampling intensity, variables evaluated, etc.)

Response 3: Thank you for pointing out this. We didn’t carry out sampling in this research. We used forest stands as data source.

Specific modification as following 3: (lines 178-180)

The data on the total volume of trees harvested as part of sanitary cutting operations in each forest stand in each year of the 2015–2022 periods were the only measure of the pine mortality in the research.

Comment 4: Specify whether the information corresponds to satellite images or processed raster type information, since due to the year of the information (2018) the study area presents changes to the present, for which the use of more recent images or control points with field sampling is recommended.

Response 4: Thank you for suggestion this comment. In this regard we are presenting the relation between phenomena’s rather than change in time. We used forest masks taken from Poland’s land cover. The forest mask was limited to forest under national management. We clarified that in the text.

 Specific modification as following 4: (lines 162-163)

We used forest masks taken from Poland’s land cover. The forest mask was limited to forest under national management.

Comment 5: It is important to present some of the plans obtained from the fragmentation indices to observe the dynamics of ecosystems in the processes of ecological deterioration or restoration.

Response 5: Thank you, your comments have been addressed and incorporated with developing maps for mortality probabilities and fragmentation effect using multi-scale index. That maps illustrates spatial distribution of the probability of mortality and forest fragmentation effect on mortality. 

 

This gives opportunities to observe the dynamics of ecosystems in the processes of ecological deterioration.

 Specific modification as following 5: (lines 411-429)

Spatial maps of tree mortality probability (Figure 8B) and the partial effects of fragmentation (Figure 8A) are essential to forest conservation efforts. These maps visually demonstrate how fragmentation increases mortality risk and triggers harmful ecological feedback loops that threaten ecosystem integrity. These maps instantly reveal geographic mortality hotspots, prompting investigation into their suggesting potential relationships through visual patterns. Furthermore, these maps offer a powerful assessment of the geographic plausibility of underlying statistical models. Spatial display of these results is crucial for research, policy, and practice because maps significantly enhance comprehension by revealing high-risk zones and fragmentation severity and providing essential landscape context. This enables targeted mitigation, helps policymakers prioritize funding, and makes complex data accessible to non-experts, fostering dialogue and collaboration to develop solutions. By pinpointing areas that require specific resilience strategies, spatial maps transform model outputs into actionable intelligence, driving concrete policies such as ecological connectivity laws, infrastructure guidelines that avoid biodiversity hotspots, and national conservation agendas. For forest managers, these maps are indispensable. They communicate complex risk information to policymakers, community leaders, and the public far more effectively than raw models do. This clarity raises critical awareness and serves as a potent catalyst for action.

Comment 6: It is recommended to consider information from dendrochronological analyses close to the study area to validate the real factors of forest mortality and thus reinforce the claims of this research.

Response 6: We appreciate your observation and insight. However, our research is based on amount of dead wood of Scots pine forest from each forest stand collected during sanitation cuts by polish National Forest Inventory. We accept your insight as limitation of this study and add recommendation for further research.

Specific modification as following 6: (lines 537-543)

While this study provides insights into Scots pine mortality driven by dry periods, several limitations must be acknowledged. First, our focus on dry-period data means the potential influence of wet periods on mortality remains not assessed. Additionally, wind destruction events and the inherent methodological considerations of chronological data analyses may contribute to mortality patterns to some degree. These limitations concerning data scope, disturbance factors, and analytical methods represent important avenues for future research.

Round 2

Reviewer 4 Report

Comments and Suggestions for Authors

After reviewing the manuscript and observing that the observations and recommendations indicated in the previous version were addressed, I consider that it meets the requirements necessary for publication.

Comments for author File: Comments.pdf

Author Response

Thank you very much for your comments and suggestions. We are glad that we were able to implement them all.