the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Drivers of Ecosystem Stability Differ with the Intensity of Extreme Climatic Events
Abstract. This study investigates how the dominant drivers of ecosystem stability metrics vary across gradients of hydroclimatic extremity. While previous studies have documented the impacts of droughts and heavy rainfall on ecosystem functioning and resilience inferred from stochastic fluctuations, less attention has been given to whether the relative importance of climatic, biotic, and landscape controls changes systematically under different levels of climatic stress. To address this question, we quantified vegetation resistance and event-scale recovery responses and compared the contributions of meteorological, biodiversity, and topographic factors across a global range of hydroclimatic conditions. We find that under normal to moderately dry conditions, vegetation stability metrics are primarily associated with meteorological variables, particularly temperature and precipitation, consistent with earlier global assessments. Under severe and extreme drought conditions, resistance decreases markedly across most regions, whereas recovery responses exhibit weaker and more spatially heterogeneous changes. Importantly, in sparsely vegetated ecosystems such as grasslands and open shrublands, the relative dominance of drivers shifts from climatic to biodiversity and topographic factors under intensified drought stress, indicating context-dependent regulation of ecosystem stability. Deciduous needle-leaf forests show consistently low resistance and recovery capacity across climatic regimes, suggesting elevated sensitivity to hydroclimatic variability. Overall, our findings demonstrate that ecosystem stability under climatic extremes cannot be explained solely by meteorological forcing and highlight the increasing importance of biodiversity and landscape heterogeneity in shaping stability responses under intensifying climate variability.
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Status: open (until 25 Apr 2026)
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RC1: 'Comment on egusphere-2026-1007', Anonymous Referee #1, 17 Mar 2026
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AC1: 'Reply on RC1', Aki Yanagawa, 18 Mar 2026
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We sincerely thank the reviewer for this constructive and thoughtful comment. We appreciate the reviewer’s positive assessment that the manuscript addresses an important question and has promise. We also understand the concern that the current version does not yet provide a sufficiently rigorous justification of the resistance metric and that the interpretations of biodiversity proxies and feature importance need to be made more cautiously in order to support the central conclusions more robustly.
In revising the manuscript, we plan to address this concern in three main ways.
First, we will strengthen the justification of the resistance metric by clarifying its conceptual basis, its relationship to previous studies, and the rationale for adopting this metric in the context of ecosystem responses to climatic stress. We will also make the assumptions and limitations of this metric more explicit.
Second, we will revise the interpretation of biodiversity-related variables throughout the manuscript. In particular, we will clarify that these variables are proxies rather than direct measures of biodiversity, and we will discuss more explicitly the limitations of interpreting them as representing biodiversity effects.
Third, we will revise the interpretation of feature importance so that it is presented more cautiously. We will clarify that feature importance indicates relative predictive contribution within the modeling framework and does not by itself demonstrate causal effects. Accordingly, we will reassess the wording of the central conclusions to ensure that the interpretation remains consistent with the scope of the data and methods.
Overall, in the revised manuscript, we intend to improve the rigor of the methodological justification and to moderate the interpretation of the results so that the conclusions are more transparent and better supported.
Citation: https://doi.org/10.5194/egusphere-2026-1007-AC1 -
AC2: 'Reply on AC1', Aki Yanagawa, 20 Mar 2026
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Regarding “strengthen the justification of the resistance metric,” revisions are highlighted in yellow; regarding “interpretation of biodiversity-related variables,” revisions are highlighted in green; and regarding “interpretation of feature importance,” revisions are highlighted in light blue.
The first file is the originally submitted manuscript with the revised portions color-highlighted so that the changes can be easily identified.
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AC3: 'Reply on AC2', Aki Yanagawa, 20 Mar 2026
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The second file is the revised manuscript with the modified sections color-highlighted.
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AC4: 'Reply on AC3', Aki Yanagawa, 20 Mar 2026
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The third file is the clean revised manuscript in which all revisions have been incorporated and the color highlighting has been removed from the main text.
We would appreciate it if you could kindly review these files.
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AC4: 'Reply on AC3', Aki Yanagawa, 20 Mar 2026
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AC3: 'Reply on AC2', Aki Yanagawa, 20 Mar 2026
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AC2: 'Reply on AC1', Aki Yanagawa, 20 Mar 2026
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AC1: 'Reply on RC1', Aki Yanagawa, 18 Mar 2026
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RC2: 'Comment on egusphere-2026-1007', Anonymous Referee #2, 21 Apr 2026
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General comments
This manuscript presents a global-scale assessment of ecosystem resistance and resilience to extreme weather events using remotely sensed vegetation dynamics (NDVI), climate characterization via Standardized Precipitation–Evapotranspiration Index, and a broad suite of climatic and environmental predictors. The use of LightGBM to integrate these multiple drivers and identify controls on ecosystem vulnerability is timely and relevant, particularly in the context of increasing climate extremes.
Overall, the study is well conceived, methodologically sound, and clearly written. The integration of diverse datasets at the global scale is a notable strength, and the results have the potential to provide meaningful insights for ecosystem management and climate adaptation strategies. The authors also demonstrate awareness of key methodological limitations.
However, some aspects of the conceptual framing and interpretation of results require further clarification. In particular, the discussion of ecosystem stability is somewhat narrow, and the implications of methodological assumptions - especially those related to temporal recovery dynamics and NDVI limitations -are not sufficiently explored. Addressing these points would significantly strengthen the robustness and interpretability of the findings.
Specific comments
1. Conceptual framing of ecosystem stability
The manuscript focuses on resistance and resilience as indicators of ecosystem stability. While these are widely used and relevant metrics, ecosystem stability is a broader concept that may also include dimensions such as temporal variability, persistence, recovery time, and potential regime shifts.
The authors are encouraged to explicitly acknowledge that their analysis captures only part of the stability framework. Clarifying this point would improve conceptual rigor and avoid potential overgeneralization of the results.
2. Temporal definition of resilience
Resilience is quantified using NDVI in the year following an extreme event relative to normal conditions. This definition implicitly assumes that ecosystem recovery occurs within a one-year timescale.
This assumption may not hold across all ecosystems. In many cases - particularly in forests, semi-arid systems, or under severe disturbances - recovery can take multiple years. Restricting the analysis to a single subsequent year may therefore:
- Underestimate resilience in slow-recovering ecosystems
- Introduce biases when comparing regions with different ecological recovery timescales
The authors should discuss this limitation more explicitly. At minimum, a justification for the choice of a one-year recovery window should be provided, along with a discussion of how longer recovery periods might influence the results.
3. Implications of NDVI limitations
The manuscript acknowledges known limitations of NDVI, such as saturation in high-biomass regions, but does not sufficiently discuss how these limitations may affect the findings.
NDVI saturation can reduce sensitivity to vegetation changes in dense canopies, potentially leading to:
- Underestimation of resistance (i.e., smaller apparent declines during extreme events), and
- Attenuation or distortion of resilience signals during recovery
This may introduce systematic biases in the spatial patterns of ecosystem vulnerability, particularly when comparing high-biomass ecosystems (e.g., tropical forests) with lower-biomass systems (e.g., grasslands).
The authors should expand the discussion to address:
- The expected direction of these biases
- The regions or ecosystem types most affected
- The potential implications for the machine learning results (e.g., feature importance, predicted spatial patterns)
Even a qualitative assessment would substantially improve the interpretation of the results.
Technical corrections
- Consider adding a short introductory paragraph at the beginning of each main section to guide the reader before presenting subsections.
- Briefly clarify how the 5° × 5° grid aggregation may influence the interpretation of heterogeneous landscapes.
- If not already included, add a short statement on the robustness of the machine learning model (e.g., sensitivity to hyperparameters or data partitioning).
- A brief mention of alternative vegetation indices (e.g., EVI) could complement the discussion of NDVI limitations.
Citation: https://doi.org/10.5194/egusphere-2026-1007-RC2
Data sets
Drivers of Resistance and Resilience under Different Intensities of Extreme Climatic Events Aki Yanagawa et al. https://doi.org/10.5281/zenodo.18428493
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The manuscript has promise and asks an important question, but the current version needs more rigorous metric justification, more careful interpretation of biodiversity proxies and feature importance before its central conclusions can be considered robust.