the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jan 2026
Balancing wetland conservation under disease risk in Indonesia: A spatial MCDA approach
Abstract. Wetlands provide essential ecosystem services but can also serve as breeding habitats for disease vectors such as mosquitoes, creating complex challenges for conservation planning. Indonesia has extensive wetlands and high malaria incidence, requiring conservation strategies that integrate both ecological and health considerations. This study implements a spatial Multi-Criteria Decision Analysis (MCDA) framework to support wetland conservation planning by integrating ecological benefits and vector-borne disease risk. The analysis integrated eight criteria using literature-informed weighting across 94.6 % of Indonesia's wetland areas. Results reveal that conservation and health factors operate largely independently (r = 0.099, p < 0.001), suggesting minimal trade-offs between objectives. The findings demonstrate that wetland conservation and health objectives are compatible in most regions, enabling strategies that optimize ecological outcomes without systematically increasing disease exposure. Papua is noted as a region of interest, being the main region where high ecological value does coincide with elevated disease risk. The framework supports conceptualizing wetlands as Nature-based Solutions that simultaneously deliver conservation and public health benefits, providing practical guidance for Indonesian policymakers and a replicable template for other tropical regions facing similar conservation-health challenges.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 30 Apr 2026)
- RC1: 'Comment on egusphere-2026-94', Anonymous Referee #1, 09 Mar 2026 reply
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RC2: 'Comment on egusphere-2026-94', Anonymous Referee #2, 25 Apr 2026
reply
This study presents an author-led desktop study GIS-MCDA of vector-borne disease risk and wetland conservation prioritisation. While such study can be of great value to planners and policy-makers, I have concerns regarding the methodological approach and the academic contribution provided by the article. In the text below I substantiate these concerns step by step, following the structure of the paper.
First of all, there is limited engagement with the field of MCDA and the philosophy within this field that was taken. That also shows in the language used. I would advise the authors to engage with the different approaches that the broad decision science, operational research field encompasses, and their position in it. Selecting a specific philosophy (such as multi-attribute value theory MAVT) and following its analytical framework more strictly could solidify the methodology and make it more aligned with academic standards while improving its reproducibility. Several questions emerge from this lack of engagement with theory, a few of which I list here:
- How did the literature review lead to the list of criteria and its categorisation (building what in MAVT we call an objectives hierarchy)? And is this process reproducible? Where can the reader find the steps that lead to the list?
- With the value scaling did you use absolute or relative scales (in other words, is there some “objective” standard for each of the criteria (but that means that the variation in your dataset might be minimal for some criteria if you data is relatively homogenous), which could be globally applied, or does it depend on the local data and is thus relative (to enable distinction within your specific case study? And why did you apply that? What are the consequences for the interpretation of your findings?
- The weighting procedure is untransparent and seems rather arbitrary, while it has decisive influence on the outcome of the study (just as the decision to use full compensation, which should also be the result of stakeholder preference elicitation). As described currently the step is not reproducible, making the results of the study speculative and pivoting on this step to a unsatisfactory degree.
- The absence of a sensitivity analysis where the effects of varying weights, but also the value scales and degree of compensation are explored (the latter often being the most impactful of all) is particularly problematic in this context (the sensitivity analysis described in line 190 is not a true, systematic sensitivity analysis following MCDA methodological standards).
Furthermore, here are my remaining questions, suggestions and comments on specific parts of the paper:
- Line 165: what was the resolution of each of the datasets, and did you need to do a lot of interpolation in this step? What are the consequences, and why was this resolution selected? What are the consequences for interpretation (e.g. do mosquito’s travel over such distances?)
- Line 192: I believe that this may be one of the most interesting contributions of the paper. However, there are no details on the methodology, making it hard to understand how this was done.
- Lines 265 – 270: An MCDA just outputs what you put in. However, the factors that you are describing here to have led to the high risk visible on the map in Papua references factors that are not part of the MCDA, at least not directly.
- 285-288: this seems to me the main contribution of the paper. The other results are merely artifacts of the decisions made by the authors and a spatial representation and combination of data, rather than a true GIS-MCDA that combines stakeholder preferences with real-world spatial data representing suitability for certain interventions.
- I don’t understand how the authors arrive at the conclusion stated in line 314
- While the conclusions in 320 seem useful to practice, I wonder if we couldn’t have arrived at the same conclusions without doing the exercise, considering it was merely a combination of information rather than a true analysis of interactions or the combination of subjective with objective knowledge. This puts the academic value of the analysis into question, and I invite the authors to reflect on the academic contribution: what knowledge gap is being addressed and why is this novel?
- I wonder what exactly the authors mean with “framework” that can be replicated in other places in the world? If it is the collection of datasets and the value scales and weights for the MCDA, I would argue this is not a proven and tested set of MCDA settings that can be considered a generalisable “framework”, as no empirical testing of this framework is described in the paper, and thus its validity remains uncertain.
- Line 423: The conclusion suggest there was a breakthrough methodologically speaking, but what the breakthrough is remains unclear. Standard MCDA methods were applied with mostly open datasets and author-assigned parameters.
Finally, please find some textual comments in the table below:
Line
Comment
64
NbS are not a framework, but a solution.
72
Would be good to refer to this massive body of literature by giving some practical examples of comparable studies done in the past (there are thousands) and cite a textbook, like Malczewski & Rinner (2015).
Figure 1
This figure is missing a legend/caption explaining what the different colours indicate. Also, I don’t understand why phase 2 and 3 are separated, as the components of phase 3 are part of spatial MCDA.
123
“standardization” in GIS-MCDA we call this “value scaling”. This is typically a process done with knowledgeable stakeholders. “normalization functions” are called “value functions” (in MAVT) or “utility functions” (in MAUT).
133
Minimum, or the maximum performance among criteria counts in non-compensatory methods. Then, there is a whole world in between of partial compensation.
Equations
Please use mathematical conventions to create the equations, e.g. using w with subscript for the different types of weights. Please use MCDA textbooks to familiarise yourself with these conventions.
Citation: https://doi.org/10.5194/egusphere-2026-94-RC2
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- 1
Yiting Zhu
Marleen de Ruiter
Sophie Buijs
Restoring wetlands mitigates climate change, yet fears persist that they spread mosquito-borne diseases. We mapped environmental data across Indonesia to test this. Surprisingly, we found no significant link between high conservation value and disease risk. This proves that protecting nature does not necessarily endanger public health. Our results identify safe areas for conservation, demonstrating that we can safeguard both ecosystems and human communities simultaneously.
Restoring wetlands mitigates climate change, yet fears persist that they spread mosquito-borne...
The manuscript is titled “Balancing wetland conservation under disease risk in Indonesia: A spatial MCDA approach”. The authors present a spatial Multi-Criteria Decision analysis framework to support wetland conservation planning. The presented framework is applied to the case of Indonesia. The topic of spatial planning for Nature-based Solutions is an emerging and relevant research topic, especially given the increasing pressure by climate change and urban densification. This manuscript focusses on vector-borne disease risk assessment and potential trade-offs to ecological value, which is both within the scope of NHSS and a highly interesting case for spatial MCDA methods. The main contributions are tied to the issue of integrated wetlands management and practical insights for decision-makers in the examined region, as the data and MCDA methodology are sourced from literature.
General comments:
1) Overall, this is a mostly well-conducted piece of research, clearly outlining the methods being used, leading to a substantial set of conclusions that is widely supported by the results being presented. It is well written, of adequate length and easy to understand for an audience with preliminary knowledge in related fields.
2) However, a few methodological details need to be clarified and could be presented in more comprehensive manner. Furthermore, some assumptions may merit better reasoning or handling in the analysis. While the line of argumentation in the discussion is overall clear and well-reasoned, the methodological approach is not discussed at all. This could be extended.
Some more specific comments on this:
1) Page 2, l. 32: You write that “wetlands can also help reduce disease risk when well-managed, [..]”. To me, this implies a potential trade-off between management effort and disease risk that should be incorporated into the system of criteria. Currently this dimension is not reflected at all in the assessment. If not represented by actual criteria when evaluation priorities for intervention, at least, it should be discussed later when talking about real-world implications of the conservation priority map.
2) Page 4, Table 1: I am wondering about the criteria selection process. Why were specifically these criteria selected? It is mentioned that they are “established criteria from literature review”. For replicability in slightly different cases, the actual process of narrowing down a literature review towards a selection of final criteria could be interesting.
3) Page 6, ll. 122: The normalization process, literature sources and assumptions are well-documented. However, as there are assumptions being taken, I suggest that the sensitivity of the results towards changes in these assumptions on the results should be examined in sensitivity analyses as often recommended for MCDA.
4) Page 6, l 138: You mention the term “compensatory elements”. What do you mean by that? To me this remains unclear throughout the paper, especially related to the property of compensation in MCDA approaches. What exactly is non-compensatory and what is the exact meaning for the MCDA aggregation?
5) Page 6, ll. 142: I may be wrong, but to me the weights don’t add up. The sum of hazardous factors is 50% according to my calculation approach (25% malaria, 15% elevation, 6% temperature, 4% precipitation).
6) Page 6, ll. 142: How exactly were weights derived from the literature? What was the process of condensing information in the literature to a set of weights? To me, this is currently not apparent.
7) Page 7, l. 172: “[…] we constructed a composite ecological benefit index by integrating biodiversity significance, carbon storage potential, and water provision capacity.” How exactly was this done? How were criteria states on these three criteria that you mention translated to the dimensionless ecological benefit index?
8) Page 8, Subsection 2.5: At this point, you might consider adding a comprehensive table, summarizing all inputs to the MCDA (criteria, S_i, w_i) for the standard analysis and the scenarios.
9) Page 8, Subsection 2.5: I find the way the scenarios are set up problematic as there is a mismatch between criteria. As the criteria set is currently set up, there is only one ecological composite criterion while there are 8 disease risk criteria. This biases the overall priority score, as defined in eq. (2) towards ecological benefits, as only one criterion must reach a score of 1 to obtain 1 ecological priority. To obtain 1 for disease risk, all 8 criteria must reach the worst state. This makes comparability of the scenarios difficult. As you can see in Figure 3, the priority index is heavily biased by the weight applied to ecological priority. To me, the scenarios require a redesign by (i) either accounting for the bias in the distribution of lower-level domain weights (individual criteria) instead of just altering higher-level domain weights (ecological weight/risk weight) and/or (ii) examining further sources of uncertainty, e.g., uncertainty in input data.
10) Page 11, Subsection 3.3: To me the inversion from conservation priority rankings to benefit scores is not completely evident. You state that areas with intact ecosystems receive the highest benefit score (approaching 1), while urgent conservation intervention areas receive lower scores (approaching 0). Here, a lower score corresponds to higher priority. For the risk indicators, it appears opposite to me. The higher the risk, the higher the score and thus the priority. I suggest to either elaborate or clarify this in the manuscript.
11) Page 13, ll. 9–10: “Despite the different weighting emphases, all three scenarios identify similar geographic regions as important, indicating robust identification of key conservation areas. Following my previous comment, this does not emerge from the previously stated information. Shouldn’t areas with currently low ecological benefits receive high priority as these areas with high disease risk? Visually observing the ecological benefit map in Figure B4, the lower left promontory of Papua is shaded in relatively dark green color, indicating high conservation benefit and thus low priority. Under very high weighting of ecological benefits, the overall conservation priority of this area should be somewhat lower, right? I suggest to clarify the purpose of the scenario comparison and further discuss the insights.
Additionally, please note the previous remark on the scope of the sensitivity analysis, which may prohibit encompassing robustness assessments.
12) Page 15, ll. 387–388: As you mention “participatory planning processes that incorporate local stakeholder priorities”, it would be interesting to researchers and practitioners which are seeking to apply the proposed framework to discuss how exactly the framework could be made participatory and how exactly stakeholder priorities could be elicited and included.” I suggest to at least briefly discuss it. Does this only refer to criteria weights or also to more spatial information that is elicited from stakeholders? Maybe even include it in further research as the elicitation of spatial preferences is currently only briefly explored in MCDA literature.