| 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.
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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.