AFSI: A Systems-Based Metric for Aquatic Food Security and SDG Governance

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We present the Aquatic Food Security Index (AFSI), a systems-based and participatory framework for diagnosing compound vulnerabilities in freshwater socio-ecological systems under climatic and institutional uncertainty. AFSI integrates ecological integrity, food access, value chain resilience, governance leverage, and spatial vulnerability through multivariate diagnostics, stakeholder-derived weighting, and probabilistic modeling. Applied to Thailand’s Yom River Basin, AFSI revealed co-located zones of ecological degradation, nutritional dependence, and weak institutional density that amplify systemic fragility. Scenario modeling indicated that intensifying floods could expand exposure by up to 19%, underscoring the urgency of risk-informed, spatially differentiated planning. By coupling ecological diagnostics with participatory governance metrics, AFSI advances from assessment to transformative decision-support, offering a replicable pathway for subnational SDG implementation and resilience-based food system governance within planetary boundaries. Earth and environmental sciences/Ecology Scientific community and society/Agriculture Biological sciences/Ecology/Ecosystem services aquatic food systems multivariate vulnerability SDG governance participatory metrics ecological resilience spatial planning Yom River Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Aquatic food systems—including freshwater fisheries, aquaculture, wetlands, and associated value chains—offer indispensable nutritional, ecological and cultural services for millions globally. Despite their centrality to livelihoods and food sovereignty in the Global South, these systems are underrepresented in prevailing food security frameworks. Dominant indices such as the Global Food Security Index, Ocean Health Index and SDG dashboards tend to prioritise terrestrial and marine sectors, masking inland dynamics and failing to reflect granular vulnerabilities driven by socio-ecological interactions and fragmented governance¹⁻³. This analytical omission presents a critical challenge. In Southeast Asia, for instance, inland fisheries supply up to 80% of animal protein for rural populations⁴, with the Lower Mekong Basin acting as both a biodiversity stronghold and socio-economic lifeline. Increasing pressures from mining, land-use change, hydropower expansion and climatic variability threaten these systems⁵⁻⁶, yet current metrics inadequately capture such layered vulnerabilities. Consequently, decision-makers lack the context-specific insights needed for integrated, ecosystem-based interventions. As sustainability transitions increasingly demand transformation pathways that are systems-aware, equity-driven, and spatially grounded, there is growing recognition that food systems must be assessed through an integrative, diagnostic lens⁷⁻⁸. In this context, we introduce the Aquatic Food Security Index (AFSI)—a participatory, multiscale framework designed as a decision-support system under uncertainty, capable of guiding both policy and planning across diverse aquatic socio-ecological landscapes. AFSI addresses three critical gaps: (1) the absence of ecosystem-sensitive food security metrics for inland aquatic systems; (2) the exclusion of local stakeholders from index co-development; and (3) the disconnection between ecological vulnerability and SDG-aligned policy planning. The framework integrates geospatial diagnostics, multivariate ecological assessments (e.g., PCA, CCA, CART), and locally derived indicators via Delphi consultation, aligning with SDGs 2, 6.6, 14.2, and 16.7. By embedding uncertainty quantification and scenario modeling, AFSI helps decision-makers navigate the dynamic interactions among food, ecology, and governance. Applied to Thailand’s Yom River Basin, AFSI reveals clusters of compound vulnerability and spatial inequity, providing a replicable foundation for transformative governance in aquatic food systems. This systems-based flowchart illustrates how the Aquatic Food Security Index (AFSI) integrates ecological, social, spatial, and institutional dimensions into a composite vulnerability metric for inland aquatic food systems. Inputs—including Ecological Integrity, Food Access, Spatial Vulnerability, and Governance Leverage—are analyzed using multivariate statistics and geospatial overlays. These inputs feed into scenario modeling, which accounts for dynamic stressors such as climate change and infrastructure risk. The AFSI output supports SDG-aligned planning under uncertainty, providing a replicable pathway for risk-informed policy design in low- and middle-income country (LMIC) river basins. Feedback loops highlight the iterative nature of governance and ecosystem responses within the index framework. 2. Conceptual Framework Aquatic food systems in inland regions of the Global South exist within complex socio-ecological landscapes. These systems face compounding stressors—trophic degradation, nutritional reliance, infrastructural fragility, and institutional fragmentation—which interact to generate multidimensional vulnerability. Conventional food security indices often fail to diagnose such risks, especially under conditions of climatic uncertainty and weak governance. As sustainability transitions increasingly require tools that are systems-aware and policy-relevant, the Aquatic Food Security Index (AFSI) is developed to address these gaps. AFSI conceptualizes vulnerability as an emergent property of interacting stressors. It operationalizes five interdependent dimensions: Ecological Integrity, Food Access, Value Chain Resilience, Governance Leverage, and Spatial Vulnerability. Each dimension aligns with specific Sustainable Development Goals (SDGs 2.1, 6.6, 14.2, 16.7) and is informed by agroecological principles and systems theory. The framework employs multivariate statistical analysis (PCA, CCA, CART), participatory indicator weighting via Delphi consultation, and spatial modeling to support decision-making under uncertainty. Compared to the Global Food Security Index (GFSI) and Ocean Health Index (OHI), AFSI introduces conceptual and methodological advances. It offers finer spatial granularity (subdistrict and watershed levels), ecosystem-specific diagnostics for inland freshwater systems, and participatory metric construction involving community stakeholders. Unlike benchmark indices that provide global comparison or awareness raising, AFSI serves as a planning tool to inform subnational SDG implementation and investment prioritization. This positioning reflects a shift towards SDG-aligned ecosystem diagnostics under uncertainty, responding to calls for transformative governance in aquatic food systems. Figure 2 illustrates the AFSI framework as a radial vulnerability wheel. Each node represents a diagnostic entry point, while arcs denote compound risk pathways and feedback loops. The modular and reflexive design enables configuration across different geographic contexts and governance structures. By visualizing vulnerability as a systems interaction rather than a summative score, AFSI facilitates cross-sectoral planning and strengthens the interface between ecological diagnostics and inclusive governance. In essence, AFSI offers a pathway for resilient governance under compound risk, bridging ecological intelligence and participatory planning in support of sustainability transitions. 3. Methods 3.1 Index Design Framework The Aquatic Food Security Index (AFSI) operationalises five core dimensions: (i) Ecological Integrity — captures degradation, biodiversity loss, and trophic vulnerability; (ii) Food Access — quantifies nutritional reliance and market reach; (iii) Value Chain Resilience — measures post-harvest loss, infrastructure and market volatility; (iv) Governance Leverage — evaluates institutional diversity, policy responsiveness and participatory capacity; (v) Spatial Vulnerability — maps compound risks, hazard exposure and ecosystem service overlap. Each dimension was informed by systems theory, agroecological principles and SDG target alignment. Indicators were reviewed for scalability, stakeholder relevance and data availability across freshwater zones (Table 1). 3.2 Participatory Delphi Process To ensure contextual appropriateness and stakeholder alignment, the development of all five index dimensions was guided by a structured Delphi consultation process (N = 30) involving representatives from fishing communities, policy institutions, academic researchers, and civil society organizations. Indicator selection and weighting were iteratively refined through three rounds of deliberation, with a consensus threshold set at ≥70%. Detailed definitions and data sources are shown in Table S1 (Supplementary Information). The process was organized into dimension-specific panels: the ecological panel assessed variables such as habitat degradation and water quality; the governance panel evaluated institutional capacity and actor heterogeneity; and the social panel reviewed indicators related to food access, social inclusion, and value chain performance. 3.3 Multivariate Statistical Analysis Each index dimension was subjected to a tailored analytical approach aligned with its underlying data structure and thematic scope. For Ecological Integrity, Principal Component Analysis (PCA) and Canonical Correspondence Analysis (CCA) were applied to assess fish species composition and water quality parameters across 60 sampling sites. The Food Access and Value Chain components employed cluster analysis to categorize subdistricts based on market accessibility, the prevalence of informal trade, and seasonal variability in supply chains. Governance dynamics were examined through Classification and Regression Tree (CART) modeling, using actor network attributes to differentiate leverage patterns. Spatial Vulnerability was assessed through composite overlays integrating hazard exposure with ecosystem service distributions. All statistical analyses were conducted in R (v4.3) utilizing the packages vegan, cluster, rpart, and factoextra. 3.4 Normalization and Aggregation by Dimension Each indicator was standardised individually via: Min–max scaling for direct indicators (e.g. protein %) Z-score for ecological indices (e.g. PCA axes) Inverted scoring where higher values indicated risk (e.g. loss rates) Dimension scores were aggregated using a weighted mean: Weights Wd were derived from stakeholder priorities in Delphi Round 3. 3.5 Spatial Mapping and Dashboard Integration Each analytical dimension was spatially resolved at the subdistrict level using QGIS. The ecological layer delineated zones of pronounced habitat degradation, while the food access layer identified nutritionally vulnerable areas characterized by limited market connectivity and infrastructure deficits. Governance mapping rendered the distribution of institutional actors and assessed their responsiveness across spatial units. The value chain layer illustrated trade flow volatility and post-harvest infrastructure fragility. Lastly, the spatial vulnerability composite integrated multi-hazard exposure with ecosystem service overlap to highlight zones of compound systemic risk. An interactive dashboard was built using R Shiny and Tableau, enabling multi-scalar assessment and SDG-aligned decision support. 3.6 Robustness and Uncertainty Modeling To enhance the reliability of AFSI under dynamic and uncertain conditions, we integrated two robustness techniques: 3.6.1 Sensitivity Analysis was applied to evaluate the influence of key input variables (e.g., cold chain proximity, post-harvest loss, governance density) on AFSI scores. Each parameter was perturbed within a ±10% range, and Sobol indices were used to rank their relative impact. Tornado plots visualized leverage points, informing policy prioritization. 3.6.2 Monte Carlo Simulation using Latin Hypercube Sampling (5,000 iterations) generated probabilistic distributions and confidence intervals (CI95) for composite scores. This revealed spatial clusters with elevated uncertainty—particularly in upland subdistricts—and provided a basis for scenario-informed decision-making. Together, these techniques complement deterministic workflows, support adaptive planning, and align the AFSI framework with SDG targets focused on climate resilience (SDG 13) and inclusive governance (SDG 16.7). 3.7 Dimension-Specific Indicator Framework and Diagnostics 3.7.1 Ecological Integrity This dimension evaluates the environmental drivers of vulnerability in aquatic food systems—such as habitat integrity, species diversity, and trophic dynamics. Data collection included water sampling, landcover overlays, and species inventories from 60 representative sites. Multivariate techniques (PCA, CCA) were applied to streamline indicators and detect ecological thresholds. [1–2] This component assesses the biophysical determinants of vulnerability within aquatic food systems, with a focus on habitat condition, species richness, and trophic stability. Data sources included water quality monitoring, land cover analysis, and comprehensive species inventories conducted at 60 representative locations. Principal Component Analysis (PCA) and Canonical Correspondence Analysis (CCA) were utilized to streamline indicator sets and detect critical ecological inflection points (Table 2). 3.7.2 Food Access & Nutritional Dependence Dimension This dimension captures both the physical and economic accessibility of aquatic food resources, alongside the degree of dietary dependence observed within local populations. Indicator values were derived from household-level survey data, dietary intake records, and spatial analyses of proximity to markets. Subsequently, cluster analysis was employed to categorize subdistricts according to levels of nutritional vulnerability (Table 3). 3.7.3 Value Chain Resilience Dimension This dimension quantifies the adaptive capacity of post-harvest systems and trade infrastructure in aquatic food networks. Indicators focus on loss rates, informal markets, and seasonal variability (Table 4). 3.7.4 Governance Dimension and Indicator Justification This dimension reflects institutional diversity, participatory mechanisms, and policy responsiveness relevant to aquatic food security. Indicators were derived through a combination of Delphi-based expert assessments, governance network mapping, and local policy audits. To capture governance leverage, we applied entropy scores to measure the diversity of actors across institutional types—such as state, civil society, private sector, and hybrid arrangements. Entropy offers a scale-independent, distribution-sensitive metric that highlights institutional heterogeneity within a given geographic unit. This approach aligns with our objective to evaluate the enabling conditions for inclusive and pluralistic governance, rather than emphasizing control or influence concentrated in a few actors. Conventional network centrality measures (e.g., degree, betweenness) are useful for analyzing communication or influence pathways, but they often require detailed relational data and tend to overrepresent dominant institutions. In contrast, entropy focuses on the presence and balance of actor types, making it better suited for assessing governance diversity, particularly in contexts where marginalized actors play critical but less visible roles (Table 5). Moreover, this indicator choice complements the participatory and spatially grounded nature of our Delphi framework. It enhances comparability across districts with varied governance architectures and supports the broader goal of promoting inclusive, participatory decision-making in line with sustainable development principles. 3.7.5 Spatial Vulnerability Dimension This dimension integrates geographic exposure to environmental hazards and ecological service loss. Geospatial overlays and ecosystem service models provided spatial vulnerability metrics (Table 6). 4. Results 4.1 Multidimensional Risk Profiling and Biophysical Clusters Across the Yom River Basin, the Aquatic Food Security Index (AFSI) revealed compound vulnerabilities integrating ecological degradation, nutritional reliance, infrastructural gaps, and institutional fragility. Cluster analysis based on ecological indicators identified three dominant biophysical risk zones. Type I zones, concentrated in upstream forest-agriculture interfaces, exhibited high species turnover, low habitat integrity (average EDI = 0.41), and seasonal flow instability. Type II zones, located along mid-basin floodplains, showed moderate resilience but declining benthic diversity. Type III zones in lowland peri-urban regions were characterized by eutrophication stress, invasive species dominance, and habitat fragmentation. 4.2 Food Access and Value Chain Fragility Spatial analysis of nutritional dependence and market accessibility revealed persistent “nutrition deserts” in upland communities. Travel time to the nearest market exceeded 60 minutes in 23% of subdistricts, correlating with low cold-chain infrastructure presence (CCI Index < 0.4). These zones also relied heavily on informal trade and dried fish substitutes, exposing them to seasonal price volatility and post-harvest losses exceeding 30%. Scenario modeling under intensified climate conditions (+20% volatility) projected a 0.09 unit drop in value chain resilience across 40% of high-dependence districts. 4.3 Governance Gradient and Institutional Responsiveness Governance leverage scores varied widely, with institutional entropy ranging from 0.17 to 0.71 across subdistricts. Zones with high ecological stress often coincided with low policy responsiveness, particularly where community organizations were sparse and decentralized planning mechanisms were absent. Decision-maker interviews emphasized that policy implementation lag (median delay = 3.2 years) undermined early response to ecosystem collapse or market failures. Composite scores showed that low-governance zones overlapped significantly with nutritional vulnerability clusters (Pearson r = 0.62, p < 0.001). 4.4 Spatial Overlay of Systemic Risk Multi-layer spatial analysis revealed critical zones where ecological degradation, food access challenges, and governance gaps converge. Figure 3 illustrates three key panels: (A) Composite AFSI scores mapped at the subdistrict level, highlighting clusters of vulnerability in both the upper and lower basin. (B) Ecological degradation hotspots are overlaid with governance entropy, revealing zones with low institutional presence and high habitat stress. (C) Modeled flood exposure under a +20% climate severity scenario identifies seven additional subdistricts that would become high-risk under future intensification. Figure 4 expands this spatial narrative with overlays of infrastructure and ecosystem service trade-offs. Panel (A) shows travel-time-based market access and cold-chain infrastructure gaps, identifying "nutrition deserts" in upland areas. Panel (B1 and B2) maps informal trade zones alongside seasonal volatility indicators. Panel (C) overlays protected areas and ecosystem service retention indices, highlighting where food access and conservation priorities intersect. Figure 5 shows the spatial overlay of systemic risks across the Yom River Basin, integrating ecological integrity, food access, governance quality, market connectivity, and spatial vulnerability indices. The composite risk map highlights upstream sub-basins in Phrae and Nan provinces as areas of elevated systemic vulnerability, while downstream floodplains show moderate to low risks due to higher governance capacity and market access. Together, these spatial overlays support subdistrict-scale prioritization of policy, infrastructure investment, and SDG-aligned governance. DISCUSSION This study provides a multidimensional diagnosis of aquatic food system (AFS) vulnerability in northern Thailand, revealing how ecological degradation, nutritional dependence, governance gaps, and spatial exposure converge to produce compound risks. These findings align with global assessments that emphasize the need for Blue Transformation—a strategic shift toward inclusive, resilient, and sustainable aquatic food systems [3]. 1. Compound Vulnerability and Climate Stress The co-location of degraded habitats, high trophic vulnerability, and protein-reliant communities reflects a triple exposure scenario. This pattern mirrors global trends where inland aquatic systems face increasing stress from climate variability, land-use change, and socio-economic marginalization [5]. A recent study found that over 90% of global aquatic food production faces substantial risk from environmental change, with Asia among the most exposed regions [7]. Scenario modeling in this study showed that intensifying flood hazards could expand high-risk zones by nearly 19%, reinforcing calls for climate-resilient zoning and adaptive infrastructure [3]. 2. Governance Leverage and Participatory Monitoring Governance emerged as a key mediating factor. Communities with higher institutional density and actor diversity exhibited stronger value chain resilience, echoing findings from the Aquaculture Governance Indicators framework [6] and the Global Aquaculture Roadmap [6]. However, policy lag and limited responsiveness in low-density zones suggest that decentralization must be coupled with community-led feedback loops to be effective [4]. Participatory monitoring and co-created science are increasingly recognized as essential for bridging the science–policy gap in aquatic food systems [4]. 3. Infrastructure Gaps and Seasonal Misalignment Post-harvest loss and informal trade dominance in remote areas highlight operational fragilities. Seasonal misalignment—where catch peaks coincide with cold-chain absence—exacerbates perishability and market volatility. Similar dynamics have been observed in LMIC contexts, where infrastructure deficits undermine food system resilience despite high production potential [12]. The FISH4ACP programme has demonstrated that resilience-enhancing strategies must go beyond technical fixes and include capacity-building for small-scale actors [4]. 4. Spatial Vulnerability and Ecosystem Buffering Spatial overlays revealed that hazard-prone districts also suffer from poor relief access and ecosystem service loss. Baseline comparisons with protected zones suggest that landscape-based food security frameworks, such as [10]’s 10 Elements of Agroecology, can offer buffering effects and guide place-based interventions [10]. Recent work emphasizes the uneven distribution of aquatic ecosystem services and the need for equitable access to foster public support for restoration [14]. 5. Toward Risk-Informed Food System Planning The inclusion of uncertainty metrics (e.g., confidence intervals, robustness checks) and scenario modeling enhances the reliability of composite indices like AFSI. This approach supports evidence-based prioritization under resource constraints and enables stakeholders to target leverage zones with the highest compound vulnerability. Recent reviews underscore that aquatic food systems must be integrated into national SDG strategies—not just as niche solutions, but as pillars of planetary health and human well-being [11]. Policy Snapshot: Leveraging AFSI for Subnational SDG Implementation The AFSI framework enables the identification of policy leverage zones—geographic areas where ecological vulnerability, food insecurity, and governance gaps co-locate and thus warrant priority intervention. By spatially integrating biophysical diagnostics with governance capacity metrics, AFSI provides actionable intelligence for subnational SDG tracking, especially for targets such as SDG 2.1 (food access), 6.6 (ecosystem protection), and 16.7 (inclusive decision-making). In this regard, AFSI serves not only as a diagnostic index but also as a planning dashboard for provincial agencies, allowing for scenario-informed investment in cold-chain infrastructure, participatory monitoring, and adaptive zoning. Given its modular structure and reliance on publicly accessible data and local consultations, the AFSI design is scalable to other river basins in the Global South, including transboundary contexts where decentralized data and stakeholder inclusion are critical to food system resilience. The spatial overlays (Figure 3–4) reinforce the need for geographically differentiated interventions, especially in zones where biophysical stress intersects with governance fragility and market access constraints. This highlights the value of spatially disaggregated metrics in supporting SDG-aligned decision-making, consistent with emerging frameworks for subnational sustainability monitoring [11]. AFSI complements recent trends in subnational sustainability assessment, where localized indices are used not only for diagnosis but also for territorial governance and SDG localization. The modularity and participatory basis of AFSI make it particularly suitable for provinces and districts aiming to align food system planning with SDG indicators 2.1, 6.6, and 16.7. This reflects a broader call for context-sensitive, spatially explicit tools that can disaggregate vulnerabilities and guide equitable resource allocation at the subnational level. The integration of scenario modeling, sensitivity analysis, and spatial diagnostics within AFSI reflects a broader paradigm shift toward resilience-based planning under uncertainty. This aligns with emerging frameworks in sustainability science that emphasize 'adaptive capacity' and 'transformability' rather than static risk reduction. By embedding probabilistic modeling and stakeholder-informed weighting, AFSI advances from a descriptive tool to a decision-support system responsive to uncertainty in socio-ecological dynamics. Moreover, the probabilistic output enables planners to navigate trade-offs among SDG targets under variable climatic and institutional futures, echoing recommendations on governing for resilience in complex systems [11]. IMPLICATIONS AND CONCLUSION This study demonstrates the utility of a multidimensional vulnerability assessment framework—AFSI—for aquatic food systems situated in environmentally sensitive and economically diverse regions. The integration of ecological, nutritional, value chain, governance, and spatial dimensions enabled the identification of compound risk zones and governance leverage points with greater precision. AFSI’s structure as a decision-support system under uncertainty—with embedded scenario modeling, confidence intervals, and spatial diagnostics—responds to the growing demand for flexible tools that reflect the dynamic nature of socio-ecological systems. It emphasizes that vulnerability is not static, but evolves under seasonal, institutional, and climate-induced pressures. Findings indicate that institutional density and ecosystem integrity can buffer communities against systemic fragility. Conversely, the co-location of degraded habitats, food insecurity, and governance gaps creates transformation chokepoints—regions where interventions must be targeted with urgency and precision. From a strategic planning perspective, the AFSI framework provides a structured foundation for designing transformation pathways by facilitating risk-informed delineation of fisheries and food assistance zones, enabling the prioritization of investments in cold-chain and processing infrastructure, and supporting adaptive governance mechanisms in areas with low institutional density. As countries seek to align national development plans with SDGs, particularly in LMIC contexts, frameworks like AFSI can facilitate cross-sectoral action and enhance spatial equity in food security planning. The system’s configurability allows it to be scaled across basins, nations, and regions, making it a valuable tool in the portfolio of sustainability transformations. Declarations Acknowledgements The authors gratefully acknowledge the support of the Department of Fisheries (DOF), Thailand, and local community leaders across Phayao, Phrae, Uttaradit, Sukhothai, Phitsanulok, Pichit, and Nakornsawan provinces for their invaluable contributions to field data collection and stakeholder mapping. Special thanks are extended to the expert panelists involved in the Delphi assessments for their time and insights. This research was supported by the Agricultural Research Development Agency (Public Organization) (ARDA) Grant No. 66-007, under the program “A Study of Economic Value, Way of Life, Food Security and Community Participation in the Development of Ecosystems in the Yom River Basin”. References Lu, H. et al. Vulnerability assessment and spatio-temporal difference of China’s inland fisheries to floods. Front. Ecol. Evol. 10, 899786 (2022). https://doi.org/10.3389/fevo.2022.899786 Folke, C. et al. Our future in the Anthropocene biosphere . Ambio 50 , 834–869 (2021). https://doi.org/10.1007/s13280-021-01544-8 Stanford FSE. Aquatic Foods in the Anthropocene: Climate Risk Assessment. https://fse.fsi.stanford.edu/publication/aquatic-foods-climate-risk (accessed 5 August 2025). Francesconi, G. N. & Albuquerque, L. FISH4ACP: Inclusive value chain development for aquatic food systems. FAO Technical Brief Series . https://www.fao.org/fish4acp (accessed 5 August 2025). FAO. 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Nutritional benefits of aquatic foods for maternal and child health. FAO Policy Brief https://www.fao.org/fisheries/nutrition (2025). CGIAR. Gender equity and inclusive governance in aquatic food systems. Ocean Action Portfolio https://sdgs.un.org/partnerships/advancing-sustainable-aquatic-food-systems-nutrition-livelihoods-and-resilient-oceans (2025). Tables Table 1. Comparative summary of food security indices highlighting methodological innovations of AFSI Criteria Global Food Security Index (GFSI) Ocean Health Index (OHI) AFSI (This Study) Ecosystem focus Terrestrial Marine & coastal Inland freshwater Spatial scale Country-level National/EEZ Subdistrict, watershed Indicator flexibility Fixed by EIU Modular, predefined Stakeholder-derived (Delphi) Ecological integration No Partial PCA, CCA, trophic metrics Spatial mapping No Limited overlays Hazard–ecosystem GIS overlays Participatory process None Experts only Community + policy co-design Uncertainty analysis No No Scenario modeling, CIs SDG alignment SDG 2 (food) SDGs 13, 14 SDGs 2, 6.6, 13, 14.2, 16.7 Use case Global benchmarking Awareness raising Decision support under uncertainty Note: AFSI demonstrates higher flexibility, ecological granularity, and participatory alignment with SDG governance compared to existing indices. Table 2. Ecological Integrity Indicators Code Indicator Name Unit / Scale Normalisation Method Source EI-1 Habitat Degradation Score % degraded area Inverted min–max scaling GIS, Thai DOF, WWF EI-2 Water Quality CCA Axis Ordination score Z-score standardisation Thai PCD, MRC EI-3 Fisheries Diversity Index Shannon H′ (0–3+) Min–max scaling Field surveys, FAO EI-4 Trophic Vulnerability Trophic level (2–5) N=5−TL3 FishBase, DOF Table 3. Food Access Indicators Code Indicator Name Unit / Scale Normalisation Method Source FA-1 Aquatic Protein Dependence % of daily intake N=X100 FAO Nutrition Profiles, NSO FA-2 Market Accessibility Index Minutes Inverted min–max scaling GIS travel time, DOAE FA-3 Cold Chain Proximity Score (0–1) Direct scoring Infrastructure maps FA-4 Income Quintile Rank 1–5 Scaled transformation NSO, community interviews Table 4. Value Chain Indicators Code Indicator Name Unit / Scale Normalisation Method Source VC-1 Post-Harvest Loss Rate % of catch N=1−X100 DOF, FAO reports VC-2 Informal Trade Volume THB/day Min–max scaling Market surveys VC-3 Processing Infrastructure Score Composite (0–3) Direct scoring DOAE, village census VC-4 Seasonal Volatility Std. deviation N=σμ DOF catch data Table 5. Governance Indicators Code Indicator Name Unit / Scale Normalisation Method Source GL-1 Delphi Consensus Score 0–1 Direct scoring Expert panel GL-2 Actor Diversity (Entropy Score) 0–1 N=Hlog(k) Stakeholder mapping GL-3 Policy Responsiveness Index 0–5 Likert scale Averaged scoring Policy review, NESDC GL-4 Institutional Density Orgs/km² Min–max scaling Provincial databases Table 6. Spatial Vulnerability Indicators Code Indicator Name Unit / Scale Normalisation Method Source SV-1 Hazard Exposure Index Composite score Zonal overlay scaling Thai Disaster Dept, GIS SV-2 Ecosystem Service Overlap Jaccard Index (0–1) Direct scoring Ecosystem mapping, QGIS SV-3 Infrastructure Risk Layer Binary Presence/absence Road + flood zone GIS layers SV-4 Access-to-Relief Index Time-distance Inverted min–max scaling GIS proximity model Additional Declarations There is NO Competing Interest. Supplementary Files SupplementTable.pdf Table S1. Indicators used in the Aquatic Food Security Index (AFSI) framework Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7956755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535494119,"identity":"291683f3-6a13-4600-89ed-4b9140b5e3d4","order_by":0,"name":"Apinun Suvarnaraksha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFACxgYgYQOhgCCBWC1pMC0GxGgBg8MwBhFa+Gc3tz3m3XE+mnlGAuOHHwx/8ghqkbhzsN2Y98zt3MYZCcySPQwGxYSddCOxTZq3DayFQRrosMQGQjrkIVrOgW35TZQWA4iWAyAtbMTZYnjnYJvk3Lbk3Maeh22WPQbGhLXI3W5/JvG2zS53Y3vy4Rs/KuQIa2GQgFnXAIpMA4LqkbTIE6N4FIyCUTAKRiYAAHu5Pf7l1GGXAAAAAElFTkSuQmCC","orcid":"","institution":"Maejo University","correspondingAuthor":true,"prefix":"","firstName":"Apinun","middleName":"","lastName":"Suvarnaraksha","suffix":""}],"badges":[],"createdAt":"2025-10-27 11:54:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7956755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7956755/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94656767,"identity":"0c2c36d8-2a35-4ed2-9ea2-405de7051593","added_by":"auto","created_at":"2025-10-29 10:50:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1298296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework of the Aquatic Food Security Index (AFSI). \u003c/strong\u003eThe framework integrates five systemic dimensions—Ecological Integrity, Food Access, Value Chain Resilience, Governance Leverage, and Spatial Vulnerability—to evaluate the overall stability of aquatic food systems. The AFSI serves as a diagnostic and decision-support tool linking ecological and socio-economic indicators to scenario modeling, which in turn informs SDG-aligned planning under uncertainty. The feedback loop between governance and ecosystem dimensions highlights the adaptive nature of AFSI for iterative learning and policy refinement.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7956755/v1/981d8d8cadbd850e9254fa59.png"},{"id":94672519,"identity":"6a08f7cb-c2cd-4ac3-b6a0-fbc42195ced9","added_by":"auto","created_at":"2025-10-29 13:40:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":579547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAFSI Framework Wheel Diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptual visualization of the Aquatic Food Security Index (AFSI), illustrating five interdependent dimensions—Ecological Integrity, Food Access, Value Chain Resilience, Governance Leverage, and Spatial Vulnerability—as interacting nodes within a systems-based diagnostic framework. Arcs between nodes represent compound risk pathways and feedback loops, capturing how ecological, institutional, and socio-economic stressors co-locate and amplify vulnerability. The wheel structure highlights AFSI's modular and reflexive design, positioning the framework as a decision-support system aligned with SDG targets and resilient governance under uncertainty.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7956755/v1/35dbfcaa7552ccdc0096a0b6.png"},{"id":94656770,"identity":"7fa3da50-b438-45d1-9844-32ce61f3f535","added_by":"auto","created_at":"2025-10-29 10:50:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":731557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of systemic risk components across the Yom River Basin. \u003c/strong\u003eEach panel represents the normalized provincial-level risk intensity (0–1 scale) for five AFSI dimensions: (A) Ecological Integrity, (B) Food Access, (C) Value Chain Resilience, (D) Governance Leverage, and (E) Spatial Vulnerability. The maps reveal distinct upstream–downstream gradients, indicating multi-dimensional risk interactions shaping overall systemic vulnerability.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7956755/v1/17799965303f491c8783b564.png"},{"id":94656768,"identity":"6f5c2c56-34bb-4e87-9988-984b189fedad","added_by":"auto","created_at":"2025-10-29 10:50:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":519173,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of systemic risk components in the Yom River Basin.\u003c/strong\u003e\u003cbr\u003e\n \u003cem\u003e(A)\u003c/em\u003e Ecological Integrity (EI) — ecological conditions are highest in the upper sub-basins and decline toward the downstream agricultural plains; \u003cem\u003e(B)\u003c/em\u003eFood Access (FA) — market accessibility and cold-chain coverage are greater in northern and mid-basin areas; \u003cem\u003e(C)\u003c/em\u003e Governance Leverage (GL) — institutional capacity and policy coherence are strongest in upstream provinces; and \u003cem\u003e(D)\u003c/em\u003e Value Chain Resilience (VCR) — midstream areas show higher resilience due to stronger local enterprise networks and diversified production systems. These four components together form the analytical foundation of the Aquatic Food Security Index (AFSI) framework for the Yom River Basin.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7956755/v1/3ebdfa0b8715b4dedefb37fe.png"},{"id":94656772,"identity":"50fc19ae-9ad8-4c48-8e8b-6422f649e532","added_by":"auto","created_at":"2025-10-29 10:50:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1689190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUncertainty and Sensitivity Analysis of Systemic Risk Components. \u003c/strong\u003ePanel (A) presents Sobol sensitivity indices showing that Food Access and Ecological Integrity exert the strongest influence on AFSI variability. Panel (B) depicts the probability distribution of AFSI composite scores from Monte Carlo simulation (N = 5,000), indicating moderate systemic uncertainty with a 95% confidence range of ±0.06. Panel (C) maps the spatial pattern of uncertainty (CI95 width) across provinces, highlighting upstream areas as the most variable due to environmental and logistical factors.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7956755/v1/7c7f522df47c2242f799d72c.png"},{"id":97668420,"identity":"4d2fbece-98b2-45aa-87fd-7cf8a44ad042","added_by":"auto","created_at":"2025-12-08 09:25:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5353746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7956755/v1/777affd2-85a0-4b6c-bf1a-5060453d9864.pdf"},{"id":94656769,"identity":"1aba905d-1b44-409c-98c4-4da98e8c798f","added_by":"auto","created_at":"2025-10-29 10:50:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":133500,"visible":true,"origin":"","legend":"Table S1. Indicators used in the Aquatic Food Security Index (AFSI) framework","description":"","filename":"SupplementTable.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7956755/v1/b0d0378da12620550d7cb2d2.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"AFSI: A Systems-Based Metric for Aquatic Food Security and SDG Governance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAquatic food systems\u0026mdash;including freshwater fisheries, aquaculture, wetlands, and associated value chains\u0026mdash;offer indispensable nutritional, ecological and cultural services for millions globally. Despite their centrality to livelihoods and food sovereignty in the Global South, these systems are underrepresented in prevailing food security frameworks. Dominant indices such as the Global Food Security Index, Ocean Health Index and SDG dashboards tend to prioritise terrestrial and marine sectors, masking inland dynamics and failing to reflect granular vulnerabilities driven by socio-ecological interactions and fragmented governance\u0026sup1;⁻\u0026sup3;.\u003c/p\u003e\n\u003cp\u003eThis analytical omission presents a critical challenge. In Southeast Asia, for instance, inland fisheries supply up to 80% of animal protein for rural populations⁴, with the Lower Mekong Basin acting as both a biodiversity stronghold and socio-economic lifeline. Increasing pressures from mining, land-use change, hydropower expansion and climatic variability threaten these systems⁵⁻⁶, yet current metrics inadequately capture such layered vulnerabilities. Consequently, decision-makers lack the context-specific insights needed for integrated, ecosystem-based interventions.\u003c/p\u003e\n\u003cp\u003eAs sustainability transitions increasingly demand transformation pathways that are systems-aware, equity-driven, and spatially grounded, there is growing recognition that food systems must be assessed through an integrative, diagnostic lens⁷⁻⁸. In this context, we introduce the Aquatic Food Security Index (AFSI)\u0026mdash;a participatory, multiscale framework designed as a decision-support system under uncertainty, capable of guiding both policy and planning across diverse aquatic socio-ecological landscapes.\u003c/p\u003e\n\u003cp\u003eAFSI addresses three critical gaps: (1) the absence of ecosystem-sensitive food security metrics for inland aquatic systems; (2) the exclusion of local stakeholders from index co-development; and (3) the disconnection between ecological vulnerability and SDG-aligned policy planning. The framework integrates geospatial diagnostics, multivariate ecological assessments (e.g., PCA, CCA, CART), and locally derived indicators via Delphi consultation, aligning with SDGs 2, 6.6, 14.2, and 16.7.\u003c/p\u003e\n\u003cp\u003eBy embedding uncertainty quantification and scenario modeling, AFSI helps decision-makers navigate the dynamic interactions among food, ecology, and governance. Applied to Thailand\u0026rsquo;s Yom River Basin, AFSI reveals clusters of compound vulnerability and spatial inequity, providing a replicable foundation for transformative governance in aquatic food systems.\u003c/p\u003e\n\u003cp\u003eThis systems-based flowchart illustrates how the Aquatic Food Security Index (AFSI) integrates ecological, social, spatial, and institutional dimensions into a composite vulnerability metric for inland aquatic food systems. Inputs\u0026mdash;including Ecological Integrity, Food Access, Spatial Vulnerability, and Governance Leverage\u0026mdash;are analyzed using multivariate statistics and geospatial overlays. These inputs feed into scenario modeling, which accounts for dynamic stressors such as climate change and infrastructure risk. The AFSI output supports SDG-aligned planning under uncertainty, providing a replicable pathway for risk-informed policy design in low- and middle-income country (LMIC) river basins. Feedback loops highlight the iterative nature of governance and ecosystem responses within the index framework.\u003c/p\u003e"},{"header":"2. Conceptual Framework","content":"\u003cp\u003eAquatic food systems in inland regions of the Global South exist within complex socio-ecological landscapes. These systems face compounding stressors—trophic degradation, nutritional reliance, infrastructural fragility, and institutional fragmentation—which interact to generate multidimensional vulnerability. Conventional food security indices often fail to diagnose such risks, especially under conditions of climatic uncertainty and weak governance. As sustainability transitions increasingly require tools that are systems-aware and policy-relevant, the Aquatic Food Security Index (AFSI) is developed to address these gaps.\u003c/p\u003e\n\u003cp\u003eAFSI conceptualizes vulnerability as an emergent property of interacting stressors. It operationalizes five interdependent dimensions: Ecological Integrity, Food Access, Value Chain Resilience, Governance Leverage, and Spatial Vulnerability. Each dimension aligns with specific Sustainable Development Goals (SDGs 2.1, 6.6, 14.2, 16.7) and is informed by agroecological principles and systems theory. The framework employs multivariate statistical analysis (PCA, CCA, CART), participatory indicator weighting via Delphi consultation, and spatial modeling to support decision-making under uncertainty.\u003c/p\u003e\n\u003cp\u003eCompared to the Global Food Security Index (GFSI) and Ocean Health Index (OHI), AFSI introduces conceptual and methodological advances. It offers finer spatial granularity (subdistrict and watershed levels), ecosystem-specific diagnostics for inland freshwater systems, and participatory metric construction involving community stakeholders. Unlike benchmark indices that provide global comparison or awareness raising, AFSI serves as a planning tool to inform subnational SDG implementation and investment prioritization. This positioning reflects a shift towards SDG-aligned ecosystem diagnostics under uncertainty, responding to calls for transformative governance in aquatic food systems.\u003c/p\u003e\n\u003cp\u003eFigure 2 illustrates the AFSI framework as a radial vulnerability wheel. Each node represents a diagnostic entry point, while arcs denote compound risk pathways and feedback loops. The modular and reflexive design enables configuration across different geographic contexts and governance structures. By visualizing vulnerability as a systems interaction rather than a summative score, AFSI facilitates cross-sectoral planning and strengthens the interface between ecological diagnostics and inclusive governance.\u003c/p\u003e\n\u003cp\u003eIn essence, AFSI offers a pathway for resilient governance under compound risk, bridging ecological intelligence and participatory planning in support of sustainability transitions.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003e\u003cstrong\u003e3.1 Index Design Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Aquatic Food Security Index (AFSI) operationalises five core dimensions: (i) Ecological Integrity \u0026mdash; captures degradation, biodiversity loss, and trophic vulnerability; (ii) Food Access \u0026mdash; quantifies nutritional reliance and market reach; (iii) Value Chain Resilience \u0026mdash; measures post-harvest loss, infrastructure and market volatility; (iv) Governance Leverage \u0026mdash; evaluates institutional diversity, policy responsiveness and participatory capacity; (v) Spatial Vulnerability \u0026mdash; maps compound risks, hazard exposure and ecosystem service overlap.\u003c/p\u003e\n\u003cp\u003eEach dimension was informed by systems theory, agroecological principles and SDG target alignment. Indicators were reviewed for scalability, stakeholder relevance and data availability across freshwater zones (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Participatory Delphi Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure contextual appropriateness and stakeholder alignment, the development of all five index dimensions was guided by a structured Delphi consultation process (N = 30) involving representatives from fishing communities, policy institutions, academic researchers, and civil society organizations. Indicator selection and weighting were iteratively refined through three rounds of deliberation, with a consensus threshold set at \u0026ge;70%. Detailed definitions and data sources are shown in Table S1 (Supplementary Information).\u0026nbsp;The process was organized into dimension-specific panels: the ecological panel assessed variables such as habitat degradation and water quality; the governance panel evaluated institutional capacity and actor heterogeneity; and the social panel reviewed indicators related to food access, social inclusion, and value chain performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Multivariate Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach index dimension was subjected to a tailored analytical approach aligned with its underlying data structure and thematic scope. For Ecological Integrity, Principal Component Analysis (PCA) and Canonical Correspondence Analysis (CCA) were applied to assess fish species composition and water quality parameters across 60 sampling sites. The Food Access and Value Chain components employed cluster analysis to categorize subdistricts based on market accessibility, the prevalence of informal trade, and seasonal variability in supply chains. Governance dynamics were examined through Classification and Regression Tree (CART) modeling, using actor network attributes to differentiate leverage patterns. Spatial Vulnerability was assessed through composite overlays integrating hazard exposure with ecosystem service distributions. All statistical analyses were conducted in R (v4.3) utilizing the packages vegan, cluster, rpart, and factoextra.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Normalization and Aggregation by Dimension\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach indicator was standardised individually via:\u003c/p\u003e\n\u003cp\u003eMin\u0026ndash;max scaling for direct indicators (e.g. protein %)\u003c/p\u003e\n\u003cp\u003eZ-score for ecological indices (e.g. PCA axes)\u003c/p\u003e\n\u003cp\u003eInverted scoring where higher values indicated risk (e.g. loss rates)\u003c/p\u003e\n\u003cp\u003eDimension scores were aggregated using a weighted mean:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eWeights Wd were derived from stakeholder priorities in Delphi Round 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Spatial Mapping and Dashboard Integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach analytical dimension was spatially resolved at the subdistrict level using QGIS. The ecological layer delineated zones of pronounced habitat degradation, while the food access layer identified nutritionally vulnerable areas characterized by limited market connectivity and infrastructure deficits. Governance mapping rendered the distribution of institutional actors and assessed their responsiveness across spatial units. The value chain layer illustrated trade flow volatility and post-harvest infrastructure fragility. Lastly, the spatial vulnerability composite integrated multi-hazard exposure with ecosystem service overlap to highlight zones of compound systemic risk. An interactive dashboard was built using R Shiny and Tableau, enabling multi-scalar assessment and SDG-aligned decision support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Robustness and Uncertainty Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enhance the reliability of AFSI under dynamic and uncertain conditions, we integrated two robustness techniques:\u003c/p\u003e\n\u003cp\u003e3.6.1 Sensitivity Analysis was applied to evaluate the influence of key input variables (e.g., cold chain proximity, post-harvest loss, governance density) on AFSI scores. Each parameter was perturbed within a \u0026plusmn;10% range, and Sobol indices were used to rank their relative impact. Tornado plots visualized leverage points, informing policy prioritization.\u003c/p\u003e\n\u003cp\u003e3.6.2 Monte Carlo Simulation using Latin Hypercube Sampling (5,000 iterations) generated probabilistic distributions and confidence intervals (CI95) for composite scores. This revealed spatial clusters with elevated uncertainty\u0026mdash;particularly in upland subdistricts\u0026mdash;and provided a basis for scenario-informed decision-making.\u003c/p\u003e\n\u003cp\u003eTogether, these techniques complement deterministic workflows, support adaptive planning, and align the AFSI framework with SDG targets focused on climate resilience (SDG 13) and inclusive governance (SDG 16.7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Dimension-Specific Indicator Framework and Diagnostics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.7.1 Ecological Integrity\u003c/p\u003e\n\u003cp\u003eThis dimension evaluates the environmental drivers of vulnerability in aquatic food systems\u0026mdash;such as habitat integrity, species diversity, and trophic dynamics. Data collection included water sampling, landcover overlays, and species inventories from 60 representative sites. Multivariate techniques (PCA, CCA) were applied to streamline indicators and detect ecological thresholds. [1\u0026ndash;2]\u003c/p\u003e\n\u003cp\u003eThis component assesses the biophysical determinants of vulnerability within aquatic food systems, with a focus on habitat condition, species richness, and trophic stability. Data sources included water quality monitoring, land cover analysis, and comprehensive species inventories conducted at 60 representative locations. Principal Component Analysis (PCA) and Canonical Correspondence Analysis (CCA) were utilized to streamline indicator sets and detect critical ecological inflection points (Table 2).\u003c/p\u003e\n\u003cp\u003e3.7.2 Food Access \u0026amp; Nutritional Dependence Dimension\u003c/p\u003e\n\u003cp\u003eThis dimension captures both the physical and economic accessibility of aquatic food resources, alongside the degree of dietary dependence observed within local populations. Indicator values were derived from household-level survey data, dietary intake records, and spatial analyses of proximity to markets. Subsequently, cluster analysis was employed to categorize subdistricts according to levels of nutritional vulnerability (Table 3).\u003c/p\u003e\n\u003cp\u003e3.7.3 Value Chain Resilience Dimension\u003c/p\u003e\n\u003cp\u003eThis dimension quantifies the adaptive capacity of post-harvest systems and trade infrastructure in aquatic food networks. Indicators focus on loss rates, informal markets, and seasonal variability (Table 4).\u003c/p\u003e\n\u003cp\u003e3.7.4 Governance Dimension and Indicator Justification\u003c/p\u003e\n\u003cp\u003eThis dimension reflects institutional diversity, participatory mechanisms, and policy responsiveness relevant to aquatic food security. Indicators were derived through a combination of Delphi-based expert assessments, governance network mapping, and local policy audits.\u003c/p\u003e\n\u003cp\u003eTo capture governance leverage, we applied entropy scores to measure the diversity of actors across institutional types\u0026mdash;such as state, civil society, private sector, and hybrid arrangements. Entropy offers a scale-independent, distribution-sensitive metric that highlights institutional heterogeneity within a given geographic unit. This approach aligns with our objective to evaluate the enabling conditions for inclusive and pluralistic governance, rather than emphasizing control or influence concentrated in a few actors.\u003c/p\u003e\n\u003cp\u003eConventional network centrality measures (e.g., degree, betweenness) are useful for analyzing communication or influence pathways, but they often require detailed relational data and tend to overrepresent dominant institutions. In contrast, entropy focuses on the presence and balance of actor types, making it better suited for assessing governance diversity, particularly in contexts where marginalized actors play critical but less visible roles (Table 5).\u003c/p\u003e\n\u003cp\u003eMoreover, this indicator choice complements the participatory and spatially grounded nature of our Delphi framework. It enhances comparability across districts with varied governance architectures and supports the broader goal of promoting inclusive, participatory decision-making in line with sustainable development principles.\u003c/p\u003e\n\u003cp\u003e3.7.5 Spatial Vulnerability Dimension\u003c/p\u003e\n\u003cp\u003eThis dimension integrates geographic exposure to environmental hazards and ecological service loss. Geospatial overlays and ecosystem service models provided spatial vulnerability metrics (Table 6).\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Multidimensional Risk Profiling and Biophysical Clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross the Yom River Basin, the Aquatic Food Security Index (AFSI) revealed compound vulnerabilities integrating ecological degradation, nutritional reliance, infrastructural gaps, and institutional fragility. Cluster analysis based on ecological indicators identified three dominant biophysical risk zones. Type I zones, concentrated in upstream forest-agriculture interfaces, exhibited high species turnover, low habitat integrity (average EDI = 0.41), and seasonal flow instability. Type II zones, located along mid-basin floodplains, showed moderate resilience but declining benthic diversity. Type III zones in lowland peri-urban regions were characterized by eutrophication stress, invasive species dominance, and habitat fragmentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Food Access and Value Chain Fragility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial analysis of nutritional dependence and market accessibility revealed persistent “nutrition deserts” in upland communities. Travel time to the nearest market exceeded 60 minutes in 23% of subdistricts, correlating with low cold-chain infrastructure presence (CCI Index \u0026lt; 0.4). These zones also relied heavily on informal trade and dried fish substitutes, exposing them to seasonal price volatility and post-harvest losses exceeding 30%. Scenario modeling under intensified climate conditions (+20% volatility) projected a 0.09 unit drop in value chain resilience across 40% of high-dependence districts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Governance Gradient and Institutional Responsiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGovernance leverage scores varied widely, with institutional entropy ranging from 0.17 to 0.71 across subdistricts. Zones with high ecological stress often coincided with low policy responsiveness, particularly where community organizations were sparse and decentralized planning mechanisms were absent. Decision-maker interviews emphasized that policy implementation lag (median delay = 3.2 years) undermined early response to ecosystem collapse or market failures. Composite scores showed that low-governance zones overlapped significantly with nutritional vulnerability clusters (Pearson r = 0.62, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Spatial Overlay of Systemic Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMulti-layer spatial analysis revealed critical zones where ecological degradation, food access challenges, and governance gaps converge. Figure 3 illustrates three key panels: (A) Composite AFSI scores mapped at the subdistrict level, highlighting clusters of vulnerability in both the upper and lower basin. (B) Ecological degradation hotspots are overlaid with governance entropy, revealing zones with low institutional presence and high habitat stress. (C) Modeled flood exposure under a +20% climate severity scenario identifies seven additional subdistricts that would become high-risk under future intensification.\u003c/p\u003e\n\u003cp\u003eFigure 4 expands this spatial narrative with overlays of infrastructure and ecosystem service trade-offs. Panel (A) shows travel-time-based market access and cold-chain infrastructure gaps, identifying \"nutrition deserts\" in upland areas. Panel (B1 and B2) maps informal trade zones alongside seasonal volatility indicators. Panel (C) overlays protected areas and ecosystem service retention indices, highlighting where food access and conservation priorities intersect.\u003c/p\u003e\n\u003cp\u003eFigure 5 shows the spatial overlay of systemic risks across the Yom River Basin, integrating ecological integrity, food access, governance quality, market connectivity, and spatial vulnerability indices. The composite risk map highlights upstream sub-basins in Phrae and Nan provinces as areas of elevated systemic vulnerability, while downstream floodplains show moderate to low risks due to higher governance capacity and market access.\u003c/p\u003e\n\u003cp\u003eTogether, these spatial overlays support subdistrict-scale prioritization of policy, infrastructure investment, and SDG-aligned governance.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides a multidimensional diagnosis of aquatic food system (AFS) vulnerability in northern Thailand, revealing how ecological degradation, nutritional dependence, governance gaps, and spatial exposure converge to produce compound risks. These findings align with global assessments that emphasize the need for Blue Transformation—a strategic shift toward inclusive, resilient, and sustainable aquatic food systems [3].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Compound Vulnerability and Climate Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe co-location of degraded habitats, high trophic vulnerability, and protein-reliant communities reflects a triple exposure scenario. This pattern mirrors global trends where inland aquatic systems face increasing stress from climate variability, land-use change, and socio-economic marginalization [5]. A recent study found that over 90% of global aquatic food production faces substantial risk from environmental change, with Asia among the most exposed regions [7].\u003c/p\u003e\n\u003cp\u003eScenario modeling in this study showed that intensifying flood hazards could expand high-risk zones by nearly 19%, reinforcing calls for climate-resilient zoning and adaptive infrastructure [3].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Governance Leverage and Participatory Monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGovernance emerged as a key mediating factor. Communities with higher institutional density and actor diversity exhibited stronger value chain resilience, echoing findings from the Aquaculture Governance Indicators framework [6] and the Global Aquaculture Roadmap [6]. However, policy lag and limited responsiveness in low-density zones suggest that decentralization must be coupled with community-led feedback loops to be effective [4].\u003c/p\u003e\n\u003cp\u003eParticipatory monitoring and co-created science are increasingly recognized as essential for bridging the science–policy gap in aquatic food systems [4].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Infrastructure Gaps and Seasonal Misalignment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-harvest loss and informal trade dominance in remote areas highlight operational fragilities. Seasonal misalignment—where catch peaks coincide with cold-chain absence—exacerbates perishability and market volatility. Similar dynamics have been observed in LMIC contexts, where infrastructure deficits undermine food system resilience despite high production potential [12].\u003c/p\u003e\n\u003cp\u003eThe FISH4ACP programme has demonstrated that resilience-enhancing strategies must go beyond technical fixes and include capacity-building for small-scale actors [4].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Spatial Vulnerability and Ecosystem Buffering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial overlays revealed that hazard-prone districts also suffer from poor relief access and ecosystem service loss. Baseline comparisons with protected zones suggest that landscape-based food security frameworks, such as [10]’s 10 Elements of Agroecology, can offer buffering effects and guide place-based interventions [10].\u003c/p\u003e\n\u003cp\u003eRecent work emphasizes the uneven distribution of aquatic ecosystem services and the need for equitable access to foster public support for restoration [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Toward Risk-Informed Food System Planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion of uncertainty metrics (e.g., confidence intervals, robustness checks) and scenario modeling enhances the reliability of composite indices like AFSI. This approach supports evidence-based prioritization under resource constraints and enables stakeholders to target leverage zones with the highest compound vulnerability.\u003c/p\u003e\n\u003cp\u003eRecent reviews underscore that aquatic food systems must be integrated into national SDG strategies—not just as niche solutions, but as pillars of planetary health and human well-being [11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy Snapshot: Leveraging AFSI for Subnational SDG Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AFSI framework enables the identification of policy leverage zones—geographic areas where ecological vulnerability, food insecurity, and governance gaps co-locate and thus warrant priority intervention. By spatially integrating biophysical diagnostics with governance capacity metrics, AFSI provides actionable intelligence for subnational SDG tracking, especially for targets such as SDG 2.1 (food access), 6.6 (ecosystem protection), and 16.7 (inclusive decision-making). In this regard, AFSI serves not only as a diagnostic index but also as a planning dashboard for provincial agencies, allowing for scenario-informed investment in cold-chain infrastructure, participatory monitoring, and adaptive zoning. Given its modular structure and reliance on publicly accessible data and local consultations, the AFSI design is scalable to other river basins in the Global South, including transboundary contexts where decentralized data and stakeholder inclusion are critical to food system resilience.\u003c/p\u003e\n\u003cp\u003eThe spatial overlays (Figure 3–4) reinforce the need for geographically differentiated interventions, especially in zones where biophysical stress intersects with governance fragility and market access constraints. This highlights the value of spatially disaggregated metrics in supporting SDG-aligned decision-making, consistent with emerging frameworks for subnational sustainability monitoring [11].\u003c/p\u003e\n\u003cp\u003eAFSI complements recent trends in subnational sustainability assessment, where localized indices are used not only for diagnosis but also for territorial governance and SDG localization. The modularity and participatory basis of AFSI make it particularly suitable for provinces and districts aiming to align food system planning with SDG indicators 2.1, 6.6, and 16.7. This reflects a broader call for context-sensitive, spatially explicit tools that can disaggregate vulnerabilities and guide equitable resource allocation at the subnational level.\u003c/p\u003e\n\u003cp\u003eThe integration of scenario modeling, sensitivity analysis, and spatial diagnostics within AFSI reflects a broader paradigm shift toward resilience-based planning under uncertainty. This aligns with emerging frameworks in sustainability science that emphasize 'adaptive capacity' and 'transformability' rather than static risk reduction. By embedding probabilistic modeling and stakeholder-informed weighting, AFSI advances from a descriptive tool to a decision-support system responsive to uncertainty in socio-ecological dynamics. Moreover, the probabilistic output enables planners to navigate trade-offs among SDG targets under variable climatic and institutional futures, echoing recommendations on governing for resilience in complex systems [11].\u003c/p\u003e"},{"header":"IMPLICATIONS AND CONCLUSION","content":"\u003cp\u003eThis study demonstrates the utility of a multidimensional vulnerability assessment framework—AFSI—for aquatic food systems situated in environmentally sensitive and economically diverse regions. The integration of ecological, nutritional, value chain, governance, and spatial dimensions enabled the identification of compound risk zones and governance leverage points with greater precision.\u003c/p\u003e\n\u003cp\u003eAFSI’s structure as a decision-support system under uncertainty—with embedded scenario modeling, confidence intervals, and spatial diagnostics—responds to the growing demand for flexible tools that reflect the dynamic nature of socio-ecological systems. It emphasizes that vulnerability is not static, but evolves under seasonal, institutional, and climate-induced pressures.\u003c/p\u003e\n\u003cp\u003eFindings indicate that institutional density and ecosystem integrity can buffer communities against systemic fragility. Conversely, the co-location of degraded habitats, food insecurity, and governance gaps creates transformation chokepoints—regions where interventions must be targeted with urgency and precision.\u003c/p\u003e\n\u003cp\u003eFrom a strategic planning perspective, the AFSI framework provides a structured foundation for designing transformation pathways by facilitating risk-informed delineation of fisheries and food assistance zones, enabling the prioritization of investments in cold-chain and processing infrastructure, and supporting adaptive governance mechanisms in areas with low institutional density.\u003c/p\u003e\n\u003cp\u003eAs countries seek to align national development plans with SDGs, particularly in LMIC contexts, frameworks like AFSI can facilitate cross-sectoral action and enhance spatial equity in food security planning. The system’s configurability allows it to be scaled across basins, nations, and regions, making it a valuable tool in the portfolio of sustainability transformations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the support of the Department of Fisheries (DOF), Thailand, and local community leaders across Phayao, Phrae, Uttaradit, Sukhothai, Phitsanulok, Pichit, and Nakornsawan provinces for their invaluable contributions to field data collection and stakeholder mapping. Special thanks are extended to the expert panelists involved in the Delphi assessments for their time and insights.\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Agricultural Research Development Agency (Public Organization) (ARDA) Grant No. 66-007, under the program “A Study of Economic Value, Way of Life, Food Security and Community Participation in the Development of Ecosystems in the Yom River Basin”.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLu, H. et al. Vulnerability assessment and spatio-temporal difference of China\u0026rsquo;s inland fisheries to floods. Front. Ecol. Evol. 10, 899786 (2022). https://doi.org/10.3389/fevo.2022.899786 \u003c/li\u003e\n\u003cli\u003eFolke, C. et al. 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A framework for complex climate change risk assessment. \u003cem\u003eOne Earth\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 489\u0026ndash;501 (2021). https://doi.org/10.1016/j.oneear.2021.03.005 \u003c/li\u003e\n\u003cli\u003eBorcard, D., Gillet, F. \u0026amp; Legendre, P. \u003cem\u003eNumerical Ecology with R\u003c/em\u003e. Springer, Cham (2018).\u003c/li\u003e\n\u003cli\u003eDudgeon, D. \u003cem\u003eet al.\u003c/em\u003e Freshwater biodiversity: importance, threats, status and conservation challenges. \u003cem\u003eBiological Reviews\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 163\u0026ndash;182 (2006). https://doi.org/10.1017/S1464793105006950 \u003c/li\u003e\n\u003cli\u003eUN Ocean Conference. Advancing sustainable aquatic food systems for nutrition, livelihoods and resilient oceans. \u003cem\u003eUnited Nations Press Briefing\u003c/em\u003e https://press.un.org/en/2025/sea2229.doc.htm (2025).\u003c/li\u003e\n\u003cli\u003eGolden, C. D. et al. Aquatic foods to nourish nations. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e598\u003c/strong\u003e, 315\u0026ndash;320 (2021). https://doi.org/10.1038/s41586-021-03917-1 \u003c/li\u003e\n\u003cli\u003eFAO Fisheries Division. Nutritional benefits of aquatic foods for maternal and child health. \u003cem\u003eFAO Policy Brief\u003c/em\u003e https://www.fao.org/fisheries/nutrition (2025).\u003c/li\u003e\n\u003cli\u003eCGIAR. Gender equity and inclusive governance in aquatic food systems. \u003cem\u003eOcean Action Portfolio\u003c/em\u003e https://sdgs.un.org/partnerships/advancing-sustainable-aquatic-food-systems-nutrition-livelihoods-and-resilient-oceans (2025).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Comparative summary of food security indices highlighting methodological innovations of AFSI\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobal Food Security Index (GFSI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOcean Health Index (OHI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAFSI (This Study)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcosystem focus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTerrestrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarine \u0026amp; coastal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInland freshwater\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpatial scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCountry-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational/EEZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubdistrict, watershed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicator flexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFixed by EIU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModular, predefined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStakeholder-derived (Delphi)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcological integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePartial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCA, CCA, trophic metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpatial mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited overlays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHazard\u0026ndash;ecosystem GIS overlays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParticipatory process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExperts only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommunity + policy co-design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUncertainty analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScenario modeling, CIs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSDG alignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSDG 2 (food)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSDGs 13, 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSDGs 2, 6.6, 13, 14.2, 16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUse case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobal benchmarking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAwareness raising\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDecision support under uncertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: AFSI demonstrates higher flexibility, ecological granularity, and participatory alignment with SDG governance compared to existing indices.\u003c/p\u003e\n\u003cp\u003eTable 2. Ecological Integrity Indicators\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicator Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnit / Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormalisation Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEI-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHabitat Degradation Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% degraded area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInverted min\u0026ndash;max scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGIS, Thai DOF, WWF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEI-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWater Quality CCA Axis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOrdination score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZ-score standardisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThai PCD, MRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEI-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFisheries Diversity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShannon H\u0026prime; (0\u0026ndash;3+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMin\u0026ndash;max scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eField surveys, FAO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEI-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrophic Vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrophic level (2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=5\u0026minus;TL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFishBase, DOF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Food Access Indicators\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicator Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnit / Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormalisation Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFA-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAquatic Protein Dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% of daily intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=X100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFAO Nutrition Profiles, NSO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFA-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarket Accessibility Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInverted min\u0026ndash;max scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGIS travel time, DOAE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFA-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCold Chain Proximity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScore (0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDirect scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfrastructure maps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFA-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncome Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRank 1\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScaled transformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNSO, community interviews\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4. Value Chain Indicators\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eIndicator Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eUnit / Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eNormalisation Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eVC-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003ePost-Harvest Loss Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e% of catch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN=1\u0026minus;X100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eDOF, FAO reports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eVC-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eInformal Trade Volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTHB/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eMin\u0026ndash;max scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eMarket surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eVC-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eProcessing Infrastructure Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eComposite (0\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eDirect scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eDOAE, village census\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eVC-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eSeasonal Volatility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eStd. deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN=\u0026sigma;\u0026mu;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eDOF catch data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Governance Indicators\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eIndicator Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eUnit / Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eNormalisation Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGL-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDelphi Consensus Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eDirect scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eExpert panel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGL-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eActor Diversity (Entropy Score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eN=Hlog(k)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eStakeholder mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGL-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003ePolicy Responsiveness Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0\u0026ndash;5 Likert scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eAveraged scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003ePolicy review, NESDC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGL-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eInstitutional Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eOrgs/km\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eMin\u0026ndash;max scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eProvincial databases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6. Spatial Vulnerability Indicators\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicator Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnit / Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormalisation Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSV-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHazard Exposure Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComposite score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZonal overlay scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThai Disaster Dept, GIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcosystem Service Overlap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJaccard Index (0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDirect scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcosystem mapping, QGIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSV-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfrastructure Risk Layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBinary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePresence/absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRoad + flood zone GIS layers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSV-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAccess-to-Relief Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTime-distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInverted min\u0026ndash;max scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGIS proximity model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"aquatic food systems, multivariate vulnerability, SDG governance, participatory metrics, ecological resilience, spatial planning, Yom River","lastPublishedDoi":"10.21203/rs.3.rs-7956755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7956755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Inland aquatic food systems underpin nutrition and resilience for millions yet remain largely invisible in global sustainability metrics. We present the Aquatic Food Security Index (AFSI), a systems-based and participatory framework for diagnosing compound vulnerabilities in freshwater socio-ecological systems under climatic and institutional uncertainty. AFSI integrates ecological integrity, food access, value chain resilience, governance leverage, and spatial vulnerability through multivariate diagnostics, stakeholder-derived weighting, and probabilistic modeling. Applied to Thailand’s Yom River Basin, AFSI revealed co-located zones of ecological degradation, nutritional dependence, and weak institutional density that amplify systemic fragility. Scenario modeling indicated that intensifying floods could expand exposure by up to 19%, underscoring the urgency of risk-informed, spatially differentiated planning. By coupling ecological diagnostics with participatory governance metrics, AFSI advances from assessment to transformative decision-support, offering a replicable pathway for subnational SDG implementation and resilience-based food system governance within planetary boundaries.","manuscriptTitle":"AFSI: A Systems-Based Metric for Aquatic Food Security and SDG Governance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 10:50:08","doi":"10.21203/rs.3.rs-7956755/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d4cc7ab-6f27-414c-9288-11a4993b3c72","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56925139,"name":"Earth and environmental sciences/Ecology"},{"id":56925140,"name":"Scientific community and society/Agriculture"},{"id":56925141,"name":"Biological sciences/Ecology/Ecosystem services"}],"tags":[],"updatedAt":"2025-12-04T17:06:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 10:50:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7956755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7956755","identity":"rs-7956755","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}