Fishery nutrient profiles provide a practical tool for nutrition-sensitive fisheries management

Fishery nutrient profiles provide a practical tool for nutrition-sensitive fisheries management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fishery nutrient profiles provide a practical tool for nutrition-sensitive fisheries management Alex Tilley, Lorenzo Longobardi, Gianna Bonis-Profumo, Hamza Altarturi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5658106/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Nature Food → Version 1 posted You are reading this latest preprint version Abstract Small-scale fisheries are a crucial source of nutrient-dense aquatic foods in low- and middle-income countries (LMICs), yet practical tools to manage these fisheries to optimize nutritional outcomes in an ecosystem approach remain limited. We present an analytical framework and predictive model of fishery nutrient profiles under typical multispecies, multi-gear situations. Using six-years of catch data from Timor-Leste, we modelled how different fishing methods, habitats, vessel types and seasons influence the yield of nutrients of public health significance. Our results demonstrate that fishing method and habitat are strong predictors of catch nutritional profiles. Importantly, different combinations of fishing strategies can achieve similar nutritional outcomes, indicating complementary management pathways to enhance nutrient availability for communities while balancing ecological, economic, and human wellbeing goals. This replicable framework provides actionable insights for nutrition-sensitive fisheries management and offers data-driven guidance for policies aimed at improving food and nutrition security in LMICs. Health sciences/Health care/Nutrition Earth and environmental sciences/Environmental social sciences/Sustainability small-scale fisheries nutrition-sensitive multispecies fisheries management malnutrition micronutrients LMIC Figures Figure 1 Figure 2 Figure 3 Figure 4 Main The evolving vision of sustainability and food systems transformation states the need to minimize the ecological impact of every fish caught and maximize its societal benefit 1 . In low- and middle-income countries (LMICs) and among populations vulnerable to malnutrition and diet-related diseases, that societal value may best be realized by maximizing the contribution of fisheries to nutrition and health. This can be achieved by minimizing waste and loss 2 and by managing the fishery to supply the micronutrients most needed to support nutrition and healthy diets in the populations accessing that aquatic food 3 . The specific management of nutrient flows in agriculture has long been highlighted 4 , 5 , and recent studies emphasize the role of blue foods specifically in providing diets that improve social, environmental and human health outcomes 6 , 7 . Fish and other harvested aquatic animals comprise thousands of species and while they all have similarly high protein content, they have very different micronutrient compositions 8 – 10 . This offers the possibility of managing fisheries to optimize the supply of the nutrients most lacking and most needed to improve the health outcomes of human diets. The nutrient yield from most of the world's marine and inland fisheries could be better targeted 11 , and nutrient-sensitive fisheries management (NSFM) that also incentivizes maintenance of biodiverse ecosystems could be a pivotal strategy within this transformation 12 , but it remains largely theoretical 13 . While there is advocacy for biodiversity conservation (and corollary protection of nutrient diversity) in small-scale fisheries (SSF), NSFM proposes the integration of nutritional goals, to sustain fish populations and ecosystems whilst also contributing to human health and wellbeing 14 . This approach recognizes the crucial role that aquatic foods play in providing essential nutrients 9 , 11 , addressing micronutrient deficiencies 15 – 18 and improving diets – particularly among vulnerable populations 13 , 19 – 21 , and seeks to optimize the nutritional benefits from fisheries. Fish and other aquatic foods are rich in bioavailable protein and iron as well as a key source of essential nutrients, such as omega-3 fatty acids, vitamin A, calcium and zinc, critical for early childhood development and overall health 22 . Such contributions cannot be overstated in the context of LMICs, who suffer most from micronutrients deficiencies, including 1.5 billion women and children globally 23 , and from stunting, affecting 148 million children in 2022 24 . Recent efforts that complement NSFM approaches to improve nutrition in the Global South include a framework on nutrition-sensitive fisheries governance 12 , trade and foreign fishing assessments on nutrients’ relocation 25 , spatial analyses of aquatic food flows to inform nutrition-sensitive policies 26 , and analyses examining the integration of fisheries, food security and nutrition policies 27 – 31 . Despite the potential, there has been limited effort to translate this into practical fisheries management tools for LMICs to enhance nutrition within an Ecosystem Approach to Fisheries (EAF) 12 . Managing fisheries for multispecies maximum nutrient yield has been suggested theoretically through a framework for nutrient-based reference points in specific contexts 32 , but in data- and capacity-limited contexts, an approach to implement this and inform policies is still lacking. Small-scale fisheries account for 40% of the global reported catch from capture fisheries 33 and play a crucial role in food and nutrition security in LMICs 9 , 11 , 15 , 34 . SSF are often subsistence 35 , target a wide range of species with multiple gears across ecologically diverse habitats, beyond commercially lucrative species 36 . These mixed-species fisheries are too complex and data-poor for conventional fisheries management of single-species stock assessments that estimate the maximum sustainable yield (of generic fish protein) in tons. Conventional fisheries management tends to overlook the intricate species interactions and competition, leading to highly selective strategies that can disrupt ecosystem balance and reduce overall productivity 37 . EAF aims to balance ecological, economic, and human wellbeing goals, providing a more sustainable and resilient fisheries management framework 38 – 40 that tacitly advocates for nutrient diversity. However, this multi-dimensionality of EAF policies makes it hard to implement and evaluate, especially in data-poor scenarios such as SSF of LMICs. Data remains a crucial part of EAF and is critical in managing fisheries for recovery and sustainability 41 , yet the quality and resolution of data from SSF hampers evidence-based fisheries management. However, emerging digital advancements support robust data systems for SSF monitoring 42 , 43 , and present new opportunities for synthesized management tools, such as incorporating nutritional or climatic considerations. Peskas is one such open-source software for managing and visualizing SSF data and has become the official fisheries national monitoring system of Timor-Leste 44 , 45 . Moreover, data on nutrient composition of aquatic foods have only recently become available 9 , 15 . LMICs such as Timor-Leste, where malnutrition is rampant 46 and child and maternal dietary quality poor 47 , could benefit from data-driven NSFM approaches to optimize nutrition from SSF. We used a six-year catch data series 44 combined with NutrientFishbase and INFOODS nutrient composition databases 48 to develop a new NSFM framework. Our approach modelled the relationship between catches from different fishing methods and corresponding nutritional outcomes, offering a tool to visualize and optimize catch nutrient supply. The framework can be replicated using multispecies fisheries catch data from other contexts, while the model enables applied policy recommendations. Results Nutrient supply from small-scale fisheries Our analysis confirmed that small pelagic fishes, particularly mackerels, flying fish, and sardines/pilchard were the most important contributors to recommended human dietary intake, providing relatively high concentrations of essential nutrients such as protein, zinc, calcium, omega-3 fatty acids, iron, and vitamin A, compared to other fish groups (Fig. 1 -A). Marine invertebrates typically caught by gleaners, such as crabs, cockles, and octopus, ranked particularly high in nutrient density, illustrating the potential of a diverse range of aquatic foods in enhancing dietary quality. Small pelagic fishes contributed most to the overall nutrient yield, due to their higher nutrient density and volumes caught (Fig. 1 -B). By linking nutrient density to catch prices, we found that small pelagic fishes were also the most affordable, costing less than USD 2 per kilogram. In contrast, larger pelagic fishes like tuna/bonito, were more expensive and modest nutritional density (Fig. 1 -C). Different marine habitats and fishing gears influenced nutrient yields considerably. Results show that deploying Fish Aggregating Devices (FADs) enhances the capture of species high in calcium, iron, and omega-3, key nutrients for addressing micronutrient deficiencies in the region (Fig. 1 -D). Catches from reef habitats also showed high average nutrient density, with notable contributions from vitamin A and calcium. These nutrients were primarily linked to the predominant gear type, gill nets and seine nets (Supplementary Fig. 1). Fisheries’ contribution to healthy diets All Timorese women of reproductive age (WRA) (337,144, ~one quarter of the population) could potentially meet their protein ‘balanced recommended nutrient intake’ (RNI) from annual catches, assuming fish was distributed equitably among WRA (54 g/day). Catches from SSF have the potential to contribute the RNI of zinc to 57.0% of WRA and to 33.3 and 28.8% for calcium and omega-3 respectively (Supplementary Table 1). In recognition that single nutrient analyses are of limited usefulness for quality diets, we present findings for multiple nutrient combinations. In Timor-Leste, where animal protein and micronutrients intakes are estimated to be well below requirements 47 , 49 , marine catches could supply the RNI for four nutrients to ~ 100,000 WRA, and to ~ 40,500 for the six examined nutrients. In terms of satisfying the national Food Based Dietary Guidelines recommendation of 250 g of cooked fish per adult/week 50 , current fisheries production has the potential to contribute to 72.5% of all WRA (Supplementary Table 2). Spatial analysis shows that four municipalities catch more fish than needed to meet the recommended quantities for WRA in the local population (Supplementary Fig. 2-B), indicating potential trade and distribution opportunities with other deficit municipalities and highlighting the importance of developing value chains for nutrition. These analyses are meaningful because WRA are not only a significant proportion of the population, but also, a nutritionally vulnerable group targeted by public health interventions aiming to improve nutrition. Whilst current catches can make significant contributions to the nutritional requirements of WRA, this analyses also highlight a large supply gap where current catches would need to increase significantly for the broader population to meet nutritional goals from aquatic foods. Fishery Nutrient Profiles prediction Box 1 . Fishery Nutrient Profiles (FNPs): what are these and why are they useful A Fishery Nutrient Profile (FNP) describes a characteristic pattern of nutrients that consistently appears in the catches from a specific fishery. In this study, FNP is defined by the specific composition and concentrations of six key nutrients: calcium, iron, omega-3, protein, vitamin A, and zinc. FNPs are defined by clustering fishing trips according to the nutrient composition of their catches. This grouping identifies trips with similar nutrient densities, revealing consistent nutritional patterns across the catches. The profiles are statistically validated to ensure each cluster represents distinct nutritional characteristics, serving as reliable analytical tool for optimizing the nutritional contributions of fisheries. Single nutrient analyses are of limited relevance for public health nutrition, as food-based approaches considering the whole-of-diet have overcome reductionist approaches that focus on specific nutrients 51 or single food commodities 28 . FNPs support this perspective and focus on a combination of multiple nutrients of public health concern. Using clustering techniques (elbow and silhouette methods), we identified three distinct FNPs within Timor-Leste's small-scale fisheries, applicable to both gill nets and other fishing gear datasets (Supplementary Fig. 3). These profiles cluster fishing trips based on the nutrient density of their catches ( Box 1 ) and were statistically significant, as confirmed by PERMANOVA analysis (Supplementary Table 3). Figure 2 illustrates the distribution of nutrient density scores (NDS) among these profiles for each gear type, highlighting variations in nutrient contributions across different fishing methods. In the gill nets dataset (Fig. 2 -A), FNP-3 stood out with the highest levels of calcium, iron, and omega-3 fatty acids, surpassing FNP-1 and FNP-2. FNP-2 was notably higher in vitamin A compared to the other profiles. While protein levels were consistent across all profiles, FNP-1 exhibited higher zinc levels than FNP-2 and FNP-3. In the dataset for other gears (Fig. 2 -B), FNP-2 demonstrated markedly higher levels of protein, omega-3, and iron. In contrast, FNP-1 and FNP-3 showed elevated levels of zinc and vitamin A. Calcium distribution was even among all profiles. The XGBoost models accurately predicted FNPs based on fishing gear, habitat, season and vessel type. The Gill Nets (GN) model achieved an Area Under the Curve (AUC) score of 0.92 and the Other Gears (OG) model achieved an AUC of 0.91 (Fig. 3 ). The OG model demonstrated slightly better reliability in classification, particularly in its accuracy and specificity (Supplementary Table 4), effectively distinguishing FNPs across different fishing methods. In both models, the interaction of habitat with fishing strategy was key (Supplementary Fig. 4). In the GN model, the interaction between habitat and mesh size was important, with mesh size alone also contributing considerably. In the OG model, gear type was the most influential predictor, closely followed by its interaction with habitat. SHAP (SHapley Additive exPlanations) values revealed the contribution of specific variables to the prediction of each FNP. Gill nets mesh sizes < 40 mm were associated with FNP-2 and FNP-3 across various habitats. FNP-2 showed higher predictive influence in reefs, beaches, and pelagic areas, indicating a strong likelihood of predicting this FNP when fishing in these environments. FNP-3 was also associated with these smaller mesh sizes, particularly within pelagic zones, followed by mangroves and FADs. At mesh sizes ranging 40–60 mm, FNP-3 was predominant, especially in reefs and FADs. Additionally, FNP-1 showed some association within the 40–60 mm mesh size range, particularly in pelagic habitats and, to a lesser extent, in mangroves. Between 60–80 mm, FNP-1 became more characteristic across all habitats, especially in littoral environments such as seagrass, reefs, and beaches. Larger mesh sizes were predominantly associated with FNP-2, especially in mangrove and reef habitats. Excluding gill nets (Fig. 4 -B), gear type emerged as the most influential predictor, with habitat contributing by interaction. SHAP analysis revealed strong associations between specific gear types and FNPs. FNP-1 was associated with spearfishing in reef and seagrass habitats, which exhibited the highest predictive influence, and to a lesser extent, gleaning. FNP-2 was strongly linked to long lines, especially in pelagic, FAD, and mangrove areas, indicating a robust association between this gear type and the FNP-2 profile. For FNP-3, seine nets had the strongest predictive contribution across multiple habitats, including beach, reef, and FAD zones. Hand lines also contributed to FNP-3 but with a weaker influence. Discussion Small-scale fisheries (SSF) are crucial for the livelihoods, culture, and well-being of half a billion people globally 11 and contribute substantially to the recommended nutrient intake (RNI) of populations 35 , 52 . Our study presents a tool that visualizes and quantifies the flow of nutrients from different fisheries that can be used to inform specific fisheries management strategies and foster interagency cooperation and policy coherence across fisheries, health, environment, and social inclusion domains. Nutrition profiles associated with fish catches are highly relevant in countries where malnutrition drives serious health concerns. Timor-Leste has one of the world’s highest stunting rates, with 47% of children not achieving their physical or neurocognitive potential 46 , and widespread anemia 49 , typically caused by dietary iron deficiency and particularly affecting female populations 53 . Aquatic foods are a rich source of multiple micronutrients including iron, zinc, calcium, vitamins and omega-3 fatty acids 8 – 10 , required for optimal brain development 54 , and aquatic foods consumption is associated with improved child health outcomes 55 such as reducing micronutrient deficiencies 9 . Malnourished populations concentrate in LMICs and those with a SSF, marine or inland, could benefit from using the proposed tool to integrate nutrition and multi-sectoral objectives into their fishery polices. Small pelagic fishes are productive, affordable and nutritious and should be promoted among nutritionally vulnerable groups, underscoring their potential to play a pivotal role in improving healthy diets, as also found in other LMICs 17 , 56 – 58 . Nearshore FADs are well documented to increase access to tunas and small pelagic species (e.g. scads, mackerels) for coastal fishers across the Pacific 59 – 62 , which can improve fish consumption when combined with social behavior change interventions 63 and be a productive and cost-effective investment 64 . We found that increased catches of small pelagic fishes from FADs increased the yield of calcium, omega-3 and iron. If deployed sufficiently close to shore, FADs can be sustainably fished using existing methods and redistribute fishing pressure away from reef-associated fisheries in response to improved catch rates 65 . This can contribute to reef recovery and the retention of nutrient-rich and diverse reef species for small-scale and women-dominated fisheries such as gleaning 66 . LMICs with SSF can apply the findings from the analytic framework to guide public health, fisheries and social inclusion policies and programs by, for example, investing in FADs and linking trade networks with government-endorsed entry points for nutrition outreach, such as mother support groups 67 , or national school meals programs 68 . The proportion of WRA that could meet their ‘balanced RNI’ for several nutrients from annual catches, indicates to what extent fisheries production can support the population’s nutritional requirements. In Timor-Leste, this analysis highlights the need to close the nutrient-gap through sustainable fisheries and aquaculture development 63 , 64 , 69 . Mesh size and gear types, in conjunction with habitat, were reliable predictors of Fishery Nutrient Profiles (FNPs). For instance, when using gill nets, FNP-3 catches returned the highest concentrations of calcium, iron, and omega-3. Combining these configurations in management interventions enhances the likelihood of obtaining FNP-3, as indicated by our predictive models. From other fishing gears, FNP-2 returned the highest levels of protein, omega-3, and iron of all FNPs, predominantly associated with long lines in pelagic habitats. Regulating gear-habitat combinations could enhance the nutrient yields of catches in different environments. Fisheries managers require a range of tools to meet the challenges posed by climate change and overfishing, while understanding the balance of gear selectivity and fishing pressure can potentially increase resilience of marine ecosystems 37 , 70 . Our results suggest that spreading fishing effort across different habitats and using diverse gear types returns optimal nutrient gains. Interestingly, this is consistent with the balanced harvest concept where moderate fishing mortality on each usable (and societally acceptable) ecological group in proportion to its production, can increase total long-term sustainable yields while minimizing impacts on ecosystem structure 71 . Our findings suggest that a suite of management regulations relating to gear types, mesh sizes, and fishing locations could be tailored to target different FNPs based on desired nutritional outcomes. However, in practice this would be difficult and complex, echoing critics of balanced harvest who suggest it is difficult to implement 72 , 73 . However, balanced harvest is likely to emerge in unmanaged SSF settings when prices and demands are roughly similar across species and sizes 74 – 76 . These characteristics are more common in SSF than industrial fisheries, and especially in contexts where fisheries are multispecies and food oriented over export-focused 77 . Yet, to achieve a balance between optimizing nutrient harvests and sustainable yields while conserving ecosystem structure – and with-it biodiversity, we suggest FNPs would be best used as a heuristic tool, rather than a management structure. Our results demonstrate the importance of novel modeling approaches for data-driven nutrition-sensitive SSF planning and management, complementing previous efforts 16 , 32 . Emerging low-cost digital tools, such as Peskas, can effectively enhance the monitoring of SSF in otherwise data-poor LMICs 44 . The proposed framework and innovative nutrition modeling exemplify an analytical approach for fisheries data, by suggesting a set of simple indicators, visualizations and models, that can identify complementary management strategies and policies that improve and combine social and ecological wellbeing. Limitations Our approach depends on the availability of time-series of species- and gear-specific catch data, so modeling with data from other contexts may not be equally robust. For data-scarce and capacity-constrained contexts, there are open-source options available like the Peskas software 44 . Nutrient composition values used include Bayesian estimates; future analyses would benefit from using primary data on aquatic foods’ nutrient content from a country or region, including ways to prepare and consume. Limited gleaning data availability underestimates its nutritional contributions, as ‘Marine invertebrates’ in Fig. 1 are particularly nutrient-dense; this women-dominated non-commercial fishery contributes to coastal nutrition security, but its inclusion in fisheries monitoring is limited by normative barriers 78 , 79 . Conclusions There is a need for fisheries policies that maximize micronutrient mass 80 . Nations with significant fisheries exports should prioritize domestic production for national consumption, especially where nutritionally vulnerable populations exist. But just as with broader Ecosystem Approaches and multispecies management, the major challenge to NSFM is implementation. We have demonstrated that a nutrition modelling approach can provide practical and applicable intervention points for NSFM. Improved data on food-critical fisheries focused on both catch volumes, fishing methods and composition, as well as local nutrient data, will be important for practical implementation of NSFM. Finally, there is a need for national policy makers to shift attention away from production and revenue as indicators of fisheries development success, and instead, emphasize the role of ‘blue foods’ for social, environmental and human health outcomes 28 . Methods Study site and fisheries characterization The small-scale fisheries (SSF) sector of Timor-Leste plays a modest role in the country's formal economy yet is crucial for food security. Comprising approximately 4,554 vessels, the fleet is predominantly motorized (about 60%) and consists of canoes (40%) 81 . Fishing activities are primarily nearshore, targeting fringing coral reefs and the pelagic forereef zone within 5 km of the coast. The most common fishing gear employed is gill nets, which catch a diverse composition of fish species. More selected gear types used in reef areas include gleaning, speargun, hook and line, long line and seine nets. In the pelagic, fish aggregating devices (FADs) are utilized in some areas to concentrate small pelagic fish and make them easier to catch. SSF contributes significantly to the livelihoods of coastal communities, particularly in rural areas where poverty rates are higher. Fishing not only provides income but also serves as a vital source of nutrition in a country with one of the highest malnutrition rates globally 46 . The average annual catch is estimated at 6,781 tons, with municipalities like Atauro, Lautem, Bobonaro, and Manatuto being the most productive. The catch composition is diverse, with larger fish species fetching higher market prices, particularly in Dili, where proximity to the capital reduces transportation costs. The fisheries sector provides crucial resilience to system shocks such as the COVID-19 pandemic, where resilience was attributed to limited export activities and a modest tourism sector, which insulated local fisheries from broader economic shocks 81 . The Peskas monitoring system, developed in partnership with the Timor-Leste Ministry of Agriculture and Fisheries, has enhanced data collection and management, allowing for better decision-making and sustainable practices within the sector. As Timor-Leste continues to invest in its fisheries sector, it is essential to balance modernization efforts with the needs of local communities, ensuring that initiatives address food security, dignified livelihoods and environmental sustainability. The ongoing development of the SSF sector can serve as a model for other Small Island Developing States facing similar challenges. Analysis overview Firstly, we analyzed the nutritional characteristics of Timor-Leste small-scale fisheries (SSF) through descriptive analyses of A) nutritional density of main species caught by functional group and volume; B) cumulative yield by nutrient from overall catches; C) affordability of key species vis-à-vis their nutrient density; and D) nutrient density of catches by marine habitat and gear type. This is the proposed nutrition-sensitive fisheries management (NSFM) analytical framework, which evaluates the nutritional properties of the overall fisheries composition (species/groups), their relative importance (production), accessibility (price) and key fisheries parameters (habitat and gears). Secondly, we estimated the contributions of marine catches to human nutrition by assessing the number of people meeting the recommended nutrient intake (RNI) and the recommended quantity of fish from the national Food-Based Dietary Guidelines, which are crucial for integrating public health nutrition policies. We focused on a population sub-group that requires more nutrient-rich diets, women of reproductive age (WRA), and conducted the analyses nationally and sub-nationally. This theoretical exercise illustrates how catches by geography can contribute to dietary quality of sub-populations and highlights opportunities for fish distribution between regions to maximize nutritional impact and equity gains. We present the percentage of WRA achieving 20% of the daily RNI for the six modeled nutrients from SSF catches 11 . This acknowledges that an adequate diet should include diverse food groups, and that fish alone is neither a realistic nor desirable sole source of 100% of a person’s RNI. Dietary diversity guidelines recommend consuming foods from at least five food groups 83 , and aquatic foods should be promoted as part of a diverse and balanced diet. We refer to the 20% RNI measure as ‘balanced RNI’. Thirdly, we proposed a method to model nutrition scenarios for managing SSF. This involved identifying and validating Fishery Nutrient Profiles (FNPs) specific to the fishery. We then predicted FNP outcomes based on fishing gear, habitat, season, and vessel type. Lastly, we explored how gear types and habitats interact to shape FNPs. Data used We utilized fishing catch data obtained from Peskas ( https://timor.peskas.org ) , an open-source web portal offering insights into Timor-Leste small-scale fisheries (SSF) 44 . This platform compiles catch data collected by local enumerators alongside vessel tracking data, enabling temporal and spatial analysis of fishing patterns. To date, the dataset comprises over 60,000 records of fishing trips along the Timor-Leste coastline, encompassing information about the fishing habitat, gear type used, number of fishers involved, catch composition, catch weight, and economic value. Launched in 2016 through a collaboration with the Timor-Leste Ministry of Agriculture and Fisheries, Peskas functions as a near-real-time, low-cost, open-access monitoring system primarily targeting SSF 45 . Data are continuously processed and validated according to a defined workflow. For this study, we used data collected from January 2018 to December 2023. Nutritional values for catch data were derived using the Fishbase Nutrient Analysis Tool ( github.com/mamacneil/NutrientFishbase ). This tool employs a Bayesian hierarchical model, incorporating phylogenetic information to represent the interconnectedness of fish species, and trait-based data, reflecting critical aspects such as fish diet, thermal regime, and energy demands. It predicts muscle flesh concentrations of seven essential nutrients: calcium, iron, omega-3 fatty acids, protein, selenium, vitamin A, and zinc, for global marine and inland fish species. Nutritional yield for each catch was ascertained by merging weight estimates for 55 fish groups defined according to the ASFIS List of Species for Fishery Statistics Purposes 84 with the model's nutrient concentration predictions. We used the median nutrient concentrations (calcium, iron, omega-3 fatty acids, protein, vitamin A, and zinc) for each species within the fish groups as a representative indicator of each fish group's nutritional value. For non-fish groups like octopuses, squids, cockles, shrimps, crabs, and lobsters, where NutrientFishbase repository models lacked nutritional data, the required information was sourced from the Global Food Composition Database for Fish and Shellfish (INFOODS) 48 . Values for selenium were excluded from the analysis because its exceptionally high contributions to nutritional requirements across species obscured other results, and it is not considered a nutrient of major public health concern. Finally, for each fishing trip, we calculated the Nutrient Density Score (NDS) 11 as the aggregated measure of the essential nutrients provided by the catch. This score was determined by summing the weighted contributions of each nutrient to the recommended nutrient intake (RNI) for women of reproductive age (19–50 years old) 85 , based on a standard portion size of 100 grams of edible product. By evaluating the combined contributions of calcium, iron, omega-3 fatty acids, protein, vitamin A, and zinc, we were able to provide a broad assessment of the nutritional value of the catch. The RNI calculations are based on several assumptions. Protein requirements are based on WHO recommendations of 0.83 g/kg/day for adult females and assume an average body weight of 55 kg 86 . This body weight is based on an observed mean BMI of 20.6 for adult women in Timor-Leste 49 and an assumed mean height of 163 cm based on WHO growth standards. Omega-3 PUFA requirements are based on FAO recommendations of 0.5-2% (taken at the midpoint of 1.25%) of dietary energy from omega-3s for adults 87 , assuming an average energy requirement of 8700 kJ/day for a 55 kg adult female and physical activity level of 1.6 86 . Iron, zinc, calcium and vitamin A requirements are based on FAO/WHO global recommendations for an adult female aged 19–50 years, assuming 10% dietary bioavailability of iron and moderate bioavailability of zinc 85 . Modeling and analyses To investigate the impact of gear type, habitat, season and vessel type on the nutritional quality of catches in small-scale fisheries (SSF), we applied a combination of K-means clustering and machine learning techniques. Using high-resolution fisheries data from Dili and Atauro—regions with intensive fishing activity and extensive data availability—we organized our dataset into two distinct subsets: one focusing on gill net usage (GN) and the other encompassing all other gear types (OG). The OG category included hand and long lines, cast and seine nets, beach seines, traps, and manual collection (gleaning). This segmentation was essential for analyzing the differential impacts of various fishing gear types on the nutritional outcomes of the catches. As a first step, we employed K-means clustering to establish consistent Fishery Nutrient Profiles (FNPs) across the datasets. Fishing trips were grouped based on similarities in their Nutritional Density Scores (NDS), which represent the contributions of six essential nutrients—calcium, iron, omega-3 fatty acids, protein, vitamin A, and zinc—to the recommended nutrient intake (RNI) per 100 grams of edible product. This clustering revealed patterns in nutrient levels, grouping fishing trips with similar nutritional characteristics. The optimal number of clusters, determined to be three distinct FNPs, was identified using the elbow and silhouette methods, which assess the compactness and separation of clusters. To validate the consistency and significance of the identified FNPs, we conducted a permutational multivariate analysis of variance (PERMANOVA) 88 . This analysis tested whether nutrient profiles within the same cluster were significantly more similar to each other than to those in different clusters, thereby confirming the robustness of our clustering approach. After clustering, we developed two eXtreme Gradient Boosting (XGBoost) models to predict FNPs based on gear type, habitat, season, and vessel type, enabling us to assess the influence of these variables on the nutritional quality of catches. For the gill net (GN) model, predictors included mesh size (to capture variations in catch composition due to different net sizes), habitat, their interaction (to capture combined effects), quarter of the year (reflecting seasonal variations), and vessel type (distinguishing between motorized and non-motorized boats). For the other gears (OG) model, predictors were gear type, habitat, their interaction, quarter of the year, and vessel type. Both datasets were divided into training (80%) and testing (20%) sets to build and evaluate the models. We applied 10-fold cross-validation on the training set to enhance the models' accuracy and generalizability, reducing the risk of overfitting 89 . Model tuning involved dynamically adjusting parameters such as the number of trees, tree depth, learning rate, loss reduction, sample size, and early stopping criteria to optimize performance. The performance of the XGBoost models was assessed using several metrics, including accuracy, the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity (recall), and specificity. We calculated SHapley Additive exPlanations (SHAP) values 90 for each model to interpret and reveal the influence of each predictor on the FNPs outcomes. SHAP values offer a unified approach to explain the output of machine learning models by quantifying the contribution of each feature to the prediction and determining the direction of its impact. In the GN subset, we focused on the impact of mesh size and habitats on FNPs. This analysis offered insights into how selecting certain mesh sizes could influence the nutritional quality of catches in different environmental contexts. For the OG subset, we examined how gear type and habitat influenced FNPs predictions. SHAP values allowed us to dissect the combined effect of specific gear types in particular habitats on the nutritional outcomes. Declarations Data availability Data used are available at https://github.com/WorldFishCenter/timor.nutrients Code availability All code is available at https://github.com/WorldFishCenter/timor.nutrients Acknowledgement We are very grateful to the Timorese fishers and community members who collaborated with us and with the Directorate General of Fisheries, Aquaculture and Aquatic Resources Management (DG-PAGRA) of the Timor-Leste Ministry of Agriculture, Livestock, Fisheries and Forestry (MALFF). We are grateful to WorldFish and the Government of Timor-Leste staff and consultants who contributed over time to the Peskas improvement, with particular thanks to Shaun P. Wilkinson, E. Fernando Cagua, Pedro Rodrigues, Acacio Guterres and many data enumerators who collected data and tested systems. This publication and continued work and scaling of Peskas beyond Timor-Leste are supported by the Aquatic Foods Initiative supported by contributors to the CGIAR Trust fund [AT, LL, HA, VS] , and the Asia–Africa BlueTech Superhighway (AABS) project led by WorldFish. Funding support for this project was provided by UK International Development from the UK government. Contributions to this study were also undertaken as part of the Nutrition-Sensitive Fisheries Management project led by WorldFish, in partnership with the CSIRO and the DG-PAGRA; funded by ACIAR FIS/2017/032 [GBP, JB] . We thank Eddie Allison for his valuable input during the manuscript development. Contributions L.L., A.T. and G.B.P. designed and conceptualized the study. V.S. and J.D.R.L supported data collection and stakeholders’ engagement. L.L. and H.A. developed statistical methods, performed the analyses and the visualizations. L.L., A.T. and G.B.P. wrote the first draft; and. J.B. and J.K. provided expert input on specific topics. All authors reviewed and approved the final paper. A.T. supervised this work. Competing interests The authors declare no competing interests. References Roberts, C. et al. Rethinking sustainability of marine fisheries for a fast-changing planet. Npj Ocean Sustain. 3, 1–11 (2024). Kruijssen, F. et al. Loss and waste in fish value chains: A review of the evidence from low and middle-income countries. Glob. Food Secur. 26, 100434 (2020). Zamborain-Mason, J. et al. A Decision Framework for Selecting Critically Important Nutrients from Aquatic Foods. Curr. Environ. Health Rep. 10, 172–183 (2023). Ruel, M. T. & Alderman, H. Nutrition-sensitive interventions and programmes: how can they help to accelerate progress in improving maternal and child nutrition? The Lancet 382, 536–551 (2013). Herrero, M. et al. Farming and the geography of nutrient production for human use: a transdisciplinary analysis. Lancet Planet. 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Each bar is the mean RNI contribution across all species that form each functional group (Panel A) or the combination of species typically caught relative to habitat and gear type (Panel D), weighted by their contribution to total catch. The cumulative Nutrient Density Score (NDS) (x-axis) is the sum of the percentage contributions for the six nutrients (Panels A, B, D). \u003cstrong\u003ePanel A\u003c/strong\u003e, bars are ranked by the cumulative NDS and divided by different functional fish-groups:Small pelagic teleost fish (red), Large pelagic teleost fish (green), Small demersal teleost fish (yellow), Large demersal teleost fish (blue), Sharks and rays (black), and Marine invertebrates (grey). Each bar is a color-segmented stacked visual, with distinct hues corresponding to individual nutrients, and white numbers indicating the specific percentage contribution of each nutrient. The chart includes the mean annual catch in metric tons for each marine species from 2018 to 2023, presented at the end of each bar, providing a view of the nutritional value and the harvest volume by functional fish-group. The transparency of these values is adjusted to reflect each species’ relative contribution to the mean annual catch. \u003cstrong\u003ePanel B\u003c/strong\u003e, distribution of nutritional yield among the functional fish-groups (small = \u0026lt;30cm, large = ≥30cm); Small Pelagic fishes (SP), Large Pelagic fishes (LP), Small Demersal fishes (SD), Large Demersal fishes (LD), Sharks and Rays (SR), and Marine Invertebrates (MI). The plot shows the ranked nutritional yield of each functional group where nutritional yield is defined as the supply volume of calcium, omega-3, iron, protein, vitamin A, and zinc (metric tons) cumulatively yielded during the period 2018-2023. \u003cstrong\u003ePanel C\u003c/strong\u003e, comparative analysis of nutrient density score (NDS) versus economic accessibility for key fish groups. This scatter plot displays the relationship between the cumulative NDS and the market price for the most important fish-group within each functional group in terms of catch tons within the Timor-Leste fishery. The x-axis quantifies the NDS for six essential nutrients from a 100 g portion of each fish group. The y-axis represents the median market price per kilogram for each group. The dot size reflects the relative catch percentage of each group, serving as an index of affordability and availability. \u003cstrong\u003ePanel D\u003c/strong\u003e, NDS relative to each habitat (left) and gear type (right), based on a 100 g portion. Each bar is a color-segmented stacked visual, with distinct hues corresponding to individual nutrients, and white numbers indicating the specific percentage contribution of each nutrient.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5658106/v1/d4001bbef6af8401e8fdce3a.png"},{"id":73181384,"identity":"8dd3ecca-0abd-442f-8ec3-ddcfe3a84a8e","added_by":"auto","created_at":"2025-01-07 12:55:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135879,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of cumulative nutrient density scores (NDS) among the Fishery Nutrient Profiles (FNP) from K-means clustering. The plot delineates the NDS per 1kg of catch for various nutrients across the identified K-means clusters (N=3), indicating contributions to the recommended nutrient intake (RNI). Each large point represents the mean NDS for a specific nutrient in each profile, while the smaller points illustrate the total distribution of the NDS within each profile. \u003cstrong\u003ePanels A\u003c/strong\u003e and \u003cstrong\u003eB\u003c/strong\u003e compare these distributions using exclusively gill nets and other gears, respectively.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5658106/v1/48322ad9405750e1adfdca93.png"},{"id":73181385,"identity":"9c687d1b-bb80-4744-87d6-82a9778d6388","added_by":"auto","created_at":"2025-01-07 12:55:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119467,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curves for evaluating the performance of a cluster-based XGBoost classification model across gill nets (\u003cstrong\u003ePanel\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e) and other gear (\u003cstrong\u003ePanel\u003c/strong\u003e \u003cstrong\u003eB\u003c/strong\u003e) data. Each curve represents one of the three Fishery Nutrient Profiles (FNPs) obtained from the classification, with different colors marking each profile. The curves display the model's performance in distinguishing the profiles, with each point depicting the balance between correctly identifying a profile (sensitivity) and the rate of incorrect identifications (1-specificity). A curve's closeness to the top-left corner denotes higher classification precision, while proximity to the diagonal dashed line indicates a performance no better than random chance. Numbers in parentheses indicate the overall ROC Area Under the Curve (AUC) performance score.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5658106/v1/d09ea8c981f29ca23daed039.png"},{"id":73181387,"identity":"1d972f1d-bea8-4d7c-9a9a-16a374175eb2","added_by":"auto","created_at":"2025-01-07 12:55:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178682,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential influence of mesh size (\u003cstrong\u003eA\u003c/strong\u003e) and gear type (\u003cstrong\u003eB\u003c/strong\u003e) based on XGBoost Models. \u003cstrong\u003ePanel A\u003c/strong\u003e: Impact of mesh size on the probability of observing various Fishery Nutrient Profiles (FNPs) based on the habitat. Each sub-panel corresponds to a specific FNP (FNP-1, FNP-2, FNP-3), showing how the contribution of mesh size to the prediction changes across a range of sizes. Data points are color-coded by habitat (Beach, Pelagic, FAD, Mangrove, Reef, and Seagrass), illustrating how the effect of mesh size varies between environments. The y-axis represents mesh size ranges, while the x-axis shows the strength of the contribution of mesh size to the prediction of each FNP, represented by SHAP values. The arrangement of mesh sizes reflects a gradient, from smaller mesh sizes (left) to larger mesh sizes (right). \u003cstrong\u003ePanel B\u003c/strong\u003e: Highlights the role of gear type in predicting FNPs, with habitats influencing the effectiveness of each gear. Each sub-panel corresponds to a specific FNP, with gear types listed on the y-axis. The x-axis represents the strength of each gear type’s contribution to the prediction of a specific FNP. Larger and more opaque points denote gear types with higher SHAP values and stronger contribution to the predictions for each FNP in different habitats.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5658106/v1/1e2e17bd6d1030bfbe2669af.png"},{"id":105446471,"identity":"77a08c68-0f89-46b3-8392-752ef7fcbacc","added_by":"auto","created_at":"2026-03-26 07:18:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1774689,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5658106/v1/3c1b4185-0194-4ecd-a758-278ae5eaf8df.pdf"},{"id":73181382,"identity":"e407a4ea-3e9c-4ed6-b81c-edea09abbd43","added_by":"auto","created_at":"2025-01-07 12:55:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1005470,"visible":true,"origin":"","legend":"SUPPLEMENTARY INFORMATION TEXT","description":"","filename":"supplementarynutritionscenarios.docx","url":"https://assets-eu.researchsquare.com/files/rs-5658106/v1/fb19af2c6e7dd99b81cd7905.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Fishery nutrient profiles provide a practical tool for nutrition-sensitive fisheries management","fulltext":[{"header":"Main","content":"\u003cp\u003eThe evolving vision of sustainability and food systems transformation states the need to minimize the ecological impact of every fish caught and maximize its societal benefit\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In low- and middle-income countries (LMICs) and among populations vulnerable to malnutrition and diet-related diseases, that societal value may best be realized by maximizing the contribution of fisheries to nutrition and health. This can be achieved by minimizing waste and loss\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and by managing the fishery to supply the micronutrients most needed to support nutrition and healthy diets in the populations accessing that aquatic food\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The specific management of nutrient flows in agriculture has long been highlighted\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and recent studies emphasize the role of blue foods specifically in providing diets that improve social, environmental and human health outcomes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Fish and other harvested aquatic animals comprise thousands of species and while they all have similarly high protein content, they have very different micronutrient compositions\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This offers the possibility of managing fisheries to optimize the supply of the nutrients most lacking and most needed to improve the health outcomes of human diets. The nutrient yield from most of the world's marine and inland fisheries could be better targeted\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and nutrient-sensitive fisheries management (NSFM) that also incentivizes maintenance of biodiverse ecosystems could be a pivotal strategy within this transformation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, but it remains largely theoretical\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile there is advocacy for biodiversity conservation (and corollary protection of nutrient diversity) in small-scale fisheries (SSF), NSFM proposes the integration of nutritional goals, to sustain fish populations and ecosystems whilst also contributing to human health and wellbeing\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This approach recognizes the crucial role that aquatic foods play in providing essential nutrients\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, addressing micronutrient deficiencies\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and improving diets \u0026ndash; particularly among vulnerable populations\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and seeks to optimize the nutritional benefits from fisheries. Fish and other aquatic foods are rich in bioavailable protein and iron as well as a key source of essential nutrients, such as omega-3 fatty acids, vitamin A, calcium and zinc, critical for early childhood development and overall health\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Such contributions cannot be overstated in the context of LMICs, who suffer most from micronutrients deficiencies, including 1.5\u0026nbsp;billion women and children globally\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and from stunting, affecting 148\u0026nbsp;million children in 2022\u003csup\u003e24\u003c/sup\u003e. Recent efforts that complement NSFM approaches to improve nutrition in the Global South include a framework on nutrition-sensitive fisheries governance\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, trade and foreign fishing assessments on nutrients\u0026rsquo; relocation\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, spatial analyses of aquatic food flows to inform nutrition-sensitive policies\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and analyses examining the integration of fisheries, food security and nutrition policies\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Despite the potential, there has been limited effort to translate this into practical fisheries management tools for LMICs to enhance nutrition within an Ecosystem Approach to Fisheries (EAF)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Managing fisheries for multispecies maximum nutrient yield has been suggested theoretically through a framework for nutrient-based reference points in specific contexts\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, but in data- and capacity-limited contexts, an approach to implement this and inform policies is still lacking.\u003c/p\u003e \u003cp\u003eSmall-scale fisheries account for 40% of the global reported catch from capture fisheries\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and play a crucial role in food and nutrition security in LMICs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. SSF are often subsistence\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, target a wide range of species with multiple gears across ecologically diverse habitats, beyond commercially lucrative species\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These mixed-species fisheries are too complex and data-poor for conventional fisheries management of single-species stock assessments that estimate the maximum sustainable yield (of generic fish protein) in tons. Conventional fisheries management tends to overlook the intricate species interactions and competition, leading to highly selective strategies that can disrupt ecosystem balance and reduce overall productivity\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. EAF aims to balance ecological, economic, and human wellbeing goals, providing a more sustainable and resilient fisheries management framework\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e that tacitly advocates for nutrient diversity. However, this multi-dimensionality of EAF policies makes it hard to implement and evaluate, especially in data-poor scenarios such as SSF of LMICs.\u003c/p\u003e \u003cp\u003eData remains a crucial part of EAF and is critical in managing fisheries for recovery and sustainability\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, yet the quality and resolution of data from SSF hampers evidence-based fisheries management. However, emerging digital advancements support robust data systems for SSF monitoring\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, and present new opportunities for synthesized management tools, such as incorporating nutritional or climatic considerations. Peskas is one such open-source software for managing and visualizing SSF data and has become the official fisheries national monitoring system of Timor-Leste\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Moreover, data on nutrient composition of aquatic foods have only recently become available\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. LMICs such as Timor-Leste, where malnutrition is rampant\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and child and maternal dietary quality poor\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, could benefit from data-driven NSFM approaches to optimize nutrition from SSF.\u003c/p\u003e \u003cp\u003eWe used a six-year catch data series\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e combined with \u003cem\u003eNutrientFishbase\u003c/em\u003e and INFOODS nutrient composition databases\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e to develop a new NSFM framework. Our approach modelled the relationship between catches from different fishing methods and corresponding nutritional outcomes, offering a tool to visualize and optimize catch nutrient supply. The framework can be replicated using multispecies fisheries catch data from other contexts, while the model enables applied policy recommendations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eNutrient supply from small-scale fisheries\u003c/p\u003e \u003cp\u003eOur analysis confirmed that small pelagic fishes, particularly mackerels, flying fish, and sardines/pilchard were the most important contributors to recommended human dietary intake, providing relatively high concentrations of essential nutrients such as protein, zinc, calcium, omega-3 fatty acids, iron, and vitamin A, compared to other fish groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-A). Marine invertebrates typically caught by gleaners, such as crabs, cockles, and octopus, ranked particularly high in nutrient density, illustrating the potential of a diverse range of aquatic foods in enhancing dietary quality. Small pelagic fishes contributed most to the overall nutrient yield, due to their higher nutrient density and volumes caught (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B). By linking nutrient density to catch prices, we found that small pelagic fishes were also the most affordable, costing less than USD 2 per kilogram. In contrast, larger pelagic fishes like tuna/bonito, were more expensive and modest nutritional density (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-C).\u003c/p\u003e \u003cp\u003eDifferent marine habitats and fishing gears influenced nutrient yields considerably. Results show that deploying Fish Aggregating Devices (FADs) enhances the capture of species high in calcium, iron, and omega-3, key nutrients for addressing micronutrient deficiencies in the region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-D). Catches from reef habitats also showed high average nutrient density, with notable contributions from vitamin A and calcium. These nutrients were primarily linked to the predominant gear type, gill nets and seine nets (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFisheries\u0026rsquo; contribution to healthy diets\u003c/p\u003e \u003cp\u003eAll Timorese women of reproductive age (WRA) (337,144, ~one quarter of the population) could potentially meet their protein \u0026lsquo;balanced recommended nutrient intake\u0026rsquo; (RNI) from annual catches, assuming fish was distributed equitably among WRA (54 g/day). Catches from SSF have the potential to contribute the RNI of zinc to 57.0% of WRA and to 33.3 and 28.8% for calcium and omega-3 respectively (Supplementary Table\u0026nbsp;1). In recognition that single nutrient analyses are of limited usefulness for quality diets, we present findings for multiple nutrient combinations. In Timor-Leste, where animal protein and micronutrients intakes are estimated to be well below requirements\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, marine catches could supply the RNI for four nutrients to ~\u0026thinsp;100,000 WRA, and to ~\u0026thinsp;40,500 for the six examined nutrients. In terms of satisfying the national Food Based Dietary Guidelines recommendation of 250 g of cooked fish per adult/week\u003csup\u003e50\u003c/sup\u003e, current fisheries production has the potential to contribute to 72.5% of all WRA (Supplementary Table\u0026nbsp;2). Spatial analysis shows that four municipalities catch more fish than needed to meet the recommended quantities for WRA in the local population (Supplementary Fig.\u0026nbsp;2-B), indicating potential trade and distribution opportunities with other deficit municipalities and highlighting the importance of developing value chains for nutrition. These analyses are meaningful because WRA are not only a significant proportion of the population, but also, a nutritionally vulnerable group targeted by public health interventions aiming to improve nutrition. Whilst current catches can make significant contributions to the nutritional requirements of WRA, this analyses also highlight a large supply gap where current catches would need to increase significantly for the broader population to meet nutritional goals from aquatic foods.\u003c/p\u003e \u003cp\u003eFishery Nutrient Profiles prediction\u003c/p\u003e \u003cp\u003e \u003cb\u003eBox 1\u003c/b\u003e. Fishery Nutrient Profiles (FNPs): what are these and why are they useful\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA Fishery Nutrient Profile (FNP) describes a characteristic pattern of nutrients that consistently appears in the catches from a specific fishery. In this study, FNP is defined by the specific composition and concentrations of six key nutrients: calcium, iron, omega-3, protein, vitamin A, and zinc.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFNPs are defined by clustering fishing trips according to the nutrient composition of their catches. This grouping identifies trips with similar nutrient densities, revealing consistent nutritional patterns across the catches. The profiles are statistically validated to ensure each cluster represents distinct nutritional characteristics, serving as reliable analytical tool for optimizing the nutritional contributions of fisheries.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSingle nutrient analyses are of limited relevance for public health nutrition, as food-based approaches considering the whole-of-diet have overcome reductionist approaches that focus on specific nutrients\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e or single food commodities\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. FNPs support this perspective and focus on a combination of multiple nutrients of public health concern.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eUsing clustering techniques (elbow and silhouette methods), we identified three distinct FNPs within Timor-Leste's small-scale fisheries, applicable to both gill nets and other fishing gear datasets (Supplementary Fig.\u0026nbsp;3). These profiles cluster fishing trips based on the nutrient density of their catches (\u003cb\u003eBox 1\u003c/b\u003e) and were statistically significant, as confirmed by PERMANOVA analysis (Supplementary Table\u0026nbsp;3). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the distribution of nutrient density scores (NDS) among these profiles for each gear type, highlighting variations in nutrient contributions across different fishing methods. In the gill nets dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A), FNP-3 stood out with the highest levels of calcium, iron, and omega-3 fatty acids, surpassing FNP-1 and FNP-2. FNP-2 was notably higher in vitamin A compared to the other profiles. While protein levels were consistent across all profiles, FNP-1 exhibited higher zinc levels than FNP-2 and FNP-3. In the dataset for other gears (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B), FNP-2 demonstrated markedly higher levels of protein, omega-3, and iron. In contrast, FNP-1 and FNP-3 showed elevated levels of zinc and vitamin A. Calcium distribution was even among all profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe XGBoost models accurately predicted FNPs based on fishing gear, habitat, season and vessel type. The Gill Nets (GN) model achieved an Area Under the Curve (AUC) score of 0.92 and the Other Gears (OG) model achieved an AUC of 0.91 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The OG model demonstrated slightly better reliability in classification, particularly in its accuracy and specificity (Supplementary Table\u0026nbsp;4), effectively distinguishing FNPs across different fishing methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn both models, the interaction of habitat with fishing strategy was key (Supplementary Fig.\u0026nbsp;4). In the GN model, the interaction between habitat and mesh size was important, with mesh size alone also contributing considerably. In the OG model, gear type was the most influential predictor, closely followed by its interaction with habitat. SHAP (SHapley Additive exPlanations) values revealed the contribution of specific variables to the prediction of each FNP. Gill nets mesh sizes\u0026thinsp;\u0026lt;\u0026thinsp;40 mm were associated with FNP-2 and FNP-3 across various habitats. FNP-2 showed higher predictive influence in reefs, beaches, and pelagic areas, indicating a strong likelihood of predicting this FNP when fishing in these environments. FNP-3 was also associated with these smaller mesh sizes, particularly within pelagic zones, followed by mangroves and FADs. At mesh sizes ranging 40\u0026ndash;60 mm, FNP-3 was predominant, especially in reefs and FADs. Additionally, FNP-1 showed some association within the 40\u0026ndash;60 mm mesh size range, particularly in pelagic habitats and, to a lesser extent, in mangroves. Between 60\u0026ndash;80 mm, FNP-1 became more characteristic across all habitats, especially in littoral environments such as seagrass, reefs, and beaches. Larger mesh sizes were predominantly associated with FNP-2, especially in mangrove and reef habitats.\u003c/p\u003e \u003cp\u003eExcluding gill nets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B), gear type emerged as the most influential predictor, with habitat contributing by interaction. SHAP analysis revealed strong associations between specific gear types and FNPs. FNP-1 was associated with spearfishing in reef and seagrass habitats, which exhibited the highest predictive influence, and to a lesser extent, gleaning. FNP-2 was strongly linked to long lines, especially in pelagic, FAD, and mangrove areas, indicating a robust association between this gear type and the FNP-2 profile. For FNP-3, seine nets had the strongest predictive contribution across multiple habitats, including beach, reef, and FAD zones. Hand lines also contributed to FNP-3 but with a weaker influence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSmall-scale fisheries (SSF) are crucial for the livelihoods, culture, and well-being of half a billion people globally\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and contribute substantially to the recommended nutrient intake (RNI) of populations\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Our study presents a tool that visualizes and quantifies the flow of nutrients from different fisheries that can be used to inform specific fisheries management strategies and foster interagency cooperation and policy coherence across fisheries, health, environment, and social inclusion domains.\u003c/p\u003e \u003cp\u003eNutrition profiles associated with fish catches are highly relevant in countries where malnutrition drives serious health concerns. Timor-Leste has one of the world\u0026rsquo;s highest stunting rates, with 47% of children not achieving their physical or neurocognitive potential\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and widespread anemia\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, typically caused by dietary iron deficiency and particularly affecting female populations\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Aquatic foods are a rich source of multiple micronutrients including iron, zinc, calcium, vitamins and omega-3 fatty acids\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, required for optimal brain development\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, and aquatic foods consumption is associated with improved child health outcomes\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e such as reducing micronutrient deficiencies\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Malnourished populations concentrate in LMICs and those with a SSF, marine or inland, could benefit from using the proposed tool to integrate nutrition and multi-sectoral objectives into their fishery polices.\u003c/p\u003e \u003cp\u003eSmall pelagic fishes are productive, affordable and nutritious and should be promoted among nutritionally vulnerable groups, underscoring their potential to play a pivotal role in improving healthy diets, as also found in other LMICs\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Nearshore FADs are well documented to increase access to tunas and small pelagic species (e.g. scads, mackerels) for coastal fishers across the Pacific\u003csup\u003e\u003cspan additionalcitationids=\"CR60 CR61\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, which can improve fish consumption when combined with social behavior change interventions\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e and be a productive and cost-effective investment\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We found that increased catches of small pelagic fishes from FADs increased the yield of calcium, omega-3 and iron. If deployed sufficiently close to shore, FADs can be sustainably fished using existing methods and redistribute fishing pressure away from reef-associated fisheries in response to improved catch rates\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. This can contribute to reef recovery and the retention of nutrient-rich and diverse reef species for small-scale and women-dominated fisheries such as gleaning\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLMICs with SSF can apply the findings from the analytic framework to guide public health, fisheries and social inclusion policies and programs by, for example, investing in FADs and linking trade networks with government-endorsed entry points for nutrition outreach, such as mother support groups\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, or national school meals programs\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The proportion of WRA that could meet their \u0026lsquo;balanced RNI\u0026rsquo; for several nutrients from annual catches, indicates to what extent fisheries production can support the population\u0026rsquo;s nutritional requirements. In Timor-Leste, this analysis highlights the need to close the nutrient-gap through sustainable fisheries and aquaculture development\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMesh size and gear types, in conjunction with habitat, were reliable predictors of Fishery Nutrient Profiles (FNPs). For instance, when using gill nets, FNP-3 catches returned the highest concentrations of calcium, iron, and omega-3. Combining these configurations in management interventions enhances the likelihood of obtaining FNP-3, as indicated by our predictive models. From other fishing gears, FNP-2 returned the highest levels of protein, omega-3, and iron of all FNPs, predominantly associated with long lines in pelagic habitats. Regulating gear-habitat combinations could enhance the nutrient yields of catches in different environments. Fisheries managers require a range of tools to meet the challenges posed by climate change and overfishing, while understanding the balance of gear selectivity and fishing pressure can potentially increase resilience of marine ecosystems\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results suggest that spreading fishing effort across different habitats and using diverse gear types returns optimal nutrient gains. Interestingly, this is consistent with the balanced harvest concept where moderate fishing mortality on each usable (and societally acceptable) ecological group in proportion to its production, can increase total long-term sustainable yields while minimizing impacts on ecosystem structure\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Our findings suggest that a suite of management regulations relating to gear types, mesh sizes, and fishing locations could be tailored to target different FNPs based on desired nutritional outcomes. However, in practice this would be difficult and complex, echoing critics of balanced harvest who suggest it is difficult to implement\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. However, balanced harvest is likely to emerge in unmanaged SSF settings when prices and demands are roughly similar across species and sizes\u003csup\u003e\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. These characteristics are more common in SSF than industrial fisheries, and especially in contexts where fisheries are multispecies and food oriented over export-focused\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Yet, to achieve a balance between optimizing nutrient harvests and sustainable yields while conserving ecosystem structure \u0026ndash; and with-it biodiversity, we suggest FNPs would be best used as a heuristic tool, rather than a management structure.\u003c/p\u003e \u003cp\u003eOur results demonstrate the importance of novel modeling approaches for data-driven nutrition-sensitive SSF planning and management, complementing previous efforts\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Emerging low-cost digital tools, such as Peskas, can effectively enhance the monitoring of SSF in otherwise data-poor LMICs\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The proposed framework and innovative nutrition modeling exemplify an analytical approach for fisheries data, by suggesting a set of simple indicators, visualizations and models, that can identify complementary management strategies and policies that improve and combine social and ecological wellbeing.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eOur approach depends on the availability of time-series of species- and gear-specific catch data, so modeling with data from other contexts may not be equally robust. For data-scarce and capacity-constrained contexts, there are open-source options available like the \u003cem\u003ePeskas\u003c/em\u003e software\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Nutrient composition values used include Bayesian estimates; future analyses would benefit from using primary data on aquatic foods\u0026rsquo; nutrient content from a country or region, including ways to prepare and consume. Limited gleaning data availability underestimates its nutritional contributions, as \u0026lsquo;Marine invertebrates\u0026rsquo; in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are particularly nutrient-dense; this women-dominated non-commercial fishery contributes to coastal nutrition security, but its inclusion in fisheries monitoring is limited by normative barriers\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere is a need for fisheries policies that maximize micronutrient mass\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Nations with significant fisheries exports should prioritize domestic production for national consumption, especially where nutritionally vulnerable populations exist. But just as with broader Ecosystem Approaches and multispecies management, the major challenge to NSFM is implementation. We have demonstrated that a nutrition modelling approach can provide practical and applicable intervention points for NSFM. Improved data on food-critical fisheries focused on both catch volumes, fishing methods and composition, as well as local nutrient data, will be important for practical implementation of NSFM. Finally, there is a need for national policy makers to shift attention away from production and revenue as indicators of fisheries development success, and instead, emphasize the role of \u0026lsquo;blue foods\u0026rsquo; for social, environmental and human health outcomes\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy site and fisheries characterization\u003c/p\u003e \u003cp\u003eThe small-scale fisheries (SSF) sector of Timor-Leste plays a modest role in the country's formal economy yet is crucial for food security. Comprising approximately 4,554 vessels, the fleet is predominantly motorized (about 60%) and consists of canoes (40%)\u003csup\u003e81\u003c/sup\u003e. Fishing activities are primarily nearshore, targeting fringing coral reefs and the pelagic forereef zone within 5 km of the coast. The most common fishing gear employed is gill nets, which catch a diverse composition of fish species. More selected gear types used in reef areas include gleaning, speargun, hook and line, long line and seine nets. In the pelagic, fish aggregating devices (FADs) are utilized in some areas to concentrate small pelagic fish and make them easier to catch.\u003c/p\u003e \u003cp\u003eSSF contributes significantly to the livelihoods of coastal communities, particularly in rural areas where poverty rates are higher. Fishing not only provides income but also serves as a vital source of nutrition in a country with one of the highest malnutrition rates globally\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The average annual catch is estimated at 6,781 tons, with municipalities like Atauro, Lautem, Bobonaro, and Manatuto being the most productive. The catch composition is diverse, with larger fish species fetching higher market prices, particularly in Dili, where proximity to the capital reduces transportation costs.\u003c/p\u003e \u003cp\u003eThe fisheries sector provides crucial resilience to system shocks such as the COVID-19 pandemic, where resilience was attributed to limited export activities and a modest tourism sector, which insulated local fisheries from broader economic shocks\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The Peskas monitoring system, developed in partnership with the Timor-Leste Ministry of Agriculture and Fisheries, has enhanced data collection and management, allowing for better decision-making and sustainable practices within the sector.\u003c/p\u003e \u003cp\u003eAs Timor-Leste continues to invest in its fisheries sector, it is essential to balance modernization efforts with the needs of local communities, ensuring that initiatives address food security, dignified livelihoods and environmental sustainability. The ongoing development of the SSF sector can serve as a model for other Small Island Developing States facing similar challenges.\u003c/p\u003e \u003cp\u003eAnalysis overview\u003c/p\u003e \u003cp\u003eFirstly, we analyzed the nutritional characteristics of Timor-Leste small-scale fisheries (SSF) through descriptive analyses of A) nutritional density of main species caught by functional group and volume; B) cumulative yield by nutrient from overall catches; C) affordability of key species vis-\u0026agrave;-vis their nutrient density; and D) nutrient density of catches by marine habitat and gear type. This is the proposed nutrition-sensitive fisheries management (NSFM) analytical framework, which evaluates the nutritional properties of the overall fisheries composition (species/groups), their relative importance (production), accessibility (price) and key fisheries parameters (habitat and gears).\u003c/p\u003e \u003cp\u003eSecondly, we estimated the contributions of marine catches to human nutrition by assessing the number of people meeting the recommended nutrient intake (RNI) and the recommended quantity of fish from the national Food-Based Dietary Guidelines, which are crucial for integrating public health nutrition policies. We focused on a population sub-group that requires more nutrient-rich diets, women of reproductive age (WRA), and conducted the analyses nationally and sub-nationally. This theoretical exercise illustrates how catches by geography can contribute to dietary quality of sub-populations and highlights opportunities for fish distribution between regions to maximize nutritional impact and equity gains. We present the percentage of WRA achieving 20% of the daily RNI for the six modeled nutrients from SSF catches\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This acknowledges that an adequate diet should include diverse food groups, and that fish alone is neither a realistic nor desirable sole source of 100% of a person\u0026rsquo;s RNI. Dietary diversity guidelines recommend consuming foods from at least five food groups\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, and aquatic foods should be promoted as part of a diverse and balanced diet. We refer to the 20% RNI measure as \u0026lsquo;balanced RNI\u0026rsquo;.\u003c/p\u003e \u003cp\u003eThirdly, we proposed a method to model nutrition scenarios for managing SSF. This involved identifying and validating Fishery Nutrient Profiles (FNPs) specific to the fishery. We then predicted FNP outcomes based on fishing gear, habitat, season, and vessel type. Lastly, we explored how gear types and habitats interact to shape FNPs.\u003c/p\u003e \u003cp\u003eData used\u003c/p\u003e \u003cp\u003eWe utilized fishing catch data obtained from Peskas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://timor.peskas.org\u003c/span\u003e\u003cspan address=\"https://timor.peskas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, an open-source web portal offering insights into Timor-Leste small-scale fisheries (SSF)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This platform compiles catch data collected by local enumerators alongside vessel tracking data, enabling temporal and spatial analysis of fishing patterns. To date, the dataset comprises over 60,000 records of fishing trips along the Timor-Leste coastline, encompassing information about the fishing habitat, gear type used, number of fishers involved, catch composition, catch weight, and economic value. Launched in 2016 through a collaboration with the Timor-Leste Ministry of Agriculture and Fisheries, Peskas functions as a near-real-time, low-cost, open-access monitoring system primarily targeting SSF\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Data are continuously processed and validated according to a defined workflow. For this study, we used data collected from January 2018 to December 2023.\u003c/p\u003e \u003cp\u003eNutritional values for catch data were derived using the Fishbase Nutrient Analysis Tool (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003egithub.com/mamacneil/NutrientFishbase\u003c/span\u003e). This tool employs a Bayesian hierarchical model, incorporating phylogenetic information to represent the interconnectedness of fish species, and trait-based data, reflecting critical aspects such as fish diet, thermal regime, and energy demands. It predicts muscle flesh concentrations of seven essential nutrients: calcium, iron, omega-3 fatty acids, protein, selenium, vitamin A, and zinc, for global marine and inland fish species. Nutritional yield for each catch was ascertained by merging weight estimates for 55 fish groups defined according to the ASFIS List of Species for Fishery Statistics Purposes\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e with the model's nutrient concentration predictions. We used the median nutrient concentrations (calcium, iron, omega-3 fatty acids, protein, vitamin A, and zinc) for each species within the fish groups as a representative indicator of each fish group's nutritional value. For non-fish groups like octopuses, squids, cockles, shrimps, crabs, and lobsters, where \u003cem\u003eNutrientFishbase\u003c/em\u003e repository models lacked nutritional data, the required information was sourced from the Global Food Composition Database for Fish and Shellfish (INFOODS)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Values for selenium were excluded from the analysis because its exceptionally high contributions to nutritional requirements across species obscured other results, and it is not considered a nutrient of major public health concern.\u003c/p\u003e \u003cp\u003eFinally, for each fishing trip, we calculated the Nutrient Density Score (NDS)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e as the aggregated measure of the essential nutrients provided by the catch. This score was determined by summing the weighted contributions of each nutrient to the recommended nutrient intake (RNI) for women of reproductive age (19\u0026ndash;50 years old)\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, based on a standard portion size of 100 grams of edible product. By evaluating the combined contributions of calcium, iron, omega-3 fatty acids, protein, vitamin A, and zinc, we were able to provide a broad assessment of the nutritional value of the catch. The RNI calculations are based on several assumptions. Protein requirements are based on WHO recommendations of 0.83 g/kg/day for adult females and assume an average body weight of 55 kg\u003csup\u003e86\u003c/sup\u003e. This body weight is based on an observed mean BMI of 20.6 for adult women in Timor-Leste\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and an assumed mean height of 163 cm based on WHO growth standards. Omega-3 PUFA requirements are based on FAO recommendations of 0.5-2% (taken at the midpoint of 1.25%) of dietary energy from omega-3s for adults\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, assuming an average energy requirement of 8700 kJ/day for a 55 kg adult female and physical activity level of 1.6\u003csup\u003e86\u003c/sup\u003e. Iron, zinc, calcium and vitamin A requirements are based on FAO/WHO global recommendations for an adult female aged 19\u0026ndash;50 years, assuming 10% dietary bioavailability of iron and moderate bioavailability of zinc\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eModeling and analyses\u003c/p\u003e \u003cp\u003eTo investigate the impact of gear type, habitat, season and vessel type on the nutritional quality of catches in small-scale fisheries (SSF), we applied a combination of K-means clustering and machine learning techniques. Using high-resolution fisheries data from Dili and Atauro\u0026mdash;regions with intensive fishing activity and extensive data availability\u0026mdash;we organized our dataset into two distinct subsets: one focusing on gill net usage (GN) and the other encompassing all other gear types (OG). The OG category included hand and long lines, cast and seine nets, beach seines, traps, and manual collection (gleaning). This segmentation was essential for analyzing the differential impacts of various fishing gear types on the nutritional outcomes of the catches.\u003c/p\u003e \u003cp\u003eAs a first step, we employed K-means clustering to establish consistent Fishery Nutrient Profiles (FNPs) across the datasets. Fishing trips were grouped based on similarities in their Nutritional Density Scores (NDS), which represent the contributions of six essential nutrients\u0026mdash;calcium, iron, omega-3 fatty acids, protein, vitamin A, and zinc\u0026mdash;to the recommended nutrient intake (RNI) per 100 grams of edible product. This clustering revealed patterns in nutrient levels, grouping fishing trips with similar nutritional characteristics. The optimal number of clusters, determined to be three distinct FNPs, was identified using the elbow and silhouette methods, which assess the compactness and separation of clusters.\u003c/p\u003e \u003cp\u003eTo validate the consistency and significance of the identified FNPs, we conducted a permutational multivariate analysis of variance (PERMANOVA)\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. This analysis tested whether nutrient profiles within the same cluster were significantly more similar to each other than to those in different clusters, thereby confirming the robustness of our clustering approach.\u003c/p\u003e \u003cp\u003eAfter clustering, we developed two eXtreme Gradient Boosting (XGBoost) models to predict FNPs based on gear type, habitat, season, and vessel type, enabling us to assess the influence of these variables on the nutritional quality of catches. For the gill net (GN) model, predictors included mesh size (to capture variations in catch composition due to different net sizes), habitat, their interaction (to capture combined effects), quarter of the year (reflecting seasonal variations), and vessel type (distinguishing between motorized and non-motorized boats). For the other gears (OG) model, predictors were gear type, habitat, their interaction, quarter of the year, and vessel type.\u003c/p\u003e \u003cp\u003eBoth datasets were divided into training (80%) and testing (20%) sets to build and evaluate the models. We applied 10-fold cross-validation on the training set to enhance the models' accuracy and generalizability, reducing the risk of overfitting\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Model tuning involved dynamically adjusting parameters such as the number of trees, tree depth, learning rate, loss reduction, sample size, and early stopping criteria to optimize performance. The performance of the XGBoost models was assessed using several metrics, including accuracy, the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity (recall), and specificity.\u003c/p\u003e \u003cp\u003eWe calculated SHapley Additive exPlanations (SHAP) values\u003csup\u003e90\u003c/sup\u003e for each model to interpret and reveal the influence of each predictor on the FNPs outcomes. SHAP values offer a unified approach to explain the output of machine learning models by quantifying the contribution of each feature to the prediction and determining the direction of its impact. In the GN subset, we focused on the impact of mesh size and habitats on FNPs. This analysis offered insights into how selecting certain mesh sizes could influence the nutritional quality of catches in different environmental contexts. For the OG subset, we examined how gear type and habitat influenced FNPs predictions. SHAP values allowed us to dissect the combined effect of specific gear types in particular habitats on the nutritional outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/WorldFishCenter/timor.nutrients\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll code is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/WorldFishCenter/timor.nutrients\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to the Timorese fishers and community members who collaborated with us and with the Directorate General of Fisheries, Aquaculture and Aquatic Resources Management (DG-PAGRA) of the Timor-Leste Ministry of Agriculture, Livestock, Fisheries and Forestry (MALFF). We are grateful to WorldFish and the Government of Timor-Leste staff and consultants who contributed over time to the Peskas improvement, with particular thanks to Shaun P. Wilkinson, E. Fernando Cagua, Pedro Rodrigues, Acacio Guterres and many data enumerators who collected data and tested systems.\u003c/p\u003e\n\u003cp\u003eThis publication and continued work and scaling of Peskas beyond Timor-Leste are supported by the Aquatic Foods Initiative supported by contributors to the CGIAR Trust fund \u003cstrong\u003e[AT, LL, HA, VS]\u003c/strong\u003e, and the Asia\u0026ndash;Africa BlueTech Superhighway (AABS) project led by WorldFish. Funding support for this project was provided by UK International Development from the UK government. Contributions to this study were also undertaken as part of the Nutrition-Sensitive Fisheries Management project led by WorldFish, in partnership with the CSIRO and the DG-PAGRA; funded by ACIAR FIS/2017/032 \u003cstrong\u003e[GBP, JB]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eWe thank Eddie Allison for his valuable input during the manuscript development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.L., A.T. and G.B.P. designed and conceptualized the study. V.S. and J.D.R.L supported data collection and stakeholders\u0026rsquo; engagement. L.L. and H.A. developed statistical methods, performed the analyses and the visualizations. L.L., A.T. and G.B.P. wrote the first draft; and. J.B. and J.K. provided expert input on specific topics. All authors reviewed and approved the final paper. 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A Unified Approach to Interpreting Model Predictions. in \u003cem\u003eProceedings of the Advances in Neural Information Processing Systems\u003c/em\u003e vol. 30 4765\u0026ndash;4774 (Curran Associates, Inc., Long Beach, CA, USA, 2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"small-scale fisheries, nutrition-sensitive, multispecies fisheries management, malnutrition, micronutrients, LMIC","lastPublishedDoi":"10.21203/rs.3.rs-5658106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5658106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall-scale fisheries are a crucial source of nutrient-dense aquatic foods in low- and middle-income countries (LMICs), yet practical tools to manage these fisheries to optimize nutritional outcomes in an ecosystem approach remain limited. We present an analytical framework and predictive model of fishery nutrient profiles under typical multispecies, multi-gear situations. Using six-years of catch data from Timor-Leste, we modelled how different fishing methods, habitats, vessel types and seasons influence the yield of nutrients of public health significance. Our results demonstrate that fishing method and habitat are strong predictors of catch nutritional profiles. Importantly, different combinations of fishing strategies can achieve similar nutritional outcomes, indicating complementary management pathways to enhance nutrient availability for communities while balancing ecological, economic, and human wellbeing goals. This replicable framework provides actionable insights for nutrition-sensitive fisheries management and offers data-driven guidance for policies aimed at improving food and nutrition security in LMICs.\u003c/p\u003e","manuscriptTitle":"Fishery nutrient profiles provide a practical tool for nutrition-sensitive fisheries management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-07 12:55:38","doi":"10.21203/rs.3.rs-5658106/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-food","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natfood","sideBox":"Learn more about [Nature Food](http://www.nature.com/natfood/)","snPcode":"","submissionUrl":"","title":"Nature Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c1112949-853d-48eb-aa18-ef9040325719","owner":[],"postedDate":"January 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42446159,"name":"Health sciences/Health care/Nutrition"},{"id":42446160,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"}],"tags":[],"updatedAt":"2026-03-26T07:18:02+00:00","versionOfRecord":{"articleIdentity":"rs-5658106","link":"https://doi.org/10.1038/s43016-026-01313-4","journal":{"identity":"nature-food","isVorOnly":false,"title":"Nature Food"},"publishedOn":"2026-03-25 04:00:00","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2025-01-07 12:55:38","video":"","vorDoi":"10.1038/s43016-026-01313-4","vorDoiUrl":"https://doi.org/10.1038/s43016-026-01313-4","workflowStages":[]},"version":"v1","identity":"rs-5658106","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5658106","identity":"rs-5658106","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}