Food beyond land: seafood contribution to human nutrition in Brazil

Food beyond land: seafood contribution to human nutrition in Brazil | 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 Food beyond land: seafood contribution to human nutrition in Brazil Malu Gallina, Cesar Cordeiro, Linda Eggertsen, Carlos Ferreira, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5139653/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Seafood is vital for tropical coastal communities, but demand and supply in Brazil remain poorly documented despite its extensive coastline and marine resources. We analyzed food consumption from over 40,000 interviews and nutritional content for six nutrients (Ca, Fe, Se, Vitamin-A, Omega-3, and Zn), comparing seafood with land-based protein sources. Our simulations show that increasing seafood consumption by 50% could meet FAO's nutritional recommendations, while a 50% increase could align all nutrient intakes. The average per capita seafood consumption is 5.3 kg/year, with lower-income populations in the North and wealthier groups in the South consuming more. Fish and shellfish are richer in most nutrients, except Zn. However, seafood production often falls short of consumption, highlighting a supply-demand mismatch. Sustainable fisheries management and expanded monitoring are essential to improving Brazil's nutrition and food security. Biological sciences/Ecology/Ecosystem services Earth and environmental sciences/Ecology/Macroecology Fisheries Ecossistemic Services Fishing Ecology Food Security Food Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Significance Statement The global reliance on oceanic resources for sustenance spans millennia, underpinning the livelihoods of billions worldwide. Yet, escalating pressures from climate change, pollution, and unsustainable fishing practices jeopardize the integrity of marine ecosystems and threaten the welfare of coastal communities. The growing demand for seafood, particularly in tropical regions, accentuates the urgency of sustainable resource management. Understanding the intricate dynamics between fishing, food and nutrition security is paramount. This research strives to unravel these complexities, shedding light on the critical interplay between marine resource utilization, societal well-being, and ecological resilience. By elucidating these connections, we aim to inform policies and practices that safeguard both oceanic ecosystems and the welfare of vulnerable populations reliant on them. Introduction Human population growth has led to significant increase in the global demand for resources, sparking intense debates on the sustainable use of nature (IPBES, 2019). Seafood is an essential source of food and livelihood for millions of people, particularly in communities along tropical coasts of low and middle-income nations (BFA, 2023). In 2020, an estimated 59.6 million people were engaged in the primary fisheries sector, and 80 million tons of seafood, excluding algae, were extracted from the oceans for human consumption (FAO , 2022). In recent decades, the demand for fish and shellfish has increased at an annual rate of 3% (FAO, 2022). However, the ongoing debate on food production predominantly focuses on terrestrial systems, largely overlooking the potential of aquatic food sources as a viable solution to address issues of food and nutritional insecurity (Golden et al., 2021; Tigchelaar et al., 2022). There is a pressing need to formulate effective strategies for the sustainable management of seafood resources. Despite the world's purported capacity to generate sufficient food to meet global energy demands, the persistent disparity in food access remains a major factor contributing to the undernourishment of 11% of the global population (HLPE, 2023). Additionally, diet-related diseases, including both undernourishment and obesity, currently account for a staggering 20% of premature deaths (USDA and USDHHS, 2020; Ritchie et al., 2023). Undernourishment harms a disproportionate part of the population, specifically, in low and middle-income countries (Ritchie et al., 2023). This highlights the urgent need for addressing disparities in food distribution and safeguarding access to healthier food items worldwide. For instance, over 40% of the population in some African nations such as Somalia, the Central African Republic, Madagascar, and the Democratic Republic of Congo are considered undernourished (Ritchie et al., 2023). In middle income countries, such as Brazil, while less than 5% of the population is undernourished, 33 million people (~58.7%) still face hunger (PENSSAN, 2022). Seafood is often minimized as a source of animal protein rather than a source of important micronutrients (Hicks et al., 2019; Tigchelaar et al., 2022). Beyond representing a healthy, nutritionally diversified and relatively low-cost animal protein source (Golden et al., 2021), Fish and shellfish may help solve most of the nutritional deficiencies faced by the world population (Béné et al., 2016; Hicks et al., 2019) at relatively low environmental costs (Robinson et al., 2022; Tigchelaar et al., 2022). Fish and shellfish are a nutrient-rich source of micronutrients (e.g., zinc, iron, calcium), vitamins, and fatty acids that are essential to human health (Golden et al., 2021; Hicks et al., 2019). These and other micronutrients play crucial roles in supporting brain function, reducing the risk of cardiovascular diseases, and even preventing some types of cancer (Chen et al., 2022; Hicks et al., 2019; Manson et al., 2019). Further, omega-3 fatty acids improve fetal and child brain development, while micronutrients, such as iron, ensure a healthy blood (Bonham et al., 2009; Tacon et al., 2020). Unlike domesticated land animals such as cattle, chicken, and pork, which offer limited variation in nutrients, seafood offers a diverse range of options that could fulfill human nutritional needs (Golden et al., 2021). For example, pelagic fish species from colder waters store higher concentrations of omega-3 fatty acids, while reef fishes are rich in calcium, iron, and zinc (Hicks et al., 2019). The inherent diversity of fish and shellfish confers adaptability to local food systems and offers more possibilities for communities to build healthier and more sustainable food provision systems (Tigchelaar et al., 2022). The vast Brazilian coast spans 8,500 km and hosts 26.6% of the country's population (IBGE, 2024). Nevertheless, the per capita consumption of seafood of approximately 10 kg per year remains relatively low when compared to countries such as Japan and Portugal, where the yearly per capita intake reaches 45 and 50 kg, respectively (Naylor et al., 2021; Ritchie & Roser, 2023). Brazilian seafood production also varies across states, consisting of both artisanal and industrial fisheries, which differ in fishing gear and targeted species (Salas et al., 2011; Eggertsen et al., 2024), leading to regional differences in nutrient composition and availability. Furthermore, the industrial fisheries sector focuses primarily on abundant and profitable targets, in particular Sardinella brasiliensis , while artisanal fisheries have a broader target spectrum ranging from shrimps and crabs in estuaries, to snappers and groupers in reef habitats (Eggertsen et al., 2024; Freire et al., 2021). Fisheries’ potential to provide abundant and varied nutrients to the human population is directly related to the stability of ecosystem functions (Heilpern et al., 2021; Heilpern et al., 2022; Robinson et al., 2022). Nevertheless, habitat degradation, climate change, and unsustainable fishing practices may cause declines in biodiversity and impact fish stocks (Cheung et al., 2022; Heilpern, DeFries, et al., 2021; Ritchie & Roser, 2023). Globally, unsustainable fishing has driven shifts in target species, with large high-trophic level species being replaced by smaller low-trophic level ones (Fogliarini et al., 2021; Freire & Pauly, 2010; Pauly et al., 1998), and regional-scale assessments (e.g., Brazil) also show signs of decline (Eggertsen et al., 2024). Utterly, these shifts potentially impact protein supply and alter the composition and availability of micronutrients to human populations (Golden, 2016; Heilpern et al., 2021). In the Seychelles, changes in reef fish composition due to climate change affected micronutrients’ availability, with a substantial negative effect on zinc concentrations (Robinson et al., 2022). Despite the recognized importance of fisheries and marine resources to human populations (FAO, 2016), there is limited understanding of seafood consumption patterns and the supply-demand relationship in many nations, particularly in low and middle-income countries. Despite its megadiverse status and abundant natural resources, Brazil continues to confront malnutrition and hunger on a nationwide scale (PENSSAN, 2022). Therefore, it is critical to understand food consumption patterns in this emerging economy, specifically the importance of seafood in addressing nutritional needs across various income classes and regions, given the substantial socioeconomic disparities among states. Protein and nutrient composition of local fisheries can provide insights into the supply-demand relationship, and help set conservation and management strategies directed towards nutrient-diverse taxa. In an innovative approach, our study combined data on food consumption profiles, landings from artisanal and industrial fisheries, and nutritional information for specific fish and shellfish species and other protein sources to investigate the role of seafood to protein and nutrient intake in Brazil. Our assessment was developed across regions, states, and among five different income classes, recognizing that some protein and food options may not be accessible to socioeconomically vulnerable individuals (Ribeiro & Corção, 2013; Robinson et al., 2022). Further, we compared: i) the daily per capita consumption of protein and macro and micro nutrients from seafood and other protein sources relative to Food and Agriculture Organization’s (FAO) daily recommendations of nutrient intake; ii) the nutritional composition of fish and shellfish relative to the composition of other animal protein sources (i.e. cattle, poultry, pork), and among the 20 most frequently harvested marine species; and iii) state-level per capita supply of landed nutrients to the per capita nutrient consumption (demand). We anticipate that nutrient intake from seafood consumption in Brazil would fall far behind FAO's daily recommendation, especially for low-income and socio-economically vulnerable classes (D, E) and states (those from north and northeast regions), and that nutrient supply would exceed demand due to the country’s low seafood consumption (Ritchie & Roser, 2023). Materials And Methods Study area Situated as the largest country in South America, Brazil is a dynamic and diverse emerging economy. With a robust mix of agriculture, natural resource abundance, and a thriving industrial sector, Brazil's economy holds significant influence in the international market. We focused on Brazilian coastal states because: 1) They concentrate landing sites, enabling a more precise association between seafood supply (i.e., landings) and demand (i.e., consumption); 2) Data on fish value chains for non-coastal states are not available; and 3) Seafood consumption is lower in non-coastal states (see Appendix S2 in Online Supporting Information). The Brazilian coast extends over 8,500 km, covering 17 coastal states, each with unique cultural, geographic, and climatic characteristics (Dominguez, 2006). The coastline is characterized by beaches, rocky and biogenic reefs, bays, estuaries, lagoons, and mangrove forests supporting considerable marine biodiversity (Salas et al., 2011; Magris et al., 2021). Such diversity of habitats and living organisms fostered the development of multiple fishing activities (Salas et al., 2011). In this exploitation scenario, fisheries play an important role in the country’s economy and food security (Eggertsen et al., 2024; Freire et al., 2021; Prates et al., 2012). Data collection Data on food consumption was obtained from the Consumer Expenditure Survey performed in Brazil during 2017-2018. This survey is locally known as “Pesquisa de Orçamento Familiar - POF” (Research on Family Budget), which is produced and published by the Brazilian Institute of Geography and Statistics (IBGE, 2020). Data was collected in metropolitan and adjacent rural areas. It consists of interviews performed with all family members, directly in the household, for up to seven days (1-7 days). Along these interviews, all food items and beverages consumed by each family member during this period were recorded. Nutrients content for meals (protein (Ptn, g), calcium (Ca, mg), iron (Fe, mg), zinc (Zn, mg), vitamin-A (Vit-A, µg), magnesium (Mg, mg)) were aggregated to these data, following the Brazilian Food Composition Table ( TBCA - Tabela Brasileira de Composição de Alimentos , 2024). The selected nutrients are broadly employed in seafood nutritional research, given their importance to human health (Chen et al., 2022; Denny Joseph & Muralidhara, 2015) and therefore, availability in nutritional datasets. We initially filtered the POF database to include only interviewees (i.e., family members): i) from coastal states (n=17), ii) that consumed meals during regular days (excluding days when meals did not represent the usual household diet, as holidays and anniversaries), and iii) who consumed animal protein in their meals. We excluded missing entries (e.g., records lacking monthly allowance data or with non-identified food items). Ultimately, our database included 43,948 out of 178,431 interviewees, where 30,701 interviewees consumed animal protein during the survey week. Regarding the income class, 84% of interviewees belonged to class E (up to U$371.28 monthly wage), 10% to class D (U$ 371.28 - 742.56), 3% to class C (U$ 742.57 - 1856.40), whereas classes B and A (U$ 1856,41 - 3,712.80 and above, respectively) represented ≤1% of the dataset (BCB, January 30, 2023 (BCB, 2024)). Some states had no interviewees from classes A, B or C (for more details see Appendix S2, Fig. S2.3). The filtered POF dataset used here had information on diet composition and nutrient quantity per meal, with 1,563 food items listed. Of these 1,563 items, 209 were food items consisting of only animal protein (no side dishes) of which 96 were fish and shellfish species, from which 27 were seafood items. Sources of animal protein in a meal were coded into eight general groups for data analyses: beef, game (wild meat, not obtained through traditional livestock farming), goat (sheep and caprine meat), pork (swine meat), poultry, freshwater fish, imported fish, and seafood (Fig. S1.1). The last three groups comprise the ‘blue food’, food extracted or cultivated in aquatic environments, used henceforth. In this coding, whenever the word “meat” appeared by itself, it was considered as ‘beef’. We complemented the POF nutritional data with three other data sources: 1) Brazilian Food Composition Table from the Brazilian Network of Food Data Systems (BRASILFOODS)( TBCA , 2024), 2) uFish1.0 from FAO/INFOODS ( FAO/INFOODS Databases , 2024); and 3) FishBase (Froese & Pauly, 2024). TBCA gathers nutritional data for more than 5,400 food items, and uFish1.0 for 515 items. Differently from TBCA and uFish1.0, FishBase data consists of the average and associated 95% Credible Intervals (CI) around species-level estimates of nutritional content. These estimates were derived from a Bayesian model accounting for the effect of fish biological traits and phylogeny on tissue nutritional content. From these three data sources, we extracted the content of ptn (g), Ca (mg), Fe (mg), Zn (mg), Vit-A (µg), selenium (Se, µg), and omega-3 (Ω-3, g) for each 100g of edible portion (EP) in different food types. We also used these datasets to determine the Ω-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content in POF food items, by calculating the ratio between the Ω-3 content in 100g of protein and the quantity of consumed protein reported in the POF data. The nutritional (POF, uFish1.0, TBCA, and FishBase), and marine fisheries landings datasets (Freire et al., 2021), were used to assess the nutritional content of harvested species and the overall nutrient supply across all fished taxa (Table S2.2). The latter dataset comprises data on taxonomic composition (at species and genus levels, or even family) and tons per year of seafood harvested from 1950 to 2015. Data organization, analyses and plots were made in the R programming environment v.4.3.0 (R Core Team, 2023). Below we present the R packages along the specific analyses. Package versions, and other packages used in this research, can be checked in GitHub (see Data Availability Statement). Data analysis Patterns of food, protein, and nutrient consumption. We explored the proportional contribution of different animal protein sources to people’s diet per region (North, Northeast, Southeast, and South) and income class (from E to A; Fig. 1) using POF data. Here, we calculated the proportion of per capita daily consumption of each protein source (beef, poultry, seafood, etc.) by dividing the protein source consumption by the total protein consumed. For example, the proportion of poultry daily consumption corresponds to the total amount of poultry consumed divided by the total protein consumption in a single day. The proportion of cephalopods, crustaceans, freshwater fishes, imported fishes, mollusks, and saltwater fishes relative to the total of blue food consumed across states was calculated by dividing the amount of each of these protein groups by the total blue food consumed. Also using the POF data, we investigated differences in nutrient consumption among regions considering two groups of interviewees: 1) those that consumed ‘all food items except seafood’ (hereafter termed 'other sources'), and 2) those that consumed ‘seafood’ in the interview week. All interviewees consumed other sources (n=30,701), and only 10% of them (n=3,037) consumed seafood. For each group, differences in the average daily per capita consumption of protein (g) and nutrients – Ca, Fe, Zn, Vit-A, Mg, and Ω-3 – between regions were tested using ANOVAs with a block factor (interviewee family) and an offset (the FAO’s recommendations according to the Codex nutrient reference value, 2019; FAO, 2019). This ANOVA enabled us to control the influence of family consumption behavior (total of 13,658 families in the dataset) by partitioning the Sum of Squares within and between regions while comparing the average nutrient consumption with FAO’s recommended quantities (Fig. S3.1). Thus, if a grand model mean (intercept) equals zero then animal protein/nutrient consumption is equal to FAO’s recommendation, whereas negative and positive values indicate deficits and surplus relative to FAO’s recommendation. Significant region effects were those with a high F-statistic value and a P-value<0.05. Given the low seafood consumption in Brazil (Fig. 1), we projected a percentage increase in this consumption to achieve FAO’s recommendations. More specifically, based on the average nutrient daily consumption for the 3,037 seafood group interviewees, we projected the nutrient intake patterns (Ptn, Ca, Fe, Zn, Vit-A, Se and Ω-3) in diets ranging from 1-100% of seafood and 1-100% of other protein sources, at intervals of 1%. First, we measured the amount of each nutrient intake and obtained the proportions derived from seafood or other protein sources in interviewees diets. Then, we projected nutrient intake under different percentages of seafood and other protein sources consumptions (e.g., 50% seafood and 50% other sources) to assess if it would still meet the FAO's recommendation. Further, we divided, for each individual, the FAO’s recommendation of each nutrient by the nutrient intake resulting from the projections. This ratio will be equal to 1 if FAO’s recommendation and the projections match exactly. It will be >1 if FAO recommendation is higher than the projected intake, and <1 otherwise. In a few cases (9% of the entries), the POF dataset had zero nutrient intake for some food items, probably caused by typing errors or issues with decimal places in the data. To enable the ratio calculation, we assumed (and set) these values were small (0.0001). We presented these ratio values at the natural-log scale because of the extremely positive values of this ratio (i.e., FAO recommendation much higher than the projected data, Table 1) and to produce more intuitive interpretations (the ratio’s log is zero when recommended and projected intakes match). Also, we built density plots for all interviewed individuals at observed (6%) and 25%, 50%, and 75% projected consumption of seafood and other protein sources. Our analyses were based on data derived from the national Consumer Expenditure Survey (POF, 2020). While this is the only dataset available at such scale, it was not specifically designed to study food consumption patterns, which might be a caveat of our study. The POF focuses on household expenditures and product pricing. It captures the socioeconomic context in large urban areas where the highest expenditures on purchasing products occur. Despite its limitations, such as reporting only in-house meals and applying broad food categories, its advantages include: i) the extensive coverage of coastal areas, where 17 out of 26 states are located ( Comex , 2023) and ii) the alignment with state-scale landing statistics (Freire et al., 2021). Nutrient variation across the most landed taxa. We aligned fisheries and nutritional datasets to evaluate the nutritional composition of the 20 most landed taxa per region, specifically, those taxa accounting for the highest landings over 65 years. These taxa represent the predominant seafood available in each region, comprising 52 bony fishes, one cartilaginous fish, and one crustacean (Table S3.2). Nutrient quantities represent the average and 95% Credible Intervals (CI) from FishBase ( Froese & Pauly, 2024) . The only exception was the swamp ghost crab, Ucides cordatus , for which nutrient data was obtained from the TBCA dataset ( TBCA , 2024). This analysis was conducted for Ptn, Ca, Fe, Zn, Vit-A, Se and Ω-3. Data for other food sources (freshwater fish, imported fish and land animals) were gathered either from uFish1.0 and/or TBCA ( FAO/INFOODS Databases , 2024; TBCA , 2024). When the nutritional data for raw food was not available, we gathered information for unseasoned cooked preparations. Whenever possible, we used the total edible portion or the mean of different meat cuts, as for beef, e.g.: Beef hump contains higher levels of calcium (7.6%) than the beef hind quarter (4.6%). Otherwise, when information was not available at the species level, we adopted the genus mean as a proxy (assuming phylogenetic correlation). We explored differences in the nutritional content of food sources using the average nutritional composition among taxa. Nutrient supply and demand. Using the quantity and nutritional content of landed and consumed seafood taxa, we calculated the per capita amount of landed and consumed nutrients – proxies for nutrient supply and demand, respectively – per coastal state. In this analysis, we contrasted the total quantity of harvested and consumed seafood, and the landed and consumed quantities of nutrients across datasets (Ca, Fe, Vit-A, Zn, and Ω-3). The annual per capita landings of each nutrient were calculated by dividing the nutrient landed in 2015 (the most recent year of landing data and closest to consumption data) by the human population size of each state (IBGE, 2024). The annual per capita consumption of each nutrient across coastal states (n=17) was calculated as follows: first, we obtained the daily per capita nutrient consumption for all interviewees (n=3,037) (see above); second, we multiplied the daily per capita consumption by the number of days in a year (e.g., 365 days); finally, we measured the average annual consumption of nutrients (in kg) across interviewees within each state. Then, we added to this analysis the annual FAO nutrient recommendations (daily recommendations were multiplied by 365 and then transformed into kilograms). Results Patterns of food, protein, and nutrient consumption along the Brazilian coast. Brazilians predominantly consume beef and poultry as animal protein sources, with regional differences (Fig.1). Notably, the proportion of seafood consumption was higher among the population of income classes E, D, and C in the North, and among classes B and A in the South and Southeast regions. The proportion of seafood consumption was similar across income classes in the Northeast region. Freshwater fish had a higher contribution to the daily diet of the North region population than elsewhere (Fig. 1). Pork was especially consumed by low-income classes in the South, but also in high income classes of the Southeast and North regions. Average annual consumption of seafood across interviewees was 5.32±3.52 kg per capita. Annual per capita seafood consumption in 2017-2018 were higher in the states of Maranhão and Pará, with an average of 14.63 and 13.2 kg, respectively. The lower averages were found in the South region (e.g., Rio Grande do Sul and Paraná), with an annual average per capita consumption of 1.65 and 1.69 kg of seafood (Fig. 1). We observed regional differences in Ca and Ω-3 intake from seafood (Appendix S3, Table S3.1), with North region interviewees consuming more Ca and less Ω-3 than interviewees from other regions (Table 1). We also detected regional differences in the intake of all nutrients from other sources, in part due to the much larger sample size used in this ANOVA. The consumption of protein, Zn, and Ω-3 from other sources was higher in the North, whereas the consumption of Vit-A was higher in the Northeast (Table S3.1, Fig. S3.1). The daily consumption of protein in other protein sources exceeded FAO’s recommendation of 50g in ~28g, whereas the consumption of protein through seafood had a deficit of ~27g from the recommendation (Table 1). The main deficiencies in nutrient intake were (the most negative intercepts for both seafood and other sources, respectively) (Table 1): 877mg and 592mg for daily Ca, 761ug and 349ug for Vit-A, and 277mg and 50mg for Mg, when considering seafood and all other sources, respectively. The comparison between FAO’s recommendations and projected nutrient intake showed that consuming more seafood would contribute to achieving FAO’s recommendations more efficiently than increasing the consumption of other food sources (Fig. 2). Overall, seafood represented only 5.9% (median = 4%, Fig. S2.2) of the total animal protein consumed, which equals to an average of 134±126g of seafood per day (range: 5 - 1500g, median of 100g), ranging from 0.17% to 71.4% protein intake. Other protein sources comprised on average 2359±1149g per day (range: 320 - 12681.0g/day). None of these 3.037 interviewees ate exclusively seafood. The ratio between FAO’s recommendations and the projected intake were overall positive (i.e., >1, or >0 at the natural-log scale), showing a nutritional deficit among Brazilians (Fig. 2.I). Current low percentages (~6%) of seafood in diets do not meet FAO’s recommendations (Fig. 2.II), whereas current percentages of other sources in diets only contributed to meet protein demands (note that the green line intersects zero at +75% of other sources). However, these curves showed that ~10% increases in the percentage of seafood in daily diets would be sufficient to meet protein demands (Fig. 2A), and increases of ~25% would help achieve Ω-3 demands (Fig. 2B). For other nutrients, such as Ca, Mg and Zn, this percentage increase should ideally be ~50% (Fig. 2D). Demands of Fe and Vit-A were not met with either seafood or other protein sources, even at percentages >75% (Fig. 2.I, D), even though seafood came closer to the thresholds compared with other protein sources (i.e., seafood projections got closer to the horizontal line at zero deviation from FAO’s recommendations) (Fig. 2.I). Notably, the ratio curves for seafood consistently remained below those for other sources (Fig. 2.I), indicating that, at equivalent percentages, seafood stands out as nutritionally richer than alternative protein sources. Nutrient composition in fisheries landings . Blue foods exhibit higher concentrations of essential nutrients compared to terrestrial protein sources (Fig. 3). In particular, Ca, Ω-3, Se, and Vit-A are found in greater amounts in blue foods. On the other hand, Fe demonstrates a more consistent concentration across various protein sources. Mollusks deviate from this pattern, showing a much higher concentration of Fe than any other taxa. Mollusks also stand out in Zn, Se, and Vit-A concentrations. Bony fish (Actinopterygii) stand out in Vit-A and Se, and imported fish in Ω-3. Among the most landed taxa by Brazilian fisheries, we found higher nutrient concentration in Mugil spp. , namely Mugil liza , and Sardinella brasiliensis, and in the crab Ucides cordatus , which inhabits mangroves. Nutrient supply vs. demand. The total annual per capita amount of landed nutrients from seafood was greater than the annual per capita seafood consumption only in four states (Amapá, Rio de Janeiro, Rio Grande do Sul and Santa Catarina) (Fig. 4). Nonetheless, landing and consumption values of different nutrients varied across states, with the aforementioned four states generally exhibiting the higher per capita landing for any nutrient. Only Rio Grande do Sul and Santa Catarina had higher per capita supply than demand for all nutrients. Fe and Zn were the two micronutrients with greater supply than demand (n=7 states), followed by Ω-3 (n=6) and all other remaining nutrients (n=4). São Paulo often presented the lowest per capita landed amount across states, and Maranhão, in the Northeast, was often among the highest per capita consumptions. Finally, the landed amount did not meet FAO’s annual recommendations for any nutrient (note the y-axis breaks in Fig. 4). Discussion In this study, we conducted a comprehensive analysis to investigate patterns of seafood consumption and associated nutrient intake in Brazil. We detected an overall low level of seafood consumption, and significant differences in nutrient intake across states and regions, either from seafood or alternative food sources. Our projections, modeling shifts in consumption patterns, indicate that even slight increases in daily seafood consumption could lead to improved nutrient intake with smaller food quantities. This result holds promise as it represents a potential optimization of fishing practices (in terms of both fish quantity and composition), while simultaneously minimizing environmental degradation from terrestrial/land food production, such as deforestation, soil erosion and water pollution (Gephart et al., 2021). Despite these positive implications, our analysis based on annual fishery landings indicates that states still exhibit a limited potential to adequately nourish their populations. In light of these findings, addressing the complex interplay between fisheries management and human nutrition requires strategic measures. These may include refining the selection of fishing targets and imported fish based on their nutritional content and cultivation/capture methods, advocating for sustainable aquaculture technologies, and promoting a well-rounded, diverse diet enriched with fruits, vegetables, legumes, nuts, and grains (Amenyogbe, 2023; Fiorella et al., 2021; WHO, 2024). In general, Brazilians are not fully accessing the potential benefits of fishery resources, given that a substantial part of the population faces food insecurity (PENSSAN, 2022) and fall below FAO’s daily recommendations for five nutrients (i.e., Ca, Fe, Mg, Vit-A, and Zn) out of the seven analyzed (S3.1). While a healthy diet should consist of approximately 35% protein, optimizing this intake with nutritionally rich options can significantly contribute to meeting these nutritional recommendations ( Dietary Guidelines for Americans, 2020-2025 , n.d.). Recent research (e.g., 4, 11 and 13) recognizes seafood as a diverse and nutrient-rich protein source, while current dietary patterns lean heavily on land animals, especially in low and middle-income countries like Brazil. Our findings highlight that with minor adjustments, such as a 10% increase in seafood consumption, could significantly enhance people’s nutritional security. Unfortunately, fisheries monitoring, statistics and knowledge of seafood markets are missing in Brazil (Eggertsen et al., 2024; Santos et al., 2023). This poses a challenge in effectively tracking the value chain of seafood production, both at international and national levels. Sustainable fishing and aquaculture practices become paramount in addressing market demands. However, to fully unlock the nutritional and economic advantages, governmental monitoring and statistical frameworks for these markets are essential, paving the way for a future where responsible blue food production contributes substantially to human well-being. In 2021, Brazil imported ~300,000 t of marine fishes, while it exported 19,237 t (plus an undefined amount of 9,267 t that possibly includes marine fish species). Imports, thus, exceeded exports and were mostly constituted by salmon (100,031 t), cod (53,235 t), sardine (70,194 t), and sharks and rays (17,691 t)(Comex, 2024). Most fishes were imported to the Southeast and South regions (mainly São Paulo and Santa Catarina), where the largest import companies are located and consumption is higher. Unfortunately, there is a paucity of information concerning internal market chains in Brazil. A study by Bevilacqua et al. (2020) conducted in Baía da Formosa (Rio Grande do Norte, Northeast region) shed light on the consumption patterns, revealed that approximately 66% of fisheries production is consumed locally, with subsistence fishers accounting for 28%, and tourists only 6% (Bevilacqua et al., 2019). While it is anticipated that production from local-scale fisheries remains predominantly local, understanding the regional and temporal (seasonal) variations, as well as determining if these fisheries contribute to international markets, is a knowledge gap. Exports of red snapper ( Lutjanus purpureus ) dominate the market, of which 85% of catch is exported, primarily to the USA (4,467 t exported in 2021; 40). Other exports include the Whitemouth croaker, tunas, and swordfish (1,111, 1,617 and 1,307 t in 2021, respectively). Parrotfish, goatfish, and several snapper species from the Northeast region also contribute substantially (Carvalho et al., 2013), but these are absent from export statistics. Brazilian seafood exports are a complex and intricate puzzle, which demands proper and continuous monitoring of marine fisheries. Brazilians generally consume high daily quantities of animal protein, exceeding FAO’s recommendations. Nevertheless, consuming proteins with low nutrient content – such as beef and poultry – and not ingesting enough vegetables, may result in nutrient deficiencies from d Ca, Fe, Mg, Vit-A, and Zn (FAO, 2019). Brazil is among the ten leading countries in beef consumption in the world, and second only to the United States in cattle raising production (Ritchie et al., 2024). Indeed, beef and poultry meat account for most of the animal protein consumed in Brazil, which is relatively homogeneous across regions and income classes. Meat consumption brings great environmental costs, as beef circulating in the national and international market often comes from deforested areas, as in the Amazon (Nunes et al., 2019), or even from overexploited grassland ecosystems (Luza et al., 2014). There were also slight regional differences in animal protein consumption, for instance, beef and pork consumption is higher in the South and Southeast, likely influenced by the history of European immigrants in these regions and their heavy-meat diets (Hötzel & Vandresen, 2022; Ribeiro & Corção, 2013). Pork consumption was prevalent among high-income classes (Class A) in the North and Southeast regions, whereas in the South it was more commonly consumed by low-income classes (Class D and E). This regional disparity may also be attributed to the concentration of swine meat production in the South, with pork being a cost-effective protein option for low-income groups (Embrapa, 2024). Among blue foods, freshwater fish consumption was higher in the North. Indeed, Amazon fisheries are fundamental to food and nutritional security in this region (Heilpern, Fiorella, et al., 2021; McIntyre et al., 2016). Food security in the Amazon basin has been threatened by overexploitation, with recently documented collapses of fish stocks and shifts in the composition of targeted species (Heilpern et al., 2022; Heilpern, Fiorella, et al., 2021). Overall, these threats may contribute to the high levels of food insecurity in this region, reaching 80% of the inhabitants (PENSSAN, 2022). Brazil ranks among the countries with the lowest per capita seafood consumption globally, with annual per capita intake of approximately 10% compared to countries such as Portugal and Spain, where seafood consumption reaches 57.1 and 42.4 kg/year per capita (Naylor et al., 2021; Ritchie & Roser, 2023). Despite differences among regions and income classes, only 10% of interviewees reported consuming seafood, at 5.32 kg/year on average, i.e., almost 7 kg below FAO’s recommendation. Elevated seafood consumption is linked to high-income classes (A and B) in the South and Southeast states, while in the North, it is more prevalent among lower-income classes. Notably, Maranhão state stands out as the leading seafood consumer in Brazil, reaching an average of ~14.6 kg/year per capita. This consumption rate underscores a discrepancy, revealing that the state's seafood production falls short of meeting the demands of its population (n=7,017,642 inhabitants; average for 2017-2018). The contrast between Brazilian regions mirrors the differences in seafood production systems. While seafood production in the North is predominantly artisanal and covers smaller scales, in the South and Southeast, it is developed at an industrial scale (Eggertsen et al., 2024; Freire et al., 2021), possibly affecting production costs and consumer behavior towards seafood (Surette, 2008). Supply exceeds demand mostly in South and Southeastern states, where seafood consumption is low and the industrial production reaches large volumes and spatial scales (i.e., exploring shallow and pelagic ecosystems) such as Rio de Janeiro, Santa Catarina, and Rio Grande do Sul. Seafood originating from Brazilian fisheries have significantly higher nutrient content and variability relative to the consumed land animals, corroborating earlier global studies (Golden et al., 2021; Hicks et al., 2019; Robinson et al., 2022). Despite the insufficient landings to meet FAO’s nutritional demands, seafood is still a viable option to supplement diets, having greater Ca, Se, Vit-A, and Ω-3 contents. Furthermore, although poultry represents an important source of Zn, this is also the case for mollusks and some bony fish (e.g., mullets) which could complement low-Zn diets. The nutritional differences among wild-caught fish protein sources highlights the importance of varied diets, and a diverse catch composition, enabling access to a complete set of macro and micronutrients. This would optimize nutrition and avoid the collapse of fish stocks (Robinson et al., 2022). Among the 20 most harvested taxa in Brazilian fisheries, pelagic shoaling fishes from lower trophic levels, such as Mugil spp . and Mugil liza , stood out for their high micronutrient levels. Ω-3 fatty acids, such as EPA and DHA fatty acids, are abundant in high trophic level, mobile, cold-water fish, such as cod and salmon, both imported species broadly consumed in Brazil, but also abundant in species such as Sardinella brasiliensis and Scomber colias (Dominguez, 2006; Robinson et al., 2022). Freshwater and farmed fish are other viable options as Ω-3 sources, even though their nutrient content significantly varies across farming practices (Heilpern et al., 2021; Tacon et al., 2020; Watters et al., 2012). For instance, the majority of imported Salmo salar in Brazil originates from aquaculture, primarily from Chile (Seafood Brasil, 2024). In that case, the nutritional composition closely aligns with the practices of each farm, leading to significant variability in nutrient content (Pohořelá et al., 2022). Our findings provide valuable insights on food supply and demand dynamics, shedding light on general food consumption patterns in Brazil. How could Brazilian fisheries contribute to micronutrient deficiencies? Given the limited role of fisheries alone to meet FAO’s nutritional recommendations, seafood might have a key role in providing a nutritionally diverse diet rather than being the main protein source in Brazilian diets. With supply exceeding demands only in four out of the 17 coastal states, a first suggestion could be increasing landings. This, however, is not a viable strategy due to multiple evidence of stocks overexploitation and the absence of temporal data on fish abundance and landings of Brazilian fish stocks (Dias, 2022; Eggertsen et al., 2024). A recent assessment showed that out of 117 species targeted by the fisheries sector, only eight stocks (comprising one lobster species and seven bony fish species) have undergone recent assessments, and more alarmingly, half of those stocks are considered overexploited (Dias, 2022). Brazil has no monitoring program and struggles with fisheries management, and any increase in fishing effort would probably result in substantial ecological consequences. Therefore, we must primarily undergo a careful analysis of the stock abundance, exploitation capacity, and recovery towards sustainable fishing activities. The first step would be for the government to recognize seafood’s importance, both for its nutritional, social, and economic value. Fish value in their natural environment, delivering multiple cultural, ecosystem services must also be considered (Spalding et al., 2017; Tribot et al., 2018; Waechter et al., in prep ). Followed by incentives for public awareness and financial support aimed at transforming the industry into a more sustainable, transparent, and efficient sector, countries such as the USA, Italy, El Salvador, and even Brazil have already promoted initiatives aimed at disseminating information and boost the sector (Global Seafood Alliance, 2024; SeafoodSource, 2024). Our findings highlight the need for monitoring fisheries through a collaborative approach, connecting information and statistics along the Brazilian coast and the economic value chain, monitoring stocks, mapping and characterizing demand, from the ocean all the way to people’s plates (Eggertsen et al., 2024). Moreover, this collaborative approach should also include a detailed analysis of catches from freshwater species, farming of both marine and freshwater species, subsistence catches, and recreational fisheries, with some nutritional value but also economic value. Declarations Acknowledgments This research was conducted by the Reef Synthesis Working Group (ReefSYN) funded by the Synthesis Center on Biodiversity and Ecosystem Services (SinBiose, CNPq, #442417/2019-5 to MGB). ALL received post-doctoral fellowships from CNPq (#153024/2022-4, #164240/2021-7, #151228/2021-3, #152410/2020-1) and CAPES (PDPG-POSDOC, #88887.800011/2022-00). Data Availability Statement All data used in this research are of free access in different sources (see Methods). Nonetheless, we provide data (those at suitable size) and R codes in GitHub in the following link: https://github.com/Sinbiose-Reefs/food_security.git. The raw POF data can be found at https://www.ibge.gov.br/en/statistics/social/health/25610-pof-2017-2018-pof-en.html?edicao=27315&t=downloads References IPBES, “Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services” (Zenodo, 2019). Blue Food Assessment. 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R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org. (2023). Tables Table 1. Table of ANOVA coefficients. The intercept consists of the difference between the reference (the South) and FAO’s nutritional recommendations (regression offset). Food type Parameter Protein (g) Calcium (mg) Iron (mg) Zinc (mg) Vitamin-A (ug) Omega-3 (mg) Magnesium (mg) Seafood FAO’s recommendation 50g 1000mg 22mg 11mg 8000ug 250mg 310mg (Intercept) -26.54 -877.33 -20.50 -10.08 -761.43 -2.17 -276.76 Southeast -0.66±2.03 -28.20±19.54 0.17±0.48 -0.13±0.12 19.96±10.39 0.05±0.03 -3.02±2.89 Northeast 0.12±1.80 -17.22±17.34 0.06±0.43 -0.06±0.11 17.13±9.22 0.06±0.03 -1.72±2.57 North -0.70±2.16 49.32±20.8 0.52±0.51 -0.05±0.13 6.42±11.05 -0.06±0.03 1.42±3.08 Other sources (Intercept) 27.84 -592.08 -11.32 -0.28 -348.64 -2.46 -50.04 Southeast -4.56±0.86 -59.98±7.04 -0.22±0.11 -1.60±0.13 18.67±37.63 0.0004±0.005 -34.98±2.86 Northeast -0.52±0.75 -79.24±6.18 -0.38±0.10 -0.55±0.12 197.91±33.03 0.03±0.004 -43.14±2.51 North 6.04±1.21 -72.95±9.92 -0.49±0.16 0.27±0.19 26.33±53.01 0.096±0.007 -31.60±4.03 Additional Declarations There is NO Competing Interest. 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Bordeaux","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"","lastName":"Luza","suffix":""},{"id":367642915,"identity":"7554cc0d-a937-4a3d-977f-c1379838fb3a","order_by":14,"name":"Mariana Bender","email":"","orcid":"","institution":"Universidade Federal de Santa Maria","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Bender","suffix":""}],"badges":[],"createdAt":"2024-09-23 17:40:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5139653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5139653/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71155543,"identity":"0ab010db-2140-4a9d-abda-2c667268c7e9","added_by":"auto","created_at":"2024-12-11 15:35:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171645,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of different animal protein sources to the daily diet of Brazilians per category of income class (E: \u0026lt;U$371.28; D: U$ 371.28 - 742.56; C: U$ 742.57 - 1856.40; B: U$ 1856,41 - 3,712.80; and A: \u0026gt;U$ 3,712.80) and region. The blue bar in the upper right represents the different contributions of food groups to blue foods: cephalopod, crustacean, freshwater fish, imported fish, mollusc, and saltwater fish. The blue color gradient of Brazilian states (AL, Alagoas; AP, Amapá; BA, Bahia; ES, Espírito Santo; MA, Maranhão; PA, Pará; PB, Paraíba; PE, Pernambuco; PI, Piauí; PR, Paraná; RJ, Rio de Janeiro; RN, Rio Grande do Norte; RS, Rio Grande do Sul; SC, Santa Catarina; SE, Sergipe; SP, São Paulo) within regions depicts the per capita annual mean consumption of seafood.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5139653/v1/2a1daa828ec149d77fa4e555.png"},{"id":71155541,"identity":"89c69add-bbd4-4c7e-a6d5-ec69eb41cf15","added_by":"auto","created_at":"2024-12-11 15:35:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149315,"visible":true,"origin":"","legend":"\u003cp\u003eProjected nutritional implications of different percentages of seafood and other protein sources in the average Brazilian diet. In the x-axis of A, we show the range of the projections, from 1 to 100% consumption of seafood or other sources. The horizontal dashed line in A and the vertical dashed line in B depicts the zone where the consumption of each nutrient equals FAO’s recommendations (zero at natural-log scale) (Lewis, 2019). Along the y-axis of A, we show the median ratio across projected percentages. The white vertical lines in A show the position of per capita projections shown in B, with A representing the observed percentage of seafood consumption of 6%, and 2 to 4 representing projections of 25%, 50%, and 75% of seafood in the diet. Along the Y-axis of B we show the density of ratio values exhibited by the interviewees in each projection, and the X-axis depicts the range of ratio values across these interviewees and projections. In all plots, seafood is represented in blue and other sources in green.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5139653/v1/b3339855a506ee8631987d46.png"},{"id":71155544,"identity":"02e61ae3-1a55-44f3-87f5-b03d109a7886","added_by":"auto","created_at":"2024-12-11 15:35:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108481,"visible":true,"origin":"","legend":"\u003cp\u003eNutrient content of the 20 most landed species from 1950 to 2015 (A), and the nutrient content of several protein sources (B). In A, the point and error bars are the average and 95% Credible Intervals of predicted nutrient content by a model that accounted for biological traits and phylogeny. In B, the values for marine Actinopterygii and Elasmobranchii are averages of data presented in MacNeil (2021). In A, Ucides cordatus (Uci cor) data were imputed based on the TBCA. In A and B the vertical lines represent the average nutrient content across taxa. Other taxa abbreviations: \u003cem\u003eMugil spp.\u003c/em\u003e (Mug spp), \u003cem\u003eMugil liza\u003c/em\u003e (Mug liz), \u003cem\u003eSardinella brasiliensis\u003c/em\u003e (Sar bra), \u003cem\u003eMacrodon atricauda\u003c/em\u003e (Mac atr), \u003cem\u003eKatsuwonus pelamis\u003c/em\u003e (Kat pel), \u003cem\u003eLutjanus purpureus \u003c/em\u003e(Lut pur), \u003cem\u003eSelene spp. \u003c/em\u003e(Sel spp), \u003cem\u003eSciades parkeri\u003c/em\u003e (Sci par), \u003cem\u003eOpisthonema oglinum\u003c/em\u003e (Opi ogl), \u003cem\u003eUmbrina canosai\u003c/em\u003e (Umb can), \u003cem\u003eScomberomorus brasiliensis\u003c/em\u003e (Sco bra), \u003cem\u003eMicropogonias furnieri \u003c/em\u003e(Mic fur), \u003cem\u003eCoryphaena hippurus\u003c/em\u003e (Cor hip), \u003cem\u003eCynoscion acoupa\u003c/em\u003e (Cyn aco), \u003cem\u003eCynoscion guatucupa\u003c/em\u003e (Cyn gua), \u003cem\u003eGenidens barbus\u003c/em\u003e(Gen bar), \u003cem\u003ePomatomus saltatrix\u003c/em\u003e (Pom sal), \u003cem\u003eScomber colias \u003c/em\u003e(Sco col), and \u003cem\u003eMerluccius hubbsi\u003c/em\u003e (Mer hub).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5139653/v1/a00d749fec103c3ed3fc46da.png"},{"id":71155540,"identity":"856a966c-7c0e-4f5f-9d92-e4c7c109b7ae","added_by":"auto","created_at":"2024-12-11 15:35:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147262,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between per capita landings and consumption of seafood and nutrients along the Brazilian coast. Light blue represents states where nutrient consumption (demand) was lower than nutrient landings (supply), and dark blue the states where nutrient demand was higher than the landings. North (AP, Amapá; PA, Pará), Northeast (AL, Alagoas; BA, Bahia; MA, Maranhão; PB, Paraíba; PE, Pernambuco; PI, Piauí; RN, Rio Grande do Norte; SE, Sergipe), Southeast (ES, Espírito Santo; RJ, Rio de Janeiro; SP, São Paulo), and South (PR, Paraná; RS, Rio Grande do Sul; SC, Santa Catarina).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5139653/v1/e93192e7ba7ab1ab76c72fd8.png"},{"id":71156844,"identity":"f55eb148-778a-42fd-a330-030e70df1f69","added_by":"auto","created_at":"2024-12-11 15:43:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1097912,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5139653/v1/b74eece2-9a21-4261-8d0a-cb2e65147beb.pdf"},{"id":71155545,"identity":"684c5b34-d9c5-412b-8ddf-9ddf518099e5","added_by":"auto","created_at":"2024-12-11 15:35:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1233810,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-5139653/v1/b466ca21cf2cbf6331d212ce.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Food beyond land: seafood contribution to human nutrition in Brazil","fulltext":[{"header":"Significance Statement","content":"\u003cp\u003eThe global reliance on oceanic resources for sustenance spans millennia, underpinning the livelihoods of billions worldwide. Yet, escalating pressures from climate change, pollution, and unsustainable fishing practices jeopardize the integrity of marine ecosystems and threaten the welfare of coastal communities. The growing demand for seafood, particularly in tropical regions, accentuates the urgency of sustainable resource management. Understanding the intricate dynamics between fishing, food and nutrition security is paramount. This research strives to unravel these complexities, shedding light on the critical interplay between marine resource utilization, societal well-being, and ecological resilience. By elucidating these connections, we aim to inform policies and practices that safeguard both oceanic ecosystems and the welfare of vulnerable populations reliant on them.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eHuman population growth has led to significant increase in the global demand for resources, sparking intense debates on the sustainable use of nature (IPBES, 2019). Seafood is an essential source of food and livelihood for millions of people, particularly in communities along tropical coasts of low and middle-income nations (BFA, 2023). In 2020, an estimated 59.6 million people were engaged in the primary fisheries sector, and 80 million tons of seafood, excluding algae, were extracted from the oceans for human consumption (FAO\u003cem\u003e,\u003c/em\u003e 2022). In recent decades, the demand for fish and shellfish has increased at an annual rate of 3% (FAO, 2022). However, the ongoing debate on food production predominantly focuses on terrestrial systems, largely overlooking the potential of aquatic food sources as a viable solution to address issues of food and nutritional insecurity (Golden et al., 2021; Tigchelaar et al., 2022). There is a pressing need to formulate effective strategies for the sustainable management of seafood resources.\u003c/p\u003e\n\u003cp\u003eDespite the world\u0026apos;s purported capacity to generate sufficient food to meet global energy demands, the persistent disparity in food access remains a major factor contributing to the undernourishment of 11% of the global population (HLPE, 2023). Additionally, diet-related diseases, including both undernourishment and obesity, currently account for a staggering 20% of premature deaths (USDA and USDHHS, 2020; Ritchie et al., 2023). Undernourishment harms a disproportionate part of the population, specifically, in low and middle-income countries (Ritchie et al., 2023). This highlights the urgent need for addressing disparities in food distribution and safeguarding access to healthier food items worldwide. For instance, over 40% of the population in some African nations such as Somalia, the Central African Republic, Madagascar, and the Democratic Republic of Congo are considered undernourished (Ritchie et al., 2023). In middle income countries, such as Brazil, while less than 5% of the population is undernourished, 33 million people (~58.7%) still face hunger (PENSSAN, 2022). Seafood is often minimized as a source of animal protein rather than a source of important micronutrients (Hicks et al., 2019; Tigchelaar et al., 2022). Beyond representing a healthy, nutritionally diversified and relatively low-cost animal protein source (Golden et al., 2021), Fish and shellfish may help solve most of the nutritional deficiencies faced by the world population (B\u0026eacute;n\u0026eacute; et al., 2016; Hicks et al., 2019) at relatively low environmental costs (Robinson et al., 2022; Tigchelaar et al., 2022).\u003c/p\u003e\n\u003cp\u003eFish and shellfish are a nutrient-rich source of micronutrients (e.g., zinc, iron, calcium), vitamins, and fatty acids that are essential to human health (Golden et al., 2021; Hicks et al., 2019). These and other micronutrients play crucial roles in supporting brain function, reducing the risk of cardiovascular diseases, and even preventing some types of cancer (Chen et al., 2022; Hicks et al., 2019; Manson et al., 2019). Further, omega-3 fatty acids improve fetal and child brain development, while micronutrients, such as iron, ensure a healthy blood (Bonham et al., 2009; Tacon et al., 2020). Unlike domesticated land animals such as cattle, chicken, and pork, which offer limited variation in nutrients, seafood offers a diverse range of options that could fulfill human nutritional needs (Golden et al., 2021). For example, pelagic fish species from colder waters store higher concentrations of omega-3 fatty acids, while reef fishes are rich in calcium, iron, and zinc (Hicks et al., 2019). The inherent diversity of fish and shellfish confers adaptability to local food systems and offers more possibilities for communities to build healthier and more sustainable food provision systems (Tigchelaar et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe vast Brazilian coast spans 8,500 km and hosts 26.6% of the country\u0026apos;s population (IBGE, 2024). Nevertheless, the per capita consumption of seafood of approximately 10 kg per year remains relatively low when compared to countries such as Japan and Portugal, where the yearly per capita intake reaches 45 and 50 kg, respectively (Naylor et al., 2021; Ritchie \u0026amp; Roser, 2023). Brazilian seafood production also varies across states, consisting of both artisanal and industrial fisheries, which differ in fishing gear and targeted species (Salas et al., 2011; Eggertsen et al., 2024), leading to regional differences in nutrient composition and availability. Furthermore, the industrial fisheries sector focuses primarily on abundant and profitable targets, in particular \u003cem\u003eSardinella brasiliensis\u003c/em\u003e, while artisanal fisheries have a broader target spectrum ranging from shrimps and crabs in estuaries, to snappers and groupers in reef habitats (Eggertsen et al., 2024; Freire et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFisheries\u0026rsquo; potential to provide abundant and varied nutrients to the human population is directly related to the stability of ecosystem functions (Heilpern et al., 2021; Heilpern et al., 2022; Robinson et al., 2022). Nevertheless, habitat degradation, climate change, and unsustainable fishing practices may cause declines in biodiversity and impact fish stocks (Cheung et al., 2022; Heilpern, DeFries, et al., 2021; Ritchie \u0026amp; Roser, 2023). Globally, unsustainable fishing has driven shifts in target species, with large high-trophic level species being replaced by smaller low-trophic level ones (Fogliarini et al., 2021; Freire \u0026amp; Pauly, 2010; Pauly et al., 1998), and regional-scale assessments (e.g., Brazil) also show signs of decline (Eggertsen et al., 2024). Utterly, these shifts potentially impact protein supply and alter the composition and availability of micronutrients to human populations (Golden, 2016; Heilpern et al., 2021). In the Seychelles, changes in reef fish composition due to climate change affected micronutrients\u0026rsquo; availability, with a substantial negative effect on zinc concentrations (Robinson et al., 2022). Despite the recognized importance of fisheries and marine resources to human populations (FAO, 2016), there is limited understanding of seafood consumption patterns and the supply-demand relationship in many nations, particularly in low and middle-income countries. Despite its megadiverse status and abundant natural resources, Brazil continues to confront malnutrition and hunger on a nationwide scale (PENSSAN, 2022). Therefore, it is critical to understand food consumption patterns in this emerging economy, specifically the importance of seafood in addressing nutritional needs across various income classes and regions, given the substantial socioeconomic disparities among states.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProtein and nutrient composition of local fisheries can provide insights into the supply-demand relationship, and help set conservation and management strategies directed towards nutrient-diverse taxa. In an innovative approach, our study combined data on food consumption profiles, landings from artisanal and industrial fisheries, and nutritional information for specific fish and shellfish species and other protein sources to investigate the role of seafood to protein and nutrient intake in Brazil. Our assessment was developed across regions, states, and among five different income classes, recognizing that some protein and food options may not be accessible to socioeconomically vulnerable individuals (Ribeiro \u0026amp; Cor\u0026ccedil;\u0026atilde;o, 2013; Robinson et al., 2022). Further, we compared: i) the daily per capita consumption of protein and macro and micro nutrients from seafood and other protein sources relative to Food and Agriculture Organization\u0026rsquo;s (FAO) daily recommendations of nutrient intake; ii) the nutritional composition of fish and shellfish relative to the composition of other animal protein sources (i.e. cattle, poultry, pork), and among the 20 most frequently harvested marine species; and iii) state-level per capita supply \u0026nbsp;of landed nutrients to the per capita nutrient consumption (demand). We anticipate that nutrient intake from seafood consumption in Brazil would fall far behind FAO\u0026apos;s daily recommendation, especially for low-income and socio-economically vulnerable classes (D, E) and states (those from north and northeast regions), and that nutrient supply would exceed demand due to the country\u0026rsquo;s low seafood consumption (Ritchie \u0026amp; Roser, 2023).\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSituated as the largest country in South America, Brazil is a dynamic and diverse emerging economy. With a robust mix of agriculture, natural resource abundance, and a thriving industrial sector, Brazil\u0026apos;s economy holds significant influence in the international market. We focused on Brazilian coastal states because: 1) They concentrate landing sites, enabling a more precise association between seafood supply (i.e., landings) and demand (i.e., consumption); 2) Data on fish value chains for non-coastal states are not available; and 3) Seafood consumption is lower in non-coastal states (see Appendix S2 in Online Supporting Information). The Brazilian coast extends over 8,500 km, covering 17 coastal states, each with unique cultural, geographic, and climatic characteristics (Dominguez, 2006). The coastline is characterized by beaches, rocky and biogenic reefs, bays, estuaries, lagoons, and mangrove forests supporting considerable marine biodiversity (Salas et al., 2011; Magris et al., 2021). Such diversity of habitats and living organisms fostered the development of multiple fishing activities (Salas et al., 2011). In this exploitation scenario, fisheries play an important role in the country\u0026rsquo;s economy and food security (Eggertsen et al., 2024; Freire et al., 2021; Prates et al., 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on food consumption was obtained from the Consumer Expenditure Survey performed in Brazil during 2017-2018. This survey is locally known as \u0026ldquo;Pesquisa de Or\u0026ccedil;amento Familiar - POF\u0026rdquo; (Research on Family Budget), which is produced and published by the Brazilian Institute of Geography and Statistics (IBGE, 2020). Data was collected in metropolitan and adjacent rural areas. It consists of interviews performed with all family members, directly in the household, for up to seven days (1-7 days). Along these interviews, all food items and beverages consumed by each family member during this period were recorded. Nutrients content for meals (protein (Ptn, g), calcium (Ca, mg), iron (Fe, mg), zinc (Zn, mg), vitamin-A (Vit-A, \u0026micro;g), magnesium (Mg, mg)) were aggregated to these data, following the Brazilian Food Composition Table (\u003cem\u003eTBCA - Tabela Brasileira de Composi\u0026ccedil;\u0026atilde;o de Alimentos\u003c/em\u003e, 2024). The selected nutrients are broadly employed in seafood nutritional research, given their importance to human health (Chen et al., 2022; Denny Joseph \u0026amp; Muralidhara, 2015)\u0026nbsp;and therefore, availability in nutritional datasets. We initially filtered the POF database to include only interviewees (i.e., family members): i) from coastal states (n=17), ii) that consumed meals during regular days (excluding days when meals did not represent the usual household diet, as holidays and anniversaries), and iii) who consumed animal protein in their meals. We excluded missing entries (e.g., records lacking monthly allowance data or with non-identified food items). Ultimately, our database included 43,948 out of 178,431 interviewees, where 30,701 interviewees consumed animal protein during the survey week. Regarding the income class, 84% of interviewees belonged to class E (up to U$371.28 monthly wage), 10% to class D (U$ 371.28 - 742.56), 3% to class C (U$ 742.57 - 1856.40), whereas classes B and A (U$ 1856,41 - 3,712.80 and above, respectively) represented \u0026le;1% of the dataset (BCB, January 30, 2023\u0026nbsp;(BCB, 2024)). Some states had no interviewees from classes A, B or C (for more details see Appendix S2, Fig. S2.3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe filtered POF dataset used here had information on diet composition and nutrient quantity per meal, with 1,563 food items listed. Of these 1,563 items, 209 were food items consisting of only animal protein (no side dishes) of which 96 were fish and shellfish species, from which 27 were seafood items. Sources of animal protein in a meal were coded into eight general groups for data analyses: beef, game (wild meat, not obtained through traditional livestock farming), goat (sheep and caprine meat), pork (swine meat), poultry, freshwater fish, imported fish, and seafood (Fig. S1.1). The last three groups comprise the \u0026lsquo;blue food\u0026rsquo;, food extracted or cultivated in aquatic environments, used henceforth. In this coding, whenever the word \u0026ldquo;meat\u0026rdquo; appeared by itself, it was considered as \u0026lsquo;beef\u0026rsquo;. We complemented the POF nutritional data with three other data sources: 1) Brazilian Food Composition Table from the Brazilian Network of Food Data Systems (BRASILFOODS)(\u003cem\u003eTBCA\u003c/em\u003e, 2024), 2) uFish1.0 from FAO/INFOODS (\u003cem\u003eFAO/INFOODS Databases\u003c/em\u003e, 2024); and 3) FishBase (Froese \u0026amp; Pauly, 2024). TBCA gathers nutritional data for more than 5,400 food items, and uFish1.0 for 515 items. Differently from TBCA and uFish1.0, FishBase data consists of the average and associated 95% Credible Intervals (CI) around species-level estimates of nutritional content. These estimates were derived from a Bayesian model accounting for the effect of fish biological traits and phylogeny on tissue nutritional content. From these three data sources, we extracted the content of ptn (g), Ca (mg), Fe (mg), Zn (mg), Vit-A (\u0026micro;g), selenium (Se, \u0026micro;g), and omega-3 (\u0026Omega;-3, g) for each 100g of edible portion (EP) in different food types. We also used these datasets to determine the \u0026Omega;-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content in POF food items, by calculating the ratio between the \u0026Omega;-3 content in 100g of protein and the quantity of consumed protein reported in the POF data.\u003c/p\u003e\n\u003cp\u003eThe nutritional (POF, uFish1.0, TBCA, and FishBase), and marine fisheries landings datasets (Freire et al., 2021), were used to assess the nutritional content of harvested species and the overall nutrient supply across all fished taxa (Table S2.2). The latter dataset comprises data on taxonomic composition (at species and genus levels, or even family) and tons per year of seafood harvested from 1950 to 2015. Data organization, analyses and plots were made in the R programming environment v.4.3.0 (R Core Team, 2023). Below we present the R packages along the specific analyses. Package versions, and other packages used in this research, can be checked in GitHub (see Data Availability Statement).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatterns of food, protein, and nutrient consumption.\u0026nbsp;\u003c/strong\u003eWe explored the proportional contribution of different animal protein sources to people\u0026rsquo;s diet per region (North, Northeast, Southeast, and South) and income class (from E to A; Fig. 1) using POF data. Here, we calculated the proportion of per capita daily consumption of each protein source (beef, poultry, seafood, etc.) by dividing the protein source consumption by the total protein consumed. For example, the proportion of poultry daily consumption corresponds to the total amount of poultry consumed divided by the total protein consumption in a single day. The proportion of cephalopods, crustaceans, freshwater fishes, imported fishes, mollusks, and saltwater fishes relative to the total of blue food consumed across states was calculated by dividing the amount of each of these protein groups by the total blue food consumed. Also using the POF data, we investigated differences in nutrient consumption among regions considering two groups of interviewees: 1) those that consumed \u0026lsquo;all food items except seafood\u0026rsquo; (hereafter termed \u0026apos;other sources\u0026apos;), and 2) those that consumed \u0026lsquo;seafood\u0026rsquo; in the interview week. All interviewees consumed other sources (n=30,701), and only 10% of them (n=3,037) consumed seafood. For each group, differences in the average daily per capita consumption of protein (g) and nutrients \u0026ndash; Ca, Fe, Zn, Vit-A, Mg, and \u0026Omega;-3 \u0026ndash; between regions were tested using ANOVAs with a block factor (interviewee family) and an offset (the FAO\u0026rsquo;s recommendations according to the Codex nutrient reference value, 2019; FAO, 2019). This ANOVA enabled us to control the influence of family consumption behavior (total of 13,658 families in the dataset) by partitioning the Sum of Squares within and between regions while comparing the average nutrient consumption with FAO\u0026rsquo;s recommended quantities (Fig. S3.1). Thus, if a grand model mean (intercept) equals zero then animal protein/nutrient consumption is equal to FAO\u0026rsquo;s recommendation, whereas negative and positive values indicate deficits and surplus relative to FAO\u0026rsquo;s recommendation. Significant region effects were those with a high F-statistic value and a P-value\u0026lt;0.05. Given the low seafood consumption in Brazil (Fig. 1), we projected a percentage increase in this consumption to achieve FAO\u0026rsquo;s recommendations. More specifically, based on the average nutrient daily consumption for the 3,037 seafood group interviewees, we projected the nutrient intake patterns (Ptn, Ca, Fe, Zn, Vit-A, Se and \u0026Omega;-3) in diets ranging from 1-100% of seafood and 1-100% of other protein sources, at intervals of 1%. First, we measured the amount of each nutrient intake and obtained the proportions derived from seafood or other protein sources in interviewees diets. Then, we projected nutrient intake under different percentages of seafood and other protein sources consumptions (e.g., 50% seafood and 50% other sources) to assess if it would still meet the FAO\u0026apos;s recommendation. Further, we divided, for each individual, the FAO\u0026rsquo;s recommendation of each nutrient by the nutrient intake resulting from the projections. This ratio will be equal to 1 if FAO\u0026rsquo;s recommendation and the projections match exactly. It will be \u0026gt;1 if FAO recommendation is higher than the projected intake, and \u0026lt;1 otherwise. In a few cases (9% of the entries), the POF dataset had zero nutrient intake for some food items, probably caused by typing errors or issues with decimal places in the data. To enable the ratio calculation, we assumed (and set) these values were small (0.0001). We presented these ratio values at the natural-log scale because of the extremely positive values of this ratio (i.e., FAO recommendation much higher than the projected data, Table 1) and to produce more intuitive interpretations (the ratio\u0026rsquo;s log is zero when recommended and projected intakes match). Also, we built density plots for all interviewed individuals at observed (6%) and 25%, 50%, and 75% projected consumption of seafood and other protein sources.\u003c/p\u003e\n\u003cp\u003eOur analyses were based on data derived from the national Consumer Expenditure Survey (POF, 2020). While this is the only dataset available at such scale, it was not specifically designed to study food consumption patterns, which might be a caveat of our study. The POF focuses on household expenditures and product pricing. It captures the socioeconomic context in large urban areas where the highest expenditures on purchasing products occur. Despite its limitations, such as reporting only in-house meals and applying broad food categories, its advantages include: i) the extensive coverage of coastal areas, where 17 out of 26 states are located (\u003cem\u003eComex\u003c/em\u003e, 2023) and ii) the alignment with state-scale landing statistics (Freire et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrient variation across the most landed taxa.\u0026nbsp;\u003c/strong\u003eWe aligned fisheries and nutritional datasets to evaluate the nutritional composition of the 20 most landed taxa per region, specifically, those taxa accounting for the highest landings over 65 years. These taxa represent the predominant seafood available in each region, comprising 52 bony fishes, one cartilaginous fish, and one crustacean (Table S3.2). Nutrient quantities represent the average and 95% Credible Intervals (CI) from FishBase (\u003cem\u003eFroese \u0026amp;\u003c/em\u003e Pauly, 2024) . The only exception was the swamp ghost crab, \u003cem\u003eUcides cordatus\u003c/em\u003e, for which nutrient data was obtained from the TBCA dataset (\u003cem\u003eTBCA\u003c/em\u003e, 2024). This analysis was conducted for Ptn, Ca, Fe, Zn, Vit-A, Se and \u0026Omega;-3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData for other food sources (freshwater fish, imported fish and land animals) were gathered either from uFish1.0 and/or TBCA (\u003cem\u003eFAO/INFOODS Databases\u003c/em\u003e, 2024; \u003cem\u003eTBCA\u003c/em\u003e, 2024). When the nutritional data for raw food was not available, we gathered information for unseasoned cooked preparations. Whenever possible, we used the total edible portion or the mean of different meat cuts, as for beef, e.g.: Beef hump contains higher levels of calcium (7.6%) than the beef hind quarter (4.6%). Otherwise, when information was not available at the species level, we adopted the genus mean as a proxy (assuming phylogenetic correlation). We explored differences in the nutritional content of food sources using the average nutritional composition among taxa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrient supply and demand.\u0026nbsp;\u003c/strong\u003eUsing the quantity and nutritional content of landed and consumed seafood taxa, we calculated the per capita amount of landed and consumed nutrients \u0026ndash; proxies for nutrient supply and demand, respectively \u0026ndash; per coastal state. In this analysis, we contrasted the total quantity of harvested and consumed seafood, and the landed and consumed quantities of nutrients across datasets (Ca, Fe, Vit-A, Zn, and \u0026Omega;-3). The annual per capita landings of each nutrient were calculated by dividing the nutrient landed in 2015 (the most recent year of landing data and closest to consumption data) by the human population size of each state (IBGE, 2024). The annual per capita consumption of each nutrient across coastal states (n=17) was calculated as follows: first, we obtained the daily per capita nutrient consumption for all interviewees (n=3,037) (see above); second, we multiplied the daily per capita consumption by the number of days in a year (e.g., 365 days); finally, we measured the average annual consumption of nutrients (in kg) across interviewees within each state. Then, we added to this analysis the annual FAO nutrient recommendations (daily recommendations were multiplied by 365 and then transformed into kilograms).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatterns of food, protein, and nutrient consumption along the Brazilian coast.\u003c/strong\u003e Brazilians predominantly consume beef and poultry as animal protein sources, with regional differences (Fig.1). Notably, the proportion of seafood consumption was higher among the population of income classes E, D, and C in the North, and among classes B and A in the South and Southeast regions. The proportion of seafood consumption was similar across income classes in the Northeast region. Freshwater fish had a higher contribution to the daily diet of the North region population than elsewhere (Fig. 1). Pork was especially consumed by low-income classes in the South, but also in high income classes of the Southeast and North regions. Average annual consumption of seafood across interviewees was 5.32\u0026plusmn;3.52 kg per capita. Annual per capita seafood consumption in 2017-2018 were higher in the states of Maranh\u0026atilde;o and Par\u0026aacute;, with an average of 14.63 and 13.2 kg, respectively. The lower averages were found in the South region (e.g., Rio Grande do Sul and Paran\u0026aacute;), with an annual average per capita consumption of 1.65 and 1.69 kg of seafood (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe observed regional differences in Ca and \u0026Omega;-3 intake from seafood (Appendix S3, Table S3.1), with North region interviewees consuming more Ca and less \u0026Omega;-3 than interviewees from other regions (Table 1). We also detected regional differences in the intake of all nutrients from other sources, in part due to the much larger sample size used in this ANOVA. The consumption of protein, Zn, and \u0026Omega;-3 from other sources was higher in the North, whereas the consumption of Vit-A was higher in the Northeast (Table S3.1, Fig. S3.1). The daily consumption of protein in other protein sources exceeded FAO\u0026rsquo;s recommendation of 50g in ~28g, whereas the consumption of protein through seafood had a deficit of ~27g from the recommendation (Table 1). The main deficiencies in nutrient intake were (the most negative intercepts for both seafood and other sources, respectively) (Table 1): 877mg and 592mg for daily Ca, 761ug and 349ug for Vit-A, and 277mg and 50mg for Mg, when considering seafood and all other sources, respectively.\u003c/p\u003e\n\u003cp\u003eThe comparison between FAO\u0026rsquo;s recommendations and projected nutrient intake showed that consuming more seafood would contribute to achieving FAO\u0026rsquo;s recommendations more efficiently than increasing the consumption of other food sources (Fig. 2). Overall, seafood represented only 5.9% (median = 4%, Fig. S2.2) of the total animal protein consumed, which equals to an average of 134\u0026plusmn;126g of seafood per day (range: 5 - 1500g, median of 100g), ranging from 0.17% to 71.4% protein intake. Other protein sources comprised on average 2359\u0026plusmn;1149g per day (range: 320 - 12681.0g/day). None of these 3.037 interviewees ate exclusively seafood. The ratio between FAO\u0026rsquo;s recommendations and the projected intake were overall positive (i.e., \u0026gt;1, or \u0026gt;0 at the natural-log scale), showing a nutritional deficit among Brazilians (Fig. 2.I). Current low percentages (~6%) of seafood in diets do not meet FAO\u0026rsquo;s recommendations (Fig. 2.II), whereas current percentages of other sources in diets only contributed to meet protein demands (note that the green line intersects zero at +75% of other sources). However, these curves showed that ~10% increases in the percentage of seafood in daily diets would be sufficient to meet protein demands (Fig. 2A), and increases of ~25% would help achieve \u0026Omega;-3 demands (Fig. 2B). For other nutrients, such as Ca, Mg and Zn, this percentage increase should ideally be ~50% (Fig. 2D). Demands of Fe and Vit-A were not met with either seafood or other protein sources, even at percentages \u0026gt;75% (Fig. 2.I, D), even though seafood came closer to the thresholds compared with other protein sources (i.e., seafood projections got closer to the horizontal line at zero deviation from FAO\u0026rsquo;s recommendations) (Fig. 2.I). Notably, the ratio curves for seafood consistently remained below those for other sources (Fig. 2.I), indicating that, at equivalent percentages, seafood stands out as nutritionally richer than alternative protein sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrient composition in fisheries landings\u003c/strong\u003e. Blue foods exhibit higher concentrations of essential nutrients compared to terrestrial protein sources (Fig. 3). In particular, Ca, \u0026Omega;-3, Se, and Vit-A are found in greater amounts in blue foods. On the other hand, Fe demonstrates a more consistent concentration across various protein sources. Mollusks deviate from this pattern, showing a much higher concentration of Fe than any other taxa. Mollusks also stand out in Zn, Se, and Vit-A concentrations. Bony fish (Actinopterygii) stand out in Vit-A and Se, and imported fish in \u0026Omega;-3. Among the most landed taxa by Brazilian fisheries, we found higher nutrient concentration in \u003cem\u003eMugil spp.\u003c/em\u003e, namely \u003cem\u003eMugil liza\u003c/em\u003e, and \u003cem\u003eSardinella brasiliensis,\u0026nbsp;\u003c/em\u003eand in the crab\u003cem\u003e\u0026nbsp;Ucides cordatus\u003c/em\u003e, which inhabits mangroves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrient supply vs. demand.\u003c/strong\u003e The total annual per capita amount of landed nutrients from seafood was greater than the annual per capita seafood consumption only in four states (Amap\u0026aacute;, Rio de Janeiro, Rio Grande do Sul and Santa Catarina) (Fig. 4). Nonetheless, landing and consumption values of different nutrients varied across states, with the aforementioned four states generally exhibiting the higher per capita landing for any nutrient. Only Rio Grande do Sul and Santa Catarina had higher per capita supply than demand for all nutrients. Fe and Zn were the two micronutrients with greater supply than demand (n=7 states), followed by \u0026Omega;-3 (n=6) and all other remaining nutrients (n=4). S\u0026atilde;o Paulo often presented the lowest per capita landed amount across states, and Maranh\u0026atilde;o, in the Northeast, was often among the highest per capita consumptions. Finally, the landed amount did not meet FAO\u0026rsquo;s annual recommendations for any nutrient (note the y-axis breaks in Fig. 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a comprehensive analysis to investigate patterns of seafood consumption and associated nutrient intake in Brazil. We detected an overall low level of seafood consumption, and significant differences in nutrient intake across states and regions, either from seafood or alternative food sources. Our projections, modeling shifts in consumption patterns, indicate that even slight increases in daily seafood consumption could lead to improved nutrient intake with smaller food quantities. This result holds promise as it represents a potential optimization of fishing practices (in terms of both fish quantity and composition), while simultaneously minimizing environmental degradation from terrestrial/land food production, such as deforestation, soil erosion and water pollution (Gephart et al., 2021). Despite these positive implications, our analysis based on annual fishery landings indicates that states still exhibit a limited potential to adequately nourish their populations. In light of these findings, addressing the complex interplay between fisheries management and human nutrition requires strategic measures. These may include refining the selection of fishing targets and imported fish based on their nutritional content and cultivation/capture methods, advocating for sustainable aquaculture technologies, and promoting a well-rounded, diverse diet enriched with fruits, vegetables, legumes, nuts, and grains (Amenyogbe, 2023; Fiorella et al., 2021; WHO, 2024). In general, Brazilians are not fully accessing the potential benefits of fishery resources, given that a substantial part of the population faces food insecurity (PENSSAN, 2022) and fall below FAO\u0026rsquo;s daily recommendations for five nutrients (i.e., Ca, Fe, Mg, Vit-A, and Zn) out of the seven analyzed (S3.1). While a healthy diet should consist of approximately 35% protein, optimizing this intake with nutritionally rich options can significantly contribute to meeting these nutritional recommendations (\u003cem\u003eDietary Guidelines for Americans, 2020-2025\u003c/em\u003e, n.d.). Recent research (e.g., 4, 11 and 13) recognizes seafood as a diverse and nutrient-rich protein source, while current dietary patterns lean heavily on land animals, especially in low and middle-income countries like Brazil. Our findings highlight that with minor adjustments, such as a 10% increase in seafood consumption, could significantly enhance people\u0026rsquo;s nutritional security. Unfortunately, fisheries monitoring, statistics and knowledge of seafood markets are missing in Brazil (Eggertsen et al., 2024; Santos et al., 2023). This poses a challenge in effectively tracking the value chain of seafood production, both at international and national levels. Sustainable fishing and aquaculture practices become paramount in addressing market demands. However, to fully unlock the nutritional and economic advantages, governmental monitoring and statistical frameworks for these markets are essential, paving the way for a future where responsible blue food production contributes substantially to human well-being.\u003c/p\u003e\n\u003cp\u003eIn 2021, Brazil imported ~300,000 t of marine fishes, while it exported 19,237 t (plus an undefined amount of 9,267 t that possibly includes marine fish species). Imports, thus, exceeded exports and were mostly constituted by salmon (100,031 t), cod (53,235 t), sardine (70,194 t), and sharks and rays (17,691 t)(Comex, 2024). Most fishes were imported to the Southeast and South regions (mainly S\u0026atilde;o Paulo and Santa Catarina), where the largest import companies are located and consumption is higher. Unfortunately, there is a paucity of information concerning internal market chains in Brazil. A study by Bevilacqua et al. (2020) conducted in Ba\u0026iacute;a da Formosa (Rio Grande do Norte, Northeast region) shed light on the consumption patterns, revealed that approximately 66% of fisheries production is consumed locally, with subsistence fishers accounting for 28%, and tourists only 6% (Bevilacqua et al., 2019). While it is anticipated that production from local-scale fisheries remains predominantly local, understanding the regional and temporal (seasonal) variations, as well as determining if these fisheries contribute to international markets, is a knowledge gap. Exports of red snapper (\u003cem\u003eLutjanus purpureus\u003c/em\u003e) dominate the market, of which 85% of catch is exported, primarily to the USA (4,467 t exported in 2021; 40). Other exports include the Whitemouth croaker, tunas, and swordfish (1,111, 1,617 and 1,307 t in 2021, respectively). Parrotfish, goatfish, and several snapper species from the Northeast region also contribute substantially (Carvalho et al., 2013), but these are absent from export statistics. Brazilian seafood exports are a complex and intricate puzzle, which demands proper and continuous monitoring of marine fisheries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrazilians generally consume high daily quantities of animal protein, exceeding FAO\u0026rsquo;s recommendations. Nevertheless, consuming proteins with low nutrient content \u0026ndash; such as beef and poultry \u0026ndash; and not ingesting enough vegetables, may result in nutrient deficiencies from d Ca, Fe, Mg, Vit-A, and Zn (FAO, 2019). Brazil is among the ten leading countries in beef consumption in the world, and second only to the United States in cattle raising production (Ritchie et al., 2024). Indeed, beef and poultry meat account for most of the animal protein consumed in Brazil, which is relatively homogeneous across regions and income classes. Meat consumption brings great environmental costs, as beef circulating in the national and international market often comes from deforested areas, as in the Amazon (Nunes et al., 2019), or even from overexploited grassland ecosystems (Luza et al., 2014). There were also slight regional differences in animal protein consumption, for instance, beef and pork consumption is higher in the South and Southeast, likely influenced by the history of European immigrants in these regions and their heavy-meat diets (H\u0026ouml;tzel \u0026amp; Vandresen, 2022; Ribeiro \u0026amp; Cor\u0026ccedil;\u0026atilde;o, 2013). Pork consumption was prevalent among high-income classes (Class A) in the North and Southeast regions, whereas in the South it was more commonly consumed by low-income classes (Class D and E). This regional disparity may also be attributed to the concentration of swine meat production in the South, with pork being a cost-effective protein option for low-income groups (Embrapa, 2024). Among blue foods, freshwater fish consumption was higher in the North. Indeed, Amazon fisheries are fundamental to food and nutritional security in this region (Heilpern, Fiorella, et al., 2021; McIntyre et al., 2016). Food security in the Amazon basin has been threatened by overexploitation, with recently documented collapses of fish stocks and shifts in the composition of targeted species (Heilpern et al., 2022; Heilpern, Fiorella, et al., 2021). Overall, these threats may contribute to the high levels of food insecurity in this region, reaching 80% of the inhabitants (PENSSAN, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrazil ranks among the countries with the lowest per capita seafood consumption globally, with annual per capita intake of approximately 10% compared to countries such as Portugal and Spain, where seafood consumption reaches 57.1 and 42.4 kg/year per capita (Naylor et al., 2021; Ritchie \u0026amp; Roser, 2023). Despite differences among regions and income classes, only 10% of interviewees reported consuming seafood, at 5.32 kg/year on average, i.e., almost 7 kg below FAO\u0026rsquo;s recommendation. Elevated seafood consumption is linked to high-income classes (A and B) in the South and Southeast states, while in the North, it is more prevalent among lower-income classes. Notably, Maranh\u0026atilde;o state stands out as the leading seafood consumer in Brazil, reaching an average of ~14.6 kg/year per capita. This consumption rate underscores a discrepancy, revealing that the state\u0026apos;s seafood production falls short of meeting the demands of its population (n=7,017,642 inhabitants; average for 2017-2018). The contrast between Brazilian regions mirrors the differences in seafood production systems. While seafood production in the North is predominantly artisanal and covers smaller scales, in the South and Southeast, it is developed at an industrial scale (Eggertsen et al., 2024; Freire et al., 2021), possibly affecting production costs and consumer behavior towards seafood (Surette, 2008). Supply exceeds demand mostly in South and Southeastern states, where seafood consumption is low and the industrial production reaches large volumes and spatial scales (i.e., exploring shallow and pelagic ecosystems) such as Rio de Janeiro, Santa Catarina, and Rio Grande do Sul.\u003c/p\u003e\n\u003cp\u003eSeafood originating from Brazilian fisheries have significantly higher nutrient content and variability relative to the consumed land animals, corroborating earlier global studies (Golden et al., 2021; Hicks et al., 2019; Robinson et al., 2022). Despite the insufficient landings to meet FAO\u0026rsquo;s nutritional demands, seafood is still a viable option to supplement diets, having greater Ca, Se, Vit-A, and \u0026Omega;-3 contents. Furthermore, although poultry represents an important source of Zn, this is also the case for mollusks and some bony fish (e.g., mullets) which could complement low-Zn diets. The nutritional differences among wild-caught fish protein sources highlights the importance of varied diets, and a diverse catch composition, enabling access to a complete set of macro and micronutrients. This would optimize nutrition and avoid the collapse of fish stocks (Robinson et al., 2022). Among the 20 most harvested taxa in Brazilian fisheries, pelagic shoaling fishes from lower trophic levels, such as \u003cem\u003eMugil spp\u003c/em\u003e. and \u003cem\u003eMugil liza\u003c/em\u003e, stood out for their high micronutrient levels. \u0026Omega;-3 fatty acids, such as EPA and DHA fatty acids, are abundant in high trophic level, mobile, cold-water fish, such as cod and salmon, both imported species broadly consumed in Brazil, but also abundant in species such as \u003cem\u003eSardinella brasiliensis\u003c/em\u003e and \u003cem\u003eScomber colias\u003c/em\u003e (Dominguez, 2006; Robinson et al., 2022). Freshwater and farmed fish are other viable options as \u0026Omega;-3 sources, even though their nutrient content significantly varies across farming practices (Heilpern \u0026nbsp;et al., 2021; Tacon et al., 2020; Watters et al., 2012). For instance, the majority of imported \u003cem\u003eSalmo salar\u003c/em\u003e in Brazil originates from aquaculture, primarily from Chile (Seafood Brasil, 2024). In that case, the nutritional composition closely aligns with the practices of each farm, leading to significant variability in nutrient content (Pohořel\u0026aacute; et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our findings provide valuable insights on food supply and demand dynamics, shedding light on general food consumption patterns in Brazil. How could Brazilian fisheries contribute to micronutrient deficiencies? Given the limited role of fisheries alone to meet FAO\u0026rsquo;s nutritional recommendations, seafood might have a key role in providing a nutritionally diverse diet rather than being the main protein source in Brazilian diets. With supply exceeding demands only in four out of the 17 coastal states, a first suggestion could be increasing landings. This, however, is not a viable strategy due to multiple evidence of stocks overexploitation and the absence of temporal data on fish abundance and landings of Brazilian fish stocks (Dias, 2022; Eggertsen et al., 2024). A recent assessment showed that out of 117 species targeted by the fisheries sector, only eight stocks (comprising one lobster species and seven bony fish species) have undergone recent assessments, and more alarmingly, half of those stocks are considered overexploited (Dias, 2022). Brazil has no monitoring program and struggles with fisheries management, and any increase in fishing effort would probably result in substantial ecological consequences. Therefore, we must primarily undergo a careful analysis of the stock abundance, exploitation capacity, and recovery towards sustainable fishing activities. The first step would be for the government to recognize seafood\u0026rsquo;s importance, both for its nutritional, social, and economic value.\u003c/p\u003e\n\u003cp\u003eFish value in their natural environment, delivering multiple cultural, ecosystem services must also be considered (Spalding et al., 2017; Tribot et al., 2018; Waechter et al.,\u003cem\u003e\u0026nbsp;in prep\u003c/em\u003e). Followed by incentives for public awareness and financial support aimed at transforming the industry into a more sustainable, transparent, and efficient sector, countries such as the USA, Italy, El Salvador, and even Brazil have already promoted initiatives aimed at disseminating information and boost the sector (Global Seafood\u003cem\u003e\u0026nbsp;\u003c/em\u003eAlliance, 2024; SeafoodSource, 2024). Our findings highlight the need for monitoring fisheries through a collaborative approach, connecting information and statistics along the Brazilian coast and the economic value chain, monitoring stocks, mapping and characterizing demand, from the ocean all the way to people\u0026rsquo;s plates (Eggertsen et al., 2024). Moreover, this collaborative approach should also include a detailed analysis of catches from freshwater species, farming of both marine and freshwater species, subsistence catches, and recreational fisheries, with some nutritional value but also economic value.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted by the Reef Synthesis Working Group (ReefSYN) funded by the Synthesis Center on Biodiversity and Ecosystem Services (SinBiose, CNPq, #442417/2019-5 to MGB). ALL received post-doctoral fellowships from CNPq (#153024/2022-4, #164240/2021-7, #151228/2021-3, #152410/2020-1) and CAPES (PDPG-POSDOC, #88887.800011/2022-00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this research are of free access in different sources (see Methods). Nonetheless, we provide data (those at suitable size) and R codes in GitHub in the following link: https://github.com/Sinbiose-Reefs/food_security.git. The raw POF data can be found at https://www.ibge.gov.br/en/statistics/social/health/25610-pof-2017-2018-pof-en.html?edicao=27315\u0026amp;t=downloads\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIPBES, \u0026ldquo;Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services\u0026rdquo; (Zenodo, 2019).\u003c/li\u003e\n\u003cli\u003eBlue Food Assessment. \u003cem\u003eBFA\u003c/em\u003e (2023). Available at: https://bluefood.earth/ [Accessed 27 May 2024].\u003c/li\u003e\n\u003cli\u003eFood and Agriculture Organization. \u003cem\u003eThe State of World Fisheries and Aquaculture 2022.\u003c/em\u003e (2022).\u003c/li\u003e\n\u003cli\u003eC.D. 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Available at: https://www.fao.org/infoods/infoods/tables-and-databases/faoinfoods-databases/en/ [Accessed 28 May 2024].\u003c/li\u003e\n\u003cli\u003eSearch FishBase. Available at: https://www.fishbase.se/search.php [Accessed 28 May 2024].\u003c/li\u003e\n\u003cli\u003eR Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org. (2023). \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eTable of ANOVA coefficients. The intercept consists of the difference between the reference (the South) and FAO\u0026rsquo;s nutritional recommendations (regression offset).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFood type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProtein (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalcium (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIron (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZinc (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVitamin-A (ug)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOmega-3 (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMagnesium (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 893px;\"\u003e\n \u003cp\u003eSeafood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFAO\u0026rsquo;s recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1000mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8000ug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e250mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e310mg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-26.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-877.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-10.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-761.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-276.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.66\u0026plusmn;2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-28.20\u0026plusmn;19.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u0026plusmn;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.13\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.96\u0026plusmn;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.02\u0026plusmn;2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u0026plusmn;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-17.22\u0026plusmn;17.34\u003c/p\u003e\n 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\u003cp\u003e-43.14\u0026plusmn;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.04\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-72.95\u0026plusmn;9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.49\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.33\u0026plusmn;53.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.096\u0026plusmn;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-31.60\u0026plusmn;4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Fisheries, Ecossistemic Services, Fishing Ecology, Food Security, Food Systems","lastPublishedDoi":"10.21203/rs.3.rs-5139653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5139653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Seafood is vital for tropical coastal communities, but demand and supply in Brazil remain poorly documented despite its extensive coastline and marine resources. We analyzed food consumption from over 40,000 interviews and nutritional content for six nutrients (Ca, Fe, Se, Vitamin-A, Omega-3, and Zn), comparing seafood with land-based protein sources. Our simulations show that increasing seafood consumption by 50% could meet FAO's nutritional recommendations, while a 50% increase could align all nutrient intakes. The average per capita seafood consumption is 5.3 kg/year, with lower-income populations in the North and wealthier groups in the South consuming more. Fish and shellfish are richer in most nutrients, except Zn. However, seafood production often falls short of consumption, highlighting a supply-demand mismatch. Sustainable fisheries management and expanded monitoring are essential to improving Brazil's nutrition and food security.","manuscriptTitle":"Food beyond land: seafood contribution to human nutrition in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-11 15:34:59","doi":"10.21203/rs.3.rs-5139653/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":"c589b945-df51-475e-9426-c7ac2e90650c","owner":[],"postedDate":"December 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":39121349,"name":"Biological sciences/Ecology/Ecosystem services"},{"id":39121350,"name":"Earth and environmental sciences/Ecology/Macroecology"}],"tags":[],"updatedAt":"2026-04-29T20:10:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-11 15:34:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5139653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5139653","identity":"rs-5139653","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}