Socio-agronomic features define the structure of socioecological seed network exchanges and “on farm” conservation of agrobiodiversity in traditional communities

Socio-agronomic features define the structure of socioecological seed network exchanges and “on farm” conservation of agrobiodiversity in traditional communities | 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 Research Article Socio-agronomic features define the structure of socioecological seed network exchanges and “on farm” conservation of agrobiodiversity in traditional communities Isabella Fernandes Fantini, Gustavo Taboada Soldati, Fernanda Vieira da Costa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7374043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2026 Read the published version in Journal of Ethnobiology and Ethnomedicine → Version 1 posted 11 You are reading this latest preprint version Abstract Background Traditional agricultural systems are rooted in the local management, selection, and conservation of agrobiodiversity. Understanding the socioecological dynamics that sustain these systems is essential for developing sustainable practices that ensure food security and sovereignty in the territories of traditional and Indigenous peoples. This study examines the role of seed exchange networks in on-farm agrobiodiversity conservation in Quilombola communities that live in environmental and political threats. We emphasize the role of socioecological networks and socio-agronomic variables in shaping how agrobiodiversity is maintained, shared, and regenerated across time and space. Methods We conducted semi-structured interviews, free listing, participant observation, and guided tours with 48 agrobiodiversity management units (AMUs) in five communities, documenting socio-agronomic variables and ethnovariety richness, with botanical identification in the field and literature. From the 359 ethnovarieties recorded, 15 were randomly selected for detailed seed exchange network analysis. The complete network of 185 AMUs (48 internal, 137 external) and 424 exchanges was analyzed using negative binomial GLMs for richness (H1), PCA and Poisson GLMs for centrality metrics (H2), and network metrics (degree, betweenness, harmonic closeness, modularity, connectivity, weighted nestedness) calculated in Gephi and R, compared against null models to assess conservation potential (H3). Results and Discussion The 48 AMUs managed an average of 40 ethnovarieties each. Agro-environmental diversity, cultivated area, and time in the community were positively associated with richness, which in turn increased AMU centrality, highlighting their role as agrobiodiversity guardians and network bridges. Despite high diversity, the seed exchange network displayed low nestedness and connectivity but high modularity, indicating cohesive subgroups with strong internal exchanges and limited intergroup seed flows. This pattern reflects both social cohesion and fragmentation, and reveals vulnerabilities, as varieties unevenly distributed across modules may not circulate widely, reducing resilience. Conclusions Historical and material conditions are critical to sustaining on-farm agrobiodiversity conservation in quilombola territories. Land tenure security and territorial rights are essential for maintaining traditional agroecosystems that integrate ecological knowledge, cultural heritage, and biodiversity management. Strengthening seed exchange connectivity, fostering collaboration across groups, and protecting these territories are urgent actions to enhance resilience, safeguard traditional knowledge, and ensure long-term biocultural justice. Biocultural heritage Ethnobiology In situ conservation Landrace seeds Political Ecology Traditional ecological knowledge Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Background Agrobiodiversity encompasses all biological diversity in agricultural ecosystems, including the inter- and intraspecific richness of domesticated, wild, ruderal, and spontaneous plants, along with the associated popular knowledge and management practices ( 1 ). Indigenous peoples and traditional communities are the primary actors responsible for generating, caring for, and maintaining agrobiodiversity (Fig. 1) through popular genetic improvement systems within their territories ( 2 , 3 ). These practices are recognized as "on-farm" conservation strategies, embodying agricultural practices' cultural and continuous aspects. They promote the dynamic and equitable establishment of agroecosystems in a decentralized manner, are low-cost, engage local guardians in decision-making processes, preserve high genetic diversity, and contribute to agricultural, food, and nutritional security and sovereignty within traditional territories ( 4 , 5 ). In this context, promoting and strengthening on-farm conservation represents a fundamental decolonial strategy to address ongoing socio-environmental crises ( 1 , 4 , 6 , 7 ). One of the key processes associated with agrobiodiversity is the concept of "seed exchange networks," where "seeds" refer to any type of plant propagule ( 1 , 8 , 9 ). These networks operate as socioecological systems, characterized by the dynamic interplay between humans and the environment ( 10 ). As such, they function as “biological corridors” ( 11 ). However, since ethnovarieties embody socio-biocultural traits, a more fitting representation would be a “tangle of sociobiodiversity.” Scholarly work on seed exchange networks has frequently aimed to identify the roles or positions of individuals within these systems, emphasizing structural properties such as node centrality metrics ( 12 – 20 ). Some contributions, aligned with agribusiness interests, have used centrality to facilitate the introduction of genetically modified organisms ( 12 , 15 , 21 – 23 ). However, the vast majority of publications have linked network analysis to agrobiodiversity conservation ( 8 , 11 , 13 , 14 , 16 , 17 , 19 , 20 , 24 – 29 ), assuming that network structure influences access, maintenance, improvement, and local distribution of ethnovarieties. This body of work highlights three key approaches: ( 1 ) nodal farmers or agrobiodiversity management units (AMUs) with high degree centrality, typically those donating or receiving the most ethnovarieties; ( 2 ) connector farmers, identified by high betweenness centrality, who act as bridges linking different modules or groups; and ( 3 ) AMUs that distribute seeds to more peripheral nodes, characterized by harmonic closeness centrality, facilitating access for more vulnerable AMUs. Building on these insights, various authors have sought to identify the sociocultural traits that distinguish AMUs with higher centrality. The concept of homophily, a foundational principle in social network theory, suggests that nodes in closer proximity tend to share more socio-environmental similarities ( 13 , 14 ). Accordingly, several analyses have demonstrated positive correlations between territorial and familial proximity and seed exchange, with AMUs more likely to exchange ethnovarieties with neighbors and relatives ( 8 , 11 – 13 , 15 , 20 , 27 ). This growing literature has consistently underscored the role of trust in seed-sharing relations, noting that strong social ties between central AMUs help support crop diversity, while social barriers can hinder it ( 9 , 12 – 17 , 25 , 30 ). Central AMUs are also often associated with greater social prestige ( 19 ). Additionally, they tend to: ( 1 ) display higher crop richness; ( 2 ) be recognized for their managers’ agrobiodiversity knowledge; and ( 3 ) be known for introducing heirloom seeds from outside the territory or having better access to local markets or seed banks. While the age of individual managers does not appear to directly influence centrality, families with longer histories in the territory tend to hold more central positions. Evidence also suggests that economically vulnerable groups rely more heavily on seed networks to remain in their territories ( 20 , 30 ). Despite this, several socio-agronomic dimensions remain underexplored, such as territorial extension, agroenvironmental diversity, and economic activity levels of AMUs.Regarding network structure, there is a significant gap in understanding how topological metrics derived from seed exchange can indicate the resilience or vulnerability of socioecological systems. Specifically, more attention is needed on how such metrics reflect a system’s capacity to maintain and generate agrobiodiversity. Understanding the architecture of interactions between AMUs is essential to inform collective management strategies for agrobiodiversity conservation ( 1 , 31 ). Some studies on seed exchange networks have utilized nesting metrics ( 22 , 24 ) connectivity, and interaction density ( 11 , 20 , 30 ) to explore how these metrics can determine contexts of threats or conservation. They suggest that: 1. Low values of nesting and connectivity can negatively impact socio-ecological systems by undermining trust among peers and eroding traditional knowledge, thus weakening the collective memory of territories; 2. Low connectivity in seed exchange networks can also demonstrate the possibility of incorporating a wider diversity of contexts and relationships between AMUs and external agents, which may foster greater diversity of ethnovarieties within agricultural systems. Other research emphasizes that future studies should further investigate the real effects of network connectivity on the resilience and conservation of agrobiodiversity in socio-ecological systems ( 30 ). Similarly, a significant gap remains in understanding how network modularity influences the functioning of socio-ecological systems. Specifically, the formation of subgroups of AMUs that interact more frequently with each other than with the rest of the network can either support or hinder agrobiodiversity conservation. Therefore, studies highlight the importance of exploring the influence of modularity within seed exchange networks to better understand how crop diversity is maintained ( 1 , 31 ). Given the above, studies of agrobiodiversity as a seed exchange network indicate that access to ethnovarieties are closely tied to the centrality of AMUs involved ( 14 – 17 , 19 , 21 , 22 , 27 , 29 , 30 ) However, the socio-agronomic variables that determine this centrality remain unclear. Furthermore, there is still limited understanding of how network architecture relates to agrobiodiversity conservation. Thus, our study evaluates the role of seed exchange networks in the "on-farm" agrobiodiversity conservation. We hypothesize that (H1) socio-agronomic characteristics influence the richness of ethnovarieties managed by AMUs. We predict that the lower total and per capita monetary income, alongside longer residence in the community and household, greater economic activities number, total managed agro-environments, and total cultivated area, will lead to greater managed ethnovarieties richness. We expect to identify what socio-agronomic characteristics of the AMUs influence their capacity to contribute to "on-farm" agrobiodiversity conservation by providing the necessary conditions and resources for the cultivation and management of ethnovarieties. Our second hypothesis (H2) evaluates the influence of socio-agronomic variables and the richness of ethnovarieties managed by AMUs on their centrality in the network., We predict that the longer residence in the community, a greater number of managed agro-environments, larger cultivated areas, and higher ethnovariety richness will result in greater centrality, measured by degree, betweenness, and by intermediation and by harmonic closeness proximity of the AMUs. With this hypothesis, we aim to analyze the potential of AMUs in the dynamics of the network, as understanding exchange events (ethnovariety flows) can help identify the variables that define fundamental processes for agrobiodiversity conservation, such as distribution and participatory improvement. Finally, our third hypothesis (H3) involves characterizing the network’s structure and its relationship with agrobiodiversity conservation. Assuming that the potential for conserving agrobiodiversity is tied to the richness and dynamics of ethnovariety exchanges, we predict that lower modularity, coupled with higher nesting and connectivity, will enhance the system’s potential for agrobiodiversity conservation, conserving the agrobiodiversity involved in the system. 2 Material and Methods 2.1 Study Area The study area lies within the phytogeographic domain of the Atlantic Forest, transitioning into the Cerrado, and is characterized by a local phytophysiognomy type known as Semideciduous Seasonal Forest (IBAMA, 2015). The main economic activities in the region include family farming and artisanal mining. The study was conducted in five quilombola communities: Castro, Embaúbas, Engenho Queimado, and Vila Santa Efigênia (I and II), all located in the rural area of the Mariana city, Minas Gerais, Brazil. These communities were officially recognized as "traditional quilombola communities" by the Palmares Cultural Foundation in 2010. However, like many traditional peoples in Brazil, they have not yet received collective land ownership ( 32 ). The territory is heavily dominated by mining operations like Vale and BHP Billiton, facing constant threats, including the collapse of the Fundão dam in 2015, considered Brazil’s largest environmental crime in Brazil ( 33 ). This disaster led to the physical and chemical contamination of the Rio Doce Basin by mining waste, which affected the Gualaxo River ( 34 ) a tributary that flows through the study area. As a result, it caused significant damage to agriculture, fishing, and recreational activities for the residents of the local communities. 2.2 Participatory Data Construction After obtaining approval from the Research Ethics Committee (CEP) of the Universidade Federal de Juiz de Fora (CAAE: 51210421.10000.5147) and registration Sistema Nacional de Gestão do Patrimônio Genético e do Conhecimento Tradicional Associado (Sisgen, code: A31BD79), the project was presented to the communities during a meeting of the territorial association, and all participants were invited to sign the Free and Informed Consent Term (TCLE). The interviews, conducted between December 2021 and March 2022, targeted the managers of the agro-environments, all of whom were over 18 years old and had lived in the territories for at least five years. We used semi-structured interviews ( 35 , 36 ) to access socio-agronomic data (gender, age, total income, per capita income, total number of people, time in the community, time in the household, total economic activities, total agricultural activities, total cultivated area, diversity of agro-environments, diversity, and total cultivated ethnovarieties). Additionally, the interviews aimed to understand ethnovariety conservation strategies and the management practices of productive backyards. All interviews were recorded, and a field notebook was used for additional documentation. Subsequently, we applied the free listing methodology ( 35 , 37 ) to detail the richness of plant ethnovarieties related to human nutrition present within the AMUs. Ethnovarieties were defined as biological species resulting from the selection and management practices of traditional farmers ( 38 ), as identified and differentiated by the farmers themselves. This concept was chosen because it is broader than the scientific nomenclature of species and varieties ( 24 ). Thus, incorporate the socio-environmental context of our research partners. To enrich the data, we utilized guided tour methodologies ( 35 ) for AMUs and participant observation ( 39 , 40 ). These methods were employed to (i) complement the free listing; (ii) gain insights into and differentiate agro-environments from the perspective of backyard managers; (iii) map out the agro-environments. Identifying each agro-environment allowed us to estimate the area managed and cultivated by each family, using GPS. Botanical identification was conducted during the guided tour. Ethnovarieties that presented identification challenges were thoroughly photographed and later identified through bibliographic consultation ( 41 , 42 ), following the APG IV classification system. Specific identification keys ( 43 , 44 ) were also used when necessary. A community guide was present throughout the research. As the data collection progressed, they became increasingly involved as a community researcher, playing a key role in describing and characterizing agro-environments and providing a holistic understanding of multi-species relationships ( 45 ). His triangulation of data is essential for dynamically understanding the complexity of socio-ecological systems ( 9 ). After consolidating the free listing for each AMU (total ethnovarieties richness), we randomly selected 15 ethnovarieties for the second phase of the study, aimed at analyzing the structure and dynamics of exchange network relations. For this, we used semi-structured interviews ( 35 , 36 ) to document the social origins of the ethnovarieties, understand their qualities and challenges, record exchange events as well as the AMUs involved in receiving and donating ethnovarieties. In addition to the AMUs interviewed within the community, external AMUs were also cited and treated as nodes within the network. 2.3 Data Analysis The data collected in the field were transcribed and categorized into electronic spreadsheets, allowing for the creation of a database that recorded the explanatory variables and responses necessary for all hypotheses. To test (H1) whether socio-agronomic variables (total income, per capita income, time in the community, time in residence, total economic activities, total agricultural activities, total agro-environments, and total cultivated area) determine the richness of ethnovarieties cultivated by an AMU, a generalized linear model (GLM) was constructed, considering for multicollinearity among the explanatory variables ( 46 – 48 ). Variables with high correlation indices (greater than 0.6) were excluded, leaving seven: total income, per capita income, time in the community, time in residence, total economic activities, total agro-environments, and total cultivated area. Initially, a GLM with a Poisson distribution was developed. However, due to overdispersion of the residuals, the negative binomial distribution was selected as a better fit for the data ( 46 , 47 ). The model was then adjusted by progressively removing variables with no significant influence, based on evaluating the Akaike Information Criterion (AIC) ( 46 – 48 ). It was compared to a null model to determine whether the adjusted model was statistically significant or due to change ( 46 ). To test whether socio-agronomic variables define the centrality attributes of AMUs (H2), only those significant in H1, along with the richness of cited species, were considered explanatory variables. Since these variables were associated with ethnovarieties richness in H1, we opted to perform a Principal Component Analysis (PCA) of the R-mode type with data standardization and Euclidean distance ( 47 , 49 ) to reduce the dimensionality of the dataset. The analysis revealed that the first two principal components were able to summarize the variables, explaining 75% of the data variance. Consequently, these components were used as the explanatory variables ( 47 , 49 ). We employed tools for analyzing ecological and social networks to calculate degree centrality, betweenness centrality, and harmonic proximity of the AMUs (nodes in the network) (Ricciardi, 2015). The exchange network was considered “open”, incorporating external AMUs, as residents frequently interact with environments outside their communities. Using the explanatory variables and estimated responses, we developed three GLMs, one for each response variable, applying a Poisson distribution ( 47 , 49 ). These models met the assumptions of variance homogeneity and residual normality, with no issues of over- or under-dispersion of residuals. Additionally, all three models were tested against a null model. To visualize the structure of the exchange network studied, we created a graph where the AMUs are represented as nodes, and the edges represent seed exchange relationships. The thickness of the edges reflects the exchange relationship—i.e., the more exchanges between two AMUs, the thicker the edge. We used degree centrality metrics to determine the size of the nodes and applied modularity to color the subgroups that interact more frequently with each other than with the rest of the network. The ForceAtlas 2 algorithm in Gephi software was used to spatially organize the nodes. To understand (H3) how the structure of the seed exchange network supports the potential of local agrobiodiversity, we calculated the network metrics of nesting, connectivity, and modularity. Nesting refers to a topology in which the network exhibits a hierarchical structure across different organizational levels ( 50 ). In our study, this metric could indicate that AMUs are organized into more central and peripheral subsets, suggesting ing socioecological stability by defining that a cohesive internal group facilitates the majority of interactions within the network ( 32 , 51 ). We specifically calculated the weighted nesting (WNODF) ( 52 ). Weighted connectivity (weighted C) is a widely used metric to assess network connectivity, representing the proportion of actual interactions relative to all possible interactions ( 53 ). In the context of our study, a network with high connectivity suggests frequent seed exchanges among the majority of AMUs. Modularity measures the extent to which AMUs are linked within a modular structure (54), meaning subsets of AMUs that interact more frequently with each other than with the rest of the network. In our study, a high modularity value indicates that AMUs are organized into subgroups, which could hinder the sharing of ethnovarieties between distinct modules (separate groups of AMUs) and challenge their preservation within the network ( 1 , 31 ). We calculate modularity using the Q algorithm with the Dormann-Strauss method ( 55 ). The values for nesting, connectivity, and modularity observed in the network were contrasted with null models, and significance was obtained by standardizing the metrics in standard deviations (Z-standardized metric) ( 56 ). All metrics were calculated using the Bipartite and Igraph packages ( 55 ) in R software. Likewise, all statistical analyses were performed using R version 2022.07.1 (R Core Team, 2022). The representative graph of the seed exchange network was developed using Geph software ( 15 , 30 ). 3 Results 3.1 Quilombola Agrobiodiversity and Socioagronomic variables that influence the structure of the seed exchange network “ I plant for myself, for others, and for the animals ” (par45, 73 years old). The 48 participants in this research ranged in age from 35 to 86 years (average = 59.4 years). On average, they have lived 55 years in their community and 32 years in their current residence. Each AMU consists of an average of 1.4 people, with a per capita income of R $ 1,448.15. The family economy is supported by various non-agricultural activities (an average of 2.85 per household), and government benefits received by 43 (85.4%) of the AMUs. The AMUs engage in diverse agricultural systems, encompassing an average of 5.77 activities, including managing gardens, backyards, roçados fields (“roçados”), raising pigs, chickens, dairy, and beef cattle, social exchanges, and selling surplus produce. These activities take place across six main agro-environments (Fig. 2), namely: (A) "Terrace": the area near the residences where delicate ornamental and food plants, such as herbs, are concentrated, requiring more care; (B) "Orchard": A space for cultivating citrus and fruit-bearing shrubs, which holds significant dietary, social, and recreational importance; (C) "Agroforestry backyard": Humid, shaded soil covered by leaf litter, where secondary forest can develop, used to grow economically important plants such as bananas ( Musa x paradisiaca L.), taro ( Colocasia esculenta L.), and mangoes (Mangifera indica L.); (D) "Roça or chácara": red latosol, typically fertilized with cow manure, comprising larger areas further from residences, used for intercropping beans, corn, squash, as well as sugarcane and cassava- essential crops for the inhabitants' food security (E) "Garden": soil rich in organic matter, closer to the house and fenced, designated for the continuous cultivation of vegetables, medicinal plants, and herbs; (F)"Piçarra land": stony, yellowish, dystrophic, and acidic soil that supports limited crop diversity, primarily peanuts ( Arachis hypogaea L.) and pineapples ( Ananas comosus (L.) Merril). Each family manages, on average, 4.12 agro-environments and has approximately 0.225 hectares (sd = 0.19) of arable land. Despite the high standard deviation (95.8%)—indicating that some AMUs possess significantly more land than others — our research partners expressed satisfaction with the size of their lands. The agricultural calendar is closely aligned with the water cycle, with the planting of "roças" and fruit trees beginning during the rainy season, from September to November. By the end of this period, in March, as the dry season sets in, garden management is carried out until the next rainy season. The products harvested from the agro-ecosystems are used for family self-consumption, with any surplus typically sold in the city of Mariana. In the studied territories, the selection of food plants for management is based on their nutritional value, economic interest, ease of cultivation, taste memory, and emotional connections to the ethnovarieties. From this agricultural system, 1,919 use citations were recorded across 359 ethnovarieties, representing 134 species from 44 botanical families. The most prominent families were Brassicaceae (35 varieties), Poaceae ( 28 ), Rutaceae ( 26 ), and Fabaceae ( 25 ). Seventeen botanical families had only one or two cultivated varieties. The species with the greatest variety were kale ( Brassica oleracea L.) (24 varieties), sugarcane ( Saccharum officinarum L.) (17 varieties), banana ( Musa x paradisiaca L.) (17 varieties), orange ( Citrus sinensis L.), and avocado ( Persea americana Mill.) (10 varieties). Sixty-six species had only one or two varieties. However, when considering use citations, banana was the most frequently cited (182 citations), followed by orange (96 citations), kale (94 citations), mango ( Mangifera indica L.) (79 citations), yam ( Colocasia esculenta L.) (69 citations), and cassava ( Manihot esculenta L.) (67 citations). Thirty-nine species were cited by only one or two research participants. When considering the total number of citations for specific varieties, the most notable were coquinho mango ( Mangifera indica L.) (41 citations), lobrobó ( Pereskia aculeata Mill.) (40 citations), purple-skinned cassava ( Manihot esculenta L.) (37 citations), campista orange ( Citrus sinensis L.) (35 citations), apple banana ( Musa x paradisiaca L.) (34 citations), devil's lime ( Citrus limonia Osbeck) (32 citations), silver banana ( Musa x paradisiaca L.) (32 citations), red acerola ( Malpighia emarginata DC.) (31 citations), and jabuticaba ( Plinia peruviana (Poir.) Govaerts) (31 citations). Notably, 195 ethnovarieties were cited by only one or two research partners. On average, we found a richness of 40 cultivated landraces per AMU. Of the 359 landraces, 53 are cultivated but not maintained by an AMU for replanting in the next agricultural cycle. Instead, when the time comes to plant again, these landraces are shared by a nearby AMU, recognized as the seed keeper. Additionally, 39 landraces are maintained by only one or two AMUs. None of the cited varieties were cultivated across all six recorded agro-environments. The most versatile varieties, cultivated in five agro-environments, were purple-skinned sweet potato ( Ipomoea batatas (L.) Lam.), purple-skinned cassava ( Manihot esculenta L.), and common and purple sugarcane ( Saccharum officinarum L.), all cultivated in gardens, backyards, orchards, fields, or piçarra land. White guava also stood out, being cultivated in the same environments, though replacing the garden with the yard. A total of 213 varieties were cultivated in only one agro-environment. Regarding potentially arable land- the total area declared by all research participants where a variety can be cultivated, coquinho mango ( M. indica ) stood out with 0.058 km², followed by devil's lime (0.046 km²), jabuticaba (0.041 km²), white and red guava (0.041 km²), and silver banana (0.044 km²). The varieties most highly regarded for their attributed qualities were purple-skinned cassava (21 citations), acerola (16 citations), coquinho mango (15 citations), apple banana (13 citations), lobrobó (13 citations), and yam (12 citations). On the other hand, the varieties with the most challenges, due to less favorable traits such as low productivity, high water demand, or bitter taste, were lettuce (Lactuca sativa L.), yam, candogueira tangerine (Citrus unshiu (Mak.) Marcov.), and pitanga (Eugenia uniflora L.), each with four citations. The implementation of a generalized model with a negative binomial distribution concluded that the variables defining species richness are: total agroenvironments (estimate = 3.057e-013; p = 0.0001), total cultivated area (estimate = 6.201e-05; p = 0.0173), and time in the community (estimate = -6.390e-03; p = 0.0466). With an AIC value of 385.82, the model explains 79% of the variation in data (R²=0.79). Biologically, the results suggest that as the diversity of agroenvironments, cultivated area, and time of residence in the community increase, so does the richness of managed ethnovarieties (Fig. 3). The generalized linear model results in the following equation: Variety richness (y) = 2.558e + 00–6.390e-03 * time in community (x1) + 3.057e-013 * total agroenvironments (x2) + 6.201e-05 * total cultivated area (x3). 3.2 Socio-agronomic variables and the richness of cultivated crop varieties influence the importance of AMUs within the seed exchange network. The PCA, which reduced the multidimensionality among the socio-agronomic variables—time living in the community, total agroenvironments managed, total cultivated area, and species richness (Fig. 4a), explains 79% of the variation in the data (R² = 0.79). The first principal component accounts for 50.06% of the variation, with the most representative variables being total agroenvironments and total varieties. The second principal component accounts for 24.38% of the variation, with time in the community and total varieties as the most representative variables. The degree centrality of the AMUs (Fig. 4b) was positively influenced by the socio-agronomic variables of time living in the community, total agroenvironments managed, cultivated area, and the richness of cultivated varieties (p = 0.0002, AIC = 293.05). The betweenness centrality (Fig. 4c) was positively explained by the first principal component from the PCA (p = 0.0109, AIC = 777.16). Similarly, harmonic closeness centrality (Fig. 4d) was positively explained by the first principal component (p = 0.00779, AIC = 365.24), with an overall AIC value of 385.82 (Fig. 4). 3.3 The structure of the seed exchange network influences its potential for agrobiodiversity conservation. We recorded a total of 137 external AMUs outside the territory, which, together with the 48 community AMUs, formed an extensive seed exchange network of 185 AMUs (Fig. 5). Among the quilombola territories, Vila Santa Efigênia II accounted for the highest number of the exchange events (23.7%), followed by Castro (18.3%), Vila Santa Efigênia I (11.4%), Embaúbas (7.4%), and Engenho Queimado (7.15%). Regarding external municipalities or districts with the most seed exchange relationships with the quilombola communities, Mariana accounted for 8.3%, Furquim 3.5%, and Acaiaca 3.1%. Through the interviews, we identified 424 seed exchange events involving the 359 ethnovarieties generated, maintained, and cared for within the quilombola territories. Despite the high ethnovariety richness, the network exhibited a low and significant degree of nestedness (WNODF = -2.20, z-score = -12.29), meaning it was 12 times lower than what would be expected by chance. Similarly, connectivity was extremely low and significant (C = 0.022; z-score = -2.73). On the other hand, we observed high modularity, which was significantly greater than expected by chance (Q = 0.60; z-score = 42.65). Thus, our third hypothesis was not corroborated. Although the network demonstrated a high potential for agrobiodiversity conservation, the emerging properties were contrary to what was expected. 4 Discussion The AMUs in this research demonstrated a high richness of food ethnovarieties managed within their agroenvironments, which are organized into a complex seed exchange network. We found that as the number of agroenvironments, cultivated areas, and duration of residency in the community increased, so did the richness of ethnovarieties maintained by the AMUs. Socio-agronomic variables and the richness of cultivated ethnovarieties positively influenced the degree, betweenness, and harmonic closeness centrality of the AMUs within the seed exchange network. Finally, we observed that the seed exchange network displayed low nestedness, low connectivity, and high modularity. This suggests a centralization of the genetic pool of agrobiodiversity, reflecting the diversity of the studied communities in terms of their life histories and socio-environmental contexts. The high richness of ethnovarieties, totaling 359, reflects our research partners’ diverse life histories and idiosyncratic characteristics. The edaphoclimatic complexity of the agroenvironments also influences the heterogeneity of agrobiodiversity, as the ethnovarieties are highly adapted to their specific environmental niches. This dynamic richness is similarly observed in other Brazilian communities ( 32 , 57 ). Our research also demonstrated that 39 ethnovarieties are maintained by only one or two research partners. This is linked to the social prestige associated with agrobiodiversity guardians and the biocultural value of these plants, indicating that the distribution of ethnovarieties across the territory is not random ( 19 , 24 ). We also found that 53 ethnovarieties have only one or two guardians responsible for maintaining the propagules for future plantings across all AMUs in the territory. This highlights the fact that AMUs in the territory already depend on exchange relationships to preserve diversity within their agroenvironments. Regarding external territories with the most seed exchange relationships with the communities, such as Mariana, Furquim, and Acaiaca, it is important to emphasize the ecological service provided by the quilombola communities in maintaining and distributing agrobiodiversity. Through these efforts, they contribute to agricultural, food, and nutritional security and sovereignty over an extensive region. When testing the relationship between socio-agronomic variables and the richness of crop varieties cultivated by an AMU, we found that the diversity of agroenvironments positively influences the richness of managed species. Specifically, the three AMUs that had six agroenvironments, yard, orchard, agroforestry backyard, roça or chacara, garden, and piçarra land, managed an average of 90 varieties (SD = 26). This result demonstrates that the heterogeneity of edaphoclimatic conditions enables the cultivation of a greater richness of crops by increasing the potential for experimentation, which in turn fosters plant diversification ( 58 ). These findings practically provide a practical illustration of the ongoing coevolutionary process within traditional agroenvironments ( 59 ). We also observed a positive relationship between the total cultivated area and the richness of managed varieties, suggesting that larger areas offer more opportunities for experimentation. For example, staple crops in the family farming system, such as beans, corn, and squash, already have designated spaces within the system. Therefore, larger land areas create surplus spaces for cultivating and managing additional food species. Additionally, larger areas increase the potential for interaction with neighboring households, facilitating seed exchanges between families. Closer proximity to neighbors reduces the time needed to acquire specific varieties, making seed procurement more economically feasible while strengthening affective and trust-based relationships ( 27 ). Another relationship identified was the positive influence of the length of residence in the territory on the total number of crop varieties managed. This suggests that the time community plays a key role in increasing the genetic pool richness of crop varieties, which are accumulated, tested, and adapted to specific agroenvironments ( 58 ). Although farming practices may become more challenging with age, the AMUs led by older farmers carefully select which varieties to manage, prioritizing those most suited to their needs and conditions. These older farmers become key references for crop species that are easier to manage (e.g., "chifre de veado" okra, which is easier to harvest), tied to gustatory memory (e.g., "manteiga" collard greens, a variety consumed since early childhood), and/or associated to emotional connections (e.g., "puteca" beans, a variety that helped the community during times of food scarcity). Together, these factors build trust and elevate the status of older residents in their territories, making them vital guardians of agrobiodiversity ( 19 ). Conversely, the total number of economic and agricultural activities, as well as total and per capita income, did not influence the management of agrobiodiversity. This may be due to the low variation in income and activity levels among the families. Additionally, the length of time living in the current residence did not impact the richness of crop varieties cultivated. This is likely because quilombola community members tend to bring their crop varieties with them when they relocate, underscoring the sociocultural importance of these varieties to the community. Lastly, the total number of people in the household did not influence the richness of managed crop varieties, indicating that, regardless of household size, the AMU remains stable, though the quantity of food produced may vary. Another key finding of this study demonstrates that socio-agronomic variables, such as the total number of agroenvironments, total cultivated area, length of residence in the community, and richness of managed crop varieties, positively affect degree centrality, betweenness centrality,y and harmonic closeness centrality. These results highlight which socio-agronomic variables define the importance of AMUs in the structure and dynamics of the seed exchange network. This study, therefore, aligns with others, demonstrating that AMUs acting as agrobiodiversity guardians are those that share the most crop varieties and serve as bridges between different territories, facilitating the flow of both genetic resources and knowledge within the seed exchange network ( 12 , 16 , 17 , 19 , 27 , 30 ). Finally, in examining the influence of the emergent dynamics of the seed exchange network on its potential for agrobiodiversity conservation, we found that the network exhibited low nestedness, low connectivity, and high modularity. This structural pattern suggests that AMUs form cohesive subgroups that exchange seeds and interact more frequently within their group with other AMUs in the system. This indicates a centralization of the genetic pool of agrobiodiversity. This segregation in seed exchanges can be explained by factors such as geographic proximity and sociocultural relationships, which align with the principle of homophily. The finding that the communities of Vila Santa Efigênia II and Castro account for the majority of exchange events (23.7% and 18.3%, respectively) can be attributed to the higher concentration of AMUs that are geographically and kinship-wise close to one another. In contrast, the communities of Embaúbas and Engenho Queimado (account for 7.4% and 7.15% of seed exchange relationships) exhibit more spatial segregation between their AMUs, despite familial ties. Thus, this structural pattern suggests that AMUs form cohesive subgroups interacting more frequently within their group than with other AMUs in the system. However, despite the high richness of crop varieties across the studied territory, the seed exchange network exhibits emergent properties that suggest some fragility. Although there is a high potential for connections among the various actors (i.e., the large number of nodes in the network), the low connectivity between them increases the risk of agrobiodiversity loss. Low levels of nestedness and connectivity in socioecological networks can negatively impact the system by compromising resilience, peer trust, and the preservation of traditional knowledge ( 30 , 47 ). As a result, the collective memory of the territories is weakened ( 51 , 60 ). We suggest, however, that high modularity in socioecological networks demonstrates that exchange dynamics are shaped by smaller groups. Since the network is poorly connected and divided into modules, it becomes more vulnerable to disturbances. As crop varieties are distributed unevenly across the network, the loss of a variety within a particular group (module) can have a significant impact on the system, given the lower likelihood of exchanges between different groups (i.e., between modules) compared to within the same group ( 1 , 31 ). This segmented structure also limits the introduction of new crop varieties into the system, thereby restricting innovation and socio-environmental adaptations ( 31 ). 5 Conclusion We can conclude that historical and material conditions play a critical role in the "on-farm" conservation of agrobiodiversity. These findings underscore the importance of land ownership and territorial security for traditional communities to continue preserving their way of life, which is rooted in ecological capital ( 6 ). This is especially relevant given that their territories are situated in a region experiencing capitalist expansion, primarily through mining. Therefore, it is crucial to protect and recognize quilombola communities as an ontologically sustainable system, essential for preserving the knowledge and cultural preservation tied to biodiversity ( 61 ). Declarations Acknowledgments: Our deepest gratitude goes to all those who welcomed us so generously into their homes, to the Quilombola Association of Vila Santa Efigênia and Adjacent Areas, and to the Saberes do Território Collective, whose support was essential for the development of this research. Author Contributions: The conception and design of the study were contributed by GTS and IFF. IFF conducted the data collection and cleaning. FVC performed the data analysis. IFF wrote the first draft of the manuscript, TSN and FRG developed a critical review of the article and both authors commented on subsequent versions. All authors reviewed and approved the final manuscript. Funding: The fieldwork activities were funded by the research project "Medicinal and Useful Plants of the Rio Doce Basin" (CAPES 881.118042/2016-01). Authors IFF, FVC, and TSN received scholarships from CAPES. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Code Availability The data collected from the interviews were organized in Microsoft® Excel and analyzed using R software. The codes (custom code) are available from the authors upon reasonable request. Ethical Approval The project was approved by the Ethics Committee for Research (CEP) of the Federal University of Juiz de Fora (CAAE: 51210421.10000.5147) and registration in Sisgen (code: A31BD79). Consent for publication Each farmer signed a Free and Informed Consent Term (TCLE), required by Brazilian Law 13.123/2015, which addresses access to traditional knowledge associated with biodiversity. References Labeyrie V, Antona M, Baudry J, Bazile D, Bodin Ö, Caillon S, et al. Networking agrobiodiversity management to foster biodiversity-based agriculture. A review. Agron Sustain Dev. 2021;41(1). 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Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2026 Read the published version in Journal of Ethnobiology and Ethnomedicine → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers invited by journal 21 Aug, 2025 Editor assigned by journal 20 Aug, 2025 Submission checks completed at journal 20 Aug, 2025 First submitted to journal 14 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7374043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505760696,"identity":"aeca787d-fb64-46df-8925-c97cb0287f1b","order_by":0,"name":"Isabella Fernandes Fantini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBACNjAqsGBgbz4A5Bow8PCDhBMKCGkxkGDgOZbAwHAAqEWyAaTFgJBFcC0gi0AEAx4tfPyHnz34YCAhz8PG/Ozxh4I7MsbnVyd+eGDAIM8vdgC7FRJp5oYzDCQMe9jYzA0OGDzjMbvxdrME0GGGM2cn4NDCYCbNYyDBuF++wUzigMFhoJazG0BaEgxu49DCf/yb9B8DCfseNvZvYC3GM85u/oFXC0OOmTTQ+4k9bDwQWwz4e7fht0Uip9ywx0AiGailTOIMUIvEDd5tFglA3+Hyi3z/8W0PflTY2AIdtk2i4s9he/7+s5tvAkXk+aWxa8ECJMAqJYhVDgL8B0hRPQpGwSgYBSMAAADkA1hEtDk8gAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":true,"prefix":"","firstName":"Isabella","middleName":"Fernandes","lastName":"Fantini","suffix":""},{"id":505760697,"identity":"ba406281-2203-485f-8faf-247821fa7dfd","order_by":1,"name":"Gustavo Taboada Soldati","email":"","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":false,"prefix":"","firstName":"Gustavo","middleName":"Taboada","lastName":"Soldati","suffix":""},{"id":505760698,"identity":"a1286f4f-b7b5-41a2-bc0a-2a8992e1853f","order_by":2,"name":"Fernanda Vieira da Costa","email":"","orcid":"","institution":"University of Brasília","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"Vieira da","lastName":"Costa","suffix":""},{"id":505760699,"identity":"9d6c3277-8e66-4312-84e3-3e7a0dbda465","order_by":3,"name":"Thiago da Silva Novato","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"da Silva","lastName":"Novato","suffix":""},{"id":505760700,"identity":"59b578f6-c889-4540-87ca-945c5ce4ef5a","order_by":4,"name":"Fátima Regina Gonçalves Salimena","email":"","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":false,"prefix":"","firstName":"Fátima","middleName":"Regina Gonçalves","lastName":"Salimena","suffix":""}],"badges":[],"createdAt":"2025-08-14 12:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7374043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7374043/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13002-025-00847-4","type":"published","date":"2026-03-11T15:58:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90311102,"identity":"511cd352-3473-47b1-9ed0-c40e49dff591","added_by":"auto","created_at":"2025-09-01 09:48:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1021020,"visible":true,"origin":"","legend":"\u003cp\u003eEthnovarieties of corn with blue ears and rosinha beans generated, managed, and maintained by quilombola communities in Mariana, Minas Gerais, Brazil.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7374043/v1/0ba5501b7bb84281ade56c82.png"},{"id":90311103,"identity":"c91d5525-6819-4faf-a789-8ae446b0796f","added_by":"auto","created_at":"2025-09-01 09:48:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":335538,"visible":true,"origin":"","legend":"\u003cp\u003eSystematization of Agro-environments: (A) Terreiro; (B) Orchard; (C) Agroforestry Backyard; (D) Roça or Chácara; (E) Garden; (F) Piçarra Land, managed by the quilombola communities of Castro, Embaúbas, Engenho Queimado, and Vila Santa Efigênia (I and II) in the municipality of Mariana, Minas Gerais, Brazil.\u003c/p\u003e","description":"","filename":"Fiure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7374043/v1/c9ed4e7c2bed57b3e6e9e9cf.jpg"},{"id":90311104,"identity":"49591034-5bd5-47e2-9884-f2f0cb7b97cf","added_by":"auto","created_at":"2025-09-01 09:48:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":427186,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized linear models showing the effect of total cultivated area (a), total managed agroenvironments (b), and time in the community (c) on the richness of agricultural ethnovarieties for human consumption cultivated in five quilombola communities in the municipality of Mariana, Minas Gerais, Brazil.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7374043/v1/0a341f1de4760538e2fee9a7.jpg"},{"id":90311107,"identity":"c4e27ee6-6720-43c5-9afa-399a4b9eb987","added_by":"auto","created_at":"2025-09-01 09:48:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":641707,"visible":true,"origin":"","legend":"\u003cp\u003eModels used to assess the relationship between socio-agronomic variables and the richness of crop varieties on the centrality of agrobiodiversity management units in five quilombola communities in Mariana, Minas Gerais, Brazil. (a) Principal Component Analysis (PCA) incorporating total cultivated area, total managed agroenvironments, time living in the community, and richness of agricultural varieties cultivated; Generalized linear models demonstrating the positive effect of the relationship between socio-agronomic variables and species richness on degree centrality (b), between centrality (c), harmonic closeness centrality (d).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7374043/v1/aa79a73923f6fa4b5063d037.jpg"},{"id":90312401,"identity":"cd4863f7-06e1-4e7c-97cc-56582f61c1d6","added_by":"auto","created_at":"2025-09-01 09:56:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":839787,"visible":true,"origin":"","legend":"\u003cp\u003eSeed exchange network among the quilombola communities of Castro, Embaúbas, Engenho Queimado, and Vila Santa Efigênia (I and II) in the municipality of Mariana, Minas Gerais, Brazil. The nodes represent agrobiodiversity managing units, with node size indicating degree centrality and color representing the network’s emerging modules. Each edge corresponds to the frequency of exchange between partners.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7374043/v1/1d704b9b3a2034722321ce25.jpg"},{"id":104739984,"identity":"d08b3065-406b-42a3-a046-dd83be2b43be","added_by":"auto","created_at":"2026-03-16 16:14:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4414732,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7374043/v1/005bfbbd-e400-4bdb-84dc-397035047d71.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socio-agronomic features define the structure of socioecological seed network exchanges and “on farm” conservation of agrobiodiversity in traditional communities","fulltext":[{"header":"1 Background","content":"\u003cp\u003eAgrobiodiversity encompasses all biological diversity in agricultural ecosystems, including the inter- and intraspecific richness of domesticated, wild, ruderal, and spontaneous plants, along with the associated popular knowledge and management practices (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Indigenous peoples and traditional communities are the primary actors responsible for generating, caring for, and maintaining agrobiodiversity (Fig.\u0026nbsp;1) through popular genetic improvement systems within their territories (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These practices are recognized as \"on-farm\" conservation strategies, embodying agricultural practices' cultural and continuous aspects. They promote the dynamic and equitable establishment of agroecosystems in a decentralized manner, are low-cost, engage local guardians in decision-making processes, preserve high genetic diversity, and contribute to agricultural, food, and nutritional security and sovereignty within traditional territories (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In this context, promoting and strengthening on-farm conservation represents a fundamental decolonial strategy to address ongoing socio-environmental crises (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of the key processes associated with agrobiodiversity is the concept of \"seed exchange networks,\" where \"seeds\" refer to any type of plant propagule (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These networks operate as socioecological systems, characterized by the dynamic interplay between humans and the environment (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). As such, they function as \u0026ldquo;biological corridors\u0026rdquo; (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, since ethnovarieties embody socio-biocultural traits, a more fitting representation would be a \u0026ldquo;tangle of sociobiodiversity.\u0026rdquo;\u003c/p\u003e\u003cp\u003eScholarly work on seed exchange networks has frequently aimed to identify the roles or positions of individuals within these systems, emphasizing structural properties such as node centrality metrics (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Some contributions, aligned with agribusiness interests, have used centrality to facilitate the introduction of genetically modified organisms (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, the vast majority of publications have linked network analysis to agrobiodiversity conservation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), assuming that network structure influences access, maintenance, improvement, and local distribution of ethnovarieties.\u003c/p\u003e\u003cp\u003eThis body of work highlights three key approaches: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) nodal farmers or agrobiodiversity management units (AMUs) with high degree centrality, typically those donating or receiving the most ethnovarieties; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) connector farmers, identified by high betweenness centrality, who act as bridges linking different modules or groups; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) AMUs that distribute seeds to more peripheral nodes, characterized by harmonic closeness centrality, facilitating access for more vulnerable AMUs.\u003c/p\u003e\u003cp\u003eBuilding on these insights, various authors have sought to identify the sociocultural traits that distinguish AMUs with higher centrality. The concept of homophily, a foundational principle in social network theory, suggests that nodes in closer proximity tend to share more socio-environmental similarities (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Accordingly, several analyses have demonstrated positive correlations between territorial and familial proximity and seed exchange, with AMUs more likely to exchange ethnovarieties with neighbors and relatives (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis growing literature has consistently underscored the role of trust in seed-sharing relations, noting that strong social ties between central AMUs help support crop diversity, while social barriers can hinder it (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Central AMUs are also often associated with greater social prestige (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, they tend to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) display higher crop richness; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) be recognized for their managers\u0026rsquo; agrobiodiversity knowledge; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) be known for introducing heirloom seeds from outside the territory or having better access to local markets or seed banks. While the age of individual managers does not appear to directly influence centrality, families with longer histories in the territory tend to hold more central positions.\u003c/p\u003e\u003cp\u003eEvidence also suggests that economically vulnerable groups rely more heavily on seed networks to remain in their territories (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Despite this, several socio-agronomic dimensions remain underexplored, such as territorial extension, agroenvironmental diversity, and economic activity levels of AMUs.Regarding network structure, there is a significant gap in understanding how topological metrics derived from seed exchange can indicate the resilience or vulnerability of socioecological systems. Specifically, more attention is needed on how such metrics reflect a system\u0026rsquo;s capacity to maintain and generate agrobiodiversity. Understanding the architecture of interactions between AMUs is essential to inform collective management strategies for agrobiodiversity conservation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome studies on seed exchange networks have utilized nesting metrics (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) connectivity, and interaction density (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) to explore how these metrics can determine contexts of threats or conservation. They suggest that: 1. Low values of nesting and connectivity can negatively impact socio-ecological systems by undermining trust among peers and eroding traditional knowledge, thus weakening the collective memory of territories; 2. Low connectivity in seed exchange networks can also demonstrate the possibility of incorporating a wider diversity of contexts and relationships between AMUs and external agents, which may foster greater diversity of ethnovarieties within agricultural systems.\u003c/p\u003e\u003cp\u003eOther research emphasizes that future studies should further investigate the real effects of network connectivity on the resilience and conservation of agrobiodiversity in socio-ecological systems (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Similarly, a significant gap remains in understanding how network modularity influences the functioning of socio-ecological systems. Specifically, the formation of subgroups of AMUs that interact more frequently with each other than with the rest of the network can either support or hinder agrobiodiversity conservation. Therefore, studies highlight the importance of exploring the influence of modularity within seed exchange networks to better understand how crop diversity is maintained (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the above, studies of agrobiodiversity as a seed exchange network indicate that access to ethnovarieties are closely tied to the centrality of AMUs involved (\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) However, the socio-agronomic variables that determine this centrality remain unclear. Furthermore, there is still limited understanding of how network architecture relates to agrobiodiversity conservation.\u003c/p\u003e\u003cp\u003eThus, our study evaluates the role of seed exchange networks in the \"on-farm\" agrobiodiversity conservation. We hypothesize that (H1) socio-agronomic characteristics influence the richness of ethnovarieties managed by AMUs. We predict that the lower total and per capita monetary income, alongside longer residence in the community and household, greater economic activities number, total managed agro-environments, and total cultivated area, will lead to greater managed ethnovarieties richness. We expect to identify what socio-agronomic characteristics of the AMUs influence their capacity to contribute to \"on-farm\" agrobiodiversity conservation by providing the necessary conditions and resources for the cultivation and management of ethnovarieties.\u003c/p\u003e\u003cp\u003eOur second hypothesis (H2) evaluates the influence of socio-agronomic variables and the richness of ethnovarieties managed by AMUs on their centrality in the network., We predict that the longer residence in the community, a greater number of managed agro-environments, larger cultivated areas, and higher ethnovariety richness will result in greater centrality, measured by degree, betweenness, and by intermediation and by harmonic closeness proximity of the AMUs. With this hypothesis, we aim to analyze the potential of AMUs in the dynamics of the network, as understanding exchange events (ethnovariety flows) can help identify the variables that define fundamental processes for agrobiodiversity conservation, such as distribution and participatory improvement.\u003c/p\u003e\u003cp\u003eFinally, our third hypothesis (H3) involves characterizing the network\u0026rsquo;s structure and its relationship with agrobiodiversity conservation. Assuming that the potential for conserving agrobiodiversity is tied to the richness and dynamics of ethnovariety exchanges, we predict that lower modularity, coupled with higher nesting and connectivity, will enhance the system\u0026rsquo;s potential for agrobiodiversity conservation, conserving the agrobiodiversity involved in the system.\u003c/p\u003e"},{"header":"2 Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThe study area lies within the phytogeographic domain of the Atlantic Forest, transitioning into the Cerrado, and is characterized by a local phytophysiognomy type known as Semideciduous Seasonal Forest (IBAMA, 2015). The main economic activities in the region include family farming and artisanal mining. The study was conducted in five quilombola communities: Castro, Emba\u0026uacute;bas, Engenho Queimado, and Vila Santa Efig\u0026ecirc;nia (I and II), all located in the rural area of the Mariana city, Minas Gerais, Brazil. These communities were officially recognized as \"traditional quilombola communities\" by the Palmares Cultural Foundation in 2010. However, like many traditional peoples in Brazil, they have not yet received collective land ownership (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The territory is heavily dominated by mining operations like Vale and BHP Billiton, facing constant threats, including the collapse of the Fund\u0026atilde;o dam in 2015, considered Brazil\u0026rsquo;s largest environmental crime in Brazil (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This disaster led to the physical and chemical contamination of the Rio Doce Basin by mining waste, which affected the Gualaxo River (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) a tributary that flows through the study area. As a result, it caused significant damage to agriculture, fishing, and recreational activities for the residents of the local communities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Participatory Data Construction\u003c/h2\u003e\u003cp\u003eAfter obtaining approval from the Research Ethics Committee (CEP) of the Universidade Federal de Juiz de Fora (CAAE: 51210421.10000.5147) and registration Sistema Nacional de Gest\u0026atilde;o do Patrim\u0026ocirc;nio Gen\u0026eacute;tico e do Conhecimento Tradicional Associado (Sisgen, code: A31BD79), the project was presented to the communities during a meeting of the territorial association, and all participants were invited to sign the Free and Informed Consent Term (TCLE). The interviews, conducted between December 2021 and March 2022, targeted the managers of the agro-environments, all of whom were over 18 years old and had lived in the territories for at least five years. We used semi-structured interviews (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) to access socio-agronomic data (gender, age, total income, per capita income, total number of people, time in the community, time in the household, total economic activities, total agricultural activities, total cultivated area, diversity of agro-environments, diversity, and total cultivated ethnovarieties). Additionally, the interviews aimed to understand ethnovariety conservation strategies and the management practices of productive backyards.\u003c/p\u003e\u003cp\u003eAll interviews were recorded, and a field notebook was used for additional documentation. Subsequently, we applied the free listing methodology (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) to detail the richness of plant ethnovarieties related to human nutrition present within the AMUs. Ethnovarieties were defined as biological species resulting from the selection and management practices of traditional farmers (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), as identified and differentiated by the farmers themselves. This concept was chosen because it is broader than the scientific nomenclature of species and varieties (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Thus, incorporate the socio-environmental context of our research partners. To enrich the data, we utilized guided tour methodologies (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) for AMUs and participant observation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These methods were employed to (i) complement the free listing; (ii) gain insights into and differentiate agro-environments from the perspective of backyard managers; (iii) map out the agro-environments. Identifying each agro-environment allowed us to estimate the area managed and cultivated by each family, using GPS.\u003c/p\u003e\u003cp\u003eBotanical identification was conducted during the guided tour. Ethnovarieties that presented identification challenges were thoroughly photographed and later identified through bibliographic consultation (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), following the APG IV classification system. Specific identification keys (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) were also used when necessary. A community guide was present throughout the research. As the data collection progressed, they became increasingly involved as a community researcher, playing a key role in describing and characterizing agro-environments and providing a holistic understanding of multi-species relationships (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). His triangulation of data is essential for dynamically understanding the complexity of socio-ecological systems (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAfter consolidating the free listing for each AMU (total ethnovarieties richness), we randomly selected 15 ethnovarieties for the second phase of the study, aimed at analyzing the structure and dynamics of exchange network relations. For this, we used semi-structured interviews (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) to document the social origins of the ethnovarieties, understand their qualities and challenges, record exchange events as well as the AMUs involved in receiving and donating ethnovarieties. In addition to the AMUs interviewed within the community, external AMUs were also cited and treated as nodes within the network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e\u003cp\u003eThe data collected in the field were transcribed and categorized into electronic spreadsheets, allowing for the creation of a database that recorded the explanatory variables and responses necessary for all hypotheses. To test (H1) whether socio-agronomic variables (total income, per capita income, time in the community, time in residence, total economic activities, total agricultural activities, total agro-environments, and total cultivated area) determine the richness of ethnovarieties cultivated by an AMU, a generalized linear model (GLM) was constructed, considering for multicollinearity among the explanatory variables (\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Variables with high correlation indices (greater than 0.6) were excluded, leaving seven: total income, per capita income, time in the community, time in residence, total economic activities, total agro-environments, and total cultivated area.\u003c/p\u003e\u003cp\u003eInitially, a GLM with a Poisson distribution was developed. However, due to overdispersion of the residuals, the negative binomial distribution was selected as a better fit for the data (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The model was then adjusted by progressively removing variables with no significant influence, based on evaluating the Akaike Information Criterion (AIC) (\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). It was compared to a null model to determine whether the adjusted model was statistically significant or due to change (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo test whether socio-agronomic variables define the centrality attributes of AMUs (H2), only those significant in H1, along with the richness of cited species, were considered explanatory variables. Since these variables were associated with ethnovarieties richness in H1, we opted to perform a Principal Component Analysis (PCA) of the R-mode type with data standardization and Euclidean distance (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) to reduce the dimensionality of the dataset. The analysis revealed that the first two principal components were able to summarize the variables, explaining 75% of the data variance. Consequently, these components were used as the explanatory variables (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe employed tools for analyzing ecological and social networks to calculate degree centrality, betweenness centrality, and harmonic proximity of the AMUs (nodes in the network) (Ricciardi, 2015). The exchange network was considered \u0026ldquo;open\u0026rdquo;, incorporating external AMUs, as residents frequently interact with environments outside their communities. Using the explanatory variables and estimated responses, we developed three GLMs, one for each response variable, applying a Poisson distribution (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). These models met the assumptions of variance homogeneity and residual normality, with no issues of over- or under-dispersion of residuals. Additionally, all three models were tested against a null model.\u003c/p\u003e\u003cp\u003eTo visualize the structure of the exchange network studied, we created a graph where the AMUs are represented as nodes, and the edges represent seed exchange relationships. The thickness of the edges reflects the exchange relationship\u0026mdash;i.e., the more exchanges between two AMUs, the thicker the edge. We used degree centrality metrics to determine the size of the nodes and applied modularity to color the subgroups that interact more frequently with each other than with the rest of the network. The ForceAtlas 2 algorithm in Gephi software was used to spatially organize the nodes.\u003c/p\u003e\u003cp\u003eTo understand (H3) how the structure of the seed exchange network supports the potential of local agrobiodiversity, we calculated the network metrics of nesting, connectivity, and modularity. Nesting refers to a topology in which the network exhibits a hierarchical structure across different organizational levels (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). In our study, this metric could indicate that AMUs are organized into more central and peripheral subsets, suggesting ing socioecological stability by defining that a cohesive internal group facilitates the majority of interactions within the network (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). We specifically calculated the weighted nesting (WNODF) (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWeighted connectivity (weighted C) is a widely used metric to assess network connectivity, representing the proportion of actual interactions relative to all possible interactions (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In the context of our study, a network with high connectivity suggests frequent seed exchanges among the majority of AMUs.\u003c/p\u003e\u003cp\u003eModularity measures the extent to which AMUs are linked within a modular structure (54), meaning subsets of AMUs that interact more frequently with each other than with the rest of the network. In our study, a high modularity value indicates that AMUs are organized into subgroups, which could hinder the sharing of ethnovarieties between distinct modules (separate groups of AMUs) and challenge their preservation within the network (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). We calculate modularity using the Q algorithm with the Dormann-Strauss method (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). The values for nesting, connectivity, and modularity observed in the network were contrasted with null models, and significance was obtained by standardizing the metrics in standard deviations (Z-standardized metric) (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll metrics were calculated using the Bipartite and Igraph packages (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) in R software. Likewise, all statistical analyses were performed using R version 2022.07.1 (R Core Team, 2022). The representative graph of the seed exchange network was developed using Geph software (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Quilombola Agrobiodiversity and Socioagronomic variables that influence the structure of the seed exchange network\u003c/h2\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eI plant for myself, for others, and for the animals\u003c/em\u003e\u0026rdquo; (par45, 73 years old).\u003c/p\u003e\n \u003cp\u003eThe 48 participants in this research ranged in age from 35 to 86 years (average\u0026thinsp;=\u0026thinsp;59.4 years). On average, they have lived 55 years in their community and 32 years in their current residence. Each AMU consists of an average of 1.4 people, with a per capita income of R\u003cspan\u003e$\u003c/span\u003e 1,448.15. The family economy is supported by various non-agricultural activities (an average of 2.85 per household), and government benefits received by 43 (85.4%) of the AMUs. The AMUs engage in diverse agricultural systems, encompassing an average of 5.77 activities, including managing gardens, backyards, ro\u0026ccedil;ados fields (\u0026ldquo;ro\u0026ccedil;ados\u0026rdquo;), raising pigs, chickens, dairy, and beef cattle, social exchanges, and selling surplus produce. These activities take place across six main agro-environments (Fig. 2), namely: (A) \u0026quot;Terrace\u0026quot;: the area near the residences where delicate ornamental and food plants, such as herbs, are concentrated, requiring more care; (B) \u0026quot;Orchard\u0026quot;: A space for cultivating citrus and fruit-bearing shrubs, which holds significant dietary, social, and recreational importance; (C) \u0026quot;Agroforestry backyard\u0026quot;: Humid, shaded soil covered by leaf litter, where secondary forest can develop, used to grow economically important plants such as bananas (\u003cem\u003eMusa x paradisiaca\u003c/em\u003e L.), taro (\u003cem\u003eColocasia esculenta\u003c/em\u003e L.), and mangoes (Mangifera indica L.); (D) \u0026quot;Ro\u0026ccedil;a or ch\u0026aacute;cara\u0026quot;: red latosol, typically fertilized with cow manure, comprising larger areas further from residences, used for intercropping beans, corn, squash, as well as sugarcane and cassava- essential crops for the inhabitants\u0026apos; food security (E) \u0026quot;Garden\u0026quot;: soil rich in organic matter, closer to the house and fenced, designated for the continuous cultivation of vegetables, medicinal plants, and herbs; (F)\u0026quot;Pi\u0026ccedil;arra land\u0026quot;: stony, yellowish, dystrophic, and acidic soil that supports limited crop diversity, primarily peanuts (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) and pineapples (\u003cem\u003eAnanas comosus\u003c/em\u003e (L.) Merril).\u003c/p\u003e\n \u003cp\u003eEach family manages, on average, 4.12 agro-environments and has approximately 0.225 hectares (sd\u0026thinsp;=\u0026thinsp;0.19) of arable land. Despite the high standard deviation (95.8%)\u0026mdash;indicating that some AMUs possess significantly more land than others \u0026mdash; our research partners expressed satisfaction with the size of their lands. The agricultural calendar is closely aligned with the water cycle, with the planting of \u0026quot;ro\u0026ccedil;as\u0026quot; and fruit trees beginning during the rainy season, from September to November. By the end of this period, in March, as the dry season sets in, garden management is carried out until the next rainy season. The products harvested from the agro-ecosystems are used for family self-consumption, with any surplus typically sold in the city of Mariana.\u003c/p\u003e\n \u003cp\u003eIn the studied territories, the selection of food plants for management is based on their nutritional value, economic interest, ease of cultivation, taste memory, and emotional connections to the ethnovarieties. From this agricultural system, 1,919 use citations were recorded across 359 ethnovarieties, representing 134 species from 44 botanical families. The most prominent families were Brassicaceae (35 varieties), Poaceae (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e), Rutaceae (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e), and Fabaceae (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). Seventeen botanical families had only one or two cultivated varieties. The species with the greatest variety were kale (\u003cem\u003eBrassica oleracea\u003c/em\u003e L.) (24 varieties), sugarcane (\u003cem\u003eSaccharum officinarum\u003c/em\u003e L.) (17 varieties), banana (\u003cem\u003eMusa\u003c/em\u003e x \u003cem\u003eparadisiaca\u003c/em\u003e L.) (17 varieties), orange (\u003cem\u003eCitrus sinensis\u003c/em\u003e L.), and avocado (\u003cem\u003ePersea americana\u003c/em\u003e Mill.) (10 varieties). Sixty-six species had only one or two varieties. However, when considering use citations, banana was the most frequently cited (182 citations), followed by orange (96 citations), kale (94 citations), mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) (79 citations), yam (\u003cem\u003eColocasia esculenta\u003c/em\u003e L.) (69 citations), and cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e L.) (67 citations). Thirty-nine species were cited by only one or two research participants.\u003c/p\u003e\n \u003cp\u003eWhen considering the total number of citations for specific varieties, the most notable were coquinho mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) (41 citations), lobrob\u0026oacute; (\u003cem\u003ePereskia aculeata\u003c/em\u003e Mill.) (40 citations), purple-skinned cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e L.) (37 citations), campista orange (\u003cem\u003eCitrus sinensis\u003c/em\u003e L.) (35 citations), apple banana (\u003cem\u003eMusa\u003c/em\u003e x \u003cem\u003eparadisiaca\u003c/em\u003e L.) (34 citations), devil\u0026apos;s lime (\u003cem\u003eCitrus limonia\u003c/em\u003e Osbeck) (32 citations), silver banana (\u003cem\u003eMusa x paradisiaca\u003c/em\u003e L.) (32 citations), red acerola (\u003cem\u003eMalpighia emarginata\u003c/em\u003e DC.) (31 citations), and jabuticaba (\u003cem\u003ePlinia peruviana\u003c/em\u003e (Poir.) Govaerts) (31 citations). Notably, 195 ethnovarieties were cited by only one or two research partners.\u003c/p\u003e\n \u003cp\u003eOn average, we found a richness of 40 cultivated landraces per AMU. Of the 359 landraces, 53 are cultivated but not maintained by an AMU for replanting in the next agricultural cycle. Instead, when the time comes to plant again, these landraces are shared by a nearby AMU, recognized as the seed keeper. Additionally, 39 landraces are maintained by only one or two AMUs. None of the cited varieties were cultivated across all six recorded agro-environments. The most versatile varieties, cultivated in five agro-environments, were purple-skinned sweet potato (\u003cem\u003eIpomoea batatas\u003c/em\u003e (L.) Lam.), purple-skinned cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e L.), and common and purple sugarcane (\u003cem\u003eSaccharum officinarum\u003c/em\u003e L.), all cultivated in gardens, backyards, orchards, fields, or pi\u0026ccedil;arra land. White guava also stood out, being cultivated in the same environments, though replacing the garden with the yard. A total of 213 varieties were cultivated in only one agro-environment.\u003c/p\u003e\n \u003cp\u003eRegarding potentially arable land- the total area declared by all research participants where a variety can be cultivated, coquinho mango (\u003cem\u003eM. indica\u003c/em\u003e) stood out with 0.058 km\u0026sup2;, followed by devil\u0026apos;s lime (0.046 km\u0026sup2;), jabuticaba (0.041 km\u0026sup2;), white and red guava (0.041 km\u0026sup2;), and silver banana (0.044 km\u0026sup2;). The varieties most highly regarded for their attributed qualities were purple-skinned cassava (21 citations), acerola (16 citations), coquinho mango (15 citations), apple banana (13 citations), lobrob\u0026oacute; (13 citations), and yam (12 citations). On the other hand, the varieties with the most challenges, due to less favorable traits such as low productivity, high water demand, or bitter taste, were lettuce (Lactuca sativa L.), yam, candogueira tangerine (Citrus unshiu (Mak.) Marcov.), and pitanga (Eugenia uniflora L.), each with four citations.\u003c/p\u003e\n \u003cp\u003eThe implementation of a generalized model with a negative binomial distribution concluded that the variables defining species richness are: total agroenvironments (estimate\u0026thinsp;=\u0026thinsp;3.057e-013; p\u0026thinsp;=\u0026thinsp;0.0001), total cultivated area (estimate\u0026thinsp;=\u0026thinsp;6.201e-05; p\u0026thinsp;=\u0026thinsp;0.0173), and time in the community (estimate = -6.390e-03; p\u0026thinsp;=\u0026thinsp;0.0466). With an AIC value of 385.82, the model explains 79% of the variation in data (R\u0026sup2;=0.79). Biologically, the results suggest that as the diversity of agroenvironments, cultivated area, and time of residence in the community increase, so does the richness of managed ethnovarieties (Fig.\u0026nbsp;3). The generalized linear model results in the following equation:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eVariety richness (y)\u0026thinsp;=\u0026thinsp;2.558e\u0026thinsp;+\u0026thinsp;00\u0026ndash;6.390e-03 * time in community (x1)\u0026thinsp;+\u0026thinsp;3.057e-013 * total agroenvironments (x2)\u0026thinsp;+\u0026thinsp;6.201e-05 * total cultivated area (x3).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2 Socio-agronomic variables and the richness of cultivated crop varieties influence the importance of AMUs within the seed exchange network.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe PCA, which reduced the multidimensionality among the socio-agronomic variables\u0026mdash;time living in the community, total agroenvironments managed, total cultivated area, and species richness (Fig.\u0026nbsp;4a), explains 79% of the variation in the data (R\u0026sup2; = 0.79). The first principal component accounts for 50.06% of the variation, with the most representative variables being total agroenvironments and total varieties. The second principal component accounts for 24.38% of the variation, with time in the community and total varieties as the most representative variables.\u003c/p\u003e\n \u003cp\u003eThe degree centrality of the AMUs (Fig. 4b) was positively influenced by the socio-agronomic variables of time living in the community, total agroenvironments managed, cultivated area, and the richness of cultivated varieties (p\u0026thinsp;=\u0026thinsp;0.0002, AIC\u0026thinsp;=\u0026thinsp;293.05). The betweenness centrality (Fig. 4c) was positively explained by the first principal component from the PCA (p\u0026thinsp;=\u0026thinsp;0.0109, AIC\u0026thinsp;=\u0026thinsp;777.16). Similarly, harmonic closeness centrality (Fig. 4d) was positively explained by the first principal component (p\u0026thinsp;=\u0026thinsp;0.00779, AIC\u0026thinsp;=\u0026thinsp;365.24), with an overall AIC value of 385.82 (Fig. 4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 The structure of the seed exchange network influences its potential for agrobiodiversity conservation.\u003c/h2\u003e\n \u003cp\u003eWe recorded a total of 137 external AMUs outside the territory, which, together with the 48 community AMUs, formed an extensive seed exchange network of 185 AMUs (Fig.\u0026nbsp;5). Among the quilombola territories, Vila Santa Efig\u0026ecirc;nia II accounted for the highest number of the exchange events (23.7%), followed by Castro (18.3%), Vila Santa Efig\u0026ecirc;nia I (11.4%), Emba\u0026uacute;bas (7.4%), and Engenho Queimado (7.15%). Regarding external municipalities or districts with the most seed exchange relationships with the quilombola communities, Mariana accounted for 8.3%, Furquim 3.5%, and Acaiaca 3.1%. Through the interviews, we identified 424 seed exchange events involving the 359 ethnovarieties generated, maintained, and cared for within the quilombola territories.\u003c/p\u003e\n \u003cp\u003eDespite the high ethnovariety richness, the network exhibited a low and significant degree of nestedness (WNODF = -2.20, z-score = -12.29), meaning it was 12 times lower than what would be expected by chance. Similarly, connectivity was extremely low and significant (C\u0026thinsp;=\u0026thinsp;0.022; z-score = -2.73). On the other hand, we observed high modularity, which was significantly greater than expected by chance (Q\u0026thinsp;=\u0026thinsp;0.60; z-score\u0026thinsp;=\u0026thinsp;42.65). Thus, our third hypothesis was not corroborated. Although the network demonstrated a high potential for agrobiodiversity conservation, the emerging properties were contrary to what was expected.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe AMUs in this research demonstrated a high richness of food ethnovarieties managed within their agroenvironments, which are organized into a complex seed exchange network. We found that as the number of agroenvironments, cultivated areas, and duration of residency in the community increased, so did the richness of ethnovarieties maintained by the AMUs. Socio-agronomic variables and the richness of cultivated ethnovarieties positively influenced the degree, betweenness, and harmonic closeness centrality of the AMUs within the seed exchange network. Finally, we observed that the seed exchange network displayed low nestedness, low connectivity, and high modularity. This suggests a centralization of the genetic pool of agrobiodiversity, reflecting the diversity of the studied communities in terms of their life histories and socio-environmental contexts.\u003c/p\u003e\u003cp\u003eThe high richness of ethnovarieties, totaling 359, reflects our research partners\u0026rsquo; diverse life histories and idiosyncratic characteristics. The edaphoclimatic complexity of the agroenvironments also influences the heterogeneity of agrobiodiversity, as the ethnovarieties are highly adapted to their specific environmental niches. This dynamic richness is similarly observed in other Brazilian communities (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur research also demonstrated that 39 ethnovarieties are maintained by only one or two research partners. This is linked to the social prestige associated with agrobiodiversity guardians and the biocultural value of these plants, indicating that the distribution of ethnovarieties across the territory is not random (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). We also found that 53 ethnovarieties have only one or two guardians responsible for maintaining the propagules for future plantings across all AMUs in the territory. This highlights the fact that AMUs in the territory already depend on exchange relationships to preserve diversity within their agroenvironments.\u003c/p\u003e\u003cp\u003eRegarding external territories with the most seed exchange relationships with the communities, such as Mariana, Furquim, and Acaiaca, it is important to emphasize the ecological service provided by the quilombola communities in maintaining and distributing agrobiodiversity. Through these efforts, they contribute to agricultural, food, and nutritional security and sovereignty over an extensive region.\u003c/p\u003e\u003cp\u003eWhen testing the relationship between socio-agronomic variables and the richness of crop varieties cultivated by an AMU, we found that the diversity of agroenvironments positively influences the richness of managed species. Specifically, the three AMUs that had six agroenvironments, yard, orchard, agroforestry backyard, ro\u0026ccedil;a or chacara, garden, and pi\u0026ccedil;arra land, managed an average of 90 varieties (SD\u0026thinsp;=\u0026thinsp;26). This result demonstrates that the heterogeneity of edaphoclimatic conditions enables the cultivation of a greater richness of crops by increasing the potential for experimentation, which in turn fosters plant diversification (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). These findings practically provide a practical illustration of the ongoing coevolutionary process within traditional agroenvironments (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe also observed a positive relationship between the total cultivated area and the richness of managed varieties, suggesting that larger areas offer more opportunities for experimentation. For example, staple crops in the family farming system, such as beans, corn, and squash, already have designated spaces within the system. Therefore, larger land areas create surplus spaces for cultivating and managing additional food species. Additionally, larger areas increase the potential for interaction with neighboring households, facilitating seed exchanges between families. Closer proximity to neighbors reduces the time needed to acquire specific varieties, making seed procurement more economically feasible while strengthening affective and trust-based relationships (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother relationship identified was the positive influence of the length of residence in the territory on the total number of crop varieties managed. This suggests that the time community plays a key role in increasing the genetic pool richness of crop varieties, which are accumulated, tested, and adapted to specific agroenvironments (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Although farming practices may become more challenging with age, the AMUs led by older farmers carefully select which varieties to manage, prioritizing those most suited to their needs and conditions. These older farmers become key references for crop species that are easier to manage (e.g., \"chifre de veado\" okra, which is easier to harvest), tied to gustatory memory (e.g., \"manteiga\" collard greens, a variety consumed since early childhood), and/or associated to emotional connections (e.g., \"puteca\" beans, a variety that helped the community during times of food scarcity). Together, these factors build trust and elevate the status of older residents in their territories, making them vital guardians of agrobiodiversity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConversely, the total number of economic and agricultural activities, as well as total and per capita income, did not influence the management of agrobiodiversity. This may be due to the low variation in income and activity levels among the families. Additionally, the length of time living in the current residence did not impact the richness of crop varieties cultivated. This is likely because quilombola community members tend to bring their crop varieties with them when they relocate, underscoring the sociocultural importance of these varieties to the community. Lastly, the total number of people in the household did not influence the richness of managed crop varieties, indicating that, regardless of household size, the AMU remains stable, though the quantity of food produced may vary.\u003c/p\u003e\u003cp\u003eAnother key finding of this study demonstrates that socio-agronomic variables, such as the total number of agroenvironments, total cultivated area, length of residence in the community, and richness of managed crop varieties, positively affect degree centrality, betweenness centrality,y and harmonic closeness centrality. These results highlight which socio-agronomic variables define the importance of AMUs in the structure and dynamics of the seed exchange network. This study, therefore, aligns with others, demonstrating that AMUs acting as agrobiodiversity guardians are those that share the most crop varieties and serve as bridges between different territories, facilitating the flow of both genetic resources and knowledge within the seed exchange network (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, in examining the influence of the emergent dynamics of the seed exchange network on its potential for agrobiodiversity conservation, we found that the network exhibited low nestedness, low connectivity, and high modularity. This structural pattern suggests that AMUs form cohesive subgroups that exchange seeds and interact more frequently within their group with other AMUs in the system. This indicates a centralization of the genetic pool of agrobiodiversity.\u003c/p\u003e\u003cp\u003eThis segregation in seed exchanges can be explained by factors such as geographic proximity and sociocultural relationships, which align with the principle of homophily. The finding that the communities of Vila Santa Efig\u0026ecirc;nia II and Castro account for the majority of exchange events (23.7% and 18.3%, respectively) can be attributed to the higher concentration of AMUs that are geographically and kinship-wise close to one another. In contrast, the communities of Emba\u0026uacute;bas and Engenho Queimado (account for 7.4% and 7.15% of seed exchange relationships) exhibit more spatial segregation between their AMUs, despite familial ties. Thus, this structural pattern suggests that AMUs form cohesive subgroups interacting more frequently within their group than with other AMUs in the system.\u003c/p\u003e\u003cp\u003eHowever, despite the high richness of crop varieties across the studied territory, the seed exchange network exhibits emergent properties that suggest some fragility. Although there is a high potential for connections among the various actors (i.e., the large number of nodes in the network), the low connectivity between them increases the risk of agrobiodiversity loss. Low levels of nestedness and connectivity in socioecological networks can negatively impact the system by compromising resilience, peer trust, and the preservation of traditional knowledge (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). As a result, the collective memory of the territories is weakened (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe suggest, however, that high modularity in socioecological networks demonstrates that exchange dynamics are shaped by smaller groups. Since the network is poorly connected and divided into modules, it becomes more vulnerable to disturbances. As crop varieties are distributed unevenly across the network, the loss of a variety within a particular group (module) can have a significant impact on the system, given the lower likelihood of exchanges between different groups (i.e., between modules) compared to within the same group (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This segmented structure also limits the introduction of new crop varieties into the system, thereby restricting innovation and socio-environmental adaptations (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eWe can conclude that historical and material conditions play a critical role in the \"on-farm\" conservation of agrobiodiversity. These findings underscore the importance of land ownership and territorial security for traditional communities to continue preserving their way of life, which is rooted in ecological capital (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This is especially relevant given that their territories are situated in a region experiencing capitalist expansion, primarily through mining. Therefore, it is crucial to protect and recognize quilombola communities as an ontologically sustainable system, essential for preserving the knowledge and cultural preservation tied to biodiversity (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eOur deepest gratitude goes to all those who welcomed us so generously into their homes, to the Quilombola Association of Vila Santa Efig\u0026ecirc;nia and Adjacent Areas, and to the Saberes do Territ\u0026oacute;rio Collective, whose support was essential for the development of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e The conception and design of the study were contributed by GTS and IFF. IFF conducted the data collection and cleaning. FVC performed the data analysis. IFF wrote the first draft of the manuscript, TSN and FRG developed a critical review of the article and both authors commented on subsequent versions. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The fieldwork activities were funded by the research project \u0026quot;Medicinal and Useful Plants of the Rio Doce Basin\u0026quot; (CAPES 881.118042/2016-01). Authors IFF, FVC, and TSN received scholarships from CAPES.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e The data collected from the interviews were organized in Microsoft\u0026reg; Excel and analyzed using R software. The codes (custom code) are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e The project was approved by the Ethics Committee for Research (CEP) of the Federal University of Juiz de Fora (CAAE: 51210421.10000.5147) and registration in Sisgen (code: A31BD79).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e Each farmer signed a Free and Informed Consent Term (TCLE), required by Brazilian Law 13.123/2015, which addresses access to traditional knowledge associated with biodiversity. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLabeyrie V, Antona M, Baudry J, Bazile D, Bodin \u0026Ouml;, Caillon S, et al. Networking agrobiodiversity management to foster biodiversity-based agriculture. A review. 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A two-year study on seed acquisition in northern Cameroon. Ecology and Society. 2016;21(2). \u003c/li\u003e\n\u003cli\u003eAbay F, De Boef W, Bj\u0026oslash;rnstad \u0026Aring;. Network analysis of barley seed flows in Tigray, Ethiopia: Supporting the design of strategies that contribute to on-farm management of plant genetic resources. Plant Genetic Resources: Characterisation and Utilisation. 2011 Dec;9(4):495\u0026ndash;505. \u003c/li\u003e\n\u003cli\u003eOtieno G, Lacasse H, Fadda C, Reynolds TW, Recha JW. Social Seed Networks for Climate Change Adaptation in Western Kenya Results from a study to better understand farmers\u0026rsquo; primary sources of seed information in the Nyando Climate-Smart Villages. 2018. \u003c/li\u003e\n\u003cli\u003eSeboka B, Deressa A. Validating farmers\u0026rsquo; indigenous social networks for local seed supply in central rift valley of Ethiopia. The Journal of Agricultural Education and Extension. 1999 Dec 15;6(4):245\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eEmperaire L, Eloy L, Seixas AC. Redes e observat\u0026oacute;rios da agrobiodiversidade, como e para quem? Uma abordagem explorat\u0026oacute;ria na regi\u0026atilde;o de Cruzeiro do Sul, Acre. Boletim do Museu Paraense Emilio Goeldi:Ciencias Humanas. 2016;11(1):159\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eFenzi M, Rog\u0026eacute; P, Cruz-Estrada A, Tuxill J, Jarvis D. Community seed network in an era of climate change: dynamics of maize diversity in Yucat\u0026aacute;n, Mexico. Agric Human Values. 2022 Mar 1;39(1):339\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eKawa NC, McCarty C, Clement CR. Manioc varietal diversity, social networks, and distribution constraints in rural Amazonia. Curr Anthropol. 2013 Dec;54(6):764\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eLlamas-Guzm\u0026aacute;n LP, Lazos Chavero E, Perales Rivera HR, Casas A. Seed Exchange Networks of Native Maize, Beans, and Squash in San Juan Ixtenco and San Luis Huamantla, Tlaxcala, Mexico. Sustainability (Switzerland). 2022 Apr 1;14(7). \u003c/li\u003e\n\u003cli\u003ePinto MFC. Caminhos da agrobiodiversidade: redes de trocas de sementes em sistemas agroecol\u0026oacute;gicos na Serra Catarinense, Alto Vale do Rio Tijucas, Santa Catarina [Disserta\u0026ccedil;\u0026atilde;o]. Universidade Federal de Santa Catarina; 2014. \u003c/li\u003e\n\u003cli\u003ePoudel D, Sthapit B, Shrestha P. An Analysis of Social Seed Network and Its Contribution to On-Farm Conservation of Crop Genetic Diversity in Nepal. International Journal of Biodiversity. 2015 Feb 23;2015:1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eOtieno G, Zebrowski WM, Recha J, Reynolds TW. Gender and social seed networks for climate change adaptation: Evidence from bean, finger millet, and sorghum seed systems in East Africa. Sustainability (Switzerland). 2021 Feb 2;13(4):1\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eBarbillon P, Thomas M, Goldringer I, Hospital F, Robin S. Network impact on persistence in a finite population dynamic diffusion model: Application to an emergent seed exchange network. J Theor Biol. 2015 Jan 1;365:365\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves MC, Da Silva FR, Cantelli D, Dos Santos MR, Aguiar PV, Pereira ES, et al. Traditional Agriculture and Food Sovereignty: Quilombola Knowledge and Management of Food Crops. J Ethnobiol. 2022 Jul 1;42(2):241\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eFabr\u0026iacute;cio SA, Ferreira DDM, Borba JA. A panorama of Mariana and Brumadinho disasters: What do we do know so far? REAd Revista Eletr\u0026ocirc;nica de Administra\u0026ccedil;\u0026atilde;o (Porto Alegre). 2021 Apr;27(1):128\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eSantos EM, Nalini HA, Abreu AT, Nogueira LB. Mobilization of heavy metals in river sediments from the region impacted by the Fund\u0026atilde;o dam rupture, Brazil. Environ Earth Sci. 2021 Dec 1;80(24). \u003c/li\u003e\n\u003cli\u003eAlbuquerque UP, Ramos MA, Lucena RFP, Alencar NL. Methods and techniques used to collect ethnobiological data. In: Albuquerque UP, Cruz LVF, Lucena RFP, Alves RRN, editors. Methods and Techniques in Ethnobiology and Ethnoecology. 1st ed. NewYork: Springer; 2014. p. 15\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eAlexiades MN. Collecting ethnobotanical data: An introduction to basic concepts and techniques. In: Alexiades MN, editor. Select guidelines for ethnobotanical research: A field manual [Internet]. New York: The New York Botanical Garden; 1996. p. 53\u0026ndash;94. Available from: https://www.researchgate.net/publication/304496311. Accessed 18 Jul 2025.\u003c/li\u003e\n\u003cli\u003eThompson EC, Juan Z. Comparative Cultural Salience: Measures Using Free-List Data. Field methods. 2006;18(4):398\u0026ndash;412. \u003c/li\u003e\n\u003cli\u003eMartins PS. Biodiversity and agriculture: patterns of domestication of brazilian native plant species. An Acad Bras Cienc. 1994;66:26\u0026ndash;219. \u003c/li\u003e\n\u003cli\u003eAmorozo MCM, Viertler RB. A abordagem qualitativa na coleta e an\u0026aacute;lise de dados em etnobiologia e etnoecologia. In: Albuquerque UP, Lucena RFP, Cunha LVFCC, editors. M\u0026eacute;todos e t\u0026eacute;cnicas na pesquisa etnobot\u0026acirc;nica. 3rd ed. Recife: NUPPEA; 2010. \u003c/li\u003e\n\u003cli\u003eCOMBESSIE JC. O m\u0026eacute;todo em sociologia o que \u0026eacute;, como se faz. Edi\u0026ccedil;\u0026otilde;es Loyola. S\u0026atilde;o Paulo; 2004. 92\u0026ndash;111 p. \u003c/li\u003e\n\u003cli\u003eBrasil M da S. Alimentos regionais brasileiros [Internet]. 2nd ed. Brasil M da S, editor. Bras\u0026iacute;lia: Minist\u0026eacute;rio da Sa\u0026uacute;de, Secretaria de Aten\u0026ccedil;\u0026atilde;o \u0026agrave; Sa\u0026uacute;de, Departamento de Aten\u0026ccedil;\u0026atilde;o B\u0026aacute;sica; 2015. Available from: www.saude.gov.br/nutricao. Accessed 30 Apr 2025.\u003c/li\u003e\n\u003cli\u003eLorenzi H, Lacerda MTC, Bacher LB. Frutas no Brasil nativas e ex\u0026oacute;ticas: (de consumo in natura). S\u0026atilde;o Paulo: Plantarum; 2015. \u003c/li\u003e\n\u003cli\u003eCarvalho SZ, Mendes L, Bonini DM. Etnoconhecimento de plantas de uso medicinal por benzedeiras/benzedores e rezadeiras/rezadores de Anhembi e Mogi das Cruzes \u0026ndash; SP. Revista Eletr\u0026ocirc;nica Correlativo. 2017;16(2):133\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eHeiden G, Barbieri L, Neitzke RSN. Chave para a identifica\u0026ccedil;\u0026atilde;o das esp\u0026eacute;cies de ab\u0026oacute;boras (Cucurbita, Cucurbitaceae) cultivadas no Brasil. 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The nested assembly of plant-animal mutualistic networks. Proceedings of the National Academy of Sciences [Internet]. 2003; Available from: www.pnas.orgcgidoi10.1073pnas.1633576100\u003c/li\u003e\n\u003cli\u003eAlmeida-Neto M, Guimar\u0026atilde;es P, Guimar\u0026atilde;es Jr PR, Loyola RD, Ulrich Almeida-Neto WM, Loyola RD, et al. A consistent metric for nestedness analysis in ecological systems:reconciling concept and measurement. Oikos. 2008;117:1227\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003ePoisot T, Gravel D. When is an ecological network complex? Connectance drives degree distribution and emerging network properties. PeerJ. 2014;2014(1). \u003c/li\u003e\n\u003cli\u003eNewman MEJ. Modularity and community structure in networks [Internet]. 2006. Available from: www.pnas.orgcgidoi10.1073pnas.0601602103. Accessed 1 Mai 2022.\u003c/li\u003e\n\u003cli\u003eBeckett SJ. Improved community detection in weighted bipartite networks. R Soc Open Sci. 2016 Jan 1;3(1). \u003c/li\u003e\n\u003cli\u003eMiyauchi A, Kawase Y. Z-score-based modularity for community detection in networks. PLoS One. 2016 Jan 1;11(1). \u003c/li\u003e\n\u003cli\u003eDuarte Almada ED. Entre as serras: Etnoecologia de duas comunidades quilombolas no Sudeste brasileiro. [Campinas]: Unicamp; 2012. \u003c/li\u003e\n\u003cli\u003eBerkes F, Colding J, Folke C. Rediscovery of Traditional Ecological Knowledge as Adaptive Management. In: Source: Ecological Applications. 2000. p. 1251\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eAltieri M. Agroecologia: bases cient\u0026iacute;ficas para agricultura sustent\u0026aacute;vel. 3rd ed. S\u0026atilde;o Paulo, Rio de Janeiro: Express\u0026atilde;o Popular; 2012. 400 p. \u003c/li\u003e\n\u003cli\u003eda Costa FV, Guimar\u0026atilde;es MFM, Messias MCTB. Gender differences in traditional knowledge of useful plants in a Brazilian community. PLoS One. 2021;16(7):e0253820. \u003c/li\u003e\n\u003cli\u003eSoldati GT, Almada ED. Political Ethnobiology. Ethnobiology and Conservation. 2024 Jul 12;13.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-ethnobiology-and-ethnomedicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jeet","sideBox":"Learn more about [Journal of Ethnobiology and Ethnomedicine](http://ethnobiomed.biomedcentral.com/)","snPcode":"13002","submissionUrl":"https://submission.nature.com/new-submission/13002/3","title":"Journal of Ethnobiology and Ethnomedicine","twitterHandle":"@ethnobiomed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biocultural heritage, Ethnobiology, In situ conservation, Landrace seeds, Political Ecology, Traditional ecological knowledge","lastPublishedDoi":"10.21203/rs.3.rs-7374043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7374043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTraditional agricultural systems are rooted in the local management, selection, and conservation of agrobiodiversity. Understanding the socioecological dynamics that sustain these systems is essential for developing sustainable practices that ensure food security and sovereignty in the territories of traditional and Indigenous peoples. This study examines the role of seed exchange networks in on-farm agrobiodiversity conservation in Quilombola communities that live in environmental and political threats. We emphasize the role of socioecological networks and socio-agronomic variables in shaping how agrobiodiversity is maintained, shared, and regenerated across time and space.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted semi-structured interviews, free listing, participant observation, and guided tours with 48 agrobiodiversity management units (AMUs) in five communities, documenting socio-agronomic variables and ethnovariety richness, with botanical identification in the field and literature. From the 359 ethnovarieties recorded, 15 were randomly selected for detailed seed exchange network analysis. The complete network of 185 AMUs (48 internal, 137 external) and 424 exchanges was analyzed using negative binomial GLMs for richness (H1), PCA and Poisson GLMs for centrality metrics (H2), and network metrics (degree, betweenness, harmonic closeness, modularity, connectivity, weighted nestedness) calculated in Gephi and R, compared against null models to assess conservation potential (H3).\u003c/p\u003e\u003ch2\u003eResults and Discussion\u003c/h2\u003e\u003cp\u003eThe 48 AMUs managed an average of 40 ethnovarieties each. Agro-environmental diversity, cultivated area, and time in the community were positively associated with richness, which in turn increased AMU centrality, highlighting their role as agrobiodiversity guardians and network bridges. Despite high diversity, the seed exchange network displayed low nestedness and connectivity but high modularity, indicating cohesive subgroups with strong internal exchanges and limited intergroup seed flows. This pattern reflects both social cohesion and fragmentation, and reveals vulnerabilities, as varieties unevenly distributed across modules may not circulate widely, reducing resilience.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHistorical and material conditions are critical to sustaining on-farm agrobiodiversity conservation in quilombola territories. Land tenure security and territorial rights are essential for maintaining traditional agroecosystems that integrate ecological knowledge, cultural heritage, and biodiversity management. Strengthening seed exchange connectivity, fostering collaboration across groups, and protecting these territories are urgent actions to enhance resilience, safeguard traditional knowledge, and ensure long-term biocultural justice.\u003c/p\u003e","manuscriptTitle":"Socio-agronomic features define the structure of socioecological seed network exchanges and “on farm” conservation of agrobiodiversity in traditional communities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:48:17","doi":"10.21203/rs.3.rs-7374043/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T18:46:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T16:32:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-04T19:57:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19676793333240973368453364837354494395","date":"2025-09-15T12:41:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181916206336029475771046573931435050956","date":"2025-09-15T07:41:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238431622143468044334136050089227417481","date":"2025-09-13T14:54:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87362533734085715852136654198752870929","date":"2025-08-21T17:51:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-21T05:53:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-21T00:45:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-21T00:45:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ethnobiology and Ethnomedicine","date":"2025-08-14T12:46:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ethnobiology-and-ethnomedicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jeet","sideBox":"Learn more about [Journal of Ethnobiology and Ethnomedicine](http://ethnobiomed.biomedcentral.com/)","snPcode":"13002","submissionUrl":"https://submission.nature.com/new-submission/13002/3","title":"Journal of Ethnobiology and Ethnomedicine","twitterHandle":"@ethnobiomed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b86306b-1123-44f9-a456-bee260c29afe","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:07:20+00:00","versionOfRecord":{"articleIdentity":"rs-7374043","link":"https://doi.org/10.1186/s13002-025-00847-4","journal":{"identity":"journal-of-ethnobiology-and-ethnomedicine","isVorOnly":false,"title":"Journal of Ethnobiology and Ethnomedicine"},"publishedOn":"2026-03-11 15:58:47","publishedOnDateReadable":"March 11th, 2026"},"versionCreatedAt":"2025-09-01 09:48:17","video":"","vorDoi":"10.1186/s13002-025-00847-4","vorDoiUrl":"https://doi.org/10.1186/s13002-025-00847-4","workflowStages":[]},"version":"v1","identity":"rs-7374043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7374043","identity":"rs-7374043","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}