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. Author manuscript; available in PMC: 2016 Sep 22.
Published in final edited form as: Ecol Soc. 2016 Mar;21(1):1. doi: 10.5751/ES-07944-210101

Social organization influences the exchange and species richness of medicinal plants in Amazonian homegardens

PMCID: PMC5033073  EMSID: EMS69861  PMID: 27668001

Abstract

Medicinal plants provide indigenous and peasant communities worldwide with means to meet their healthcare needs. Homegardens often act as medicine cabinets, providing easily accessible medicinal plants for household needs. Social structure and social exchanges have been proposed as factors influencing the species diversity that people maintain in their homegardens. Here, we assess the association between the exchange of medicinal knowledge and plant material and medicinal plant richness in homegardens. Using Tsimane’ Amazonian homegardens as a case study, we explore whether social organization shapes exchanges of medicinal plant knowledge and medicinal plant material. We also use network centrality measures to evaluate people’s location and performance in medicinal plant knowledge and plant material exchange networks. Our results suggest that social organization, specifically kinship and gender relations, influences medicinal plant exchange patterns significantly. Homegardens total and medicinal plant species richness are related to gardeners’ centrality in the networks, whereby people with greater centrality maintain greater plant richness. Thus, together with agroecological conditions, social relations among gardeners and the culturally specific social structure seem to be important determinants of plant richness in homegardens. Understanding which factors pattern general species diversity in tropical homegardens, and medicinal plant diversity in particular, can help policy makers, health providers, and local communities to understand better how to promote and preserve medicinal plants in situ. Biocultural approaches that are also gender sensitive offer a culturally appropriate means to reduce the global and local loss of both biological and cultural diversity.

Keywords: exchange networks, gender, plant diversity, social networks analysis, tropical homegardens, Tsimane’

Introduction

Medicinal plants provide locally accessible, culturally appropriate, and economically affordable health care options for people with scarce access to biomedical healthcare systems. Indeed, most indigenous and peasant communities meet their primary health care needs through the use of medicinal plants. While some medicinal plants are obtained from the wild, many are also obtained - both for household consumption and for sale - from agricultural fields and homegardens (e.g. Bernholt et al. 2009, Aceituno-Mata 2010, Thomas and van Damme 2010, Yang et al. 2014). Especially, tropical homegardens support high species diversity and help communities to meet health needs, constituting in situ germplasm banks, biodiversity reservoirs, and medicine cabinets (Finerman and Sackett 2003, Huai and Hamilton 2009).

Diversity in homegardens

Tropical homegardens are renowned for their typically high levels of biological diversity. This species diversity is the result of gardeners’ meticulous selection and management, which is aimed at providing products they consider to be important to subsistence and livelihoods (Nair and Kumar 2006). Homegarden diversity partly depends on climatic conditions, altitude, size and age of the garden, remoteness from urban centers, and village size, among other factors (Wezel and Bender 2003, Kehlenbeck and Maass 2004, Wezel and Ohl 2005, Rao and Rao 2006). Furthermore, socio-cultural and economic characteristics of gardeners are important for explaining plant diversity in homegardens. For example, Howard (2006) showed that, in Latin American homegardens, the division of labor, knowledge, access to garden resources, and degree of commoditization help to explain the structure, composition, and functions of homegardens. The sex of the gardener and the gendered distribution of gardening tasks are related to diversity in homegardens in the Iberian Peninsula (Reyes-García et al. 2010) where, despite being smaller and closer to the dwelling, gardens managed mainly by women have greater species diversity per unit area compared with those mainly manage by men. In Peruvian Amazonian gardens, differences in homegarden diversity are related to ethnicity (Uranina, mestizos and Achuar) in terms of species richness, homegarden composition, and the presence of medicinal plants, where some medicinal species are exclusively cultivated by one or another ethic group (Perrault-Archambault and Coomes 2008). Finerman and Sackett (2003) have also observed that, in the Ecuadorian Andes, where gardens are managed by women and are largely devoted to medicinal plant production, species composition reflects household demographics (e.g. age, composition) and stage in the life cycle (e.g. reproductive status) as well as specific health needs of individuals in the household.

Homegarden diversity is also strongly influenced by access to and exchange of planting material (seeds, stakes, stems, and cuttings) (Aguilar-Støen et al. 2009, Coomes 2010), which are critical for developing and maintaining plant diversity. Peoples’ movements and migratory patterns are typically accompanied by flows of seeds and plants, which modify, enrich, and diversify migrants’ homegardens (Voeks 2004; Kujawska and Pardo-de-Santayana 2015). For example, in a study of planting material exchange networks among indigenous peoples in the Peruvian Amazon, Lerch (1999) found a positive association between plant diversity in homegardens and households’ frequency of plant exchanges; Ban and Coomes (2004) found similar results in the same region. However, the exchange of homegarden planting material is usually contained within certain social networks. Most exchanges occur between kin, relatives, close friends, and neighbors (Buchmann 2009; Aguilar-Støen et al. 2009), predominately between women (Boster 1985, Sereni Murrieta and Winklerprins 2003, Lope-Alzina 2014).

Navigating social exchange through social network analysis

Only recently have researchers begun to apply social network analysis (SNA) to investigate the exchange of homegarden products (goods and planting materials) and the related knowledge. Calvet-Mir et al. (2012) explored the seed exchange network in homegardens in the Catalan Pyrenees and evaluated its contribution to agrobiodiversity conservation. They found that people who were mentioned more often in seed exchange networks and who had a higher level of intermediation conserved more local landraces and had more knowledge of such varieties compared with people who were less central in the network. In a similar study among gardeners in the Iberian Peninsula, Reyes-García et al. (2013) found that the number of contacts that an individual had in the germplasm exchange network was positively associated with their agroecological knowledge. Lope-Alzina and Howard (2014) reported that, among members of a Yucatec-Maya community in Mexico, homegardens are the main source of exchanged planting material. They found that, despite strong market participation, gift-giving continues to be the predominant form of exchange, with most gifts coming from homegardens and with most exchanges occurring between women in kinship-based networks. Elderly women at the top of the hierarchy within their own kin networks were the most outstanding givers.

Social network analysis has also been used to explore medicinal plant knowledge transmission pathways. For example, Hopkins’ (2011) study among Yucatec-Maya in Mexico suggests that an individual’s knowledge of herbal medicines is positively associated with that individual’s structural position within the herbal remedy network. Other researchers have assessed selective learning biases in cultural transmission pathways through social network modeling. Henrich and Broesch (2011) asked Fijian villagers about who they would go to for advice if they have a question about how to use medicinal plants. Their results suggest that, being knowledgeable, older, and a woman, and lacking formal education, increase the chances of being selected as model for learning about medicinal plants. In summary, findings from previous research suggest 1) that the individual structural position in social networks is associated with medicinal plant knowledge, and 2) that kinship, sex, and cultural learning pathways shape social networks.

In this study, we seek to contribute to these lines of research by assessing the influence that the exchange of medicinal knowledge and plant material through social networks have for medicinal plant diversity in Tsimane’ Amazonian homegardens. We explore whether social organization (i.e. kinship, gender relations and the division of labor and tasks in gardening) patterns the exchange of medicinal plant knowledge and/or medicinal plant material. We use network centrality measures to evaluate people’s location and performance in knowledge and plant material exchange networks, hypothesizing that people with higher centrality in the knowledge/plant material network maintain a higher diversity of medicinal plants in their homegarden.

Methods

Our research was carried out among Tsimane’ forager-horticulturalists in the Amazonian lowland forest of Beni Department, Bolivia. We selected two villages located along the Maniqui River, within the Tsimane’ Indigenous Territory. Although both villages are relatively isolated and self-sufficient, they differ in their degree of isolation. One village is closer to the market town (it can be reached after a one-day canoe trip) whereas the other is more isolated (it can only be reached after a three-day canoe trip) (Fig. 1).

Figure 1.

Figure 1

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Map of the study area

Social organization in Tsimane’ villages is largely kinship based, where most Tsimane’ practice cross-cousin marriage (Daillant 2003), and residence is commonly matrilocal (couples live with or near the wife’s parents). Traditionally, the Tsimane’s semi-nomadic settlements were small, consisting of clusters of two to three extended family households that were often considerable distances apart (Chiccón 1992, Ellis 1996). The influence of Protestant missionaries and the introduction of formal education in the mid-20th century fostered the settlement and confluence of different clans or clusters around schools. Today, the Tsimane’ still change residence very frequently, even within villages, moving closer to their agricultural plots in the harvest season and to rivers in the dry season, when fish are plentiful.

In these villages, livelihoods are mostly subsistence-oriented and depend on foraging and swidden agriculture. Besides having swidden plots located at varying distances from the household, the Tsimane’ cultivate and manage a diversity of species in homegardens. While there are many and diverse ways to define homegardens (see e.g. Nair and Kumar 2006), we use a concept that coincides well with the type of land use practiced by the Tsimane’: “the peridomestic area belonging to the household where members plant and/or tend useful plants” (Perrault-Archambault and Coomes 2008). Frequently used or common medicinal plants are found in homegardens together with fruit trees, cotton, and chili pepper (Reyes-García et al. 2003, Reyes-García et al. 2005).

Since their access to biomedical health care is very limited, medicinal plants provide the Tsimane’ with locally accessible and socio-culturally relevant options for treating health complaints. Ailments are firstly treated in the household, where women are the principle healers (Chiccón 1992). However, both women and men cultivate plants in the area around their houses. Quite interestingly, the Tsimane’ recognize customary ownership of these medicinal plants and have detailed knowledge of such rights (see also Howard and Nabanoga 2006). In the Tsimane’s customary usufruct tenure system, gardens belong to the families who originally established them (e.g. former residents). Abandoned gardens are usually reoccupied by the families that previously abandoned them or by their closest relatives, who obtain permission from the previous occupants to use them (Piland 2000). When a family member dies, to get rid of bad spirits and avoid visits by the deceased’s spirit, the Tsimane’ move to another location (Chicchón 1992); the garden that remains behind is left intact (Piland 2000).

Data collection

The first and third authors lived in the area for 18 months (January 2012- November 2013) allowing them to actively observe as well as participate and interact with the Tsimane’ while gardening. Different tasks were performed with some of the informants; for example, we accompanied them while gathering products from their gardens, and helped with tasks such as planting and weeding.

Between August and December 2012, individual inventories were made of all plants in homegardens that were planted or managed by household heads. A total of 86 informants were interviewed (46 women and 40 men), which represented about 80% of all household heads. Of these, 55 lived in the village closer to town village and 31 lived in the more isolated village. Each informant was asked individually to show the plants kept in the garden, and to provide their vernacular or common names and uses. Uses were classified into four categories: food, medicine, artisanal (including plants use for making bags, carpets, and bows and arrows), and others (including fish poisoning, ornamental, and construction uses). A given plant could fall into more than one category (e.g. a plant with both food and medicinal uses). When the informants indicated a plant with medicinal uses, they were asked about the ailments it was used to treat.

Social network data were compiled through individual interviews. We used recall methods that employed a set of name generators to collect network data in relation to knowledge (e.g. information and advice about medicinal plants) and plant material (propagates, seeds, plants) exchange (hereinafter medicinal plant exchange networks) (Table 1). The names collected were limited to people who reside within the village, as a boundary for a whole network analytical approach. In addition to data on social relations, we collected demographic data on each informant, including sex, age (in years), kinship relations, years of residence in the village and years residing in the household. Because some informants in each village were not members of a village clan (e.g. the teacher and his wife, who are Tsimane’ from another village but reside in the studied village), these were considered as a separate clan for the descriptive analysis (clans 5 and 9) and were not taken into account in statistical analysis.

Table 1.

Name generating questions used to elicit information on knowledge and medicinal plant material exchange social networks in homegardens in two Tsimane’ villages

Network Question asked (name generator)
Medicinal plant knowledge network Q1: Could you tell me the names of anyone who has ever given you advice about medicinal plants?
Q2: Could you list the name of people to whom you have ever given advice about medicinal plants?
Medicinal plant material exchange network Q3: Could you list the names of people who gave you medicinal plants for your homegarden?
Q4: Could you list the names of people to whom you have ever given medicinal plant material or remedies from your homegarden?
Q5: While doing the inventory, for each medicinal plant the informants showed us, we asked: Has someone given you this plant? If so, Could you tell me the name of the person who gave you this plant?
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We also assessed the medicinal plant knowledge of garden managers. To do so, we first asked 20 men and women from both villages to free-list the medicinal plants they knew, in order to design a knowledge survey that consisted of structured questions regarding some 16 medicinal plants, which were chosen according to their frequency and position in the free-listing or their ‘salience’ (Thompson and Zhang 2006). We created three salience groups by randomly selecting the three species with the highest and lowest salience, and four species with medium salience. Additionally, we analyzed women’s and men’s free-listings separately and selected three more species that were listed only by women and three listed only by men. During the knowledge survey (available at http://icta.uab.cat/Etnoecologia/Docs/[423]-lektests.pdf), local assistants read out the vernacular names of the selected medicinal plants, asking gardeners whether they knew the plant and, if so, to list up to three different medicinal uses for that plant. The average number of uses known per known species was used to assess individuals’ medicinal plant knowledge.

Analysis

We use richness as a proxy for diversity in homegardens, i.e. the number of different species inventoried per informant’s garden. The richness of plant species in homegardens was measured for each informant using inventory data. Total richness is the number of distinct species (including those with medicinal, food, artisanal and other uses) inventoried per informant garden. Medicinal plant richness is the number of distinct plant species with medicinal use(s) inventoried by informant garden.

We recorded the vernacular names given by interviewees (Hanazaki et al. 2000, Perrault-Archambault and Coomes 2008), and then identified their scientific equivalents using previous ethnobotanical studies in the area (see Appendix 1), and assigned codes to calculate richness. For example, the local names seviria and vira’ vira’ are synonyms that refer to a single botanical species, Cymbopogon citratus, so the same code was assigned to both vernacular names to avoid double counting. When it was not possible to link vernacular names to botanical nomenclature since this information was not available, we assigned unique codes to all of the vernacular names given by informants. This might lead to the over-estimation of species richness as some of these vernacular names probably refer to the same species. Also, it might have led us to under-estimate the actual number of species since a single vernacular name may refer to different species, which has been called “hidden diversity” (Cavalcanti and Alburquerque 2013). We described the overall composition of homegardens by village, clan, sex of the gardener, and age groups. To this end, kinship data was used to assign informants to one of nine different clans identified, and informants were also classified into one of four age groups (younger or equal to 25, 26-35, 36-45, and > 45 years).

Social network analysis

Using information on social exchange networks, we built a whole network matrix and calculated a set of graph-based measures (McCarty and Molina 2014) for each village (group level) and informant (individual level). Information was treated as undirected and analyzed with UCINET6-Netdraw for Windows. Nominations elicited with a multiple name generator approach were aggregated in a single file by village, since we consider that planting material often flows together with the associated knowledge - in other words, when people give/receive planting material, they typically also give/receive explanations on how to grow and use the species (Reyes-García et al. 2013). For each village exchange network, we calculated 1) Size, or number of people in the network; 2) Density, or the proportion of existing connections in the network relative to the maximum possible number of connections (from 0 to 1); 3) Centralization, or tendency for a few people to centralize the existing connections (expressed as a percentage); and 4) Reciprocity, or the extent of reciprocated ties. We calculated three centrality measures for each person in the network (Wasserman and Faust 1994, Scott 2000, Freeman 1977,1979): 1) Degree, or number of people with whom a person is directly connected; 2) Betweenness or the extent to which a given person (ego) appears in the path connecting other people in the network; and 3) Egobetweeness, or the number of people connected to each other only through the ego, a measure that captures the importance of a person in her or his personal network. In order to capture the existence of a relation regardless the direction of the nomination, data were treated as undirected.

To explore the effect of clan membership, sex, and age of the gardener on exchanges of knowledge and plant material, we calculated an External-Internal index (E-I index) (Krackhardt and Stern 1988). The E-I index is proposed as follows:

E−I index=EL−ILEL+IL (1)

where

EL= number of external exchanges of medicinal knowledge and plant material

EI= number of internal exchanges of medicinal knowledge and plant material

Therefore, given a partition of a network into a number of mutually exclusive groups (here, clans, sex, or age groups), the E-I index evaluates the relation between external and internal exchanges (relative homophily, or people’s tendency to relate to others who are similar to themselves, leading to preferential exchanges within groups). The value of the E-I index can range from -1 (in which all ties are within the group) to 1 (all ties are external to the group): the index equal zero when a group has the same number of internal and external ties. A permutation test (n=5000) was performed to assess whether the network E-I index was significantly different than expected.

Statistical analysis

To estimate the association between medicinal plant richness managed by an informant and informants’ centrality measures, we ran a Poisson multivariate regression, which is adequate for count data. We first tested whether degree centrality was associated with medicinal plant richness while controlling for additional factors that research suggests affect diversity in homegarden. Specifically, controls in our regression include: village or residence, clan membership, sex, age (in years), and age squared (Age2), (to control for non-linearity in the relation between age and medicinal knowledge, as cognitive ability might decrease among elders), years of residence in the village (to control for mobility), years residing in the same house (as a proxy for homegarden age), and, individuals’ medicinal plant knowledge. We use STATA 13 for Mac for the statistical analysis.

Results

Richness in Tsimane’ homegardens

A total of 111 plants were inventoried in this study, 45 of which were used as medicines. The total richness in gardens in the two villages is relatively high and evenly distributed, with 86 and 83 plants encountered in the closer and more isolated villages, respectively. Food was the most common use reported followed by medicinal, artisanal and other uses. Fig. 2 illustrates the distribution of the number of plants inventoried and the corresponding uses reported by village, clan, and sex-age groups. On average, a resident of the closer-to-town village maintained 11.58 (SD=8.53) plants, including 1.90 (SD= 2.27) with medicinal uses. In the more isolated village, an informant on average maintained 13.67 (SD=7.49) plants, 3.54 (SD=2.87) of those with medicinal uses.

Figure 2.

Figure 2

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Richness of plants in different use categories inventoried by village (A closer to town, B more isolated); clans (C: clans 1 to 5, closer to town village; clans 6 to 9 more isolated village), and sex-age groups (D: women left side, men right side).

In the village closer to town, women maintained 2.75 (SD=2.58) medicinal plants and men 0.96 (SD=1.39). One woman had 12 medicinal plants in her homegarden, but 17 informants (30.90%) had none (12 of whom were men). A similar pattern was found in the isolated village, where women also maintained more medicinal plants in homegardens (4.29; SD=3.07) compared with men (2.64; SD=2.37), and five informants had none (16 %), three of whom were men.

The species most frequently found plants in homegardens were citruses, such as orange (Citrus sinensis) and grapefruit (Citrus paradisi), along with peach palm (Bactris gasipaes), mango (Magnifera indica), and cotton (Gosipyum barbadense) – the latter was almost exclusively planted by women. The medicinal plants most frequently found in homegardens were ginger (Zingiber officinale), tobacco (Nicotina tabacum) and garlic weed (Petiveria alliacea). Of the total number of times that medicine was reported as a use, 15% were used for treating common flu, 10% for general pain, 10% for fungal infections of the skin, and five percent each for diarrhea and stomach afflictions, injuries, wasps’ stings and skin parasites.

Structure of medicinal knowledge and plant material exchange networks

There were 48 gardeners involved in medicinal plant exchange networks in the village closer to town and 37 in the more isolated village (Fig. 3). These networks were characterized by low density (0.034 in the closer village and 0.063 in the more isolated), low centralization indexes (8.08% vs. 6.28%) and low reciprocity (0.0317 vs. 0.109), meaning that connections in the networks are relatively low and not reciprocal. Overall, both networks show asymmetry and hierarchy, meaning that some people have many more connections than others.

Figure 3.

Figure 3

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Medicinal plant knowledge and exchange networks (undirected) by village: A) closer to town village and B) more isolated village. Nodes color shows sex of the person (women: purple; men: green). Nodes size shows degree centrality.

We found different patterns in the exchanges of medicinal knowledge and plant material between E-I indexes calculated by clan membership and sex, but not by age groups (Fig. 4). When grouping by clan membership, larger clans (2, 3, and 8) tended to have more exchanges within the same clan, whereas, smaller clans (1, 4, 6, and 7) had mostly external exchanges. The permutation tests revealed statistically significant differences for the E-I indexes between clans for the closer village (p< 0.05), meaning that different clans had dissimilar exchange patterns. Sex groups presented homophily, with most exchanges occurring within the same-sex group; this difference was significant for both villages (p<0.05). Most exchanges occurred outside of the age group, and differences among E-I indexes for age groups were not statistically significant.

Figure 4.

Figure 4

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Graphic representation of E-I indexes by clans (A, B,); sex (C, D); and age-groups (E, F). Figures on the left-hand side correspond to village closer to town, and right-hand side to more isolated village. E-I index evaluates the relationship between external and internal exchanges, ranging from -1 (all ties within the group) to 1(all ties external to the group); if the ties are divided equally, the index will equal zero.

Centrality measures

On average, centrality measures were higher in the isolated village and for women. The average degree values were 2.96 (SD= 2.90) and 4.17 (SD=2.48) for women in the closer and more isolated villages, respectively, and 1.15 (SD=1.36) and 3.35 (SD=3.12) for men, indicating that women exchanged (gave or received) medicinal plants with more people compared with men (Fig. 3). The average value for betweenness centrality followed a similar pattern, with women in both villages having a similar value (mean= 80.70, SD=127.56 in the closer village; mean= 81.59, SD=76.50 in the more isolated village), meaning that, on average, each woman connected 80 pairs of otherwise unconnected informants. There was high variation in this variable, indicating that some women had a much more pronounced centralizing role in the network than others. Average betweenness values were lower for men (closer village 27.30, SD=56.99; more isolated 39.84, SD=184.02). Betweenness displayed greater variation for men than for women, suggesting greater variation in men’s bridging role, particularly in the isolated village. Similarly, the average value of the variable egobetweenness was considerably higher for women (closer village 5.78, SD=10.98; more isolated 8.17, SD=9.59) compared with men (closer village 0.82, SD=1.94; more isolated village 6.71, SD=12.38) although, again, men’s egobetweenness displayed greater variation than women’s.

Medicinal plant diversity in homegardens and centrality in the exchange network

We analyzed the link between informants’ medicinal richness in homegardens and informants’ locations in the medicinal plant exchange networks (assessed through centrality measures). Degree centrality, which measures the number of people with whom a person is directly connected, has a statistically significant relation with medicinal richness in homegardens. The association is robust for all regressions. Across all models, the variable man displays a greater and more consistent association with medicinal plant richness, suggesting that women have a prominent role in these networks.

In model [A], we tested the association between a person’s degree centrality and the richness of medicinal plants species that they maintain in their homegarden, controlling for village, sex, and age (Table 3). Results indicate that a person’s degree centrality has a positive and statistically significant association with medicinal plant richness (coef = 0.122; p=0.000). In other words, the higher the number of connections that a person has in the exchange networks, the higher the richness of medicinal plants the person maintains in her/his homegarden. The statistical significance of the “closer village” dummy variable (coef=0.454; p=0.004) denotes that informants in the closer village have higher medicinal richness compared with informants in the more isolated village. Results also indicate that women have higher homegarden medicinal richness than men (coef= -0.621; p=0.000), and that people with greater medicinal plant knowledge (coef=0.502; p=0.008) tend to maintain greater medicinal species richness in their homegardens. Age, however, was not significantly associated with homegarden medicinal richness.

Table 3.

Poisson multivariable regressions between informants’ medicinal plant richness in homegardens and individual centrality measures.

Medicinal plant richness
Model [A] [B] [C] [D]
Number of observations (n) 80 80 76 74
Explanatory variable
Degree 0.122 (0.024)*** 0.127 (0.026)*** 0.126 (0.025)*** 0.136 (0.030)***
Control variables
Closer village 0.454 (0.156)*** ^ ^ ^
Clan (omitted clan 6)
1 ^ 0.187 (0.326) -0.234 (0.353) 0.035 (0.340)
2 ^ -0.068 (0.214) -0.195 (0.216) -0.424 (0.265)
3 ^ -0.447 (0.240)* -0.075 (0.245)*** -0.637 (0.256)**
4 ^ -1.012 (0.533)* -1.107 (0.534)** -0.956 (0.538)*
7 ^ 0.389 (0.208)* 0.095 (0.240) 0.225 (0.213)
8 ^ -0.011 (0.313) -0.295 (0.376) -0.252 (0.317)
Man -0.621(0.160)*** -0.626 (0.161)*** -0.641 (0.168)*** -0.472 (0.168)***
Age 0.029 (0.022) 0.029 (0.023) ^ ^
Age2 -0.000 (0.000) -0.000 (0.000) ^ ^
Medicinal knowledge 0.502 (0.188)*** 0.390 (0.203)* 0.290 (0.204) 0.079 (0.221)
Years in household ^ ^ 0.032 (0.011)*** ^
Years in village ^ ^ ^ 0.015 (0.005)***
R2 0.22 0.25 0.25 0.25
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†

For definition of variables see Table 2. Robust standard errors in parenthesis.

* ,**, and *** significant at the 10%, 5% and 1% level.

^

variable intentionally omitted. Model C used in robustness analysis.

Model [B] resembles model [A], except that instead of village, we used a set of dummies to control for clan membership. As in the previous model, we found that degree centrality is associated with greater medicinal richness in homegardens (coef=0.127; p=0.000). Compared with people in clan 6, people in clans 2,3,4 and 8 have less homegarden medicinal richness and people in clan 1and 7 have more (see Table 3).

In model [C], we excluded the variables age and age squared (not significant in previous models) and added the number of years a person has resided in the household. As in the two previous models, we found that degree centrality is associated with higher medicinal plant richness (coef= 0.126; p=0.000). In this model, the variable man (coef=-0.641; p=0.000) and years residing in the household (coef=0.032; p=0.003) are associated with medicinal richness, which suggests that women who have had gardens for longer periods also have more medicinal plants in their gardens.

In our final model [D], we controlled for the years residing in the village. Again, we found a positive and statistically significant association between degree centrality and richness (coef=0.136; p=0.000). As in previous models, the variable man is also significantly associated with richness as is years residing in the village, meaning that people who have longer residency in the same village maintain more medicinal plant richness in their homegardens.

We tested the robustness of the findings by running a set of variations of our best model (Table 3, model C; R2= 0.25). In our two first robustness tests (see Table 4, models [a] and [b]) we changed the explanatory variable using betweenness centrality and egobetweenness instead of degree centrality. In the third robustness model ([c]), we changed the outcome variable to total richness and kept the same controls as in the Model C. The last robustness model ([d]) explored the possible effect of having censoring in the data (18 people did not have any medicinal plants) by fitting a Tobit multivariate regression rather than a Poisson multivariate regression model. Results from the robustness analysis confirm that other centrality measures are also associated with medicinal richness. Robustness analysis also suggests that the variable degree centrality has a positive association with total richness in homegardens (coef=0.092; p=0.000). Finally, the association between degree centrality and richness is also maintained when running a Tobit multivariate regression model (coef=0.457; p=0.000). In summary, results suggest that the associations found in Table 3 are robust to changes in the specification model.

Table 4.

Robustness analysis, variations of Model C.

[a] [b] [c] [d]
Medicinal plant richness Medicinal plant richness Total richness Tobit regression
Number of observations (n) 76 76 76 76
Explanatory variables
Degree ^ ^ 0.092 (0.012)*** 0.457 (0.112)***
Betweenness 0.002 (0.000)*** ^ ^ ^
Egobetweenness ^ 0.023 (0.006)*** ^ ^
Control variables
Clan (omitted clan 6)
1 -0.570 (0.339)* -.0404 (0.344) 0.446 (0.147)*** -0.422 (1.257)
2 -0.334 (0.215) -0.166 (0.218) 0.339 (0.097)*** -0.104 (0.856)
3 -1.028 (0.252)*** -0.797(0.243)*** -0.311(0.111)*** -1.596 (0.849)*
4 -1.419 (0.524)*** -1.289 (0.529) -0.457(0.203)** -1.753 (1.310)
7 0.069 (0.243) 0.218 (0.242) 0.199 (0.120)* 0.691 (1.053)
8 -0.344 (0.374) -0.241 (0.378) 0.079 (0.165) -0.820 (1.289)
Man -0.676 (0.170)*** -0.739 (0.165)*** -0.346 (0.072)*** -1.631 (0.581)***
Medicinal knowledge 0.330 (0.205) 0.295 (0.204) 0.274 (0.089)*** 0.731 (0.746)
Years in household 0.037 (0.011)*** 0.032 (0.011)*** 0.006 (0.005) 0.088 (0.042)**
R2 0.22 0.22 0.33 0.13
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†

For definition of variables see Table 2. Robust standard errors in parenthesis.

* ,**, and *** significant at the 10%, 5% and 1% level.

^

variable intentionally omitted.

Discussion

In this work, we aimed to assess the influence that medicinal plant exchanges through social networks have for homegarden medicinal plant richness by applying social network analysis methods. Our results suggest that Tsimane’ social organization, specifically kinship and gender relations, notably influences exchange patterns. Our findings also show that people who are more central in the network (i.e. who hold higher centrality measures) maintained greater medicinal plant richness, as well as total richness, in their homegardens. Women also maintain a higher richness of medicinal plants in their homegardens than men.

Previous studies suggest that social organization shapes the pattern of social exchanges in small-scales societies affecting, for example, crop diversity (Leclerc and D’Eeckenbrugge 2012, Labeyrie et al. 2013) and local ecological knowledge (Salpeteur et al. 2015). Researchers have also argued that planting material exchanges are by no means ‘free-flowing’ (Coomes and Ban 2004) but, rather, are usually confined to kinship networks (Buchmann 2009; Aguilar-Støen et al. 2009) in which women often have a prominent role (Boster, 1985; Sereni Murrieta and Winklerprins 2003). As has been shown elsewhere (Coomes and Ban 2004) it is possible that this pattern also increases the opportunities to access new planting material for homegardens. For example, among the Achuar in the Peruvian Amazon, planting material, such as seeds or cuttings, moves mostly through matrilineal kin networks, particularly from female-to-female (Perrault-Archambault and Coomes 2008). For the Achuar, gardening is traditionally a woman’s responsibility and, as in other Amazonian societies, high agrobiodiversity in gardens confers prestige to its owners (Descola 1986 as cited in, Perrault-Archambault and Coomes 2008).

Our results support these previous studies, showing that exchange of knowledge and plant material exchanges among the Tsimane’ are not random, but embedded within networks based on kinship and gender relations. Results suggest that networks are gendered, presenting homophily, where women performance is prominent. Tsimane’ social organization can then help to explain our findings. It is mostly based on kinship and, within a village, extended families’ households are spatially clustered. Socializing among the Tsimane’ consists of visits, which are an essential means to maintain close relations. Visiting usually occurs between same-sex kin and affines (Ellis 1996), which would facilitate exchanges among members of the same sex and clan, and also explain why is it that larger kin groups tend to have more exchanges. Tsimane’ women are considered as the main health custodians who are responsible for meeting the health needs of their families in the first instance (Chiccón 1992). Gardening also seems to be also primarily a women’s domain, a productive role that is related to their duties as caregivers in the domestic sphere. Women are prominent garden managers across the Latin American region (see Howard 2006 for a review), which is also related to the maintenance of traditional communal social relations, community food security, and health (Lope-Alzina and Howard 2014, Finerman and Sackett 2003). Homegardening provides women with an opportunity to engage in subsistence production that does not violate gendered norms about men’s privileges in the productive sphere (e.g. as ‘principle providers’) or about women’s ‘domesticity’, offering women sources of authority, autonomy, and status, and a place where they can develop specialized knowledge and provide visible means of recognition according to their cultural roles (Howard 2006, Lope-Alzina and Howard 2014). Homegardens are also considered as arenas for sociality and experimentation and are a source of pride and self-esteem for women (Heckler 2004). The Tsimane’ do not seem to deviate from this pattern.

Locations in a social network provide both possibilities and constraints for accessing resources and knowledge through other people in the network (Calvet-Mir et al. 2012; Kawa et al. 2013), given that, in each particular situation, networks can either support or constrain access to these people. Access to other people’s planting material is important for developing and maintaining diversity in homegardens (Coomes 2010). In this study, network centrality seems to be associated with a person’s performance in medicinal plant exchange networks, as people with higher centrality in the network also maintain higher medicinal plant richness in their homegardens. Compared with men, women are more central in the exchange networks, a finding that fits well with women’s prominent role as main garden managers. The gendered networks, in which women have higher centrality measures, may indicate that they have more access to medicinal planting material and associated knowledge. Other factors, such as the number of years that a garden has been tended by its owner and the number of years that a person has resided in the same village, also explain medicinal plant richness in Tsimane’ homegardens.

We would like to end by acknowledge the potential shortcomings of our interpretations since our data capture only a snapshot of network structure at a single point in time which, to be valid, assumes that network structures are stable (Howison et al. 2011). Data were also limited in that they were only collected on exchanges that occurred within the same village; exchanges with Tsimane’ residing in other villages were not considered, and nor were exchanges with non-Tsimane’ (i.e. with merchants and researchers). This limits the breadth and thus explanatory power of our results, since social networks are dynamic and are embedded within networks at higher local and regional scales.

Conclusion

This research suggests that social network analysis is an appropriate and useful tool for tracing the uneven flow of homegarden medicinal planting material and knowledge among the Tsimane’. Homegarden medicinal plant richness and total plant species richness are related to gardeners’ centrality in the exchange networks, meaning that people with greater centrality maintain greater richness. As women generally hold higher centrality, they also maintain greater richness than men. Similarly, the number of years the garden has been tended and the number of years a person has resided in the same village are positively related with greater medicinal plant and total plant species richness. This study also shows that social organization, specifically around kinship and gender, notably influence medicinal plant knowledge and planting material exchange patterns, highlighting that, together with agroecological conditions, social relations among gardeners and the culturally specific social structure are important determinants of plant species diversity in homegardens. This suggests that agrodiversity and culture are closely interrelated (Howard 2006, Leclerc and D’Eeckenbrugge 2012).

Understanding which factors pattern general species diversity in tropical homegardens, and medicinal plant diversity in particular, can help policy makers, health providers, and local communities to better understand how to promote and preserve medicinal plants in situ, so that they can continue to provide locally accessible, culturally appropriate, and economically affordable health care options for people with scarce access to biomedical healthcare systems. Such understandings promote the use of biocultural approaches, that as well as being gender sensitive, also offer a culturally appropriate means to reduce the global and local loss of both biological and cultural diversity.

Supplementary Material

Appendix 1

Table 2.

Definition and descriptive statistics of variables used in multivariate analysis

Variable Definition N Mean SD Min Max
Outcome variables
Medicinal richness Total number of medicinal plant species inventoried by informant homegarden 86 2.5 2.611062 0 12
Total Richness Total number of plant species inventoried by informant homegarden 86 12.33721 8.189822 2 39
Explanatory variables
Degree Number of people with whom a person is directly connected 86 4.184989 2.83004 1 11
Betweenness Grade of intermediation among people where each person is directly and indirectly connected 86 94.01606 111.9947 0 585.767
Egobetwenness Grade of intermediation among people where each person is directly connected 86 8.90222 11.74843 0 50
Controls
Age Age of the person, in years 85 39.27 16.62 14 88
Age2 Age squared term to control for non-linearity in the relation between age and richness in homegardens 85 1815 1687.38 196 7744
Medicinal knowledge Average number of medicinal uses known per plant known from a knowledge survey including 16 medicinal plants 80 0.87 0.41 0.125 2
Years in household Number of years a person resided in the in the same household 81 6.69 7.09 0.1 25
Years in village Number of years that the person resided in the village 79 23.24 13.50 1 66
N %
Closer village Village of residence
closer to town 55
more isolated 31
Clan clan membership
1 8 9.30
2 17 19.77
3 22 25.58
4 6 6.98
5 2 2.33
6 11 12.79
7 7 8.14
8 11 12.79
9 2 2.33
Man Dummy variable that captures the sex of the informant, (1=man, 0=woman)
women 46 53.49
men 40 46.51
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Acknowledgements

This research was funded by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement FP7-261971-LEK to Reyes-García. We are grateful to all of our informants for their willingness to share their time and knowledge, and for kindly showing us their homegardens. We also thank the Gran Consejo Tsimane’ and the CBIDSI for providing logistical support and office facilities in San Borja; and we thank Marta Pache, Paulino Pache, Vicente Cuata, Sascha Huditz, and Giuliana Castañeda for field assistance, as well as Maximilien Guéze for assistance with mapping. We are very grateful to two anonymous reviewers for their thoughtful suggestions.

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Supplementary Materials

Appendix 1
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