Indigenous Siberian Food Sharing Networks:
Social Innovation in a Transforming Economy
John P. Zikera1 and Karen S. Fulkaa

Department of Anthropology, Boise State University, Boise, ID 83725-1950

Abstract The sustainability of indigenous communities in the Arctic, and thevulnerable households within, is in large part dependent on their continuing foodsecurity. A social food-sharing network within the Ust’-Avam community on the
Taimyr Peninsula in northern Siberia is analyzed for underlying patterns of resilience and key evolutionarily stable strategies supporting cooperative behavior. Factors influencing the network include interhousehold relatedness, reciprocal sharing, and interaction effects. Economic-need and social association also influence
sharing. In sum, these findings illustrate the robustness of self-organizing distribution networks in an economic context of uncertainty.

1 Introduction
Anthropologists have documented traditions encouraging generous transfers of essential foodstuffs among a wide variety of indigenous groups as diverse as the
Hadza of Tanzania (Wood and Marlow 1982), Ache of Paraguay (Kaplan and Hill
1985) and Apache of southwestern United States (Basso 1996). These traditions ofsharing are conventions that prioritize social relationships, and such customs still
are observed by many households in Siberia. The present article identifies themechanisms by which sharing networks developed to provide food security during
the economic crisis that ensued following the collapse of the Soviet Union. Here,
we present results from a social network analysis of food sharing events documented among 10 women in the community of Ust’-Avam in 2001, and discussthese results in light of comparable network analysis studies.
Ust’-Avam is situated near the confluence of the Avam and Dudypta Rivers on
the Taimyr Peninsula in north central Siberia. Two indigenous small-numbering
peoples are represented in the community—the Dolgan and the Nganasan—alongwith a small minority of ethnic Russians and other former Soviet nationalities. The
community is approximately 250 km by air from the regional center, Dudinka, and
1 Corresponding author: John P. Ziker, email: jziker@boisestate.edu

2

400 km by water from the large industrial city of Noril’sk (Ziker 2002). Transportation to and from the cities is expensive and time-constrained, and the community
depends primarily on the production from hunting, fishing, and trapping activities
for nutrient dense foods. Approximately 60% of caloric intake (and almost all theprotein) is derived from local hunting and fishing (Ziker 2014).
Ichikawa (2004) describes three common phases of food sharing for huntergatherers: 1) obligatory sharing after the kill; 2) voluntary sharing of raw meat
(large, first-butchering portions) to those not participating in the kill; and 3) sharing portions prepared for consumption. Individual decisions and structural constraints vary across the distribution cycles of large-game kills. Making these distinctions is important because each phase of distribution is the potential outcomeof varying socio-ecological pressures, and may be dependent on the resources being procured and residence patterns.
In the Ust’-Avam context, obligatory sharing amongst individuals taking part in
the hunt immediately follows the kill. A second wave of sharing is done after
hunters return to their homes. Normally, the wife or mother of the hunter will
manage the distributions at this phase. Women often share portions with theirfriends, and they, in turn, share to their friends. Interhousehold sharing in the third
phase of distribution is also a common occurrence in Ust’-Avam (Ziker andSchnegg 2005). In the present article, we focus on the second wave of sharing in
Ust’-Avam, particularly the women’s sharing network, whereby food portions are
redistributed to households without a resident hunter. We consider the crosscultural work on food sharing and evolutionary hypotheses for cooperation to examine the sharing network. The research questions can be summarized thus: 1)
What is the relative importance of kinship, reciprocity, indirect reciprocity, demand sharing, and costly signaling in women’s sharing networks?; and 2) How
does the evidence for food sharing in Ust'-Avam compare with other kinds of social networks?

2 Explanatory Hypotheses and Comparative Studies
Studies of food sharing among other hunter-gatherer groups have demonstrated avariety of correlations relevant to explanatory hypotheses for helping behavior:
kin selection, reciprocal altruism, indirect reciprocity, signaling, and demand sharing (Gurven 2004). Indicators of kinship also are linked to food sharing behaviors
in numerous studies. For example, among the Indonesian Lamalera whale hunters,
Nolin (2011) found that biological kinship is a better predictor of food-sharing relationships than social kinship (i.e., patrilineage membership). Consanguineal(blood) relatedness predicts food sharing on Ifaluk atoll (Betzig and Turke 1986),
Ache farmer-foragers in Paraguay (Gurven Allen-Arave Hill and Hurtado 2001),
Hadza hunter-gatherers in Tanzania (Wood and Marlowe, 2013), and Mayangnaand Miskito horticulturalists in Nicaragua (Koster and Leckie 2014). Favoring relatives in food sharing follows the psychology of nepotism and the predictions of

3inclusive fitness theory for the evolution of altruistic behaviors via improved outcomes for descendants and collateral relatives (Hamilton 1964).
According to theory of reciprocal altruism (Trivers 1971), rewards accrue directly to cooperative individuals, benefits can be delayed and favoring kin is notnecessary. In their work with the reservation Ache, Gurven et al (2000) foundsignificant positive correlations between the amounts of food transferred among
pairs of families—demonstrating the contingency component required of reciprocal altruism models. Reciprocal food sharing has been postulated to be a mechanism that reduces the variance in daily food intake among regularly cooperating
members of a community through delayed returns (Cashdan 1985; Kaplan and
Hill 1985). Nolin (2010) also found that reciprocity was the strongest predictor of
Lamalera food sharing patterns.
In indirect reciprocity based on image scoring (reputation), individuals invest
only in partners that have sufficiently helped others in the past and who are, therefore, interested in how others view them (Alexander 1987). Among Ache, Gurven
et al (2000) found that consistently high food producers give more than they receive. However, these hunters gain the least on a daily basis, because on any given
day they are more likely to have their own supplies. The explanation Gurven et alprovided for this apparent generosity is that high producers receive additional foodduring hard times. Generosity signaling is hypothesized to provide long-term riskbuffering benefits to the signaler by maintaining social norms of sharing and may
represent indirect reciprocity via image scoring. A recent study of men and women Martu hunters in the western desert of Australia (Bliege Bird and Power 2015)
also found support for prosocial signaling. Those who “consistently pay higher
costs to share,” were not necessarily better hunters, but they were preferred forcooperative hunting activities. Another form of hypothesized indirect reciprocity
is generalized reciprocity (Bshary and Bergmüller 2008). Rather than purposefully
investing to receive benefits in the future via a good image score, individuals who
receive help are simply willing to invest into third parties. This is synonymouswith the concept of pay it forward, so wonderfully represented in the film by thesame name (Abrams et al 2000).
The costly signaling model proposes that big-game hunting evolved as part ofcompetitive displays, rather than as part of provisioning relatives, risk buffering,
signaling, or avoidance of defense costs (Zahavi 1975). Marcel Mauss (1954[1925]) discusses the obligatory, public, and sometimes antagonistic nature ofgifts, particularly amongst hunter gatherers on the northwest coast of North America, and notes that this type of gifting is likely a form of costly signaling. Hawkes
and Bliege Bird (2002), using data from Torres Strait Meriam and TanzanianHadza, argue that the distribution of meat can best be seen in light of the evolutionof men’s subsistence work, where “honesty is at a premium” and political alliances contribute to status acquisition. Similarly, Smith, Bliege Bird, and Bird (2003),
found that Meriam turtle hunters gain social and reproductive benefits via meat
distribution, which is consistent with the idea that hunting is a form of costly signaling. Nolin’s (2012) most recent analysis of sharing by Lamalera men withleadership positions found excessive giving by leaders is consistent with the shar-

4

ing-as-signaling hypothesis. Among Lamalerans, however, status did not explain
much of the variation in the sharing because the exchanges observed in high-statushouseholds were best explained by the same factors that defined the activities ofother households. This pattern suggests that multiple mechanisms may operate
simultaneously to promote sharing in Lamalera.
When the consumption of food stores exhibits declining marginal value to the
producer, then marginal portions are worth more to other individuals who have nofood. As a result, there may be a cost associated with defending these food reserves and a producer should relinquish marginal portions to other individuals if
the price of defense is greater than the additional value gained by others (Blurton
Jones 1984). Blurton Jones referred to this as tolerated scrounging. An additional
prediction of this model is that the disparity in amounts should be low, since portions are given out until the marginal consumption value or utility is equal for all
potential recipients (Winterhalder 1996). For example, Bliege Bird, Bird, Smith,and Kushnick (2002) found that the marginal valuation of the food to the acquirer
conditioned Meriam sharing, but was only weakly affected by harvest variance—
leading authors to conclude that sharing on Mer does not function to reduce foraging risk. Similarly, Peterson’s (1993) concept of demand sharing emphasized thesocial and symbolic significance of requests for food and other resources. Whileacknowledging its correspondence to the tolerated-scrounging model, Peterson
viewed demand sharing as part of a wider testing behavior that is used to establish
relationships by incurring debt. Both tolerated scrounging and costly signaling
models were more egocentric in their hypothesized benefits than were the kinship,
reciprocal altruism, indirect reciprocity, and generosity signaling models. However, economic need can intersect with kinship to drive sharing without the presenceof tolerated theft or demand sharing, as Koster (2011) demonstrated for the
Mayangna and Miskito horticulturalists in Nicaragua.
Furthermore, most empirical research on indigenous food sharing networks indicates a multiplicity of mechanisms at play (Nolin 2012). The particular combination of sharing strategies in any given society is likely to make the most sensewhen viewed in light of the local socio-ecology of food production andembeddedness in surrounding economies and societies. A case in point that looks
at the multiplicity of mechanisms involved in food sharing in northern Canada isElspeth Ready’s (2016) research in Kangiqsujuaq. She found that food sharing
does not serve a single function, such as reciprocity. Instead, she argued that foodsharing emerged as a complex of social, political and economic phenomena that
accomplish different objectives for actors based on their social position. The network approach adopted in Ready’s research highlights the conjugate role of individual decisions and structural constraints on the economic strategies available tohouseholds. Her detailed analysis demonstrated that the benefits of food sharing
were concentrated among high income/high harvest households—those who wereable to give the most. Likewise, a basic premise of the Ust’-Avam research wasthat a multiplicity of factors is likely informing decisions to share food beyond thehousehold.

5

3 Methods
John Ziker’s (JPZ) research in the Ust’-Avam community comprised a sum of 36
months from 1994 through 2007. During field trips in 2001 and 2003, Ziker investigated the primary distributions of hunters and their respective households (Ziker
et al 2015), as well as women’s sharing patterns discussed here. Women residing
in a household without a hunter (n = 10) were asked to complete a diary (a survey
developed specifically for this investigation) by making entries for seven days,
every three weeks. Diary responses and the results of interviews and observations
JPZ conducted over a 12 week period (August - October, 2001) were combinedwith community census and genealogical data for our analyses. These data included 162 distributions among 69 household dyads. One report from August, 2002
and the remainder of the 2003 data were not included in this analysis.
To analyze the independent variables influencing the Ust’-Avam sharing patterns, we used matrix regression, specifically the MRQAP (double-Dekker semipartialling) process in UCINET (Borgatti Everett and Freeman 2002). The independent variables used in the matrix regressions included: maximum genealogicalrelatedness between households (kinship, or rmax), the transpose of the dependent
variable matrix representing reciprocal food transfers (reciprocity), ego-to-sharer
returned gifts in non-food goods and services (returned gifts), sharer-to-ego visitation frequency (social association), the differences in the number of active individual sharers in sharing households (active sharers differences), and the differences in the number of total household occupants (occupant differences). Thesevariables were used to represent the predictions derived from explanatory hypotheses (Gurven 2004; Ziker and Schnegg 2005). Interhousehold relatedness and reciprocal food sharing were relevant to kin selection (Hamilton 1964) and reciprocal altruism (Trivers 1971). The returned gifts variable was relevant to the costly
signaling hypothesis (Zahavi 1975). The active sharers differences were relevant
to relative economic need, and thus, the demand sharing hypothesis (Peterson
1993). The number of actively sharing household members and the sum of household members were used to provide indices of relative need (Blurton Jones 1984).
The sharer-to-ego visitation frequency provided an independent measure of social
association (following Koster and Leckie 2014). An additional attribute matrix included the sum of active individual sharers in sharing households to represent productive capacity similarities. This variable was relevant to the hypothesized riskbuffering function of reciprocal altruism.

4 Results
The following independent variable matrices were found to individually predict
the overall food sharing pattern (see Table 1): maximum genealogical relatedness
between households (rmax), reciprocal food transfers (reciprocity), ego-to-sharer

6

returned gifts in non-food goods and services (returned gifts), sharer-to-ego visitation frequency (social association), and the differences in the number of active individual sharers in sharing households (active sharers differences). The variables
in Table 1 were arranged by the strength of each individual model’s R2, providing
an indication of the relative magnitude of the variance explained in the frequencyof sharing.
Table 1. Variance explained for interhousehold food transfers (main effects)
Independent Variables

Model R2

Kinship (rmax).072***

Reciprocity.052***

Returned gifts.014**

Social Association.013**

Active sharers differences.012**

Occupant differences. 001aa** p < 0.01, *** p < 0.00, not significant p >.05

Taken alone, each of the main independent variable’s effects tells a limited story. To explore underlying patterns in more detail, we introduced interaction terms
using the product of each pair of independent variable matrices. The new set of
matrix regressions included the paired main effects along with their interaction toreveal more about the structural features underlying the pattern. Finally, a series ofcombined models were generated to arrive at a model explaining the most variation in the derivative sharing network with the least number of variables. Our best,
combined model (see Table 2) included the following main effects and one interaction: interhousehold genealogical relatedness (rmax), reciprocal food transfers(reciprocity), the dissimilarity in number of active household members (active
sharers differences), an interaction term (rmax * reciprocity), and sharer-to-ego visitation frequency (social association). The model R2 = .117 (p < .001) indicatedthat this set of independent variables explained 12% of the variance in the total
food sharing pattern.
Table 2. Best combined model for frequency of interhousehold food transfers
Independent Variables

Unstandardized CoefficientStandardized Coefficient

Kinship (rmax).720.210***

Active sharers differences.024.122**

Reciprocal food transfers.126.126**

rmax * Reciprocity

.161

.053*

Social Association.085* p < .05 ** p < .01 *** p < .001

.053*

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There are two things to note about this combined model in comparison to themain effects presented in Table 1. First, the variable representing the frequency ofreturned gifts, which appeared to be strong in the individual results, dropped outof significance (also found in Ziker et al 2015). When we included the frequencyof returned gifts in the model, the overall model coefficient was unchanged andthe standardized variable coefficient (p = .135) was not statistically significant.
The fact that this variable drops out of significance in the multiple regression
model indicated that the variation in food sharing explained by returned gifts was
better understood by other variables. This had obvious implications for the hypotheses under consideration. Second, an interaction between kinship and reciprocity was statistically significant and it remained in the combined model alongwith the main effects of kinship and reciprocity. Ziker and Schnegg (2005) andZiker et al (2015) found a similar interaction in food shared at meals and in the
primary distributions in Ust’-Avam. In both studies this effect represented something more than generous giving to kin—likely childcare and meat pooling among
extended households, respectively. Correspondingly, Axelrod and Hamilton(1981) theorized that kinship can help initiate systems of reciprocity in small
groups.
The combined model in Table 2 shows that interhousehold relatedness (asmeasured by the strongest genealogical link between households) and economic
need (as measured by the differences in the number of active sharers in eachhousehold pair) were statistically the most significant variables to explain the foodsharing pattern. In addition, reciprocal food sharing, the interaction of kinship andreciprocity, and the social association indicator were relevant variables in thissharing network. Uncooked portions of meat and fish provided to egos by otherhouseholds were shared to additional households by the pathways of kinship andsocial association (friendship) and were also influenced by need. Unlike many
ethnographically documented food-sharing networks, status striving did not appear to be a factor in conditioning resource flows in this network. While economic
need and reciprocal exchange were utilitarian functions that the network served tofulfill, kinship and friendship ties provided a social matrix through which resources were redistributed in this community.

5 Discussion
A few words from the sharing diaries of the women in Ust’-Avam help to contextualize these findings. Regarding the question, “Why did you share? What do you
get out of sharing?” answers include: “Pleasure, joy;” “Nina also shared;” “I simply gave it when I was outside;” “You need meat, just take it;” and “She’s a neighbor, I simply gifted it.” Regarding the question “What did you or do you do for the
person who shared?” answers include: “I thanked them;” “I give to her too—if she
has it she gives it to me and if I have it I give it to her;” and “Together we dranktea, ate breakfast, and went to gather berries.” As can be seen here, there are a8combination of factors that leads to sharing, but the desire to attain status or leverage over other households is not among them. This finding is consistent with traditional knowledge about sharing patterns in the community (Ziker et al 2015).
Comparing the results of three food sharing studies in Ust’-Avam, we find thatthe women’s food sharing network is influenced by some of the same variables
that condition the network of primary distributions as reported by hunters (Ziker et
al 2015) and the consumption events observed by JPZ in 1994 through 1997
(Ziker 2002; Ziker and Schnegg 2005). There are important distinctions that illustrate the relevance of sharing phase. One difference relates to the inclusion of thermax * reciprocity interaction. Ziker et al (2015) report that when the rmax * reciprocity interaction is added into a combined model for primary distributions withthe main effects, the coefficient on the main effect of reciprocity changes signs
from positive to negative. This means that the frequency of food sharing by hunters increases as reciprocal sharing by recipient households decreases. This result
provides evidence for three hypothesized kinds of sharing: 1) nepotistic food sharing; 2) food sharing increasing with reciprocal relationships between relatedhouseholds; and 3) food sharing as indirect reciprocity, as either generosity signaling (Gurven et al 2000; Bliege Bird and Power 2015) or indirect reciprocity (i.e.,
paying it forward) (Bshary and Bergmüller 2008).
We find the analogous effects for the first two types of sharing in this women’ssharing network, but the opposite effect for the third type. There is a positive correlation between interhousehold relatedness and food sharing, as well as a positive
correlation between food sharing and the interaction of interhousehold relatedness
and reciprocal sharing. However, when controlling for kinship and the interaction
effect in our final model, the sign on reciprocal sharing remains positive. This indicates that reciprocal sharing (i.e., giving back) with relatedness held constant is
the predominant pathway for sharing, rather than indirect reciprocity. Furthermore, since we are controlling for economic need, social association, and gifts returned, it is likely that such reciprocal sharing is related to a risk-buffering function, rather than from incurring debt for prestige (Hawkes and Bliege Bird 2002;
Nolin 2012; Smith Bliege Bird and Bird 2003).
Overall, household economic need appears to be prompting the later phases offood sharing (i.e., the women’s sharing and meal sharing), rather than initial phases. Although providing goods or services back to givers is individually predictivein the primary distribution and women’s sharing networks, it drops out of significance in the combined models, thereby suggesting that this effect is part of the reciprocal relationships households have as kin or friends, rather than payback forfood transfers or status-seeking activities as predicted by the costly signaling hypothesis. All in all, sharing is more prosocial in Ust’-Avam and less influenced byegocentric pathways.
One limitation of this study is that it is based on a partial snowball sample ofthe community. Although our analysis finds several independent variables that arehighly significant predictors of flows in this sample, the use of social network variables (as in Ready 2016) is not justified without a complete network of the community. This may be why the amount of total variation explained is low. Future

9studies should integrate network statistics from a complete network sample to test
the relative importance of network position of households versus other kinds of
independent variables, such as the interhousehold relatedness, as analyzed here.
A second limitation of our study is in comparing the novel sharing patterns developed in this economically challenged community with that of other remote, resource challenged groups. While our study relies heavily on the foundational research of traditional hunter-gather groups, comparisons with more industrialized
populations needs further exploration. For example, virtual networks are allowing
people to overcome distance constraints and garner access to a broader range ofresources, making them a conduit for social innovation. There are similarities insome of the patterns of sharing behaviors identified in online communities andthose found in our study. For instance, while status striving is often an attribute ofmembers in online groups (Hanson and Jiang 2016; Utz and Jankowski 2016), it is
not the only reason for group participation. Porter et al (2011) report that status
striving is but one of the common needs fulfilled—others being information seeking, desire to help others, relationship building, belonging, enjoyment, and social
identity. In short, participation in virtual networks permit individuals to fulfill
psychological needs, whether utilitarian or hedonic (Hanson and Jiang 2016; Porter et al 2011; Utz and Jankowski 2016).
Regardless of an individual’s motivation, successful virtual networks, likethose of face-to-face systems, require member contribution. Porter et al 2011 notesthat successful virtual networks demonstrate reciprocity through member contributions—allowing individual fulfillment needs—and when member needs are supported group trust increases. Once trust is established, an environment exists tofoster cooperation and continued sharing within the group. Further work shouldlook at the relationship between online sharing and sharing of material resources.

6 Conclusion
Following Ichikawa’s (2004) typology, we have found that the second phase offood distribution operating through women’s networks is a social innovation facilitating household resilience in the face of change. In the Ust’-Avam women’s
network, social association, kinship, and friendship are the pathways by whichfood is shared beyond the household. These food sharing events appear to be driven by social relationships and cooperation, rather than status striving in the Ust’Avam community.
Importantly, this research illustrates the resilience of traditional sharing strategies. Cooperation amongst community members is essential for survival in the Siberian Arctic as Russia continues its economic transformation. We found that successive waves of food sharing serve a slightly different array of economic andsocial functions. Food sharing helps to establish and maintain these important social ties, providing a buffer against the unpredictable economic conditions. This

10social innovation of food sharing provides a safety net for vulnerable households
and is founded in multiple evolutionarily stable strategies promoting cooperation.
Acknowledgments JPZ is grateful to friends in Taimyr who participated in the research andhosted repeated visits. JPZ wrote the article. KSF conducted data manipulation and commented
on earlier drafts. JPZ’s field research was supported by grants from the National Science Foundation OPP 0631970 and the L.B.S. Leakey Foundation.

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