Cooperative pastoral production — the importance of kinship

Evolution and Human Behavior 31 (2010) 246 – 258

Original Articles

Cooperative pastoral production — the importance of kinship☆ Marius Warg Næssa,⁎, Bård-Jørgen Bårdsenb , Per Fauchaldb,c , Torkild Tveraab

a

Department of Archaeology and Social Anthropology, Faculty of Humanities, Social Sciences and Education, University of Tromsø, 9037 Tromsø, Norway b Norwegian Institute for Nature Research (NINA), Arctic Ecology Department, Polar Environmental Centre, 9296 Tromsø, Norway c Department of Biology, Faculty of Science and Technology, University of Tromsø, 9037 Tromsø, Norway Initial receipt 4 September 2009; final revision received 6 December 2009

Abstract While there is a general assumption that labour has a positive effect on pastoral production, studies that have quantified this relationship have been characterized by ambiguous results. This is most likely related to the fact that possible cooperative pastoral production has been little explored in the literature, although it is well documented that nomadic pastoralist households share and exchange labour in so-called cooperative herding groups. Consequently, this study aims at investigating possible cooperative labour-related effects on production among Saami reindeer herders in Norway by using kinship relations as a proxy for cooperation. This study found that cooperative labour investment is important for Saami reindeer herders, but that the effect of kinship and labour needs to be understood in relation to each other. When assessing the effect of labour and kinship simultaneously, both labour and genealogical relationship had positive effects on herd size. We also found a positive interaction between kinship and labour suggesting that high levels of relatedness coupled with a large potential labour pool had an increasingly positive effect on herd size. © 2010 Elsevier Inc. All rights reserved. Keywords: Nomadic pastoralism; Labour; Production; Cooperation; Rangifer tarandus; Saami reindeer husbandry; Tragedy of the commons; Negative density dependence

1. Introduction It has been argued that pastoralists maximize long-term household survival by minimizing the risk of destitution (see Mace & Houston, 1989, p. 188). In a previous study of Saami reindeer husbandry, it was found that one viable path for obtaining this goal is to accumulate reindeer numbers (Næss & Bårdsen, 2009). In essence, this implies that keeping of large herds is an efficient risk-reducing strategy for nomadic pastoralists (cf. Fratkin & Roth, 1990; Roth, 1996; Hjort, 1981; Templer et al., 1993; Barth, 1964; McPeak, 2005). Furthermore, it has also been argued that pastoralists build large herds in order to transfer them in bridewealth (McCabe, 2004) or to increase reproductive success (Mace, 1996; see also Borgerhoff Mulder & Sellen, 1994). Additionally, Coughenour et al. (1985, p. 619) has argued that pastoralists keep large herds because: (1) milk ☆ The present study is part of the Ecosystem Finnmark project and was funded by the Research Council of Norway (the FRIMUF program). ⁎ Corresponding author. E-mail address: [email protected] (M.W. Næss).

1090-5138/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.evolhumbehav.2009.12.004

requirements, for both humans and young animals, necessitate a large fraction of mature females, giving herds excessive reproductive capability; and (2) individual animals are in general characterized by low productivity, necessitating more animals per person. One important mechanism facilitating the accumulation of large herds of livestock may be argued to be pastoral labour investment. Nevertheless, for pastoralists in general it has been argued that above some minimum labour requirement pastoral production is not influenced by additional labour investment (Helland, 1980, p. 15). Furthermore, it has been observed that a single herder can just as easily herd a flock of 20 sheep as one of 200, indicating that labour inputs are not central to pastoral production (Sieff, 1997; Borgerhoff Mulder & Sellen, 1994). A large part of the literature is, however, concerned with discussions of what aspects of pastoral production are sensitive to labour inputs, and Næss, Fauchald and Tveraa (2009) have argued that there exists a prevalent assumption of a positive effect of labour investment on various areas of pastoral production. Moreover, Bonte and Galaty (1991, p. 7) have argued that labour is a major constraint in all pastoral systems.

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Nevertheless, the studies that have tried to quantify this relationship have found conflicting evidence for a positive effect of pastoral labour on production (e.g., Yi et al., 2008; Scoones, 1992; Sieff, 1997; Roth, 1990; Turner & Hiernaux, 2008; Berhanu, Colman & Fayissa, 2007). In essence, this may be taken to imply that detecting pastoral labour-related effects is far from straightforward and Næss et al. (2009) have argued that nonsignificant results may be an effect of small sample sizes (see, e.g., Johnson, 2002; Anderson, Burnham &Thompson, 2000). This is especially pertinent if the effect of pastoral labour is marginal as some evidence suggests (Berhanu et al., 2007; Turner & Hiernaux, 2008; Næss et al., 2009). Moreover, previous studies have used different measures of pastoral production and as such do not represent clear-cut evidence for or against the assumed positive relationship (e.g., Roth, 1990; Turner & Hiernaux, 2008; Berhanu et al., 2007; Sieff, 1997). 1.1. Cooperative labour investment and production Moreover, previous quantitative studies have had an explicit bias toward within-household labour availability, and Næss et al. (2009) have argued that this has resulted in a neglect of possible cooperative production among pastoralists. Furthermore, Næss et al. (2009) argued that cooperative herding groups are a prominent feature of pastoral social organization, and that the prevalence of cooperative herding groups may indicate the presence of scale-dependent effects of pastoral labour on production. In essence, this implies that important mechanisms facilitating cooperative behaviour, such as kinship relations (see, e.g., Borgerhoff Mulder & Coppolillo, 2005; Alvard, 2003; Hamilton, 1964), may be an important factor influencing pastoral production. According to Axelrod (1984), benefits not easily obtainable by individuals may be available to cooperating groups. The problem, however, is related to ‘free riding’ where individuals who can benefit from mutual cooperation can do better by exploiting the cooperative behaviour of others (Axelrod, 1984, p. 92). As such, cooperative behaviour needs an explanation and one important mechanism argued to underlie cooperative behaviour is kin selection and inclusive fitness (see, e.g., Hamilton, 1964; Alvard, 2003; for a review, see Griffin & West, 2002). Apart from kinship relations, other prominent mechanisms explaining cooperation from this point of view are (1) reciprocity (Trivers, 1971), (2) signalling (for a brief review, see Smith & Bird, 2005) and (3) punishment (Axelrod, 1986). Moreover, (4) asymmetry in social relations has been argued to play a part in the emergence of cooperative social institutions (Richerson, Boyd & Henrich, 2003; see also Borgerhoff Mulder & Coppolillo, 2005, pp. 141–142), where some individuals have both the means and the incentives to enforce, e.g., costly punishment that facilitates cooperative behaviour. Following Smith (2003, p. 402), cooperation can be defined as collective action for mutual benefit, where collective action can be defined as when two or more

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individuals have to interact so as to achieve the specific goal. Furthermore, the simplest form of cooperation entails some form of coordination, i.e., when individuals share preferences so that they always benefit from cooperation (Smith, 2003, p. 402). This is usually referred to as mutualism and can be characterized as a broad definition of cooperation (Richerson et al., 2003, p. 358; but see Bowles & Gintis, 2003, p. 429, for a restricted definition of cooperation). In terms of cooperative production and cooperative labour investment, Smith (1981), for example, has argued that group formation by huntergatherers can potentially benefit individual foraging behaviour (see also Smith, 1997). The same has also been observed for whale hunters in Indonesia where cooperative whale hunting resulted in greater per capita returns than solitary fishing (Alvard & Nolin, 2002). By extension, by cooperating and sharing labour, individual pastoral households may reap additional benefits in terms of pastoral production. In other words, this approach assumes a mutualistic view of cooperative labour investment where individual households cooperate to increase their own production. Assuming a benefit from increasing labour inputs, however small, and that cooperation is based on sharing and exchange of labour, it is possible that the cost–benefit relationship of pastoral labour investment changes when working cooperatively, i.e., cooperation could be a least-cost combination among a set of feasible labour input combinations incorporating herding alone and herding cooperatively (Næss, submitted for publication Næss & Bårdsen, 2009). Assuming the validity of this statement, it may be argued that rather than expect that households with a large pool of available labourers should do better than households with few (as suggested by Bonte & Galaty, 1991, p. 8; Fratkin & Smith, 1994, p. 91), we should expect that households with extensive cooperative networks do better than households with less. Nevertheless, little research has been undertaken to quantify the possible cooperative aspects of pastoral production (Næss et al., 2009). While Næss et al. (2009) found evidence for a scale-dependent effects of pastoral labour on production among Saami reindeer herders in Finnmark, Norway, where a number of husbandry units within reindeer districts had a positive effect on both density of female reindeer and calf carcass body mass, the effect of possible cooperative behaviour was not explicitly measured. As a consequence, this study aims to explore the importance of cooperative production by using measures of kinship relations within reindeer districts as a proxy for cooperation as kinship has usually been conceptualized as an important prerequisite for cooperation (Gintis, Bowles, Boyd & Fehr, 2005; Griffin & West, 2002; Alvard, 2003; Smith, 2003; Hamilton, 1964). 1.2. Predictions To gain an increased understanding of possible cooperative production efforts in the Saami reindeer husbandry, we undertook three statistical analyses: (1) Labour

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investment, where we expected potential labour to be positively related to herd size. (2) degree of kinship relations (see Appendix B), where we expect that kinship is positively related to herd size, and (3) labour investment and degree of kinship relations, where we expect that both kinship and labour are positively related to herd size. Moreover, from a cooperative point of view, we also expect a positive interaction between kinship and labour, as districts with many husbandry units with a large degree of relatedness should cooperate more effectively. 2. Methods 2.1. Study area Saami reindeer husbandry has been said to be the cornerstone of the Saami culture in northern Fennoscandia (Bostedt, 2001). Reindeer husbandry is, however, historically relatively recent, only 3–400 years old (Paine, 1994; see, e.g., Bostedt, 2001; Bjørklund, 1990, p. 76, for different estimates) and probably evolved from a hunting culture based on wild reindeer. Over the years, the Saami reindeer husbandry has undergone changes, perhaps the most important being the change from milk and meat production with smaller herds to meat production alone with larger herds (Paine 1994). Traditionally, reindeer pastoralism was based on families, or households, which followed their herds year round where the pastoral economy was primarily tied to reindeer products (Vorren, 1978). During 1960–1990, the reindeer husbandry underwent major technological, economical and political changes where the production system changed from being subsistence based to a motorized and market-oriented industry (Riseth, 2003). The changes have been summarized by Vorren (1962, in Riseth 2003): (1) more extensive management; (2) a more money-based economy; (3) sedentarisation; (4) reindeer husbandry became an occupation and not a way of life; and (5) increased use of modern technology. At present, the Saami reindeer husbandry can be distinguished into three different levels of social organization: (1) husbandry unit; (2) siida; and (3) district. The husbandry unit is the basic unit in the social organization of the reindeer husbandry and operates on the basis of a license given by the government which entitles a person to manage a herd of reindeer within a delimited area (Ulvevadet & Klokov, 2004). The husbandry unit is similar to a household, as defined by Dahl (1979, p. 70), but as it can also consist of reindeer belonging to family members of the husbandry unit's manager, a husbandry unit is similar to an extended family unit. In contrast to the husbandry unit, the siida is a cooperative unit composed of one or more reindeer management families and is part of the traditional reindeer husbandry system (this level of social organization is, however, formally recognized by the Norwegian government in the new reindeer husbandry law as what has previously been designated as husbandry unit will change to

siida shares; Ulvevadet, 2008; Anonymous, 2007). The siida is usually organized on the basis of kinship joined together in social and labour communities for keeping control of herds of reindeer through herding (Bjørklund, 2004; Nilsen & Mosli, 1994; Paine, 1994; Pehrson, 1964; Ingold, 1980). The Saami kinship system is extensive and includes terms for consanguineal and affinal relationships (Pehrson, 1964). Traditionally, the Saami kinship system was bilateral (i.e., kinship defined through both the male and female lines), which has been argued to be more flexible than unilineal systems as the bilateral system is more adaptable to changing environmental conditions (Gjessing, 1975, p. 326). Sibling solidarity, however, could be extended to include cousins and other affinal relatives of the same generation (Paine 1964, pp. 256–257, in Bergman, Liedgren, Östlund & Zackrisson, 2008, p. 101). The Saami reindeer husbandry districts are formal management units with the responsibility to provide the Norwegian reindeer husbandry administration with information connected to the reindeer husbandry and also to help the authorities with the administration of the reindeer husbandry (Bull, 1997). The district also has the responsibility of ensuring that the reindeer husbandry is managed in accordance with the rules and regulations that are stipulated by the Norwegian government (Bull, 1997). As such, the district may be argued to not be a level of social organization, but rather as the lowest level of governmental management for the reindeer husbandry (Ulvevadet, 2008). However, members of reindeer districts have to cooperate in, e.g., maintaining fences or fulfilling governmental demands on the maximum number of reindeer per districts, and as such may also be conceptualized as a level of social organization (Næss et al., 2009). 2.2. Study design Different reindeer summer pasture districts represent heterogeneous units that are subject to differences in both climatic regimes and herding strategies (e.g., Tveraa et al., 2007). Previous studies have used a design that has accounted for temporally persistent high- and low-reindeer density and offspring body mass between districts located through latitude and longitude gradients in Finnmark, northern Norway (see Ims et al., 2007; Bråthen et al., 2007a,b for details on the study design applied in the present study). We do not estimate the effects of the design directly in our analysis, but as we have based our analyses on the districts included in this design (see Fig. 1), we ensure that we base our analyses on a subsample of districts representing the heterogeneous nature of reindeer husbandry. A potential confounder in our analyses may be district variation in quantity and quality of natural resources across districts. This effect can be controlled for by including information about vegetation productivity from satellite images. This has been done earlier, but it did not have a large impact on reindeer density (Tveraa et al., 2007, p. 711) or

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Fig. 1. Map showing the summer and winter grazing areas for the 20 reindeer husbandry districts in Finnmark, Norway, included in this study. Grey-shaded area designates the 20 reindeer husbandry districts and also serves as their individual summer pastures. Hatched area shows the common area used for winter pastures (spring and autumn pastures are also included in this area). Arrow-headed lines indicate general seasonal movement patterns between different grazing areas for all husbandry units in this study and do not refer to the movement pattern of specific husbandry units within specific districts.

herd size (Næss et al., 2009, pp. 198–199). Nevertheless, the use of mixed-effects models does control for between-district variations (using district as a random effect). This means that all factors affecting between-district variability, e.g., differences in quality and quantity of natural resources, have been estimated in our models (see below for details). Moreover, while Saami reindeer husbandry can formally be separated into three different levels of social organisation: (1) husbandry unit level, (2) siida level and (3) district level, our main focus is to test for cooperative labour effects on individual husbandry unit's production with the use of district level predictors (i.e., labour investment, kinship relations and reindeer density). The districts are not cooperative units explicitly aimed at sharing labour costs. Consequently, it can be argued that by using predictors pertaining to cooperation from the district level we are overestimating possible cooperative networks among Saami reindeer herders. As previously argued, the siida and not districts, was traditionally an important social institution for solving labour-related issues on a daily basis (see, e.g., Bjørklund, 1990; Paine, 1964; Paine, 1972; Pehrson, 1964). Næss et al. (2009), however, have argued that the distinction between the siida and the district as distinct managerial units may be less clear cut: some labour costs have to be shared by the different husbandry units within districts regardless of differing siida affiliations, e.g., the responsibility of maintaining and monitoring the state of district fences shared by all husbandry units within a district. While this example is not connected to active herding-related labour investment per se, it is not impossible that this form of cooperation across siida affiliations may have also, in some cases, been

extended to incorporate more herding-related cooperation. Moreover, Næss et al. (2009) found that a number of siidas within districts were not a good predictor of possible labour cooperation. Moreover, it is well known that population density has negative effects on an individual, i.e., reproduction and mortality, and population dynamics for large herbivores (Albon, Mitchell & Staines, 1983; CluttonBrock et al., 1996; Saether, 1997) such as reindeer (Tveraa et al., 2007; Bårdsen et al., in press). Information pertaining to the extent of area available for reindeer herding and reindeer density was only available for the summer districts. While this study is mainly concerned with the possible cooperative aspects of labour investment (between-husbandry unit cooperation), Næss et al. (2009) found a positive relationship between within-husbandry labour (number of people per husbandry unit above 16 years) and herd size. Nevertheless, Næss et al. (2009, p. 202) argued that it may not be the number of people per husbandry units that influenced herd size, but rather that the size of livestock holdings positively influenced the number of people per husbandry unit. It has, for example, been demonstrated that for the Gabbra pastoralists in the northern part of Kenya, wealth (measured as herd size) was positively correlated with reproductive success (Mace, 1996). In short, this indicates that including labour measurements from within-husbandry units cause interpretative difficulties that are diminished by focusing on between-husbandry unit measurement of labour. It has also been suggested that the effect of labour has a temporal component as the effect of cooperative labour may vary between years because cooperation will be more critical during periods of crisis like, e.g., droughts (see, e.g.,

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Scoones, 1992). The database incorporates observations from several years (1998–2004), and years can be classified as a variable that incorporates a variety of effects, i.e., it is well known that, e.g., herd size, climatic effects and pastures vary from year to year in pastoral systems (Tveraa et al., 2007). Consequently, we statistically controlled for this potential confounder by using year as a factor in the study. 2.3. Study protocol This study is based on two datasets: the first dataset consists of governmental statistics compiled and published annually by the Norwegian reindeer husbandry administration (31st of March; e.g., Anonymous, 2005). This dataset contains data pertaining to husbandry unit numbers, herd size (total number of reindeer in spring herds) and density. This data covers the period 1998–2004 with data from 20 reindeer husbandry summer districts and number of husbandry units ranging from 209 (1998) to 231 (2000). Data on husbandry unit numbers and herd size are based on counts made by herders which are regularly checked by the authorities (Anonymous, 2005). The second dataset consists of data pertaining to the genealogical relatedness between active reindeer herders within summer districts, i.e., herders that have a licence to practice reindeer husbandry (see above). This data was compiled by making a list over all active reindeer herders within each summer district (based on the first dataset). We then interviewed key personnel in the reindeer husbandry administration as well as reindeer herders to map kinship relations between the active herders in the list up to second- cousin relationships (see Appendix B for details of the calculation). Data pertaining to kinship denote the average coefficient of relatedness within a reindeer husbandry district. We were only interested in husbandry units that received operating subsidies from the Norwegian government, as we assume that receipt of subsidies reflects a high level of dependence on reindeer husbandry for making a living. Husbandry units have to sell a quota of reindeer meat to receive operating subsidies. As of 2007, this quota is set to the value of $50 000 NOK1 (Anonymous, 2008, p. 56). Consequently, we selected only husbandry units with a minimum total of 70 reindeer, a relatively conservative estimate of the number of animals needed for fulfilling the meat quota and still maintaining viable herds (see Næss et al., 2009 for a more thorough discussion). The dataset, which is organized at the husbandry unit level, contains the following variables: Nt (response) — A continuous (husbandry unit level) variable denoting the total spring herd size in each husbandry unit in a given year (t). rDistrict — A continuous (district level) variable denoting the average coefficient of relatedness within a district.

1

100 NOK=US$16.34 per 2 Sept. 2009.

This variable measures kinship between members of a given district [see Appendix B and Smith (1997, pp. 67–68) for details on how this was calculated]. Daroundt — A continuous (district level) variable denoting the density, individuals per square kilometer, of reindeer in the district (after subtracting the number of animals in the husbandry unit itself). This variable thus measures the density of animals around each husbandry unit per year (t). t — A factor variable with each year from 1998 to 2004 acting as levels. Labourt — A continuous (district level) variable denoting the number of husbandry units per reindeer husbandry district per year (t). This variable acts as a proxy for potential labour cooperation at the district level (for a detailed discussion on the operationalization of this variable see Næss et al., 2009). IDdistrict — This design (district level) variable labels each summer district in study. This is a factor variable with each district number from 1:20 acting as levels.2 2.4. Statistical analyses 2.4.1. An overview of the statistical analyses Our predictions can statistically be tested using the following models: (1) Nt=Labourt (analysis of labour); (2) Nt=rDistrict (analysis of kinship); and (3) Nt=rDistrict+Labourt+ rDistrict×Labourt (analysis of labour and kinship). Covariates, i.e., t, Daroundt and meaningful interactions, were included and excluded within the ‘paradigm’ of model selection (see Appendix A). Statistical analyses and plotting of results were carried out in R (R Development Core Team, 2007), and all tests were two tailed and the null hypothesis was rejected at an α level of 0.05. 2.4.2. Analyses of herd size: linear mixed-effect models Linear mixed-effect models (lme) applied using the nlme package in R (Pinheiro & Bates, 2000; Pinheiro et al., 2007) were used to analyze how herd size was related to the set of predictors presented above. rDistrcit, Daroundt, t and Labourt were applied as fixed effects, whereas IDdistrict (the design variable) was included as a random effect (Pinheiro & Bates, 2000). All mixed-effect models in the present study were fitted with random intercepts only. Our aim was to assess whether herd size was related to labour and kinship, i.e., whether these two factors can enhance building large herds which again has been found to be an effective buffer strategy in a variable environment (Næss & Bårdsen, 2009). Our predictions can then be tested statistically by estimating and comparing three effects: (1) the main effect of labour (Analyses 1 and 3); (2) the main effect of kinship (Analyses 2 and 3); and (3) the two-way interaction between labour and kinship (Analyses 3). Consequently, these effects were

2 However, the analyses are based on information from only 19 districts.

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Table 1 Estimates from linear mixed-effect models (lme) relating herd size as a function of (a) labour (Labourt), (b) kinship (rDistrict) and (c) both Labourt, rDistrict, and their two-way interaction Parameter

(A) Analysis of labour Fixed effects Intercept Labourtb t (1999) t (2000) t (2001) t (2002) t (2003) t (2004) Daroundtb Labourtb×t (1999) Labourtb×t (2000) Labourtb×t (2001) Labourtb×t (2002) Labourtb×t (2003) Labourtb×t (2004) Random effectsc Among IDdistrict S.D. Within group S.D. (residuals) (B) Analysis of kinship Fixed effects Intercept rDistrictb t (1999) t (2000) t (2001) t (2002) t (2003) t (2004) Daroundtb rDistrictb×t (1999) rDistrictb×t (2000) rDistrictb×t (2001) rDistrictb×t (2002) rDistrictb×t (2003) rDistrictb×t (2004) Random effectsc Among IDdistrict S.D. Within group S.D. (residuals) (C) Analysis of labour and kinship Fixed effects Intercept rDistrictb Labourtb Daroundtb t (1999) t (2000) t (2001) t (2002) t (2003) t (2004) rDistrictb×Labourtb Random effectsc Among IDdistrict S.D. Within group S.D. (residuals)

Response: Nta Value (95% CI)

df

p

5.614 (5.367 to 5.862) 0.020 (−0.002 to 0.041) −0.070 (−0.183 to 0.043) −0.237 (−0.355 to −0.118) −0.355 (−0.477 to −0.233) −0.096 (−0.208 to 0.015) 0.069 (−0.041 to 0.18) 0.307 (0.192 to 0.422) −0.099 (−0.132 to −0.066) 7.5×10−5 (−0.015 to 0.015) 0.002 (−0.014 to 0.017) 2.2×10−4 (−0.017 to 0.018) 0.004 (−0.013 to 0.021) 0.008 (−0.009 to 0.025) 0.002 (−0.015 to 0.019)

1334 1334 1334 1334 1334 1334 1334 1334 1334 1334 1334 1334 1334 1334 1334

b.001 .072 .224 b.001 b.001 .090 .217 b.001 b.001 .992 .836 .980 .614 .360 .819

0.475 (0.316 to 0.715) 0.550 (0.53 to 0.572)

5.510 (5.275 to 5.745) 0.010 (−4.394 to 4.413) −0.046 (−0.156 to 0.065) −0.198 (−0.311 to −0.084) −0.326 (−0.446 to −0.207) −0.083 (−0.194 to 0.028) 0.075 (−0.035 to 0.186) 0.304 (0.188 to 0.419) −0.086 (−0.117 to −0.055) 0.264 (−2.140 to 2.669) 0.496 (−1.911 to 2.904) 0.926 (−1.490 to 3.341) 0.364 (−1.996 to 2.723) −0.386 (−2.728 to 1.957) 0.318 (−2.043 to 2.678) 0.463 (0.303 to 0.707) 0.551 (0.531 to 0.573)

5.821 (5.526 to 6.117) 11.53 (2.475 to 20.585) 0.044 (0.018 to 0.071) −0.111 (−0.145 to −0.076) −0.076 (−0.189 to 0.037) −0.258 (−0.378 to −0.138) −0.381 (−0.505 to −0.257) −0.113 (−0.225 to −0.001) 0.058 (−0.053 to 0.168) 0.308 (0.193 to 0.422) 1.003 (0.213 to 1.793) 0.432 (0.289 to 0.648) 0.549 (0.529 to 0.570)

nObs=19 nObs=1367

1335 17 1335 1335 1335 1335 1335 1335 1335 1335 1335 1335 1335 1335 1335

b.001 .996 .420 .001 b.001 .143 .180 b.001 b.001 .829 .686 .452 .762 .747 .792

nObs=19 nObs=1367

1339 17 1339 1339 1339 1339 1339 1339 1339 1339 1339

b.001 .016 .001 b.001 .186 b.001 b.001 .048 .305 b.001 .013

nObs=19 nObs=1367 (continued on next page)

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Fig. 2. Scatter plot showing herd size in 1998 (Nt) as a function of (A) labour and (B) kinship and (C) both predictors. The lines in (A) and (B) show predicted values for 1998 from the models presented in Table 1A–B. The contour plot (C) shows how herd size was related to both predictors in Table 1C. The value of other continuous predictors, i.e., density, was set to 0. Note the lack of data in the upper right corner of (C), but the relationship between kinship and labour still holds for in the region of the plot where data on both predictors were present.

kept in all candidate models based on our a priori expectations (Appendix A; e.g., Anderson, Link, Johnson & Burnham, 2001; Burnham & Anderson, 2002). Akaike's Information Criterion (AIC) was used to assess the fit of several candidate models (see, e.g., Anderson et al., 2001; Burnham & Anderson, 2002), and whenever the difference in AIC (Δi) was b1.5 we used the simplest candidate model for inference. Following Pinheiro and Bates (2000), maximum likelihood fitted models were used when models were compared to each other, whereas a restricted maximum likelihood fitted model was used for parameter estimation. We used the treatment contrast comparing each year with the baseline level for year (1998), and Wald statistics to test the hypothesis that the contrasts were not significantly different from 0 (see Pinheiro & Bates, 2000, for details).

3. Results 3.1. Labour Labour was a positive, but not a statistically significant, predictor of herd size (Table 1A and Fig. 2A). Even though a model containing the interaction between year and labour was selected over a model without this interaction, none of these interactions was significant, revealing that the effect of labour was similar across years. Herd size, however, was different between years as some main effects of year were statistically significant. The density of reindeer around each herding unit had a negative effect on herd size (Table 1A), indicating that negative density dependence was a limiting factor. In sum, when labour was evaluated as the single predictor of a priori interest it was a poor predictor of herd size.

Notes to Table 1. Covariates, i.e., year (t), density of reindeer in the district (Daroundt) and meaningful interactions involving Nt and rDistrict, were included if they led to a more parsimonious model (see Appendix A for details). The intercept represents Nt for 1998, whereas the other coefficients for year are the estimated difference between the intercept and the average value for each level of that factor. If other continuous predictors are included, the intercept represents the mean level of Nt for 1998 when these predictors are set to 0. a Variable was transformed using the natural logarithm [i.e., loge (Nt)]. b Variable was centred (i.e., subtracting the average). c Random terms involved only the constant term (i.e., random intercepts fitted per group).

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3.2. Kinship Kinship did have a positive but nonsignificant effect on herd size (Table 1B and Fig. 2B). A model containing the interaction between year and kinship was preferred over a model without these interactions, but none of these interactions was statistically significant as in the analysis above. The effect of year and density of reindeer was similar as in the analysis of labour (Table 1B). In sum, when kinship was evaluated as the single predictor of a priori interest it was a poor predictor of herd size. 3.3. Labour and kinship When both labour and kinship were simultaneously included in a model, both of them had clear positive effects on herd size (main effects of labour and kinship; Table 1C and Fig. 2C). Moreover, a positive interaction between kinship and labour revealed that herding units in districts characterized by many husbandry units that were highly related possessed the largest herds (Table 1C and Fig. 2C). Reindeer density and year had similar effects as in the two previous analyses (Table 1C). In sum, the effects of kinship and labour with respect to herd size need to be understood in relation to each other as a positive interaction between them was of importance. 4. Discussion The main finding in this study was that cooperative labour investment was important for Saami reindeer herders, but that the effect of kinship and labour needs to be understood in relation to each other. When we assessed the effect of labour and kinship simultaneously, both had positive effects on herd size. Moreover, a positive interaction between them showed that high levels of relatedness coupled with a large potential labour pool had increasingly positive effects on herd size. We also found evidence of negative density dependence as reindeer density had a negative effect on herd size, and this negative effect was more important in some years compared to others. 4.1. Labour The effect of labour on herd size was weakly positive when assessed alone. This result suggests that, if cooperative production is important for pastoralists, measuring possible cooperation by the use of crude number of households/husbandry units within a geographically delimited area provides a poor estimate of potential cooperative labour investment. 4.2. Kinship Kinship also had a weak and positive effect on individual herd size. This result suggests that, if cooperative production is important for pastoralist, measuring possible cooperation by the use of average kinship

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relations among husbandry units within districts alone provides a poor estimate of potential pastoral cooperative labour investment. 4.3. Labour and kinship The situation changed, however, when assessing both labour and kinship together as both had positive effects on individual herd size. The clear positive interaction between them made it evident that husbandry units within districts with both a high degree of relatedness and a large number of husbandry units performed better than husbandry units in districts with a lower degree of relatedness and/or smaller number of husbandry units. The implication of these findings together is that pastoral labour cooperation needs to be understood with reference to both kinship and number of husbandry units, and that neither of them are good proxies for labour investment when assessed alone. In other words, our results confirm the importance of kinship relations as a basis for cooperation in the Saami reindeer husbandry (Bjørklund, 2004; Nilsen & Mosli, 1994; Paine, 1994; Pehrson, 1964; Ingold, 1980). Nevertheless, cooperation in relation to the herding of reindeer (defined as the relationship between herd/pasture in connection with the welfare of the animals; Paine, 1964) was traditionally the responsibility of the siida group, which was both a social and a working community normally consisting of several households/husbandry units organized on the basis of kinship relations (Nilsen & Mosli, 1994). In contrast, since this study found that kinship relations within summer districts were important, summer districts could arguably have become an important organisational level where cooperation is of importance (see also Næss et al., 2009, p. 206). 4.4. Using kinship as a proxy for cooperative behaviour Biological kinship has been argued to provide poor measurement of human social relations as genetically and culturally the definition of kin can differ substantially (e.g., Sahlins, 1976). For example, from a biological point of view unilineal descent groups make little sense as genetic relatedness does not differ depending on parental lineage, i.e., unilineal descent system underestimates genetic relatedness. Nevertheless, Alvard (2003, p. 131) found that unilineal descent systems were common among humans where 47% of societies (n=857) are patrilineal, while 14% are matrilineal descent systems. In contrast, he found that only 36% of societies have bilateral kinship systems. Furthermore, Alvard (2003, p. 134) in a study of whale hunters in Lamalera, Indonesia, found that partners during cooperative hunting were predominantly determined according to lineage membership rather than to genetic kinship. In essence, this seems to indicate that not only biological measures of kinship are important for humans. Consequently, measuring cooperative effort between Saami reindeer herders with reference to genetic relatedness can

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be viewed as problematic. As previously mentioned, Saami cooperative groups (siida) were based on conjugal and sibling solidarity (which could be extended to include cousins and other affinal relatives of the same generation; see Bergman et al., 2008, p. 101, for details). The kinship system in the Saami reindeer husbandry is, however, predominantly bilateral and quite extensive. We are confident that our measure of kinship is conservative as we are more likely to underestimate the cultural understandings of kinship as the Saami descent system is bilateral and not unilineal (e.g., Alvard, 2003). Nevertheless, while kin selection predicts that small cooperative groups can easily form (Alvard, 2003, p. 133), Paciotti and Hadley (2004, p. 122) argue that it is insufficient to explain large-scale cooperation as the problem of cooperation in small groups is substantially different from cooperation in large groups. Similarly, Bowles and Gintis (2003, p. 432) argue that the conditions under which repetition (reciprocity, kinship) and retaliation (punishment) explain cooperation is not met with a large number of possible cooperators as: (1) the number of probable defections increases as the number of interactions increases; (2) the probability that a large fraction of a population is so foresighted so as to make cooperation profitable decreases exponentially with the number of persons; and (3) the mechanisms to ensure punishment of defectors become more and more difficult and complex as the number of persons increases (Bowles & Gintis, 2003, p. 432; Boyd & Richerson, 2005; Gintis et al., 2005). Nevertheless, labour cooperation among Saami reindeer herders, and among pastoralists in general, is first and foremost undertaken by relatively small groups consisting of individuals with close kinship ties (the number of husbandry units per district ranged from 2 to 28 in this study; see also Paine, 1994; Sperling, 1985; Swidler, 1972; Fratkin, 1987; Sato, 1980; Barth, 1964; for a review, see Næss, 2009, Paper 3). As a consequence, possible problems connected to explaining large-scale cooperative behaviour were not important in this study. Finally, Griffin and West (2002, pp. 15–16) argue that while kin selection theory has provided a compelling explanation for helping behaviour, this behavioural pattern is well documented in species that do not live in family groups. Consequently, it could be argued that individuals may cooperate because it is in their own direct self-interest and not necessarily because of the benefits indirectly acquired through kin relations (Griffin & West, 2002, p. 20; Alvard, 2003, p. 147). In other words, kin-related benefits may be secondary to the development of cooperative behaviour (Alvard, 2003, p. 147). Moreover, it has also been argued that being part of a cooperative group may provide individuals with benefits independent of benefits accrued through kin selection (Smith, 1997; Alvard, 2003). If cooperative labour investment represents a reduced cost for individual households, this form of cooperation may be defined as a classical example of a

coordination game where the players' payoff is related to one another in such a way that the players' preferences coincide (Colman, 1995, p. 11). This means that there is a common interest among players where benefits accrue to individuals by acting collectively, i.e., individuals are always better off cooperating (Alvard, 2003, p. 149). Thus, if cooperative production among Saami reindeer herders is characterized by mutualism, it is to be expected that cooperative labour investment is important regardless of possible kinship relations among cooperating partners. While this may be very well true, and partly corroborated by Næss et al. (2009), this does not necessarily invalidate the more general assumption that kinship relations impact the efficiency of cooperative behaviour. The results from this study suggest that, while both labour and kinship had a positive effect on individual husbandry units' herd size when assessed simultaneously, the interaction between them was important. Moreover, when assessing labour alone the effect was not significant, indicating that cooperative labour investment is mediated through kinship relations. 5. Pastoral cooperation and competition — prospects for future research The results from the present study indicate that one important mechanism facilitating the accumulation of large herds of reindeer is cooperative labour investment. Nevertheless, there are reasons to believe that investing cooperative labour in herd building may be suboptimal in the Saami reindeer husbandry. For example, it is relatively well known that density has a negative effect on herbivore body mass (Albon et al., 1983; CluttonBrock et al., 1996; Saether, 1997). As for the reindeer husbandry, it has been demonstrated that reindeer density and climatic conditions have negative effects on individual body mass (Næss et al., 2009; Tveraa et al., 2007; Bårdsen, 2009; Bårdsen et al., in press) and, consequently, survival (Tveraa et al., 2003). From a risk perspective, it could be argued that for the Saami reindeer herders, the best long-term strategy may be to invest in livestock body mass and not in herd size. One of the reasons why this pattern was not observed in the present study may be explained with reference to between-district competition over access to common winter pastures (Fig. 1). Most of the summer districts in this study share the same winter grazing area where access is, to a large degree, determined by herd size (Anonymous, 2009; Ulvevadet, 2000, p. 65; Nilsen & Mosli, 1994, pp. 102–103). Riseth, Johansen and Vatn (2004), for example, argue that from 1957 to 1997 some herders expanded their relative share of winter pastures at the cost of others, mainly due to both the differential use of technology and variations in herd size. Larger herds use more extensive pasture areas and may thereby exclude

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other herds from grazing in the same area. Building of large herds through cooperative labour investment during summer may thus be a viable strategy for gaining access to winter pastures. Furthermore, while summer districts consist of relatively few husbandry units and with the possibility of efficient cooperation through kinship relations, common winter pastures are used by a relatively large number of husbandry units. As such, the use of common winter grazing may represent a problem of largescale cooperation (Paciotti & Hadley, 2004; Bowles & Gintis, 2003) where kinship relations are not sufficient to provide the basis for cooperative behaviour. From a theoretical point of view, competition rather than cooperation for access to common winter grazing could easily develop if just one or a few herders initiate herd accumulation (cf. Hardin, 1968 and ‘The tragedy of the commons’; see also Ostrom, 1998). If this is the case, other herders have to adopt the same strategy so as to not be excluded from the common grazing areas. Consequently, access to winter grazing areas can be characterized as a classical social dilemma (cf. Ostrom, 1998). As such, competition for access to winter pastures may explain the adoption of herd accumulation as the only viable risk-reducing strategy (Næss & Bårdsen, 2009). Moreover, this article demonstrates that herd accumulation may be constrained by cooperative labour investment in summer districts and that husbandry units within summer districts with a lower degree of relatedness have less access to a cooperative labour pool. This entails that access to more cooperative labour enables husbandry units to be more successful in following whatever strategy is best in a given context. For example, we could expect that in contexts where between-district competition for access to winter grazing is highly controlled, e.g., by the existence of formal and informal regulations in terms of access to common grazing areas, husbandry units experiencing high degrees of cooperation should invest cooperative labour into livestock condition. At present, the principal challenge facing the Saami reindeer husbandry in Norway, both from a governmental management perspective and from the perspective of individual herders, is the perceived notion of overstocking in terms of number of reindeer (Næss & Bårdsen, 2009). In short, it could be hypothesized that one of the reasons for this is that reindeer herders adopt herd accumulation as the best available risk-reducing strategy when access to common winter pastures is characterized by competition. Acknowledgment We thank the Norwegian Reindeer Administration for providing the data that this study is based on, and the area reindeer husbandry administrations in Kautokeino and Karasjok for providing additional information. We would also like to thank Ellen Margrete Oskal for help with data collection, and Rolf A. Ims and Bjørn Bjerkli for comments

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and discussions that improved this manuscript. Finally, we also thank Brian Paciotti and one anonymous reviewer whose comments helped to improve the quality of the article. Appendix A. Model selection and the set of candidate models Selecting the models used for inference in the three analyses presented in Table 1 was performed within a model selection framework (e.g., Anderson et al., 2000; Buckland, Burnham & Augustin, 1997; Burnham & Anderson, 2002): First, a pool of candidate models was defined. Defining the set of candidate models is an important but often underemphasized part of a statistical analysis: ‘models without biological support should not be included in the set of candidate models’ (Burnham & Anderson, 2002). Thus, we kept the average coefficient of relatedness (rDistrict) and the number of husbandry units per reindeer husbandry district per year (Labourt) in all analyses based on our a priori expectations (see Tables AI.1–3 for details). In the final analysis where both predictors were used, we also kept the interaction between the two based on our a priori expectations (see Table AI.3 for details). Second, in each analysis, rescaling and ranking models relative to the value of the model with the lowest AIC value were performed (Burnham & Anderson 2002; Δi denotes this difference for model i). Table AI.1 The relative evidence for each candidate model (i) in Table 1A based on differences in AIC values (Δi) i

Labourta

t

Daroundt

Labourt×t

Kb

Δi

1. 2. 3. 4.

× × × ×

× × ×

×

× ×

17 10 16 4

0.00 19.66 9.92 63.59

The model in italics was selected and used for inference in each analysis as it was the simplest model with an Δi ≤1.5. The predictors included in the different models are marked with an ‘×’. a This predictor was kept in all models based on our a priori expectations. b K denotes the number of parameters, whereas the number of observations (n) was 1367 for all models. Table AI.2 The relative evidence for each candidate model (i) in Table 1B based on differences in AIC values (Δi) i

rDistricta

t

Daroundt

rDistrict×t

Kb

Δi

1. 2. 3. 4.

× × × ×

× × ×

×

× ×

17 10 16 4

0.00 6.05 15.94 59.58

The model in italics was selected and used for inference in each analysis as it was the simplest model with an Δi ≤1.5. The predictors included in the different models are marked with an ‘×’. a This predictor was kept in all models based on our a priori expectations. b K denotes the number of parameters, whereas the number of observations (n) was 1367 for all models.

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Table AI.3 The relative evidence for each candidate model (i) in Table 1C based on differences in AIC values (Δi) i

rDistricta

Labourta

rDistrict×Labourta

t

Daroundt

rDistrict×t

Labourt×t

rDistrict×Labourt×t

Kb

Δi

1. 2. 3. 4. 5. 6.

× × × × × ×

× × × × × ×

× × × × × ×

× × × ×

× × × × ×

× × ×

× ×

×

31 25 19 13 7 6

24.90 20.61 10.37 0.00 80.18 80.00

The model in italics was selected and used for inference in each analysis as it was the simplest model with an Δi ≤1.5. The predictors included in the different models are marked with an ‘×’. a The predictors were kept in all models based on our a priori expectations. b K denotes the number of parameters, whereas the number of observations (n) was 1367 for all models.

Appendix B. Measuring relatedness

References

Following Smith (1997, p. 67; see also Krebs & Davies, 1993), we measure kinship as the coefficient of genetic relatedness, r, as was developed in Hamilton's (1964) theory of kin selection. Again following Smith (1997, p. 67), we calculated r by counting the genealogical pathways between each pair of active reindeer herders within each reindeer husbandry districts, i.e., herders that have a licence to practice reindeer husbandry, up to second-cousin relationships. The rationale for this can be found in Paine (1994, pp. 104–105) who argues that the building blocks for herding partnership are ‘[…] brother/sister, brother/sister-in-law, and cousin’. In essence, this indicates that cooperative labour arrangements among Saami reindeer herders are based on relatively close kinship relations. For any dyad then, the genetic relationship can be given by

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N 1 r= ; 2

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where N represents the number of kinship relations between two individuals. Following Smith (1997, p. 67), we calculated the average coefficient of relatedness for each reindeer husbandry district consisting of n individuals by calculating r for each dyad in the district and then averaged the values, given by the following expression: Pd rn =

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rn is denoted rDistrict in the main text.

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