Thursday, February 7, 2013

Kline & Boyd (2010) Population size predicts technological complexity

Kline, M. A., & Boyd, R. (2010). Population size predicts technological complexity in Oceania. Proc. R. Soc. Lond. B, 277, 2559-2564.


Models of cumulative cultural adaptation predict larger populations will have more diverse and complex tool kits than small, isolated populations.

  •      Cultural drift
a.     The number of people adopting a variant is affected by sampling variation
b.     Some cultural variants will be lost by chance when their practitioners are not imitated.
c.     The rate of loss from cultural drift will be higher in smaller populations, because random losses are more likely.
d.     Contact between populations replenishes adaptive variants lost by change, leading to higher levels of standing variation and thus more adaptive traits
e.     (See models by Neiman, 1995 and Shennan, 2001)

  •      Error in social learning
a.     Errors usually degrade complex adaptive traits
b.     Most pupils will not attain the level of expertise of their teachers.
c.     Inaccurate learning creates a “treadmill” of cultural loss, against which learners must work to maintain the current level of expertise.
d.     Countered by selection of expert models for learning.
e.     Cumulative cultural adaptation happens when a rare pupil surpasses his teachers.
f.      Learners in larger populations have access to more experts, making such improvements more likely.
g.     Contact between populations replenishes adaptive variants lost by change, leading to higher levels of standing variation and thus more adaptive traits.
h.     (see models by Henrich, 2004 and Powell et al., 2009).
  • Economic and ecological factors may also influence technological adaptations, but these are not discussed here.

Previous empirical tests:
  • ·      Neither of two previous systematic tests of the population size/technological complexity hypothesis found any relationship between population size and tool kit diversity or complexity.
  • ·      However, the sample used in both analyses did not include any measure of contact between populations, and was drawn mostly from northern coastal regions of the western North America where intergroup contact was probably common (but hard to estimate).

Methods of the present study:
  • Broad outline:

o   Examine effects of population size and contact on the complexity of marine foraging tool kits among island populations in Oceania.
o   The groups exploit similar marine ecosystems, minimizing the effect of ecological variation.
o   Groups also share a common cultural descent, minimizing the effects of cultural history.
o   Analysis indicates that both the number of tools and the average complexity of tools are higher in large populations than in small, isolated ones.
  •      Methods:

o   Using an available database, info on toolkits from 10 indigenous marine foraging societies (with known rates of contact – coarse grained as “high” or “low”). Collected excerpts indexed as fishing, marine foraging, or fishing gear.
o   Tool types were established with following criteria: (i) tools had different names and at least 1 non-overlapping function, (ii) tools had different mechanical structures, or (iii) tools were made through different production processes.
§  The number of tool types varied from 13 to 71.
o   Tool complexity was quantified by the number of “techno-units” (Oswalt, 1976), defined as “an integrated, physically distinct and unique structural configuration that contributes to the form of a finished artifact.”
§  Techno-unit counts based on verbal descriptions, illustrations, and photographs.
§  In contrast to Oswalt, they included decorative elements, because the production of any part of the tool may be socially learned, and thus subject to the dynamics of cultural transmission (as per the above mentioned models).
Results:
  • Larger island populations have a larger repertoire of tools than smaller island populations. The linear regression was highly significant.

o   AIC statistic indicates that population size is a much better predictor than any other single explanatory variable.
  • Both models of cultural adaptation predict that contact will be less important in larger populations. The data provide some support for this.

o   4/5 high-contact societies have more tool types than expected based on their population size. These societies all fall in the intermediate range of population size.
o   4/5 low-contact groups have fewer tools than expected by their population size.
o   AICs suggest that contact is important, with the model that includes contact and population size ranked as the second best (after population size and fish genera, indicating that ecological factors also matter).
  • Both models of cultural adaptation predict that complex tools will be especially prone to loss because it is harder to learn to make them, and they will be more affected by cultural drift if component parts of a tool are the units of inheritance. This prediction is supported by the data.

o   The mean number of techno-units per tool is significantly higher in larger populations than in smaller populations.
o   The standard deviation of rainfall has a substantial effect on tool complexity – the AICc indicates that population size is the best individual predictor, but the SD of rainfall is a close second. 


Discussion
  • The ability of human populations to evolve the optimal toolkit as determined by ecological factors will depend on constraints imposed by cultural adaptation by population size and the rate of contact between populations.
  • Hill et al. (2009) have argued that the sporadic appearance of sophisticated tools during the Late Stone Age in Africa can be understood as the result of climate-induced fluctuations in population size. This study provides empirical support for this. 

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