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293 W e began the survey and excavation projects in Bandelier with a particular interest in understanding why larger settlements (eventually including towns such as Tyuonyi) appeared out of local precedents that included only much smaller settlements . We organized the research to collect data that would be useful in examining a simple model of aggregation , set out in chapter . This model was heavily influenced by Kohler and Orcutt’s prior experience in another upland area, investigated by the Dolores Archaeological Project (Kohler et al. ; Kohler and Matthews ; Orcutt et al. ), as well as by a body of theory surrounding subsistence intensification (e.g., Johnson and Earle ). This model suggested that population growth leads to wild resource depletion and decreased mobility, agricultural intensification, and ultimately to aggregation , under the assumption that aggregates have three crucial advantages in this context. First, they can provide “social safety nets” that replace recourse to wild resources in less intensified regimes.(Households in close proximity can also monitor those who carry reciprocal obligations to them, helping ensure they honor those obligations as they are able.) Second, aggregates have an advantage over smaller settlements in competition for scarce resources, especially prime agricultural land, but possibly also hunting territories. Third, and somewhat C H A P T E R E I G H T Bandelier from Hamlets to Towns Timothy A. Kohler, Robert P. Powers, and Janet D. Orcutt For history is a pontoon bridge. Every man walks and works at its building end, and has come as far as he has over the pontoons laid by others he may never have heard of. Events have a way of making other events inevitable; the actions of men are consecutive and indivisible. —Wallace Stegner (1962:29) paradoxically,aggregation might also defuse competition by pulling in a dispersed community living in settlements whose agricultural catchments risked overlapping with those of adjacent communities. By this model,“it should be possible to accurately predict the degree of Monumentwide aggregation in any period from a knowledge of the current population density, some measure of the degree of depletion of wild plant and animal resources,and some measure of average length of residential occupation” (Kohler b:). Data from the Bandelier Archeological Survey provide us with measures of population aggregation and total population (Table .). It falls to the excavations to generate measures of wild resource depletion and length of residential occupation.We generate and discuss measures of those parameters in the first part of this chapter,which provides an opportunity to summarize some of the data from chapters  through  over time. Then we test our original model.Finally,we discuss the strengths and weaknesses of that model in light of the preceding chapters. Estimating Duration of Occupation from the Probability Sample One of the predictions of the model is that increasing populations will deplete wild resources both regionally, through the mechanism of more areas being farmed and hunted,and locally,through the effect of longer durations of occupation engendered by decreased opportunities for mobility. Unfortunately it is impossible to measure durationof occupationfairlydirectlyexceptinafewstrongcases of nearlycompleteexcavationandabundanttree-ringdates (e.g., Lightfoot ). On the other hand, such cases can be useful in calibrating estimates of duration of occupation based on rates of accumulation of various materials (Varien and Mills ). Here we will use the total accumulations of sherds, estimable for our three probabilistically sampled locations. Point estimates and  percent confidence intervals for sherd and flake populations from these three locations are shown in Table ..(We call these 294 / TIMOTHY A. KOHLER, ROBERT P. POWERS, and JANET D. ORCUTT TABLE 8.1. Summary Data for Population Size, Degree of Aggregation, Duration of Occupation, and PDSI Mean and Variance 1 275 12.8 4 15 –.21 4.91 2 500 18.2 8 15 –.10 4.60 3 1,100 13.6 16 15 –.15 4.12 4 3,240 15.2 26 35 –.12 4.42 5 710 15.7 9 35 –.46 3.85 6 3,610 21.5 15.9 35 .14 4.20 7 3,230 22.3 15 50 .05 3.80 8 2,100 70.9 34 50 –.02 2.78 9 2,400 56.3 13.8 50 –.32 3.90 10 500 113.9 9.5 50 –.275 4.32 11 550 90.1 12 50 –.50 4.15 aOrcutt 1999a:Figure 5.6 (top). bVan Zandt 1999:Table 6.12. cOrcutt 1999a:Figure 5.14. dOrcutt 1999a:(derived from excavation data). eOrcutt 1999a:Figure 5.8. Period...

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