Behavioral ecologists study the relationship between the behavioral consequences of natural selection and circumstances that tend towards optimization. This type of analysis focuses on choices among suites of behaviors that have predictable outcomes, and the results of those behaviors in conferring a selective advantage at some desirable goal (for example, reproduction; Boone and Smith 1998; Foley 1985). Most applications of behavioral ecology have been concerned with resource selection and the degree of optimization or the tendency for selection to promote behaviors that enhance fitness through the optimization of resource use (for example: Barlow 1997; Beck 1999; Broughton and O'Connell 1999; Hemphill and Larsen 1999; Kelly 1995; Simms 1987). There has also been moderate success in applying behavioral ecology to human social organization in archaeological cases (Diehl 2000; Hegmon 1991; Storey 2000)—a logical outgrowth of success in primate, broadly construed here to include humans, behavioral studies that focus on resource selection or aspects of social organization (Berkovitch 1991; Boehm 1992; Boehm 2000; Boone 2000; Hawkes 2000; Krebs and Davies 1991; Winterhalder 1993; Winterhalder et al. 1988; Winterhalder and Smith 2000).
A tendency towards the optimization of resource use should occur when one or more of the following conditions is met (Kelly 1995, 54-57): (1) there is a threat of starvation, (2) specific nutrients (calories in most studies) are in short supply, (3) constraints limit the amount of time available for obtaining food, (4) subsistence activities expose people to risk, or (5) surplus food or time may be used to enhance reproductive fitness. When optimization occurs one expects rational decision-making to favor the use of the most efficient food acquisition systems. It is not necessary to assume, a priori, that this was likely to be the case. It is sufficient to show that resources were used commensurate with their ranking based on their energy returns, and that diet breadths changed through time in ways consistent with optimization models.
To apply this behavioral ecology model to prehistoric southern Arizona, or to any other archaeological case, two problems must be solved. First, it needs to be demonstrated that at least one of the aforementioned five conditions existed during or prior to the prehistoric period under consideration. Second, a model for the return rates of different resources must be derived. Both tasks are quite difficult.
Could prehistoric southern Arizonans have benefited from optimization? While there is no basis for assessing the intensity of the selective pressure in favor of optimization, it is probably the case that optimization would have conferred selective benefits. Analyses of human skeletal remains suggest that Early Agricultural Period forager-farmers suffered episodes of impaired health, either as a result of infectious disease, injury, or malnutrition (Minturn et al. 1998). Enamel hypoplasia—which is attributable to early weaning among agriculturists, early onset of infections, or infant malnutrition—has also been observed (Guthrie and Lincoln-Babb 1997, 142-143). Interestingly, Minturn et al. (1998, 754-755) found that Early Agricultural Period burials exhibited osteological development (related to musculature) that was more consistent with a foraging or mixed foraging and farming pattern than with a highly agricultural one; that observation contravenes the proposition that the hypoplasias may be attributed to early weaning. That leaves infection and malnutrition as potential causes of observed human osteopathologies. In general, good nutrition promotes greater disease resistance and longer life spans, as well as increasing human fertility.
It is more difficult to rank the returns of prehistoric resources because so little is known about the precise ways that each resource was acquired and used. In addition, little is known about the frequency with which the resources occurred on the prehistoric landscape. Studies of the yields of various wild plant taxa, even economically important ones such as rangeland grasses, are rather rare. To construct a rank order of the energy returns from resources requires detailed knowledge of the performance characteristics of plants and animals and detailed knowledge of the ways that they were used. One must rely on return rates of plants and animals that are estimated from studies in agricultural science and range management, by analogy to other resources whose return rates have been studied, or by experimentation. It is understood that further research on the phenology, circumstances of availability, concentration, duration and timing on the landscape, of these resources may result in subsequent changes to our model. The rank orders of six indicator plants or plant groups, each of which represents a different land use strategy (Diehl 2001) are presented in Table 4.2. These ranks were determined on the basis of energetic returns per unit of time, after accounting for gross yields, harvesting, processing and transport costs. The assumptions used
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