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«Item type text; Dissertation-Reproduction (electronic) Authors Munro, Natalie Dawn Publisher The University of Arizona. Rights Copyright © is held ...»

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This chapter also presents the results of a new simulation for gazelle (Gazella gazella), the most common ungulate species in Natufian assemblages, using the same methods employed for small game predator-prey simulations. In the gazelle case, the population models are used to monitor the impact of human harvest pressure on living age structures. As in the small prey models, simulated gazelle populations are subjected to incremental increases in hunting pressure. However, the proportion of juveniles in the population, in particular, is tracked, rather than population resilience. While the living age structures of prehistoric prey populations are not directly preserved in the age profiles of archaeological assemblages ~ potential distortions are imposed by the human cultural filter ~ it is possible to estimate the degree of hunting pressure prey were exposed to when hunted. Distortions of gazelle living structures caused by human hunting and/or other factors such as seasonality in the Natufian are addressed in Chapter

8. The simulation results for small game are employed in an analysis of these animals in Natufian assemblages in Chapter 7. The objective here is to use faunal data to test ideas about site occupation intensity within the Mediterranean hill zone during two phases of

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The following discussion summarizes the mechanics of the simulations presented by Stiner et al. (1999, 2000). The population characteristics of prey taxa were obtained from wildlife population studies on Mediterranean and closely related species. When available, longitudinal studies or those focusing on non-hunted wildlife populations were preferred, as they are likely to capture a more complete range of the natural variation in prey population cycles which fluctuate, often dramatically, in response to changes in food supply, climatic conditions, and other factors. To capture the full range of variation, best case (High Growth Model) and worst case (Low Growth Model) scenarios were modeled for each simulated population.

The simulations were written by Todd Surovell using Visual Basic macros in Microsoft Excel 7.0. The populations were simulated as a group of individuals, each assigned a sex and an age that increased by a fixed amount with each iteration of the model. The prey simulations operate according to a series of fertility and mortality parameters that determine rates of population growth and recovery. The fertility parameters include the age of females at first reproduction, and the minimum and maximum number of offspring produced per female each year. Once a female reaches reproductive age she adds infants to the populations at the beginning of each iteration of the model, until she dies. The number of babies a female produces each year is randomly selected from within a range defined by the minimum and maximum number of offspring

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natural fluctuations in the number of offspring produced from year to year or female to female, as some proportion of females may not bear young in any given year.

Mortality is determined by the combined influence of juvenile mortality; adult mortality; and maximum potential lifespan variables. The shift from youth to adulthood also determines the age of onset of adult mortality. All individuals below this age are subjected to a generalized juvenile mortality rate, and those above it to adult mortality.

The mortality values determine the proportion of adults and juveniles that are randomly removed from the population in each iteration. Individuals are also removed once they attain the maximum potential lifespan.

The growth rate of the prey populations was modeled by plugging taxa-specific values for the preceding parameters into the simulation, and allowing a small randomly generated population (n = 25) to grow to carrying capacity. Carrying capacity was set so that in the absence of human hunting, the stable population size for each species equaled approximately 800 individuals in the high growth (HGM) and 400 in the low growth models (LGM). Once carrying capacity was reached, the model was cycled for at least an additional 100 iterations to ensure that the structure had stabilized. Population size was then plotted from year 0 to 100 to depict the growth rate for each population.

The stable populations created by the growth simulations are used as starting points for the models investigating the impact of human hunting pressure on prey population structure. Hunting pressure is introduced as a mortality variable that preferentially removes a fixed percentage of adults from the population with each

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contain the most fat in some seasons. If there are not enough adults in the population to fill the hunting quota, older juveniles are also hunted. In each successive run of the model, the proportion of hunting is increased incrementally to find the crash threshold of the prey population under HGM and LGM conditions. The results of the HGM and LGM simulations for each prey species thereby provide the upper and lower limits of hunting pressure that can be sustained by its population.

The gazelle simulations introduced below follow the same methods developed previously for tortoises, hares, and partridges. Parameters derived fi-om modem gazelle populations were plugged into the model to generate a stable age structure at carrying capacity. Hunting was then introduced and increased incrementally with each succeeding run. When hunting pressure is added, the populations first begin to shrink and then restabilize at a lower population size.





The Small Game Simulations : Population Growth and Resilience

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Stiner et al. (1999,2000) present the results of the predator-prey simulation models for the most common small game species from Paleolithic Mediterranean archaeofaunas, the tortoises, partridges, and hares. The derivation of the parameters used in the HGM and LGM for each of these species is discussed in detail in Stiner et al.

(2000), and summarized in Table 6.1. The table emphasizes the salient differences in the reproductive capacities and resilience of the prey species.

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Figure 6.1: Growth curves of tortoise and hare populations created by LGM and HGM simulations.

Growth rates for partridge populations are not included, but are nearly identical to hares. Partridge populations grow slightly faster than hare populations. Note, there is no overlap in the growth ranges of tortoises and hares. Figure reprinted from Stiner et al. (2000: 56).

Figure 6.1 diagrams the growth of the tortoise and hare populations from a size of 25 individuals to carrying capacity as determined by the LGM and HGM.

Although, partridge populations are not depicted here, their growth potential is virtually identical to

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slow- and fast-growing populations further still. One point is of great importance;

though partridges and hares race to carrying capacity in 25 years or less even under the most unfavorable conditions, the tortoise take at least 7 times longer. Despite the broad extremes afforded by the HGM and LGM models, there is no overlap whatsoever in the growth rates of tortoise populations versus partridge and hare populations.

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Figure 6.2; The response of the tortoise HGM and LGM populations to incremental increases in hunting pressure.

Each line in the series represents population size when subjected to a set percentage of hunting each year. The proportion of the population hunted in each run is indicated by the numbers in parentheses at the right side of the graph. The lowermost line that extends to 200 years, indicates the ma.ximum percentage of hunting pressure the population can sustain each year without crashing. Figure reprinted from Stiner et al. (2000: 53).

Figures 6.2 to 6.

4 illustrate the affects of increasing hunting intensity on the sizes of tortoise, hare, and partridge populations for the HGM and LGM simulations. Whether

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much lower percentage of annual off-take (between 3 and 8%) than hares and partridge populations. The sustainability of hare and partridge populations is great, with partridges showing slightly greater resilience (between 20% and 65% off-take per annum) than hares (between 18% and 50% off-take per annum; Figure 6.5).

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Figure 6J: The response of hare HGM and LGM populations to incremental increases in hunting pressure.

Each line in the series represents the population size when subjected to a set percentage of hunting each year. The proportion of the population hunted in each run is indicated by the numbers in parentheses at the right side of graph. The lowermost line that extends to 200 years, indicates the ma.ximum percentage of hunting pressure the population can sustain each year without crashing. Figure reprinted from Stiner et al.

(2000; 55).

The results of the simulations highlight two important points. First, the reproductive potential of tortoises is significantly lower than for either partridges or

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carrying capacity after periodic disturbances. Second, tortoise populations show poor overall resilience and can be put at risk from much lower intensities of hunting pressure.

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Figure 6.4: The response of partridge HGM and LGM populations to incremental increases in hunting pressure.

Each line in the series represents the population size when subjected to a set percentage of hunting each year. The proportion of the population hunted in each run is indicated by the numbers in parentheses at the right side of graph. The lowermost line that extends to 200 years, indicates the maximum percentage of hunting pressure the population can sustain each year without crashing. Figure reprinted from Stiner et al. (2000: 54).

Tortoises replace themselves slowly and thus have low-turnover populations.

This is largely the product of slow maturation rates (age of first reproduction in females is on average 10 years) and high juvenile mortality, which prevent most young recruits from reaching reproductive age and contributing to future generations. Those tortoises that do reach reproductive maturity live extremely long lives (up to 60 years) however, and

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mostly due to rapid maturation (the majority of females begin reproducing in their first year of life), and extremely high reproductive rates (see Table 6.1). While many individuals may reach reproductive maturity, they may reproduce only a few times on average before they die, owing to high adult mortality rates and short lifespans (12 years for maximum for hares and 8 years for partridges).

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Figure 6.5: Range of tolerance of tortoise, hare, and partridge populations to hunting pressure.

Lower bar represents ma.ximum percentage of hunting sustained by the LGM populations and the upper bar indicates the maximum percentage of hunting sustained by the HGM populations. Figure reprinted from Stiner et al.

(2000: 56).

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partridges are of great significance for archaeologists, as they provide a baseline from which to predict the relative availability of prey species under a range of hunting conditions. Because hunting intensity differentially affects the abundance of prey species due to variation in prey population resilience (i.e., tortoises are affected more than hares and partridges), it is possible to predict the impact of increased hunting intensity on the relative availability of these three small game types. For example, when human hunting

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partridges, and thus the relative availability of the three species should change and tortoise abundance will decrease in relation to hares and partridges. Human preference for some prey species over others (i.e., high- versus low-ranked prey), also enters the equation, allowing the generation of even more precise predictions about past hunting behavior (see Chapter 7).

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Two gazelle species, the mountain gazelle {Gazella gazella gazella) and the Dorcas gazelle {G. dorcas) occupy the modem day Levant. Though there is a small zone of sympatry between the two species, their niches are roughly separated by the boundary separating regions which receive more and less than 150mm of precipitation per annum (Mendelssohn 1974). The mountain gazelle lives in the wetter areas, primarily in the hilly Mediterranean zone of Lebanon and Israel, and much of the steppic Irano-Turanian belt throughout Israel, Jordan, and Syria, while the Dorcas gazelle is arid-adapted, occupying desertic areas including the Negev and the Arava Valley of Jordan and Israel.

The Dorcas gazelle also occurs across the Arabian peninsula and northern Africa (AlHazmi and Ghandour 1992; Grettenberger 1988; Loggers 1992; Marraha 1996).

Tchemov et al. (1987) re-examined elements attributed to G. dorcas from Levantine Paleolithic and early Neolithic sites, concluding that there is no good evidence for Dorcas gazelles in the Levant until after the Pre-Pottery Neolithic B period, and reassigning all G. dorcas specimens from sites recovered before this date to G. gazella. Priority for the

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Dorcas gazelles were consulted when the literature on the mountain gazelle proved inadequate. A perusal of the literature on several gazelle species indicates that, despite some variation, most species share similar reproductive parameters (Baharav 1974, 1983a, 1983b; Dittrich 1972; Loggers 1992; Zhaowen et al. 1998).

Pristine gazelle populations are non-existent today. Though many modem herds are protected, most are managed and live in reserves or areas inhabited by humans. In Israel, three well-studied mountain gazelle populations provide the bulk of the information provided here. The populations inhabit the Golan Heights, the lower Galilee (Ramat Yissakhar), and the Hula Valley (Ramat Qedesh; Ayal and Baharav 1983;

Baharav 1974,1983a, 1983b, 1988; Frankenburg 1992; Mendelssohn 1974; Shy et al.



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