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Analysis of hunting options by the use of general food taboos
Ecological Modelling 110 (1998) 5 – 17
Analysis of hunting options by the use of general food taboos Johan Colding * Department of Systems Ecology, Stockholm Uni6ersity, S-106 91 Stockholm, Sweden
Abstract A hypothetical model was built, using the STELLA II software program, to test several hunting options for a human hunting group. Different outcomes of possible hunting modes are analysed, such as a change in hunting rate, prey hunted, or species avoided or not avoided by taboos. The model consists of five sectors that reflect a short food chain in an upper Amazonian ecosystem. There is a vegetation sector, a predator sector, and two sectors consisting of browsers and grazers. The last sector represents a human group, known as the Ecuador Achuar. The critical factor analysed is how differences in hunting rate affect a target resource, and how this resource may be affected by general food taboos. The major results of the model are that general food taboos may not be an adaptive short term strategy for hunters, but that a ‘moderate’ hunting mode may be the most effective option for the human group. Since the model is a simplification of the real world, no general conclusions for management should be drawn from the results. © 1998 Elsevier Science B.V. All rights reserved. Keywords: General food taboos; Hunting options; Amazone ecosystems
1. Introduction Many traditional societies employ food taboos on animal species for a number of reasons. Some researchers suggest that there are nature management motives behind them (Rappaport, 1967, 1968; Reichel-Dolmatoff, 1976; McDonald, 1977; Johaness, 1978; Ross, 1978; Harris, 1979), while others resent any such ecological motives (Rea,
* Present address: The Beijer International Institute of Ecological Economics, The Royal Swedish Academy of Sciences, PO Box 50005, S-104 05 Stockholm, Sweden. Tel.: +46 8 6739500; fax: + 46 8 152464; e-mail: [email protected]. se
1981; Edgerton, 1992). Hunting among some traditional groups may be conducted in a highly efficient way, consistent with predicators of foraging theory (Alvard, 1993, 1994). While species are thus pursued with short-term harvest rate maximization, one may very well ask why some native hunting groups employ food taboos. McDonald (1977), in a study of 11 South American tropical groups, suggests that taboos may function as conservation mechanisms for reducing the hunting pressure on larger mammals in environments where such species are low in abundance. General food taboos—applying to all members within a community—may play a role in biodiversity con-
0304-3800/98/$19.00 © 1998 Elsevier Science B.V. All rights reserved. PII S 0 3 0 4 - 3 8 0 0 ( 9 8 ) 0 0 0 3 8 - 6
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J. Colding / Ecological Modelling 110 (1998) 5–17
Fig. 1. Approximate location of the Ecuador Achuar territory (marked in grey).
servation. For example, Colding and Folke (1996), found that a number of threatened populations of species, including endemic and keystone species, benefit from such taboos. The purpose of this modelling exercise with STELLA II, has been to analyse possible relations between general food taboos and different hunting modes. The STELLA II program may be a suitable tool for an investigation of this kind. While real world data on wild populations are available in the literature (see Emmons, 1990; Nowak, 1991; Redford and Eisenberg, 1992), it is nevertheless problematic to generalise from such data. For example, data on numbers of density are measured in habitats where species thrive. To generalise from such data, from one habitat to another, may be highly awkward. Among other things, this is due to the differences in the heterogenity of landscapes within and between ecosystems. Perhaps more realistic data for the presented model could be obtained using site-spe-
cific data from field studies. Unfortunately, this has not been possible. Thus, the estimates used here are highly artificial. The Ecuador Achuar represents the human group of this model. This group, inhabiting a vast region at the Ecuador/Peru border (see Fig. 1), has been well studied by DeScola (1986). The Ecuador Achuar impose general food taboos on several species of mammals. They abstain from the consumption of all carnivorous mammals and regard several other such species as inedible, referring to them as ‘sickening meat’ (DeScola, 1986). Among such species is the rodent Capybara, Hydrochoerus hydrochoerus, which is widely hunted by other neotropical forest groups (see Redford, 1993; Bodmer 1994). However, other species of rodents are hunted, e.g. agoutis (Agouti paca) and acouchis (Dasyprocta and Myoprocta). Ungulates, such as Brocket deer (Mazama Americana) and Tapirs (Tapirus terrestris and T. pinchaque) are avoided by general food taboos, due to reincarnation beliefs (DeScola, 1986).
J. Colding / Ecological Modelling 110 (1998) 5–17
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narios are presented in Section 3. Individual model runs will also be discussed in Section 3, while an overall discussion will follow in Section 4.
2. Basic structure and data
Fig. 2. The basic structure and key actors of the model.
The results obtained in this study are highly artificial and should not be taken as indicators of the Ecuador Achuar hunting mode. The model is far too incomplete for any such inferences to be made. This complies for both the structure of the ecosystem, as well as for the various populations of species described in the model. Some possible hunting modes and sce-
The main sectors and key actors of the model are presented in Fig. 2. This figure illustrates the basic food chain investigated. The population of jaguars is regarded as the top-predator in this extremely simplified ecosystem. Jaguars prey on both the rodent and tapir populations respectively, that, in turn, feed from the vegetation sector. Although all carnivore species are avoided by the Achuar hunter, the jaguars in this model are thought of representing a fraction of all the carnivores in this ecosystem. The same holds true for both the rodent sector and the
Fig. 3. A model for examining different hunting options for a human group.
J. Colding / Ecological Modelling 110 (1998) 5–17
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Table 1 Sensitivity runs on population stocks of the model, using STELLA II Months
1: Vegetation
2: Vegetation
3: Vegetation
1: Rodent
2: Rodent
3: Rodent
0 50 100 150 200
3 000 000 6 617 864 6 605 182 6 557 093 6 620 185
6 000 000 6 618 420 6 605 278 6 557 171 6 620 226
9 000 000 6 618 413 6 605 277 6 557 170 6 620 225
9000 11 955 11 772 14 512 12 272
18 000 12 009 11 785 14 521 12 279
27 000 12 035 11 791 14 525 12 281
0 50 100 150 200
1: Tapir 300 300 294 335 338
2: Tapir 600 541 511 530 503
3: Tapir 900 651 566 561 521
1: Jaguar 10 28 28 31 27
2: Jaguar 25 28 28 31 27
3: Jaguar 40 28 28 31 27
0 50 100 150 200
1: Tapir* 300 218 67 0 0
2 Tapir* 600 485 410 400 333
3: Tapir* 900 603 487 463 392
An increase in the rate of tapirs per jag from RANDOM (0, 0.4, 0.4) to RANDOM (0, 0.5, 0.5) is marked as * in the table.
tapir sector. The former represents mainly a lumping of larger rodents that the Ecuador Achuar pursue, and the latter a fraction of the ungulates avoided by general food taboos.
2.1. Climatic conditions of the ecosystem The ecosystem is a simplified version of the Ecuador Achuar territory, situated at the Ecuador/Peru border. It lies north of the Pastaza river, and covers an area of about 9000 km2 (see Fig. 1). The region has a typical equatorial climate, constantly humid, no dry season, and a monthly rainfall always over 60 mm. It is situated 2° south of the equator, where days and nights are nearly the same length, and temperatures remain constant throughout the year. Despite its proximity to the Andean barrier, this ecosystem is not directly affected by the special meteorological conditions of the foothills. Mean annual daytime temperatures vary between 24 and 25°C. The annual average low temperature fluctuates between 19 and 20°C, depending on altitude, and the average annual high tempera-
ture is between 29 and 31°C. It is marginally warmer from October to February. The average annual rainfall is no more than 3000 mm for the highest latitudes, and not less than 2000 mm for the lowest. However, rainfall varies from year to year. Periods of too little or too much precipitation have no noteworthy effect on the vegetative activity of wild and cultivated plants, since the duration is too short to have any long-term influence. However, animal populations may be affected by this variation, since droughts rapidly dries up secondary branches of rivers, normally filled with water, affecting the fish that live there, and other species of animals that normally receive water at these places. In the opposite case, heavy continuous rainfall tends to accelerate the decomposition of organic bedding, rapidly destroying the fruit and seeds eaten by large terrestrial herbivores such as tapirs and peccaries. The ecosystem is in a state of dynamic equilibrium, as its system of energetic exchange operates theoretically in a closed circuit (Odum, 1971). The organic matter and minerals are con-
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Fig. 4. A case with a zero hunt on all the three populations of species. The ecosystem is in balance.
A detailed structure of the model, describing the various in- and outflows between and within stocks and sectors is presented in Fig. 3. The model has been run at a monthly time step for 200 months. This length was chosen on the basis that it would allow a human population to adjust to changes in hunting modes, since possible effects of a taboo would thus be known in a fairly short notice of time.
Section 2.1 the vegetation sector is considered to be largely unaffected by climatic variability. Nutrients are thus tightly recycled and closely associated to the standing living biomass. This has been accounted for in the model by the design of a nutrient cycle circuit (see Fig. 3). The vegetation sector was constructed to be robust, and largely unaffected by different levels of population changes within other sectors. Sensitivity runs indicate that population levels of the herbivores (rodents and tapirs) are constrained by their respective carrying capacities, and do not pose a threat to the vegetation, even if initial numbers greatly overshoot carrying capacity levels (see Table 1). This sector is thought to consist largely of evergreen leaves, flowering plants, fruits and seeds, and constitutes the first trophic level of this ecosystem (for information on input data used throughout Section 2.2, see equations in the Appendix).
2.2.1. The 6egetati6e sector Based on the climatic conditions described in
2.2.2. The rodent sector The rodent sector of the model represents ro-
tinually recycled by a complex network of microorganisms and specialised bacteria. Given that almost 90% of the nutrients of tropical forest ecosystems are stored in the standing plant biomass (Begon et al., 1990), this closed circuit system is sensitive to larger clearings and deforestation activities.
2.2. Description of model sectors
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J. Colding / Ecological Modelling 110 (1998) 5–17
Fig. 5. A case where rodents are hunted at the hunting rate of 0.17, equal to an average of pursued rodents by other neotropical forest groups (Redford 1993). Jaguars and tapirs are not hunted at all.
dents that may be hunted by the Ecuador Achuar (mainly those of a weight between 2 and 10 kg). Species under consideration include Agouti paca, and Dasyprocta spp. and Myoprocta spp. They represent part of the second trophic level. Rodents are widely hunted among tropical Amazonian groups (Redford, 1993) and are important sources of protein in the Ecuador Achuar diet. Initial population of rodents were set at 18 000. This estimate is based on adding the density numbers for the above rodent species (see estimates of Nowak (1991), Redford (1993), and Bodmer (1994)) (Density of Agouti is 5.1 per km2, for Dasyprocta and Myoprocta 6.1 per km2.) For an ecosystem 9000 km2 in size, the estimate will end up being about 110 000 rodents, which is about 12.2 individuals per km2. This is considered as an extremely high estimate. Densities for rodents such as hares in Sweden,
are considered very high at 4 per km2 (ibid), which is about 1/6 of the number above. For this reason the estimated number on density has been set at about 2 rodents per km2, which gives a total value of about 18 000 rodents. Mean litter size of rodents was set at four (Eisenberg, 1981), with 3–4 litters per year (ibid). Based on these numbers the specific birth rate was set at about 6.0, which gives a monthly birth rate of 0.5. The death rate was estimated by using the survivorship curve based on that of Odum (1983), and set at a monthly rate of 0.46.
2.2.3. The tapir sector Tapirs are both grazers and browsers. They represent part of the second trophic level of this model. The Ecuador Achuar employ a general food taboo on the killing of tapirs. Densities for tapirs have been estimated at 0.4 per km2 (Bodmer, 1994). As was the case for rodents, 1/6 of this estimate has
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Fig. 6. A case displaying the maximum hunting rode on rodents. At a hunting rate of 0.92, both rodents and jaguars are severely affected. Above this rate these populations go extinct. Jaguars and tapirs are not hunted at all.
been used as an input value for this model. Therefore, the initial population of tapirs is set at about 600. For the calculation of birth and death rates, the survivorship curve of the species of Black-tail deer was used (see Odum, 1983). This species is a herbivore of approximately the same size, and was assumed to have a survivorship curve of the same kind as tapirs. Litter size of tapirs was set at one, with an average of about one litter per year (Eisenberg, 1981). Monthly birth rate was calculated at 0.04, and monthly death rate at 0.017.
2.2.5. The Achuar sector The Achuar of this particular region of Ecuador has a steady population of about 1000 (DeScola, 1986). The group has many options for hunting which will be analysed below.
2.2.4. The jaguar sector Jaguars represent the top-predator of this ecosystem, preying on both rodents and tapirs. No information on rates of predation, birth and death were found in the literature. Ecological density of jaguars is estimated at 0.013 per km (Redford and Eisenberg, 1992). One sixth of this value sets the initial jaguar population at about 20 individuals.
3.1. Scenario one
3. Scenarios and individual outcomes of runs The results of the different runs are presented in Figs. 4–9. Following are three different scenarios, each representing possible hunting options for the Ecuador Achuar population.
As has been stated, the Ecuador Achuar obey general food taboos on the hunting of tapirs and jaguars. In this first scenario, this has been accounted for by setting hunting rates on these species at zero (see Figs. 4–6). In this scenario, consisting of three runs, the Achuar only hunt rodents. Different outcomes will be obtained, de-
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J. Colding / Ecological Modelling 110 (1998) 5–17
Fig. 7. A case where jaguars are pursued at a hunting rate of 0.0036, which turned out to be the maximum rate allowed for not creating a collapse of the jaguar population. Rodents are pursued at a hunting rate of 0.17. Tapirs are not pursued at all.
pending on the different hunting rates of rodents. Fig. 4 represents a case where the Achuar employ a zero value in the hunting rate of rodents. This run indicates that the model ecosystem is in balance. In the second run (see Fig. 5), the hunting rate on rodents employed by the Achuar was set at 170 per month, which is about the normal hunting rate on rodents by other neotropical forest groups (Redford and Robinson, 1987). In the third run (see Fig. 6), the numbers of rodents hunted was set at 920 per month, which was the highest number of rodents that could be hunted without collapsing this population. If the rodent population collapses it will make the jaguar population collapse as well, since jaguars predominantly prey on rodents in this model. These results indicate that the Ecuador Achuar sector of the model may hunt rodents sustainably at a rate of \0.17 to 0.92.
3.2. Scenario two If rodents are hunted at a rate of 170 per month, and the taboo on tapirs is still employed but the taboo on jaguars is not obeyed, the run presented in Fig. 7 is obtained. In this run the hunting rate on jaguars is set at 3.6 per month, which is the maximum hunting rate allowed without collapsing the population.
3.3. Scenario three In this last scenario two different runs were made. In Fig. 8 the taboo on the hunting of jaguars is observed by the hunters, but not the taboo on tapirs. The result from this run indicates that if tapirs are hunted at a rate of two per month, or greater, the number of the tapir population will gradually decline and eventually go extinct. Additional runs were made in which
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Fig. 8. A case displaying a zero hunting rate of jaguars (by taboo). With a hunting rate on tapirs of 0.002, the population declines steadily over time. Rodents are hunted at the rate of 0.17.
it was possible for hunters to hunt all three populations of species simultaneously at rather high hunting rates in a sustainable manner. For example rodents, jaguars and tapirs, may all be pursued at corresponding hunting rates of 170, 3 and 2 per month, without threatening the long time survival of these populations. Fig. 9 shows that over-hunting of jaguars may be a rational hunting option for groups that hunt species, such as rodents and ungulates. The interspecific competition between the human hunters and jaguars is thus reduced. This situation does not pertain to the Ecuador Achuar.
4. Discussion There are several limitations to this exercise. For example, input data on consumption rates are simply estimated without any real references. The system was sensitivity tested and analysed in this regard, which demonstrated
that if the numbers of tapirs taken per jaguar are slightly increased from a random value of 0.4 to 0.5, it could have a dramatic impact on a tapir population of about 300 individuals (see Table 1). At this level the whole tapir population collapsed and became extinct. Estimates of vegetation, below and above values in the model, revealed that carrying capacity is set at about 6.6 million vegetative units. Sensitivity runs on the rodent population at numbers below or above the estimated population size, showed that the rodent population established itself close to a carrying capacity at about 12 000–14 500 in most runs (see Table 1). Despite the limitations of this exercise, what can be said about the overall results of this modelling exercise? Firstly, the results indicate that general taboos imposed on species of jaguars and tapirs protect them from becoming extinct. However, the taboos of this model seem not to have any ‘hidden’ ecological functions by, e.g. enhancing other populations of
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J. Colding / Ecological Modelling 110 (1998) 5–17
Fig. 9. A case showing that it may be advantageous for hunters to pursue a predator species such as the jaguar. This may reduce the interspecific competition between two populations feeding on the same resource. Hunting rate on jaguars is set at 0.0036, on rodents at 0.17, and on tapirs at 0.002.
species pursued by the Ecuador Achuar. It seems to be more efficient to pursue jaguars whenever encountered, than not to. As Fig. 8 also indicates (and several other runs have demonstrated), it may be most efficient to pursue all three species of mammals at moderate hunting rates.
based on mythologies and perceptions of reality, may be the reason behind these taboos in the real world (see also DeScola, 1986). On the other hand, for the preservation of species that may go extinct from over hunting, the use of a moderate hunting mode, or the use of general food taboos, seem to be viable options. Any final conclusion on these relationships cannot be drawn from this model.
5. Conclusion General food taboos may be ecologically adaptive for hunters in ways which are not disclosed by the nature of this simplified model. The STELLA model used here indicates that a ‘moderate’ hunting mode may be the most effective option for the neotropical group of this model. However, no inferences should be drawn based on this result for tropical forest groups such as the Ecuador Achuar. In the model of this paper, general food taboos do not increase the yield for hunters of species which are not surrounded by taboos. Cultural reasons,
Acknowledgements I would like to thank Professor Robert Costanza. at the Institute for Ecological Economics, University of Maryland, for giving me valuable advice and feedback in the construction of this model. Without his insistence this paper would never have been completed. Warm thanks also to Professor Carl Folke, at the Beijer Institute of Ecological Economics, Stockholm, for valuable suggestions and thoughtful considerations.
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Appendix A. Equations for the model Human ° Human – group(t)= Human – group(t − dt) INIT Human – group = 1000 Jaguar – kill – per – human = 0 Rodent – kill – per – human = 0 Tapir – kill – per – human = 0 Jaguars ° Jaguar – pop(t) = Jaguar – pop(t − dt) + (births – jaguar − deaths – jaguar)*dt INIT Jaguar – pop = 20 INFLOWS: births – jag = Jaguar – pop*(birth – frac + jag – of – tap+birth – fract – jag – of – rod) *(1−(Jaguar – pop/car – cap – jag)) OUTFLOWS: deaths – jaguar = Jaguar – pop*(death – frac – jag – of – rod+death – fract – jag – of
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(40.0, 0.245), (50.0, 0.305), (60.0, 0.35), (70.0, 0.37), (80.0, 0.383), (90.0, 385), (100, 0.385) * death – fract – jag – of – tapirs= GRAPH(Tapir – pop) (0.00, 0.11), (60.0, 0.105), (120, 0.09), (180, 0.075), (240, 0.06), (300, 0.05), (360, 0.04), (420, 0.032), (480, 0.0235), (540, 0.016), (600, 0.00) * death – frac – jag – of – rod= GRAPH(Rodent – pop) (0.00, 0.91), (1800, 0.855), (3600, 0.76), (5400, 0.625), (7200, 0.45), (9000, 0.295), (10 800, 0.18), (12 600, 0.105), (14 400, 0.06), (16 200, 0.025), (18 000, 0.0005) Rodents ° Rodent – pop(t)=Rodent – pop(t− dt)+(births−deaths)*dt INIT Rodent – pop=18 000 INFLOWS: births=Rodent – pop*birth –fraction*(1 −Rodent – pop/car – cap –rod)) OUTFLOWS:
– tapirs)+Human – group*Jaguar – kill – per – human car – cap – jag = 40 * birth – fract – jag – of – rod = GRAPH(Rodent – pop) (0.00, 0.0265), (100, 0.0285), (200, 0.033), (300, 0.0375), (400, 0.0425), (500, 0.048), (600, 0.0515), (700, 0.0575), (800, 0.059), (900, 0.06), (1000, 0.06) * birth – frac – jag – of – tap = GRAPH(Tapir – pop) (0.00, 0.13), (10.0, 0.145), (20.0, 0.174), (30.0, 0.205),
deaths=Jaguar – pop*rod – per –jaguar+Rodent – pop*death –fraction+Human – group*Rodent – kill –per – human car – cap – rod=36 000 rod – per – jaguar=RANDOM(0.100, 50) * birth – fraction=GRAPH(Vegetation) (0.00, 0.12), (700 000, 0.165), (1.4e+006, 0.23), (2.1e+ 006, 0.285), (2.8e+006, 0.33), (3.5e+006, 0.395), (4.2e+
J. Colding / Ecological Modelling 110 (1998) 5–17
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006, 0.43), (4.9e + 006, 0.455), (5.6e + 006, 0.485), (6.3e + 006, 0.5), (7e + 006, 0.5) * death – fraction = GRAPH(Vegetation) (0.00, 0.815), (80 000, 0.63), (160 000, 0.49), (240 000, 0.395), (320 000, 0.32), (400 000, 0.27), (480 000, 0.24), (560 000, 0.215), (640 000, 0.213), (720 000, 0.215), (800 000, 0.213) Tapirs ° Tapir – pop(t) = Tapir – pop(t − dt) + (births – tapir − deaths – tapir)*dt INIT Tapir – pop = 600 INFLOWS: births – tapir = Tapir – pop*birth – fract – tapir*(1 −(Tapir – pop/car – cap – tapirs)) OUTFLOWS: deaths – tapir = Jaguar – pop*tapirs – per – jag + Tapir – pop*death – fract – tapir + Human – group*Tapir – kill – per – human car – cap – tapirs = 1200 tapirs – per – jag = RANDOM(0, 0.4, 0.4) * birth – fract – tapir = GRAPH(Vegetation) (0.00, 0.0005), (80 000, 0.0185), (160 000, 0.0275), (240 000, 0.033), (320 000, 0.0358), (400 000, 0.0388), (480 000, 0.0403), (560 000, 0.0425), (640 000, 0.0433), (720 000, 0.0438), (800 000, 0.044) * death – fract – tapir = GRAPH(Vegetation) (0.00, 0.9), (600 000, 0.8), (1.2e + 006, 0.7), (1.8e + 006, 0.4), (2.4e + 006, 0.0498), (3e + 006, 0.0305), (3.6e+006, 0.019), (4.2e +
006, 0.016), (4.8e+006, 0.0155), (5.4e+006, 0.015), (6e+006, 0.0145) Vegetation ° Vegetation(t) =Vegetation(t −dt)+ (regeneration −consumption)*dt INIT Vegetation=6 000 000 INFLOWS: regeneration =seeding+(Vegetation*regen – per –plant) *(1−(Vegetation/car – cap – veg)) OUTFLOWS: consumption =Rodent – pop*veget –per – rodent+Tapir – pop*veget – per –tapir car – cap – veg=7 000 000 seeding=1 veget – per – rodent=20 veget – per – tapir=20 * nutrients=GRAPH(Vegetation) (0.00, 0.015), (600 000, 0.07), (1.2e+006, 0.135), (1.8e+ 006, 0.23), (2.4e+006, 0.365), (3e+ 006, 0.565), (3.6e+006, 0.745), (4.2e+006, 0.815), (4.8e+ 006, 0.855), (5.4e+006, 0.88), (6e+ 006, 0.9) * regen – per – piant=GRAPH(nutrients) (0.00, 0.085), (0.1, 0.125), (0.3, 0.145), (0.3, 0.185), (0.4, 0.215), (0.5, 0.275), (0.6, 0.385), (0.7, 0.505), (0.8, 0.645), (0.9, 0.705), (1, 0.735)
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Analysis of hunting options by the use of general food taboos Johan Colding * Department of Systems Ecology, Stockholm Uni6ersity, S-106 91 Stockholm, Sweden
Abstract A hypothetical model was built, using the STELLA II software program, to test several hunting options for a human hunting group. Different outcomes of possible hunting modes are analysed, such as a change in hunting rate, prey hunted, or species avoided or not avoided by taboos. The model consists of five sectors that reflect a short food chain in an upper Amazonian ecosystem. There is a vegetation sector, a predator sector, and two sectors consisting of browsers and grazers. The last sector represents a human group, known as the Ecuador Achuar. The critical factor analysed is how differences in hunting rate affect a target resource, and how this resource may be affected by general food taboos. The major results of the model are that general food taboos may not be an adaptive short term strategy for hunters, but that a ‘moderate’ hunting mode may be the most effective option for the human group. Since the model is a simplification of the real world, no general conclusions for management should be drawn from the results. © 1998 Elsevier Science B.V. All rights reserved. Keywords: General food taboos; Hunting options; Amazone ecosystems
1. Introduction Many traditional societies employ food taboos on animal species for a number of reasons. Some researchers suggest that there are nature management motives behind them (Rappaport, 1967, 1968; Reichel-Dolmatoff, 1976; McDonald, 1977; Johaness, 1978; Ross, 1978; Harris, 1979), while others resent any such ecological motives (Rea,
* Present address: The Beijer International Institute of Ecological Economics, The Royal Swedish Academy of Sciences, PO Box 50005, S-104 05 Stockholm, Sweden. Tel.: +46 8 6739500; fax: + 46 8 152464; e-mail: [email protected]. se
1981; Edgerton, 1992). Hunting among some traditional groups may be conducted in a highly efficient way, consistent with predicators of foraging theory (Alvard, 1993, 1994). While species are thus pursued with short-term harvest rate maximization, one may very well ask why some native hunting groups employ food taboos. McDonald (1977), in a study of 11 South American tropical groups, suggests that taboos may function as conservation mechanisms for reducing the hunting pressure on larger mammals in environments where such species are low in abundance. General food taboos—applying to all members within a community—may play a role in biodiversity con-
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Fig. 1. Approximate location of the Ecuador Achuar territory (marked in grey).
servation. For example, Colding and Folke (1996), found that a number of threatened populations of species, including endemic and keystone species, benefit from such taboos. The purpose of this modelling exercise with STELLA II, has been to analyse possible relations between general food taboos and different hunting modes. The STELLA II program may be a suitable tool for an investigation of this kind. While real world data on wild populations are available in the literature (see Emmons, 1990; Nowak, 1991; Redford and Eisenberg, 1992), it is nevertheless problematic to generalise from such data. For example, data on numbers of density are measured in habitats where species thrive. To generalise from such data, from one habitat to another, may be highly awkward. Among other things, this is due to the differences in the heterogenity of landscapes within and between ecosystems. Perhaps more realistic data for the presented model could be obtained using site-spe-
cific data from field studies. Unfortunately, this has not been possible. Thus, the estimates used here are highly artificial. The Ecuador Achuar represents the human group of this model. This group, inhabiting a vast region at the Ecuador/Peru border (see Fig. 1), has been well studied by DeScola (1986). The Ecuador Achuar impose general food taboos on several species of mammals. They abstain from the consumption of all carnivorous mammals and regard several other such species as inedible, referring to them as ‘sickening meat’ (DeScola, 1986). Among such species is the rodent Capybara, Hydrochoerus hydrochoerus, which is widely hunted by other neotropical forest groups (see Redford, 1993; Bodmer 1994). However, other species of rodents are hunted, e.g. agoutis (Agouti paca) and acouchis (Dasyprocta and Myoprocta). Ungulates, such as Brocket deer (Mazama Americana) and Tapirs (Tapirus terrestris and T. pinchaque) are avoided by general food taboos, due to reincarnation beliefs (DeScola, 1986).
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narios are presented in Section 3. Individual model runs will also be discussed in Section 3, while an overall discussion will follow in Section 4.
2. Basic structure and data
Fig. 2. The basic structure and key actors of the model.
The results obtained in this study are highly artificial and should not be taken as indicators of the Ecuador Achuar hunting mode. The model is far too incomplete for any such inferences to be made. This complies for both the structure of the ecosystem, as well as for the various populations of species described in the model. Some possible hunting modes and sce-
The main sectors and key actors of the model are presented in Fig. 2. This figure illustrates the basic food chain investigated. The population of jaguars is regarded as the top-predator in this extremely simplified ecosystem. Jaguars prey on both the rodent and tapir populations respectively, that, in turn, feed from the vegetation sector. Although all carnivore species are avoided by the Achuar hunter, the jaguars in this model are thought of representing a fraction of all the carnivores in this ecosystem. The same holds true for both the rodent sector and the
Fig. 3. A model for examining different hunting options for a human group.
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Table 1 Sensitivity runs on population stocks of the model, using STELLA II Months
1: Vegetation
2: Vegetation
3: Vegetation
1: Rodent
2: Rodent
3: Rodent
0 50 100 150 200
3 000 000 6 617 864 6 605 182 6 557 093 6 620 185
6 000 000 6 618 420 6 605 278 6 557 171 6 620 226
9 000 000 6 618 413 6 605 277 6 557 170 6 620 225
9000 11 955 11 772 14 512 12 272
18 000 12 009 11 785 14 521 12 279
27 000 12 035 11 791 14 525 12 281
0 50 100 150 200
1: Tapir 300 300 294 335 338
2: Tapir 600 541 511 530 503
3: Tapir 900 651 566 561 521
1: Jaguar 10 28 28 31 27
2: Jaguar 25 28 28 31 27
3: Jaguar 40 28 28 31 27
0 50 100 150 200
1: Tapir* 300 218 67 0 0
2 Tapir* 600 485 410 400 333
3: Tapir* 900 603 487 463 392
An increase in the rate of tapirs per jag from RANDOM (0, 0.4, 0.4) to RANDOM (0, 0.5, 0.5) is marked as * in the table.
tapir sector. The former represents mainly a lumping of larger rodents that the Ecuador Achuar pursue, and the latter a fraction of the ungulates avoided by general food taboos.
2.1. Climatic conditions of the ecosystem The ecosystem is a simplified version of the Ecuador Achuar territory, situated at the Ecuador/Peru border. It lies north of the Pastaza river, and covers an area of about 9000 km2 (see Fig. 1). The region has a typical equatorial climate, constantly humid, no dry season, and a monthly rainfall always over 60 mm. It is situated 2° south of the equator, where days and nights are nearly the same length, and temperatures remain constant throughout the year. Despite its proximity to the Andean barrier, this ecosystem is not directly affected by the special meteorological conditions of the foothills. Mean annual daytime temperatures vary between 24 and 25°C. The annual average low temperature fluctuates between 19 and 20°C, depending on altitude, and the average annual high tempera-
ture is between 29 and 31°C. It is marginally warmer from October to February. The average annual rainfall is no more than 3000 mm for the highest latitudes, and not less than 2000 mm for the lowest. However, rainfall varies from year to year. Periods of too little or too much precipitation have no noteworthy effect on the vegetative activity of wild and cultivated plants, since the duration is too short to have any long-term influence. However, animal populations may be affected by this variation, since droughts rapidly dries up secondary branches of rivers, normally filled with water, affecting the fish that live there, and other species of animals that normally receive water at these places. In the opposite case, heavy continuous rainfall tends to accelerate the decomposition of organic bedding, rapidly destroying the fruit and seeds eaten by large terrestrial herbivores such as tapirs and peccaries. The ecosystem is in a state of dynamic equilibrium, as its system of energetic exchange operates theoretically in a closed circuit (Odum, 1971). The organic matter and minerals are con-
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Fig. 4. A case with a zero hunt on all the three populations of species. The ecosystem is in balance.
A detailed structure of the model, describing the various in- and outflows between and within stocks and sectors is presented in Fig. 3. The model has been run at a monthly time step for 200 months. This length was chosen on the basis that it would allow a human population to adjust to changes in hunting modes, since possible effects of a taboo would thus be known in a fairly short notice of time.
Section 2.1 the vegetation sector is considered to be largely unaffected by climatic variability. Nutrients are thus tightly recycled and closely associated to the standing living biomass. This has been accounted for in the model by the design of a nutrient cycle circuit (see Fig. 3). The vegetation sector was constructed to be robust, and largely unaffected by different levels of population changes within other sectors. Sensitivity runs indicate that population levels of the herbivores (rodents and tapirs) are constrained by their respective carrying capacities, and do not pose a threat to the vegetation, even if initial numbers greatly overshoot carrying capacity levels (see Table 1). This sector is thought to consist largely of evergreen leaves, flowering plants, fruits and seeds, and constitutes the first trophic level of this ecosystem (for information on input data used throughout Section 2.2, see equations in the Appendix).
2.2.1. The 6egetati6e sector Based on the climatic conditions described in
2.2.2. The rodent sector The rodent sector of the model represents ro-
tinually recycled by a complex network of microorganisms and specialised bacteria. Given that almost 90% of the nutrients of tropical forest ecosystems are stored in the standing plant biomass (Begon et al., 1990), this closed circuit system is sensitive to larger clearings and deforestation activities.
2.2. Description of model sectors
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Fig. 5. A case where rodents are hunted at the hunting rate of 0.17, equal to an average of pursued rodents by other neotropical forest groups (Redford 1993). Jaguars and tapirs are not hunted at all.
dents that may be hunted by the Ecuador Achuar (mainly those of a weight between 2 and 10 kg). Species under consideration include Agouti paca, and Dasyprocta spp. and Myoprocta spp. They represent part of the second trophic level. Rodents are widely hunted among tropical Amazonian groups (Redford, 1993) and are important sources of protein in the Ecuador Achuar diet. Initial population of rodents were set at 18 000. This estimate is based on adding the density numbers for the above rodent species (see estimates of Nowak (1991), Redford (1993), and Bodmer (1994)) (Density of Agouti is 5.1 per km2, for Dasyprocta and Myoprocta 6.1 per km2.) For an ecosystem 9000 km2 in size, the estimate will end up being about 110 000 rodents, which is about 12.2 individuals per km2. This is considered as an extremely high estimate. Densities for rodents such as hares in Sweden,
are considered very high at 4 per km2 (ibid), which is about 1/6 of the number above. For this reason the estimated number on density has been set at about 2 rodents per km2, which gives a total value of about 18 000 rodents. Mean litter size of rodents was set at four (Eisenberg, 1981), with 3–4 litters per year (ibid). Based on these numbers the specific birth rate was set at about 6.0, which gives a monthly birth rate of 0.5. The death rate was estimated by using the survivorship curve based on that of Odum (1983), and set at a monthly rate of 0.46.
2.2.3. The tapir sector Tapirs are both grazers and browsers. They represent part of the second trophic level of this model. The Ecuador Achuar employ a general food taboo on the killing of tapirs. Densities for tapirs have been estimated at 0.4 per km2 (Bodmer, 1994). As was the case for rodents, 1/6 of this estimate has
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Fig. 6. A case displaying the maximum hunting rode on rodents. At a hunting rate of 0.92, both rodents and jaguars are severely affected. Above this rate these populations go extinct. Jaguars and tapirs are not hunted at all.
been used as an input value for this model. Therefore, the initial population of tapirs is set at about 600. For the calculation of birth and death rates, the survivorship curve of the species of Black-tail deer was used (see Odum, 1983). This species is a herbivore of approximately the same size, and was assumed to have a survivorship curve of the same kind as tapirs. Litter size of tapirs was set at one, with an average of about one litter per year (Eisenberg, 1981). Monthly birth rate was calculated at 0.04, and monthly death rate at 0.017.
2.2.5. The Achuar sector The Achuar of this particular region of Ecuador has a steady population of about 1000 (DeScola, 1986). The group has many options for hunting which will be analysed below.
2.2.4. The jaguar sector Jaguars represent the top-predator of this ecosystem, preying on both rodents and tapirs. No information on rates of predation, birth and death were found in the literature. Ecological density of jaguars is estimated at 0.013 per km (Redford and Eisenberg, 1992). One sixth of this value sets the initial jaguar population at about 20 individuals.
3.1. Scenario one
3. Scenarios and individual outcomes of runs The results of the different runs are presented in Figs. 4–9. Following are three different scenarios, each representing possible hunting options for the Ecuador Achuar population.
As has been stated, the Ecuador Achuar obey general food taboos on the hunting of tapirs and jaguars. In this first scenario, this has been accounted for by setting hunting rates on these species at zero (see Figs. 4–6). In this scenario, consisting of three runs, the Achuar only hunt rodents. Different outcomes will be obtained, de-
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J. Colding / Ecological Modelling 110 (1998) 5–17
Fig. 7. A case where jaguars are pursued at a hunting rate of 0.0036, which turned out to be the maximum rate allowed for not creating a collapse of the jaguar population. Rodents are pursued at a hunting rate of 0.17. Tapirs are not pursued at all.
pending on the different hunting rates of rodents. Fig. 4 represents a case where the Achuar employ a zero value in the hunting rate of rodents. This run indicates that the model ecosystem is in balance. In the second run (see Fig. 5), the hunting rate on rodents employed by the Achuar was set at 170 per month, which is about the normal hunting rate on rodents by other neotropical forest groups (Redford and Robinson, 1987). In the third run (see Fig. 6), the numbers of rodents hunted was set at 920 per month, which was the highest number of rodents that could be hunted without collapsing this population. If the rodent population collapses it will make the jaguar population collapse as well, since jaguars predominantly prey on rodents in this model. These results indicate that the Ecuador Achuar sector of the model may hunt rodents sustainably at a rate of \0.17 to 0.92.
3.2. Scenario two If rodents are hunted at a rate of 170 per month, and the taboo on tapirs is still employed but the taboo on jaguars is not obeyed, the run presented in Fig. 7 is obtained. In this run the hunting rate on jaguars is set at 3.6 per month, which is the maximum hunting rate allowed without collapsing the population.
3.3. Scenario three In this last scenario two different runs were made. In Fig. 8 the taboo on the hunting of jaguars is observed by the hunters, but not the taboo on tapirs. The result from this run indicates that if tapirs are hunted at a rate of two per month, or greater, the number of the tapir population will gradually decline and eventually go extinct. Additional runs were made in which
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Fig. 8. A case displaying a zero hunting rate of jaguars (by taboo). With a hunting rate on tapirs of 0.002, the population declines steadily over time. Rodents are hunted at the rate of 0.17.
it was possible for hunters to hunt all three populations of species simultaneously at rather high hunting rates in a sustainable manner. For example rodents, jaguars and tapirs, may all be pursued at corresponding hunting rates of 170, 3 and 2 per month, without threatening the long time survival of these populations. Fig. 9 shows that over-hunting of jaguars may be a rational hunting option for groups that hunt species, such as rodents and ungulates. The interspecific competition between the human hunters and jaguars is thus reduced. This situation does not pertain to the Ecuador Achuar.
4. Discussion There are several limitations to this exercise. For example, input data on consumption rates are simply estimated without any real references. The system was sensitivity tested and analysed in this regard, which demonstrated
that if the numbers of tapirs taken per jaguar are slightly increased from a random value of 0.4 to 0.5, it could have a dramatic impact on a tapir population of about 300 individuals (see Table 1). At this level the whole tapir population collapsed and became extinct. Estimates of vegetation, below and above values in the model, revealed that carrying capacity is set at about 6.6 million vegetative units. Sensitivity runs on the rodent population at numbers below or above the estimated population size, showed that the rodent population established itself close to a carrying capacity at about 12 000–14 500 in most runs (see Table 1). Despite the limitations of this exercise, what can be said about the overall results of this modelling exercise? Firstly, the results indicate that general taboos imposed on species of jaguars and tapirs protect them from becoming extinct. However, the taboos of this model seem not to have any ‘hidden’ ecological functions by, e.g. enhancing other populations of
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J. Colding / Ecological Modelling 110 (1998) 5–17
Fig. 9. A case showing that it may be advantageous for hunters to pursue a predator species such as the jaguar. This may reduce the interspecific competition between two populations feeding on the same resource. Hunting rate on jaguars is set at 0.0036, on rodents at 0.17, and on tapirs at 0.002.
species pursued by the Ecuador Achuar. It seems to be more efficient to pursue jaguars whenever encountered, than not to. As Fig. 8 also indicates (and several other runs have demonstrated), it may be most efficient to pursue all three species of mammals at moderate hunting rates.
based on mythologies and perceptions of reality, may be the reason behind these taboos in the real world (see also DeScola, 1986). On the other hand, for the preservation of species that may go extinct from over hunting, the use of a moderate hunting mode, or the use of general food taboos, seem to be viable options. Any final conclusion on these relationships cannot be drawn from this model.
5. Conclusion General food taboos may be ecologically adaptive for hunters in ways which are not disclosed by the nature of this simplified model. The STELLA model used here indicates that a ‘moderate’ hunting mode may be the most effective option for the neotropical group of this model. However, no inferences should be drawn based on this result for tropical forest groups such as the Ecuador Achuar. In the model of this paper, general food taboos do not increase the yield for hunters of species which are not surrounded by taboos. Cultural reasons,
Acknowledgements I would like to thank Professor Robert Costanza. at the Institute for Ecological Economics, University of Maryland, for giving me valuable advice and feedback in the construction of this model. Without his insistence this paper would never have been completed. Warm thanks also to Professor Carl Folke, at the Beijer Institute of Ecological Economics, Stockholm, for valuable suggestions and thoughtful considerations.
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Appendix A. Equations for the model Human ° Human – group(t)= Human – group(t − dt) INIT Human – group = 1000 Jaguar – kill – per – human = 0 Rodent – kill – per – human = 0 Tapir – kill – per – human = 0 Jaguars ° Jaguar – pop(t) = Jaguar – pop(t − dt) + (births – jaguar − deaths – jaguar)*dt INIT Jaguar – pop = 20 INFLOWS: births – jag = Jaguar – pop*(birth – frac + jag – of – tap+birth – fract – jag – of – rod) *(1−(Jaguar – pop/car – cap – jag)) OUTFLOWS: deaths – jaguar = Jaguar – pop*(death – frac – jag – of – rod+death – fract – jag – of
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(40.0, 0.245), (50.0, 0.305), (60.0, 0.35), (70.0, 0.37), (80.0, 0.383), (90.0, 385), (100, 0.385) * death – fract – jag – of – tapirs= GRAPH(Tapir – pop) (0.00, 0.11), (60.0, 0.105), (120, 0.09), (180, 0.075), (240, 0.06), (300, 0.05), (360, 0.04), (420, 0.032), (480, 0.0235), (540, 0.016), (600, 0.00) * death – frac – jag – of – rod= GRAPH(Rodent – pop) (0.00, 0.91), (1800, 0.855), (3600, 0.76), (5400, 0.625), (7200, 0.45), (9000, 0.295), (10 800, 0.18), (12 600, 0.105), (14 400, 0.06), (16 200, 0.025), (18 000, 0.0005) Rodents ° Rodent – pop(t)=Rodent – pop(t− dt)+(births−deaths)*dt INIT Rodent – pop=18 000 INFLOWS: births=Rodent – pop*birth –fraction*(1 −Rodent – pop/car – cap –rod)) OUTFLOWS:
– tapirs)+Human – group*Jaguar – kill – per – human car – cap – jag = 40 * birth – fract – jag – of – rod = GRAPH(Rodent – pop) (0.00, 0.0265), (100, 0.0285), (200, 0.033), (300, 0.0375), (400, 0.0425), (500, 0.048), (600, 0.0515), (700, 0.0575), (800, 0.059), (900, 0.06), (1000, 0.06) * birth – frac – jag – of – tap = GRAPH(Tapir – pop) (0.00, 0.13), (10.0, 0.145), (20.0, 0.174), (30.0, 0.205),
deaths=Jaguar – pop*rod – per –jaguar+Rodent – pop*death –fraction+Human – group*Rodent – kill –per – human car – cap – rod=36 000 rod – per – jaguar=RANDOM(0.100, 50) * birth – fraction=GRAPH(Vegetation) (0.00, 0.12), (700 000, 0.165), (1.4e+006, 0.23), (2.1e+ 006, 0.285), (2.8e+006, 0.33), (3.5e+006, 0.395), (4.2e+
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006, 0.43), (4.9e + 006, 0.455), (5.6e + 006, 0.485), (6.3e + 006, 0.5), (7e + 006, 0.5) * death – fraction = GRAPH(Vegetation) (0.00, 0.815), (80 000, 0.63), (160 000, 0.49), (240 000, 0.395), (320 000, 0.32), (400 000, 0.27), (480 000, 0.24), (560 000, 0.215), (640 000, 0.213), (720 000, 0.215), (800 000, 0.213) Tapirs ° Tapir – pop(t) = Tapir – pop(t − dt) + (births – tapir − deaths – tapir)*dt INIT Tapir – pop = 600 INFLOWS: births – tapir = Tapir – pop*birth – fract – tapir*(1 −(Tapir – pop/car – cap – tapirs)) OUTFLOWS: deaths – tapir = Jaguar – pop*tapirs – per – jag + Tapir – pop*death – fract – tapir + Human – group*Tapir – kill – per – human car – cap – tapirs = 1200 tapirs – per – jag = RANDOM(0, 0.4, 0.4) * birth – fract – tapir = GRAPH(Vegetation) (0.00, 0.0005), (80 000, 0.0185), (160 000, 0.0275), (240 000, 0.033), (320 000, 0.0358), (400 000, 0.0388), (480 000, 0.0403), (560 000, 0.0425), (640 000, 0.0433), (720 000, 0.0438), (800 000, 0.044) * death – fract – tapir = GRAPH(Vegetation) (0.00, 0.9), (600 000, 0.8), (1.2e + 006, 0.7), (1.8e + 006, 0.4), (2.4e + 006, 0.0498), (3e + 006, 0.0305), (3.6e+006, 0.019), (4.2e +
006, 0.016), (4.8e+006, 0.0155), (5.4e+006, 0.015), (6e+006, 0.0145) Vegetation ° Vegetation(t) =Vegetation(t −dt)+ (regeneration −consumption)*dt INIT Vegetation=6 000 000 INFLOWS: regeneration =seeding+(Vegetation*regen – per –plant) *(1−(Vegetation/car – cap – veg)) OUTFLOWS: consumption =Rodent – pop*veget –per – rodent+Tapir – pop*veget – per –tapir car – cap – veg=7 000 000 seeding=1 veget – per – rodent=20 veget – per – tapir=20 * nutrients=GRAPH(Vegetation) (0.00, 0.015), (600 000, 0.07), (1.2e+006, 0.135), (1.8e+ 006, 0.23), (2.4e+006, 0.365), (3e+ 006, 0.565), (3.6e+006, 0.745), (4.2e+006, 0.815), (4.8e+ 006, 0.855), (5.4e+006, 0.88), (6e+ 006, 0.9) * regen – per – piant=GRAPH(nutrients) (0.00, 0.085), (0.1, 0.125), (0.3, 0.145), (0.3, 0.185), (0.4, 0.215), (0.5, 0.275), (0.6, 0.385), (0.7, 0.505), (0.8, 0.645), (0.9, 0.705), (1, 0.735)
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