Megafaunal Extinction Climate Humans And Assumptions And Critical Thinking


The late Quaternary period saw the rapid extinction of the majority of the world's terrestrial megafauna. The cause of these dramatic losses, especially the relative importance of climatic change and the impacts of newly arrived people, remains highly controversial, with geographically restricted analyses generating conflicting conclusions. By analyzing the distribution and timing of all megafaunal extinctions in relation to climatic variables and human arrival on five landmasses, we demonstrate that the observed pattern of extinctions is best explained by models that combine both human arrival and climatic variables. Our conclusions are robust to uncertainties in climate data and in the dates of megafaunal extinctions and human arrival on different landmasses, and strongly suggest that these extinctions were driven by both anthropogenic and climatic factors.

Most of the terrestrial megafauna present 100,000 years (100 ky) ago are now extinct (1). The extinctions were geologically rapid, and almost all occurred in the past 50 ky, but their exact timing varied among different parts of the world (2). Climatic change, and overhunting, habitat alteration, or the introduction of a novel disease by recently arrived people have been put forward as competing, and sometimes interacting, explanations (3). In addition to its enormous paleontological significance, this debate has drawn wide interest for its relevance to the relationship of humans with nature and to our understanding of the current anthropogenic extinction episode (4⇓⇓⇓–8).

Attempts to explain megafaunal extinctions have, in addition to examining the effect of factors such as size and reproductive rate on extinction probability (9, 10), often focused on matching them in space and time with either climatic change or human arrival (11⇓–13). However, most studies have been limited to single regions and limited numbers of taxa (e.g., 14⇓⇓⇓–18), and have been beset by uncertainties in the accurate dating of human and/or megafaunal remains [e.g., the Cuddie Springs site in Australia (19⇓–21)]. We believe that the problem is better approached by considering several landmasses simultaneously and dealing explicitly with uncertainty.

We did this by analyzing the relationship, across different areas and time periods, between variation in extinction rate and variations in human arrival and climatic conditions. Specifically, we compiled a dataset of human arrival (Table 1) and megafaunal extinction dates (Table S1) from the literature. We used the Antarctic Dome C core (22) as our main source of information on climatic variability; this dataset is the most complete among available time series and is well correlated with other time series at the scale used for our analysis (Tables S2–S4). We used generalized linear models (GLMs) with a binomial error structure and a logit link function to explore the role of human arrival (classified as either just arrived or not) and climatic variables in predicting the probability of extinction for a given landmass and time period (quantified as the proportion of taxa becoming extinct during a time period). We also allowed for landmasses to exhibit different background extinction rates (by including landmass as a block factor in the GLMs). To disentangle the roles of human arrival and climate, we compared the ability of models containing arrival or climate variables in isolation, or both of them simultaneously, to predict the pattern and severity of megafaunal extinctions. To explore the importance of uncertainty in extinction and human arrival dates, we reran the analysis for 10,000 combinations of first and last appearances of our taxa (for both the 700-ky and 100-ky time scales) and for the 32 most plausible combinations of human arrival dates for the 100-ky time scale only (Table S5).

Results and Discussion

We modeled megafaunal extinction rates on five landmasses (North America, South America, Palaearctic Eurasia, Australia, and New Zealand) during the past 700 ky at 100-ky resolution and during the past 100 ky at 10-ky resolution. Over the 700-ky time scale, both climatic variables and human arrival were important predictors of extinction rates. When considered in isolation, both climate and human arrival were informative in all our 10,000 extinction scenarios (Table 2) and predicted extinction very well. Predicted extinction rates were close to observed ones (Fig. 1), and a high percentage of deviance was explained by the models (92.5% and 91.4%, respectively; Fig. 2). Combining both climate and human arrival simultaneously led only to a marginal improvement in fit (Fig. 1): The deviance explained by models with all predictors increased little (to 93.0%) compared with models that only included either climate or human arrival (Fig. 2), even though climate improved the fit of models with human arrival alone in 28.8% of scenarios and human arrival improved models with climate alone in 33.4% of scenarios (Table 2). Of the climatic variables, the strongest predictor of extinction rate was the most rapid rate of temperature decrease within a time period, which had an effect almost double that of the SD and mean of temperature (Fig. 3; note that mean temperature has a negative coefficient, implying that extinctions were more likely to happen at lower temperatures). The maximum rate of temperature increase, on the other hand, had only a limited effect (Fig. 3). The effect of human arrival was of the same order of magnitude as that of mean temperature. Although both climate and human arrival are informative predictors of extinctions across the past 700 ky, the power of the analysis at this time scale to separate their effects is limited by the co-occurrence in all landmasses of peak extinction rate and human arrival in the past 100-ky time interval.

Fig. 1.

Observed and predicted extinction rates (proportion of megafauna that become extinct) for each region and time interval in the 700-ky analysis. Observed extinctions (open circle) are the mean of the 10,000 extinction scenarios to take account of date uncertainties. Colored circles show the extinction rates predicted by models containing climate only (green), human arrival only (orange), or both human arrival and climate (blue). The time interval in which humans have an effect is shaded. Prob., probability.

Fig. 2.

Proportion (Prop.) of deviance explained by climate only (yellow), human arrival only (red), and shared deviance (orange) for the 700- and 100-ky analyses. For the 100-ky analysis, the results for each of the 32 human arrival scenarios are shown. The deviance explained by climate variables (yellow + orange combined) is independent of human arrival scenario.

Fig. 3.

Strength of the effect of four climatic variables and human arrival in predicting extinctions. The absolute magnitude of the median standardized coefficients for each scenario is given by the diameter of the circles, which are scaled such that the largest coefficient is represented by a circle filling a whole square in the grid. The sign and magnitude of the coefficients are given by the color of the circles, according to the scale at the bottom of the graph (positive coefficients represent increased extinctions, negative coefficients decreased extinctions). Note that the size and color scales differ between models covering the past 700 ky vs. the ones covering the last 100 ky. Max, maximum; SD, standard deviation.

At the 100-ky time scale, in which there was variation among landmasses in both human arrival and the timing of peak extinction rates, human arrival and climatic variables were both important predictors of extinction rate in the vast majority of cases. In all 320,000 extinction scenarios tested [10,000 for each of 32 human arrival scenarios (Table S5) designed to reflect uncertainty in human arrival dates], models forced to contain only climatic variables were improved by adding the effect of human arrival (Table 2). On the other hand, depending on which human arrival scenario was used, adding climatic variables improved human-only models in 92–100% of extinction scenarios (Table 2). Models including human arrival explained more deviance (Fig. 2) and generally gave more accurate predictions (Fig. 4) than climate-only models for most time intervals in all continents, with very few exceptions. Climate-only models, on the other hand, sometimes made inaccurate predictions for nonpeak extinction intervals (Fig. 4). This could be the result of assuming that climate covaried consistently, and had consistent effects, across all landmasses. The climate effect was almost completely attributable to the fastest rate of decrease in temperature, which had much larger coefficients than other climatic variables in almost all scenarios (Fig. 3), with steeper temperature declines being associated with greater extinction rates. Human arrival had an even stronger negative effect, which was consistent for all scenarios (Fig. 3).

Fig. 4.

Observed and predicted extinction rates (proportion of megafauna that become extinct) for each region and time interval in the 100-ky analysis. Observed extinctions (open circle) are the means of the 10,000 extinction scenarios. Colored circles show the extinction rates predicted by models containing climate only (green), human arrival only (orange), or both human arrival and climate (blue). For time intervals in which there is uncertainty over the timing of human arrival, the interquartile range of predicted values is shown, with the circle representing the median of these predictions. The ranges of time intervals in which humans may have had an effect are shaded. Prob, probability.

Together, human arrival and climatic variables explained a large proportion of the deviance (93.0% and 65.4–85.0% for the 700-ky and 100-ky analyses, respectively; Fig. 2), especially for an ecological dataset with many inherent uncertainties. Our approach is conservative in attributing importance to human arrival because this forms one explanatory variable (compared with four climatic variables), which can only act in one (700-ky time scale) or two (100-ky time scale) time intervals, whereas climatic variables can act in all of them.

It would also be interesting to repeat the analysis with climatic records or reconstructions for each of the different areas. However, simulated reconstructions of climate covering the past 100 ky (23) are currently of insufficient resolution (i.e., fewer than 10 data points per 10-ky interval), especially in older time periods. Furthermore, there are no local climate records of sufficient length and resolution to cover our analysis, which is why we could only use the Antarctic Dome C ice core (22), which covers eight glacial cycles over the past 700 ky. However, it is possible to use the North Greenland Ice Core Project (NGRIP) record (24) for the past 100 ky. To ensure that our results were not biased by using only records from one hemisphere, we repeated the 100-ky analysis using the NGRIP record for all continents [note that it was not possible to use both records in the same analysis because they measure different climate proxies (Table S2) and there was insufficient power to treat the two hemispheres separately].

The results from the NGRIP 100-ky analysis were strikingly similar to those obtained using the Antarctic Dome C record (Figs. S1–S3). Human arrival always improved models forced to contain climatic variables; adding climate to human-only models improved them, on average, in 81.7% of extinction scenarios (ranging from 8.2–100%, depending on human arrival scenario; Table S6). Climate and human arrival together account for 55.9–78.8% of the deviance [depending on the extinction scenario, with 24.2–47.1% attributable solely to anthropogenic effects and 1.7–19.7% attributable solely to climate effects (Fig. S1)]. As with the analysis using the Antarctic record, human arrival was always associated with an increase in extinction rate and its effect had the strongest effect of all the predictors across all 32 human arrival scenarios (Fig. S2). Among the climatic variables, the maximum rate of temperature decrease was again often the most important factor (with large values associated with higher extinction rates), whereas the maximum rate of temperature increase had the smallest effect (Fig. S2). This suggests that our choice of the one hemisphere's climate record does not influence our conclusions. This result might appear surprising, given that temperature changes in the two hemispheres are known to be asynchronous. However, temperature increases in the Southern Hemisphere only preceded those in the Northern Hemisphere by 1.5–3 ky (25), and the two climatic records are highly correlated (Table S2) at the resolution of our analysis (10-ky intervals).

In addition to being robust to the choice of climatic record, our results are robust to uncertainty over exact extinction and arrival dates, being remarkably consistent across different permutations. This is interesting in light of previous work in which the argument for the relative importance of climatic conditions or human arrival has hinged on the precise dating of megafaunal or human remains (21, 26). Although this may still hold true for specific taxa and locations, the analysis we have carried out suggests that arguments over precise dates are unlikely to affect the general result.

We have demonstrated that extinctions were correlated in space and time with both certain climatic conditions and human arrival. There remains a debate as to the severity of the most recent glacial cycle in comparison to previous cycles, and to the extent to which this matters for climatic explanations of the extinctions (27). Our results show that for the 700-ky analysis in particular, the unique combination of a rapid period of cooling, high variance in temperature, and low mean temperature in the past 100 ky predicted higher levels of extinction than in previous periods. Such conditions are likely to have severe impacts on vegetation (28). For example, falling temperature and the expansion of the Scandinavian and Alpine ice sheets during the Last Glacial Maximum converted previously wooded areas into treeless “mammoth steppe,” with severe impacts on species such as Megaloceros giganteus (the “Irish elk”) (29). However, the strong and consistent effect of human arrival, particularly at the 100-ky scale, and the more accurate predictions made by combined models support the view that humans, either directly through overhunting (30) or indirectly by bringing disease (31) or altering habitat (32), also contributed to the extinctions.

Table 2.

Percentage of extinction scenarios in which climate and human arrival are informative predictors on their own (“climate only” and “human arrival only”)

Materials and Methods

Following an extensive literature review, we estimated the extinction rates of megafaunal genera for five landmasses (North America, South America, Palaearctic Eurasia, Australia, and New Zealand) on two time scales: (i) the past 700 ky, split into intervals of 100 ky, and (ii) the past 100 ky, split into intervals of 10 ky (SI Materials and Methods). All first and last appearance dates were taken from the published literature (a list of dates and the relevant references is provided in Table S1). Because of the uncertainty in the exact timing of the first and last appearances of many genera, we generated 10,000 datasets (which we term “extinction scenarios”) for each time scale by randomly sampling dates from the ranges of first and last appearances available from the literature. We then modeled the extinction rate (in each time interval) in each extinction scenario by building GLMs with four climatic variables (mean temperature, its SD, and the fastest decreasing and increasing rates of change in temperature) derived from ice core data from Dome C in Antarctica (22) (Tables S3 and S4) and the occurrence of human arrival (presence/absence) during the time interval as explanatory variables. We used the Antarctic ice record because it remains the longest record of adequate resolution, allowing us to investigate the effects of several glacial cycles, and we used only the presence/absence of humans because there are insufficient data on prehistoric human densities. We also repeated the 100-ky analysis using an ice record from Greenland (24) to ensure that our conclusions were not biased by using only a Southern Hemisphere climatic record.

Although human arrival is known to have occurred only during the past 100 ky, the exact dates of human arrival are less certain when expressed in the 10-ky intervals of our shorter time scale. We therefore considered 32 different human arrival scenarios (Table S5), covering all plausible permutations of arrival dates proposed in the literature, and fitted models for each of them (SI Materials and Methods and Table S5). In these shorter time scale models, the effect of human arrival was considered to last for two time intervals (i.e., 20 ky) to ensure that humans had enough time to colonize the whole landmass. Important predictors of extinction rates were determined by comparing models using Akaike's information criterion. Additional details are provided in SI Materials and Methods.


We thank Richard Preece, Adrian Friday, Rob Asher, Ian Craigie, Andrew Clarke, and Tony Stuart for general help and advice; and Paul Hesse, Colin Prentice, Tony Stuart, Sam Turvey, Kate Lyons, Chris Johnson, Trevor Worthy, and Adrian Lister for also helping us collect data.


  • Author contributions: A.B. and R.E.G. designed research; G.W.P. and D.R.W. performed research; G.W.P., D.R.W., and A.M. analyzed data; and G.W.P. and D.R.W. wrote the paper.

  • The authors declare no conflict of interest.

  • ↵*This Direct Submission article had a prearranged editor.

  • This article contains supporting information online at


Table 1.

Range of human arrival dates used in our analysis, with references

The correlation between human immigration into the Americas, the invention of Clovis spear points and the extinctions of megafauna (large mammals exceeding 44 kg) at the end of the Pleistocene has caused many to implicate human hunting in the event. Correlation, however, does not necessarily imply causation. The close of the Pleistocene also marked a prolonged period of global warming and climate changes. In the 1990s the “overkill” hypothesis fell out of favor both because of its original ties to the “Clovis First” hypothesis (1) and paleontological evidence regarding the pattern of megafauna extinctions (2). At the turn of the millennium however, mathematical models (3,4), indirect evidence for human and megafauna occupations (5), and evidence for the reality of Clovis specialization as hunters of large game (6) made human hunting a viable hypothesis again. Recently Barnosky et al.  argued that humans caused extinctions through multiple synergistic effects, and were likely responsible for extinctions in North America and Australia (although both the timing of human immigration and the extinctions in Australia have yet to be firmly determined), whereas extinctions in Europe were likely due primarily to climate change (7). Barnosky et al. also argue that megafauna extinctions in South America have yet to be properly studied, but are coincident with both human immigration and climate change (7). The debate however is far from closed.

Before delving into the arguments for and against human responsibility for megafauna extinctions, it is important to distinguish four potential causes of the extinctions. The first is that climate change was responsible for the extinctions. The remaining three are all in some form anthropogenic extinction models. The first of these is the blitzkrieg (rapid overkill) hypothesis, in which human hunters armed with Clovis spear points overhunted naïve American megafauna. Clovis spear point technology is named for a site in Clovis, New Mexico, where fluted spear points were found with mammoth remains (1). Clovis sites date generally between 11,500 and 11,000 B.P., around when a corridor opened in the glaciation between Alaska and the rest of North America (1). A second possibility is the overkill hypothesis, which does not necessarily involve Clovis technology or immediate extinction of megafauna falling before an expanding lens of human immigration. A third is a sitzkrieg model in which human immigration resulted in extinctions through a combination of hunting, fire, habitat fragmentation, introduction of exotic species, diseases, and modifications to food webs (7).

Large mammals such as the mammoth used to roam North America, but whether hunting played a role in their extinction is a subject of controversy.

Meltzer largely argues that the presence of humans in the Americas prior to the invention of Clovis spear point technology refutes the blitzkrieg hypothesis (1). At the time, pre-Clovis archaeological sites, including Monte Verde in Chile, were still suspect (1), but Monte Verde is now generally accepted as pre-Clovis (7). Meltzer argues that the “apparent chronological correlation” between extinction of megafauna and the appearance of Clovis may not exist. He further further argues that, as defined by Martin, the hypothesis requires Clovis to be the first human culture in North America, and that mtDNA molecular clock, linguistic, and archaeological evidence make this unlikely (1). The confirmation of pre-Clovis cultures in the Americas would therefore appear to refute the overkill hypothesis. I would argue that while this may refute the hypothesis as presented at the time, this does not actually refute that humans or even human hunting using Clovis technology were responsible for megafauna extinctions. 

Beck takes a paleontological approach by looking at the geographic patterns of megafauna extinctions in North America (2). Beck does support the timing of megafauna extinctions as coincident with Clovis technology  (12-10 thousand years ago), but attempts to test the blitzkrieg models by looking at the distribution of the most recent remains of megafauna. If the blitzkrieg model is correct and humans advanced from an ice-free corridor in southwestern Canada, eliminating megafauna as they expanded into the United States, then megafauna would have been more likely to survive to later dates further from the initial point of intrusion. This implies that even though the fossil record is unlikely to preserve the last individual or even population of a particular species, statistically more terminal sites should be found in the Southern and Eastern portions of the ranges of megafauna. Only three of 11 megafauna taxa sampled by Beck have terminal sites in the southeastern halves of their ranges (2). Again this seems to refute the Clovis blitzkrieg model quite nicely, but rejecting this model of overkill does not mean that humans or even human hunting were not implicated in megafauna extinctions. Interestingly, Beck notes that the killing front he presumes is necessary for an overkill hypothesis is not needed by a mathematical model constructed by Whittington and Dyke (8), but that this model requires longer coexistence between humans and megafauna, which is precisely what Meltzer (1) uses in an attempt to refute the overkill hypothesis in general.   

Revival of anthropogenic models of megafauna extinctions consisted of further confirmations of the timing of megafauna extinctions and new mathematical models, which made overkill appear to be a reasonable mechanism for the extinctions. John Alroy argues that the coincidence of evidence for large human populations in the Americas by at least 13,400 years before present and confirmation of human hunting of at least some megafauna makes the overkill hypothesis plausible (3). Alroy therefore constructed a mathematical model of 41 prey species, some of which went extinct and others of which survived the end of the Pleistocene.  He incorporated differences in prey body mass, geographic ranges, population densities, population growth rates (as predicted by body mass), rates of primary production and caloric values of plants and small game food resources, human nutritional needs, maximal rates of increase for human populations, and the first appearance of significant human populations in the United States (3). Additional, less constrained components of the model included the number of humans entering the region, hunting ability, and hunting effort (it was assumed to be related to handling time and nutritional requirements and so “per capita kill rates never exceeded a low ceiling”). The single best fit scenario in the original paper correctly predicts the survival or extinction of 32 out of 41 species (78 percent). Extinction times are also accurately predicted. It also takes 410 years for human populations to exceed 10,000, which may mean that early human occupation was at levels unlikely to appear in the archaeological record. Alroy further states that if the model is modified so that humans were already present at low densities before the extinctions, rather than arriving as a small founding population, extinction rates increase (3).Grayson criticizes the model for assuming that no megafauna went extinct before Clovis times and states that only 15 of 35 extinct genera can be confirmed to have survived into Clovis times and that only mammoth kill sites are confirmed (9). Alroy counters that radiocarbon dates are scarce for all but six genera, all of which overlapped with Clovis, and (without directly referencing it) cites the Signor-Lipps effect which shows that extinctions which take place in rapid periods of time would appear to be staggered in the fossil record. Alroy also notes that kill sites are likely for mastodons and giant tortoises as well and that “kill sites for smaller species are not expected because smaller bones are fragile.” In addition, the model is conservative in predicting how the deaths of some animals came as the result of human hunting.  For example, it predicts that only nine percent of M. columbi deaths were due to hunting, while in reality nine out of 61 fossil sites are associated with kills (15 percent) (3). Slaughter and Skulan also point out an unrealistically low rm (species specific growth constant) used for some species, and argue that this would make megafauna extinctions too likely in the model (10). Alroy accepts their correction, noting that it was a programming error, and thanks them as the corrected model actually increases the predicted number of extinctions from 27 to 29 (3).

Johnson (2002) sought to identify characteristics of species that made them more likely to go extinct when compared with close relatives. Low reproductive rates were associated with both likelihood of extinction, and relatively large body size, but this was not a uniform threshold (10 kg in lemurs compared with 350 kg in bovid) (11). Low reproductive rates could have predisposed some species to extinction, even under low hunting pressures. Johnson goes so far as to state that the number of deaths due to human hunting necessary to cause the extinction of some species may have been so low that “archaeological evidence of killing would be very sparse and in many cases could well be effectively veiled by its rarity” (11). The percentages of survivors from arboreal or closed habitats when compared with extinct species from those habitats (68 percent versus 22.8 percent, p< 0.001 and 75 percent versus 42 percent p< 0.01, respectively) also support anthropogenic models (11). Barnosky et al. also acknowledge that since almost all the slow-breeding survivors in Australia, the Americas and Madagascar are nocturnal, arboreal, alpine, and/or deep forest dwellers, climate change alone seems less able to explain these extinctions than an overkill hypothesis (7).

It does not seem possible for models alone to resolve the question of whether megafauna were hunted to extinction, due to the number of assumptions in the models (4). The number of assumptions and the apparently ethereal nature of mathematical models lead those who oppose anthropogenic or overkill models of megafauna extinctions to demand archaeological evidence that megafauna extinctions could have been caused by humans, and could not have been caused by climate change. Guy Robinson, David Burney, and Lida Pigott use indirect evidence to show that humans arrive very close to the megafauna collapse, and that climate change occurred much later than the collapse (5). This evidence comes in the form of spores of the fungus Sporormiella, which is partial to the dung of large animals and is used to determine the population levels of megafauna; tree pollen, which is used to track climate; and microscopic pieces of charcoal, which indicate human activity at four sites in southeastern New York. Microscopic bits of charcoal have been shown to be good indicators of human arrivals on islands around the world. Using the much more abundant charcoal and fungal spore information allowed better temporal resolution than the vertebrate fossil record and provided a means of estimating megafauna abundance that would not be affected by the Signor-Lipps effect. However, this method is unable to distinguish between individual species. Their analysis revealed that first the megafauna population collapsed, indicated by a 10-fold decrease in Sporormiella spores. Soon after this, charcoal abundances jumped 10-fold. According to Robinson, Burney and Burney, human hunting of megafauna would have led to an overabundance of fuel for both human and natural fires. Only around 1,000 years later did pollen data show the last major cooling in the Pleistocene. The last megafauna bones in the fossil record appear during this last cooling period, indicating that a combination of anthropogenic effects and climate may have led to the extinction of the megafauna, but only after humans had drastically reduced the megafauna population numbers. Similar patterns of extinctions indicated by fungal spores and charcoal in Madagascar, where extinctions occurred in the last 2,000 years without climate change, show that climate change may not even be necessary to provide a “knock out punch” (5). 

Now that there does appear to be some evidence for anthropogenic reductions in megafauna populations, it is worth reevaluating whether Clovis peoples were specialized as big game hunters. Waguespack and Surovell note that while nearly everyone acknowledges that Clovis peoples killed megafauna occasionally, the bulk of their diet was made up of small game and plant resources (6). Many critics argue that big-game hunting would not have been a reliable subsistence strategy for a colonizing population. According to Waguespack and Surovell, a number of archaeological sites indicate that Clovis hunter-gatherers did utilize plants, small mammals, birds, fish, and reptiles for food, but presence of small game does not provide an adequate indicator of generalized foraging (6). Other arguments against big-game specialization include: 1. ig-game hunting is ethnographically rare and only occurs in restricted, homogeneous, low biodiversity environments; 2. Big-game hunting is sustainable as a subsistence strategy only when targeted prey is abundant and has high renewal rates; 3. The diverse environments present during Clovis times would have favored a generalized resource acquisition strategy (6).

Proboscidians like this mastodon were found in 79 percent of Clovis sites.

The argument that big game hunting would have been unreliable for Clovis peoples assumes that big game were rare and potentially dangerous, and this may not have been the case. Additionally, although hunters are occasionally wounded by elephants, this “does not appear to have deterred modern hunter-gatherers from pursuing them” (6). 

Waguespack and Surovell’s analysis of whether Clovis peoples were specialized or generalized foragers compares the number of individuals hunted with the predicted encounter rates for particular prey items (encounter rate assumes that small prey are far more common than large prey) (6). By this definition a big game specialist may have a relatively small proportion of the diet made up of large prey, so long as it was utilized above the predicted encounter rate. If high ranked, but relatively large game dominated Clovis faunal assemblages, Clovis peoples would qualify as big game specialists. Small game would have been utilized, but at lower levels than expected based on encounter rate. Estimated population densities were used as proxy measures of encounter rates, and only relative population densities are used (these are estimated using relationships between body size and population density and ecological principles). Proboscidians, bison, and ungulates are the most consistent members of Clovis sites (79 percent, 52 percent, and 45 percent respectively), with rodents, tortoises and birds next (39 percent, 30 percent, and 30 percent respectively). A strong negative correlation exists between body size and presence in a Clovis assemblage, “indicating that the largest, least diverse and least abundant taxa are the most consistent members of Clovis faunal assemblages,” which is indicative of specialization (6). Testing the actual minus expected number of sites for each size class (assuming a generalized subsistence strategy) using a two-tailed Spearman’s ρ yields a value of -1, which strongly rejects the null hypothesis (a generalized subsistence strategy). Waguespack and Surovell acknowledge that the archaeological record may be unduly biased towards megafaunal kill sites, but state that this is “the only direct source of information on Clovis hunting behaviors” (6).  I find the biases towards megafauna kill sites, including the increased likelihood of finding kill sites relative to gathering communities, biases towards the preservation of large bones and biases towards finding large bones even when some bones from small game are present, to be too numerous and influential to take this information at face value as no attempts are made to correct for these biases, but the strength of the pattern is intriguing.

Furthermore, Waguespack and Surovell studied 92 hunter-gatherer populations and compared the proportion of large game in their diets with population density (6). Groups which derive more than 46 percent of their diets from hunting had mean population densities less than 0.25 people per 100 km2 (6). They argue that since Clovis peoples were colonizing an uninhabited landscape, big-game hunting was a viable subsistence strategy. Although there is now evidence for pre-Clovis inhabitation of the Americas, big game hunting may still have been a viable subsistence strategy for the initial colonizers (6). 

In light of this, it is worth reexamining the assumptions of Alroy’s 2001 model with regard to specialization. Alroy’s best fit model required that only 0.111 percent of the human diet come from meat at 5.82 to 10.86 persons per 100 km2. In fact, various iterations have 8.8 to 13.2 percent of human dietary needs (i.e., 194 to 290 kcal/person/day or about 64 to 97 g meat/person/day) being met by hunting large game (3). For comparison, the model used by Barnosky et al. correctly predicted the fate of 34 of 41 megafauna species with human population densities ~28 people per 100 km2 obtaining 30 percent of their diet from meat (7). 

As the mathematical models now seem quite plausible and the patterns of survivors versus extinct species seem inexplicable by climate change and easily explicable by hunting (7,11), it is worth considering comparisons to other systems. Barnosky et al. note that on islands, humans cause extinctions through multiple synergistic effects, including predation and sitzkrieg, and “only rarely have island megafauna been demonstrated to go extinct because of environmental change without human involvement,” while acknowledging that the extrapolation from islands to continents is often disputed (7). The case for human contribution to extinction is now much better supported by chronology (both radiometric and based on trace fossils like fungal spores), mathematical simulations, paleoclimatology, paleontology, archaeology, and the traits of extinct species when compared with survivors than when Meltzer and Beck rejected it in the 1990s, although the blitzkrieg model which assumes Clovis-first can be thoroughly rejected by confirmation of pre-Clovis sites. Grayson and Meltzer (12) argue that the overkill hypothesis has become irrefutable, but the patterns by which organisms went extinct (7,11), the timing of megafauna population reductions and human arrival when compared with climate change (5), and the assumptions necessary to make paleoecologically informed mathematical models for the extinctions to make accurate predictions all provide opportunities to refute the overkill hypothesis, or at least make it appear unlikely. However, all of these indicate human involvement in megafauna extinctions as not only plausible, but likely.


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