Relative demographic susceptibility does not explain the extinction chronology of Sahul’s megafauna

The myriad mechanisms driving species extinctions (Sodhi et al., 2009) are often synergistic (Brook et al., 2008), spatially variable (Saltré et al., 2019), phylogenetically clumped (Fritz and Purvis, 2010), correlated with population size (O'Grady et al., 2004), and dependent on biotic interactions (Strona and Bradshaw, 2018). This complexity means that even in contemporary settings involving closely monitored species, identifying the ecological mechanisms underlying the causes of a particular extinction can be difficult (Caughley and Gunn, 1996; Fagan and Holmes, 2006). This challenge is considerably greater for palaeo-extinctions because of the restricted ecological knowledge of extinct species. In the case of prehistoric extinctions, we can only infer the environmental conditions likely operating at the estimated time of the events from rare and sparsely distributed proxies (Johnson et al., 2016).

The rapid and widespread disappearance of megafauna in the late Quaternary on most continents is one of the best-studied extinction events of the past. Many plausible causes of megafaunal extinction have been proposed (Koch and Barnosky, 2006). The main drivers of these extinctions appear to differ depending on taxon, region, and time period (Lorenzen et al., 2011; Metcalf et al., 2016; Wan and Zhang, 2017), but there is growing consensus that multiple drivers were involved, including the interactions between climatic shifts and novel human pressure as dominant mechanisms (Bartlett et al., 2016; Johnson et al., 2016; Saltré et al., 2019; Sandom et al., 2014; Villavicencio et al., 2016). This consensus mostly relies on approaches examining extinction chronologies relative to indices of temporal and spatial environmental variation. While such correlative approaches can suggest potential causes of extinction, they cannot by themselves provide strong inference on the plausible ecological processes involved. Instead, approaches that construct mechanistic models of environmental and other processes that drive extinctions could reveal the relative susceptibility of species over the course of a large extinction event (Alroy, 2001; Timmermann, 2020). Revealing such mechanisms not only provides evidence-based explanations of how and why past extinction events unfolded, it also assists contemporary and future ecological analyses by describing extinction processes over longer timeframes than historical records permit. This can further contextualise baselines for conservation and management targets, and assist in predicting the magnitude and sequences of future extinctions (Pardi and Smith, 2012; Willis and Birks, 2006; Wingard et al., 2017) as the current crisis unfolds (Bradshaw et al., 2021).

Existing mechanistic models applied to megafauna systems differ in their complexity, ranging from predator-prey models (Frank et al., 2015; Nogués-Bravo et al., 2008), to fully age-structured stochastic models (Prowse et al., 2013), or stochastic predator-prey-competition functions (Prowse et al., 2014). If sufficiently comprehensive, such models can be useful tools to compare the likelihood of the processes driving extinctions, even in the deeper past. Although measuring the demographic rates of long-extinct species is impossible, robust estimates can be approximated from modern analogues and allometric relationships derived from extant species (Prowse et al., 2014; Prowse et al., 2013). This makes it possible to construct stochastic demographic models of both extinct and related extant species, and compare their relative susceptibility to perturbations by mimicking particular environmental processes in silico.

Despite these methodological advances, unravelling the causes of the disappearance of megafauna from Sahul (the combined landmass of Australia and New Guinea joined during periods of low sea level) is still a major challenge. This is because of the event’s antiquity (Johnson et al., 2016) and the sparse palaeo-ecological information on megafauna extinctions compared to other parts of the world (Johnson et al., 2016; Saltré et al., 2019). However, based on the expectation that if high demographic susceptibility is an important feature of a species’ actual extinction dynamics, the most susceptible species should have gone extinct before more resilient species did.

Stochastic demographic models can quantify the relative support for the hypothesized mechanisms potentially involved in the megafauna disappearances in Sahul (summarized in Figure 1), which could assist in explaining the observed extinction chronology. These mechanisms include: (i) There is a life history pattern in which the slowest-reproducing species succumbed first to changing conditions (e.g., novel and efficient human hunting and/or climate change) (Cardillo et al., 2005; Purvis et al., 2000; Sodhi et al., 2009). This mechanism assumes that human hunting or climate change, even if non-selective, would differentially remove species that were more demographically sensitive to increased mortality (González-Suárez et al., 2013; Johnson, 2002). (ii) The most susceptible species were those whose life histories conferred the highest sensitivity to human hunting, such as species most sensitive to the loss of juveniles (Brook and Johnson, 2006). (iii) Bottom-up processes drove the extinctions, as supported by differences in the extinction timing between carnivores and their herbivore prey. Under this mechanism, prey-specialist carnivores should be more susceptible than their prey (i.e. because they depend on declining prey populations), whereas more generalist carnivores that could switch food sources would be less susceptible than their main prey (Chamberlain et al., 2005; Ripple and Van Valkenburgh, 2010). (iv) Species susceptible to temporal variation in climate succumbed before those most able to adapt to changing conditions. Under this mechanism, we expect the largest species — that is, those possessing traits associated with diet/habitat generalism (Monaco et al., 2020), physiological resilience to fluctuating food availability (Herfindal et al., 2006; Morris and Doak, 2004), high endurance, and rapid, efficient dispersal away from stressful conditions (Johnson, 2006) — would persist the longest in the face of catastrophic environmental change, independent of the intensity of human predation or changing climates. However, there are many other ways that climate change can alter demography (reviewed in Selwood et al., 2015). For example, heat stress can disadvantage juveniles by reducing parent capacity to raise neonates, or by affecting relatively heat-intolerant juveniles directly; species with more-sensitive juveniles are therefore expected to go extinct before more heat-tolerant species (Selwood et al., 2015). An increasing frequency of climate-induced catastrophes can also drive relatively smaller populations toward extinction faster, meaning large-bodied species with smaller populations are potentially more susceptible (Cardillo et al., 2005; Liow et al., 2008; Tomiya, 2013). (v) If none of the aforementioned mechanisms explains the extinction event’s chronology, non-demographic mechanisms such as differential selection of or ease of access by human hunters could have played more important roles.

Figure 1

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To examine the relative support of these hypotheses, we developed the first stochastic, age-structured demographic models ever constructed for 13 extinct megafauna species in Sahul broadly categorized into five functional/taxonomic groups: (i) 4 vombatiform herbivores, (ii) 5 macropodiform herbivores, (iii) 1 large, flightless bird, (iv) 2 marsupial carnivores, and (v) 1 monotreme invertivore. We also built demographic models for 8 of some of the largest, local, extant species, including representatives from each of the functional/taxonomic groups described above for comparison. Our null hypothesis is that these extant species should demonstrate higher resilience to perturbations than the extinct species, given that they persisted through the main extinction event to the present. Subjecting each species’ model stochastically to different types of demographic perturbations, we tested seven scenarios (described in more details in Materials and methods) regarding the processes that could lead to extinction (see also Figure 1): an allometric relationship between the time of extinction and species’ body mass and/or generation length (Scenario ‘LH’), reducing juvenile survival (Scenario ‘↓S_j’) (Brook and Johnson, 2006; Munn and Dawson, 2001), reducing fertility (Scenario ‘↓F’) (Gittleman and Thompson, 1988; Miller et al., 2016; Oftedal, 1985), reducing survival across all ages (Scenario ‘↓S’), individual offtake from the population via hunting (Scenario ‘↓ind’), increasing environmental variability generating extreme climate events causing catastrophic mortality (Scenario ‘↑cat_f’), and increasing the magnitude of environment-driven, catastrophic mortality events (Scenario ‘↑cat_M’) (Reed et al., 2003).

We hypothesize that one, or several, of these types of perturbations would provide a better match than the others between relative demographic susceptibility and the continental-scale chronology of extinctions as inferred from the fossil record. Identifying which, if any, of the scenarios best matches the chronology would therefore indicate higher relative support for those mechanisms being the most likely involved in driving the observed extinctions. We first compared the expectation of larger (and therefore, slower life-history; Scenario LH, Figure 1) species more likely to go extinct than smaller species when faced with novel mortality sources (Brook and Bowman, 2005), followed by the outcomes of all other scenarios (Figure 1) to test if sensitivity to specific demographic changes supported other mechanisms.

There was no indication that relatively heavier (Figure 2a) or slower life history (longer-generation; Figure 2b) species went extinct before lighter, faster life-history species (Scenario LH), even considering that the two mid- and small-sized carnivores Thylacinus and Sarcophilus went extinct on the mainland late in the Holocene at approximately the same time (~3200 years before present; Figure 2; White et al., 2018).

Figure 2

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To distil overall extinction risk per species in each scenario, we progressively increased the relevant perturbation and calculated the proportion of 10,000 stochastic model runs where the final population size fell below a quasi-extinction threshold (E_q) of 50 females (Frankham et al., 2014). The area under the resulting quasi-extinction-probability curves provides a scenario-specific representation of extinction risk across the entire range of the specific perturbation. The quasi-extinction curves for each species differed markedly in each perturbation scenario (Appendix 6—figure 1), although there were some similarities among scenarios. For example, in all scenarios except for fertility reduction (Scenario ↓F) and offtake (Scenario ↓ind), the smallest, extant carnivore Dasyurus was the least susceptible, whereas Genyornis was one of the most susceptible in four of the six scenarios (Appendix 6—figure 1).

Across all species, log₁₀ body mass explained some of the variance in the total area under the quasi-extinction curve (Figure 3) for the individual-removal (↓ind: evidence ratio [ER]=41.22, R² = 0.35; Figure 3d) and catastrophe-magnitude (↑cat_M: ER = 19.92, R² = 0.31; Figure 3f) scenarios, but less variance for the scenarios with reductions in juvenile (↓S_j: ER = 2.04, R² = 0.14; Figure 3a) and all-ages survival (↓S: ER = 5.05, R² = 0.21; Scenario iv; Figure 3c). There was little to no evidence for a relationship in the fertility-reduction (↓F) and catastrophe-frequency (↑cat_f) scenarios (ER ≲ 1; Figure 3b,e). The relationships between area under the quasi-extinction curve and log₁₀ generation length (G) were generally stronger (Figure 4). The strongest relationships here were for all-ages survival-reduction (↓S) and magnitude-of-catastrophe (↑cat_M) scenarios (ER >490, R² ≥0.49; Figure 4c,f), followed by weaker relationships (ER <11, R² ≤0.26) for Scenarios ↓S_j (Figure 4a), ↓ind (Figure 4d), and ↑cat_f (Figure 4e), and no evidence for a relationship in Scenario ↓F (ER <1; Figure 4b).

Figure 3

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Figure 4

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Allometric scaling of extinction risk was also apparent within most taxonomic/functional groups. For example, Diprotodon had the highest extinction risk among the extinct vombatiform herbivores in every scenario except fertility reduction (Scenario ↓F; Figures 3b and 4b). Most species were relatively immune even to large reductions in fertility, except for Sarcophilus, Dasyurus, Vombatus, Alectura, Dromaius, and Genyornis (Figures 3b and 4b). In the offtake sub-scenario where we removed only juvenile individuals (↓ind_j), the results were qualitatively similar to Scenario ↓S_j where we progressively decreased juvenile survival (Appendix 7—figure 1), although the relative susceptibility for most species decreased from Scenario ↓S_j to ↓ind_j. However, susceptibility increased for the extinct carnivores Thylacinus and Thylacoleo, and remained approximately the same for Dasyurus and Notamacropus (Appendix 7—figure 1).

In the fertility-reduction sub-scenario (↓F_e) where we progressively increased the mean number of eggs removed per female per year in the bird species to emulate egg harvesting by humans, there was a progressively increasing susceptibility with body mass (Figure 5). Genyornis was clearly the most susceptible to extinction from this mechanism compared to the other two bird species (Figure 4).

Figure 5

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When the extinction dates are viewed relative to the extinction risk (quasi-extinction integrals) calculated for each scenario, there is no indication that the most susceptible species went extinct earlier in any perturbation scenario (Figure 6). Taking the sum of the quasi-extinction integrals across scenarios indicated that five of the eight extant species examined (Sarcophilus [extinct on mainland; extant in Tasmania], Dromaius, Alectura, Vombatus, Tachyglossus) had extinction risks that were equivalent or higher than most of the extinct species (Figure 7a). Taking the median rank of the quasi-extinction integral across scenarios generally indicated the lowest susceptibility in the macropodiformes (although the small, extant carnivore Dasyurus was consistently the least susceptible for all scenarios except fertility reduction; Figure 7a), followed by the carnivores (except Dasyurus), monotremes, vombatiform herbivores, and finally, large birds (Figure 7b). The carnivores had susceptibility ranks spread across most of the spectrum (Figure 7b).

Figure 6

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Figure 7

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Expressed as a function of log₁₀ generation length, there was evidence for a moderate relationship across all species (ER = 12.07, R² = 0.27; Figure 7c), but removing the outlier species Sarcophilus and Dromaius resulted in a much stronger relationship (ER = 3.61 × 10⁵, R² = 0.76; Figure 7c). Susceptibility also tended to increase with body mass within a group, except for carnivores (Sarcophilus being the anomaly) and monotremes (Figure 7c). There was no indication of a pattern when extinction date is plotted against median susceptibility rank (Figure 7d).

The megafauna species of Sahul demonstrate demographic susceptibility to extinction largely following expectations derived from threat risk in modern species — species with slower life histories have higher demographic risk to extinction on average (Cardillo et al., 2005; Liow et al., 2008; Purvis et al., 2000; Tomiya, 2013). Indeed, our models show a convincing relationship between a taxon’s overall relative demographic susceptibility to extinction and its generation length. While demography clearly must have played a role in the extinction of Sahul’s megafauna given that mainly the largest species succumbed (Lyons et al., 2016), our stochastic models revealed no clear relationship between relative demographic susceptibility and the order in which the taxa we considered went extinct (even after considering alternative approaches to calculate the window of extinction — Appendix 8—figure 1–3). In particular, we did not find that the most demographically susceptible species went extinct before more resilient species. We can only conclude that the actual extinction chronology must have instead been an emergent property of many interacting demographic rates, temporal and spatial variation in population abundance, particular environmental contexts, community interactions, and likely other traits (Sodhi et al., 2009). As different perturbations compromise different aspects of a species’ life history, its relative susceptibility to extinction compared to other species in its community varies in often unpredictable ways.

We can therefore reject the hypothesis that the continent-wide extinction chronology is explained by species’ relative demographic susceptibility and non-selective hunting by humans. If demographic susceptibility coupled with non-selective hunting were the primary causes of these extinctions, we would expect a relationship between vulnerability and extinction date whereby the more vulnerable species went extinct earlier — but we found no such relationship (Figure 7d). However, the comparison of susceptibility under increasing intensities of egg harvesting revealed the highest demographic risk from this type of activity for the extinct Genyornis compared to the extant Dromaius and Alectura birds, supporting the notion that egg harvesting (and not hunting of adults) might have been at least partially responsible for the demise of Genyornis (Miller et al., 2016). We can also reject the hypothesis that the largest, and therefore the most physiologically buffered and mobile species, were the most resilient given the lack of relationship with the inferred extinction chronology (Figures 2 and 7d). Neither did the species with the highest sensitivities to reductions arising from the various perturbation scenarios succumb earlier (Figures 5 and 6d).

This lack of relationship to the chronology, combined with the result that many of the extant species had some of the highest extinction susceptibilities, suggest that no obvious demographic properties can explain the taxon-specific timing within the Sahul extinction event of the Late Pleistocene. This opens the possibility that the chronology instead reflects either a random set of circumstances — that species succumbed to circumstantial combinations of stressors depending on the local perturbations experienced by particular populations (Saltré et al., 2019) — or even that the chronology is still insufficiently resolved. However, this conclusion does not accord well with the notion that Late Pleistocene megafauna extinctions were non-random and occurred at a much higher pace than background extinction rates (Johnson, 2006; Koch and Barnosky, 2006).

Another possibility is that in the case of human hunting, preferences for selecting or avoiding particular species, such as targeting larger species for more efficient returns (Broughton et al., 2011), could have overridden or interacted with intrinsic demographic susceptibility. Indeed, Lyons et al., 2016 concluded that either large-bodied mammals were selectively targeted by humans during the Late Quaternary, or that these species were relatively more vulnerable to human hunting than smaller-bodied species, or both. In addition, specific behavioural adaptations could potentially have rendered demographically high-risk species in fact less vulnerable to human hunting, such as the behavior of Vombatus to dig and defend burrows that were difficult to access by humans (Garvey et al., 2016) compared to larger burrowing or non-burrowing vombatiformes. In the case of the macropodiformes, interspecific variation in the type of locomotion — a trait not captured by our demographic models — could have contributed more to their relative susceptibility to human hunters. For example, the ability to hop at high velocities as in Osphranter rufus could have given it an escape advantage over the relatively slower sthenurine macropodiformes that likely employed more bipedal striding than hopping (Janis et al., 2020). Similar hypotheses regarding risk-persistence mismatches in multispecies simulations have been proposed for the Late Pleistocene megafauna extinction event in North America (Alroy, 2001).

Although marsupials are widely included in studies estimating the types of mammalian demographic relationships like those we used here (de Magalhães et al., 2007; Healy et al., 2014; McCarthy et al., 2008), more explicit consideration of their reproductive differences compared to placentals might further improve the resolution of future models. In particular, marsupials are born at the extreme altricial stage and complete most of their development ex utero through lactation (Tyndale-Biscoe, 2005). This might change the way the cost of reproduction is borne, because the unusually long period of marsupial lactation can reduce the cost of raising offspring per unit time (Cork and Dove, 1989; Weisbecker and Goswami, 2010). There is also an increasing number of small marsupial and placental mammals known to have gone extinct during the Pleistocene in Sahul (e.g. Cramb et al., 2009; Cramb et al., 2018). The inclusion of these smaller species in future demographic susceptibility/chronology of extinction analyses will likely be insightful, but unfortunately estimates of extinction dates — which require at least 8–10 reliability dated specimens (Bradshaw et al., 2012; Saltré et al., 2015) — are not yet available for these animals.

Our models, although age-structured, stochastic, and incorporating compensatory density feedback, are still simplified expressions of a species’ particular ecological and environmental contexts. As stated, our models are aspatial, yet we know that spatial processes are correlated with local extinctions across the landscape (Saltré et al., 2019). For example, large proboscideans like mammoths managed to persist well into the Holocene on island refugia despite having a high intrinsic extinction risk (Nogués-Bravo et al., 2008). It is therefore plausible that more localized measures of extinction risk, timing, and particular climate and habitat contexts (see Appendix 9 for an examination of demographic susceptibility relative to hindcasted climate trends) could reveal subtler demographic processes at work (Chase et al., 2020). However, Sahul’s fossil record is still generally too sparse at a regional level to test this properly (Peters et al., 2019; Rodríguez-Rey et al., 2016), nor do we have data indicating how spatial variation might have altered local expressions of demographic rates in long-extinct species.

Our models also ignore biotic dependencies such as predator-prey, plant-herbivore, and competition relationships that could have modified relative susceptibility in different ways depending on the community in question (Brook and Bowman, 2004; Choquenot and Bowman, 1998). Trophic community networks constructed for south-eastern Sahul show that bottom-up processes most strongly affect lower trophic levels, with their influence diminishing at higher trophic levels, although extinct carnivores were more vulnerable to coextinction than extant carnivores (Llewelyn et al., 2020).

The particulars of the Genyornis extinction are also still debatable given the possibility that the egg shells used to date the species (Miller et al., 2005) are potentially confounded with an extinct Progura megapode (Grellet-Tinner et al., 2016; Shute et al., 2017). However, removing ‘Genyornis’ from the extinction chronology makes no difference to our overall conclusions, but it is problematic for comparing relative extinction risk between the extinct Genyornis and the extant Dromaius and Alectura. In fact, by including an extant megapode (Alectura) in our model simulations, we determined that this much smaller (2.2 kg) and faster-reproducing species had a much lower extinction susceptibility than both Genyornis and Dromaius.

That we found no clear patterns among the extinct megafauna of Sahul to explain their relative extinction chronology supports the notion that, at least for mammals, risk can be high across all body masses depending on a species’ particular ecology (Davidson et al., 2009), even if relative extinction risk appears to follow allometric expectations (Brook and Bowman, 2005; Cardillo et al., 2005; Liow et al., 2008; Purvis et al., 2000; Tomiya, 2013) as we demonstrated clearly here (Figures 3, 5 and 7). By definition, the megafauna were generally large (>45 kg) species, yet neither their body mass or their correspondingly higher relative demographic susceptibility explains the extinction chronology in Sahul. Our approach also provides a template for assessing relative demographic susceptibility to extinction for other Sahul species that we did not consider here, and for those in other continents, that could reveal previously underappreciated dynamics and drivers. Nonetheless, more spatially and community-dependent models are still needed to provide a more complete picture of the dynamics of Late Pleistocene megafauna extinctions.

All data and are R code needed to reproduce the analyses are available for download at http://github.com/cjabradshaw/MegafaunaSusceptibility.

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Acceptance summary:

This manuscript deploys novel ecological modelling to test hypotheses about the extinction of Sahul's megafauna. It estimates the demographic parameters of several extinct groups, and then introduces perturbations that disrupt these populations in various ways (simulating the effects of changing variables such as ecological conditions, human hunting, and predator-prey relationships). The authors then compare the predictions of when different taxa should go extinct under each scenario to current estimates of the extinction windows of these groups, and find that no single scenario explains the overall chronology of extinction. This is valuable information for palaeontologists, archaeologists, and other palaeo-scientists, but also has implications for understanding biodiversity loss in the present day.

Decision letter after peer review:

Thank you for submitting your article "Relative demographic susceptibility does not explain the extinction chronology of Sahul's megafauna" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and George Perry as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Summary:

The reviewers agree that this is an interesting and useful contribution for understanding LQ extinctions, and that it is generally well-presented. It shows that the factors that increase extinction risk are de-coupled from the factors that eventually lead to extinction and thus in its timing. However, the reviewers also note that although the modelling approach is novel, it is reliant on datasets that are biased and at times these biases are not well-accounted for. Because much of the conclusions drawn from the modelling could already be drawn from existing records and using literature that is glossed over here, attention to that literature should be improved and the contributions beyond the megafauna debate should be emphasized. Furthermore, the authors should take care to improve clarity in the framing of the models, the presentation and interpretation of results, figures, and discussion. The reviewers have provided extensive and detailed comments about how to go about doing this, divided below by the Reviewing Editor into major issues additional analyses, conceptual issues, framing.

Essential revisions:

1) Additional analyses (no additional data collection). The reviewers had specific concerns about the effects of sampling on the extinction chronology and the influence of body mass on a number of things (recovery potential, life history/demographic correlates, etc). Specifically, the analytical issues that present the biggest problems revolve around sampling uncertainty and body mass correlation. The former could be addressed by introducing some sensitivity tests. These could be directed towards chronological biases (how does removing one date affect the confidence intervals?), as well as geographical sampling biases (how does removing a region affect the trends?). The latter in particular would be important in the claims of a continental trend. It is also possible that biases are a function of taxon sampling. There are an increasing number of small mammal Pleistocene extinctions being recognized in Australia, and it is unclear if these follow the same trends as the megafauna. If so, that would indeed remove the body size issues.

2) Better framing of the five putative drivers of extinctions (these are not considered or glossed over in the present manuscript)):

i. Appears to assume that only human hunting will differentially affect demographically sensitive species. However, novel or extreme climate change can also affect such species (e.g. Selwood, K.E., McGeoch, M.A. and Mac Nally, R., 2015. The effects of climate change and land‐use change on demographic rates and population viability. Biological Reviews, 90(3), pp.837-853.)

ii. This mechanism is predicated on using a modelling result [ref. 25] as data. It also makes the bold claim that species inhabiting certain habitats are less accessible to human hunters without any consideration of the archaeological or modern record on this point (e.g. Roberts, P., Hunt, C., Arroyo-Kalin, M., Evans, D. and Boivin, N., 2017. The deep human prehistory of global tropical forests and its relevance for modern conservation. Nature Plants, 3(8), pp.1-9; Fa, J.E. and Brown, D., 2009. Impacts of hunting on mammals in African tropical moist forests: a review and synthesis. Mammal Review, 39(4), pp.231-264).

iii. Many of the supporting references here do not seem like logical choices for this argument. E.g. [28] refers to coral-reef fishes. Moreover, this hypothesis conflicts with much modern data showing that extinction risk and body size are correlated under climate and environmental change (e.g. Cardillo, M., Mace, G.M., Jones, K.E., Bielby, J., Bininda-Emonds, O.R., Sechrest, W., Orme, C.D.L. and Purvis, A., 2005. Multiple causes of high extinction risk in large mammal species. Science, 309(5738), pp.1239-1241. Liow, L.H., Fortelius, M., Bingham, E., Lintulaakso, K., Mannila, H., Flynn, L. and Stenseth, N.C., 2008. Higher origination and extinction rates in larger mammals. Proceedings of the National Academy of Sciences, 105(16), pp.6097-6102. Tomiya, S., 2013. Body size and extinction risk in terrestrial mammals above the species level. The American Naturalist, 182(6), pp.E196-E214.)

3) More nuanced interpretation of model output.

The major weakness in this manuscript is in the discussion. The authors should be very clear in their discussion that their model does not indicate that demographic factors had no part in extinct events per se, but rather that they don't explain extinction chronology. Extinction chronologies reflect a number of different factors and processes, but they don't take away from the fact that certain life history traits can make a species more likely to go extinct from those factors.

The authors seem to argue that demographics don't explain the megafaunal extinction in the Sahul, but in fact, their results suggest that they do; the only thing demographics by themselves don't explain is the chronology. Extinction risk as determined by demographic susceptibility is highly related to body mass and generation time (which in turn is also affected by body mass) but differential survival (timing of extinction) is determined by factors such as geographic range size, dispersal ability, access to refugia, and behavioral and morphological adaptations against hunting, and the ability to survive catastrophic events. A reiteration of this point would be beneficial to the clarity of this otherwise well written manuscript.

The authors clearly (and elegantly) show that extinct species, which were all large, and had long generation times, had demographic traits that made them more susceptible to extinction. This is evident in figures 3 and 4. However, in the discussion, in lines 301-303, they state that no demographic trends explain the extinction. This is not supported by the results. While the timing of when species go extinct doesn't correlate with demographic susceptibility, the peculiar nature of the extinction-a large size biased extinction-is explained by demographic factors, and is a phenomenon that has been explored in a global analysis by Lyons et al. 2016 Biol. Lett. Therefore, demographic trends DO explain why certain species go extinct, while others survive. The authors should be careful when they say that "that no obvious demographic trends can explain the great Sahul mass extinction event"; instead, they should re-iterate that no obvious demographic trend explains the extinction chronology.

4) More careful discussion of results relative to literature. The authors further go on to suggest that their results suggest that the extinctions were random, but the size-selectivity clearly shows that the extinctions were in fact not random with respect to body size. Their analyses do show that the rate of extinction doesn't exceed background to the same degree that it's been suggested in prior studies, and this is something that researchers need to explore further. Also, the authors raise an important point in lines 309-311 that human hunting could have interacted with demographic susceptibility, something that Lyons et al. 2016 Biol. Lett. show, and the results of the present study should be discussed in light of the 2016 paper.

They also raise an important point in lines 312-320 that behavioral or morphological adaptations may have allowed some seemingly "high risk" species to persist despite anthropogenic pressure. These model "mis-matches" have been reported by Alroy 2001 Science as well in a multispecies overkill simulation. It would be beneficial to discuss the present results within the context of other examples of model mismatches, such as those from Alroy 2001.

In lines 353-358, the authors once again state that their results show no clear relationship between body-mass and demographic disadvantage, despite clearly showing these relationships in Figures 3 and 4, and even stating as much in the beginning of the discussion. The plots clearly show that large bodied taxa were at a demographic disadvantage. There is a difference between explaining why certain taxa go extinct vs. why they go extinct at a certain point in time, and this should be made clear. The authors are correct in stating that demographic factors don't explain the relative extinction chronology, i.e. when species go extinction relative to each other, but they do explain why large species go extinct, and why these extinctions take place after human arrival. Moreover, generation length, which is also correlated with demographic susceptibility, is highly correlated with body mass (Brook and Bowman 2005 Pop. Ecol), once again showing that body mass-related effects do help explain the extinctions.

The authors rightfully point out earlier in the discussion that spatial variation, local climates, ecological interactions, etc. all influence how and why a particular population disappears. Extinction chronologies reflect a number of different factors and processes, but they don't take away from the fact that certain life history traits can make a species more likely to go extinct from those factors. Large proboscideans like mammoths had a high risk of extinction based on life history traits, but managed to survive on island refugia into the mid-Holocene. Similar other examples exist, and show that extinction chronologies can vary vastly.

Therefore, the lack of correlation can be explained by these factors, and the authors need to expand on these in their discussion, perhaps if possible, by giving specific examples. They should be more careful in their discussion by clearly distinguishing drivers of extinction risk, and how these drivers can be de-coupled from timing, but at the same time providing a good explanation for the biological factors leading to the extinction. Here again the authors should consider the work of Brook and Bowman and Lyons et al.

Essential revisions:

1) Additional analyses (no additional data collection). The reviewers had specific concerns about the effects of sampling on the extinction chronology and the influence of body mass on a number of things (recovery potential, life history/demographic correlates, etc). Specifically, the analytical issues that present the biggest problems revolve around sampling uncertainty and body mass correlation. The former could be addressed by introducing some sensitivity tests. These could be directed towards chronological biases (how does removing one date affect the confidence intervals?)

Hidden chronological biases in time series of dated palaeontological specimens will always exist, which are particularly compounded at the limits of radiocarbon dating. That said, we agree that another treatment of the chronologies is warranted to demonstrate that different sets of assumptions do not change our general conclusions about the lack of a chronological signal.

To this end, we have revisited all the dates we derived from Saltré et al. [1], which were predicted regions of extinction-timing agreement among six frequentist models correcting for the Signor-Lipps effect (apart from the two updated extinction dates for mainland Thylacinus and Sarcophilus from White et al. [2] that have already been recalculated). For the remaining taxa (Diprotodon, Genyornis, Megalibgwilia, Metasthenurus, Palorchestes, Phascolonus, Procoptodon, Protemnodon, Sismosthenurus, Sthenurus, Thylacoleo, and Zygomaturus), we adapted the Gaussian-resampled, inverse-weighted McInerny (GRIWM) approach we designed and tested previously [3, 4] to derive new estimates and uncertainties of extinction time. Here, we jack-knifed the estimates 10,000 times for each taxon, calculating the median and 95% lower and upper confidence bounds.

This approach does not depend on distributional assumptions given that it is a resampling approach [4], even if it often provides wider uncertainty windows (especially for older dates). The main effect of this approach was to push back the extinction date for Metasthenurus (from a median of 50 ka to 89 ka), and push forward some of the dates within the other macropodiformes and vombatiforms. However, the net effect when we related these new estimates to any of the different risk metrics (mass, generation length, vital rate-specific extinction risk, or median susceptibility rank) was to maintain our general conclusion of no relationship for the chronology. We therefore did not repeat the supplementary analysis of the climate conditions based on these new extinction estimates.

We have placed these new results in the supplementary information, essentially making a new version of Figure 1 (in new Appendix S8; new Figure S9), Figure 6 (in new Appendix S8; new Figure S10), and Figure 7 (in new Appendix S8; new Figure S11). We also now refer to these additional results in the Discussion section, and have added a description of the approach in this new Appendix and a brief overview in the Methods. We have also included the GRIWM code in the updated Github respository (github.com/cjabradshaw/MegafaunaSusceptibility), as well as the raw dates for calculating the new estimates for each taxon (.csv files).

Methods:

“We also examined the sensitivity of our overall results to uncertainty in extinction estimates by deriving a jack-knifed version of the Gaussian-Resampled, Inverse-Weighted McInerny (GRIWM) model [3, 4] (Supplementary Information Appendix S8).”

Discussion:

“… went extinct (even after considering alternative approaches to calculate the window of extinction – Supplementary Information Appendix 8; Figure S9–S11).”

As well as geographical sampling biases (how does removing a region affect the trends?). The latter in particular would be important in the claims of a continental trend.

We would dearly love to do this. Even our ‘regional’ (south-eastern Sahul) assessment of spatial patterns of extinction [5] required us to lump taxa into one large group because of a lack of spatial replication of dated fossil specimens. Therefore, it is not yet currently possible to generate regional estimates of extinction time at the taxonomic scale of genus yet for Sahul megafauna. We have already indicated this limitation in paragraph six of the Discussion.

It is also possible that biases are a function of taxon sampling. There are an increasing number of small mammal Pleistocene extinctions being recognized in Australia, and it is unclear if these follow the same trends as the megafauna. If so, that would indeed remove the body size issues.

This is true, but the unfortunate reality is that for the small mammals known to have gone extinct in Pleistocene Sahul, there are few or no accurately dated specimens, no estimates for final extinction dates, and much of the research on these species is yet to be published. Thus, although the reviewer is correct in saying size-biased taxon sampling could affect our results, we are unable to include smaller extinct species in our analyses because we do not know when they went extinct. We have partly mitigated this bias by pairing extinct species with the largest extant species from the same functional/taxonomic group in our analyses. Nonetheless, we now also state in the Discussion that this is an issue and point to addressing it in the future:

“There is also an increasing number of small marsupial and placental mammals known to have gone extinct during the Pleistocene in Sahul [e.g., 6, 7]. The inclusion of these smaller species in future demographic susceptibility/chronology of extinction analyses will likely be insightful, but unfortunately estimates of extinction dates – which require at least 8 – 10 reliability dated specimens [3, 4] – are not yet available for these animals.”

2) Better framing of the five putative drivers of extinctions (these are not considered or glossed over in the present manuscript)):

i. Appears to assume that only human hunting will differentially affect demographically sensitive species. However, novel or extreme climate change can also affect such species (e.g. Selwood, K.E., McGeoch, M.A. and Mac Nally, R., 2015. The effects of climate change and land‐use change on demographic rates and population viability. Biological Reviews, 90(3), pp.837-853.)

This is a good suggestion for inclusion. We have now reworded our description of scenario (i) and updated Figure 1 to include the ways in which climate change can affect demography as described in Selwood et al. [8]. We have also modified the main text to include the reference and some of the most-pertinent conclusions from that paper (paragraph immediately before Figure 1 in the Introduction):

“However, there are many other ways that climate change can alter demography (reviewed in [8]). […] An increasing frequency of climate-induced catastrophes can also drive relatively smaller populations toward extinction faster, meaning large-bodied species with smaller populations are potentially more susceptible.”

ii. This mechanism is predicated on using a modelling result [ref. 25] as data. It also makes the bold claim that species inhabiting certain habitats are less accessible to human hunters without any consideration of the archaeological or modern record on this point (e.g. Roberts, P., Hunt, C., Arroyo-Kalin, M., Evans, D. and Boivin, N., 2017. The deep human prehistory of global tropical forests and its relevance for modern conservation. Nature Plants, 3(8), pp.1-9; Fa, J.E. and Brown, D., 2009. Impacts of hunting on mammals in African tropical moist forests: a review and synthesis. Mammal Review, 39(4), pp.231-264).

Perhaps a debatable concept given previous writings in this area for Sahul [9], but considering we cannot examine this mechanism explicitly with our approach, we have decided to remove reference to habitat access here in the Introduction (including removing the last mechanism in Figure 1). However, we maintained the text regarding the hypothesised relative susceptibility to human hunting in terms of mode of travelling (gait) and burrowing behaviour (Discussion).

iii. Many of the supporting references here do not seem like logical choices for this argument. E.g. [28] refers to coral-reef fishes. Moreover, this hypothesis conflicts with much modern data showing that extinction risk and body size are correlated under climate and environmental change (e.g. Cardillo, M., Mace, G.M., Jones, K.E., Bielby, J., Bininda-Emonds, O.R., Sechrest, W., Orme, C.D.L. and Purvis, A., 2005. Multiple causes of high extinction risk in large mammal species. Science, 309(5738), pp.1239-1241. Liow, L.H., Fortelius, M., Bingham, E., Lintulaakso, K., Mannila, H., Flynn, L. and Stenseth, N.C., 2008. Higher origination and extinction rates in larger mammals. Proceedings of the National Academy of Sciences, 105(16), pp.6097-6102. Tomiya, S., 2013. Body size and extinction risk in terrestrial mammals above the species level. The American Naturalist, 182(6), pp.E196-E214.)

In terms of reference 28 (Monaco et al.), the taxon is somewhat irrelevant. Rather, that paper focuses on the mechanisms of dispersal and establishment to new regions of climate suitability. For this reason, we think it is a useful citation.

For the other suggested papers, we have added them to the relevant text in the Introduction (including Figure 1) and the Discussion.

We acknowledge that scenario (iv) is in conflict with a widely observed pattern — climate change selects for a faster pace of life history (including faster growth, earlier maturation and decreased adult survival) and leads to smaller maximum adult body size. Indeed, this pattern corresponds to scenario (i) in which species with a slower life history (generally larger animals) succumbed first.

On the other hand, Scenario (iv) relates to another widely hypothesised pattern: that species with lower dispersal and higher specialisation are more vulnerable to climate change. In this scenario, larger species (which tend to have higher dispersal and less specialisation) are predicted to have later extinction dates (i.e., opposite pattern to i). We have now reworded scenario (i) to make the distinction between these scenarios clearer.

3) More nuanced interpretation of model output.

The major weakness in this manuscript is in the discussion. The authors should be very clear in their discussion that their model does not indicate that demographic factors had no part in extinct events per se, but rather that they don't explain extinction chronology. Extinction chronologies reflect a number of different factors and processes, but they don't take away from the fact that certain life history traits can make a species more likely to go extinct from those factors.

The authors seem to argue that demographics don't explain the megafaunal extinction in the Sahul, but in fact, their results suggest that they do; the only thing demographics by themselves don't explain is the chronology. Extinction risk as determined by demographic susceptibility is highly related to body mass and generation time (which in turn is also affected by body mass) but differential survival (timing of extinction) is determined by factors such as geographic range size, dispersal ability, access to refugia, and behavioral and morphological adaptations against hunting, and the ability to survive catastrophic events. A reiteration of this point would be beneficial to the clarity of this otherwise well written manuscript.

We agree that we are testing whether demographic susceptibility explains the chronology, and we did not intend to suggest that demographic variability played no role in the extinctions. That said, we understand now that perhaps we were not clear enough on the distinction between demographic susceptibility to extinction (something we clearly showed) versus testing for a relationship between demographic vulnerability and extinction chronology (a hypothesis we specifically rejected).

The relevant paragraphs of the Discussion appear to be mainly the first and last, which we have now updated to read as follows:

First paragraph:

“The megafauna species of Sahul demonstrate demographic susceptibility to extinction largely following expectations derived from threat risk in modern species – species with slower life histories have higher demographic risk to extinction on average [10-13]. […] As different perturbations compromise different aspects of a species’ life history, its relative susceptibility to extinction compared to other species in its community varies in often unpredictable ways.”

last paragraph:

“That we found no clear patterns among the extinct megafauna of Sahul to explain their relative extinction chronology supports the notion that, at least for mammals, extinction risk can be high across all body masses depending on a species’ particular ecology [15], even if relative extinction risk appears to follow allometric expectations [10-13, 16] as we demonstrated clearly here (Figure 3, 5, 7). […] Nonetheless, more spatially and community-dependent models are still needed to provide a more complete picture of the dynamics of Late Pleistocene megafauna extinctions”

We also modified the 3^rd paragraph of the Discussion as follows:

“This lack of relationship to the chronology, combined with the result that many of the extant species had, in fact, some of the highest extinction susceptibilities, suggest that no obvious demographic properties can explain the taxon-specific timing within the Sahul extinction event of the Late Pleistocene. […] However, this conclusion does not accord well with the notion that Late Pleistocene megafauna extinctions were non-random and occurred at a much higher pace than background extinction rates [18, 19].”

The authors clearly (and elegantly) show that extinct species, which were all large, and had long generation times, had demographic traits that made them more susceptible to extinction. This is evident in figures 3 and 4. However, in the discussion, in lines 301-303, they state that no demographic trends explain the extinction. This is not supported by the results. While the timing of when species go extinct doesn't correlate with demographic susceptibility, the peculiar nature of the extinction-a large size biased extinction-is explained by demographic factors, and is a phenomenon that has been explored in a global analysis by Lyons et al. 2016 Biol. Lett. Therefore, demographic trends DO explain why certain species go extinct, while others survive. The authors should be careful when they say that "that no obvious demographic trends can explain the great Sahul mass extinction event"; instead, they should re-iterate that no obvious demographic trend explains the extinction chronology.

As indicated in Response #7, we did not intend to give this impression, but agree that our wording needs to be clearer. We have amended this paragraph to:

“This lack of relationship to the chronology, combined with the result that many of the extant species had, in fact, some of the highest extinction susceptibilities, suggest that no obvious demographic properties can explain the taxon-specific timing within the Sahul extinction event of the Late Pleistocene. […] However, this conclusion does not accord well with the notion that Late Pleistocene megafauna extinctions were non-random and occurred at a much higher pace than background extinction rates [18, 19].”

We have also now cited Lyons et al. [20] in the first paragraph of the Discussion.

4) More careful discussion of results relative to literature. The authors further go on to suggest that their results suggest that the extinctions were random, but the size-selectivity clearly shows that the extinctions were in fact not random with respect to body size. Their analyses do show that the rate of extinction doesn't exceed background to the same degree that it's been suggested in prior studies, and this is something that researchers need to explore further. Also, the authors raise an important point in lines 309-311 that human hunting could have interacted with demographic susceptibility, something that Lyons et al. 2016 Biol. Lett. show, and the results of the present study should be discussed in light of the 2016 paper.

We had meant that the chronology of the extinctions might have arisen at random, not that the extinctions themselves were random (we later dismiss this suggestion). But we agree that our wording was unclear. We have reworded this to:

“This opens the possibility that the chronology instead reflects either a random set of circumstances – that species succumbed to circumstantial combinations of stressors depending on the local perturbations experienced by particular populations [5, 17] – or even that the chronology is still insufficiently resolved.”

We are uncertain what the reviewer is referring to with respect to extinction rate. While we mentioned that previous work [18, 19] concluded that the Sahul megafauna extinction rate was higher than the background rate, we offer no such data or analysis on rates of extinction (i.e., extinctions per unit time); rather, we demonstrate how quickly populations of certain sizes could have gone extinct at increasing magnitudes of perturbations to demographic rates.

For the Lyons et al. [20] conclusions, we have added a new sentence in the paragraph mentioned after the first sentence:

“Indeed, Lyons et al. [20] concluded that either large-bodied mammals were selectively targeted by humans during the Late Quaternary, that these species were relatively more vulnerable to human hunting than smaller-bodied species, or both.”

They also raise an important point in lines 312-320 that behavioral or morphological adaptations may have allowed some seemingly "high risk" species to persist despite anthropogenic pressure. These model "mis-matches" have been reported by Alroy 2001 Science as well in a multispecies overkill simulation. It would be beneficial to discuss the present results within the context of other examples of model mismatches, such as those from Alroy 2001.

This is a good reference for mismatches, although they only describe hypotheses as opposed to modelled mechanisms. We have therefore added the following sentence to the end of the relevant paragraph:

“Similar hypotheses regarding risk-persistence mismatches in multispecies simulations have been proposed for the Late Pleistocene megafauna extinction event in North America [21].”

In lines 353-358, the authors once again state that their results show no clear relationship between body-mass and demographic disadvantage, despite clearly showing these relationships in Figures 3 and 4, and even stating as much in the beginning of the discussion. The plots clearly show that large bodied taxa were at a demographic disadvantage. There is a difference between explaining why certain taxa go extinct vs. why they go extinct at a certain point in time, and this should be made clear. The authors are correct in stating that demographic factors don't explain the relative extinction chronology, i.e. when species go extinction relative to each other, but they do explain why large species go extinct, and why these extinctions take place after human arrival. Moreover, generation length, which is also correlated with demographic susceptibility, is highly correlated with body mass (Brook and Bowman 2005 Pop. Ecol), once again showing that body mass-related effects do help explain the extinctions.

We have completely redrafted this paragraph as explained in Response #7. We have also added the citation to Brook and Bowman [16] at the end of the first sentence in that paragraph.

The authors rightfully point out earlier in the discussion that spatial variation, local climates, ecological interactions, etc. all influence how and why a particular population disappears. Extinction chronologies reflect a number of different factors and processes, but they don't take away from the fact that certain life history traits can make a species more likely to go extinct from those factors. Large proboscideans like mammoths had a high risk of extinction based on life history traits, but managed to survive on island refugia into the mid-Holocene. Similar other examples exist, and show that extinction chronologies can vary vastly.

Good point. We have added the following sentence encapsulating this idea, and added a new citation:

“For example, large proboscideans like mammoths managed to persist well into the Holocene on island refugia despite having a high intrinsic extinction risk [22].”

Therefore, the lack of correlation can be explained by these factors, and the authors need to expand on these in their discussion, perhaps if possible, by giving specific examples. They should be more careful in their discussion by clearly distinguishing drivers of extinction risk, and how these drivers can be de-coupled from timing, but at the same time providing a good explanation for the biological factors leading to the extinction. Here again the authors should consider the work of Brook and Bowman and Lyons et al.

The changes we have made throughout the Discussion suffice to make this distinction clear now, especially with the addition of the suggested references. Thank you.

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Author details

1. Corey JA Bradshaw

   1. Global Ecology Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Tarndanya (Adelaide), Australia
   2. ARC Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, Australia

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing, C.J.A.B conceptualized and wrote the paper, designed the research, wrote the code, sourced the data, and did the analysis

   For correspondence

   corey.bradshaw@flinders.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5328-7741

2. Christopher N Johnson

   1. ARC Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, Australia
   2. Dynamics of Eco-Evolutionary Pattern, University of Tasmania, Hobart, Australia

   Contribution

   Conceptualization, Resources, Methodology, Writing - review and editing, C.J. provided additional analytical approaches and data, and contributed to writing the manuscript

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9719-3771

3. John Llewelyn

   1. Global Ecology Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Tarndanya (Adelaide), Australia
   2. ARC Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, Australia

   Contribution

   Conceptualization, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5379-5631

4. Vera Weisbecker

   1. ARC Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, Australia
   2. College of Science and Engineering, Flinders University, Adelaide, Australia

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2370-4046

5. Giovanni Strona

   Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2294-4013

6. Frédérik Saltré

   1. Global Ecology Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Tarndanya (Adelaide), Australia
   2. ARC Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, Australia

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Methodology, Writing - review and editing, Conceptualised and assisted writing the paper, and provided climate data

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5040-3911

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