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.

1. 1. Allainé D
   2. Pontier D
   3. Gaillard JM
   4. Lebreton JD
   5. Trouvilliez J
   6. Clobert J

   (1987) The relationship between fecundity and adult body weight in homeotherms

   Oecologia 73:478–480.

   https://doi.org/10.1007/BF00385268

   • PubMed
   • Google Scholar
2. 1. Allentoft ME
   2. Bunce M
   3. Scofield RP
   4. Hale ML
   5. Holdaway RN

   (2010) Highly skewed sex ratios and biased fossil deposition of moa: ancient DNA provides New insight on New Zealand's extinct megafauna

   Quaternary Science Reviews 29:753–762.

   https://doi.org/10.1016/j.quascirev.2009.11.022

   • Google Scholar
3. 1. Alonso-Llamazares C
   2. Pablos A

   (2019) Sex estimation from the calcaneus and talus using discriminant function analysis and its possible application in fossil remains

   Archaeological and Anthropological Sciences 11:4927–4946.

   https://doi.org/10.1007/s12520-019-00855-y

   • Google Scholar
4. 1. Alroy J

   (2001) A multispecies overkill simulation of the end-Pleistocene megafaunal mass extinction

   Science 292:1893–1896.

   https://doi.org/10.1126/science.1059342

   • PubMed
   • Google Scholar
5. 1. Asier L

   (2016) Shoulder height, body mass, and shape of proboscideans

   Acta Palaeontologica Polonica 61:537–574.

   https://doi.org/10.4202/app.00136.2014

   • Google Scholar
6. Data

   1. Atlas of Living Australia

   (authors) (2020) Dromaius novaehollandiae (Latham, 1790)

   Atlas of Living Australia. ID 2714924-4fd5-456e-bb04-d23edbcf888f.

   https://bie.ala.org.au/species/urn:lsid:biodiversity.org.au:afd.taxon:c2714924-4fd5-456e-bb04-d23edbcf888f
7. 1. Bartlett LJ
   2. Williams DR
   3. Prescott GW
   4. Balmford A
   5. Green RE
   6. Eriksson A
   7. Valdes PJ
   8. Singarayer JS
   9. Manica A

   (2016) Robustness despite uncertainty: regional climate data reveal the dominant role of humans in explaining global extinctions of late Quaternary megafauna

   Ecography 39:152–161.

   https://doi.org/10.1111/ecog.01566

   • Google Scholar
8. Book

   1. Belcher C
   2. Burnett S
   3. Jones M

   (2008)

   Dasyurus maculatus

   In: van Dyck S, Strahan R, editors. The Mammals of Australia. New Holland. pp. 60–62.

   • Google Scholar
9. 1. Bode M
   2. Brennan KEC

   (2011) Using population viability analysis to guide research and conservation actions for Australia's threatened malleefowl Leipoa ocellata

   Oryx 45:513–521.

   https://doi.org/10.1017/S0030605311000688

   • Google Scholar
10. 1. Bradshaw CJA
    2. Cooper A
    3. Turney CSM
    4. Brook BW

    (2012) Robust estimates of extinction time in the geological record

    Quaternary Science Reviews 33:14–19.

    https://doi.org/10.1016/j.quascirev.2011.11.021

    • Google Scholar
11. 1. Bradshaw CJA
    2. Field IC
    3. McMahon CR
    4. Johnson GJ
    5. Meekan MG
    6. Buckworth RC

    (2013) More analytical bite in estimating targets for shark harvest

    Marine Ecology Progress Series 488:221–232.

    https://doi.org/10.3354/meps10375

    • Google Scholar
12. 1. Bradshaw CJA
    2. Ehrlich PR
    3. Beattie A
    4. Ceballos G
    5. Crist E
    6. Diamond J
    7. Dirzo R
    8. Ehrlich AH
    9. Harte J
    10. Harte ME
    11. Pyke G
    12. Raven PH
    13. Ripple WJ
    14. Saltré F
    15. Turnbull C
    16. Wackernagel M
    17. Blumstein DT

    (2021) Underestimating the challenges of avoiding a ghastly future

    Frontiers in Conservation Science 1:9.

    https://doi.org/10.3389/fcosc.2020.615419

    • Google Scholar
13. 1. Bradshaw CJA
    2. Brook BW

    (2005) Disease and the devil: density-dependent epidemiological processes explain historical population fluctuations in the Tasmanian devil

    Ecography 28:181–190.

    https://doi.org/10.1111/j.0906-7590.2005.04088.x

    • Google Scholar
14. 1. Brook BW
    2. Traill LW
    3. Bradshaw CJA

    (2006) Minimum viable population sizes and global extinction risk are unrelated

    Ecology Letters 9:375–382.

    https://doi.org/10.1111/j.1461-0248.2006.00883.x

    • PubMed
    • Google Scholar
15. 1. Brook BW
    2. Sodhi N
    3. Bradshaw CJA

    (2008) Synergies among extinction drivers under global change

    Trends in Ecology & Evolution 23:453–460.

    https://doi.org/10.1016/j.tree.2008.03.011

    • Google Scholar
16. 1. Brook BW
    2. Bowman DMJS

    (2004) The uncertain blitzkrieg of Pleistocene megafauna

    Journal of Biogeography 31:517–523.

    https://doi.org/10.1046/j.1365-2699.2003.01028.x

    • Google Scholar
17. 1. Brook BW
    2. Bowman DMJS

    (2005) One equation fits overkill: why allometry underpins both prehistoric and modern body size-biased extinctions

    Population Ecology 47:137–141.

    https://doi.org/10.1007/s10144-005-0213-4

    • Google Scholar
18. 1. Brook BW
    2. Johnson CN

    (2006) Selective hunting of juveniles as a cause of the imperceptible overkill of the Australian Pleistocene megafauna

    Alcheringa 30:39–48.

    https://doi.org/10.1080/03115510609506854

    • Google Scholar
19. 1. Broughton JM
    2. Cannon MD
    3. Bayham FE
    4. Byers DA

    (2011) Prey body size and ranking in zooarchaeology: theory, empirical evidence, and applications from the northern Great Basin

    American Antiquity 76:403–428.

    https://doi.org/10.7183/0002-7316.76.3.403

    • Google Scholar
20. 1. Cardillo M
    2. Mace GM
    3. Jones KE
    4. Bielby J
    5. Bininda-Emonds OR
    6. Sechrest W
    7. Orme CD
    8. Purvis A

    (2005) Multiple causes of high extinction risk in large mammal species

    Science 309:1239–1241.

    https://doi.org/10.1126/science.1116030

    • PubMed
    • Google Scholar
21. Book

    1. Caswell H

    (2001)

    Matrix Population Models: Construction, Analysis, and Interpretation (2nd edn)

    Sunderland, USA: Sinauer Associates, Inc.

    • Google Scholar
22. 1. Catt DC

    (1977) The breeding biology of Bennett's wallaby (Macropus rufogriseus jruticus) in South Canterbury, New Zealand

    New Zealand Journal of Zoology 4:401–411.

    https://doi.org/10.1080/03014223.1977.9517965

    • Google Scholar
23. Book

    1. Caughley G
    2. Gunn A

    (1996)

    Conservation Biology in Theory and Practice

    Cambridge, USA: Blackwell Science.

    • Google Scholar
24. 1. Chamberlain CP
    2. Waldbauer JR
    3. Fox-Dobbs K
    4. Newsome SD
    5. Koch PL
    6. Smith DR
    7. Church ME
    8. Chamberlain SD
    9. Sorenson KJ
    10. Risebrough R

    (2005) Pleistocene to recent dietary shifts in California condors

    PNAS 102:16707–16711.

    https://doi.org/10.1073/pnas.0508529102

    • PubMed
    • Google Scholar
25. 1. Chase JM
    2. Blowes SA
    3. Knight TM
    4. Gerstner K
    5. May F

    (2020) Ecosystem decay exacerbates biodiversity loss with habitat loss

    Nature 584:238–243.

    https://doi.org/10.1038/s41586-020-2531-2

    • PubMed
    • Google Scholar
26. 1. Choquenot D
    2. Bowman DMJS

    (1998) Marsupial megafauna, Aborigines and the overkill hypothesis: application of predator-prey models to the question of Pleistocene extinction in Australia

    Global Ecology and Biogeography Letters 7:167–180.

    https://doi.org/10.2307/2997372

    • Google Scholar
27. Book

    1. Cockburn A

    (1997)

    Living slow and dying young: senescence in marsupials

    In: Saunders N, Hinds L, editors. Marsupial Biology: Recent Research, New Perspectives. Sydney: University of New South Wales Press. pp. 163–171.

    • Google Scholar
28. Book

    1. Corbett L

    (1995)

    The Dingo in Australia and Asia

    Sydney: University of New South Wales Press.

    • Google Scholar
29. 1. Cork SJ
    2. Dove H

    (1989) Lactation in the tammar wallaby (Macropus eugenii). II. Intake of milk components and maternal allocation of energy

    Journal of Zoology 219:399–409.

    https://doi.org/10.1111/j.1469-7998.1989.tb02588.x

    • Google Scholar
30. 1. Cramb J
    2. Hocknull S
    3. Webb GE

    (2009) High diversity Pleistocene rainforest dasyurid assemblages with implications for the radiation of the Dasyuridae

    Austral Ecology 34:663–669.

    https://doi.org/10.1111/j.1442-9993.2009.01972.x

    • Google Scholar
31. 1. Cramb J
    2. Price GJ
    3. Hocknull SA

    (2018) Short-tailed mice with a long fossil record: the genus Leggadina (Rodentia: Muridae) from the Quaternary of Queensland, Australia

    PeerJ 6:e5639.

    https://doi.org/10.7717/peerj.5639

    • PubMed
    • Google Scholar
32. 1. Cremona T
    2. Crowther MS
    3. Webb JK

    (2017) High mortality and small population size prevent population recovery of a reintroduced mesopredator

    Animal Conservation 20:555–563.

    https://doi.org/10.1111/acv.12358

    • Google Scholar
33. 1. Damuth J

    (1981) Population density and body size in mammals

    Nature 290:699–700.

    https://doi.org/10.1038/290699a0

    • Google Scholar
34. 1. Davidson AD
    2. Hamilton MJ
    3. Boyer AG
    4. Brown JH
    5. Ceballos G

    (2009) Multiple ecological pathways to extinction in mammals

    PNAS 106:10702–10705.

    https://doi.org/10.1073/pnas.0901956106

    • PubMed
    • Google Scholar
35. 1. de Magalhães JP
    2. Costa J
    3. Church GM

    (2007) An analysis of the relationship between metabolism, developmental schedules, and longevity using phylogenetic independent contrasts

    Journal of Gerontology: Series A 62:149–160.

    https://doi.org/10.1093/gerona/62.2.149

    • Google Scholar
36. 1. Dillingham PW
    2. Moore JE
    3. Fletcher D
    4. Cortés E
    5. Curtis KA
    6. James KC
    7. Lewison RL

    (2016) Improved estimation of intrinsic growth r_max for long-lived species: integrating matrix models and allometry

    Ecological Applications 26:322–333.

    https://doi.org/10.1890/14-1990

    • PubMed
    • Google Scholar
37. 1. Fagan WF
    2. Holmes EE

    (2006) Quantifying the extinction vortex

    Ecology Letters 9:51–60.

    https://doi.org/10.1111/j.1461-0248.2005.00845.x

    • PubMed
    • Google Scholar
38. 1. Fisher DO
    2. Owens IPF
    3. Johnson CN

    (2001) The ecological basis of life history variation in marsupials

    Ecology 82:3531–3540.

    https://doi.org/10.1890/0012-9658(2001)082[3531:TEBOLH]2.0.CO;2

    • Google Scholar
39. 1. Frank M
    2. Slaton A
    3. Tinta T
    4. Capaldi A

    (2015) Investigating anthropogenic mammoth extinction with mathematical models

    SPORA: A Journal of Biomathematics 1:8–16.

    https://doi.org/10.30707/SPORA1.1Frank

    • Google Scholar
40. 1. Frankham R
    2. Bradshaw CJA
    3. Brook BW

    (2014) Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses

    Biological Conservation 170:56–63.

    https://doi.org/10.1016/j.biocon.2013.12.036

    • Google Scholar
41. 1. Frith HJ

    (1959) Breeding of the Mallee-fowl, Leipoa ocellata gould (Megapodiidae)

    CSIRO Wildlife Research 4:31–60.

    https://doi.org/10.1071/CWR9590031

    • Google Scholar
42. 1. Fritz SA
    2. Purvis A

    (2010) Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits

    Conservation Biology 24:1042–1051.

    https://doi.org/10.1111/j.1523-1739.2010.01455.x

    • PubMed
    • Google Scholar
43. 1. Garvey J
    2. Roberts G
    3. Cosgrove R

    (2016) Economic utility and nutritional value of the common wombat (Vombatus ursinus): evaluating Australian Aboriginal hunting and butchery patterns

    Journal of Archaeological Science: Reports 7:751–763.

    https://doi.org/10.1016/j.jasrep.2015.10.037

    • Google Scholar
44. 1. Gittleman JL
    2. Thompson SD

    (1988) Energy allocation in mammalian reproduction

    American Zoologist 28:863–875.

    https://doi.org/10.1093/icb/28.3.863

    • Google Scholar
45. 1. Glen AS

    (2008) Population attributes of the spotted-tailed quoll (Dasyurus maculatus) in north-eastern New South Wales

    Australian Journal of Zoology 56:137–142.

    https://doi.org/10.1071/ZO08025

    • Google Scholar
46. 1. Glen AS
    2. Dickman CR

    (2013) Population viability analysis shows spotted-tailed quolls may be vulnerable to competition

    Australian Mammalogy 35:180–183.

    https://doi.org/10.1071/AM12045

    • Google Scholar
47. 1. González-Suárez M
    2. Gómez A
    3. Revilla E

    (2013) Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes

    Ecosphere 4:1–16.

    https://doi.org/10.1890/ES12-00380.1

    • Google Scholar
48. 1. Goosse H
    2. Brovkin V
    3. Fichefet T
    4. Haarsma R
    5. Huybrechts P
    6. Jongma J
    7. Mouchet A
    8. Selten F
    9. Barriat P-Y
    10. Campin J-M
    11. Deleersnijder E
    12. Driesschaert E
    13. Goelzer H
    14. Janssens I
    15. Loutre M-F
    16. Morales Maqueda MA
    17. Opsteegh T
    18. Mathieu P-P
    19. Munhoven G
    20. Pettersson EJ
    21. Renssen H
    22. Roche DM
    23. Schaeffer M
    24. Tartinville B
    25. Timmermann A
    26. Weber SL

    (2010) Description of the Earth system model of intermediate complexity LOVECLIM version 1.2

    Geoscientific Model Development 3:603–633.

    https://doi.org/10.5194/gmd-3-603-2010

    • Google Scholar
49. 1. Gower G
    2. Fenderson LE
    3. Salis AT
    4. Helgen KM
    5. van Loenen AL
    6. Heiniger H
    7. Hofman-Kamińska E
    8. Kowalczyk R
    9. Mitchell KJ
    10. Llamas B
    11. Cooper A

    (2019) Widespread male sex bias in mammal fossil and museum collections

    PNAS 116:19019–19024.

    https://doi.org/10.1073/pnas.1903275116

    • PubMed
    • Google Scholar
50. 1. Grellet-Tinner G
    2. Spooner NA
    3. Worthy TH

    (2016) Is the “Genyornis” egg of a mihirung or another extinct bird from the Australian dreamtime?

    Quaternary Science Reviews 133:147–164.

    https://doi.org/10.1016/j.quascirev.2015.12.011

    • Google Scholar
51. Book

    1. Grzimek B

    (1990)

    Grzimek's Animal Life Encyclopedia

    New York: McGraw-Hill Publishing Company.

    • Google Scholar
52. 1. Guiler ER

    (1978)

    Observations on the Tasmanian devil, Sarcophilus harrisii (Dasyuridae: Marsupiala) at Granville Harbour, 1966-75

    Papers and Proceedings of the Royal Society of Tasmania 112:161–188.

    • Google Scholar
53. 1. Gurven M
    2. Kaplan H

    (2007) Longevity among hunter-gatherers: a cross-cultural examination

    Population and Development Review 33:321–365.

    https://doi.org/10.1111/j.1728-4457.2007.00171.x

    • Google Scholar
54. 1. Healy K
    2. Guillerme T
    3. Finlay S
    4. Kane A
    5. Kelly SBA
    6. McClean D
    7. Kelly DJ
    8. Donohue I
    9. Jackson AL
    10. Cooper N

    (2014) Ecology and mode-of-life explain lifespan variation in birds and mammals

    Proceedings of the Royal Society B: Biological Sciences 281:20140298.

    https://doi.org/10.1098/rspb.2014.0298

    • Google Scholar
55. 1. Hennemann WW

    (1983) Relationship among body mass, metabolic rate and the intrinsic rate of natural increase in mammals

    Oecologia 56:104–108.

    https://doi.org/10.1007/BF00378224

    • PubMed
    • Google Scholar
56. 1. Herfindal I
    2. Sæther B-E
    3. Solberg EJ
    4. Andersen R
    5. Høgda KA

    (2006) Population characteristics predict responses in moose body mass to temporal variation in the environment

    Journal of Animal Ecology 75:1110–1118.

    https://doi.org/10.1111/j.1365-2656.2006.01138.x

    • Google Scholar
57. 1. Holz PH
    2. Little PB

    (1995) Degenerative leukoencephalopathy and myelopathy in dasyurids

    Journal of Wildlife Diseases 31:509–513.

    https://doi.org/10.7589/0090-3558-31.4.509

    • PubMed
    • Google Scholar
58. 1. Hone J
    2. Duncan RP
    3. Forsyth DM

    (2010) Estimates of maximum annual population growth rates (r_m) of mammals and their application in wildlife management

    Journal of Applied Ecology 47:507–514.

    https://doi.org/10.1111/j.1365-2664.2010.01812.x

    • Google Scholar
59. 1. Janis CM
    2. Napoli JG
    3. Billingham C
    4. Martín-Serra A

    (2020) Proximal humerus morphology indicates divergent patterns of locomotion in extinct giant kangaroos

    Journal of Mammalian Evolution 27:627–647.

    https://doi.org/10.1007/s10914-019-09494-5

    • Google Scholar
60. 1. Johnson CN

    (2002) Determinants of loss of mammal species during the late Quaternary ‘megafauna’ extinctions: life history and ecology, but not body size

    Proceedings of the Royal Society B: Biological Sciences 269:2221–2227.

    https://doi.org/10.1098/rspb.2002.2130

    • Google Scholar
61. Book

    1. Johnson CN

    (2006)

    Australia's Mammal Extinctions: A 50 000 Year History

    Cambridge, United Kingdom: Cambridge University Press.

    • Google Scholar
62. 1. Johnson CN
    2. Alroy J
    3. Beeton NJ
    4. Bird MI
    5. Brook BW
    6. Cooper A
    7. Gillespie R
    8. Herrando-Pérez S
    9. Jacobs Z
    10. Miller GH
    11. Prideaux GJ
    12. Roberts RG
    13. Rodríguez-Rey M
    14. Saltré F
    15. Turney CSM
    16. Bradshaw CJA

    (2016) What caused extinction of the Pleistocene megafauna of Sahul?

    Proceedings of the Royal Society B: Biological Sciences 283:20152399.

    https://doi.org/10.1098/rspb.2015.2399

    • Google Scholar
63. 1. Johnson CN
    2. Prideaux GJ

    (2004) Extinctions of herbivorous mammals in the late Pleistocene of Australia in relation to their feeding ecology: no evidence for environmental change as cause of extinction

    Austral Ecology 29:553–557.

    https://doi.org/10.1111/j.1442-9993.2004.01389.x

    • Google Scholar
64. 1. Jones DN

    (1988) Hatching success of the Australian brush-turkey Alectura lathami in south-east Queensland

    Emu 88:260–263.

    https://doi.org/10.1071/MU9880260

    • Google Scholar
65. Book

    1. Jones DN
    2. Dekker WRJ
    3. Roselaar CS

    (1995)

    The Megapodes: Megapodiidae, Bird Families of the World

    Oxford: Oxford University Press.

    • Google Scholar
66. 1. Jones ME
    2. Stoddart DM

    (1998) Reconstruction of the predatory behaviour of the extinct marsupial thylacine (Thylacinus cynocephalus)

    Journal of Zoology 246:239–246.

    https://doi.org/10.1111/j.1469-7998.1998.tb00152.x

    • Google Scholar
67. 1. Jonzén N
    2. Pople T
    3. Knape J
    4. Sköld M

    (2010) Stochastic demography and population dynamics in the red kangaroo Macropus rufus

    Journal of Animal Ecology 79:109–116.

    https://doi.org/10.1111/j.1365-2656.2009.01601.x

    • Google Scholar
68. 1. Juanes F

    (1986) Population density and body size in birds

    The American Naturalist 128:921–929.

    https://doi.org/10.1086/284613

    • Google Scholar
69. 1. Kennou Sebei S
    2. Bergaoui R
    3. Ben Hamouda M
    4. Cooper RG

    (2009) Wild ostrich (Struthio camelus australis) reproduction in Orbata, a nature reserve in Tunisia

    Tropical Animal Health and Production 41:1427–1438.

    https://doi.org/10.1007/s11250-009-9331-x

    • PubMed
    • Google Scholar
70. 1. Koch PL
    2. Barnosky AD

    (2006) Late Quaternary extinctions: state of the debate

    Annual Review of Ecology, Evolution, and Systematics 37:215–250.

    https://doi.org/10.1146/annurev.ecolsys.34.011802.132415

    • Google Scholar
71. 1. Lachish S
    2. McCallum H
    3. Jones M

    (2009) Demography, disease and the devil: life-history changes in a disease-affected population of Tasmanian devils (Sarcophilus harrisii)

    Journal of Animal Ecology 78:427–436.

    https://doi.org/10.1111/j.1365-2656.2008.01494.x

    • Google Scholar
72. 1. Latham ADM
    2. Latham MC
    3. Wilmshurst JM
    4. Forsyth DM
    5. Gormley AM
    6. Pech RP
    7. Perry GLW
    8. Wood JR

    (2020) A refined model of body mass and population density in flightless birds reconciles extreme bimodal population estimates for extinct moa

    Ecography 43:353–364.

    https://doi.org/10.1111/ecog.04917

    • Google Scholar
73. 1. Liow LH
    2. Fortelius M
    3. Bingham E
    4. Lintulaakso K
    5. Mannila H
    6. Flynn L
    7. Stenseth NC

    (2008) Higher origination and extinction rates in larger mammals

    PNAS 105:6097–6102.

    https://doi.org/10.1073/pnas.0709763105

    • PubMed
    • Google Scholar
74. Preprint

    1. Llewelyn J
    2. Strona G
    3. McDowell MC
    4. Johnson Christopher N
    5. Peters KJ
    6. Stouffer DB
    7. de Visser SN
    8. Saltré F
    9. Bradshaw CJA

    (2020) Sahul’s megafauna were vulnerable to extinction due to their position in the trophic network

    bioRxiv.

    https://doi.org/10.1101/2021.01.19.427338

    • Google Scholar
75. 1. Lorenzen ED
    2. Nogués-Bravo D
    3. Orlando L
    4. Weinstock J
    5. Binladen J
    6. Marske KA
    7. Ugan A
    8. Borregaard MK
    9. Gilbert MT
    10. Nielsen R
    11. Ho SY
    12. Goebel T
    13. Graf KE
    14. Byers D
    15. Stenderup JT
    16. Rasmussen M
    17. Campos PF
    18. Leonard JA
    19. Koepfli KP
    20. Froese D
    21. Zazula G
    22. Stafford TW
    23. Aaris-Sørensen K
    24. Batra P
    25. Haywood AM
    26. Singarayer JS
    27. Valdes PJ
    28. Boeskorov G
    29. Burns JA
    30. Davydov SP
    31. Haile J
    32. Jenkins DL
    33. Kosintsev P
    34. Kuznetsova T
    35. Lai X
    36. Martin LD
    37. McDonald HG
    38. Mol D
    39. Meldgaard M
    40. Munch K
    41. Stephan E
    42. Sablin M
    43. Sommer RS
    44. Sipko T
    45. Scott E
    46. Suchard MA
    47. Tikhonov A
    48. Willerslev R
    49. Wayne RK
    50. Cooper A
    51. Hofreiter M
    52. Sher A
    53. Shapiro B
    54. Rahbek C
    55. Willerslev E

    (2011) Species-specific responses of late Quaternary megafauna to climate and humans

    Nature 479:359–364.

    https://doi.org/10.1038/nature10574

    • PubMed
    • Google Scholar
76. 1. Lyons SK
    2. Miller JH
    3. Fraser D
    4. Smith FA
    5. Boyer A
    6. Lindsey E
    7. Mychajliw AM

    (2016) The changing role of mammal life histories in late Quaternary extinction vulnerability on continents and islands

    Biology Letters 12:20160342.

    https://doi.org/10.1098/rsbl.2016.0342

    • PubMed
    • Google Scholar
77. 1. Marshall CR

    (1997) Confidence intervals on stratigraphic ranges with nonrandom distributions of fossil horizons

    Paleobiology 23:165–173.

    https://doi.org/10.1017/S0094837300016766

    • Google Scholar
78. 1. McCarthy MA

    (1998) Identifying declining and threatened species with museum data

    Biological Conservation 83:9–17.

    https://doi.org/10.1016/S0006-3207(97)00048-7

    • Google Scholar
79. 1. McCarthy MA
    2. Citroen R
    3. McCall SC

    (2008) Allometric scaling and Bayesian priors for annual survival of birds and mammals

    The American Naturalist 172:216–222.

    https://doi.org/10.1086/588074

    • Google Scholar
80. Book

    1. McIlroy JC

    (1996)

    Common wombat

    In: Strahan R, editors. The Australian Museum Complete Book of Australian Mammals. Sydney: Reed Books. pp. 205–206.

    • Google Scholar
81. Book

    1. McIlroy JC

    (2008)

    Vombatus ursinus

    In: van Dyck S, Strahan R, editors. The Mammals of Australia. Sydney: Reed New Holland. pp. 207–208.

    • Google Scholar
82. 1. McInerny GJ
    2. Roberts DL
    3. Davy AJ
    4. Cribb PJ

    (2006) Significance of sighting rate in inferring extinction and threat

    Conservation Biology 20:562–567.

    https://doi.org/10.1111/j.1523-1739.2006.00377.x

    • PubMed
    • Google Scholar
83. 1. Metcalf JL
    2. Turney C
    3. Barnett R
    4. Martin F
    5. Bray SC
    6. Vilstrup JT
    7. Orlando L
    8. Salas-Gismondi R
    9. Loponte D
    10. Medina M
    11. De Nigris M
    12. Civalero T
    13. Fernández PM
    14. Gasco A
    15. Duran V
    16. Seymour KL
    17. Otaola C
    18. Gil A
    19. Paunero R
    20. Prevosti FJ
    21. Bradshaw CJA
    22. Wheeler JC
    23. Borrero L
    24. Austin JJ
    25. Cooper A

    (2016) Synergistic roles of climate warming and human occupation in patagonian megafaunal extinctions during the last deglaciation

    Science Advances 2:e1501682.

    https://doi.org/10.1126/sciadv.1501682

    • PubMed
    • Google Scholar
84. 1. Miller GH
    2. Fogel ML
    3. Magee JW
    4. Gagan MK
    5. Clarke SJ
    6. Johnson BJ

    (2005) Ecosystem collapse in Pleistocene Australia and a human role in megafaunal extinction

    Science 309:287–290.

    https://doi.org/10.1126/science.1111288

    • PubMed
    • Google Scholar
85. 1. Miller G
    2. Magee J
    3. Smith M
    4. Spooner N
    5. Baynes A
    6. Lehman S
    7. Fogel M
    8. Johnston H
    9. Williams D
    10. Clark P
    11. Florian C
    12. Holst R
    13. DeVogel S

    (2016) Human predation contributed to the extinction of the Australian megafaunal bird Genyornis newtoni ∼47 ka

    Nature Communications 7:10496.

    https://doi.org/10.1038/ncomms10496

    • PubMed
    • Google Scholar
86. 1. Monaco CJ
    2. Bradshaw CJA
    3. Booth DJ
    4. Gillanders BM
    5. Schoeman DS
    6. Nagelkerken I

    (2020) Dietary generalism accelerates arrival and persistence of coral-reef fishes in their novel ranges under climate change

    Global Change Biology 26:5564–5573.

    https://doi.org/10.1111/gcb.15221

    • PubMed
    • Google Scholar
87. 1. Moro D
    2. Dunlop J
    3. Williams MR

    (2019) Northern quoll persistence is most sensitive to survivorship of juveniles

    Wildlife Research 46:165–175.

    https://doi.org/10.1071/WR18010

    • Google Scholar
88. 1. Morris WF
    2. Doak DF

    (2004) Buffering of life histories against environmental stochasticity: accounting for a spurious correlation between the variabilities of vital rates and their contributions to fitness

    The American Naturalist 163:579–590.

    https://doi.org/10.1086/382550

    • PubMed
    • Google Scholar
89. 1. Munn AJ
    2. Dawson TJ

    (2001) Thermoregulation in juvenile red kangaroos (Macropus rufus) after pouch exit: higher metabolism and evaporative water requirements

    Physiological and Biochemical Zoology 74:917–927.

    https://doi.org/10.1086/324568

    • PubMed
    • Google Scholar
90. 1. Nicol S
    2. Andersen NA

    (2007) The life history of an egg-laying mammal, the echidna (Tachyglossus aculeatus)

    Ecoscience 14:275–285.

    https://doi.org/10.2980/1195-6860(2007)14[275:TLHOAE]2.0.CO;2

    • Google Scholar
91. Book

    1. Nicol SC
    2. Morrow GE

    (2012) Sex and seasonality: reproduction in the echidna (Tachyglossus aculeatus)

    In: Ruf T, Bieber C, Arnold W, Millesi E, editors. Living in a Seasonal World: Thermoregulatory and Metabolic Adaptations. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 143–153.

    https://doi.org/10.1007/978-3-642-28678-0_13

    • Google Scholar
92. 1. Nogués-Bravo D
    2. Rodríguez J
    3. Hortal J
    4. Batra P
    5. Araújo MB

    (2008) Climate change, humans, and the extinction of the woolly mammoth

    PLOS Biology 6:e79.

    https://doi.org/10.1371/journal.pbio.0060079

    • PubMed
    • Google Scholar
93. 1. O'Grady JJ
    2. Reed DH
    3. Brook BW
    4. Frankham R

    (2004) What are the best correlates of predicted extinction risk?

    Biological Conservation 118:513–520.

    https://doi.org/10.1016/j.biocon.2003.10.002

    • Google Scholar
94. 1. Oakwood M
    2. Bradley AJ
    3. Cockburn A

    (2001) Semelparity in a large marsupial

    Proceedings of the Royal Society B: Biological Sciences 268:407–411.

    https://doi.org/10.1098/rspb.2000.1369

    • Google Scholar
95. Book

    1. Oftedal OT

    (1985)

    Pregnancy and lactation

    In: Hudson R. J, White R. G, editors. Bioenergetics of Wild Herbivores. Boca Raton, Florida: CRC Press. pp. 215–238.

    • Google Scholar
96. 1. Packer C
    2. Tatar M
    3. Collins A

    (1998) Reproductive cessation in female mammals

    Nature 392:807–811.

    https://doi.org/10.1038/33910

    • PubMed
    • Google Scholar
97. Book

    1. Pardi MI
    2. Smith FA

    (2012) Paleoecology in an era of climate change: how the past can provide insights into the future

    In: Louys J, editors. Paleontology in Ecology and Conservation. Berlin, Heidelber: Springer Berlin Heidelberg. pp. 93–116.

    https://doi.org/10.1007/978-3-642-25038-5_6

    • Google Scholar
98. 1. Patodkar VR
    2. Rahane SD
    3. Shejal MA
    4. Belhekar DR

    (2009)

    Behavior of emu bird (Dromaius novaehollandiae)

    Veterinary World 2:439–440.

    • Google Scholar
99. 1. Peters KJ
    2. Saltré F
    3. Friedrich T
    4. Jacobs Z
    5. Wood R
    6. McDowell M
    7. Ulm S
    8. Bradshaw CJA

    (2019) FosSahul 2.0, an updated database for the late Quaternary fossil records of Sahul

    Scientific Data 6:272.

    https://doi.org/10.1038/s41597-019-0267-3

    • PubMed
    • Google Scholar
100. 1. Prowse TAA
     2. Johnson CN
     3. Lacy RC
     4. Bradshaw CJA
     5. Pollak JP
     6. Watts MJ
     7. Brook BW

     (2013) No need for disease: testing extinction hypotheses for the thylacine using multi-species metamodels

     Journal of Animal Ecology 82:355–364.

     https://doi.org/10.1111/1365-2656.12029

     • Google Scholar
101. 1. Prowse TAA
     2. Johnson CN
     3. Bradshaw CJA
     4. Brook BW

     (2014) An ecological regime shift resulting from disrupted predator-prey interactions in Holocene Australia

     Ecology 95:693–702.

     https://doi.org/10.1890/13-0746.1

     • PubMed
     • Google Scholar
102. 1. Purvis A
     2. Gittleman JL
     3. Cowlishaw G
     4. Mace GM

     (2000) Predicting extinction risk in declining species

     Proceedings of the Royal Society B: Biological Sciences 267:1947–1952.

     https://doi.org/10.1098/rspb.2000.1234

     • Google Scholar
103. 1. Reed DH
     2. O'Grady JJ
     3. Ballou JD
     4. Frankham R

     (2003) The frequency and severity of catastrophic die-offs in vertebrates

     Animal Conservation 6:109–114.

     https://doi.org/10.1017/S1367943003003147

     • Google Scholar
104. 1. Richards HL
     2. Wells RT
     3. Evans AR
     4. Fitzgerald EMG
     5. Adams JW

     (2019) The extraordinary osteology and functional morphology of the limbs in Palorchestidae, a family of strange extinct marsupial giants

     PLOS ONE 14:e0221824.

     https://doi.org/10.1371/journal.pone.0221824

     • PubMed
     • Google Scholar
105. 1. Ripple WJ
     2. Van Valkenburgh B

     (2010) Linking top-down forces to the Pleistocene megafaunal extinctions

     BioScience 60:516–526.

     https://doi.org/10.1525/bio.2010.60.7.7

     • Google Scholar
106. 1. Rismiller PD
     2. McKelvey MW

     (2000) Frequency of breeding and recruitment in the short-beaked echidna, Tachyglossus aculeatus

     Journal of Mammalogy 81:1–17.

     https://doi.org/10.1644/1545-1542(2000)081<0001:FOBARI>2.0.CO;2

     • Google Scholar
107. 1. Rodríguez-Rey M
     2. Herrando-Pérez S
     3. Gillespie R
     4. Jacobs Z
     5. Saltré F
     6. Brook BW
     7. Prideaux GJ
     8. Roberts RG
     9. Cooper A
     10. Alroy J
     11. Miller GH
     12. Bird MI
     13. Johnson CN
     14. Beeton N
     15. Turney CSM
     16. Bradshaw CJA

     (2015) Criteria for assessing the quality of middle Pleistocene to Holocene vertebrate fossil ages

     Quaternary Geochronology 30:69–79.

     https://doi.org/10.1016/j.quageo.2015.08.002

     • Google Scholar
108. 1. Rodríguez-Rey M
     2. Herrando-Pérez S
     3. Brook BW
     4. Saltré F
     5. Alroy J
     6. Beeton N
     7. Bird MI
     8. Cooper A
     9. Gillespie R
     10. Jacobs Z
     11. Johnson CN
     12. Miller GH
     13. Prideaux GJ
     14. Roberts RG
     15. Turney CS
     16. Bradshaw CJA

     (2016) A comprehensive database of quality-rated fossil ages for Sahul's Quaternary vertebrates

     Scientific Data 3:160053.

     https://doi.org/10.1038/sdata.2016.53

     • PubMed
     • Google Scholar
109. 1. Roger E
     2. Laffan SW
     3. Ramp D

     (2011) Road impacts a tipping point for wildlife populations in threatened landscapes

     Population Ecology 53:215–227.

     https://doi.org/10.1007/s10144-010-0209-6

     • Google Scholar
110. 1. Sales J

     (2007) The emu (Dromaius novaehollandiae): a review of its biology and commercial products

     Avian and Poultry Biology Reviews 18:1–20.

     https://doi.org/10.3184/147020607X245048

     • Google Scholar
111. 1. Saltré F
     2. Brook BW
     3. Rodríguez-Rey M
     4. Cooper A
     5. Johnson CN
     6. Turney CSM
     7. Bradshaw CJA

     (2015) Uncertainties in dating constrain model choice for inferring extinction time from fossil records

     Quaternary Science Reviews 112:128–137.

     https://doi.org/10.1016/j.quascirev.2015.01.022

     • Google Scholar
112. 1. Saltré F
     2. Rodríguez-Rey M
     3. Brook BW
     4. Johnson CN
     5. Turney CS
     6. Alroy J
     7. Cooper A
     8. Beeton N
     9. Bird MI
     10. Fordham DA
     11. Gillespie R
     12. Herrando-Pérez S
     13. Jacobs Z
     14. Miller GH
     15. Nogués-Bravo D
     16. Prideaux GJ
     17. Roberts RG
     18. Bradshaw CJA

     (2016) Climate change not to blame for late Quaternary megafauna extinctions in Australia

     Nature Communications 7:10511.

     https://doi.org/10.1038/ncomms10511

     • PubMed
     • Google Scholar
113. 1. Saltré F
     2. Chadoeuf J
     3. Peters KJ
     4. McDowell MC
     5. Friedrich T
     6. Timmermann A
     7. Ulm S
     8. Bradshaw CJA

     (2019) Climate-human interaction associated with southeast Australian megafauna extinction patterns

     Nature Communications 10:5311.

     https://doi.org/10.1038/s41467-019-13277-0

     • PubMed
     • Google Scholar
114. 1. Sandom C
     2. Faurby S
     3. Sandel B
     4. Svenning J-C

     (2014) Global late Quaternary megafauna extinctions linked to humans, not climate change

     Proceedings of the Royal Society B: Biological Sciences 281:20133254.

     https://doi.org/10.1098/rspb.2013.3254

     • Google Scholar
115. 1. Saran KA
     2. Parker G
     3. Parker R
     4. Dickman CR

     (2011) Rehabilitation as a conservation tool: a case study using the common wombat

     Pacific Conservation Biology 17:310–319.

     https://doi.org/10.1071/PC110310

     • Google Scholar
116. 1. Selwood KE
     2. McGeoch MA
     3. Mac Nally R

     (2015) The effects of climate change and land-use change on demographic rates and population viability

     Biological Reviews 90:837–853.

     https://doi.org/10.1111/brv.12136

     • PubMed
     • Google Scholar
117. 1. Shute E
     2. Prideaux GJ
     3. Worthy TH

     (2017) Taxonomic review of the late Cenozoic megapodes (Galliformes: Megapodiidae) of Australia

     Royal Society Open Science 4:170233.

     https://doi.org/10.1098/rsos.170233

     • PubMed
     • Google Scholar
118. Book

     1. Sodhi NS
     2. Brook BW
     3. Bradshaw CJA

     (2009) Causes and consequences of species extinctions

     In: Levin S. A, editors. The Princeton Guide to Ecology. Princeton, USA: Princeton University Press. pp. 514–520.

     https://doi.org/10.1515/9781400833023.514

     • Google Scholar
119. 1. Solow AR
     2. Roberts DL
     3. Robbirt KM

     (2006) On the Pleistocene extinctions of Alaskan mammoths and horses

     PNAS 103:7351–7353.

     https://doi.org/10.1073/pnas.0509480103

     • PubMed
     • Google Scholar
120. 1. Stephens PA
     2. Vieira MV
     3. Willis SG
     4. Carbone C

     (2019) The limits to population density in birds and mammals

     Ecology Letters 22:654–663.

     https://doi.org/10.1111/ele.13227

     • PubMed
     • Google Scholar
121. Book

     1. Strahan R

     (1991)

     The Australian Museum Complete Book of Australian Mammals

     North Ryde, New South Wales: Cornstalk Publications.

     • Google Scholar
122. 1. Strauss D
     2. Sadler PM

     (1989) Classical confidence intervals and Bayesian probability estimates for ends of local taxon ranges

     Mathematical Geology 21:411–427.

     https://doi.org/10.1007/BF00897326

     • Google Scholar
123. 1. Strona G
     2. Bradshaw CJA

     (2018) Co-extinctions annihilate planetary life during extreme environmental change

     Scientific Reports 8:16724.

     https://doi.org/10.1038/s41598-018-35068-1

     • PubMed
     • Google Scholar
124. 1. Timmermann A

     (2020) Quantifying the potential causes of Neanderthal extinction: abrupt climate change versus competition and interbreeding

     Quaternary Science Reviews 238:106331.

     https://doi.org/10.1016/j.quascirev.2020.106331

     • Google Scholar
125. 1. Timmermann A
     2. Friedrich T

     (2016) Late Pleistocene climate drivers of early human migration

     Nature 538:92–95.

     https://doi.org/10.1038/nature19365

     • PubMed
     • Google Scholar
126. 1. Tomiya S

     (2013) Body size and extinction risk in terrestrial mammals above the species level

     The American Naturalist 182:E196–E214.

     https://doi.org/10.1086/673489

     • Google Scholar
127. 1. Traill LW
     2. Brook BW
     3. Frankham RR
     4. Bradshaw CJA

     (2010) Pragmatic population viability targets in a rapidly changing world

     Biological Conservation 143:28–34.

     https://doi.org/10.1016/j.biocon.2009.09.001

     • Google Scholar
128. 1. Turbill C
     2. Ruf T

     (2010) Senescence is more important in the natural lives of long- than short-lived mammals

     PLOS ONE 5:e12019.

     https://doi.org/10.1371/journal.pone.0012019

     • PubMed
     • Google Scholar
129. Book

     1. Tyndale-Biscoe CH

     (2005)

     Life of Marsupials

     Clayton, Victoria: CSIRO Publishing.

     • Google Scholar
130. 1. Villavicencio NA
     2. Lindsey EL
     3. Martin FM
     4. Borrero LA
     5. Moreno PI
     6. Marshall CR
     7. Barnosky AD

     (2016) Combination of humans, climate, and vegetation change triggered late Quaternary megafauna extinction in the Última Esperanza region, southern Patagonia, Chile

     Ecography 39:125–140.

     https://doi.org/10.1111/ecog.01606

     • Google Scholar
131. 1. Wan X
     2. Zhang Z

     (2017) Climate warming and humans played different roles in triggering late Quaternary extinctions in east and west Eurasia

     Proceedings of the Royal Society B: Biological Sciences 284:20162438.

     https://doi.org/10.1098/rspb.2016.2438

     • Google Scholar
132. 1. Way KC

     (1988) Captive management and breeding of the tiger quoll: Dasyurus maculatus

     International Zoo Yearbook 27:108–119.

     https://doi.org/10.1111/j.1748-1090.1988.tb03203.x

     • Google Scholar
133. 1. Weisbecker V
     2. Goswami A

     (2010) Brain size, life history, and metabolism at the marsupial/placental dichotomy

     PNAS 107:16216–16221.

     https://doi.org/10.1073/pnas.0906486107

     • PubMed
     • Google Scholar
134. 1. White LC
     2. Saltré F
     3. Bradshaw CJA
     4. Austin JJ

     (2018) High-quality fossil dates support a synchronous, late Holocene extinction of devils and thylacines in mainland Australia

     Biology Letters 14:20170642.

     https://doi.org/10.1098/rsbl.2017.0642

     • PubMed
     • Google Scholar
135. 1. Willis KJ
     2. Birks HJ

     (2006) What is natural? the need for a long-term perspective in biodiversity conservation

     Science 314:1261–1265.

     https://doi.org/10.1126/science.1122667

     • PubMed
     • Google Scholar
136. Book

     1. Wilson DE
     2. Mittermeier RA

     (2001)

     Handbook of the Mammals of the World, 2

     Barcelona: Lynx Edicions.

     • Google Scholar
137. 1. Wingard GL
     2. Bernhardt CE
     3. Wachnicka AH

     (2017) The role of paleoecology in restoration and resource management—the past as a guide to future decision-making: review and example from the Greater Everglades Ecosystem, U.S.A

     Frontiers in Ecology and Evolution 5:11.

     https://doi.org/10.3389/fevo.2017.00011

     • Google Scholar
138. 1. Wroe S
     2. Crowther M
     3. Dortch J
     4. Chong J

     (2004) The size of the largest marsupial and why it matters

     Proceedings of the Royal Society B: Biological Sciences 271:S34–S36.

     https://doi.org/10.1098/rsbl.2003.0095

     • Google Scholar

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.

References:

1. Saltré F, Rodríguez-Rey M, Brook BW, Johnson CN, Turney CSM, Alroy J, et al. Climate change not to blame for late Quaternary megafauna extinctions in Australia. Nature Communications. 2016. doi: 10.1038/ncomms10511.2. White LC, Saltré F, Bradshaw CJA, Austin JJ. High-quality fossil dates support a synchronous, Late Holocene extinction of devils and thylacines in mainland Australia. Biology Letters. 2018. doi: 10.1098/rsbl.2017.0642.3. Bradshaw CJA, Cooper A, Turney CSM, Brook BW. Robust estimates of extinction time in the geological record. Quaternary Science Reviews. 2012;33:14-9.4. Saltré F, Brook BW, Rodríguez-Rey M, Cooper A, Johnson CN, Turney CSM, et al. Uncertainties in dating constrain model choice for inferring extinction time from fossil records. Quaternary Science Reviews. 2015. doi: 10.1016/j.quascirev.2015.01.022.5. Saltré F, Chadoeuf J, Peters KJ, McDowell MC, Friedrich T, Timmermann A, et al. Climate-human interaction associated with southeast Australian megafauna extinction patterns. Nat Comm. 2019;10(1):5311. doi: 10.1038/s41467-019-13277-0.6. Cramb J, Price GJ, Hocknull SA. Short-tailed mice with a long fossil record: the genus Leggadina (Rodentia: Muridae) from the Quaternary of Queensland, Australia. PeerJ. 2018;6:e5639. doi: 10.7717/peerj.5639.7. Cramb J, Hocknull S, Webb GE. High diversity Pleistocene rainforest Dasyurid assemblages with implications for the radiation of the dasyuridae. Austral Ecol. 2009;34(6):663-9. doi: 10.1111/j.1442-9993.2009.01972.x.8. Selwood KE, McGeoch MA, Mac Nally R. The effects of climate change and land-use change on demographic rates and population viability. Biol Rev. 2015;90(3):837-53. doi: 10.1111/brv.12136.9. Johnson CN. Determinants of loss of mammal species during the late Quaternary 'megafauna' extinctions: life history and ecology, but not body size. Proc R Soc Lond B. 2002;269(1506):2221-7.10. Purvis A, Gittleman JL, Cowlishaw G, Mace GM. Predicting extinction risk in declining species. Proc R Soc Lond B. 2000;267(1456):1947-52. doi: 10.1098/rspb.2000.1234.11. Tomiya S. Body size and extinction risk in terrestrial mammals above the species level. Am Nat. 2013;182(6):E196-E214. doi: 10.1086/673489.12. Cardillo M, Mace GM, Jones KE, Bielby J, Bininda-Emonds ORP, Sechrest W, et al. Multiple causes of high extinction risk in large mammal species. Science. 2005;309(5738):1239-41. PubMed PMID: ISI:000231395400045.13. Liow LH, Fortelius M, Bingham E, Lintulaakso K, Mannila H, Flynn L, et al. Higher origination and extinction rates in larger mammals. Proc Natl Acad Sci USA. 2008;105(16):6097. doi: 10.1073/pnas.0709763105.14. Sodhi NS, Brook BW, Bradshaw CJA. Causes and consequences of species extinctions. In: Levin SA, editor. The Princeton Guide to Ecology. Princeton, USA: Princeton University Press; 2009. p. 514-20.15. Davidson AD, Hamilton MJ, Boyer AG, Brown JH, Ceballos G. Multiple ecological pathways to extinction in mammals. Proc Natl Acad Sci USA. 2009;106(26):10702. doi: 10.1073/pnas.0901956106.16. Brook BW, Bowman DMJS. One equation fits overkill: why allometry underpins both prehistoric and modern body size-biased extinctions. Popul Ecol. 2005;47:137-41. doi: 10.1007/s10144-005-0213-4.17. Peters KJ, Bradshaw CJA, Chadœuf J, Ulm S, Bird MI, Friedrich T, et al. Landscape of fear explains trade-off between distance to water and human predation for extinct Australian megafauna. Comm Biol. 2020;in press.18. Koch PL, Barnosky AD. Late Quaternary extinctions: state of the debate. Annu Rev Ecol Evol Syst. 2006;37:215-50. doi: 10.1146/annurev.ecolsys.34.011802.132415.19. Johnson CN. Australia's Mammal Extinctions: A 50 000 Year History. Cambridge, United Kingdom: Cambridge University Press; 2006.20. Lyons SK, Miller JH, Fraser D, Smith FA, Boyer A, Lindsey E, et al. The changing role of mammal life histories in Late Quaternary extinction vulnerability on continents and islands. Biol Lett. 2016;12(6):20160342. doi: 10.1098/rsbl.2016.0342.21. Alroy J. A multispecies overkill simulation of the end-Pleistocene megafaunal mass extinction. Science. 2001;292(5523):1893-6. PubMed PMID: ISI:000169200700043.22. Nogués-Bravo D, Rodríguez J, Hortal J, Batra P, Araújo MB. Climate change, humans, and the extinction of the woolly mammoth. PLoS Biol. 2008;6(4):e79. doi: 10.1371/journal.pbio.0060079.23. Pardi MI, Smith FA. Paleoecology in an era of climate change: how the past can provide insights into the future. In: Louys J, editor. Paleontology in Ecology and Conservation. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 93-116.24. Willis KJ, Birks HJB. What is natural? The need for a long-term perspective in biodiversity conservation. Science. 2006;314(5803):1261. doi: 10.1126/science.1122667.25. Wingard GL, Bernhardt CE, Wachnicka AH. The role of paleoecology in restoration and resource management—the past as a guide to future decision-making: review and example from the Greater Everglades ecosystem, U.S.A. Front Ecol Evol. 2017;5:11. doi: 10.3389/fevo.2017.00011.26. Bradshaw CJA, Ehrlich PR, Beattie A, Ceballos G, Crist E, Diamond J, et al. Underestimating the challenges of avoiding a ghastly future. Frontiers in Conservation Science. 2021;1:9. doi: 10.3389/fcosc.2020.615419.27. Frankham R, Bradshaw CJA, Brook BW. Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv. 2014;170(0):56-63. doi: 10.1016/j.biocon.2013.12.036.28. Saltré F, Rodríguez-Rey M, Brook BW, Johnson CN, Turney CSM, Alroy J, et al. Climate change not to blame for late Quaternary megafauna extinctions in Australia. Nat Comm. 2016;7:10511. doi: 10.1038/ncomms10511. PubMed PMID: WOS:000369019400003.29. Strauss D, Sadler PM. Classical confidence intervals and Bayesian probability estimates for ends of local taxon ranges. Math Geol. 1989;21(4):411-27. doi: 10.1007/BF00897326.30. McCarthy MA. Identifying declining and threatened species with museum data. Biol Conserv. 1998;83(1):9-17. doi: 10.1016/S0006-3207(97)00048-7.31. Marshall CR. Confidence intervals on stratigraphic ranges with nonrandom distributions of fossil horizons. Paleobiology. 1997;23(2):165-73.32. Solow AR, Roberts DL, Robbirt KM. On the Pleistocene extinctions of Alaskan mammoths and horses. Proc Natl Acad Sci USA. 2006;103(19):7351-3. doi: 10.1073/pnas.0509480103.33. McInerny GJ, Roberts DL, Davy AJ, Cribb PJ. Significance of sighting rate in inferring extinction and threat. Conserv Biol. 2006;20(2):562-7. doi: 10.1111/j.1523-1739.2006.00377.x.34. Bradshaw CJA, Cooper A, Turney CSM, Brook BW. Robust estimates of extinction time in the geological record. Quat Sci Rev. 2012;33(0):14-9. doi: 10.1016/j.quascirev.2011.11.021.35. Peters KJ, Saltré F, Friedrich T, Jacobs Z, Wood R, McDowell M, et al. FosSahul 2.0, an updated database for the Late Quaternary fossil records of Sahul. Sci Dat. 2019;6(1):272. doi: 10.1038/s41597-019-0267-3.36. Rodríguez-Rey M, Herrando-Perez S, Gillespie R, Jacobs Z, Saltré F, Brook BW, et al. Criteria for assessing the quality of Middle Pleistocene to Holocene vertebrate fossil ages. Quat Geochronol. 2015;30:69-79. doi: 10.1016/j.quageo.2015.08.002. PubMed PMID: WOS:000366538100009.37. Bird MI, Condie SA, O’Connor S, O’Grady D, Reepmeyer C, Ulm S, et al. Early human settlement of Sahul was not an accident. Sci Rep. 2019;9(1):8220. doi: 10.1038/s41598-019-42946-9.38. Bradshaw CJA, Ulm S, Williams AN, Bird MI, Roberts RG, Jacobs Z, et al. Minimum founding populations for the first peopling of Sahul. Nat Ecol Evol. 2019;3:1057-63. doi: 10.1038/s41559-019-0902-6.39. Bradshaw CJA, Johnson CN, Llewelyn J, Weisbecker V, Strona G, Saltré F. Relative demographic susceptibility does not explain the extinction chronology of Sahul’s megafauna. bioRxiv. 2020:2020.10.16.342303. doi: 10.1101/2020.10.16.342303.40. Bradshaw CJA, Norman K, Ulm S, Williams AN, Clarkson C, Chadoeuf J, et al. Stochastic models support rapid peopling of Late Pleistocene Sahul. Nat Comm. 2021. doi: 10.1038/s41467-021-21551-3.41. Shabani F, Ahmadi M, Peters KJ, Haberle S, Champreux A, Saltré F, et al. Climate-driven shifts in the distribution of koala-browse species from the Last Interglacial to the near future. Ecography. 2019;42(9):1587-99. doi: 10.1111/ecog.04530.

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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