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

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Version of Record published: April 13, 2021 (This version)
Accepted Manuscript published: March 30, 2021 (Go to version)
Accepted: March 29, 2021
Received: October 9, 2020

1. Of interest
Sensory collectives in natural systems

Hannah J Williams, Vivek H Sridhar ... Amanda D Melin

Review Article Nov 29, 2023
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Abstract

The causes of Sahul’s megafauna extinctions remain uncertain, although several interacting factors were likely responsible. To examine the relative support for hypotheses regarding plausible ecological mechanisms underlying these extinctions, we constructed the first stochastic, age-structured models for 13 extinct megafauna species from five functional/taxonomic groups, as well as 8 extant species within these groups for comparison. Perturbing specific demographic rates individually, we tested which species were more demographically susceptible to extinction, and then compared these relative sensitivities to the fossil-derived extinction chronology. Our models show that the macropodiformes were the least demographically susceptible to extinction, followed by carnivores, monotremes, vombatiform herbivores, and large birds. Five of the eight extant species were as or more susceptible than the extinct species. There was no clear relationship between extinction susceptibility and the extinction chronology for any perturbation scenario, while body mass and generation length explained much of the variation in relative risk. Our results reveal that the actual mechanisms leading to the observed extinction chronology were unlikely related to variation in demographic susceptibility per se, but were possibly driven instead by finer-scale variation in climate change and/or human prey choice and relative hunting success.

Introduction

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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Description of five dominant mechanisms by which megafauna could have been driven to extinction and the associated seven perturbation scenarios examined.

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.

Results

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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Relationship between estimated date of species extinction (across the entire continent) and (a) body mass (M, kg) or (b) generation length (G, years) (Scenario LH).

Extinction-timing windows are estimated based on the agreement among six different models that correct for the Signor-Lipps effect (described in Materials and methods) in chronologies of quality-rated (Rodríguez-Rey et al., 2015) fossil dates for the studied taxa described in Peters et al., 2019. Here, we have depicted *Sarcophilus* as ‘extant’, even though it went extinct on the mainland >3000 years ago. Also shown are the approximate major climate periods and transitions: Marine Isotope Stage 3 (MIS 3), MIS 2 (including the Last Glacial Maximum, Antarctic Cold Reversal, and Younger Dryas), and the Holocene (including the period of sea level flooding when Tasmania separated from the mainland, and the relatively warm, wet, and climatically stable Holocene optimum).

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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Area under the quasi-extinction curve (from Appendix 6—figure 1) — extinction risk: ∫Pr(E_q)d(x) — as a function of body mass (M, kg) for (a) (Scenario ↓S_j) decreasing juvenile survival, (b) (Scenario ↓F) decreasing fertility, (c) (Scenario ↓S) decreasing survival across all age classes, (d) (Scenario ↓ind) increasing number of individuals removed year⁻¹, (e) (Scenario ↑cat_f) increasing frequency of catastrophic die-offs per generation, and f: (Scenario ↑cat_M) increasing magnitude of catastrophic die-offs.

Shown are the information-theoretic evidence ratios (ER) and variation explained (R²) for the lines of best fit (grey dashed) in each scenario. Here, we have depicted *Sarcophilus* as ‘extant’, even though it went extinct on the mainland >3000 years ago.

Figure 4

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Area under the quasi-extinction curve (from Appendix 6—figure 1) — extinction risk: ∫Pr(E_q)d(x) — as a function of generation length (G, years) for (a) (Scenario ↓S_j) decreasing juvenile survival, (b) (Scenario ↓F) decreasing fertility, (c) (Scenario ↓S) decreasing survival across all age classes, (d) (Scenario ↓ind) increasing number of individuals removed year⁻¹, (e) (Scenario ↑cat_f) increasing frequency of catastrophic die-offs per generation, and f: (Scenario ↑cat_M) increasing magnitude of catastrophic die-offs.

Shown are the information-theoretic evidence ratios (ER) and variation explained (R²) for the lines of best fit (grey dashed) in each scenario. Here, we have depicted *Sarcophilus* as ‘extant’, even though it went extinct on the mainland >3000 years ago.

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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Inset: Increasing extinction risk for birds — quasi-extinction probability: Pr(E_q) — as a function of increasing the mean number of eggs harvested per female per year (Scenario ↓F_e).

The main graph shows the area under the quasi-extinction curve — extinction risk: ∫Pr(E_q)d(x) — as a function body mass (M, kg).

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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Relationship between estimated date of species extinction (across entire mainland) and area under the quasi-extinction curve (from Appendix 7—figure 1) — extinction risk: ∫Pr(E_q)d(x) — for (a) (Scenario ↓S_j) decreasing juvenile survival, (b) (Scenario ↓F) decreasing fertility, (c) (Scenario ↓S) decreasing survival across all age classes, (d) (Scenario ↓ind) increasing number of individuals removed year⁻¹, (e) (Scenario ↑cat_f) increasing frequency of catastrophic die-offs per generation, and f: (Scenario **↑cat_M**) increasing magnitude of catastrophic die-offs.

Extinction-timing windows are estimated based on the agreement among six different models that correct for the Signor-Lipps effect (described in Materials and methods) in chronologies of quality-rated (Rodríguez-Rey et al., 2015) fossil dates for the studied taxa described in Peters et al., 2019.

Figure 7

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Extinction susceptibility of the 21 modelled species.

(a) Sum of the areas under the quasi-extinction curve for each of the six scenarios considered — total extinction risk: Σ∫Pr(E_q)d(x) — for each of the 21 modelled species (^†extinct; ^♀extant; scenario abbreviations: ↓S_j = reducing juvenile survival; ↓F = reducing fertility; ↓S = reducing survival; ind = reducing number of individuals; ↑cat_f = increasing frequency of catastrophe; ↑cat_M = increasing magnitude of catastrophe); (b) median susceptibility rank across the six scenarios considered (where higher ranks = higher susceptibility to extinction) for each species (red = extinct; black = extant; outline-only bars = macropodiformes; solid bars = vombatiforms; angled crosshatching = birds; vertical crosshatching = carnivores; brick crosshatching = monotremes); (c) median susceptibility rank as a function of log₁₀ generation length (G, kg) — there was a weak correlation including all species (solid grey line 1), but a strong relationship removing *Sarcophilus* and *Dromaius* (dashed gray line 2) (information-theoretic evidence ratio [ER] and variance explained [R²] shown for each); (d) estimated date of species extinction (across entire continent) as a function of median susceptibility rank; taxonomic/functional groupings are indicated by coloured symbols and convex hulls (macropodids: brown; monotremes: grey; vombatiforms: green; birds: blue; carnivores: red). Extinction-timing windows are estimated based on the agreement among six different models that correct for the Signor-Lipps effect (described in Materials and methods) in chronologies of quality-rated (Rodríguez-Rey et al., 2015) fossil dates for the studied taxa described in Peters et al., 2019.

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).

Discussion

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.

Materials and methods

Choice of species

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Our first step was to choose enough extinct and extant species from the Sahul fossil record (Peters et al., 2019; Rodríguez-Rey et al., 2016) to represent a diversity of clades that were particularly affected during the main extinction event (estimated between 60 and 40 ka, where one ka = 1000 years ago) (Saltré et al., 2016). We also aimed to include at least one extant species within each functional/taxonomic group to compare extant with extinct species’ susceptibility. We settled on a total of 21 species (13 extinct; 8 extant) from five different functional/taxonomic groups: (i) 5 vombatiform herbivores, (ii) 7 macropodiform herbivores, (iii) 3 large birds, (iv) 4 carnivores, and (v) 2 monotreme invertivores. For a full list and justification of species chosen as well as the distribution of mean body masses, refer to Appendix 1.

Estimating demographic rates

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To build age-structured population models for extinct taxa, we relied on different allometric, phylogenetic, and measured relationships to predict the plausible range of component demographic rates. For most extant marsupials, we relied mainly on the marsupial life-history database published in Fisher et al., 2001, but updated some values for some species with more recent sources (see below). A detailed description of how we estimated the necessary demographic rates and other ecological data to build the stochastic models is provided in Appendix 2, and a full table of all demographic values is provided in Appendix 2—table 1. We also provide a correlation matrix among demographic values across species (Appendix 2—table 2).

Age-structured (Leslie) population models

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From the estimated demographic rates for each species, we constructed a pre-breeding, ω+1 (i) × ω+1 (j) element (representing ages from 0 to ω years old), Leslie transition matrix (M) for females only (males are demographically irrelevant assuming equal sex ratios). Fertilities (m_x) occupied the first row of the matrix, survival probabilities (S_x) occupied the sub-diagonal, and we set the final diagonal transition probability (M_i,j) to S_ω for all species except Vombatus, Thylacinus, and Sarcophilus for which we instead set the value to 0 to limit unrealistically high proportions of old individuals in the population, and the evidence for catastrophic mortality at ω for the latter two species (dasyurids) (Cockburn, 1997; Holz and Little, 1995; Oakwood et al., 2001). Multiplying M by a population vector n estimates total population size at each forecasted time step (Caswell, 2001). Here, we used n₀ = ADMw, where w = the right eigenvector of M (stable stage distribution), and A = the surface area of the study zone applied in the stochastic extinction scenarios — we arbitrarily chose A = 250,000 km² (500 km × 500 km; approximately 10% larger than the state of Victoria) so that the species with the lowest n₀ would have a population of at least several thousand individuals at the start of the simulations (see Appendix 3). We also included a compensatory density-feedback function in all simulations to avoid exponentially increasing populations (see Appendix 4).

Stochastic extinction scenarios

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With the base M including density feedback tailored for each species, we perturbed various elements of their life histories to examine the relative support for hypotheses regarding plausible extinction drivers and pathways (see Figure 1). We first tested the relationship between extinction date and speed of life history as a baseline without any perturbation (Scenario LH), and then we generated six additional scenarios with perturbations. The second scenario (↓S_j) decreased juvenile (x = 0 to α−1) survival (plus a sub-scenario [↓ind_j] where we progressively removed individual juveniles from the population as we did for all individuals in Scenario ↓ind — see below). This scenario aims to emulate either food shortages of sufficient magnitude to make growing juveniles with higher relative energy and water demand (Munn and Dawson, 2001) succumb to environmental change more than adults, or from targeted hunting of juveniles by humans (Brook and Johnson, 2006). The third scenario (↓F) progressively reduces fertility to emulate food shortages lowering energetically demanding reproduction/lactation (Gittleman and Thompson, 1988; Oftedal, 1985). We also considered a sub-scenario (Scenario ↓F_e, see details below) for the category of large birds where we progressively increased the number of eggs harvested by humans (Miller et al., 2016). In Scenario ↓S, we progressively reduced survival across all age classes to examine the influence of an age-independent environmental stressor. Scenario ↓ind progressively removed individuals from the n population vector emulating offtake where animals are directly removed from the population to simulate human hunting (with age-relative offtake following the stable stage distribution of the target species). In Scenario ↑cat_f, we emulated how environmental variability would compromise populations via an increased relative (i.e. per generation) frequency of catastrophic die-offs by progressively increasing the number of catastrophic ~50% mortality events occurring per generation. Finally, Scenario ↑cat_M progressively increased the magnitude of the catastrophic mortality events to examine species’ responses to rising severity of catastrophes (Reed et al., 2003).

For Scenario ↓F_e, we estimated the egg-production component for Genyornis by calculating the proportion of total fecundity contributed by individual egg production in Dromaius (nest success of 0.406 × hatching probability of 0.419 = 0.17), and then multiplying this proportion by the total fertility estimated for Genyornis from Equation 11 — this produced an estimated per-individual annual egg production of 7.74 eggs for Genyornis (or, 7.74/2 = 3.87 eggs resulting in daughters). For Scenario ↑cat_M, we randomly allocated a multiplier of the expected frequency per generation (uniform sampling) derived from the species-specific range of multipliers identified in Scenario ↑cat_f (i.e. from one to the value where the species has an extinction probability = 1). In this way, we both standardized the relative risk among species and avoided cases where catastrophe frequency was insufficient to elicit any iterations without at least one extinction.

We ran 10,000 stochastic iterations of each model starting with allometrically predicted stable population size (see Appendix 3) divided into age classes according to the stable stage distribution. We projected all runs to $40 ⌊ G ⌉$ for each species (removing the first $⌈ G ⌋$ values as burn-in). 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 (E_q) of 50 female individuals (100 total individuals total assuming 1:1 sex ratios). This threshold is based on the updated minimum size below which a population cannot avoid inbreeding depression (Frankham et al., 2014). After calculating the per-increment probability of E_q in each of the seven scenarios, we calculated the total area under the quasi-extinction curve (integral) for each species as a scenario-specific representation of extinction risk across the entire range of the specific perturbation — this provides a single, relative value per species for comparison. Finally, we ranked the integrals among species in each scenario (lower ranks = higher resilience), and took the median rank as an index of resilience to extinction incorporating all scenario sensitivities into one value for each species.

Extinction dates

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We compared the relative susceptibilities among all extinction scenarios, as well as the combined extinction-susceptibility rank of each species, to estimates of continental extinction times for the genera we examined. We took all estimates of continental extinction dates from the Signor-Lipps corrected values provided in Saltré et al., 2016; however, more recent continent-wide disappearance dates for Thylacinus and Sarcophilus are provided in White et al., 2018. The Signor-Lipps-correction of extinction timing is explained in detail in Saltré et al., 2016, but we briefly summarize it here. The mean date of extinction and its confidence intervals are derived from six frequentist methods to infer the timing of extinction — Strauss and Sadler, 1989, McCarthy, 1998, Marshall, 1997, Solow et al., 2006, McInerny et al., 2006, and the Gaussian-Resampled, Inverse-Weighted McInerny (GRIWM) model (Bradshaw et al., 2012) — from chronologies of dated fossils described in the FosSahul 2.0 database (Peters et al., 2019). All dates in a taxon’s chronology are first assessed for reliability, with dates of less than ‘A’ rejected for the calculation of the extinction window (Rodríguez-Rey et al., 2015). Each method calculates an extinction window for each species (taxon), and from these a window of cross-model agreement is calculated for every year (assuming that higher cross-model agreement indicates a greater likelihood of the true extinction date occurring during those times). We also examined the sensitivity of our overall results to uncertainty in extinction estimates by deriving a jack-knifed version of the GRIWM model (Bradshaw et al., 2012; Saltré et al., 2015) (Appendix 8).

We hypothesize that one, or several, of these types of perturbation scenarios would lead to a better match between the continental-scale chronology of extinctions as inferred from the fossil record compared to the simpler expectation of larger species with slower life-histories being more likely to go extinct than smaller species with faster life histories when faced with novel mortality sources (Scenario LH) (Brook and Bowman, 2005).

Data and code availability

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

Appendix 1

Choice of species and body mass distribution

Given data availability, we settled on a total of 21 species (13 extinct; 8 extant) from five different functional/taxonomic groups: (i) 5 vombatiform herbivores: Diprotodon optatum (2786 kg; extinct) (Wroe et al., 2004), Palorchestes azael (1000 kg; extinct) (Richards et al., 2019), Zygomaturus trilobus (500 kg; extinct) (Johnson, 2006), Phascolonus gigas (200 kg; extinct) (Johnson, 2006), and Vombatus ursinus (common wombat; 25 kg; extant) (McIlroy, 1996; Saran et al., 2011); (ii) 7 macropodiform herbivores: Procoptodon goliah (250 kg; extinct) (Johnson and Prideaux, 2004), Sthenurus stirlingi (150 kg; extinct) (Johnson and Prideaux, 2004), Protemnodon anak (130 kg; extinct) (Johnson, 2006), Simosthenurus occidentalis (120 kg; extinct) (Johnson, 2006), Metasthenurus newtonae (55 kg; extinct) (Johnson, 2006), Osphranter rufus (red kangaroo; 25 kg; extant) (McIlroy, 2008), and Notamacropus rufogriseus (red-necked wallaby; 14 kg; extant) (Strahan, 1991); (iii) 3 omnivorous (but primarily plant-eating) large birds: Genyornis newtoni (200 kg; extinct) (Johnson, 2006), Dromaius novaehollandiae (emu; 55 kg; extant) (Sales, 2007), and Alectura lathami (brush turkey; 2.2 kg; extant) (Jones et al., 1995); (iv) 4 carnivores: Thylacoleo carnifex (marsupial ‘lion’; 110 kg; extinct) (Johnson, 2006), Thylacinus cynocephalus (marsupial ‘tiger’; 20 kg; extinct) (Jones and Stoddart, 1998), Sarcophilus harrisii (devil; 6.1 kg; extinct in mainland Australia, but extant in Tasmania — see below) (Guiler, 1978), and Dasyurus maculatus (spotted-tail quoll; 2.0 kg; extant) (Belcher et al., 2008); and (v) 2 monotreme invertivores: Megalibgwilia ramsayi (11 kg; extinct) (Johnson, 2006), and Tachyglossus aculeatus (short-beaked echidna; 4.0 kg; extant) (Nicol and Andersen, 2007).

For each species, we identified the body mass of mature females. However, the sex of an extinct individual from its fossilized remains in many species is difficult to determine, especially when sample sizes are small (Alonso-Llamazares and Pablos, 2019). As such, we might have inadvertently assigned a female mass based on an estimated male mass, given evidence that there is a male bias in many fossil collections (Allentoft et al., 2010; Gower et al., 2019). For this reason, we attempted to cover a broad range of body masses among species to maximize the relative difference between them for comparison.

The two genera Thylacinus and Sarcophilus require special consideration in both the design of the analysis and the interpretation of the results. While Sarcophilus is extant, it is restricted to the island state of Tasmania that has been separated from mainland Australia since approximately 8–10 ka. However, the species went extinct on the mainland 3179 (±24) years ago, whether considering the species complex Sarcophilus harrisii and S. laniarius together or separately (White et al., 2018). Like Sarcophilus, Thylacinus went extinct on the mainland just over 3000 years ago (White et al., 2018) and persisted in Tasmania. However, unlike Sarcophilus, Thylacinus also went extinct in Tasmania in the 1930s. In our analyses we treat Sarcophilus as ‘extant’, with the proviso that it should be considered extinct on the mainland. Although we could also have treated Thylacinus as ‘extant’ in the sense that it persisted into historical times in Tasmania, we treat this species as extinct in our analyses.

The distribution of the body masses (M) across the 21 species (range: 1.68–2786 kg) was approximately log-Normal (Shapiro-Wilk Normality test on log₁₀M: W = 0.9804; p=0.9305; Appendix 1—figure 1). Median dates of continental (i.e., total species) extinction ranged from 51,470 (±3167 standard deviation) for Metasthenurus, to 3179 (±24) for Sarcophilus (mainland only; currently extant in Tasmania) (Saltré et al., 2016; White et al., 2018).

Appendix 1—figure 1

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Histogram of log₁₀ adult body masses (in kg) for the 21 species examined.

The distribution is approximately log-Normal (Shapiro-Wilk Normality test on log₁₀*M: W* = 0.9804; p=0.9305).

Appendix 2

Estimating demographic rates and population data as input parameters for the stochastic models

For each species, we first calculated the maximum rate of instantaneous population growth (r_m) using the following equation:

r_{m} = 10^{0.6914 - 0.2622 {log}_{10} M}

for mammals, where M = mass (g) (Hennemann, 1983). For birds, we used an optimization of the objective function based on age at first breeding (α, estimated as shown below), adult survival (s_ad, estimated as shown below), and the allometric constant for birds (Dillingham et al., 2016) a_rT = 1.107:

r_{m} = \log_{e} λ_{1} = \log_{e} (min | λ_{1} - e^{\frac{a_{r T}}{α + \frac{s_{ad}}{λ_{1} - s_{ad}}}} |)

We then calculated theoretical equilibrium population densities (D, km⁻²) based on the following:

D = 10^{4.196 - 0.740 \log_{10} M} / 2

for mammalian herbivores (M = body mass in g), where dividing by two predicts for females only (i.e., assumed 1:1 sex ratio) (Damuth, 1981). For large, flightless birds (i.e., Genyornis and Dromaius) (Latham et al., 2020), we applied:

D = 10^{3.65 - 0.82 \log_{10} M} / 2

and:

D = 10^{1.63 - 0.23 \log_{10} M} / 2

for omnivorous birds (i.e., Alectura) (Juanes, 1986) where M is in g. For mammalian carnivores, we applied:

D = e^{1.930 - 1.026 \log M} / 2

(M in kg), which we derived from Stephens et al., 2019. There were no specific invertivore or taxonomically specific equations to estimate D; however, we determined that the equation for the fitted 97.5 percentile in mammalian carnivores:

D = 10^{1.91 - 1.02 \log_{10} M} / 2

Stephens et al., 2019 provided is a reasonable D for female Tachyglossus = 9.9 km⁻². This is comparable to echidna densities measured for Kangaroo Island (4.4 females km⁻²) (Rismiller and McKelvey, 2000) and Tasmania (8.4 females km⁻²) (Nicol and Andersen, 2007). We therefore also used Equation 7 to predict D for Megalibgwilia. For a detailed description of the distribution and trends of equilibrium densities, population sizes, and biomasses across the modelled species, see Appendix 3.

We estimated the maximum age (ω) of each species according to:

ω = 10^{0.89 + 0.13 \log_{10} M}

for non-volant birds and mammals (M in g) (Healy et al., 2014), or

ω = {7.02 M}^{0.174}

for Alectura (M in g) (de Magalhães et al., 2007), the latter of which produces a ω that closely matches the maximum longevity of 25 years estimated for the similar-sized megapode Leipoa ocellata (malleefowl) (Bode and Brennan, 2011).

For other species, we made species- or group-specific adjustments to the estimates of ω: for Vombatus (McIlroy, 2008), we set ω = 26; the disparity between this and the ω derived from the allometric prediction (Equation 7 gives ω = 29) means we adjusted ω′=26/29ω for all vombatiform herbivores. Similarly for the macropodiformes, we scaled the predicted ω according to the degree of overprediction of the parameter from the equation for Osphranter rufus (for the latter, the equation predicted ω = 30, but in reality it is closer to 13) (Jonzén et al., 2010), meaning we adjusted ω′=13/30ω for all macropodiformes except Notamacropus; for Notamacropus rufogriseus, we took the mean maximum age (Grzimek, 1990) of 16. For Dromaius (Atlas of Living Australia, 2020),we set ω = 17; for Thylacoleo, we set ω = 17 to match female lion Panthera leo longevity (Packer et al., 1998); for Thylacinus (Corbett, 1995; Prowse et al., 2014), we set ω = 10, for Sarcophilus (Bradshaw and Brook, 2005) ω = 5, and Dasyurus ω = 4 given the catastrophic mortality at maximum lifespan characteristic of dasyurids (Cockburn, 1997; Holz and Little, 1995; Oakwood et al., 2001). In the case of Dasyurus (both D. maculatus and D. hallucatus), most females die in their third year, although some can persist into the fourth year (Cremona et al., 2017; Glen, 2008; Glen and Dickman, 2013; Moro et al., 2019) and maximum longevity in captivity can be up to 5 years (Way, 1988). For Tachyglossus, ω is extremely high compared to similar-sized marsupials or placentals: up to 50 years in captivity, and possibly 45 years in the wild (Nicol and Andersen, 2007); we set the latter value of ω = 45 to be conservative. For Megalibgwilia, we assumed that the underestimation for Tachyglossus according to Equation 8 would also apply, so here we set Megalibgwilia ω = (45/23)26 = 51 years.

We estimated fecundity (F; mean number of female neonates produced per year and per breeding female) for mammals (Allainé et al., 1987) as:

F = e^{2.719 - 0.211 \log M} / 2

dividing by two for daughters only (M in g). Although we used well-establish allometric relationships to derive our input parameters, most of these relationships are based on placental species. It is accepted that average life-history traits differ between similar-sized marsupials and placentals (Fisher et al., 2001), and we therefore estimated a correction factor for F and age at first breeding (α) (see Equation 12) for both the vombatiform and macropodiform herbivore groups separately (see Appendix 5 figures 1–3 for approach) using demographic data describing marsupial life histories (Fisher et al., 2001).

For Vombatus (McIlroy, 1996), we set F = 0.25 given a litter size = 1, an inter-birth interval of 2 years, and an assumed sex ratio of 1:1, from which we derived F for the extinct vombatiform herbivores (Appendix 5—figure 2). For Notamacropus rufogriseus, we set the average annual number of offspring = 1 multiplied by a 2.8% twinning rate (Catt, 1977), 1.3 based on an interbirth interval of 286 days, and an assumed 1:1 sex ratio. For Genyornis, we applied the following equation:

F = e^{2.35 + 0.17 \log M} / 2

(M in g) (Allainé et al., 1987). For Dromaius (Sales, 2007), we used the average of 6.7 eggs clutch⁻¹ and 3.4 clutches year⁻¹, an assumed sex ratio of 1:1, and nest success (0.406) and hatching probabilities (0.419) for ostriches (Kennou Sebei et al., 2009). For Alectura, we used the annual mean of 16.6 eggs pair⁻¹ for Leipoa ocellata (Bode and Brennan, 2011), a hatching success of 0.866 for Alectura lathami (Jones, 1988), and an assumed 1:1 sex ratio. For Thylacoleo, we applied the average litter size of 1 for large vombatiforms, and a 1:1 sex ratio; for Thylacinus and Sarcophilus, we applied the values of 3.42 progeny litter⁻¹ and the proportion (Lachish et al., 2009; Prowse et al., 2014) of adults reproducing year⁻¹ (0.91), and a 1:1 sex ratio. The allometric prediction (Equation 10) nearly matched the product of mean litter size (4.9) (Glen, 2008) and proportion females breeding (0.643) (Glen and Dickman, 2013) for Dasyurus, so we used the former. For Tachyglossus (Nicol and Morrow, 2012), we used the production of 1 egg breeding event (divided by two for daughters) multiplied by the probability of breeding = 0.55. For Megalibgwilia, we assumed that the overestimation for Tachyglossus according to Equation 10 would also apply, so here we set Megalibgwilia F = (0.275/0.659)0.532 = 0.222.

To estimate the age at first breeding (α), we used the following relationship for mammals (de Magalhães et al., 2007):

α = e^{- 1.34 + 2.14 \log M}

and

α = 0.214 M^{0.303}

for birds (de Magalhães et al., 2007) (M in g). For the macropodiforms, Equation 12 appeared to overestimate α by ~ 20% (see Appendix 5—figure 2), so we adjusted the extinct macropodiforms accordingly. For the vombatiform herbivores, Equation 12 performed more according to expectation. For example, the 1800–2000 kg female white rhinoceros (Ceratotherium simum) has α = 6–7 years (Wilson and Mittermeier, 2001), which is similar to the 7 years predicted by equation 12 for the 2786 kg Diprotodon optatum (but < α = 10–12 years for the > 6000 kg female African savanna elephant Loxodonta africana) (Asier, 2016). We also made species-specific adjustments to α for the extant species (or recently extinct in the case of Thylacinus). In the case of Vombatus (Roger et al., 2011), we set α = 2, α = 2 for Osphranter rufus (Jonzén et al., 2010), α = 1 for Notamacropus rufogriseus (Catt, 1977), α = 3 for Dromaius (Patodkar et al., 2009), α = 2 for Alectura based on data from Leipoa ocellata (Bode and Brennan, 2011; Frith, 1959), and α = 1 for Thylacinus (Lachish et al., 2009; Prowse et al., 2014), Sarcophilus (Bradshaw and Brook, 2005), and Dasyurus (Glen and Dickman, 2013). For Thylacinus and Sarcophilus, although α = 1, only a small proportion of females breed at this age (see below), so for most females α is in fact 2. But for Dasyurus, some females can become sexually mature and breed at < 12 months old (Moro et al., 2019), so we incorporated a modest capacity to reproduce in the year prior to age 1 (40% of total fertility). For Tachyglosssus, we set α = 3 based on evidence that echidnas take 3–5 years to reach adult mass (Nicol and Andersen, 2007), and only adults are observed to breed (Rismiller and McKelvey, 2000); as such, we set m = 0.5F in year 3, 0.75F in year 4, and m = F thereafter. As we did for ω and F, we estimated the bias between the allometrically predicted and measured α (equation 12) for Tachyglossus, and applied this to Megalibgwilia; however, rounding to the nearest year also means α = 3 for Meglibgwilia. The log₁₀ of the resulting α among species predicted their respective log₁₀ r_m well (R² = 0.73) (Appendix 5—figure 4), with the fitted parameters similar to theoretical expectation (Hone et al., 2010), and thus, supporting our estimates of r_m as realistic.

To estimate age-specific fertilities (m_x) from F and α, we fit a logistic power function of the general form:

m_{x} = \frac{a}{1 + {(\frac{x}{b})}^{c}}

where x = age in years, and a, b, c are constants estimated for each species, to a vector composed of (α−1) values at 0F, $⌊ α / 2 ⌉$ values at 0.75F, and for the remaining ages up to ω, the full value of F. This produced a continuous increase in m_x up to maximum rather than a less-realistic stepped series. For Sarcophilus, we instead used the parameters from an existing devil model (Bradshaw and Brook, 2005) to populate the m_x vector.

To estimate realistic survival schedules, we first used the allometric prediction of adult survival (s_ad) as:

S_{ad} = e^{- e^{- 0.5 - 0.25 \log M}}

for mammals, and:

S_{ad} = e^{- e^{- 1.78 - 0.21 \log M}}

for birds, where M = body mass (g) (McCarthy et al., 2008). For Tachyglossus, we used the mean S_ad = 0.96 based on upper and lower estimates of mortality for tagged individuals over 15 years in Tasmania (Nicol and Morrow, 2012), and applying the allometric-bias correction for Megalibgwilia as for ω, F, and α as described above. We then applied the Siler hazard model (Gurven and Kaplan, 2007) to estimate the age- (x-) specific proportion of surviving individuals (l_x); this combines survival schedules for immature, mature, and senescent individuals within the population:

l_{x} = e^{(\frac{- a_{1}}{b_{1}}) (1 - e^{- b_{1} x})} e^{- a_{2} x} e^{(\frac{a_{3}}{b_{3}}) (1 - e^{b_{3} x})}

where a₁ = initial immature mortality, b₁ = rate of mortality decline in immatures, a₂ = the age-independent mortality due to environmental variation, a₃ = initial adult mortality, and b₃ = the rate of mortality increase (senescence). From l_x, age-specific survival can be estimated as:

S_{x} = 1 - \frac{(l_{x} - l_{x + 1})}{l_{x}}

We estimated the component parameters starting with 1 – S_ad for a₁ and a₂, adjusting the other parameters in turn to produce a dominant eigen value (λ₁) from the transition matrix containing S_x (see Materials and methods) such that log_e λ₁ ≈ r_m. However, in many cases, marsupial and monotreme life histories were incapable of reproducing predicted r_m (see Appendix 2—table 1 below), although we attempted to maximize log_e λ₁ wherever possible. This appears to be biologically justified given the slower life histories of vombatiforms and monotremes in particular compared to macropodiforms. We also generally favoured a more pronounced senescence component of l_x in the longer-lived species given evidence for survival senescence in long-lived mammals (Turbill and Ruf, 2010). For Sarcophilus, we instead used the parameters from an existing devil model (Bradshaw and Brook, 2005) to populate the S_x vector.

Appendix 2—table 1

Predicted demographic values for each species (equations provided in Materials and methods and this appendix).

M = mass, r_m = maximum rate of instaneous exponential population growth predicted allometrically, $r_{m}^{'}$ = realised r_m predicted from the constructed matrix (see text), ω = longevity, F = fertility (daughters per breeding female per year), α = age at first reproduction (primiparity), S_ad = yearly adult survival, G = generation length. ^†extinct; ^♀extant. See Appendix 2—table 2 for rank correlations among demographic values across species.

Species	M (kg)	r_m	$r_{m}^{'}$	D (km⁻²)	ω (yrs)	F (n_♀yr⁻¹♀⁻¹)	α (yrs)	S_ad (yr⁻¹)	G (yrs)
vombatiform herbivores
Diprotodon^†	2786	0.100	0.061	0.134	48	0.1311	7	0.985	18.1
Palorchestes^†	1000	0.131	0.077	0.285	42	0.1705	6	0.981	15.1
Zygomaturus^†	500	0.157	0.095	0.476	39	0.2038	5	0.977	13.2
Phascolonus^†	200	0.200	0.121	0.938	34	0.2586	4	0.972	10.7
Vombatus^♀	25	0.345	0.119	4.370	26	0.2500	2	0.953	10.0
macropodiform herbivores
Proctoptodon^†	250	0.189	0.188	0.795	17	0.524	3	0.973	8.3
Sthenurus^†	150	0.216	0.215	1.161	17	0.617	3	0.970	8.1
Protemnodon^†	130	0.224	0.224	1.290	16	0.646	3	0.969	7.8
Simosthenurus^†	120	0.229	0.226	1.369	16	0.663	3	0.968	7.8
Metasthenurus^†	55	0.281	0.280	2.438	14	0.858	2	0.961	6.0
Osphranter^♀	25	0.345	0.343	4.370	13	0.750	2	0.953	5.5
Notamacropus^♀	14	0.402	0.351	6.712	16	0.668	1	0.993	6.3
large birds
Genyornis^†	200	0.041	0.041	0.101	38	0.658	9	0.987	20.0
Dromaius^♀	55	0.100	0.100	0.290	17	1.938	3	0.983	5.9
Alectura^♀	2.2	0.176	0.175	3.633	27	7.188	2	0.967	6.8
carnivores
Thylacoleo^†	110	0.234	0.201	0.028	14	0.500	4	0.967	9.1
Thylacinus^†	20	0.366	0.368	0.159	10	1.556	1	0.950	5.2
Sarcophilus^†,♀	6.1	0.500	0.094	0.539	5	1.205	1	0.820	3.1
Dasyurus^♀	2.0	0.701	0.644	2.023	4	1.582	1	0.910	2.3
monotremes
Megalibgwilia^†	11.0	0.307	0.107	3.522	51	0.222	3	0.977	16.4
Tachyglossus^♀	4.0	0.400	0.112	9.883	45	0.275	3	0.950	14.1

Sarcophilus harrisii is extinct in mainland Australia, but extant in the island state of Tasmania.

Thylacinus could also be treated like Sarcophilus in that Thylacinus survived in Tasmania until historical times (1930s).
In the case of the vombatiform and macropodiform herbivores, ω shown in the table is in fact the downscaled ω′ calculated for each group (see below). Likewise, both allometric predictions of F and α are corrected for these groups (see Supplementary Information Appendix 5).

Appendix 2—table 2

Kendall’s rank correlation (τ) among demographic values given in Appendix 2—table 1 across species.

	M (kg)	r_m	rm'	D (km⁻²)	ω (yrs)	F (n_♀yr⁻¹♀⁻¹)	α (yrs)	S_ad (yr⁻¹)
r_m	−0.684
r_m'	−0.350	0.507
D (km⁻²)	−0.476	0.499	0.358
ω (yrs)	0.390	−0.511	−0.605	−0.068
F (n_♀yr⁻¹♀⁻¹)	−0.484	0.278	0.486	0.148	−0.567
α (yrs)	0.671	−0.685	−0.583	−0.459	0.556	−0.562
S_ad (yr⁻¹)	0.552	−0.681	−0.394	−0.328	0.484	−0.327	0.572
G (yrs)	0.471	−0.446	−0.616	−0.201	0.762	−0.711	0.730	0.487

Appendix 3

Allometric predictions of equilibrium population density, total population size, and biomass

The allometric predictions of stable population size (N_stable) for each species in the 500 km × 500 km study area showed the largest populations for some of the smallest, extant species (e.g., N_stable > 600,000,000 for Vombatus, Osphranter, Notamacropus, Alectura, Dasyurus, Tachyglossus) (Appendix 3—figure 1a). There was a clear separation in the allometric predictions of N_stable among the species in each group (Appendix 3—figure 1b). When expressed as total biomass across the study area, the four carnivores had approximately equal biomasses (~10⁶ kg), as did the macropodiformes and monotremes (Appendix 3—figure 1c). For the large birds and herbivore vombatiformes, biomass increased with body mass (Appendix 3—figure 1c).

Appendix 3—figure 1

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Mass-predicted abundance and biomass for the 21 modelled species.

(a) Stable population sizes (N_stable) for each modelled species predicted from allometric estimates of population density for a 500 km × 500 km (250,000 km²) landscape (outline-only bars = macropodiformes; solid bars = vombatiforms; angled crosshatching = birds; vertical crosshatching = carnivores; brick crosshatching = monotremes); (b) N_stable plotted against body mass (M, in kg), showing the allometric scaling separating the vombatiform herbivores (green)/macropodiformes (brown), flightless birds (blue), carnivores (red), and monotremes (grey); (c) predicted landscape biomass (N_stable × M) for each species (outline-only bars = macropodiformes; solid bars = vombatiforms; angled crosshatching = birds; vertical crosshatching = carnivores; brick crosshatching = monotremes).

Here, we have depicted *Sarcophilus* as ‘extant’, even though it went extinct on the mainland >3000 years ago.

Appendix 4

Compensatory density feedback

To avoid an exponentially increasing population without limit generated by a transition matrix optimized to produce values as close to r_m as possible, we applied a theoretical compensatory density-feedback function. This procedure ensures that the long-term population dynamics were approximately stable by creating a second logistic function of the same form as m_x to calculate a modifier (S_mod) of the S_x vector according to total population size (Σn):

S_{mod} = \frac{a}{1 + {(\frac{\sum n}{b})}^{c}}

We adjusted the a, b, and c constants for each species in turn so that a stochastic projection of the population remained stable on average for 40 generations ( $40 ⌊ G ⌉$ ), where:

G = \frac{\log ({(v^{T} M)}_{1})}{λ_{1}}

and (v^TM)₁ = the dominant eigen value of the reproductive matrix R derived from M, and v = the left eigenvector (Caswell, 2001) of M. Although arbitrary, we chose a $40 ⌊ G ⌉$ projection time as a convention of population viability analysis to standardize across different life histories (Brook et al., 2006; Traill et al., 2010).

The projections were stochastic in that we β-resampled the S_x vector assuming a 5% standard deviation of each S_x and Gaussian-resampled the m_x vector at each yearly time step to $40 ⌊ G ⌉$ . We also added a catastrophic die-off function to account for the probability of catastrophic mortality events (C) scaling to generation length among vertebrates (Reed et al., 2003):

C = \frac{p_{C}}{G}

where p_C = probability of catastrophe (Reed et al., 2003) (set at 0.14). Once invoked at probability C, we applied a β-resampled proportion centred on 0.5 to the β-resampled S_x vector to induce a ~ 50% mortality event for that year (Bradshaw et al., 2013), as we assumed that a catastrophic event is defined as “… any 1 yr peak-to-trough decline in estimated numbers of 50% or greater” (Reed et al., 2003). Finally, for each species we rejected the first $⌊ G ⌉$ years of the projection as a burn-in to allow the initial (deterministic) stable stage distribution to stabilize to the stochastic expression of stability under compensatory density feedback.

Appendix 5

Deriving marsupial correction factors for fecundity (F) and age at first breeding (α)

Given that allometric predictions of various life-history traits in mammals are based primarily on data from extant placentals, we investigated the degree of potential bias in our estimates of longevity (ω) fecundity, (F) and age at first breeding (α) based on a comparison of theoretical and observed data for extant marsupials. We discuss the bias correction ω for in the main text for the vombatiform and macropodiform herbivores (see Appendix 2), so here we report how we derived group-specific corrections for F and α.

Fertility (F) correction

We first collected adult female mass, inter-birth interval (I_b), and age at first breeding data for twenty-three extant species in the database compiled by Fisher et al., 2001. We included all species for which data were listed in the genera: Macropus (Osphranter and Notamacropus), Dorcopsis, Lagorchestes, Petrogale, Thylogale, and Wallabia. We excluded the genus Dendrolagus (tree kangaroos) because they represent a distinct clade and differ ecologically from most other macropodids. We also excluded the tammar wallaby (Macropus eugenii) because it is a strongly seasonal-breeding species that potentially strong leverage on estimating the allometric slope.

To correct F, we first examined the relationship between I_b and body mass (M) for these species (Appendix 5—figure 1):

We therefore concluded that there was sufficient evidence for an allometric relationship between the two variables for this group, which we used to project the degree to which F was overestimated by the allometric relationship (Equation 10) used to estimate F for the extinct macropodidiform herbivores. Using the intercept and slope estimated in the relationship shown in Appendix 5—figure 1, we predicted an inter-birth interval of 384 days for Procoptodon, 363 days for Sthenurus, 357 days for Protemnodon, 354 days for Simosthenurus, and 322 days for Metasthenurus. These changed the allometrically predicted F by −4.9%, +0.5%, +2.2%, +3.1%, and +13.3%, respectively (corrected F shown in Appendix 2—table 1).

Appendix 5—figure 1

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Relationship between the logarithm of adult female body mass (M, kg) and inter-birth interval (I_b, in days) for 23 extant macropodid herbivores (Fisher et al., 2001).

The estimated parameters of the linear fit (y ~ *α + βx*) are: α = 159.3 ± 31.5 days (± SE) and β = 93.6 ± 36.4, explaining 24.2% of the variation ( $R_{adj}^{2}$ ), with the information-theoretic evidence ratio (ER) of the slope *versus* intercept-only model = 11.0.

For the vombatiforms, there are only four extant phascolarctid (koala Phascolarctos cinereus) and vombatiform herbivores (common or bare-nosed wombat Vombatus ursinus, northern hairy-nosed wombat Lasiorhinus krefftii, and southern hairy-nosed wombat L. latifrons). There were not enough species to estimate an allometric relationship that might predict the expected I_b for extinct vombatiform herbivores, so instead we assumed that the extinct vombatiform herbivores we considered would scale allometrically relative to Vombatus ursinus, which has a measured I_b of 730 days (Fisher et al., 2001). For this, we assumed the same slope as measured for the extant macropodiforms (Appendix 5—figure 1), and an intercept that aligned Vombatus with the relationship (Appendix 5—figure 2):

Appendix 5—figure 2

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Relationship between the logarithm of adult female body mass (M, kg) and inter-birth interval (I_b, in days) for four large, extant phoscolarctid and vombatiform herbivores (black circles): koala *Phascolarctos cinereus*; common wombat *Vombatus ursinus*; northern hairy-nosed wombat *Lasiorhinus krefftii*; southern hairy-nosed wombat *L. latifrons*.

Shown is the assumed relationship between I_b and log₁₀M setting the slope to that estimated for the extant macropodiforms (β = 93.6; Appendix 5—figure 1) and an intercept that aligned with *Vombatus* (α = 599 days) to estimate the inter-birth interval for the four extinct vombatiform herbivores (red circles) considered in this analysis.

Age at first breeding (α) correction

Next, we estimated the bias in the predicted age at first breeding (α) for the macropodiforms. A similar correction for the vombatiform herbivores was not warranted given that the allometric predictions were close to expectation for placentals of similar mass (see main text). We plotted α predicted from the allometric Equation 12 against those observed from the marsupial database (Fisher et al., 2001) for the extant macropodiform species as described above for F (Appendix 5—figure 3):

Appendix 5—figure 3

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Relationship between the predicted age (years) at first breeding (α_pred) and observed α (α_obs) for 23 extant macropodid herbivores (Fisher et al., 2001).

The allometric prediction over-estimated α by and average of ~20%. Also shown is the 1:1 line (dashed).

Calculating the average disparity between the predicted and observed α across species, the allometric prediction over-estimated α by 20% for the macropodiform herbivores. We therefore applied this correction factor to the estimated α for the extinct macropodiform species (corrected values in Appendix 2—table 1). The corrected α predicted maximum rate of population growth (r_m) approximately following theoretical expectation (see Appendix 5—figure 4).

Appendix 5—figure 4

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Negative relationship between the logarithm of the maximum rate of intrinsic population growth (log₁₀r_m) and the logarithm of the age at primiparity (log₁₀α) for the 21 species examined.

The estimated parameters of the linear fit (y ~ *α + βx*) including all species (black lines) are: α = -0.388 ± 0.075 (± SE) and β = -0.931 ± 0.146, and explaining 66.4% of the variation ( $R_{adj}^{2}$ ), with the information-theoretic evidence ratio (ER) of the slope *versus* intercept-only model = 4.052×10⁴. This relationship is similar to the theoretical expectation for the intercept = -0.15 and slope = -1.0 for mammals (Hone et al., 2010). Birds (*Alectura*, *Dromaius*, *Genyornis*; ◆) potentially fall outside this relationship, so just considering the remaining mammals (●), the parameters for the linear fit (red lines) become: α = -0.388 ± 0.083 (± SE), β = -0.875 ± 0.170, $R_{adj}^{2}$ = 60.1%, and ER = 1.567×10³.

Appendix 6

Quasi-extinction curves for each species in each of the six perturbation scenarios

Appendix 6—figure 1

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Increasing probabilities of quasi-extinction — quasi-extinction probability: Pr(E_q) — as a function of (a) decreasing juvenile survival (Scenario ↓S_j), (b) decreasing fertility (Scenario ↓F), (c) decreasing survival across all age classes (Scenario ↓S), (d) increasing number of individuals removed year⁻¹ (Scenario ↓ind), (e) increasing frequency of catastrophic die-offs per generation (Scenario ↑cat_f), and (f) increasing magnitude of catastrophic die-offs (Scenario ↑cat_M).

Species notation: DP = *Diprotodon optatum*, PA = *Palorchestes azael*, ZT = *Zygomaturus trilobus*, PH = *Phascolonus gigas*, VU = *Vombatus ursinus* (vombatiform herbivores); PG = *Procoptodon goliah*, SS = *Sthenurus stirlingi*, PT = *Protemnodon anak*, SO = *Simosthenurus occidentalis*, MN = *Metasthenurus newtonae*, OR = *Osphranter rufus*, NR = *Notamacropus rufogriseus* (macropodiformes); GN = *Genyornis newtoni*, DN = *Dromaius novaehollandiae*, AL = *Alectura lathami* (large birds); TC = *Thylacoleo carnifex*, TH = *Thylacinus cynocephalus*, SH = *Sarcophilus harrisii*, DM = *Dasyurus maculatus* (carnivores); TA = *Tachyglossus aculeatus*, MR = *Megalibgwilia ramsayi* (monotreme invertivores). Here, we have depicted SH as ‘extant’, even though it went extinct on the mainland >3000 years ago.

Appendix 7

Comparing increasing mortality in juveniles and increasing offtake of juvenile individuals

Appendix 7—figure 1

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Appendix 8

Sensitivity analysis of extinction dates using a jack-knife approach based on the Gaussian-Resampled, Inverse-Weighted McInerny (GRIWM) Signor-Lipps correction method

To test the sensitivity of our conclusion to uncertainty in the dates of final extinction for each taxon taken from Saltré et al., 2016 and White et al., 2018, we adapted the Gaussian-resampled, inverse-weighted McInerny (GRIWM) approach (Bradshaw et al., 2012; Saltré et al., 2015) 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. We applied this new approach to the following taxa: Diprotodon, Genyornis, Megalibgwilia, Metasthenurus, Palorchestes, Phascolonus, Procoptodon, Protemnodon, Sismosthenurus, Sthenurus, Thylacoleo, and Zygomaturus (but we kept the original dates for Sarcophilus and Thylacinus).

GRIWM does not depend on distributional assumptions given that it is a resampling approach (Saltré et al., 2015), even if it often provides wider uncertainty windows (especially for older dates) compared to other approaches. 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 (Appendix 8 figures 1–3). However, the net effect when we related these new estimates to any of the different risk metrics — mass (Appendix 8—figure 1a), generation length (Appendix 8—figure 1b), vital rate-specific extinction risk (Appendix 8—figure 2), or median susceptibility rank (Appendix 8—figure 3) — was to maintain our general conclusion of no relationship with the chronology.

Appendix 8—figure 1

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Relationship between estimated date of species extinction (across the entire continent) based on a jack-knifed GRIWM approach (Bradshaw et al., 2012; Saltré et al., 2015) and (a) body mass (kg) or (b) generation length (years) (Scenario LH).

Extinction-timing windows are estimated based on the agreement among six different models that correct for the Signor-Lipps effect (described in Materials and methods) in chronologies of quality-rated (Rodríguez-Rey et al., 2015) fossil dates for the studied taxa described in Peters et al., 2019. Here, we have depicted *Sarcophilus* as ‘extant’, even though it went extinct on the mainland >3000 years ago. Also shown are the approximate major climate periods and transitions: Marine Isotope Stage 5 (MIS 5), MIS 4, MIS 3, MIS 2 (including the Last Glacial Maximum, Antarctic Cold Reversal, and Younger Dryas), and the Holocene (including the period of sea level flooding when Tasmania separated from the mainland, and the relatively warm, wet, and climatically stable Holocene optimum).

Appendix 8—figure 2

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Relationship between estimated date of species extinction (across the entire mainland) based on a jack-knifed GRIWM approach (Bradshaw et al., 2012; Saltré et al., 2015) and area under the quasi-extinction curve (from Fig. S7) — extinction risk: ∫Pr(E_q)d(x) — for (a) (Scenario ↓S_j) decreasing juvenile survival, (b) (Scenario ↓F) decreasing fertility, (c) (Scenario ↓S) decreasing survival across all age classes, (d) (Scenario ↓ind) increasing number of individuals removed year⁻¹, (e) (Scenario ↑cat_f) increasing frequency of catastrophic die-offs per generation, and (f) (Scenario ↑cat_M) increasing magnitude of catastrophic die-offs.

Extinction-timing windows are estimated based on the agreement among six different models that correct for the Signor-Lipps effect (described in Materials and methods) in chronologies of quality-rated (Rodríguez-Rey et al., 2015) fossil dates for the studied taxa described in Peters et al., 2019.

Appendix 8—figure 3

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(a) Sum of the areas under the quasi-extinction curve for each of the six scenarios considered — total extinction risk: Σ∫Pr(E_q)d(x) — for each of the 21 modelled species (^†extinct; ^♀extant; scenario abbreviations: ↓S_j = reducing juvenile survival; ↓F = reducing fertility; ↓S = reducing survival; ind = reducing number of individuals; ↑cat_f = increasing frequency of catastrophe; ↑cat_M = increasing magnitude of catastrophe); (b) median susceptibility rank across the six scenarios considered (where higher ranks = higher susceptibility to extinction) for each species (red = extinct; black = extant; outline-only bars = macropodiformes; solid bars = vombatiforms; angled crosshatching = birds; vertical crosshatching = carnivores; brick crosshatching = monotremes); (c) median susceptibility rank as a function of log₁₀ generation length (G, kg) — there was a weak correlation including all species (solid grey line 1), but a strong relationship after removing *Sarcophilus* and *Dromaius* (dashed grey line 2) (information-theoretic evidence ratio [ER] and variance explained [R²] shown for each); (d) estimated date of species extinction (across entire continent) as a function of median susceptibility rank; taxonomic/functional groupings are indicated by colored symbols and convex hulls (macropodids: brown; monotremes: grey; vombatiforms: green; birds: blue; carnivores: red).

Extinction-timing windows are on a jack-knifed GRIWM approach (Bradshaw et al., 2012; Saltré et al., 2015) that corrects for the Signor-Lipps effect in chronologies of quality-rated (Rodríguez-Rey et al., 2015) fossil dates for the studied taxa described in Peters et al., 2019.

Appendix 9

Comparing demographic susceptibility to climate variation

Failing to observe any relationship between overall demographic susceptibility and the extinction chronology for Sahul, we might alternatively expect species’ demographic susceptibility to align with increasing environmental stress expressed as hotter, drier climates ( Saltré et al., 2019). We therefore hypothesized that the most extreme (hottest/driest) climates of the past would eventually drive the most-resilient species to extinction, which would manifest as a negative relationship between demographic susceptibility and warming/drying conditions (i.e., only when conditions became bad enough did the least-susceptible species succumb).

To test this hypothesis, we compiled climate indices hindcasted for the estimated extinction windows for the species we considered here. To this end, we acquired four hindcasted, continentally averaged climate variables from the intermediate-complexity, three-dimensional, Earth-system model known as LOVECLIM (Goosse et al., 2010; Timmermann and Friedrich, 2016). LOVECLIM hindcasts various climatic conditions by incorporating representations of the atmosphere, ocean and sea ice, land surface (including a vegetation submodel), ice sheets, and the carbon cycle. These variables were — mean annual temperature (°C), mean annual precipitation (mm), net primary production (kg C m⁻² year⁻¹), and fraction of the landscape designated as ‘desert’ — all expressed as anomalies of their respective average values calculated relative to 120 ka (i.e., a time when all species we considered were extant). We downscaled the original spatial resolution of LOVECLIM (5.625°×5.625°) to an output resolution of 1°×1° using bilinear interpolation because it retains the integrity and limitations of the original model output data.

We then calculated the information-theoretic evidence ratios (ER) for all relationships between the mean value of the climate variable across the entirely of Sahul and the sum of the extinction integrals across scenarios as the Akaike’s information criterion (AIC) of the slope model: y = α + βx divided by the AIC of the intercept-only model: y = α (i.e., ER_mean = AIC_slope/AIC_intercept). To incorporate full uncertainty in the climate variables (y), we developed a randomization test where we uniformly resampled the y values between y_min and y_max, estimating the residual sum of squares of the resampled values at each iteration compared to a randomized order of these residuals. We then calculated the probability (p_u) of producing a randomly generated relationship between the climate variable and the sum of the extinction integrals as the number of iterations when the randomized order produced a residual sum of squares ≤ the residual sum of squares of the resampled (ordered) climate variables divided by the total number of iterations (10,000).

When we plotted the four climate variables against the sum of the quasi-extinction integrals across scenarios, there was evidence for a weak, negative relationship between mean annual precipitation anomaly and relative extinction susceptibility (ER_mean = 9.6; Appendix 9—figure 1b), and a weak, positive relationship with desert-fraction anomaly (ER_mean = 4.2; Fig. Appendix 9—figure 1d). There was no evidence for a relationship between the means for temperature (Appendix 9—figure 1a) and net primary production (Appendix 9—figure 1c) anomalies and quasi-extinction integrals. The relationship with desert fraction supports the hypothesis that a drier (more desert-like) environment might have been related to extinction susceptibility. However, when we took full uncertainty of the climate variables into account in a randomization least-squares regression, none of the relationships could not be differentiated from a random process (p_u >0.17; Appendix 9—figure 1).

Appendix 9—figure 1

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Sum of the areas under the quasi-extinction curve over the six scenarios considered — total extinction risk: Σ∫Pr(E_q)d(x) — for each of the 13 extinct (mainland only) modelled species relative to (a) mean annual temperature anomaly (°C): information-theoretic evidence ratio of the slope model relative to the intercept-only model (ER_mean) = 0.75 for the mean climate values; probability of a non-random slope relationship incorporating full uncertainty in the climate variable p_u = 0.603.

(b) mean annual precipitation anomaly (mm): ER_mean = 9.6; p_u = 0.461. (c) net primary production anomaly (kg C m⁻² year⁻¹): ER_mean <0.01; p_u = 0.411. (d) desert fraction anomaly: ER_mean = 4.2; p_u = 0.425. The dashed red line in panels a, b, and d indicate evidence for a slope model versus the intercept-only model for these variables (ER_mean >2). Error bars indicate ±1 standard deviation.

The lack of evidence for a relationship between extinction susceptibility and warming/drying conditions for the mean climate conditions across the continent contrasts with recent evidence that water availability potentially exacerbated mortality from novel human hunting (Saltré et al., 2019). However, there is too much uncertainty in the climate hindcasts to test this hypothesis definitively. Another weakness of this approach is that we were obliged to take continental-scale averages of average climate conditions, which obviously ignores spatial complexity previously established as an important element in explaining the chronology and directionality of megafauna extinctions, at least in south-eastern Sahul (Saltré et al., 2019). Thus, this enticing, but still unsupported hypothesis that warming and drying conditions were related to intrinsic extinction susceptibility, will require more precise estimates of extinction timing and hindcasted climate conditions, and perhaps greater sample sizes across more species.

Data availability

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

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Decision letter

Jessica C Thompson

Reviewing Editor; Yale University, United States
George H Perry

Senior Editor; Pennsylvania State University, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

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.

https://doi.org/10.7554/eLife.63870.sa1

Author response

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:

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:

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https://doi.org/10.7554/eLife.63870.sa2

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Author details

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
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
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
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
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
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

Funding

Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage (CE170100015)

Corey JA Bradshaw
Christopher N Johnson
Vera Weisbecker

Australian Research Council (DP170103227)

Vera Weisbecker

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was supported by the Australian Research Council through a Centre of Excellence grant (CE170100015) to CJAB, CNJ, and VW, and a Discovery Project grant (DP170103227) to VW. We acknowledge the Indigenous Traditional Owners of the land on which Flinders University is built — the Kaurna people of the Adelaide Plains.

Senior Editor

George H Perry, Pennsylvania State University, United States

Reviewing Editor

Jessica C Thompson, Yale University, United States

Version history

Received: October 9, 2020
Accepted: March 29, 2021
Accepted Manuscript published: March 30, 2021 (version 1)
Version of Record published: April 13, 2021 (version 2)

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This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Corey JA Bradshaw
Christopher N Johnson
John Llewelyn
Vera Weisbecker
Giovanni Strona
Frédérik Saltré

(2021)

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

eLife 10:e63870.

https://doi.org/10.7554/eLife.63870

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Description of five dominant mechanisms by which megafauna could have been driven to extinction and the associated seven perturbation scenarios examined.

Relationship between estimated date of species extinction (across the entire continent) and (a) body mass (M, kg) or (b) generation length (G, years) (Scenario LH).

Inset: Increasing extinction risk for birds — quasi-extinction probability: Pr(Eq) — as a function of increasing the mean number of eggs harvested per female per year (Scenario ↓Fe).

Extinction susceptibility of the 21 modelled species.

Histogram of log10 adult body masses (in kg) for the 21 species examined.

Predicted demographic values for each species (equations provided in Materials and methods and this appendix).

Kendall’s rank correlation (τ) among demographic values given in Appendix 2—table 1 across species.

Mass-predicted abundance and biomass for the 21 modelled species.

Relationship between the logarithm of adult female body mass (M, kg) and inter-birth interval (Ib, in days) for 23 extant macropodid herbivores (Fisher et al., 2001).

Relationship between the predicted age (years) at first breeding (αpred) and observed α (αobs) for 23 extant macropodid herbivores (Fisher et al., 2001).

Negative relationship between the logarithm of the maximum rate of intrinsic population growth (log10rm) and the logarithm of the age at primiparity (log10α) for the 21 species examined.

Increasing probabilities of quasi-extinction — quasi-extinction probability: Pr(Eq) — as a function of (a) decreasing juvenile survival (Scenario ↓Sj), and (b) increasing number of juvenile individuals removed year−1 (Scenario ↓indj).

Relationship between estimated date of species extinction (across the entire continent) based on a jack-knifed GRIWM approach (Bradshaw et al., 2012; Saltré et al., 2015) and (a) body mass (kg) or (b) generation length (years) (Scenario LH).

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Corey JA Bradshaw

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For correspondence

Competing interests

Christopher N Johnson

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Competing interests

John Llewelyn

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Competing interests

Vera Weisbecker

Contribution

Competing interests

Giovanni Strona

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Competing interests

Frédérik Saltré

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Competing interests

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Inset: Increasing extinction risk for birds — quasi-extinction probability: Pr(E_q) — as a function of increasing the mean number of eggs harvested per female per year (Scenario ↓F_e).

Histogram of log₁₀ adult body masses (in kg) for the 21 species examined.

Relationship between the logarithm of adult female body mass (M, kg) and inter-birth interval (I_b, in days) for 23 extant macropodid herbivores (Fisher et al., 2001).

Relationship between the predicted age (years) at first breeding (α_pred) and observed α (α_obs) for 23 extant macropodid herbivores (Fisher et al., 2001).

Negative relationship between the logarithm of the maximum rate of intrinsic population growth (log₁₀r_m) and the logarithm of the age at primiparity (log₁₀α) for the 21 species examined.

Increasing probabilities of quasi-extinction — quasi-extinction probability: Pr(E_q) — as a function of (a) decreasing juvenile survival (Scenario ↓S_j), and (b) increasing number of juvenile individuals removed year⁻¹ (Scenario ↓ind_j).