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Figure 2

Patch characteristics for NPC with (N=20). (a) individual patch connectivity versus normalized patch area parameter (beta _i). (b) pair-wise connectivity for the homogeneous patch case with (beta _i=0.5) (forall) i, versus normalized inter-patch distances (dfrac{D_{ij}}{L}) (forall) (i ne j).

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In the following section, we start by discussing some important characteristics of NPC. In “Homogeneous patches” section, we look at the system behavior for homogeneous patch parameters and contrast between different connectivity modes, namely (1) all-to-all connected, (2) fixed NBM, and (3) rewiring NBM. We study the homogeneous patch case first to focus on the influence of NPC on system dynamics without any confounding effects of patch heterogeneity. In “Heterogeneous patches” section, we discuss some general results for NBMs with heterogeneous patches, expressed through different patch carrying capacities.
NPC characteristics
For the case of (N=20) patches, individual patch (C_i) and pair-wise patch (C_{ij}) connectivity estimates are shown as functions of patch areas (beta _i) and inter-patch distances (D_{ij}), in Fig. 2a,b respectively. We observe a nearly linear growth in (C_i) as a function of patch areas in Fig. 2a, and a nonlinear (exponential) decrease in (C_{ij}) as a function of inter-patch distances in Fig. 2b. These results nicely illustrate the essential features of the NPC as formulated in Eq. (3): (a) larger patches have more incoming/outgoing connections as compared to smaller patches, and (b) closely located patches connect more frequently as compared to distant ones.
Homogeneous patches
The influence of dispersal rate d on species persistence are investigated and compared for the (1) all-to-all connected model, (2) fixed NBM, and (3) rewiring NBM. For uniformity in comparison, we consider the dispersal efficiency parameter (delta =1) in the following “Probability of persistence” and “Influence of patch density and network rewiring rates on persistence” sections.
Probability of persistence
Figure 3

Persistence probabilities (P_{mathrm{per}}) as a function of dispersal rate d, for different connectivity configurations with (N=20) and dispersal efficiency (delta =1). Curves: all-to-all connected (black), fixed NBM (green), and rewiring NBM (red) (rewiring every 100 time units). The ensemble sizes (number of network realizations) for these calculations are fixed at (N_{text{ensemble}}=100) for all three cases. Standard error values (mathrm{SE}(P_{mathrm{per}})), for NBM calculations are highlighted by error bars every four data points to avoid graph overcrowding. Corresponding biomass calculations can be seen in the Online Appendix Fig. A.1.

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Figure 3 highlights the influence of NPC on the system dynamics for the (N=20) case. For the (1) deterministic all-to-all connected case, species persistence (P_{{rm per}}=1) in the entire d range, and is mathematically expected to stay at the same value for arbitrarily high (d rightarrow infty) values—which is extremely counter intuitive considering the physical bounds and limitations in natural systems. Notably for an all-to-all connected system with (delta =1), the second term in Eq. (1) vanishes due to the parameter symmetry and the diffusive mean field nature of the term. Consequently, the dynamics of this system is essentially independent of the dispersal rate d, and therefore not affected by any changes in d. Hence in this case, local populations in each patch independently follow their logistic growth, and settle on the respective carrying capacity (K_i = K) (forall) i—which constitutes the stable equilibrium for the system. On the contrary, for the other two cases of (2) fixed and (3) rewiring NBMs, we observe decreasing (P_{mathrm{per}}) estimates for increasing d values. These results suggest that several network realizations in the NPC ensembles can lead to species extinction with higher dispersal rates in the NBMs. Furthermore, for rewiring NBM, (P_{mathrm{per}}) approaches very low values for higher d, before exhibiting species extinction (P_{mathrm{per}}=0) for (d approx 28) (not shown). On the contrary, we do not observe species extinction (P_{mathrm{per}}=0) for quite high d values for the fixed NBM case. Please see Online Appendix: Fig. A.1 for the corresponding average biomass calculations.
Influence of patch density and network rewiring rates on persistence
Figure 4

Persistence probabilities (P_{mathrm{per}}) as a function of the dispersal rate d for (a) fixed, and (b) rewiring (every 100 time units) NBMs. (N_{text{ensemble}}=100) network realizations were used for the simulations, for (N=10) (blue), (N=20) (red) and (N=40) (black) network sizes. Standard error values (mathrm{SE}(P_{mathrm{per}})) for these calculations are highlighted by error bars every four data points. Related biomass calculations can be seen in Online Appendix Fig. A.2.

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Figure 4 shows the relationship between species persistence probability (P_{mathrm{per}}), and dispersal rate d for fixed and rewiring NBMs. Since the value of largest possible distance in the system (L) is fixed (fixed landscape) for all calculations, an increase in number of patches N corresponds to an increase in patch density in the meta-population. The results suggest that a higher patch density supports species persistence in the system for higher dispersal rates d. For (N=10), (P_{mathrm{per}} > 0) (non-zero) for the fixed NBM case over the entire investigated range of d, extending to even higher d values (not shown here). This implies that there are always some network realizations in the ensemble that support species persistence. In contrast, the species go extinct at (d approx 5) for the rewiring NBM case. For (N=20), both fixed and rewiring NBMs yield non-zero (P_{mathrm{per}}) estimates in the investigated d range. However we observe a steeper decrease in persistence with increasing d, and consequently, extinction for (d approx 28) (not shown) for the rewiring NBM. For higher number of patches: (N=40), (P_{mathrm{per}} approx 1) for the entire d range for both fixed and rewiring NBMs, and this behavior extends to even higher d values above the range shown in Fig. 4. Our calculations suggest an exponential growth in the total number of connections as a function of number of patches N for a fixed landscape, and hence for increasing patch densities (see Online Appendix B, Fig. B.1). Therefore, we can conclude that higher patch densities tend to shift the (P_{mathrm{per}}) behavior closer to a highly connected case, which yields (P_{mathrm{per}}) results similar to the all-to-all connected situation. Overall, (N=20) seems to provide an appropriate trade-off between a system which is neither too sparse, nor a densely filled landscape, where a sparse system might lead to a higher proportion of isolated patches, and a dense system can mask the effects of spatially explicit connectivity. Therefore, we use (N=20) as the standard meta-population size in most of our calculations for the given set of parameter values.
For the case of rewiring NBMs, persistence probability (P_{mathrm{per}}) is also influenced by the rewiring time intervals of the connectivity matrix. Results in Fig. 5 compare the (P_{mathrm{per}}) estimates for fixed, and rewiring NBMs with different rewiring rates—every 10 and 100 time units. These results show that a faster rewiring NBM promotes species persistence in comparison to the fixed and slower rewiring NBMs, for a range of dispersal rates (d in (5,10))—a faster rewiring provides higher (P_{mathrm{per}}) estimates in this d range. However, the average biomass estimates for all these three cases are quite similar in this dispersal rate range (Online Appendix: Fig. A.3). However, for higher d values, species persistence for both rewiring NBMs is lower compared to the fixed NBM. Eventually, the rewiring NBMs exhibit species extinction for higher d, whereas, the fixed NBM still yields non-vanishing (P_{mathrm{per}}) estimates (not shown).
Figure 5

(P_{mathrm{per}}) estimates for (N=20), as a function of dispersal rate d, and (mathrm{SE}(P_{mathrm{per}})) are highlighted by error bars every four data points, for identical patches with fixed (black) versus different rewiring rates—every 10 (green) and 100 (red) time units. (N_{text{ensemble}}=100) network realizations were used for these calculations. Corresponding biomass estimates are provided in Online Appendix Fig. A.3.

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To better understand the mechanism of species extinction in the meta-population, we need to focus on the critical role of the between-patch interaction (second) term in Eq. (1), i.e. (-dleft( x_i-dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_jright)). As mentioned before, this term assumes that the dispersal process is diffusive in nature, and the diffusion occurs along a gradient from higher to lower values. With this assumption, the following situations can occur for any (d >0): (1) average input from other patches is higher than the patch population, i.e. (dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j > x_i) (implies) (dleft( dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j – x_iright) >0), the interaction term is positive implying an increase in the number of individuals within the patch via dispersal, i.e. net species movement is directed into the patch due to the population gradient, (2) average input from other patches is less than the patch population, i.e. (x_i >dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j) (implies) (dleft( dfrac{delta _i}{k_{mathrm{in}}^{i}}sum nolimits _{j=1}^{N} A_{ij} x_j – x_iright) 0), the underlying NPC has a strong influence on the observed dynamics. The underlying connectivity matrix can lead to a situation where some patches have no incoming connections—such a situation is more likely in a sparsely connected network and/or for a network with low patch density. Absence of any incoming connections will lead to case (3) as discussed above, implying to net loss in the local biomass. Consequently, local populations will go extinct in these source-only patches once the dispersal rate is higher than the species growth rate for case (3). This extinction will decrease the biomass flux from these patches to the connected sink patches. With increasing dispersal rates, this will lead to case (2) for these sink patches and eventually they will also experience local extinction, thereby, enabling a cascade which leads to the extinction of the entire meta-population. In terms of bifurcation analysis, this extinction corresponds to a transcritical bifurcation (stability exchange) between equilibria with species persistence and extinction. An important point to consider is that for a fixed landscape, different NPC realizations can give rise to completely different persistence/extinction scenarios. Considering an NPC realization where all patches have at least one incoming connection, and another realization where one/some patches have no incoming connections (source-only case) or are isolated, the described mechanism indicates that species can persist for comparatively higher d values in the former case, as compared to the latter.
Figure 6

(i) (P_{mathrm{per}}) projection on the ((d,delta )) plane for homogeneous patches, and (ii) for the heterogeneous patch case. [(a.i),(a.ii)] correspond to all-to-all connected system, [(b.i),(b.ii)] to fixed NBM, and [(c.i),(c.ii)] to rewiring NBM with a rewire every 100 time units. The blue shaded area corresponds to meta-population extinction, i.e. (P_{mathrm{per}}=0), whereas red regimes correspond to (P_{mathrm{per}}=1). The boundary between persistence and extinction for the all-to-all connected system is indicated by a yellow curve which for comparison, is indicated in all panels. (N_{text{ensemble}}=100) for these calculations. Corresponding biomass calculations are shown in Online Appendix Fig. A.4.

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Figure 7

(i) (mathrm{SE}(P_{mathrm{per}})) projection on the ((d,delta )) plane for homogeneous patches, and (ii) for the heterogeneous patch case. [(a.i),(a.ii)] correspond to all-to-all connected system, [(b.i),(b.ii)] to fixed NBM, and [(c.i),(c.ii)] to NBM rewiring every 100 time units. The blue shaded area corresponds to regimes where the error is zero. Positive values are highlighted by colors as per the attached color bar. The boundary between persistence and extinction for the all-to-all connected system is again indicated by a yellow curve for comparison in all the panels. (N_{text{ensemble}}=100) for these calculations. The dynamical differences between all-to-all connected, and NBMs are even more obvious in these calculations.

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Dependence of persistence patterns on dispersal rate and efficiency
So far, the results were presented for cases with a dispersal efficiency (delta _i = delta = 1). However, an assumption of 100% efficiency of dispersal is unrealistic. Losses during the dispersal process are likely to occur in natural systems and there is a chance of the species not establishing after arrival in a new patch. To take these losses into account, we introduced (delta) in our system Eq. (1). In the following, we investigate species persistence for different connectivity scenarios for (delta in [0,1]), and (d in [0,20]). (P_{mathrm{per}}) ((mathrm{SE}(P_{mathrm{per}}))) behaviors for the homogeneous case are shown in the top row of Fig. 6 (Fig. 7), for (a.i) all-to-all connected, (b.i) fixed NBM, and (c.i) rewiring NBM, respectively.
Starting with the all-to-all connected case, we do not observe any transitions to (P_{mathrm{per}}=0) for (delta =1). The reason is related to parameter symmetries leading to a vanishing interaction term in Eq. (1) as discussed before. Therefore, the species in their respective patches can grow to the patch carrying capacities (K_i = K) (forall) i—see also the average biomass estimates in Online Appendix Fig. A.4. On considering dispersal efficiency (delta < 1), the interaction term in Eq. (1) does not vanish, and therefore, species extinction can arise, as seen in Fig. 6(a.i), where red (blue) areas correspond to persistence probabilities of one (zero). Linear stability analysis of the extinction equilibrium, (mathbf{x} ^*=mathbf{0} implies (x_1^*=0,x_2^*=0,ldots , x_N^*=0)^T), and any general (N ge 2) yields the following eigenspectrum, $$begin{aligned} begin{aligned} lambda _i={left{ begin{array}{ll}(r-d) + ddelta , &{}quad i=1 (largest) \ (r-d)-dfrac{ddelta }{(N-1)} &{}quad i=2,ldots ,N. end{array}right. } end{aligned} end{aligned}$$ (4) Note that these eigenvalues (lambda _i)s are independent of patch carrying capacities, therefore the persistence–extinction boundary is unaffected by parameter mismatch in the carrying capacities. Using the largest eigenvalue (lambda _1), one can follow the transition boundary of the transcritical bifurcation between persistence and extinction in the ((d,delta )) plane, which satisfies the expression (lambda _1 = (r-d) + ddelta =0). This boundary (yellow curve) is highlighted in Fig. 6(a.i) and is in excellent agreement with the theoretical estimates of this transition boundary (see Online Appendix Fig. A.4 for biomass calculations). Additionally, corresponding (mathrm{SE}(P_{mathrm{per}})) calculations in Fig. 7(a.i) show that the error values for the homogeneous all-to-all connected case are uniformly vanishing in the entire considered ((d,delta )) plane. This is due to the behavior of (P_{mathrm{per}}) which is 1 before the bifurcation boundary and 0 after the bifurcation leading to meta-population extinction. For the spatially explicit Fig. 6(b.i) fixed NBM, and Fig. 6(c.i) rewiring NBM cases, we observe that the persistence–extinction transition boundary is not as sharp as for the all-to-all connected case. For comparison, the transition boundary for the all-to-all case is indicated by a yellow curve in these figures. For the fixed NBM case, starting from lower (delta) and d values, the transition becomes more uncertain as we increase the value of (delta)—which can be seen by the presence of lighter colored regimes corresponding to low, non-zero persistence probabilities. This ambiguity between persistence and extinction increases even further for higher (delta). These results suggest that for high (delta), and d values, species extinction is possible for the fixed NBM case contrary to the all-to-all connected network, where extinction is impossible in the similar parameter regime. The reasoning behind these results is quite straightforward considering how (P_{mathrm{per}}) is estimated. For high (delta) and d values, there is a proportion of connectivity configurations which lead to species extinction following the mechanism discussed in “Influence of patch density and network rewiring rates on persistence” section. Due to these configurations, we obtain (0< P_{mathrm{per}} < 1) in this case. This effect is even more pronounced for the rewiring network, where the blue (extinction) regimes extend to even higher (delta) and d values, thereby comparatively reducing the regimes of persistence in the parameter space. These results are quite contrary to the all-to-all connected system where the persistence is ensured with (P_{mathrm{per}} = 1) along the entire range of d values for high (delta). The dynamical differences between the all-to-all connected and NBMs are even more conspicuous in the corresponding (mathrm{SE}(P_{mathrm{per}})) calculations, see Fig. 7(b.i),(c.i). For the parameter regimes where (P_{mathrm{per}} = 1 (0)), the (mathrm{SE}(P_{mathrm{per}})) values are either zero or very small. For (0< P_{mathrm{per}} < 1), (mathrm{SE}(P_{mathrm{per}})) exhibit higher values implying a higher variability in the (P_{mathrm{per}}) estimates for NBMs. This comparison also highlights the differences between the fixed and rewiring NBMs. In correspondence to (P_{mathrm{per}}) estimates, fixed NBM results exhibit a higher variability for high (delta) and high d values, as compared to the rewiring NBM case. This is due to the fact that in these ranges of high variability for fixed NBM, the rewiring NBM predicts extinction of the meta-population. It is quite natural that a meta-population with a local finite growth rate r and a biomass loss during dispersal, cannot sustain a population for ever increasing dispersal rates. For lower (delta) regimes, all three connectivity scenarios follow this reasoning. For higher (delta), the all-to-all connected system can sustain the meta-population for arbitrary high (d rightarrow infty) values with (P_{mathrm{per}} = 1). In comparison, our NBM implementations yield reasonable estimates of (P_{mathrm{per}} < 1) in high (delta) and d ranges. Additionally, rewiring NBMs show a higher likelihood of extinction than the fixed NBM—which highlights the fact that, everything else being constant, it is highly likely for a meta-population to exhibit extinction depending on the changes in underlying connectivity alone, which here, correspond to different NPC realizations. Heterogeneous patches Results for the heterogeneous patch case are shown in the bottom row of Fig. 6 for (a.ii) all-to-all connected, (b.ii) fixed NBM, and (c.ii) rewiring NBM. Here we investigate these three configurations for different patch areas (beta _i) (in) [0.3, 0.7], and consequently different patch carrying capacities, (K_i in [K_{min },K_{max }]), assigned to the patches in increments of (left( K_{max }-K_{min }right) /N). For our calculations in Fig. 6(a.ii),(b.ii),(c.ii), we chose (K_{min }=1.5), (K_{max }=3.5), and (N=20). We observe that the results for heterogeneous patches are quite similar to the results with identical carrying capacities. The similarity in the transition boundary can be explained by looking at the eigenspectrum in Eq. (4). The eigenspectrum for the all-to-all connected system is independent of the patch carrying capacities for the extinction equilibrium and therefore, the extinction threshold is not affected by the dissimilarity in the carrying capacities. Accordingly, for the all-to-all connected cases in Fig. 6(a.i),(a.ii), there are no differences, and (P_{mathrm{per}}) behaves identically in both the homogeneous [Fig. 6(a.i)] and heterogeneous patch [Fig. 6(a.ii)] cases. Like for the homogeneous case, (mathrm{SE}(P_{mathrm{per}})) calculations in Fig. 7(a.ii) again show that the error values for the heterogeneous all-to-all connected case are uniformly vanishing in the entire considered ((d,delta )) plane. For the fixed NPC case [Fig. 6(b.i),(b.ii)], we observe some differences for the transition boundaries for higher (delta) values. The (P_{mathrm{per}}) estimates at the transition boundaries for high (delta) and d values are lower (lighter blue dots) in the heterogeneous case [Fig. 6(b.ii)] when compared to the homogeneous case [Fig. 6(b.i)], thereby signifying that in the heterogeneous case, more realizations in the ensemble close to the transition lead to extinction. At the same time, we observe that patch heterogeneity shifts the extinction threshold towards higher (delta) values, as compared to the homogeneous case. This implies that for the case of heterogeneous patches, we will observe species extinction for higher (delta) values where the homogeneous system still supports persistence. A similar pattern can be observed for the homogeneous [Fig. 6(c.i)] and heterogeneous [Fig. 6(c.ii)] rewiring SEMs. Similar to the homogeneous case, dynamical differences between all-to-all and SEMs are yet again more obvious in (mathrm{SE}(P_{mathrm{per}})) calculations in Fig. 7(b.ii),(c.ii). These observations suggest that unlike in the all-to-all connected case, patch heterogeneity appears to play an essential role in determining the extinction threshold for meta-populations as a function of d and (delta) in fixed, as well as rewiring NBMs. More

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Ethnographic accounts from around the world have reported the widespread use of insects as food by people1,2,3. In some cases, such as among the Shoshone and other Great Basin tribes of the U.S., swarms of grasshoppers and crickets were driven into pits and blankets4, while among the Paiute the larvae of Pandora moths (Coloradia pandora lindseyi) were smoked out of trees to fall into prepared trenches, where they would be cooked5. Across the world, insects could be mass-harvested, often seasonally, offering high nutritional value especially in fat, protein and vitamins6. The harvesting of insects in the past has ranged from opportunities to feed large communal gatherings during times of plenty, to more individualistic economic pursuits such as in the search for delicacies or the exploitation of low-ranked resources when other foods were scarce or depleted7,8,9. Irrespective of the catch, insects often represented an important component of the diet, and of the reliability and thus dependability of locales as resource zones, with implications for social scheduling and cultural practice. However, a paucity of archaeological studies of insect food remains has resulted in a downplay or omission of the use of insects from archaeological narratives and deep-time community histories10.
In Australia, a wide range of insects is known to have been eaten by Aboriginal groups, in particular the larvae (‘witchetty grubs’) of cossid moths (especially Endoxyla leucomochla) in arid and semi-arid areas11,12,13. Of particular interest to archaeologists and behavioural ecologists has been the seasonal consumption of Bogong moths by mass gatherings of Aboriginal groups in the southern portions of the Eastern Uplands14 (Fig. 1). However, no conclusive archaeological evidence has ever been reported for the processing or use of Bogong moths.
Figure 1

(A) Bogong moth, Agrotis infusa (photo: Ajay Narendra). (B) Thousands of moths per square metre aestivating on a rock surface (photo: Eric Warrant).

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The Cloggs Cave grindstone
Cloggs Cave is located 72 m above sea level in the southern foothills of the Australian Alps, in the lands of the Krauatungalung clan of the GunaiKurnai Aboriginal peoples of southeastern Australia (Fig. 2). The cave is a small, 12 m long × 5 m wide × 6.8 m high limestone karst formation that is today entered through a walk-through opening on the side of a cliff (Fig. 3). Indirect sunlight dimly illuminates the cave for much of the day (Supplementary Fig. S1).
Figure 2

Location of Cloggs Cave and the area of the GunaiKurnai Land and Waters Aboriginal Corporation, at the southern foothills of the Australian Alps. Esri ArcMap 10.5 (https://desktop.arcgis.com/en/arcmap/) and Adobe Illustrator CC 2017 (21.0) (https://helpx.adobe.com/au/illustrator/release-note/illustrator-cc-2017-21-0-release-notes.html) were used by CartoGIS Services, College of Asia and the Pacific at the Australian National University, to create the map.

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Figure 3

Cloggs Cave cliffline above the Buchan River flood plain, showing location of cave entrance (white rectangle) (photo: Bruno David).

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Archaeological excavations were first undertaken in 1971–197214, followed by a new program of excavations in 2019–2020, initiated by the GunaiKurnai Land and Waters Aboriginal Corporation and directed by Bruno David. The new excavations were aimed at better determining the site’s stratigraphy and the antiquity of Aboriginal occupation (Supplementary Fig. S2). An intensive dating programme showed that the oldest excavated evidence for human activity dates to between 19,330–19,730 cal BP (median age of 19,530 cal BP; cal BP = before AD1950) and 20,590–23,530 cal BP (median age of 21,690 cal BP) (all calibrated radiocarbon ages in the text are presented at 95.4% probability range. See “Methods”; Supplementary Fig. S3)15,16,17.
During the 2019 excavations, a small, flat grindstone was found. The finely stratified hearth layers of stratigraphic unit (SU) 2 in which it was found were radiocarbon-dated to 1567–1696 cal BP at their top (uncalibrated: 1724 ± 16 BP; median age of 1632 cal BP) and 2002–2117 cal BP at their base (uncalibrated: 2091 ± 16 BP; median age of 2062 cal BP). The grindstone therefore dates to between 1600 and 2100 years ago (see “Methods”; Supplementary Figs. S3 and S4)17. No other grindstone has been found at Cloggs Cave.
The grindstone is a tabular fragment of sandstone with two flat and parallel ground surfaces (Surfaces A and B), in the form of a flat dish (Fig. 4). It measures 10.5 cm long × 8.3 cm wide × 2.2 cm thick and weighs 304 g. The outer, intact margin is elliptical in plan view; the other three margins indicate old breaks that have been subsequently worn from use. Therefore, prior to its deposition at Cloggs Cave, the grindstone had been used in its current form.
Figure 4

The Cloggs Cave grindstone. (A) Surface A, with the accretion that formed across parts of the surface after its use. (B) Surface B. (C) Margin A. (D) Margin B. (E) Narrow end. The numbers in circles are the residue sample numbers; the ‘control’ samples are in areas where grinding did not take place (photos: Richard Fullagar).

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To understand how the grindstone was used, we undertook use-wear and residue analyses (see “Methods”). The central area of both its surfaces contain fine unidirectional striations (Supplementary Figs. S5A and S5B), a lowered but not levelled topography, and areas of missing or ripped quartz grains (Supplementary Figs. S5C and S5D). Its use to shape ground stone axes is an unlikely function because the Cloggs Cave grindstone surfaces are relatively flat with only very slight concavities, and the lowered surface topography (Fig. 4) lacks broad grooves typical of axe grinding.
When viewed at lower (up to 5 ×) magnification under a stereozoom microscope with a point source of light, each surface appears relatively rough compared with grindstones used for processing seeds, which, in Australia, tend to be highly smoothed and polished18,19. There are numerous ‘pits’ where sand grains have been plucked from the surface during use (Supplementary Fig. S5D). The presence of a lowered surface topography (Supplementary Fig. S5C) with a lack of smooth, developed polish suggests that the stone was not used to process siliceous plants.
The repeated mechanical action of grinding has been shown to force residues into the voids and interstitial spaces of ground surfaces, where they become trapped20,21,22. Residue analyses conducted on grindstones worldwide have relied on microscopic observations of individual residue morphologies. However, visually diagnostic features can be altered by the mechanical forces of grinding, heat, and contact with water and various environmental factors, which can cause residues to swell or become amorphous21,22,23,24. The distinctiveness of residue identifications can be enhanced significantly with the introduction of biochemical staining that can be observed under high-power microscopy and is best used in conjunction with microscopic use-wear analysis and identification of residue morphologies22.
We extracted nine samples, or ‘lifts’, for residue analysis from across Surface A and Surface B of the Cloggs Cave grindstone, including a control sample from an unworked part of each surface (Fig. 4; see “Methods”). These samples were analysed using a recently developed biochemical staining technique that enables residues to be identified from colorimetric changes occurring at a cellular level, rather than relying solely on structural features (see “Methods”)22. We used the collagen stain Picrosirius Red (PSR) to differentiate between plant and animal residues (see “Methods”). When PSR comes into contact with collagen (a protein unique to animals), it reacts to produce clear and distinctive staining and enhanced birefringence in cross-polarised light22,25.
Residues extracted from the grindstone
A range of residues were identified in the lifts, including amorphous collagen, collagen fibres, collagen structures, partially woven collagen, possible bone-like fragments, moth wing segments, a possible moth hind leg, amorphous cellulose, wood-like structures with pits, carbonised material, bordered pits and minerals (see below).
We found collagenous residues in mid-range densities across Samples 1 and 4 from Surface B and across Sample 5 from Surface A (Supplementary Fig. S6). These extractions were taken from central areas across each modified surface. In all cases, the frequency of the collagenous residues was approximately three times greater than the collagenous residues associated with the control samples. Residues include damaged collagen fibres of varying thicknesses, including some reticular fibres.
Woven collagen structures clearly show birefringence in cross-polarised light across Sample 1. Woven collagen, which forms quickly, is mechanically weak and usually associated with immature bone. Although woven collagen may persist as tendon and ligament attachments to bone, it is generally replaced by organised parallel collagen fibre bundles at skeleton maturity26. Collagen fibrils are found in the connective tissues of vertebrates as well as in invertebrates such as insects27, and may be present as individual strands, woven structures or parallel bundles, including among the Lepidoptera (moths and butterflies)28.
The density and combination of collagenous residues on the Cloggs Cave grindstone indicates that it was used to process fauna. A variety of collagenous materials (including woven collagen) were found in association with carbonised residues across Sample 2, which was extracted from a crystalline layer. The residues present on Samples 1 and 2 suggest that an insect or immature vertebrate was prepared and cooked using the grindstone.
We identified a moderate density of carbonised plant residues across Sample 2, in particular, wood-like structures with pits. These ranged from being partially to completely carbonised. Partially carbonised residues were also seen across Sample 4. In addition, bordered pits in small clusters were identified, along with pits within the carbonised structures. Bordered pits are cavities that are essential components in the water-transport system of higher-order plants and are found in the lignified cell walls of xylem conduits (vessels and tracheids). The pit membrane allows water to pass between xylem conduits, but limits the spread of embolism and vascular pathogens in the xylem29. Small quantities of lignin were also present (see “Methods”). Lignin is found in the cell walls of vascular plants (especially in wood and bark) and is responsible for the rigidity of plant structures.
The residues identified via biochemical staining are consistent with the use of twigs and bark as fuel for fires such as those of the microstratified ashy layers in which the grindstone was found (see Supplementary Fig. S3)17. Partially carbonised wood-like material was also noted across Sample 5. The density and distribution of carbonised residues varies across extractions. Our observations suggest either that: (a) the stone has been placed in or near fires; or (b) ash, embers or fires of varying heat were placed or lit across the stone, for varied durations of time.
We identified especially high densities (frequency of residue particles per unit volume of sample) of amorphous cellulose across Samples 1, 2, 4 and 5 (Supplementary Fig. S7). The presence of partially carbonised amorphous cellulose indicates that the plant residues were associated with fire. While the high density is indicative of a plant-processing event, there is no evidence of combinations of plant residues normally expected from plant processing. In particular, no starch grain or phytolith was seen in any of the extractions. While low heat can damage starch and cause its structure to be disrupted and its characteristic extinction-cross to be lost, low heat does not completely destroy starch visibility30. Similarly, phytoliths can be reshaped but not destroyed by fire31. The presence of animal and mineral residues but absence of starches and phytoliths is thus interpreted as a true absence of plant processing activities rather than a taphonomic effect of environmental factors negatively impacting their preservation.
We found a high density of variably carbonised insect wings in Sample 6 (Surface A), and lower densities in Samples 2 and 4. These wing fragments contain regular patterning or structure and exhibit distinct birefringence in cross-polarised light. A portion of proteinaceous material was associated with a ‘tangle’ of these structures (Fig. 5). To assess whether the insect remains were those of the Bogong moth, we compared the residues on Samples 2, 4 and 6 with a comparative reference sample (see “Methods”). All 26 cases of wing segments from the grindstone matched the metrical and morphological characteristics of those from Bogong moths in the reference material. The recorded damage on the archaeological wing segments, such as ripped wing structures, small rectangular wing fragments and tearing in various states of carbonisation, is what would be expected from ethnohistoric accounts of Bogong moth processing. Aboriginal people from across the region are known to have cooked Bogong moths on heated earth during the early and mid-nineteenth century. The moths were stirred during cooking, causing the wings and legs to be broken off by friction and heat. Some of the moths were pounded and ground into a paste which could then be smoked to preserve the food for weeks1,2.
Figure 5

Examples of Bogong moth segments from lifted samples (all at × 400 magnification). (A) Partially carbonised wing structures from Sample 2 (pp). (B) Partially carbonised wing structure and carbonised material from Sample 2 (pp). (C) Partially carbonised moth wing segment from Sample 4 (pp). (D–E) Damaged moth wing segment from Sample 6 (D pp; E xp). (F–G) Damaged moth wing segment from Sample 6 (F pp; G xp). (H) Damaged moth wing segment with proteinaceous material, from Sample 6 (pp). (I) Unburnt moth wing segment from Sample 4 (pp). (J) Damaged moth wing segment with attachment, from Sample 6 (pp). (K) Damaged moth wing segments from Sample 6 (pp). (L–M) Probable moth hind leg from Sample 6 (L pp; M xp). (N) Damaged moth wing segment from Sample 6 (pp). (O) Damaged moth wing segment with attachment, from Sample 6 (pp). Light source = plane (pp), part polarised (part pol) and cross-polarised (xp) (photos: Birgitta Stephenson).

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Though the time and place of the origin of this bloom is unknown, the presumable causes of it were high temperatures, abundant nutrients, low tidal amplitude, and little current. According to fishermen, these bioluminescent blooms were first seen about 15 nautical miles offshore of the Mandapam coast between India and Sri Lanka on 6th September, and subsequently moved towards the shore (Fig. 2). Bloom of N. scintillans in 2008 was reported to affect all the marine organisms including corals in GoM12. On 14th September, our preliminary assessment revealed that corals in Shingle and Krusadai islands were possibly affected by the bloom. A great multitude of N. scintillans cells were found settled on corals and other benthic organisms in the affected areas. A greenish settlement was observable on live coral colonies and other benthic organisms including macro algae, coralline algae and sponges etc.(Fig. S2). Settling of N. scintillans on benthic organisms has been reported to cause significant damage to the reef organisms through asphyxiation12. At Shingle Island, the area of significant impact was about 8.1 hectares on the shoreward side of the Island (79°14′14.38″E, 9°14′44.23″N) at depths between 1 and 3 m (Fig. 3). At Krusadai Island, an area of 2.1 hectares in the shoreward side was found affected by the bloom (79°13′20.78″E, 9°15′00.88″N) at depths between 1 and 2 m. The rest of the reef areas in both of these islands were healthy without any impact. The settled cells of N. scintillans were found to be washed ashore during subsequent surveys. In addition to dead fishes, a multitude of benthic communities such as crustaceans, mollusks and echinoderms were also found dead on the bottom in the impacted areas. Surveys between 15 and 18th September 2019 confirmed that corals in other islands (Pullivasal, Poomarichan, Manoliputti, Manoli and Hare) were in good health, and without any noticeable impact due to the bloom. Shingle and Krusadai islands occur closest to the mainland, and the concentrated bloom appeared to get trapped by currents between the mainland shore and islands.
Figure 2

(a) Green tide of Noctiluca scintillans in the Gulf of Mannar; (b) image of N.scintillans cells; size of the grid is 1 mm2 (N. scintillans exhibits bioluminescence when disturbed).

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Figure 3

Map showing the affected islands in the Mandapam group shown in Fig. 1. Base map was prepared by digitizing the georeferred Toposheet of Survey of India (http://www.surveyofindia.gov.in/) and field data using Open source GIS software (QGIS 3.10.6; https://qgis.org/en/site/forusers/download.html).

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On 14th September, coral mortality was not observed in the affected areas though the colonies were observed to be disturbed by the settling N. scintillans cells. Low dissolved oxygen levels have been reported to be the primary cause of benthic mortality during algal blooms22. Dissolved oxygen levels were 1.48 mg l−1 at Shingle Island and 2.02 mg l−1 at Krusadai Island in the affected areas. This compares to ‘normal’ levels for coral reefs of 5–8 mg l−1, and Haas et al.11 found that dissolved oxygen content less than 4 mg l−1 is detrimental to acroporid corals. Moreover, branching coral forms have been reported to be more susceptible to hypoxic episodes than spherical or massive forms5. Corals are routinely exposed to fluctuations in oxygen levels at the tissue level due to photosynthesis and respiration processes of endosymbionts7, but are negatively impacted when (sub-) lethal thresholds of hypoxia exposure are exceeded1,5,11. Lethal hypoxia thresholds appear to differ considerably between coral species, ranging between 0.5 and 4 mg O2 l−11,5,11, while sub-lethal hypoxia thresholds for corals are almost entirely unknown5.
Seawater temperature can significantly impact dissolved oxygen levels23,24. Water temperature was 29.9 and 29.8º C (Table 1) at Shingle and Krusadai islands respectively and these levels are marginally higher than the normal levels for this particular time of the year. Apart from the summer months (April to June), temperature levels in GoM do not go higher than 29º C20. The concentration of N. scintillans was 43.4 × 105 and 27.3 × 105 cells l−1 at Shingle and Krusadai Islands respectively; pH and TDS were also high in the affected area (Table 1). Dissolved oxygen levels in other sites of these two islands and in other five islands were higher than 5 mg l−1.
Table 1 Environmental characterization at the affected sites in Shingle and Krusadai Islands.
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During the next assessment on 17th of September 2019, severe coral mortality was observed at the affected sites. At Shingle Island, overall coral colony density was 134.25 (SE ± 3.28) no.100 m−2 (n = 537) within ten 20 m belt transects which is dominated by Acropora (64%) followed by Montipora (15%). Out of total 537 colonies, 33.52% (n = 180) were found dead (Fig. 4), which include 34.5 (SE ± 1.05) no.100 m−2 (n = 138) of Acropora, 7.75 (SE ± 0.75) no.100 m−2 (n = 31) of Montipora and 2.75 (SE ± 0.35) no.100 m−2 (n = 11) of Pocillopora. The death of coral colonies was so rapid that the coral tissue was intact on the colony surface and still had its natural colour (Fig. 5). When wafted with water by hand or with scuba air, the tissue peeled off exposing the skeleton (Supplementary video). Other observed genera such as Dipsastraea, Favites, Porites, Hydnophora, Goniastrea, Echinopora, Turbinaria, Platygyra, Goniopora and Symphyllia in the same site were all alive (Fig. S3), though with excess mucus production. This may be explained by differential lethal thresholds for oxygen levels at species and growth form levels5,19. At Krusadai Island, the overall coral density on 17th September was 66 (SE ± 2.54) no.100 m−2 (n = 132), dominated by Acropora. Among the counted colonies, 6 (SE ± 1.03) no.100 m−2 of Acropora were found recently dead while mortality was not observed in other available genera such as Montipora, Pocillopora, Dipsastraea, Favites, Porites and Turbinaria. Dissolved oxygen levels had increased to 3.78 mg l−1 at Shingle Island and to 4.02 mg l−1 at Krusadai Island at the affected sites and the water had started to become clear. The concentration of N. scintillans had reduced to 1.63 × 103 cells l−1 and 0.88 × 103 cells l−1 at Shingle and Krusadai Islands, respectively (Table 1).
Figure 4

Density of live and dead colonies of affected coral genera (Acropora, Montipora and Pocillopora) in Shingle Island, by date; the green line indicates the drastic decline of Acropora density between 17.09.2019 and 27.09.2019.

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Figure 5

Rapid mortality of corals presumably due to low oxygen levels caused by Noctiluca scintillans; (a, b) Acropora; (c) Montipora; (d) Pocillopora.

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Assessment on 27th September 2019 at the impacted area in Shingle Island, showed that the overall density of coral colonies within ten 20 m transects was 135.75 (SE ± 2.82) no.100 m−2 (n = 543) and of them 70.35% (n = 382) of colonies belonging to Acropora, Montipora and Pocillopora were found dead revealing that the impact of algal bloom was more severe than expected (Fig. 4). It was almost two weeks since the corals had died and hence secondary algae had started colonizing the dead colonies. On the same day at the impacted area of Krusadai Island, overall coral density within five belt transects was 65.5 (SE ± 1.83) no.100 m−2 (n = 131), of which 9.09% (n = 12) of colonies belonging to Acropora were found dead. By 27th September, dissolved oxygen levels had increased to 6.02 and 5.73 mg l−1 respectively at the affected areas of Shingle and Krusadai islands (Table 1). N. scintillans cells were absent in all the sites indicating the end of bloom. On 04th October 2019, the overall coral colony density within 20 m belt transects was 138 (SE ± 2.08) no.100 m−2 (n = 552) and of them 71.23% (n = 393) colonies belonging to Acropora, Montipora and Pocillopora were found dead at the area of impact in Shingle Island (Fig. 4). No further mortality was witnessed in the affected area of Krusadai Island. Secondary algae have completely overgrown the dead coral colonies making the reef look green (Fig. S4). Dissolved oxygen levels were reasonably high at 7.13 and 7.24 mg l−1 respectively at Shingle and Krusadai Islands during this time (Table 1).
Coral mortality due to algal bloom and consequent hypoxia has rarely been reported12,13,25. The present study reports that the impact of blooms can be severe on corals. Different coral species respond differently to low oxygen levels according to their respiration and photosynthesis5,26. Thus, low oxygen levels can orchestrate the coral mortality by affecting coral’s productivity and respiration7. Further, fast growing corals such as Acropora and Pocillopora have been reported to be more susceptible to low oxygen levels11,13,27. Fast growing coral species have faster metabolism rates28 and hence metabolic oxygen requirements are higher11,29. Thus, the mortality of fast growing species in the present study was presumably due to the low oxygen levels induced by N.scintillans bloom.
Bleaching episodes in 2010 and 2016 had also caused significant mortality to these fast growing species in GoM19,20. Corals in GoM start to bleach when water temperature exceeds 30º C and the temperature levels during this bloom period ranged between 28.4 and 29.9º C. Though bleaching was not observed, heat stress might also have played its role in coral mortality along with low oxygen levels as the temperature level almost reached 30º C. Similar temperature levels were reported during the bloom of N. scintillans in 2008 in GoM12.
Corals in Gulf of Mannnar are still recovering from the 2016 bleaching episode20 and hence the present decline is significant. Phase shifts on coral reefs are predominantly associated with shifts from hard coral-dominated communities to macroalgae-dominated ones30. Space competition between corals and other organisms such as algae and sponges has been reported to negatively impact the corals of GoM after the 2016 bleaching event20,31. At present, secondary algae have completely occupied the dead coral colonies, which will affect the coral recovery by hindering the attachment of new coral recruits during the next spawning season32. Recent studies suggest hypoxia increases coral susceptibility to bleaching27, and may increase disease prevalence and algal proliferation7. Thus algal blooms add to the existing array of threats to corals of GoM that needs to be understood more with further focused research.
On account of the problems related to climate change, there has been a steady and severe decline of coral reefs in the past two decades. Bleaching and diseases have been reported to cause mass coral mortalities within a very short time. The observations of the present study alert us to possible mass mortality due to short-term hypoxic condition caused by algal blooms. Algal blooms and hypoxic conditions are predicted to occur more frequently in the future due to climate change14. Hence, it is likely that shallow water coral reefs will be affected more frequently by temporary low oxygen levels caused by algal blooms. More studies are, however, required to understand the mechanism of coral mortality due to algal blooms, impacts on community composition and the potential for subsequent recovery. More

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