Ecology

Population dynamicsThe abundance of A. omorii peaked in April 2016, March 2017, and April–May 2018, ranging from 2.99 × 104 to 6.73 × 104 individuals m−3 (Fig. 3, Table 1). Anakubo and Murano28 reported that the abundance of A. omorii, including individuals from the nauplius to adult stage, peaked at 5.34 × 104 m−3 in April 1981 in Tokyo Bay. Itoh and Aoki18 surveyed A. omorii in Tokyo Bay from March 1990 to November 1992 and found that abundance peaked at 2.13 × 104 individuals m−3 in March. Tachibana et al.40 reported that abundance peaked at 2.60 × 104 individuals m−3 in April in 2007. The peak abundance in Tokyo Bay apparently occurs between March and May; this pattern was also observed in the Seto Inland Sea (Table 1). According to Kasahara et al.15, the peak period of abundance of adults and later-stage copepodites in the Seto Inland Sea occurred in mid-May at  > 3.5 × 104 individuals m−3. The abundance of adults and later-stage copepodites significantly decreased in July. The planktonic population disappeared from the water column in mid-August and recovered in November, when the water temperature was  50 g wet weight m−3). Hence summer jellyfish bloom probably has a strong negative impact on the abundance of A. omorii. Unfortunately, we do not have enough information on jellyfish abundance in Onagawa Bay, effect of jellyfish should be considered for the better understanding of A. omorii dynamics in future.A. omorii abundance became zero only from September to November 2016 in the 3 years of the present study. However, in other years, A. omorii appeared at low abundance in summer, as reported in previous studies conducted in Tokyo Bay 16,18,28,40. The period of complete disappearance was short, and low densities were recorded throughout the year in most studies (Table 1), indicating that the population is maintained in the planktonic stages even under unfavorable conditions in Tokyo Bay.EPR and egg type dynamicsSEPR peaked in winter (January or February) and May in both years at both stations (Fig. 8a). The second peaks were caused by increase in unhatched egg production, except for station CB in 2017 (Figs. 7b,c and 8b,c). Increase in unhatched egg production occurred when the surface water temperature exceeded 18 °C (Fig. 2a) and day length increased to  > 14 h, similar to that reported by Uye5. From January to May 1982, 80% of eggs that were newly spawned by A. omorii females collected in Fukuyama Harbor of the Seto Inland Sea were subitaneous; resting eggs appeared in June when the surface water temperature exceeded 17.5 °C. In the present study, on June 9, 32% of the total eggs produced were resting and did not hatch within 14 days but hatched after being reincubated at 15 °C for 2 weeks. Uye5 also demonstrated the effect of photoperiod on egg production by A. omorii via laboratory experiments under two temperature conditions (15 °C and 20 °C ± 1 °C). Approximately half were resting eggs under l4L–10D photoperiodic conditions at both water temperatures, indicating that photoperiod is important for shift to resting eggs. Many copepods have dormancy strategies at the thermal limits of species distribution46. In Tokyo Bay, the photoperiod exceeds 14 h in mid-May, when the water temperature usually exceeds 18 °C (Fig. 2a). Therefore, higher water temperatures and increased day length periods are synchronized in early summer in Tokyo Bay and may serve as cues for diapause egg production.Unhatched PEPR exceeded subitaneous PEPR from May to June (Fig. 9), when abundance drastically decreased to  45%) (Fig. 6) in the late period of A. omorii appearance (in May at station CB and in early June at station F3). However, in early June at station CB and late June at station F3, when the abundance sharply decreased, the proportion of females producing subitaneous eggs increased, whereas those producing quiescent eggs decreased from the previous month (Fig. 11). Uye5 reported a similar result; the proportion of diapause eggs to total eggs produced by A. omorii in the Seto Inland Sea peaked on June 9 and then reduced by half by June 30. Quiescent eggs have been defined as subitaneous eggs with arrested development that remain in a quiescent stage in unsuitable environmental conditions49. Uye5 defined diapause eggs as eggs that did not hatch during 2 weeks at in situ water temperatures but hatched within 2 weeks when the incubation temperature decreased to 15 °C. These eggs may be classified as quiescent eggs in the present study. The results of Uye5 and the present study suggest that not all produced eggs of this species shift from subitaneous to quiescent eggs at higher water temperatures.As mentioned in the previous subsection, a planktonic population of A. omorii has been found in mid-summer at low abundance in Tokyo Bay16,18,28,40 (Fig. 3). Similar results were reported in Maizuru Bay50. Itoh et al.45 investigated the vertical distribution of copepods at 1-m depth intervals at station F3 in Tokyo Bay in mid-summer, when hypoxia develops near the bottom, and showed that A. omorii population had a sharp peak, with densities exceeding 1.5 × 103 individuals m−3, at 8 m at a water temperature of 18 °C and just above the hypoxic zone45. This suggests that A. omorii maintains a planktonic stage even at low density in mid-summer, whereas most of the population estivates by forming resting eggs in bottom sediments. These mid-summer populations are presumably hatched from subitaneous eggs spawned in mid-July (Figs. 7, 10). Uye5 also reported that more than half of the eggs were still subitaneous in late July in the Seto Inland Sea. Therefore, we tentatively think that this phenomenon is a bet-hedging strategy of A. omorii in an unfavorable and uncertain environment. In contrast, Ueda50 stated that the increase in subitaneous EPR in summer was due to immature development of this species. Thus, these remaining populations may not contribute to the autumnal development of the population. To understand how A. omorii survive in mid-summer, more detailed field investigations are warranted, including egg and nauplii dynamics in the water column, egg hatching process from sediments, and differences in endogenous factors in individual females producing subitaneous and diapause eggs in summer.Information on A. omorii’s delayed-hatching eggs is strictly limited. Delayed-hatching eggs are eggs hatching over a wide time span regardless of environmental conditions4,11. Takayama and Toda4 defined the unhatched eggs of A. japonica hatching during 72 h–50 days as “delayed-hatching eggs.” Thus, delayed-hatching eggs may have been included in the quiescent and diapause eggs defined in this study. Our results showed that no eggs hatched between 48–96 h and 7 days in the experiment at in situ water temperature; many quiescent eggs hatched within a few days after reincubation at lower water temperatures (Figs. 10, 11). Therefore, delayed-hatching eggs may not have been produced in the present study.Effects of water temperature on the production of subitaneous and diapause eggsMultiple regression analysis revealed that subitaneous SEPR negatively correlated with bottom water temperature, inconsistent with the results of Uye3. EPR and copepod growth generally increase with increased water temperature51. Uye3 reported that EPR of A. omorii also increased with water temperature; they developed a simple model equation describing the fecundity of A. omorii in Onagawa Bay via a laboratory experiment:$${text{F}} = 0.000{331 }left( {{text{T}} + {12}.0} right)^{{{3}.{25}}} {text{SW}}_{{text{f}}} /left( {0.{47}0 + {text{S}}} right),$$
where F is daily fecundity (eggs female−1 day−1), T is water temperature (°C), S is chlorophyll a concentration (µg L−1), and Wf is female carbon content (µg). The fecundity predicted by the above described model was similar to the observed EPR of this species in Onagawa Bay3.Many studies have used Uye’s equation to estimate A. omorii egg production. Kang et al.52 reported A. omorii’s EPR in Ilkwang Bay to be 22–57 eggs female−1 day−1, which was higher than that in the present study (1.6–18.7 eggs female−1 day−1) (Fig. 7a). Liang and Uye17 estimated A. omorii’s EPR in the Seto Inland Sea by two methods: the above described model (estimated incubation fecundity)3 and based on the number of eggs remaining in the water column and the adult female population (egg-ratio fecundity)53. In the Seto Inland Sea, the estimated incubation fecundity was 26–60 eggs female−1 day−1 and the egg-ratio fecundity was 0.5–25 eggs female−1 day−1; the estimated incubation fecundity was always greater than the egg-ratio fecundity17.Suspension-feeding copepods may ingest their own eggs and nauplii. In the Seto Inland Sea, possible egg predators were the dominant copepods Centropages abdominalis and A. omorii54. Based on their abundance (0.2–39 predators L−1) and assuming a predator clearance rate of 50 mL d−1, C. abdominalis and A. omorii could remove 1–86% of eggs in the water column per day. Liang and Uye17 noted that their predators were abundant when the abundance of surviving eggs in the water column was low; therefore, they tentatively concluded that the difference between the two estimates was due to egg loss by predation, including cannibalism. However, it is unlikely that fecundity reached its highest value ( > 50 eggs female−1 day−1) in mid-July when the population disappeared from the water column17. At that time, the water temperature was 25 °C, which also does not support the increase in fecundity observed by Liang and Uye17.The model equation of Uye3 was derived from Onagawa Bay, where the average water temperature is 7.7–21.9°C55. In laboratory experiments using A. omorii from Onagawa Bay, EPR decreased when the water temperature exceeded 22.5°C3. In Uye’s equation3, the decrease in egg production above 22.5 °C was not foreseen, whereas water temperature exceeded 22.5 °C in the Seto Inland Sea17, Ikkwang Bay52, and Tokyo Bay (Fig. 2). Thus, Uye’s model equation3 is not applicable to these warm environments.Based on the temperature regime, seasonal population dynamics and egg types produced are divided into two types: no resting egg production in the colder Onagawa Bay and resting egg production in the warmer Tokyo Bay and Seto Inland Sea. As mentioned above, A. omorii in Onagawa Bay exists throughout the year, even in summer14 and hardly produces diapause eggs5,7. However, the population almost disappears in late summer in Tokyo Bay16,18,28,40 and the Seto Inland Sea15,17. Furthermore, in these warm coastal waters, A. omorii produced diapause eggs just before copepodite disappearance from the water column. Therefore, a separate equation for estimating egg production should be developed, depending on the temperature regime of the habitat.Recent climate change, particularly global warming, may affect A. omorii’s egg production. In Tokyo Bay, between 1955 and 2015, the water temperature increased by 1.0 °C and 0.94 °C at the surface and bottom layers, respectively, in winter and autumn56,57. In summer, the water temperature at both the surface and bottom layers decreased, probably due to strengthened estuary circulation56,57. Considering the response of A. omorii to water temperature, the increase in winter temperature might reduce subitaneous egg production, resulting in delayed population increase. In contrast, the decrease in summer temperature might lead to reduced diapause egg production per amount of body carbon. The long-term trends of water temperature might have different effects on each egg type production and alter the dynamics of A. omorii egg production.Effects of phytoplankton composition on the production of subitaneous and diapause eggsThe EPR of A. omorii may be saturated at low (1–2 µg L−1) chlorophyll a concentrations3,19. However, multiple regression analysis revealed that small diatoms stimulate subitaneous SEPR (Figs. 8, 12). The EPR at station CB was quite high ( > 14 eggs female−1 day−1) in January and February 2018, when the diatoms comprised Skeletonema and Chaetoceros. In contrast, at station F3, EPR drastically decreased from 18.7 ± 6.3 eggs female−1 day−1 in January to 8.4 ± 4.6 eggs female−1 day−1 in February 2018. The EPR at station F3 in February was significantly lower than that at station CB (Tukey’s post hoc test, p  95%) at station CB in January and February 2018, suggesting that small diatoms ingestion enhances A. omorii’s egg production.It is also likely that small nanoflagellates have an adverse effect on egg production. At station F3, the proportion of nanoflagellates to total phytoplankton carbon biomass was high ( > 93%) in February and March (Fig. 12), when EPR was quite low ( More

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The illumination system often is considered the key setting in most image acquisitions30. Thus, we first studied this system under three different settings, namely 2Pt_Rf, RRf_P and Trans due to their practical and scientific merits. The schematic ray diagrams showing the lighting conditions and their experimental set-ups have already been illustrated (Fig. 1). The 2Pt_Rf system is one of the most-used illumination systems in the field of entomology and food-filth detection, for it allows the operator to view a wide area of samples, even when they are dark and thick9. Beetle elytra (and other external surfaces of beetles) are coated with natural waxy substances and sometimes also with setae (hair-like features), which scatter reflected light, causing glare spots in their images31. Due to its reflective nature, this system, produced significant glare spots, often overshadowing the actual patterns of the elytra as seen on the top rows of Fig. 2a,b, and rendering it the least advantageous for our application.Figure 2Comparing three different illumination systems. Images of elytra under three different lighting conditions (top: 2Pt_Rf, middle: RRf_P and bottom: Trans), for two different families (a) Anobiidae, with species L. serricorne (1st column) and S. paniceum (2nd column) and (b) Tenebrionidae with species G. Cornutus (3rd column) and T. castaneum (4th column). All images were captured at 50x magnification, with the scale bars being 250 µm. The glare in images due to 2Pt_Rf illumination is evident. The RRf_P and Trans systems provide a much better alternative that significantly reduces glare, which obstruct elytral patterns. The extent of glare has been quantified as (c) percentage of elytra area obstructed due to glare, (d) number of glare spots (i.e. artifacts) due to setae when compared against three different illumination systems for two different families, which further highlights the advantages of RRf_P and Trans systems.Full size imageThe use of 2Pt_Rf light produced so much glare that it obstructed about 25% of the pattern area (Fig. 2c). Moreover, the dorsal setae produced a large number of tiny glare spots that could easily be confused for feature points (Fig. 2d), and led to misidentifications in previous studies21,22,23,24. Furthermore, we observed that the position of the glare spots did not remain constant, and varied with the angle of incident light and/or the ambient lighting conditions (Supplementary Figure S1). Such randomness in image illumination (i.e., brightness and contrast) makes this setting very inconsistent, (e.g., highly dependent on random, external factors) and rendering it ineffective for automated pattern analysis, particularly in developing computer models for pattern recognition27,28.The RRf_P system was a significant improvement over the 2Pt_Rf system, as it allowed us to remove many of the illumination inconsistencies (Fig. 2a,b, the middle row). The use of a ring light instead of two light bulbs provided a more uniform illumination, and the polarized filter helped minimize glare spots from the images by imaging in circular polarized light (also minimizing the incoherent scattering of light, responsible for glare)32. However, upon closer observation, we found that the illumination system could not completely remove all the glare spots, especially for the species with dorsal setae. The orientation of the polarizing filter can be adjusted to remove random scattering of light only from one particular plane and/or angle. Since dorsal setae lie at a slightly different angle to the elytra surface, it was not possible to simultaneously remove glare spots originating from both elytra and dorsal setae. However, this setting was found to be excellent in imaging whole insects and/or other thick, dark or shiny objects that may have been challenging when using the 2Pt_Rf setting33.Compared to RRf_P and 2Pt_Rf, the Trans system was a much simpler and more economical set-up that produced excellent glare-free images (Fig. 2a,b, the bottom row). The images of elytra revealed patterns clearly as they did not suffer from any reflective glares (Fig. 2c,d). Unlike the RRf_P system, images captured in Trans remained mostly unaffected by the polarizing filter or its orientation (Supplementary Figure S2). This makes Trans least susceptible to variations such as those in operating personnel and ambient lighting conditions; and it remained relatively unaffected by the change in intensity of the transmitted beam, (i.e., due to the camera’s automatic intensity balance setting). Methods that enable consistent acquisition of data are highly desirable for any automated processing as they help avoid any batch effect or unknown error34,35. In this regard, the Trans system provides an effective, yet simple solution for consistently capturing clear, high-quality images.We also captured images under the combined illumination of RRf_P and Trans systems. This combined lighting system, however, fell short by revealing both surface and internal features with equal precision, and its images more closely resembled the RRf_P-based images (Supplementary Figure S3). It was not possible to balance or equalize the illumination intensities of the two (i.e., reflected and transmitted) light beams. The ring light was higher in illumination intensity compared to the transmitted beam, which also underwent sample adsorption, possibly resulting from the camera’s greater expose to reflected light, producing images similar to those captured using RRf_P.The key to discerning between similar patterns lies in acquiring images that enable the visualization of fine pattern details. This would permit maximum information to be extracted from each image and could help identify a species with a much larger set of characteristic features. To ensure visualization of the most patterns, both sides (D and V) of elytra were imaged. We noted that both of these illumination systems were excellent in revealing the pattern details, as one can clearly observe differences in patterns for D and V sides of the same elytron. The lack of glare spots made distinguishing between beetles belonging to the same family relatively easy, as their pattern features such as color, design, and setae are quite different from each other when observed through either the RRf_P or Trans illumination systems (Supplementary Figure S4).Differences between species were subtler for beetles belonging to the same genus however. For instance, rectangular patterns are only slightly lighter with marginally thicker ridge lines between T. castaneum and T. confusum (both genus Tribolium), irrespective of the illumination system used (Fig. 3a,b). Differences also were found to be subtle between O. mercator and O. surinamensis (genus Oryzaephilus) (Supplementary Figure S4e). For the same species, the variation in elytra patterns due to two different illumination settings (RRf_P and Trans) could only be perceived during careful observation of the images (refer to first row for D & second row for V side in images Fig. 3a,b). This difference, though minute, originated from the variation in the imaging mechanism and deserves comprehensive interpretation. The RRf_P system uses a light beam that reflects back from the specimen surface to capture an image, which allows better visualization of surface features. On the contrary, the Trans system uses a transmitted light beam that traverses through the specimen allowing better visualization of internal structures, in our case this is the cytoskeletal pattern on each elytron. That may explain why the images captured through the Trans system did not appear significantly different between the D and V sides, as they did for RRf_P lighting (Fig. 3a,b).Figure 3Acquired patterns on either side of elytra dorsal (D) & ventral (V) sides for maximum visualization of the patterns and to compare the two different illumination systems, RRf_P and Trans, for (a) T. castenium, (b) T. confusum both belonging to the genus Tribolium; and are well known for their remarkably similar appearance; and (c) in visualizing surface features in O. surinamensis (OS), L. serricorne (LS), and T. confusum (TCo). Although, both systems seemed equally good for visualizing elytral patterns, the Trans system was found to be the least affected by minor surface features such as setae, thin surface coatings or occasional adherents, making it more advantageous for the imaging application.Full size imageIn an ideal scenario the RRf_P system should be used for imaging surface features such as setae, contours and the like, and the Trans system for such internal structures as ridges and bulges. In practice however we observed that the RRf_P system is more susceptible to dorsal setae or artifacts such as contaminants or adherents (Fig. 3c). In several of the elytron samples, especially in the case of deeply concave samples such as L. serricorne, we found thin adherent coatings (possibly from the medium in which the insect lived) on the V side of the elytra persisted even after thorough ultrasonic cleaning. We also observed occasional dust-like adherent on either side of the elytra, all of which masked the actual elytral patterns. Additionally, we foresee the loss of non-structural surface features such as setae in real-world samples that undergo aggressive sample preparation steps prior to analysis. The internal structures of elytra are made of well crosslinked chitin (or its modified version), which is known to have excellent mechanical and structural properties16,36. Thus, capturing images through the Trans system is advantageous in this regard, as it captures the internal structures which usually remain unaffected by minor surface adhesions, contamination or even vigorous sample preparation steps.Having studied the illumination systems, we focused on another important imaging parameter, namely, magnification. Figure 4b shows images of elytra from O. mercator and O. surinamensis at magnifications 20 x to 160 x under the Trans system. It is obvious that the patterns are visibly better and more prominent at higher magnifications. Moreover, the size and shape of an elytron varies from one species to another (Fig. 4a,c). For the seven beetle species examined in this study, their sizes varied from ~ 5 mm2 for genus Oryzaephilus to ~ 13 mm2 for genus Tribolium. At these sizes, 50 x to 100 x magnification seemed a good choice, as it enabled us to capture most of the elytra (Fig. 4b,d). Imaging at higher magnifications (such as 160 x) allowed significantly better visualization of individual patterns. On the other hand, it only revealed parts of the elytral patterns, with a large portion remaining out of the field of view, and thus unanalyzed.Figure 4Magnification considerations: (a) Elytra images captured at 50 ×  magnification, showing their overall appearance; (b) Elytra images of T. castaneum (TCa) and T. confusum (TCo) at various magnifications showing area of elytra captured in each frame/magnification, (c) variation in average elytra sizes (of 10 images per species) for the seven different species used in this study, (d) proportion (area imaged/total area) of elytra captured at various magnifications, (e) number of images required (elytra size/frame size) to visually represent the whole elytra and (f) change in frame area (area captured in an image) by magnification. Even though there is a variation in elytral sizes, collectively they can be represented effectively at a magnification of 100 x.Full size imageTo remedy this inconvenience, many images must be captured in order to visually represent the entire elytra (about five or so in some cases as shown in Fig. 4e). This would increase overlapping errors (i.e. same parts being imaged in multiple images) without significantly increasing the pattern information. It also makes the process more susceptible to vibrations, thus increasing the likelihood of introducing undesirable artifacts. Increasing the magnification also significantly reduces the frame area. The average size of pantry beetle elytra is approximately 10 mm2, which is similar in dimension to the area of the image frame at 100 x magnification (Fig. 4f). Therefore, images captured at this magnification (i.e. 100 x), would reveal all or most of the elytra structure for a majority of the pantry beetles (Fig. 5a). We thus concluded that this a good starting point for a more detailed investigation.Figure 5Elytra and their patterns at various magnifications; (a) elytral images of species O. mercator (OM) and O. surinamensis (OS) at magnifications 20x to 160x; (b) elytra patterns captured for six different species at 100x magnification under the Trans illumination system; and (c) various elytra patterns for the same six species at higher display resolution, which highlights that this setup (Trans at 100x) is capable of resolving the finest pattern details required to distinguish species belonging to the same genus or family from one another.Full size imageLike the sizes of elytra, morphology of the patterns also varied widely across family and genera, and even within the same genera, which could clearly be visible at 100 x magnification across all the species (Fig. 5b). For instance, the central bulges were more circular in shape and measured on average ~ 100 µm diameter for T. confusum compared to a less circular bulge shape that measured less than ~ 50 µm in diameter for T. castaneum. Interestingly, L. serricorne completely lacked any such bulges (a feature we observed on very few pantry beetles) and had distances of less than 20 µm between the seatal pits (hair roots). Such small feature size could not be resolved adequately at 50 x magnification (even at ~ 2400 × 1900 dpi image resolution). Imaging at 100 x magnification (and at ~ 2400 × 1900 dpi resolution) allowed us to visualize the patterns clearly enough to visibly distinguish one elytra pattern from another (Fig. 5c). We therefore, chose this magnification as optimal, as it struck a reasonable balance between appropriate field of view and adequate resolution of detailed elytral patterns. At this point we also began to concur that magnification of 100 x under Trans illumination yielded high quality elytra images that allowed one to visualize fine patterns in beetle elytra in a consistent manner. It could be the optimal imaging condition, as images captured this way enabled us to visually differentiate one species from another, at least for the set of beetle species under investigation.After optimizing the illumination and magnification settings (which were the hardware-based parameters), we focused our attention to digital parameters for improving the image quality. Amongst these parameters, sharpness, distortion and aberration were important for their significance in image processing. They also contribute to the visual clarity, by revealing the finest elytral patterns required for distinguishing one species from another. The corresponding FFT images of the images obtained using conventional (low resolution 2Pt_Rf), 2Pt-Rf and Trans settings (Supplementary Figure S5) highlights the difference in the pattern clarity. The central vertical line represents the horizonal groves in the pattern and the smaller spots on both sides possibly represent the rectangular box like features patterns. Their distinct nature for the FFT image, indicates that the pattern (obtained using the Trans setting) is clear due to the good image quality. On the contrary, the FFT patterns are increasingly diffused (due to glare and scattering, less sharpness and clarity) for 2Pt-Rf setting and lower resolution. The FFT patterns from species belong to different families shows variation. The FFTs from those belong to the same genus and family, shows some similarity (due to the similarity in their elytral patterns) but are not identical, further bolsters the superior image quality of Trans setting that allows distinction of one species from another.Image resolution, another of the image quality parameter, was kept at 2592 × 1944 dpi (~ 5-megapixel, image size: 14 MB per image). This was found to be adequate for our applications, as higher resolution (greater than 10 megapixel) increased the images size significantly without providing any additional information. This may also cause difficulty in handling and storage for a repository aimed to contain about 2000 images. Fortunately, most modern cameras, including the one we used, comes with well-documented, user-guided software interface that allows convenient optimization of the ‘soft’ parameters enabling easy capturing of good quality images. So, it was less arduous for us to obtain good quality elytral images of once the illumination and magnification settings were worked out. A step-wise details on how to capture a good quality image has been elaborated (Supplementary Information-Step-Wise Imaging Method) for consistency and reproducibility.Our ultimate objective is to obtain large number of high-quality elytral images for species identification through elytral pattern recognition using artificial intelligence (AI) based machine learning methods. However, developing such methods require both time and high-performing computational capability, which could be expensive. It may be prudent to run a ‘quick-check’ to observe if improving the image quality indeed showed any promise in improving species-level identification. Therefore, as a proof of concept, we used ImageJ to analyze the elytral patterns based on their difference in sizes and shapes. For this test, we analyzed elytra images of G. cornutus, T. castaneum and T. confusum, all three belonging to the same family Tenebrionidae, thus showing very similar elytral patterns (Fig. 6a). The processed images yield corresponding pattern outlines whose size and shape have been quantified by the area within the outlines and their circularity (area/perimeter2, 0 for line and 1 for circle) (Fig. 6b). It can be noted that their distributions created three different bell curves for the three species analyzed. The shape distributions did show three peaks having good overlap, probably indicating the similarity in pattern shapes for species belonging to the same family (Fig. 6c). The size distribution curves however are further apart for the species belonging to the same family but different genus and quite close for those of the same genus (Fig. 6d). This species wise classification using rudimentary size analysis of elytra patterns indicated that improving image quality may indeed help improve species-level identification.Figure 6Analysis of elytra patterns for 3 different species, namely G. cornutus (GC), T. confusum (TCo), & T. castaneum (TCa) belonging to the same family Tenebrionidae. (a) optical images captured in Trans illumination at 100x magnification showing elytra patterns, with the scale bars being 250 µm; (b) the corresponding binary image, that shows only the dominant patterns. The log-normal distribution of (c) ‘circularity’ (shape) and (d) ‘size’ of the patterns for the three different species. It can be noted that the shape of the patterns are similar (close ‘circularity’ distribution) for the species belonging to the same family. But the ‘size’ distribution varied for three species with TCo & TCa belonging to the same genus Tribolium, much closer compared to the other family member GC. The indexed numbers show the mean and SD of the distribution, with the size values bearing units of  10–3 mm2.Full size imageAny optimization technique must be tested for its robustness before being extended to a much wider range of samples. Thus, we wanted to observe whether the Trans setting at a 100x magnification could be used for other species of beetles. For this setting to work the transmitted light must pass through the elytra, which may seem difficult if the elytron is too dark and/or thick. Thus, we tested this system to image elytral patterns on rainbow scrap beetle (Phanaeus vindex), black dung beetle (Onitis aygulus) and click beetle (Orthostethus infuscatus), (none of which are pantry beetles), for they have some of the biggest, darkest and thickest elytral known to entomologists (Fig. 7a,b)36. It was indeed possible to capture elytra patterns for all three species using this setup. For comparison, images also were obtained using the RRf_P setting further highlighting the advantage of imaging in transmitted light, Trans remains least influenced by the presence of surface features such as pigmentation, excessive setae and the like as shown in the bottom row images in Fig. 7a–c. Since the setup worked well for some of the biggest and darkest elytra samples, presumably it could be used for imaging any species of pantry beetle (or other class of beetles) which are roughly 10 times smaller in size and have far thinner elytra.Figure 7Control study to evaluate imaging capability when using Trans at 100x magnification, which was employed to image (a) Rainbow Scrap Beetle (Phanaeus vindex); (b) Black Dung Beetle (Onitis aygulus) and (c) Click Beetle (Orthostethus infuscatus), which possess some of the thickest and darkest known elytra in beetles. For comparison, the bottom row imaged using the RRf_P system, shows colored pigmentation, waxy coating and excessive setae. The top row represents images captured using the Trans system showing only the cytoskeletal structure, and which remain mostly unaffected by nonuniform surface features.Full size imageHaving tested the Trans system at 100x magnification on thicker and darker elytral samples, we moved forward in capturing elytral images of other pantry beetles to further expand the robustness of this system. Figure 8 shows representative images of six more species, listed in Table 1, by their family and genus names. We observed that this system (100 x magnification with the Trans setting) yielded excellent images for most of the beetles. The only exception was Zabrotes subfasciatus (Z. subfasciatus- commonly known as Mexican bean weevil of family Chrysomelidae). Its dark dorsal setae with white stripes, combined with a deeply concave shape probably made auto-focusing difficult during image acquisition. This could have contributed to slightly imperfect stacking of multi-layer images resulting in a poorer 3D montage construction. Images of the same species obtained through the RRf_P system also were of poorer quality, leading us to conclude that elytra with surface setae of contrasting colors combined with deep concave shapes are more difficult to image compared to elytra with more uniform color, flatter shape and clear patterns. This, however, may not pose a significant challenge, as in a real-world scenario, elytra often lose both their dorsal setae and concave shape due to food processing steps and/or fragmentation.Figure 8Extending this imaging method to other species of food contaminating beetles. Elytra images of species belonging to (a) two completely different families; (b,c) of same genus. Sitophilus granarius and Sitophilus oryzae of genus Sitophilus; T. freemani and T. madens of genus Tribolium. The optimized imaging technique works well for imaging elytra of most species of pantry beetles. However, species with variegated dorsal setae and deep concave shapes are most difficult to image properly. The images are at 100x magnification, with the scale bars being 250 µm; the insets show the magnified patters with scale bar being 100 µm.Full size imageWe currently are extending this imaging method to a total of 40 different species and plan to create a publicly available database of these images. Such database is meant to serve the food safety and entomology communities by providing good quality images for referencing and taxonomical applications. The optimization of parameters required for machine learning methods for species identification through pattern recognition are quite different from those in image acquisition and is far beyond the scope of this manuscript. But this study is aimed to serve as a forerunner for such study as it provides the means to obtain a large set of good-quality, noise-free images for developing a good computational model, which has been one of the primary challenges in the field of AI and machine leaning. We hope that a publicly available database of images will encourage data scientists to develop state-of-the art species identification algorithms, thus eventually contribute to our long-term goal of efficient automated species identification of food contaminating beetles. We believe this work lays a solid foundation for such an exhaustive study, which would require more resources and would help predict the correct species of pantry pests with greater accuracy. We hope such efforts ultimately would expedite the entire process of taxonomical analysis and help better manage contamination scenarios or catalog ecological systems, in the foreseeable future. More

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Escherichia coli is amongst the dominant pan-aminoglycoside-resistant bacteria from wastewater and river waterA total of 168 bacteria highly resistant to aminoglycosides were obtained by growing collected samples in MacConkey media supplemented with high aminoglycoside concentrations, from both WWTPs (66 isolates) and rivers (102 isolates) sampled at the same period of time and located in the Barcelona region. Most of the isolates were identified as Escherichia coli, being the predominant Enterobacteriaceae species in WWTPs (50 isolates, 75.76%) and the second most prevalent in rivers (20 isolates, 19.61%). Klebsiella pneumoniae was the predominant bacterium identified in river environments (50 isolates, 49.02%), although its presence in WWTP samples was scarce (7 isolates, 10.61%). The remaining aminoglycoside-resistant species, members of genus Enterobacter, Citrobacter and Aeromonas among others, represented a low fraction in WWTPs (9 isolates, 13.64%) comparing to river environments (32 isolates, 31.37%), showing a higher bacterial diversity of natural over WWTP niches (Fig. 1, Supplementary Data 1).Fig. 1: Geographical distribution of sampled points in the region of Barcelona (Spain).The location of Barcelona city is indicated by black circled city icon. The Llobregat river is presented in light blue and the Cardener river in dark blue. The three river sampled locations are indicated by black circled river icon. The two sampled WWTPs are indicated by black circled droplet icon. The collection area of the El Prat WWTP is highlighted in dark green, whereas the area of the Gavà WWTP is highlighted in pale green, collecting the wastewater of 2,000,000 and 370,000 inhabitants, respectively. Total number of pan-aminoglycoside-resistant bacteria collected from each sampling location is indicated in the center of the sunburn diagrams. Inner rings represent the proportion of different pan-aminoglycoside-resistant bacterial species identified in each sampling location, indicating the total number of E. coli, K. pneumoniae and other species, and the number of E. coli isolates selected for later Illumina (I) and Nanopore sequencing (N). Outer rings show the 16S-RMTase gene harbored by these bacteria.Full size imageAntimicrobial resistance in E. coli varies between wastewater and river water environmentsResistance to diverse antibiotic compounds was related to the sampling origin and the species of the isolates. All isolates were highly resistant to 4,6-DOS aminoglycosides, since they were selected according to their resistance to this family of antibiotics. The 16S-RMTase gene identified in most wastewater isolates was rmtB (55 isolates, 83.33%), whereas armA was the responsible in the remainder (11 isolates, 16.67%). On the contrary, 100% of isolates from river environments harbored the armA gene (Fig. 1, Supplementary Data 1). No other 16S-RMTase gene was detected neither in wastewater nor river isolates. Bacteria from wastewater samples showed higher MIC values to all tested antibiotics (e.g., 77.3% isolates resistant to cefotaxime) than bacteria from freshwater environments (e.g., 0% isolates resistant to cefotaxime), even when comparing isolates belonging to the same species. This was also the case for E. coli, where isolates from WWTPs were among the most resistant (e.g., 90% isolates resistant to cefotaxime) while those from rivers were among the most susceptible ones (e.g., 0% isolates resistant to cefotaxime), highlighting key differences between bacteria from these different ecological niches (Supplementary Data 1).Distinct E. coli populations exist in wastewater and river water environmentsPulsed-field gel electrophoresis (PFGE) of E. coli isolates revealed the existence of two highly prevalent pulsotypes present in the two WWTPs (31 isolates, 62%), and a total of 5 characterized pulsotypes (≥90% similarity in the PFGE pattern) from wastewater samples. Interestingly, 15 isolates (30%) from both WWTPs could not be typed by PFGE. In contrast, river environments showed higher clonal diversity, with up to 10 different pulsotypes characterized, despite the lower number of E. coli isolates recovered from these samples (Supplementary Fig. 1). Of the 70 E. coli isolates, 43 were selected for further analyses and sequencing by Illumina technology (25 from WWTPs and 18 from river environments), ensuring wide representation in relation to the origins, clonal relatedness, and antibiogram resistance profiles.Multi-locus sequence typing (MLST) results were highly correlated with PFGE profiles. Almost all E. coli isolates from wastewater samples (23 isolates, 92%) belonged to two predominant sequence types (STs) (Fig. 2). The most prevalent one, ST1196/ST632 (Warwick/Pasteur MLST scheme), encompassed the two main pulsotypes identified by PFGE. ST1196 is an increasingly prevalent ST related to OXA-48-carbapenemase production and associated with the mcr-1 colistin-resistance gene expansion in clinical settings9, hospital wastewater10 and companion animals11. Minimal inhibitory concentration (MIC) values of the isolates belonging to this ST showed resistance to numerous antibiotic classes, including clinically critical antibiotics such as 3rd generation cephalosporins and colistin6. The other predominant ST in WWTPs, ST224/ST479, comprised all the isolates which were non-typeable by PFGE. ST224 is a pandemic multi-drug resistant ST previously associated with NDM-, CTX-M- and KPC-carbapenemase production, found in both clinical human12 and animal13 samples, but also in natural environments14. ST224 E. coli isolates identified exhibited a common resistance profile, including resistance to 3rd generation cephalosporins. However, two isolates from river samples belonging to this same ST showed a different resistance pattern, revealing the influence of the niche in the bacterial resistance phenotype, even within the same ST. One of the E. coli isolates from El Prat WWTP was also identified as ST131, a clonal group present in multiple environments with a plethora of resistance mechanisms and virulence factors, representing a major public health concern15. River-related E. coli showed a higher number of E. coli STs compared to wastewater isolates, comprising up to 6 different E. coli STs (Fig. 2). All river E. coli STs presented similar MIC values for most of the tested antibiotics, which were lower than those exhibited by wastewater E. coli. ST607 (Warwick MLST scheme) was the most prevalent ST found in rivers. This ST has been scarcely reported, although it has been already detected in river sediments16. MICs of plazomicin for all isolates, both from wastewater and river environments, were ≥512 mg/L, demonstrating the high-level resistance to aminoglycosides conferred by 16S-RMTases, even to an aminoglycoside that has not yet been approved for clinical use in the EU (Fig. 2).Fig. 2: Sequenced E. coli data.The source of the isolates is specified by different colors in the genomic SNP-tree branches, as well as the related sequence type. Level of resistance to all tested antibiotics is shown in a gradient of colors: PLZ (plazomicin), GEN (gentamicin), AMP (ampicillin), FOT (cefotaxime), TAZ (ceftazidime), MERO (meropenem), CHL (chloramphenicol), TMP (trimethoprim), AZI (azithromycin), COL (colistin), CIP (ciprofloxacin), NAL (nalidixic acid), TET (tetracycline), TGC (tigecycline), and SMX (sulfamethoxazole). The presence and absence of antibiotic resistance genes and plasmid incompatibility groups are indicated by circle and triangle symbols, respectively. The presence and absence of specific 16S-RMTase genes are indicated by star symbols.Full size imageResistome analysis results were highly correlated with resistance phenotypic profiles. Thus, isolates from WWTPs harbored an heterogenous antibiotic resistance gene content depending on the E. coli ST, such as the strong association between the mcr-1 gene and isolates belonging to ST119617. This heterogenicity was even present between isolates belonging to ST1196, which exhibited different resistance levels to chloramphenicol and tetracycline depending on the presence of cmlA1/floR and tet(A), respectively. However, all wastewater isolates showed a common high-level resistance to β-lactam compounds, including third generation cephalosporins, which could be attributed to the presence of blaCTX-M-55 (ST1196) and blaCMY-2 (ST224) (Fig. 2). On the contrary, all isolates from river environments possessed a uniform antibiotic resistance gene content, despite comprising different STs (Fig. 2). Almost all of them (17 isolates, 94.4%) were susceptible to β-lactams, including the isolates belonging to ST224, contrasting with wastewater isolates, which have been under anthropogenic pressure, such as clinical treatments with a combination of aminoglycosides and β-lactams18, that led to this resistance associations. Likewise, plasmidome analysis revealed that the total plasmid content, based on the plasmid incompatibility groups, was heterogeneously distributed among isolates from WWTPs according to E. coli STs and closely correlated with the resistance gene content (an average of 6.36 different plasmid replicons per isolate). ST1196 isolates carried a higher plasmid content, exhibiting different plasmid profiles among them. The only plasmid incompatibility group carried by all wastewater isolates was IncFII type, specifically a pC15-1a-like plasmid (Fig. 2). Considering river E. coli STs, the total plasmid content was generally lower (an average of 3.72 different plasmid replicons per isolate) and more uniform compared to wastewater STs, similar to the pattern of the resistome (these differences are addressed in the E. coli pan-genome structure and genome, plasmid and antibiotic resistance gene diversities section). The most prevalent plasmid incompatibility group found among them was IncHI2A, present in all river isolates except ST224, which harbored a completely different plasmid content (Fig. 2).
E. coli STs have a similar genomic complexity in wastewater and river water, but the diversity of plasmids and resistance genes is higher in wastewater STsThe pan-genome of all 43 E. coli isolates, with the independence of the origin, was constituted by a total of 13,819 different genes. The pan-genome was distributed in a common core-genome of 3109 genes (22.5%) and a variable accessory-genome of 10,710 genes (77.5%), covering a considerable E. coli diversity, considering that the estimation of the global E. coli core-genome comprises around 1500 genes19 (Supplementary Fig. 2). Attending to the pan-genome configuration depending on the water source, 3340 out of a total of 9099 genes (36.71%) were included in the core-genome of wastewater isolates, and the core-genome of river isolates was formed by 3410 out of 9927 genes (34.35%), showing that river isolates presented a larger total gene pool and a smaller relative core-genome comparing to WWTP isolates. The genes conforming the pan-genomes of wastewater and river water E. coli were statistically different, considering the genes that were present and absent in each niche from the total pan-genome (Jaccard, P-value = 0.001) (Fig. 3a), indicating that the different environments, and/or upstream environments from which they have seeded, led to the selection of specific genomic populations, even between members of E. coli ST224 originating from the two different environments (Jaccard, P-value = 0.049). The genomic diversity of the whole E. coli population from river water was significantly higher than the one found in wastewater (Jaccard, P-value = 3.599 × 10−8) (Supplementary Fig. 3a). However, considering the number of different STs constituting the genomic pool of each environment, the genetic diversity of an E. coli ST from wastewater was statistically similar to the diversity of an E. coli ST from freshwater (Jaccard, P-value = 0.3123) (Fig. 3b). This model, which took into account the number of STs from each source in the diversity analysis, was previously checked by random sampling analysis, obtaining similar results. Essentially, the diversity of the total E. coli population was dependent on the number of different STs that defined the population, but the diversity of a specific ST was independent of this factor and more suitable to estimate the variability of specific bacterial clones according to the origin. Likewise, the diversity of plasmid content, taking into account the different plasmid incompatibility groups identified, was distinctive for each water type (Jaccard, P-value = 0.001) (Fig. 3c). Thus, the ecological niche also influenced the presence of specific plasmid types. The level of complexity of the E. coli plasmid pool circulating in each aquatic environment was statistically similar (Jaccard, P-value = 0.2447) (Supplementary Figure 3b). However, the number of different plasmid types carried by each E. coli ST was significantly higher in WWTPs than in rivers (Jaccard, P-value = 1.037 × 10−14) (Fig. 3d). Both wastewater and river E. coli populations also showed a great divergence in the antibiotic resistance gene content (Jaccard, P-value = 0.001) (Fig. 3e), showing different resistance mechanisms to particular antimicrobial classes. Furthermore, this diversity was significantly higher in the total E. coli population from wastewater comparing to the E. coli population from river water (Jaccard, P-value = 5.093 × 10−14) (Supplementary Fig. 3c), even when the latter possessed a higher total genomic diversity. Thus, the sum of genetic resistance determinants carried by each E. coli ST from wastewater environments was much higher than for each E. coli ST from natural effluents (Jaccard, P-value  More

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Establishing the variables involved in rock weathering and fire behaviour is a key aspect of developing an accurate fire-induced rock spalling hypothesis. We expand on these variables by drawing on field observations and existing findings outlined below.Mechanical weatheringThe physical breakup and removal of rocks of varying hardness and degrees of weathering via mechanical weathering is the primary process that denudes and sculpts uplifted regions of Earth’s surface. Sub-critical cracking describes the slow propagation of microfractures through a rock in low-stress, near-surface conditions as a result of thermal stress, ice wedging, mineral alteration (volumetric expansion) and biomechanical processes such as root growth25. Sheeting is characterised by thick (0.1–1 m) layers of rock peeling off exposed surfaces roughly parallel to the surface topography. There is debate as to whether sheeting is related to gradual unloading and release of stresses near the surface or a combination of other stresses26,27. The physical process of thermal expansion and contraction of rocks over thousands of years is responsible for the thinner, gradual flaking (exfoliation) of rock surfaces, which can be observed all over the surface of inselbergs in central Australia5 and presumably the main process responsible for the slow rates of erosion at the tops of inselbergs14.Fracture propagation is facilitated by the presence of water28, which helps to break chemical bonds leading to more fractured rock at shallow, superficial levels of the crust. Thus, rocks are generally more fractured in the superficial, near-surface environments than at deeper levels. Spontaneous rock-burst events were captured on video during a hot summer of 2014 in California when a granite dome at Twain Harte began explosively exfoliating29. Extreme thermal stresses associated with fire and lightning strikes are acknowledged as mechanisms of critical stress fracturing in rocks but generally considered to be a rare form of rapid and catastrophic mechanical weathering25. Our observations of rock surfaces following wildfires are that fire-related rock spalling is a commonly observed phenomenon wherever high-intensity fire has swept across rocky outcrops (for example, Figs. 5 and 6). We suggest that fire-spalling is a significant driving mechanism of physical weathering in arid, fire-prone environments and has been overlooked as an important agent of geomorphic change and landscape evolution.Fig. 6: Existing and new models of flared slope development using Uluru as an example and a graphical representation of the formula for erosion due to fire-spalling in different fire regimes.a The model of Twidale and Bourne 199822 involving subsurface weathering via shallow groundwaters to form soft regolith or unconsolidated soil that is subsequently removed by erosion and landscape lowering; b a new model of flared slope development via fire-induced rock spalling associated with episodic wildfire events. Note the charcoal on the recently burnt trees is the same height as the flared slope; c inverse correlation relationship between rates of erosion E(t) plotted against fire recurrence interval (t) using the formula E = W.A/t (see https://www.geogebra.org/calculator/uwa68amr). Rock-type and fire temperatures tend to control the thickness of spalled sheets (W) whilst fire intensity and duration are the main controls on the surface area spalled (0–100%). The inverse correlation of rates of fire-spalling erosion with average recurrence intervals (t) results in an increasing rate of weathering with smaller average fire recurrence intervals. Fire recurrence intervals are largely controlled by climatic and vegetation regimes and examples from Figs. 2 and 4 are shown and plotted on the graph according to the fire recurrence interval for that region.Full size imageWildfire temperaturesA detailed study of high-intensity wildfires in eucalypt forests of SW Australia30 revealed that these fires burn at temperatures between 300 °C at the tips of visible flames and up to a maximum of 1100 °C near the flame base, while temperatures of up to 1330 °C were recorded in Canadian crown fires31. Experimental fires conducted in jarrah forests of south-west Western Australia (Project VESTA) reveal that temperature correlates directly with the rate of spread, fire intensity, flame height and surface fuel bulk density30. This single case study measured the average flame-front residence time in eucalypt forest fuels of about 37 s. However, radiant heat and hot winds fanning out in front of the fire have the ability to pre-heat the rock surface and vegetation before and after the arrival of the fire front31 particularly along cliff lines.We report the first documented case of spalling in basalt from Mount Kaputar in northern N.S.W. (Fig. 4d). Basalt is a high-temperature volcanic rock with no quartz content. Fire-spalling was minimal across most of the outcrops and generally consisted of dislodged pyroxene phenocrysts. However, a few basalt outcrops adjacent to nearby fallen burnt logs were intensely scorched and displayed thin (1–4 mm) spalled flakes of basalt indicating that fire-spalling is not restricted entirely to quartz-rich lithologies. In mature eucalypt forests with large, woody fuels, termed ‘down wood’32, fires can burn or smoulder for days, providing prolonged heat required for extensive spalling. Some cliff faces record distinct ghosted impressions of nearby tree trunks with the resultant spalling hollowing out the line and shape of a tree trunk in an otherwise flat, vertical rockface (Fig. 5a—right-hand side). A discarded brown glass bottle adjacent to the basalt spalling had softened and undergone ductile collapse and partially melted. The glass had cooled slowly enough to avoid shattering indicating prolonged heating from the smouldering downward. This glass was collected and placed in a high-temperature oven where it was observed to become soft and malleable at 750 °C and completely collapsed and started melting at 830 °C indicating that this fire sustained ground surface temperatures of between 750 and 830 °C next to the smouldering tree and fallen logs.Fire-induced rock spallingFire is known to accelerate the rock flaking process25,33,34,35,36,37,38 resulting in rock spalling36,39 and shattering38. Conflagration leads to the rapid disintegration of the rock surface due to the differential expansion of the hot rock surface compared with the cooler interior. Fire-spalling can remove between 10 and 100% of the burnt rock surface in sheets between 5 and 50 mm thick37 depending upon rock type and fire intensity. Detailed measurements of post-fire rock spalling after the Esperanza chaparral fire in California revealed that 7–55% of the granodiorite boulder surfaces were spalled to a depth of 11–24 mm33. They found that the thickest spalled sheets occurred around the flanks of the boulders and cautioned that, if sampled for cosmogenic dating, these freshly exposed, spalled surfaces would produce a significant underestimate of exposure age. These figures match our own observations of spalled granite following the fires in Cobargo, Moonbi and Thredbo N.S.W. (Fig. 4) in which granite boulders spalled sheets between 5 and 50 mm thick, while sandstones from the Blue Mountains spalled sheets between 5 and 22 mm thick (Fig. 5).Quartz expands four times more than feldspar and twice as much as hornblende and shows a 3.76% volume expansion when heated from room temperature to 570 °C40. Thus, quartz-rich rocks have a greater expansion potential and are more likely to spall. Experimental studies41 show that rock elasticity reduces significantly at temperatures as low as 200 °C, over a relatively short period of time. Goudie et al.41 postulated that rock outcrops subject to intense fires would have an increased susceptibility to erosion via spalling and weathering. However, these findings have not been applied to broader landscape models or the formation of flared slopes around inselbergs.Fire regimesThe potential rate of erosion due to fire-spalling at the base of inselbergs will be strongly influenced by fire severity and recurrence intervals, which vary greatly across Australia from 1- to 5-year recurrence intervals and 100-year intervals and >10,000 kW m−1 for tall, open forests of the cool, temperate south42. Accurately calculating the fire return period is difficult due to limited historical records but estimates for arid, spinifex-dominated regions such as the Tanami are in the order of every 7–9 years43. Analyses of satellite data between 1998 and 2004 revealed that 27% of arid Australia burnt at least once over that 6-year period44. Figure 1a shows the areas burnt in Australia since 2001. The surface area of the rock affected by spalling depends on the rock-type and severity of the fire. Fire severity is strongly determined by the bulk density45, height and proximity of the adjacent vegetation to rock surfaces and the surrounding slope gradient. All the examples of flared slopes shown in Figs. 1 and 2 reveal a close relationship between the height of the encroaching vegetation and the height of the concavity. Katter Kich, Pildappa Rock and Walga Rock form distinct embayments where the flared slopes are most pronounced, which appear to promote denser, taller vegetation growth and hence greater fuel loading and thus higher fire severity (Fig. 3).The impermeable nature of inselbergs results in rapid and efficient water runoff from the bare-rock surface before draining into adjacent, thin soil profiles. This creates a “roof and gutter” effect around the periphery of many inselbergs which creates permanent water holes and shallow groundwater within easy reach of deep-rooted plants. Inselbergs create important geodiversity within otherwise flat landscapes and thus host important niche ecosystems that add to the overall biodiversity of desert regions46. Accessible groundwater around the fringes of the inselbergs encourages denser, taller vegetation at the interface between bare rock and unconsolidated surficial sediments which in turn increases the fuel load. Inselbergs are prominent topographic features in flat deserts that provide sources of permanent water, abundant flora and fauna and shelter.Grassy plains and savannahs of central Australia are characterised by regular, low-intensity fires with fire recurrence intervals between 1 and 5 years42. However, where these fires encounter inselbergs they move into thicker, taller vegetation regimes with greater fuel loads (Figs. 2 and 3). Inselbergs are topographic highs within relatively flat landscapes and the slight increase in slope gradient around the inselberg will accelerate and intensify an approaching fire front. Steep slopes around the margins of inselbergs possibly act as chimneys, drawing in hot air from the surrounding plains and channelling them upwards. These factors possibly help to draw in fires from the surrounding plains into and around topographically high inselbergs where the intensity is enhanced at the base of inselbergs due to the denser vegetation and greater fuel load.A fire-induced spalling weathering formulaFire-spalling leads to physical weathering (erosion) and disintegration of exposed rock faces36,37 as shown in Figs. 3–5. The degree and extent of spalling on different rock types and at varying temperatures and durations is less well understood and requires further experimental work41 but essentially fire-spalling is a function of fire intensity (temperature), duration and rock type with quartz-rich rocks having a greater propensity to expand and spall40.We developed a simple fire-spalling erosion formula to estimate a long-term rate of fire-induced spalling that broadly considers the net result of fire-spalling in terms of the thickness (width) of the spalled flakes produced by a single fire event, the total surface area as a percentage of the exposed rock face affected by a single fire-spalling event, and the average fire recurrence interval for a given region. Together these variables can give some indication as to the long-term rates of erosion due to fire-spalling at the base of an inselberg or cliff face where there is significant vegetation to fuel a wildfire.The formula for erosion due to fire-spalling.$$E = frac{{W times A}}{t}$$
(1)
where, E = rate of erosion due to fire-spalling (mm yr−1), W = average width (thickness) of spalled sheets (mm) for a single fire event. Dependent on rock type (quartz content and texture), rock strength, fire temperature and duration, A = area of rock surface affected by fire-spalling as a percentage (%) of total surface area. Dependent on temperature and duration of the fire, t = average fire recurrence interval (years). Determined from regional, historic fire records or palaeofire records for longer time periods. Dependent on vegetation, climatic regimes and land management practices.Limitations: this equation applies to a near-vertical rock face at ground level which receives uniform heat radiation from a fire that burns right up to the rock face at ground level. The intensity of radiation will vary according to the dynamics of the fire front, fuel loading, vegetation type and slope gradient. Flame height is not critical to the overall rate of retreat of the cliff face because fire-spalling at the base of the cliff will gradually remove material supporting the cliff resulting in over-steepening at the base of the cliff and periodic sheeting and rockfalls as the overhanging cliff face becomes gravitationally unstable. The formula assumes that fire recurrence intervals have remained constant but we know from palaeofire records47,48 that fire intensity and recurrence intervals are largely controlled by long-term climatic variations which affect vegetation types and thus fuel loads. Below, we give two end-member examples of long-term rates of spalling-related erosion for low and high-frequency fire regimes that may apply to temperate and arid environments, respectively.Example 1. Low intensity, irregular fire regime. In this scenario, the average fire against a cliff results in spalling and flaking of ~10 mm sheets off ~20% of the surface area at ground level during a single fire event. Fire recurrence interval is one event every 50 years.$$E = frac{{W times A}}{t} = frac{{10;{mathrm{mm}} times 0.2}}{{50}} = 0.04;{rm{mm}};{rm{yr}}^{ – 1} = 40;{rm{m}};{rm{Ma}}^{ – 1}$$Example 2. High intensity, high-frequency fire regime. In this scenario, the average fire against a cliff results in spalling and flaking of ~20 mm sheets (Fig. 5) off ~80% of the surface area at ground level. Fire recurrence interval is one event every 5 years.$$E = frac{{W times A}}{t} = frac{{20;{mathrm{mm}} times 0.8}}{5} = 3.2;{rm{mm}};{rm{yr}}^{ – 1} = 3200;{rm{m}};{rm{Ma}}^{ – 1}$$In an intensely fire-prone environment such as example 2 above, it may only take about 625 years of fire-induced spalling to weather out a 2 m deep flared slope at the base of a vertical rock face. The point at which undercutting due to fire-spalling would trigger massive sheeting of the unsupported, overhanging rock ledge and subsequent rockfall event is not well constrained but some of the flared slopes around Uluru and Walga Rock are at least 2–3 m deep (Fig. 2h).Sediment production ratesIf rates of erosion due to fire-spalling around the periphery of an inselberg are orders of magnitude greater than those across the top of the inselberg, then this has implications for mechanisms of sediment production in flat, arid environments like Central Australia.Spalling of a 20 mm sheet from a 1 m2 area of granite with a density of 2691 kg m−3 will yield 0.02 m3 (53.82 kg) of rock. A flared slope around an inselberg such as Uluru with a circumference of ~10,000 m and a height of 2 m, would produce 400 m3 (1,076,400 kg) in a single event in which 100% of the 2 m high flared slope was spalled. Obviously, 100% spalling of the entire flared slope would never occur in a single event, so we use the long-term erosion rate based on fire recurrence intervals and average area spalled calculated in Eq. 1. This long-term estimate of sediment production from a single inselberg is compared with quantitative measurements of spalled granite surfaces sampled after the 2019–2020 fires in Cobargo on the south coast of N.S.W., Australia.The formula for sediment production.$$S_{rm{FS}} = P.H.E$$where, SFS = sediment production from fire-spalled rock surface (cubic metres per year), P = perimeter of the inselberg (metres), H = height of the flared slope around the inselberg as determined by vegetation and fire height, E =  rate of erosion due to fire-spalling (Eq. 1).Fire-spalling sediment production around the periphery of an inselberg such as Uluru with a perimeter of roughly 10,000 m and flared slope height of 2 m, would be$$S_{mathrm{FS}}=10,000, {mathrm{m}}times 2, {mathrm{m}} times 0.0032, {mathrm{m}}, {mathrm{yr}}^{-1}=64, {mathrm{m}}^{3}, {mathrm{yr}}^{-1}=172,224, {mathrm{kg}}, {mathrm{yr}}^{-1}$$This can be standardised to give a volume of rock spalled per year per square metre, which is the same as the erosion rate but in cubic metres per year. Given the density of the rock (granite = 2691 kg m−3 and compacted, meta-arkose sandstone (Uluru) are about the same) we can calculate the average mass of rock spalled each year. In the above scenario, it equals 8.61 kg per square metre per year.The rate of background (non-fire related) sediment production (SBA) from erosion of the surface area of an inselberg such as Uluru is equivalent to the surface area (~3,440,000 m2) multiplied by the average denudation rate of ~0.3–0.6 m/Ma (0.0003 mm yr−1) as established from cosmogenic studies.$$S_{mathrm{BA}}=3,440,000, {mathrm{m}}^{2}times 0.0000003, {mathrm{m}}, {mathrm{yr}}^{-1} =, sim! 1, {mathrm{m}}^{3}, {mathrm{yr}}^{-1}=2691, {mathrm{kg}}, {mathrm{yr}}^{-1}$$This equates to only 0.00081 kg per square metre per year. We estimate that fire-spalling on a 2 m high perimeter produces in the order of 64 times more sediment than the erosion of the entire surface of the inselberg due to background (non-fire related) processes.Spalled granite material was collected from two locations following the 2019–2020 fires in the Cobargo region along the south coast of N.S.W. (Fig. 4) to assist in quantifying the amount of rock spalled from a single rock face. Spalled surface area can be estimated simply by measuring the maximum height and width of the spalled surface in the field. We also created a digital surface using photogrammetry MetaShapePro software to calculate a precise surface area of the spalled surface. All of the spalled material was weighed and a standard granite density of 2691 kg m−3 was used to determine total volume. Generally spalling occurs as thin (1–3 cm) sheets but occasionally includes large 20–30 cm thick slabs that substantially add to the overall weight of spalled material. Whilst complete spalling of a 2 cm sheet from one square metre of granite surface will produce 53.82 kg m−2 of rock, our two sites (Cobargo2 and 3A) produced 23.65 kg total (16.89 kg m−2) and 41.60 kg m−2 total (33.55 kg m−2), respectively, indicating an average spalling thickness of 0.63–1.25 cm although the spalled thickness was highly variable with spalling distinctly more prominent along sharp or protruding edges than on flat surfaces. Large logs or tree trunks have the potential to continue burning long after the fire front moves through and their presence near rock surfaces significantly increases the degree of spalling. The most intense spalling was observed at Moonbi Granite near Tamworth in northern N.S.W. where some granite boulders had 100% surface spalling up to 2 m above ground level and not one but several spalled sheets (5–20 cm total thickness) exfoliating off during a single, intense fire creating several hundred kilograms of spalled rock debris on the granite surface facing the fire front (Fig. 4). Likewise, lichen coated granites from Australia’s most elevated alpine regions in the Snowy Mountains (Thredbo) displayed intense spalling but were covered in snow six months later. The fire recurrence interval for these alpine regions is probably in the order of one every 20–100 years thus the effects of fire-spalling are less pronounced than in arid regions and less evident than other forms of fluvial or chemical weathering that dominate in wetter climates. However, the abundant spalled surfaces shown in Fig. 4 reveal that large, intense fires such as the Black Summer fires of 2019–2020 will result in significant erosion and sediment production even in alpine environments. More