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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

Combined culture-dependent and culture-independent approach identifies the prevalent and viable bacterial community members of the human lung post-transplantTo characterize the bacterial community composition of the lung microbiota post-transplant, we performed 16S rRNA gene amplicon sequencing of 234 longitudinal BALF samples from 64 lung transplant recipients collected over a 49-month period (Fig. 1a, Supplementary Table 1). A total of 7164 operational taxonomic units (OTUs) were identified, excluding OTUs contributing to reads in 11 negative control samples32 (see “Methods”, Supplementary Fig. 1a, Supplementary Data 1 and 2). In accordance with previous studies on BALF samples from healthy non-transplant individuals4,5,6,26, we found that Bacteroidetes and Firmicutes followed by Proteobacteria and Actinobacteria are the most abundant phyla in the post-transplant lung (Fig. 1b). Prevalence analysis across all BALF samples showed that the community composition is highly variable with only 22 OTUs shared by ≥50% of the samples (Supplementary Fig. 1b, Supplementary Data 3). However, these 22 OTUs constituted 42% of the total number of rarefied reads, indicating that they are predominant members of the post-transplant lung microbiota (Fig. 1c, Supplementary Fig. 1c, Supplementary Table 2, Supplementary Data 3). They belonged to the genera Prevotella 7, Streptococcus, Veillonella, Neisseria, Alloprevotella, Pseudomonas, Gemella, Granulicatella, Campylobacter, Porphyromonas and Rothia, the majority of which are also prevailing community members in the healthy human lung3,5,7,26, suggesting a considerable overlap in the overall composition of the lung microbiota between the healthy and the transplanted lung.Fig. 1: Combining BALF amplicon sequencing and bacterial culturing to deduce the microbial ecology of deep lung microbiota.a Schematic of the sampling of Bronchoalveolar lavage fluid (BALF) from lung transplant recipients over time (months post-transplant). b Relative abundances (%) of most abundant phyla across BALF samples. Box plots show median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers) as well as outliers (single points). c Prevalence (% samples) vs contribution to total reads across samples for most abundant phyla. Dot color shows different genera and size show total rarefied reads. Gray dashed horizontal line shows prevalence ≥50%. d Scatter plot shows correlation between number of observed OTUs and bacterial counts per BALF sample obtained by quantifying 16S rRNA gene copies with qPCR. Linear regression is shown by the blue line with gray shaded area showing 95% confidence interval (n = 234, two-sided, F(1, 232) = 91.04, P = 2.2 × 10−16), Coefficient of correlation; R2 = 0.28. e Bar chart shows lung taxa (genera; OTU IDs) that contributed ≥75% of total bacterial biomass across samples (n = 234). Venn diagram inset shows overlap (yellow) between the most prevalent (≥50% incidence, light blue) and the most abundant (≥75% total count, red) taxa in the transplanted lung. Bar colors also show the same.Full size imageDifferences in bacterial loads between samples can skew community analyses when based on relative abundance profiling alone. Therefore, we used qPCR to determine the total copies of the 16S rRNA gene as an estimate for bacterial counts, and normalized the abundances of each OTU across the 234 samples (absolute abundance). We found that the bacterial counts vastly differed between samples, ranging between 101 and 106 gene copies per ml of BALF (Supplementary Fig. 1d). The number of observed OTUs increased with decreasing counts (Fig. 1d) suggesting that a large fraction of the OTUs were detected in samples of low bacterial biomass and hence represent either transient or extremely low-abundant community members, or sequencing artefacts and contaminations. In turn, 19 of the 7164 OTUs constituted >75% of the total bacterial biomass detected across the 234 BALF samples (Fig. 1e). This included 11 of the 22 most prevalent OTUs (see above) plus eight OTUs that were detected in only a few samples but at very high abundance (Staphylococcus; OTU_2, Corynebacterium 1; OTU_16 and OTU_24, Anaerococcus; OTU_49 and OTU_234, Haemophilus; OTU_78, Streptococcus; OTU_6768, Peptoniphilus; OTU_63, Supplementary Table 2). It is important to differentiate these opportunistic colonizers from other community members with low incidence, as they reached very high bacterial counts in some samples with potential implications for lung health.To demonstrate the viability of prevalent lung microbiota members and to establish a reference catalogue of bacterial isolates from the human lung for experimental studies, we complemented the amplicon sequencing with a bacterial culturing approach (Supplementary Fig. 2). We cultivated 21 random BALF samples from 18 individuals, on 15 different semi-solid media (both general and selective) in combination with 3 oxygen concentrations; aerobic, 5% CO2, and anaerobic (See “Methods” and Supplementary Table 3), representing 26 different conditions. We cultured fresh BALF immediately upon extraction (within 2 h), as we observed loss in bacterial diversity upon cultivating frozen samples. This resulted in a total of 300 bacterial isolates, representing 5 phyla, 7 classes, 13 orders, and 17 families from which we built an open-access biobank called the Lung Microbiota culture Collection (LuMiCol, Supplementary Data 4, https://github.com/sudu87/Microbial-ecology-of-the-transplanted-human-lung).To examine the extent of overlap between bacteria in LuMiCol and the diversity obtained by amplicon sequencing, we included 16S rRNA gene sequences from 215 isolates that passed our quality filter into the community analysis, which allowed for the retrieval of OTU-isolate matching pairs32 (Methods). We found that 213 isolates matched to 47 OTUs (Fig. 2a, c, Supplementary Data 5), including 17 of the most prevalent and abundant bacteria (Fig. 1e, Supplementary Table 2). As expected, bacteria with high abundance in the amplicon sequencing-based community analysis were isolated more frequently, with Firmicutes revealing the highest isolate diversity (Fig. 2a–c, Supplementary Data 4, 5) and being recovered under the most diverse culturing conditions.Fig. 2: A lung microbiota culture collection (LuMiCol) reveals extended diversity and phenotypic characteristics of the lower airway bacterial community.a Phylogenetic tree of the 47 OTU-isolate matching pairs inferred with FastTree. Branch bootstrap support values (size of dark gray circles) ≥80% are displayed. b Growth characteristics of each OTU-isolate matching pair in three different oxygen conditions (Anaerobic – light brown, 5% CO2-yellow, aerobic-light blue, n = 3). Column with pie charts shows growth on semi-solid agar. Heatmap shows median change in Optical Density (OD) at 600 nm growth in three different liquid media (THY, RPMI, RPMI without glucose) over 3 days. c Cumulative counts of each OTU-isolate matching pair across all BALF samples (gray). d Number of isolates in Lumicol (black) per OTU-isolate matching pair. Taxa are labeled as genus; OTU ID, with an indication of whether they are prevalent (gray rectangle) or opportunistic (magenta rectangle) in the lower airway community. The names of the closest hit in databases: eHOMD and SILVA are used as species descriptor.Full size imageIn summary, our results from the combined culture-dependent and culture-independent approach show that the lung microbiota post-transplant is highly variable in terms of both bacterial load and community composition with many transient and low-abundant bacterial taxa. However, a few community members display relatively high prevalence and/or abundance suggesting that they represent important colonizers of the human lung.LuMiCol informs on the diversity and metabolic preferences of culturable human lung bacteriaWe characterized the culturable community members of the lower respiratory tract contained in LuMiCol by testing a wide range of growth conditions and phenotypic properties (see “Methods”). The majority of the cultured isolates could taxonomically be assigned at the species level based on genotyping of the 16S rRNA gene V1-V5 region. However, the limited taxonomic resolution offered by this method does not allow to discriminate between closely related strains, which can include both pathogenic and non-pathogenic bacteria. Hence for Streptococcus, we additionally tested for type of hemolysis (alpha, beta, or gamma) and resistance to optochin, which differentiates the pathogenic pneumococcus and the non-pathogenic viridans groups (Fig. 2a, Supplementary Fig. 2b, c). This demonstrated that the 16 Streptococcus OTU-isolate pairs belong to the viridans group of streptococci (VS)33. Interestingly, these isolates exhibited the highest genotypic and phenotypic diversity throughout our collection and belonged to five OTUs among the 22 most prevalent community members, with Streptococcus mitis (OTU_11) present in 93.6% of all samples.BALF from healthy individuals contains amino acids, citrate, urate, fatty acids, and antioxidants such as glutathione but no detectable glucose34, which is associated with increased bacterial load and infection35,36,37. To get insights into basic bacterial metabolism, we assessed the growth of all 47 isolates matching an OTU under different oxygen concentrations. We used undefined rich media (Todd-Hewitt Yeast extract) and defined low-complexity liquid media (RPMI 1640), including a glucose-free version to mimic the deep lung environment (see “Methods”). Despite the presence of oxygen in the human lung, the majority of the isolates were either obligate or facultative anaerobes (Fig. 2a), including some of the most prevalent members (Prevotella melaninogenica (OTU_3), Streptococcus mitis (OTU_11), Veillonella atypica (OTU_6) and Granulicatella adiacens (OTU_17). A similar trend was also observed in liquid media under anaerobic conditions, with the exception of the genera Prevotella, Veillonella and Granulicatella. Most streptococci from the human lung grew best in complex liquid media containing glucose under anaerobic conditions, including the most prevalent species in our cohort, S. mitis (OTU_11) (Fig. 2b). However, noticeable exceptions were S. vestibularis (OTU_34), S. oralis (OTU_3427 and OTU_1567), and S. gordonii (OTU_10031), which grew equally well in the presence of oxygen and in low-complexity liquid medium (Fig. 2b). Most Actinobacteria grew best on rich medium in the presence of 5% CO2, with an exception of Actinomyces odontolyticus (OTU_39), which required anaerobic conditions. Some Actinobacteria grew equally well in anaerobic conditions as in the presence of 5% CO2, i.e., Corynebacterium durum (OTU_501), Actinobacteria sp. oral taxon (OTU_328 and OTU_228).The two most predominant opportunistic pathogens in our lung cohort, P. aeruginosa (OTU_1) and S. aureus (OTU_2), grew best in rich liquid medium in the presence of oxygen (Fig. 2c), although these also grew to lower degree under anaerobic conditions. These results indicate that changes in the physicochemical conditions in the lung may favor the growth of these two opportunistic pathogens. In summary, our observations from the bacterial culture collection provide first insights into the phenotypic properties of human lung bacteria and will serve as a basis for future experimental work.Identification of four compositionally distinct pneumotypes post-transplant using machine learning based on ecological metricsTo detect and characterize differences in bacterial community composition between BALF samples from transplant patients, we clustered the samples using an unsupervised machine learning algorithm based on pairwise Bray–Curtis dissimilarity32 (beta diversity, See “Methods”, Supplementary Data 6). This segregated the samples into four partitions around medoids (PAMs) at both phylum and OTU level (Fig. 3a, b, Supplementary Fig. 3a, b). We refer to these clusters as “pneumotypes” PAM1, PAM2, PAM3, and PAM4 (Supplementary Table 4). PAM1 formed the largest cluster consisting of the majority of samples (n = 115) followed by PAM3 (n = 76), PAM2 (n = 19), and PAM4 (n = 24) (Supplementary Data 7). Examination of various diversity measures (Species occurrence, OTU diversity, OTU richness, Fig. 3c–e), distribution of the dominant community members (Fig. 3f), and bacterial counts (16S rRNA gene copies, Fig. 3g) revealed distinctive characteristics between the four pneumotypes.Fig. 3: Bacterial communities of the lung post-transplant fall into four ‘pneumotypes’ with distinct community characteristics.a, b Principal component analysis shows Partition around medoids (PAMs) at phylum and OTU level respectively generated by k-medoid-based unsupervised machine learning using Bray–Curtis dissimilarity (occurrence and abundance). Pneumotypes are color coded: Balanced (red, n = 115), Staphylococcus (green, n = 19), Microbiota-depleted (MD, blue, n = 76), and Pseudomonas (orange, n = 24). c–g Violin plots show distributions of pairwise species occurrence (Sorenson’s index, PERMANOVA, two-sided, F(3, 229) = 8.49, P = 9.9 × 10−5), OTU diversity (Kruskal–Wallis test, χ2 = 89.2, df = 3, two-sided, P = 2.2 × 10−16), OTU richness (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), proportion of most dominant OTUs (Kruskal–Wallis test, χ2 = 94.45, df = 3, two-sided, P = 2.2 × 10−16), and total bacterial counts (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), respectively, across the four pneumotypes. h, i Enrichment analysis of prevalence (green dotted line ≥50%) and absolute abundance across all samples of the 30 most dominant taxa (i.e., OTUs) in PneumotypeBalanced and PneumotypeMD respectively, when each was compared to the other three combined pneumotypes (gray boxes). Differential abundances after enrichment analysis was calculated between each PAM and the other three PAMs combined, using ART-ANOVA. j Heatmap shows relative percentage of taxa (right colored panel) cultured from paired samples of Bronchial aspiration (BA) and Bronchoalveolar lavage fluid (BALF) from each pneumotype (left colored panel). Oropharyngeal flora mainly corresponds to PneumotypeBalanced (i.e., Streptococcus, Prevotella, Veillonella). All box plots including insets show median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers) as well as outliers (single points). Multiple comparison of beta diversity indices was done by pairwise PERMANOVA (adonis) with False Discovery rate (FDR). Post hoc analyses (95% Confidence Interval) were done by using Tukey’s test (ANOVA) or Dunn’s test (Kruskal test) with False Discovery Rate (FDR) or least-squares means (ART-ANOVA) with False Discovery Rate (FDR). * P  More

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