More stories

  • in

    Spider mites avoid caterpillar traces to prevent intraguild predation

    All the materials followed relevant institutional and national guidelines and legislation.MitesWe used a T. kanzawai population collected from trifoliate orange trees (Poncirus trifoliata [L.] Raf.) in 2018 in Kyoto, Japan, and a T. urticae population collected from chrysanthemum plants (Chrysanthemum morifolium Ramat.) in 1998 in Nara, Japan. These populations were reared on adaxial surfaces of kidney bean (Phaseolus vulgaris L.) primary leaves, which were pressed onto water-saturated cotton in Petri dishes (90 mm diameter, 14 mm depth). The water-saturated cotton served as a barrier to prevent mites from escaping. The dishes were maintained at 25 °C, 50% relative humidity, and a 16L:8D photoperiod. All experiments were conducted under these conditions. We only used mated adult females (i.e., the dispersal stage) of T. kanzawai or T. urticae mites.CaterpillarsWe used caterpillars of four lepidopteran species: Bombyx mori L., P. Xuthus, Spodoptera litura Fabricius and T. oldenlandiae. We collected eggs and larvae of T. oldenlandiae from C. japonica in 2021 in Kyoto, Japan, and reared them on C. japonica leaves until pupation. Theretra oldenlandiae shares Vitaceae host plants with T. kanzawai and T. urticae8,15. We collected eggs and larvae of P. xuthus from Ptelea trifoliata in 2021 in Kyoto, Japan, and reared them on Citrus unshiu Markov. leaves until pupation. Papilio. xuthus and T. kanzawai share P. trifoliata as a host plant in Kyoto (Kinto, personal observation).We obtained commercial populations of the B. mori Kinshu × Showa strain (Ueda-sanshu Co., Ltd, Nagano, Japan) or the w1-pnd strain. We reared B. mori larvae on an artificial diet produced at the Kyoto Institute of Technology. Although T. kanzawai use Morus alba, a food plant for the B. mori strain, the mite and the strain never encounter one another in the wild, because the B. mori strain has been domesticated for hundreds of years.We obtained a sub-cultured population of S. litura from the Kyoto Institute of Technology. We reared first to fourth instars of S. litura on an artificial diet (Insecta LFM, Nosan Insect Materials, Kanagawa, Japan), while final instars were fed P. vulgaris leaves. Because S. litura feeds on various wild and cultivated plants22,23, it may share some host plants with T. kanzawai and T. urticae, both of which also feed on many host plant species8,9,10.We reared caterpillars of T. oldenlandiae, P. xuthus, and S. litura in 900 mL transparent plastic cups and caterpillars of B. mori in transparent plastic containers (140 × 220 × 35 mm). All caterpillars were maintained under the same laboratory conditions described above.PlantsWe used several parts of P. vulgaris plants in the following experiments. This species is a preferred food for both mite species16,17 and S. litura24, but the other three caterpillar species do not feed on it (Kinto, personal observation). We thus used P. vulgaris rather than shared host plants, because some caterpillars and mites (T. urticae and P. xuthus, for example) do not share any host plant.Avoidance of caterpillar traces on leaf surfaces by spider mitesTo examine whether spider mites avoid settling on host plant surfaces bearing caterpillar traces, we conducted dual-choice tests using paired adjacent leaf squares with and without caterpillar traces. We did not use whole plants because, in practice, it was difficult to induce caterpillar traces on whole plants. We used two spider mite species (T. kanzawai and T. urticae) and four caterpillar species (T. oldenlandiae, P. xuthus, B. mori, and S. litura). We cut a 10 × 20 mm leaf piece from a fully expanded primary kidney bean leaf and then cut the piece into two equal squares (10 × 10 mm). To introduce caterpillar traces to one square, we arranged them on a separate piece of paper towel on water-saturated cotton. This procedure was necessary because the caterpillars used were larger than individual leaf squares. Then we placed a fourth or final instar caterpillar on the squares and induced the caterpillar to walk across every leaf square three times (Fig. 1a). We carefully removed all caterpillar-produced silk threads from the squares. Within 30 min, we arranged the square (trace +) to touch against the other square (trace −) on water-saturated cotton in a Petri dish. Subsequently, a 2- to 4-day-old mated adult female of T. kanzawai or T. urticae was introduced onto a pointed piece of Parafilm in contact with both leaf edges using a fine brush (Fig. 1a). We recorded the leaf square onto which the mite had settled at 2 h after its introduction, as preliminary observations confirmed that all females would settle on a particular leaf within that period. Each female mite and pair of leaf squares were used only once. All tests described below were conducted between 13:00 and 17:00 h, when adult female spider mites actively disperse by walking. There were 14 replicates using traces of T. oldenlandiae, 48 of P. xuthus, 20 of B. mori, and 26 of S. litura for T. kanzawai, as well as 18, 32, 16, and 47, respectively, for T. urticae. Data were subjected to two-tailed binomial tests with the common null hypothesis that a spider mite would settle on the two squares with equal probability (i.e., 0.5).Figure 1(a) Procedure used to observe avoidance of caterpillar traces by spider mites. (b) Experimental setup used to observe avoidance of B. mori traces on plant stems by T. kanzawai. (c) Experimental setup used to observe avoidance of B. mori trace extracts by T. kanzawai.Full size imageDuration of B. mori trace avoidance by T. kanzawai
    To examine whether the effects of caterpillar traces on spider mite avoidance decline over time, we used T. kanzawai mites and B. mori caterpillars. We used B. mori because populations can be easily maintained over many generations. We prepared bean leaf squares with B. mori traces in the same manner descried above and preserved the traced square on water-saturated cotton for 0 h (n = 30), 24 h (n = 29), 48 h (n = 28), or 72 h (n = 28). Then we arranged the square (trace +) to lie in close proximity to the control square (trace −) that had been preserved for the same periods of time. Then we compared the avoidance response of T. kanzawai females in the same manner described above.Avoidance of B. mori traces on plant stems by T. kanzawai
    To examine whether T. kanzawai females avoid walking along plant stems bearing caterpillar traces, we used Y-shaped kidney bean stems (Fig. 1b). We cut symmetric bean plants ca. 15 days after sowing from their base and inserted them perpendicularly into a 5 mL glass bottle filled with water and wet cotton. To induce caterpillar traces on one branch of the stem, we allowed a silkworm to crawl from the branching point to the far end of one branch three times for each stem (n = 20). Then we introduced a T. kanzawai adult female at a release point 35 mm below the branch point (Fig. 1b). We recorded the branch along which the female walked to the far end. Each female mite and each Y-shaped stem were used only once. The numbers of females were compared using binomial tests in the same manner described above.Avoidance of B. mori trace extracts by T. kanzawai
    To extract chemical traces of caterpillar, we introduced 10 third instar B. mori to a glass Petri dish (120 mm diameter, 60 mm depth). After 1 h, we removed all caterpillars and washed the inside bottom of the dish with 1.0 mL acetone. We replicated the procedure twice using different individuals to combine all extracts and to acquire enough extract for the following experiment.To examine avoidance of B. mori trace extracts by T. kanzawai females, we conducted dual-choice experiments using T-shaped pathways of filter paper (35 × 35 mm; width, 2 mm; Fig. 1c). Using disposable micropipettes (Drummond Scientific Co., PA, USA), 1.75 caterpillar equivalents (i.e., 60 µL) of acetone extract were applied to an alternately selected branch (17.5 mm long) of each pathway (i.e., 0.10 caterpillar equivalent/mm), with control acetone applied to the other branch. We applied each solution dropwise at the junction point to minimize mixing. After evaporating the solvent from those pathways, we perpendicularly suspended them (Fig. 1c) and introduced an adult female mite at 2 days post-maturation onto the bottom of each pathway using a fine brush and recorded the branch along which the female first walked to the far end. Each female mite and each T-shaped filter paper were used only once, with 19 replicates. Each female mite made a choice within 10 min. The avoidance response of T. kanzawai was analysed in the same manner described above.Indirect effects of B. mori traces on T. kanzawai via plantsTo determine whether B. mori traces on plants indirectly affect the performance of T. kanzawai on plants, we introduced 70–80 randomly selected quiescent female deutonymphs of T. kanzawai onto kidney bean leaf disks. Immediately after synchronized adult emergence, we introduced the same number of adult males to allow mating; the detailed procedure is described elsewhere25. After 24 h, we transferred the females singly onto 10 × 10 mm bean leaf squares with or without B. mori traces prepared as described above. Because the number of eggs laid within a certain period is considered the most sensitive performance index of spider mite females26,27, any plant-mediated indirect interaction, such as defence induction in response to caterpillar traces, should result in lower egg numbers laid by the test females. We counted the eggs laid on the leaf squares 24 h after their introduction. One female that laid no eggs during the 24 h period (n = 1, trace +) was excluded from the analysis. We obtained 33 and 36 replicates for the trail+ and trail– conditions, respectively. We compared the numbers of eggs laid on leaves with and without B. mori traces using a generalized linear model with a Poisson error distribution using the SAS 9.22 software (SAS Institute Inc., Cary, NC, USA).EthicsThis article does not contain any studies with human participants or animals. More

  • in

    Urban agriculture in walkable neighborhoods bore fruit for health and food system resilience during the COVID-19 pandemic

    During the COVID-19 pandemic, behavioral restrictions were imposed, after which various health problems were reported in many countries45,46. The pandemic has also increased food insecurity worldwide; consequently, panic buying has been observed in many countries, including Japan47. However, even in such situations, we found that diversity in local food access, ranging from self-cultivation to direct-to-consumer sales, was significantly associated with health and food security variables. Specifically, our results revealed the following five key discussion points.Urban agriculture in walkable neighborhoods bore fruit for health and food system resilience. However, the magnitude of its contribution differed depending on the type of urban agricultureThe results of this study showed that those who grew food by themselves at allotment farms and home gardens had significantly better subjective well-being and physical activity levels than those who did not. This result is in line with previous studies conducted during times free from the impact of infectious disease pandemics38,39,40. The use of direct sales was not related to subjective well-being but was significantly associated with physical activity. The reason might be that farm stand users tend to live in areas with farmland and travel to purchase fruits and vegetables at farm stands on foot or by bicycle. This result is consistent with that of a previous study demonstrating that the food environment in neighborhoods is an important component in promoting physical activity17.Our results also showed that those who grew food by themselves at allotment farms and those who purchased local foods at farm stands were significantly less anxious about the availability of fresh food both during the state of emergency and in the future than their counterparts. In contrast, home garden users showed significant differences only for the state of emergency. This result might be due to the differences in the size and yield of cultivation at allotment farms and home gardens. One lot in allotment farms in Tokyo can produce as much as or more than the average annual vegetable consumption per household in Japan48. However, home gardens are generally smaller and produce limited fresh foods for consumption, which may have influenced food security concerns.As in other countries, Japan imports much food from overseas and is deeply integrated into the large-scale global food system. However, as shown in this study, urban agriculture in Japanese suburbs forms small-scale, decentralized, and community-based local food systems. This multilayered food system can complement the disruptions and shortages of the global system when various problems occur for climatic, sociopolitical, or other reasons, such as pandemics. In fact, our empirical evidence suggests that urban agriculture in walkable neighborhoods, particularly allotment farms and direct-to-consumer sales at farm stands, contributed to the mitigation of food security concerns in neighborhood communities. This means that urban agriculture could enhance the resilience of the urban food system at a time when the global food system has been disrupted due to a pandemic. This validates recent discussions about the potential of urban agriculture to facilitate food system resilience10. Furthermore, our findings imply that the types of urban agriculture employed matter in determining the degree of contribution to food system resilience.To summarize the overall results, urban agriculture in walkable neighborhoods bore fruit for health and food system resilience during the COVID-19 pandemic. However, different types of urban agriculture exhibited varying associations with health and resilience. Allotment farms were positively related to all of the following: subjective well-being, physical activity, and food security concerns, both during the state of emergency and in the future. Home gardens were positively related to subjective well-being, physical activity, and food security concerns only during the state of emergency. Farm stands were positively related to physical activity and food security concerns both during the state of emergency and in the future.These differences may be due to the characteristics of the respective spaces. It is suggested that this diversity of urban agriculture has led to different types of people benefiting from various kinds of urban agriculture. Allotment farms were found to be associated with high subjective well-being, physical activity, and food security, but they may not be feasible for those who do not have enough physical strength because users are responsible for cultivating their lots, which measure 10–30 square meters40. In contrast, home gardens can be created even by those who are not confident in their physical strength. In fact, our study showed that women and older people engaged in home gardening more than men and younger people. In addition, direct-to-consumer sales at farm stands are the easiest way to obtain local fresh foods for those who do not have the time and space for allotment farms and home gardens. The need for urban agriculture has been argued in many countries2,3. However, little attention has been paid to its scale, accessibility, and diversity. Our study suggests that it is worthwhile to create diverse food production spaces within walkable neighborhoods while considering the diversity of people who access these spaces.Compared to other urban greenery and food retailers, the benefits of urban agriculture on subjective well-being and food security could be greaterCompared to the use of other urban green spaces, including urban parks, our results indicated that self-cultivation at allotment farms and home gardens was more strongly associated with subjective well-being. Previous studies have offered limited perspectives on the differences among various types of urban green spaces33. Our study further suggests that urban parks, allotment farms, and home gardens are differently associated with human health. However, as the reason was not determined, further research is needed.Furthermore, compared to other food retailers, such as supermarkets, convenience stores, and co-op deliveries, allotment farms and farm stands were more strongly associated with less anxiety about fresh food availability in the future. The availability of local fresh foods within walkable neighborhoods might have mitigated food security concerns because residents could grow food by themselves or directly observe farmers’ production processes, which may have made the difference from purchasing at places where the food systems were not visible.Flexibility in work style might promote urban agriculture in walkable neighborhoodsThere was an association between work style—working from home—and access to local food. According to the Ministry of Health, Labor and Welfare (https://www.mhlw.go.jp/english), 52% of Tokyo office workers worked from home during the first emergency declaration. Long commute times and high train congestion rates have been a problem in Tokyo suburbs, but remote workers have gained more time at and around their homes by reducing their commute times, increasing their opportunities to access local food in their walkable neighborhoods. Those who worked from home sought outdoor activities for refreshment and exercise and used a variety of urban green spaces during the pandemic49. Allotment farms and home gardens might be used as such urban green spaces. This result is consistent with previous studies assessing the characteristics of Canadian gardeners during the COVID-19 pandemic28,30.Until now, urban planners and policymakers have rarely taken work style into account. However, the flexibility of work styles and work hours may bring new insights; for example, those who work from home may become important players in urban agriculture. It has been pointed out that cities have a large hidden potential for urban agriculture by cultivating underused lands50. Our study suggests that such underused lands could be converted into productive urban landscapes for remote workers to engage in farming or gardening in between jobs as a hobby or as a side business.Food equity might be improved by urban agriculture in walkable neighborhoodsLocal fresh food is generally considered more expensive than junk food in high-income countries, creating social issues of food inequity. Therefore, past discussions on urban agriculture and food security have focused primarily on low-income households in socioeconomically disadvantaged areas24,25,26.In contrast, our study covered people from all income groups and found no statistically significant relationship between access to local food and income. This finding might be due to two urban cultural backgrounds regarding local food in Tokyo, that is, accessibility and affordability. First, residential segregation by income levels is not noteworthy in Tokyo and people from various income brackets live mixed in the same neighborhoods51. Therefore, most urban residents living in the suburbs have geographically equitable opportunities to access local foods. Second, local foods sold at farm stands are affordable. Prices are almost the same or cheaper than buying food at food retailers. While prices increase because of middleman margins related to shipping in the wholesale market, such increases are unnecessary when selling directly to consumers at farm stands. In addition, the allotment farm lots are not expensive to rent, particularly those operated by local municipalities (Supplementary Note 1).These two backgrounds make local fresh food physically and economically accessible to consumers of all income levels, resulting in food equity. This is particularly important because the concept of food system resilience includes the equitability perspective27.The integration of urban agriculture into walkable neighborhoods is a fruitful wayWhile the current discussion on walkable neighborhoods does not emphasize urban agriculture, our evidence indicated its effectiveness. The concept of walkable neighborhoods (e.g., the 15-min city model) stresses the decarbonization benefit of limiting vehicle travel, as well as the health benefits of promoting walking and cycling13,14,15,16. In addition, our research indicated that urban agriculture in walkable neighborhoods benefited health and well-being by increasing recreational outdoor opportunities to neighborhood communities, including remote workers. It also contributed to food system resilience by providing local foods to all people, including low-income households, when the global food system was disrupted due to the pandemic. Furthermore, recent studies on urban agriculture reported the decarbonization benefit of reducing carbon footprints in food production and distribution7,8. Small-scale and community-based urban agriculture in walkable neighborhoods might especially bring this benefit because neighborhood communities travel to farms on foot or by bicycle, which means almost no emission by distribution. While urban green spaces have various health benefits32,33,34,35, urban agriculture also contributes to food system resilience as well as carbon emission reduction, which makes it unique.Urban agriculture was once considered a failure of urban planning in Japan because it symbolized uncontrolled sprawl. This is analogous to the Western view, as urban agriculture was once considered the ultimate oxymoron1. However, our empirical evidence suggests that the urban‒rural mixture at neighborhood scales is a reasonable urban form that contributes to the resilience of the urban food system and to the health and well-being of neighborhood communities. It is no longer a failure of urban planning but a legacy of urban sprawl in the current urban context.Our study showed that integrating urban agriculture into walkable neighborhoods is a fruitful way of creating healthier cities and developing more resilient urban food systems during times of uncertainty. In cities where there is no farmland in intraurban areas, it would be considered effective to utilize underused spaces such as vacant lots and rooftops as productive urban landscapes. In growing cities where urban areas are still expanding, it would be advantageous to conserve agricultural landscapes within their urban fabrics. Our study could provide referential insights and robust evidence for urban policy to integrate urban agriculture into walkable neighborhoods.This study has potential limitations, including the timing of the survey and the measurement method that was utilized. We conducted the survey between June 4 and 8, 2020, just after the end of the first declaration of a state of emergency by the Japanese government. During this period, the main cultivation activities were planting and growing, and the harvest was just beginning. This seasonal constraint may have influenced the results. Because the survey was conducted during the pandemic, we used subjective methods to measure health and well-being status. However, the results might be different using objective methods52, thus further research is necessary. In addition, a longitudinal study is needed to determine whether the trends observed in this study were specific to the emergency period or whether they will persist after the COVID-19 pandemic. More

  • in

    Using click chemistry to study microbial ecology and evolution

    Saxon E, Bertozzi C. Cell surface engineering by a modified Staudinger reaction. Science. 2000;287:2007–10.CAS 

    Google Scholar 
    Staudinger H, Meyer J. Über neue organische Phosphorverbindungen III. Phosphinmethylenderivate und Phosphinimine. Helv Chim Acta. 1919;2:635–46. https://doi.org/10.1002/hlca.19190020164.Article 
    CAS 

    Google Scholar 
    Laughlin ST, Bertozzi CR. Metabolic labeling of glycans with azido sugars and subsequent glycan-profiling and visualization via Staudinger ligation. Nat Protoc. 2007;2:2930–44.CAS 

    Google Scholar 
    Oliveira BL, Guo Z, Bernardes GJL. Inverse electron demand Diels–Alder reactions in chemical biology. Chem Soc Rev. 2017;46:4895–950.CAS 

    Google Scholar 
    Lang K, Chin JW. Bioorthogonal reactions for labeling proteins. ACS Chem Biol. 2014;9:16–20. https://doi.org/10.1021/cb4009292.Article 
    CAS 

    Google Scholar 
    Kolb HC, Finn MG, Sharpless K. Click chemistry: diverse chemical function from a few good reactions. Angew Chemie-Int Ed. 2001;40:2004–21.CAS 

    Google Scholar 
    Tornøe C, Christensen C, Meldal M. Peptidotriazoles on Solid Phase: [1,2,3]-Triazoles by regiospecific Copper(I)-Catalyzed 1,3-Dipolar Cycloadditions of Terminal Alkynes to Azides. J Org Chem. 2002;67:3057–64. https://doi.org/10.1021/jo011148j.Article 
    CAS 

    Google Scholar 
    Bakkum T, Leeuwen T, van, Sarris AJC, Elsland DM, van, Poulcharidis D, Overkleeft HS, et al. Quantification of bioorthogonal stability in immune phagocytes using flow cytometry reveals rapid degradation of strained alkynes. ACS Chem Biol. 2018;13:1173–9. https://doi.org/10.1021/acschembio.8b0035.Article 
    CAS 

    Google Scholar 
    Wang Q, Chan T, Hilgraf R, Fokin R, Sharpless K, Finn M. Bioconjugation by copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:3192–3.CAS 

    Google Scholar 
    Link A, Tirrell D. Cell surface labeling of Escherichia coli via copper(I)-catalyzed [3+2] cycloaddition. J Am Chem Soc. 2003;125:11164–5.CAS 

    Google Scholar 
    Dieterich D, Link A, Tirrell D, Schuman E. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc Natl Acad Sci USA. 2006;103:9482–7.CAS 

    Google Scholar 
    McKay C, Finn M. Click chemistry in complex mixtures: bioorthogonal bioconjugation. Chem Biol. 2014;21:1075–101.CAS 

    Google Scholar 
    Agard N, Prescher J, Bertozzi C. A strain-promoted [3 + 2] Azide−Alkyne cycloaddition for covalent modification of biomolecules in living systems. J Am Chem Soc. 2004;126:15046–7. https://doi.org/10.1021/ja044996f.Article 
    CAS 

    Google Scholar 
    Weissleder R, Hilderbrand S. Tetrazine-based cycloadditions: application to pretargeted live cell imaging. Bioconjug Chem. 2008;19:2297–9.
    Google Scholar 
    Scinto SL, Bilodeau DA, Hincapie R, Lee W, Nguyen SS, Xu M, et al. Bioorthogonal chemistry. Nat Rev Methods. 2021;1:1–23.
    Google Scholar 
    Sletten E, Bertozzi C. Bioorthogonal chemistry: fishing for selectivity in a sea of functionality. Angew Chem Int Ed Engl. 2009;48:6974–98.CAS 

    Google Scholar 
    Moses JE, Moorhouse AD. The growing applications of click chemistry. Chem Soc Rev. 2007;36:1249–62.CAS 

    Google Scholar 
    Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
    CAS 

    Google Scholar 
    Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol. 2020;184:241–56.
    Google Scholar 
    Siegrist M, Whiteside S, Jewett J, Aditham A, Cava F, Bertozzi C. (D)-Amino acid chemical reporters reveal peptidoglycan dynamics of an intracellular pathogen. ACS Chem Biol. 2013;8:500–5.CAS 

    Google Scholar 
    Liechti G, Kuru E, Hall E, Kalinda A, Brun YV, VanNieuwenhze M, et al. A new metabolic cell wall labeling method reveals peptidoglycan in Chlamydia trachomatis. Nature. 2014;506:507. https://doi.org/10.1038/nature12892.Article 
    CAS 

    Google Scholar 
    Pilhofer M, Aistleitner K, Biboy J, Gray J, Kuru E, Hall E, et al. Discovery of chlamydial peptidoglycan reveals bacteria with murein sacculi but without FtsZ. Nat Commun. 2013;4:1–7.
    Google Scholar 
    Taylor JA, Bratton BP, Sichel SR, Blair KM, Jacobs HM, Demeester KE, et al. Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in helicobacter pylori. Elife. 2020;9:e52482.CAS 

    Google Scholar 
    Kuru E, Hughes HV, Brown PJ, Hall E, Tekkam S, Cava F, et al. In situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent D-amino acids. Angew Chemie Int Ed. 2012;51:12519–23. https://doi.org/10.1002/anie.201206749.Article 
    CAS 

    Google Scholar 
    van Teeseling MCF, Mesman RJ, Kuru E, Espaillat A, Cava F, Brun YV, et al. Anammox Planctomycetes have a peptidoglycan cell wall. Nat Commun. 2015;6:6878. https://doi.org/10.1038/ncomms7878.Article 
    CAS 

    Google Scholar 
    Wang W, Yang Q, Du Y, Zhou X, Du X, Wu Q. et al. Metabolic labeling of Peptidoglycan with NIR-II dye enables in vivo imaging of gut microbiota. Angew Chemie Int Ed. 2020;59:2628–33. https://doi.org/10.1002/anie.201910555.Article 
    CAS 

    Google Scholar 
    Wang W, Zhu Y, Chen X. imaging of gram-negative and gram-positive microbiotas in the mouse gut. Biochemistry. 2017;56:3889–93.CAS 

    Google Scholar 
    Geva-Zatorsky N, Alvarez D, Hudak JE, Reading NC, Erturk-Hasdemir D, Dasgupta S, et al. In vivo imaging and tracking of host-microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat Med. 2015;21:1091–100.CAS 

    Google Scholar 
    Besanceney-Webler C, Jiang H, Wang W, Baughn AD, Wu P. Metabolic labeling of fucosylated glycoproteins in Bacteroidales species. Bioorg Med Chem Lett. 2011;21:4989–92.CAS 

    Google Scholar 
    Han Z, Thuy-Boun PS, Pfeiffer W, Vartabedian VF, Torkamani A, Teijaro JR, et al. Identification of an N-acetylneuraminic acid-presenting bacteria isolated from a human microbiome. Sci Rep. 2021;11:1–12.
    Google Scholar 
    Becam J, Walter T, Burgert A, Schlegel J, Sauer M, Seibel J, et al. Antibacterial activity of ceramide and ceramide analogs against pathogenic Neisseria. Sci Rep. 2017;7:1–12.CAS 

    Google Scholar 
    Nilsson I, Lee SY, Sawyer WS, Baxter Rath CM, Lapointe G, Six DA. Metabolic phospholipid labeling of intact bacteria enables a fluorescence assay that detects compromised outer membranes. J Lipid Res. 2020;61:870–83.CAS 

    Google Scholar 
    Evershed RP, Crossman ZM, Bull ID, Mottram H, Dungait JAJ, Maxfield PJ, et al. 13C-Labelling of lipids to investigate microbial communities in the environment. Curr Opin Biotechnol. 2006;17:72–82.CAS 

    Google Scholar 
    Salic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc Natl Acad Sci USA. 2008;105:2415–20. https://doi.org/10.1073/pnas.0712168105.Article 

    Google Scholar 
    Smriga S, Samo TJ, Malfatti F, Villareal J, Azam F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat Microb Ecol. 2014;72:269–80.
    Google Scholar 
    Beauchemina ET, Hunter C, Maurice CF. Actively replicating gut bacteria identified by 5-ethynyl-2’-deoxyuridine (EdU) click chemistry and cell sorting. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.07.20.500840v2.Sinclair L, Barthelemy C, Cantrell D. Single cell glucose uptake assays: a cautionary tale. Immunometabolism. 2020;2. https://pubmed.ncbi.nlm.nih.gov/32879737/.Hu F, Chen DZ, Zhang DL, Shen Y, Wei L, Min PW. Vibrational imaging of glucose uptake activity in live cells and tissues by stimulated Raman scattering. Angew Chem Int Ed Engl. 2015;54:9821.CAS 

    Google Scholar 
    Kiick K, Saxon E, Tirrell D, Bertozzi C. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc Natl Acad Sci USA. 2002;99:19–24.CAS 

    Google Scholar 
    Kiick K, Tirrell D. Protein engineering by in vivo incorporation of non-natural amino acids: control of incorporation of methionine analogues by Methionyl-tRNA Synthetase. Tetrahedron. 2000;56:9487–93.CAS 

    Google Scholar 
    Ignacio B, Bakkum T, Bonger K, Martin N, van Kasteren S. Metabolic labeling probes for interrogation of the host-pathogen interaction. Org Biomol Chem. 2021;19:2856–70.CAS 

    Google Scholar 
    Bagert JD, Kessel JC, van, Sweredoski MJ, Feng L, Hess S, Bassler BL, et al. Time-resolved proteomic analysis of quorum sensing in Vibrio harveyi. Chem Sci. 2016;7:1797–806.CAS 

    Google Scholar 
    Babin BM, Atangcho L, Van Eldijk MB, Sweredoski MJ, Moradian A, Hess S, et al. Selective proteomic analysis of antibiotic-tolerant cellular subpopulations in pseudomonas aeruginosa biofilms. 2017. https://doi.org/10.1128/mBio.01593-17.Hatzenpichler R, Scheller S, Tavormina PL, Babin BM, Tirrell DA, Orphan VJ. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol. 2014;16:2568–90. https://doi.org/10.1111/1462-2920.12436.Article 
    CAS 

    Google Scholar 
    Samo TJ, Smriga S, Malfatti F, Sherwood BP, Azam F. Broad distribution and high proportion of protein synthesis active marine bacteria revealed by click chemistry at the single cell level. Front Mar Sci. 2014;0:48.
    Google Scholar 
    Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal-bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–78. https://doi.org/10.1073/pnas.1603757113.Article 
    CAS 

    Google Scholar 
    Couradeau E, Sasse J, Goudeau D, Nath N, Hazen TC, Bowen BP, et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun. 2019;10:1–10.CAS 

    Google Scholar 
    Leizeaga A, Estrany M, Forn I, Sebastián M. Using click-chemistry for visualizing in situ changes of translational activity in planktonic marine bacteria. Front Microbiol. 2017;0:2360.
    Google Scholar 
    Lindivat M, Larsen A, Hess-Erga OK, Bratbak G, Hoell IA. Bioorthogonal non-canonical amino acid tagging combined with flow cytometry for determination of activity in aquatic microorganisms. Front Microbiol. 2020;0:1929.
    Google Scholar 
    Chen L, Zhao B, Li X, Cheng Z, Wu R, Xia Y. Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging. Chem Eng J. 2021;418:129411.CAS 

    Google Scholar 
    McKay LJ, Smith HJ, Barnhart EP, Schweitzer HD, Malmstrom RR, Goudeau D, et al. Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane. ISME J. 2021;16:915–26.
    Google Scholar 
    Du Z, Behrens SF. Tracking de novo protein synthesis in the activated sludge microbiome using BONCAT-FACS. Water Res. 2021;205:117696.CAS 

    Google Scholar 
    Valentini TD, Lucas SK, Binder KA, Cameron LC, Motl JA, Dunitz JM, et al. Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota. Nat Commun. 2020;11:1–11.
    Google Scholar 
    Taguer M, Shapiro BJ, Maurice CF. Translational activity is uncoupled from nucleic acid content in bacterial cells of the human gut microbiota. Gut Microbes. 2021;13:1–15.
    Google Scholar 
    Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
    CAS 

    Google Scholar 
    Kavunja HW, Piligian BF, Fiolek TJ, Foley HN, Nathan TO, Swarts BM. A chemical reporter strategy for detecting and identifying O-mycoloylated proteins in Corynebacterium. Chem Commun. 2016;52:13795–8.CAS 

    Google Scholar 
    Demeester KE, Liang H, Jensen MR, Jones ZS, D’Ambrosio EA, Scinto SL, et al. Synthesis of functionalized N-Acetyl Muramic acids to probe bacterial cell wall recycling and biosynthesis. J Am Chem Soc. 2018;140:9458–65. https://doi.org/10.1021/jacs.8b03304.Article 
    CAS 

    Google Scholar 
    Moulton KD, Adewale AP, Carol HA, Mikami SA, Dube DH. Metabolic glycan labeling-based screen to identify bacterial glycosylation genes. ACS Infect Dis. 2020;6:3247–59. https://doi.org/10.1021/acsinfecdis.0c00612.Article 
    CAS 

    Google Scholar 
    Keller LJ, Babin BM, Lakemeyer M, Bogyo M. Activity-based protein profiling in bacteria: Applications for identification of therapeutic targets and characterization of microbial communities. Curr Opin Chem Biol. 2020;54:45–53.CAS 

    Google Scholar 
    Speers AE, Adam GC, Cravatt BF. Activity-based protein profiling in vivo using a copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:4686–7. https://doi.org/10.1021/ja034490.Article 
    CAS 

    Google Scholar 
    Krysiak J, Sieber SA. Activity-based protein profiling in bacteria. Methods Mol Biol. 2017;1491:57–74.CAS 

    Google Scholar 
    Jariwala PB, Pellock SJ, Cloer EW, Artola M, Simpson JB, Bhatt AP, et al. Discovering the microbial enzymes driving drug toxicity with activity-based protein profiling. ACS Chem Biol. 2020;15:217–25. https://doi.org/10.1021/acschembio.9b00788.Article 
    CAS 

    Google Scholar 
    Kovalyova Y, Hatzios SK. Activity-based protein profiling at the host-pathogen interface. Curr Top Microbiol Immunol. 2019;420:73–91.CAS 

    Google Scholar 
    Sakoula D, Smith GJ, Frank J, Mesman RJ, Kop LFM, Blom P, et al. Universal activity-based labeling method for ammonia- and alkane-oxidizing bacteria. ISME J. 2021;16:958–71.
    Google Scholar 
    Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2020;19:55–71.
    Google Scholar 
    Fitzpatrick CR, Salas-González I, Conway JM, Finkel OM, Gilbert S, Russ D, et al. The plant microbiome: from ecology to reductionism and beyond. 101146/annurev-micro-022620-014327. 2020;74:81–100. https://www.annualreviews.org/doi/abs/10.1146/annurev-micro-022620-014327.Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, Whitlock MC. Experimental evolution. Trends Ecol Evol. 2012;27:547–60.
    Google Scholar 
    Lenski RE. Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations. ISME J. 2017;11:2181–94.CAS 

    Google Scholar 
    Rodríguez-Verdugo A. Evolving Interactions and Emergent Functions in Microbial Consortia. mSystems. 2021;6. https://pubmed.ncbi.nlm.nih.gov/34427521/.Pascual-García A, Bonhoeffer S, Bell T. Metabolically cohesive microbial consortia and ecosystem functioning. Philos Trans R Soc B. 2020;375. https://royalsocietypublishing.org/doi/full/10.1098/rstb.2019.0245.Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 

    Google Scholar 
    Balaban NQ, Helaine S, Lewis K, Ackermann M, Aldridge B, Andersson DI, et al. Definitions and guidelines for research on antibiotic persistence. Nat Rev Microbiol. 2019;17:441–8.CAS 

    Google Scholar 
    Vermeersch L, Perez-Samper G, Cerulus B, Jariani A, Gallone B, Voordeckers K, et al. On the duration of the microbial lag phase. Curr Genet. 2019;65:721–7.CAS 

    Google Scholar 
    Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, et al. Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA. 2014;111:7427–32.CAS 

    Google Scholar 
    Zhang Z, Du C, de Barsy F, Liem M, Liakopoulos A, van Wezel GP, et al. Antibiotic production in Streptomyces is organized by a division of labor through terminal genomic differentiation. Sci Adv. 2020;6:eaay5781.CAS 

    Google Scholar 
    Mavridou DAI, Gonzalez D, Kim W, West SA, Foster KR. Bacteria use collective behavior to generate diverse combat strategies. Curr Biol. 2018;28:345–355.e4.CAS 

    Google Scholar 
    Levin AM, de Vries RP, Conesa A, de Bekker C, Talon M, Menke HH, et al. Spatial differentiation in the vegetative mycelium of Aspergillus niger. Eukaryot Cell. 2007;6:2311–22.CAS 

    Google Scholar 
    Zacchetti B, Wösten HAB, Claessen D. Multiscale heterogeneity in filamentous microbes. Biotechnol Adv. 2018;36:2138–49.CAS 

    Google Scholar 
    Bleichrodt R-J, Vinck A, Read ND, Wösten HAB. Selective transport between heterogeneous hyphal compartments via the plasma membrane lining septal walls of Aspergillus niger. Fungal Genet Biol. 2015;82:193–200.CAS 

    Google Scholar 
    Nürnberg DJ, Mariscal V, Bornikoel J, Nieves-Morión M, Krauß N, Herrero A, et al. Intercellular diffusion of a fluorescent sucrose analog via the septal junctions in a Filamentous Cyanobacterium. MBio. 2015;6. https://journals.asm.org/doi/full/10.1128/mBio.02109-14.Pasulka AL, Thamatrakoln K, Kopf SH, Guan Y, Poulos B, Moradian A, et al. Interrogating marine virus-host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Environ Microbiol. 2018;20:671–92. https://doi.org/10.1111/1462-2920.13996.Article 
    CAS 

    Google Scholar 
    Berjón-Otero M, Duponchel S, Hackl T, Fischer M. Visualization of giant virus particles using BONCAT labeling and STED microscopy. bioRxiv. 2020;2020.07.14.202192. https://www.biorxiv.org/content/10.1101/2020.07.14.202192v1.Steward KF, Eilers B, Tripet B, Fuchs A, Dorle M, Rawle R, et al. Metabolic implications of using BioOrthogonal Non-Canonical Amino Acid Tagging (BONCAT) for tracking protein synthesis. Front Microbiol. 2020;0:197.
    Google Scholar 
    van Elsland DM, Pujals S, Bakkum T, Bos E, Oikonomeas-Koppasis N, Berlin I, et al. Ultrastructural Imaging of Salmonella–Host interactions using super-resolution correlative light-electron microscopy of bioorthogonal pathogens. ChemBioChem. 2018;19:1766–70. https://doi.org/10.1002/cbic.201800230.Article 
    CAS 

    Google Scholar 
    Michels DE, Lomenick B, Chou T-F, Sweredoski MJ, Pasulka A. Amino acid analog induces stress response in marine Synechococcus. Appl Environ Microbiol. 2021;87:1–18. https://doi.org/10.1128/AEM.00200-21.Article 

    Google Scholar 
    Hong V, Steinmetz NF, Manchester M, Finn MG. Labeling live cells by copper-catalyzed alkyne−azide click chemistry. Bioconjug Chem. 2010;21:1912–6. https://doi.org/10.1021/bc100272z.Article 
    CAS 

    Google Scholar 
    van Geel R, Pruijn G, van Delft F, Boelens W. Preventing thiol-yne addition improves the specificity of strain-promoted azide-alkyne cycloaddition. Bioconjug Chem. 2012;23:392–8.
    Google Scholar 
    Patterson DM, Nazarova LA, Prescher JA. Finding the Right (Bioorthogonal) Chemistry. ACS Chem Biol. 2014;9:592–605. https://doi.org/10.1021/cb400828a.Article 
    CAS 

    Google Scholar 
    Ignacio BJ, Dijkstra J, Garcia NM, Slot EFJ, van Weijsten MJ, Storkebaum E, et al. THRONCAT: Efficient metabolic labeling of newly synthesized proteins using a bioorthogonal threonine analog. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.03.29.486210v1.Wright MH. Chemical proteomics of host–microbe interactions. Proteomics. 2018;18:1700333. https://doi.org/10.1002/pmic.201700333.Article 
    CAS 

    Google Scholar 
    Yu H, Schomaker J. Recent developments and strategies for mutually orthogonal bioorthogonal reactions. Chembiochem. 2021;22:3254–62.
    Google Scholar 
    Willems LI, Li N, Florea BI, Ruben M, van der Marel GA, Overkleeft HS. Triple bioorthogonal ligation strategy for simultaneous labeling of multiple enzymatic activities. Angew Chemie Int Ed. 2012;51:4431–4. https://doi.org/10.1002/anie.201200923.Article 
    CAS 

    Google Scholar 
    Simon C, Lion C, Spriet C, Baldacci-Cresp F, Hawkins S, Biot C. One, two, three: a bioorthogonal triple labelling strategy for studying the dynamics of plant cell wall formation in vivo. Angew Chemie Int Ed. 2018;57:16665–71. https://doi.org/10.1002/anie.201808493.Article 
    CAS 

    Google Scholar 
    Chio TI, Gu H, Mukherjee K, Tumey LN, Bane SL. Site-specific bioconjugation and multi-bioorthogonal labeling via rapid formation of a boron–nitrogen heterocycle. Bioconjug Chem. 2019;30:1554–64. https://doi.org/10.1021/acs.bioconjchem.9b0024.Article 
    CAS 

    Google Scholar 
    Bakkum T, Heemskerk MT, Bos E, Groenewold M, Oikonomeas-Koppasis N, Walburg KV, et al. Bioorthogonal correlative light-electron microscopy of mycobacterium tuberculosis in macrophages reveals the effect of antituberculosis drugs on subcellular bacterial distribution. ACS Cent Sci. 2020;6:1997–2007. https://doi.org/10.1021/acscentsci.0c00539.Article 
    CAS 

    Google Scholar  More

  • in

    Artificial lighting affects the landscape of fear in a widely distributed shorebird

    Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399 (1999).
    Google Scholar 
    Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the ‘landscape of fear’ in Yellowstone National Park, US.A. Can. J. Zool. 79, 1401–1409 (2001).
    Google Scholar 
    Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).CAS 

    Google Scholar 
    Laundre, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).
    Google Scholar 
    Loggins, A. A., Shrader, A. M., Monadjem, A. & McCleery, R. A. Shrub cover homogenizes small mammals’ activity and perceived predation risk. Sci. Rep. 9, 16857 (2019).
    Google Scholar 
    Whittingham, M. J. & Evans, K. L. The effects of habitat structure on predation risk of birds in agricultural landscapes. Ibis 146, 210–220 (2004).
    Google Scholar 
    Marshall, K. L. A., Philpot, K. E. & Stevens, M. Microhabitat choice in island lizards enhances camouflage against avian predators. Sci. Rep. 6, 19815 (2016).CAS 

    Google Scholar 
    Stevens, M., Troscianko, J., Wilson-Aggarwal, J. K. & Spottiswoode, C. N. Improvement of individual camouflage through background choice in ground-nesting birds. Nat. Ecol. Evol. 1, 1325–1333 (2017).
    Google Scholar 
    Wilson-Aggarwal, J. K., Troscianko, J. T., Stevens, M. & Spottiswoode, C. N. Escape distance in ground-nesting birds differs with individual level of camouflage. Am. Nat. 188, 231–239 (2016).
    Google Scholar 
    Troscianko, J., Wilson-Aggarwal, J., Stevens, M. & Spottiswoode, C. N. Camouflage predicts survival in ground-nesting birds. Sci. Rep. 6, 19966 (2016).CAS 

    Google Scholar 
    Gaston, K. J., Duffy, J. P., Gaston, S., Bennie, J. & Davies, T. W. Human alteration of natural light cycles: causes and ecological consequences. Oecologia 176, 917–931 (2014).
    Google Scholar 
    Gaston, K. J., Davies, T. W., Nedelec, S. L. & Holt, L. A. Impacts of artificial light at night on biological timings. Annu. Rev. Ecol. Evol. Syst. 48, 49–68 (2017).
    Google Scholar 
    Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).
    Google Scholar 
    Gaston, K. J. et al. Pervasiveness of biological impacts of artificial light at night. Integr. Comp. Biol. 61, 1098–1110 (2021).
    Google Scholar 
    Sanders, D., Frago, E., Kehoe, R., Patterson, C. & Gaston, K. J. A meta-analysis of biological impacts of artificial light at night. Nat. Ecol. Evol. 5, 74–81 (2021).
    Google Scholar 
    Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160248 (2017).
    Google Scholar 
    Underwood, C. N., Davies, T. W. & Queir Os, A. M. Artificial light at night alters trophic interactions of intertidal invertebrates. J. Anim. Ecol. 86, 781–789 (2017).
    Google Scholar 
    Burger, J., Howe, M. A., Hahn, D. C. & Chase, J. Effects of tide cycles on habitat selection and habitat partitioning by migrating shorebirds. Auk 94, 743–758 (1977).
    Google Scholar 
    Granadeiro, J. P., Dias, M. P., Martins, R. C. & Palmeirim, J. M. Variation in numbers and behaviour of waders during the tidal cycle: implications for the use of estuarine sediment flats. Acta Oecologica 29, 293–300 (2006).
    Google Scholar 
    Lourenço, P. M. et al. The energetic importance of night foraging for waders wintering in a temperate estuary. Acta Oecologica 34, 122–129 (2008).
    Google Scholar 
    McNeil, R., Drapeau, P. & Goss-Custard, J. D. The occurrence and adaptive significance of nocturnal habits in waterfowl. Biol. Rev. 67, 381–419 (1992).
    Google Scholar 
    Martin, G. R. Visual fields and their functions in birds. J. Ornithol. 148, 547–562 (2007).
    Google Scholar 
    Martin, G. R. What is binocular vision for? A birds’ eye view. J. Vis. 9, 1–19 (2009).
    Google Scholar 
    Davies, T. W., Duffy, J. P., Bennie, J. & Gaston, K. J. The nature, extent, and ecological implications of marine light pollution. Front. Ecol. Environ. 12, 347–355 (2014).
    Google Scholar 
    Leopold, M. F., Philippart, C. J. M. & Yorio, P. Nocturnal feeding under artificial light conditions by Brown-hooded Gull (Larus maculipennis) in Puerto Madryn harbour (Chubut Province, Argentina). Hornero 25, 55–60 (2010).
    Google Scholar 
    Pugh, A. R. & Pawson, S. M. Artificial light at night potentially alters feeding behaviour of the native southern black-backed gull (Larus dominicanus). Notornis 63, 37–39 (2016).
    Google Scholar 
    Santos, C. D. et al. Effects of artificial illumination on the nocturnal foraging of waders. Acta Oecologica 36, 166–172 (2010).
    Google Scholar 
    Montevecchi, W. A. Influences of Artificial Light on Marine Birds. in Ecological Consequences of Artificial Night Lighting (eds. Rich, C. & Longcore, T.) 94–113 (Island Press, 2006).Dwyer, R. G., Bearhop, S., Campbell, H. A. & Bryant, D. M. Shedding light on light: benefits of anthropogenic illumination to a nocturnally foraging shorebird. J. Anim. Ecol. 82, 478–485 (2013).
    Google Scholar 
    Blumstein, D. T. Developing an evolutionary ecology of fear: how life history and natural history traits affect disturbance tolerance in birds. Anim. Behav. 71, 389–399 (2006).
    Google Scholar 
    Stankowich, T. & Blumstein, D. T. Fear in animals: a meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 272, 2627–2634 (2005).
    Google Scholar 
    Caro, T. Antipredator Defenses in Birds and Mammals. (University of Chicago Press, 2005).Tillmann, J. E. Fear of the dark: night-time roosting and anti-predation behaviour in the grey partridge (Perdix perdix L.). Behaviour 146, 999–1023 (2009).
    Google Scholar 
    IUCN. The IUCN Red List of Threatened Species. Version 2022-1. https://www.iucnredlist.org/species/22693190/117917038 (2022).Brown, D. et al. The Eurasian Curlew—the most pressing bird conservation priority in the UK? Br. Birds 108, 660–668 (2015).
    Google Scholar 
    Franks, S. E., Douglas, D. J. T., Gillings, S. & Pearce-Higgins, J. W. Environmental correlates of breeding abundance and population change of Eurasian Curlew Numenius arquata in Britain. Bird. Study 64, 393–409 (2017).
    Google Scholar 
    Desholm, M. & Kahlert, J. Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005).
    Google Scholar 
    Clarke, J. A. Moonlight’s influence on predator/prey interactions between short-eared owls (Asio flammeus) and Deermice (Peromyscus maniculatus). Behav. Ecol. Sociobiol. 13, 205–209 (1983).
    Google Scholar 
    Mandelik, Y., Jones, M. & Dayan, T. Structurally complex habitat and sensory adaptations mediate the behavioural responses of a desert rodent to an indirect cue for increased predation risk. Evol. Ecol. Res. 5, 501–515 (2003).
    Google Scholar 
    Alexander, R. D. The Evolution of Social Behavior | Annual Review of Ecology, Evolution, and Systematics. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).
    Google Scholar 
    Pulliam, H. R. On the advantages of flocking. J. Theor. Biol. 38, 419–422 (1973).CAS 

    Google Scholar 
    Barnard, C. J. Flock feeding and time budgets in the house sparrow (Passer domesticus L.). Anim. Behav. 28, 295–309 (1980).
    Google Scholar 
    Cooper, W. E. Jr. et al. Effects of risk, cost, and their interaction on optimal escape by nonrefuging Bonaire whiptail lizards, Cnemidophorus murinus. Behav. Ecol. 14, 288–293 (2003).
    Google Scholar 
    Lagos, P. A. et al. Flight initiation distance is differentially sensitive to the costs of staying and leaving food patches in a small-mammal prey. Can. J. Zool. 87, 1016–1023 (2009).
    Google Scholar 
    Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).
    Google Scholar 
    Tucker, V. A., Tucker, A. E., Akers, K. & Enderson, J. H. Curved flight paths and sideways vision in peregrine falcons (Falco peregrinus). J. Exp. Biol. 203, 3755–3763 (2000).CAS 

    Google Scholar 
    Carr, J. M. & Lima, S. L. Wintering birds avoid warm sunshine: predation and the costs of foraging in sunlight. Oecologia 174, 713–721 (2014).
    Google Scholar 
    van den Hout, P. J. & Martin, G. R. Extreme head-tilting in shorebirds: predator detection and sun avoidance. Wader Study Group Bull. 118, 18–21 (2011).
    Google Scholar 
    Ferguson, J. W. H., Galpin, J. S. & de Wet, M. J. Factors affecting the activity patterns of black-backed jackals Canis mesomelas. J. Zool. 214, 55–69 (1988).
    Google Scholar 
    Pyke, G. H. Optimal foraging theory: a critical review. Annu. Rev. Ecol. Syst. 15, 523–575 (1984).
    Google Scholar 
    Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton University Press, 1986).Mouritsen, K. N. Predator avoidance in night-feeding dunlins calidris alpina: a matter of concealment. Ornis Scand. 23, 195–198 (1992).
    Google Scholar 
    Blumstein, D. T. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manag. 67, 852–857 (2003).
    Google Scholar 
    Troscianko, J. OSpRad; an open-source, low-cost, high-sensitivity spectroradiometer (p. 2022.12.09.519768). bioRxiv https://doi.org/10.1101/2022.12.09.519768 (2022).Article 

    Google Scholar 
    Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.4. http://florianhartig.github.io/DHARMa/ (2022).Core Team, R. R: a Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2022).
    Google Scholar  More

  • in

    Predicting the potential suitable distribution area of Emeia pseudosauteri in Zhejiang Province based on the MaxEnt model

    Daskalova, G. N. et al. Landscape-scale forest loss as a catalyst of population and biodiversity change. Science 368(6497), 1341–1347 (2020).ADS 
    CAS 

    Google Scholar 
    Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366(6470), 1236–1239 (2019).ADS 
    CAS 

    Google Scholar 
    Siddig, A. A., Ellison, A. M., Ochs, A., Villar-Leeman, C. & Lau, M. K. How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecol. Ind. 60, 223–230 (2016).
    Google Scholar 
    Thancharoen, A. Well managed firefly tourism: A good tool for firefly conservation in Thailand. Lampyrid. 2, 142–148 (2012).
    Google Scholar 
    Hwang, Y. T., Moon, J., Lee, W. S., Kim, S. A. & Kim, J. Evaluation of firefly as a tourist attraction and resource using contingent valuation method based on a new environmental paradigm. J. Qual. Assur. Hosp. Tour. 21(3), 320–336 (2019).Carlson, A. D. & Copeland, J. Flash communication in fireflies. Q. Rev. Biol. 60(4), 415–436 (1985).
    Google Scholar 
    Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).
    Google Scholar 
    Lewis, S. M. et al. A global perspective on firefly extinction threats. Bioscience 70(2), 157–167 (2020).
    Google Scholar 
    Cao, C. Q., Zhang, Y., Wang, Y. Z. & He, H. Progress in the research, protection, development and utilization of fireflies. J. Environ. Entomol.1–36 (2022).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403(6772), 853–858 (2000).ADS 
    CAS 

    Google Scholar 
    Thorn, J. S., Nijman, V., Smith, D. & Nekaris, K. A. I. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates:Nycticebus). Divers. Distrib. 15(2), 289–298 (2009).
    Google Scholar 
    Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40(1), 677–697 (2009).
    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).
    Google Scholar 
    Hirzel, A. H., Hausser, J., Chessel, D. & Perrin, N. Ecological-Niche Factor Analysis: How to compute habitat-suitability maps without absence data?. Ecology 83(7), 2027–2036 (2002).
    Google Scholar 
    Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (General). 135(3), 370–384 (1972).
    Google Scholar 
    Hastie, T. J. Generalized additive models. Statistical models in S. Routledge. 249–307 (2017).Stockwell, D. R. & Noble, I. R. Induction of sets of rules from animal distribution data: A robust and informative method of data analysis. Math. Comput. Simul. 33(5–6), 385–390 (1992).
    Google Scholar 
    Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186(2), 251–270 (2005).
    Google Scholar 
    Jung, J. M., Lee, W. H. & Jung, S. Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomol. Res. 46(4), 223–235 (2016).
    Google Scholar 
    Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2), 161–175 (2008).
    Google Scholar 
    Moreno, R., Zamora, R., Molina, J. R., Vasquez, A. & Herrera, M. Á. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Eco. Inform. 6(6), 364–370 (2011).
    Google Scholar 
    Wang, Z. et al. Prediction of potential distribution of the invasive Chrysanthemum Lace Bug, Corythucha marmorata in China based on Maxent. J. Environ. Entomol. 41(3), 626–633 (2019).
    Google Scholar 
    Li, A. et al. MaxEnt modeling to predict current and future distributions of Batocera lineolata (Coleoptera: Cerambycidae) under climate change in China. Ecoscience 27(1), 23–31 (2020).
    Google Scholar 
    Sutherland, L. N., Powell, G. S. & Bybee, S. M. Validating species distribution models to illuminate coastal fireflies in the South Pacific (Coleoptera: Lampyridae). Sci. Rep. 11(1), 1–12 (2021).ADS 

    Google Scholar 
    Fu, X. H., Ballantyne, L. A. & Lambkin, C. Emeia gen. nov., a new genus of Luciolinae fireflies from China (Coleoptera: Lampyridae) with an unusual trilobite-like larva, and a redescription of the genus Curtos Motschulsky. Zootaxa. 3403(1), 1–53 (2012).Idris, N. S. et al. The dynamics of landscape changes surrounding a firefly ecotourism area. Glob. Ecol. Conserv. 29, e01741 (2021).
    Google Scholar 
    Santiago-Blay, J. A. Silent Sparks: The Wondrous World of Fireflies. Life: The Excitement of Biology. (2016).Picchi, M. S., Avolio, L., Azzani, L., Brombin, O. & Camerini, G. Fireflies and land use in an urban landscape: the case of Luciola italica L.(Coleoptera: Lampyridae) in the city of Turin. J. Insect Conserv. 17(4), 797–805 (2013).Pearsons, K. A., Lower, S. E. & Tooker, J. F. Toxicity of clothianidin to common Eastern North American fireflies. PeerJ 9, e12495 (2021).
    Google Scholar 
    Madruga Rios, O. & Hernández Quinta, M. Larval Feeding Habits of the Cuban Endemic FireflyAlecton discoidalisLaporte (Coleoptera: Lampyridae). Psyche J. Entomol. 2010, 1–5 (2010).Roberge, J. M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18(1), 76–85 (2004).
    Google Scholar 
    Bowen-Jones, E. & Entwistle, A. Identifying appropriate flagship species: The importance of culture and local contexts. Oryx 36(2), 189–195 (2002).
    Google Scholar 
    Walpole, M. J. & Leader-Williams, N. Tourism and flagship species in conservation. Biodivers. Conserv. 11(3), 543–547 (2002).Zhejiang Provincial Bureau of Statistics. Zhejiang physical geography profile, http://tjj.zj.gov.cn/col/col1525489/index.html (2022).Zhejiang Provincial Forestry Department. Announcement of Forest Resources and Their Ecological Function Value in Zhejiang Province. Zhejiang Daily. https://doi.org/10.38328/n.cnki.nzjrb.2016.002829 (2016).Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).
    Google Scholar 
    Brown, J. L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5(7), 694–700 (2014).
    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).
    Google Scholar 
    Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C. & Taneja, J. Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sens. 13(5), 922 (2021).ADS 

    Google Scholar 
    WAN, J. et al. Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling. J. Integr. Agric. 19(8), 2072–2082 (2020).Zhou, R. et al. Projecting the potential distribution of glossina morsitans (Diptera: Glossinidae) under climate change using the MaxEnt model. Biology. 10(11), 1150 (2021).
    Google Scholar 
    Hill, M. P., Hoffmann, A. A., McColl, S. A. & Umina, P. A. Distribution of cryptic blue oat mite species in Australia: current and future climate conditions. Agric. For. Entomol. 14(2), 127–137 (2011).
    Google Scholar 
    Su, H., Bista, M. & Li, M. Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models. Sci. Rep. 11(1), 1 (2021).ADS 
    CAS 

    Google Scholar 
    Proosdij, A. J., Sosef, M., Wieringa, J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).
    Google Scholar 
    Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2), 145–151 (2008).
    Google Scholar 
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43(6), 1223–1232 (2006).
    Google Scholar 
    Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6(1), 337–348 (2016).
    Google Scholar 
    Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988).ADS 
    CAS 
    MATH 

    Google Scholar 
    Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133(3), 225–245 (2000).
    Google Scholar 
    Gama, M., Crespo, D., Dolbeth, M. & Anastácio, P. M. Ensemble forecasting of Corbicula fluminea worldwide distribution: projections of the impact of climate change. Aquat. Conserv. Mar. Freshwat. Ecosyst. 27(3), 675–684 (2017).
    Google Scholar 
    Zhao, Y., Deng, X., Xiang, W., Chen, L. & Ouyang, S. Predicting potential suitable habitats of Chinese fir under current and future climatic scenarios based on Maxent model. Eco. Inform. 64, 101393 (2021).
    Google Scholar 
    Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).Chettri, B., Bhupathy, S. & Acharya, B. K. Distribution pattern of reptiles along an eastern Himalayan elevation gradient India. Acta Oecol. 36(1), 16–22 (2010).ADS 

    Google Scholar 
    Brown, J. H. Mammals on mountainsides: elevational patterns of diversity. Global Ecol. Biogeogr. 10(1), 101–109 (2001).Gairola, S., Sharma, C. M., Ghildiyal, S. K. & Suyal, S. Tree species composition and diversity along an altitudinal gradient in moist tropical montane valley slopes of the Garhwal Himalaya India. For. Sci. Technol. 7(3), 91–102 (2011).
    Google Scholar 
    Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5), 773–785 (2006).
    Google Scholar 
    Abe, N. Kansei estimation on luminescence of Firefly-Kansei information measurement and welfare utilization. J. Japan Soc. Kansei Eng. 3(2), 41–50 (2004).
    Google Scholar 
    Buckley, R. et al. Economic value of protected areas via visitor mental health. Nat. Commun. 10(1), 1 (2019).
    Google Scholar 
    Lewis, S. M. et al. Firefly tourism: Advancing a global phenomenon toward a brighter future. Conserv. Sci. Pract. 3(5), 1 (2021).
    Google Scholar  More

  • in

    Elevated alpha diversity in disturbed sites obscures regional decline and homogenization of amphibian taxonomic, functional and phylogenetic diversity

    Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
    CAS 

    Google Scholar 
    McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the Anthropogene. Trends Ecol. Evol. 30, 104–113 (2015).
    Google Scholar 
    Bradshaw, C. J. A., Sodhi, N. S. & Brook, B. W. Tropical turmoil: A biodiversity tragedy in progress. Front. Ecol. Environ. 7, 79–87 (2009).
    Google Scholar 
    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
    CAS 

    Google Scholar 
    Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294, 804–808 (2001).ADS 
    CAS 

    Google Scholar 
    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
    Google Scholar 
    Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS 
    CAS 

    Google Scholar 
    Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
    Google Scholar 
    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
    CAS 

    Google Scholar 
    Pasari, J. R., Levi, T., Zavaleta, E. S. & Tilman, D. Several scales of biodiversity affect ecosystem multifunctionality. Proc. Natl. Acad. Sci. U.S.A. 110, 10219–10222 (2013).ADS 
    CAS 

    Google Scholar 
    Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
    Google Scholar 
    Murphy, G. E. P. & Romanuk, T. N. A meta-analysis of declines in local species richness from human disturbances. Ecol. Evol. 4, 91–103 (2014).
    Google Scholar 
    Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).ADS 
    CAS 

    Google Scholar 
    de Coster, G., Banks-Leite, C. & Metzger, J. P. Atlantic forest bird communities provide different but not fewer functions after habitat loss. Proc. R. Soc. B 282, 20142844 (2015).
    Google Scholar 
    Riemann, J. C., Ndriantsoa, S. H., Rödel, M.-O. & Glos, J. Functional diversity in a fragmented landscape—habitat alterations affect functional trait composition of frog assemblages in Madagascar. Global Ecol. Conserv. 10, 173–183 (2017).
    Google Scholar 
    McKinney, M. L. & Lockwood, J. L. Biotic homogenization: A few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).CAS 

    Google Scholar 
    Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).
    Google Scholar 
    van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multi-functionality. Proc. Natl. Acad. Sci. U.S.A. 113, 3557–3562 (2016).ADS 

    Google Scholar 
    Mori, A. S., Isbell, F. & Seidl, R. β-diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).
    Google Scholar 
    Dehling, J. M. & Dehling, D. M. Conserving ecological functions of frog communities in Borneo requires diverse forest landscapes. Global Ecol. Conserv. 26, e01481 (2021).
    Google Scholar 
    Hector, A. & Bagchi, R. Biodiversity and ecosystem multifunctionality. Nature 448, 188–190 (2007).ADS 
    CAS 

    Google Scholar 
    Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).ADS 
    CAS 

    Google Scholar 
    Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl. Acad. Sci. U.S.A. 100, 12765–12770 (2003).ADS 
    CAS 

    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
    CAS 

    Google Scholar 
    Felipe-Lucia, M. R. et al. Land-use intensity alters networks between biodiversity, ecosystem functions, and services. Proc. Natl. Acad. Sci. U.S.A. 117, 28140–28149 (2020).ADS 
    CAS 

    Google Scholar 
    Tilman, D. Functional diversity in Encyclopedia of biodiversity, Vol. 3. (ed. Levin S. A.) 109–120 (Academic Press, 2001)Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).
    Google Scholar 
    Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity-ecosystem function relationships. Ecology 92, 1573–1581 (2011).
    Google Scholar 
    Lean, C. & Maclaurin, J. The value of phylogenetic diversity in Biodiversity conservation and phylogenetic systematics. Topics in Biodiversity and Conservation 14. (eds. Pellens, R., Grandcolas, P.) 19–38 (Springer, 2016).Owen, N. R., Gumbs, R., Gray, C. L. & Faith, D. P. Global conservation of phylogenetic diversity captures more than just functional diversity. Nat. Commun. 10, 859 (2019).ADS 

    Google Scholar 
    Gumbs, R., Williams, R. C., Lowney, A. M. & Smith, D. Spatial and species-level metrics reveal global patterns of irreplaceable and imperiled gecko phylogenetic diversity. Israel J. Ecol. Evolut. 66, 239–252 (2020).
    Google Scholar 
    Brooks, D. R., Mayden, R. L. & McLennan, D. A. Phylogeny and biodiversity: Conserving our evolutionary legacy. Trends Ecol. Evol. 7, 55–59 (1992).CAS 

    Google Scholar 
    Phillimore, A. B. et al. Biogeographical basis of recent phenotypic divergence among birds: a global study of subspecies richness. Evolution 61, 942–957 (2007).
    Google Scholar 
    Miraldo, A. et al. An Anthropocene map of genetic diversity. Science 353, 1532–1535 (2016).ADS 
    CAS 

    Google Scholar 
    Smith, B. T., Seeholzer, G. F., Harvey, M. G., Cuervo, A. M. & Brumfield, R. T. A latitudinal phylogeographic diversity gradient in birds. PLoS Biol. 15, e2001073 (2017).
    Google Scholar 
    Tucker, C. M. et al. Assessing the utility of conserving evolutionary history. Biol. Rev. 94, 1740–1760 (2019).
    Google Scholar 
    Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).
    Google Scholar 
    Villéger, S., Miranda, J. R., Hernández, D. F. & Mouillot, D. Contrasting changes in taxonomic vs. functional diversity of tropical fish communities after habitat degradation. Ecological Applications 20, 1512–1522 (2010).Gibbons, J. W. et al. Remarkable amphibian biomass and abundance in an isolated wetland: Implications for wetland conservation. Conserv. Biol. 20, 1457–1465 (2006).
    Google Scholar 
    Hocking, D. J. & Babbitt, K. J. Amphibian contributions to ecosystem services. Herpetol. Conserv. Biol. 9, 1–17 (2014).
    Google Scholar 
    Beebee, T. J. C. Amphibian breeding and climate change. Nature 374, 219–220 (1995).ADS 
    CAS 

    Google Scholar 
    Kiesecker, J. M., Blaustein, A. R. & Belden, L. K. Complex causes of amphibian population declines. Nature 410, 681–684 (2001).ADS 
    CAS 

    Google Scholar 
    Cheng, T. L., Rovito, S. M., Wake, D. B. & Vredenburg, V. T. Coincident mass extirpation of neotropical amphibians with the emergence of the infection fungal pathogen Batrachochytrium dendrobatidis. Proc. Natl. Acad. Sci. U.S.A. 108, 9502–9507 (2011).ADS 
    CAS 

    Google Scholar 
    Wake, D. B. & Vredenburg, V. T. Are we in the midst of the sixth mass extinction? A view from the world of amphibians. Proc. Natl. Acad. Sci. U.S.A. 105, 11466–11473 (2008).ADS 
    CAS 

    Google Scholar 
    Ernst, R. & Rödel, M.-O. Patterns of community composition in two tropical tree frog assemblages: Separating spatial structure and environmental effects in disturbed and undisturbed forests. J. Trop. Ecol. 24, 111–120 (2008).
    Google Scholar 
    Gardner, T. A. et al. The value of primary, secondary, and plantation forests for a Neotropical Herpetofauna. Conserv. Biol. 21, 775–787 (2007).
    Google Scholar 
    Gardner, T. A., Fitzherbert, E. B., Drewes, R. C., Howell, K. M. & Caro, T. Spatial and temporal patterns of abundance and diversity of an East African leaf litter amphibian fauna. Biotropica 39, 105–113 (2007).
    Google Scholar 
    Gillespie, G. R. et al. Conservation of amphibians in Borneo: relative value of secondary tropical forest and non-forest habitats. Biol. Cons. 152, 136–144 (2012).
    Google Scholar 
    Angarita-M., O., Montes-Correa, A. C. & Renjifo, J. M. Amphibians and reptiles of an agroforestry system in the Colombian Caribbean. Amphibian & Reptile Conservation 8, 33–52 (2015).Jiménez-Robles, O., Guayasamin, J. M., Ron, S. R. & De la Riva, I. Reproductive traits associated with species turnover of amphibians in Amazonia and its Andean slopes. Ecol. Evol. 7, 2489–2500 (2017).
    Google Scholar 
    Ernst, R., Linsenmair, K. E. & Rödel, M.-O. Diversity erosion beyond the species level: dramatic loss of functional diversity after selective logging in two tropical amphibian communities. Biol. Cons. 133, 143–155 (2006).
    Google Scholar 
    Oda, F. H. et al. Anuran species richness, composition, and breeding habitat preferences: a comparison between forest remnants and agricultural landscapes in Southern Brazil. Zool. Stud. 55, 34 (2016).
    Google Scholar 
    Sinsch, U., Lümkemann, K., Rosar, K., Schwarz, C. & Dehling, J. M. Acoustic niche partitioning in an anuran community inhabiting an Afromontane wetland (Butare, Rwanda). African Zool. 47, 60–73 (2012).
    Google Scholar 
    Tumushimire, L., Mindje, M., Sinsch, U. & Dehling, J. M. The anuran diversity of cultivated wetlands in Rwanda: Melting pot of generalists?. Salamandra 56, 99–112 (2020).
    Google Scholar 
    REMA. Rwanda State of Environment and Outlook Report 2017 – Achieving Sustainable Urbanization. (Rwanda Environment Management Authority, Government of Rwanda, 2017).Su, J. C., Debinski, D. M., Jakubauskas, M. E. & Kindscher, K. Beyond species richness: Community similarity as a measure of cross-taxon congruence for coarse-filter conservation. Conserv. Biol. 18, 167–173 (2004).
    Google Scholar 
    Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).ADS 
    CAS 

    Google Scholar 
    Zimkus, B. M., Rödel, M.-O. & Hillers, A. Complex patterns of continental speciation: Molecular phylogenetics and biogeography of sub-Saharan puddle frogs (Phrynobatrachus). Mol. Phylogenet. Evol. 55, 883–900 (2010).
    Google Scholar 
    Dehling, J. M. & Sinsch, U. Partitioning of morphospace in larval and adult reed frogs (Anura: Hyperoliidae: Hyperolius) of the Central African Albertine Rift. Zool. Anz. 280, 65–77 (2019).
    Google Scholar 
    Mazel, F. et al. Prioritizing phylogenetic diversity captures functional diversity unreliably. Nat. Commun. 9, 2888 (2018).ADS 

    Google Scholar 
    Haddad, C. F. B. & Prado, C. P. A. Reproductive modes and their unexpected diversity in the Atlantic forest of Brazil. Bioscience 55, 207–217 (2005).
    Google Scholar 
    Capinha, C., Essl, F., Seebens, H., Moser, D. & Pereira, H. M. The dispersal of alien species redefines biogeography in the Anthropocene. Science 348, 1248–1251 (2015).ADS 
    CAS 

    Google Scholar 
    Alroy, J. Effects of habitat disturbance on tropical forest biodiversity. Proc. Natl. Acad. Sci. U.S.A. 114, 6056–6061 (2017).ADS 
    CAS 

    Google Scholar 
    Dehling, J. M. & Sinsch, U. Diversity of Ptychadena in Rwanda and taxonomic status of P. chrysogaster Laurent, 1954 (Amphibia, Anura, Ptychadenidae). ZooKeys 356, 69–102 (2013).IUCN. The IUCN Red List of Threatened Species. Version 2020–1. https://www.iucnredlist.org (2020).Portillo, F., Greenbaum, E., Menegon, M., Kusamba, C. & Dehling, J. M. Phylogeography and species boundaries of Leptopelis (Anura: Arthroleptidae) from the Albertine Rift. Mol. Phylogenet. Evol. 82, 75–86 (2015).
    Google Scholar 
    Channing, A., Dehling, J. M., Lötters, S. & Ernst, R. Species boundaries and taxonomy of the African River Frogs (Anura: Pyxicephalidae: Amietia). Zootaxa 4155, 1–76 (2016).CAS 

    Google Scholar 
    Rödel, M.-O. & Ernst, R. Measuring and monitoring amphibian diversity in tropical forests. I. An evaluation of methods with recommendations for standardization. Ecotropica 10, 1–14 (2004).Channing, A. & Howell, K. M. Amphibians of East Africa. (Chimaira, 2006).Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evolut. 2, 850–858 (2018).
    Google Scholar 
    Villéger, S., Mason, N. W. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).
    Google Scholar 
    Maire, E., Grenouillet, G., Brosse, S. & Villéger, S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Glob. Ecol. Biogeogr. 24, 728–740 (2015).
    Google Scholar 
    Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Cons. 61, 1–10 (1992).
    Google Scholar 
    Dehling, D. M. et al. Functional and phylogenetic diversity and assemblage structure of frugivorous birds along an elevational gradient in the tropical Andes. Ecography 37, 1047–1055 (2014).
    Google Scholar 
    Baselga, A. et al. betapart: partitioning beta diversity into turnover and nestedness components. R package version 1.5.6. https://CRAN.R-project.org/package=betapart (2022).Dehling, D. M. et al. Specialists and generalists fulfil important and complementary functional roles in ecological processes. Funct. Ecol. 35, 1810–1821 (2021).CAS 

    Google Scholar 
    Dehling, D. M., Barreto, E. & Graham, C. H. The contribution of mutualistic interactions to functional and phylogenetic diversity. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.05.006 (2022).Article 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2021). More

  • in

    TRPM8 thermosensation in poikilotherms mediates both skin colour and locomotor performance responses to cold temperature

    Lovegrove, B. G. A phenology of the evolution of endothermy in birds and mammals. Biol. Rev. 92, 1213–1240 (2017).
    Google Scholar 
    Cuthill, I. C. et al. The biology of color. Science 357, 1–7 (2017).
    Google Scholar 
    Stuart-Fox, D., Newton, E. & Clusella-Trullas, S. Thermal consequences of colour and near-infrared reflectance. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160345 (2017).
    Google Scholar 
    Smith, K. R. et al. Color change for thermoregulation versus camouflage in free-ranging lizards. Am. Nat. 188, 668–678 (2016).
    Google Scholar 
    Rudh, A. & Qvarnström, A. Adaptive colouration in amphibians. Semin. Cell Dev. Biol. 24, 553–561 (2013).
    Google Scholar 
    Geen, M. R. S. & Johnston, G. R. Coloration affects heating and cooling in three color morphs of the Australian bluetongue lizard, Tiliqua scincoides. J. Therm. Biol. 43, 54–60 (2014).
    Google Scholar 
    Tattersall, G. J., Eterovick, P. C. & de Andrade, D. V. Tribute to R. G. Boutilier: skin colour and body temperature changes in basking Bokermannohyla alvarengai (Bokermann 1956). J. Exp. Biol. 209, 1185–1196 (2006).
    Google Scholar 
    Tattersall, G. J., Hillman, S. S., Drewes, R. C. & Sokol, O. M. The thermogenesis of digestion in rattlesnakes. J. Exp. Biol. 207, 579–585 (2004).
    Google Scholar 
    Seebacher, F. & Murray, S. A. Transient receptor potential ion channels control thermoregulatory behaviour in reptiles. PLoS One 2, e281, 1–7 (2007).Forget-Klein, É. & Green, D. M. Toads use the subsurface thermal gradient for temperature regulation underground. J. Therm. Biol. 99, 1–9 (2021).
    Google Scholar 
    Kiefer, M. C., Van Sluys, M. & Rocha, C. F. D. Thermoregulatory behaviour in Tropidurus torquatus (Squamata, Tropiduridae) from Brazilian coastal populations: an estimate of passive and active thermoregulation in lizards. Acta Zool. 88, 81–87 (2007).
    Google Scholar 
    Spencer, K. et al. Growth at cold temperature increases the number of motor neurons to optimize locomotor function. Curr. Biol. 29, 1787–1799.e5 (2019).CAS 

    Google Scholar 
    Herrel, A. & Bonneaud, C. Temperature dependence of locomotor performance in the tropical clawed frog, Xenopus tropicalis. J. Exp. Biol. 215, 2465–2470 (2012).
    Google Scholar 
    Casterlin, M. E. & Reynolds, W. W. Diel activity and thermoregulatory behavior of a fully aquatic frog: Xenopus laevis. Hydrobiologia 75, 189–191 (1980).
    Google Scholar 
    Guo, K. et al. The thermal dependence and molecular basis of physiological color change in Takydromus septentrionalis (Lacertidae). Biol. Open 10, 1–9 (2021).
    Google Scholar 
    De Velasco, J. B. & Tattersall, G. J. The influence of hypoxia on the thermal sensitivity of skin colouration in the bearded dragon, Pogona vitticeps. J. Comp. Physiol. B. 178, 867–875 (2008).CAS 

    Google Scholar 
    Stuart-Fox, D. & Moussalli, A. Camouflage, communication and thermoregulation: lessons from colour changing organisms. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 463–470 (2009).
    Google Scholar 
    Sanabria, E. A., Vaira, M., Quiroga, L. B., Akmentins, M. S. & Pereyra, L. C. Variation of thermal parameters in two different color morphs of a diurnal poison toad, Melanophryniscus rubriventris (Anura: Bufonidae). J. Therm. Biol. 41, 1–5 (2014).
    Google Scholar 
    Clusella-Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal benefits of melanism in cordylid lizards: a theoretical and field test. Ecology 90, 2297–2312 (2009).
    Google Scholar 
    Duarte, R. C., Flores, A. A. V. & Stevens, M. Camouflage through colour change: mechanisms, adaptive value and ecological significance. Philos. Trans. R. Soc. B: Biol. Sci. 372, 1–7 (2017).Bertolesi, G. E. & McFarlane, S. Seeing the light to change colour: an evolutionary perspective on the role of melanopsin in neuroendocrine circuits regulating light-mediated skin pigmentation. Pigment Cell Melanoma Res. 31, 354–373 (2018).CAS 

    Google Scholar 
    Bertolesi, G. E. et al. The regulation of skin pigmentation in response to environmental light by pineal type II opsins and skin melanophore melatonin receptors. J. Photochem. Photobiol. B Biol. 212, 112024 (2020).CAS 

    Google Scholar 
    Bagnara, J. T. Pineal regulation of the body lightening reaction in amphibian larvae. Sci. (80-.). 132, 1481–1483 (1960).CAS 

    Google Scholar 
    Bertolesi, G. E., Song, Y. N., Atkinson-Leadbeater, K., Yang, J.-L. J. & McFarlane, S. Interaction and developmental activation of two neuroendocrine systems that regulate light-mediated skin pigmentation. Pigment Cell Melanoma Res. 30, 413–423 (2017).CAS 

    Google Scholar 
    Wang, H. & Siemens, J. TRP ion channels in thermosensation, thermoregulation and metabolism. Temp. (Austin, Tex.) 2, 178–187 (2015).
    Google Scholar 
    Hoffstaetter, L. J., Bagriantsev, S. N. & Gracheva, E. O. TRPs et al.: a molecular toolkit for thermosensory adaptations. Pflug. Arch. Eur. J. Physiol. 470, 745–759 (2018).CAS 

    Google Scholar 
    Kashio, M. Thermosensation involving thermo-TRPs. Mol. Cell. Endocrinol. 520, 1–8 (2021).
    Google Scholar 
    Señarís, R., Ordás, P., Reimúndez, A. & Viana, F. Mammalian cold TRP channels: impact on thermoregulation and energy homeostasis. Pflug. Arch. 470, 761–777 (2018).
    Google Scholar 
    Guo, H., Carlson, J. A. & Slominski, A. Role of TRPM in melanocytes and melanoma. Exp. Dermatol. 21, 650–654 (2012).CAS 

    Google Scholar 
    Kadowaki, T. Evolutionary dynamics of metazoan TRP channels. Pflug. Arch. 467, 2043–2053 (2015).CAS 

    Google Scholar 
    Saito, S. & Tominaga, M. Evolutionary tuning of TRPA1 and TRPV1 thermal and chemical sensitivity in vertebrates. Temp. (Austin, Tex.) 4, 141–152 (2017).
    Google Scholar 
    Saito, S. et al. Analysis of transient receptor potential ankyrin 1 (TRPA1) in frogs and lizards illuminates both nociceptive heat and chemical sensitivities and coexpression with TRP vanilloid 1 (TRPV1) in ancestral vertebrates. J. Biol. Chem. 287, 30743–30754 (2012).CAS 

    Google Scholar 
    Saito, S. et al. Evolution of heat sensors drove shifts in thermosensation between xenopus species adapted to different thermal niches. J. Biol. Chem. 291, 11446–11459 (2016).CAS 

    Google Scholar 
    Gracheva, E. O. et al. Molecular basis of infrared detection by snakes. Nature 464, 1006–1011 (2010).CAS 

    Google Scholar 
    Laursen, W. J., Anderson, E. O., Hoffstaetter, L. J., Bagriantsev, S. N. & Gracheva, E. O. Species-specific temperature sensitivity of TRPA1. Temp. (Austin, Tex.) 2, 214–226 (2015).
    Google Scholar 
    Bertolesi, G. E., Hehr, C. L. & McFarlane, S. Melanopsin photoreception in the eye regulates light-induced skin colour changes through the production of α-MSH in the pituitary gland. Pigment Cell Melanoma Res. 28, 559–571 (2015).CAS 

    Google Scholar 
    Bagnara, J. T. The pineal and the body lightening reaction of larval amphibians. Gen. Comp. Endocrinol. 3, 86–100 (1963).CAS 

    Google Scholar 
    Nisembaum, L. et al. In the heat of the night: thermo-TRPV channels in the salmonid pineal photoreceptors and modulation of melatonin secretion. Endocrinology 156, 4629–4638 (2015).CAS 

    Google Scholar 
    Schartl, M. et al. What is a vertebrate pigment cell? Pigment Cell Melanoma Res. 29, 8–14 (2016).
    Google Scholar 
    Slominski, A. Cooling skin cancer: menthol inhibits melanoma growth. Focus on ‘TRPM8 activation suppresses cellular viability in human melanoma’. Am. J. Physiol. – Cell Physiol. 295, C293–C295 (2008).CAS 

    Google Scholar 
    Yamamura, H., Ugawa, S., Ueda, T., Morita, A. & Shimada, S. TRPM8 activation suppresses cellular viability in human melanoma. Am. J. Physiol. Cell Physiol. 295, C296–C301 (2008).CAS 

    Google Scholar 
    Knowlton, W. M. et al. A sensory-labeled line for cold: TRPM8-expressing sensory neurons define the cellular basis for cold, cold pain, and cooling-mediated analgesia. J. Neurosci. 33, 2837–2848 (2013).CAS 

    Google Scholar 
    Weyer-Menkhoff, I., Pinter, A., Schlierbach, H., Schänzer, A. & Lötsch, J. Epidermal expression of human TRPM8, but not of TRPA1 ion channels, is associated with sensory responses to local skin cooling. Pain 160, 2699–2709 (2019).Kumasaka, M., Sato, S., Yajima, I. & Yamamoto, H. Isolation and developmental expression of tyrosinase family genes in Xenopus laevis. Pigment Cell Res. 16, 455–462 (2003).CAS 

    Google Scholar 
    Rodionov, V. I., Hope, A. J., Svitkina, T. M. & Borisy, G. G. Functional coordination of microtubule-based and actin-based motility in melanophores. Curr. Biol. 8, 165–169 (1998).CAS 

    Google Scholar 
    Session, A. M. et al. Genome evolution in the allotetraploid frog Xenopus laevis. Nature 538, 336–343 (2016).CAS 

    Google Scholar 
    Gosset, J. R. et al. A cross-species translational pharmacokinetic-pharmacodynamic evaluation of core body temperature reduction by the TRPM8 blocker PF-05105679. Eur. J. Pharm. Sci. 109S, S161–S167 (2017).
    Google Scholar 
    Winchester, W. J. et al. Inhibition of TRPM8 channels reduces pain in the cold pressor test in humans. J. Pharmacol. Exp. Ther. 351, 259–269 (2014).
    Google Scholar 
    Bianchi, B., Smith, P. A. & Abriel, H. The ion channel TRPM4 in murine experimental autoimmune encephalomyelitis and in a model of glutamate-induced neuronal degeneration. Mol. Brain 11, 1–10 (2018).
    Google Scholar 
    Li, K., Shi, Y., Gonye, E. C. & Bayliss, D. A. TRPM4 contributes to subthreshold membrane potential oscillations in multiple mouse pacemaker neurons. eNeuro 8, 1–13 (2021).
    Google Scholar 
    Dong, W. et al. Visual avoidance in Xenopus tadpoles is correlated with the maturation of visual responses in the optic tectum. J. Neurophysiol. 101, 803–815 (2009).
    Google Scholar 
    Bertolesi, G. E., Debnath, N., Atkinson-Leadbeater, K., Niedzwiecka, A. & McFarlane, S. Distinct type II opsins in the eye decode light properties for background adaptation and behavioural background preference. Mol. Ecol. 30, 6659–6676 (2021).CAS 

    Google Scholar 
    Viczian, A. S. & Zuber, M. E. A simple behavioral assay for testing visual function in xenopus laevis. J. Vis. Exp. 12, 51726 (2014).
    Google Scholar 
    Myers, B. R., Sigal, Y. M. & Julius, D. Evolution of thermal response properties in a cold-activated TRP channel. PLoS One 4, e5741 (2009).
    Google Scholar 
    Furman, B. L. S. et al. Pan-African phylogeography of a model organism, the African clawed frog ‘Xenopus laevis’. Mol. Ecol. 24, 909–925 (2015).CAS 

    Google Scholar 
    Wilson, R. S., James, R. S. & Johnston, I. A. Thermal acclimation of locomotor performance in tadpoles and adults of the aquatic frog Xenopus laevis. J. Comp. Physiol. B. 170, 117–124 (2000).CAS 

    Google Scholar 
    Kashiwagi, K. et al. Xenopus tropicalis: an ideal experimental animal in amphibia. Exp. Anim. 59, 395–405 (2010).CAS 

    Google Scholar 
    Martínez-Freiría, F., Toyama, K. S., Freitas, I. & Kaliontzopoulou, A. Thermal melanism explains macroevolutionary variation of dorsal pigmentation in Eurasian vipers. Sci. Rep. 10, 72871–1 (2020).Tanaka, K. Does the thermal advantage of melanism produce size differences in color-dimorphic snakes? Zool. Sci. 26, 698–703 (2009).
    Google Scholar 
    Moreno Azócar, D. L., Nayan, A. A., Perotti, M. G. & Cruz, F. B. How and when melanic coloration is an advantage for lizards: the case of three closely-related species of Liolaemus. Zool. (Jena.) 141, 125774 (2020).
    Google Scholar 
    Azócar, D. L. M. et al. Effect of body mass and melanism on heat balance in Liolaemus lizards of the goetschi clade. J. Exp. Biol. 219, 1162–1171 (2016).
    Google Scholar 
    Smith, K. R. et al. Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards. Proc. R. Soc. B Biol. Sci. 283, 20160626 (2016).
    Google Scholar 
    Rowe, J. W. et al. Thermal and substrate color-induced melanization in laboratory reared red-eared sliders (Trachemys scripta elegans). J. Therm. Biol. 61, 125–132 (2016).
    Google Scholar 
    Larsen, E. H. Dual skin functions in amphibian osmoregulation. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 253, 110869 (2021).CAS 

    Google Scholar 
    Franco-Belussi, L., Sköld, H. N. & De Oliveira, C. Internal pigment cells respond to external UV radiation in frogs. J. Exp. Biol. 219, 1378–1383 (2016).
    Google Scholar 
    Langhelle, A., Lindell, M. J. & Nyström, P. Effects of ultraviolet radiation on amphibian embryonic and larval development. J. Herpetol. 33, 449–456 (1999).
    Google Scholar 
    Mueller, K. P. & Neuhauss, S. C. F. Sunscreen for fish: co-option of UV light protection for camouflage. PLoS One 9, e87372 (2014).
    Google Scholar 
    Perotti, M. G., Diéguez, M. & Del, C. Effect of UV-B exposure on eggs and embryos of patagonian anurans and evidence of photoprotection. Chemosphere 65, 2063–2070 (2006).CAS 

    Google Scholar 
    Nilsson Sköld, H., Aspengren, S. & Wallin, M. Rapid color change in fish and amphibians – function, regulation, and emerging applications. Pigment Cell Melanoma Res. 26, 29–38 (2013).
    Google Scholar 
    Vences, M. et al. Field body temperatures and heating rates in a montane frog population: the importance of black dorsal pattern for thermoregulation on JSTOR. Ann. Zool. Fennici 39, 209–220 (2002).
    Google Scholar 
    Lindgren, J. et al. Skin pigmentation provides evidence of convergent melanism in extinct marine reptiles. Nature 506, 484–488 (2014).CAS 

    Google Scholar 
    Bonino, M. F., Cruz, F. B. & Perotti, M. G. Does temperature at local scale explain thermal biology patterns of temperate tadpoles? J. Therm. Biol. 94, 102744 (2020).
    Google Scholar 
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 

    Google Scholar 
    Liu, T. et al. RNA interference-mediated depletion of TRPM8 enhances the efficacy of epirubicin chemotherapy in prostate cancer LNCaP and PC3 cells. Oncol. Lett. 15, 4129–4136 (2018).
    Google Scholar 
    Kashina, A. S. et al. Protein Kinase A, which regulates intracellular transport, forms complexes with molecular motors on organelles. Curr. Biol. 14, 1877–1881 (2004).CAS 

    Google Scholar  More

  • in

    High abundance of hydrocarbon-degrading Alcanivorax in plumes of hydrothermally active volcanoes in the South Pacific Ocean

    German CR, Von Damm KL. Hydrothermal processes. In: Holland HD, Turekian KK and Elderfield H, editors. Treatise geochem, Vol. 6. The oceans and marine geochemistry. Oxford, UK:Elsevier-Pergamon, 2004;181–222.Bell JB, Woulds C, Oevelen DV. Hydrothermal activity, functional diversity and chemoautotrophy are major drivers of seafloor carbon cycling. Sci Rep. 2017;7:1–3.
    Google Scholar 
    McCollom TM. Geochemical constraints on primary productivity in submarine hydrothermal vent plumes. Deep Res Part I Oceanogr Res Pap. 2000;47:85–101.CAS 

    Google Scholar 
    Tunnicliffe V, Baross JA, Gebruk AV, Giere O, Holland ME, Koschinsky A, et al. Group report: what are the interactions between biotic processes at vents and physical, chemical, and geological conditions. In: Halbach PE, Tunnicliffe V, and Hein JR, editors. Energy and Mass Transfer in Marine Hydrothermal Systems. Berlin-Dahlem:University Press; 2003;251–70.Nakamura K, Takai K. Theoretical constraints of physical and chemical properties of hydrothermal fluids on variations in chemolithotrophic microbial communities in seafloor hydrothermal systems. Prog Earth Planet Sci. 2014;1:1–24.
    Google Scholar 
    Wang W, Li Z, Zeng L, Dong C, Shao Z. The oxidation of hydrocarbons by diverse heterotrophic and mixotrophic bacteria that inhabit deep-sea hydrothermal ecosystems. ISME J. 2020;14:1994–2006.CAS 

    Google Scholar 
    Sinha RK, Krishnan KP, Kurian PJ. Complete genome sequence and comparative genome analysis of Alcanivorax sp. IO_7, a marine alkane-degrading bacterium isolated from hydrothermally-influenced deep seawater of southwest Indian ridge. Genomics 2021;113:884–91.CAS 

    Google Scholar 
    Li J, Yang J, Sun M, Su L, Wang H, Gao J, et al. Distribution and succession of microbial communities along the dispersal pathway of hydrothermal plumes on the Southwest Indian Ridge. Front Mar Sci. 2020;7:581381.
    Google Scholar 
    Meier DV, Bach W, Girguis PR, Gruber-Vodicka HR, Reeves EP, Richter M, et al. Heterotrophic Proteobacteria in the vicinity of diffuse hydrothermal venting. Environ Microbiol. 2016;18:4348–68.
    Google Scholar 
    Li WL, Huang JM, Zhang PW, Cui GJ, Wei ZF, Wu YZ, et al. Periodic and spatial spreading of alkanes and Alcanivorax bacteria in deep waters of the Mariana Trench. Appl Environ Microbiol. 2019;85:e02089–18.CAS 

    Google Scholar 
    Brooijmans RJW, Pastink MI, Siezen RJ. Hydrocarbon-degrading bacteria: The oil-spill clean-up crew. Micro Biotechnol. 2009;2:587.CAS 

    Google Scholar 
    Scoma A, Barbato M, Borin S, Daffonchio D, Boon N. An impaired metabolic response to hydrostatic pressure explains Alcanivorax borkumensis recorded distribution in the deep marine water column. Sci Rep. 2016;6:1–3.
    Google Scholar 
    Lai Q, Wang L, Liu Y, Fu Y, Zhong H, Wang B, et al. Alcanivorax pacificus sp. nov., isolated from a deep-sea pyrene-degrading consortium. Int J Syst Evol Microbiol. 2011;61:1370–4.CAS 

    Google Scholar 
    Wu Y, Lai Q, Zhou Z, Qiao N, Liu C, Shao Z. Alcanivorax hongdengensis sp. nov., an alkane-degrading bacterium isolated from surface seawater of the straits of Malacca and Singapore, producing a lipopeptide as its biosurfactant. Int J Syst Evol Microbiol. 2009;59:1474–9.CAS 

    Google Scholar 
    Fernández-Martínez J, Pujalte MJ, García-Martínez J, Mata M, Garay E, Rodríguez-Valera F. Description of Alcanivorax venustensis sp. nov. and reclassification of Fundibacter jadensis DSM 12178T (Bruns and Berthe-Corti 1999) as Alcanivorax jadensis comb. nov., members of the emended genus Alcanivorax. Int J Syst Evol Microbiol. 2003;53:331–8.
    Google Scholar 
    Radwan SS, Khanafer MM, Al-Awadhi HA. Ability of the so-called obligate hydrocarbonoclastic bacteria to utilize nonhydrocarbon substrates thus enhancing their activities despite their misleading name. BMC Microbiol. 2019;19:1–2.
    Google Scholar 
    Kalscheuer R, Stöveken T, Malkus U, Reichelt R, Golyshin PN, Sabirova JS, et al. Analysis of storage lipid accumulation in Alcanivorax borkumensis: Evidence for alternative triacylglycerol biosynthesis routes in bacteria. J Bacteriol. 2007;189:918–28.CAS 

    Google Scholar 
    Timm C, Davy B, Haase K, Hoernle KA, Graham IJ, De Ronde CEJ, et al. Subduction of the oceanic Hikurangi Plateau and its impact on the Kermadec arc. Nat Commun. 2014;5:1–9.
    Google Scholar 
    Haase KM, Beier C, Bach W, Kleint C, Anderson MO, Rubin K, et al. SO-263 Cruise Report: Tonga Rift. 2018. https://doi.org/10.13140/RG.2.2.23035.16169.Gartman A, Hannington M, Jamieson JW, Peterkin B, Garbe-Schönberg D, Findlay AJ, et al. Boiling-induced formation of colloidal gold in black smoker hydrothermal fluids. Geology 2018;46:39–42.CAS 

    Google Scholar 
    Falkenberg JJ, Keith M, Haase KM, Bach W, Klemd R, Strauss H, et al. Effects of fluid boiling on Au and volatile element enrichment in submarine arc-related hydrothermal systems. Geochim Cosmochim Acta. 2021;307:105–32.CAS 

    Google Scholar 
    Peters C, Strauss H, Haase K, Bach W, de Ronde CEJ, Kleint C, et al. SO2 disproportionation impacting hydrothermal sulfur cycling: Insights from multiple sulfur isotopes for hydrothermal fluids from the Tonga-Kermadec intraoceanic arc and the NE Lau Basin. Chem Geol. 2021;586:120586.CAS 

    Google Scholar 
    Baker ET, Walker SL, Massoth GJ, Resing JA. The NE Lau Basin: Widespread and abundant hydrothermal venting in the back-arc region behind a superfast subduction zone. Front Mar Sci. 2019;6:382.
    Google Scholar 
    Kim J, Lee KY, Kim JH. Metal-bearing molten sulfur collected from a submarine volcano: Implications for vapor transport of metals in seafloor hydrothermal systems. Geology 2011;39:351–4.CAS 

    Google Scholar 
    Klose L, Keith M, Hafermaas D, Kleint C, Bach W, Diehl A, et al. Trace element and isotope systematics in vent fluids and sulphides from Maka volcano, North Eastern Lau Spreading Centre: Insights into three-component fluid mixing. Front Earth Sci. 2021;9:1–26.
    Google Scholar 
    Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS 

    Google Scholar 
    Dede B, Hansen CT, Neuholz R, Schnetger B, Kleint C, Walker S, et al. Niche differentiation of sulfur-oxidizing bacteria (SUP05) in submarine hydrothermal plumes. ISME J. 2022;16:1479–90.CAS 

    Google Scholar 
    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.
    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 

    Google Scholar 
    McMurdie PJ, Holmes S. Phyloseq: An R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 

    Google Scholar 
    Diehl A, Bach W. MARHYS (MARine HYdrothermal Solutions) Database: A global compilation of marine hydrothermal vent fluid, end member, and seawater compositions. Geochem Geophys Geosystems. 2020;21:e2020GC009385.
    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 

    Google Scholar 
    Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 2012;28:1823–9.CAS 

    Google Scholar 
    Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar A, et al. ARB: A software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 

    Google Scholar 
    Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.CAS 

    Google Scholar 
    Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014;30:1312–3.CAS 

    Google Scholar 
    Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS 

    Google Scholar 
    Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.CAS 

    Google Scholar 
    Daims H, Brühl A, Amann R, Schleifer KH, Wagner M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: Development and evaluation of a more comprehensive probe set. Syst Appl Microbiol. 1999;22:434–44.CAS 

    Google Scholar 
    Wallner G, Amann R, Beisker W. Optimizing fluorescent in situ hybridization with rRNA‐targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 1993;14:136–43.CAS 

    Google Scholar 
    Stahl DA, Amann R. Development and application of nucleic acid probes in bacterial systematics. In: Nucleic acid techniques in bacterial systematics. Stackebrandt, E, Goodfellow M, editors. Chichester, UK: John Wiley & Sons Ltd; 1991. pp. 205–48.Manz W, Amann R, Ludwig W, Wagner M, Schleifer KH. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: Problems and solutions. Syst Appl Microbiol. 1992;15:593–600.
    Google Scholar 
    Eilers H, Pernthaler J, Glöckner FO, Amann R. Culturability and in situ abundance of pelagic Bacteria from the North Sea. Appl Environ Microbiol. 2000;66:3044–51.CAS 

    Google Scholar 
    Syutsubo K, Kishira H, Harayama S. Development of specific oligonucleotide probes for the identification and in situ detection of hydrocarbon-degrading Alcanivorax strains. Environ Microbiol. 2001;3:371–9.CAS 

    Google Scholar 
    Morris RM, Rappé MS, Urbach E, Connon SA, Giovannoni SJ. Prevalence of the Chloroflexi-related SAR202 bacterioplankton cluster throughout the mesopelagic zone and deep ocean. Appl Environ Microbiol. 2004;70:2836–42.CAS 

    Google Scholar 
    Bushnell B BBMap (version 35.14). 2015. https://sourceforge.net/projects/bbmap/.Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinforma. 2010; http://www.bioinformatics.babraham.ac.uk/projects/.Rodriguez-R LM, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: Fast estimation of metagenomic coverage and sequence diversity. mSystems 2018;3:e00039–18.
    Google Scholar 
    Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.
    Google Scholar 
    Kopylova E, Noé L, Touzet H. SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012;28:3211–7.CAS 

    Google Scholar 
    Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.CAS 

    Google Scholar 
    Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 2013;29:1072–5.CAS 

    Google Scholar 
    Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 

    Google Scholar 
    Eren AM, Kiefl E, Shaiber A, Veseli I, Miller SE, Schechter MS, et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol. 2021;6:3–6.CAS 

    Google Scholar 
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 

    Google Scholar 
    Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotech. 2017;35:725–31.CAS 

    Google Scholar 
    Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
    Google Scholar 
    Seemann T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 

    Google Scholar 
    Priest T, Heins A, Harder J, Amann R, Fuchs BM. Niche partitioning of the ubiquitous and ecologically relevant NS5 marine group. ISME J. 2022;16:1570–82.CAS 

    Google Scholar 
    Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.CAS 

    Google Scholar 
    Karthikeyan S, Rodriguez‐R LM, Heritier‐Robbins P, Hatt JK, Huettel M, Kostka JE, et al. Genome repository of oil systems: An interactive and searchable database that expands the catalogued diversity of crude oil‐associated microbes. Environ Microbiol. 2020;22:2094–106.CAS 

    Google Scholar 
    Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.CAS 

    Google Scholar 
    Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.CAS 

    Google Scholar 
    Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 

    Google Scholar 
    Gomes AÉI, Stuchi LP, Siqueira NMG, Henrique JB, Vicentini R, Ribeiro ML, et al. Selection and validation of reference genes for gene expression studies in Klebsiella pneumoniae using Reverse Transcription Quantitative real-time PCR. Sci Rep. 2018;8:1–4.
    Google Scholar 
    Guidi L, Chaffron S, Bittner L, Eveillard D, Larhlimi A, Roux S, et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 2016;532:465–70.CAS 

    Google Scholar 
    Duarte CM. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol Oceanogr Bull. 2015;24:11–4.
    Google Scholar 
    Anantharaman K, Breier JA, Dick GJ. Metagenomic resolution of microbial functions in deep-sea hydrothermal plumes across the Eastern Lau Spreading Center. ISME J. 2016;10:225–39.CAS 

    Google Scholar 
    Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front Microbiol. 2017;8:682.
    Google Scholar 
    Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Addendum: Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl.nov.). Front Microbiol. 2017;9:772.
    Google Scholar 
    Green DH, Llewellyn LE, Negri AP, Blackburn SI, Bolch CJS. Phylogenetic and functional diversity of the cultivable bacterial community associated with the paralytic shellfish poisoning dinoflagellate Gymnodinium catenatum. FEMS Microbiol Ecol. 2004;47:345–57.CAS 

    Google Scholar 
    Ramasamy KP, Rajasabapathy R, Lips I, Mohandass C, James RA. Genomic features and copper biosorption potential of a new Alcanivorax sp. VBW004 isolated from the shallow hydrothermal vent (Azores, Portugal). Genomics 2020;112:3268–73.CAS 

    Google Scholar 
    Barbato M, Scoma A, Mapelli F, De Smet R, Banat IM, Daffonchio D, et al. Hydrocarbonoclastic Alcanivorax isolates exhibit different physiological and expression responses to N-dodecane. Front Microbiol. 2016;7:2056.
    Google Scholar 
    Sevilla E, Yuste L, Rojo F. Marine hydrocarbonoclastic bacteria as whole-cell biosensors for n-alkanes. Micro Biotechnol. 2015;8:693–706.CAS 

    Google Scholar 
    Tivey MK. Black and white smokers. In: Harff J, Meschede M, Petersen S, Thiede Jö, editors. Encyclopedia of Marine Geosciences. Dordrecht: Springer Netherlands; 2016. p. 58–62.Djurhuus A, Mikalsen SO, Giebel HA, Rogers AD. Cutting through the smoke: The diversity of microorganisms in deep-sea hydrothermal plumes. R Soc Open Sci. 2017;4:160829.
    Google Scholar 
    Leahy JG, Colwell RR. Microbial degradation of hydrocarbons in the environment. Microbiol Rev. 1990;54:305–15.CAS 

    Google Scholar 
    Atlas R, Bragg J. Bioremediation of marine oil spills: When and when not – The Exxon Valdez experience. Micro Biotechnol. 2009;2:213–21.CAS 

    Google Scholar 
    Reva ON, Hallin PF, Willenbrock H, Sicheritz-Ponten T, Tümmler B, Ussery DW. Global features of the Alcanivorax borkumensis SK2 genome. Environ Microbiol. 2008;10:614–25.CAS 

    Google Scholar 
    Gregory GJ, Morreale DP, Carpenter MR, Kalburge SS, Boyd EF. Quorum sensing regulators AphA and OpaR control expression of the operon responsible for biosynthesis of the compatible solute ectoine. Appl Environ Microbiol. 2019;85:e01543–19.CAS 

    Google Scholar 
    Richter AA, Mais CN, Czech L, Geyer K, Hoeppner A, Smits SHJ, et al. Biosynthesis of the stress-protectant and chemical chaperon ectoine: biochemistry of the transaminase EctB. Front Microbiol. 2019;10:2811.
    Google Scholar 
    Schneiker S, Dos Santos VAPM, Bartels D, Bekel T, Brecht M, Buhrmester J, et al. Genome sequence of the ubiquitous hydrocarbon-degrading marine bacterium Alcanivorax borkumensis. Nat Biotechnol. 2006;24:997–1004.CAS 

    Google Scholar 
    Wang W, Shao Z. Enzymes and genes involved in aerobic alkane degradation. Front Microbiol. 2013;4:116.
    Google Scholar 
    Barclay W, Rodd JA, Pflueger JC, Havard KR, Helu SP. Oil plays in the kingdom of Tonga, Southwest Pacific. PESA J. 1993;21:79–92.
    Google Scholar 
    Chadwick WW, Rubin KH, Merle SG, Bobbitt AM, Kwasnitschka T, Embley RW. Recent eruptions between 2012-2018 discovered at West Mata submarine volcano (NE Lau Basin, SW Pacific) and characterized by new ship, AUV, and ROV data. Front Mar Sci. 2019;6:495.
    Google Scholar 
    Baumberger T, Lilley MD, Lupton JE, Baker ET, Resing JA, Buck NJ, et al. Dissolved gas and metal composition of hydrothermal plumes from a 2008 submarine eruption on the Northeast Lau Spreading Center. Front Mar Sci. 2020;7:171.
    Google Scholar 
    Lupton J, Rubin KH, Arculus R, Lilley M, Butterfield D, Resing J, et al. Helium isotope, C/3 He, and Ba‐Nb‐Ti signatures in the northern Lau Basin: Distinguishing arc, back‐arc, and hotspot affinities. Geochem Geophys. 2015;16:1133–55.CAS 

    Google Scholar 
    Graham DW. Noble gas isotope geochemistry of mid-ocean ridge and ocean island basalts: Characterization of mantle source reservoirs. In: Porcelli D, Wieler R, Ballentine C, editors. Noble gases in Geochemistry and cosmochemistry, Rev Mineral Geochem. Vol 47. Washington D.C.: Mineral Soc. Of Am; 2002. p. 247–318.Lupton JE, Arculus RJ, Greene RR, Evans LJ, Goddard CI. Helium isotope variations in seafloor basalts from the Northwest Lau Backarc Basin: Mapping the influence of the Samoan hotspot. Geophys Res Lett. 2009;36:L17313.
    Google Scholar 
    Gordon GW. Naturally occurring organohalogen compounds – A comprehensive survey. Prog Chem Org Nat Prod. 1996;68:1–423.
    Google Scholar 
    Spietz RL, Butterfield DA, Buck NJ, Larson BI, Chadwick WW, Walker SL, et al. Deep-sea volcanic eruptions create unique chemical and biological linkages between the subsurface lithosphere and the oceanic hydrosphere. Oceanography. 2018;31:128–35.
    Google Scholar 
    Huber JA, Butterfield DA, Baross JA. Bacterial diversity in a subseafloor habitat following a deep-sea volcanic eruption. FEMS Microbiol Ecol. 2003;43:393–409.CAS 

    Google Scholar  More