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

    Tamarixia radiata global distribution to current and future climate using the climate change experiment (CLIMEX) model

    Arunrat, N., Sereenonchai, S., Chaowiwat, W. & Wang, C. Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand. Sci. Total Environ. 807, 150741 (2022).ADS 
    CAS 

    Google Scholar 
    Chandio, A. A., Shah, M. I., Sethi, N. & Mushtaq, Z. Assessing the effect of climate change and financial development on agricultural production in ASEAN-4: the role of renewable energy, institutional quality, and human capital as moderators. Environ. Sci. Pollut. Res. 29, 13211–13225 (2022).
    Google Scholar 
    Masood, N., Akram, R., Fatima, M., Mubeen, M., Hussain, S., Shakeel, M., Khan, N., Adnan, M., Wahid, A., Shah, A. N. and Ihsan, M. Z. (2022) Insect pest management under climate change. In Building climate resilience in agriculture. Springer, ChamOzdemir, D. The impact of climate change on agricultural productivity in Asian countries: A heterogeneous panel data approach. Environ. Sci. Pollut. Res. 29, 8205–8217 (2022).
    Google Scholar 
    Aidoo, O. F. et al. Climate-induced range shifts of invasive species (Diaphorina citri Kuwayama). Pest Manag. Sci. 78, 2534–2549 (2022).CAS 

    Google Scholar 
    Hebbar, K. B. et al. Predicting the Potential Suitable Climate for Coconut (Cocos nucifera L.) Cultivation in India under Climate Change Scenarios Using the MaxEnt Model. Plants. 11, 731 (2022).
    Google Scholar 
    Martín-Vélez, V. & Abellán, P. Effects of climate change on the distribution of threatened invertebrates in a Mediterranean hotspot. Insect Conserv. Divers. 15, 370–379 (2022).
    Google Scholar 
    Williams, J. J., Freeman, R., Spooner, F. & Newbold, T. Vertebrate population trends are influenced by interactions between land use, climatic position, habitat loss and climate change. Glob. Chang. Biol. 28, 797–815 (2022).CAS 

    Google Scholar 
    Aidoo, O. F. et al. Lethal yellowing disease: insights from predicting potential distribution under different climate change scenarios. J. Plant Dis. Prot. 128, 1313–1325 (2021).
    Google Scholar 
    Sofaer, H. R. et al. Development and delivery of species distribution models to inform decision-making. Bioscience 69, 544–557 (2019).
    Google Scholar 
    Mead FW, The Asiatic citrus psyllid, Diaphorina citri Kuwayama (Homoptera: Psyllidae). Florida Department of Agriculture Conservation Service, Division of Plant Industry Entomological Circular No. 180.Bové, J. M. Huanglongbing: A destructive, newly-emerging, century-old disease of citrus. Plant Pathol. J. 1, 7–37 (2006).
    Google Scholar 
    Li, S., Wu, F., Duan, Y., Singerman, A. & Guan, Z. Citrus greening: Management strategies and their economic impact. HortScience 55, 604–612 (2020).
    Google Scholar 
    Jia, H. et al. Genome editing of the disease susceptibility gene Cs LOB 1 in citrus confers resistance to citrus canker. Plant Biotechnol. J. 15, 817–823 (2017).CAS 

    Google Scholar 
    Ehsani, R., Dewdney, M. & Johnson, E. Controlling HLB with thermotherapy: What have we learned so far?. Citrus Ind. News 9, 26–28 (2016).
    Google Scholar 
    Spreen, T. H., Baldwin, J. P. & Futch, S. H. An economic assessment of the impact of Huanglongbing on citrus tree plantings in Florida. J. Hortic. Sci. 49, 1052–1055 (2014).
    Google Scholar 
    Djeddour, D., Pratt, C., Constantine, K., Rwomushana, I. and Day, R., (2021) The Asian citrus greening disease (Huanglongbing). Evidence note on invasiveness and potential economic impacts for East Africa. CABI Working Paper, 24, 94Hu, J., Jiang, J. & Wang, N. Control of citrus Huanglongbing via trunk injection of plant defense activators and antibiotics. Phytopathology 108, 186–195 (2018).CAS 

    Google Scholar 
    Fan, G. C. et al. Evaluation of thermotherapy against Huanglongbing (citrus greening) in the greenhouse. J. Integr. Agric. 15, 111–119 (2016).
    Google Scholar 
    Nguyen, V. A., Bartels, D. & Gilligan, C. Modelling the spread and mitigation of an emerging vector-borne pathogen: citrus greening in the US. Biorxiv https://doi.org/10.1101/2022.05.04.490566 (2022).Article 

    Google Scholar 
    Milosavljević, I. et al. Post-release evaluation of Diaphorencyrtus aligarhensis (Hymenoptera: Encyrtidae) and Tamarixia radiata (Hymenoptera: Eulophidae) for biological control of Diaphorina citri (Hemiptera: Liviidae) in Urban California, USA. Agronomy 12, 583 (2022).
    Google Scholar 
    Maluta, N., Castro, T. & Lopes, J. R. S. Entomopathogenic fungus disrupts the phloem-probing behavior of Diaphorina citri and may be an important biological control tool in citrus. Sci. Rep. 12, 1–10 (2022).
    Google Scholar 
    Hall, D. G., Richardson, M. L., Ammar, E. D. & Halbert, S. E. Asian citrus psyllid, Diaphorina citri, vector of citrus huanglongbing disease. Entomol. Exp. Appl. 146, 207–223 (2013).
    Google Scholar 
    Vázquez-García, M. et al. Insecticide resistance in adult Diaphorina citri Kuwayama1 from lime orchards in central west Mexico. Southwest. Entomol. 38, 579–596 (2013).
    Google Scholar 
    Naeem, A., Freed, S., Jin, F. L., Akmal, M. & Mehmood, M. Monitoring of insecticide resistance in Diaphorina citri Kuwayama (Hemiptera: Psyllidae) from citrus groves of Punjab Pakistan. Crop Prot. 86, 62–68 (2016).CAS 

    Google Scholar 
    Hulme, P. E. et al. Grasping at the routes of biological invasions: A framework for integrating pathways into policy. J. Appl. Ecol. 45, 403–414 (2008).
    Google Scholar 
    Oke, A. O., Oladigbolu, A. A., Kunta, M., Alabi, O. J. & Sétamou, M. First report of the occurrence of Asian citrus psyllid Diaphorina citri (Hemiptera: Liviidae), an invasive species in Nigeria. West Africa. Sci. Rep. 10, 1–8 (2020).
    Google Scholar 
    Tang, Y.Q. (1990) On the parasite complex of Diaphorina citri Kuwayama (Homoptera: Psyllidae) in Asian-Pacific and other areas. In proceedings 4th international conference on citrus rehabilitation, Chiang Mai, Thailand. 4: 240 245Chien, C. C., Chiu, S. C. & Ku, S. C. Biological control of Diaphorina citri in Taiwan. Fruits 44, 401–407 (1989).
    Google Scholar 
    Hoddle, M. S. Foreign exploration for natural enemies of Asian citrus psyllid, Diaphorina citri (Hemiptera: Psyllidae), in the Punjab of Pakistan for use in a classical biological control program in California USA. Pakistan Entomol. 34, 1–5 (2012).
    Google Scholar 
    Étienne, J., Quilici, S., Marival, D., Franck, A. & Gonzalez Fernandez, C. Biological control of Diaphorina citri (Hemiptera: Psyllidae) in Guadeloupe by imported Tamarixia radiata (Hymenoptera: Eulophidae). Fruits 56, 307–315 (2001).
    Google Scholar 
    Qureshi, J. A., Rogers, M. E., Hall, D. G. & Stansly, P. A. Incidence of invasive Diaphorina citri (Hemiptera: Psyllidae) and its introduced parasitoid Tamarixia radiata (Hymenoptera: Eulophidae) in Florida citrus. J. Econ. Entomol. 102, 247–256 (2009).
    Google Scholar 
    Chen, X., Triana, M. & Stansly, P. A. Optimizing production of Tamarixia radiata (Hymenoptera: Eulophidae), a parasitoid of the citrus greening disease vector Diaphorina citri (Hemiptera: Psylloidea). Biol. Control. 105, 13–18. https://doi.org/10.1016/j.biocontrol.2016.10.010 (2017).Article 

    Google Scholar 
    Kistner, E. J., Amrich, R., Castillo, M., Strode, V. & Hoddle, M. S. Phenology of Asian citrus psyllid (Hemiptera: Liviidae), with special reference to biological control by Tamarixia radiata, in the residential landscape of southern California. J. Econ. Entomol. 109, 1047–1057. https://doi.org/10.1093/jee/tow021 (2016).Article 

    Google Scholar 
    Ramos Aguila, L. C. et al. Temperature-dependent biological control effectiveness of Tamarixia radiata (Hymenoptera: Eulophidea) under laboratory conditions. J. Econ. Entomol. 114, 2009–2017 (2021).
    Google Scholar 
    Ramos Aguila, L. C. et al. Temperature-dependent demography and population projection of Tamarixia radiata (Hymenoptera: Eulophidea) reared on Diaphorina citri (Hemiptera: Liviidae). J. Econ. Entomol. 113, 55–63 (2020).
    Google Scholar 
    Ashraf, H. J. et al. Comparative microbiome analysis of Diaphorina citri and its associated parasitoids Tamarixia radiata and Diaphorencyrtus aligarhensis reveals Wolbachia as a dominant endosymbiont. Environ. Microbiol. 24, 1638–1652 (2022).CAS 

    Google Scholar 
    Chow, A. & Sétamou, M. Parasitism of Diaphorina citri (Hemiptera: Liviidae) by Tamarixia radiata (Hymenoptera: Eulophidae) on residential citrus in Texas: Importance of colony size and instar composition. Biol. Control 165, 104796 (2022).
    Google Scholar 
    Ajene, I. J. et al. Habitat suitability and distribution potential of Liberibacter species (“Candidatus Liberibacter asiaticus” and “Candidatus Liberibacter africanus”) associated with citrus greening disease. Environ. Microbiol. 26, 575–588 (2020).
    Google Scholar 
    Shabani, F., Kumar, L. & Ahmadi, M. A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecol. Evol. 6, 5973–5986 (2016).
    Google Scholar 
    Kearney, M. & Porter, W. Mechanistic niche modelling: Combining physiological and spatial data to predict species’ ranges. Ecol 12, 334–350 (2009).
    Google Scholar 
    Byeon, D. H., Jung, S. & Lee, W. H. Review of CLIMEX and MaxEnt for studying species distribution in South Korea. J. Asia-Pac. Biodivers. 1, 325–333 (2018).
    Google Scholar 
    Kriticos, D. J., Yonow, T. & McFadyen, R. E. The potential distribution of Chromolaena odorata (Siam weed) in relation to climate. Weed Res 45, 246–254 (2005).
    Google Scholar 
    Wharton, T. N. & Kriticos, D. J. The fundamental and realized niche of the Monterey pine aphid, Essigella californica (Essig) (Hemiptera: Aphididae): implications for managing softwood plantations in Australia. Divers. Distrib. 10, 253–262 (2004).
    Google Scholar 
    Sutherst, R., Maywald, G. and Kriticos, D., CLIMEX version 3: user’s guide. (2007).Ramirez-Cabral, N. Y., Kumar, L. & Shabani, F. Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Sci. Rep. 7, 1–3 (2017).CAS 

    Google Scholar 
    McCalla, K. A., Keçeci, M., Milosavljević, I., Ratkowsky, D. A. & Hoddle, M. S. The influence of temperature variation on life history parameters and thermal performance curves of Tamarixia radiata (Hymenoptera: Eulophidae), a parasitoid of the Asian citrus psyllid (Hemiptera: Liviidae). J. Econ. Entomol. 112, 1560–1574 (2019).
    Google Scholar 
    Gonzalez-Cabrera, J., Moreno-Carrillo, G., Sanchez-Gonzalez, J. A. & Bernal, H. C. Natural and augmented parasitism of tamarixia radiata (Hymenoptera Eulophidae) in Urban Areas of western Mexico. Entomol. Sci. 53, 486–492. https://doi.org/10.18474/JES17-112.1 (2018).Article 

    Google Scholar 
    Chavez, Y. et al. Tamarixia radiata (Waterston) and Cheilomenes sexmaculata (Fabricius) as biological control agents of Diaphorina citri Kuwayama in Ecuador. Chil. J. Agric. Res. 77, 180–184. https://doi.org/10.4067/S0718-58392017000200180 (2017).Article 

    Google Scholar 
    Flores, D. & Ciomperlik, M. Biological control using the ectoparasitoid, Tamarixia radiata, against the Asian citrus psyllid, Diaphorina citri, in the lower Rio Grande valley of Texas. Southwest. Entomol. 42, 49–59. https://doi.org/10.3958/059.042.0105 (2017).Article 

    Google Scholar 
    Parra, J. R., Alves, G. R., Diniz, A. J. & Vieira, J. M. Tamarixia radiata (Hymenoptera: Eulophidae) × Diaphorina citri (Hemiptera: Liviidae): Mass rearing and potential use of the parasitoid in Brazil. J. Integr. Pest. Manag. https://doi.org/10.1093/jipm/pmw003 (2016).Article 

    Google Scholar 
    Diniz, A. J. F., Otimização da criação de Diaphorina citri Kuwayama, 1908 (Hemiptera: Liviidae) e de Tamarixia radiata (Waterston, 1922) (Hymenoptera: Eulophidae), visando a produção em larga escala do parasitoide e avalliação do seu estabelecimento em campo. Tese (Doutorado em Entomologia)—Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, São Paulo. (2013)Hoddle, M. S. & Pandey, R. Host range testing of Tamarixia radiata (Hymenoptera: Eulophidae) sourced from the Punjab of Pakistan for classical biological control of Diaphorina citri (Hemiptera: Liviidae: Euphyllurinae: Diaphorinini) in California. J. Econ. Entomol. 107, 125–136. https://doi.org/10.1603/EC13318 (2014).Article 

    Google Scholar 
    Gómez-Torres, M. L., Nava, D. E. & Parra, J. R. Thermal hygrometric requirements for the rearing and release of Tamarixia radiata (Waterston) (Hymenoptera, Eulophidae). Rev. Bras. Entomol. 58, 291–295. https://doi.org/10.1590/S0085-56262014000300011 (2014).Article 

    Google Scholar 
    Gómez-Torres, M. L., Nava, D. E. & Parra, J. R. Life table of Tamarixia radiata (Hymenoptera: Eulophidae) on Diaphorina citri (Hemiptera: Psyllidae) at different temperatures. J. Econ. Entomol. 105, 338–343 (2012).
    Google Scholar 
    Chong, J. H., Roda, A. L. & Mannion, C. M. Density and natural enemies of the Asian Citrus Psyllid, Diaphorina citri (Hemiptera: Psyllidae), in the residential landscape of Southern Florida. J. Agric. Urban Entomol. 27, 33–49. https://doi.org/10.3954/11-05.1 (2010).Article 

    Google Scholar 
    Pluke, R. W., Qureshi, J. A. & Stansly, P. A. Citrus flushing patterns, Diaphorina citri (Hemiptera: Psyllidae) populations and parasitism by Tamarixia radiata (Hymenoptera: Eulophidae) in Puerto Rico. Florida Entomol. 91, 36–42 (2008).
    Google Scholar 
    Ashraf, H. J. et al. Genetic diversity of Tamarixia radiata populations and their associated endosymbiont Wolbachia species from China. Agronomy 11, 2018 (2021).CAS 

    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, 223–235 (2016).
    Google Scholar 
    Kriticos, D. J. et al. CLIMEX Version 4, 184p (2015).
    Google Scholar 
    Gomez-Marco, F., Gebiola, M., Baker, B. G., Stouthamer, R. & Simmons, G. S. Impact of the temperature on the phenology of Diaphorina citri (Hemiptera: Liviidae) and on the establishment of Tamarixia radiata (Hymenoptera: Eulophidae) in urban areas in the lower Colorado Desert in Arizona. Environ. Entomol. 48, 514–523 (2019).
    Google Scholar 
    Vieira, J. M. Biologia em temperaturas alternantes e exigências térmicas de Diaphorina citri Kuwayama, 1908 (Hemiptera: Liviidae) e Tamarixia radiata (Waterston, 1922) (Hymenoptera: Eulophidae) visando ao seu zoneamento em regiões citrícolas do estado (Doctoral dissertation, Universidade de São Paulo).Castillo, J., Jacas, J. A., Peña, J. E., Ulmer, B. J. & Hall, D. G. Effect of temperature on life history of Quadrastichus haitiensis (Hymenoptera: Eulophidae), an endoparasitoid of Diaprepes abbreviatus (Coleoptera: Curculionidae). Biol. Control. 36, 189–196 (2006).
    Google Scholar 
    McFarland, C. D. & Hoy, M. A. Survival of Diaphorina citri (Homoptera: Psyllidae), and its two parasitoids, Tamarixia radiata (Hymenoptera: Eulophidae) and Diaphorencyrtus aligarhensis (Hymenoptera: Encyrtidae), under different relative humidities and temperature regimes. Fla. Entomol. 84, 227–233 (2001).
    Google Scholar 
    Fauvergue, X. & Quilici, S. Etude de certains parametres de la biologie de Tamarixia radiata (Waterston, 1992)(Hymenoptera: Eulophidae), ectoparasitoide primaire de Diaphorina citri Kuwayama (Hemiptera: Psyllidae) vecteur du greening des agrumes. Paris Fruits 46, 179–179 (1991).
    Google Scholar 
    Araújo, F. H. et al. Modelling climate suitability for Striga asiatica, a potential invasive weed of cereal crops. Crop Prot. 1(160), 106050 (2022).
    Google Scholar 
    Silva, D. A. & RS, Kumar L, Shabani F and Picanço MC,. Potential risk levels of invasive Neoleucinodes elegantalis (small tomato borer) in areas optimal for open-field Solanum lycopersicum (tomato) cultivation in the present and under predicted climate change. Pest Manag. Sci 73, 616–627 (2017).
    Google Scholar 
    Kumar, S., Neven, L. G. & Yee, W. L. Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment. Ecosphere 5, 1–23. https://doi.org/10.1890/ES14-00050.1 (2014).Article 
    CAS 

    Google Scholar 
    Kriticos, D. J. et al. CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 1, 53–64 (2012).
    Google Scholar 
    Santana Júnior PA, Worldwide spatial distribution of Tuta absoluta (Lepidoptera: Gelechiidae) and its natural enemies under current and future climatic change conditions through modelling. 136 f 2019 (Tese (Doutorado em Fitotecnia) – Universidade Federal de Viçosa, 2019).
    Google Scholar 
    Kriticos, D. J., Maywald, G. F., Yonow, T., Zurcher, E. J., Herrmann, N. I. and Sutherst, R. W., CLIMEX Version 4: Exploring the effects of climate on plants, animals and diseases. CSIRO, Canberra.156, (2015)Ramos Aguila, L. C. et al. Temperature-dependent demography and population projection of Tamarixia radiata (Hymenoptera: Eulophidea) reared on Diaphorina citri (Hemiptera: Liviidae). J. Econ. Entomol. 113, 55–63 (2019).
    Google Scholar 
    Oliveira, R. C., Modelagem de nicho ecológico para Helicoverpa punctigera (Wallengren, 1860) (Lepidoptera: Noctuidae) no mundo: Potencial invasão e riscos diante das mudanças climáticas. (2021). http://www.repositorio.ufc.br/handle/riufc/61961Bazzocchi, G. G., Lanzoni, A., Burgio, G. & Fiacconi, M. R. Effects of temperature and host on the pre-imaginal development of the parasitoid Diglyphus isaea (Hymenoptera: Eulophidae). Biol. Control 26, 74–82 (2003).
    Google Scholar 
    Hondo, T., Koike, A. & Sugimoto, T. Comparison of thermal tolerance of seven native species of parasitoids (Hymenoptera: Eulophidae) as biological control agents against Liriomyza trifolii (Diptera: Agromyzidae) in Japan. Appl. Entomol. Zool. 41, 73–82 (2006).
    Google Scholar 
    Duale, A. Effect of temperature and relative humidity on the biology of the stem borer parasitoid Pediobius furvus (Gahan) (Hymenoptera: Eulophidae) for the management of stem borers. Environ. Entomol. 34, 1–5 (2005).
    Google Scholar 
    Ashraf, H. J. et al. Comparative transcriptome analysis of Tamarixia radiata (Hymenoptera: Eulophidae) reveals differentially expressed genes upon heat shock. Comp. Biochem. Physiol. D: Genom. Proteom. 41, 100940 (2022).CAS 

    Google Scholar 
    van Doan, C. et al. Natural enemies of herbivores maintain their biological control potential under short-term exposure to future CO2, temperature, and precipitation patterns. Ecol. Evol. 11, 4182–4192 (2021).
    Google Scholar 
    Thomson, L. J., Macfadyen, S. & Hoffmann, A. A. Predicting the effects of climate change on natural enemies of agricultural pests. Biol. Control. 52, 296–306 (2010).
    Google Scholar 
    Rosenblatt, A. E. & Schmitz, O. J. Climate change, nutrition, and bottom-up and top-down food web processes. Trends Ecol. Evol. 31, 965–975 (2016).
    Google Scholar 
    Aidoo, O. F. et al. A machine learning algorithm-based approach (MaxEnt) for predicting invasive potential of Trioza erytreae on a global scale. Ecol. Inform. 71, 101792 (2022).
    Google Scholar 
    Aidoo, O. F. et al. The Impact of Climate Change on Potential Invasion Risk of Oryctes monoceros Worldwide. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2022.895906 (2022).Article 

    Google Scholar 
    Hao, M. et al. Global potential distribution of Oryctes rhinoceros, as predicted by Boosted Regression Tree model. Glob. Ecol. Conserv. 1(37), e02175 (2022).
    Google Scholar 
    Aidoo, O. F. et al. Model-based prediction of the potential geographical distribution of the invasive coconut mite, Aceria guerreronis Keifer (Acari: Eriophyidae) based on MaxEnt. Agric. For. Entomol. 24, 390–404 (2022).
    Google Scholar  More

  • in

    Climate extremes drive negative vegetation growth

    Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
    the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
    Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
    and JavaScript. More

  • in

    Viral infection switches the balance between bacterial and eukaryotic recyclers of organic matter during coccolithophore blooms

    Methods for data analysis in figuresAll analyses in figures were performed using Mathematica 12.3 (Wolfram Research, Inc., Champaign, IL, USA).Analysis in Fig. 1
    C&D. To calculate integrated abundances of E. huxleyi cells and EhV, we first selected days for which all the bags had a non-null value. Values were then summed up to obtain the integrated abundance.E&J. We computed a standard linear fit between the E. huxleyi total abundances and total EhV abundances for covered and uncovered bags separately. We followed the same procedure for the correlations in panel J and provide a comparison between different models in Supplementary Fig. 5.Analysis in Fig. 2
    A. The ASVs that were selected appeared at a relative abundance of at least 2% in at least 4 samples for the 0.2–2 µm 16S sequences and at least in 8 samples for the 2–20 µm 18S sequences. Abundances were concatenated for each time point and normalized by row, to have maximum relative abundance of 1 across all samples. ASVs were sorted by the position of their individual center of mass ({t}_{{CM}}) defined by$${t}_{{CM}}=,frac{mathop{sum}limits_{i}{t}_{i}f({t}_{i})}{mathop{sum}limits_{i}f({t}_{i})}$$
    (1)
    with i representing the different time points and f(({t}_{i})) the relative abundance of the ASV. The same figure for the individual bags in shown in Supplementary Fig. 14 and Supplementary Fig. 15.B. We selected 18S ASVs with a maximum relative abundance of at least 2% and observed in at least five samples. We averaged relative abundance across bags and then smoothed the time series with a moving average filter (width 2). Then, we grouped all ASVs into clusters based on their cosine distance using Mathematica’s FindClusters function and the KMeans method. The number of possible clusters ranged from 2 to 12, and the final number of clusters was decided using the silhouette method71. Only silhouette scores for 2 and 6 clusters were positive (between-cluster distance minus within-cluster distance).D. We subset reads that map to either Flavobacteriales or Fhodobacterales, then renormalized within each class, taking the mean over bags. Results per bag are shown in Supplementary Fig. 9.F. The turnover time was defined by the exponential rate k at which the Bray-Curtis similarity ({BC}(t)) declined over time. To this end, for a given bag, we computed the Bray–Curtis similarity between the composition vector at a starting day t’ with all following days t, giving a curve that declined roughly exponentially. For earlier starting days (for which the similarity curves declined the furthest), we found that the Bray–Curtis similarity never reached 0 but instead leveled out around ({{BC}}_{infty }=0.05) (due to ASVs that are constantly present in all the samples and maintain a minimal level of similarity between bags). Thus, we imposed an offset at(,{{BC}}_{infty }) for all fits (using Mathematica’s FindFit function) with the function:$${BC}(t)=(1-{{BC}}_{{{infty }}}) times {e}^{-kleft({t}^{{prime} }-tright)}+{{BC}}_{{{infty }}}$$
    (2)
    The turnover is averaged over bags, showing the standard deviation as error bars in the figure.G. To find differentially abundant ASVs, we first selected a subset of ASVs that had a maximum abundance of at least 10%, and performed Mann–Whitney U-Tests between the relative abundance values of a given ASV in the focal bag and all the other bags over all timepoints of the bloom’s demise. Correcting for multiple testing, we found four 16S ASVs that were differentially abundant in any of the bags, three of which were specific to bag 7, shown in Fig. 2g; and five 18S ASVs, two specific to bags 5 and 6 (Rhizosolenia delicatula and Aplanochytrium), one specific to bag 4 (Pterosperma), and two specific to bag 7 (MAST-1C and Woloszynskia halophila, shown in Fig. 2g).H. The divergence between bags was calculated as follows: we first measured, for each bag, the Bray–Curtis distance between this given bag and all the other bags at the end of the experiment (Supplementary Fig. 13). In order to control for the existing differences between bags at the beginning of the bloom, Bray–Curtis distances were normalized according to the differences between bags at the starting day of the E. huxleyi bloom. As the exact starting days of the bloom is not clear, we normalized for starting days 11, 12, or 13. The plot shows averages with the standard deviation as error bars. For the 18S microbiome, we first removed reads that map to E. huxleyi to reduce bias toward bag 7 (which had by far the lowest E. huxleyi abundance, Fig. 1c).Analysis in Fig. 3
    A. Functional annotation of dominant 18S ASVs was based on manual literature search for the 100 most abundant 18S ASVs. Automatic annotation using the functional database created by72 gave qualitatively identical results but contained fewer organisms (covering about 50% of reads). The relative abundance of each trait was obtained by summing up the relative abundance of all the species harboring a specific trait. We used the annotations from72 to further subdivide heterotrophs into osmotrophs, saprotrophs, and other types of heterotrophy (e.g., grazing), ignoring ASVs with missing annotations.D. Growth rates were computed by fitting a linear model to the log-transformed absolute abundances. For thraustochytrids, we measured growth rates until the abundances reached their maximum, i.e., for days indicated by solid lines in Fig. 3b. For bacteria in the 0.2–2 micron fraction, we measured growth rates during the bloom and demise of E. huxleyi, i.e., for the time period after day 15 until the final day, except for bag 4 (until day 22) and bag 7 (until day 18) to account for their different bloom and demise dynamics. For bacteria in the 2–20 micron fraction, we measured growth rates similarly, starting after day 10 until the final day, except bags 4 and 7 (until day 22).E. To quantify the rate of change k of the biomass ratio of thraustochytrids to bacteria we fit a linear function to the log of biomass ratio from day 10 to the time point t where the ratio was maximal; for bag 7, this was day 18, for all others, day 23. We thus have:$$,{{log }},{BR},(t)={kt},+,{{log }},{BR},(0)$$
    (3)
    Analysis in Fig. 4
    C&D. Since TEP accumulates over time, it cannot be expressed as a weighted sum of phytoplankton abundances. Instead, we formulate the model as a recursive relation where TEP can be produced by E. huxleyi, naked nanophytoplankton, and picophytoplankton, and degraded or lost through sinking:$${TEP}left(tright)=left(1-dright){TEP}left(t-1right)+{a}_{E}Eleft(tright)+{a}_{N}Nleft(tright)+{a}_{P}Pleft(tright),$$
    (4)
    The amount of TEP at time t is given by the fraction (1-d) of TEP at time t-1, where d corresponds to the fraction of TEP that is degraded between time points, plus the amount of TEP produced by the phytoplankton cells present at time t (or time t-1, which gives equivalent results). E, N, and P correspond to E. huxleyi, naked nanophytoplankton, and picophytoplankton, respectively. The parameter ({a}_{E}) corresponds to the amount of TEP produced per E. huxleyi cell, reported in panel D. ({a}_{E}) is set to be fixed through time, and different for each bag. This recursion can be solved to give an explicit expression for TEP(t):$${TEP}left(tright)=mathop{sum }limits_{{t}^{{prime} }=0}^{t}{left(1-dright)}^{t-{t}^{{prime} }}[{a}_{E}Eleft({t}^{{prime} }right)+{a}_{N}Nleft({t}^{{prime} }right)+{a}_{P}Pleft({t}^{{prime} }right)].$$
    (5)
    This functional form was then used to perform a linear model fitting with the constraint ({a}_{i}ge 0) for various values of the parameter d. The best fit, defined by maximum ({R}^{2}) over the resulting linear model, was used to fix d = 0.12. Our model considers that the fraction of non-calcified E. huxleyi cells in the nanophytoplankton counts is small.Larger phytoplankton cells ( >40 μm) filtered out from flow-cytometry measurements can also be a major source of TEP, despite low cell density. In order to verify this, FlowCam data was analyzed. None of the identified classes of larger phytoplankton (such as Phaeocystis or Dinobryon) increased in a systematic manner toward later stages of the bloom, explaining why larger phytoplankton were not included in the TEP model (Supplementary Fig. 24 and Supplementary Fig. 25).E. Using the smFISH method that reports the proportion of infected E. huxleyi cells, we estimated the amount of TEP produced from infected cells. We first used the least infected uncovered bags (bags 1 and 3) as a baseline to fix model parameters such as how much TEP does a non-infected cell produce. We then split the E. huxleyi abundance into an uninfected subpopulation producing T TEP/cell as in the uninfected bags, and an infected subpopulation producing I×T TEP/cells. To define I, we combined the fixed model parameters (i.e., amount of TEP produced per cell from Fig. 4d for bags 1 and 3) with the measured fraction of infected cells. We adjusted the factor I = 4 to minimize deviation of the measure total TEP concentration from the model prediction including the two subpopulations. The same procedure was used for panel H, using the corresponding model for PIC.F&G. To model the amount of PIC produced per cell we assume that the measured PIC only increases via new E. huxleyi coccoliths. The equivalent model for PIC reads$${PIC}left(tright)=left(1-dright){PIC}left(t-1right)+{a}_{E}{{max }}left(Eleft(tright)-Eleft(t-1right)right).$$
    (6)
    Where ({a}_{E}) is the amount of PIC produced per cell, and displayed in panel G. Using the same procedure as for TEP, we obtain the best fit for d = 0.0075. Our PIC model assumes that all PIC production comes from E. huxleyi, supported by large occurrence of E. huxleyi cells observed in scanning electron microscopy (Supplementary Fig. 1).Methods for data collectionMesocosm core setupThe mesocosm experiment AQUACOSM VIMS-Ehux was carried out for 24 days between 24th May (day 0) and 16th June (day 23) 2018 in Raunefjorden at the University of Bergen’s Marine Biological Station Espegrend, Norway (60°16′11 N; 5°13′07E). The experiment consisted of seven enclosure bags made of transparent polyethylene (11 m3, 4 m deep and 2 m wide, permeable to 90% photosynthetically active radiation) mounted on floating frames and moored to a raft in the middle of the fjord. The bags were filled with surrounding fjord water (day −1; pumped from 5 m depth) and continuously mixed by aeration (from day 0 onwards). Each bag was supplemented with nutrients at a nitrogen to phosphorus ratio of 16:1 according to the optimal Redfield Ratio (1.6 µM NaNO3 and 0.1 µM KH2PO4 final concentration) on days 0–5 and 14–17, whereas on days 6, 7 and 13 only nitrogen was added to limit the growth of pico-eukaryotes and favor the growth of E. huxleyi that is more resistant to phosphate limited conditions. Silica was not added as a nutrient source in order to suppress the growth of diatoms and to enhance E. huxleyi proliferation. Bags 5, 6, 7 were covered to collect aerosols and guarantee minimal contamination while sampling for core variables. Bags 1, 2, 3, 4 were sampled for additional assays such as metabolomics, polysaccharides profiling, and vesicles, which increase sampling time and potential for contamination.Measurement of dissolved inorganic nutrientsUnfiltered seawater aliquots (10 mL) were collected from each bag and the surrounding fjord water in 12 mL polypropylene tubes and stored frozen at −20 °C. Dissolved inorganic nutrients were measured with standard segmented flow analysis with colorimetric detection73, using a Bran & Luebe autoanalyser. Data are available in ref. 74 and values for individual bags are plotted in Supplementary Fig. 26.Measurement of water temperature and salinityWater temperature and salinity were measured in each bag and the surrounding fjord water using a SD204 CTD/STD (SAIV A/S, Laksevag, Norway). Data points were averaged for 1–3 m depth (descending only). When this depth was not available, the available data points were taken. Data are missing for the fjord in days 0–1. Outliers were removed for the following samples: bag 1 at days 0, 4, 15; bag 7 at day 15. Data are available in ref. 74.Flow cytometry measurementsSamples for flow cytometric counts were collected twice a day, in the morning (7:00 a.m.) and evening (8:00–9:00 p.m.) from each bag and the surrounding fjord, which served as an environmental reference. Water samples were collected in 50 mL centrifugal tubes from 1 m depth, pre-filtered using 40 µm cell strainers, and immediately analyzed with an Eclipse iCyt (Sony Biotechology, Champaign, IL, USA) flow cytometer. A total volume of 300 µL with a flow rate of 150 µL/min was analyzed with the machine’s software ec800 v1.3.7. A threshold was applied based on the forward scatter signal to reduce the background noise.Phytoplankton populations were identified by plotting the autofluorescence of chlorophyll versus phycoerythrin and side scatter: calcified E. huxleyi (high side scatter and high chlorophyll), Synechococcus (high phycoerythrin and low chlorophyll), nano- and picophytoplankton (high and low chlorophyll, respectively). Chlorophyll fluorescence was detected by FL4 (excitation (ex): 488 nm and emission (em): 663–737 nm). Phycoerythrin was detected by FL3 (ex: 488 nm and em: 570–620 nm). Raw.fcs files were extracted and analyzed in R using ‘flowCore’ and ‘ggcyto’ packages and all data are available on Dryad74. In particular, the gating strategy was adapted to each day and each bag and individual plots for each days and each bag can be found in the Dryad link.For bacteria and viral counts, 200 µL of sample were fixed with 4 µL of 20% glutaraldehyde (final concentration of 0.5%) for 1 h at 4 °C and flash frozen. They were thawed and stained with SYBR gold (Invitrogen) that was diluted 1:10,000 in Tris-EDTA buffer, incubated for 20 min at 80 °C and cooled to room temperature. Bacteria and viruses were counted and analyzed using a Cytoflex and identified based on the Violet SSC-A versus FITC-A by comparing to reference samples containing fixed bacteria and viruses from lab cultures. A total volume of 60 µL with a flow rate of 10 µL/min was analyzed. A threshold was applied based on the forward scatter signal to reduce the background noise. For plotting bacteria (Fig. 1h), a moving average of three successive days was used.Enumeration of extracellular EhV abundance by qPCRDNA extracts from filters from the core sampling (see above) were diluted 100 times, and 1 µL was then used for qPCR analysis. EhV abundance was determined by qPCR for the major capsid protein (mcp) gene: 5′-acgcaccctcaatgtatggaagg-3′ (mcp1F) and 5′-rtscrgccaactcagcagtcgt -3′ (mcp94Rv). All reactions were carried out in technical triplicates using water as a negative control. For all reactions, Platinum SYBER Green qPCR SuperMix-UDG with ROX (Invitrogen, Carlsbad, CA, USA) was used as described by the manufacturer. Reactions were performed on a QuantStudio 5 Real-Time PCR System equipped with the QuantStudio Design and Analysis Software version 1.5.1 (Applied Biosystems, Foster City, CA, USA) as follows: 50 °C for 2 min, 95 °C for 5 min, 40 cycles of 95 °C for 15 s, and 60 °C for 30 s. Results were calibrated against serial dilutions of EhV201 DNA at known concentrations, enabling exact enumeration of viruses. Samples showing multiple peaks in melting curve analysis or peaks that were not corresponding to the standard curves were omitted. Data are available in ref. 74. A comparison of viral counts based on flow-cytometry and qPCR is shown in Supplementary Fig. 2.FlowCam analysisSamples for automated flow imaging microcopy were collected once a day in the morning (7:00 a.m.) from each bag and the surrounding fjord, which served as an environmental reference. Water samples were collected in 50 mL centrifugal tubes from 1 m depth, kept at 12 °C in darkness, and analyzed within 2 h of sampling, using a FlowCAM II (Fluid Imaging Technologies Inc., Scarborough, ME, USA) fitted with a 300 µm path length flow cell and a 4× microscope objective. Images were collected using auto-image mode at a rate of 7 frames/second. A sample volume of 10 mL was processed at a flow rate of 0.7 mL/min. Individual objects within each sample were clustered and annotated using the Ecotaxa platform75. Absolute counts for major groups, including the most abundant ciliate category Ciliophora U04, were then exported and normalized by the individual amount of water volume processed for each sample.Data are available under “Flowcam Composite Aquacosm_2018_VIMS-Ehux” project on Ecotaxa.Scanning electron microscopy50 ml of water samples from bags or fjord were collected on polycarbonate filters (0.2 µm pore size, 47 mm diameter, Millipore). The filters were air dried and stored on petri-slides (Millipore) at room temperature. Prior to observation, a small fraction of the filter was cut and coated with 2 nm of iridium using a Safematic CCU-010 coater (Safematic GMBH, Switzerland). Samples were observed on a Zeiss Ultra SEM that was set at a working distance of 6.2 ± 0.1 mm, an acceleration voltage of 3.0 kV and an aperture size of 30 mm. The secondary electron detector was used for image acquisition.Paired dilution experimentPhytoplankton growth and microzooplankton grazing rates were estimated using the dilution method76,77. A slightly modified version of the method was used with only one low dilution level (20%) and an undiluted treatment used78. Rates calculated using this method are considered conservative but accurate when compared with those using multiple dilution levels and a linear regression. Water from bags 1–4 was collected using a peristaltic pump at ~1 m depth and mixed into a 20 L clean carboy. Water was screened through a 200 µm mesh to remove larger mesozooplankton. The collected water was shaded with black plastic and returned to shore. Dilution experiments were set-up in a temperature-controlled room, set to ambient water temperature (±2 °C). Particle-free diluent (FSW) was prepared by gravity filtering whole seawater (WSW) through a 0.45 µm inline filter (PALL Acropak™ Membrane capsule) into a clean carboy. To the FSW, WSW was gently siphoned at a proportion of 20%. The 20% dilution and 100% WSW treatments were prepared in single carboys and then siphoned into triplicate 1.2 L Nalgene™ incubation bottles. To control for nutrient limitation, additional triplicate bottles of 100% WSW were incubated without added nutrients (10 µM nitrate and 1 µM phosphate). The incubation bottles were incubated for 24 h in an outdoor tank maintained at in-situ water temperatures by a flow-through system of ambient seawater. Bottles could float freely, and the seawater inflow caused gentle agitation throughout the 24 h period. A screen was used to mimic light conditions experienced within the mesocosm bags.To quantify viral mortality, we used the paired dilution method79 which involves setting up an extra low dilution level (20%) containing water filtered through a tangential flow filter (TFF) of 100 kDå to remove viral particles. During this experiment, TFF water was produced 1–2 days prior to the dilution experiment, to ensure the chemical composition of the water was as similar as possible, and experiments could be set up in a timely manner.At T0 hours and T24 hours from all dilution experiments, sub-samples were taken for the determination of chlorophyll-a and flow cytometry. For chlorophyll-a, 100–150 mL of seawater was filtered under low vacuum pressure through a 47 mm Whatman GF/F filters (effective pore size 0.7 µm), and then extracted in 7 mL of 97% methanol at 4 °C in the dark for 12 h. All chlorophyll readings were conducted on a Turner TD700 fluorometer80. Methanol blanks were included, and all samples were corrected for phaeophytin using a drop of 10% hydrochloric acid and then reading the sample again81.Water samples (2 × 1 mL) for flow cytometry were taken at T0 and T24 of dilution experiments for the determination of phytoplankton abundances. Water samples were taken in triplicate from T0, and from each bottle at T24. Samples were immediately fixed in 20 µL of glutaraldehyde (final concentration 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

    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

  • 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

    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