More stories

  • in

    Safeguarding nutrients from coral reefs under climate change

    Burke, L., Reytar, K., Spalding, M. & Perry, A. Reefs at Risk Revisited (World Resource Institute, 2011).Bell, J. D. et al. Planning the use of fish for food security in the Pacific. Mar. Policy 33, 64–76 (2009).Article 

    Google Scholar 
    Gillett, R. Fisheries in the Economies of the Pacific Island Countries and Territories (Asian Development Bank, 2016).The Regional State of the Coast Report: Western Indian Ocean (UNEP, Nairobi Convention & WIOMSA, 2015).Wabnitz, C. C. C., Cisneros-Montemayor, A. M., Hanich, Q. & Ota, Y. Ecotourism, climate change and reef fish consumption in Palau: benefits, trade-offs and adaptation strategies. Mar. Policy 88, 323–332 (2018).Article 

    Google Scholar 
    Cinner, J. E. et al. Building adaptive capacity to climate change in tropical coastal communities. Nat. Clim. Change 8, 117–123 (2018).Article 

    Google Scholar 
    Thilsted, S. H. et al. Sustaining healthy diets: the role of capture fisheries and aquaculture for improving nutrition in the post-2015 era. Food Policy 61, 126–131 (2016).Article 

    Google Scholar 
    Beal, T., Massiot, E., Arsenault, J. E., Smith, M. R. & Hijmans, R. J. Global trends in dietary micronutrient supplies and estimated prevalence of inadequate intakes. PLoS ONE 12, e0175554 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Calder, P. C. Marine omega-3 fatty acids and inflammatory processes: effects, mechanisms and clinical relevance. Biochim. Biophys. Acta 1851, 469–484 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Haddad, L. et al. A new global research agenda for food. Nature 540, 30–32 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Golden, C. D. et al. Aquatic foods to nourish nations. Nature 598, 315–320 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    MacNeil, M. et al. Recovery potential of the world’s coral reef fishes. Nature 520, 341–344 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Crona, B. I., Van Holt, T., Petersson, M., Daw, T. M. & Buchary, E. Using social–ecological syndromes to understand impacts of international seafood trade on small-scale fisheries. Glob. Environ. Change 35, 162–175 (2015).Article 

    Google Scholar 
    Okemwa, G. M., Kaunda-Arara, B., Kimani, E. N. & Ogutu, B. Catch composition and sustainability of the marine aquarium fishery in Kenya. Fish. Res. 183, 19–31 (2016).Article 

    Google Scholar 
    Cinner, J. E., Folke, C., Daw, T. & Hicks, C. C. Responding to change: using scenarios to understand how socioeconomic factors may influence amplifying or dampening exploitation feedbacks among Tanzanian fishers. Glob. Environ. Change 21, 7–12 (2011).Article 

    Google Scholar 
    Hicks, C. C., Graham, N. A. J., Maire, E. & Robinson, J. P. W. Secure local aquatic food systems in the face of declining coral reefs. One Earth 4, 1214–1216 (2021).Article 

    Google Scholar 
    Albert, J. et al. Malnutrition in rural Solomon Islands: an analysis of the problem and its drivers. Matern. Child Nutr. 16, e12921 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Golden, C. D. et al. Social–ecological traps link food systems to nutritional outcomes. Glob. Food Security 30, 100561 (2021).Article 

    Google Scholar 
    Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 

    Google Scholar 
    Robinson, J. P. W., Wilson, S. K., Jennings, S. & Graham, N. A. J. Thermal stress induces persistently altered coral reef fish assemblages. Glob. Change Biol. 25, 2739–2750 (2019).Article 

    Google Scholar 
    Robinson, J. P. W. et al. Productive instability of coral reef fisheries after climate-driven regime shifts. Nat. Ecol. Evol. 3, 183–190 (2019).PubMed 
    Article 

    Google Scholar 
    Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring along the Great Barrier Reef following mass coral bleaching. Nature 560, 92–96 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Morais, R. et al. Severe coral loss shifts energetic dynamics on a coral reef. Funct. Ecol. 34, 1507–1518 (2020).Article 

    Google Scholar 
    Robinson, J. P. W. et al. Habitat and fishing control grazing potential on coral reefs. Funct. Ecol. 34, 240–251 (2020).Article 

    Google Scholar 
    Fontoura, L. et al. Climate-driven shift in coral morphological structure predicts decline of juvenile reef fishes. Glob. Change Biol. 26, 557–567 (2020).Article 

    Google Scholar 
    Rogers, A., Blanchard, J. L. & Mumby, P. J. Fisheries productivity under progressive coral reef degradation. J. Appl. Ecol. 55, 1041–1049 (2018).Article 

    Google Scholar 
    Bates, A. E. et al. Climate resilience in marine protected areas and the ‘protection paradox’. Biol. Conserv. 236, 305–314 (2019).Article 

    Google Scholar 
    Darling, E. S. et al. Social–environmental drivers inform strategic management of coral reefs in the Anthropocene. Nat. Ecol. Evol. 3, 1341–1350 (2019).PubMed 
    Article 

    Google Scholar 
    Soliño, L. & Costa, P. R. Global impact of ciguatoxins and ciguatera fish poisoning on fish, fisheries and consumers. Environ. Res. 182, 109111 (2020).PubMed 
    Article 

    Google Scholar 
    Rogers, A. et al. Anticipative management for coral reef ecosystem services in the 21st century. Glob. Change Biol. 21, 504–514 (2015).Article 

    Google Scholar 
    Thiault, L. et al. Escaping the perfect storm of simultaneous climate change impacts on agriculture and marine fisheries. Sci. Adv. 5, eaaw9976 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Souter, D. et al. Status of Coral Reefs of the World: 2020 (Global Coral Reef Monitoring Network & International Coral Reef Initiative, 2021).Hicks, C. C. et al. Harnessing global fisheries to tackle micronutrient deficiencies. Nature 574, 95–98 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bierwagen, S. L., Heupel, M. R., Chin, A. & Simpfendorfer, C. A. Trophodynamics as a tool for understanding coral reef ecosystems. Front. Mar. Sci. 5, 24 (2018).Article 

    Google Scholar 
    Flombaum, P. et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc. Natl Acad. Sci. USA 110, 9824–9829 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lehane, L. & Lewis, R. J. Ciguatera: recent advances but the risk remains. Int. J. Food Microbiol. 61, 91–125 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fraser, K. M. et al. Production of mobile invertebrate communities on shallow reefs from temperate to tropical seas. Proc. R. Soc. B Biol. Sci. 287, 20201798 (2020).CAS 
    Article 

    Google Scholar 
    Ullah, H., Nagelkerken, I., Goldenberg, S. U. & Fordham, D. A. Climate change could drive marine food web collapse through altered trophic flows and cyanobacterial proliferation. PLoS Biol. 16, e2003446 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kang, J. X. Omega-3: a link between global climate change and human health. Biotechnol. Adv. 29, 388–390 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hixson, S. M. & Arts, M. T. Climate warming is predicted to reduce omega-3, long-chain, polyunsaturated fatty acid production in phytoplankton. Glob. Change Biol. 22, 2744–2755 (2016).Article 

    Google Scholar 
    Tan, K., Zhang, H. & Zheng, H. Climate change and n-3 LC-PUFA availability. Prog. Lipid Res. 86, 101161 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pethybridge, H. R. et al. Spatial patterns and temperature predictions of tuna fatty acids: tracing essential nutrients and changes in primary producers. PLoS ONE 10, e0131598 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hempson, T. N., Graham, N. A. J., MacNeil, M. A., Bodin, N. & Wilson, S. K. Regime shifts shorten food chains for mesopredators with potential sublethal effects. Funct. Ecol. 32, 820–830 (2018).Article 

    Google Scholar 
    Bellwood, D. R., Hughes, T. & Hoey, A. S. Sleeping functional group drives coral-reef recovery. Curr. Biol. 16, 2434–2439 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18, 944–953 (2015).PubMed 
    Article 

    Google Scholar 
    Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).Article 

    Google Scholar 
    Cheung, W. W., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stuart-Smith, R. D., Mellin, C., Bates, A. E. & Edgar, G. Habitat loss and range shifts contribute to ecological generalization amongst reef fishes. Nat. Ecol. Evol. 5, 656–662 (2021).PubMed 
    Article 

    Google Scholar 
    Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

    Google Scholar 
    Du Pontavice, H., Gascuel, D., Reygondeau, G., Maureaud, A. & Cheung, W. W. L. Climate change undermines the global functioning of marine food webs. Glob. Change Biol. 26, 1306–1318 (2020).Article 

    Google Scholar 
    Jones, J. et al. The microbiome of the gastrointestinal tract of a range-shifting marine herbivorous fish. Front. Microbiol. 9, 2000 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Littman, R., Willis, B. L. & Bourne, D. G. Metagenomic analysis of the coral holobiont during a natural bleaching event on the Great Barrier Reef. Environ. Microbiol. Rep. 3, 651–660 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Robinson, J. P. W. et al. Climate-induced increases in micronutrient availability for coral reef fisheries. One Earth 5, 98–108 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Froese, R. & Pauly, D. FishBase (FishBase, 2021); www.fishbase.orgMacNeil, M. A. NutrientFishbase dataset. GitHub https://github.com/mamacneil/NutrientFishbase (2021).Waldock, C., Stuart-Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).PubMed 
    Article 

    Google Scholar 
    Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cheung, W. W. L., Reygondeau, G. & Frölicher, T. L. Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science 354, 1591–1594 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Golden, C. et al. Nutrition: fall in fish catch threatens human health. Nature 534, 317–320 (2016).PubMed 
    Article 

    Google Scholar 
    Nash, K. L. & Graham, N. A. J. Ecological indicators for coral reef fisheries management. Fish Fish. 17, 1029–1054 (2016).Article 

    Google Scholar 
    Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Brandl, S. J. et al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445–454 (2019).Article 

    Google Scholar 
    Maire, E. et al. Micronutrient supply from global marine fisheries under climate change and overfishing. Curr. Biol. 31, 4132–4138 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Change Biol. 24, 2416–2433 (2018).Article 

    Google Scholar 
    Graham, N. A. J. & Nash, K. L. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326 (2013).Article 

    Google Scholar 
    Nash, K. L., Graham, N. A. J., Wilson, S. K. & Bellwood, D. R. Cross-scale habitat structure drives fish body size distributions on coral reefs. Ecosystems 16, 478–490 (2013).Article 

    Google Scholar 
    Pratchett, M. S. et al. in Oceanography and Marine Biology: An Annual Review Vol. 46 (eds Gibson, R. N. et al.) 251–296 (CRC Press, 2008).Graham, N. A. J. et al. Dynamic fragility of oceanic coral reef ecosystems. Proc. Natl Acad. Sci. USA 103, 8425–8429 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Richardson, L. E., Graham, N. A. J., Pratchett, M. S., Eurich, J. G. & Hoey, A. S. Mass coral bleaching causes biotic homogenization of reef fish assemblages. Glob. Change Biol. 24, 3117–3129 (2018).Article 

    Google Scholar 
    Graham, N. A. et al. Lag effects in the impacts of mass coral bleaching on coral reef fish, fisheries, and ecosystems. Conserv. Biol. 21, 1291–1300 (2007).PubMed 
    Article 

    Google Scholar 
    Hempson, T., Graham, N., Macneil, A., Hoey, A. & Wilson, S. Ecosystem regime shifts disrupt trophic structure. Ecol. Appl. 28, 191–200 (2018).PubMed 
    Article 

    Google Scholar 
    Jouffray, J.-B. et al. Identifying multiple coral reef regimes and their drivers across the Hawaiian archipelago. Phil. Trans. R. Soc. B Biol. Sci. 370, 20130268 (2015).Article 

    Google Scholar 
    McLean, M. et al. Trait structure and redundancy determine sensitivity to disturbance in marine fish communities. Glob. Change Biol. 25, 3424–3437 (2019).Article 

    Google Scholar 
    Nash, K. L., Graham, N. A. J., Jennings, S., Wilson, S. K. & Bellwood, D. R. Herbivore cross-scale redundancy supports response diversity and promotes coral reef resilience. J. Appl. Ecol. 53, 646–655 (2016).Article 

    Google Scholar 
    Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 3742 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kissling, W. D. et al. Towards global data products of essential biodiversity variables on species traits. Nat. Ecol. Evol. 2, 1531–1540 (2018).PubMed 
    Article 

    Google Scholar 
    Edgar, G. J. et al. Reef Life Survey: establishing the ecological basis for conservation of shallow marine life. Biol. Conserv. 252, 108855 (2020).Article 

    Google Scholar 
    Pauly, D. & Zeller, D. Accurate catches and the sustainability of coral reef fisheries. Curr. Opin. Environ. Sustain. 7, 44–51 (2014).Article 

    Google Scholar 
    Worm, B. & Branch, T. A. The future of fish. Trends Ecol. Evol. 27, 594–599 (2012).PubMed 
    Article 

    Google Scholar 
    McClanahan, T. R. et al. Critical thresholds and tangible targets for ecosystem-based management of coral reef fisheries. Proc. Natl Acad. Sci. USA 108, 17230–17233 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cinner, J. E. et al. Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science 368, 307–311 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Robinson, J. P. W. et al. Managing fisheries for maximum nutrient yield. Fish Fish. 23, 800–811 (2022).Article 

    Google Scholar 
    Graham, N. A. et al. Extinction vulnerability of coral reef fishes. Ecol. Lett. 14, 341–348 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schartup, A. T. et al. Climate change and overfishing increase neurotoxicant in marine predators. Nature 572, 648–650 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pinsky Malin, L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Thorson, J. T. Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data-integrated life-history model. Fish Fish. 21, 237–251 (2020).Article 

    Google Scholar 
    Ahern, M. B. et al. Locally-procured fish is essential in school feeding programmes in sub-Saharan Africa. Foods 10, 2080 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    UNEP-WCMC, WorldFish Centre, WRI & TNC. Global Distribution of Coral Reefs. Version 4.1. Ocean Data Viewer https://doi.org/10.34892/t2wk-5t34 (UN Environment World Conservation Monitoring Centre, 2021).Morillo-Velarde, P. S. et al. Habitat degradation alters trophic pathways but not food chain length on shallow Caribbean coral reefs. Sci. Rep. 8, 4109 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kumar, M. et al. Minerals, PUFAs and antioxidant properties of some tropical seaweeds from Saurashtra coast of India. J. Appl. Phycol. 23, 797–810 (2011).CAS 
    Article 

    Google Scholar 
    Coleman, M. A. et al. Climate change does not affect the seafood quality of a commonly targeted fish. Glob. Change Biol. 25, 699–707 (2019).Article 

    Google Scholar 
    Sissener, N. H. Are we what we eat? Changes to the feed fatty acid composition of farmed salmon and its effects through the food chain. J. Exp. Biol. 221, jeb161521 (2018).PubMed 
    Article 

    Google Scholar 
    Hadj-Hammou, J., Mouillot, D. & Graham, N. A. J. Response and effect traits of coral reef fish. Front. Mar. Sci. 8, 640619 (2021).Article 

    Google Scholar 
    Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).PubMed 
    Article 

    Google Scholar 
    McMahon, K. W., Thorrold, S. R., Houghton, L. A. & Berumen, M. L. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180, 809–821 (2016).PubMed 
    Article 

    Google Scholar 
    McMahon, K., Hamady, L. L. & Thorrold, S. Ocean ecogeochemistry—a review. Oceanogr. Mar. Biol. 51, 327–374 (2013).
    Google Scholar 
    Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 
    Article 

    Google Scholar 
    Bowes, R. E. & Thorp, J. H. Consequences of employing amino acid vs. bulk-tissue, stable isotope analysis: a laboratory trophic position experiment. Ecosphere 6, 14 (2015).Article 

    Google Scholar 
    Blanchard, J. L., Heneghan, R. F., Everett, J. D., Trebilco, R. & Richardson, A. J. From bacteria to whales: using functional size spectra to model marine ecosystems. Trends Ecol. Evol. 32, 174–186 (2017).PubMed 
    Article 

    Google Scholar 
    Kleiber, D., Harris, L. M. & Vincent, A. C. J. Gender and small-scale fisheries: a case for counting women and beyond. Fish Fish. 16, 547–562 (2015).Article 

    Google Scholar  More

  • in

    A tripartite model system for Southern Ocean diatom-bacterial interactions reveals the coexistence of competing symbiotic strategies

    Saba GK, Fraser WR, Saba VS, Iannuzzi RA, Coleman KE, Doney SC, et al. Winter and spring controls on the summer food web of the coastal West Antarctic Peninsula. Nat Commun. 2014;5:4318.CAS 
    PubMed 
    Article 

    Google Scholar 
    Behrenfeld MJ, Randerson JT, McClain CR, Feldman GC, Los SO, Tucker CJ, et al. Biospheric primary production during an ENSO transition. Science. 2001;291:2594–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cho BC, Azam F. Major role of bacteria in biogeochemical fluxes in the ocean’s interior. Nature. 1988;332:441–3.CAS 
    Article 

    Google Scholar 
    Amin S, Hmelo L, Van Tol H, Durham B, Carlson L, Heal K, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.CAS 
    PubMed 
    Article 

    Google Scholar 
    Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci. 2015;112:453–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Mühlenbruch M, Grossart HP, Eigemann F, Voss M. Mini‐review: Phytoplankton‐derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ Microbiol. 2018;20:2671–85.PubMed 
    Article 

    Google Scholar 
    Seymour JR, Amin SA, Raina J-B, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat Microbiol. 2017;2:1–12.Article 

    Google Scholar 
    Azam F, Fenchel T, Field JG, Gray JS, Meyer-Reil L-A, Thingstad F. The ecological role of water-column microbes in the sea. Marine ecology progress series. 1983;10:257–63.Ratnarajah L, Blain S, Boyd PW, Fourquez M, Obernosterer I, Tagliabue A. Resource colimitation drives competition between phytoplankton and bacteria in the Southern Ocean. Geophys Res Lett. 2021;48:e2020GL088369.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oulhen N, Schulz BJ, Carrier TJ. English translation of Heinrich Anton de Bary’s 1878 speech, ‘Die Erscheinung der Symbiose’ (‘De la symbiose’). Symbiosis. 2016;69:131–9.Article 

    Google Scholar 
    Cooper MB, Smith AG. Exploring mutualistic interactions between microalgae and bacteria in the omics age. Curr Opin Plant Biol. 2015;26:147–53.PubMed 
    Article 

    Google Scholar 
    Croft MT, Lawrence AD, Raux-Deery E, Warren MJ, Smith AG. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature. 2005;438:90–3.CAS 
    PubMed 
    Article 

    Google Scholar 
    Cole JJ. Interactions between bacteria and algae in aquatic ecosystems. Ann Rev Ecol Syst. 1982;13:291–314.Article 

    Google Scholar 
    Durham B. Deciphering metabolic currencies that support marine microbial networks. mSystems. 2021;6:e00763-21.Bell W, Mitchell R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol Bull. 1972;143:265–77.Article 

    Google Scholar 
    Baker LJ, Kemp PF. Exploring bacteria–diatom associations using single-cell whole genome amplification. Aquat Microb Ecol. 2014;72:73–88.Article 

    Google Scholar 
    Graff JR, Rines JE, Donaghay PL. Bacterial attachment to phytoplankton in the pelagic marine environment. Mar Ecol Prog Ser. 2011;441:15–24.Article 

    Google Scholar 
    Baker LJ, Alegado RA, Kemp PF. Response of diatom-associated bacteria to host growth state, nutrient concentrations, and viral host infection in a model system. Environ Microbiol Rep. 2016;8:917–27.PubMed 
    Article 

    Google Scholar 
    Shibl AA, Isaac A, Ochsenkühn MA, Cárdenas A, Fei C, Behringer G, et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc Natl Acad Sci. 2020;117:27445–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Leinweber K, Kroth PG. Capsules of the diatom Achnanthidium minutissimum arise from fibrillar precursors and foster attachment of bacteria. PeerJ. 2015;3:e858.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guo S, Stevens CA, Vance TDR, Olijve LLC, Graham LA, Campbell RL, et al. Structure of a 1.5-MDa adhesin that binds its Antarctic bacterium to diatoms and ice. Sci Adv. 2017;3:e1701440.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rao D, Webb JS, Kjelleberg S. Microbial colonization and competition on the Marine Alga Ulva australis. Appl Environ Microbiol. 2006;72:5547–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhou J, Chen G-F, Ying K-Z, Jin H, Song J-T, Cai Z-H, et al. Phycosphere microbial succession patterns and assembly mechanisms in a marine Dinoflagellate bloom. Appl Environ Microbiol. 2019;85:e00349–19.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seyedsayamdost MR, Case RJ, Kolter R, Clardy J. The Jekyll-and-Hyde chemistry of Phaeobacter gallaeciensis. Nat Chem. 2011;3:331–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Frölicher TL, Sarmiento JL, Paynter DJ, Dunne JP, Krasting JP, Winton M. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J Clim. 2015;28:862–86.Article 

    Google Scholar 
    Strzepek RF, Hunter KA, Frew RD, Harrison PJ, Boyd PW. Iron‐light interactions differ in Southern Ocean phytoplankton. Limnol Oceanogr. 2012;57:1182–200.CAS 
    Article 

    Google Scholar 
    Andrew SM, Strzepek RF, M Whitney S, Chow WS, Ellwood MJ. Divergent physiological and molecular responses of light‐and iron‐limited Southern Ocean phytoplankton. Limnol Oceanogr Lett. 2022;7:150–8.CAS 
    Article 

    Google Scholar 
    Bertrand EM, Saito MA, Rose JM, Riesselman CR, Lohan MC, Noble AE, et al. Vitamin B12 and iron colimitation of phytoplankton growth in the Ross Sea. Limnol Oceanogr. 2007;52:1079–93.CAS 
    Article 

    Google Scholar 
    Bertrand EM, McCrow JP, Moustafa A, Zheng H, McQuaid JB, Delmont TO, et al. Phytoplankton–bacterial interactions mediate micronutrient colimitation at the coastal Antarctic sea ice edge. Proc Natl Acad Sci. 2015;112:9938–43.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bates SSB, Hubbard KA, Lundholm N, Montresor M, Leaw CP. Pseudo-nitzschia, Nitzschia, and domoic acid: new research since 2011. Harmful Algae. 2018;79:3–43.PubMed 
    Article 

    Google Scholar 
    Almandoz GO, Ferreyra GA, Schloss IR, Dogliotti AI, Rupolo V, Paparazzo FE, et al. Distribution and ecology of Pseudo-nitzschia species (Bacillariophyceae) in surface waters of the Weddell Sea (Antarctica). Polar Biol. 2008;31:429–42.Article 

    Google Scholar 
    Jabre LJ, Allen AE, McCain JSP, McCrow JP, Tenenbaum N, Spackeen JL, et al. Molecular underpinnings and biogeochemical consequences of enhanced diatom growth in a warming Southern Ocean. Proc Natl Acad Sci. 2021;118:e2107238118.Malviya S, Scalco E, Audic S, Vincent F, Veluchamy A, Poulain J, et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc Natl Acad Sci. 2016;113:E1516–25.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moreno CM, Lin Y, Davies S, Monbureau E, Cassar N, Marchetti A. Examination of gene repertoires and physiological responses to iron and light limitation in Southern Ocean diatoms. Polar Biol. 2018;41:679–96.Article 

    Google Scholar 
    Ellis KA, Cohen NR, Moreno C, Marchetti A. Cobalamin-independent methionine synthase distribution and influence on vitamin B12 growth requirements in marine diatoms. Protist. 2017;168:32–47.CAS 
    PubMed 
    Article 

    Google Scholar 
    Price NM, Harrison GI, Hering JG, Hudson RJ, Nirel PM, Palenik B, et al. Preparation and chemistry of the artificial algal culture medium Aquil. Biol Oceanogr. 1989;6:443–61.Article 

    Google Scholar 
    Hubbard KA, Rocap G, Armbrust EV. Inter- and intraspecific community structure within the diatom genus Pseudo-nitzschia (Bacillariophyceae). J Phycol. 2008;44:637–49.CAS 
    Article 

    Google Scholar 
    Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 2019;47:W636–41.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    Brand LE, Guillard RR, Murphy LS. A method for the rapid and precise determination of acclimated phytoplankton reproduction rates. J Plankton Res. 1981;3:193–201.Article 

    Google Scholar 
    Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25:1189–91.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nguyen L-T, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kalyaanamoorthy S, Minh BQ, Wong TK, Von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hoang DT, Chernomor O, Von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
    PubMed 
    Article 

    Google Scholar 
    Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    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 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rodriguez-R LM, Gunturu S, Harvey WT, Rosselló-Mora R, Tiedje JM, Cole JR, et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucl Acids Res. 2018;46:W282–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:1–8.Article 

    Google Scholar 
    Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Noble RT, Fuhrman JA. Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria. Aquat Microb Ecol. 1998;14:113–8.Article 

    Google Scholar 
    Alcamán-Arias ME, Fuentes-Alburquenque S, Vergara-Barros P, Cifuentes-Anticevic J, Verdugo J, Polz M, et al. Coastal bacterial community response to glacier melting in the Western Antarctic Peninsula. Microorganisms. 2021;9:88.PubMed Central 
    Article 

    Google Scholar 
    Bowman JP, Gosink JJ, McCAMMON SA, Lewis TE, Nichols DS, Nichols PD, et al. Colwellia demingiae sp. nov., Colwellia hornerae sp. nov., Colwellia rossensis sp. nov. and Colwellia psychrotropica sp. nov.: psychrophilic Antarctic species with the ability to synthesize docosahexaenoic acid (22: ω63). Int J Syst Evol Microbiol. 1998;48:1171–80.CAS 

    Google Scholar 
    Reisch CR, Moran MA, Whitman WB. Bacterial catabolism of dimethylsulfoniopropionate (DMSP). Front Microbiol. 2011;2:172.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Diaz J, Ingall E, Benitez-Nelson C, Paterson D, de Jonge MD, McNulty I, et al. Marine polyphosphate: a key player in geologic phosphorus sequestration. Science. 2008;320:652–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    Nichols CM, Bowman JP, Guezennec J. Olleya marilimosa gen. nov., sp. nov., an exopolysaccharide-producing marine bacterium from the family Flavobacteriaceae, isolated from the Southern Ocean. Int J Syst Evol Microbiol. 2005;55:1557–61.CAS 
    PubMed 
    Article 

    Google Scholar 
    von Scheibner M, Sommer U, Jürgens K. Tight coupling of Glaciecola spp. and diatoms during cold-water Phytoplankton spring blooms. Front Microbiol. 2017;8:27.Holmstrom C, Kjelleberg S. Marine Pseudoalteromonas species are associated with higher organisms and produce biologically active extracellular agents. FEMS Microbiol Ecol. 1999;30:285–93.CAS 
    PubMed 
    Article 

    Google Scholar 
    Methe BA, Nelson KE, Deming JW, Momen B, Melamud E, Zhang X, et al. The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses. Proc Natl Acad Sci. 2005;102:10913–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kirchman DL. The ecology of Cytophaga–Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 2002;39:91–100.CAS 
    PubMed 

    Google Scholar 
    Hong Z, Lai Q, Luo Q, Jiang S, Zhu R, Liang J, et al. Sulfitobacter pseudonitzschiae sp. nov., isolated from the toxic marine diatom Pseudo-nitzschia multiseries. Int J Syst Evol Microbiol. 2015;65:95–100.CAS 
    PubMed 
    Article 

    Google Scholar 
    Brussaard CPD, Riegman R. Influence of bacteria on phytoplankton cell mortality with phosphorus or nitrogen as the algal-growth-limiting nutrient. Aqua Microb Ecol. 1998;14:271–80.Article 

    Google Scholar 
    Cohen NR, A. Ellis K, Burns WG, Lampe RH, Schuback N, Johnson Z, et al. Iron and vitamin interactions in marine diatom isolates and natural assemblages of the Northeast Pacific Ocean. Limnol Oceanogr. 2017;62:2076–96.CAS 
    Article 

    Google Scholar 
    Hunken M, Harder J, Kirst G. Epiphytic bacteria on the Antarctic ice diatom Amphiprora kufferathii Manguin cleave hydrogen peroxide produced during algal photosynthesis. Plant Biol. 2008;10:519–26.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gourinchas G, Etzl S, Winkler A. Bacteriophytochromes–from informative model systems of phytochrome function to powerful tools in cell biology. Curr Opin Struct Biol. 2019;57:72–83.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gourion B, Rossignol M, Vorholt JA. A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth. Proc Natl Acad Sci. 2006;103:13186–91.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mukherjee S, Bassler BL. Bacterial quorum sensing in complex and dynamically changing environments. Nat Rev Microbiol. 2019;17:371–82.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dong YH, Zhang LH. Quorum sensing and quorum-quenching enzymes. J Microbiol. 2005;43:101–9.CAS 
    PubMed 

    Google Scholar 
    Núñez-Montero K, Barrientos L. Advances in Antarctic research for antimicrobial discovery: a comprehensive narrative review of bacteria from Antarctic environments as potential sources of novel antibiotic compounds against human pathogens and microorganisms of industrial importance. Antibiotics. 2018;7:90.Kieft B, Li Z, Bryson S, Hettich RL, Pan C, Mayali X, et al. Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits. Proc Natl Acad Sci. 2021;118:e2101178118.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Maranger R, Bird DF. Viral abundance in aquatic systems: a comparison between marine and fresh waters. Mar Ecol Prog Ser. 1995;121:217–26.Article 

    Google Scholar 
    Sharpe GC, Gifford SM, Septer AN. A model roseobacter, Ruegeria pomeroyi DSS-3, employs a diffusible killing mechanism to eliminate competitors. Msystems. 2020;5:e00443–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cude WN, Mooney J, Tavanaei AA, Hadden MK, Frank AM, Gulvik CA, et al. Production of the antimicrobial secondary metabolite indigoidine contributes to competitive surface colonization by the marine roseobacter Phaeobacter sp. strain Y4I. Appl Environ Microbiol. 2012;78:4771–80.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Long RA, Rowley DC, Zamora E, Liu J, Bartlett DH, Azam F. Antagonistic interactions among marine bacteria impede the proliferation of Vibrio cholerae. Appl Environ Microbiol. 2005;71:8531–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bruhn JB, Gram L, Belas R. Production of antibacterial compounds and biofilm formation by Roseobacter species are influenced by culture conditions. Appl Environ Microbiol. 2007;73:442–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gromek SM, Suria AM, Fullmer MS, Garcia JL, Gogarten JP, Nyholm SV, et al. Leisingera sp. JC1, a bacterial isolate from Hawaiian bobtail squid eggs, produces indigoidine and differentially inhibits vibrios. Front Microbiol. 2016;7:1342.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sharifah EN, Eguchi M. The phytoplankton Nannochloropsis oculata enhances the ability of Roseobacter clade bacteria to inhibit the growth of fish pathogen Vibrio anguillarum. PLoS One. 2011;6:e26756.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kerwin AH, Gromek SM, Suria AM, Samples RM, Deoss DJ, O’Donnell K, et al. Shielding the next generation: symbiotic bacteria from a reproductive organ protect bobtail squid eggs from fungal fouling. MBio. 2019;10:e02376–19.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tonelli M, Signori CN, Bendia A, Neiva J, Ferrero B, Pellizari V, et al. Climate projections for the southern ocean reveal impacts in the marine microbial communities following increases in sea surface temperature. Front Mar Sci. 2021;8:636226.Andrew SM, Morell HT, Strzepek RF, Boyd PW, Ellwood MJ. Iron availability influences the tolerance of southern ocean phytoplankton to warming and elevated irradiance. Front Mar Sci. 2019;6:681.Andrew SM, Strzepek RF, Branson O, Ellwood MJ. Ocean acidification reduces the growth of two Southern Ocean phytoplankton. Mar Ecol Prog Ser. 2022;682:51–64.CAS 
    Article 

    Google Scholar 
    Blin K, Shaw S, Kloosterman AM, Charlop-Powers Z, van Weezel GP, Medema MH, et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucl Acids Res. 2021;49:W29–35.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ferrer-González FX, Widner B, Holderman NR, Glushka J, Edison AS, Kujawinski EB, et al. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. 2021;15:762–73.PubMed 
    Article 

    Google Scholar  More

  • in

    Ancient marine sediment DNA reveals diatom transition in Antarctica

    Sampling location and sediment coringSamples were collected during IODP Exp. 382 ‘Iceberg Alley and Subantarctic Ice and Ocean Dynamics’ on-board RV Joides Resolution between 20 March and 20 May 2019. Specifically, we collected samples at Site U1534 (Falkland Plateau, 606 m water depth), U1536 (Dove Basin, Scotia Sea, 3220 m water depth), and Site U1538 (Pirie Basin, Scotia Sea, 3130 m water depth) (Fig. 1). Site U1534 is located at the Subantarctic Front on a contourite drift at the northern limit of the Scotia Sea. This setting is ideal to study the poorly understood role of Antarctic Intermediate Water (AAIC) and its impact on the Atlantic Meridional Overturning Circulation (AMOC) along the so-called ‘cold water route’ that connects to the Pacific Ocean through the Drake Passage, as opposed to the ‘warm water route’ that connects to the Indian Ocean via the Agulhas Current42. Sites U1536 and U1538 are located in the southern and central Scotia Sea, respectively, and were drilled to study the Neogene flux of icebergs through ‘Iceberg Alley’, the main pathway along which icebergs calved from the margin of the AIS travel as they move equatorward into the warmer waters of the Antarctic Circumpolar Current (ACC)23. sedaDNA samples collected at Site U1534 were from Hole C, at Site U1536 from Hole B, and at Site U1538 from Holes C and D (Table 1), and in the following we refer to site names only. IODP Expedition proposals undergo a rigorous environmental protection and safety review, which is approved by the IODP’s Environmental Protection and Safety Panel (EPSP) and/or the Safety Panel. The same procedure was applied to IODP Exp. 382 and approval was provided by the EPSP. Sediment samples for sedaDNA analyses were imported to Australia under Import Permit number 0002658554 provided by the Australian Government Department for Agriculture and Water Resources (date of issue: 19 September 2018), and were stored and extracted at a quarantine approved facility (AA Site No. S1253, Australian Centre for Ancient DNA). No ethical approval was required for this study.Table 1 Sampling location and sample detailsFull size tableSample age determinationAge control for Site U1534 is based on tuning of benthic foraminifera δ18O to the LR04 stack43. Wherever present specimens of Uvigerina bifurcata were picked from samples at 10 cm intervals. During warmer periods when U. bifurcata was not present, Melonis affinis and/or Hoeglundina elegans were analysed. Sedimentation rates over the intervals sampled for sedaDNA typically range between 6 and 30 cm/kyr, with rates exceeding 100 cm/kyr during the Last Glacial Maximum ~20,000 years ago (20 ka). For our deepest sample, U1534C-10H-6_115cm (90.95 mbsf), we only have biostratigraphically assigned ages available (shipboard data), which date this sample as early Pleistocene (~2.5–0.7 million years ago, Ma44).Low-resolution age control for both Sites U1536 and U1538 was established using shipboard magneto- and biostratigraphy21,23. Average sedimentation rates are ~10 cm/kyr for Site U1536, with elevated values (up to 20 cm/kyr) in the upper ~80 mbsf (the last ~400 ka). Site U1538 average sedimentation rates are twice as high, averaging ~20 cm/kyr. Especially in the upper ~430 mbsf (the last 1.8 Ma), rates are up to 40 cm/kyr. Higher resolution age models are based on dust climate couplings, correlating sedimentary dust proxy records such as magnetic susceptibility and sedimentary Ca and Fe records to ice-core dust proxy records over the last 800 ka45 and to a benthic isotopic stack26 before that. These age models were established for Site U1537 (adjacent to Site U1536) and provide orbital to millennial scale resolution. For this study we correlated sedimentary cycles of Sites U1536 and U1538 to U1537 to achieve similar resolution and to be able to determine if a sample originates from a glacial or interglacial period (Table 1).Sampling of sedaDNAA detailed description of sedaDNA sampling methods can be found in ref. 24. In brief, we used advanced piston coring (APC) to acquire sediment cores, which recovers the least disturbed sediments46,47,48 and is thus the preferred technique for sedaDNA sampling. All samples were taken on the ship’s ‘catwalk’, where, once the core was on deck, the core liners were wiped clean twice (3% sodium hypochlorite, ‘bleach’) at each cutting point. Core cutting tools were sterilised before each cut (3% bleach and 80% ethanol) of the core in 1 m sections. The outer ~3 mm of surface material were removed from the bottom of each core section to be sampled, using sterilised scrapers (~4 cm wide; bleach and ethanol treated). A cylindrical sample was taken from the core centre using a sterile (autoclaved) 10 mL cut-tip syringe, providing ~5 cm3 of sediment material. The syringe was placed in a sterile plastic bag (Whirl-Pak) and immediately frozen at −80 °C. The mudline (sediment/seawater interface) was transferred from the core liner into a sterile bucket (3% bleach treated), and 10 mL sample was retained in a sterile 15 mL centrifuge tube (Falcon) and frozen at −80 °C. Samples were collected at various depth intervals depending on the site to span the Holocene up to ~1 million years (Table 1). This lower depth/age limit was determined by switching coring system from APC to the extended core barrel (XCB) system.To test for potential airborne contamination, at least one air control was taken during the sedaDNA sampling process per site. For this, an empty syringe was held for a few seconds in the sampling area and then transferred into a sterile plastic bag and frozen at −80 °C. The air controls were processed, sequenced and analysed alongside the sediment samples.Contamination control using perfluoromethyldecalin tracersAs part of the APC process, drill fluid (basically, seawater) is pumped into the borehole to trigger the hydraulic coring system, therefore, the potential for contamination exists due to drill fluid making contact with the core liner. To assess the latter, we added the non-toxic chemical tracer perfluoromethyldecalin (PFMD) to the drill fluid at a rate of ~0.55 mL min−1 for cores collected at Sites U1534 and U153649. As we found that PFMD concentrations were very low at these sites (Results section), the infusion rate was doubled prior to sedaDNA sampling at Site U1538 to ensure low PFMD concentrations represent low contamination and not delivery failure of PFMD to the core. At each sedaDNA sampling depth, one PFMD sample was taken from the periphery of the core (prior to scraping, to test whether drill fluid reached the core pipe), and one next to the sedaDNA sample in the centre of the core (after scraping, to minimise differences to the sedaDNA sample, and testing if drill fluid had reached the core centre). We transferred ~3 cm3 of sediment using a disposable, autoclaved 5 mL cut-tip syringe into a 20 mL headspace vial with metal caps and Teflon seals. We also collected a sample of the tracer-infused drill fluid at each site, by transferring ~10 mL of the fluid collected at the injection pipe on the rig floor via a sterile plastic bottle into a 15 mL centrifuge tube (inside a sterile plastic bag) and freezing it at −80 °C. These drill fluid controls were processed and analysed in the same way as the sedaDNA samples including sequencing. Samples were analysed using gas chromatography (GC-µECD; Hewlett-Packard 6890).A detailed description of the PFMD GC measurements is provided in ref. 24. Briefly, PFMD measurements were undertaken in batches per site for U1534, U1536 and U1538. This included the analyses of PFMD samples collected at two additional holes at these sites, U1534D and U1536C, from which we also collected sedaDNA samples but that are not part of this study. PFMD is categorised as the stereoisomers of PFMD (C11F20), which add up to 87-88% (and with the remaining 12% being additional perfluoro compounds unable to be separated by the manufacturer). We exclusively refer to the first and measurable PFMD category, calibrating for the 88% in bottle concentrations during concentration calculations. Each GC analysis run included the measurement of duplicate blanks and duplicate PFMD standards. Due to a large sample number, PFMD at Site U1538 was measured in three separate runs, with the first and last run including triplicate blank and triplicate PFMD standards (duplicates in the second run), and the last run also containing a drill-fluid sample. To blank-correct PFMD concentrations, we subtracted the average PFMD concentration of all blanks per run from PFMD measurements in that run. To determine the detection limit of PFMD, we used three times the standard deviation of the average blank PFMD values per run; due to all blank values for the U1538 runs being 0, we used three times the standard deviation of the lowest PFMD standard for this site in this calculation. This provided us with a PFMD detection limit of 0.2338 ng mL−1. Any PFMD measurements of samples below this limit were rejected.
    sedaDNA extractions and metagenomic library preparationsA total of 80 sedaDNA extracts and metagenomic shotgun libraries (Table 1) were prepared following8,10. For the sedaDNA extractions, we randomised our samples and controls and extracted sedaDNA in batches of 16 extracts/libraries at a time, with each batch including at least one air control and one extraction blank control (EBC), and the last batch including mudline and PFMD samples to avoid contamination of the sedaDNA samples. In brief, we used 20 µL sedaDNA extracts in a repair reaction (using T4 DNA polymerase, New England Biolabs, USA; 15 min, 25 °C), then purified the sedaDNA (MinElute Reaction Cleanup Kit, Qiagen, Germany), ligated adaptors (T4 DNA ligase, Fermentas, USA, where truncated Illumina-adaptor sequences containing two unique 7 base-pair (bp) barcodes were attached to the double-stranded DNA; 60 min, 22 °C), purified the sedaDNA again (MinElute Reaction Cleanup Kit, Qiagen), and then added a fill-in reaction with adaptor sequences (Bst DNA polymerase, New England Biolabs, USA; 30 min, 37 °C, with polymerase deactivation for 10 min, 80 °C). We amplified the barcoded libraries using IS7/IS8 primers50 (8 replicates per sample, where each replicate was a 25 µL reaction containing 3 µL DNA template; using 22 cycles), purified (AxyPrep magnetic beads, Axygen Biosciences, USA; 1:1.8 library:beads) and quantified them (Qubit dsDNA HS Assay, Invitrogen, Molecular Probes, USA). We amplified the libraries (8 replicates per sample, 13 amplification cycles) using IS4 and GAII Indexing Primers50, purified (AxyPrep magnetic beads, at a ratio of 1:1.1 library:beads), quantified and quality-checked using Qubit (dsDNA HS Assay, Invitrogen, USA) and TapeStation (Agilent Technologies, USA). We combined the libraries into an equimolar pool (volume of 68 µL in total), diluted this pool with nuclease-free H2O to 100 µL, and performed a ‘reverse’ AxyPrep clean-up to retain only the small DNA fragments typical for ancient DNA (≤ 500 bp; initial library:beads ratio of 1:0.6, followed by 1:1.1, and double-eluted in 30 µL nuclease-free H2O8,51). We added one more AxyPrep clean-up to remove primer-dimer (library:beads ratio of 1:1.05) and checked sedaDNA quantity and quality via TapeStation and qPCR (QuantStudio, Applied Biosystems, USA). The libraries sequenced at the Garvan Institute for Medical Research, Sydney, Australia (Illumina NovaSeq 2 × 100 bp).
    sedaDNA data processingThe sequencing data was processed and filtered as described in detail in refs. 8, 10. Briefly, data filtering involved the removal of sequences More

  • in

    Revealing the uncharacterised diversity of amphibian and reptile viruses

    Benton MJ, Donoghue PCJ. Paleontological evidence to date the tree of life. Mol Biol Evol. 2006;24:26–53.PubMed 

    Google Scholar 
    Roll U, Feldman A, Novosolov M, Allison A, Bauer AM, Bernard R, et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat Ecol Evol. 2017;1:1677–82.PubMed 

    Google Scholar 
    IUCN, The IUCN Red List of Threatened Species. Version 2021-3. 2021.Medicine, N.L.o., NCBI Genome. 2022, National Center for Biotechnology Information.Hotaling S, Kelley JL, Frandsen PB. Toward a genome sequence for every animal: Where are we now? Proc Natl Acad Sci. 2021;118:e2109019118.PubMed 
    PubMed Central 

    Google Scholar 
    Shi M, Lin XD, Chen X, Tian JH, Chen LJ, Li K, et al. The evolutionary history of vertebrate RNA viruses. Nature. 2018;556:197–202.PubMed 

    Google Scholar 
    Parry R, Wille M, Turnbull OMH, Geoghegan JL, Holmes EC. Divergent influenzalike viruses of amphibians and fish support an ancient evolutionary association. Viruses. 2020;12:1042.PubMed Central 

    Google Scholar 
    Peck KM, Lauring AS, Christopher S, Complexities of viral mutation rates. J Virol. 92: e01031-17.Latney LV, Klaphake E. Selected emerging infectious diseases of amphibians. Vet Clin N Am—Exotic Animal Pract. 2020;23:397–412.
    Google Scholar 
    Zhang J, Finlaison DS, Frost MJ, Gestier S, Gu X, Hall J, et al. Identification of a novel nidovirus as a potential cause of large scale mortalities in the endangered Bellinger River snapping turtle (Myuchelys georgesi). PLOS ONE. 2018;13:e0205209.PubMed 
    PubMed Central 

    Google Scholar 
    Parrish K, Kirkland PD, Skerratt LF, Ariel E. Nidoviruses in reptiles: a review. Front Vet Sci. 2021;8:733404.PubMed 
    PubMed Central 

    Google Scholar 
    Chang WS, Li CX, Hall J, Eden JS, Hyndman TH, Holmes EC, et al. Metatranscriptomic discovery of a divergent circovirus and a chaphamaparvovirus in captive reptiles with proliferative respiratory syndrome. Viruses. 2020;12:1073.PubMed Central 

    Google Scholar 
    Mendoza-Roldan JA, Mendoza-Roldan MA, Otranto D. Reptile vector-borne diseases of zoonotic concern. Int J Parasitol: Parasites Wildl. 2021;15:132–42.
    Google Scholar 
    Essbauer S, Ahne W. Viruses of lower vertebrates. J Vet Med Ser B. 2001;48:403–75.
    Google Scholar 
    Mercer LK, Harding EF, Yan GJH, White PA. Novel viruses discovered in the transcriptomes of agnathan fish. J Fish Dis. 2022;45:931–8.PubMed 
    PubMed Central 

    Google Scholar 
    Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat biotechnol. 2011;29:644–52.PubMed 
    PubMed Central 

    Google Scholar 
    Harding EF, Russo AG, Yan GJH, Waters PD, White PA. Ancient viral integrations in marsupials: a potential antiviral defence. Virus Evol. 2021;7:veab076.PubMed 
    PubMed Central 

    Google Scholar 
    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.PubMed 

    Google Scholar 
    Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.PubMed 
    PubMed Central 

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

    Google Scholar 
    Kelly AG, Netzler NE, White PA. Ancient recombination events and the origins of hepatitis E virus. BMC Evol Biol. 2016;16:210.PubMed 
    PubMed Central 

    Google Scholar 
    Rector A, Van, Ranst M. Animal papillomaviruses. Virology. 2013;445:213–23.PubMed 

    Google Scholar 
    Blahak S, Jenckel M, Höper D, Beer M, Hoffmann B, Schlottau K. Investigations into the presence of nidoviruses in pythons. Virol J. 2020;17:6.PubMed 
    PubMed Central 

    Google Scholar 
    Marschang RE. Viruses infecting reptiles. Viruses. 2011;3:2087–126.PubMed 
    PubMed Central 

    Google Scholar 
    Horie M, Akashi H, Kawata M, Tomonaga K. Identification of a reptile lyssavirus in Anolis allogus provided novel insights into lyssavirus evolution. Virus Genes. 2021;57:40–49.PubMed 

    Google Scholar 
    Stenglein MD, Sanders C, Kistler AL, Ruby JG, Franco JY, Reavill DR, et al. Identification, characterization, and in vitro culture of highly divergent arenaviruses from boa constrictors and annulated tree boas: candidate etiological agents for snake inclusion body disease. mBio. 2012;3:e00180–12.PubMed 
    PubMed Central 

    Google Scholar 
    Garver KA, Leskisenoja K, Macrae R, Hawley LM, Subramaniam K, Waltzek TB, et al. An alloherpesvirus infection of european perch perca fluviatilis in Finland. Dis Aquat Org. 2018;128:175–85.
    Google Scholar 
    Hellebuyck T, Couck L, Ducatelle R, Broeck WV, Marschang RE. Cheilitis associated with a novel herpesvirus in two panther chameleons (Furcifer pardalis). J Comp Pathol. 2021;182:58–66.PubMed 

    Google Scholar 
    Altan E, Kubiski SV, Burchell J, Bicknese E, Deng X, Delwart E. The first reptilian circovirus identified infects gut and liver tissues of black-headed pythons. Vet Res. 2019;50:35.PubMed 
    PubMed Central 

    Google Scholar 
    Russo AG, Harding EF, Yan GJH, Selechnik D, Ducatez S, DeVore JL, et al. Discovery of novel viruses associated with the invasive cane toad (Rhinella marina) in its native and introduced ranges. Front Microbiol. 2021;12:733631.PubMed 
    PubMed Central 

    Google Scholar 
    Chen X-X, Wu W-C, Shi M. Discovery and characterization of actively replicating DNA and retro-transcribing viruses in lower vertebrate hosts based on RNA sequencing. Viruses. 2021;13:1042.PubMed 
    PubMed Central 

    Google Scholar 
    Russo AG, Eden JS, Tuipulotu DE, Shi M, Selechnik D, Shine R, et al. Viral discovery in the invasive Australian cane toad (Rhinella marina) using metatranscriptomic and genomic approaches. J Virol. 2018;92:e00768–18.PubMed 
    PubMed Central 

    Google Scholar 
    López-Bueno A, Mavian C, Labella AM, Castro D, Borrego JJ, Alcami A, et al. Concurrence of Iridovirus, Polyomavirus, and a unique member of a new group of fish Papillomaviruses in Lymphocystis disease-affected gilthead sea bream. Journal of virology. 2016;90:8768–79.PubMed 
    PubMed Central 

    Google Scholar 
    Bentley K, Evans DJ. Mechanisms and consequences of positive-strand RNA virus recombination. J Gen Virol. 2018;99:1345–56.PubMed 

    Google Scholar 
    Diemer GS, Stedman KM. A novel virus genome discovered in an extreme environment suggests recombination between unrelated groups of RNA and DNA viruses. Biol Direct. 2012;7:13.PubMed 
    PubMed Central 

    Google Scholar 
    Welch, NL, MJ Tisza, GJ Starrett, AK Belford, DV Pastrana, Y-YS Pang, et al. Identification of Adomavirus Virion proteins. bioRxiv. 2020:341131. https://doi.org/10.1101/341131Dill JA, Camus AC, Leary JH, Ng TFF, Zheng Z-M, Meng X-J. Microscopic and Molecular Evidence of the First Elasmobranch Adomavirus, the Cause of Skin Disease in a Giant Guitarfish, Rhynchobatus djiddensis. mBio. 2018;9:e00185–18.PubMed 
    PubMed Central 

    Google Scholar 
    Yang J-X, Chen X, Li Y-Y, Song T-Y, Ge J-Q. Isolation of a novel adomavirus from cultured American eels, Anguilla rostrata, with haemorrhagic gill necrosis disease. J Fish Dis. 2021;44:1811–8.PubMed 

    Google Scholar 
    King AMQ, Adams MJ, Carstens EB & Lefkowitz EJ, Order – Nidovirales, in virus taxonomy: classification and nomenclature of viruses. 2012, Elsevier/Academic Press: San Diego.Lyu S, Yuan X, Zhang H, Shi W, Hang X, Liu L, et al. Complete genome sequence and analysis of a new lethal arterivirus, Trionyx sinensis hemorrhagic syndrome virus (TSHSV), amplified from an infected Chinese softshell turtle. Arch Virol. 2019;164:2593–7.PubMed 
    PubMed Central 

    Google Scholar 
    Sinzelle L, Carradec Q, Paillard E, Bronchain OJ, Pollet N. Characterization of a Xenopus tropicalis endogenous retrovirus with developmental and stress-dependent expression. J Virol. 2011;85:2167–79.PubMed 

    Google Scholar 
    Wei X, Chen Y, Duan G, Holmes EC, Cui J. A reptilian endogenous foamy virus sheds light on the early evolution of retroviruses. Virus Evol. 2019;5:vez001.PubMed 
    PubMed Central 

    Google Scholar 
    Debat HJ, Ng TFF. Complete genome sequence of a divergent strain of Tibetan frog hepatitis B virus associated with a concave-eared torrent frog (Odorrana tormota). Arch Virol. 2019;164:1727–32.PubMed 

    Google Scholar 
    Reuter G, Boros Á, Tóth Z, Gia Phan T, Delwart E, Pankovics P. A highly divergent picornavirus in an amphibian, the smooth newt (Lissotriton vulgaris). J Gen Virol. 2015;96:2607–13.PubMed 
    PubMed Central 

    Google Scholar 
    ICTV. Subfamily: Secondpapillomavirinae. 2021 [cited 2022 15/06/2022]; Virus Taxonomy: 2021 Release:[Available from: https://talk.ictvonline.org/ictv-reports/ictv_online_report/dsdna-viruses/w/papillomaviridae/894/subfamilysecondpapillomavirinae.Willemsen A, Bravo IG. Origin and evolution of papillomavirus (onco)genes and genomes. Philos Trans R Soc B: Biol Sci. 2019;374:20180303.
    Google Scholar 
    Agius JE, Phalen DN, Rose K, Eden J-S. New insights into Sauropsid Papillomaviridae evolution and epizootiology: discovery of two novel papillomaviruses in native and invasive Island geckos. Virus Evol. 2019;5:vez051.PubMed 
    PubMed Central 

    Google Scholar 
    Bienentreu J-F, Lesbarrères D. Amphibian disease ecology: are we just scratching the surface? Herpetologica. 2020;76:153–66.
    Google Scholar 
    Mashkour N, Jones K, Wirth W, Burgess G, Ariel E. The concurrent detection of Chelonid Alphaherpesvirus 5 and Chelonia mydas Papillomavirus 1 in tumoured and non-tumoured green turtles. Animals. 2021;11:697.PubMed 
    PubMed Central 

    Google Scholar 
    Hoon-Hanks LL, Layton ML, Ossiboff RJ, Parker JSL, Dubovi EJ, Stenglein MD. Respiratory disease in ball pythons (Python regius) experimentally infected with ball python nidovirus. Virology. 2018;517:77–87.PubMed 

    Google Scholar 
    Dervas E, Hepojoki J, Smura T, Prähauser B, Windbichler K, Blümich S, et al. Serpentoviruses: More than respiratory pathogens. J Virol. 2020;94:e00649–20.PubMed 
    PubMed Central 

    Google Scholar 
    O’Dea MA, Jackson B, Jackson C, Xavier P, Warren K. Discovery and Partial Genomic Characterisation of a Novel Nidovirus Associated with Respiratory Disease in Wild Shingleback Lizards (Tiliqua rugosa). PloS One. 2016;11:e0165209.PubMed 
    PubMed Central 

    Google Scholar 
    Dervas E, Hepojoki J, Laimbacher A, Romero-Palomo F, Jelinek C, Keller S, et al. Nidovirus-associated proliferative pneumonia in the green tree python (Morelia viridis). J Virol. 2017;91:e00718–17.PubMed 
    PubMed Central 

    Google Scholar 
    Oberhuber M, Schopf A, Hennrich AA, Santos-Mandujano R, Huhn AG, Seitz S, et al. Glycoproteins of predicted amphibian and reptile lyssaviruses can mediate infection of mammalian and reptile cells. Viruses. 2021;13:1726.PubMed 
    PubMed Central 

    Google Scholar 
    Ritchie BW, Niagro FD, Lukert PD, Steffens WL, Latimer KS. Characterization of a new virus from cockatoos with psittacine beak and feather disease. Virology. 1989;171:83–88.PubMed 

    Google Scholar 
    Eleni C, Corteggio A, Altamura G, Meoli R, Cocumelli C, Rossi G, et al. Detection of Papillomavirus DNA in cutaneous squamous cell carcinoma and multiple papillomas in captive reptiles. J Comp Pathol. 2017;157:23–26.PubMed 

    Google Scholar 
    Tessier TM, Dodge MJ, MacNeil KM, Evans AM, Prusinkiewicz MA, Mymryk JS. Almost famous: Human adenoviruses (and what they have taught us about cancer). Tumour. Virus Res. 2021;12:200225.
    Google Scholar 
    Chen XX, Wu WC, Shi M. Discovery and characterization of actively replicating dna and retro-transcribing viruses in lower vertebrate hosts based on rna sequencing. Viruses. 2021;13:1042.PubMed 
    PubMed Central 

    Google Scholar 
    Liu W, Zhang Y, Ma J, Jiang N, Fan Y, Zhou Y, et al. Determination of a novel parvovirus pathogen associated with massive mortality in adult tilapia. PLOS Pathogens. 2020;16:e1008765.PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Device for automatic measurement of light pollution of the night sky

    For several years, systematic research has been carried out on the pollution of the night sky by artificial light in the city of Toruń11,36,38. The main objective is to monitor this phenomenon, including its spatial and temporal variability and the most important factors affecting it. Based on previous experience, an in-house measurement device was constructed to automate the process of data acquisition.Genesis of the projectThe first measurements pertaining to the phenomenon of night sky pollution in Toruń were made in autumn 2017, followed by regular observations using a handheld SQM photometer (Unihedron, Canada) as part of a project implemented in 2017–2018. To this end, a permanent measurement network distributed throughout the city was established, consisting of 24 locations. During a one night measurement session taking place during the astronomical night (there is no such period at the latitude of Toruń in the summer), sky brightness was measured at all sites. The results of spot monitoring were plotted using interpolation methods and visualisation tools available in GIS systems11,39, which helped to determine the spatial distribution and extent of night sky light pollution. The intensity of this phenomenon at each of the surveyed points was also explored in relation to the distinguished landcover categories and types of urban development.Repeatable measurements performed regularly over such a long period of time were characterised by significant limitations. One measurement session was very time-consuming, as it lasted about two hours, during which time all measurement locations were visited, covering a distance of almost 50 km each night by car. Despite the observance of all time frames and sticking to the plan of fieldwork, measurements were not carried out simultaneously at all the locations, which affected the results, especially during the night with changing cloud cover. Although the measurements were carried out with great consistency and care, they were performed in a spatial buffer of about 5 m, which could unintentionally slightly affect the obtained results. An additional limitation was also a one-time night measurement at one point, instead of a whole series of measurements at specific time intervals. Inaccuracies in the readings within a single session could have been caused by sudden changes in meteorological parameters. In the adopted procedure, it was not possible to carry out simultaneous measurements in identical time and weather conditions at all the locations, not to mention the involvement of the personnel in each tour of the measurement network points.Using the experience gained and after an analysis of the identified constraints and the technical capabilities at hand, work began in 2019 on developing a network for automatic remote monitoring of light pollution of the night sky in Toruń, based on designed in-house recording devices.Design, functional and utility features of the deviceTo enhance the research on light pollution in urban space, work has begun on the construction of a device that would perform automatic measurements, would be mobile, battery-powered and use long-range wireless communication. All the aforementioned features are in line with the strategy of Industry 4.0 and modern solutions proposed as part of the Smart City concept.The concept of Industry 4.0 assumes the more and more common use of process automation as well as the processing and exchange of data with the use of new transmission technologies26. LoRaWAN is one of the solutions used for communication of Internet of Things (IoT) devices, which supports the development of Smart Cities in the Smart Environment area. As a result, the interactivity, frequency, and scope of measurements carried out in urbanized areas are increased40,41.According to the developed project, the device was to serve as a meter of very low intensity light observed in the night sky. In this respect, it was necessary to use a sensor with technical parameters suitable for very accurate measurements of light intensity. To locally verify the weather conditions occurring during the operation of the device, it was decided to carry out additional simultaneous measurements of other environmental parameters—temperature and moisture content. The analysis of the spatial coverage of the study area indicated that 36 measurement devices should be deployed to provide full coverage of Toruń. The concept of creating an urban measurement network assumes the selection of points covering the whole city relatively evenly and representing different types of housing development and elements of land cover. It was assumed that measurements will be made only at night, between 21 p.m. and 6 a.m. on the following day, at 15 min intervals, and in addition, weather conditions will be recorded twice a day.Construction and technical parameters of the device, and selected characteristics of its componentsA prototype device meeting all the predefined functionalities was constructed based on available electronic modules. The B-L072Z-LRWAN Discovery developmental board from STMicroelectronics42 was selected as the main electronic component providing wireless communication. This board has an integrated LoRa communication module, enabling low-power wireless messaging, and also allows the board to enter a low-power state during hibernation, and thus target long-term battery-powered operation. This module is fully programmable, which enables future expansion of the set with other functionalities. The TSL2591 light sensor from AMS, which has high sensitivity and registration accuracy, was selected as a component implementing the light intensity measurement. Its great advantage is a wide measurement range of 188 μlx to 88 000 lx, sensitivity reaching 0.000377 lx, and a wide dynamic range (WDR) of 600 M:143. The sensor used has two diodes with different spectral properties. One of them registers visible light together with infrared (in the range from 400 to 1 100 nm), while the other is responsible for the registration of infrared light (between 500 and 1 100 nm). Thanks to this solution, we can use the results in various ways. The use of the formula provided by the manufacturer allows us to obtain spectral characteristics similar to the human eye. The presence of a compensating diode makes a difference compared to the sensor used in the SQM device, so the results obtained in the measurements may be slightly different.To measure additional environmental parameters, the X-NUCLEO-IKS01A2 development board from STMicroelectronics was used, which is connected to the STM32 microcontroller via the I2C interface44. This board enables the recording of a number of parameters, however, in the constructed device it is only responsible for reading the temperature and humidity of the environment. This results from the necessity to limit the size of the message packets sent, while at the same time improving the operating range and reducing the power consumption of the device.Once all the components had been selected, tested and integrated, the process of final connection and programming was carried out. The base of the device, i.e. the B-L072Z-LRWAN development board was connected to the X-NUCLEO-IKS01A2 environmental sensor board, using Arduino connectors. Using standard wires, a TSL2591 light sensor was added by connecting the corresponding I2C (SCL and SDA), power supply (VIN), sensor ground (GND) pins and the X-NUCLEO-IKS01A2 board.All components used were placed in a standard external casing with dimensions of 8.0 × 5.4 × 15.8 cm. In its lower part an opening was made for an external antenna, while in the upper part a specifically selected opening was cut out, protected with a glass pane, through which measurements are performed by the light sensor (Fig. 2).Figure 2(photo by Dominika Karpińska).Constructed device viewFull size imageFollowing the above steps, an automatic device was constructed to record light intensity in the lower troposphere, i.e. to measure the pollution of the night sky by artificial light coming from the Earth’s surface. Selected technical parameters of the device are presented in Table 1.Table 1 Selected technical parameters of the device for measuring light pollution of the night sky.Full size tableFlowchart of the system operationAfter constructing the device and writing the control software, the construction of the entire measurement system was started. Each of the measuring instruments is ultimately connected to the communication gateway using LoRa technology. A MultiTech communication gateway with a LoRaWAN module was used as an access device. To successfully connect the gateway to the measuring device, it was necessary to configure the communication gateway software. To this end, the information about the unique device number (Dev EUI) and the application key and its number (App EUI and App Key) was used. Once the unit is configured, it is possible to send data to the communication gateway and read them using NodeRED, a programming tool where data are redirected to a selected server, which stores all measurement results. Figure 3 shows a schematic representation of the constructed measurement system.Figure 3Schematic diagram of the measurement system.Full size image More

  • in

    Wolf risk fails to inspire fear in two mesocarnivores suggesting facilitation prevails

    Elmhagen, B. & Rushton, S. P. Trophic control of mesopredators in terrestrial ecosystems: Top-down or bottom-up?. Ecol. Lett. 10, 197–206 (2007).PubMed 
    Article 

    Google Scholar 
    Newsome, T. M. et al. Top predators constrain mesopredator distributions. Nat. Commun. 8, 15469 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Prugh, L. R. & Sivy, K. J. Enemies with benefits: Integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 
    Article 

    Google Scholar 
    Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

    Google Scholar 
    Suraci, J. P., Clinchy, M., Dill, L. M., Roberts, D. & Zanette, L. Y. Fear of large carnivores causes a trophic cascade. Nat. Commun. 7, 10698 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).PubMed 
    Article 

    Google Scholar 
    Selva, N., Jȩdrzejewska, B., Jȩdrzejewski, W. & Wajrak, A. Factors affecting carcass use by a guild of scavengers in European temperate woodland. Can. J. Zool. 83, 1590–1601 (2005).Article 

    Google Scholar 
    McArthur, C., Banks, P. B., Boonstra, R. & Forbey, J. S. The dilemma of foraging herbivores: Dealing with food and fear. Oecologia 176, 667–689 (2014).ADS 
    Article 

    Google Scholar 
    Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
    Article 

    Google Scholar 
    Kuijper, D. P. J. et al. Paws without claws? Ecological effects of large carnivores in anthropogenic landscapes. Proc. R. Soc. B Biol. Sci. 283, 20161625 (2016).Article 

    Google Scholar 
    Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolfes, elk, and bison: Reestablishing the ‘landscape of fear’ in Yellowstone National Park, U.S.A. Can. J. Zool. 79, 1401–1409 (2001).Article 

    Google Scholar 
    Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: Spatial patterns of risk perception and response. Trends Ecol. Evol. 34, 355–368 (2019).PubMed 
    Article 

    Google Scholar 
    Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. Ecol. Lett. 12, 982–998 (2009).PubMed 
    Article 

    Google Scholar 
    Leo, V., Reading, R. P. & Letnic, M. Interference competition: Odours of an apex predator and conspecifics influence resource acquisition by red foxes. Oecologia 179, 1033–1040 (2015).ADS 
    PubMed 
    Article 

    Google Scholar 
    Clinchy, M. et al. Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore. Behav. Ecol. 27, 1826–1832 (2016).
    Google Scholar 
    Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: how mesopredators avoid costly interactions with apex predators. Oecologia 187, 573–583 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Switalski, T. A. Coyote foraging ecology and vigilance in response to gray wolf reintroduction in Yellowstone National Park. Can. J. Zool. 81, 985–993 (2003).Article 

    Google Scholar 
    Wikenros, C., Jarnemo, A., Frisén, M., Kuijper, D. P. J. & Schmidt, K. Mesopredator behavioral response to olfactory signals of an apex predator. J. Ethol. 35, 161–168 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Palomares, F., Ferreras, P., Fedriani, J. M. & Delibes, M. Spatial relationships between Iberian Lynx and other carnivores in an area of south-western Spain. J. Appl. Ecol. 33, 5–13 (1996).Article 

    Google Scholar 
    Salo, P., Nordström, M., Thomson, R. L. & Korpimäki, E. Risk induced by a native top predator reduces alien mink movements. J. Anim. Ecol. 77, 1092–1098 (2008).PubMed 
    Article 

    Google Scholar 
    Haswell, P. M., Kusak, J. & Hayward, M. W. Large carnivore impacts are context-dependent. Food Webs 12, 3–13 (2017).Article 

    Google Scholar 
    Parsons, M. H. et al. Biologically meaningful scents: A framework for understanding predator–prey research across disciplines. Biol. Rev. 93, 98–114 (2018).PubMed 
    Article 

    Google Scholar 
    Sivy, K. J., Pozzanghera, C. B., Grace, J. B. & Prugh, L. R. Fatal attraction? Intraguild facilitation and suppression among predators. Am. Nat. 190, 663–679 (2017).PubMed 
    Article 

    Google Scholar 
    Ruprecht, J. et al. Variable strategies to solve risk-reward tradeoffs in carnivore communities. Proc. Natl. Acad. Sci. USA. 118, e2101614118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jędrzejewska, B. & Jędrzejewski, W. Predation in Vertebrate Communities Vol. 135 (Springer, 1998).Book 

    Google Scholar 
    Jȩdrzejewski, W. et al. Kill rates and predation by wolves on ungulate populations in Białowieża primeval forest (Poland). Ecology 83, 1341–1356 (2002).
    Google Scholar 
    Selva, N. The role of scavenging in the predator community of Białowieża Primeval Forest (Poland). PhD Thesis. (University of Sevilla, 2004).Kowalczyk, R., Zalewski, A., Jędrzejewska, B., Ansorge, H. & Bunevich, A. N. Reproduction and mortality of invasive raccoon dogs (Nyctereutes procyonoides) in the Biatowieža Primeval Forest (eastern Poland). Ann. Zool. Fennici 46, 291–303 (2009).Article 

    Google Scholar 
    Ballard, W. B., Carbyn, L. N. & Smith, D. W. Wolf interactions with non-prey. In Wolves: Behavior, Ecology, and Conservation (eds Mech, D. & Boitani, L.) 259–271 (University of Chicago Press, 2003).
    Google Scholar 
    Brown, J. S. Patch use as an indicator of habitat preference, predation risk, and competition. Behav. Ecol. Sociobiol. 22, 37–47 (1988).Article 

    Google Scholar 
    Bedoya-Perez, M. A., Carthey, A. J. R., Mella, V. S. A., McArthur, C. & Banks, P. B. A practical guide to avoid giving up on giving-up densities. Behav. Ecol. Sociobiol. 67, 1541–1553 (2013).Article 

    Google Scholar 
    Kwiatkowski, W. Vegetation landscapes of Białowieża Forest. Phytocoen. Suppl. Cart. Geobot 6, 35–87 (1994).
    Google Scholar 
    European Court of Justice Judgment of the Court (Grand Chamber) of 17 April 2018. European Commission vs. Republic of Poland. Case C-441/17. https://curia.europa.eu/jcms/upload/docs/application/pdf/2018-04/cp180048en.pdf.Bubnicki, J. W., Churski, M., Schmidt, K., Diserens, T. A. & Kuijper, D. P. J. Linking spatial patterns of terrestrial herbivore community structure to trophic interactions. Elife 8, e44937 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kowalczyk, R., Bunevich, A. N. & Jędrzejewska, B. Badger density and distribution of setts in Bialowieza Primeval Forest (Poland and Belarus) compared to other Eurasian populations. Acta Theriol. 45, 395–408 (2000).Article 

    Google Scholar 
    Jędrzejewski, W., Schmidt, K., Theuerkauf, J., Jędrzejewska, B. & Kowalczyk, R. Territory size of wolves Canis lupus: Linking local (Białowieża Primeval Forest, Poland) and holarctic-scale patterns. Ecography 30, 66–67 (2007).
    Google Scholar 
    Schmidt, K., Jędrzejewski, W., Okarma, H. & Kowalczyk, R. Spatial interactions between grey wolves and Eurasian lynx in Białowieża Primeval Forest, Poland. Ecol. Res. 24, 207–214 (2009).Article 

    Google Scholar 
    Bytheway, J. P., Carthey, A. J. R. & Banks, P. B. Risk vs reward: How predators and prey respond to aging olfactory cues. Behav. Ecol. Sociobiol. 67, 715–725 (2013).Article 

    Google Scholar 
    Carthey, A. J. R. & Banks, P. B. Naiveté is not forever: responses of a vulnerable native rodent to its long term alien predators. Oikos 125, 918–926 (2016).Article 

    Google Scholar 
    Blanchard, C. D. & Blanchard, R. J. Antipredator DEFENSE. In The Behavior of the Laboratory Rat: A Handbook with Tests (eds Whishaw, I. Q. & Kolb, B.) 335–343 (Oxford University Press, 2004).Chapter 

    Google Scholar 
    Masini, C. V., Sauer, S. & Campeau, S. Ferret odor as a processive stress model in rats: Neurochemical, behavioral, and endocrine evidence. Behav. Neurosci. 119, 280–292 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bubnicki, J. W., Churski, M. & Kuijper, D. P. J. Trapper: An open source web-based application to manage camera trapping projects. Methods Ecol. Evol. 7, 1209–1216 (2016).Article 

    Google Scholar 
    Jędrzejewski, W., Schmidt, K., Theuerkauf, J., Jędrzejewska, B. & Okarma, H. Daily movements and territory use by radio-collared wolves (Canis lupus) in Bialowieza Primeval Forest in Poland. Can. J. Zool. 79, 1993–2004 (2001).Article 

    Google Scholar 
    Theuerkauf, J., Jędrzejewski, W., Schmidt, K. & Gula, R. Spatiotemporal segregation of wolves from humans in the Bialowieza Forest (Poland). J. Wildl. Manage. 67, 706–716 (2003).Article 

    Google Scholar 
    Theuerkauf, J., Rouys, S. & Jędrzejewski, W. Selection of den, rendezvous, and resting sites by wolves in the Bialowieza Forest, Poland. Can. J. Zool. 81, 163–167 (2003).Article 

    Google Scholar 
    Miller, B. J., Harlow, H. J., Harlow, T. S., Biggins, D. & Ripple, W. J. Trophic cascades linking wolves (Canis lupus), coyotes (Canis latrans), and small mammals. Can. J. Zool. 90, 70–78 (2012).Article 

    Google Scholar 
    Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: An R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).Article 

    Google Scholar 
    Zoller, H. & Drygala, F. Activity patterns of the invasive raccoon dog (Nyctereutes procyonoides) in North East Germany. Folia Zool. 62, 290–296 (2013).Article 

    Google Scholar 
    Díaz-Ruiz, F., Caro, J., Delibes-Mateos, M., Arroyo, B. & Ferreras, P. Drivers of red fox (Vulpes vulpes) daily activity: Prey availability, human disturbance or habitat structure?. J. Zool. 298, 128–138 (2016).Article 

    Google Scholar 
    Mukherjee, S., Zelcer, M. & Kotler, B. P. Patch use in time and space for a meso-predator in a risky world. Oecologia 159, 661–668 (2009).ADS 
    PubMed 
    Article 

    Google Scholar 
    Tredennick, A. T., Hooker, G., Ellner, S. P. & Adler, P. B. A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology 102, e03336 (2021).PubMed 
    Article 

    Google Scholar 
    Team, R. C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).Magnusson, A. et al. R Package ‘glmmTMB’. (2020).Hartig, F. R Package ‘DHARMa: Residual Diagnostics for Hierarchical (Multi-level/Mixed) Regression Models’ (2021).Fox, J. et al. R Package ‘effects’. (2020).Hawlena, D. & Schmitz, O. J. Physiological stress as a fundamental mechanism linking predation to ecosystem functioning. Am. Nat. 176, 537–556 (2010).PubMed 
    Article 

    Google Scholar 
    Diserens, T. A. et al. Fossoriality in a risky landscape: Badger sett use varies with perceived wolf risk. J. Zool. 313, 76–85 (2021).Article 

    Google Scholar 
    Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: The predation risk allocation hypothesis. Am. Nat. 153, 649–659 (1999).PubMed 
    Article 

    Google Scholar 
    Scheinin, S., Yom-Tov, Y., Motro, U. & Geffen, E. Behavioural responses of red foxes to an increase in the presence of golden jackals: A field experiment. Anim. Behav. 71, 577 (2006).Article 

    Google Scholar 
    Vanak, A. T., Thaker, M. & Gompper, M. E. Experimental examination of behavioural interactions between free-ranging wild and domestic canids. Behav. Ecol. Sociobiol. 64, 279–287 (2009).Article 

    Google Scholar 
    Creel, S., Winnie, J. A., Christianson, D. & Liley, S. Time and space in general models of antipredator response: Tests with wolves and elk. Anim. Behav. 76, 1139–1146 (2008).Article 

    Google Scholar 
    Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128 (2017).PubMed 
    Article 

    Google Scholar 
    Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Fitness consequences of chronic exposure to different light pollution wavelengths in nocturnal and diurnal rodents

    Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Holker, F., Wolter, C., Perkin, E. K. & Tockner, K. Light pollution as a biodiversity threat. Trends Ecol. Evol. 25, 681–682. https://doi.org/10.1016/j.tree.2010.09.007 (2010).Article 
    PubMed 

    Google Scholar 
    Kyba, C., Mohar, A. & Posch, T. How bright is moonlight?. Astron. Geophys. 58, 1.31-1.32 (2017).
    Google Scholar 
    Hölker, F. et al. The dark side of light: A transdisciplinary research agenda for light pollution policy. Ecol. Soc. 15, 150413 (2010).
    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).PubMed 

    Google Scholar 
    Gaston, K. J., Bennie, J., Davies, T. W. & Hopkins, J. The ecological impacts of nighttime light pollution: A mechanistic appraisal. Biol. Rev. 88, 912–927. https://doi.org/10.1111/brv.12036 (2013).Article 
    PubMed 

    Google Scholar 
    Gaston, K. J. & Bennie, J. Demographic effects of artificial nighttime lighting on animal populations. Environ. Rev. 22, 323–330. https://doi.org/10.1139/er-2014-0005 (2014).Article 

    Google Scholar 
    Gaston, K. J., Visser, M. E. & Hoelker, F. The biological impacts of artificial light at night: The research challenge. R. Soc. Philos. Trans. Biol. Sci. 370, 20140133–20140133 (2015).
    Google Scholar 
    Ouyang, J. Q. et al. Stressful colours: Corticosterone concentrations in a free-living songbird vary with the spectral composition of experimental illumination. Biol. Lett. https://doi.org/10.1098/rsbl.2015.0517 (2016).Article 

    Google Scholar 
    Ouyang, J. Q., Davies, S. & Dominoni, D. Hormonally mediated effects of artificial light at night on behavior and fitness: Linking endocrine mechanisms with function. J. Exp. Biol. https://doi.org/10.1242/jeb.156893 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dominoni, D., Quetting, M. & Partecke, J. Artificial light at night advances avian reproductive physiology. Proc. Biol. Sci. 280(1756), 20123017. https://doi.org/10.1098/rspb.2012.3017 (2012).CAS 
    Article 

    Google Scholar 
    Ayalon, I. et al. Coral gametogenesis collapse under artificial light pollution. Curr. Biol. 31, 413–419 (2021).CAS 
    PubMed 

    Google Scholar 
    Ayalon, I., de Barros Marangoni, L. F., Benichou, J. I., Avisar, D. & Levy, O. Red Sea corals under Artificial Light Pollution at Night (ALAN) undergo oxidative stress and photosynthetic impairment. Glob. Change Biol. 25, 4194–4207 (2019).ADS 

    Google Scholar 
    Amichai, E. & Kronfeld-Schor, N. Artificial light at night promotes activity throughout the night in nesting common swifts (Apus apus). Sci. Rep. 9, 11052 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kronfeld-Schor, N. et al. Drivers of infectious disease seasonality: Potential implications for COVID-19. J. Biol. Rhythms 36, 35–54 (2021).CAS 
    PubMed 
    PubMed Central 

    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. Lond. B https://doi.org/10.1098/rstb.2016.0248 (2017).Article 

    Google Scholar 
    Kronfeld-Schor, N. et al. Chronobiology by moonlight. Proc. R. Soc. B 280, 20123088 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Stevenson, T. J. et al. Disrupted seasonal biology impacts health, food security and ecosystems. Proc. R. Soc. Lond. B. https://doi.org/10.1098/rspb.2015.1453 (2015).Article 

    Google Scholar 
    Kaniewska, P., Alon, S., Karako-Lampert, S., Hoegh-Guldberg, O. & Levy, O. Signaling cascades and the importance of moonlight in coral broadcast mass spawning. eLife 4, e09991 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Liu, J. A., Meléndez-Fernández, O. H., Bumgarner, J. R. & Nelson, R. J. Effects of light pollution on photoperiod-driven seasonality. Horm. Behav. 141, 105150. https://doi.org/10.1016/j.yhbeh.2022.105150 (2022).Article 
    PubMed 

    Google Scholar 
    Grubisic, M. et al. Light pollution, circadian photoreception, and melatonin in vertebrates. Sustainability 11, 6400 (2019).CAS 

    Google Scholar 
    Stevenson, T. J. & Prendergast, B. J. Photoperiodic time measurement and seasonal immunological plasticity. Front. Neuroendocrinol. 37, 76–88. https://doi.org/10.1016/j.yfrne.2014.10.002 (2015).Article 
    PubMed 

    Google Scholar 
    Bumgarner, J. R. & Nelson, R. J. Light at night and disrupted circadian rhythms alter physiology and behavior. Integr. Comp. Biol. 61, 1160–1169 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Mishra, I. et al. Light at night disrupts diel patterns of cytokine gene expression and endocrine profiles in zebra finch (Taeniopygia guttata). Sci. Rep. 9, 1–12 (2019).
    Google Scholar 
    Grunst, M. L. et al. Early-life exposure to artificial light at night elevates physiological stress in free-living songbirds. Environ. Pollut. 259, 113895 (2020).CAS 
    PubMed 

    Google Scholar 
    Bedrosian, T., Galan, A., Vaughn, C., Weil, Z. M. & Nelson, R. J. Light at night alters daily patterns of cortisol and clock proteins in female Siberian hamsters. J. Neuroendocrinol. 25, 590–596 (2013).CAS 
    PubMed 

    Google Scholar 
    Touzot, M. et al. Artificial light at night alters the sexual behaviour and fertilisation success of the common toad. Environ. Pollut. 259, 113883 (2020).CAS 
    PubMed 

    Google Scholar 
    de Jong, M. et al. Effects of nocturnal illumination on life-history decisions and fitness in two wild songbird species. Philos. Trans. R. Soc. B 370, 20140128 (2015).
    Google Scholar 
    Spoelstra, K. et al. Experimental illumination of natural habitat: An experimental set-up to assess the direct and indirect ecological consequences of artificial light of different spectral composition. Philos. Trans. R. Soc. Lond. B 370, 20140129 (2015).
    Google Scholar 
    Hattar, S., Liao, H. W., Takao, M., Berson, D. M. & Yau, K. W. Melanopsin-containing retinal ganglion cells: Architecture, projections, and intrinsic photosensitivity. Science 295, 1065–1070. https://doi.org/10.1126/science.1069609 (2002).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gutman, R., Dayan, T., Levy, O., Schubert, I. & Kronfeld-Schor, N. The effect of the lunar cycle on fecal cortisol metabolite levels and foraging ecology of nocturnally and diurnally active spiny mice. PLoS ONE 6, e23446 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dhairykar, M., Singh, K. P., Kumar Jadav, K. & Rajput, N. Comparison of cortisol level in Asian elephants of different tiger reserves of Madhya Pradesh. Int. J. Vet. Sci. Anim. Husb. 5, 152–155 (2020).
    Google Scholar 
    Sosnowski, M. J., Benítez, M. E. & Brosnan, S. F. Endogenous cortisol correlates with performance under pressure on a working memory task in capuchin monkeys. Sci. Rep. 12, 1–10. https://doi.org/10.1038/s41598-022-04986-6 (2022).CAS 
    Article 

    Google Scholar 
    Bewick, V., Cheek, L. & Ball, J. Statistics review 12: survival analysis. Crit. care 8, 1–6 (2004).
    Google Scholar 
    Shkolnik, A. Studies in the Comparative Biology of Israel’s Two Species of Spiny Mice (genus Acomys). Hebrew (1966).Shkolnik, A. Diurnal activity in a small desert rodent. Int. J. Biometeorol. 15, 115–120 (1971).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Levy, O., Dayan, T. & Kronfeld-Schor, N. The relationship between the golden spiny mouse circadian system and its diurnal activity: An experimental field enclosures and laboratory study. Chronobiol. Int. 24, 599–613. https://doi.org/10.1080/07420520701534640 (2007).Article 
    PubMed 

    Google Scholar 
    Levy, O., Dayan, T. & Kronfeld-Schor, N. Interspecific competition and torpor in golden spiny mice: Two sides of the energy-acquisition coin. Integr. Comp. Biol. 51, 441–448. https://doi.org/10.1093/icb/icr071 (2011).Article 
    PubMed 

    Google Scholar 
    Jones, M. & Dayan, T. Foraging behavior and microhabitat use by spiny mice, Acomys cahirinus and A. russatus, in the presence of Blanford’s fox (Vulpes cana) odor. J. Chem. Ecol. 26, 455–469 (2000).CAS 

    Google Scholar 
    Jones, M., Mandelik, Y. & Dayan, T. Coexistence of temporally partitioned spiny mice: Roles of habitat structure and foraging behavior. Ecology 82, 2164–2176 (2001).
    Google Scholar 
    Kronfeld, N., Dayan, T., Zisapel, N. & Haim, A. Coexisting populations of Acomys cahirinus and A. russatus: A preliminary report. Isr. J. Zool. 40, 177–183 (1994).
    Google Scholar 
    Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181. https://doi.org/10.1146/annurev.ecolsys.34.011802.132435 (2003).Article 

    Google Scholar 
    Kronfeld-Schor, N. & Dayan, T. The dietary basis for temporal partitioning: Food habits of coexisting Acomys species. Oecologia 121, 123–128 (1999).ADS 
    PubMed 

    Google Scholar 
    Pinter-Wollman, N., Dayan, T., Eilam, D. & Kronfeld-Schor, N. Can aggression be the force driving temporal separation between competing common and golden spiny mice?. J. Mammal. 87, 48–53 (2006).
    Google Scholar 
    Shargal, E., Kronfeld-Schor, N. & Dayan, T. Population biology and spatial relationships of coexisting spiny mice (Acomys) in Israel. J. Mammal. 81, 1046–1052 (2000).
    Google Scholar 
    Pasco, R., Gardner, D. K., Walker, D. W. & Dickinson, H. A superovulation protocol for the spiny mouse (Acomys cahirinus). Reprod. Fertil. Dev. 24, 1117–1122 (2012).CAS 
    PubMed 

    Google Scholar 
    Lee, T. E., Watkins, J. F. & Cash, C. G. Acomys russatus. Mammal. Species 550, 1–4 (1998).
    Google Scholar 
    Dominoni, D., Quetting, M. & Partecke, J. Artificial light at night advances avian reproductive physiology. Proc. R. Soc. B 280, 20123017 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Kempenaers, B., Borgström, P., Loës, P., Schlicht, E. & Valcu, M. Artificial night lighting affects dawn song, extra-pair siring success, and lay date in songbirds. Curr. Biol. 20, 1735–1739. https://doi.org/10.1016/j.cub.2010.08.028 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Le Tallec, T., Théry, M. & Perret, M. Melatonin concentrations and timing of seasonal reproduction in male mouse lemurs (Microcebus murinus) exposed to light pollution. J. Mammal. 97, 753–760 (2016).
    Google Scholar 
    Vonshak, M., Dayan, T. & Kronfeld-Schor, N. Arthropods as a prey resource: Patterns of diel, seasonal, and spatial availability. J. Arid Environ. 73, 458–462. https://doi.org/10.1016/j.jaridenv.2008.11.013 (2009).ADS 
    Article 

    Google Scholar 
    Levy, O., Dayan, T. & Kronfeld-Schor, N. Adaptive thermoregulation in golden spiny mice: The influence of season and food availability on body temperature. Physiol. Biochem. Zool. 84, 175–184 (2011).PubMed 

    Google Scholar 
    Levy, O., Dayan, T., Rotics, S. & Kronfeld-Schor, N. Foraging sequence, energy intake and torpor: An individual-based field study of energy balancing in desert golden spiny mice. Ecol. Lett. 15, 1240–1248. https://doi.org/10.1111/j.1461-0248.2012.01845.x (2012).Article 
    PubMed 

    Google Scholar 
    Katz, N., Dayan, T. & Kronfeld-Schor, N. Fitness effects of interspecific competition between two species of desert rodents. Zoology 128, 62–68 (2018).PubMed 

    Google Scholar 
    Brzezinski, A. Melatonin in humans. N. Engl. J. Med. 336, 186–195 (1997).CAS 
    PubMed 

    Google Scholar 
    Hastings, M., Vance, G. & Maywood, E. Some reflections on the phylogeny and function of the pineal. Experientia 45, 903–909 (1989).CAS 
    PubMed 

    Google Scholar 
    Oster, H. et al. The circadian rhythm of glucocorticoids is regulated by a gating mechanism residing in the adrenal cortical clock. Cell Metab. 4, 163–173 (2006).CAS 
    PubMed 

    Google Scholar 
    Mora, F., Segovia, G., Del Arco, A., de Blas, M. & Garrido, P. Stress, neurotransmitters, corticosterone and body–brain integration. Brain Res. 1476, 71–85 (2012).CAS 
    PubMed 

    Google Scholar 
    Farrell, M. R. Sex Differences and Stress Effects in Corticolimbic Structure and Function (Indiana University, 2013).
    Google Scholar 
    Son, G. H., Chung, S. & Kim, K. The adrenal peripheral clock: Glucocorticoid and the circadian timing system. Front. Neuroendocrinol. 32, 451–465 (2011).CAS 
    PubMed 

    Google Scholar 
    Schradin, C. Seasonal changes in testosterone and corticosterone levels in four social classes of a desert dwelling sociable rodent. Horm. Behav. 53, 573–579 (2008).CAS 
    PubMed 

    Google Scholar 
    Zatra, Y. et al. Seasonal changes in plasma testosterone and cortisol suggest an androgen mediated regulation of the pituitary adrenal axis in the Tarabul’s gerbil Gerbillus tarabuli (Thomas, 1902). Gen. Comp. Endocrinol. 258, 173–183 (2018).CAS 
    PubMed 

    Google Scholar 
    Richardson, C. S., Heeren, T. & Kunz, T. H. Seasonal and sexual variation in metabolism, thermoregulation, and hormones in the big brown bat (Eptesicus fuscus). Physiol. Biochem. Zool. 91, 705–715 (2018).PubMed 

    Google Scholar 
    Touitou, S., Heistermann, M., Schülke, O. & Ostner, J. Triiodothyronine and cortisol levels in the face of energetic challenges from reproduction, thermoregulation and food intake in female macaques. Horm. Behav. 131, 104968 (2021).CAS 
    PubMed 

    Google Scholar 
    Rotics, S., Dayan, T. & Kronfeld-Schor, N. Effect of artificial night lighting on temporally partitioned spiny mice. J. Mammal. 92, 159–168. https://doi.org/10.1644/10-mamm-a-112.1 (2011).Article 

    Google Scholar 
    Rotics, S., Dayan, T., Levy, O. & Kronfeld-Schor, N. Light masking in the field: An experiment with nocturnal and diurnal spiny mice under semi-natural field conditions. Chronobiol. Int. 28, 70–75. https://doi.org/10.3109/07420528.2010.525674 (2011).Article 
    PubMed 

    Google Scholar 
    Padgett, D. A. & Glaser, R. How stress influences the immune response. Trends Immunol. 24, 444–448 (2003).CAS 
    PubMed 

    Google Scholar 
    Khansari, D. N., Murgo, A. J. & Faith, R. E. Effects of stress on the immune system. Immunol. Today 11, 170–175 (1990).CAS 
    PubMed 

    Google Scholar 
    Zozaya, S. M., Alford, R. A. & Schwarzkopf, L. Invasive house geckos are more willing to use artificial lights than are native geckos. Austral. Ecol. 40, 982–987 (2015).
    Google Scholar 
    Komine, H., Koike, S. & Schwarzkopf, L. Impacts of artificial light on food intake in invasive toads. Sci. Rep. 10, 1–8 (2020).
    Google Scholar 
    Murphy, S., Vyas, D., Sher, A. & Grenis, K. Light pollution affects invasive and native plant traits important to plant competition and herbivorous insects. Biol. Invasions 24, 599–602. https://doi.org/10.1007/s10530-021-02670-w (2022).Article 

    Google Scholar 
    Murphy, S. M. et al. Streetlights positively affect the presence of an invasive grass species. Ecol. Evol. 11, 10320–10326 (2021).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Influence of green technology, green energy consumption, energy efficiency, trade, economic development and FDI on climate change in South Asia

    Kejun, J. et al. Transition of the Chinese economy in the face of deep greenhouse gas emissions cuts in the future. Asian Econ. Policy Rev. 16(1), 142–162 (2021).
    Google Scholar 
    COP26, United nations climate change. https://unfccc.int/news/cop26-facts-and-figures, (2020).Dong, Y., Coleman, M. and Miller, S. A. Greenhouse gas emissions from air conditioning and refrigeration service expansion in developing countries. Annual Rev. Environ. Resour. 46 (2021).Azam, M. & Khan, A. Q. Testing the Environmental Kuznets Curve hypothesis: A comparative empirical study for low, lower middle, upper middle and high income countries. Renew. Sustain. Energy Rev. 63, 556–567 (2016).CAS 

    Google Scholar 
    Li, Z. et al. An economic analysis software for evaluating best management practices to mitigate greenhouse gas emissions from cropland. Agric. Syst. 186, 102950 (2021).
    Google Scholar 
    Dinda, S. Environmental Kuznets curve hypothesis: A survey. Ecol. Econ. 49(4), 431–455 (2004).
    Google Scholar 
    Xia, Q. et al. Drivers of global and national CO2 emissions changes 2000–2017. Climate Policy 21(5), 604–615 (2021).
    Google Scholar 
    Fatima, T., Shahzad, U. & Cui, L. Renewable and nonrenewable energy consumption, trade and CO2 emissions in high emitter countries: Does the income level matter?. J. Environ. Planning Manage. 64(7), 1227–1251 (2021).
    Google Scholar 
    Kılavuz, E. & Doğan, İ. Economic growth, openness, industry and CO2 modelling: Are regulatory policies important in Turkish economies?. Int. J. Low-Carbon Technol. 16(2), 476–487 (2021).
    Google Scholar 
    Setyari, N. P. W. & Kusuma, W. G. A. Economics and environmental development: Testing the environmental Kuznets Curve hypothesis. Int. J. Energy Econ. Policy 11(4), 51 (2021).
    Google Scholar 
    Gołasa, P. et al. Sources of greenhouse gas emissions in agriculture, with particular emphasis on emissions from energy used. Energies 14(13), 3784 (2021).
    Google Scholar 
    Liobikienė, G. & Butkus, M. The challenges and opportunities of climate change policy under different stages of economic development. Sci. Total Environ. 642, 999–1007 (2018).ADS 
    PubMed 

    Google Scholar 
    Koondhar, M. A. et al. A visualization review analysis of the last two decades for environmental Kuznets curve “EKC” based on co-citation analysis theory and pathfinder network scaling algorithms. Environ. Sci. Pollut. Res. 28(13), 16690–16706 (2021).CAS 

    Google Scholar 
    Bilgili, F., Koçak, E. & Bulut, Ü. The dynamic impact of renewable energy consumption on CO2 emissions: A revisited Environmental Kuznets Curve approach. Renew. Sustain. Energy Rev. 54, 838–845 (2016).
    Google Scholar 
    Gorus, M. S. & Aydin, M. The relationship between energy consumption, economic growth, and CO2 emission in MENA countries: Causality analysis in the frequency domain. Energy 168, 815–822 (2019).
    Google Scholar 
    Kirikkaleli, D. & Adebayo, T. S. Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence. Sustain. Develop. 29(4), 583–594 (2021).
    Google Scholar 
    Godil, D. I. et al. Investigate the role of technology innovation and renewable energy in reducing transport sector CO2 emission in China: A path toward sustainable development. Sustain. Develop. (2021).An, T., Xu, C. & Liao, X. The impact of FDI on environmental pollution in China: Evidence from spatial panel data. Environ. Sci. Pollut. Res. 1–13 (2021).Halliru, A. M., Loganathan, N. and Golam Hassan, A. A. Does FDI and economic growth harm environment? Evidence from selected West African countries. Trans. Corp. Rev., 13(2), 237–251 (2021.).Al-Mulali, U., Ozturk, I. & Solarin, S. A. Investigating the environmental Kuznets curve hypothesis in seven regions: The role of renewable energy. Ecol. Ind. 67, 267–282 (2016).
    Google Scholar 
    Zhang, D. et al. Public spending and green economic growth in BRI region: Mediating role of green finance. Energy Policy 153, 112256 (2021).
    Google Scholar 
    Usman, M. et al. How do financial development, energy consumption, natural resources, and globalization affect Arctic countries’ economic growth and environmental quality? An advanced panel data simulation. Energy, 122515 (2021).Rehman, A. et al. The impact of globalization, energy use, and trade on ecological footprint in Pakistan: does environmental sustainability exist?. Energies 14(17), 5234 (2021).CAS 

    Google Scholar 
    Bremond, U. et al. A vision of European biogas sector development towards 2030: Trends and challenges. J. Clean. Prod. 287, 125065 (2021).
    Google Scholar 
    Abdul Latif, S. N. et al. The trend and status of energy resources and greenhouse gas emissions in the malaysia power generation mix. Energies 14(8), 2200 (2021).CAS 

    Google Scholar 
    Chen, P.-Y. et al. Modeling the global relationships among economic growth, energy consumption and CO2 emissions. Renew. Sustain. Energy Rev. 65, 420–431 (2016).CAS 

    Google Scholar 
    Kais, S. & Sami, H. An econometric study of the impact of economic growth and energy use on carbon emissions: Panel data evidence from fifty eight countries. Renew. Sustain. Energy Rev. 59, 1101–1110 (2016).
    Google Scholar 
    Rüstemoğlu, H. & Andrés, A. R. Determinants of CO2 emissions in Brazil and Russia between 1992 and 2011: A decomposition analysis. Environ. Sci. Policy 58, 95–106 (2016).
    Google Scholar 
    Yao, C., Feng, K. & Hubacek, K. Driving forces of CO2 emissions in the G20 countries: An index decomposition analysis from 1971 to 2010. Eco. Inform. 26, 93–100 (2015).
    Google Scholar 
    González, P. F., Landajo, M. & Presno, M. The driving forces behind changes in CO2 emission levels in EU-27. Differences between member states. Environ. Sci. Policy 38, 11–16 (2014).
    Google Scholar 
    Nathaniel, S. P. Environmental degradation in ASEAN: assessing the criticality of natural resources abundance, economic growth and human capital. Environ. Sci. Pollut. Res. 28(17), 21766–21778 (2021).
    Google Scholar 
    Baloch, M. A., Mahmood, N. & Zhang, J. W. Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries. Sci. Total Environ. 678, 632–638 (2019).ADS 
    PubMed 

    Google Scholar 
    Balsalobre-Lorente, D. et al. How economic growth, renewable electricity and natural resources contribute to CO2 emissions?. Energy Policy 113, 356–367 (2018).
    Google Scholar 
    Bekun, F. V., Alola, A. A. & Sarkodie, S. A. Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci. Total Environ. 657, 1023–1029 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Baloch, M. A. & Meng, F. Modeling the non-linear relationship between financial development and energy consumption: Statistical experience from OECD countries. Environ. Sci. Pollut. Res. 26(9), 8838–8846 (2019).
    Google Scholar 
    Dong, K., Sun, R. & Hochman, G. Do natural gas and renewable energy consumption lead to less CO2 emission? Empirical evidence from a panel of BRICS countries. Energy 141, 1466–1478 (2017).
    Google Scholar 
    Omri, A. et al. Determinants of environmental sustainability: Evidence from Saudi Arabia. Sci. Total Environ. 657, 1592–1601 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Zhu, H. et al. The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression. Econ. Model. 58, 237–248 (2016).
    Google Scholar 
    Cheng, C. et al. Heterogeneous impacts of renewable energy and environmental patents on CO2 emission-evidence from the BRIICS. Sci. Total Environ. 668, 1328–1338 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Zhang, C. & Zhou, X. Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China. Renew. Sustain. Energy Rev. 58, 943–951 (2016).
    Google Scholar 
    Phung, T. Q., Rasoulinezhad, E. and Luong Thi Thu, H. How are FDI and green recovery related in Southeast Asian economies? Econ. Change Restruct. 1–21 (2022).Quang, P.T. and Thao, D. P. Analyzing the green financing and energy efficiency relationship in ASEAN. J. Risk Financ. (2022)(ahead-of-print).Ahmad, M. et al. Modelling the dynamic linkages between eco-innovation, urbanization, economic growth and ecological footprints for G7 countries: Does financial globalization matter?. Sustain. Cities Soc. 70, 102881 (2021).
    Google Scholar 
    Murshed, M. An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia. Environ. Sci. Pollut. Res. 27(29), 36254–36281 (2020).CAS 

    Google Scholar 
    Díaz-García, C., González-Moreno, Á. & Sáez-Martínez, F. J. Eco-innovation: Insights from a literature review. Innovation 17(1), 6–23 (2015).
    Google Scholar 
    Wang, L. et al. Are eco-innovation and export diversification mutually exclusive to control carbon emissions in G-7 countries?. J. Environ. Manage. 270, 110829 (2020).PubMed 

    Google Scholar 
    Su, H.-N. & Moaniba, I. M. Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions. Technol. Forecast. Soc. Chang. 122, 49–62 (2017).
    Google Scholar 
    Ding, Q., Khattak, S. I. & Ahmad, M. Towards sustainable production and consumption: assessing the impact of energy productivity and eco-innovation on consumption-based carbon dioxide emissions (CCO2) in G-7 nations. Sustain. Prod. Consum. 27, 254–268 (2021).
    Google Scholar 
    Zhang, Y.-J. et al. Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100, 18–28 (2017).
    Google Scholar 
    Solarin, S. A. & Bello, M. O. Energy innovations and environmental sustainability in the US: the roles of immigration and economic expansion using a maximum likelihood method. Sci. Total Environ. 712, 135594 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hashmi, R. & Alam, K. Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation. J. Clean. Prod. 231, 1100–1109 (2019).
    Google Scholar 
    Sinha, A., Sengupta, T. & Alvarado, R. Interplay between technological innovation and environmental quality: Formulating the SDG policies for next 11 economies. J. Clean. Prod. 242, 118549 (2020).
    Google Scholar 
    Gormus, S. & Aydin, M. Revisiting the environmental Kuznets curve hypothesis using innovation: New evidence from the top 10 innovative economies. Environ. Sci. Pollut. Res. 27(22), 27904–27913 (2020).
    Google Scholar 
    Usman, M. & Hammar, N. Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: Fresh insights based on the STIRPAT model for Asia Pacific Economic Cooperation countries. Environ. Sci. Pollut. Res. 28(12), 15519–15536 (2021).
    Google Scholar 
    Shahbaz, M., Mutascu, M. & Azim, P. Environmental Kuznets curve in Romania and the role of energy consumption. Renew. Sustain. Energy Rev. 18, 165–173 (2013).
    Google Scholar 
    Kong, Q. et al. Trade openness and economic growth quality of China: Empirical analysis using ARDL model. Financ. Res. Lett. 38, 101488 (2021).
    Google Scholar 
    Kasman, A. & Duman, Y. S. CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Econ. Model. 44, 97–103 (2015).
    Google Scholar 
    Ali, S. et al. Impact of trade openness, human capital, public expenditure and institutional performance on unemployment: Evidence from OIC countries. Int. J. Manpower, (2021).Chen, F., Jiang, G. & Kitila, G. M. Trade openness and CO2 emissions: The heterogeneous and mediating effects for the belt and road countries. Sustainability 13(4), 1958 (2021).
    Google Scholar 
    Sun, H. et al. Nexus between environmental infrastructure and transnational cluster in one belt one road countries: Role of governance. Bus. Strategy Develop. 1(1), 17–30 (2018).
    Google Scholar 
    Jebli, M. B. & Youssef, S. B. The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia. Renew. Sustain. Energy Rev. 47, 173–185 (2015).
    Google Scholar 
    Jebli, M. B., Youssef, S. B. & Ozturk, I. Testing environmental Kuznets curve hypothesis: The role of renewable and non-renewable energy consumption and trade in OECD countries. Ecol. Ind. 60, 824–831 (2016).
    Google Scholar 
    Shahbaz, M. et al. Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates. Ecol. Ind. 45, 622–631 (2014).CAS 

    Google Scholar 
    Xu, B. & Lin, B. How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models. Energy Econ. 48, 188–202 (2015).
    Google Scholar 
    Ertugrul, H. M. et al. The impact of trade openness on global carbon dioxide emissions: Evidence from the top ten emitters among developing countries. Ecol. Ind. 67, 543–555 (2016).
    Google Scholar 
    Najarzadeh, R. et al. Kyoto Protocol and global value chains: Trade effects of an international environmental policy. Environ. Develop. 40, 100659 (2021).
    Google Scholar 
    Liobikienė, G. & Butkus, M. Environmental Kuznets Curve of greenhouse gas emissions including technological progress and substitution effects. Energy 135, 237–248 (2017).
    Google Scholar 
    Liobikienė, G. The revised approaches to income inequality impact on production-based and consumption-based carbon dioxide emissions: Literature review. Environ. Sci. Pollut. Res. 27(9), 8980–8990 (2020).
    Google Scholar 
    Li, G., Zakari, A. & Tawiah, V. Energy resource melioration and CO2 emissions in China and Nigeria: Efficiency and trade perspectives. Resour. Policy 68, 101769 (2020).
    Google Scholar 
    Ali, M. U. et al. Fossil energy consumption, economic development, inward FDI impact on CO2 emissions in Pakistan: Testing EKC hypothesis through ARDL model. Int. J. Financ. Econ. 26(3), 3210–3221 (2021).
    Google Scholar 
    Özbuğday, F. C. & Erbas, B. C. How effective are energy efficiency and renewable energy in curbing CO2 emissions in the long run? A heterogeneous panel data analysis. Energy 82, 734–745 (2015).
    Google Scholar 
    Wang, Q., Chiu, Y.-H. & Chiu, C.-R. Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis. Energy Econ. 51, 252–260 (2015).
    Google Scholar 
    Dong, K. et al. Energy intensity and energy conservation potential in China: A regional comparison perspective. Energy 155, 782–795 (2018).
    Google Scholar 
    Tan, R. & Lin, B. What factors lead to the decline of energy intensity in China’s energy intensive industries?. Energy Econ. 71, 213–221 (2018).
    Google Scholar 
    Tariq, G. et al. Energy consumption and economic growth: Evidence from four developing countries. Am. J. Multidiscip. Res. 7(1), (2018).Sharif, A. et al. Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from quantile ARDL approach. Sustain. Cities Soc. 57, 102138 (2020).
    Google Scholar 
    Khan, I., Hou, F. & Le, H. P. The impact of natural resources, energy consumption, and population growth on environmental quality: Fresh evidence from the United States of America. Sci. Total Environ. 754, 142222 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bölük, G. & Mert, M. Fossil & renewable energy consumption, GHGs (greenhouse gases) and economic growth: Evidence from a panel of EU (European Union) countries. Energy 74, 439–446 (2014).
    Google Scholar 
    Sugiawan, Y. & Managi, S. The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy 98, 187–198 (2016).
    Google Scholar 
    Bölük, G. & Mert, M. The renewable energy, growth and environmental Kuznets curve in Turkey: An ARDL approach. Renew. Sustain. Energy Rev. 52, 587–595 (2015).
    Google Scholar 
    Sebri, M. & Ben-Salha, O. On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: Fresh evidence from BRICS countries. Renew. Sustain. Energy Rev. 39, 14–23 (2014).
    Google Scholar 
    Tiwari, A. K. A structural VAR analysis of renewable energy consumption, real GDP and CO2 emissions: Evidence from India. Econ. Bull. 31(2), 1793–1806 (2011).
    Google Scholar 
    Apergis, N. & Payne, J. E. Renewable energy consumption and growth in Eurasia. Energy Econ. 32(6), 1392–1397 (2010).
    Google Scholar 
    Menyah, K. & Wolde-Rufael, Y. CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38(6), 2911–2915 (2010).CAS 

    Google Scholar 
    Fareed, Z. et al. Financial inclusion and the environmental deterioration in Eurozone: The moderating role of innovation activity. Technol. Soc. 69, 101961 (2022).
    Google Scholar 
    Adebayo, T. S. Renewable energy consumption and environmental sustainability in Canada: does political stability make a difference? Environ. Sci. Pollut. Res., 1–16 (2022).Shahbaz, M. et al. Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries?. Energy Econ. 51, 275–287 (2015).
    Google Scholar 
    Tariq, G. et al. Trade liberalization, FDI inflows economic growth and environmental sustanaibility in Pakistan and India. J. Agric. Environ. Int. Develop. (JAEID) 112(2), 253–269 (2018).
    Google Scholar 
    Lee, J. W. The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth. Energy Policy 55, 483–489 (2013).
    Google Scholar 
    Sun, H.-P. et al. Evaluating the environmental effects of economic openness: Evidence from SAARC countries. Environ. Sci. Pollut. Res. 26(24), 24542–24551 (2019).CAS 

    Google Scholar 
    Adebayo, T. S. Environmental consequences of fossil fuel in Spain amidst renewable energy consumption: a new insights from the wavelet-based Granger causality approach. Int. J. Sustain. Develop. World Ecol. 1–14 (2022).Adebayo, T. S. et al. Impact of tourist arrivals on environmental quality: A way towards environmental sustainability targets. Current Issues Tourism, 1–19 (2022).Akadiri, S.S. et al. Testing the role of economic complexity on the ecological footprint in China: A nonparametric causality-in-quantiles approach. Energy Environ. 0958305X221094573 (2022).Xie, Q. et al. Race to environmental sustainability: Can renewable energy consumption and technological innovation sustain the strides for China? Renew. Energy (2022).Du, L. et al. Asymmetric effects of high-tech industry and renewable energy on consumption-based carbon emissions in MINT countries. Renew. Energy 196, 1269–1280 (2022).CAS 

    Google Scholar 
    Al-Mulali, U. & Tang, C. F. Investigating the validity of pollution haven hypothesis in the gulf cooperation council (GCC) countries. Energy Policy 60, 813–819 (2013).
    Google Scholar 
    Jiang, Y. Foreign direct investment, pollution, and the environmental quality: A model with empirical evidence from the Chinese regions. Int. Trade J. 29(3), 212–227 (2015).
    Google Scholar 
    Ren, S. et al. International trade, FDI (foreign direct investment) and embodied CO2 emissions: A case study of Chinas industrial sectors. China Econ. Rev. 28, 123–134 (2014).
    Google Scholar 
    Tang, C. F. & Tan, B. W. The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam. Energy 79, 447–454 (2015).
    Google Scholar 
    Omri, A. & Kahouli, B. Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models. Energy Policy 67, 913–922 (2014).
    Google Scholar 
    Dong, K.-Y. et al. A review of China’s energy consumption structure and outlook based on a long-range energy alternatives modeling tool. Pet. Sci. 14(1), 214–227 (2017).
    Google Scholar 
    WDI, World Development Indicator. https://data.worldbank.org/, (2022).OECD, Organisation for Economic Co-operation and Development. https://data.oecd.org/, (2021).Levin, A., Lin, C.-F. & Chu, C.-S.J. Unit root tests in panel data: Asymptotic and finite-sample properties. J. Econom. 108(1), 1–24 (2002).MathSciNet 
    MATH 

    Google Scholar 
    Breitung, J. The local power of some unit root tests for panel data. (Emerald Group Publishing Limited, 2001).Im, K. S., Pesaran, M. H. & Shin, Y. Testing for unit roots in heterogeneous panels. J. Econom. 115(1), 53–74 (2003).MathSciNet 
    MATH 

    Google Scholar 
    Hlouskova, J. & Wagner, M. The performance of panel unit root and stationarity tests: Results from a large scale simulation study. Economet. Rev. 25(1), 85–116 (2006).MathSciNet 
    MATH 

    Google Scholar 
    Narayan, P. K. & Narayan, S. Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy 38(1), 661–666 (2010).
    Google Scholar 
    Pedroni, P. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bull. Econ. Stat. 61(S1), 653–670 (1999).
    Google Scholar 
    Pedroni, P. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Economet. Theor. 20(3), 597–625 (2004).MathSciNet 
    MATH 

    Google Scholar 
    Kao, C. Spurious regression and residual-based tests for cointegration in panel data. J. Econom. 90(1), 1–44 (1999).MathSciNet 
    MATH 

    Google Scholar 
    Breusch, T. S. & Pagan, A. R. The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 47(1), 239–253 (1980).MathSciNet 
    MATH 

    Google Scholar 
    Baltagi, B. H. and Hashem Pesaran, M. Heterogeneity and cross section dependence in panel data models: Theory and applications introduction. 229–232 (Wiley Online Library, 2007).Levine, S. & Kendall, K. Energy efficiency and conservation: Opportunities, obstacles, and experiences. Vt. J. Envtl. L. 8, 101 (2006).
    Google Scholar 
    Stock, J. H. and Watson, M. W. A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: J. Econom. Soc. 783–820 (1993).Phillips, P.C. and Hansen, B.E. Estimation and inference in models of cointegration: A simulation study. (Cowles Foundation for Research in Economics, Yale University, 1988).Pedroni, P. Fully modified OLS for heterogeneous cointegrated panels, in Nonstationary panels, panel cointegration, and dynamic panels. (Emerald Group Publishing Limited, 2001).Kao, C. and Chiang, M.-H. On the estimation and inference of a cointegrated regression in panel data, in Nonstationary panels, panel cointegration, and dynamic panels. (Emerald Group Publishing Limited, 2001).Liobikienė, G. & Butkus, M. Scale, composition, and technique effects through which the economic growth, foreign direct investment, urbanization, and trade affect greenhouse gas emissions. Renew. Energy 132, 1310–1322 (2019).
    Google Scholar 
    Balsalobre-Lorente, D. et al. The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew. Energy 185, 1441–1455 (2022).CAS 

    Google Scholar 
    Sarkodie, S. A. & Adams, S. Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. Sci. Total Environ. 643, 1590–1601 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Mohamued, E. A. et al. Global oil price and innovation for sustainability: The impact of R&D spending, oil price and oil price volatility on GHG emissions. Energies 14(6), 1757 (2021).
    Google Scholar 
    Iqbal, N. et al. Does exports diversification and environmental innovation achieve carbon neutrality target of OECD economies?. J. Environ. Manage. 291, 112648 (2021).PubMed 

    Google Scholar 
    Edenhofer, O. et al. Renewable energy sources and climate change mitigation: Special report of the intergovernmental panel on climate change. (Cambridge University Press, 2011).Owusu, P. A. & Asumadu-Sarkodie, S. A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng. 3(1), 1167990 (2016).
    Google Scholar  More