More stories

  • in

    Modeling the spatial distribution of Culicoides species (Diptera: Ceratopogonidae) as vectors of animal diseases in Ethiopia

    MacLachlan, N. J. & Guthrie, A. J. Re-emergence of bluetongue, African horse sickness, and other Orbivirus diseases. Vet. Res. https://doi.org/10.1051/vetres/2010007 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Koenraadt, C. J. M. et al. Bluetongue, Schmallenberg—What is next? Culicoides-borne viral diseases in the 21st Century. BMC Res. Notes 10, 77 (2014).
    Google Scholar 
    Dennis, S. J., Meyers, A. E., Hitzeroth, I. I. & Rybicki, E. P. African horse sickness: A review of current understanding and vaccine development in the. Viruses 11, 844 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Collins, Á. B., Doherty, M. L., Barrett, D. J. & Mee, J. F. Schmallenberg virus: A systematic international literature review (2011–2019) from an Irish perspective. Ir. Vet. J. 72, 1–22 (2019).Article 

    Google Scholar 
    Tkuwet, G. & Firesbhat, A. A review on African horse sickness. Eur. J. Appl. Sci. 7, 213–219 (2015).CAS 

    Google Scholar 
    Mellor, P. S. & Hamblin, C. African horse sickness. Vet. Res. 35, 445–466 (2004).PubMed 
    Article 

    Google Scholar 
    Coetzee, P., Stokstad, M., Venter, E. H., Myrmel, M. & Van Vuuren, M. Bluetongue: A historical and epidemiological perspective with the emphasis on South Africa. Virol. J. 9, 198 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cagienard, A., Griot, C., Mellor, P. S., Denison, E. & Stärk, K. D. Bluetongue vector species of Culicoides in Switzerland. Med. Vet. Entomol. 20, 239–247 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Oluwayelu, D., Adebiyi, A. & Tomori, O. Endemic and emerging arboviral diseases of livestock in Nigeria: A review. Parasit. Vectors 11, 1–12 (2018).Article 

    Google Scholar 
    Sibhat, B., Ayelet, G., Gebremedhin, E. Z., Skjerve, E. & Asmare, K. Seroprevalence of Schmallenberg virus in dairy cattle in Ethiopia. Acta Trop. 178, 61–67 (2018).PubMed 
    Article 

    Google Scholar 
    Aklilu, N. et al. African horse sickness outbreaks caused by multiple virus types in Ethiopia. Transbound. Emerg. Dis. 61, 185–192 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rojas, J. M., Rodríguez-Martín, D., Martín, V. & Sevilla, N. Diagnosing bluetongue virus in domestic ruminants: Current perspectives. Vet. Med. Res. Rep. 10, 17 (2019).
    Google Scholar 
    Gizaw, D., Sibhat, D., Ayalew, B. & Sehal, M. Sero-prevalence study of bluetongue infection in sheep and goats in selected areas of Ethiopia. Ethiop. Vet. J. 20, 105 (2016).Article 

    Google Scholar 
    Abera, T. et al. Bluetongue disease in small ruminants in south western Ethiopia: Cross-sectional sero-epidemiological study. BMC Res. Notes 11, 112 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mellor, P. S., Boorman, J. & Baylis, M. Culicoides biting midges: Their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Carpenter, S., Groschup, M. H., Garros, C., Felippe-Bauer, M. L. & Purse, B. V. Culicoides biting midges, arboviruses and public health in Europe. Antivir. Res. 100, 102–113 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sick, F., Beer, M., Kampen, H. & Wernike, K. Culicoides biting midges—Underestimated vectors for arboviruses of public health and veterinary importance. Viruses 11, 376 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Blanda, V. et al. Geo-statistical analysis of Culicoides spp. distribution and abundance in Sicily, Italy. Parasit. Vectors 11, 78 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vasić, A. et al. Species diversity, host preference and arbovirus detection of Culicoides (Diptera: Ceratopogonidae) in south-eastern Serbia. Parasit. Vectors 12, 1–9 (2019).Article 

    Google Scholar 
    Martin, E. et al. Culicoides species community composition and infection status with parasites in an urban environment of east central Texas, USA. Parasit. Vectors 12, 1–10 (2019).Article 

    Google Scholar 
    Gusmão, G. M. C., Brito, G. A., Moraes, L. S., Bandeira, M. D. C. A. & Rebêlo, J. M. M. Temporal variation in species abundance and richness of Culicoides (Diptera: Ceratopogonidae) in a tropical equatorial area. J. Med. Entomol. https://doi.org/10.1093/jme/tjz015 (2019).Article 
    PubMed 

    Google Scholar 
    Sghaier, S. et al. New species of the genus Culicoides (Diptera Ceratopogonidae) for Tunisia, with detection of Bluetongue viruses in vectors. Vet. Ital. 53, 357–366 (2017).PubMed 

    Google Scholar 
    Gordon, S. J. G. et al. The occurrence of Culicoides species, the vectors of arboviruses, at selected trap sites in Zimbabwe. Onderstepoort J. Vet. Res. 82, e1–e8 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Villard, P. et al. Modeling Culicoides abundance in mainland France: Implications for surveillance. Parasit. Vectors 12, 1–10 (2019).Article 

    Google Scholar 
    Diarra, M. et al. Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal. Parasit. Vectors 11, 341 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Calvete, C. et al. Spatial distribution of Culicoides imicola, the main vector of bluetongue virus, Spain. Vet. Rec. 158, 130–131 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Purse, B. V. et al. Modelling the distributions of Culicoides bluetongue virus vectors in Sicily in relation to satellite-derived climate variables. Med. Vet. Entomol. 18, 90–101 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Purse, B. V. et al. Spatial and temporal distribution of bluetongue and its Culicoides vectors in Bulgaria. Med. Vet. Entomol. 20, 335–344 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Leta, S. et al. Modeling the global distribution of Culicoides imicola: An ensemble approach. Sci. Rep. 9, 1–9 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Mulatu, T. & Hailu, A. The occurrence and identification of Culicoides species in the Western Ethiopia. Acad. J. Entomol. 12, 40–43 (2019).
    Google Scholar 
    Khamala, C. P. M. & Kettle, D. S. The Culicoides Latreille (Diptera: Ceratopogonidae) of East Africa. Trans. R. Entomol. Soc. Lond. 123, 1–95 (1971).Article 

    Google Scholar 
    Venter, G. J. Specie di Culicoides (Diptera: Ceratopogonidae) vettori del virus della Bluetongue in Sud Africa. Vet. Ital. 51, 325–333 (2015).PubMed 

    Google Scholar 
    Mathieu, B. et al. Development and validation of IIKC: An interactive identification key for Culicoides (Diptera: Ceratopogonidae) females from the Western Palaearctic region. Parasit. Vectors 5, 137 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography (Cop.) 32, 369–373 (2009).Article 

    Google Scholar 
    Baylis, M., Bouayoune, H., Touti, J. & El Hasnaoui, H. Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco. Med. Vet. Entomol. 12, 255–266 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Diarra, M. et al. Modelling the abundances of two major culicoides (Diptera: Ceratopogonidae) species in the Niayes area of Senegal. PLoS One 10, e0131021 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ramilo, D. W., Nunes, T., Madeira, S., Boinas, F. & da Fonseca, I. P. Geographical distribution of Culicoides (DIPTERA: CERATOPOGONIDAE) in mainland Portugal: Presence/absence modelling of vector and potential vector species. PLoS One 12, e0180606 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ben Rais Lasram, F. et al. The Mediterranean Sea as a ‘cul-de-sac’ for endemic fishes facing climate change. Glob. Chang. Biol. 16, 3233–3245 (2010).ADS 
    Article 

    Google Scholar 
    Tiffin, P. & Ross-Ibarra, J. Goal-oriented evaluation of species distribution models accuracy and precision: True Skill Statistic profile and uncertainty maps. PeerJ PrePints https://doi.org/10.7287/peerj.preprints.488v1 (2014).Article 

    Google Scholar 
    Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).Article 

    Google Scholar 
    Demissie, G. H. Seroepidemiological study of African horse sickness in southern Ethiopia. Open Sci. Repos. Vet. Med. 10, e70081919 (2013).
    Google Scholar 
    Zeleke, A., Sori, T., Powel, K., Gebre-Ab, F. & Endebu, B. Isolation and identification of circulating serotypes of African horse sickness virus in Ethiopia. J. Appl. Res. Vet. Med. 3, 40–43 (2005).
    Google Scholar 
    Ayelet, G. et al. Outbreak investigation and molecular characterization of African horse sickness virus circulating in selected areas of Ethiopia. Acta Trop. 127, 91–96 (2013).PubMed 
    Article 

    Google Scholar 
    Gulima, D. Seroepidemiological study of bluetongue in indigenous sheep in selected districts of Amhara National Regional State, north western Ethiopia. Ethiop. Vet. J. 13, 1–15 (2009).
    Google Scholar 
    Borkent, A. & Dominiak, P. Catalog of the biting midges of the world (Diptera: Ceratopogonidae). Zootaxa 4787, 1–377 (2020).Article 

    Google Scholar 
    Borkent, A. & Wirth, W. W. World species of biting midges (Diptera: Ceratopogonidae). Bull. Am. Museum Nat. Hist. 233, 5–195 (1997).
    Google Scholar 
    Guichard, S. et al. Worldwide niche and future potential distribution of Culicoides imicola, a major vector of bluetongue and African horse sickness viruses. PLoS One 9, e112491 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Becker, E. E. E., Venter, G. J., Labuschagne, K., Greyling, T. & van Hamburg, H. Occurrence of Culicoides species Diptera: Ceratopogonidae) in the Khomas region of Namibia during the winter months. Vet. Ital. 48, 45–54 (2012).PubMed 

    Google Scholar 
    Capela, R. et al. Spatial distribution of Culicoides species in Portugal in relation to the transmission of African horse sickness and bluetongue viruses. Med. Vet. Entomol. 17, 165–177 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Calvete, C. et al. Modelling the distributions and spatial coincidence of bluetongue vectors Culicoides imicola and the Culicoides obsoletus group throughout the Iberian peninsula. Med. Vet. Entomol. 22, 124–134 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Riddin, M. A., Venter, G. J., Labuschagne, K. & Villet, M. H. Culicoides species as potential vectors of African horse sickness virus in the southern regions of South Africa. Med. Vet. Entomol. 33, 498–511 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Foxi, C. et al. Role of different Culicoides vectors (Diptera: Ceratopogonidae) in bluetongue virus transmission and overwintering in Sardinia (Italy). Parasit. Vectors 9, 440 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Musuka, G. N., Mellor, P. S., Meiswinkel, R., Baylis, M. & Kelly, P. J. Prevalence of Culicoides imicola and other species (Diptera: Ceratopogonidae) ateight sites in Zimbabwe: To the editor. J. S. Afr. Vet. Assoc. 72, 62–63 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Meiswinkel, R. The 1996 outbreak of African horse sickness in South Africa—the entomological perspective. Arch. Virol. Suppl. 14, 69–83 (1998).CAS 
    PubMed 

    Google Scholar 
    Jean Pierre, T. et al. Characteristics, classification and genesis of vertisols under seasonally contrasted climate in the Lake Chad Basin, Central Africa. J. Afr. Earth Sci. 150, 176–193 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Elias, E. Characteristics of Nitisol profiles as affected by land use type and slope class in some Ethiopian highlands. Environ. Syst. Res. 6, 1–15 (2017).Article 

    Google Scholar 
    Nachtergaele, F. The classification of leptosols in the world reference base for soil resources.Veronesi, E., Venter, G. J., Labuschagne, K., Mellor, P. S. & Carpenter, S. Life-history parameters of Culicoides (Avaritia) imicola Kieffer in the laboratory at different rearing temperatures. Vet. Parasitol. 163, 370–373 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Verhoef, F. A. A., Venter, G. J. & Weldon, C. W. Thermal limits of two biting midges, Culicoides imicola Kieffer and C. bolitinos Meiswinkel (Diptera: Ceratopogonidae). Parasites Vectors 7, 384 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Conte, A., Goffredo, M., Ippoliti, C. & Meiswinkel, R. Influence of biotic and abiotic factors on the distribution and abundance of Culicoides imicola and the Obsoletus Complex in Italy. Vet. Parasitol. 150, 333–344 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Martinez-de la Puente, J., Navarro, J., Ferraguti, M., Soriguer, R. & Figuerola, J. First molecular identification of the vertebrate hosts of Culicoides imicola in Europe and a review of its blood-feeding patterns worldwide: Implications for the transmission of bluetongue disease and African horse sickness. Med. Vet. Entomol. 31, 333–339 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Purse, B. V. et al. Impacts of climate, host and landscape factors on Culicoides species in Scotland. Med. Vet. Entomol. 26, 168–177 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Leta, S. et al. Updating the global occurrence of Culicoides imicola, a vector for emerging viral diseases. Sci. Data 6, 1–8 (2019).CAS 
    Article 

    Google Scholar  More

  • in

    Ordering and topological defects in social wasps’ nests

    Camazine, S. et al. Self-organization in Biological Systems (Princeton University Press, Princeton, 2001).
    Google Scholar 
    Tschinkel, W. R. The nest architecture of the Florida harvester ant, Pogonomyrmex badius. J. Insect Sci. 4(1), 21 (2004).MathSciNet 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reid, C. R. et al. Army ants dynamically adjust living bridges in response to a cost-benefit trade-off. Proc. Natl. Acad. Sci. 112(49), 15113–15118 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grassé, P. P. Termitology. Termite anatomy-physiology-biology-systematics. Vol. II. Colony foundation-construction. Termitology. Termite anatomy-physiology-biology-systematics. Vol. II. Colony foundation-construction. Masson, Paris, (1984).Theraulaz, G., Bonabeau, E. & Deneubourg, J. L. The mechanisms and rules of coordinated building in social insects (In Information Processing in Social Insects, Birkhäuser, Basel, 1999).Hansell, M. & Hansell, M. H. Animal Architecture (Oxford University Press, Oxford, 2005).Book 

    Google Scholar 
    Peters, J. M., Peleg, O. & Mahadevan, L. Collective ventilation in honeybee nests. J. R. Soc. Interface 16(150), 20180561 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grassé, P. P. La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insectes Sociaux, 6(1):41–80 (1959).Theraulaz, G. & Bonabeau, E. Coordination in Distributed Building. Science 269(5224), 686–688 (1995).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Bonabeau, E., Theraulaz, G., Deneubourg, J. L. & Camazine, S. Self-organization in social insects. Trends Ecol. Evol. 12(5), 188–193 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Khuong, A. et al. Stigmergic construction and topochemical information shape ant nest architecture. Proc. Natl. Acad. Sci. 113(5), 1303–1308 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pénzes, Z. & Karsai, I. Round shape combs produced by Stigmergic scripts in social wasp. Proc. Eur. Conf. Artif. Life 93, 896–905 (1993).
    Google Scholar 
    Karsai, I. Decentralized control of construction behavior in paper wasps: an overview of the Stigmergy Approach. Artif. Life 5(2), 117–136 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Perna, A. & Theraulaz, G. When social behaviour is moulded in clay: On growth and form of social insect nests. J. Exp. Biol. 220(1), 83–91 (2017).PubMed 
    Article 

    Google Scholar 
    Gallo, V. & Chittka, L. Cognitive Aspects of Comb-Building in the Honeybee?. Front. Psychol. 9, 900 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hales, T. C. The Honeycomb Conjecture. Discrete Comput. Geom. 25(1), 1–22 (2001).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Tóth, L. F. What the bees know and what they do not know. Bull. Am. Math. Soc. 70(4), 468–481 (1964).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Jeanne, R. L. The Adaptiveness of Social Wasp Nest Architecture. Q. Rev. Biol. 50(3), 267–287 (1975).Article 

    Google Scholar 
    Karsai, I. & Pénzes, Z. Optimality of cell arrangement and rules of thumb of cell initiation in Polistes dominulus: A modeling approach. Behav. Ecol. 11(4), 387–395 (1999).Article 

    Google Scholar 
    Pirk, C., Hepburn, H., Radloff, S. & Tautz, J. Honeybee combs: construction through a liquid equilibrium process? Naturwissenschaften, 91(7) (2004).Karihaloo, B. L., Zhang, K. & Wang, J. Honeybee combs: How the circular cells transform into rounded hexagons. J. R. Soc. Interface 10(86), 20130299 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bauer, D. & Bienefeld, K. Hexagonal comb cells of honeybees are not produced via a liquid equilibrium process. Naturwissenschaften 100(1), 45–49 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Mermin, N. D. The topological theory of defects in ordered media. Rev. Mod. Phys. 51(3), 591–648 (1979).ADS 
    MathSciNet 
    CAS 
    Article 

    Google Scholar 
    Bhattacharjee, S. M. Use of Topology in physical problems. In Topology and Condensed Matter Physics (eds Bhattacharjee, S. M. et al.) 171–216 (Springer, Singapore, 2017).MATH 
    Chapter 

    Google Scholar 
    Griffin, S. M. & Spaldin, N. A. On the relationship between topological and geometric defects. J. Phys.: Condens. Matter 29(34), 343001 (2017).
    Google Scholar 
    Harris, W. F. Disclinations. Sci. Am. 237(6), 130–145 (1977).MathSciNet 
    Article 

    Google Scholar 
    de Gennes, P.-G. The Physics of liquid crystals (Clarendon Press, Oxford, 1979).
    Google Scholar 
    Iorio, A. & Sen, S. Virus Structure: From Crick and Watson to a New Conjecture. In arXiv 0707, 3690 (2007).Lee, K. C., Yu, Q. & Erb, U. Mesostructure of Ordered Corneal Nano-nipple Arrays: The Role of 5–7 Coordination Defects. Sci. Rep. 6(1), 28342 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stone, A. J. & Wales, D. J. Theoretical studies of icosahedral C60 and some related species. Chem. Phys. Lett. 128(5), 501–503 (1986).ADS 
    CAS 
    Article 

    Google Scholar 
    Ma, J., Alfè, D., Michaelides, A. & Wang, E. Stone-Wales defects in graphene and other planar sp2 -bonded materials. Phys. Rev. B 80(3), 033407 (2009).ADS 
    Article 
    CAS 

    Google Scholar 
    Heggie, M. I., Haffenden, G. L., Latham, C. D. & Trevethan, T. The Stone-Wales transformation: From fullerenes to graphite, from radiation damage to heat capacity. Philos.Trans. Royal Soc. A Math. Phys. Eng. Sci. 374(2076), 20150317 (2016).ADS 
    Article 
    CAS 

    Google Scholar 
    Eberhard, M. J. W. The Social Biology of Polistine Wasps. Misc. Publ. Museum Zoology Univ. Michigan 140, 110 (1969).
    Google Scholar 
    Jeanne, R. L. A latitudinal gradient in rates of ant predation. Ecology 60(6), 1211–1224 (1979).Article 

    Google Scholar 
    Seeley, T. & Heinrich, B. (1981). Regulation of temperature in the nests of social insects. John Wiley and Sons, Inc, pp. 224–234.Wenzel, J. W. Evolution of nest architecture. In The Social Biology Wasps (eds Ross, K. G. & Matthews, R. W.) 480–519 (Cornell University Press, Ithaca, New York, 1991).
    Google Scholar 
    Karsai, I. & Pénzes, Z. (1998). Nest shapes in paper wasps: Can the variability of forms be deduced from the same construction algorithm? Proceedings of the Royal Society of London. Series B: Biological Sciences, 265(1402):1261–1268.Carpenter, J. M. Phylogeny and biogeography of Polistes. In Natural History and Evolution of Paper-Wasps (eds Turillazzi, S. & Eberhard, M. J. W.) 18–57 (Oxford University Press, Oxford, Newyork, 1996).
    Google Scholar 
    Ceccolini, F. New records and distribution update of Polistes (Gyrostoma) wattii Cameron, 1900 (Hymenoptera: Vespidae: Polistinae). Caucasian Entomol. Bull. 15(2), 323–326 (2019).Article 

    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC Press, London, 2015).MATH 
    Book 

    Google Scholar 
    Steinhardt, P. J., Nelson, D. R. & Ronchetti, M. Bond-orientational order in liquids and glasses. Phys. Rev. B 28(2), 784–805 (1983).ADS 
    CAS 
    Article 

    Google Scholar 
    Schilling, T., Pronk, S., Mulder, B. & Frenkel, D. Monte Carlo study of hard pentagons. Phys. Rev. E 71(3), 036138 (2005).ADS 
    Article 
    CAS 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).Bishop, M. & Bruin, C. The pair correlation function: A probe of molecular order. Am. J. Phys. 52(12), 1106–1108 (1984).ADS 
    CAS 
    Article 

    Google Scholar 
    Fleury, P. A. Phase Transitions, Critical Phenomena, and Instabilities. Science 211, 125–131 (1981).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    Wenzel, J. W. Endogenous factors, external cues, and eccentric construction in Polistes annularis (Hymenoptera: Vespidae). J. Insect Behavior 2(5), 679–699 (1989).Article 

    Google Scholar 
    Zsoldos. Effect of topological defects on graphene geometry and stability. Nanotechnol. Sci. Appl., p. 101 (2010).Ophus, C., Shekhawat, A., Rasool, H. & Zettl, A. Large-scale experimental and theoretical study of graphene grain boundary structures. Phys. Rev. B 92(20), 205402 (2015).ADS 
    Article 
    CAS 

    Google Scholar 
    Kosterlitz, J. M. (2016). Commentary on ‘Ordering, metastability and phase transitions in two-dimensional systems’ J M Kosterlitz and D J Thouless (1973 J. Phys. C: Solid State Phys. 6 1181-203)-the early basis of the successful Kosterlitz-Thouless theory. Journal of Physics: Condensed Matter28:481001.Hepburn, H. R. & Whiffler, L. A. Construction defects define pattern and method in comb building by honeybees. Apidologie 22(4), 381–388 (1991).Article 

    Google Scholar 
    Smith, M. L., Napp, N. & Petersen, K. H. Imperfect comb construction reveals the architectural abilities of honeybees. Proc. Natl. Acad. Sci. 118(31), e2103605118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nazzi, F. The hexagonal shape of the honeycomb cells depends on the construction behavior of bees. Sci. Rep. 6(1), 28341 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tarnai, T. Buckling patterns of shells and spherical honeycomb structures. Symmetry, pp. 639–652 (1989).Downing, H. & Jeanne, R. The regulation of complex building behaviour in the paper wasp, Polistes fuscatus (Insecta, Hymenoptera, Vespidae). Anim. Behav. 39(1), 105–124 (1990).Article 

    Google Scholar  More

  • in

    Post-foraging in-colony behaviour of a central-place foraging seabird

    Naef-Daenzer, B. Patch time allocation and patch sampling by foraging great and blue tits. Anim. Behav. 59, 989–999 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kotler, B. P., Brown, J. S. & Bouskila, A. Apprehension and time allocation in gerbils: The effects of predatory risk and energetic state. Ecology 85, 917–922 (2004).Article 

    Google Scholar 
    Wajnberg, E., Bernhard, P., Hamelin, F. & Boivin, G. Optimal patch time allocation for time-limited foragers. Behav. Ecol. Sociobiol. 60, 1–10 (2006).Article 

    Google Scholar 
    Embar, K., Kotler, B. P. & Mukherjee, S. Risk management in optimal foragers: The effect of sightlines and predator type on patch use, time allocation, and vigilance in gerbils. Oikos 120, 1657–1666 (2011).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 
    Beauchamp, G. & Ruxton, G. D. A reassessment of the predation risk allocation hypothesis: A comment on Lima and Bednekoff. Am. Nat. 177, 143–146 (2011).PubMed 
    Article 

    Google Scholar 
    Ferrari, M. C. O., Sih, A. & Chivers, D. P. The paradox of risk allocation: A review and prospectus. Anim. Behav. 78, 579–585 (2009).Article 

    Google Scholar 
    Wolf, L. L. & Hainsworth, F. R. Foraging efficiencies and time budgets in nectar-feeding birds. Ecology 56, 117–128 (1975).Article 

    Google Scholar 
    Litzow, M. A. & Piatt, J. F. Variance in prey abundance influences time budgets of breeding seabirds: Evidence from pigeon guillemots Cepphus columba. J. Avian Biol. 34, 54–64 (2003).Article 

    Google Scholar 
    Rishworth, G. M., Tremblay, Y. & Green, D. B. Drivers of time-activity budget variability during breeding in a pelagic seabird. PLoS One 9, e116544 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Stephens, D. W., Brown, J. S. & Ydenberg, R. C. Foraging: Behavior and Ecology. (The University of Chicago Press, 2007).Orians, G. & Pearson, N. On the theory of central place foraging. In Analysis of Ecological Systems (eds. Horn, D., Mitchell, R. & Stairs, G.) 154–177 (The Ohio State University Press, 1979).Chaurand, T. & Weimerskirch, H. The regular alternation of short and long foraging trips in the blue petrel Halobaena caerulea: A previously undescribed strategy of food provisioning in a pelagic seabird. J. Anim. Ecol. 63, 275–282 (1994).Article 

    Google Scholar 
    Weimerskirch, H. et al. Alternate long and short foraging trips in pelagic seabird parents. Anim. Behav. 47, 472–476 (1994).Article 

    Google Scholar 
    Welcker, J., Beiersdorf, A., Varpe, Ø. & Steen, H. Mass fluctuations suggest different functions of bimodal foraging trips in a central-place forager. Behav. Ecol. 23, 1372–1378 (2012).Article 

    Google Scholar 
    Welcker, J. et al. Flexibility in the bimodal foraging strategy of a high Arctic alcid, the little auk Alle alle. J. Avian Biol. 40, 388–399 (2009).Article 

    Google Scholar 
    Jakubas, D., Wojczulanis-Jakubas, K., Iliszko, L. M. & Kidawa, D. Flexibility of little auks foraging in various oceanographic features in a changing Arctic. Sci. Rep. https://doi.org/10.1038/s41598-020-65210-x (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shoji, A. et al. Dual foraging and pair coordination during chick provisioning by Manx shearwaters: Empirical evidence supported by a simple model. J. Exp. Biol. 218, 2116–2123 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Phillips, R. A., Wakefield, E. D., Croxall, J. P., Fukuda, A. & Higuchi, H. Albatross foraging behaviour: No evidence for dual foraging, and limited support for anticipatory regulation of provisioning at South Georgia. Mar. Ecol. Prog. Ser. 391, 279–292 (2009).ADS 
    Article 

    Google Scholar 
    Brown, Z. W., Welcker, J., Harding, A. M. A., Walkusz, W. & Karnovsky, N. J. Divergent diving behavior during short and long trips of a bimodal forager, the little auk Alle alle. J. Avian Biol. 43, 215–226 (2012).Article 

    Google Scholar 
    Baduini, C. L. & Hyrenbach, K. D. Biogeography of procellariiform foraging strategies: Does ocean productivity influence provisioning?. Mar. Ornithol. 31, 101–112 (2003).
    Google Scholar 
    Navarro, J. & González-Solís, J. Environmental determinants of foraging strategies in Cory’s shearwaters Calonectris diomedea. Mar. Ecol. Prog. Ser. 378, 259–267 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Ochi, D., Oka, N. & Watanuki, Y. Foraging trip decisions by the streaked shearwater Calonectris leucomelas depend on both parental and chick state. J. Ethol. 28, 313–321 (2010).Article 

    Google Scholar 
    Congdon, B. C., Krockenberger, A. K. & Smithers, B. V. Dual-foraging and co-ordinated provisioning in a tropical Procellariiform, the wedge-tailed shearwater. Mar. Ecol. Prog. Ser. 301, 293–301 (2005).ADS 
    Article 

    Google Scholar 
    Peck, D. R. & Congdon, B. C. Colony-specific foraging behaviour and co-ordinated divergence of chick development in the wedge-tailed shearwater Puffinus pacificus. Mar. Ecol. Prog. Ser. 299, 289–296 (2005).ADS 
    Article 

    Google Scholar 
    Weimerskirch, H. How can a pelagic seabird provision its chick when relying on a distant food resource? Cyclic attendance at the colony, foraging decision and body condition in sooty shearwaters. J. Anim. Ecol. 67, 99–109 (1998).Article 

    Google Scholar 
    Stempniewicz, L. BWP update. Little Auk (Alle alle). J. Birds West. Palearct. 3, 175–201 (2001).
    Google Scholar 
    Wojczulanis-Jakubas, K. & Jakubas, D. When and why does my mother leave me? The question of brood desertion in the Dovekie (Alle Alle). Auk 129, 632–637 (2012).Article 

    Google Scholar 
    Harding, A. M. A., Van Pelt, T. I., Lifjeld, J. T. & Mehlum, F. Sex differences in little auk Alle alle parental care: Transition from biparental to paternal-only care. Ibis (Lond. 1859). 146, 642–651 (2004).Article 

    Google Scholar 
    Wojczulanis-Jakubas, K. et al. Duration of female parental care and their survival in the little auk Alle alle—Are these two traits linked ?. Behav. Ecol. Sociobiol. 74, 1–11 (2020).Article 

    Google Scholar 
    Wojczulanis, K., Dariusz, J. & Lech, S. The Little Auk Alle alle: An ecological indicator of a changing Arctic and a model organism. Polar Biol. https://doi.org/10.1007/s00300-021-02981-7 (2021).Article 

    Google Scholar 
    Steen, H., Vogedes, D., Broms, F., Falk-Petersen, S. & Berge, J. Little auks (Alle alle) breeding in a High Arctic fjord system: Bimodal foraging strategies as a response to poor food quality?. Polar Res. 26, 118–125 (2007).Article 

    Google Scholar 
    Wojczulanis-Jakubas, K., Jakubas, D., Karnovsky, N. J. & Walkusz, W. Foraging strategy of little auks under divergent conditions on feeding grounds. Polar Res. 29, 22–29 (2010).Article 

    Google Scholar 
    Jakubas, D., Wojczulanis-Jakubas, K., Iliszko, L., Darecki, M. & Stempniewicz, L. Foraging strategy of the little auk Alle alle throughout breeding season—switch from unimodal to bimodal pattern. J. Avian Biol. 45, 551–560 (2014).Article 

    Google Scholar 
    Jakubas, D., Iliszko, L., Wojczulanis-Jakubas, K. & Stempniewicz, L. Foraging by little auks in the distant marginal sea ice zone during the chick-rearing period. Polar Biol. 35, 73–81 (2012).Article 

    Google Scholar 
    Jakubas, D. et al. Intra-seasonal variation in zooplankton availability, chick diet and breeding performance of a high Arctic planktivorous seabird. Polar Biol. 391, 1547–1561 (2016).Article 

    Google Scholar 
    Jakubas, D. et al. Foraging closer to the colony leads to faster growth in little auks. Mar. Ecol. Prog. Ser. 489, 263–278 (2013).ADS 
    Article 

    Google Scholar 
    Stempniewicz, L. Predator-prey interactions between Glaucous Gull Larus hyperboreus and Little Auk Alle alle in Spitsbergen. Acta Ornithol. 29, 155–170 (1995).
    Google Scholar 
    Wojczulanis-Jakubas, K., Jakubas, D. & Stempniewicz, L. Changes in the glaucous gull predatory pressure on little auks in Southwest Spitsbergen. Waterbirds 28, 430–435 (2005).Article 

    Google Scholar 
    Kharitonov, S. Methods and Theoretical Aspects of Seabird Studies. (Proc 5 All-Russian Mar Biol School, Marine Biological Institute, 2007).Wojczulanis-Jakubas, K., Jakubas, D. & Stempniewicz, L. Avifauna of Hornsund area, SW Spitsbergen: Present state and recent changes. Polish Polar Res. 29, 187–197 (2008).
    Google Scholar 
    Keslinka, K. L., Wojczulanis-Jakubas, K., Jakubas, D. & Neubauer, G. Determinants of the little auk (Alle alle) breeding colony location and size in W and NW coast of Spitsbergen. PLoS One 14, 1–20 (2019).
    Google Scholar 
    Kidawa, D., Barcikowski, M. & Palme, R. Parent-offspring interactions in a long-lived seabird, the Little Auk (Alle alle): Begging and provisioning under simulated stress. J. Ornithol. 158, 145–157 (2017).Article 

    Google Scholar 
    Welcker, J., Beiersdorf, A., Varpe, Ø. & Steen, H. Mass fluctuations suggest different functions of bimodal foraging trips in a central-place forager. Behav. Ecol. https://doi.org/10.1093/beheco/ars131 (2012).Article 

    Google Scholar 
    Jakubas, D. & Wojczulanis, K. Predicting the sex of Dovekies by discriminant analysis. Waterbirds 30, 92–96 (2007).Article 

    Google Scholar 
    Grissot, A. et al. Parental coordination of chick provisioning in a planktivorous arctic seabird under divergent conditions on foraging grounds. Front. Ecol. Evol. 7, 349 (2019).Article 

    Google Scholar 
    Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: Repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. R. (2019).Wojczulanis-Jakubas, K., Jakubas, D. & Stempniewicz, L. Sex-specific parental care by incubating Little Auks (Alle alle). Ornis Fenn. 86, 140–148 (2009).
    Google Scholar 
    Welcker, J., Steen, H., Harding, A. M. A. & Gabrielsen, G. W. Sex-specific provisioning behaviour in a monomorphic seabird with a bimodal foraging strategy. Ibis (Lond. 1859). 151, 502–513 (2009).Article 

    Google Scholar 
    Kidawa, D. et al. Parental efforts of an Arctic seabird, the little auk Alle alle under variable foraging conditions. Mar. Biol. Res. 11, 349–360 (2015).Article 

    Google Scholar 
    Wickham, H. Hadley Wickham. Media 35, 211 (2009).
    Google Scholar 
    Karnovsky, N. J. et al. Inter-colony comparison of diving behavior of an Arctic top predator: Implications for warming in the Greenland Sea. Mar. Ecol. Prog. Ser. 440, 229–240 (2011).ADS 
    Article 

    Google Scholar 
    Karnovsky, N. et al. Foraging distributions of little auks Alle alle across the Greenland Sea: Implications of present and future Arctic climate change. Mar. Ecol. Prog. Ser. 415, 283–293 (2010).ADS 
    Article 

    Google Scholar 
    Gremillet, D. et al. Little auks buffer the impact of current Arctic climate change. Mar. Ecol. Prog. Ser. 454, 197–206 (2012).ADS 
    Article 

    Google Scholar 
    Harding, A. M. A. et al. Flexibility in the parental effort of an Arctic-breeding seabird. Funct. Ecol. 23, 348–358 (2009).Article 

    Google Scholar 
    Jakubas, D. et al. Foraging effort does not influence body condition and stress level in little auks. Mar. Ecol. Prog. Ser. 432, 277–290 (2011).ADS 
    Article 

    Google Scholar 
    Jakubas, D., Wojczulanis-Jakubas, K., Iliszko, L. M., Strøm, H. & Stempniewicz, L. Habitat foraging niche of a High Arctic zooplanktivorous seabird in a changing environment. Sci. Rep. 7, 1–14 (2017).CAS 
    Article 

    Google Scholar  More

  • in

    Flow patterns in circular fish tanks and its relations with flow rate and nozzle features

    Rotational velocityFigure 3 shows the effect of flow rate, nozzle diameter and number of nozzles on the rotational velocity of water in a circular tank. The results indicate that the rotational velocity increases with increasing flow rates and deceasing nozzle diameter. It could be seen that, the rotational velocity decreased from 10.1 to 5.0 cm s−1, when the nozzle diameter increased from 10 to 20 mm, respectively for 5 nozzles used, and it decreased from 5.1 to 4.0 cm s−1, when the nozzle diameter increased from 10 to 15 mm, respectively, for 10 nozzles used with 5 m3 h−1 flow rate. At 15 m3 h−1, the rotational velocity was decreased from 23.5 to 17.5, 12.0 to 7.5, 10.0 to 6.9, 7.6 to 4.7 and 5.9 to 4.0 cm s−1 when the nozzle diameter increased from 10 to 20 mm, respectively, for 5, 10, 15, 20 and 25 nozzles, respectively. The results also indicate that when the nozzle diameter increased from 20 to 25 mm, the rotational velocity decreased from 19.0 to 16.5, 12.0 to 10.0 and 7.1 to 5.5 cm s−1 for 3, 6 and 9 nozzles, respectively, with 15 m3 h−1 flow rate.Figure 3Effect of flow rate, nozzle diameter and number of nozzles on the rotational velocity of water in a circular tank.Full size imageAt 30 m3 h−1 flow rate, the highest value of the rotational velocity was 33.5 cm s−1 was found for 5 nozzles and 10 mm nozzle diameter. While, the lowest value of the rotational velocity was 7.3 cm s−1 was found for 25 nozzles and 25 mm nozzle diameter. At 45 m3 h−1 flow rate, the rotational velocity ranged from 11.0 to 49.9 cm s−1 for all treatments under study.At 60 m3 h−1 flow rate, the rotational velocity deceased from 61.0 to 50.1, 47.7 to 34.0, 36.3 to 23.0, 23.5 to 17.5, 21.0 to 15.0 and 17.0 to 11.5 cm s−1 when the nozzle diameter increased from 10 to 20 mm, respectively at 5, 10, 15, 20, 25 and 30 number of nozzles. The results also indicate that, when the nozzle diameter increased from 20 to 25 mm, the rotational velocity decreased from 56.0 to 47.0, 43.0 to 33.0, 27.0 to 22.0 and 19.0 to 16.5 cm s−1 at 3, 6, 9 and 12 nozzles, respectively.At 75 m3 h−1 flow rate, the rotational velocity deceased from 60.9 to 49.1, 48.4 to 38.0, 39.0 to 30, 31.8 to 23.0, 23.5 to 17.5 and 22.0 to 15.0 cm s−1 when the nozzle diameter increased from 10 to 20 mm, respectively for 5, 10, 15, 20, 25 and 30 nozzles, respectively. The results also indicate that, when the nozzle diameter increased from 20 to 25 mm, the rotational velocity decreased from 50.48 to 43.0 to 38.5, 33.0 to 27.5 and 23.5 to 22.0 cm s−1 for 3, 6, 9 and 12 nozzles, respectively.The results also indicate that the highest values of the rotational velocities were 10.1, 23.5, 33.5, 49.9, 60.9 and 61.0 cm s−1 were found for 5 nozzles and 10 mm nozzle diameter at 5, 15, 30, 45, 60 and 75 m3 h−1 flow rate, respectively. While, the lowest values of the rotational velocities were 4.0, 7.5 and 11.5 cm s−1 for 25 nozzles and 15 mm nozzle diameter at 5, 15 and 30 m3 h−1 flow rate, respectively. They were 11.5 and 15 cm s−1 were found for 30 nozzles and 15 mm nozzle diameter at 60 and 75 m3 h−1 flow rate, respectively. The velocity of water obtained seemed to be in the recommended range of safe and proper velocity for fish according to12. Due to it is effective compromise to allow heavy solids settle rapidly, yet sufficiently fast to create “good” hydraulics. Timmons and Youngs18 mentioned that the water velocity needed to maintain self-cleaning properties ranges from 3 to 40 cm s−1 varying greatly according to the physical properties of the biosolids. When fish swims at lower speed than its optimal, a large amount of energy will be used for higher spontaneous activity such as aggression. In contrast, when fish swim at higher speed than optimal, they become stressful, unstable, increase lactate production and fatigue6.Multiple regression analysis was carried out to obtain a relationship between the rotational velocity of water as dependent variable and different both of flow rate and nozzle diameter as independent variables. The best fit for this relationship with coefficient of determination of 0.95 and an error of 1.06% is in the following form:-$$ RV = 6.97 + 0.41Q – 0.19Dquad {text{R}}^{{2}} = 0.95 $$
    (3)
    where RV is the rotational velocity of water, cm s−1, Q is the water flow rate, m3 h−1, D is the nozzle diameter, mm.This equation could be applied in the range of 5 to 75 m3 h−1 water flow rate and from 10 to 25 mm of nozzle diameter.Impulse force of waterFigure 4 shows the effect of flow rate, diameter and number of nozzles on the impulse force of water in a circular tank. The results indicate that the impulse force of water increases with increasing flow rates and deceasing nozzle diameter and number of nozzles. It could be seen that, the impulse force of water decreased from 5.1 to 1.7 N, when the number of nozzles increased from 5 to 15, respectively at 10 nozzle diameter, and it decreased from 2.3 to 1.2 N, when the number of nozzles increased from 5 to 10, respectively, at 15 diameter nozzle with 5 m3 h−1 flow rate. At 15 m3 h−1, the impulse force of water was decreased from 84.7 to 9.4 N when the number of nozzles increased from 5 to 30, respectively 10 mm diameter nozzle. The results also indicate that when the number of nozzles increased from 5 to 25, the impulse force of water decreased from 14.8 to 1.4 N at 15 mm nozzle diameter, respectively, and it decreased from 9.5 to 1.9 and 5.3 to 1.3 N at 20 and 25 mm, respectively, when the number of nozzles increased from 3 to 9.Figure 4Effect of flow rate, nozzle diameter and number of nozzles on the impulse force of water in a circular tank.Full size imageAt 30 m3 h−1 flow rate, the impulse force of water deceased from 84.7 to 46.9, 56.9 to 14.8, 28.5 to 5.3, 14.9 to 3.0 and 11.8 to 2.2 N when the nozzle diameter increased from 10 to 15 mm, respectively at 5, 10, 15, 20 and 25 nozzles. The results also indicate that, when the nozzle diameter increased from 20 to 25 mm, the impulse force of water decreased from 21.4 to 14.9, 14.8 to 5.4, 5.3 to 2.2 and 2.3 to 1.9 N for 3, 6, 9 and 12 nozzles, respectively.At 45 m3 h−1 flow rate, the impulse force of water was ranged from 2.1 to 111.2 N for all treatments under this study. Also, at 60 m3 h−1 flow rate, the impulse force of water ranged from 5.1 to 151.3 N for all treatments under this study. At 75 m3 h−1 flow rate, the highest value of the impulse force of water 211.2 N was found for 5 numbers of nozzles and 10 mm nozzle diameter, respectively. While, the lowest value of the impulse force of water was 9.1 N was found for 12 nozzles and 25 mm nozzle diameter, respectively.The results also indicate that the highest value of the impulse force of water 211.2 N was found for 5 nozzles and 10 mm nozzle diameter at 75 m3 h−1 flow rate, respectively. While, the lowest value of the impulse force of water was 1.2 N was found for 10 nozzles and 15 mm nozzle diameter at 5 m3 h−1 flow rate, respectively.The results indicated that, the relationship between the rotational velocity and impulse force of water is linear relationship at the same treatments. When the rotational velocity increased from 10.7 to 37.6, 8.1 to 28.8, 10.2 to 36.0 and 11.0 to 31.9 cm s−1, the impulse force of water increased from 3.1 to 106.6, 1.8 to 31.1, 1.3 to 32.5 and 1.4 to 22.8 N, respectively, at the same treatments. The trend of these results agreed with those obtained by19.Multiple regression analysis was carried out to obtain a relationship between the impulse force of water as dependent variable and different both of flow rate and nozzle diameter as independent variables. The best fit for this relationship with coefficient of determination of 0.88 and an error of 2.13% is in the following form:-$$ F_{i} = 38.18 + 0.67Q – 2.35Dquad {text{R}}^{{2}} = 0.88 $$
    (4)
    This equation could be applied in the range of 5 to 75 m3 h−1 water flow rate and from 10 to 25 mm of nozzle diameter.Average velocity of waterFigure 5 shows the effect of flow rate, diameter and number of nozzles on the average velocity of water in a circular tank. The results indicate that the average velocity of water increases with increasing flow rates and deceasing nozzle diameter and number of nozzles. It could be seen that, the average velocity of water decreased from 3.32 to 1.59 cm s−1, when the number of nozzles increased from 5 to 15, respectively at 10 nozzle diameter, and it decreased from 1.13 to 1.07 cm s−1, when the number of nozzles increased from 5 to 10, respectively, at 15 diameter nozzle with 5 m3 h−1 flow rate. At 15 m3 h−1, the average velocity of water was decreased from 12.03 to 4.33 cm s−1 when the number of nozzles increased from 5 to 30, respectively 10 mm diameter nozzle. The results also indicate that when the number of nozzles increased from 5 to 25, the average velocity of water decreased from 6.93 to 2.89 cm s−1 at 15 mm nozzle diameter, respectively, and it decreased from 7.55 to 4.00 and 4.89 to 2.95 cm s−1 at 20 and 25 mm, respectively, when the number of nozzles increased from 3 to 9.Figure 5Effect of flow rate, nozzle diameter and number of nozzles on the average velocity of water in a circular tank.Full size imageAt 30 m3 h−1 flow rate, the highest value of the average velocity of water 18.51 cm s−1 was found for 5 nozzles and 10 mm nozzle diameter. While, the lowest value of the average velocity of water was 4.65 cm s−1 was found for 12 nozzles and 25 mm nozzle diameter. At 45 m3 h−1 flow rate, the average velocity of water ranged from 6.66 to 23.26 for all treatments under study, also, at 60 m3 h−1 flow rate, the average velocity of water ranged from 9.23 to 34.82 for all treatments under study. At 75 m3 h−1 flow rate, the average velocity of water ranged from 10.00 to 48.76 for all treatment of this study.The results also indicate that the highest value of the average velocity of water 48.76 cm s−1 was found for 5 nozzles and 10 mm nozzle diameter at 75 m3 h−1 flow rate, respectively. While, the lowest value of the average velocity of water was 1.07 cm s−1 was found for 10 nozzles and 15 mm nozzle diameter at 5 m3 h−1 flow rate, respectively. These results agreed with those obtained by18,20. Fish distribution in the circular tank is influenced by the heterogeneity of water velocity in the area between inlet flow and the center of the tank9. Fish distribution in the circular tank is mostly concentrated in the area between high and low velocity area. The high velocity area will be avoided by most fishes as it requires high swimming energy, while dead volumes (low velocity area) are unfavorable condition for fish (low DO and higher metabolites accumulation)21.Multiple regression analysis was carried out to obtain a relationship between the average velocity of water as dependent variable and different both of flow rate and nozzle diameter as independent variables. The best fit for this relationship with coefficient of determination of 0.91 and an error of 1.48% is in the following form:$$ V_{avg} = 6.53 + 0.26Q – 0.37Dquad {text{R}}^{{2}} = 0.91 $$
    (5)
    This equation could be applied in the range of 5 to 75 m3 h−1 water flow rate and from 10 to 25 mm of nozzle diameter. More

  • in

    Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change

    El Bilali, H., Callenius, C., Strassner, C. & Probst, L. Food and nutrition security and sustainability transitions in food systems. Food Energy Secur 8, e00154 (2019).Article 

    Google Scholar 
    De Raymond, A. B. & Goulet, F. Science, technology and food security: An introduction. Sci. Technol. Soc. 25, 7–18 (2020).Article 

    Google Scholar 
    Wang, C. et al. Occurrence of crop pests and diseases has largely increased in China since 1970. Nat. Food 3, 57–65 (2022).Article 

    Google Scholar 
    Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Verger, P. J. P. & Boobis, A. R. Reevaluate pesticides for food security and safety. Science 341, 717–718 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Humann‐Guilleminot, S. et al. A nation‐wide survey of neonicotinoid insecticides in agricultural land with implications for agri‐environment schemes. J. Appl. Ecol. 56, 1502–1514 (2019).Article 
    CAS 

    Google Scholar 
    Haynes, K. J., Allstadt, A. J. & Klimetzek, D. Forest defoliator outbreaks under climate change: Effects on the frequency and severity of outbreaks of five pine insect pests. Glob. Change Biol. 20, 2004–2018 (2014).Article 

    Google Scholar 
    Sheppard, L., Bell, J. R., Harrington, R. & Reuman, D. C. Changes in large-scale climate alter spatial synchrony of aphid pests. Nat. Clim. Change 6, 610–613 (2016).Article 

    Google Scholar 
    Skendžić, S. et al. The impact of climate change on agricultural insect pests. Insects 12, 440 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    WASDE. World Agricultural Supply and Demand Estimates 1554–9089 (World Agricultural Outlook Board, 2012).FAOSTAT. Food and agriculture organisation of the United Nations. http://faostat.fao.org/ (2018).Bellard, C. et al. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bebber, D. P. Range-expanding pests and pathogens in a warming world. Annu. Rev. Phytopathol. 53, 335–356 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jactel, H., Koricheva, J. & Castagneyrol, B. Responses of forest insect pests to climate change: Not so simple. Curr. Opin. Insect Sci. 35, 103–108 (2019).PubMed 
    Article 

    Google Scholar 
    Stephane, A. P., Derocles, D. H., Lunt Sophie, C. F. & Moss., B. Climate warming alters the structure of farmland tritrophic ecological networks and reduces crop yield. Mol. Ecol. 27, 4931–4946 (2018).Article 

    Google Scholar 
    Nechols, J. R. The potential impact of climate change on non-target risks from imported generalist natural enemies and on biological control. Bio. Control 66, 37–44 (2021).
    Google Scholar 
    Tian, B. et al. Elevated temperature reduces wheat grain yield by increasing pests and decreasing soil mutualists. Pest Manag. Sci. 75, 466–475 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lehmann, P. et al. Complex responses of global insect pests to climate warming. Front. Ecol. Environ. 18, 141–150 (2020).Article 

    Google Scholar 
    Zhao, F., Zhang, W., Hoffmann, A. A. & Ma, C. Night warming on hot days produces novel impacts on development, survival, and reproduction in a small arthropod. J. Anim. Ecol. 83, 769–778 (2014).PubMed 
    Article 

    Google Scholar 
    Marini, L. et al. Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography 40, 1426–1435 (2017).Article 

    Google Scholar 
    Bale, J. S. et al. Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).Article 

    Google Scholar 
    Jamieson, M. A., Trowbridge, A. M., Raffa, K. F. & Lindroth, R. L. Consequences of climate warming and altered precipitation patterns for plant-insect and multitrophic interactions. Plant Physiol. 160, 1719–1727 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gagic, V. et al. Better outcomes for pest pressure, insecticide use, and yield in less intensive agricultural landscapes. Proc. Natl Acad. Sci. USA 118, 1–6 (2021).Article 
    CAS 

    Google Scholar 
    Paredes, D. et al. Landscape simplification increases vineyard pest outbreaks and insecticide use. Ecol. Lett. 24, 73–83 (2021).PubMed 
    Article 

    Google Scholar 
    Brattsten, L. B., Holyoke, C. W., Leeper, J. R. & Raffa, K. F. Insecticide resistance: Challenge to pest management and basic research. Science 231, 1255–1260 (1986).CAS 
    PubMed 
    Article 

    Google Scholar 
    Haddi, K. et al. Rethinking biorational insecticides for pest management: Unintended effects and consequences. Pest Manag. Sci. 76, 2286–2293 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gould, F., Brown, Z. S. & Kuzma, J. Wicked evolution: Can we address the sociobiological dilemma of pesticide resistance? Science 360, 728–732 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wei, N. et al. Transcriptome analysis and identification of insecticide tolerance-related genes after exposure to insecticide in Sitobion avenae. Genes 1012, 951 (2019).Article 
    CAS 

    Google Scholar 
    Gong, X. et al. Feasibility of reinforced post-endogenous denitrification coupling with synchronous nitritation, denitrification and phosphorus removal for high-nitrate sewage treatment using limited carbon source in municipal wastewater. Chemosphere 269, 128687 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tilman, D. et al. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Geiger, F. et al. Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland. Basic Appl. Ecol. 11, 97–105 (2010).CAS 
    Article 

    Google Scholar 
    Muneret, L. et al. Evidence that organic farming promotes pest control. Nat. Sustain 1, 361–368 (2018).Article 

    Google Scholar 
    Lu, Y. et al. Widespread adoption of Bt cotton and insecticide decrease promotes biocontrol services. Nature 487, 362–365 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chaplin‐Kramer, R., O’Rourke, M. E., Blitzer, E. J. & Kremen, C. A meta‐analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).PubMed 
    Article 

    Google Scholar 
    Baillod, A. B., Tscharntke, T., Clough, Y. & Batary, P. Landscape‐scale interactions of spatial and temporal cropland heterogeneity drive biological control of cereal aphids. J. Appl. Ecol. 54, 1804–1813 (2017).Article 

    Google Scholar 
    Gagic, V. et al. Combined effects of agrochemicals and ecosystem services on crop yield across Europe. Ecol. Lett. 20, 1427–1436 (2017).PubMed 
    Article 

    Google Scholar 
    Zhang, W. et al. Multidecadal, county-level analysis of the effects of land use, Bt cotton, and weather on cotton pests in China. Proc. Natl Acad. Sci. USA 115, 700–7709 (2018).
    Google Scholar 
    Horgan, F. G. et al. Population development of rice black bug, Scotinophara latiuscula (Breddin), under varying nitrogen in a field experiment. Entomol. Gen. 37, 19–33 (2018).Article 

    Google Scholar 
    Butler, J., Garratt, M., & Leather, S. Fertilisers and insect herbivores: A meta‐analysis. Ann. Appl. Biol. 161, 223–233 (2012).Article 

    Google Scholar 
    Aqueel, M. A. et al. Effect of plant nutrition on aphid size, prey consumption, and life history characteristics of green lacewing. Insect Sci. 21, 74–82 (2014).PubMed 
    Article 

    Google Scholar 
    Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: Is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

    Google Scholar 
    Winqvist, C. et al. Mixed effects of organic farming and landscape complexity on farmland biodiversity and biological control potential across Europe. J. Appl. Ecol. 48, 570–579 (2011).Article 

    Google Scholar 
    Tscharntke, T. et al. Landscape perspectives on agricultural intensification and biodiversity-ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

    Google Scholar 
    Meehan, T. D., Werling, B. P., Landis, D. A. & Gratton, C. Agricultural landscape simplification and insecticide use in the Midwestern United States. Proc. Natl Acad. Sci. USA 108, 11500–11505 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Macfadyen, S. et al. Do differences in food web structure between organic and conventional farms affect the ecosystem service of pest control? Ecol. Lett. 12, 229–238 (2009).PubMed 
    Article 

    Google Scholar 
    Liu, J., Ning, J., Kuang, W. & Xu, X. Spatio-temporal patterns and characteristics of land-use change in China during 2010-2015. J. Geogr. Sci. 73, 789–802 (2018).
    Google Scholar 
    Ma, C., Ma, G. & Zhao, F. Impact of global warming on cereal aphids. Chin. J. Appl. Entomol. 51, 1435–1443 (2014).
    Google Scholar 
    Han, Z. et al. Effects of simulated climate warming on the population dynamics of Sitobion avenae (Fabricius) and its parasitoids in wheat fields. Pest Manag. Sci. 75, 3252–3259 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Meisner, M. H., Harmon, J. P. & Ives, A. R. Temperature effects on long‐term population dynamics in a parasitoid-host system. Ecol. Monogr. 84, 457–476 (2014).Article 

    Google Scholar 
    Xiao, H. et al. Exposure to mild temperatures decreases overwintering larval survival and post-diapause reproductive potential in the rice stem borer Chilo suppressalis. J. Pest Sci. 90, 117–125 (2017).Article 

    Google Scholar 
    Senior, V. L. et al. Phenological responses in a sycamore-aphid-parasitoid system and consequences for aphid population dynamics: A 20 year case study. Glob. Change Biol. 26, 2814–2828 (2020).Article 

    Google Scholar 
    Chiu, M. C., Chen, Y. H. & Kuo, M. H. The effect of experimental warming on a low‐latitude aphid, Myzus varians. Entomol. Exp. Appl. 142, 216–222 (2012).Article 

    Google Scholar 
    Adler, L. S., De Valpine, P., Harte, J. & Call, J. Effects of long-term experimental warming on aphid density in the field. J. Kans. Entomol. Soc. 80, 156–169 (2007).Article 

    Google Scholar 
    Clement, S. L., Husebye, D. S. & Eigenbrode, S. D. Aphid Biodiversity under Environmental Change 107–129 (Springer, 2010).Van der Putten, W. H., Macel, M. & Visser, M. E. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Philos. T. Roy. Soc. B. 365, 2025–2034 (2010).Article 

    Google Scholar 
    Evans, E. W. Multitrophic interactions among plants, aphids, alternate prey and shared natural enemies—a review. Eur. J. Entomol. 105, 369–380 (2013).Article 

    Google Scholar 
    Sigsgaard, L. A survey of aphids and aphid parasitoids in cereal fields in Denmark, and the parasitoids’ role in biological control. J. Appl. Entomol. 126, 101–107 (2002).Article 

    Google Scholar 
    Diehl, E., Sereda, E., Wolters, V. & Birkhofer, K. Effects of predator specialization, host plant and climate on biological control of aphids by natural enemies: a meta‐analysis. J. Appl. Ecol. 50, 262–270 (2013).Article 

    Google Scholar 
    Hopper, K. R. et al. Natural enemy impact on the abundance of Diuraphis noxia (Homoptera: Aphididae) in wheat in Southern France. Environ. Entomol. 24, 402–408 (1995).Article 

    Google Scholar 
    Latham, D. R. & Mills, N. J. Quantifying aphid predation: The mealy plum aphid Hyalopterus pruni in California as a case study. J. Appl. Ecol. 47, 200–208 (2010).Article 

    Google Scholar 
    Östman, Ö., Ekbom, B. & Bengtsson, J. Yield increase attributable to aphid predation by ground-living polyphagous natural enemies in spring barley in Sweden. Ecol. Econ. 45, 149–158 (2003).Article 

    Google Scholar 
    Snyder, W. E. & Ives, A. R. Interactions between specialist and generalist natural enemies: Parasitoids, predators, and pea aphid control. Ecology 84, 91–107 (2003).Article 

    Google Scholar 
    Freier, B., Triltsch, H., Möwes, M. & Moll, E. The potential of predators in natural control of aphids in wheat: results of a ten-year field study in two German landscapes. Biocontrology 52, 775–788 (2007).Article 

    Google Scholar 
    Barczak, T., Dębek-Jankowska, A. & Bennewicz, J. Primary parasitoid and hyperparasitoid guilds (Hymenoptera) of grain aphid (Sitobion avenae F.) in northern Poland. Arch. Biol. Sci. 66, 1141–1148 (2014).Article 

    Google Scholar 
    Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

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

    Google Scholar 
    Zhang, W., Jiang, F. & Ou, J. Global pesticide consumption and pollution: With China as a focus. P. Intern. Acad. Ecol. Environ. Sci. 1, 125–144 (2011).CAS 

    Google Scholar 
    El-Wakeil, N., Gaafar, N., Sallam, A. & Volkmar, C. Side Effects of Insecticides on Natural Enemies and Possibility of their Integration in Plant Protection Strategies. Insecticides: Development of Safer and More Effective Technologies Agricultural and Biological Sciences (ed Trdan, S.) 1–56 (Intech Open Access Publisher, 2013).Peshin, R. & Dhawan, A. K. Integrated Pest Management: Innovation-Development Process (Springer Science & Business Media, 2009).Jia, B., Hong, S., Zhang, Y. & Cao, Y. Toxicity and safety of 12 insecticides to Diadegma semiclausum. J. Shanxi Agric. Sci. 43, 999–1002 (2015).
    Google Scholar 
    Emery, S. E. et al. High agricultural intensity at the landscape scale benefits pests, but low intensity practices at the local scale can mitigate these effects. Agric. Ecosyst. Environ. 306, 107199 (2021).Article 

    Google Scholar 
    Aqueel, M. A. & Leather, S. R. Effect of nitrogen fertilizer on the growth and survival of Rhopalosiphum padi (L.) and Sitobion avenae (F.)(Homoptera: Aphididae) on different wheat cultivars. Crop. Prot. 30, 216–221 (2011).Article 

    Google Scholar 
    Gao, J., Guo, H. J., Sun, Y. C. & Ge, F. Juvenile hormone mediates the positive effects of nitrogen fertilization on weight and reproduction in pea aphid. Pest Manag. Sci. 74, 2511–2519 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Barnett, K. L. & Facey, S. L. Grasslands, invertebrates, and precipitation: A review of the effects of climate change. Front. Plant. Sci. 7, 1196 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yu, X. et al. Engineering plants for aphid resistance: Current status and future perspectives. Theor. Appl. Genet. 127, 2065–2083 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Martin, E. A. et al. The interplay of landscape composition and configuration: New pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094 (2019).PubMed 
    Article 

    Google Scholar 
    Steckel, J. et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol. Conserv. 172, 56–64 (2014).Article 

    Google Scholar 
    Lu, Y. H. et al. Major ecosystems in China: Dynamics and challenges for sustainable management. Environ. Manag. 48, 13–27 (2011).Article 

    Google Scholar 
    Wood, G. A. et al. Real-time measures of canopy size as a basis for spatially varying nitroge applications to winter wheat sown at different seed rates. Biosyst. Eng. 84, 513–531 (2003).Article 

    Google Scholar 
    NOAA. https://www.ncdc.noaa.gov/cdo-web/ (2018).WORLD BANK GROUP. https://climateknowledgeportal.worldbank.org/download-data (2018). More

  • in

    Strategic planning to mitigate mining impacts on protected areas in the Brazilian Amazon

    Adams, V. M., Iacona, G. D. & Possingham, H. P. Weighing the benefits of expanding protected areas versus managing existing ones. Nat. Sustain. 2, 404–411 (2019).Article 

    Google Scholar 
    Blicharska, M. et al. Biodiversity’s contributions to sustainable development. Nat. Sustain. 2, 1083–1093 (2019).Article 

    Google Scholar 
    Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).CAS 
    Article 

    Google Scholar 
    Sonter, L. J., Barrett, D. J., Soares-filho, B. S. & Moran, C. J. Global demand for steel drives extensive land-use change in Brazil’ s Iron Quadrangle. Glob. Environ. Change 26, 63–72 (2014).Article 

    Google Scholar 
    Siqueira-Gay, J., Soares-Filho, B., Sánchez, L. E., Oviedo, A. & Sonter, L. J. Proposed legislation to mine Brazil’s Indigenous lands will threaten Amazon forests and their valuable ecosystem services. One Earth 3, 356–362 (2020).Article 

    Google Scholar 
    El Bizri, H. R., Macedo, J. C. B. M., Plaglia, A. P. & Morcatty, T. Q. Mining undermining Brazil’s environment. Science 353, 2–3 (2016).Article 

    Google Scholar 
    Ferreira, J. et al. Brazil’s environmental leadership at risk. Science 346, 706–707 (2014).CAS 
    Article 

    Google Scholar 
    Rudke, A. P. et al. Impact of mining activities on areas of environmental protection in the southwest of the Amazon: a GIS- and remote sensing-based assessment. J. Environ. Manage. 263, 110392 (2020).Article 

    Google Scholar 
    Naughton-Treves, L. & Holland, M. B. Losing ground in protected areas? Science 364, 832–833 (2019).CAS 
    Article 

    Google Scholar 
    Kroner, R. E. G. et al. The uncertain future of protected lands and waters. Science 364, 881–886 (2019).Article 
    CAS 

    Google Scholar 
    Pack, S. M. et al. Protected area downgrading, downsizing, and degazettement (PADDD) in the Amazon. Biol. Conserv. 197, 32–39 (2016).Article 

    Google Scholar 
    PADDDtracker.org Data Release Version 2.0 (Conservation International and World Wildlife Fund, 2019); https://doi.org/10.5281/zenodo.3371733Bebbington, A. J., Humphreys, D., Aileen, L., Rogan, J. & Agrawal, S. Resource extraction and infrastructure threaten forest cover and community rights. Proc. Natl Acad. Sci. USA 115, 13164–13173 (2018).CAS 
    Article 

    Google Scholar 
    Paiva, P. F. P. R. et al. Deforestation in protect areas in the Amazon: a threat to biodiversity. Biodivers. Conserv. 29, 19–38 (2020).Article 

    Google Scholar 
    Boldy, R., Santini, T., Annandale, M., Erskine, P. D. & Sonter, L. J. Understanding the impacts of mining on ecosystem services through a systematic review. Extr. Ind. Soc. https://doi.org/10.1016/j.exis.2020.12.005 (2020).Murguía, D. I., Bringezu, S. & Schaldach, R. Global direct pressures on biodiversity by large-scale metal mining: spatial distribution and implications for conservation. J. Environ. Manage. 180, 409–420 (2016).Article 

    Google Scholar 
    Kobayashi, H., Watando, H. & Kakimoto, M. A global extent site-level analysis of land cover and protected area overlap with mining activities as an indicator of biodiversity pressure. J. Clean. Prod. 84, 459–468 (2014).Article 

    Google Scholar 
    Craig, M. D., White, D. A., Stokes, V. L. & Prince, J. Can postmining revegetation create habitat for a threatened mammal? Ecol. Manage. Restor. 18, 149–155 (2017).Article 

    Google Scholar 
    Sonter, L. J. et al. Mining drives extensive deforestation in the Brazilian Amazon. Nat. Commun. 8, 1013 (2017).Article 
    CAS 

    Google Scholar 
    Siqueira-Gay, J., Sonter, L. J. & Sánchez, L. E. Exploring potential impacts of mining on forest loss and fragmentation within a biodiverse region of Brazil’s northeastern Amazon. Resour. Policy 67, 101662 (2020).Article 

    Google Scholar 
    Siqueira-Gay, J. & Sánchez, L. E. Keep the Amazon niobium in the ground. Environ. Sci. Policy 111, 1–6 (2020).CAS 
    Article 

    Google Scholar 
    Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).Article 

    Google Scholar 
    Raiter, K. G., Possingham, H. P., Prober, S. M. & Hobbs, R. J. Under the radar: mitigating enigmatic ecological impacts. Trends Ecol. Evol. 29, 635–644 (2014).Article 

    Google Scholar 
    Whitehead, A. L., Kujala, H. & Wintle, B. A. Dealing with cumulative biodiversity impacts in strategic environmental assessment: a new frontier for conservation planning. Conserv. Lett. 10, 195–204 (2017).Article 

    Google Scholar 
    Jenner, N. Making Mining ‘Forest-Smart’: Executive Summary Report (World Bank, 2019); http://documents.worldbank.org/curated/en/369711560319906622/Making-Mining-Forest-Smart-Executive-Summary-ReportRenca: Situação legal dos direitos minerários da reserva nacional do cobre (WWF, 2017).Soares-Filho, B. S., Cerqueira, G. C. & Pennachin, C. L. DINAMICA—a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecol. Modell. 154, 217–235 (2002).Article 

    Google Scholar 
    Strand, J. et al. Spatially explicit valuation of the Brazilian Amazon forest’s ecosystem services. Nat. Sustain. 1, 657–664 (2018).Article 

    Google Scholar 
    Barber, C. P., Cochrane, M. A., Souza, C. M. & Laurance, W. F. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol. Conserv. 177, 203–209 (2014).Article 

    Google Scholar 
    Rorato, A. C. et al. Brazilian Amazon Indigenous peoples threatened by mining bill. Environ. Res. Lett. 15, 1040a3 (2020).Article 

    Google Scholar 
    Villén-Pérez, S., Anaya-Valenzuela, L., Conrado da Cruz, D. & Fearnside, P. M. Mining threatens isolated Indigenous peoples in the Brazilian Amazon. Glob. Environ. Change 72, (2022).Siqueira-Gay, J. & Sánchez, L. E. The outbreak of illegal gold mining in the Brazilian Amazon boosts deforestation. Reg. Environ. Change 21, 28 (2021).Article 

    Google Scholar 
    Sonter, L. J., Dade, M. C., Watson, J. E. M. & Valenta, R. K. Renewable energy production will exacerbate mining threats to biodiversity. Nat. Commun. 11, 4174 (2020).CAS 
    Article 

    Google Scholar 
    Tallis, H., Kennedy, C. M., Ruckelshaus, M., Goldstein, J. & Kiesecker, J. M. Mitigation for one & all: an integrated framework for mitigation of development impacts on biodiversity and ecosystem services. Environ. Impact Assess. Rev. 55, 21–34 (2015).Article 

    Google Scholar 
    Bull, J. W. et al. Quantifying the “avoided” biodiversity impacts associated with economic development. Front. Ecol. Environ. https://doi.org/10.1002/fee.2496 (2022).Gastauer, M. et al. Mine land rehabilitation: modern ecological approaches for more sustainable mining. J. Clean. Prod. 172, 1409–1422 (2018).Article 

    Google Scholar 
    Souza, B. A., Rosa, J. C. S., Siqueira-Gay, J. & Sánchez, L. E. Mitigating impacts on ecosystem services requires more than biodiversity offsets. Land Use Policy 105, 105393 (2021).Article 

    Google Scholar 
    Ritter, C. D. et al. Environmental impact assessment in Brazilian Amazonia: challenges and prospects to assess biodiversity. Biol. Conserv. 206, 161–168 (2017).Article 

    Google Scholar 
    Good Practice Handbook: Cumulative Impact Assessment and Management, Guidance for the Private Sector in Emerging Markets (IFC, 2013).Gunn, J. H. & Noble, B. F. Integrating cumulative effects in regional strategic environmental assessment frameworks: lessons from practice. J. Environ. Assess. Policy Manage. 11, 267–290 (2009).Article 

    Google Scholar 
    Ferrante, L. & Fearnside, P. M. The Amazon’ s road to deforestation. Science 20, 20–22 (2020).
    Google Scholar 
    Runge, C. A., Tulloch, A. I. T., Gordon, A. & Rhodes, J. R. Quantifying the conservation gains from shared access to linear infrastructure. Conserv. Biol. 31, 1428–1438 (2017).Article 

    Google Scholar 
    Kiesecker, J. M., Copeland, H., Pocewicz, A. & McKenney, B. Development by design: blending landscape-level planning with the mitigation hierarchy. Front. Ecol. Environ. 8, 261–266 (2010).Article 

    Google Scholar 
    Heiner, M. et al. Moving from reactive to proactive development planning to conserve Indigenous community and biodiversity values. Environ. Impact Assess. Rev. 74, 1–13 (2019).Article 

    Google Scholar 
    Pfaff, A., Robalino, J., Herrera, D. & Sandoval, C. Protected areas’ impacts on Brazilian Amazon deforestation: examining conservation–development interactions to inform planning. PLoS ONE 10, 1–17 (2015).Article 
    CAS 

    Google Scholar 
    Almeida, C. A. et al. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5 / TM and MODIS data. Acta Amazon. 46, 291–302 (2008).Article 

    Google Scholar 
    Asner, G. P. & Tupayachi, R. Accelerated losses of protected forests from gold mining in the Peruvian Amazon. Environ. Res. Lett. 12, 094004 (2016).Article 

    Google Scholar 
    Boham-Carter, G. F. Geographic Information Systems for Geoscientists: Modelling with GIS (Elsevier, 1994).Soares-Filho, B., Rodrigues, H. & Follador, M. A hybrid analytical–heuristic method for calibrating land-use change models. Environ. Model. Softw. 43, 80–87 (2013).Article 

    Google Scholar 
    INPE. TerraClass https://www.terraclass.gov.br/geoportal-aml/ (2021).INPE. Slope http://www.dsr.inpe.br/topodata/acesso.php (2020).Ministério do Meio Ambiente (MMA). Conservation units http://mapas.mma.gov.br/i3geo/datadownload.htm (2022).Fundação Nacional do Índio (FUNAI). Indigenous lands http://www.funai.gov.br/index.php/shape (2021).Leite-Filho, A., Soares-filho, B. S., Davis, J. & Rodrigues, H. Dinamica EGO Guidebook (Centro de Sensoriamento Remoto, UFMG, 2020).Serviço Geológico do Brasil. Mineral deposits https://geosgb.cprm.gov.br/ (2020).Soares-Filho, B. et al. Simulating the response of land-cover changes to road paving and governance along a major Amazon highway: the Santarém-Cuiabá corridor. Glob. Change Biol. 10, 745–764 (2004).Article 

    Google Scholar 
    Centro de Sensoriamento Remoto. Biodiversity https://csr.ufmg.br/amazones/biodiversity/ (2021).Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).Pardini, R., de Bueno, A. A., Gardner, T. A., Prado, P. I. & Metzger, J. P. Beyond the fragmentation threshold hypothesis: regime shifts in biodiversity across fragmented landscapes. PLoS ONE 5, e13666 (2010).Montibeller, B., Kmoch, A., Virro, H., Mander, Ü. & Uuemaa, E. Increasing fragmentation of forest cover in Brazil’s Legal Amazon from 2001 to 2017. Sci. Rep. 10, 5803 (2020).CAS 
    Article 

    Google Scholar 
    Cabral, A. I. R., Saito, C., Pereira, H. & Laques, A. E. Deforestation pattern dynamics in protected areas of the Brazilian Legal Amazon using remote sensing data. Appl. Geogr. 100, 101–115 (2018).Article 

    Google Scholar 
    Colson, F., Bogaert, J. & Ceulemans, R. Fragmentation in the Legal Amazon, Brazil: can landscape metrics indicate agricultural policy differences? Ecol. Indic. 11, 1467–1471 (2011).Article 

    Google Scholar 
    Monmonier, M. S. Measures of pattern complexity for choroplethic maps. Am. Cartogr. 1, 159–169 (1974).Article 

    Google Scholar 
    Werner, T. T. et al. Global-scale remote sensing of mine areas and analysis of factors explaining their extent. Glob. Environ. Change 60, 102007 (2020).Article 

    Google Scholar 
    Soares-Filho, B. et al. Roads, http://maps.csr.ufmg.br/ (2016). More

  • in

    Increased incompatibility of heterologous algal symbionts under thermal stress in the cnidarian-dinoflagellate model Aiptasia

    Sylvan, J. How to protect a coral reef: the public trust doctrine and the law of the sea recommended citation. Sustain. Dev. Law Policy 7, 12 (2006).
    Google Scholar 
    LaJeunesse, T. C. et al. Systematic revision of symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580.e6 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kopp, C. et al. Highly dynamic cellular-level response of symbiotic coral to a sudden increase in environmental nitrogen. mBio 4, e00052–13 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muscatine, L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reef. 25, 75–87 (1990).
    Google Scholar 
    Dubinsky, Z. & Stambler, N. Coral reefs: an ecosystem in transition. (Springer, 2011).Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. https://doi.org/10.1038/NCLIMATE1661 (2012).Suggett, D. J., Warner, M. E. & Leggat, W. Symbiotic dinoflagellate functional diversity mediates coral survival under ecological crisis. Trends Ecol. Evolution 32, 735–745 (2017).Article 

    Google Scholar 
    Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient availability and metabolism affect the stability of coral–symbiodiniaceae symbioses. Trends Microbiol. 27, 678–689 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lehnert, E. M. et al. Extensive differences in gene expression between symbiotic and aposymbiotic cnidarians. G3 (Bethesda) 4, 277–95 (2014).CAS 
    Article 

    Google Scholar 
    Dubinsky, Z. & Berman-Frank, I. Uncoupling primary production from population growth in photosynthesizing organisms in aquatic ecosystems. in. Aquat. Sci. 63, 4–17 (2001).CAS 
    Article 

    Google Scholar 
    Burriesci, M. S., Raab, T. K. & Pringle, J. R. Evidence that glucose is the major transferred metabolite in dinoflagellate–cnidarian symbiosis. J. Exp. Biol. 215, 3467–3477 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Davy, S. K., Allemand, D. & Weis, V. M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–61 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Cui, G. et al. Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. PLOS Genet. 15, e1008189 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc. Natl Acad. Sci. USA 118, https://doi.org/10.1073/pnas.2022653118 (2021).Weis, V. M. Cellular mechanisms of Cnidarian bleaching: stress causes the collapse of symbiosis. J. Exp. Biol. 211, 3059–3066 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wooldridge, S. A. Breakdown of the coral-algae symbiosis: towards formalising a linkage between warm-water bleaching thresholds and the growth rate of the intracellular zooxanthellae. Biogeosciences Discuss. 9, 8111–8139 (2012).
    Google Scholar 
    Cziesielski, M. J., Schmidt‐Roach, S. & Aranda, M. The past, present, and future of coral heat stress studies. Ecol. Evol. https://doi.org/10.1002/ece3.5576 (2019).Leggat, W. et al. Differential responses of the coral host and their algal symbiont to thermal stress. PLoS ONE 6, e26687 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pinzón, J. H. et al. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. R. Soc. Open Sci. 2, 140214 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bang, C. et al. Metaorganisms in extreme environments: do microbes play a role in organismal adaptation? Zoology 127, 1–19 (2018).PubMed 
    Article 

    Google Scholar 
    Berkelmans, R. & van Oppen, M. J. H. The role of zooxanthellae in the thermal tolerance of corals: a “nugget of hope” for coral reefs in an era of climate change. Proc. Biol. Sci./R. Soc. 273, 2305–12 (2006).
    Google Scholar 
    Sampayo, E. M., Ridgway, T., Bongaerts, P. & Hoegh-Guldberg, O. Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc. Natl Acad. Sci. 105, 10444–10449 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Change https://doi.org/10.1038/nclimate1330 (2011).Cziesielski, M. J. et al. Multi-omics analysis of thermal stress response in a zooxanthellate cnidarian reveals the importance of associating with thermotolerant symbionts. Proc. Biol. Sci. 285, 20172654 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Baker, A. C., Starger, C. J., McClanahan, T. R. & Glynn, P. W. Corals’ adaptive response to climate change. Nature 430, 741–741 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Thornhill, D. J., LaJeunesse, T. C., Kemp, D. W., Fitt, W. K. & Schmidt, G. W. Multi-year, seasonal genotypic surveys of coral-algal symbioses reveal prevalent stability or post-bleaching reversion. Mar. Biol. 148, 711–722 (2006).Article 

    Google Scholar 
    Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to environmental stress,making its relative ability to acclimate or adapt extremely important to the to future climate change. Science 344, 895–898 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Herrera, M. et al. Temperature transcends partner specificity in the symbiosis establishment of a cnidarian. ISME J. 15, 141–153 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Howells, E. J. et al. Corals in the hottest reefs in the world exhibit symbiont fidelity not flexibility. Mol. Ecol. 29, 899–911 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hume, B. C. C., Mejia-Restrepo, A., Voolstra, C. R. & Berumen, M. L. Fine-scale delineation of Symbiodiniaceae genotypes on a previously bleached central Red Sea reef system demonstrates a prevalence of coral host-specific associations. Coral Reefs 1–19 https://doi.org/10.1007/s00338-020-01917-7 (2020).Perez, S. F., Cook, C. B. & Brooks, W. R. The role of symbiotic dinoflagellates in the temperature-induced bleaching response of the subtropical sea anemone Aiptasia pallida. J. Exp. Mar. Biol. Ecol. 256, 1–14 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mieog, J. C. et al. The roles and interactions of symbiont, host and environment in defining coral fitness. PLoS ONE 4, e6364 (2009).Cantin, N. E., van Oppen, M. J. H., Willis, B. L., Mieog, J. C. & Negri, A. P. Juvenile corals can acquire more carbon from high-performance algal symbionts. Coral Reefs 28, 405–414 (2009).Article 

    Google Scholar 
    Herrera, M. et al. Unfamiliar partnerships limit cnidarian holobiont acclimation to warming. Glob. Change Biol. 26, 5539–5553 (2020).Article 

    Google Scholar 
    LaJeunesse, T. et al. Closely related Symbiodinium spp. differ in relative dominance in coral reef host communities across environmental, latitudinal and biogeographic gradients. Mar. Ecol. Prog. Ser. 284, 147–161 (2004).Article 

    Google Scholar 
    Parkinson, J. E. & Baums, I. B. The extended phenotypes of marine symbioses: ecological and evolutionary consequences of intraspecific genetic diversity in coral-algal associations. Front. Microbiol. 5, 445 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Coffroth, M. A., Poland, D. M., Petrou, E. L., Brazeau, D. A. & Holmberg, J. C. Environmental symbiont acquisition may not be the solution to warming seas for reef-building corals. PLoS ONE 5, e13258 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bellantuono, A. J., Granados-Cifuentes, C., Miller, D. J., Hoegh-Guldberg, O. & Rodriguez-Lanetty, M. Coral thermal tolerance: tuning gene expression to resist thermal stress. PLoS ONE 7, e50685 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sunagawa, S. et al. Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics 10, 258 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Baumgarten, S. et al. The genome of Aiptasia, a sea anemone model for coral symbiosis. Proc. Natl Acad. Sci. 112, 201513318 (2015).
    Google Scholar 
    Matthews, J. L. et al. Menthol-induced bleaching rapidly and effectively provides experimental aposymbiotic sea anemones (Aiptasia sp.) for symbiosis investigations. J. Exp. Biol. jeb.128934 https://doi.org/10.1242/JEB.128934 (2015).Kenkel, C. D. et al. Evidence for a host role in thermotolerance divergence between populations of the mustard hill coral (Porites astreoides) from different reef environments. Mol. Ecol. 22, 4335–4348 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Polato, N. R., Altman, N. S. & Baums, I. B. Variation in the transcriptional response of threatened coral larvae to elevated temperatures. Mol. Ecol. 22, 1366–1382 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    DeSalvo, M., Sunagawa, S., Voolstra, C. R. & Medina, M. Transcriptomic resonses to heat stress and bleaching in the elkhorn coral Acropora palmata. Mar. Ecol. Prog. Ser. 402, 97–113 (2010).CAS 
    Article 

    Google Scholar 
    Maor-Landaw, K. & Levy, O. Gene expression profiles during short-term heat stress; branching vs. massive Scleractinian corals of the Red Sea. PeerJ 4, e1814 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Yamamoto, K. et al. Control of the heat stress-induced alternative splicing of a subset of genes by hnRNP K. Genes Cells 21, 1006–1014 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Seneca, F. O. & Palumbi, S. R. The role of transcriptome resilience in resistance of corals to bleaching. Mol. Ecol. 24, 1467–1484 (2015).PubMed 
    Article 

    Google Scholar 
    Meyer, E. & Weis, V. M. Study of cnidarian-algal symbiosis in the “omics” age. Biol. Bull. 223, 44–65 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Oakley, C. A. et al. Thermal shock induces host proteostasis disruption and endoplasmic reticulum stress in the model symbiotic Cnidarian Aiptasia. J. Proteome Res. 16, 2121–2134 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Robbart, M. L., Peckol, P., Scordilis, S. P., Curran, H. A. & Brown-Saracino, J. Population recovery and differential heat shock protein expression for the corals Agaricia agaricites and A-tenuifolia in Belize. Mar. Ecol. Prog. Ser. 283, 151–160 (2004).Article 

    Google Scholar 
    Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. 110, 1387–1392 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Traylor-Knowles, N., Rose, N. H. & Palumbi, S. R. The cell specificity of gene expression in the response to heat stress in corals. J. Exp. Biol. 220, 1837–1845 (2017).PubMed 

    Google Scholar 
    Benchimol, S. p53-dependent pathways of apoptosis. Cell Death Differ. 8, 1049–1051 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Moya, A. et al. Functional conservation of the apoptotic machinery from coral to man: The diverse and complex Bcl-2 and caspase repertoires of Acropora millepora. BMC Genomics 17, 62 (2016).Elmore, S. Apoptosis: a review of programmed cell death. Toxicol. Pathol. 35, 495–516 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Karim, W., Nakaema, S. & Hidaka, M. Temperature effects on the growth rates and photosynthetic activities of symbiodinium cells. J. Mar. Sci. Eng. 3, 368–381 (2015).Article 

    Google Scholar 
    Cunning, R. & Baker, A. C. Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 259–262 (2013).Article 

    Google Scholar 
    Rehman, A. U. et al. Symbiodinium sp. cells produce light-induced intra- and extracellular singlet oxygen, which mediates photodamage of the photosynthetic apparatus and has the potential to interact with the animal host in coral symbiosis. N. Phytologist 212, 472–484 (2016).CAS 
    Article 

    Google Scholar 
    Lesser, K. B. & Garcia, F. A. Association between polycystic ovary syndrome and glucose intolerance during pregnancy. J. Matern. Fetal Med. 6, 303–307 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dunn, S. R., Schnitzler, C. E. & Weis, V. M. Apoptosis and autophagy as mechanisms of dinoflagellate symbiont release during cnidarian bleaching: every which way you lose. Proc. R. Soc. Lond. B: Biol. Sci. 274, 3079–3085 (2007).
    Google Scholar 
    DeSalvo, M. K. et al. Coral host transcriptomic states are correlated with Symbiodinium genotypes. Mol. Ecol. 19, 1174–1186 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Levin, R. A. et al. Engineering strategies to decode and enhance the genomes of coral symbionts. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.01220 (2017).Yuyama, I., Ishikawa, M., Nozawa, M., Yoshida, M. & Ikeo, K. Transcriptomic changes with increasing algal symbiont reveal the detailed process underlying establishment of coral-algal symbiosis. Sci. Rep. 8, 16802 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Sproles, A. E. et al. Sub-cellular imaging shows reduced photosynthetic carbon and increased nitrogen assimilation by the non-native endosymbiont Durusdinium trenchii in the model cnidarian Aiptasia. Environ. Microbiol. 22, 3741–3753 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rädecker, N. et al. Using Aiptasia as a model to study metabolic interactions in Cnidarian-Symbiodinium symbioses. Front. Physiol. 9, 214 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Falkowski, P. G., Dubinsky, Z., Muscatine, L. & McCloskey, L. Population control in symbiotic corals. BioScience 43, 606–611 (1993).Article 

    Google Scholar 
    Wang & Douglas. Nitrogen recycling or nitrogen conservation in an alga-invertebrate symbiosis? J. Exp. Biol. 201, 2445–53 (1998).Loram, J. E., Trapido-Rosenthal, H. G. & Douglas, A. E. Functional significance of genetically different symbiotic algae Symbiodinium in a coral reef symbiosis. Mol. Ecol. 16, 4849–4857 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Karako-Lampert, S. et al. Transcriptome analysis of the scleractinian coral Stylophora pistillata. PLoS One 9, e88615 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hillyer, K. E., Tumanov, S., Villas-Bôas, S. & Davy, S. K. Metabolite profiling of symbiont and host during thermal stress and bleaching in a model cnidarian-dinoflagellate symbiosis. J. Exp. Biol. 219, 516–27 (2016).PubMed 

    Google Scholar 
    Bertucci, A., Forêt, S., Ball, E. E. & Miller, D. J. Transcriptomic differences between day and night in Acropora millepora provide new insights into metabolite exchange and light-enhanced calcification in corals. Mol. Ecol. 24, 4489–4504 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Matthews, J. L. et al. Optimal nutrient exchange and immune responses operate in partner specificity in the cnidarian-dinoflagellate symbiosis. Proc. Natl Acad. Sci. 114, 13194–13199 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lin, M.-F., Takahashi, S., Forêt, S., Davy, S. K. & Miller, D. J. Transcriptomic analyses highlight the likely metabolic consequences of colonization of a cnidarian host by native or non-native Symbiodinium species. Biol. Open 8, bio038281 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Medrano, E., Merselis, D. G., Bellantuono, A. J. & Rodriguez-Lanetty, M. Proteomic Basis of Symbiosis: A Heterologous Partner Fails to Duplicate Homologous Colonization in a Novel Cnidarian– Symbiodiniaceae Mutualism. Front. Microbiol. 10, 1153 (2019).Schoepf, V., Stat, M., Falter, J. L. & McCulloch, M. T. Limits to the thermal tolerance of corals adapted to a highly fluctuating, naturally extreme temperature environment. Sci. Rep. 5, 17639 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xiang, T., Hambleton, E. A., DeNofrio, J. C., Pringle, J. R. & Grossman, A. R. Isolation of clonal axenic strains of the symbiotic dinoflagellate Symbiodinium and their growth and host specificity1. J. Phycol. 49, 447–458 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    RNA viromes from terrestrial sites across China expand environmental viral diversity

    Shi, M. et al. Redefining the invertebrate RNA virosphere. Nature 540, 539–543 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, Y.-Z., Shi, M. & Holmes, E. C. Using metagenomics to characterize an expanding virosphere. Cell 172, 1168–1172 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, C.-X. et al. Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. eLife 4, e05378 (2015).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Starr, E. P., Nuccio, E. E., Pett-Ridge, J., Banfield, J. F. & Firestone, M. K. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc. Natl Acad. Sci. USA 116, 25900–25908 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wolf, Y. I. et al. Doubling of the known set of RNA viruses by metagenomic analysis of an aquatic virome. Nat. Microbiol. 5, 1262–1270 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zayed, A. A. et al. Cryptic and abundant marine viruses at the evolutionary origins of Earth’s RNA virome. Science 376, 156–162 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Simmonds, P. et al. Virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 15, 161–168 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Trubl, G., Hyman, P., Roux, S. & Abedon, S. T. Coming-of-age characterization of soil viruses: a user’s guide to virus isolation, detection within metagenomes, and viromics. Soil Syst. 4, 23 (2020).CAS 
    Article 

    Google Scholar 
    Jin, M. et al. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome 7, 58 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Trubl, G. et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 3, e00076-18 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Steward, G. F. et al. Are we missing half of the viruses in the ocean? ISME J. 7, 672–679 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Paul, K. I., Scott Black, A. & Conyers, M. K. in Advances in Agronomy. Sparks, D.L., Vol. 78 187–214 (Elsevier, 2003).Urayama, S., Takaki, Y. & Nunoura, T. FLDS: a comprehensive dsRNA sequencing method for intracellular RNA virus surveillance. Microbes Environ. 31, 33–40 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Armbrust, E. V. The life of diatoms in the world’s oceans. Nature 459, 185–192 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wu, W., Jin, Y., Bai, F. & Jin, S. in Molecular Medical Microbiology. Tang, Y.W., Liu, D., Schwartzman, J., Sussman, M., Poxton, I., 753–767 (Elsevier, 2015).Cooney, S., O’Brien, S., Iversen, C. & Fanning, S. in Encyclopedia of Food Safety. Motarjemi, Y., 433–441 (Elsevier, 2014).Geoghegan, J. L. et al. Hidden diversity and evolution of viruses in market fish. Virus Evol. 4, vey031 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lauber, C. et al. Deciphering the origin and evolution of hepatitis B viruses by means of a family of non-enveloped fish viruses. Cell Host Microbe 22, 387–399.e6 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shi, M., Zhang, Y.-Z. & Holmes, E. C. Meta-transcriptomics and the evolutionary biology of RNA viruses. Virus Res. 243, 83–90 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Turnbull, O. M. H. et al. Meta-transcriptomic identification of divergent Amnoonviridae in Fish. Viruses 12, 1254 (2020).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Bauermann, F. V., Hause, B., Buysse, A. R., Joshi, L. R. & Diel, D. G. Identification and genetic characterization of a porcine hepe-astrovirus (bastrovirus) in the United States. Arch. Virol. 164, 2321–2326 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Oude Munnink, B. B. et al. A novel astrovirus-like RNA virus detected in human stool. Virus Evol. 2, vew005 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Williamson, K. E. et al. Estimates of viral abundance in soils are strongly influenced by extraction and enumeration methods. Biol. Fertil. Soils 49, 857–869 (2013).Article 

    Google Scholar 
    Wang, C., Liu, D. & Bai, E. Decreasing soil microbial diversity is associated with decreasing microbial biomass under nitrogen addition. Soil Biol. Biochem. 120, 126–133 (2018).CAS 
    Article 

    Google Scholar 
    Wang, Q. et al. Effects of nitrogen and phosphorus inputs on soil bacterial abundance, diversity, and community composition in Chinese fir plantations. Front. Microbiol. 9, 1543 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Payne, S. in Viruses. Payne, S., 219–226 (Elsevier, 2017).Hillman, B. I. & Cai, G. The family Narnaviridae. Adv. Virus Res. 86, 149–176 (2013).PubMed 
    Article 

    Google Scholar 
    Wolf, Y. I. et al. Origins and evolution of the global RNA virome. mBio 9, e02329-18 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analysis in R. Bioinformatics 35, 526–528 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

    Google Scholar 
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gupta, R., Jung, E. & Brunak, S. NetNGlyc 1.0 Server (2017). DTU Health Tech. http://www.cbs.dtu.dk/services/NetNGlyc/Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, D170–D176 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9, 173–175 (2012).CAS 
    Article 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lagkouvardos, I., Fischer, S., Kumar, N. & Clavel, T. Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons. PeerJ 5, e2836 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McLeod, A., Xu, C. & Lai, Y. Package ‘bestglm’. CRAN. (2020).Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More