More stories

  • in

    Moroccan entomopathogenic nematodes as potential biocontrol agents against Dactylopius opuntiae (Hemiptera: Dactylopiidae)

    Spodek, M., Ben-Dov, Y., Protasov, A., Carvalho, C. J. & Mendel, Z. First record of Dactylopius opuntiae (Cockerell) (Hemiptera: Coccoidea: Dactylopiidae) from Israel. Phytoparasitica 42(3), 377–379. https://doi.org/10.1007/s12600-013-0373-2 (2014).Article 

    Google Scholar 
    García Morales, M., Denno, B. D., Miller, D. R., Miller, G. L., Ben-Dov, Y. & Hardy, N. B. ScaleNet: a literature-based model of scale insect biology and systematic (2016).Bouharroud, R., Amarraque, A. & Qessaoui, R. First report of the Opuntia cochineal scale Dactylopius opuntiae (Hemiptera: Dactylopiidae) in Morocco. EPPO Bull. 46(2), 308–310. https://doi.org/10.1111/epp.12298 (2016).Article 

    Google Scholar 
    Vanegas-Rico, J. M. et al. Biology and life history of Hyperaspis trifurcata feeding on Dactylopius opuntiae. Biocontrol 61(6), 691–701. https://doi.org/10.1007/s10526-016-9753-0 (2016).Article 

    Google Scholar 
    Mann, J. Cactus-feeding insects and mites. Bull. US. Nat. Mus. 256, 1–15 (1969).
    Google Scholar 
    Vanegas-Rico, J. M. et al. Hyperaspis trifurcata (Coleoptera: Coccinellidae) and its parasitoids in Central Mexico. Rev. Colomb. Entomol. 41(2), 194–199 (2015).
    Google Scholar 
    Lopes, E. B., Albuquerque, I. C., Brito, C. H. & Batista, J. D. L. Velocidade de dispersão de dactylopius opuntiae em palma gigante (opuntia fícus-indica). Rev. Bras. Eng. Agric. Ambient. 6(2), 644–649 (2009).
    Google Scholar 
    Badii, M. H. & Flores, A. E. Prickly pear cacti pests and their control in Mexico. Fla. Entomol. 84, 503–505. https://doi.org/10.2307/3496379 (2001).Article 

    Google Scholar 
    Sbaghi, M., Bouharroud, R., Boujghagh, M. & El Bouhssini, M. Sources de résistance d’Opuntia spp. contre la cochenille à carmin, Dactylopius opuntiae, au Maroc. EPPO Bull. 49(3), 585–592. https://doi.org/10.1111/epp.12606 (2019).Article 

    Google Scholar 
    Khan, H. A. A., Sayyed, A. H., Akram, W., Razald, S. & Ali, M. Predatory potential of Chrysoperla carnea and Cryptolaemus montrouzieri larvae on different stages of the mealybug, Phenacoccus solenopsis: A threat to cotton in South Asia. J. Insect. Sci. 12(1), 147. https://doi.org/10.1673/031.012.14701 (2012).Article 
    PubMed Central 

    Google Scholar 
    El Aalaoui, M., Bouharroud, R., Sbaghi, M., El Bouhssini, M. & Hilali, L. Seasonal biology of Dactylopius opuntiae (Hemiptera: Dactylopiidae) on Opuntia ficus-indica (Caryophyllales: Cactaceae) under field and semi-field conditions in Morocco. Ponte. 1, 259–327. https://doi.org/10.21506/j.ponte.2020.1.17 (2020).Article 

    Google Scholar 
    Flores, A., Olvera, H., Rodríguez, S. & Barranco, J. Predation potential of Chilocorus cacti (Coleoptera: Coccinellidae) to the prickly pear cacti pest Dactylopius opuntiae (Hemiptera: Dactylopiidae). Neotrop. Entomol. 42(4), 407–411. https://doi.org/10.1007/s13744-013-0139-z (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Galloway, T. & Handy, R. Immunotoxicity of organophosphorous pesticides. Ecotoxicology 12(1), 345–363. https://doi.org/10.1023/A:1022579416322 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    Arias-Estévez, M. et al. The mobility and degradation of pesticides in soils and the pollution of groundwater resources. Agric. Ecosyst. Environ. 123(4), 247–260. https://doi.org/10.1016/j.agee.2007.07.011 (2008).CAS 
    Article 

    Google Scholar 
    Palacios-Mendoza, C., Nieto-Hernández, R., Llanderal-Cázares, C. & González-Hernández, H. Efectividad biológica de productos biodegradables para el control de la cochinilla silvestre Dactylopius opuntiae (Cockerell) (Homoptera: Dactylopiidae). Acta. Zool. Mex. 20(3), 99–106 (2004).
    Google Scholar 
    Borges, L. R. et al. Use of biodegradable products for the control of Dactylopius opuntiae (Hemiptera: Dactylopiidae) in cactus pear. Acta. Hortic. 995, 379–386. https://doi.org/10.17660/ActaHortic.2013.995.49 (2013).Article 

    Google Scholar 
    Carneiro-Leão, M. P., Tiago, P. V., Medeiros, L. V., da Costa, A. F. & de Oliveira, N. T. Dactylopius opuntiae: Control by the Fusarium incarnatum–equiseti species complex and confirmation of mortality by DNA fingerprinting. J. Pest. Sci. 90(3), 925–933. https://doi.org/10.1007/s10340-017-0841-4 (2017).Article 

    Google Scholar 
    da Silva Santos, A. C., Oliveira, R. L. S., da Costa, A. F., Tiago, P. V. & de Oliveira, N. T. Controlling Dactylopius opuntiae with Fusarium incarnatum–equiseti species complex and extracts of Ricinus communis and Poincianella pyramidalis. J. Pest. Sci. 89(2), 539–547. https://doi.org/10.1007/s10340-015-0689-4 (2016).Article 

    Google Scholar 
    Tiago, P. V. et al. Polymorphisms in entomopathogenic fusaria based on inter simple sequence repeats. Biocontrol Sci. Technol. 26(10), 1401–1410. https://doi.org/10.1080/09583157.2016.1210084 (2016).Article 

    Google Scholar 
    Ramdani, C., Bouharroud, R., Sbaghi, M., Mesfioui, A. & El Bouhssini, M. Field and laboratory evaluations of different botanical insecticides for the control of Dactylopius opuntiae (Cockerell) on cactus pear in Morocco. Int. J. Trop. Insect. Sci. 41(2), 1623–1632. https://doi.org/10.1007/s42690-020-00363-w (2021).Article 

    Google Scholar 
    El-Aalaoui, M. et al. Comparative toxicity of different chemical and biological insecticides against the scale insect Dactylopius opuntiae and their side effects on the predator Cryptolaemus montrouzieri. Arch. Phytopathol. Plant. Prot. 52(1–2), 155–169. https://doi.org/10.1080/03235408.2019.1589909 (2019).CAS 
    Article 

    Google Scholar 
    El-Aalaoui, M., Bouharroud, R., Sbaghi, M., El Bouhssini, M. & Hilali, L. Predatory potential of eleven native Moroccan adult ladybird species on different stages of Dactylopius opuntiae (Cockerell)(Hemiptera: Dactylopiidae). EPPO Bull. 49(2), 374–379. https://doi.org/10.1111/epp.12565 (2019).Article 

    Google Scholar 
    El-Aalaoui, M., Bouharroud, R., Sbaghi, M., El Bouhssini, M. & Hilali, L. First study of the biology of Cryptolaemus montrouzieri and its potential to feed on the mealybug Dactylopius opuntiae (Hemiptera: Dactylopiidae) under laboratory conditions in Morocco. Arch. Phytopathol. Plant. Prot. 52(13–14), 1112–1124. https://doi.org/10.1080/03235408.2019.1691904 (2019).CAS 
    Article 

    Google Scholar 
    Lester, P. J., Thistlewood, H. M. A. & Harmsen, R. Some effects of pre-release host-plant on the biological control of Panonychus ulmi by the predatory mite Amblyseius fallacis. Exp. Appl. Acarol. 24(1), 19–33. https://doi.org/10.1023/A:1006345119387 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    Poinar, G. O. Description and biology of a new insect parasitic rhabditoid, Heterorhabditis bacteriophora n. Gen., n. Sp. (Rhabditida: Heterorhabditidae n. Fam.). Nematol. 21(4), 463–470. https://doi.org/10.1163/187529275X00239 (1976).Article 

    Google Scholar 
    Boemare, N., Akhurst, R. & Mourant, R. DNA relatedness between Xenorhabdus spp. (Enterobacteriaceae), symbiotic bacteria of entomopathogenic nematodes, and a proposal to transfer Xenorhabdus luminescens to a new genus, Photorhabdus gen-nov.. Int. J. Syst. Bacteriol. 43(2), 249–255. https://doi.org/10.1099/00207713-43-2-249 (1993).CAS 
    Article 

    Google Scholar 
    Gulzar, S., Wakil, W. & Shapiro-Ilan, D. I. Potential use of entomopathogenic nematodes against the soil dwelling stages of onion thrips, Thrips tabaci Lindeman: Laboratory, greenhouse and field trials. Biol. Control. 161, 104677. https://doi.org/10.1016/j.biocontrol.2021.104677 (2021).Article 

    Google Scholar 
    Adams, B. J. & Nguyen, K. B. Taxonomy and systematics. In Entomopathogenic Nematology (ed. Gaugler, R.) 1–34 (CABI Publishing, 2002).
    Google Scholar 
    Dowds, B. C. A. & Peters, A. Virulence mechanisms. In Entomopathogenic Nematology (ed. Gaugler, R.) 79–90 (CABI Publishing, 2003).
    Google Scholar 
    Bal, H. K. & Grewal, P. S. Lateral dispersal and foraging behavior of entomopathogenic nematodes in the absence and presence of mobile and non-mobile hosts. PLoS ONE 10(6), e0129887. https://doi.org/10.1371/journal.pone.0129887 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lewis, E. E., Gaugler, R. & Harrison, R. Entomopathogenic nematode host finding—response to host contact cues by cruise and ambush foragers. Parasitology 105, 309–315. https://doi.org/10.1017/S0031182000074230 (1992).Article 

    Google Scholar 
    Campbell, J. F. & Gaugler, R. Nictation behavior and its ecological implications in the host search strategies of entomopathogenic nematodes (Heterorhabditidae and Steinernematidae). Behaviour 126, 155–169 (1993).Article 

    Google Scholar 
    Lewis, E. E., Gaugler, R. & Harrison, R. Response of cruiser and ambusher entomopathogenic nematodes (Steinernematidae) to host volatile cues. Can. J. Zool. 71, 765–769 (1993).Article 

    Google Scholar 
    Grewal, P. S., Lewis, E. E., Gaugler, R. & Campbell, J. F. Host finding behavior as a predictor of foraging strategy in entomopathogenic nematodes. Parasitology 108, 207–215 (1994).Article 

    Google Scholar 
    Poinar, G. O. Biology and taxonomy of Steinernematidae and Heterorhabditidae. In Entomopathogenic Nematodes in Biological cOntrol (eds Gaugler, R. & Kaya, H. K.) 23–62 (CRC Press, 1990).
    Google Scholar 
    De Waal, J. Y., Wolhlfarter, M. & Malan, A. P. Laboratory bioassays for the differential susceptibility of Planococcus ficus and Pseudococcus viburni (Hemiptera: Pseudococcidae) to entomopathogenic nematodes (Rhabditida: Heterorhabditidae and Steinernematidae). S. Afr. J. Plant. Soil. 24, 243–244 (2007).
    Google Scholar 
    Lacey, L. A. & Shapiro-Ilan, D. I. Microbial control of insect pests in temperate orchard systems: Potential for incorporation into IPM. Annu. Rev. Entomol. 53(1), 121–144. https://doi.org/10.1146/annurev.ento.53.103106.093419 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Van Niekerk, S. & Malan, A. P. Potential of South African entomopathogenic nematodes (Heterorhabditidae and Steinernematidae) for control of the citrus mealybug, Planococcus citri (Pseudococcidae). J. Invertebr. Pathol. 111(2), 166–174. https://doi.org/10.1016/j.jip.2012.07.023 (2012).Article 
    PubMed 

    Google Scholar 
    Půža, V. Control of insect pests by entomopathogenic nematodes. In Principles of Plant Microbe Interactions (ed. Lugtenberg, B.) 175–183 (Springer, 2015).
    Google Scholar 
    Gulzar, S. et al. Environmental tolerance of entomopathogenic nematodes differs among nematodes arising from host cadavers versus aqueous suspension. J. Invertebr. Pathol. 175, 107452. https://doi.org/10.1016/j.jip.2020.107452 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gulzar, S. et al. Virulence of entomopathogenic nematodes to pupae of Frankliniella fusca (Thysanoptera: Thripidae). J. Econ. Entomol. 114(5), 2018–2023. https://doi.org/10.1093/jee/toab132 (2021).Article 
    PubMed 

    Google Scholar 
    Gulzar, S., Wakil, W. & Shapiro-Ilan, D. I. Combined effect of entomopathogens against Thrips tabaci Lindeman (Thysanoptera: Thripidae): laboratory, greenhouse and field trials. Insects 12(5), 456. https://doi.org/10.3390/insects12050456 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Usman, M. et al. Virulence of entomopathogenic fungi to Rhagoletis pomonella (Diptera: Tephritidae) and interactions with entomopathogenic nematodes. J. Econ. Entomol. 113(6), 2627–2633. https://doi.org/10.1093/jee/toaa209 (2020).Article 
    PubMed 

    Google Scholar 
    Usman, M. et al. Potential of entomopathogenic nematodes against the pupal stage of the apple maggot Rhagoletis pomonella (Walsh) (Diptera: Tephritidae). J. Nematol. 52, e2020–e2079. https://doi.org/10.21307/jofnem-2020-079 (2020).Article 
    PubMed Central 

    Google Scholar 
    Usman, M., Wakil, W. & Shapiro-Ilan, D. I. Entomopathogenic nematodes as biological control agent against Bactrocera zonata and Bactrocera dorsalis (Diptera: Tephritidae). Biol. Control. 163, 104706. https://doi.org/10.1016/j.biocontrol.2021.104706 (2021).Article 

    Google Scholar 
    Grewal, P. S., Wang, X. & Taylor, R. A. J. Dauer juvenile longevity and stress tolerance in natural populations of entomopathogenic nematodes: Is there a relationship?. Int. J. Parasitol. 32(6), 717–725. https://doi.org/10.1016/S0020-7519(02)00029-2 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    Benseddik, Y. et al. Occurrence and distribution of entomopathogenic nematodes (Steinernematidae and Heterorhabditidae) in Morocco. Biocontrol. Sci. Technol. 30(10), 1060–1072. https://doi.org/10.1080/09583157.2020.1787344 (2020).Article 

    Google Scholar 
    Mokrini, F. et al. Potential of Moroccan entomopathogenic nematodes for the control of the Mediterranean fruit fly Ceratitis capitata Wiedemann (Diptera: Tephritidae). Sci. Rep. 10(1), 1–11. https://doi.org/10.1038/s41598-020-76170-7 (2020).CAS 
    Article 

    Google Scholar 
    Gorgadze, O., Bakhtadze, G., Kereselidze, M. & Lortkipanidze, M. The efficacy of entomopathogenic agents against Halyomorpha halys. Int. J. Curr. Res. 9, 62177–62180 (2017).
    Google Scholar 
    Tarasco, E. & Triggiani, O. Use of Italian EPNs in controlling Rhytidoderes plicatus Oliv, (Coleoptera, Curculionidae) in potted savoy cabbages. IOBC. WPRS. Bull. OILBN. 28, 9–12 (2005).
    Google Scholar 
    Moreno Salguero, C. A., Bustillo Pardey, A. E., Lopez Nunez, J. C., Castro Valderrama, U. & Ramirez Sanchez, G. D. Virulence of entomopathogenic nematodes to control Aeneolamia varia (Hemiptera: Cercopidae) in sugarcane. Rev. Colomb. Entomol. 38(2), 260–265 (2012).
    Google Scholar 
    Julià, I., Morton, A., Roca, M. & Garcia-del-Pino, F. Evaluation of three entomopathogenic nematode species against nymphs and adults of the sycamore lace bug, Corythucha ciliata. Biocontrol 65(5), 623–633. https://doi.org/10.1007/s10526-020-10045-8 (2020).CAS 
    Article 

    Google Scholar 
    Sirjani, F. O., Lewis, E. E. & Kaya, H. K. Evaluation of entomopathogenic nematodes against the olive fruit fly, Bactrocera oleae (Diptera: Tephritidae). Biol. Control. 48, 274–7280. https://doi.org/10.1016/j.biocontrol.2008.11.002 (2009).Article 

    Google Scholar 
    Guide, B. A., Soares, E. A., Itimura, C. R. & Alves, V. S. Entomopathogenic nematodes in the control of cassava root mealybug Dysmicoccus sp. (Hemiptera: Pseudococcidae). Rev. Colomb. Entomol. 42(1), 16–21. https://doi.org/10.25100/socolen.v42i1.6664 (2016).CAS 
    Article 

    Google Scholar 
    Le Vieux, P. D. & Malan, A. P. The potential use of entomopathogenic nematodes to control Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae). S. J. Enol. Vitic. 34(2), 296–306. https://doi.org/10.21548/34-2-1108 (2013).Article 

    Google Scholar 
    Lewis, E. D., Campbell, J., Griffin, C., Kaya, H. & Peters, A. Behavioral ecology of entomopathogenic nematodes. Biol. Control. 38(1), 66–79. https://doi.org/10.1016/j.biocontrol.2005.11.007 (2006).Article 

    Google Scholar 
    Rahoo, A. M., Tariq Mukhta, T., Gowen, S. R., Rahoo, R. K. & Abro, S. A. Reproductive potential and host searching ability of entomopathogenic nematode Steinernema feltiae. Pak. J. Zool. 49(1), 229–234. https://doi.org/10.17582/journal.pjz/2017.49.1.229.234 (2017).Article 

    Google Scholar 
    Selvan, S., Campbell, J. F. & Gaugler, R. Density-dependent effects on entomopathogenic nematodes (Heterorhabditidae and Steinernematidae) within an insect host. J. Invertebr. Pathol. 62(3), 278–284. https://doi.org/10.1006/jipa.1993.1113 (1993).Article 

    Google Scholar 
    Gaugler, R., Wang, Y. & Campbell, J. F. Aggressive and evasive behaviors in Popillia japonica (Coleoptera: Scarabaeidae) larvae: Defences against entomopathogenic nematode attack. J. Invertebr. Pathol. 64(3), 193–199. https://doi.org/10.1016/S00222011(94)90150-3 (1994).Article 

    Google Scholar 
    Burjanadze, M., Kharabadze, N. & Chkhidze, N. Testing local isolates of entomopathogenic microorganisms against brown marmorated stink Bug Halyomorpha halys in Georgia. BIO Web Conf. 18, 00006. https://doi.org/10.1051/bioconf/20201800006 (2020).Article 

    Google Scholar 
    Del Valle, E. E., Dolinski, C. & Souza, R. M. Dispersal of Heterorhabditis baujardi LPP7 (Nematoda: Rhabditida) applied to the soil as infected host cadavers. Int. J. Pest. Manag. 54(2), 115–122. https://doi.org/10.1080/09670870701660579 (2008).Article 

    Google Scholar 
    Griffin, C. T., Boemare, N. E. & Lewis, E. E. Biology and behavior. In Nematodes as Biocontrol Agents 1st edn (eds Grewal, P. S. et al.) 47–59 (CABI Publishing, 2005).Chapter 

    Google Scholar 
    Bastidas, B., Portillo, E. & San-Blas, E. Size does matter: The life cycle of Steinernema spp. in micro-insect hosts. J. Invertebr. Pathol. 121, 46–55. https://doi.org/10.1016/j.jip.2014.06.010 (2014).Article 
    PubMed 

    Google Scholar 
    Stokwe, N. F. & Malan, A. P. Susceptibility of the obscure mealybug, Pseudococcus viburni (Signoret) (Pseudococcidae), to South African isolates of entomopathogenic nematodes. Int. J. Pest. Manag. 62(2), 119–128. https://doi.org/10.1080/09670874.2015.1122250 (2016).Article 

    Google Scholar 
    Stokwe, N. F. & Malan, A. P. Laboratory bioassays to determine susceptibility of woolly apple aphid, Eriosoma lanigerum (Hausmann) (Hemiptera: Aphididae), to entomopathogenic nematodes. Afr. Entomol. 25(1), 123–136. https://doi.org/10.4001/003.025.0123 (2017).Article 

    Google Scholar 
    Cuthbertson, A. G. et al. Bemisia tabaci: The current situation in the UK and the prospect of developing strategies for eradication using entomopathogens. Insect Sci. 18(1), 1–10. https://doi.org/10.1111/j.1744-7917.2010.01383.x (2011).Article 

    Google Scholar 
    Van Niekerk, S. & Malan, A. P. Compatibility of Heterorhabditis zealandica and Steinernema yirgalemense with agrochemicals and biological control agents. Afr. Entomol. 22, 49–56 (2014).Article 

    Google Scholar 
    Van Niekerk, S. & Malan, A. P. Adjuvants to improve aerial control of the citrus mealybug Planococcus citri (Hemiptera: Pseudococcidae) using entomopathogenic nematodes. J. Helminthol. 89(2), 189–195. https://doi.org/10.1017/S0022149X13000771 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Aldama-Aguilera, C. & Llanderal-Cázares, C. Grana cochinilla: comparación de métodos de producción en penca cortada. Agrociencia 37(1), 11–19 (2003).
    Google Scholar 
    Kaya, H. K. & Stock, S. P. Techniques in insect nematology. In Manual of Techniques in Insect Pathology, Biological Techniques Series (ed. Lacey, L. A.) 281–324 (Academic Press, 1997).Chapter 

    Google Scholar 
    White, C. F. A method for obtaining infective larvae from culture. Science 66, 302–303. https://doi.org/10.1126/science.66.1709.302-a (1927).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Shapiro-Ilan, D. I., Morales-Ramos, J. A. & Rojas, M. G. In vivo production of entomopathogenic nematodes. In Microbial-Based Biopesticides 137–158 (Humana Press, 2016).Chapter 

    Google Scholar 
    Henderson, C. F. & Tilton, E. W. Tests with acaricides against the brown wheat mite. J. Econ. Entomol. 48(2), 157–161 (1955).CAS 
    Article 

    Google Scholar 
    Abbot, W. S. Method of computing the effectiveness of an insecticide. J. Econ. Entomol. 18(2), 265–267. https://doi.org/10.1093/jee/18.2.265a (1925).Article 

    Google Scholar 
    Finney, D. J. Probit analysis 3rd edn, 20–63 (Cambridge University Press, 1971).MATH 

    Google Scholar 
    Haye, T., Wyniger, D. & Gariepy, T. D. Recent range expansion of brown marmorated stink bug in Europe. In Proceedings of the Eighth International Conference on Urban Pests (eds Müller, G. et al.) 309–314 (OOK Press, 2014).
    Google Scholar 
    Carver, R. H. & Nash, J. G. Doing data analysis with SPSS: version 18.0. (Cengage Learning, 2011). More

  • in

    An allometric model-based approach for estimating biomass in seven Indian bamboo species in western Himalayan foothills, India

    Vorontsova, M. S., Clark, L. G., Dransfield, J., Govaerts, R. H. A. & Baker, W. J. World Checklist of Bamboos and Rattans 102 (Science Press, 2017).
    Google Scholar 
    Lobovikov, M., Paudel, S., Ball, L., Piazza, M., Guardia, M., Ren, H., Russo, L. & Wu, J. World bamboo resources: a thematic study prepared in the framework of the global forest resources assessment 2005. Food & Agriculture Org., (2007).FAO. Global Forest Resources Assessment 2020: Main report, Rome. Accessed 18 Nov 2021. https://www.fao.org/3/ca9825en/ca9825en.pdf. https://doi.org/10.4060/ca9825en (2020).ISFR http://www.indiaenvironmentportal.org.in/files/file/isfr-fsi-vol1.pdf (Accessed November 18 2021) (2019).Salam, K. Connecting the poor: bamboo, problems and prospect. South Asia Bamboo Foundation (SABF) (2013) retrieved 17 December 2013 from jeevika.org/bamboo/2g-article-fornbda.docx.INBAR. Accessed 18 Nov 2021. https://www.inbar.int/global-programmes/.Osman, A. I., Abdelkader, A., Johnston, C. R., Morgan, K. & Rooney, D. W. Thermal investigation and kinetic modeling of lignocellulosic biomass combustion for energy production and other applications. Ind. Eng. Chem. Res. 56, 12119–12130 (2017).CAS 
    Article 

    Google Scholar 
    Fawzy, S., Osman, A., Doran, J. & Rooney, D. W. Strategies for mitigation of climate change: a review. Environ. Chem. Lett. 18, 2069–2094 (2020).CAS 
    Article 

    Google Scholar 
    IPCC. Global warming of 1.5 °C. In: Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., & Waterfeld, T. (eds) An IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and eforts to eradicate poverty (2018). https://www.ipcc.ch/site/assets/uploads/sites/2/2019/06/SR15_Full_Report_High_Res.pdf (Accessed 22 Dec 2019).Osman, A. et al. Conversion of biomass to biofuels and life cycle assessment: a review. Environ. Chem. Lett. 19, 4075–4118 (2021).CAS 
    Article 

    Google Scholar 
    Balajii, M. & Niju, S. Biochar-derived heterogeneous catalysts for biodiesel production. Environ. Chem. Lett. 17, 1447–1469. https://doi.org/10.1007/s10311-019-00885-x (2019).CAS 
    Article 

    Google Scholar 
    Gunarathne, V., Ashiq, A., Ramanayaka, S., Wijekoon, P. & Vithanage, M. Biochar from municipal solid waste for resource recovery and pollution remediation. Environ. Chem. Lett. 17, 1225–1235. https://doi.org/10.1007/s10311-019-00866-0 (2019).CAS 
    Article 

    Google Scholar 
    Lobovikov, M., Schoene, D. & Yping, L. Bamboo in climate change and rural livelihood. Mitig. Adapt. Strateg. Glob. Change 17, 261–276 (2012).Article 

    Google Scholar 
    Yuen, J. Q., Fung, T. & Ziegler, A. D. Carbon stocks in bamboo ecosystems worldwide: estimates and uncertainties. For. Ecol. Manag. 393, 113–138 (2017).Article 

    Google Scholar 
    Devi, A. S. & Singh, K. S. Carbon storage and sequestration potential in aboveground biomass of bamboos in North East India. Sci. Rep. 11, 837 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nath, A. J., Lal, R. & Das, A. K. Managing woody bamboos for carbon farming and carbon trading. Glob. Ecol. Conserv. 3, 654–663 (2015).Article 

    Google Scholar 
    UNFCCC. Thirty-ninth Meeting of the Clean Development Mechanism Executive Board. UN Campus, Langer Eugen, Hermann-Ehlers-Str. 10, 53113 Bonn, Germany (2008).FTFA. Food and Trees for Africa. World’s First Bamboo Carbon Offset Credits Issued under the VCS in the Voluntary Carbon Market. In: trees.co.za (2012).Sharma, R., Wahono, J. & Baral, H. Bamboo as an alternative bioenergy crop and powerful ally for land restoration in Indonesia. Sustainability 10, 4367 (2018).Article 

    Google Scholar 
    Chin, K. L. et al. Bioenergy production from bamboo: potential source from Malaysia’s perspective. Bioresources 12, 6844–6867 (2017).CAS 
    Article 

    Google Scholar 
    Littlewood, J., Wang, L., Tumbull, C. & Murphy, R. J. Techno-economic potential of bioethanol from bamboo in China. Biotechnol. Biofuels 6, 173–173 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Buckingham, K. et al. The potential of bamboo is constrained by outmoded policy frames. Ambio 40, 544–548 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    IPCC shorturl.at/bguxF (Accessed November 18 2021) (2003).Kempes, C. P., West, G. B., Crowell, K. & Girvan, M. Predicting maximum tree heights and other traits from allometric scaling and resource limitations. PLoS ONE 6(6), e20551 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sileshi, G. W. A critical review of forest biomass estimation models, common mistakes and corrective measures. For. Ecol. Manag. 329, 237–254 (2014).Article 

    Google Scholar 
    Verma, A. et al. Predictive models for biomass and carbon stocks estimation in Grewia optiva on degraded lands in western Himalaya. Agrofor. Syst. 88(5), 895–905 (2014).Article 

    Google Scholar 
    Gao, X. et al. Modeling of the height–diameter relationship using an allometric equation model: a case study of stands of Phyllostachys edulis. J. For. Res. 27, 339–347 (2016).CAS 
    Article 

    Google Scholar 
    Huy, B. & Long, T. T. A manual for bamboo forest biomass and carbon assessment, INBAR technical report (2019).https://www.inbar.int/resources/inbar_publications/a-manual-for-bamboo-forest-biomass-and-carbon-assessment/ (Accessed November 18 2021).Brahma, B. et al. A critical review of forest biomass estimation equations in India. Trees For. People 5, 100098. https://doi.org/10.1016/j.tfp.2021.100098 (2021).Article 

    Google Scholar 
    Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllostachys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344. https://doi.org/10.1016/j.foreco.2010.04.021 (2010).Article 

    Google Scholar 
    FAO. Guidelines on Destructive Measurement for Forest Biomass Estimation (FAO, Rome, 2012).Yen, T. M. Comparing aboveground structure and aboveground carbon storage of an age series of moso bamboo forests subjected to different management strategies. J. For. Res. 20, 1–8 (2015).CAS 
    Article 

    Google Scholar 
    Yuen, J. Q., Fung, T. & Ziegler, A. D. Carbon stocks in bamboo ecosystem worldwide: estimates and uncertainties. For. Ecol. Manag. 393, 113–138 (2017).Article 

    Google Scholar 
    Nath, A. J., Das, G. & Das, A. K. Above ground standing biomass and carbon storage in village bamboos in North East India. Biomass Bioenergy 33, 1188–1196 (2009).Article 

    Google Scholar 
    Rawat, R. S., Arora, G., Rawat, V. R. S., Borah, H. R., Singson, M. Z., Chandra, G., Nautiyal, R. & Rawat, J. Estimation of biomass and carbon stock of bamboo species through development of allometric equations. Indian Council of Forestry Research and Education, Dehradun, INDIA (2018).Tripathi, S. K. & Singh, K. P. Productivity and nutrient cycling in recently harvested and mature bamboo savannas in the dry tropics. J. Appl. Ecol. 31, 109–124 (1994).Article 

    Google Scholar 
    Kaushal, R. et al. Predictive models for biomass and carbon stock estimation in male bamboo (Dendrocalamus strictus L.) in Doon valley, India. Acta Ecol. Sin. 36, 469–476 (2016).Article 

    Google Scholar 
    Das, D. & Chaturvedi, O. P. Bambusa bambos (L.) Voss plantation in eastern India: I. Culm recruitment, dry matter dynamics and carbon flux. J. Bamboo Rattan 5(1&2), 47–59 (2006).
    Google Scholar 
    Shanmughavel, P. & Francis, K. Above ground biomass production and nutrient distribution in growing bamboo (Bambusa bambos (L.) Voss). Biomass Bioenergy 10(5/6), 383–91 (1996).CAS 
    Article 

    Google Scholar 
    Seethalakshmi, K. K. & Kumar, M. Bamboos of India: A Compendium. Kerala Forest Research Institute, Peechi and International Network for Bamboo and Rattan, Beijing (1998).Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllostachys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344. https://doi.org/10.1016/j.foreco.2010.04.021 (2010).Article 

    Google Scholar 
    FAO. Guidelines on Destructive Measurement for Forest Biomass Estimation (FAO, Rome, 2012).Huy, B. et al. Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Vietnam. For. Ecol. Manag. 382, 193–205 (2016).Article 

    Google Scholar 
    Huy, B. et al. Allometric equations for estimating tree aboveground biomass in tropical dipterocarp forests of Vietnam’. Forests 7(180), 1–19 (2016).
    Google Scholar 
    Huy, B., Poudel, K. P. & Temesgen, H. Aboveground biomass equations for evergreen broadleaf forests in South Central coastal ecoregion of Vietnam: selection of eco-regional or pantropical models’. For. Ecol. Manag. 376, 276–283 (2016).Article 

    Google Scholar 
    Akaike, H. Information theory as an extension of the maximum likelihood principle’. In Petrov, B. N. & Csaki, F. E. (eds) Proceedings of the 2nd international symposium on information theory. Budapest: Akademiai Kiado, 267–281 (1973).Schwarz, G. E. Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Huy, B. Methodology for developing and cross-validating allometric equations for estimating forest tree biomass. HCM City: Science & Technology, 238 (2017a).Huy, B. Statistical informatics in forestry. HCM City: Science & Technology, 282 (2017b).Huy, B., Tinh, N. T., Poudel, K. P., Frank, B. M. & Temesgen, H. Taxon-specific modeling systems for improving reliability of tree aboveground biomass and its components estimates in tropical dry dipterocarp forests. For. Ecol. Manag. 437, 156–174 (2019).Article 

    Google Scholar 
    Huy, B., Thanh, G. T., Poudel, K. P. & Temesgen, H. Individual plant allometric equations for estimating aboveground biomass and its components for a common bamboo species (Bambusa procera A. Chev. and A Camus) in tropical forests. Forests 10, 1–17 (2019).Article 

    Google Scholar 
    Mayer, D. G. & Butler, D. G. Statistical validation. Ecol. Model. 68, 21–32 (1993).Article 

    Google Scholar 
    Chave, J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Basuki, T. M., Van Laake, P. E., Skidmore, A. K. & Hussin, Y. A. Allometric equations for estimating the aboveground biomass in the tropical lowland Dipterocarp forests’. For. Ecol. Manag. 257, 1684–1694 (2009).Article 

    Google Scholar 
    Kaushal, R. et al. Rooting behavior and soil properties in different bamboo species of Western Himalayan Foothils, India. Sci. Rep. 10, 4966 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kramer, P. J. & Kozlowski, T. T. Physiology of Wood Plants 628–702 (McGraw Hill, 1979).
    Google Scholar 
    IPCC Available at http://www.ipcc.ch. AccessedOctober2008 (2008).Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllostachys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344 (2010).Article 

    Google Scholar 
    Inoue, A., Sakamoto, S., Suga, H., Kitazato, H. & Sakuta, K. Construction of one-way volume table for the three major useful bamboos in Japan. J. For. Res. 18, 323–334 (2013).Article 

    Google Scholar 
    Kralicek, K., Huy, B., Poudel, K. P., Temesgen, H. & Salas, C. Simultaneous estimation of above- and below-ground biomass in tropical forests of Vietnam. For. Ecol. Manag. 390, 147–156 (2017).Article 

    Google Scholar 
    Montes, N., Gauquelin, W., Badri, V., Bertaudiere, E. H. & Zaoui, A. A non-destructive method for estimating aboveground forest biomass in threatended woodlands. For. Ecol. Manag. 130, 37–46 (2000).Article 

    Google Scholar 
    Verma, A. et al. Predictive models for biomass and carbon stocks estimation in Grewia optiva on degraded lands in western Himalaya. Agrofor. Syst. 88, 895–905. https://doi.org/10.1007/s10457-014-9734-1 (2014).Article 

    Google Scholar 
    Singnar, P. et al. Allometric scaling, biomass accumulation and carbon stocks in different aged stands of thin-walled bamboos Schizostachyum dullooa Pseudostachyum polymorphum and Melocanna baccifera. For. Ecol. Manag. 395, 81–91. https://doi.org/10.1016/j.foreco.2017.04.001 (2017).Article 

    Google Scholar 
    Huang, S., Price, D. & Titus, S. J. Development of ecoregion-based height diameter models for white spruce in boreal forests. For. Ecol. Manag. 129, 125–141 (2000).Article 

    Google Scholar 
    Yen, T. M. Culm height development, biomass accumulation and carbon storage in an initial growth stage for a fast-growing moso bamboo (Phyllostachy pubescens). Bot. Stud. 57, 10 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tripathi, S. K. & Singh, K. P. Culm recruitment, dry matter dynamics and carbon flux in recently harvested and mature bamboo savannas in the Indian dry tropics. Ecol. Res. 11, 149–164 (1996).Article 

    Google Scholar 
    Singh, A. N. & Singh, J. S. Biomass, net primary production and impact of bamboo plantation on soil redevelopment in a dry tropical region. For. Ecol. Manag. 119, 195–207 (1999).Article 

    Google Scholar 
    Das, D. K. & Chaturvedi, O. P. Bambusa bambos (L.) Voss plantation in eastern India: I. Culm recruitment, dry matter dynamics and carbon flux. J. Bamboo Rattan 5, 47–59 (2006).
    Google Scholar 
    Shanmughavel, P. & Francis, K. Above ground biomass production and nutrient distribution in growing bamboo (Bambusa bambos (L.) Voss). Biomass Bioenergy 10, 383–391 (1996).CAS 
    Article 

    Google Scholar 
    Arnoult, S. & Brancourt-Hulmel, M. A review on miscanthus biomass production and composition for bioenergy use: genotypic and environmental variability and implications for breeding. Bioenergy Res. 8, 502–526 (2015).CAS 
    Article 

    Google Scholar 
    Nath, A. J., Das, G. & Das, A. K. Above ground standing biomass and carbon storage in village bamboos in North East India. Biomass Bioenergy 33, 1188–1196 (2009).Article 

    Google Scholar 
    Bargali, S. S., Singh, S. P. & Singh, R. Structure and function of an age series of eucalyptus plantations in central Himalaya I. Dry matter dynamics. Ann. Bot. 69, 405–411 (1992).Article 

    Google Scholar 
    Rizvi, R. H., Dhyani, S. K., Yadav, R. S. & Ramesh, S. Biomass production and carbon stock of poplar agroforestry systems in Yamunanagar and Saharanpur districts of North western India. Curr. Sci. 100, 736–742 (2011).CAS 

    Google Scholar 
    Kanime, N. et al. Biomass production and carbon sequestration in different tree-based systems of Central Himalayan Tarai region. For Trees Livelihoods 22(1), 38–50 (2013).Article 

    Google Scholar 
    Arora, G. et al. Growth, biomass, carbon stocks and sequestration in age series Populus deltoides plantations in Tarai region of central Himalaya. Turk. J. Agric. For. https://doi.org/10.3906/tar-1307-94 (2013).Article 

    Google Scholar 
    Song, X. et al. Carbon sequestration by Chinese bamboo forests and their ecological benefits: assessment of potential, problems, and future challenges. Environ. Rev. 19, 418–428 (2011).CAS 
    Article 

    Google Scholar 
    Winjum, J. K., Dixon, R. C. & Schroeder, P. E. Carbon storage in forest plantations and their wood products. J. World Resour. Manag. 8, 1–19 (1997).
    Google Scholar 
    Yadava, A. K. Biomass production and carbon sequestration in different agroforestry systems of Tarai region. Indian For. 136(2), 234–244 (2010).
    Google Scholar 
    Lou, Y., Li, Y., Buckingham, K., Henley, G. & Zhou, G. Bamboo and Climate change mitigation: a comparative analysis of carbon sequestration. In International Network for Bamboo and Rattan (INBAR), Beijing (2010).Nair, P. K. R., Kumar, B. M. & Nair, V. D. Agroforestry as a strategy for carbon sequestration. J. Plant Nutr. Soil Sci. 172, 10–23 (2009).CAS 
    Article 

    Google Scholar  More

  • in

    The effect of climate variability in the efficacy of the entomopathogenic fungus Metarhizium acridum against the desert locust Schistocerca gregaria

    Biological control in IPM systems in Africa. (CABI, 2002). https://doi.org/10.1079/9780851996394.0000Kvakkestad, V., Sundbye, A., Gwynn, R. & Klingen, I. Authorization of microbial plant protection products in the Scandinavian countries: A comparative analysis. Environ. Sci. Policy 106, 115–124 (2020).Article 

    Google Scholar 
    Barzman, M. et al. Eight principles of integrated pest management. Agron. Sustain. Dev. 35, 1199–1215 (2015).Article 

    Google Scholar 
    Popp, J., Pető, K. & Nagy, J. Pesticide productivity and food security. A review. Agron. Sustain. Dev. 33, 243–255 (2013).Article 

    Google Scholar 
    Bale, J., van Lenteren, J. & Bigler, F. Biological control and sustainable food production. Philos. Trans. R. Soc. B Biol. Sci. 363, 761–776 (2008).CAS 
    Article 

    Google Scholar 
    Vacante, V. & Bonsignore, C. P. Natural enemies and pest control. In Handbook of Pest Management in Organic Farming 60–77 (CABI, 2018). https://doi.org/10.1079/9781780644998.0060Eilenberg, J., Hajek, A. & Lomer, C. Suggestions for unifying the terminology in biological control. Biocontrol 46, 387–400 (2001).Article 

    Google Scholar 
    Lacey, L. A. et al. Insect pathogens as biological control agents: Back to the future. J. Invertebr. Pathol. 132, 1–41 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hatting, J. L., Moore, S. D. & Malan, A. P. Microbial control of phytophagous invertebrate pests in South Africa: Current status and future prospects. J. Invertebr. Pathol. 165, 54–66 (2019).PubMed 
    Article 

    Google Scholar 
    Karimi, S., Askari Seyahooei, M., Izadi, H., Bagheri, A. & Khodaygan, P. Effect of arsenophonus endosymbiont elimination on fitness of the date palm hopper, ommatissus lybicus (Hemiptera: Tropiduchidae). Environ. Entomol. 48, 614–622 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kumar, K. K. et al. Microbial biopesticides for insect pest management in India: Current status and future prospects. J. Invertebr. Pathol. 165, 74–81 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mascarin, G. M. et al. Current status and perspectives of fungal entomopathogens used for microbial control of arthropod pests in Brazil. J. Invertebr. Pathol. 165, 46–53 (2019).PubMed 
    Article 

    Google Scholar 
    Shapiro-Ilan, D. I., Bruck, D. J. & Lacey, L. A. Principles of epizootiology and microbial control. Insect Pathol. https://doi.org/10.1016/B978-0-12-384984-7.00003-8 (2012).Article 

    Google Scholar 
    Hawkins, B. A. & Cornell, H. V. Theoretical Approaches to Biological Control. https://doi.org/10.1017/CBO9780511542077 (Cambridge University Press, 2009).Tonnang, H. E. Z., Nedorezov, L. V., Ochanda, H., Owino, J. & Löhr, B. Assessing the impact of biological control of Plutella xylostella through the application of Lotka—Volterra model. Ecol. Model. 220, 60–70 (2009).Article 

    Google Scholar 
    Hesketh, H., Roy, H. E., Eilenberg, J., Pell, J. K. & Hails, R. S. Challenges in modelling complexity of fungal entomopathogens in semi-natural populations of insects. Biocontrol 55, 55–73 (2010).Article 

    Google Scholar 
    Fuxa, J. R. & Tanada, Y. Epizootiology of Insect Diseases (Wiley, 1987).
    Google Scholar 
    Lacey, L. A. Manual of Techniques in Insect Pathology. Manual of Techniques in Insect Pathology (Academic, 1997). https://doi.org/10.1016/b978-0-12-432555-5.x5000-3.Book 

    Google Scholar 
    Lomer, C. J., Bateman, R. P., Johnson, D. L., Langewald, J. & Thomas, M. Biological control of locusts and grasshoppers. Annu. Rev. Entomol. 46, 667–702 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Arthurs, S. & Thomas, M. B. Effects of a mycoinsecticide on feeding and fecundity of the brown locust Locustana pardalina. Biocontrol Sci. Technol. 10, 321–329 (2000).Article 

    Google Scholar 
    Jiang, W. et al. Effects of the entomopathogenic fungus Metarhizium anisopliae on the mortality and immune response of Locusta migratoria. Insects 11, 36 (2020).Article 

    Google Scholar 
    Thomas, M. B. & Blanford, S. Thermal biology in insect-parasite interactions. Trends Ecol. Evol. 18, 344–350 (2003).Article 

    Google Scholar 
    Douthwaite, M. B. Development and Commercialization of the Green Muscle Biopesticide 21 (2001).Douthwaite, B., Langewald, J., & Harris, J. Development and commercialization of the Green Muscle biopesticide. (International Institute of Tropical Agriculture, 2002).CABI. Green Muscle providing strength against devastating locusts in the horn of Africa—CABI.org. CABI.org https://www.cabi.org/news-article/green-muscle-providing-strength-against-devastating-locusts-in-the-horn-of-africa/ (2020).Geoff, G. & Steve, W. Biological Control (Springer, 1996). https://doi.org/10.1007/978-1-4613-1157-7.Book 

    Google Scholar 
    Fargues, J., Ouedraogo, A., Goettel, M. S. & Lomer, C. J. Effects of temperature, humidity and inoculation method on susceptibility of Schistocerca gregaria to Metarhizium flavoviride. Biocontrol Sci. Technol. 7, 345–356 (1997).Article 

    Google Scholar 
    Aragón, P., Coca-Abia, M. M., Llorente, V. & Lobo, J. M. Estimation of climatic favourable areas for locust outbreaks in Spain: Integrating species’ presence records and spatial information on outbreaks. J. Appl. Entomol. 137, 610–623 (2013).Article 

    Google Scholar 
    Arthurs, S. & Thomas, M. B. Effect of dose, pre-mortem host incubation temperature and thermal behaviour on host mortality, mycosis and sporulation of Metarhizium anisopliae var. acridum in Schistocerca gregaria. Biocontrol Sci. Technol. 11, 411–420 (2001).Article 

    Google Scholar 
    van der Valk, H. Review of the efficacy of Metarhizium anisopliae var. acridum. FAO—U.N. Publ. (2007).Klass, J. I., Blanford, S. & Thomas, M. B. Development of a model for evaluating the effects of environmental temperature and thermal behaviour on biological control of locusts and grasshoppers using pathogens. Agric. For. Entomol. 9, 189–199 (2007).Article 

    Google Scholar 
    Devi, K. U., Sridevi, V., Mohan, C. M. & Padmavathi, J. Effect of high temperature and water stress on in vitro germination and growth in isolates of the entomopathogenic fungus Beauveria bassiana (Bals.) Vuillemin. J. Invertebr. Pathol. 88, 181–189 (2005).PubMed 
    Article 

    Google Scholar 
    Dimbi, S., Maniania, N. K., Lux, S. A. & Mueke, J. M. Effect of constant temperatures on germination, radial growth and virulence of Metarhizium anisopliae to three species of African tephritid fruit flies. Biocontrol 49, 83–94 (2004).Article 

    Google Scholar 
    Ekesi, S., Maniania, N. K. & Ampong-Nyarko, K. Effect of temperature on germination, radial growth and virulence of Metarhizium anisopliae and Beauveria bassiana on Megalurothrips sjostedti. Biocontrol Sci. Technol. 9, 177–185 (1999).Article 

    Google Scholar 
    Thomas, M. B. & Jenkins, N. E. Effects of temperature on growth of Metarhizium flavoviride and virulence to the variegated grasshopper Zonocerus variegatus. Mycol. Res. 101, 1469–1474 (1997).Article 

    Google Scholar 
    Klass, J. I., Blanford, S. & Thomas, M. B. Use of a geographic information system to explore spatial variation in pathogen virulence and the implications for biological control of locusts and grasshoppers. Agric. For. Entomol. 9, 201–208 (2007).Article 

    Google Scholar 
    Castro, T., Moral, R., Demétrio, C., Delalibera, I. & Klingen, I. Prediction of sporulation and germination by the spider mite pathogenic fungus Neozygites floridana (Neozygitomycetes: Neozygitales: Neozygitaceae) based on temperature, humidity and time. Insects 9, 69 (2018).PubMed Central 
    Article 

    Google Scholar 
    Hajek, A. E., Larkin, T. S., Carruthers, R. I. & Soper, R. S. Modelling the dynamics of Entomophaga maimaga (Zygomycetes: Entomophtorales) epizootics in gypsy moth (Lepidoptera: Lymantridae) populations. Environ. Entomol. 22, 1172–1187 (1993).Article 

    Google Scholar 
    Gul, H. T., Saeed, S. & Khan, F. A. Z. Entomopathogenic fungi as effective insect pest management tactic: A review. Appl. Sci. Bus. Econ. 1, 10–18 (2014).
    Google Scholar 
    Davidson, G. et al. Study of temperature—Growth interactions of entomopathogenic fungi with potential for control of Varroa destructor (Acari: Mesostigmata) using a nonlinear model of poikilotherm development. J. Appl. Microbiol. 94, 816–825 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hallsworth, J. E. & Magan, N. Water and temperature relations of growth of the entomogenous fungi Beauveria bassiana, Metarhizium anisopliae, and Paecilomyces farinosus. J. Invertebr. Pathol. 74, 261–266 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fargues, J. et al. Climatic factors on entomopathogenic hyphomycetes infection of Trialeurodes vaporariorum (Homoptera: Aleyrodidae) in Mediterranean glasshouse tomato. Biol. Control 28, 320–331 (2003).Article 

    Google Scholar 
    Boulard, T. et al. Effect of greenhouse ventilation on humidity of inside air and in leaf boundary-layer. Agric. For. Meteorol. 125, 225–239 (2004).ADS 
    Article 

    Google Scholar 
    Mishra, S., Kumar, P. & Malik, A. Effect of temperature and humidity on pathogenicity of native Beauveria bassiana isolate against Musca domestica L. J. Parasit. Dis. 39, 697–704 (2015).PubMed 
    Article 

    Google Scholar 
    Klingen, I., Westrum, K. & Meyling, N. V. Effect of Norwegian entomopathogenic fungal isolates against Otiorhynchus sulcatus larvae at low temperatures and persistence in strawberry rhizospheres. Biol. Control 81, 1–7 (2015).Article 

    Google Scholar 
    Thaochan, N., Benarlee, R., Shekhar Prabhakar, C. & Hu, Q. Impact of temperature and relative humidity on effectiveness of Metarhizium guizhouense PSUM02 against longkong bark eating caterpillar Cossus chloratus Swinhoe under laboratory and field conditions. J. Asia. Pac. Entomol. 23, 285–290 (2020).Article 

    Google Scholar 
    Kryukov, V. et al. Ecological preferences of Metarhizium spp. from Russia and neighboring territories and their activity against Colorado potato beetle larvae. J. Invertebr. Pathol. 149, 1–7 (2017).PubMed 
    Article 

    Google Scholar 
    Saldarriaga Ausique, J. J., D’Alessandro, C. P., Conceschi, M. R., Mascarin, G. M. & Delalibera Júnior, I. Efficacy of entomopathogenic fungi against adult Diaphorina citri from laboratory to field applications. J. Pest Sci. 2017 903 90, 947–960 (2017).
    Google Scholar 
    Dwyer, G. Density dependence and spatial structure in the dynamics of insect pathogens. Am. Nat. 143, 533–562 (1994).ADS 
    Article 

    Google Scholar 
    Dwyer, G., Elkinton, J. & Hajek, A. Spatial scale and the spread of a fungal pathogen of gypsy moth. Am. Nat. 152, 485–494 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Knudsen, G. R. & Schotzko, D. J. Spatial simulation of epizootics caused by Beauveria bassiana in Russian wheat aphid populations. Biol. Control 16, 318–326 (1999).Article 

    Google Scholar 
    Weseloh, R. M. Effect of conidial dispersal of the fungal pathogen Entomophaga maimaiga (Zygomycetes: Entomophthorales) on survival of its gypsy moth (Lepidoptera: Lymantriidae) host. Biol. Control 29, 138–144 (2004).Article 

    Google Scholar 
    Meynard, C. N. et al. Climate-driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change. Glob. Chang. Biol. 23, 4739–4749 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    Anderson, R. M. & May, R. M. Infectious diseases of humans: Dynamics and control. Aust. J. Public Health 16, 208–212 (1991).
    Google Scholar 
    Cáceres, C. E. et al. Complex Daphnia interactions with parasites and competitors. Math. Biosci. 258, 148–161 (2014).MathSciNet 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    Briggs, C. J. & Godfray, H. C. J. The dynamics of insect-pathogen interactions stage-structured populations c. J. Am. Nat. 145, 855–887 (1995).Article 

    Google Scholar 
    Rapti, Z. & Cáceres, C. E. Effects of intrinsic and extrinsic host mortality on disease spread. Bull. Math. Biol. 78, 235–253 (2016).MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    Hartemink, N. A., Randolph, S. E., Davis, S. A. & Heesterbeek, J. A. P. The basic reproduction number for complex disease systems: Defining R0 for tick-borne infections. Am. Nat. 171, 743–754 (2014).Article 

    Google Scholar 
    Arthur, F. H. Toxicity of diatomaceous earth to red flour beetles and confused flour beetles (Coleoptera: Tenebrionidae): Effects of temperature and relative humidity. J. Econ. Entomol. 93, 526–532 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Arthurs, S. & Thomas, M. B. Effects of temperature and relative humidity on sporulation of Metarhizium anisopliae var. acridum in mycosed cadavers of Schistocerca gregaria. J. Invertebr. Pathol. 78, 59–65 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Whipps, J. M. & Davies, K. G. Success in Biological Control of Plant Pathogens and Nematodes by Microorganisms. In Biological Control: Measures of Success 1st edn, (eds Gurr, G. & Wratten, S.) 429. https://doi.org/10.1007/978-94-011-4014-0_8 (Springer, Dordrecht, 2000).Gilchrist, M. A., Sulsky, D. L. & Pringle, A. Identifying fitness and optimal life-history strategies for an asexual filamentous fungus. Evolution 60, 970–979 (2006).PubMed 
    Article 

    Google Scholar 
    Frank, S. A. Spatial processes in host-parasite genetics. In Metapopulation Biology, 1st edn, (eds Hanski, I. A. & Gilpin, M. E.) 325–352. https://doi.org/10.1016/B978-012323445-2/50018-3 (Elsevier, 1997).Yan, Y., Wang, Y.-C., Feng, C.-C., Wan, P.-H.M. & Chang, K.T.-T. Potential distributional changes of invasive crop pest species associated with global climate change. Appl. Geogr. 82, 83–92 (2017).Article 

    Google Scholar 
    Inglis, G. D., Johnson, D. L. & Goettel, M. S. Effects of temperature and thermoregulation on mycosis by Beauveria bassianain grasshoppers. Biol. Control 7, 131–139 (1996).Article 

    Google Scholar 
    Lactin, D. J. & Johnson, D. L. Temperature-dependent feeding rates of Melanoplus sanguinipes nymphs (Orthoptera: Acrididae) laboratory trials. Environ. Entomol. 24, 1291–1296 (1995).Article 

    Google Scholar 
    FAO. Biopesticides for locust control | FAO Stories | Food and Agriculture Organization of the United Nations. Food and Agriculture Organisation of the UN http://www.fao.org/fao-stories/article/en/c/1267098/ (2021).Kimathi, E. et al. Prediction of breeding regions for the desert locust Schistocerca gregaria in East Africa. Sci. Rep. 10, 11937 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cordovez, J. M., Rendon, L. M., Gonzalez, C. & Guhl, F. Using the basic reproduction number to assess the effects of climate change in the risk of Chagas disease transmission in Colombia. Acta Trop. 129, 74–82 (2014).PubMed 
    Article 

    Google Scholar 
    Hartemink, N. A. et al. Mapping the basic reproduction number ( R 0) for vector-borne diseases: A case study on bluetongue virus. EPIDEM 1, 153–161 (2009).CAS 
    Article 

    Google Scholar 
    Jamison, A., Tuttle, E., Jensen, R., Bierly, G. & Gonser, R. Spatial ecology, landscapes, and the geography of vector-borne disease: A multi-disciplinary review. Appl. Geogr. 63, 418–426 (2015).Article 

    Google Scholar 
    Moukam Kakmeni, F. M. et al. Spatial panorama of malaria prevalence in Africa under climate change and interventions scenarios. Int. J. Health Geogr. 17, 2 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ngarakana-Gwasira, E. T., Bhunu, C. P., Masocha, M. & Mashonjowa, E. Transmission dynamics of schistosomiasis in Zimbabwe: A mathematical and GIS approach. Commun. Nonlinear Sci. Numer. Simul. 35, 137–147 (2016).ADS 
    MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Ogden, N. H. & Radojevic, M. Estimated effects of projected climate change on the basic reproductive number of the Lyme disease vector ixodes scapularis. Environ. Health Perspect. 122, 631–639 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Parham, P. E. & Michael, E. Modeling the effects of weather and climate change on malaria transmission. Environ. Health Perspect. 118, 620–626 (2010).PubMed 
    Article 

    Google Scholar 
    Phillips, J. Climate change and surface mining: A review of environment-human interactions & their spatial dynamics. Appl. Geogr. 74, 95–108 (2016).Article 

    Google Scholar 
    Rogers, D. J. & Randolphz, S. E. The global spread of malaria in a future. Warmer World Sci. 2, 1763–1766 (2000).
    Google Scholar 
    Wu, X. et al. Developing a temperature-driven map of the basic reproductive number of the emerging tick vector of Lyme disease Ixodes scapularis in Canada. J. Theor. Biol. 319, 50–61 (2013).ADS 
    MathSciNet 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    CABI. Green Muscle providing strength against devastating locusts in the horn of Africa. https://www.cabi.org/news-article/green-muscle-providing-strength-against-devastating-locusts-in-the-horn-of-africa/ (2020).Piou, C. et al. Mapping the spatiotemporal distributions of the Desert Locust in Mauritania and Morocco to improve preventive management. Basic Appl. Ecol. 25, 37–47 (2017).Article 

    Google Scholar 
    FAO. FAO Locust Hub. https://locust-hub-hqfao.hub.arcgis.com/ (2021).Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    DeJesus, E. X. & Kaufman, C. Routh-Hurwitz criterion in the examination of eigenvalues of a system of nonlinear ordinary differential equations. Phys. Rev. A 35, 5288–5290 (1987).ADS 
    MathSciNet 
    CAS 
    Article 

    Google Scholar 
    QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org. Qgisorg (2014).RCoreTeam. R: A language and environment for statistical computing. The R Foundation for Statistical Computing. (2020).Marino, S., Hogue, I. B., Ray, C. J. & Kirschner, D. E. A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254, 178–196 (2008).ADS 
    MathSciNet 
    PubMed 
    PubMed Central 
    MATH 
    Article 

    Google Scholar  More

  • in

    The effect of reducing per capita water and energy uses on renewable water resources in the water, food and energy nexus

    This work formulates a general framework of the WFE Nexus at the national level, which includes all pertinent interactions between water, food, and energy sources and demands. Figure 1 depicts the feedbacks involving resource availability and consumption. The causal loops of the developed model for national-scale assessment are shown in Fig. 2. The model depicted in Fig. 2 proposes reducing consumption to reduce the water crisis to the extent possible. By reducing water use and pollution the environmental water requirement can be reduced, thus alleviating the water crisis. This paper’s objective is sustainable management by reducing per capita water use (in the residential section) and per capita energy use (in the domestic, public, and commercial section). The WFE nexus is modeled as a dynamic system for demand management applied to the stocks of energy, surface water, and groundwater resources to calculate their input and output rates (flows) at the national level while providing for environmental flow requirements (Fig. 3). The national modeling approach is of the lumped type, meaning that inputs and outputs to the stocks of water and energy represent totals over an entire country (in the case study, Iran); therefore, the models does not consider intra-country regional variations. The units of water resources and energy resources are expressed in cubic meters and MWh, respectively.Figure 1Feedbacks between resources and uses in the WFE nexus taking into account environmental considerations.Full size imageFigure 2The causal loops of the model developed for simulating the WFE nexus.Full size imageFigure 3Flow diagram of the WFE Nexus system.Full size imageBalance of water resourcesThe study of water exchanges in a country is based on the law of conservation of matter. The following sections present calculations pertinent to the annual balance of surface and groundwater resources.Surface water resourcesThe national runoff generated in a country’s high-elevation areas (or high terrain) and low-elevation areas (plains) is quantified with the following equations:$${preheight}_{t}=HeightCotimes {Precipitation}_{t}$$
    (1)

    in which ({preheight}_{t}) = volume of precipitation that falls in high-elevation areas during period t, (HeightCo) = the percentage of total precipitation that falls in high-elevation areas, and ({Precipitation}_{t}) = volume of precipitation during period t.$${preplain}_{t}=PlainCotimes {Precipitation}_{t}$$
    (2)

    in which ({preplain}_{t}) = volume of precipitation that falls in the plains during period t, and (PlainCo) = the percentage of total precipitation that falls in plains (low elevation areas).$${SInflow}_{t}=HeighSInflowCotimes {preheight}_{t}+PlainSInflowCotimes {preplain}_{t}+{OutCSW}_{t}+{Dr}_{t}$$
    (3)

    in which ({SInflow}_{t}) = the total volume of surface flows during period t, (HeighSInflowCo) = the runoff coefficient in high-elevation areas, (PlainSInflowCo) = the runoff coefficient in the plains, ({OutCSW}_{t}) = the difference between the volume of surface inflow and outflow through a country’s border during period t; and ({Dr}_{t}) = the flow of groundwater resources to surface water resources (i.e., baseflow) during period t.It is possible to calculate the water use after calculating the annual surface water originating by precipitation. Some of the water use by the agricultural, industrial, and municipal sectors becomes return flows. Equations (4) through (9) show how to calculate the surface water use and the water return flows to the surface water sources.$${DomWD}_{t}={Population}_{t}times PerCapitaWatertimes 365$$
    (4)

    in which ({DomWD}_{t}) = the volume of water use in the municipal sector during period t, ({Population}_{t}) = the population of the country during period t, and (PerCapitaWater) = per capita drinking water use (cubic meters per person per day).$${IndDomWD}_{t}={DomWD}_{t}+{IndWD}_{t}$$
    (5)

    in which ({IndDomWD}_{t}) = the volume of water use in the municipal and industrial sectors during period t, and ({IndWD}_{t}) = the volume of water use in the industrial sector during period t.The water use by the agricultural sector accounts for the water footprint of agricultural products, which measures their water use per mass of produce, and adjusting the water use by including water losses and agricultural return flows. A separate sub-agent (AGR agent) is introduced to perform the calculations related to the agricultural sector to simplify the dynamic-system model (main model), and the required outputs (BWAgr, GWAgr) of the dynamic system model are called by the agent in the main model (see Figs. 3 and 4). The BWAgr is given by the expression within parentheses in Eq. (6).Figure 4Agricultural subsystem modeled in the AGR agent (shows how to calculate the blue and gray water footprints of agricultural products).Full size image$${AgrWD}_{t}=left(sum_{iin A}{BW}_{i}times {Product}_{i,t}right)times frac{1}{{E}_{Agr}}+OtherAgrWD$$
    (6)

    in which ({AgrWD}_{t}) = the volume of agricultural water use during period t, ({BW}_{i}) = blue water footprint of agricultural product i (cubic meters per ton), ({Product}_{i,t}) = the amount of production of agricultural product i during period t (tons), ({E}_{Agr}) = the overall irrigation efficiency, (OtherAgrWD) = the volume of water consumed by agricultural products not included in the set A of agricultural products (in cubic meters). The set A includes those agricultural products with the largest yields and shares of the national food basket.$${AgrReW}_{t}={AgrWD}_{t}times AgrReCo$$
    (7)

    in which ({AgrReW}_{t}) = the volume of water returned from agricultural water use during the period t, and (AgrReCo) = the coefficient of water returned from agricultural water use.$${IndDomReW}_{t}={IndDomWD}_{t}times IndDomReCo$$
    (8)

    in which ({IndDomReW}_{t}) = the volume of water returned from industrial and municipal water use during period t, and (IndDomReCo) = the coefficient of water returned from industrial and municipal water uses.$${ReSW}_{t}=IndDomReSWCotimes {IndDomReW}_{t}+AgrReSWCotimes {AgrReW}_{t}$$
    (9)

    in which ({ReSW}_{t}) = the volume of water returned from water uses to surface water resources during period t, (IndDomReSWCo) = the percentage of water returned from municipal and industrial water use to surface water resources, and (AgrReSWCo) = the percentage of water returned from agricultural water use to surface water resources.Water is applied to produce energy, and Eqs. (10) through (15) perform the related calculations. The ({WEIF}_{t}) variable in Eq. (14) is necessary to account for the volume of water saved as a result of the energy savings. A PR model is introduced to account for such water savings (see Fig. 3).$${Diff}_{t} ={OutputE}_{t}-{OutputE}_{t}^{P}$$
    (10)

    in which ({Diff}_{t})= the difference between the energy used in the main model during period t and the energy used in period t in the PR model, ({OutputE}_{t}) = the sum of energy uses during period t in the main model (the method of calculating ({OutputE}_{t}) is described in detail in “Energy uses”), and ({OutputE}_{t}^{P}) = the sum of energy uses during period t in the PR model. Equations (11) and (12) account for the case when energy use exceeds energy production under current conditions, in which case energy exports are reduced. This prevents additional energy production to meet excess demand, and, consequently, there would not be increases in water use.$${Diff}_{t} le 0,,,{if,,func}_{t}=0$$
    (11)
    $${Diff}_{t} >0,,,{ if,,func}_{t}={Diff}_{t}$$
    (12)

    in which ({ iffunc}_{t}) = the amount of energy saved during period t.Equation (13) calculates the water required to produce energy:$${{TotalWE}_{t}=Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN+{Elec}_{t}times ENwateruseE$$
    (13)

    in which ({TotalWE}_{t}) = the volume of water required to produce the energy demand during period t,({Elec}_{t}) = the amount of electricity production during period t (MWh), and (ENwateruseE) = the water required per unit of energy generated by electricity (cubic meters per MWh), all other terms were previously defined.Equation (14) calculates the water savings:$${WEIF}_{t}=sum_{t=1}^{T}frac{{TotalWE}_{t}}{{OutputE}_{t}^{0}}times {if,,func}_{t}$$
    (14)

    in which ({WEIF}_{t})= the volume of water saved as a result of the energy saved during period t, T = the number of periods of simulation (T = 5 years).Part of the water used to produce energy from coal, oil, petroleum products, and nuclear fuel is accounted for in the industrial sector water use. For this reason, the volume of water to produce energy calculated with Eq. (15) is reduced by that part of water already accounted for in the industrial water use to avoid double accounting.$${WE}_{t}={Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN-INDEtimes {IndWD}_{t}-{WEIF}_{t}$$
    (15)

    in which ({WE}_{t}) = the volume of water required to produce different types of energy (except those included in the industrial sector) during period t, ({Coal}_{t}) = the energy produced with coal during period t (MWh), (ENwateruseC) = the water required per unit of energy produced with coal (cubic meters per MWh),({Gas}_{t}) = the amount of energy produced with natural gas during period t (MWh), (ENwateruseG) = the water required per unit of energy produced with natural gas (cubic meters per MWh), ({OilPetroleumP}_{t}) = the amount of energy produced with crude oil and other petroleum products during period t (MWh), (ENwateruseO) = the water required per unit of energy produced with crude oil and petroleum products (cubic meters per MWh),({Nuclear}_{t}) = the amount of nuclear energy produced during period t (MWh), (ENwateruseN) = the water required per unit of nuclear energy produced (cubic meters per MWh), and (INDE) = the percentage of industrial water use already accounted for in Eq. (5) (which pertains to water used in the coke coal, oil refineries, and nuclear fuel industries).Part of the discharge of springs enters the surface water sources, and this enters the calculation of the input to the surface water-resources stock in Eq. (16):$${InputSW}_{t}= SInflow+{ReSW}_{t}{+ Fountain}_{t}$$
    (16)

    in which ({InputSW}_{t}) = the volume of inflow water to surface water sources during period t, and ({Fountain}_{t}) = discharge of springs to surface water sources during period t, other terms previously defined.The output of the surface water resources includes water use and the infiltration of surface water into groundwater, the latter calculated with Eq. (17):$${SInflowInf}_{t}={SInflow}_{t}times SInflowInfCo$$
    (17)

    in which ({SInflowInf}_{t}) = the infiltration volume of surface water during period t, and (SInflowInfCo) = the infiltration coefficient of surface water.The output of the surface water resources stock is calculated using Eq. (18):$${OutputSW}_{t}={AgrSWDCo}_{t}times {AgrWD}_{t}+{IndSWDCo}_{t}times {IndWD}_{t}+{DomSWDCo}_{t}times {DomWD}_{t}+{mathrm{ WE}}_{t}+{SInflowInf}_{t}-{EvSwSea}_{t}$$
    (18)

    in which ({OutputSW}_{t}) = the output volume of surface water during period t, ({AgrSWDCo}_{t}) = the percentage of gross agricultural water use from surface water resources during period t, ({IndSWDCo}_{t}) = the percentage of industrial water use from surface water resources during period t, ({DomSWDCo}_{t})= the percentage of gross drinking water consumption from surface water sources during period t, and ({EvSwSea}_{t}) = the total volume of evaporation from surface water plus the discharge of surface water to the sea during period t.The balance of surface water resources is calculated based on Eq. (19):$$SWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputSW}_{t}left(Sright)-{OutputSW}_{t}(S)right]dt+SWater(0)$$
    (19)

    in which (SWaterleft(tright)) = the stock of surface water resources at time t, (SWater(0)) denotes the stock of surface water at the initial time (t = 0).Groundwater resourcesGroundwater resources gain water from deep infiltration of precipitation in the plains and elevated areas from (1) inflows from outside of the study area, (2) infiltration from surface flows and return waters. Groundwater output factors also include the discharge of groundwater resources (wells, springs, and aqueducts), groundwater flow that moves outside the study area and evaporation. Infiltration of precipitation in the plains and in high terrain into groundwater resources is calculated with Eq. (20):$${Inf}_{t}=PrePInfCotimes {preplain}_{t}+PreHInfCotimes {preheight}_{t}$$
    (20)

    in which ({Inf}_{t}) = the volume of water entering groundwater sources through infiltration of precipitation during period t, (PrePInfCo) = the infiltration coefficient of precipitation in the plains, and (PreHInfCo) = the infiltration coefficient of rainfall in high terrain.Equation (21) calculates the volume of return water that accrues to groundwater resources:$${ReGW}_{t}=IndDomReGWCotimes {IndDomReW}_{t}+AgrReGWCotimes {AgrReW}_{t}$$
    (21)

    in which ({ReGW}_{t}) = the volume of water returned from water use that accrues to groundwater resources during period t, (IndDomReGWCo) = the percentage of water returned from municipal and industrial water use accruing to groundwater resources, and (AgrReGWCo) = the percentage of water returned from agricultural water use accruing to groundwater resources.The volume of groundwater input is calculated with Eq. (22):$${InputGW}_{t}={Inf}_{t}+{ReGW}_{t}+{SInflowInf}_{t}+{OutCGw }_{t}$$
    (22)

    in which ({InputGW}_{t}) = the volume of groundwater input during period t, and ({OutCGw }_{t}) = the difference between the volume of groundwater leaving and that entering the country during period t.The volume of groundwater output is calculated with Eq. (23):$${OutputGW}_{t}={AgrGWDCo}_{t}times {AgrWD}_{t}+IndGWDCotimes {IndWD}_{t}+DomGWDCotimes {DomWD}_{t}+{EvGwDr}_{t}$$
    (23)

    in which ({OutputGW}_{t}) = the volume of groundwater output during period t, ({AgrGWDCo}_{t}) = the percentage of gross agricultural water use from groundwater resources during period t, IndGWDCo = the percentage of industrial water use from groundwater resources during period t, DomGWDCo = the percentage of municipal water use from groundwater resources during period t, and ({EvGwDr }_{t}) = the total volume of evaporation from groundwater plus the drainage of groundwater resources to surface water resources at time t.Equation (24) calculates the annual balance of groundwater resources:$$GWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputGW}_{t}left(Sright)-{OutputGW}_{t}left(Sright)right]dt+GWater(0)$$
    (24)

    in which GWater(t) = the groundwater resources stock at time t, (GWater(0)) denotes the stock of groundwater at the initial time (t = 0).Energy usesEnergy uses are calculated with Eqs. (25)–(27). The total national energy use includes the agricultural, industrial, transportation, and exports sectors’ energy demands. The energy uses by these sectors do not change during the implementation of the policy, and, consequently do not change the WFE Nexus in that period; therefore, they are not included in the calculations.$${WDTP}_{t}={DomWD}_{t}times {CEIntensity}_{t}$$
    (25)

    in which ({WDTP}_{t}) = the energy used in the extraction, transmission, distribution, and treatment of water in the water and wastewater system during period t, and ({CEIntensity}_{t}) = the energy intensity in the extraction, transmission, distribution, and treatment of water in water and wastewater systems during the period t (MWh per cubic meter).$${ResComPubED}_{t}=ResComPubPerCapitatimes {Population}_{t}$$
    (26)

    in which ({ResComPubED}_{t}) = the energy use by the domestic, commercial, and public sectors during period t, and (ResComPubPerCapita) = the per capita energy consumption by the domestic, commercial, and public sectors (MWh per person per year).$${OutputE}_{t}={ResComPubED}_{t}+{WDTP}_{t}$$
    (27)
    Environmental water needsThe gray water footprint is defined as the volume of freshwater that is required to assimilate the load of pollutants based on natural background concentrations and existing ambient water quality standards. The estimation of the gray water footprint associated with discharges from agricultural production is based on the load of nitrogen fertilizers, which are pervasive in agriculture. The gray water footprint in terms of nitrogen concentration has been estimated by Mekonnen and Hoekstra24,25, as written in Eq. (28):$${GW}_{t}^{Agr}=sum_{iin A}{GW}_{i}times {Product}_{i,t}$$
    (28)

    in which ({GW}_{t}^{Agr})= the volume of gray water in the agricultural sector during period t, and ({GW}_{i}) = the volume of gray water associated with the production of one ton of agricultural product i (cubic meters per ton)(.)There are no accurate estimates of the concentrations of pollutants per unit of industrial production, or of the concentration of pollutants in municipal wastewater. Therefore, the conservative dilution factor (DF), which is equal to 1 for untreated returned water from the municipal and industrial sectors, is applied in this work. Equation (29) is a simplified equation of the gray water footprint26. The fraction appearing on the right-hand side of Eq. (29) is equal to the DF.$${GW}_{t}^{IndDom}= frac{{C}_{eff}-{C}_{nat}}{{C}_{max}-{C}_{nat}}times {IndDomReW}_{t}times IndDomReUT$$
    (29)

    in which ({GW}_{t}^{IndDom}) = the gray water footprint of the municipal and industrial sectors during period t, ({C}_{eff}) = the nitrogen concentration in return water (mg/L), ({C}_{nat}) = the natural concentrations of contaminant in surface water (mg/L), ({C}_{max}) = the maximum allowable concentration contaminant in surface water (mg/L), and (IndDomReUT) = the percentage of untreated returned water from the municipal and industrial sectors.The total gray water footprint is obtained by summing the footprints associated with the municipal/industrial and agricultural sectors:$${TotalGW}_{mathrm{t}}={GW}_{t}^{IndDom}+{GW}_{t}^{Agr}$$
    (30)

    in which ({TotalGW}_{mathrm{t}}) = the volume of gray water from all sectors during period t.This work considers qualitative and quantitative environmental water needs. Equation (31) is used to calculate the total environmental water need. The Tennant method for calculating the riverine environmental flow requirement (or instream flow) stipulates that, based on the conditions of each basin, between 10 to 30% of the average long-term flow of rivers represents the environmental flow requirement27. The sum of these requirements across all the basins equals the environmental requirement of the entire region or country. Yet, by providing 10 to 30% of the average long-term flow of rivers the riverine ecosystem barely emerges from critical conditions, and is far from optimal ecologic functioning. The total environmental water need is equal to the sum of the environmental flow requirement plus the volume of water needed to dilute the contaminants entering the surface water sources:$${ENV}_{t}={TotalGW}_{t}+Tennant$$
    (31)

    in which ({ENV}_{t}) = the environmental flow requirement during period t, and Tennant = the environmental flow requirement calculated by the Tennant (1976) method.The policy evaluation indexThe available renewable water is calculated with Eq. (32):$${IN}_{t}={OutCGW }_{t}+ {SInflow }_{t}+{ Inf}_{t}-{EvGwDr}_{t}$$
    (32)

    in which ({IN}_{t})= the renewable water available before the application of environmental constraints during period t.The volume of manageable water is calculated with Eq. (33):$$REWleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[INleft(tright)-ENVleft(tright)right]dt$$
    (33)

    in which REW (t) = the (cumulative) manageable and exploitable renewable water in the period t-t0.Equation (34) calculates the total water withdrawals by the agricultural, industrial, municipal, and energy production sectors:$${WDW}_{t}={OutputSW }_{t}+ {OutputGW}_{t}- {cheshmeh}_{t}$$
    (34)

    in which ({WDW}_{t}) = the sum of the withdrawals by the agricultural, industrial, municipal, and energy production sectors during period t.The cumulative water withdrawals are calculated with Eq. (35):$$withdleft(tright)=underset{{t}_{0}}{overset{t}{int }}WDWleft(tright)dt$$
    (35)

    in which (withdleft(tright)) = the sum of the withdrawals by the agricultural, industrial, municipal and energy production sectors in the horizon t-t0.Equation (36) calculates the water stress index:$${index}_{{t}_{f}}^{MRW}=frac{withd({t}_{f})}{REWleft({t}_{f}right)}times 100$$
    (36)

    in which ({index}_{{t}_{f}}^{MRW}) = the renewable water stress index at the end of the study period, and ({t}_{f}) = the period marking the end of the study horizon.Once the water and energy model is developed it must be calibrated with observational data prior to its use in predictions, as shown below. More

  • in

    Phylotype diversity within soil fungal functional groups drives ecosystem stability

    Singh, B. K., Bardgett, R. D., Smith, P. & Reay, D. S. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat. Rev. Microbiol. 8, 779–790 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
    Article 

    Google Scholar 
    Chen, W. et al. Fertility-related interplay between fungal guilds underlies plant richness-productivity relationships in natural grasslands. New Phytol. 226, 1129–1143 (2020).PubMed 
    Article 

    Google Scholar 
    Semchenko, M. et al. Fungal diversity regulates plant–soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kohli, M. et al. Stability of grassland production is robust to changes in the consumer food web. Ecol. Lett. 22, 707–716 (2019).PubMed 
    Article 

    Google Scholar 
    Liang, M. et al. Soil microbes drive phylogenetic diversity–productivity relationships in a subtropical forest. Sci. Adv. 5, eaax5088 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. How soil biota drive ecosystem stability. Trends Plant Sci. 23, 1057–1067 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).PubMed 
    Article 
    CAS 

    Google Scholar 
    Pörtner, H.O. et al. Scientific outcome of the IPBES-IPCC co-sponsored workshop on biodiversity and climate change (IPBES, 2021).Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).PubMed 
    Article 

    Google Scholar 
    Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Anthony, M. A. et al. Forest tree growth is linked to mycorrhizal fungal composition and function across Europe. ISME J. https://doi.org/10.1038/s41396-021-01159-7 (2022).Jia, Y. Y., van der Heijden, M. G. A., Wagg, C., Feng, G. & Walder, F. Symbiotic soil fungi enhance resistance and resilience of an experimental grassland to drought and nitrogen deposition. J. Ecol. 109, 3171–3181 (2020).Article 
    CAS 

    Google Scholar 
    Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).Article 

    Google Scholar 
    Tedersoo, L., Bahram, M. & Zobel, M. How do mycorrhizal associations drive plant population and community biology? Science 367, eaba1223 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Guo, X. et al. Climate warming leads to divergent succession of grassland microbial communities. Nat. Clim. Change 8, 813–818 (2018).Article 

    Google Scholar 
    Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

    Google Scholar 
    Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. Nat. Commun. 10, 2369 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1078–1088 (2014).CAS 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. The influence of soil age on ecosystem structure and function across biomes. Nat. Commun. 11, 4721 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wagg, C. et al. Diversity and asynchrony in soil microbial communities stabilizes ecosystem functioning. Elife 10, 3207 (2021).Article 

    Google Scholar 
    Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. Plant and soil biodiversity have non-substitutable stabilizing effects on biomass production. Ecol. Lett. 24, 1582–1593 (2021).PubMed 
    Article 

    Google Scholar 
    Chen, L. T. et al. Above- and belowground biodiversity jointly drive ecosystem stability in natural alpine grasslands on the Tibetan Plateau. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13307 (2021).Garcia-Palacios, P., Gross, N., Gaitan, J. & Maestre, F. T. Climate mediates the biodiversity-ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Natl Acad. Sci. USA 117, 24345–24351 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).PubMed 
    Article 

    Google Scholar 
    Naeem, S. & Li, S. B. Biodiversity enhances ecosystem reliability. Nature 390, 507–509 (1997).CAS 
    Article 

    Google Scholar 
    Hautier, Y. et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 508, 521–525 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jousset, A., Schmid, B., Scheu, S. & Eisenhauer, N. Genotypic richness and dissimilarity opposingly affect ecosystem performance. Ecol. Lett. 14, 537–624 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jiang, L., Pu, Z. & Nemergut, D. R. On the importance of the negative selection effect for the relationship between biodiversity and ecosystem functioning. Oikos 117, 488–493 (2008).Article 

    Google Scholar 
    Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
    Article 

    Google Scholar 
    Lekberg, Y. et al. Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils. Nat. Commun. 12, 3484 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bastida, F. et al. Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes. ISME J. 15, 2081–2091 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Paruelo, J., Epstein, H. E., Lauenroth, W. K. & Burke, I. C. ANPP estimates from NDVI for the central grassland region of the United States. Ecology 78, 953–958 (1997).Article 

    Google Scholar 
    Jobbágy, E. G., Sala, O. E. & Paruelo, J. M. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83, 307–319 (2002).
    Google Scholar 
    Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bastos, A., Running, S. W., Gouveia, C. & Trigo, R. M. The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J. Geophys. Res. Biogeosci. 118, 1247–1255 (2013).Article 

    Google Scholar 
    Orwin, K. H. & Wardle, D. A. New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances. Soil Biol. Biochem. 36, 1907–1912 (2004).CAS 
    Article 

    Google Scholar 
    Frankenberg, C. et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sens. Environ. 147, 1–12 (2014).Article 

    Google Scholar 
    Sun, Y. et al. Overview of solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens. Environ. 209, 808–823 (2018).Article 

    Google Scholar 
    Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).CAS 
    Article 

    Google Scholar 
    Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

    Google Scholar 
    Beguería, S. et al. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

    Google Scholar 
    Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Luo, H. et al. Contrasting responses of planted and natural forests to drought intensity in Yunnan, China. Remote Sens. 8, 635 (2016).Article 

    Google Scholar 
    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

    Google Scholar 
    Allen, R. G. et al. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements (FAO, 1998); https://www.fao.org/3/x0490e/x0490e00.htmOksanen, J. et al. Vegan: Community Ecology Package (R Foundation for Statistical Computing, 2013).Legendre, P. et al. Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis. J. Plant Ecol. 1, 3–8 (2008).Article 

    Google Scholar 
    Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).Article 

    Google Scholar 
    Lefcheck., J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579.Bates, D. et al. lme4: linear mixed-effects models using Eigen and S4. J. Stat. Soft. 67, 1–48 (2014).
    Google Scholar  More

  • in

    Crabs retreat from heat

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

  • in

    Gentle-giant sharks are on a collision course with mighty ships

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

  • in

    Elemental analyses reveal distinct mineralization patterns in radular teeth of various molluscan taxa

    Runham, N. A study of the replacement mechanism of the pulmonate radula. J. Cell Sci. 3(66), 271–277 (1963).Article 

    Google Scholar 
    Runham, N. & Isarankura, K. Studies on radula replacement. Malacologia 5, 73 (1966).
    Google Scholar 
    Mackenstedt, U. & Märkel, K. Radular structure and function. In The Biology of Terrestrial Molluscs (ed. Barker, G. M.) 213–236 (CABI Publishing, Oxon, United Kingdom, 2001).Chapter 

    Google Scholar 
    Crampton, D. M. Functional anatomy of the buccal apparatus of Onchidoris bilamellata (Mollusca: Opisthobranchia). Trans. Zool. Soc. Lond. 34(1), 45–86 (1977).Article 

    Google Scholar 
    Steneck, R. S. & Watling, L. Feeding capabilities and limitation of herbivorous molluscs: A functional group approach. Mar. Biol. 68(3), 299–319 (1982).Article 

    Google Scholar 
    Jensen, K. R. Evolution of the sacoglossa (Mollusca, Opisthobranchia) and the ecological associations with their food plants. Evol. Ecol. 11, 301–335 (1997).Article 

    Google Scholar 
    Nishi, M. & Kohn, A. J. Radular teeth of Indo-Pacific molluscivorous species of Conus: A comparative analysis. J. Molluscan Stud. 65(4), 483–497 (1999).Article 

    Google Scholar 
    Duda, T. F., Kohn, A. J. & Palumbi, S. R. Origins of diverse feeding ecologies within Conus, a genus of venomous marine gastropods. Biol. J. Linn. Soc. Lond. 73, 391–409 (2001).Article 

    Google Scholar 
    von Rintelen, T., Wilson, A. B., Meyer, A. & Glaubrecht, M. Escalation and trophic specialization drive adaptive radiation of freshwater gastropods in ancient lakes on Sulawesi, Indonesia. Proc. R. Soc. B 271, 2541–2549 (2004).Article 

    Google Scholar 
    Ekimova, I. et al. Diet-driven ecological radiation and allopatric speciation result in high species diversity in a temperate-cold water marine genus Dendronotus (Gastropoda: Nudibranchia). Mol. Phylogenet. Evol. 141, 106609 (2019).PubMed 
    Article 

    Google Scholar 
    Mikhlina, A., Ekimova, I. & Vortsepneva, E. Functional morphology and post-larval development of the buccal complex in Eubranchus rupium (Nudibranchia: Aeolidia: Fionidae). Zoology 143, 125850 (2020).PubMed 
    Article 

    Google Scholar 
    Krings, W. Trophic specialization of paludomid gastropods from ‘ancient’ Lake Tanganyika reflected by radular tooth morphologies and material properties, Thesis, Universität Hamburg (2020).Krings, W., Brütt, J.-O., Gorb, S. N. & Glaubrecht, M. Tightening it up: Diversity of the chitin anchorage of radular-teeth in paludomid freshwater-gastropods. Malacologia 63(1), 77–94 (2020).Article 

    Google Scholar 
    Bleakney, J. S. Indirect evidence of a morphological response in the radula of Placida dendritica (Alder & Hancock, 1843) (Opisthobranchia: Ascoglossa/ Sacoglossa) to different algae prey. Veliger 33(1), 111–115 (1990).
    Google Scholar 
    Jensen, K. R. Morphological adaptations and plasticity of radular teeth of the Sacoglossa (= Ascoglossa) (Mollusca: Opisthobranchia) in relation to their food plants. Biol. J. Linn. Soc. Lond. 48, 135–155 (1993).Article 

    Google Scholar 
    Reid, D. G. & Mak, Y.-M. Indirect evidence for ecophenotypic plasticity in radular dentition of Littorina species (Gastropoda: Littorinidae). J. Molluscan Stud. 65, 355–370 (1999).Article 

    Google Scholar 
    Padilla, D. K., Dilger, E. K. & Dittmann, D. E. Phenotypic plasticity of feeding structures in species of Littorina. Am. Zool. 40, 1161 (2000).
    Google Scholar 
    Ito, A., Ilano, A. S., Goshima, S. & Nakao, S. Seasonal and tidal height variations in body weight and radular length in Nodilittorina radiata (Eydoux and Souleyet, 1852). J. Molluscan Stud. 68, 197–203 (2002).Article 

    Google Scholar 
    Padilla, D. K. Form and function of radular teeth of herbivorous molluscs: Focus on the future. Am. Malacol. Bull. 18(1/2), 163–168 (2003).
    Google Scholar 
    Krings, W. & Gorb, S. N. Substrate roughness induced wear pattern in gastropod radulae. Biotribology 26, 100164 (2021).Article 

    Google Scholar 
    Krings, W., Hempel, C., Siemers, L., Neiber, M. T. & Gorb, S. N. Feeding experiments on Vittina turrita (Mollusca, Gastropoda, Neritidae) reveal tooth contact areas and bent radular shape during foraging. Sci. Rep. 11, 9556 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lu, D. & Barber, A. H. Optimized nanoscale composite behaviour in limpet teeth. J. R. Soc. Interface 9(71), 1318–1324 (2012).PubMed 
    Article 

    Google Scholar 
    Grunenfelder, L. K. et al. Biomineralization: Stress and damage mitigation from oriented nanostructures within the radular teeth of Cryptochiton stelleri. Adv. Funct. Mater. 24(39), 6093–6104 (2014).CAS 
    Article 

    Google Scholar 
    Barber, A. H., Lu, D. & Pugno, N. M. Extreme strength observed in limpet teeth. J. R. Soc. Interface 12(105), 20141326 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Herrera, S. A., Grunenfelder, L., Escobar, E., Wang, Q., Salinas, C., Yaraghi, N., Geiger, J., Wuhrer, R., Zavattieri, P. & Kisailus, D. Stylus support structure and function of radular teeth. In Cryptochiton Stelleri, 20th International Conference on Composite Materials Copenhagen, 19–24th July, 2015.Ukmar-Godec, T. et al. Materials nanoarchitecturing via cation-mediated protein assembly: Making limpet teeth without mineral. Adv. Mater. 29(27), 1701171 (2017).Article 
    CAS 

    Google Scholar 
    Pohl, A. et al. Radular stylus of Cryptochiton stelleri: A multifunctional lightweight and flexible fiber-reinforced composite. J. Mech. Behav. Biomed. Mater. 111, 103991 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stegbauer, L. et al. Persistent polyamorphism in the chiton tooth: From a new biomineral to inks for additive manufacturing. PNAS 118(23), e2020160118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Weaver, J. C. et al. Analysis of an ultra hard magnetic biomineral in chiton radular teeth. Mater. Today 13(1–2), 42–52 (2010).CAS 
    Article 

    Google Scholar 
    Wang, Q. et al. Phase transformations and structural developments in the radular teeth of Cryptochiton stelleri. Adv. Fun. Mater. 23, 2908–2917 (2013).CAS 
    Article 

    Google Scholar 
    Ukmar-Godec, T. Mineralization of goethite in limpet radular teeth. In Iron Oxides: From Nature to Applications (eds Faivre, D. & Frankel, R. B.) 207–224 (Wiley-VCH, Weinheim, 2016).Chapter 

    Google Scholar 
    Krings, W., Brütt, J.-O. & Gorb, S. N. Ontogeny of the elemental composition and the biomechanics of radular teeth in the chiton Lepidochitona cinerea. Under review at Frontiers in Zoology (2022).Brooker, L. R. & Shaw, J. A. The chiton radula: A unique model for biomineralization studies. In Advanced Topics in Biomineralization (ed. Seto, J.) 65–84 (Intech Open, Rijeka, Croatia, 2012).
    Google Scholar 
    Joester, D. & Brooker, L. R. The chiton radula: A model system for versatile use of iron oxides. In Iron Oxides: From Nature to Applications (ed. Seto, J.) 177–205 (Wiley-VCH, Weinheim, 2016).Chapter 

    Google Scholar 
    Kisailus, D. & Nemoto, M. Structural and proteomic analyses of iron oxide biomineralization in chiton teeth. In Biological Magnetic Materials and Applications (eds Matsunaga, T. et al.) 53–73 (Springer, Singapore, 2018).Chapter 

    Google Scholar 
    Moura, H. M. & Unterlass, M. M. Biogenic metal oxides. Biomimetics 5(2), 29 (2020).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Krings, W., Kovalev, A., Glaubrecht, M. & Gorb, S. N. Differences in the Young modulus and hardness reflect different functions of teeth within the taenioglossan radula of gastropods. Zoology 137, 125713 (2019).PubMed 
    Article 

    Google Scholar 
    Krings, W., Neiber, M. T., Kovalev, A., Gorb, S. N. & Glaubrecht, M. Trophic specialisation reflected by radular tooth material properties in an ‘ancient’ Lake Tanganyikan gastropod species flock. BMC Ecol. Evol. 21, 35 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Krings, W., Marcé-Nogué, J. & Gorb, S. N. Finite element analysis relating shape, material properties, and dimensions of taenioglossan radular teeth with trophic specialisations in Paludomidae (Gastropoda). Sci. Rep. 11, 22775 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gorb, S. N. & Krings, W. Mechanical property gradients of taenioglossan radular teeth are associated with specific function and ecological niche in Paludomidae (Gastropoda: Mollusca). Acta Biomater. 134, 513–530 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Troschel, F. H. Das Gebiss Der Schnecken Zur Begründung Einer Natürlichen Classification (Nicolaische Verlagsbuchhandlung, Berlin, Germany, 1863).
    Google Scholar 
    Sollas, I. B. The molluscan radula: Its chemical composition, and some points in its development. Q. J. Microsc. Sci. 51, 115–136 (1907).
    Google Scholar 
    Jones, E., McCance, R. & Shackleton, L. The role of iron and silica in the structure of the radular teeth of certain marine molluscs. J. Exp. Biol. 12(1), 59–64 (1935).CAS 
    Article 

    Google Scholar 
    Tillier, S. & Cuif, J.-P. L’animal-conodonte est-il un Mollusque Aplacophore. C. R. Acad. Sci. Sér. 2 Méc. Phys. Chim. Sci. Univ. Sci. Terre 303(7), 627–632 (1986).Cruz, R., Lins, U. & Farina, M. Minerals of the radular apparatus of Falcidens sp. (Caudofoveata) and the evolutionary implications for the phylum mollusca. Biol. Bull. 194(2), 224–230 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Smith, I. F. Lepidochitona cinerea, identification and biology, 2020. https://doi.org/10.13140/RG.2.2.28288.58889.Smith, I. F. Acanthochitona fascicularis (Linnaeus, 1767), identification and biology, 2020. https://doi.org/10.13140/RG.2.2.10640.64005.Quetglas, A., de Mesa, A., Ordines, F. & Grau, A. Life history of the deep-sea cephalopod family Histioteuthidae in the western Mediterranean. Deep Res. Part I Oceanogr. Res. Pap. 57, 999–1008 (2010).ADS 
    Article 

    Google Scholar 
    Coelho, M., Domingues, P., Balguerias, E., Fernandez, M. & Andrade, J. P. A comparative study of the diet of Loligo vulgaris (Lamarck, 1799) (Mollusca: Cephalopoda) from the south coast of Portugal and the Saharan Bank (Central-East Atlantic). Fish. Res. 29(3), 245–255 (1997).Article 

    Google Scholar 
    Notman, G. M., McGill, R. A., Hawkins, S. J. & Burrows, M. T. Macroalgae contribute to the diet of Patella vulgata from contrasting conditions of latitude and wave exposure in the UK. Mar. Ecol. Prog. Ser. 549, 113–123 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Marchais, V. et al. New tool to elucidate the diet of the ormer Haliotis tuberculata (L.): Digital shell color analysis. Mar. Biol. 164, 71 (2017).Article 

    Google Scholar 
    Eichhorst, T. E. Neritidae of the World: Volume 1 and 2 (ConchBooks, 2016).Bourguignat, M. J. R. Notice Prodromique sur les Mollusques Terrestres et Fluviatiles (Savy, Paris, 1885).
    Google Scholar 
    Bourguignat, M. J. R. Iconographie Malacologiques des Animaux Mollusques Fluviatiles du Lac Tanganika (Corbeil, Crété, 1888).Book 

    Google Scholar 
    West, K., Michel, E., Todd, J., Brown, D. & Clabaugh, J. The gastropods of Lake Tanganyika: Diagnostic key, classification and notes on the fauna (Special publications: Societas Internationalis Limnologiae – Int. Assoc. of Theoretical and Applied Limnology, 2003)Glaubrecht, M. Adaptive radiation of thalassoid gastropods in Lake Tanganyika, East Africa: Morphology and systematization of a paludomid species flock in an ancient lake. Zoosyst. Evol. 84, 71–122 (2008).Article 

    Google Scholar 
    Moore, J. E. S. The Tanganyika Problem (Burst and Blackett, London, 1903).Book 

    Google Scholar 
    Leloup, E. Exploration Hydrobiologique du Lac Tanganika (1946–1947) (Bruxelles, 1953).Brown, D. Freshwater Snails of Africa and their Medical Importance (Taylor and Francis, London, 1994).Book 

    Google Scholar 
    Germain, L. Mollusques du Lac Tanganyika et de ses environs. Extrait des resultats secientifiques des voyages en Afrique d’Edouard Foa. Bull. Mus. Natl. Hist. Nat. 14, 1–612 (1908).
    Google Scholar 
    Coulter, G. W. Lake Tanganyika and its Life (Oxford University Press, Oxford, 1991).
    Google Scholar 
    Bandel, K. Evolutionary history of East African fresh water gastropods interpreted from the fauna of Lake Tanganyika and Lake Malawi. Zent. Geol. Paläontol. Teil I, 233–292 (1997).
    Google Scholar 
    Pilsbry, H. A. & Bequaert, J. The aquatic mollusks of the Begian Congo. With a geographical and ecological account of Congo malacology. Bull. Am. Mus. Nat. Hist. 53, 69–602 (1927).
    Google Scholar 
    Lok, A. F. S. L., Ang, W. F., Ng, P. X., Ng, B. Y. Q. & Tan, S. K. Status and distribution of Faunus ater (Linnaeus, 1758) (Mollusca: Cerithioidea) in Singapore. NiS 4, 115–121 (2011).
    Google Scholar 
    Das, R. R. et al. Limited distribution of devil snail Faunus ater (Linnaeus, 1758) in tropical mangrove habitats of India. IJMS 47(10), 2002–2007 (2018).
    Google Scholar 
    Watson, D. C. & Norton, T. A. Dietary preferences of the common periwinkle, Littorina littorea (L.). J. Exp. Mar. Biol. Ecol. 88, 193–211 (1985).Article 

    Google Scholar 
    Imrie, D. W., McCrohan, C. R. & Hawkins, S. J. Feeding behaviour in Littorina littorea: A study of the effects of ingestive conditioning and previous dietary history on food preference and rates of consumption. Hydrobiologia 193, 191–198 (1990).Article 

    Google Scholar 
    Olsson, M., Svärdh, L. & Toth, G. B. Feeding behaviour in Littorina littorea: The red seaweed Osmundea ramosissima may not prevent trematode infection. Mar. Ecol. Prog. Ser. 348, 221–228 (2007).ADS 
    Article 

    Google Scholar 
    Lauzon-Guay, J. S. & Scheibling, R. E. Food-dependent movement of periwinkles (Littorina littorea) associated with feeding fronts. J. Shellfish Res. 28, 581–587 (2009).Article 

    Google Scholar 
    Bogan, A. E. & Hanneman, E. H. A carnivorous aquatic gastropod in the pet trade in North America: The next threat to freshwater gastropods?. Ellipsaria 15, 18–19 (2013).
    Google Scholar 
    Strong, E. E., Galindo, L. A. & Kantor, Y. I. Quid est Clea helena? Evidence for a previously unrecognized radiation of assassin snails (Gastropoda: Buccinoidea: Nassariidae). PeerJ 11(5), e3638 (2017).Article 

    Google Scholar 
    Himmelman, J. H. & Hamel, J. R. Diet behaviour and reproduction of the whelk Buccinum undatum in the northern Gulf of St Lawrence, eastern Canada. Mar. Biol. 116, 423–430 (1993).Article 

    Google Scholar 
    Barnes, H. & Powell, H. T. Onchidoris fusca (Müller); A predator of barnacles. J. Anim. Ecol. 23(2), 361–363 (1954).Article 

    Google Scholar 
    Waters, V. L. Food-preference of the nudibranch Aeolidia papillosa, and the effect of the defenses of the prey on predation. Veliger 15(3), 174–192 (1973).
    Google Scholar 
    Edmunds, M., Potts, G., Swinfen, R. & Waters, V. The feeding preferences of Aeolidia papillosa (L.) (Mollusca, Nudibranchia). J. Mar. Biol. Assoc. U. K. 54(4), 939–947 (1974).Article 

    Google Scholar 
    Edmunds, M. Advantages of food specificity in Aeolidia papillosa. J. Molluscan Stud. 49(1), 80–81 (1983).Article 

    Google Scholar 
    Sørensen, C. G., Rauch, C., Pola, M. & Malaquias, M. A. E. Integrative taxonomy reveals a cryptic species of the nudibranch genus Polycera (Polyceridae) in European waters. J. Mar. Biol. Assoc. U. K. 100(5), 733–752 (2020).Article 
    CAS 

    Google Scholar 
    Forrest, J. E. On the feeding habits and the morphology and mode of functioning of the alimentary canal in some littoral dorid nudibranchiate. Mollusca. Proc. Linn. Soc. Lond. 164(2), 225–235 (1953).Article 

    Google Scholar 
    Rose, R. M. Functional morphology of the buccal mass of the nudibranch Archidoris pseudoargus. J. Zool. 165(3), 317–336 (1971).Article 

    Google Scholar 
    Faivre, D. & Ukmar-Godec, T. From bacteria to mollusks: The principles underlying the biomineralization of iron oxide materials. Angew. Chem. Int. Ed. Engl. 54(16), 4728–4747 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Towe, K. M. & Lowenstam, H. A. Ultrastructure and development of iron mineralization in the radular teeth of Cryptochiton stelleri (Mollusca). J. Ultrastruct. Res. 17(1–2), 1–13 (1967).CAS 
    PubMed 
    Article 

    Google Scholar 
    Evans, L. A., Macey, D. J. & Webb, J. Distribution and composition of the matrix protein in the radula teeth of the chiton Acanthopleura hirtosa. Mar. Biol. 109, 281–286 (1991).CAS 
    Article 

    Google Scholar 
    Macey, D. J. & Brooker, L. R. The junction zone: Initial site of mineralization in radula teeth of the chiton Cryptoplax striata (Mollusca: Polyplacophora). J. Morphol. 230, 33–42 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lee, A. P. et al. In situ Raman spectroscopic studies of the teeth of the chiton Acanthopleura hirtosa. J. Biol. Inorg. Chem. 3, 614–619 (1998).CAS 
    Article 

    Google Scholar 
    Brooker, L. R. & Macey, D. J. Biomineralization in chiton teeth and its usefulness as a taxonomic character in the genus Acanthopleura Guilding, 1829 (Mollusca: Polyplacophora). Am. Malacol. Bull. 16(1/2), 203–215 (2001).
    Google Scholar 
    Lee, A. P., Brooker, L. R., Macey, D. J., Webb, J. & van Bronswijk, W. A new biomineral identified in the cores of teeth from the chiton Plaxiphora albida. J. Biol. Inorg. Chem. 8(3), 256–262 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Shaw, J. A. et al. The chiton stylus canal: An element delivery pathway for tooth cusp biomineralization. J. Morphol. 270(5), 588–600 (2009).PubMed 
    Article 

    Google Scholar 
    Gordon, L. & Joester, D. Nanoscale chemical tomography of buried organic-inorganic interfaces in the chiton tooth. Nature 469, 194–198 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Emmanuel, S., Schuessler, J. A., Vinther, J., Matthews, A. & von Blanckenburg, F. A preliminary study of iron isotope fractionation in marine invertebrates (chitons, Mollusca) in near-shore environments. Biogeosciences 11(19), 5493–5502 (2014).ADS 
    Article 

    Google Scholar 
    Shaw, J. A., Macey, D. J. & Brooker, L. R. Radula synthesis by three species of iron mineralizing molluscs: Production rate and elemental demand. J. Mar. Biol. Assoc. U. K. 88(3), 597–601 (2008).CAS 
    Article 

    Google Scholar 
    Brooker, L. R., Lee, A. P., Macey, D. J., van Bronswijk, W. & Webb, J. Multiple-front iron-mineralisation in chiton teeth (Acanthopleura echinata: Mollusca: Polyplacophora). Mar. Biol. 142, 447–454 (2003).CAS 
    Article 

    Google Scholar 
    Lee, A. P., Brooker, L. R., Macey, D. J., van Bronswijk, W. & Webb, J. Apatite mineralization in teeth of the chiton Acanthopleura echinata. Calcif. Tissue Int. 67, 408–415 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Brooker, L. R., Lee, A. P., Macey, D. J., Webb, J. & van Bronswijk, W. In situ studies of biomineral deposition in the radula teeth of chitons of the suborder Chitonina. Venus 65(1–2), 71–80 (2006).
    Google Scholar 
    van der Wal, P. Structure and formation of the magnetite-bearing cap of the polyplacophoran tricuspid radula teeth. In Iron Biominerals (eds Frankel, R. B. & Blakemore, R. P.) 221–229 (Plenum Press, New York, 1990).
    Google Scholar 
    Saunders, M., Kong, C., Shaw, J. A. & Clode, P. L. Matrix-mediated biomineralization in marine mollusks: A combined transmission electron microscopy and focused ion beam approach. Microsc. Microanal. 17, 220–225 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Lowenstam, H. A. Phosphatic hard tissues of marine invertebrates, their nature and mechanical function, and some fossil implications. Chem. Geol. 9, 153–166 (1972).ADS 
    CAS 
    Article 

    Google Scholar 
    Macey, D. J., Webb, J. & Brooker, L. R. The structure and synthesis of biominerals in chiton teeth. Bull. Inst. Océanogr. (Monaco) 4(1), 191–197 (1994).
    Google Scholar 
    Lowenstam, H. A. & Weiner, S. Transformation of amorphous calcium phosphate to crystalline dahllite in the radula teeth of chitons. Science 227, 51–52 (1985).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Lowenstam, H. A. & Weiner, S. Mollusca. In On biomineralization (eds Lowenstam, H. A. & Weiner, S.) 88–305 (Oxford University Press, Oxford, 1989).Chapter 

    Google Scholar 
    Evans, L. A. & Alvarez, R. Characterization of the calcium biomineral in the radular teeth of Chiton pelliserpentis. J. Biol. Inorg. Chem. 4(2), 166–170 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Evans, L. A., Macey, D. J. & Webb, J. Calcium biomineralization in the radula teeth of the chiton, Acanthopleura hirtosa. Calcif. Tissue Int. 51, 78–82 (1992).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kim, K. S., Webb, J., Macey, D. J. & Cohen, D. D. Compositional changes during biomineralization of the radula of the chiton Clavarizona hirtosa. J. Inorg. Biochem. 28(2–3), 337–345 (1986).CAS 
    Article 

    Google Scholar 
    Runham, N. W. The histochemistry of the radula of Patella vulgata. Q. J. Microsc. Sci. 102(3), 371–380 (1961).
    Google Scholar 
    Runham, N. W., Thronton, P. R., Shaw, D. A. & Wayte, R. C. The mineralization and hardness of the radular teeth of the limpet Patella vulgate L. Z. Zellforsch. 99, 608–626 (1969).CAS 
    PubMed 
    Article 

    Google Scholar 
    Grime, G. et al. Biological applications of the Oxford scanning proton microprobe. Trends Biochem. Sci. 10(1), 6–10 (1985).CAS 
    Article 

    Google Scholar 
    St Pierre, T. G. et al. Iron oxide biomineralization in the radula teeth of the limpet Patella vulgata; Mössbauer spectroscopy and high resolution transmission electron microscopy studies. Proc. R. Soc. B 228, 31–42 (1986).ADS 
    CAS 

    Google Scholar 
    Mann, S., Perry, C. C., Webb, J., Luke, B. & Williams, R. J. P. Structure, morphology, composition and organization of biogenic minerals in limpet teeth. Proc. R. Soc. B 227(1247), 179–190 (1986).ADS 
    CAS 

    Google Scholar 
    van der Wal, P. Structural and material design of mature mineralized radula teeth of Patella vulgata (Gastropoda). J. Ultrastruct. Mol. Struct. Res. 102(2), 147–161 (1989).Article 

    Google Scholar 
    Huang, C., Li, C.-W., Deng, M. & Chin, T. Magnetic properties of goethite in radulae of limpets. IEEE Trans. Magn. 28(5), 2409–2411 (1992).ADS 
    CAS 
    Article 

    Google Scholar 
    Rinkevich, B. Major primary stages of biomineralization in radular teeth of the limpet Lottia gigantea. Mar. Biol. 117, 269–277 (1993).Article 

    Google Scholar 
    Liddiard, K. J., Hockridge, J. G., Macey, D. J., Webb, J. & van Bronswijk, W. Mineralisation in the teeth of the limpets Patelloida alticostata and Scutellastra laticostata (Mollusca: Patellogastropoda). Molluscan Res. 24, 21–31 (2004).CAS 
    Article 

    Google Scholar 
    Cruz, R. & Farina, M. Mineralization of major lateral teeth in the radula of a deep-sea hydrothermal vent limpet (Gastropoda: Neolepetopsidae). Mar. Biol. 147, 163–168 (2005).CAS 
    Article 

    Google Scholar 
    Davies, M. S., Proudlock, D. J. & Mistry, A. Metal concentrations in the radula of the common limpet, Patella vulgata L., from 10 sites in the UK. Ecotoxicology 14(4), 465–475 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sone, E. D., Weiner, S. & Addadi, L. Biomineralization of limpet teeth: A cryo-TEM study of the organic matrix and the onset of mineral deposition. J. Struct. Biol. 158, 428–444 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hua, T.-E. & Li, C.-W. Silica biomineralization in the radula of a limpet Notoacmea schrenckii (Gastropoda: Acmaeidae). Zool. Stud. 46(4), 379–388 (2007).CAS 

    Google Scholar 
    Krings, W. et al. In slow motion: Radula motion pattern and forces exerted to the substrate in the land snail Cornu aspersum (Mollusca, Gastropoda) during feeding. R. Soc. Open Sci. 6(7), 2054–5703 (2019).Article 
    CAS 

    Google Scholar 
    Mikovari, A. et al. Radula development in the giant key-hole limpet Megathura crenulate. J. Shellfish Res. 34(3), 893–902 (2015).Article 

    Google Scholar 
    Ukmar-Godec, T., Kapun, G., Zaslansky, P. & Faivre, D. The giant keyhole limpet radular teeth: A naturally-grown harvest machine. J. Struct. Biol. 192, 392–402 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Macey, D. J., Brooker, L. R. & Cameron, V. Mineralisation in the teeth of the gastropod mollusc Nerita atramentosa. Molluscan Res. 18(1), 33–41 (1997).Article 

    Google Scholar 
    Barkalova, V. O., Fedosov, A. E. & Kantor, Y. I. Morphology of the anterior digestive system of tonnoideans (Gastropoda: Caenogastropoda) with an emphasis on the foregut glands. Molluscan Res. 36, 54–73 (2016).Article 

    Google Scholar 
    Ponte, G. & Modica, M. V. Salivary glands in predatory mollusks: Evolutionary considerations. Front. Physiol. 8, 580 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Haszprunar, G. On the origin and evolution of major gastropod groups, with special reference to the Streptoneura. J. Molluscan Stud. 54, 367–441 (1988).Article 

    Google Scholar 
    Sasaki, T. Comparative anatomy and phylogeny of the recent Archaeogastropoda (Mollusca: Gastropoda). Univ. Tokyo Bull. 38, 1–224 (1998).
    Google Scholar 
    Simone, L. R. L. Phylogeny of the Caenogastropoda (Mollusca), based on comparative morphology. Arq. Zool. 42(4), 161–323 (2011).Article 

    Google Scholar 
    Meirelles, C. A. & Matthews-Cascon, H. Relations between shell size and radula size in marine prosobranchs (Mollusca: Gastropoda). Thalassas 19(2), 45–53 (2003).
    Google Scholar 
    Peile, A. J. Some radula problems. J. Conchol. 20, 292–304 (1937).
    Google Scholar 
    Marcus, E. & Marcus, E. Mesogastropoden von der Küste São Paulos. Abh Math Naturwissenschaftlichen Kl Akad Wiss Lit Mainz 1963(1), 1–105 (1963).
    Google Scholar 
    Reid, D. G. The Littorinid Molluscs of Mangrove Forests in the Indo-Pacific Region: The Genus LITTORARIA (British Museum Natural History, London, 1986).
    Google Scholar 
    Reid, D. G. The comparative morphology, phylogeny and evolution of the gastropod family Littorinidae. Philos. Trans. R. Soc. Lond. B 324, 1–110 (1989).ADS 
    Article 

    Google Scholar 
    Reid, D. G. & Mak, Y.-M. Indirect evidence for ecophenotypic plasticity in radular dentition of Littoraria species (Gastropoda: Littorinidae). J. Molluscan Stud. 65(3), 355–370 (1999).Article 

    Google Scholar 
    Fretter, V. & Graham, A. British Prosobranch Molluscs (The Ray Society, London, 1994).
    Google Scholar 
    Cabral, J. P. Shape and growth in European Atlantic Patella limpets (Gastropoda, Mollusca). Ecological implications for survival. Web Ecol. 7, 11–21 (2007).Article 

    Google Scholar 
    Nesson, M. H. Studies on radula tooth mineralization in the Polyplacophora, thesis, California Institute of Technology, Pasadena, USA (1969).Shaw, J. A., Brooker, L. R. & Macey, D. J. Radular tooth turnover in the chiton Acanthopleura hirtosa (Blainville, 1825) (Mollusca: Polyplacophora). Molluscan Res. 22, 93–99 (2002).Article 

    Google Scholar 
    Isarankura, K. & Runham, N. Studies on the replacement of the gastropod radula. Malacologia 7(1), 71–91 (1968).
    Google Scholar 
    Padilla, D. K., Dittman, D. E., Franz, J. & Sladek, R. Radular production rates in two species of Lacuna Turton (Gastropoda: Littorinidae). J. Molluscan Stud. 62(3), 275–280 (1996).Article 

    Google Scholar 
    Runham, N. W. Rate of replacement of the molluscan radula. Nature 194, 992–993 (1962).ADS 
    Article 

    Google Scholar 
    Mackenstedt, U. & Märkel, K. Experimental and comparative morphology of radula renewal in pulmonates (Mollusca, Gastropoda). Zoomorphology 107(4), 209–239 (1987).Article 

    Google Scholar 
    Mischor, B. & Märkel, K. Histology and regeneration of the radula of Pomacea bridgesi (Gastropoda, Prosobranchia). Zoomorphology 104, 42–66 (1984).Article 

    Google Scholar 
    Fujioka, Y. Seasonal aberrant radular formation in Thais bronni (Dunker) and T. clavigera (Küster) (Gastropoda: Muricidae). J. Exp. Mar. Biol. Ecol. 90(1), 43–54 (1985).Article 

    Google Scholar 
    Liu, Z., Meyers, M. A., Zhang, Z. & Ritchie, R. O. Functional gradients and heterogeneities in biological materials: Design principles, functions, and bioinspired applications. Progr. Mater. Sci. 88, 467–498 (2017).CAS 
    Article 

    Google Scholar 
    Vincent, J. F. V. The hardness of the tooth of Patella vulgata L. Radula: A Reappraisal. J. Molluscan Stud. 46, 129–133 (1980).
    Google Scholar 
    Evans, L. A., Macey, D. J. & Webb, J. Characterization and structural organization of the organic matrix of radula teeth of the chiton Acanthopleura hirtosa. Philos. Trans. R. Soc. Lond. B 329, 87–96 (1990).ADS 
    Article 

    Google Scholar 
    Evans, L. A., Macey, D. J. & Webb, J. Matrix heterogeneity in the radular teeth of the chiton Acanthopleura hirtosa. Acta Zool. 75(1), 75–79 (1994).Article 

    Google Scholar 
    Wealthall, R. J., Brooker, L. R., Macey, D. J. & Griffin, B. J. Fine structure of the mineralized teeth of the chiton Acanthopleura echinata (Mollusca: Polyplacophora). J. Morphol. 265, 165–175 (2005).PubMed 
    Article 

    Google Scholar 
    Krings, W., Kovalev, A. & Gorb, S. N. Influence of water content on mechanical behaviour of gastropod taenioglossan radulae. Proc. R. Soc. B 288, 20203173 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Krings, W., Kovalev, A. & Gorb, S. N. Collective effect of damage prevention in taenioglossan radular teeth is related to the ecological niche in Paludomidae (Gastropoda: Cerithioidea). Acta Biomater. 135, 458–472 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Radwin, G. E. & Wells, H. W. Comparative radular morphology and feeding habits of muricid gastropods from the Gulf of Mexico. Bull. Mar. Sci. 18(1), 72–85 (1968).
    Google Scholar 
    Grünbaum, D. & Padilla, D. K. An integrated modeling approach to assessing linkages between environment, organism, and phenotypic plasticity. Integr. Comp. Biol. 54(2), 323–335 (2014).PubMed 
    Article 

    Google Scholar 
    Scheel, C., Gorb, S. N., Glaubrecht, M. & Krings, W. Not just scratching the surface: Distinct radular motion patterns in Mollusca. Biol. Open 9, bio055699 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gray, J. On the division of ctenobranchous gasteropodous Mollusca into larger groups and families. Ann. Mag. Nat. Hist. 11(2), 124–133 (1853).Article 

    Google Scholar 
    Hyman, L. H. Mollusca I. Aplacophora polyplacophora monoplacophora. Gastropoda, the coelomate bilateria. The invertebrates 6 (McGraw-Hill Book Company, New York, 1967).
    Google Scholar 
    Nixon, M. A nomenclature for the radula of the Cephalopoda (Mollusca) – living and fossil. J. Zool. 236, 73–81 (1995).Article 

    Google Scholar 
    Haszprunar, G. & Götting, K. J. Mollusca, Weichtiere. In Spezielle Zoologie Teil Einzeller und wirbellose Tiere (eds Westheide, W. & Rieger, R.) 305–362 (Springer, Berlin, Germany, 2007).
    Google Scholar 
    Lowenstam, H. A. Magnetite in denticle capping in recent chitons (Polyplacophora). Geol. Soc. Am. Bull. 73, 435–438 (1962).ADS 
    CAS 
    Article 

    Google Scholar 
    Kirschvink, J. L. & Lowenstam, H. A. Mineralization and magnetization of chiton teeth: Paleomagnetic, sedimentalogic and biologic implications of organic magnetite. EPSL 44, 193–204 (1979).ADS 
    Article 

    Google Scholar 
    Han, Y. et al. Magnetic and structural properties of magnetite in radular teeth of chiton Acanthochiton rubrolinestus. Bioelectromagnetics 32, 226–233 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nemoto, M. et al. Integrated transcriptomic and proteomic analyses of a molecular mechanism of radular teeth biomineralization in Cryptochiton stelleri. Sci. Rep. 9, 856 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    McCoey, J. M. et al. Quantum magnetic imaging of iron biomineralization in teeth of the chiton Acanthopleura hirtosa. Small Methods 4, 1900754 (2020).CAS 
    Article 

    Google Scholar 
    Lowenstam, H. A. Lepidocrocite, an apatite mineral, and magnetite in teeth of chitons (Polyplacophora). Science 56, 1373–1375 (1967).ADS 
    Article 

    Google Scholar 
    Brooker, L. R., Lee, A. P., Macey, D. J. & Webb, J. Molluscan and other marine teeth. In Encyclopedia of Materials: Science and Technology (eds Buschow, K. H. J. et al.) 5186–5189 (Elsevier Science Ltd., Oxford, 2001).Chapter 

    Google Scholar 
    Shaw, J. A. et al. Ultrastructure of the epithelial cells associated with tooth biomineralization in the chiton Acanthopleura hirtosa. Microsc. Microanal. 15(2), 154–165 (2009).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Creighton, T. E. Protein folding coupled to disulphide bond formation. Biol. Chem. 378(8), 731–744 (1997).CAS 
    PubMed 

    Google Scholar 
    Harding, M. M. Metal-ligand geometry relevant to proteins and in proteins: Sodium and potassium. Acta Cryst. D 58, 872–874 (2002).Article 
    CAS 

    Google Scholar 
    Hayes, T. The influence of diet on local distributions of Cypraea. Pac. Sci. 37(1), 27–36 (1983).
    Google Scholar 
    Padilla, D. K. The importance of form: Differences in competitive ability, resistance to consumers and environmental stress in an assemblage of coralline algae. J. Exp. Mar. Biol. Ecol. 79(2), 105–127 (1984).Article 

    Google Scholar 
    Kesler, D. H., Jokinen, E. H. & Munns, W. R. Jr. Trophic preferences and feeding morphology of two pulmonate snail species from a small New England pond, USA. Can. J. Zool. 64, 2570–2575 (1986).Article 

    Google Scholar 
    Blinn, W., Truitt, R. E. & Pickart, A. Feeding ecology and radular morphology of the freshwater limpet Ferrissia fragilis. J. N. Am. Benthol. Soc. 8(3), 237–242 (1989).Article 

    Google Scholar 
    Hawkins, S. J. et al. A comparison of feeding mechanisms in microphagous, herbivorous, intertidal, prosobranchs in relation to resource partitioning. J. Molluscan Stud. 55(2), 151–165 (1989).Article 

    Google Scholar 
    Franz, C. J. Feeding patterns of Fissurella species on Isla de Margarita, Venezuela: Use of radulae and food passage rates. J. Molluscan Stud. 56(1), 25–35 (1990).Article 

    Google Scholar 
    Thompson, R. C., Johnson, L. E. & Hawkins, S. J. A method for spatial and temporal assessment of gastropod grazing intensity in the field: The use of radula scrapes on wax surfaces. J. Exp. Mar. Biol. Ecol. 218(1), 63–76 (1997).Article 

    Google Scholar 
    Iken, K. Feeding ecology of the Antarctic herbivorous gastropod Laevilacunaria antarctica Martens. J. Exp. Mar. Biol. Ecol. 236(1), 133–148 (1999).Article 

    Google Scholar 
    Forrest, R. E., Chapman, M. G. & Underwood, A. J. Quantification of radular marks as a method for estimating grazing of intertidal gastropods on rocky shores. J. Exp. Mar. Biol. Ecol. 258(2), 155–171 (2001).PubMed 
    Article 

    Google Scholar 
    Dimitriadis, V. K. Structure and function of the digestive system in Stylommatophora. In The Biology of Terrestrial Molluscs (ed. Barker, G. M.) 237–258 (CABI Publishing, Wallingford, UK, 2001).Chapter 

    Google Scholar 
    Speiser, B. Food and feeding behaviour. In The Biology of Terrestrial Molluscs (ed. Barker, G. M.) 259–288 (CABI Publishing, Wallingford, UK, 2001).Chapter 

    Google Scholar 
    Sitnikova, T. et al. Resource partitioning in endemic species of Baikal gastropods indicated by gut contents, stable isotopes and radular morphology. Hydrobiologia 682, 75–90 (2012).CAS 
    Article 

    Google Scholar 
    Bergmeier, F. S., Ostermair, L. & Jörger, K. M. Specialized predation by deep-sea Solenogastres revealed by sequencing of gut contents. Curr. Biol. 31(13), R836–R837 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Goodheart, J. A., Bazinet, A. L., Valdés, Á., Collins, A. G. & Cummings, M. P. Prey preference follows phylogeny: Evolutionary dietary patterns within the marine gastropod group Cladobranchia (Gastropoda: Heterobranchia: Nudibranchia). BMC Evol. Biol. 17, 221 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Padilla, D. K. Structural resistance of algae to herbivores. A biomechanical approach. Mar. Biol. 90, 103–109 (1985).Article 

    Google Scholar 
    Padilla, D. K. Algal structural defenses: Form and calcification in resistance to tropical limpets. Ecology 70(4), 835–842 (1989).Article 

    Google Scholar 
    Wilson, A. B., Glaubrecht, M. & Meyer, A. Ancient lakes as evolutionary reservoirs: Evidence from the thalassoid gastropods of Lake Tanganyika. Proc. R. Soc. B 271(1538), 529–536 (2004).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ponder, W. & Lindberg, D. R. Phylogeny and Evolution of the Mollusca (University of California Press, Berkeley, California, 2008).Book 

    Google Scholar 
    Jörger, K. M. et al. On the origin of Acochlidia and other enigmatic euthyneuran gastropods, with implications for the systematics of Heterobranchia. BMC Evol. Biol. 10, 323 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kocot, K. et al. Phylogenomics reveals deep molluscan relationships. Nature 477, 452–456 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kocot, K. M., Poustka, A. J., Stöger, I., Halanych, K. M. & Schrödl, M. New data from Monoplacophora and a carefully-curated dataset resolve molluscan relationships. Sci. Rep. 10, 101 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Smith, S. et al. Resolving the evolutionary relationships of molluscs with phylogenomic tools. Nature 480, 364–367 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Haszprunar, G. & Wanninger, A. Molluscs. Curr Biol. 22(13), R510-514 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wanninger, A. & Wollesen, T. The evolution of molluscs. Biol. Rev. 94, 102–115 (2019).Article 

    Google Scholar 
    Irisarri, I., Uribe, J. E., Eernisse, D. J. & Zardoya, R. A mitogenomic phylogeny of chitons (Mollusca: Polyplacophora). BMC Evol. Biol. 20, 22 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More