Philippa Kaur

Sakamoto, M. Primary production by phytoplankton community in some Japanese lakes and its dependence on lake depth. Archiv für Hydrobilogie. 62, 1–28 (1966).
Google Scholar 
Vollenweider, R. A. Scientific fundamentals of the eutrophication of lakes and flowing waters, with particular reference to nitrogen and phosphorus as factors in eutrophication (Organisation for Economic Co-operation and Development, 1968).
Google Scholar 
Edmondson, W. T. Phosphorus, nitrogen, and algae in Lake Washington after diversion of sewage. Science 169, 690–691 (1970).ADS 
CAS 
Article 

Google Scholar 
Dillon, P. J. & Rigler, F. H. The phosphorus-chlorophyll relationship in lakes. Limnol. Oceanogr. 19, 767–773 (1974).ADS 
CAS 
Article 

Google Scholar 
Jones, J. R. & Bachmann, R. W. Prediction of phosphorus and chlorophyll levels in lakes. J. Water Pollut. Control Feder. 48, 2176–2182 (1976).CAS 

Google Scholar 
Schindler, D. W. Evolution of phosphorus limitation in lakes. Science 195, 260–262 (1977).ADS 
CAS 
Article 

Google Scholar 
Filstrup, C. T. & Downing, J. A. Relationship of chlorophyll to phosphorus and nitrogen in nutrient-rich lakes. Inland Waters. 7, 385–400 (2017).CAS 
Article 

Google Scholar 
Schindler, D. W. Recent advances in the understanding and management of eutrophication. Limnol. Oceanogr. 51, 356–363 (2006).ADS 
Article 

Google Scholar 
Quinlan, R. et al. Relationships of total phosphorus and chlorophyll in lakes worldwide. Limnol. Oceanogr. 66, 392–404 (2020).ADS 
Article 

Google Scholar 
Yuan, L. L. & Jones, J. R. Rethinking phosphorus–chlorophyll relationships in lakes. Limnol. Oceanogr. 65, 1847–1857 (2020).ADS 
CAS 
Article 

Google Scholar 
Carlson, R. E. A trophic state index for lakes. Limnol. Oceanogr. 11, 361–369 (1977).ADS 
Article 

Google Scholar 
Neveux, J. et al. Comparison of chlorophyll and phaeopigment determinations by spectrophotometric, fluorometric, spectrofluorometric and HPLC methods. Mar. Microb. Food Webs 4, 217–238 (1990).
Google Scholar 
Lampert, W. & Sommer, U. Limnoecology (Oxford University, 2007).
Google Scholar 
Kovalevskaya, R. Z., Zhukava, H. A. & Adamovich, B. V. Modification of the method of spectrophotometric determination of chlorophyll a in the suspended matter of water bodies. J. Appl. Spectrosc. 87, 72–78 (2020).ADS 
CAS 
Article 

Google Scholar 
Søndergaard, M., Lauridsen, T. L., Johansson, L. S. & Jeppesen, E. Nitrogen or phosphorus limitation in lakes and its impact on phytoplankton biomass and submerged macrophyte cover. Hydrobiologia 795, 35–48 (2017).Article 

Google Scholar 
Søndergaard, M., Jensen, J. P., Jeppesen, E. & Møller. P. H. Seasonal dynamics in the concentrations and retention of phosphorus in shallow Danish lakes after reduced loading. Aquat. Ecosyst. Health Manag. 5(1), 19–29 (2002).Magumba, D., Atsushi, M., Michiko, T., Akira, K. & Masao, K. Relationships between Chlorophyll-a, phosphorus and nitrogen as fundamentals for controlling phytoplankton biomass in lakes. Environ. Control. Biol. 51(4), 179–185 (2013).CAS 
Article 

Google Scholar 
Smith, V. H. & Shapiro, J. Chlorophyll-phosphorus relations in individual lakes. Their importance to lake restoration strategies. Environ. Sci. Technol. 15(4), 444–451 (1981).Pothoven, S. A. & Vanderploeg, H. A. Seasonal patterns for Secchi depth, chlorophyll a, total phosphorus, and nutrient limitation differ between nearshore and offshore in Lake Michigan. J. Great Lakes Res. 46, 519–527 (2020).CAS 
Article 

Google Scholar 
Søndergaard, M. & Jeppesen, E. Lake Søbygaard, Denmark: phosphorus dynamics during the first 35 years after an external loading reduction. In: Internal Phosphorus Loading: Causes, Case Studies, and Management (ed. Steinman, A.D. & Spears, B. M.) 285–299 (J. Ross, Plantation, 2020).Guildford, S. J. & Hecky, R. E. Total nitrogen, total phosphorus, and nutrient limitation in lakes and oceans: Is there a common relationship?. Limnol. Oceanogr. 45, 1213–1223 (2000).ADS 
CAS 
Article 

Google Scholar 
Jones, J.R. et al. Nutrients, seston, and transparency of Missouri reservoirs and oxbow lakes. An analysis of regional limnology. Lake Reser. Manag. 24, 155–180 (2008).Pikovsky, A., Rosenblum, M. & Kurths, J. Synchronization. A universal concept in nonlinear sciences (Cambridge University, 2001).Book 

Google Scholar 
Kuramoto, Y. Chemical Oscillations, Waves, and Turbulence (Springer, 1984).Book 

Google Scholar 
Sazonov, A. V. et al. An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG. Biol. Cybern. 100, 129–146 (2009).Article 

Google Scholar 
Medvinsky, A. B. et al. Temperature as a factor affecting fluctuations and predictability of the abundance of lake bacterioplankton. Ecol. Complex. 32, 90–98 (2017).Article 

Google Scholar 
Zhukova, T. V. & Ostapenya, A. P. Estimation of efficiency of nature protection measures in water catchment area of the Naroch lakes. Natural Resources. 3, 68–73 (2000) ((in Russian)).
Google Scholar 
Burlakova, L. E., Karatayev, A. Y. & Padilla, D. K. Changes in the distribution and abundance of Dreissena polymorpha within lakes through time. Hydrobiologia 571, 133–146 (2006).Article 

Google Scholar 
Ostapenia, A. P. et al. Bentification of lake ecosystem: causes, mechanisms, possible consequences, prospect for future research. Trudy BGU. 7, 135–148 (2012) ((in Russian)).
Google Scholar 
Karatayev, A.Y., Burlakova, L.E. & Padilla, D.K. Impacts of Zebra Mussels on aquatic communities and their role as ecosystem engineers. In: Leppäkoski, E., Gollasch, S., Olenin, S. (eds) Invasive Aquatic Species of Europe. Distribution, Impacts and Management (Springer, Dordrecht, 2002).Adamovich, B. V. et al. The divergence of chlorophyll dynamics in the Naroch Lakes. Biophysics 60, 632–638 (2015).CAS 
Article 

Google Scholar 
Zhukova, T. V. et al. Long-term dynamics of suspended matter in Naroch Lakes: Trend or intervation. Inland Water Biol. 10, 250–257 (2017).Article 

Google Scholar 
Adamovich, B. V. et al. Eutrophication, oligotrophication, and benthiphication in Naroch Lakes: 40 years of monitoring. J. Siber. Federal Univ. Biol. 10, 379–394 (2017).Article 

Google Scholar 
Ostapenya A.P. et al. Ecological passport of Lake Myastro (EcoMir, Minsk, 1994) (in Russian).Kantz, H. & Schreiber, T. Nonlinear time series analysis (Cambridge University, 1997).MATH 

Google Scholar 
Kot, M. Elements of mathematical ecology (Cambridge University, 2001).Book 

Google Scholar 
Turchin, P. Complex population dynamics. A Theoretical/Empirical Synthesis (Princeton University, Princeton, 2003).MATH 

Google Scholar 
Cazelles, B. & Stone, L. Detection of imperfect population synchrony in an uncertain world. J. Anim. Ecol. 72, 953–968 (2003).Article 

Google Scholar 
Karatayev, A. Y., Burlakova, L. & Padilla, D. K. The effects of Dreissena polymorpha (Pallas) invasion on aquatic communities in Eastern Europe. J. Shellfish Res. 16, 187–203 (1997).
Google Scholar 
Lia, J. et al. Benthic invaders control the phosphorus cycle in the world’s largest freshwater ecosystem. PNAS 118(6), e2008223118. https://doi.org/10.1073/pnas.2008223118 (2021).CAS 
Article 

Google Scholar 
Mikheyeva, T. M. et al. The dynamics of freshwater phytoplankton stability in the Naroch Lakes (Belarus). Ecol. Ind. 81, 481–490 (2017).Article 

Google Scholar 
Harris, P. H. Phytoplankton ecology. Structure, functioning and flucttuation (Chapman & Hall, London, New York, 1986).Jeppesen, E., Jensen, J. P., Søndergaard, M. & Lauridsen, T. L. Response of fish and plankton to nutrient loading reduction in eight shallow Danish lakes with special emphasis on seasonal dynamics. Freshw. Biol. 50, 1616–1627 (2005).CAS 
Article 

Google Scholar 
Nezlin, N.P. & Li, B-L. Time-series analysis of remote-sensed chlorophyll and environmental factors in the Santa Monica–San Pedro Basin off Southern California. J. Mar. Syst. 39, 185–202 (2003).French, T. D. & Petticrew, E. L. Chlorophyll a seasonality in four shallow eutrophic lakes (northern British Columbia, Canada) and the critical roles of internal phosphorus loading and temperature. Hydrobiologia 575, 285–299 (2007).CAS 
Article 

Google Scholar 
SCOR-UNESCO Working Group no. 17. Determination of photosynthetic pigments in sea-water. Monographs on Oceanologic Methodology 9–18 (UNESSCO, Paris, 1966).Semenov, A. D. Guide on the chemical analysis of continental surface waters (Gidrometeoizdat, 1977) ((in Russian)).
Google Scholar 
Wetzel, R. G. & Likens, G. E. Limnological analysis (Springer, 2000).Book 

Google Scholar 
Steffen, M. & Bartz-Beielstein, T. imputeTS: time series missing value imputation in R. R J. 9(1), 207–218 (2017).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020). More

Karlsson, O. et al. Pesticide-induced multigenerational effects on amphibian reproduction and metabolism. Sci. Total Environ. 775, 145771 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2021-3. https://www.iucnredlist.org (2022).Wake, D. B. & Koo, M. S. Amphibians. Curr. Biol. 28, R1237–R1241 (2018).CAS 
PubMed 
Article 

Google Scholar 
Campbell Grant, E. H., Miller, D. A. & Muths, E. A synthesis of evidence of drivers of amphibian declines. Herpetologica 76, 101–107 (2020).Article 

Google Scholar 
Green, D. M., Lannoo, M. J., Lesbarrères, D. & Muths, E. Amphibian population declines: 30 years of progress in confronting a complex problem. Herpetologica 76, 97–100 (2020).Article 

Google Scholar 
Mason, R., Tennekes, H., Sánchez-Bayo, F. & Jepsen, P. U. Immune suppression by neonicotinoid insecticides at the root of global wildlife declines. J. Environ. Immunol. Toxicol. 1, 3–12 (2013).Article 

Google Scholar 
Adams, E., Leeb, C. & Brühl, C. A. Pesticide exposure affects reproductive capacity of common toads (Bufo bufo) in a viticultural landscape. Ecotoxicology 30, 213–223 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frost, D. R. Amphibian species of the world 6,1, an online reference. Electron. Datab. https://doi.org/10.5531/db.vz.0001 (American Museum of Natural History, 2021).Article 

Google Scholar 
Eterovick, P. C., Souza, A. M. & Sazima, I. Anfíbios da Serra do Cipó [Amphibians from the Serra do Cipó]. http://herpeto.org/wp-content/uploads/2020/11/ANFIBIOS-DA-SERRA-DO-CIPO.pdf (PUCMINAS, 2020).Mijares, A., Rodrigues, M. T. & Baldo, D. Physalaemus cuvieri The IUCN Red List of Threatened Species, version 2014.3. http://www.iucnredlist.org (2010). Accessed 9 Jan 2015.de Sá, F. P., Zina, J. & Haddad, C. F. B. Reproductive dynamics of the Neotropical treefrog Hypsiboas albopunctatus (Anura, Hylidae). J. Herpetol. 48, 181–185 (2014).Article 

Google Scholar 
Herek, J. S. et al. Can environmental concentrations of glyphosate affect survival and cause malformation in amphibians? Effects from a glyphosate-based herbicide on Physalaemus cuvieri and P. gracilis (Anura: Leptodactylidae). Environ. Sci. Pollut. Res. 27, 22619–22630 (2020).CAS 
Article 

Google Scholar 
Silva, F. L. et al. Swimming ability in tadpoles of Physalaemus cf. cuvieri, Scinax x-signatus and Leptodactylus latrans (Amphibia: Anura) exposed to the insecticide chlorpyrifos. Ecotoxicol. Environ. Contam. 16, 13–18 (2021).
Google Scholar 
Pavan, F. A. et al. Morphological, behavioral and genotoxic effects of glyphosate and 2,4-D mixture in tadpoles of two native species of South American amphibians. Environ. Toxicol. Pharmacol. 85, 103637 (2021).CAS 
PubMed 
Article 

Google Scholar 
Simon-Delso, N. et al. Systemic insecticides (Neonicotinoids and fipronil): Trends, uses, mode of action and metabolites. Environ. Sci. Pollut. Res. 22, 5–34 (2015).CAS 
Article 

Google Scholar 
Pietrzak, D., Kania, J., Malina, G., Kmiecik, E. & Wątor, K. Pesticides from the EU first and second watch lists in the water environment. Clean 47, 1–10 (2019).
Google Scholar 
IBAMA: Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis. Relatório de comercialização de agrotóxicos 2019 [Brazilian Pesticide Marketing Report 2019] https://www.ibama.gov.br/agrotoxicos/relatorios-de-comercializacao-de-agrotoxicos#boletinsanuais (2021).IBAMA: Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis. Vendas de ingredientes ativos por UF [Active ingredient sales by UF in Brazil]. http://ibama.gov.br/phocadownload/qualidadeambiental/relatorios/2019/Vendas_ingredientes_ativos_UF_2019.x (2021).IBAMA – Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis. Boletins anuais de produção, importação, exportação e vendas de agrotóxicos no Brasil [Annual bulletins of production, import, export and sales of pesticides in Brazil]. http://ibama.gov.br/index.php?option=com_content&view=article&id=594&Itemid=54 (2021).Pietrzak, D., Kania, J., Kmiecik, E., Malina, G. & Wątor, K. Fate of selected neonicotinoid insecticides in soil–water systems: Current state of the art and knowledge gaps. Chemosphere 255, 126981 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
ANVISA: Agência Nacional de Vigilância Sanitária; Índice Monográfico I13. Imidacloprido. http://portal.anvisa.gov.br/documents/111215/117782/I13+%E2%80%93+Imidacloprido/9d08c7e5-8979-4ee9-b76c-1092899514d7 (2021).Kagabu, S. Discovery of imidacloprid and further developments from strategic molecular designs. J. Agric. Food Chem. 59, 2887–2896 (2011).CAS 
PubMed 
Article 

Google Scholar 
Tomizawa, M. & Casida, J. E. Neonicotinoid insecticide toxicology: Mechanisms of selective action. Annu. Rev. Pharmacol. Toxicol. 45, 247–268 (2005).CAS 
PubMed 
Article 

Google Scholar 
Hashimoto, F. et al. Occurrence of imidacloprid and its transformation product (imidacloprid-nitroguanidine) in rivers during an irrigating and soil puddling duration. Microchem. J. 153, 12 (2020).Article 
CAS 

Google Scholar 
Hladik, M. L. et al. Year-round presence of neonicotinoid insecticides in tributaries to the Great Lakes, USA. Environ. Pollut. 235, 1022–1029 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jurado, A., Walther, M. & Díaz-Cruz, M. Occurrence, fate and environmental risk assessment of the organic microcontaminants included in the Watch Lists set by EU Decisions 2015/495 and 2018/840 in the groundwater of Spain. Sci. Total Environ. 663, 285–296 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Montagner, C. C. et al. Ten years-snapshot of the occurrence of emerging contaminants in drinking, surface and ground waters and wastewaters from São Paulo State, Brazil. J. Braz. Chem. Soc. 30, 614–632 (2019).CAS 

Google Scholar 
CCME. Council of Ministers of the Environment. Canadian water quality guidelines for the protection of aquatic life. Imidacloprid. In Canadian water quality guidelines, Council of Ministers of the Environment. Winnipeg. https://ccme.ca/en/res/imidacloprid-en-canadian-water-quality-guidelines-for-the-protection-of-aquatic-life.pdf (2007).RIVM. Water quality standards for imidacloprid: Proposal for an update according to the Water Framework Directive in National Institute for Public Health and the Environment. https://www.rivm.nl/bibliotheek/rapporten/270006001.pdf (2014).PAN. Pesticide Action Network. International Consolidated List of Banned Pesticides. https://pan-international.org/pan-international-consolidated-list-of-banned-pesticides/ (2021).Brazil. Secretaria Estadual da Saúde do Rio Grande do Sul. Portaria SES RS nº 320, de 28 de abril de 2014. https://www.cevs.rs.gov.br/upload/arquivos/201705/11110603-portaria-agrotoxicos-n-320-de-28-de-abril-de-2014.pdf. (2014).Kobashi, K. et al. Comparative ecotoxicity of imidacloprid and dinotefuran to aquatic insects in rice mesocosms. Ecotoxicol. Environ. Saf. 138, 122–129 (2017).CAS 
PubMed 
Article 

Google Scholar 
Islam, M. A., Hossen, M. S., Sumon, K. A. & Rahman, M. M. Acute toxicity of imidacloprid on the developmental stages of common carp Cyprinus carpio. Toxicol. Environ. Health Sci. 11, 244–251 (2019).Article 

Google Scholar 
Pérez-Iglesias, J. M. et al. The genotoxic effects of the imidacloprid-based insecticide formulation Glacoxan Imida on Montevideo tree frog Hypsiboas pulchellus tadpoles (Anura, Hylidae). Ecotoxicol. Environ. Saf. 104, 120–126 (2014).PubMed 
Article 
CAS 

Google Scholar 
Sievers, M., Hale, R., Swearer, S. E. & Parris, K. M. Contaminant mixtures interact to impair predator-avoidance behaviours and survival in a larval amphibian. Ecotoxicol. Environ. Saf. 161, 482–488 (2018).CAS 
PubMed 
Article 

Google Scholar 
USEPA. United States Environmental Protection Agency. Aquatic Life Benchmarks and Ecological Risk Assessments for Registered Pesticides. https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/aquatic-life-benchmarks-and-ecological-risk. (2021).Feng, S., Kong, Z., Wang, X., Zhao, L. & Peng, P. Acute toxicity and genotoxicity of two novel pesticides on amphibian, Rana N. Hallwell. Chemosphere 56, 457–463 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
De Arcaute, C. R. et al. Genotoxicity evaluation of the insecticide imidacloprid on circulating blood cells of Montevideo tree frog Hypsiboas pulchellus tadpoles (Anura, Hylidae) by comet and micronucleus bioassays. Ecol. Indic. 45, 632–639 (2014).Article 
CAS 

Google Scholar 
Nkontcheu, D. B. K., Tchamadeu, N. N., Ngealekeleoh, F. & Nchase, S. Ecotoxicological effects of imidacloprid and lambda-cyhalothrin (insecticide) on tadpoles of the African common toad, Amietophrynus regularis (Reuss, 1833) (Amphibia: Bufonidae). Emerg. Sci. J. 1, 49–53 (2017).
Google Scholar 
Bortoluzzi, E. C. et al. Contaminação de águas superficiais por agrotóxicos em função do uso do solo numa microbacia hidrográfica de Agudo, RS. Rev. Bras. Eng. Agric. Ambient. 10, 881–887 (2006).Article 

Google Scholar 
Bortoluzzi, E. C. et al. Investigation of the occurrence of pesticide residues in rural wells and surface water following application to tobacco. Quim. Nova 30, 1872–1876 (2007)CAS 
Article 

Google Scholar 
La, N., Lamers, M., Bannwarth, M., Nguyen, V. V. & Streck, T. Imidacloprid concentrations in paddy rice fields in northern Vietnam: measurement and probabilistic modeling. Paddy Water Environ. 13, 191–203 (2015).Article 

Google Scholar 
Sweeney, M. R., Thompson, C. M. & Popescu, V. D. Sublethal, behavioral, and developmental effects of the neonicotinoid pesticide imidacloprid on larval wood frogs (Rana sylvatica). Environ. Toxicol. Chem. 40, 1838–1847 (2021).Article 
CAS 

Google Scholar 
Gibbons, D., Morrissey, C. & Mineau, P. A review of the direct and indirect effects of neonicotinoids and fipronil on vertebrate wildlife. Environ. Sci. Pollut. Res. 22, 103–118 (2015).CAS 
Article 

Google Scholar 
Morrissey, C. A. et al. Neonicotinoid contamination of global surface waters and associated risk to aquatic invertebrates: A review. Environ. Int. 74, 150920 (2015).Article 
CAS 

Google Scholar 
Stinson, S. A. et al. Agricultural surface water, imidacloprid, and chlorantraniliprole result in altered gene expression and receptor activation in Pimephales promelas. Sci. Total Environ. 806, 150920. (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
DiGiacopo, D. G. & Hua, J. Evaluating the fitness consequences of plasticity in tolerance to pesticides. Ecol. Evol. 10, 4448–4456 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Carlson, B. E. & Langkilde, T. Body size variation in aquatic consumers causes pervasive community effects, independent of mean body size. Ecol. Evol. 7, 9978–9990 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Phung, T. X., Nascimento, J. C. S., Novarro, A. J. & Wiens, J. J. Correlated and decoupled evolution of adult and larval body size in frogs. Proc. Royal Soc. B 287, 20201474 (2020).Article 

Google Scholar 
Beasley, V. R. Direct and indirect effects of environmental contaminants on amphibians. In Reference Module in Earth Systems and Environmental Sciences https://doi.org/10.1016/b978-0-12-409548-9.11274-6 (Elsevier, 2020).Toledo, L. F., Sazima, I. & Haddad, C. F. B. Behavioural defences of anurans: An overview. Ethol. Ecol. Evol. 23, 1–25 (2011).Article 

Google Scholar 
Hartmann, M. T., Hartmann, P. A. & Haddad, C. F. B. Reproductive modes and fecundity of an assemblage of anuran amphibians in the Atlantic rainforest, Brazil. Inheringia 100, 207–215 (2010).Article 

Google Scholar 
Pupin, N. C., Gasparini, J. L., Bastos, R. P., Haddad, C. F. B. & Prado, C. P. A. Reproductive biology of an endemic Physalaemus of the Brazilian Atlantic forest, and the trade-off between clutch and egg size in terrestrial breeders of the P. signifer group. Herpetol. J. 20, 147–156 (2010).
Google Scholar 
Pereira, G. & Maneyro, R. Size-fecundity relationships and reproductive investment in females of Physalaemus riograndensis Milstead, 1960 (Anura, Leiuperidae) in Uruguay. Herpetol. J. 22, 145–150 (2012).
Google Scholar 
Tolledo, J., Silva, E. T., Nunes-de-Almeida, C. H. L. & Toledo, L. F. Anomalous tadpoles in a Brazilian oceanic archipelago: implications of oral anomalies on foraging behaviour, food intake and metamorphosis. Herpetol. J. 24, 237–243 (2014).
Google Scholar 
Annibale, F. S. et al. Smooth, striated, or rough: how substrate textures affect the feeding performance of tadpoles with different oral morphologies. Zoomorphology 139, 97–110 (2020).Article 

Google Scholar 
Venesky, M. D., Wassersug, R. J. & Parris, M. J. The impact of variation in labial tooth number on the feeding kinematics of tadpoles of southern leopard frog (Lithobates sphenocephalus). Copeia 3, 481–486 (2010).Article 

Google Scholar 
Venesky, M. D. et al. Comparative feeding kinematics of tropical hylid tadpoles. J. Exp. Biol. 216, 1928–1937 (2013).PubMed 

Google Scholar 
Jones, S. K. C., Munn, A. J., Penman, T. D. & Byrne, P. G. Long-term changes in food availability mediate the effects of temperature on growth, development and survival in striped marsh frog larvae: implications for captive breeding programmes. Conserv. Physiol. 3, cov029 (2015).Article 
CAS 

Google Scholar 
Bach, N. C., Natale, G. S., Somoza, G. M. & Ronco, A. E. Effect on the growth and development and induction of abnormalities by a glyphosate commercial formulation and its active ingredient during two developmental stages of the South-American Creole frog, Leptodactylus latrans. Environ. Sci. Pollut. Res. 23, 23959–23971 (2016).CAS 
Article 

Google Scholar 
Capellán, E. & Nicieza, A. G. Non-equivalence of growth arrest induced by predation risk or food limitation: context-dependent compensatory growth in anuran tadpoles. J. Anim. Ecol. 76, 1026–1035 (2007).PubMed 
Article 

Google Scholar 
Chin, A. M., Hill, D. R., Aurora, M. & Spence, J. R. Morphogenesis and maturation of the embryonic and postnatal intestine. Semin. Cell Dev. Biol. 66, 81–93 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Sun, Y., Zhang, J., Song, W. & Shan, A. Vitamin E alleviates phoxim-induced toxic effects on intestinal oxidative stress, barrier function, and morphological changes in rats. Environ. Sci. Pollut. Res. 25, 26682–26692 (2018).
Google Scholar 
Ouellet, M. Amphibian deformities: current state of knowledge. In Ecotoxicology of Amphibians and Reptiles (eds Sparling, D. W. et al.) 617–661 (Society of Environmental Toxicology and Chemistry, 2000).Hussein, M. & Singh, V. Effect on chick embryos development after exposure to neonicotinoid insecticide imidacloprid. J. Anat. Soc. India 65, 83–89 (2016).Article 

Google Scholar 
Crosby, E. B., Bailey, J. M., Oliveri, A. N. & Levin, E. D. Neurobehavioral impairments caused by developmental imidacloprid exposure in zebrafish. Neurotoxicol. Teratol. 49, 81–90 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lonare, M. et al. Evaluation of imidacloprid-induced neurotoxicity in male rats: A protective effect of curcumin. Neurochem. Int. 78, 122–129 (2014).CAS 
PubMed 
Article 

Google Scholar 
Žegura, B., Lah, T. T. & Filipič, M. The role of reactive oxygen species in microcystin-LR-induced DNA damage. Toxicology 200, 59–68 (2004).PubMed 
Article 
CAS 

Google Scholar 
Odetti, L. M., López González, E. C., Romito, M. L., Simoniello, M. F. & Poletta, G. L. Genotoxicity and oxidative stress in Caiman latirostris hatchlings exposed to pesticide formulations and their mixtures during incubation period. Ecotoxicol. Environ. Saf. 193, 110312 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rutkoski, C. F. et al. Morphological and biochemical traits and mortality in Physalaemus gracilis (Anura: Leptodactylidae) tadpoles exposed to the insecticide chlorpyrifos. Chemosphere 250, 126162 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Herek, J. S. et al. Genotoxic effects of glyphosate on Physalaemus tadpoles. Environ. Toxicol. Pharmacol. 81, 103516 (2021).CAS 
PubMed 
Article 

Google Scholar 
Natale, G. S. et al. Lethal and sublethal effects of the pirimicarb-based formulation Aficida® on Boana pulchella (Duméril and Bibron, 1841) tadpoles (Anura, Hylidae). Ecotoxicol. Environ. Saf. 147, 471–479 (2018)
Google Scholar 
Gilbert, S. F. Developmental Biology, 8th edn. (Sinauer Associates, 2006).Soto, M., García-Santisteban, I., Krenning, L., Medema, R. H. & Raaijmakers, J. A. Chromosomes trapped in micronuclei are liable to segregation errors. J. Cell Sci. 131, 214742 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Crott, J. & Fenech, M. Preliminary study of the genotoxic potential of homocysteine in human lymphocytes in vitro. Mutagenesis 16, 213–217 (2001).CAS 
PubMed 
Article 

Google Scholar 
Benvindo-Souza, M. et al. Micronucleus test in tadpole erythrocytes: Trends in studies and new paths. Chemosphere 240, 124910 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fenech, M. The in vitro micronucleus technique. Mutat. Res. 455, 81–95 (2000).CAS 
PubMed 
Article 

Google Scholar 
Podratz, J. L. et al. Drosophila melanogaster: A new model to study cisplatin-induced neurotoxicity. Neurobiol. Dis. 43, 330–337 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Iturburu, F. G. et al. Uptake, distribution in different tissues, and genotoxicity of imidacloprid in the freshwater fish Australoheros facetus. Environ. Toxicol. Chem. 36, 699–708 (2017).CAS 
PubMed 
Article 

Google Scholar 
Vieira, C. E. D., Pérez, M. R., Acayaba, R. D. A., Raimundo, C. C. M. & Martinez, C. B. R. DNA damage and oxidative stress induced by imidacloprid exposure in different tissues of the Neotropical fish Prochilodus lineatus. Chemosphere 195, 125–134 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sanchéz-Bayo, F., Goka, K. & Hayasaka, D. Contamination of the aquatic environment with neonicotinoids and its implication for ecosystems. Front. Environ. Sci. 4, 71 (2016).Article 

Google Scholar 
Wood, T. & Goulson, D. The environmental risks of neonicotinoid pesticides: a review of the evidence post-2013. Environ. Sci. Pollut. Res. 24, 17285–17325 (2017).CAS 
Article 

Google Scholar 
Craddock, H. A., Huang, D., Turner, P.C., Quirós-Alcalá, L. & Payne-Sturges, D. C. Trends in neonicotinoid pesticide residues in food and water in the United States, 1999–2015. Environ. Health 18, 7 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Heyer, R. et al. Leptodactylus latrans. IUCN Red List https://doi.org/10.2305/IUCN.UK.2010-2.RLTS.T57151A11592655.en (2010).Ade, C. M., Boone, M. D. & Puglis, H. J. Effects of an insecticide and potential predators on green frogs and northern cricket frogs. J. Herpetol. 44, 591–600 (2010).Article 

Google Scholar 
Sarkar, M. A., Roy, S., Kole, R. K. & Chowdhury, A. Persistence and metabolism of imidacloprid in different soils of West Bengal. Pest Manag. Sci. 57, 598–602 (2001).CAS 
PubMed 
Article 

Google Scholar 
Goulson, D. Review: An overview of the environmental risks posed by neonicotinoid insecticides. J. Appl. Ecol. 50, 977–987 (2013).Article 

Google Scholar 
Mineau, P. Neonic insecticides and invertebrate species endangerment. In Reference Module in Earth Systems and Environmental Sciences https://doi.org/10.1016/B978-0-12-821139-7.00126-4 (2021).Yamamuro, M. et al. Neonicotinoids disrupt aquatic food webs and decrease fishery yields. Science 366, 620–623 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gosner. K. L. A simplified table for staging anuran embryos and larvae with notes on identification. Herpetologica 16, 183–190 (1960).
Google Scholar 
Percie-du-Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020). CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Herkovits, J. & Pérez-Coll, C. S. AMPHITOX: A customized set of toxicity tests employing amphibian embryos. Symposium on multiple stressor effects in relation to declining amphibian populations. In Multiple Stressor Effects in Relation to Declining Amphibian Populations (eds Linder, G. et al.) 46–60 (ASTM International STP 1443, 2003).Merga, L. B. & Van den Brink, P. J. Ecological effects of imidacloprid on a tropical freshwater ecosystem and subsequent recovery dynamics. Sci. Total Environ. 784, 147167 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bonmatin, J.-M. et al. Environmental fate and exposure; neonicotinoids and fipronil. Environ. Sci. Pollut. Res. 22, 35–67 (2015).CAS 
Article 

Google Scholar 
Sumon, K. A. et al. Effects of imidacloprid on the ecology of sub-tropical freshwater microcosms. Environ. Pollut. 236, 432–441 (2018).CONCEA – Conselho Nacional de Controle e Experimentação Animal. Resolução normativa Nº 25, 29 de setembro de 2015. Guia Brasileiro de Produção, Manutenção ou Utilização de Animais para Atividades de Ensino ou Pesquisa Científica do Conselho Nacional de Controle e Experimentação Animal. http://www.mctic.gov.br/mctic/export/sites/institucional/institucional/concea/arquivos/legislacao/resolucoes_normativas/Resolucao-Normativa-CONCEA-n-27-de-23.10.2015-D.O.U.-de-27.10.2015-Secao-I-Pag.-10.pdf. (2015).Rutkoski, C. F. et al. Lethal and sublethal effects of the herbicide atrazine in the early stages of development of Physalaemus gracilis (Anura: Leptodactylidae). Arch. Environ. Contam. Toxicol. 74, 587–593 (2018).CAS 
PubMed 
Article 

Google Scholar 
Pérez-Iglesias, J. M., Soloneski, S., Nikoloff, N., Natale, G. S. & Larramendy, M. L. Toxic and genotoxic effects of the imazethapyr-based herbicide formulation Pivot H® on montevideo tree frog Hypsiboas pulchellus tadpoles (Anura, Hylidae). Ecotoxicol. Environ. Saf. 119, 15–24 (2015).PubMed 
Article 
CAS 

Google Scholar 
Montalvão, M. F. et al. The genotoxicity and cytotoxicity of tannery effluent in bullfrog (Rana catesbeianus). Chemosphere 183, 491–502 (2017).ADS 
PubMed 
Article 
CAS 

Google Scholar  More

Ryan MJ, Rand AS. Species recognition and sexual selection as a unitary problem in animal communication. Evolution. 1993;47:647–57.PubMed 
Article 

Google Scholar 
Trivers RL. Parental Investment and Sexual Selection. In: Campbell BG, (ed). Sexual Selection and the Descent of Man. Aldine Publishing Company; 1972. p. 136–79.
Google Scholar 
Andersson M. Sexual selection. Sexual Selection. Princeton: Princeton University Press; 1994.Chapter 

Google Scholar 
Schiestl FP, Ayasse M, Paulus HF, Löfstedt C, Hansson BS, Ibarra F, et al. Sex pheromone mimicry in the early spider orchid (Ophrys sphegodes): Patters of hydrocarbons as the key mechanism for pollination by sexual deception. J Comp Physiol – A Sens, Neural, Behav Physiol. 2000;186:567–74.CAS 
Article 

Google Scholar 
Cohen C, Liltved WR, Colville JF, Bytebier B, Johnson SD. Sexual deception of a beetle pollinator through floral mimicry. Curr Biol. 2021;31:1962–1969. e6.CAS 
PubMed 
Article 

Google Scholar 
Hayashi T, Bohman B, Scaffidi A, Peakall R, Flematti GR. An unusual tricosatriene is crucial for male fungus gnat attraction and exploitation by sexually deceptive Pterostylis orchids. Curr Biol. 2021;31:1954–1961. e7.CAS 
PubMed 
Article 

Google Scholar 
Hansen AN, De Fine Licht HH. Logistic growth of the host-specific obligate insect pathogenic fungus Entomophthora muscae in house flies (Musca domestica). J Appl Entomol. 2017;141:583–6.CAS 
Article 

Google Scholar 
Schmid-Hempel P Evolutionary parasitology. 2011. Oxford University Press.Helluy S, Thomas F. Effects of Microphallus papillorobustus (Platyhelminthes: Trematoda) on serotonergic immunoreactivity and neuronal architecture in the brain of Gammarus insensibilis (Crustacea: Amphipoda). Proc R Soc B: Biol Sci. 2003;270:563–8.CAS 
Article 

Google Scholar 
Hoover K, Grove M, Gardner M. A gene for an extended phenotype. Science. 2011;333:1401. others.CAS 
PubMed 
Article 

Google Scholar 
Adamo SA. Parasites: evolution’s neurobiologists. J Exp Biol. 2013;216:3–10.CAS 
PubMed 
Article 

Google Scholar 
de Bekker C, Ohm RA, Loreto RG. Gene expression during zombie ant biting behavior reflects the complexity underlying fungal parasitic behavioral manipulation. BMC Genomics. 2015;16:620. others.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ros VID, Van Houte S, Hemerik L, Van Oers MM. Baculovirus-induced tree-top disease: How extended is the role of egt as a gene for the extended phenotype? Mol Ecol. 2015;24:249–58.CAS 
PubMed 
Article 

Google Scholar 
Botnevik CF, Malagocka J, Jensen AB, Fredensborg BL. Relative effects of temperature, light, and humidity on clinging behavior of metacercariae-infected ants. J Parasitol. 2016;102:495–500.CAS 
PubMed 
Article 

Google Scholar 
Małagocka J, Jensen AB, Eilenberg J. Pandora formicae, a specialist ant pathogenic fungus: New insights into biology and taxonomy. J Invertebr Pathol. 2017;143:108–14.PubMed 
Article 
CAS 

Google Scholar 
Hughes DP, Libersat F. Neuroparasitology of parasite-insect associations. Annu Rev Entomol. 2018;63:471–87.CAS 
PubMed 
Article 

Google Scholar 
Hojo MK, Pierce NE, Tsuji K. Lycaenid caterpillar secretions manipulate attendant ant behavior. Curr Biol. 2015;25:2260–4.CAS 
PubMed 
Article 

Google Scholar 
Gal R, Libersat F. A wasp manipulates neuronal activity in the sub-esophageal ganglion to decrease the drive for walking in its cockroach prey. PLoS ONE. 2010;5:e10019.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Keesey IW, Koerte S, Khallaf MA, Retzke T, Guillou A, Grosse-Wilde E, et al. Pathogenic bacteria enhance dispersal through alteration of Drosophila social communication. Nat Commun. 2017;8:265.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhang X, Machado RAR, Van Doan C, Arce CCM, Hu L, Robert CAM. Entomopathogenic nematodes increase predation success by inducing cadaver volatiles that attract healthy herbivores. eLife. 2019;8:e46668.PubMed 
PubMed Central 
Article 

Google Scholar 
George J, Jenkins NE, Blanford S, Thomas MB, Baker TC. Malaria mosquitoes attracted by fatal fungus. PLoS ONE. 2013;8:e62632.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Trandem N, Bhattarai UR, Westrum K, Knudsen GK, Klingen I. Fatal attraction: male spider mites prefer females killed by the mite-pathogenic fungus Neozygites floridana. J Invertebr Pathol. 2015;128:6–13.PubMed 
Article 

Google Scholar 
Evans WS, Wong A, Hardy M, Currie RW, Vanderwel D. Evidence that the factor used by the tapeworm, Hymenolepis diminuta, to direct the foraging of its intermediate host, Tribolium confusum, is a volatile attractant. J Parasitol. 1998;84:1098–101.CAS 
PubMed 
Article 

Google Scholar 
Shostak AW, Smyth KA. Activity of flour beetles (Tribolium confusum) in the presence of feces from rats infected with rat tapeworm (Hymenolepis diminuta). Can J Zool. 1998;76:1472–9.Article 

Google Scholar 
Shea JF. Lack of preference for infective faeces in Hymenolepis diminuta-infected beetles (Tenebrio molitor). J Helminthol. 2007;81:293–9.PubMed 
Article 

Google Scholar 
Mauck KE, De Moraes CM, Mescher MC. Deceptive chemical signals induced by a plant virus attract insect vectors to inferior hosts. Proc Natl Acad Sci USA. 2010;107:3600–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dawkins R. The extended phenotype. Oxford: Oxdord University Press; 1982.
Google Scholar 
Van Houte S, Ros VID, Van Oers MM. Walking with insects: Molecular mechanisms behind parasitic manipulation of host behaviour. Mol Ecol. 2013;22:3458–75.PubMed 
Article 

Google Scholar 
de Bekker C, Beckerson WC, Elya C. Mechanisms behind the madness: how do zombie-making fungal entomopathogens affect host behavior to increase transmission? mBio. 2021;12:e01872–21.PubMed Central 
Article 

Google Scholar 
Lefévre T, Lebarbenchon C, Gauthier-Clerc M, Missé D, Poulin R, Thomas F, et al. The ecological significance of manipulative parasites. Trends Ecol Evolution. 2009;24:41–48.Article 

Google Scholar 
Kalsbeek V, Pell JK, Steenberg T. Sporulation by Entomophthora schizophorae (Zygomycetes: Entomophthorales) from housefly cadavers and the persistence of primary conidia at constant temperatures and relative humidities. J Invertebr Pathol. 2001;77:149–57.CAS 
PubMed 
Article 

Google Scholar 
de Ruiter J, Arnbjerg-Nielsen SF, Herren P, Høier F, De Fine Licht HH, Jensen KH. Fungal artillery of zombie flies: infectious spore dispersal using a soft water cannon. J R Soc Interface. 2019;16:20190448.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lovett B, Macias A, Stajich JE, Cooley J, Eilenberg J, de Fine Licht HH, et al. Behavioral betrayal: how select fungal parasites enlist living insects to do their bidding. PLoS Pathog. 2020;16:e1008598.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moller AP. A fungus infecting domestic flies manipulates sexual behaviour of its host. Behav Ecol Sociobiol. 1993;33:403–7.
Google Scholar 
Murvosh CM, Fye RL, LaBrecque GC. Studies on the mating behavior of the house fly, Musca Domestica L. Ohio J Sci. 1964;64:264–71.
Google Scholar 
Tobin EN, Stoffolano JG. The courtship of Musca species found in North America. II. The face fly, Musca autumnalis, and a comparison. Ann Entomological Soc Am. 1973;66:1329–34.Article 

Google Scholar 
Goulson D, Bristow L, Elderfield E, Brinklow K, Parry-Jones B, Chapman JW. Size, Symmetry, and sexual selection in the housefly, Musca domestica. Evolution. 1999;53:527–34.PubMed 
Article 

Google Scholar 
Zurek L, Wes Watson D, Krasnoff SB, Schal C. Effect of the entomopathogenic fungus, Entomophthora muscae (Zygomycetes: Entomophthoraceae), on sex pheromone and other cuticular hydrocarbons of the house fly, Musca domestica. J Invertebr Pathol. 2002;80:171–6.CAS 
PubMed 
Article 

Google Scholar 
Rogoff WM, Beltz AD, Johnsen JO, Plapp FW. A sex pheromone in the housefly, Musca domestica L. J Insect Physiol. 1964;10:239–46.CAS 
Article 

Google Scholar 
Adams TS, Holt GG. Effect of pheromone components when applied to different models on male sexual behaviour in the housefly, Musca domestica. J Insect Physiol. 1987;33:9–18.CAS 
Article 

Google Scholar 
Carlson DA, Mayer MS, Silhacek DL, James JD, Beroza M, Bierl BA, et al. Sex attractant pheromone of the house fly: Isolation, identification and synthesis. Science. 1971;174:76–78.CAS 
PubMed 
Article 

Google Scholar 
Adams TS, Nelson DR, Fatland CL. Effect of methylalkanes on male house fly, Musca domestica, sexual behavior. J Insect Physiol. 1995;41:443–9.CAS 
Article 

Google Scholar 
Noorman N, Otter CJ. The effects of laboratory culturing on (Z)-9-tricosene (muscalure) quantities on female houseflies. Entomologia Experimentalis et Applicata. 2001;101:69–80.CAS 
Article 

Google Scholar 
Uebel EC, Schwarz M, Lusby WR, Miller RW, Sonnet PE. Cuticular nonhydrocarbons of the female house fly and their evaluation as mating stimulants. Lloydia. 1978;41:63–67.CAS 

Google Scholar 
Blomquist GJ, Ginzel MD. Chemical ecology, biochemistry, and molecular biology of insect hydrocarbons. Annu Rev Entomol. 2021;66:45–60.CAS 
PubMed 
Article 

Google Scholar 
Lebreton S, Borrero-Echeverry F, Gonzalez F, Solum M, Wallin EA, Hedenström E, et al. A Drosophila female pheromone elicits species-specific long-range attraction via an olfactory channel with dual specificity for sex and food. BMC Biol. 2017;15:88.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Krasnoff SB, Watson DW, Gibson DM, Kwan EC. Behavioral effects of the entomopathogenic fungus, Entomophthora muscae on its host Musca domestica: Postural changes in dying hosts and gated pattern of mortality. J Insect Physiol. 1995;41:895–903.CAS 
Article 

Google Scholar 
Friard O, Gamba M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol Evolution. 2016;7:1325–30.Article 

Google Scholar 
Quan AS, Eisen MB. The ecology of the Drosophila-yeast mutualism in wineries. PLOS ONE. 2018;13:e0196440.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
van Den Dool H, Dec, Kratz P. A generalization of the retention index system including linear temperature programmed gas-liquid partition chromatography. J Chromatogr A. 1963;11:463–71.Article 

Google Scholar 
Nelson DR, Dillwith JW, Blomquist GJ. Cuticular hydrocarbons of the house fly, Musca domestica. Insect Biochem. 1981;11:187–97.CAS 
Article 

Google Scholar 
Bagnères AG, Morgan ED. A simple method for analysis of insect cuticular hydrocarbons. J Chem Ecol. 1990;16:3263–76.PubMed 
Article 

Google Scholar 
Stránský K, Jursík T, Vítek A, Skořepa J. An improved method of characterizing fatty acids by equivalent chain length values. J High Resolut Chromatogr. 1992;15:730–40.Article 

Google Scholar 
Stránský K, Zarevúcka M, Valterová I, Wimmer Z. Gas chromatographic retention data of wax esters. J Chromatogr A. 2006;1128:208–19.PubMed 
Article 
CAS 

Google Scholar 
Carlson DA, Bernier UR, Sutton BD. Elution patterns from capillary GC for methyl-branched alkanes. J Chem Ecol. 1998;24:1845–65.CAS 
Article 

Google Scholar 
Mpuru S, Blomquist GJ, Schal C, Roux M, Kuenzli M, Dusticier G, et al. Effect of age and sex on the production of internal and external hydrocarbons and pheromones in the housefly, Musca domestica. Insect Biochem Mol Biol. 2001;31:139–55.CAS 
PubMed 
Article 

Google Scholar 
Gulias Gomes CC, Trigo JR, Eiras ÁE. Sex pheromone of the American warble fly, Dermatobia hominis: The role of cuticular hydrocarbons. J Chem Ecol. 2008;34:636–46.CAS 
PubMed 
Article 

Google Scholar 
Zhang LX, Yun YF, Liang YZ, Cao DS. Discovery of mass spectral characteristics and automatic identification of wax esters from gas chromatography mass spectrometry data. J Chromatogr A. 2010;1217:3695–701.CAS 
PubMed 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7.CAS 
PubMed 
Article 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.Article 
CAS 

Google Scholar 
Becher PG, Verschut V, Bibb MJ, Bush MJ, Molnár BP, Barane E, et al. Developmentally regulated volatiles geosmin and 2-methylisoborneol attract a soil arthropod to Streptomyces bacteria promoting spore dispersal. Nat Microbiol. 2020;5:821–9.CAS 
PubMed 
Article 

Google Scholar 
Lê S, Josse J, Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw. 2008;25:1–18.Article 

Google Scholar 
Darbro JM, Millar JG, McElfresh JS, Mullens BA. Survey of muscalure [(Z)-9-tricosene] on house flies (Diptera: Muscidae) from field populations in California. Environ Entomol. 2005;34:1418–25.CAS 
Article 

Google Scholar 
Butler SM, Moon RD, Hinkle NC, Millar JG, Mcelfresh JS, Mullens BA. Characterization of age and cuticular hydrocarbon variation in mating pairs of house fly, Musca domestica, collected in the field. Med Vet Entomol. 2009;23:426–42.CAS 
PubMed 
Article 

Google Scholar 
Eder M, Sanchez I, Brice C, Camarasa C, Legras JL, Dequin S. QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation. BMC Genomics. 2018;19:166.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vranová E, Coman D, Gruissem W. Network analysis of the MVA and MEP pathways for isoprenoid synthesis. Annu Rev Plant Biol. 2013;64:665–700.PubMed 
Article 
CAS 

Google Scholar 
Saerens SMG, Verstrepen KJ, Van Laere SDM, Voet ARD, Van Dijck P, Delvaux FR, et al. The Saccharomyces cerevisiae EHT1 and EEB1 genes encode novel enzymes with medium-chain fatty acid ethyl ester synthesis and hydrolysis capacity. J Biol Chem. 2006;281:4446–56.CAS 
PubMed 
Article 

Google Scholar 
Saerens SMG, Delvaux F, Verstrepen KJ, Van Dijck P, Thevelein JM, Delvaux FR. Parameters affecting ethyl ester production by Saccharomyces cerevisiae during fermentation. Appl Environ Microbiol. 2008;74:454–61.CAS 
PubMed 
Article 

Google Scholar 
Cooley JR, Marshall DC, Hill KBR. A specialized fungal parasite (Massospora cicadina) hijacks the sexual signals of periodical cicadas (Hemiptera: Cicadidae: Magicicada). Sci Rep. 2018;8:1432.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhang X-M. Floral volatile sesquiterpenes of Elsholtzia rugulosa (Lamiaceae) selectively attract Asian honey bees. J Appl Entomol. 2018;142:359–62.CAS 
Article 

Google Scholar 
Haber AI, Sims JW, Mescher MC, De Moraes CM, Carr DE. A key floral scent component (β-trans-bergamotene) drives pollinator preferences independently of pollen rewards in seep monkeyflower. Funct Ecol. 2019;33:218–28.Article 

Google Scholar 
Mithöfer A, Boland W. Plant defense against herbivores: chemical aspects. Annu Rev Plant Biol. 2012;63:431–50.PubMed 
Article 
CAS 

Google Scholar 
Stanjek V, Herhaus C, Ritgen U, Boland W, Städler E. Changes in the leaf surface chemistry of Apium graveolens (apiaceae) stimulated by jasmonic acid and perceived by a specialist insect. Helvetica Chim Acta. 1997;80:1408–20.CAS 
Article 

Google Scholar 
Ding Y, Huffaker A, Köllner TG, Weckwerth P, Robert CAM, Spencer JL, et al. Selinene volatiles are essential precursors for maize defense promoting fungal pathogen resistance. Plant Physiol. 2017;175:1455–68.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Könen PP, Wüst M. Analysis of sesquiterpene hydrocarbons in grape berry exocarp (Vitis vinifera L.) using in vivo-labeling and comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC–MS). Beilstein J Org Chem. 2019;15:1945–61.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lam K, Tsang M, Labrie A, Gries R, Gries G. Semiochemical-mediated oviposition avoidance by female house flies, Musca domestica, on animal feces colonized with harmful fungi. J Chem Ecol. 2010;36:141–7.CAS 
PubMed 
Article 

Google Scholar 
Phillips RD, Bohman B, Peakall R. Pollination by nectar‐foraging pompilid wasps: a new specialized pollination strategy for the Australian flora. Plant Biology 2021;23:702–10.Spieth HT. Courtship behavior in Drosophila. Annu Rev Entomol. 1974;19:385–405.CAS 
PubMed 
Article 

Google Scholar 
Grosjean Y, Rytz R, Farine JP, Abuin L, Cortot J, Jefferis GSXE, et al. An olfactory receptor for food-derived odours promotes male courtship in Drosophila. Nature. 2011;478:236–40.CAS 
PubMed 
Article 

Google Scholar 
Mullens BA, Rodrigues JL, Meyer JA. An epizootiological study of Entomophthora muscae in muscoid fly populations on southern california poultry facilities, with emphasis on Musca domestica. Hilgardia. 1987;55:1–41.Article 

Google Scholar 
Watson DW, Petersen JJ. Sexual activity of male Musca domestica (Diptera: Muscidae) infected with Entomophthora muscae (Entomophthoraceae: Entomophthorales). Biol Control. 1993;3:22–26.Article 

Google Scholar 
van Huis A, Oonincx DGAB, Rojo S, Tomberlin JK. Insects as feed: house fly or black soldier fly? J Insects Food Feed. 2020;6:221–9.Article 

Google Scholar 
Khamesipour F, Lankarani KB, Honarvar B, Kwenti TE. A systematic review of human pathogens carried by the housefly (Musca domestica L.). BMC Public Health. 2018;18:1049.PubMed 
PubMed Central 
Article 

Google Scholar 
Biedermann PHW, De Fine Licht HH, Rohlfs M. Evolutionary chemo-ecology of insect-fungus interactions: still in its infancy but advancing. Fungal Ecol. 2019;38:1–6.Article 

Google Scholar  More

Palaeogeographical modelWe use palaeogeographical reconstructions describing Earth’s palaeotopography and palaeobathymetry for a series of time slices from 541 Ma to the present day. The reconstructions merge existing models from two published global reconstruction datasets—those of ref. 32 and ref. 33 (https://doi.org/10.5281/zenodo.5348492), which themselves are syntheses of a wealth of previous work.For continental regions, estimates of palaeoelevation and continental flooding rely on a diverse range of geological evidence, such as sedimentary depositional environments and the spatiotemporal distribution of volcanic activity. For a full description, see a recent review34. Together, these data can be used to define the past locations of mountain ranges and palaeoshorelines34. For this part of our reconstruction, we used the compilation of ref. 33 with updated palaeoshorelines based on depositional environment information in current fossil databases35. This compilation comprises 82 palaeotopography maps covering the entire Phanerozoic. Note that each palaeogeographical map is a time slice representing the concatenation of geological data over several million years36.We quantified the impact of using the original compilation of ref. 33 on our model results and found only small changes with respect to using the reconstructions with updated palaeoshorelines (Extended Data Fig. 3a–c). Similarly, eustatic sea level is thought to have varied by around 100 m at timescales much shorter than the duration of the time-slices throughout the Phanerozoic37, such that the extent of continental flooding could have varied within each time slice by an amount significant for our analysis. For this reason, and to assess the uncertainty of our results to continental palaeogeography in general, we computed additional maps of continental flooding in which the sea level is raised or lowered by 100 m compared with the original palaeo–digital elevation model grids of ref. 33 (Extended Data Fig. 3d–f).For deep-ocean regions, the primary control on seafloor depth is the age of the seafloor, so reconstructing palaeobathymetry relies on constructing maps of seafloor age back in time38. As a consequence, we rely on reconstruction models that incorporate a continuous network of plate boundaries. For this part, we used the reconstruction of ref. 32 and derived maps of seafloor age from the plate tectonic model using the method of ref. 39 for which source code is available at GitHub (https://github.com/siwill22/agegrid-0.1). Palaeobathymetry is derived from the seafloor age maps following the steps outlined in ref. 38. It is important to note that seafloor age maps for most of the Phanerozoic (that is, pre-Pangaea times) are not directly constrained by data due to recycling of oceanic crust at subduction zones. Rather, they are model predictions generated by constructing plate motions and plate boundary configurations from the geological and palaeomagnetic record of the continents. Nonetheless, the first-order trends in ocean-basin volume and mean seafloor age are consistent with independent estimates for at least the last 410 million years (Myr)39.The reconstructions of refs. 32,33 differ in the precise locations of the continents through time. To resolve this discrepancy, we reverse reconstructed the continental palaeoelevation model of ref. 33 to present-day coordinates using their rotation parameters, then reconstructed them back in time using the rotations of ref. 32. Owing to the differences in how the continents are divided into different tectonic units, this process leads to some gaps and overlaps in the results40, which we resolved primarily through a combination of data interpolation and averaging. Manual adjustments were made to ensure that the flooding history remained consistent with the original palaeotopography in areas in which interpolation gives a noticeably different history of seafloor ages. The resulting palaeotopography maps are therefore defined in palaeomagnetic reference frame32 appropriate for use in Earth system models.For the biodiversity modelling, we generate estimates of the age of the seafloor for discrete points within the oceans and flooded continents, and track these ages through the lifetime of each point (Supplementary Video 5). For the oceans, this is achieved using the method described in ref. 39 in which the seafloor is represented by points that are incrementally generated at the mid-ocean ridges for a series of time steps 1 Myr apart, with each point tracked through subsequent time steps based on Euler poles of rotation until either the present-day is reached, or they arrive at a subduction zone and are considered to be destroyed.For the continents, tracking the location of discrete points is generally simpler as most crust is conserved throughout the timespan of the reconstruction. In contrast to the deep oceans (where we assume that crust is at all times submerged), we model the ‘age’ of the seafloor from the history of continental flooding and emergence within the palaeogeographical interpretation33. The continents are seeded with uniformly distributed points at the oldest timeslice (541 Ma) at which they are assigned an age of zero. These points are tracked to subsequent time slices of which the palaeogeography is used to determine whether the point lies within a flooded or emergent region. Points within flooded regions of continents are considered to be seafloor, and the age of this seafloor is accumulated across consecutive time slices where a given point lies within a flooded region. When a point is within an emergent region, the seafloor age is reset to zero. Following this approach, individual points within stable continents may undergo several cycles of seafloor age increasing from zero before being reset. At the continental margins formed during the Pangaea breakup, the age of the seafloor continuously grows from the onset of rifting. Intraoceanic island arcs represent an additional case, which can appear as new tectonic units with the reconstructions at various times. In these cases, we assume that the seafloor has a zero-age at the time at which the intraoceanic arc first develops, then remains predominantly underwater for the rest of its lifetime.Thus, for each of the 82 palaeogeographical reconstructions, we annotate 0.5° by 0.5° grids as continental, flooded continental shelf or oceanic for later use in model coupling and production of regional diversity maps.Palaeoenvironmental conditions under the cGENIE Earth system modelWe use cGENIE41, an Earth system model of intermediate complexity, to simulate palaeoenvironmental conditions of seawater temperature and organic carbon export production (as a surrogate for food supply) throughout the Phanerozoic (from 541 Ma to the present day).cGENIE is based on a three-dimensional (3D) ocean circulation model coupled to a 2D energy–moisture balance atmospheric component and a sea-ice module. We configured the model on a 36 × 36 (latitude, longitude) equal area grid with 17 unevenly spaced vertical levels in depth, down to a maximum ocean depth of 5,900 m. The cycling of carbon and associated tracers in the ocean is based on a size-structured plankton ecosystem model with a single (phosphate) nutrient42,43, and adopts an Arrhenius-type temperature-dependent scheme for the remineralization of organic matter exported to the ocean interior44.cGENIE provides a spatially resolved representation of ocean physics and biogeochemistry, which is a prerequisite for the present study to be able to reconstruct the spatial patterns of biodiversity in deep time. However, owing to the computational impracticality of generating a single transient simulation of physics (that is, temperature) and biogeochemistry (that is, export production) over the entire Phanerozoic, we therefore generate 30 model equilibria at regular time intervals throughout the Phanerozoic that are subsequently used as inputs for the regional diversification model (see the ‘Model coupling’ section).We used 30 Phanerozoic palaeogeographical reconstructions through time (~20 Myr evenly spaced time intervals) to represent key time periods. For each continental configuration corresponding to a given age in Earth history, we generate idealized 2D (but zonally averaged) wind speed and wind stress, and 1D zonally averaged albedo forcing fields45 required by the cGENIE model using the ‘muffingen’ open-source software (see the ‘Code availability’ section). For each palaeogeographical reconstruction, the climatic forcing (that is, solar irradiance and carbon dioxide concentration) is adapted to match the corresponding geological time interval. The partial pressure of CO2 is taken from the recent update of the GEOCARB model46. Solar luminosity is calculated using the model of stellar physics of ref. 47. We impose modern-day orbital parameters (obliquity, eccentricity and precession). The simulations are initialized with a sea-ice-free ocean, homogeneous oceanic temperature (5 °C) and salinity (34.9‰). As variations in the oceanic concentration of bio-available phosphate remain challenging to reconstruct in the geological past48,49, we impose a present-day mean ocean phosphate concentration (2.159 μmol kg−1) in our baseline simulations. We quantify the impact of this uncertainty on our model results by conducting additional simulations using half and twice the present-day ocean phosphate concentration (Extended Data Fig. 3g–i). For each ocean phosphate scenario (that is, 0.5×, 1× and 2× the present-day value), each of the 30 model simulations is then run for 20,000 years, a duration ensuring that deep-ocean temperature and geochemistry reach equilibrium. For each model simulation, the results of the mean annual values of the last simulated year are used for the analysis. Note that, although cGENIE makes projections of the distribution of dissolved oxygen ([O2]) in the ocean, our diversification model does not currently consider oxygenation to be a limit on diversity. Thus, we assumed a modern atmospheric partial pressure of O2 in all 30 palaeo simulations and did not use the resulting projected [O2] fields.Regional diversification modelWe tested two models of diversification—the logistic model and the exponential model—describing the dynamics of regional diversity over time. In both models, the net diversification rate (ρ), with units of inverse time (Myr−1), varies within a pre-fixed range of values as a function of seawater temperature and food availability. The net diversification rate is then calculated for a given location and time according to the following equation:$$rho ={rho }_{max }-({rho }_{max }-{rho }_{min })(1-{Q}_{{rm{temp}}}{Q}_{{rm{food}}})$$
(1)
where ρmin and ρmax set the lower and upper net diversification rate limits within which ρ is allowed to vary, and Qtemp and Qfood are non-dimensional limitation terms with values between 0 and 1 that define the dependence of ρ on temperature and food, respectively (Extended Data Table 1).The model considers a direct relationship between seawater temperature, food supply and the rate of net diversification on the basis of the theoretical control that temperature and food supply exert on the rates of origination and extinction (Supplementary Fig. 1). Temperature rise is expected to accelerate the biochemical kinetics of metabolism50 and shorten the development times of individuals51, leading to higher rates of mutation and origination. Greater food availability increases population sizes, which increases the rates of mutation and reduces the probability of extinction52. Furthermore, a large body of observations shows the existence of a positive relationship between resource availability (that is, food supply) and the standing stock of species in marine and terrestrial communities53,54. A larger food supply would support a greater number of individuals. A greater diversity of food resources could also lead to a finer partitioning of available resources55.The temperature dependence of ρ is calculated using the following equation:$${Q}_{{rm{temp}}}=frac{{Q}_{10}^{frac{T-Tmin }{10}}}{{Q}_{10}^{frac{Tmax -Tmin }{10}}}$$
(2)
where the Q10 coefficient measures the temperature sensitivity of the origination rate. In equation (2) above, T is the seawater temperature (in °C) at a given location and time, and Tmin and Tmax are the 0.01 percentile and the 0.99 percentile, respectively, of the temperature frequency distribution in each time interval. In the model, the values of Tmin and Tmax used to calculate Qtemp are therefore recomputed for every time interval (~5 Myr) according to the temperature frequency distribution of the corresponding time interval. This enables us to use updated Tmin and Tmax values in each Phanerozoic time interval and to account for the thermal adaptation of organisms to ever changing climate conditions.The food limitation term is parameterized using a Michaelis–Menten formulation as follows:$${Q}_{{rm{food}}}=frac{text{POC flux}}{left({K}_{{rm{food}}},+,text{POC flux}right)}$$
(3)
where POC flux (mol m−2 yr−1) is the particulate organic carbon export flux, which is used as a surrogate for food availability, at a given location and time of the simulated seafloor. The parameter Kfood (mol m−2 yr−1) in equation (3) is the half-saturation constant, that is, the POC flux at which the diversification rate is half its maximum value, provided that other factors were not limiting. These temperature and food supply limitation terms vary in space and time as a result of changes in seawater temperature and particulate organic carbon export rate, respectively, thereby controlling the spatial and temporal variability of ρ (Supplementary Video 6).The net diversification rate becomes negative (1) in the event of mass extinctions or (2) in response to regional-scale processes, such as sea-level fall and/or seafloor deformation along convergent plate boundaries. Mass extinction events are imposed as external perturbations to the diversification model by imputing negative net diversification rates to all active seafloor points (ocean points and flooded continental points) and assuming non-selective extinction. The percentage of diversity loss as well as the starting time and duration of mass extinctions are extracted from three fossil diversity curves of reference20,21,22 (Source Data for Fig. 1). Each of these fossil diversity curves provides different insights into the Phanerozoic history of marine animal diversity based on uncorrected range-through genus richness estimates20,22 and sampling standardized estimates21. Regional-scale processes—such as sea level fall during marine regressions and/or seafloor destruction at plate boundaries, either by subduction or uplift—are simulated by the combined plate tectonic–palaeoelevation model, and constrain the time that seafloor habitats have to accumulate diversity.The model assumes non-selective extinction during mass extinction events (that is, the field of bullets model of extinction; everything is equally likely to die, no matter the age of the clade and regardless of adaptation)56. However, there is much fossil evidence supporting extinction selectivity57,58. It could be argued that higher extinction rates at diversity hotspots would have delayed their subsequent recovery, flattening global diversity trends. This argument is difficult to reconcile with Sepkoski’s genus-level global diversity curve but could be consistent with the standardized diversity curve of ref. 21. Similarly, the model is also not suitable for reproducing the explosive radiations of certain taxonomic groups after mass extinctions, which could explain the offset between the model and fossil observations in the early Mesozoic (Fig. 1).Letting D represent regional diversity (number of genera within a given seafloor point) and t represent time, the logistic model is formalized by the following differential equation:$$frac{partial Dleft(tright)}{partial t}=rho Dleft[1-frac{D}{{K}_{{rm{eff}}}}right]$$
(4)
where D(t) is the number of genera at time t and Keff is the effective carrying capacity or maximum number of genera that a given seafloor point (that is, grid cell area after gridding) can carry at that time, t. In our logistic model, Keff is allowed to vary within a fixed range of values (from Kmin to Kmax) as a positive linear function of the POC flux at a given location and time as follows:$${K}_{{rm{eff}}}={K}_{max }-left({K}_{max }-{K}_{min }right)frac{{text{POC flux}}_{max }-text{POC flux}}{{text{POC flux}}_{max }-{text{POC flux}}_{min }}$$
(5)
where POC fluxmin and POC fluxmax correspond to the 0.01 and 0.99 quantiles of the POC flux range in the whole Phanerozoic dataset.In the logistic model, the net diversification rate decreases as regional diversity approaches its Keff. The exponential model is a particular case of the logistic model when Keff approaches infinity and, therefore, neither the origination rate nor the extinction rate depend on the standing diversities. In this scenario, diversity grows in an unlimited manner over time only truncated by the effect of mass extinctions and/or by the dynamics of the seafloor (creation versus destruction). Thus, the exponential model is as follows:$$frac{partial Dleft(tright)}{partial t}=rho D$$
(6)
where the rate of change of diversity (the time derivative) is proportional to the standing diversity D such that the regional diversity will follow an exponential increase in time at a speed controlled by the temperature- and food-dependent net diversification rate. Even if analytical solutions exist for the steady-state equilibrium of the logistic and exponential functions, we solved the ordinary differential equations (4) and (6) using numerical methods with a time lag of 1 Myr to account for the spatially and temporally varying environmental constraints, seafloor dynamics and mass extinction events.As the analysis of global fossil diversity curves is unable to discern the causes of diversity loss during mass extinctions, our imputation of negative diversification rates could have overestimated the loss of diversity in those cases in which sea level fall, a factor already accounted for by our model, contributed to mass extinction. This effect was particularly recognizable across the Permian–Triassic mass extinction (Extended Data Fig. 6d–f), and supports previous claims that the decline in the global area of the shallow water shelf exacerbated the severity of the end-Permian mass extinction34.Model couplingAs stated above, the coupled plate tectonic–palaeoelevation (palaeogeographical) model corresponds to a tracer-based model (a Lagrangian-based approach) that simulates and tracks the spatiotemporal dynamics of ocean and flooded continental points. The diversification models start at time 541 Ma with all active points having a D0 = 1 (one single genus everywhere) and we let points accumulate diversity heterogeneously with time according to seafloor age distributions (for ocean points) and the time that continents have been underwater (for flooded continental points). The ocean points are created at mid-ocean ridges and disappear primarily at subduction zones. Between their origin and demise, the points move following plate tectonic motions and we trace their positions while accumulating diversity. The flooded continental points begin to accumulate diversity from the moment that they are submerged, starting with a D value equal to the nearest neighbour flooded continental point with D  > 1, thereby simulating a process of coastal recolonization (or immigration). The diversification process remains active while the seafloor points remain underwater, but it is interrupted, and D set to 0, in those continental points that emerge above sea level. Similarly, seafloor points corresponding to ocean domains disappear in subduction zones, and their diversity is lost. We track the geographical position of the ocean and flooded continental points approximately every 5 Myr, from 541 Ma to the present. Each and every one of the tracked points accumulates diversity over time at a different rate, which is modulated by the environmental history (seawater temperature and food availability) of each point, as described in equations (1)–(3). When a point arrives in an environment with a carrying capacity lower than the diversity it has accumulated through time, we reset the diversity of the point to the value of the carrying capacity, thereby simulating local extinction.Seawater temperature (T) and food availability (POC flux) are provided by the cGENIE model, which has a spatial and temporal resolution coarser than the palaeogeographical model. The cGENIE model provides average seawater T and POC flux values in a 36 × 36 equal area grid (grid cell area equivalent to 2° latitude by 10° longitude at the equator) and 30 time slices or snapshots (from 541 Ma to the present: each ~20 Myr time intervals). To have environmental inputs for the 82 time slices of the plate tectonic–palaeoelevation model, we first interpolate the cGENIE original model output data on a 0.5° by 0.5° grid to match the annotated grids provided by the plate tectonic–palaeoelevation model. As the relatively coarse spatial resolution of the cGENIE model prevents rendering the coast–ocean gradients, we assign surface T and POC flux at the base of the euphotic zone to the flooded continental shelf grid cells, and deep ocean T and POC flux at the bottom of the ocean to the ocean grid cells. As there are time slices without input data of seawater T and POC flux, we interpolate/extrapolate seawater T and POC flux values into the 0.5° by 0.5° flooded continental shelf and ocean grids independently. Finally, we interpolate values from these 0.5° by 0.5° flooded continental shelf and ocean grids into the exact point locations in each time frame. Thus, each active point is tracked with its associated time-varying T and POC flux values throughout its lifetime. On average, 6,000 flooded continental points and 44,000 oceanic points were actively accumulating diversity in each time frame. The model cannot simulate the singularities of relatively small enclosed seas for which the spatial resolution of the palaeogeographical and Earth system models is insufficient to capture relevant features (such as palaeobathymetry, seawater temperature) in detail. The method is also likely to underestimate the diversity of epeiric (inland) seas due to the difficulty of simulating immigration, a process that is strongly influenced by the effect of surface ocean currents and is not considered here. However, as stated above, the model considers recolonization of recently submerged areas by marine biota from nearby coastal environments, which partially explains coastal immigration.Estimation of global diversity from regional diversityOur regional diversity maps are generated by separately interpolating ocean point diversity and flooded continental point diversity into the 0.5° by 0.5° annotated grids provided by the palaeogeographical model. We calculate global diversity at each time step from each of the regional diversity maps following a series of steps to integrate diversity along line transects from diversity peaks (maxima) to diversity troughs (minima) (Extended Data Fig. 1). To select the transects, first, we identify on each of the regional diversity maps the geographical position of the diversity peaks. We identify local maxima (that is, grid cells with diversity greater than their neighbour cells), and define the peaks as those local maxima with diversity greater than the 0.75 quantile of diversity values in all local maxima in the map. In the case of grid cells with equal neighbour diversity, the peak is assigned to the grid cell in the middle. We subsequently identify the geographical position of the diversity troughs, which are defined as newly formed ocean grid cells (age = 0 Myr) and, therefore, with diversities equal to one. The troughs are mostly located at mid-ocean ridges.On each of the 82 spatial diversity maps, we trace a line transect from each diversity peak to its closest trough, provided that the transect does not cross land in more than 20% of the grid cells along the linear path (Supplementary Video 7). On average, for each spatial diversity map, we trace 400 (σ = ±75) linear transects. This sampling design gives rise to transects of different lengths, which may bias the estimates of global diversity. To minimize this bias, we cut the tail of the transects to have a length of 555 km equivalent to 5° at the equator. We tested an alternative cut-off threshold, 1,110 km, and the results do not alter the study’s conclusions.We apply Bresenham’s line algorithm59 to detect the grid cells crossed by the transects and annotate their diversity. To integrate regional diversity along the transects, we developed a method to simplify the scenario of peaks and troughs heterogeneously distributed on the 2D diversity maps. The method requires (1) a vector (the transect) of genus richness (αn) at n different locations (grids) arranged in a line (1D) of L grids, and (2) a coefficient of similarity (Vn,n + 1) between each two neighbouring locations, n and n + 1. Vn,n + 1, the coefficient of similarity, follows a decreasing exponential function with distance between locations. The number of shared genera between n and n + 1 is Vn,n + 1 × min(αn; αn + 1). We integrate diversity from peaks to troughs and assume that, along the transect, αn + 1 is lower than αn. We further assume that the genera present in n and n + 2 cannot be absent from n + 1. Using this method, we integrate the transect’s diversity (γi) using the following equation:$${gamma }_{i}={ {mathbf{upalpha}}}_{1}+{sum }_{n=1}^{L-1}left(1-{V}_{n,n+1}right){ {mathbf{upalpha}}}_{n+1}$$
(7)
To integrate the diversity of all transects (γi) on each 2D diversity map (or time slice), we apply the same procedure as described above (Extended Data Fig. 1). We first sort the transects in descending order from the highest to the lowest diversity. We then assume that the number of shared genera between transect i and the rest of the transects with greater diversity {1, 2, …, i − 1} is given by the distance of its peak to the nearest neighbour peak (NN(i)) of those already integrated {1, 2, …, i − 1}. Thus, we perform a zigzag integration of transects’ diversities down gradient, from the greatest to the poorest, weighted by the nearest neighbour distance among the peaks already integrated. As a result, the contribution of each transect to global diversity will depend on its diversity and its distance to the closest transect out of all those transects already integrated. Using this method, we linearize the problem to simplify the cumbersome procedure of passing from a 2D regional diversity map to a global diversity estimate without knowing the identity (taxonomic affiliation) of the genera. If γtotal is the global diversity at time t:$${gamma }_{{rm{total}}}={gamma }_{1}+{sum }_{i=2}^{j}left(1-{V}_{{rm{NN}}left(iright),i}right){gamma }_{i}$$
(8)
Finally, the resulting global estimates are plotted against the midpoint value of the corresponding time interval to generate a synthetic global diversity curve. To compare the global diversity curves produced by the diversification models with those composed from the fossil record, Lin’s CCC60 is applied to the data normalized to the min–max values of each time series (that is, rescaled within the range 0–1). Lin’s CCC combines measures of both precision and accuracy to determine how far the observed data deviate from the line of perfect concordance (that is, the 1:1 line). Lin’s CCC increases in value as a function of the nearness of the data’s reduced major axis to the line of perfect concordance (the accuracy of the data) and of the tightness of the data around its reduced major axis (the precision of the data).The time series of global diversity generated from the fossil record and from the diversification model exhibit serial correlation and the resulting CCCs are therefore inflated. The use of methods for analysing non-zero autocorrelation time series data, such as first differencing or generalised least squares regression, enables high-frequency variations along the time series to be taken into account. However, the relative simplicity of our model, which was designed to reproduce the main Phanerozoic trends in global diversity, coupled with the fact that biases in the fossil data would introduce uncertainty into the analysis, leads us to focus our analysis on the long-term trends, obviating the effect of autocorrelation.Model parameterization and calibrationThe diversification models are parameterized assuming a range of values that constrain the lower and upper limits of the genus-level net diversification rate (ρmin and ρmax, respectively) (Extended Data Table 1) according to previously reported estimates from fossil records (figures 8 and 11 of ref. 5). A range of realistic values is assigned for the parameters Q10 and Kfood, determining, respectively, the thermal sensitivity and food dependence of the net diversification rate. We test a total of 40 different parameter combinations (Extended Data Table 2). The resulting estimates of diversity are then compared against the fossil diversity curves of ref. 20, ref. 21 or ref. 22, and the 15 parameter combinations providing the highest CCCs are selected.The results of the logistic diversification model rely on the values of the minimum and maximum carrying capacities (Kmin and Kmax, respectively) within which the spatially resolved effective carrying capacities (Keff) are allowed to vary. The values of Kmin and Kmax are therefore calibrated by running 28 simulations of pair-wise Kmin and Kmax combinations increasing in a geometric sequence of base 2, from 2 to 256 genera (Extended Data Fig. 4). We perform these simulations independently for each of the 15 parameter settings selected previously (Extended Data Fig. 4 and Extended Data Table 2). Each combination of Kmin and Kmax produces a global diversity curve, which is evaluated as described above using Lin’s CCC.Calculating estimates of global diversity from regional diversity maps in the absence of information on genus-level taxonomic identities requires that we assume a spatial turnover of taxa with geographical distance (Extended Data Fig. 1). Distance-decay curves are routinely fitted by calculating the ecological similarity (for example, the Jaccard similarity index) between each pair of sampling sites, and fitting an exponential decay function to the points on a scatter plot of similarity (y axis) versus distance (x axis). Following this method, we fit an exponential decay function to the distance–decay curves reported in ref. 61, depicting the decrease in the Jaccard similarity index (J) of fossil genera with geographical distance (great circle distance) at different Phanerozoic time intervals:$$J={J}_{{rm{o}}{rm{f}}{rm{f}}}+(,{J}_{max}text{-}{J}_{{rm{o}}{rm{f}}{rm{f}}}){{rm{e}}}^{-lambda times {rm{d}}{rm{i}}{rm{s}}{rm{t}}{rm{a}}{rm{n}}{rm{c}}{rm{e}}}$$
(9)
where Joff = 0.06 (n.d.) is a small offset, Jmax = 1.0 (n.d.) is the maximum value of the genus-based Jaccard similarity index and λ = 0.0024 (km−1) is the distance-decay rate.The Jaccard similarity index (J) between consecutive points n and n + 1 is bounded between 0 and min(αn; αn + 1)/max(αn; αn + 1). A larger value for J would mean that there are more shared genera between the two communities than there are genera within the least diverse community, which is ecologically absurd. However, using a single similarity decay function can lead the computed value of J to be locally larger than min(αn; αn + 1)/max(αn; αn + 1). To prevent this artefact, we use the Simpson similarity index or ‘overlap coefficient’ (V) instead of J. V corresponds to the percentage of shared genera with respect to the least diverse community (min(αn ; αn + 1)). V is bounded between 0 and 1, whatever the ratio of diversities. As the pre-existing estimates of similarity are expressed using J (ref. 61), we perform the conversion from J to V using the algebraic expression V = (1 + R) × J/(1 + J) where R = max(αn; αn + 1)/min(αn; αn + 1) (Supplementary Note 1). In the cases in which J exceeds the min(αn; αn + 1)/max(αn; αn + 1), V becomes >1 and, in those cases, we force V to be More

Benchmark regression resultsParallel trend testThe premise of using DID is that the treatment group and control group meet the assumptions of parallel trend, which means that before ETS is officially implemented, the evolution trend of ecologicalization efficiency of industry of the control group and the experimental group is consistent and does not show a systematic difference. This study uses a more rigorous empirical test in parallel trend test: if the interaction coefficient is not significant and is different from zero before the implementation of ETS; and if the interaction coefficient is significant and is different from zero after the implementation of ETS, it indicates that there is no significant difference in ecologicalization efficiency of industry between the control group and the experimental group before the implementation of ETS. Results are shown in Table 4: before ETS was officially implemented, the difference coefficient was not significant; after the official implementation of ETS in 2013, the difference coefficient was significant and not equal to 0, and the ecologicalization efficiency of industry was improved significantly, which met the parallel trend of the DID. Therefore, it is scientific and reasonable to evaluate the effectiveness of ETS with DID.Table 4 Parallel trend test.Full size tableDynamic effect analysisTo compare the conditions of the experimental group and the control group before and after the implementation of ETS, dynamic graphs are drawn in this study, as shown in Fig. 1, which shows the impact of ETS on the regional ecologicalization efficiency of industry. The vertical line represents a 95% confidence interval and the broken line shows the marginal effect of regional ecologicalization efficiency, which means that the confidence interval contains is 0 before ETS’s implementation, and the result is not significant. In contrast, after 2013, the effect of ETS became apparent, the marginal effect gradually increased and the results became significant, perhaps owing to the implementation of ETS.Figure 1Dynamic analysis diagram.Full size imageThe effect of ETS on ecologicalization efficiency of industryControlling time effect and fixed effect, this study collected the data of pilot and non-pilot provinces of ETS from 2007 to 2019 to analyze the impact of ETS on the regional ecologicalization efficiency of industry and regional heterogeneity. The results are shown in Table 5. According to the results in the first column, ETS has significantly promoted the regional ecologicalization efficiency of industry, and the national implementation of ETS has achieved remarkable results. Compared with the regions that are not ETS pilot areas, the ecologicalization efficiency of industry of pilot provinces and cities has increased by 35%. Results also show that ETS has different effects on the ecologicalization efficiency of industry in different regions. Specifically, ETS significantly promoted regional ecologicalization efficiency of industry in the eastern and central regions, and the efficiency in the eastern region was more significant than that of the central region. However, the impact of ETS on the regional ecologicalization efficiency of industry in the western region was negative which may result from the fact that compared to the central and western regions, the east region has better economic development, advanced technology, and lots of talents that can respond to the implementation of ETS, accelerate the upgrade of industries, and improve the utilization level of regional resources. There are many traditional industries in the central and western regions, and the development of scientific and technological levels as well as the resource utilization efficiency there are relatively slow. Besides, it is difficult for the central and western regions to adapt to ETS in a short-term of time leading to the failure of improving the regional ecologicalization efficiency of industry in a short time.Table 5 Influence of ETS on ecologicalization efficiency of industry.Full size tableRobustness testPropensity matching score—double difference method (PSM-DID)The assumption of homogeneity and randomness between the control group and the experimental group is the premise of using the DID model. However, due to the large economic and regional differences among provinces and cities, there may be systematic differences between the experimental group and the control group, which may cause deviations in the results. Therefore, the data after propensity score matching is used in this study, making the matched individuals have no other significant differences unless they have been treated or not. The dual difference is conducted again to avoid self-selection bias, and the robustness of the above results is verified according to the measurement results. Control variables were used to match characteristic variables, the experimental group was matched with the control group, and the Logit model was adopted to delete the samples that fail to meet the matching criteria. After the matching, there are 168 observation values. The regression results of PSM-DID model show that, ETS has positive effects on the regional ecologicalization of industry (0.460***), which again proves that the conclusion that ETS improves regional ecologicalization of industry efficiency is reliable. The results are shown in Table 6.Table 6 The result of the PSM-DID.Full size tableCounterfactual testTo verify the robustness of the results again, six provinces and cities are randomly selected as experimental groups for multiple tests to construct new dummy variables of ETS, and the DID model was used again to verify the credibility of the above results. Four random samples were conducted in this study, and the results are shown in Table 7. It can be seen that the results are not significant, which also reversely proves that ETS improves the regional ecologicalization efficiency of industry.Table 7 Counterfactual test results.Full size tableActing pattern analysis of ETS on the regional ecologicalization efficiency of industryFirst, ETS may improve the regional ecologicalization efficiency of industry through industrial structure optimization and upgrading. Promoting upgrading of the industrial structure is one of the important approaches of social and economic development during the 14th Five-Year Plan formulation and is the only way to promote low-carbon and sustainable development of modern national industries. The upgrading of the industrial structure has been promoted to the national strategic level, contributing to the healthy development of the national economy system. ETS bring costs and benefits to enterprises, forcing them to transform and upgrade, increase investment in environmental protection and use clean energy, and accelerate the pace of energy conservation and emission reduction31. Second, ETS may improve the regional ecologicalization efficiency of industry through the coordinated agglomeration of resources. Marshall’s theory of scale economy, Krugman’s theory of new economic geography, Weber’s theory of agglomeration economy, Coase’s transaction cost theory, and so on reflect the importance of resource aggregation of economic activities through cost-saving, resource sharing, and other ways to improve industrial input–output efficiency, enhance industrial competitiveness, increase regional comprehensive strength and strengthen the competitive advantage of regional industrial clusters32. The benefits generated by resource aggregation far exceed the sum of benefits generated by various industries in the decentralized state. Under the pressure of ETS, enterprises may alleviate the mismatch between labor and capital through the collaborative aggregation of industrial resources, aiming to improve economic benefits and regional resource allocation efficiency and promote regional ecologicalization efficiency of industry. Third, ETS may improve the regional ecologicalization efficiency of industry by supporting ecological optimization. The sustainable development of the ecological environment is closely related to emission reduction policy. To alleviate the bad effects on the ecology, environmental protection is more and more brought to the attention of society and government. Policies for ecological protection have been introduced to reduce pollution20. All regions take effective and targeted measures to control environmental pollution and optimize the investment structure in light of their actual conditions. The purpose of ecological optimization is to improve the regional environment and strengthen pollution control which is one of the important parts of China’s fiscal spending. The government must guide the market to carry out ecological protection and environmental governance according to ETS. Studies have found that a low-carbon pilot policy helps to enhance the level of regional pollution control, promote the harmonious development of regional economy and environment, and then improve the regional ecologicalization efficiency of industry.To explore the transmission mechanism of ETS on the regional ecologicalization of industry efficiency, Baron and Kenny (1986)’s mediating effect model was referred to explore and verify whether there exists a structural optimization upgrade effect, resource synergistic agglomeration effect, ecological optimization support effect when ETC promotes regional ecologicalization efficiency of industry. Table 8 shows the regression results of the influence mechanism of ETS on the regional ecologicalization efficiency of industry. This study refers to the definition and research of industrial optimization and upgrading by Wang Qunwei, Huang Xianglan, and others, and the proportion of tertiary industry added value accounting for industrial added value is selected to measure the effectiveness of industrial optimization and upgrading. For resource synergistic agglomeration effect, this study refers to the calculation methods of Cui Shuhui, Chen Jianjun et al. and adopts the collaborative aggregation index of manufacturing and producer services to measure the collaborative aggregation effect of resources, which effectively avoids the scale difference between different regions. It can be seen from the table that the implementation of ETS has significantly influenced the three effects proposed by this study: the optimization and upgrading effect of industrial structure, the synergistic aggregation effect of resources, and the support effect of ecological optimization. In addition, ETS has a positive and significant impact on the regional ecologicalization efficiency of industry. The results in Columns 3, 5, and 7 of the table show the industrial optimization and upgrading effect, resource synergistic aggregation effect, structural upgrading effect, and resource allocation effect generated in the process of low-carbon pilot policy operation can significantly promote regional ecologicalization efficiency of industry and have an obvious intermediary effect. The mediating effect produced by industrial structure optimization and upgrading is about 0.042, the mediating effect produced by resource synergy agglomeration is about 0.148, and the mediating effect produced by ecological optimization support is about 0.166. According to the Sobal test results, all of them have passed the test, indicating that the above results are reliable.Table 8 Mediating effect test results.Full size table More

Brandes, N. & Linial, M. Giant viruses—big surprises. Viruses 11, 404 (2019).CAS 
Article 

Google Scholar 
Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
Article 

Google Scholar 
Madsen, E. L. Microorganisms and their roles in fundamental biogeochemical cycles. Curr. opinion biotechnology 22, 456–464 (2011).CAS 
Article 

Google Scholar 
Gummow, B. Challenges posed by new and re-emerging infectious diseases in livestock production, wildlife and humans. Livest. Sci. 130, 41–46 (2010).CAS 
Article 

Google Scholar 
Becker, D. J., Streicker, D. G. & Altizer, S. Linking anthropogenic resources to wildlife–pathogen dynamics: a review and meta-analysis. Ecol. letters 18, 483–495 (2015).Article 

Google Scholar 
Woolhouse, M. E. & Gowtage-Sequeria, S. Host range and emerging and reemerging pathogens. Emerg. infectious diseases 11, 1842 (2005).Article 

Google Scholar 
Foster, R. et al. Pathogens co-transported with invasive non-native aquatic species: implications for risk analysis and legislation. NeoBiota 69, 79–102 (2021).Article 

Google Scholar 
Brasier, C. The biosecurity threat to the uk and global environment from international trade in plants. Plant Pathol. 57, 792–808 (2008).Article 

Google Scholar 
Ruiz, G. M. et al. Global spread of microorganisms by ships. Nature 408, 49–50 (2000).ADS 
CAS 
Article 

Google Scholar 
Essl, F. et al. Which taxa are alien? criteria, applications, and uncertainties. BioScience 68, 496–509 (2018).Article 

Google Scholar 
Blackburn, T. M., Bellard, C. & Ricciardi, A. Alien versus native species as drivers of recent extinctions. Front. Ecol. Environ. 17, 203–207 (2019).Article 

Google Scholar 
Hawkins, C. L. et al. Framework and guidelines for implementing the proposed iucn environmental impact classification for alien taxa (eicat). Divers. Distributions 21, 1360–1363 (2015).Article 

Google Scholar 
Corrales, X. et al. Advances and challenges in modelling the impacts of invasive alien species on aquatic ecosystems. Biol. Invasions 22, 907–934 (2020).Article 

Google Scholar 
Regulation, E. Regulation (eu) no 1143/2014 of the European Parliament and of the Council of 22 October 2014 on the prevention and management of the introduction and spread of invasive alien species. Off. J. Eur. Union 57, 35–55 (2014).
Google Scholar 
EU. Regulation (eu) 2016/2031 of the European Parliament of the Council of 26 October 2016 on protective measures against pests of plants, amending regulations (eu) 228/2013,(eu) 652/2014 and (eu) 1143/2014 and repealing council directives 69/464/eec, 74/647/eec, 93/85/eec, 98/57/ec, 2000/29/ec, 2006/91/ec and 2007/33/ec. Off. J. 317, 4–104 (2016).
Google Scholar 
Murtaugh, M. P. et al. The science behind one health: at the interface of humans, animals, and the environment. Tech. Rep. (2017).Ogden, N. H. et al. Emerging infectious diseases and biological invasions: a call for a one health collaboration in science and management. Royal Soc. open science 6, 181577 (2019).ADS 
Article 

Google Scholar 
Roy, H. E. et al. Alien pathogens on the horizon: Opportunities for predicting their threat to wildlife. Conserv. Lett. 10, 477–484 (2017).Article 

Google Scholar 
Ikner, L. A., Gerba, C. P. & Bright, K. R. Concentration and recovery of viruses from water: a comprehensive review. Food Environ. Virol. 4, 41–67 (2012).
Google Scholar 
Taylor, M. W. Introduction: A short history of virology. In Viruses and Man: A History of Interactions, 1–22 (Springer, 2014).Thakur, M. P., Van der Putten, W. H., Cobben, M. M., van Kleunen, M. & Geisen, S. Microbial invasions in terrestrial ecosystems. Nat. Rev. Microbiol. 17, 621–631 (2019).CAS 
Article 

Google Scholar 
Desprez-Loustau, M.-L. et al. The fungal dimension of biological invasions. Trends ecology & evolution 22, 472–480 (2007).Article 

Google Scholar 
Rivett, D. W. et al. Elevated success of multispecies bacterial invasions impacts community composition during ecological succession. Ecol. Lett. 21, 516–524 (2018).Article 

Google Scholar 
Dunn, A. M. & Hatcher, M. J. Parasites and biological invasions: parallels, interactions, and control. TRENDS Parasitol. 31, 189–199 (2015).Article 

Google Scholar 
Pyšek, P. et al. Macroecological framework for invasive aliens (mafia): disentangling large-scale context dependence in biological invasions. (2020).Hulme, P. E. et al. Blurring alien introduction pathways risks losing the focus on invasive species policy. Conserv. Lett. 10, 265–266 (2017).Article 

Google Scholar 
Gilroy, J. J., Avery, J. D. & Lockwood, J. L. Seeking international agreement on what it means to be “native”. Conserv. Lett. 10, 238–247 (2017).Article 

Google Scholar 
Webber, B. L. & Scott, J. K. Rapid global change: implications for defining natives and aliens. Glob. Ecol. Biogeogr. 21, 305–311 (2012).Article 

Google Scholar 
CBD Secretariat. Decision VI/23: Alien species that threaten ecosystems, habitats and species. Document UNEP/CBD/COP/6/23 (2002).World Health Organization. A brief guide to emerging infectious diseases and zoonoses. Tech. Rep. https://apps.who.int/iris/handle/10665/204722 (2014).Firrao, G. et al. Candidatus phytoplasma’, a taxon for the wall-less, non-helical prokaryotes that colonize plant phloem and insects. Int. J. Syst. Evol. Microbiol. 54, 1243–1255 (2004).CAS 
Article 

Google Scholar 
CBD. Pathways of introduction of invasive species, their prioritization and management (Secretariat of the Convention on Biological Diversity Montreal, 2014).OIE. Terrestrial Animal Health Code 2021 (OIE, 2021).Magliozzi, C. et al. bacteria and viruses traits and species-related factors. figshare https://doi.org/10.6084/m9.figshare.18550907.v2 (2022).Katsanevakis, S. et al. Implementing the European policies for alien species: networking, science, and partnership in a complex environment. Manag. Biol. Invasions 4, 3–6 (2013).Article 

Google Scholar 
Tsiamis, K. et al. The EASIN Editorial Board: quality assurance, exchange and sharing of alien species information in europe. Manag. Biol. invasions 7, 321–328 (2016).Article 

Google Scholar 
Wieczorek, J. et al. Darwin core: an evolving community-developed biodiversity data standard. PloS one 7, e29715 (2012).ADS 
CAS 
Article 

Google Scholar 
Darwin Core. Darwin Core quick reference guide. https://dwc.tdwg.org/terms/ (2018).R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis, https://ggplot2.tidyverse.org (Springer-Verlag New York, 2016).Schwarzl, T. ggBubbles: Mini Bubble Plots for Comparison of Discrete Data with ‘ggplot2’ R package version 0.1.4 (2019).Moon, K. R statistics and graphs for medical papers (Hannarae Seoul, 2015).Current, C. Invasive species compendium. Wallingford, UK: CAB Int. Available online: www.cabi.org/isc (accessed on 19 August 2020) (2011).Adams, M. J. & Antoniw, J. F. Dpvweb: An open access internet resource on plant viruses and virus diseases. Outlooks on Pest Manag. 16, 268 (2005).Article 

Google Scholar 
Adams, M. J. & Antoniw, J. F. Dpvweb: a comprehensive database of plant and fungal virus genes and genomes. Nucleic acids research 34, D382–D385 (2006).CAS 
Article 

Google Scholar 
Benson, D. A. et al. Genbank. Nucleic acids research 41, D36–D42 (2012).Article 

Google Scholar  More

Giorgi, A., Pentimalli, D., Giupponi, L. & Panseri, S. Quality traits of saffron (Crocus sativus L.) produced in the Italian Alps. Open Agric. 2(1), 52–57 (2017).Article 

Google Scholar 
Winterhalter, P. & Straubinger, M. Saffron—Renewed interest in an ancient spice. Food Rev. Intl. 16(1), 39–59 (2000).CAS 
Article 

Google Scholar 
Schmidt, M., Betti, G. & Hensel, A. Saffron in phytotherapy: Pharmacology and clinical uses. Wien Med. Wochenschr. 157, 315–319 (2007).PubMed 
Article 

Google Scholar 
Siddique, H. R., Fatma, H. & Khan, M. A. Medicinal properties of saffron with special reference to cancer—A review of preclinical studies. in Saffron: The Age-Old Panacea in a New Light (ed. Sarwat,
M. & Sumaiya, S.) 233–244 (Academic Press, 2020).Chapter 

Google Scholar 
Abdullaev, F. I. Cancer chemopreventive and tumoricidal properties of saffron (Crocus sativus L.). Exp. Biol. Med. 227(1), 20–25 (2002).CAS 
Article 

Google Scholar 
Kafi, M., Koocheki, A. & Rashed, M. H. Saffron (Crocus sativus): Production and Processing (Science Publishers, 2006).Book 

Google Scholar 
Mir, G.M. Saffron Agronomy in Kashmir (1992).Melnyk, J. P., Wang, S. & Marcone, M. F. Chemical and biological properties of the world’s most expensive spice: Saffron. Food Res. Int. 43(8), 1981–1989 (2010).CAS 
Article 

Google Scholar 
Menia, M. et al. Production technology of saffron for enhancing productivity. J. Pharmacognosy Phytochem. 7(1), 1033–1039 (2018).
Google Scholar 
Tanra, M. A., Dar, B. A. & Sing, S. Economic analysis of Production and Marketing of saffron in Jammu and Kashmir. J. Social Relevance Concern 5(10), 12–19 (2017).
Google Scholar 
Husaini, A. M., Hassan, B., Ghani, M. Y., Teixeira da Silva, J. A. & Kirmani, N. A. saffron (Crocus sativus Kashmirianus) cultivation in Kashmir: Practices and problems. Functional Plant Sci. Biotechnol. 4(2), 108–115 (2010).
Google Scholar 
Amirnia, R., Bayat, M. & Tajbakhsh, M. Effects of nano fertilizer application and maternal corm weight on flowering at some saffron (Crocus sativus L.) ecotypes. Turkish J. Field Crops. 19(2), 158–168 (2014).Article 

Google Scholar 
Kumar, R. et al. State of art of saffron (Crocus sativus L.) agronomy: A comprehensive review. Food Rev. Int. 25(1), 44–85 (2009).Article 

Google Scholar 
Dhar, A. K. Saffron breeding and agrotechnology. Status Rep. PAFAI J. 12, 18–22 (1990).
Google Scholar 
Ehsanzadeh, P., Yadollahi, A. A. & Maibodi, A. M. Productivity, growth and quality attributes of 10 Iranian saffron accessions under climatic conditions of Chahar-Mahal Bakhtiari, Central Iran. Int. Symp. Saffron Biol. Biotechnol. 650, 183–188 (2003).
Google Scholar 
Duke, J. A. Ecosystematic data on economic plants. Quart. J. Crude Drug Res. 17(3–4), 91–109 (1979).Article 

Google Scholar 
Kanth, R.H., Khanday, B.A. & Tabassum, S. Crop weather relationship for saffron production. Saffron Production in Jammu and Kashmir, Directorate of Extension Education. SKUAST-K, India 170–188 (2008).Shinde, D. A., Talib, A. R. & Gorantiwar, S. M. Composition and classification of some typical soils of saffron growing areas of Jammu and Kashmir. J. Indian Soc. Soil Sci. 32(3), 473–477 (1984).CAS 

Google Scholar 
Nazir, N. A., Khitrov, N. B. & Chizhikova, N. P. Statistical evaluation of soil properties which influence saffron growth in Kashmir. Eurasian Soil Sci. 28(4), 120–138 (1996).
Google Scholar 
Ganai, M. R., Wani, M. A. & Zargar, G. H. Characterization of saffron growing soils of Kashmir. Appl. Biol. Res. 2(1/2), 27–30 (2000).
Google Scholar 
Ganai, M.R.D. Nutrient status of saffron soils and their management. in Proceedings of Seminar-cum-Workshop on saffron (Crocus sativus) 51–54 (2001).Molina, R. V., Valero, M., Navarro, Y., Guardiola, J. L. & Garcia-Luis, A. Temperature effects on flower formation in saffron (Crocus sativus L.). ScientiaHorticulturae 103(3), 361–379 (2005).
Google Scholar 
Galavi, M., Soloki, M., Mousavi, S. R. & Ziyaie, M. Effect of planting depth and soil summer temperature control on growth and yield of saffron (Crocus sativus L.). Asian J. Plant Sci. 7(8), 747 (2008).Article 

Google Scholar 
Kamyabi, S., Habibi Nokhandan, M. & Rouhi, A. Effect of climatic factors affecting saffron using analytic hierarchy process (AHP); Case Study Roshtkhar Region, Iran. (2014).Gupta, R. K. Saffron status and cultivation in northwestern Himalayas. Vegetos 20(1), 1–7 (2007).
Google Scholar 
Qin, A. et al. Maxent modelling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Glob. Ecol. Conserv. 10, 139–146 (2017).Article 

Google Scholar 
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24(1), 38–49 (1997).Article 

Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988).ADS 
MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evaluat. 5(11), 1198–1205 (2014).Article 

Google Scholar 
Hao, T., Elith, J., Arroita, G. G. & Monfort, J. J. L. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25(5), 839–852 (2019).Article 

Google Scholar 
Thuiller, W. BIOMOD—Optimizing predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9, 1353–1362 (2003).ADS 
Article 

Google Scholar 
Mykhailenko, O., Desenko, V., Ivanauskas, L. & Georgiyants, V. Standard operating procedure of Ukrainian saffron cultivation according to with good agricultural and collection practices to assure quality and traceability. Ind. Crops Prod. 151, 112376. https://doi.org/10.1016/j.indcrop.2020.112376 (2020).CAS 
Article 

Google Scholar 
Kothari, D., Thakur, M., Joshi, R., Kumar, A. & Kumar, R. Agro-climatic suitability evaluation for saffron production in areas of western Himalaya. Front. Plant Sci. 12, 657819. https://doi.org/10.3389/fpls.2021.657819 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Mir, J. I., Ahmed, N., Wafai, A. H. & Qadri, R. A. Variability in stigma length and apocarotenoid content in Crocus sativus L. selections of Kashmir. J. Spices Aromatic Crops. 21(2), 169–171 (2012).
Google Scholar 
Nehvi, F. A. et al. New emerging trends on production technology of saffron. II Int. Symp. Saffron Biol. Technol. 739, 375–381 (2006).
Google Scholar 
Golmohammadi, F. Sustainable agriculture and rural development in Iran, Some modern issues in sustainable agriculture and rural development in Iran Germany, LAP LAMBERT Academic Publishing GmbH & Co. LAP Lambert Academic Publishing. Germany. ISBN-13, 978-3 (2012).Golmohammadi, F. Saffron and its importance, medical uses and economical export situation in Iran. in Oral Article Presented in: International Conference on Advances in Plant Sciences 14–18 (2012).Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modelling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).Article 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species distributions from occurrence data. Ecography 29(2), 129–151 (2006).Article 

Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Article 

Google Scholar 
Wisz, M. S. et al. NCEAS Predicting species distributions working group. Effects of sample size on the performance of species distribution models. Diversity Distributions. 14(5), 763–773 (2008).Article 

Google Scholar 
Rebelo, H. & Jones, G. Ground validation of presence only modelling with rare species: A case study on Barbastella barbastellus (Chiroptera: Vespertilionidae). J. Appl. Ecol. 47(2), 410–420 (2010).Article 

Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).Article 

Google Scholar 
Palomera, S. et al. Mapping from heterogeneous biodiversity monitoring data sources. Biodiversity Conservation 21(11), 2927–2948 (2012).Article 

Google Scholar 
Garcia, K., Lasco, R., Ines, A., Lyon, B. & Pulhin, F. Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Appl. Geogr. 44, 12–22 (2013).Article 

Google Scholar 
Marcer, A., Sáez, L., Molowny-Horas, R., Pons, X. & Pino, J. Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biol. Cons. 166, 221–230 (2013).Article 

Google Scholar 
Phillips, S.J., Dudík, M. & Schapire, R.E. A maximum entropy approach to species distribution modelling. in Proceedings of the Twenty-First International Conference on Machine Learning 83 (2004).Baldwin, R. A. Use of maximum entropy modelling in wildlife research. Entropy 11(4), 854–866 (2009).ADS 
Article 

Google Scholar 
Izadpanah, F., Kalantari, S., Hassani, M. E., Naghavi, M. R. & Shokrpour, M. Variation in Saffron (Crocus sativus L.) accessions and Crocus wild species by RAPD analysis. Plant Syst. Evolut. 300, 1941–1944 (2014).Article 

Google Scholar 
Nemati, Z., Harpke, D., Gemicioglu, A., Kerndorff, H. & Blattner, F. R. Saffron (Crocus sativus) is an autotriploid that evolved in Attica (Greece) from wild Crocus cartwrightianus. Mol. Phylogenet. Evol. 136, 14–20 (2019).PubMed 
Article 

Google Scholar 
Proosdij, A. S. J. V., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).Article 

Google Scholar  More

The whiskers of seals are known to function as vibration receptors. Earlier experiments with blindfolded harbour seals in captivity have for example revealed that the animals can detect small water movements, and follow the hydrodynamic trails created by passing objects. But it is unclear if seals in the wild actively use this ability to find prey.
This is a preview of subscription content More