Philippa Kaur

McElroy, C. R., Constantinou, A., Jones, L. C., Summerton, L. & Clark, J. H. Towards a holistic approach to metrics for the 21st century pharmaceutical industry. Green Chem. 17, 3111–3121. https://doi.org/10.1039/C5GC00340G (2015).Article 
CAS 

Google Scholar 
Zimmerman, J. B., Anastas, P. T., Erythropel, H. C. & Leitner, W. Designing for a green chemistry future. Science 367, 397–400. https://doi.org/10.1126/science.aay3060 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sheldon, R. A. Metrics of green chemistry and sustainability: Past, present, and future. ACS Sustain. Chem. Eng. 6, 32–48. https://doi.org/10.1021/acssuschemeng.7b03505 (2018).Article 
CAS 

Google Scholar 
Anastas, P. T. & Williamson, T. C. in Green Chemistry, Vol. 626 ACS Symposium Series Ch. 1, 1–17 (American Chemical Society, 1996). https://doi.org/10.1021/bk-1996-0626.ch001.Clark, H. J. Green chemistry: Challenges and opportunities. Green Chem. 1, 1–8. https://doi.org/10.1039/A807961G (1999).Article 
CAS 

Google Scholar 
Dekamin, M. G. & Eslami, M. Highly efficient organocatalytic synthesis of diverse and densely functionalized 2-amino-3-cyano-4 H-pyrans under mechanochemical ball milling. Green Chem. 16, 4914–4921 (2014).Article 
CAS 

Google Scholar 
Eze, A. A. et al. Wet ball milling of niobium by using ethanol, determination of the crystallite size and microstructures. Sci. Rep. 11, 1–8 (2021).Article 

Google Scholar 
Gorrasi, G. & Sorrentino, A. Mechanical milling as a technology to produce structural and functional bio-nanocomposites. Green Chem. 17, 2610–2625 (2015).Article 
CAS 

Google Scholar 
Li, L. H., Glushenkov, A. M., Hait, S. K., Hodgson, P. & Chen, Y. High-efficient production of boron nitride nanosheets via an optimized ball milling process for lubrication in oil. Sci. Rep. 4, 1–6 (2014).
Google Scholar 
Mac Naughton, G. E., Rolfe, S. A. & Siraj-Blatchford, I. E. Doing Early Childhood Research: International Perspectives on Theory and Practice (Open University Press, 2001).Evangelisti, L. et al. The borderline between reactivity and pre-reactivity of binary mixtures of gaseous carboxylic acids and alcohols. Angew. Chem. 129, 3930–3933 (2017).Article 
ADS 

Google Scholar 
Gaspa, S., Porcheddu, A. & De Luca, L. Metal-free oxidative cross esterification of alcohols via acyl chloride formation. Adv. Synth. Catal. 358, 154–158 (2016).Article 
CAS 

Google Scholar 
Fiorio, J. L., Braga, A. H., Guedes, C. L. S. B. & Rossi, L. M. Reusable heterogeneous tungstophosphoric acid-derived catalyst for green esterification of carboxylic acids. ACS Sustain. Chem. Eng. 7, 15874–15883 (2019).Article 
CAS 

Google Scholar 
Karimi, B., Mirzaei, H. M. & Mobaraki, A. Periodic mesoporous organosilica functionalized sulfonic acids as highly efficient and recyclable catalysts in biodiesel production. Catal. Sci. Technol. 2, 828–834 (2012).Article 
CAS 

Google Scholar 
Tran, T. T. V. et al. Selective production of green solvent (isoamyl acetate) from fusel oil using a sulfonic acid-functionalized KIT-6 catalyst. Mol. Catal. 484, 110724 (2020).Article 
CAS 

Google Scholar 
Afshar, S. et al. Optimization of catalytic activity of sulfated titania for efficient synthesis of isoamyl acetate by response surface methodology. Mon. Chem. Chem. Mon. 146, 1949–1957 (2015).Article 
CAS 

Google Scholar 
Chng, L. L., Yang, J. & Ying, J. Y. Efficient synthesis of amides and esters from alcohols under aerobic ambient conditions catalyzed by a Au/mesoporous Al2O3 nanocatalyst. Chemsuschem 8, 1916–1925 (2015).Article 
CAS 
PubMed 

Google Scholar 
Lozano, P., Bernal, J. M. & Navarro, A. A clean enzymatic process for producing flavour esters by direct esterification in switchable ionic liquid/solid phases. Green Chem. 14, 3026–3033 (2012).Article 
CAS 

Google Scholar 
Su, L., Hong, R., Guo, X., Wu, J. & Xia, Y. Short-chain aliphatic ester synthesis using Thermobifida fusca cutinase. Food Chem. 206, 131–136 (2016).Article 
CAS 
PubMed 

Google Scholar 
Güvenç, A., Kapucu, N., Kapucu, H., Aydoğan, Ö. & Mehmetoğlu, Ü. Enzymatic esterification of isoamyl alcohol obtained from fusel oil: Optimization by response surface methodolgy. Enzyme Microb. Technol. 40, 778–785 (2007).Article 

Google Scholar 
Torres, S., Baigorí, M. D., Swathy, S., Pandey, A. & Castro, G. R. Enzymatic synthesis of banana flavour (isoamyl acetate) by Bacillus licheniformis S-86 esterase. Food Res. Int. 42, 454–460 (2009).Article 
CAS 

Google Scholar 
Ando, H., Kurata, A. & Kishimoto, N. Antimicrobial properties and mechanism of volatile isoamyl acetate, a main flavour component of Japanese sake (Ginjo-shu). J. Appl. Microbiol. 118, 873–880 (2015).Article 
CAS 
PubMed 

Google Scholar 
Ghamgui, H., Karra-Chaâbouni, M., Bezzine, S., Miled, N. & Gargouri, Y. Production of isoamyl acetate with immobilized Staphylococcus simulans lipase in a solvent-free system. Enzyme Microb. Technol. 38, 788–794 (2006).Article 
CAS 

Google Scholar 
Romero, M., Calvo, L., Alba, C., Daneshfar, A. & Ghaziaskar, H. Enzymatic synthesis of isoamyl acetate with immobilized Candida antarctica lipase in n-hexane. Enzyme Microb. Technol. 37, 42–48 (2005).Article 
CAS 

Google Scholar 
Borges, M. E. & Díaz, L. Recent developments on heterogeneous catalysts for biodiesel production by oil esterification and transesterification reactions: A review. Renew. Sustain. Energy Rev. 16, 2839–2849 (2012).Article 
CAS 

Google Scholar 
Li, K.-T., Wang, C.-K., Wang, I. & Wang, C.-M. Esterification of lactic acid over TiO2–ZrO2 catalysts. Appl. Catal. A 392, 180–183 (2011).Article 
CAS 

Google Scholar 
Clark, J. H. & Rhodes, C. N. In Clean Synthesis Using Porous Inorganic Solid Catalysts and Supported Reagents, Vol. 4, (Royal Society of Chemistry, London, 2000). https://doi.org/10.1039/9781847550569Dekamin, M. G. et al. Sodium alginate: An efficient biopolymeric catalyst for green synthesis of 2-amino-4H-pyran derivatives. Int. J. Biol. Macromol. 87, 172–179 (2016).Article 
CAS 
PubMed 

Google Scholar 
Melfi, D. T., dos Santos, K. C., Ramos, L. P. & Corazza, M. L. Supercritical CO2 as solvent for fatty acids esterification with ethanol catalyzed by Amberlyst-15. J. Supercrit. Fluids 158, 104736 (2020).Article 
CAS 

Google Scholar 
Azudin, N. Y., Mashitah, M. & Abd Shukor, S. R. Optimization of isoamyl acetate production in a solvent-free system. J. Food Qual. 36, 441–446 (2013).Article 
CAS 

Google Scholar 
Ćorović, M. et al. Immobilization of Candida antarctica lipase B onto Purolite® MN102 and its application in solvent-free and organic media esterification. Bioprocess Biosyst. Eng. 40, 23–34 (2017).Article 
PubMed 

Google Scholar 
Liu, C. & Luo, G. Synthesis of isoamyl acetate catalyzed by ferric tri-dodecylsulfonate. Riyong Huaxue Gongye 34, 403–405 (2004).
Google Scholar 
Narwal, S. K., Saun, N. K., Dogra, P. & Gupta, R. Green synthesis of isoamyl acetate via silica immobilized novel thermophilic lipase from Bacillus aerius. Russ. J. Bioorg. Chem. 42, 69–73 (2016).Article 
CAS 

Google Scholar 
Pizzio, L., Vázquez, P., Cáceres, C. & Blanco, M. Tungstophosphoric and molybdophosphoric acids supported on zirconia as esterification catalysts. Catal. Lett. 77, 233–239 (2001).Article 
CAS 

Google Scholar 
Saha, B., Alqahtani, A. & Teo, H. T. R. Production of iso-Amyl Acetate: Heterogeneous Kinetics and Techno-feasibility Evaluation for Catalytic Distillation. Int. J. Chem. React. Eng. 3(1), https://doi.org/10.2202/1542-6580.1231 (2005).Osorio-Viana, W., Ibarra-Taquez, H. N., Dobrosz-Gomez, I. & Gómez-García, M. Á. Hybrid membrane and conventional processes comparison for isoamyl acetate production. Chem. Eng. Process. 76, 70–82 (2014).Article 
CAS 

Google Scholar 
Fang, M. et al. Synthesis of isoamyl acetate using polyoxometalate-based sulfonated ionic liquid as catalyst. Indian J. Chem. Sect. A 53A, 1485–1492 (2014).Yang, Z., Zhou, C., Zhang, W., Li, H. & Chen, M. β-MnO2 nanorods: A new and efficient catalyst for isoamyl acetate synthesis. Colloids Surf., A 356, 134–139 (2010).Article 
CAS 

Google Scholar 
Yang, Z. et al. Kinetic study and process simulation of transesterification of methyl acetate and isoamyl alcohol catalyzed by ionic liquid. Ind. Eng. Chem. Res. 54, 1204–1215 (2015).Article 
CAS 

Google Scholar 
Dohendou, M., Pakzad, K., Nezafat, Z., Nasrollahzadeh, M. & Dekamin, M. G. Progresses in chitin, chitosan, starch, cellulose, pectin, alginate, gelatin and gum based (nano)catalysts for the Heck coupling reactions: A review. Int. J. Biol. Macromol. 192, 771–819. https://doi.org/10.1016/j.ijbiomac.2021.09.162 (2021).Article 
CAS 
PubMed 

Google Scholar 
Valiey, E., Dekamin, M. G. & Alirezvani, Z. Melamine-modified chitosan materials: An efficient and recyclable bifunctional organocatalyst for green synthesis of densely functionalized bioactive dihydropyrano[2,3-c]pyrazole and benzylpyrazolyl coumarin derivatives. Int. J. Biol. Macromol. 129, 407–421. https://doi.org/10.1016/j.ijbiomac.2019.01.027 (2019).Article 
CAS 
PubMed 

Google Scholar 
Dekamin, M. G., Kazemi, E., Karimi, Z., Mohammadalipoor, M. & Naimi-Jamal, M. R. Chitosan: An efficient biomacromolecule support for synergic catalyzing of Hantzsch esters by CuSO4. Int. J. Biol. Macromol. 93, 767–774. https://doi.org/10.1016/j.ijbiomac.2016.09.012 (2016).Article 
CAS 
PubMed 

Google Scholar 
Valiey, E., Dekamin, M. G. & Bondarian, S. Sulfamic acid grafted to cross-linked chitosan by dendritic units: A bio-based, highly efficient and heterogeneous organocatalyst for green synthesis of 2,3-dihydroquinazoline derivatives. RSC Adv. 13, 320–334. https://doi.org/10.1039/D2RA07319F (2023).Article 
ADS 
CAS 

Google Scholar 
Dekamin, M. G., Azimoshan, M. & Ramezani, L. Chitosan: A highly efficient renewable and recoverable bio-polymer catalyst for the expeditious synthesis of α-amino nitriles and imines under mild conditions. Green Chem. 15, 811–820. https://doi.org/10.1039/C3GC36901C (2013).Article 
CAS 

Google Scholar 
Alirezvani, Z., Dekamin, M. G. & Valiey, E. Cu (II) and magnetite nanoparticles decorated melamine-functionalized chitosan: A synergistic multifunctional catalyst for sustainable cascade oxidation of benzyl alcohols/Knoevenagel condensation. Sci. Rep. 9, 17758 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Rostami, N., Dekamin, M., Valiey, E. & Fanimoghadam, H. Chitosan-EDTA-Cellulose network as a green, recyclable and multifunctional biopolymeric organocatalyst for the one-pot synthesis of 2-amino-4H-pyran derivatives. Sci. Rep. 12, 8642–8642 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frindy, S., el Kadib, A., Lahcini, M., Primo, A. & García, H. Copper nanoparticles stabilized in a porous chitosan aerogel as a heterogeneous catalyst for C−S cross-coupling. ChemCatChem 7, 3307–3315 (2015).Article 
CAS 

Google Scholar 
Pettignano, A. et al. Alginic acid aerogel: A heterogeneous Brønsted acid promoter for the direct Mannich reaction. New J. Chem. 39, 4222–4226 (2015).Article 
CAS 

Google Scholar 
Schnepp, Z. Biopolymers as a flexible resource for nanochemistry. Angew. Chem. Int. Ed. 52, 1096–1108 (2013).Article 
CAS 

Google Scholar 
Khrunyk, Y., Lach, S., Petrenko, I. & Ehrlich, H. Progress in modern marine biomaterials research. Mar. Drugs 18, 589 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, I. Molecular self-assembly: Smart design of surface and interface via secondary molecular interactions. Langmuir 29, 2476–2489. https://doi.org/10.1021/la304123b (2013).Article 
CAS 
PubMed 

Google Scholar 
Shaheed, N., Javanshir, S., Esmkhani, M., Dekamin, M. G. & Naimi-Jamal, M. R. Synthesis of nanocellulose aerogels and Cu-BTC/nanocellulose aerogel composites for adsorption of organic dyes and heavy metal ions. Sci. Rep. 11, 18553 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Abdullah, M. A. et al. Processing Aspects and biomedical and environmental applications of sustainable nanocomposites containing nanofillers. In Sustainable Polymer Composites and Nanocomposites, (eds Inamuddin et al.) 727–757 (Springer, Cham, 2019). https://doi.org/10.1007/978-3-030-05399-4_25Dekamin, M. G. et al. Alginic acid: A highly efficient renewable and heterogeneous biopolymeric catalyst for one-pot synthesis of the Hantzsch 1,4-dihydropyridines. RSC Adv. 4, 56658–56664. https://doi.org/10.1039/C4RA11801D (2014).Article 
ADS 
CAS 

Google Scholar 
Ilkhanizadeh, S., Khalafy, J. & Dekamin, M. G. Sodium alginate: A biopolymeric catalyst for the synthesis of novel and known polysubstituted pyrano[3,2-c]chromenes. Int. J. Biol. Macromol. 140, 605–613. https://doi.org/10.1016/j.ijbiomac.2019.08.154 (2019).Article 
CAS 
PubMed 

Google Scholar 
Dekamin, M. G. et al. Alginic acid: A mild and renewable bifunctional heterogeneous biopolymeric organocatalyst for efficient and facile synthesis of polyhydroquinolines. Int. J. Biol. Macromol. 108, 1273–1280. https://doi.org/10.1016/j.ijbiomac.2017.11.050 (2018).Article 
CAS 
PubMed 

Google Scholar 
Rostami, N., Dekamin, M. G. & Valiey, E. Chitosan-EDTA-cellulose bio-based network: A recyclable multifunctional organocatalyst for green and expeditious synthesis of Hantzsch esters. Carbohydr. Polym. Technol. Appl. 5, 100279. https://doi.org/10.1016/j.carpta.2022.100279 (2023).Article 
CAS 

Google Scholar 
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S. & Escaleira, L. A. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76, 965–977. https://doi.org/10.1016/j.talanta.2008.05.019 (2008).Article 
CAS 
PubMed 

Google Scholar 
Hill, W. J. & Hunter, W. G. A review of response surface methodology: A literature survey. Technometrics 8, 571–590. https://doi.org/10.1080/00401706.1966.10490404 (1966).Article 
MathSciNet 

Google Scholar 
Hamidi, F. et al. Acid red 18 removal from aqueous solution by nanocrystalline granular ferric hydroxide (GFH); optimization by response surface methodology & genetic-algorithm. Sci. Rep. 12, 1–15 (2022).Article 

Google Scholar 
Han, X.-X. et al. Syntheses of novel halogen-free Brønsted–Lewis acidic ionic liquid catalysts and their applications for synthesis of methyl caprylate. Green Chem. 17, 499–508 (2015).Article 
CAS 

Google Scholar 
Rehman, K. et al. Operational parameters optimization for remediation of crude oil-polluted water in floating treatment wetlands using response surface methodology. Sci. Rep. 12, 1–11 (2022).Article 

Google Scholar 
Kamari, S., Ghorbani, F. & Sanati, A. M. Adsorptive removal of lead from aqueous solutions by amine–functionalized magMCM-41 as a low–cost nanocomposite prepared from rice husk: Modeling and optimization by response surface methodology. Sustain. Chem. Pharm. 13, 100153. https://doi.org/10.1016/j.scp.2019.100153 (2019).Article 

Google Scholar 
Sanati, A. M., Kamari, S. & Ghorbani, F. Application of response surface methodology for optimization of cadmium adsorption from aqueous solutions by Fe3O4@SiO2@APTMS core–shell magnetic nanohybrid. Surf. Interfaces 17, 100374. https://doi.org/10.1016/j.surfin.2019.100374 (2019).Article 
CAS 

Google Scholar 
Guner, S. G. & Dericioglu, A. Nacre-mimetic epoxy matrix composites reinforced by two-dimensional glass reinforcements. RSC Adv. 6, 33184–33196 (2016).Article 
ADS 
CAS 

Google Scholar 
Shao, Y., Zhao, H.-P. & Feng, X.-Q. Optimal characteristic nanosizes of mineral bridges in mollusk nacre. RSC Adv. 4, 32451–32456 (2014).Article 
ADS 
CAS 

Google Scholar 
Jaji, A. Z. et al. Synthesis, characterization, and cytocompatibility of potential cockle shell aragonite nanocrystals for osteoporosis therapy and hormonal delivery. Nanotechnol. Sci. Appl. 10, 23 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Çam, M. & Aaby, K. Optimization of extraction of apple pomace phenolics with water by response surface methodology. J. Agric. Food Chem. 58, 9103–9111 (2010).Article 
PubMed 

Google Scholar 
Iwuchukwu, I. J. et al. Optimization of photosynthetic hydrogen yield from platinized photosystem I complexes using response surface methodology. Int. J. Hydrog. Energy 36, 11684–11692 (2011).Article 
CAS 

Google Scholar 
Hu, C. et al. Characterization and photocatalytic activity of noble-metal-supported surface TiO2/SiO2. Appl. Catal. A 253, 389–396 (2003).Article 
CAS 

Google Scholar 
Noda, L. K., de Almeida, R. M., Probst, L. F. D. & Gonçalves, N. S. Characterization of sulfated TiO2 prepared by the sol–gel method and its catalytic activity in the n-hexane isomerization reaction. J. Mol. Catal. A Chem. 225, 39–46 (2005).Article 
CAS 

Google Scholar 
Jalali-Heravi, M., Parastar, H. & Ebrahimi-Najafabadi, H. Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography–mass spectrometry analysis. J. Chromatogr. A 1216, 6088–6097 (2009).Article 
CAS 
PubMed 

Google Scholar 
Sendzikiene, E., Sinkuniene, D., Kazanceva, I. & Kazancev, K. Optimization of low quality rapeseed oil transesterification with butanol by applying the response surface methodology. Renew. Energy 87, 266–272 (2016).Article 
CAS 

Google Scholar 
Das, R., Sarkar, S. & Bhattacharjee, C. Photocatalytic degradation of chlorhexidine—a chemical assessment and prediction of optimal condition by response surface methodology. J. Water Process Eng. 2, 79–86 (2014).Article 

Google Scholar 
Nandiwale, K. Y., Galande, N. D. & Bokade, V. V. Process optimization by response surface methodology for transesterification of renewable ethyl acetate to butyl acetate biofuel additive over borated USY zeolite. RSC Adv. 5, 17109–17116 (2015).Article 
ADS 
CAS 

Google Scholar 
Soltani, R. D. C. & Safari, M. Periodate-assisted pulsed sonocatalysis of real textile wastewater in the presence of MgO nanoparticles: Response surface methodological optimization. Ultrason. Sonochem. 32, 181–190 (2016).Article 

Google Scholar 
Tan, K. T., Lee, K. T. & Mohamed, A. R. A glycerol-free process to produce biodiesel by supercritical methyl acetate technology: An optimization study via response surface methodology. Biores. Technol. 101, 965–969 (2010).Article 
CAS 

Google Scholar 
Nagaraju, N., Peeran, M. & Prasad, D. Synthesis of isoamyl acetate usin NaX and NaY zeolites as catalysts. React. Kinet. Catal. Lett. 61, 155–160 (1997).Article 
CAS 

Google Scholar 
Pizzio, L. R. & Blanco, M. N. Isoamyl acetate production catalyzed by H3PW12O40 on their partially substituted Cs or K salts. Appl. Catal. A 255, 265–277 (2003).Article 
CAS 

Google Scholar 
Dekamin, M. G., Karimi, Z. & Farahmand, M. Tetraethylammonium 2-(N-hydroxycarbamoyl)benzoate: A powerful bifunctional metal-free catalyst for efficient and rapid cyanosilylation of carbonyl compounds under mild conditions. Catal. Sci. Technol. 2, 1375–1381. https://doi.org/10.1039/C2CY20037F (2012).Article 
CAS 

Google Scholar 
Dekamin, M. G., Sagheb-Asl, S. & Reza Naimi-Jamal, M. An expeditious synthesis of cyanohydrin trimethylsilyl ethers using tetraethylammonium 2-(carbamoyl)benzoate as a bifunctional organocatalyst. Tetrahedron Lett. 50, 4063–4066. https://doi.org/10.1016/j.tetlet.2009.04.090 (2009).Article 
CAS 

Google Scholar 
Alirezvani, Z., Dekamin, M. G. & Valiey, E. New hydrogen-bond-enriched 1,3,5-tris(2-hydroxyethyl) isocyanurate covalently functionalized MCM-41: An efficient and recoverable hybrid catalyst for convenient synthesis of acridinedione derivatives. ACS Omega 4, 20618–20633. https://doi.org/10.1021/acsomega.9b02755 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Hallegraeff, G. M. Ocean climate change, phytoplankton community responses, and harmful algal blooms: A formidable predictive challenge 1. J. Phycol. 46, 220–235 (2010).Article 
CAS 

Google Scholar 
Itakura, S. & Imai, I. Economic impacts of harmful algal blooms on fisheries and aquaculture in western Japan—An overview of interannual variability and interspecies comparison. PICES Sci. Rep. 47, 17 (2014).
Google Scholar 
Haigh, N. & Esenkulova, S. Economic losses to the British Columbia salmon aquaculture industry due to harmful algal blooms, 2009–2012. PICES Sci. Rep. 47, 2 (2014).
Google Scholar 
Sharma, N. K. et al. (eds) Cyanobacteria: An Economic Perspective 245–256 (Wiley, 2014).
Google Scholar 
O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334. https://doi.org/10.1016/j.hal.2011.10.027 (2012).Article 
CAS 

Google Scholar 
Paerl, H. W. & Huisman, J. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environ. Microbiol. Rep. 1, 27–37. https://doi.org/10.1111/j.1758-2229.2008.00004.x (2009).Article 
CAS 
PubMed 

Google Scholar 
Hallegraeff, G. M. et al. Perceived global increase in algal blooms is attributable to intensified monitoring and emerging bloom impacts. Commun. Earth Environ. 2, 117. https://doi.org/10.1038/s43247-021-00178-8 (2021).Article 
ADS 

Google Scholar 
Hennon, G. M. M. & Dyhrman, S. T. Progress and promise of omics for predicting the impacts of climate change on harmful algal blooms. Harmful Algae 91, 101587. https://doi.org/10.1016/j.hal.2019.03.005 (2020).Article 
CAS 
PubMed 

Google Scholar 
Kudela, R., Berdalet, E. & Urban, E. Harmful Algal Blooms: A Scientific Summary for Policy Makers (2015).Lezcano, M., Velázquez, D., Quesada, A. & El-Shehawy, R. Diversity and temporal shifts of the bacterial community associated with a toxic cyanobacterial bloom: An interplay between microcystin producers and degraders. Water Res. 125, 52–61. https://doi.org/10.1016/j.watres.2017.08.025 (2017).Article 
CAS 
PubMed 

Google Scholar 
Scherer, P. I. et al. Temporal dynamics of the microbial community composition with a focus on toxic cyanobacteria and toxin presence during harmful algal blooms in two South German Lakes. Front. Microbiol. 8, 02387. https://doi.org/10.3389/fmicb.2017.02387 (2017).Article 

Google Scholar 
Woodhouse, J. N. et al. Microbial communities reflect temporal changes in cyanobacterial composition in a shallow ephemeral freshwater lake. ISME J. 10, 1337–1351. https://doi.org/10.1038/ismej.2015.218 (2016).Article 
CAS 
PubMed 

Google Scholar 
Beaver, J. R. et al. Land use patterns, ecoregion, and microcystin relationships in U.S. lakes and reservoirs: A preliminary evaluation. Harmful Algae 36, 57–62. https://doi.org/10.1016/j.hal.2014.03.005 (2014).Article 
CAS 

Google Scholar 
Loftin, K. A. et al. Cyanotoxins in inland lakes of the United States: Occurrence and potential recreational health risks in the EPA National Lakes Assessment 2007. Harmful Algae 56, 77–90. https://doi.org/10.1016/j.hal.2016.04.001 (2016).Article 
CAS 
PubMed 

Google Scholar 
Casero, M. C., Velázquez, D., Medina-Cobo, M., Quesada, A. & Cirés, S. Unmasking the identity of toxigenic cyanobacteria driving a multi-toxin bloom by high-throughput sequencing of cyanotoxins genes and 16S rRNA metabarcoding. Sci. Total Environ. 665, 367–378. https://doi.org/10.1016/j.scitotenv.2019.02.083 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chaffin, J. D., Sigler, V. & Bridgeman, T. B. Connecting the blooms: Tracking and establishing the origin of the record-breaking Lake Erie Microcystis bloom of 2011 using DGGE. Aquat. Microb. Ecol. 73, 29–39 (2014).Article 

Google Scholar 
Stanley, E. H. & Jones, J. B. (eds) Stream Ecosystems in a Changing Environment 321–348 (Elsevier, 2016).Book 

Google Scholar 
Giblin, S. M. & Gerrish, G. A. Environmental factors controlling phytoplankton dynamics in a large floodplain river with emphasis on cyanobacteria. River Res. Appl. 36, 1137–1150. https://doi.org/10.1002/rra.3658 (2020).Article 

Google Scholar 
Graham, J. L., Ziegler, A. C., Loving, B. L. & Loftin, K. A. Fate and Transport of Cyanobacteria and Associated Toxins and Taste-and-Odor Compounds from Upstream Reservoir Releases in the Kansas River, Kansas, September and October 2011 65 (US Geological Survey, 2012).
Google Scholar 
Knowlton, M. F. & Jones, J. R. Seston, light, nutrients and chlorophyll in the lower Missouri River, 1994–1998. J. Freshw. Ecol. 15, 283–297. https://doi.org/10.1080/02705060.2000.9663747 (2000).Article 

Google Scholar 
Otten, T. G., Crosswell, J. R., Mackey, S. & Dreher, T. W. Application of molecular tools for microbial source tracking and public health risk assessment of a Microcystis bloom traversing 300 km of the Klamath River. Harmful Algae 46, 71–81 (2015).Article 

Google Scholar 
Preece, E. P., Hardy, F. J., Moore, B. C. & Bryan, M. A review of microcystin detections in Estuarine and Marine waters: Environmental implications and human health risk. Harmful Algae 61, 31–45. https://doi.org/10.1016/j.hal.2016.11.006 (2017).Article 
CAS 

Google Scholar 
Reinl, K. L., Sterner, R. W., Lafrancois, B. M. & Brovold, S. Fluvial seeding of cyanobacterial blooms in oligotrophic Lake Superior. Harmful Algae 100, 101941. https://doi.org/10.1016/j.hal.2020.101941 (2020).Article 
CAS 
PubMed 

Google Scholar 
Bridgeman, T. B. et al. From River to Lake: Phosphorus partitioning and algal community compositional changes in Western Lake Erie. J. Great Lakes Res. 38, 90–97 (2012).Article 
CAS 

Google Scholar 
Brown, B. L. et al. Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River. Stand Genomic Sci. 10, 65. https://doi.org/10.1186/s40793-015-0062-5 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Hamner, S. et al. Metagenomic profiling of microbial pathogens in the Little Bighorn River, Montana. Int. J. Environ. Res. Public Health 16, 071097. https://doi.org/10.3390/ijerph16071097 (2019).Article 
CAS 

Google Scholar 
Staley, C. et al. Application of Illumina next-generation sequencing to characterize the bacterial community of the Upper Mississippi River. J. Appl. Microbiol. 115, 1147–1158. https://doi.org/10.1111/jam.12323 (2013).Article 
CAS 
PubMed 

Google Scholar 
Winter, C., Hein, T., Kavka, G., Mach, R. L. & Farnleitner, A. H. Longitudinal changes in the bacterial community composition of the Danube River: A whole-river approach. Appl. Environ. Microbiol. 73, 421–431. https://doi.org/10.1128/aem.01849-06 (2007).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jackson, C. R., Millar, J. J., Payne, J. T., Ochs, C. A. & Wommack, K. E. Free-living and particle-associated bacterioplankton in large rivers of the Mississippi River basin demonstrate biogeographic patterns. Appl. Environ. Microbiol. 80, 7186–7195. https://doi.org/10.1128/AEM.01844-14 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Payne, J. T., Jackson, C. R., Millar, J. J. & Ochs, C. A. Timescales of variation in diversity and production of bacterioplankton assemblages in the Lower Mississippi River. PLoS ONE 15, e0230945. https://doi.org/10.1371/journal.pone.0230945 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Payne, J. T., Millar, J. J., Jackson, C. R. & Ochs, C. A. Patterns of variation in diversity of the Mississippi river microbiome over 1,300 kilometers. PLoS ONE 12, e0174890. https://doi.org/10.1371/journal.pone.0174890 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Read, D. S. et al. Catchment-scale biogeography of riverine bacterioplankton. ISME J. 9, 516–526. https://doi.org/10.1038/ismej.2014.166 (2015).Article 
CAS 
PubMed 

Google Scholar 
Reddington, K. et al. Metagenomic analysis of planktonic riverine microbial consortia using nanopore sequencing reveals insight into river microbe taxonomy and function. GigaScience 9, 53. https://doi.org/10.1093/gigascience/giaa053 (2020).Article 
CAS 

Google Scholar 
Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Staley, C. et al. Species sorting and seasonal dynamics primarily shape bacterial communities in the Upper Mississippi River. Sci. Total Environ. 505, 435–445. https://doi.org/10.1016/j.scitotenv.2014.10.012 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Van Rossum, T. et al. Year-long metagenomic study of river microbiomes across land use and water quality. Front. Microbiol. 6, 1405 (2015).PubMed 
PubMed Central 

Google Scholar 
Kim, K. H. et al. Application of metagenome analysis to characterize the molecular diversity and saxitoxin-producing potentials of a cyanobacterial community: A case study in the North Han River, Korea. Appl. Biol. Chem. 61, 153–161. https://doi.org/10.1007/s13765-017-0342-4 (2018).Article 
CAS 

Google Scholar 
Graham, J. L. et al. Cyanotoxin occurrence in large rivers of the United States. Inland Waters 10, 109–117. https://doi.org/10.1080/20442041.2019.1700749 (2020).Article 
CAS 

Google Scholar 
Zuellig, R. E., Graham, J. L., Stelzer, E. A., Loftin, K. A. & Rosen, B. H. Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence Among Selected Large River Sites of the Conterminous United States, 2017–18 22 (US Geological Survey, 2021).
Google Scholar 
Kramer, B. J. et al. Nitrogen limitation, toxin synthesis potential, and toxicity of cyanobacterial populations in Lake Okeechobee and the St. Lucie River Estuary, Florida, during the 2016 state of emergency event. PLoS ONE 13, e0196278 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Bouma-Gregson, K. et al. Impacts of microbial assemblage and environmental conditions on the distribution of anatoxin-a producing cyanobacteria within a river network. ISME J. 13, 1618–1634. https://doi.org/10.1038/s41396-019-0374-3 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tillett, D. et al. Structural organization of microcystin biosynthesis in Microcystis aeruginosa PCC7806: An integrated peptide–polyketide synthetase system. Chem. Biol. 7, 753–764 (2000).Article 
CAS 
PubMed 

Google Scholar 
Dittmann, E., Fewer, D. P. & Neilan, B. A. Cyanobacterial toxins: Biosynthetic routes and evolutionary roots. FEMS Microbiol. Rev. 37, 23–43. https://doi.org/10.1111/j.1574-6976.2012.12000.x (2013).Article 
CAS 
PubMed 

Google Scholar 
Jungblut, A. D. & Neilan, B. A. Molecular identification and evolution of the cyclic peptide hepatotoxins, microcystin and nodularin, synthetase genes in three orders of cyanobacteria. Arch. Microbiol. 185, 107–114. https://doi.org/10.1007/s00203-005-0073-5 (2006).Article 
CAS 
PubMed 

Google Scholar 
Meriluoto, J. et al. (eds) Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis 501–525 (Wiley, 2017).Book 

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643. https://doi.org/10.1038/ismej.2017.119 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Graham, J. L., Dubrovsky, N. M., Loftin, K. A., Rosen, B. H. & Stelzer, E. A. Cyanotoxin, Chlorophyll-a, and Cyanobacterial Toxin Genetic Data for Samples Collected at Twelve Large River Sites Throughout the United States, June Through October 2019 (U.S. Geological Survey, 2022).
Google Scholar 
Dodds, W. K. & Smith, V. H. Nitrogen, phosphorus, and eutrophication in streams. Inland Waters 6, 155–164. https://doi.org/10.5268/IW-6.2.909 (2016).Article 
CAS 

Google Scholar 
Debroas, D. et al. Overview of freshwater microbial eukaryotes diversity: A first analysis of publicly available metabarcoding data. FEMS Microbiol. Ecol. 93, 23. https://doi.org/10.1093/femsec/fix023 (2017).Article 
CAS 

Google Scholar 
Henson, M. W. et al. Nutrient dynamics and stream order influence microbial community patterns along a 2914 kilometer transect of the Mississippi River. Limnol. Oceanogr. 63, 1837–1855. https://doi.org/10.1002/lno.10811 (2018).Article 
ADS 
CAS 

Google Scholar 
Ghai, R. et al. Metagenomics of the water column in the pristine upper course of the Amazon river. PLoS ONE 6, e23785. https://doi.org/10.1371/journal.pone.0023785 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Liao, J. et al. Cyanobacteria in lakes on Yungui Plateau, China are assembled via niche processes driven by water physicochemical property, lake morphology and watershed land-use. Sci. Rep. 6, 36357. https://doi.org/10.1038/srep36357 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Monchamp, M.-E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324. https://doi.org/10.1038/s41559-017-0407-0 (2018).Article 
PubMed 

Google Scholar 
Pessi, I. S., Maalouf, P. D. C., LaughinghouseBaurain, H. D. D. & Wilmotte, A. On the use of high-throughput sequencing for the study of cyanobacterial diversity in Antarctic aquatic mats. J. Phycol. 52, 356–368. https://doi.org/10.1111/jpy.12399 (2016).Article 
CAS 
PubMed 

Google Scholar 
Tanvir, R. U., Hu, Z., Zhang, Y. & Lu, J. Cyanobacterial community succession and associated cyanotoxin production in hypereutrophic and eutrophic freshwaters. Environ. Pollut. 290, 118056. https://doi.org/10.1016/j.envpol.2021.118056 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chételat, J., Pick, F. R. & Hamilton, P. B. Potamoplankton size structure and taxonomic composition: Influence of river size and nutrient concentrations. Limnol. Oceanogr. 51, 681–689 (2006).Article 
ADS 

Google Scholar 
Heiskary, S. & Markus, H. Establishing relationships among nutrient concentrations, phytoplankton abundance, and biochemical oxygen demand in Minnesota, USA, rivers. Lake Reserv. Manag. 17, 251–262 (2001).Article 
CAS 

Google Scholar 
Smith, V. H. Eutrophication of freshwater and coastal marine ecosystems a global problem. Environ. Sci. Pollut. Res. 10, 126–139 (2003).Article 
CAS 

Google Scholar 
Verspagen, J. M. et al. Rising CO2 levels will intensify phytoplankton blooms in eutrophic and hypertrophic lakes. PLoS ONE 9, e104325 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Zepernick, B. N. et al. Elevated pH conditions associated with Microcystis spp. blooms decrease viability of the cultured diatom Fragilaria crotonensis and natural diatoms in Lake Erie. Front. Microbiol. 12, 598736. https://doi.org/10.3389/fmicb.2021.598736 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Urban, L. et al. Freshwater monitoring by nanopore sequencing. Elife 10, 61504. https://doi.org/10.7554/eLife.61504 (2021).Article 

Google Scholar 
Lee, C. J. & Henderson, R. J. Tracking Water-Quality in U.S. Streams and Rivers: U.S. Geological Survey National Water Quality Network. https://nrtwq.usgs.gov/nwqn (2020).Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (2010).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257. https://doi.org/10.1186/s13059-019-1891-0 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu, J. B. F., Thielen, P. & Salzberg, S. L. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, 104. https://doi.org/10.7717/peerj-cs.104 (2017).Article 

Google Scholar 
Bagley, M. et al. High-throughput environmental DNA analysis informs a biological assessment of an urban stream. Ecol. Ind. 104, 378–389. https://doi.org/10.1016/j.ecolind.2019.04.088 (2019).Article 
CAS 

Google Scholar 
Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. https://doi.org/10.1093/bioinformatics/btr507 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nübel, U., Garcia-Pichel, F. & Muyzer, G. PCR primers to amplify 16S rRNA genes from cyanobacteria. Appl. Environ. Microbiol. 63, 3327–3332. https://doi.org/10.1128/aem.63.8.3327-3332.1997 (1997).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Neilan, B. A. et al. rRNA sequences and evolutionary relationships among toxic and nontoxic cyanobacteria of the genus Microcystis. Int. J. Syst. Bacteriol. 47, 693–697. https://doi.org/10.1099/00207713-47-3-693 (1997).Article 
CAS 
PubMed 

Google Scholar 
Team R Core. R: A Language and Environment for Statistical Computing (2013).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.5-2 (2018).Wickham, H. ggplot2-Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
U.S. Geological Survey. National Water Information System Database. https://doi.org/10.5066/F7P55KJN (2022). More

Study siteThe study was conducted in the Brasília National Park (PNB), Federal District, Brazil (15º39′57″ S; 47º59′38″ W), a 42.355 ha Protected Area with a typical vegetation configuration found in the Cerrado of the central highlands of Brazil, i.e., a mosaic of gallery forest patches along rivers surrounded by a matrix of savannas and grasslands34. The climate in the region falls into the Aw category in the Köppen scale, categorizing a tropical wet savanna, with marked rainy (October to March) and dry (April to September) seasons.We carried out the study in eight fixed sampling sites scattered evenly throughout the PNB and separated by at least two kilometers from one another (Supplementary Fig. S1). The sites consisted of four cerrado sensu stricto sites (bushy savanna containing low stature trees); two gallery forest edges sites (ca. 5 m from forest edges, containing a transitional community), and two gallery forest interior sites. These three types reflect the overall availability of habitat types in the reserve (excluding grasslands) and are the most appropriate foraging areas to sample interactions as bat-visited plants are either bushes, trees, or epiphytes, but rarely herbs35.Bat and interaction samplingsWe sampled bat-plant interactions using pollen loads collected from bat individuals captured in the course of one phenological year, thus configuring an animal-centered sampling. We carried out monthly field campaigns to capture bats from October 2019 to February 2020, from August to September 2020, and from March to July 2021. In each month, we carried out eight sampling nights during periods of low moonlight intensity, each associated with one of the eight sites. Each night, we set 10 mist nets (2.6 × 12 m, polyester, denier 75/2, 36 mm mesh size, Avinet NET-PTX, Japan) at ground level randomly within the site, which were opened at sunset and closed after six hours. We accumulated a total sampling effort of 552 net-hours, 28,704 m2 of net area, or 172,224 m2h sensu Straube and Bianconi36.All captured bats were sampled for pollen, irrespective of family or feeding guild. We used glycerinated and stained gelatin cubes to collect pollen grains from the external body of bats (head, torso, wings, and uropatagium). Samples were stored individually, and care was taken not to cross-contaminate samples. Pollen types were identified by light microscopy, and palynomorphs were identified to the lowest-possible taxonomical level using an extensive personal reference pollen collection from plants from the PNB (details in next section). Palynomorphs were sometimes classified to the genus or family level or grouped in entities representing more than one species. Any palynomorph numbering five or fewer grains in one sample was considered contamination, alongside any anemophilous species irrespective of pollen number.Bats were identified using a specialized key37 and four ecomorphological variables were measured for each individual. (i) Forearm length and (ii) body mass were used to calculate the body condition index (BCI), a proxy of body robustness38, where higher BCI values indicate larger and heavier bats, which are less effective in interacting with flowers in general due to a lack of hovering behavior, the incapability of interacting with delicate flowers that cannot sustain them, a lower maneuverability and higher energetic requirements39. Moreover, we measured (iii) longest skull length (distance from the edge of the occipital region to the anterior edge of the lower lip) and (iv) rostrum length (distance from the anterior edge of the eye to the anterior edge of the lower lip) to calculate the rostrum-skull ratio (RSR), a proxy of morphological specialization to nectar consumption23. Higher RSR values indicate bats with proportionally longer rostra in relation to total skull length. Longer rostra in bats are associated with a weaker bite force and thus less effective in consuming harder food items such as fruits and insects, thus suggesting a higher adaptation to towards nectar40,41. Bats were then tagged with aluminum bands for individualization and released afterward. To evaluate the sampling completeness of the bat community and of the pollen types found on bats, we employed the Chao1 asymptotic species richness estimator and an individual-based sampling effort to estimate and plot rarefaction curves, calculating sampling completeness according to Chacoff et al.42.All methods were carried out in accordance with relevant guidelines and regulations. The permits to capture, handle and collect bats were granted by the Ethical Council for the Usage of Animals (CEUA) of the University of Brasília (permit 23106.119660/2019-07) and the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) (permit: SISBIO 70268). Vouchers of each species, when the collection was possible, were deposited in the Mammal Collection of the University of Brasília.Assessment of the plant communityIn each of the eight sampling sites, we delimited a 1000 × 10 m transect, each of which was walked monthly for one phenological year (January and February 2020, August to December 2020, and March to July 2021) to build a floristic inventory of plants of interest and to estimate their monthly abundance of flowering individuals. Plant species of interest were any potential partner for bats, which included species already known to be pollinated by bats, presenting chiropterophilous traits sensu Faegri and Van Der Pijl43, or any plant that could be accessed by and reward bats, whose flowers passes all the three following criteria:(i) Nectar or pollen is presented as the primary reward to visitors. (ii) Corolla diameter of 1 cm or more. This criterion excludes small generalist and insect-pollinated flowers where the visitation by bats is mechanically unlikely. It applies to the corolla diameter in non-tubular flowers or the diameter of the tube opening. Exceptions were small and actinomorphic flowers aggregated in one larger pollination unit (pseudanthia) where the 1 cm threshold was applied to inflorescence diameter. (iii) Reward must be promptly available for bats. This criterion excludes species with selective morphological mechanisms, such as quill-shaped bee-pollinated flowers or flowers with long and narrow calcars.All flowering individuals of interest species found in the transects were registered. A variable number of flowers/inflorescences (n = 5–18) were collected per species for morphometric analysis. For each species, we calculated floral tube length (FTL), corresponding to the distance between the base of the corolla, calyx, or hypanthium (depending on the species) to its opening, and the corolla’s outermost diameter (COD), which corresponds to the diameter of the corolla opening (tubular flowers) or simply the corolla diameter (non-tubular flowers). For pseudanthia-forming species, inflorescence width was measured. Pseudanthia and non-tubular flowers received a dummy FTL value of 0.1 mm to represent low restriction and enable later calculations. Finally, we collected reference pollen samples from all species from anthers of open flowers, which were used to identify pollen types found on bats. For plant species found in pollen loads but not in the PNB, measures were taken from plants found either on the outskirts of the site (Inga spp.) or from dried material in an online database (Ceiba pentandra, in https://specieslink.net/) using the ImageJ software44. Vouchers were deposited in the Herbarium of the Botany Department, University of Brasília.Data analysisNetwork macrostructureWe built a weighted adjacency matrix i x j, where cells corresponded to the number of individuals of bat species i that interacted with plant species or morphotype j. All edges corresponding to legitimate interactions were included. With this matrix, we calculated three structural metrics to describe the network’s macrostructure. First, weighted modularity (Qw), calculated by the DIRTLPAwb + algorithm45. A modular network comprises subgroups of species in which interactions are stronger and more frequent than species out of these subgroups10, which may reveal functional groups in the network9. Qw varies from zero to one, the latter representing a perfectly modular network.Second, complementary specialization through the H2′ metric46. It quantifies how unique, on average, are the interactions made by species in the network, considering interaction weights and correcting for network size. It varies from zero to one, the latter corresponding to a specialized network where interactions perfectly complement each other because species do not share partners.Lastly, nestedness, using the weighted WNODA metric25. Nested networks are characterized by interaction asymmetries, where peripheral species are only a subset of the pool of species with which generalists interact47. The index was normalized to vary from zero to one, with one representing a perfectly nested network. Given that the network has a modular structure, we also tested for a compound topology, i.e., the existence of distinct network patterns within network modules, by calculating intra-module WNODA and between-module WNODA36. Internally nested modules appear in networks in which consumers specialize in groups of dissimilar or clustered resources and suggest the existence of distinct functional groups of consumers25,48. Metric significance (Qw, H2′, and WNODA) was assessed using a Monte Carlo procedure based on a null model. We used the vaznull model3, where random matrices are created by preserving the connectance of the observed matrix but allowing marginal totals to vary. One thousand matrices were generated and metrics were calculated for each of them. Metric significance (p) corresponded to the number of times the null model delivered a value equal to or higher than the observed metric, divided by the number of matrices. The significance threshold was considered p ≤ 0.05.Given a modular structure, we followed the framework of Phillips et al.49 that correlates network concepts (especially modularity) with the distribution of morphological variables of pollinators to unveil patterns of niche divergence in pollination networks. Given the most parsimonious module configuration suggested by the algorithm, we compared modules in terms of the distribution of morphological variables of the bat (RCR and BCI) and plant (FTL and COD) species that composed the module. Differences between modules means were tested with one-way ANOVAs.Drivers of network microstructureThe role of different ecological variables in determining pairwise interaction frequencies was assessed using a probability matrices approach3. This framework considers that an interaction matrix Y is a product of several probability matrices of the same size as Y, with each matrix representing the probability of species interacting based on an ecological mechanism. Thus, adapting it to our objectives, we have Eq. (1):$$mathrm{Y}=mathrm{f}(mathrm{A},mathrm{ M },mathrm{P},mathrm{ S})$$
(1)
where Y is the observed interaction matrix, and a function of interaction probability matrices based on species relative abundances (A), representing neutrality as species interact by chance; species morphological specialization (M), phenological overlap (P), and spatial overlap (S). We built models containing each of these matrices in the following ways:Relative abundance (A): matrix cells were the products of the relative abundances of bat and plant species. The relative abundances of bats were determined through capture frequencies (each species’ capture frequency divided by all captures, excluding recaptures) and the relative abundances of plants were determined by the number of flowering individuals recorded in transections (each species’ summed abundance in all transects and all months divided by the pooled abundance of all species in the network). Cell values were normalized to sum one.Morphological specialization (M): cells were the probability of species interacting based on their matching degree of morphological specialization. Morphologically specialized bats (i.e., longer rostra and smaller size) are more likely to interact with morphologically specialized flowers (i.e., longer tubes and narrower corollas), while unspecialized bats are more likely to interact with unspecialized, accessible flowers. For this purpose, we calculated a bat specialization index (BSI) as the ratio between RCR and BCI, where higher BSI values indicate overall lower body robustness and longer snout length. Likewise, the flower specialization index (FSI) was calculated for plants as the ratio between FTL and COD, where higher values indicate smaller, narrower, long-tubed flowers that require specialized morphology and behavior from bats for visitation. BSI and FTL were normalized to range between zero and one and were averaged between individuals of each species of bat or plant. Therefore, interaction probabilities were calculated as in Eq. (2):$${P}_{i,j}=1-|{BSI}_{i}-{FSI}_{j}|$$
(2)
where Pi,j is the interaction probability between bat species i and plant species j and |BSIi – FSIj| is the absolute difference between bat and plant specialization indexes. Similar index values (two morphologically specialized or unspecialized species interacting) lead to a low difference in specialization and thus to a high probability of interaction (Pi,j → 1), whereas the interaction between a morphologically specialized and a morphologically unspecialized species leads to a high absolute difference and thus lower probability of interaction (Pi,j → 0). Cell values of the resulting matrix were normalized to sum one.Phenological overlap (P): cells were the probability of species interacting based on temporal synchrony, calculated as the number of months that individuals of bat species i and flowering individuals of plant species j co-occurred in the research site, pooling all capture sites/transections. Cell values were normalized to sum one.Spatial overlap (S): cells were the probability of species interacting based on their co-occurrence over small-scale distances and vegetation types, calculated as the number of individuals from a bat species i captured in sampling sites where the plant species j was registered in the transection, considering all capture months. Cell values were normalized to sum one.Because more than one ecological mechanism may simultaneously drive interactions3,9, we built an additional set of seven models resultant from the element-wise multiplication of individual probability matrices:

SP: The spatial and temporal distribution of species work simultaneously in driving a resource turnover in the community, driving interactions.

AS: Abundance drives interactions between bats and plants, but within spatially clustered resources in the landscape caused by a turnover in species distributions.

AP: Abundance drives interactions between bats and plants, but within temporally clustered resources caused by a seasonal distribution of resources.

APS: Abundance drives interactions between bats and plants, but within resource clusters that emerge by a simultaneous temporal and spatial aggregation.

MS: Similar to AS, but morphology drives interactions within spatial clusters.

MP: Similar to MP, but morphology drives interactions within temporal clusters.

MPS: Similar to APS, but morphology drives interactions within spatiotemporal clusters.

Finally, we created a benchmark null model in which all cells in the matrix had the same probability value. All the compound matrices and the null model were also normalized to sum one.To compare the fit of these probability models with the real data, we conducted a maximum likelihood analysis3,9. We calculated the likelihood of each of these models in predicting the observed interaction matrix, assuming a multinomial distribution for the probability of interaction between species12. To compare model fit, we calculated the Akaike Information Criterion (AIC) for each model and their variation in AIC (ΔAIC) in relation to the best-fitting model. The number of species used in the probability matrices was considered the number of model parameters to penalize model complexity. Intending to assess whether nectarivorous bats and non-nectarivorous bats assembly sub-networks with different assembly rules, we created two partial networks from the observed matrix. One contained nectarivores only (subfamilies Glossophaginae and Lonchophyllinae) and their interactions, and the other contained frugivore and insectivore bats and their interactions. We repeated the likelihood procedure for these two partial networks.To conduct the likelihood analysis, we excluded plant species from the network that could not have their interaction probabilities measured, such as species found in pollen samples but not registered in the park or pollen types that could not be identified to the species level. Therefore, the interaction network Y and probability matrices did not include these species (details in Supplementary Table S1).SoftwareAnalyses were performed in R 3.6.050. Network metrics and null models were generated with the bipartite package51, and the sampling completeness analysis was performed with the vegan package52. Gephi 0.9.253 was used to draw the graph. More

Reilly, S. M. & Lauder, G. V. The evolution of tetrapod feeding behavior: kinematic homologies in prey transport. Evolution 44, 1542–1557 (1990).Article 

Google Scholar 
Iwasaki, S. Evolution of the structure and function of the vertebrate tongue. J. Anat. 201, 1–13 (2002).Article 

Google Scholar 
Fitch, W. T. & Suthers, R. A. In Vertebrate Sound Production and Acoustic Communication (eds Suthers, R. A., Fitch, W. T., Fay, R. R., & Popper, A. N.) 1–18 (Springer, 2016).Carroll, R. L. The Palaeozoic ancestry of salamanders, frogs and caecilians. Zool. J. Linn. Soc. 150, 1–140 (2007).Article 

Google Scholar 
Schwenk, K. in Feeding: Form, Function and Evolution in Tetrapod Vertebrates (ed. Schwenk, K.) 175–291 (Academic Press, 2000).Schwenk, K. & Rubega, M. In Physiological and ecological adaptations to feeding in vertebrates, (eds. Starck, M. & Wang, T.) 1–41 (Science Pub. Inc., 2005).Schumacher, G. H. In Biology of the Reptilia, 4 (ed Gans, C.) 101–200 (Academic Press, 1973).Reese, A. M. The laryngeal region of Alligator mississippiensis. Anat. Rec. 92, 273–277 (1945).Article 

Google Scholar 
Riede, T., Li, Z., Tokuda, I. & Farmer, C. G. Functional morphology of the Alligator mississippiensis larynx with implications for vocal production. J. Exp. Biol. 218, 991–998 (2015).Article 

Google Scholar 
McLelland, J. In Form and Function in Birds, 4 (eds King, A. S. & McLelland, J.) 69–103 (Academic Press, 1989).Homberger, D. G. In The Biology of the Avian Respiratory System (ed Maina, J. N.) 27–97 (Springer, 2017).Fitch, W. T. In Encyclopedia of Language & Linguistics (ed Brown, K.) 115–121 (Elsevier, 2006).Clarke, J. A. et al. Fossil evidence of the avian vocal organ from the Mesozoic. Nature 538, 502–505 (2016).Article 

Google Scholar 
Kingsley, E. P. et al. Identity and novelty in the avian syrinx. Proc. Natl Acad. Sci. USA 115, 10209–10217 (2018).Article 
CAS 

Google Scholar 
Riede, T., Thomson, S. L., Titze, I. R. & Goller, F. The evolution of the syrinx: an acoustic theory. PLoS Biol. 17, e2006507 (2019).Nowicki, S. Vocal tract resonances in oscine bird sound production: evidence from birdsongs in a helium atmosphere. Nature 325, 53–55 (1987).Article 
CAS 

Google Scholar 
Hill, R. V. et al. A complex hyobranchial apparatus in a Cretaceous dinosaur and the antiquity of avian paraglossalia. Zool. J. Linn. Soc. 175, 892–909 (2015).Article 

Google Scholar 
Li, Z. H., Zhou, Z. H. & Clarke, J. A. Convergent evolution of a mobile bony tongue in flighted dinosaurs and pterosaurs. PLoS One 13, e0198078 (2018).Article 

Google Scholar 
Bonaparte, J. F., Novas, F. E. & Coria, R. A. Carnotaurus sastrei Bonaparte, the horned, lightly built carnosaur from the Middle Cretaceous of Patagonia. Contrib. in Sci. Nat. Hist. Mus. L. A. 416, 1–42 (1990).Maryanska, T. Ankylosauridae (Dinosauria) from Mongolia. Palaeontol. Pol. 37, 85–151 (1977).
Google Scholar 
Mori, C. A comparative anatomical study on the laryngeal cartilages and laryngeal muscles of birds, and a developmental study on the larynx of the domestic fowl. Acta Med. 27, 2629–2678 (1957).
Google Scholar 
Siebenrock, F. Über den Kehlkopf und die Luftröhre der Schildkröten. Sitzungsberichte Der Kais. 108, 581–595 (1899).
Google Scholar 
Soley, J. T., Tivane, C. & Crole, M. R. Gross morphology and topographical relationships of the hyobranchial apparatus and laryngeal cartilages in the ostrich (Struthio camelus). Acta Zool. 96, 442–451 (2015).Article 

Google Scholar 
Olson, S. L. & Feduccia, A. Presbyornis and the origin of the Anseriformes (Aves: Charadriomorphae). Smithson. Contrib. Zool. 323, 1–24 (1980).Soley, J. T., Tivane, C. & Crole, M. R. A Gross morphology and topographical relationships of the hyobranchial apparatus and laryngeal cartilages in the ostrich (Struthio camelus). Acta Zool. 94, 442–451 (2015).Article 

Google Scholar 
Hogg, D. A. Ossification of the laryngeal, tracheal and syringeal cartilages in the domestic fowl. J. Anat. 134, 57–71 (1982).CAS 

Google Scholar 
Gaunt, A. S., Stein, R. C. & Gaunt, S. L. Pressure and air flow during distress calls of the starling, Sturnus vulgaris (Aves; Passeriformes). J. Exp. Zool. 183, 241–261 (1973).Article 

Google Scholar 
Sacchi, R., Galeotti, P., Fasola, M. & Gerzeli, G. Larynx morphology and sound production in three species of Testudinidae. J. Morphol. 261, 175–183 (2004).Article 

Google Scholar 
Titze, I. R. The physics of small-amplitude oscillation of the vocal folds. J. Acoust. Soc. Am. 83, 1536–1552 (1988).Article 
CAS 

Google Scholar 
Russell, A. P., Hood, H. A. & Bauer, A. M. Laryngotracheal and cervical muscular anatomy in the genus Uroplatus (Gekkota: Gekkonidae) in relation to distress call emission. Afr. J. Herpetol. 63, 127–151 (2014).Article 

Google Scholar 
Russell, A. P., Rittenhouse, D. R. & Bauer, A. M. Laryngotracheal morphology of Afro‐Madagascan Geckos: a comparative survey. J. Morphol. 245, 241–268 (2000).Article 
CAS 

Google Scholar 
Gans, C. & Maderson, P. F. Sound producing mechanisms in recent reptiles: review and comment. Am. Zool. 13, 1195–1203 (1973).Article 

Google Scholar 
Galeotti, P., Sacchi, R., Fasola, M. & Ballasina, D. Do mounting vocalisations in tortoises have a communication function? A comparative analysis. Herpetol. J. 15, 61–71 (2005).
Google Scholar 
Fletcher, N. H. Bird song—a quantitative acoustic model. J. Theor. Biol. 135, 455–481 (1988).Article 

Google Scholar 
Vergne, A. L., Pritz, M. B. & Mathevon, N. Acoustic communication in crocodilians: from behaviour to brain. Biol. Rev. 84, 391–411 (2009).Article 
CAS 

Google Scholar 
Marler, P. R. & Slabbekoorn, H. Nature’s music: The science of birdsong (Academic Press, San Diego, USA, 2004).White, S. S. In Sisson and Grossman’s The Anatomy of the Domestic Animals. 2 (ed Getty, R.) 1891–1897 (Saunders, Philadelphia, USA 975).Kirchner, J. A. The vertebrate larynx: adaptations and aberrations. Laryngoscope 103, 1197–1201 (1993).Article 
CAS 

Google Scholar 
Mackelprang, R. & Goller, F. Ventilation patterns of the songbird lung/air sac system during different behaviors. J. Exp. Biol. 216, 3611–3619 (2013).
Google Scholar 
Brocklehurst, R. J., Schachner, E. R. & Sellers, W. I. Vertebral morphometrics and lung structure in non-avian dinosaurs. R. Soc. Open Sci. 5, 180983 (2018).Article 

Google Scholar 
Cerda, I. A., Salgado, L. & Powell, J. E. Extreme postcranial pneumaticity in sauropod dinosaurs from South America. Paläontol. Z. 86, 441–449 (2012).Article 

Google Scholar 
Sereno, P. C. et al. Evidence for avian intrathoracic air sacs in a new predatory dinosaur from Argentina. PLoS One 3, e3303 (2008).Chiari, Y., Cahais, V., Galtier, N. & Delsuc, F. Phylogenomic analyses support the position of turtles as the sister group of birds and crocodiles (Archosauria). BMC Biol. 10, 65 (2012).Article 

Google Scholar  More

Wallace, A. R. Tropical Nature and Other Essays (Macmillan, 1878).
Google Scholar 
von Humboldt, A. Ansichten der Natur: mit wissenschaftlichen Erläuterungen (Cotta, 1808).
Google Scholar 
Brown, J. H. J. Biogeogr. 41, 8–22 (2014).Article 
PubMed 

Google Scholar 
Fenton, I. S., Aze, T., Farnsworth, A., Valdes, P. & Saupe, E. E. Nature https://doi.org/10.1038/s41586-023-05712-6 (2023).Article 

Google Scholar 
Woodhouse, A., Swain, A., Fagan, W. F., Fraass, A. J. & Lowery, C. M. Nature https://doi.org/10.1038/s41586-023-05694-5 (2023).Article 

Google Scholar 
Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Biol. Rev. 92, 199–215 (2017).Article 
PubMed 

Google Scholar 
Yasuhara, M. et al. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
PubMed 

Google Scholar 
Song, H. et al. Proc. Natl Acad. Sci. USA 117, 17578–17583 (2020).Article 
PubMed 

Google Scholar 
Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Science 362, eaat1327 (2018).Article 
PubMed 

Google Scholar 
Janzen, D. H. Am. Nat. 101, 233–249 (1967).Article 

Google Scholar 
Hahn, L. C., Armour, K. C., Zelinka, M. D., Bitz, C. M. & Donohoe, A. Front. Earth Sci. 9, 710036 (2021).Article 

Google Scholar 
Penn, J. L. & Deutsch, C. Science 376, 524–526 (2022).Article 
PubMed 

Google Scholar  More

Chromosomal-scale genome assembly and full spidroin gene set of T. clavata
To explore dragline silk production in T. clavata, we sought to assemble a high-quality genome of this species. Thus, we first performed a cytogenetic analysis of T. clavata captured from the wild in Dali City, Yunnan Province, China, and found a chromosomal complement of 2n = 26 in females and 2n = 24 in males, comprising eleven pairs of autosomal elements and unpaired sex chromosomes (X1X1X2X2 in females and X1X2 in males) (Fig. 1a). Then, DNA from adult T. clavata was used to generate long-read (Oxford Nanopore Technologies (ONT)), short-read (Illumina), and Hi-C data (Supplementary Data 1). A total of 349.95 Gb of Nanopore reads, 199.55 Gb of Illumina reads, and ~438.41 Gb of Hi-C raw data were generated. Our sequential assembly approach (Supplementary Fig. 1c) resulted in a 2.63 Gb genome with a scaffold N50 of 202.09 Mb and a Benchmarking Universal Single-Copy Ortholog (BUSCO) genome completeness score of 93.70% (Table 1; Supplementary Data 3). Finally, the genome was assembled into 13 pseudochromosomes. Sex-specific Pool-Seq analysis of spiders indicated that Chr12 and Chr13 were sex chromosomes (Fig. 1b; Supplementary Fig. 2). Based on the MAKER2 pipeline34 (Supplementary Fig. 1e), we annotated 37,607 protein-encoding gene models and predicted repetitive elements with a collective length of 1.42 Gb, accounting for 53.94% of the genome.Table 1 Characteristics of the T. clavata genome assemblyFull size tableTo identify T. clavata spidroin genes, we searched the annotated gene models for sequences similar to 443 published spidroins (Supplementary Data 6) and performed a phylogenetic analysis of the putative spidroin sequences for classification (Supplementary Fig. 12a). Based on the knowledge that a typical spidroin gene consists of a long repeat domain sandwiched between the nonrepetitive N/C-terminal domains16, 128 nonrepetitive hits were primarily identified. These candidates were further validated and reconstructed using full-length transcript isoform sequencing (Iso-seq) and transcriptome sequencing (RNA-seq) data. We thus identified 28 spidroin genes, among which 26 were full-length (Supplementary Fig. 11a), including 9 MaSps, 5 minor ampullate spidroins (MiSps), 2 flagelliform spidroins (FlSps), 1 tubuliform spidroin (TuSp), 2 aggregate spidroins (AgSp), 1 aciniform spidroin (AcSp), 1 pyriform spidroin (PySp), and 5 other spidroins. This full set of spidroin genes was located across nine of the 13 T. clavata chromosomes. Interestingly, we found that the MaSp1a–c & MaSp2e, MaSp2a–d, and MiSp-a–e genes were distributed in three independent groups on chromosomes 4, 7, and 6, respectively (Fig. 1c). Notably, using the genomic data of another orb-weaving spider species, Trichonephila antipodiana35, we identified homologous group distributions of spidroin genes on T. antipodiana chromosomes (Fig. 1d), which indicated the reliability of the grouping results of our study. When we compared the spidroin gene catalog of T. clavata and those of five other orb-web spider species with genomic data28,29,36,37, we found that T. clavata and Trichonephila clavipes possessed the largest number of spidroin genes (28 genes in both species; Fig. 1e).To further explore the expression of spidroin genes in different glands, all morphologically distinct glands (major and minor ampullate- (Ma and Mi), flagelliform- (Fl), tubuliform- (Tu), and aggregate (Ag) glands) were cleanly and separately dissected from adult female T. clavata spiders except for the aciniform and pyriform glands, which could not be cleanly separated because of their proximal anatomical locations and were therefore treated as a combined sample (aciniform & pyriform gland (Ac & Py)). After RNA sequencing of these silk glands, we performed expression clustering analysis of transcriptomic data and found that the Ma and Mi glands showed the closest relationship in terms of both morphological structure (Fig. 1g) and gene expression (Fig. 1f, h). We noted that the expression profiles of spidroin genes were largely consistent with their putative roles in the corresponding morphologically distinct silk glands; for example, MaSp expression was found in the Ma gland (Fig. 1h). However, some spidroin transcripts, such as MiSps and TuSp, were expressed in several silk glands (Fig. 1h). Unclassified spidroin genes, such as Sp-GP-rich, did not appear to show gland-specific expression (Fig. 1h).In summary, the chromosomal-scale genome of T. clavata allowed us to obtain detailed structural and location information for all spidroin genes of this species. We also found a relatively diverse set of spidroin genes and a grouped distribution of MaSps and MiSps in T. clavata.Dragline silk origin and the functional character of the Ma gland segmentsTo further evaluate the detailed molecular characteristics of the Ma gland-mediated secretion of dragline silk, we performed integrated analyses of the transcriptomes of the three T. clavata Ma gland segments and the proteome and metabolome of T. clavata dragline silk (Fig. 2a). Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) analysis of dragline silk mainly showed a thick band above 240 kDa, suggesting a relatively small variety of total proteins (Fig. 2b). Subsequent liquid chromatography–mass spectrometry (LC–MS) analysis identified 28 proteins, including ten spidroins (nine MaSps and one MiSp) and 18 nonspidroin proteins (one glucose dehydrogenase (GDH), one mucin-19, one venom protein, and 15 SpiCEs of dragline silk (SpiCE-DS)) (Fig. 2b; Supplementary Data 10). Among these proteins, we found that the core protein components of dragline silk in order of intensity-based absolute quantification (iBAQ) percentages were MaSp1c (37.7%), MaSp1b (12.2%), SpiCE-DS1 (11.9%, also referred to as SpiCE-NMa1 in a previous study28), MaSp1a (10.4%), and MaSp-like (7.2%), accounting for approximately 80% of the total protein abundance in dragline silk (Fig. 2b). These results revealed potential protein components that might be highly correlated with the excellent strength and toughness of dragline silk.Fig. 2: Dragline silk origin and the functional character of the Ma gland segments.a Schematic illustration of Ma gland segmentation. b Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) (left) and LC–MS (right) analyses of dragline silk protein. iBAQ, intensity-based absolute quantification. Similar results were obtained in three independent experiments and summarized in Source data. c Classification of the identified metabolites in dragline silk. d LC–MS analyses of the metabolites. e LC–MS analyses of the golden extract from T. clavata dragline silk. The golden pigment was extracted with 80% methanol. The extracted ion chromatograms (EICs) showed a peak at m/z 206 [M + H]+ for xanthurenic acid. f Pearson correlation of different Ma gland segments (Tail, Sac, and Duct). g Expression clustering of the Tail, Sac, and Duct. The transcriptomic data were clustered according to the hierarchical clustering (HC) method. h Combinational analysis of the transcriptome and proteome showing the expression profile of the dragline silk genes in the Tail, Sac, and Duct. i Concise biosynthetic pathway of xanthurenic acid (tryptophan metabolism) in the T. clavata Ma gland. Gene expression levels mapped to tryptophan metabolism are shown in three segments of the Ma gland. Enzymes involved in the pathway are indicated in red, and the genes encoding the enzymes are shown beside them. j Gene Ontology (GO) enrichment analysis of Ma gland segment-specific genes indicating the biological functions of the Tail, Sac, and Duct. The top 12 significantly enriched GO terms are shown for each segment of the Ma gland. A P-value  2) were identified in the 2 kb regions upstream and downstream of genes, and 10,501,151 (Tail), 11,356,55 (Sac), and 9,778,368 (Duct) significant ATAC peaks (RPKM  > 2) were identified at the whole-genome level. The Tail (mean RPKM: 1.78) and Sac (mean RPKM: 2.04) plots showed genes with more accessible chromatin than the Duct (mean RPKM: 1.59) plots (Fig. 3a). We then analyzed the genome-wide DNA methylation level in the Tail, Sac, and Duct. We found the highest levels of DNA methylation in the CG context (beta value: 0.12 in Tail, 0.13 in Sac, and 0.10 in Duct) and only a small amount in the CHH (beta value: 0.04 in Tail, 0.05 in Sac, and 0.03 in Duct) and CHG (beta value: 0.04 in Tail, 0.05 in Sac, and 0.04 in Duct) contexts (Fig. 3b). Overall, there was no significant difference in methylation levels among the Tail, Sac, and Duct. Taken together, our results suggest a potential regulatory role of CA rather than DNA methylation in the transcription of dragline silk genes.Fig. 3: Comprehensive epigenetic features and ceRNA network of the tri-sectional Ma gland.a Metagene plot of ATAC-seq signals and heatmap of the ATAC-seq read densities in the Tail, Sac, and Duct. The chromatin accessibility was indicated by the mean RPKM value (upper) and the blue region (bottom). b Metagene plot of DNA methylation levels in CG/CHG/CHH contexts in the Tail, Sac, and Duct. (c, d) Screenshots of the methylation and ATAC-seq tracks of the MaSp1b (c) and MaSp2b (d) genes within the Tail, Sac, and Duct. The potential TF motifs (E-value More

Insect rearingWT B. dorsalis were collected from Haikou, Hainan province, China, in 2008. They were maintained at the Key Laboratory of Entomology and Pest Control Engineering in Chongqing at 27 ± 1 °C, 70 ± 5% relative humidity, with a 14-h photoperiod. Adult flies were reared on an artificial diet containing honey, sugar, yeast powder, and vitamin C. Newly hatched larvae were transferred to an artificial diet containing corn and wheat germ flour, yeast powder, agar, sugar, sorbic acid, linoleic acid, and filter paper.Behavioral assaysDouble trap lure assays were set up to compare the olfactory preferences of gravid and virgin females in a 20 × 20 × 20 cm transparent cage with evenly distributed holes (diameter = 1.5 mm) on the side walls. The traps were refitted from inverted 50-mL centrifuge tubes and were placed along the diagonal of the cage. The top of each trap was pierced with a 1-mL pipette tip, which was shortened to ensure flies could access the trap from the pipette. For the olfactory preference assay with mango, one trap was loaded with 60 mg mango flesh and the other trap with 20 μL MO in the cap of a 200-μL PCR tube. For the olfactory preference assay with 1-octen-3-ol (≥98%, sigma, USA), one trap was loaded with 20 μL 10% (v/v) 1-octen-3-ol diluted in MO, and the other with 20 μL MO. A cotton ball soaked in water was placed at the center of the cage to provide water for the flies. Groups of 30 female flies were introduced into the cage for each experiment, and each experiment was repeated to provide eight biological replicates. All experiments commenced at 10 am to ensure circadian consistency. The number of flies in each trap was counted every 2 h for 24 h. We compared the preferences of 3-day-old immature females, 15-day-old virgin females, and 15-day-old mated females. The olfactory preference index was calculated using the following formula41: (number of flies in mango/odorant trap – number of flies in control trap)/total number of flies.Oviposition behavior was monitored in a 10 × 10 × 10 cm transparent cage with evenly distributed holes on the side walls as above. A 9-cm Petri dish filled with 1% agar was served as an oviposition substrate, and the mango flesh, 10% (v/v) 1-octen-3-ol or MO were added at opposite edges of the dish. We tested the preference of flies for different substrates: (1) ~60 mg of mango flesh on one edge and 20 μL of MO on the other; (2) 20 μL of 1-octen-3-ol on one edge and 20 μL of MO on the other; (3) ~60 mg mango flesh on one edge and 20 μL of 1-octen-3-ol on the other; and (4) ~60 mg mango flesh plus 20 μL 1-octen-3-ol on one side and ~60 mg of mango flesh plus 20 μL MO on the other. The agar disc was covered in a pierced plastic wrap to mimic fruit skin, encouraging flies extend their ovipositor into the plastic wrap to lay eggs. The agar disc was placed at the center of the cage, and we introduced eight 15-day-old gravid females. Two Sony FDR-AX40 cameras recorded the behavior of the flies for 24 h, one fixed above the cage to record the tracks and the other placed in front of the cage to record the oviposition behavior. Based on the results from double traps luring assays, a 3 h duration (6–9 h) of the videos was selected to analyze the tracks and spent time of all flies in observed area (the surface of Petri dish). The videos were analyzed using EthoVision XT v16 (Noldus Information Technology) to determine the total time of all flies spent on each side in seconds and the total distance of movement in centimeters, and the tracks were visualized in the form of heat maps17. The number of eggs laid by the eight flies in each experiment was counted under a CNOPTEC stereomicroscope, and each experimental group comprised 7–16 replicates.Annotation of B. dorsalis OR genesD. melanogaster amino acid sequences downloaded from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/) were used as BLASTP queries against the B. dorsalis amino acid database with an identity cut-off of 30%. The candidate OR genes were compared with deep transcriptome data from B. dorsalis antennae42, maxillary palps and proboscis, and other tissues.Cloning of candidate B. dorsalis OR genesHigh-fidelity PrimerSTAR Max DNA polymerase (TaKaRa, Dalian, China) was used to amplify the full open reading frame of BdorOR genes by nested PCR using primers (Supplementary Table 2) designed according to B. dorsalis genome data. Each 25-μL reaction comprised 12.5 μL 2 × PrimerSTAR Max Premix (TaKaRa), 10.5 μL ultrapure water, 1 μL of each primer (10 μM), and 1 μL of the cDNA template. An initial denaturation step at 98 °C for 2 min was followed by 35 cycles of 10 s at 98 °C, 15 s at 55 °C and 90 s at 72 °C, and a final extension step of 10 min at 72 °C. Purified PCR products were transferred to the vector pGEM-T Easy (Promega, Madison, WI) for sequencing (BGI, Beijing, China).Transcriptional profilingTotal RNA was extracted from (i) male and female antennae, maxillary palps, head cuticle (without antenna, maxillary palps, and proboscis), proboscis, legs, wings and ovipositors, and (ii) from the heads of 15-day-old virgin and mated females using TRIzol reagent (Invitrogen, Carlsbad, CA). Genomic DNA was eliminated with RNase-free DNase I (Promega) and first-strand cDNA was synthesized from 1 µg total RNA using the PrimeScript RT reagent kit (TaKaRa). Standard curves were used to evaluate primer efficiency (Supplementary Table 3) with fivefold serial dilutions of cDNA. Quantitative real-time PCR (qRT-PCR) was carried out using a CFX Connect Real-Time System (Bio-Rad, Hercules, CA) in a total reaction volume of 10 µL containing 5 μL SYBR Supermix (Novoprotein, Shanghai, China), 3.9 μL nuclease-free water, 0.5 μL cDNA (~200 ng/μL) and 0.3 μL of the forward and reverse primers (10 μM). We used α-tubulin (GenBank: GU269902) and ribosomal protein S3 (GenBank: XM_011212815) as internal reference genes. Four biological replicates were prepared for each experiment. Relative expression levels were determined using the 2−∆∆Ct method43, and data were analyzed using SPSS v20.0 (IBM).Two-electrode voltage clamp electrophysiological recordingsVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pT7Ts for expression in oocytes. The plasmids were linearized for the synthesis of cRNAs using the mMESSAGE mMACHINE T7 Kit (Invitrogen, Lithuania). The purified cRNA was diluted to 2 µg/µL and ~60 ng cRNA was injected into X. laevis oocytes. The oocytes were pre-treated with 1.5 mg/mL collagenase I (GIBCO, Carlsbad, CA) in washing buffer (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, pH 7.6) for 30–40 min at room temperature before injection. After incubation for 2 days at 18 °C in Ringer’s solution (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, 0.8 mM CaCl2), the oocytes were exposed to different concentrations of 1-octen-3-ol diluted in Ringer’s solution from a 1 M stock in DMSO. Odorant-induced whole-cell inward currents were recorded from injected oocytes using a two-electrode voltage clamp and an OC-725C amplifier (Warner Instruments, Hamden, CT) at a holding potential of –80 mV. The signal was processed using a low-pass filter at 50 Hz and digitized at 1 kHz. Oocytes injected with nuclease-free water served as a negative control. Data were acquired using a Digidata 1550 A device (Warner Instruments, Hamden, CT) and analyzed using pCLAMP10.5 software (Axon Instruments Inc., Union City, CA).Calcium imaging assayVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pcDNA3.1(+) along with an mCherry tag that confers red fluorescence to confirm transfection. High-quality plasmid DNA was prepared using the Qiagen plasmid MIDIprep kit (QIAgen, Düsseldorf, Germany). The B. dorsalis OR and BdorOrco plasmids were co-transfected into HEK 293 cell using TransIT-LT1 transfection reagent (Mirus Bio LLC, Japan) in 96-well plates. The fluorescent dye Fluo-4 AM (Invitrogen) was prepared as a 1 mM stock in DMSO and diluted to 2.5 μM in Hanks’ balanced salt solution (HBSS, Invitrogen, Lithuania) to serve as a calcium indicator. The cell culture medium was removed 24–30 h after transfection and cells were rinsed three times with HBSS before adding Fluo 4-AM and incubating the cells for 1 h in the dark. After three rinses in HBSS, 99 μL of fresh HBSS was added to each well before testing in the dark with 1 μL of diluted 1-octen-3-ol. Fluorescent images were acquired on a laser scanning confocal microscope (Zeiss, Germany). Fluo 4-AM was excited at 488 nm and mCherry at 555 nm. The relative change in fluorescence (ΔF/F0) was used to represent the change in Ca2+, where F0 is the baseline fluorescence and ΔF is the difference between the peak fluorescence induced by 1-octen-3-ol stimulation and the baseline. The healthy and successfully transfected cells (red when excited at 555 nm) were used for analysis. The final concentration of 10−4 M was initially used to screen corresponding ORs, and then to determine the response of screened ORs to stimulation with different concentrations of 1-octen-3-ol. Each concentration of 1-octen-3-ol was tested in triplicate. Concentration–response curves were prepared using GraphPad Prism v8.0 (GraphPad Software).Genome editingThe exon sequences of BdorOR7a-6 and BdorOR13a were predicted using the high-quality B. dorsalis genome assembly. Each gRNA sequence was 20 nucleotides in length plus NGG as the protospacer adjacent motif (PAM). The potential for off-target mutations was evaluated by using CasOT to screen the B. dorsalis genome sequence. Each gRNA was synthesized using the GeneArt Precision gRNA Synthesis Kit (Invitrogen) and purified using the GeneArt gRNA Clean-up Kit (Invitrogen). Embryos were microinjected as previously described20. Purified gRNA and Cas9 protein from the GeneArt Platinum Cas9 Nuclease Kit (Invitrogen) were mixed and diluted to final concentrations of 600 and 500 ng/µL, respectively. Fresh eggs (laid within 20 min) were collected and exposed to 1% sodium hypochlorite for 90 s to soften the chorion. The eggs were fixed on glass slides and injected with the mix of gRNA and Cas9 protein at the posterior pole using an IM-300 device (Narishige, Tokyo, Japan) and needles prepared using a Model P-97 micropipette puller (Sutter Instrument Co, Novato, CA). Eggs were injected with nuclease-free water as a negative control. Injection was completed within 2 h. The injected embryos were cultured in a 27 °C incubator and mortality was recorded during subsequent development.G0 mutants were screened as previously described20. G0 adult survivors were individually backcrossed to WT flies (single pair) to collect G1 offspring. Genomic DNA was extracted from G0 individuals after oviposition using the DNeasy Blood & Tissue Kit (Qiagen). The region surrounding each gRNA target was amplified by PCR using the extracted DNA as a template, the specific primers listed in Supplementary Table 2, and 2 × Taq PCR MasterMix (Biomed, Beijing, China). PCR products were analyzed by capillary electrophoresis using the QIAxcel DNA High Resolution Kit (Qiagen). PCR products differing from the WT alleles were purified and transferred to the vector pGEM-T Easy for sequencing. To confirm the mutation was inherited, genomic DNA was also extracted from one mesothoracic leg of G1 flies using InstaGene Matrix (Bio-Rad, Hercules, CA) and was analyzed as above. To avoid potential off-target mutations, heterozygous G1 mutants were backcrossed to WT flies more than 10 generations before self-crossing to generate homozygous mutant flies.Electroantennogram (EAG) recordingThe antennal responses of 15-day-old B. dorsalis adults to 1-octen-3-ol were determined by EAG recording (Syntech, the Netherlands) as previously reported20. Briefly, antennae were fixed to two electrodes using Spectra 360 electrode gel (Parker, Fairfield, NJ, USA). The signal response was amplified using an IDAC4 device and collected using EAG-2000 software (Syntech). Before each experiment, 1-octen-3-ol and other three volatiles (ethyl tiglate, ethyl acetate, ethyl butyrate) were diluted to 10%, 1% and 0.1% (v/v) with MO to serve as the electrophysiological stimulus, and MO was used as a negative control. A constant air flow (100 mL/min) was produced using a controller (Syntech) to stimulate the antenna. We then placed 10 µL of each dilution or MO onto a piece of filter paper (5 × 1 cm), and the negative control (MO) was applied before and after the diluted odorants to calibrate the response signal. The EAG responses at each concentration were recorded for 15–20 antennae, and each concentration was recorded twice. Each test lasted 1 s, and the interval between tests was 30 s. EAG response data from WT and mutant flies for the diluted odorants were analyzed using Student’s t test with SPSS v20.0.Molecular docking and site-directed mutagenesisThe three dimensional-structures of BdorOR7a-6 and BdorOR13a were modeled using AlphaFold 2.044. The quality and rationality of each protein structure was evaluated online using a PROCHECK Ramachandran plot in SAVES 6.0 (https://saves.mbi.ucla.edu/). AutoDock Vina 1.1.2 was used for docking analysis, and the receptor protein structure and ligand molecular structure were pre-treated using AutoDock 4.2.6. The docking parameters were set according to the protein structure and active sites, and the optimal docking model was selected based on affinity (kcal/mol). Docking models were imported into Pymol and Discovery Studio 2016 Client for analysis and image processing. Based on the molecular docking data, three residues (Asn86 in OR7a-6, Asp320, and Lys323 in OR13a) were replaced with alanine by site-directed mutagenesis45 using the primers listed in Supplementary Table 2. Calcium imaging assays and molecular docking of mutated proteins were then carried out as described above.Statistics reproducibilityAll of the olfactory preference assays, oviposition bioassays, expression profiles analysis, EAG recording assays were analyzed using Student’s t-test (*p  More

Fine, P. V. Ecological and evolutionary drivers of geographic variation in species diversity. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015).Article 

Google Scholar 
Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).Article 
PubMed 

Google Scholar 
Mittelbach, G. G. et al. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecol. Lett. 10, 315–331 (2007).Article 
PubMed 

Google Scholar 
Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309 (2003).Article 

Google Scholar 
Pontarp, M. et al. The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).Article 
PubMed 

Google Scholar 
Crame, J. A. Taxonomic diversity gradients through geological time. Divers Distrib. 7, 175–189 (2011).
Google Scholar 
Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).Article 
PubMed 

Google Scholar 
Powell, M. G. Latitudinal diversity gradients for brachiopod genera during late Palaeozoic time: links between climate, biogeography and evolutionary rates. Glob. Ecol. Biogeogr. 16, 519–528 (2007).Article 

Google Scholar 
Powell, M. G., Beresford, V. P. & Colaianne, B. A. The latitudinal position of peak marine diversity in living and fossil biotas. J. Biogeogr. 39, 1687–1694 (2012).Article 

Google Scholar 
Hillebrand, H. Strength, slope and variability of marine latitudinal gradients. Mar. Ecol. Prog. Ser. 273, 251–267 (2004).Article 
ADS 

Google Scholar 
Beaugrand, G., Rombouts, I. & Kirby, R. R. Towards an understanding of the pattern of biodiversity in the oceans. Glob. Ecol. Biogeogr. 22, 440–449 (2013).Article 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).Article 

Google Scholar 
Saupe, E. E. et al. Spatio-temporal climate change contributes to latitudinal diversity gradients. Nat. Ecol. Evol. 3, 1419–1429 (2019).Article 
PubMed 

Google Scholar 
Stehli, F. G., Douglas, R. G. & Newell, N. D. Generation and maintenance of gradients in taxonomic diversity. Science 164, 947–949 (1969).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Rutherford, S., D’Hondt, S. & Prell, W. Environmental controls on the geographic distribution of zooplankton diversity. Nature 4000, 749–752 (1999).Article 
ADS 

Google Scholar 
Klopfer, P. H. Environmental determinants of faunal diversity. Am. Nat. 93, 337–342 (1959).Article 

Google Scholar 
Haffer, J. & Prance, G. T. Climatic forcing of evolution in Amazonia during the Cenozoic: on the refuge theory of biotic differentiation. Amazoniana 16, 579–607 (2001).
Google Scholar 
Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115–9120 (2000).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).
Google Scholar 
Williams, C. B. Patterns in the Balance of Nature (Academic Press, 1964).Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).Article 

Google Scholar 
Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).Article 

Google Scholar 
Currie, D. J. Energy and large-scale patterns of animal and plant species richness. Am. Nat. 137, 27–49 (1991).Article 

Google Scholar 
Connell, J. H. & Orias, E. The ecological regulation of species diversity. Am. Nat. 98, 399–414 (1964).Article 

Google Scholar 
Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge Univ. Press, 1995).Fenton, I. S. et al. The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera. Phil. Trans. R. Soc. B 371, 20150224 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yasuhara, M., Hunt, G., Dowsett, H. J., Robinson, M. M. & Stoll, D. K. Latitudinal species diversity gradient of marine zooplankton for the last three million years. Ecol. Lett. 15, 1174–1179 (2012).Article 
PubMed 

Google Scholar 
Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Combining marine macroecology and palaeoecology in understanding biodiversity: microfossils as a model. Biol. Rev. 92, 199–215 (2017).Article 
PubMed 

Google Scholar 
Fenton, I. S. et al. Triton, a new species-level database of Cenozoic planktonic foraminiferal occurrences. Sci. Data 8, 160 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Yasuhara, M. & Deutsch, C. A. Paleobiology provides glimpses of future ocean. Science 375, 25–26 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Yasuhara, M. et al. Time machine biology cross-timescale integration of ecology, evolution, and oceanography. Oceanography 33, 16–28 (2020).Article 

Google Scholar 
Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Al-Sabouni, N., Kucera, M. & Schmidt, D. N. Vertical niche separation control of diversity and size disparity in planktonic foraminifera. Mar. Micropaleontol. 63, 75–90 (2007).Article 
ADS 

Google Scholar 
Lowery, C. M., Bown, P. R., Fraass, A. J. & Hull, P. M. Ecological response of plankton to environmental change: thresholds for extinction. Annu. Rev. Earth Planet. Sci. 48, 403–429 (2020).Article 
ADS 
CAS 

Google Scholar 
Lear, C. H., Elderfield, H. & Wilson, P. A. Cenozoic deep-sea temperatures and global ice volumes from Mg/Ca in benthic foraminiferal calcite. Science 287, 269–272 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Weiner, A., Aurahs, R., Kurasawa, A., Kitazato, H. & Kučera, M. Vertical niche partitioning between cryptic sibling species of a cosmopolitan marine planktonic protist. Mol. Ecol. 21, 4063–4073 (2012).Article 
PubMed 

Google Scholar 
Schneider, E. & Kennett, J. P. Segregation and speciation in the Neogene planktonic foraminiferal clade Globoconella. Paleobiology 25, 383–395 (1999).Article 

Google Scholar 
Raja, N. B. & Kiessling, W. Out of the extratropics: the evolution of the latitudinal diversity gradient of Cenozoic marine plankton. Proc. Biol. Sci. 288, 20210545 (2021).PubMed 
PubMed Central 

Google Scholar 
Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).Article 
PubMed 

Google Scholar 
Irigoien, X., Huisman, J. & Harris, R. P. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–886 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Schiebel, R. & Hemleben, C. Planktic Foraminifers in the Modern Ocean (Springer-Verlag, 2017).Ruddimann, W. F. Recent planktonic foraminifera: dominance and diversity in North Atlantic surface sediments. Science 164, 1164–1167 (1969).Article 
ADS 

Google Scholar 
Bé, A. W. H. & Tolderlund, D. S. in Micropaleontology of Marine Bottom Sediments (eds Funnell, B. M. & Riedel, W. K.) 105–149 (Cambridge Univ. Press, 1971).Sibert, E., Norris, R., Cuevas, J. & Graves, L. Eighty-five million years of Pacific Ocean gyre ecosystem structure: long-term stability marked by punctuated change. Proc. Biol. Sci. 283, 20160189 (2016).PubMed 
PubMed Central 

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

Google Scholar 
Worm, B. & Tittensor, D. P. A Theory of Global Biodiversity (Princeton Univ. Press, 2018).Boscolo-Galazzo, F. et al. Temperature controls carbon cycling and biological evolution in the ocean twilight zone. Science 371, 1148–1152 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Boscolo-Galazzo, F. et al. Late Neogene evolution of modern deep-dwelling plankton. Biogeosciences 19, 743–762 (2022).Article 
ADS 

Google Scholar 
Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
PubMed 

Google Scholar 
Matthews, K. J. et al. Global plate boundary evolution and kinematics since the late Paleozoic. Glob. Planet. Change 146, 226–250 (2016).Article 
ADS 

Google Scholar 
Gyldenfeldt, A.-B. V., Carstens, J. & Meincke, J. Estimation of the catchment area of a sediment trap by means of current meters and foraminiferal tests. Deep Sea Res. Part II 47, 1701–1717 (2000).Article 
ADS 

Google Scholar 
Qiu, Z., Doglioli, A. M. & Carlotti, F. Using a Lagrangian model to estimate source regions of particles in sediment traps. Sci. China Earth Sci. 57, 2447–2456 (2014).Article 
ADS 

Google Scholar 
Siegel, D. A. & Deuser, W. G. Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps. Deep Sea Res. Part I 44, 1519–1541 (1997).Article 

Google Scholar 
Waniek, J., Koeve, W. & Prien, R. D. Trajectories of sinking particles and the catchment areas above sediment traps in the Northeast Atlantic. J. Mar. Res. 58, 983–1006 (2000).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2019).Alroy, J. The fossil record of North American mammals: evidence for a Paleocene evolutionary radiation. Syst. Biol. 48, 107–118 (1999).Article 
CAS 
PubMed 

Google Scholar 
Marcot, J. D. The fossil record and macroevolutionary history of North American ungulate mammals: standardizing variation in intensity and geography of sampling. Paleobiology 40, 238–255 (2014).Article 

Google Scholar 
Gaston, K. J., Williams, P. H., Eggleton, P. & Humphries, C. J. Large scale patterns of biodiversity: spatial variation in family richness. Proc. R. Soc. Lond. B 260, 149–154 (1995).Article 
ADS 

Google Scholar 
Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).Article 
ADS 
CAS 

Google Scholar 
Cox, P. M. et al. The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim. Dyn. 15, 183–203 (1999).Article 

Google Scholar 
Sagoo, N., Valdes, P., Flecker, R. & Gregoire, L. J. The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions? Phil. Trans. R. Soc. A 371, 20130123 (2013).Article 
ADS 
PubMed 

Google Scholar 
Kiehl, J. T. & Shields, C. A. Sensitivity of the Palaeocene–Eocene thermal maximum climate to cloud properties. Phil. Trans. R. Soc. A 371, 20130093 (2013).Article 
ADS 
PubMed 

Google Scholar 
Cox, M. D. A Primitive Equation, 3-Dimensional Model of the Ocean. GFDL Ocean Group Technical Report No. 1 (GFDL Princeton Univ., 1984).Collins, M., Tett, S. F. B. & Cooper, C. The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim. Dyn. 17, 61–81 (2001).Article 

Google Scholar 
Farnsworth, A. et al. Climate sensitivity on geological timescales controlled by nonlinear feedbacks and ocean circulation. Geophys. Res. Lett. 46, 9880–9889 (2019).Article 
ADS 

Google Scholar 
Valdes, P. J., Scotese, C. R. & Lunt, D. J. Deep ocean temperatures through time. Clim. Past 17, 1483–1506 (2021).Article 

Google Scholar 
Farnsworth, A. et al. Past East Asian monsoon evolution controlled by paleogeography, not CO2. Sci. Adv. 5, eaax1697 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones, L. A., Mannion, P. D., Farnsworth, A., Bragg, F. & Lunt, D. J. Climatic and tectonic drivers shaped the tropical distribution of coral reefs. Nat. Commun. 13, 3120 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scotese, C. R. & Wright, N. PALEOMAP paleodigital elevation models (PaleoDEMS) for the Phanerozoic. Zenodo https://doi.org/10.5281/zenodo.5460860 (2018).Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gough, D. O. Solar interior structure and luminosity variations. Sol. Phys. 74, 21–34 (1981).Article 
ADS 
CAS 

Google Scholar 
Farnsworth, A. et al. Paleoclimate model-derived thermal lapse rates: towards increasing precision in paleoaltimetry studies. Earth Planet. Sci. Lett. 564, 116903 (2021).Article 
CAS 

Google Scholar 
Bahcall, J. N., Pinsonneault, M. H. & Basu, S. Solar models: current epoch and time dependences, neutrinos, and helioseismological properties. Astrophys. J. 555, 990–1012 (2001).Article 
ADS 
CAS 

Google Scholar 
Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).Article 
ADS 

Google Scholar 
Kraus, E. B. & Turner, J. S. A one-dimensional model of the seasonal thermocline II. The general theory and its consequences. Tellus 19, 98–105 (1967).ADS 

Google Scholar 
Foreman, S. J. The Ocean Model Report. Unified Model Documentaiton Paper Number 40 (The Met Office, 2005).HH: Statistical Analysis and Data Display: Heiberger and Holland. R package version 3.1-47 (2022).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Bivand, R., Millo, G. & Piras, G. A review of software for spatial econometrics in R. Mathematics 9, 1276 (2021).Article 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Cooper, N. & Purvis, A. Body size evolution in mammals: complexity in tempo and mode. Am. Nat. 175, 727–738 (2010).Article 
PubMed 

Google Scholar 
geosphere: Spherical Trigonometry. R package version 1.5-14 (2021).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Wade, B. S., Pearson, P. N., Berggren, W. A. & Pälike, H. Review and revision of Cenozoic tropical planktonic foraminiferal biostratigraphy and calibration to the geomagnetic polarity and astronomical time scale. Earth Sci. Rev. 104, 111–142 (2011).Article 
ADS 

Google Scholar  More