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MacFarlane, G. R., Koller, C. E. & Blomberg, S. P. Accumulation and partitioning of heavy metals in mangroves: A synthesis of field-based studies. Chemosphere 69, 1454–1464 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Krauss, K. W. & Osland, M. J. Tropical cyclones and the organization of mangrove forests: A review. Ann. Bot. 125, 213–234 (2020).PubMed 

Google Scholar 
Kirk, G. J. D. & Krinzucker, H. J. The potential for nitrification and nitrate uptake in the rhizosphere of wetland plants: A modeling study. Ann. Bot. 96, 639–646 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wu, C., Huang, L., Xue, S. G. & Pan, W. S. Oxic and anoxic conditions affect arsenic (As) accumulation and arsenite transporter expression in rice. Chemosphere 168, 969–975 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Tripathi, R. D. et al. Roles for root iron plaque in sequestration and uptake of heavy metals and metalloids in aquatic and wetland plants. Metallomics 6, 1789–1800 (2014).CAS 
PubMed 
Article 

Google Scholar 
Xiao, W. et al. Continuous flooding stimulates root iron plaque formation and reduces chromium accumulation in rice (Oryza sativa L.). Sci. Total Environ. 788, 147786 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lee, C. H., Hsieh, Y. C., Lin, T. H. & Lee, D. Y. Iron plaque formation and its effect on arsenic uptake by different genotypes of paddy rice. Plant Soil 363, 231–241 (2013).CAS 
Article 

Google Scholar 
Dai, M. Y. et al. Phosphorus effects on radial oxygen loss, root porosity and iron plaque in two mangrove seedlings under cadmium stress. Mar. Pollut. Bull. 119, 262–269 (2017).CAS 
PubMed 
Article 

Google Scholar 
Liu, C. Y., Chen, C. L., Gong, X. F., Zhou, W. B. & Yang, J. Y. Progress in research of iron plaque on root surface of wetland plants. Acta Ecol. Sin. 34, 2470–2480 (2014).CAS 

Google Scholar 
Li, J., Liu, J. C., Yan, C. L., Du, D. L. & Li, H. L. The alleviation effect of iron on cadmium phytotoxicity on mangrove A. marina. Alleviation effect of rion on cadmium phytotoxicity in mangrove Avicennia marina (Forsk.) Vierh. Chemosphere 226, 413–420 (2019).ADS 
CAS 
Article 

Google Scholar 
Zhang, J. Y. et al. Effects of nano-Fe3O4-modified biochar on iron plaque formation and Cd accumulation in rice (Oryza sativa L.). Environ. Pollut. 260, 113970 (2020).CAS 
PubMed 
Article 

Google Scholar 
Farhat, Y. A., Kim, S. H., Seyfferth, A. L., Zhang, L. & Neumann, R. B. Altered arsenic availability, uptake, and allocation in rice under elevated temperature. Sci. Total Environ. 763, 143049 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wu, X. Y. et al. A review of toxicity and mechanisms of individual and mixtures of heavy metals in the environment. Environ. Sci. Pollut. R. 23, 8244–8259 (2016).CAS 
Article 

Google Scholar 
Abubakar, U. S., Zulkifli, S. Z. & Ismail, A. Heavy metals bioavailability and pollution indices evaluation in the mangrove surface sediment of Sungai Puloh Malaysia. Environ. Earth. Sci. 77, 225 (2018).CAS 
Article 

Google Scholar 
Kulkarni, R., Deobagkar, D. & Zinjarde, S. Metals in mangrove ecosystems and associated biota: A global perspective. Ecotox. Environ. Safe. 153, 215–228 (2018).CAS 
Article 

Google Scholar 
Shi, C., Ding, H., Zan, Q. J. & Li, R. L. Spatial variation and ecological risk assessment of heavy metals in mangrove sediments across China. Mar. Pollut. Bull. 143, 115–124 (2019).CAS 
PubMed 
Article 

Google Scholar 
Cheng, H. et al. Mixture of Pb, Zn and Cu on root permeability and radial oxygen loss in the mangrove Bruguiera gymnorrhiza. Ecotoxicology 29, 691–697 (2020).CAS 
PubMed 
Article 

Google Scholar 
Cheng, S. S. et al. Temporal variations in physiological responses of kandelia obovata seedlings exposed to multiple heavy metals. Mar. Pollut. Bull. 124, 1089–1095 (2017).CAS 
PubMed 
Article 

Google Scholar 
Shen, X. X. et al. Does combined heavy metal stress enhance iron plaque formation and heavy metal bioaccumulation in Kandelia obovata?. Environ. Exp. Bot. 186, 104463 (2021).CAS 
Article 

Google Scholar 
Shen, X. X. et al. Interactive effects of single, binary and trinary trace metals (lead, zinc and copper) on the physiological responses of Kandelia obovata seedlings. Environ. Geochem. Hlth. 41, 135–148 (2019).CAS 
Article 

Google Scholar 
Youssef, T. & Saenger, P. Anatomical adaptive strategies to flooding and rhizosphere oxidation in mangrove seedlings. Aust. J. Bot. 44, 297–313 (1996).Article 

Google Scholar 
Cheng, H., Wang, Y. S., Fei, J., Jiang, Z. Y. & Ye, Z. H. Differences in root aeration, iron plaque formation and waterlogging tolerance in six mangroves along a continues tidal gradient. Ecotoxicology 24, 1659–1667 (2015).CAS 
PubMed 
Article 

Google Scholar 
Takahashi, H., Yamauchi, T., Colmer, T. D. & Nakazono, M. Aerenchyma formation in plants. In Low-Oxygen Stress in Plants (eds van Dongen, J. T. & Licausi, F.) 247–265 (Springer, 2014).Chapter 

Google Scholar 
Yamauchi, T., Colmer, T. D., Pedersen, O. & Nakazono, M. Regulation of root traits for internal aeration and tolerance to soil waterlogging-flooding stress. Plant Physiol. 176, 1118–1130 (2018).CAS 
PubMed 
Article 

Google Scholar 
Cheng, H. et al. The role of radial oxygen loss and root anatomy on zinc uptake and tolerance in mangrove seedlings. Environ. Pollut. 158, 1189–1196 (2010).CAS 
PubMed 
Article 

Google Scholar 
Liu, Y. et al. Mixed heavy metals tolerance and radial oxygen loss in mangrove seedlings. Mar. Pollut. Bull. 58, 1843–1849 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wu, C., Li, H., Ye, Z., Wu, F. & Wong, M. H. Effects of As levels on radial oxygen loss and as speciation in rice. Environ. Sci. Pollut R. 20, 8334–8341 (2013).CAS 
Article 

Google Scholar 
Mendellshn, A., Kleiss, B. A. & Wakeley, J. S. Factors controlling the formation of oxidized root channels—a review. Wetlands 15, 37–46 (1995).Article 

Google Scholar 
Moller, C. L. & Sand-Jesen, K. Iron plaques improve the oxygen supply to root meristems of the freshwater plant Lobelia dortmanna. New Phytol. 179, 848–856 (2008).PubMed 
Article 
CAS 

Google Scholar 
Yang, J. X., Liu, Y. & Ye, Z. H. Root-induced changes of pH, eh, Fe (II) and fractions of Pb and Zn in rhizosphere soils of four wetland plants with different radial oxygen losses. Pedosphere 22, 518–527 (2012).CAS 
Article 

Google Scholar 
Hu, M., Li, F., Liu, C. & Wu, W. The diversity and abundance of as (III) oxidizers on root iron plaque is critical for arsenic bioavailability to rice. Sci. Rep. 5, 13611 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huang, G. X., Ding, C. F., Li, Y. S., Zhang, T. L. & Wang, X. X. Selenium enhances iron plaque formation by elevating the radial oxygen loss of roots to reduce cadmium accumulation in rice (Oryza sativa L.). J. Hazard. Mater. 398, 122860 (2020).CAS 
PubMed 
Article 

Google Scholar 
Thakur, S. et al. Plant-driven removal of heavy metals from soil: Uptake, translocation, tolerance mechanism, challenges, and future perspectives. Environ. Monit. Assess. 188, 206–212 (2016).PubMed 
Article 

Google Scholar 
Huang, H., Zhu, Y., Chen, Z., Yin, X. & Sun, G. Arsenic mobilization and speciation during iron plaque decomposition in a paddy soil. J. Soil. Sediment. 12, 402–410 (2012).CAS 
Article 

Google Scholar 
Zhong, S. Q. Effect of iron plaque on root growth and activity of two wetland plants. J. Hydroecol. 36, 74–79 (2015).
Google Scholar 
Khan, N. et al. Root iron plaque on wetland plants as a dynamic pool of nutrients and contaminants. Adv. Agron. 138, 1–96 (2016).Article 

Google Scholar 
Ma, H. H. et al. Formation of iron plaque on roots of Iris pseudacorus and its consequence for cadmium immobilization is impacted by zinc concentration. Ecotox. Environ. Safe. 193, 110306 (2020).CAS 
Article 

Google Scholar 
Martinez, S., Sáenz, M. E., Alberdi, J. L. & Di Marzio, W. D. Comparative ecotoxicity of single and binary mixtures exposures of cadmium and zinc on growth and biomarkers of Lemna gibba. Ecotoxicology 29, 571–583 (2020).CAS 
PubMed 
Article 

Google Scholar 
Huang, Y. Z., Hu, Y. & Liu, Y. X. Heavy metal accumulation in iron plaque and growth of rice plants upon exposure to single and combined contamination by copper, cadmium and lead. Acta Ecol. Sin. 29, 320–326 (2009).Article 

Google Scholar 
Deraison, H., Badenhausser, I., Börger, L. & Gross, N. Herbivore effect traits and their impact on plant community biomass: An experimental test using grasshoppers. Funct. Ecol. 29, 650–661 (2015).Article 

Google Scholar 
Wang, F., Wang, X. & Song, N. Polythylene microplastics increase cadmium uptake in lettuce (Lactuca sativa L.) by altering the soil microenvironment. Sci. Total Environ. 784, 147133 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yu, H. et al. Microplastic residues in wetland ecosystems: Do they truly threaten the plant-microbe-soil system?. Environ. Int. 156, 106708 (2021).CAS 
PubMed 
Article 

Google Scholar 
He, B., Li, R. L., Chai, M. W. & Qiu, G. Y. Threat of heavy metal contamination in eight mangrove plants from the Futian mangrove forest, China. Environ. Geochem. Hlth. 36, 467–476 (2014).CAS 
Article 

Google Scholar 
Du, J. N., Yan, C. L. & Li, Z. D. Formation of iron plaque on mangrove Kandalar. Obovata (S.L.) Root surfaces and its role in cadmium uptake and translocation. Mar. Pollut. Bull. 74, 105–109 (2013).CAS 
PubMed 
Article 

Google Scholar 
Hao, Z. B., Cang, J. & Xu, Z. Plant Physiology Experiment (Harbin Institute of Technology Press, 2004).
Google Scholar 
Kludze, H. K., Delaune, R. D. & Patrick, W. H. A colorimetric method for assaying dissolved oxygen loss from container-grown rice roots. Agron. J. 86, 483–487 (1994).CAS 
Article 

Google Scholar 
Kludze, H. K., Delaune, R. D. & Patrick, W. H. Aerenchyma formation and methane and oxygen-exchange in rice. Soil Sci. Soc. Am. J. 57, 386–391 (1993).ADS 
CAS 
Article 

Google Scholar 
Mei, X. Q., Yang, Y., Tam, N. F. Y., Wang, Y. W. & Li, L. Roles of root porosity, radial oxygen loss, Fe plaque formation on nutrient removal and tolerance of wetland plants to domestic wastewater. Water Res. 50, 147–159 (2014).CAS 
PubMed 
Article 

Google Scholar 
Taylor, G. J. & Crowder, A. Use of the DCB technique for extraction of hydrous iron oxides from roots of wetland plants. Am. J. Bot. 70, 1254–1257 (1983).CAS 
Article 

Google Scholar 
USEPA (United States Environmental Protection Agency). Method 3052: microwave assisted acid digestion of siliceous and organically based matrices SW-846. DC: Washington (1996). More

Bromley, R. G. & Gale, A. S. The lithostratigraphy of the English Chalk Rock. Cretac. Res. 3, 273–306 (1982).Article 

Google Scholar 
Scholle, P. A., Arthur, M. A. & Ekdale, A. A. Pelagic environment. In Carbonate Depositional Environments (eds Scholle, P. A. et al.) 619–691 (Am. Ass. Petrol. Geol. Mem. 33, 1983).Chapter 

Google Scholar 
Gealy, E. L., Winterer, E. L. & Moberly, R. Methods, conventions, and general observations. Initial Rep. Deep Sea Drill. Proj. 7, 9–26 (1971).
Google Scholar 
Kroenke, L. W. et al. Ocean Drilling Program. Proc. ODP, Init. Repts. 130, College Station (1991).Dunham, R. L. Classification of carbonate rocks according to depositional texture. Mem. Am. Assoc. Petrol. Geol. 1, 108–121 (1962).
Google Scholar 
Quine, M. & Bosence, D. Stratal geometries, facies and sea-floor erosion in Upper Cretaceous chalk, Normandy, France. Sedimentology 38, 1113–1152 (1991).ADS 
Article 

Google Scholar 
Røgen, B., Gommesen, L. & Fabricius, I. L. Grain size distributions of Chalk from Image analysis of electron micrographs. Comput. Geosci. 27, 1071–1080 (2001).ADS 
Article 

Google Scholar 
Saïag, J. et al. Classifying chalk microtextures: Sedimentary versus diagenetic origin (Cenomanian–Santonian, Paris Basin, France). Sedimentology 66, 2976–3007 (2019).Article 
CAS 

Google Scholar 
Scholle, P. A. Chalk diagenesis and its relation to petroleum exploration: Oil from chalks, a modern miracle?. Bull. Am. Assoc. Petrol. Geol. 61, 982–1009 (1977).CAS 

Google Scholar 
Tagliavento, M., John, C. M., Anderskouv, K. & Stemmerik, L. Towards a new understanding of the genesis of chalk: Diagenetic origin of micarbs confirmed by clumped isotope analysis. Sedimentology 68, 513–530 (2021).CAS 
Article 

Google Scholar 
Bramlette, M. N. Significance of coccolithophorids in calcium-carbonate deposition. Bull. Geol. Soc. Am. 69, 121–126 (1958).Article 

Google Scholar 
Hattin, D. E. & Darko, D. A. Technique for determining coccolith abundance in shaly chalk of Greenhorn Limestone (Upper Cretaceous) of Kansas. Kansas Geol. Surv. Bull. 202, 1–11 (1971).
Google Scholar 
Houghton, S. D. Calcareous nannofossils. In Calcareous algae and Stromatolites (ed. Riding, R.) 217–266 (Springer, 1991).Chapter 

Google Scholar 
Bown, P. R., Lees, J. A. & Young, J. R. Calcareous nannoplankton evolution and diversity through time. In Coccolithophores—From Molecular Processes to Global Impact (eds Thierstein, H. R. & Young, J. R.) 481–508 (Springer, 2004).
Google Scholar 
Roth, P. H. Mesozoic paleoceanography of the North Atlantic and Tethys Oceans. In North Atlantic Paleoceanography (eds Summerhayes, C. P. & Shackleton, N. J.) 299–320 (Geological Society Special Publications, 1986).
Google Scholar 
Baumann, K.-H., Andruleit, H., Böckel, B., Geisen, M. & Kinkel, H. The significance of extant coccolithophores as indicators of ocean water masses, surface water temperature, and paleoproductivity: A review. Paläontol. Z. 79, 93–112 (2005).Article 

Google Scholar 
Miller, K. G. et al. The phanerozoic record of global sea-level change. Science 310, 1293–1298 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ando, A., Huber, B. T., MacLeod, K. G. & Watkins, D. K. Early Cenomanian “hot greenhouse” revealed by oxygen isotope record of exceptionally well-preserved foraminifera from Tanzania. Paleoceanography 30, 1556–1572 (2015).ADS 
Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Comparative ichnology of shelf-sea and deep-sea chalk. J. Paleontol. 58, 322–332 (1984).
Google Scholar 
Savrda, C. E. Chalk and related deep-marine carbonates. In Trace Fossils as Indicators of Sedimentary Environments (eds Knaust, D. & Bromley, R. G.) 777–806 (Elsevier, 2012).Chapter 

Google Scholar 
Savrda, C. E., Foster, C. & Fluegeman, R. A unique Lower Paleocene shelf-sea chalk in the eastern U.S. Gulf coastal plain (Clayton Formation, western Alabama): Implications for depositional environment, sea-level dynamics and paleogeography. Palaeogeogr. Palaeoclimatol. Palaeoecol. 538, 109439 (2020).Article 

Google Scholar 
Erba, E., Watkins, D. & Mutterlose, J. Campanian dwarf calcareous nannofossils from Wodejebato Guyot. In Proc. Ocean Drill. Program Sci. Results (eds Haggerty, J. A. et al.) 141–155 (Ocean Drilling Program, 1995).
Google Scholar 
Hancock, J. M. The petrology of chalk. Proc. Geol. Assoc. 86, 499–535 (1975).Article 

Google Scholar 
Stanley, S. M. & Hardie, L. A. Secular oscillations in the carbonate mineralogy of reef-building and sediment-producing organisms driven by tectonically forced shifts in seawater chemistry. Palaeogeogr. Palaeoclimatol. Palaeoecol. 144, 3–19 (1998).Article 

Google Scholar 
Stanley, S. M., Ries, J. B. & Hardie, L. A. Seawater chemistry, coccolithophore population growth, and the origin of Cretaceous chalk. Geology 33, 593–596 (2005).ADS 
CAS 
Article 

Google Scholar 
Pemberton, S. G. et al. Ichnology and sedimentology of shallow to marginal marine systems: Ben Nevis and Avalon Reservoirs, Jeanne d’Arc Basin. Geol. Assoc. Can. Short Course Notes 15, 1–343 (2001).
Google Scholar 
Buatois, L. A. & Mángano, M. G. Ichnology: Organism–Substrate Interactions in Space and Time (Cambridge Press University, 2011).Book 

Google Scholar 
Frey, R. W. & Bromley, R. G. Ichnology of American chalks: The Selma Group (Upper Cretaceous), western Alabama. Can. J. Earth Sci. 22, 801–828 (1985).ADS 
Article 

Google Scholar 
Savrda, C. E. & Bottjer, D. Trace-fossil model for reconstructing oxygenation histories of ancient marine bottom waters: Application to Upper Cretaceous Niobrara Formation, Colorado. Palaeogeogr. Palaeoclimatol. Palaeoecol. 74, 49–74 (1989).Article 

Google Scholar 
Kennedy, W. J. Trace fossils in carbonate rocks. In The Study of Trace Fossils (ed. Frey, R. W.) 377–398 (Springer, 1975).Chapter 

Google Scholar 
Loucks, R. G., Gates, B. G. & Zahm, C. K. Depositional systems, lithofacies, nanopore to micropore matrix network, and reservoir quality of the Upper Cretaceous (Cenomanian) Buda Limestone in Dimmit County, southwestern Texas. Gulf Coast Assoc. Geol. Soc. 8, 281–300 (2019).
Google Scholar 
Valencia, F. L. et al. Depositional environments and controls on the stratigraphic architecture of the Cenomanian Buda Limestone in west Texas, U.S.A. Mar. Petrol. Geol. 133, 105275 (2021).Article 

Google Scholar 
Valencia, F. L., Laya, J. C., Buatois, L. A., Mángano, M. G. & Valencia, G. L. Sedimentology and stratigraphy of the Cenomanian Buda Limestone in central Texas, U.S.A.: Implications on regional and global depositional controls. Cretac. Res. 137, 105231 (2022).Article 

Google Scholar 
Martin, K. G. Stratigraphy of the Buda Limestone, south-central Texas. In Comanchean (Lower Cretaceous) Stratigraphy and Paleontology of Texas (ed. Hendricks, L.) 287–299 (Permian Basin Section SEPM 67 (8), 1967).
Google Scholar 
Mallon, A. J. & Swarbrick, R. E. Diagenetic characteristics of low permeability, non-reservoir chalks from the Central North Sea. Mar. Petrol. Geol. 25, 1097–1108 (2008).CAS 
Article 

Google Scholar 
Brasher, J. E. & Vagle, K. R. Influence of lithofacies and diagenesis on Norwegian North Sea chalk reservoirs. Am. Assoc. Petrol. Geol. Bull. 80, 746–769 (1996).CAS 

Google Scholar 
Hentz, T. F. & Ruppel, S. C. Regional stratigraphic and rock characteristics of eagle ford shale in its play area: Maverick Basin to East Texas Basin. Am. Ass. Petrol. Geol. Search and Discovery 10325 (2011).Robinson, W. C. Petrography and depositional environments of the Buda Limestone, northern Coahuila, Mexico. MS Thesis. The University of Texas, 156 (1982).Reaser, D. F. & Robinson, W. C. Cretaceous Buda Limestone in west Texas and northern Mexico. In Cretaceous Stratigraphy and Paleoecology, Texas and Mexico (ed. Scott, R. W.) 337–373 (Perkins Memorial volume, GCSSEPM Foundation, Special Publications in Geology 1, 2003).
Google Scholar 
Young, K. P. Cretaceous paleogeography: Implications of endemic ammonite faunas. Geol. Circ. (University of Texas at Austin, Bureau of Economic Geology) 72, 1–13 (1972).
Google Scholar 
Buatois, L. A. & Mángano, M. G. Ichnodiversity and ichnodisparity: Significance and caveats. Lethaia 46, 281–292 (2013).Article 

Google Scholar 
Buatois, L. A., Wisshak, M., Wilson, M. A. & Mángano, M. G. Categories of architectural designs in trace fossils: A measure of ichnodisparity. Earth Sci. Rev. 164, 102–181 (2017).ADS 
CAS 
Article 

Google Scholar 
Swinbanks, D. D. & Luternauer, J. L. Burrow distribution of thalassinidean shrimp on a Fraser Delta tidal flat, British Columbia. J. Paleontol. 61, 315–333 (1987).Article 

Google Scholar 
Carmona, N. B., Buatois, L. A. & Mángano, M. G. The trace fossil record of burrowing decapod crustaceans: Evaluating evolutionary radiations and behavioural convergence. In Trace Fossils in Evolutionary Palaeoecology (eds Webby, B. D. et al.) 141–153 (Wiley, 2004).
Google Scholar 
Baucon, A. et al. Ethology of the trace fossil Chondrites: Form, function and environment. Earth Sci. Rev. 202, 102989 (2020).CAS 
Article 

Google Scholar 
Pemberton, S. G. & Frey, R. W. Trace fossil nomenclature and the Planolites–Palaeophycus dilemma. J. Paleontol. 56, 843–881 (1982).
Google Scholar 
Rodríguez-Tovar, F. J. & Pérez-Valera, F. Trace fossil Rhizocorallium from the Middle Triassic of the Betic Cordillera, Southern Spain: Characterization and environmental implications. Palaios 23, 78–86 (2008).ADS 
Article 

Google Scholar 
Bown, T. M. & Kraus, M. J. Ichnofossils of the alluvial Willwood Formation (lower Eocene), Bighorn Basin, northwest Wyoming, USA. Palaeogeogr. Palaeoclimatol. Palaeoecol 43, 95–128 (1983).Article 

Google Scholar 
Uchman, A. Taxonomy and palaeoecology of flysch trace fossils: The Marnoso-arenacea Formation and associated facies (Miocene, Northern Apennines, Italy). Beringeria 15, 3–115 (1995).
Google Scholar 
Demírcan, H. & Uchman, A. The miniature trace fossil Bichordites kuzunensis isp. Nov., from early Oligocene prodelta sediments of the Mezardere Formation, Gökçeada Island, NW Turkey. Acta Geol. Pol. 62, 205–215 (2012).
Google Scholar 
Plaziat, J.-C. & Mahmoudi, M. Trace fossils attributed to burrowing echinoids: A revision including new ichnogenus and ichnospecies. Geobios 21, 209–233 (1988).Article 

Google Scholar 
Chamberlain, C. K. Morphology and ethology of trace fossils from the Ouachita Mountains, southeast Oklahoma. J. Paleontol. 45, 212–246 (1971).
Google Scholar 
Farrow, G. E. Bathymetric zonation of Jurassic trace fossils from the coast of Yorkshire, England. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2, 103–151 (1966).Article 

Google Scholar 
Mángano, M. G., Buatois, L. A., West, R. R. & Maples, C. G. Contrasting behavioral and feeding strategies recorded by tidal-flat bivalve trace fossils from the upper carboniferous of eastern Kansas. Palaios 13, 335–351 (1998).ADS 
Article 

Google Scholar 
Pemberton, S. G., Frey, R. W. & Bromley, R. G. The ichnotaxonomy of Conostichus and other plug-shaped ichnofossils. Can. J. Earth Sci. 25, 866–892 (1988).ADS 
Article 

Google Scholar 
Nara, M. Rosselia socialis: A dwelling structure of a probable terebellid polychaete. Lethaia 28, 171–178 (1995).Article 

Google Scholar 
Wilson, M. A., Curran, H. A. & White, B. Paleontological evidence of a brief global sea-level event during the last interglacial. Lethaia 31, 241–250 (1998).Article 

Google Scholar 
Santos, A., Mayoral, E., Marques da Silva, C., Cachão, M. & Kullberg, J. C. Trypanites ichnofacies: Palaeoenvironmental and tectonic implications. A case study from the Miocene disconformity at Foz da Fonte (Lower Tagus Basin, Portugal). Palaeogeogr. Palaeoclimatol. Palaeoecol. 292, 35–43 (2010).Article 

Google Scholar 
Wilson, J. L. Carbonate Facies in Geological History (Springer, 1975).Book 

Google Scholar 
Tucker, M. E. & Wright, V. P. Carbonate Sedimentology (Blackwell Science, 1990).Book 

Google Scholar 
MacEachern, J. A. & Gingras, M. K. Recognition of brackish-water trace fossil assemblages in the Cretaceous western interior seaway of Alberta. In Sediment-Organism Interactions: A Multifaceted Ichnology (eds Bromley, R. G. et al.) 149–194 (Society for Sedimentary Geology Special Publication, 2007).
Google Scholar 
MacEachern, J. A., Zaitlin, B. A. & Pemberton, S. G. High-resolution sequence stratigraphy of early transgressive deposits, Viking Formation, Joffre Field, Alberta, Canada. Bull. Am. Assoc. Petrol. Geol. 82, 729–756 (1998).
Google Scholar 
Buatois, L. A., Netto, R. G. & Mángano, M. G. Ichnology of Permian marginal-marine to shallow-marine coal-bearing successions: Rio Bonito and Palermo formations, Parana Basin, Brazil. In Applied Ichnology (eds MacEachern, J. A. et al.) 167–177 (Society for Sedimentary Geology Short Course Notes, 2007).
Google Scholar 
Buatois, L. A. et al. Colonization of brackish-water systems through time: Evidence from the trace-fossil record. Palaios 20, 321–347 (2005).ADS 
Article 

Google Scholar 
Pemberton, S. G. & Wightman, D. M. Ichnological characteristics of brackish water deposits. In Applications of Ichnology to Petroleum Exploration: A Core Work-shop (ed. Pemberton, S. G.) 141–167 (Society of Economic Paleontologists and Mineralogists Core Workshop, 1992).Chapter 

Google Scholar 
Anderson, B. G. & Droser, M. L. Ichnofabrics and geometric configurations of Ophiomorpha within a sequence stratigraphic framework: An example from the Upper Cretaceous US western interior. Sedimentology 45, 379–396 (1998).ADS 
Article 

Google Scholar 
Buatois, L. A., Mángano, M. G. & Pattison, S. A. J. Ichnology of prodeltaic hyperpycnite–turbidite channel complexes and lobes from the Upper Cretaceous Prairie Canyon Member of the Mancos Shale, Book Cliffs, Utah, USA. Sedimentology 66, 1825–1860 (2019).Article 

Google Scholar 
Bhattacharya, J. P. & MacEachern, J. A. Hyperpycnal rivers and prodeltaic shelves in the Cretaceous seaway of North America. J. Sediment. Res. 79, 184–209 (2009).ADS 
Article 

Google Scholar 
Savrda, C. E. Ichnosedimentologic evidence for a noncatastrophic origin of Cretaceous-Tertiary boundary sand in Alabama. Geology 21, 1075–1078 (1993).ADS 
Article 

Google Scholar 
Schlager, W. Accommodation and supply-a dual control on stratigraphic sequences. Sediment. Geol. 86, 111–136 (1993).ADS 
Article 

Google Scholar 
Strasser, A. & Samankassou, E. Carbonate sedimentation rates today and in the past: Holocene of Florida Bay, Bahamas, and Bermuda vs. Upper Jurassic and Lower Cretaceous of the Jura Mountains (Switzerland and France). Geol. Croat. 56, 1–18 (2003).Article 

Google Scholar 
Moyano-Paz, D., Richiano, S., Varela, A. N., Gómez-Dacal, A. R. & Poire, D. G. Ichnological signatures from wave- and fluvial-dominated deltas: The La Anita Fromation, Upper Cretaceous, Austral-Magallanes Basin, Patagonia. Mar. Pet. Geol. 114, 104168 (2020).CAS 
Article 

Google Scholar 
De Gibert, J. M. & Ekdale, A. A. Trace fossil assemblages reflecting stressed environments in the Middle Jurassic Carmel Seaway of Central Utah. J. Paleontol. 73, 711–720 (1999).Article 

Google Scholar 
Gingras, M. K., MacEachern, J. A. & Dashtgard, S. E. Process ichnology and the elucidation of physico-chemical stress. Sediment. Geol. 237, 115–134 (2011).ADS 
CAS 
Article 

Google Scholar 
Smith, C. R., Levin, L. A., Hoover, D. J., McMurty, G. & Gage, J. D. Variations in bioturbation across the oxygen minimum zone in the northwest Arabian Sea. Deep-Sea Res. II 47, 227–257 (2000).ADS 
CAS 
Article 

Google Scholar 
Wignall, P. B., Newton, R. & Brookfield, M. E. Pyrite framboid evidence for oxygen-poor deposition during the Permian-Triassic crisis in Kashmir. Palaeogeogr. Palaeoclimatol. Palaeoecol. 216, 183–188 (2005).Article 

Google Scholar 
Kennedy, W. J. Burrows and surface traces from the Lower Chalk of southern England. Bull. Br. Mus. Nat. Hist. Geol. 15, 127–167 (1967).
Google Scholar 
Kennedy, W. J. & Garrison, R. E. Morphology and genesis of nodular chalks and hardgrounds in the Upper Cretaceous of southern England. Sedimentology 22, 311–386 (1975).ADS 
CAS 
Article 

Google Scholar 
Bromley, R. G. Some observations on burrows of thalassinidean Crustacea in chalk hardgrounds. Geol. Soc. Lond. Q. J. 123, 157–182 (1967).Article 

Google Scholar 
Bromley, R. G. Trace fossils at omission surfaces. In The Study of Trace Fossils (ed. Frey, R. W.) 399–428 (Springer, 1975).Chapter 

Google Scholar 
Hart, M. B., Harries, P. J. & Cárdenas, A. L. The Cretaceous/Paleogene boundary events in the Gulf Coast: Comparisons between Alabama and Texas. Gulf Coast Assoc. Geol. Trans. 63, 235–255 (2013).
Google Scholar 
Al Balushi, S. A. K. & Macquaker, J. H. S. Sedimentological evidence for bottom-water oxygenation during deposition of the Natih-B Member intrashelf-basinal sediments: Upper Cretaceous carbonate source rock, Natih Formation, North Sultanate of Oman. GeoArabia 16, 47–84 (2011).Article 

Google Scholar 
Lasseur, E. et al. A relative water-depth model for the Normandy Chalk (Cenomanian–Middle Coniacian, Paris Basin, France) based on facies patterns of metre-scale cycles. Sediment. Geol. 213, 1–26 (2009).ADS 
Article 

Google Scholar 
Dawson, W. C. & Reaser, D. F. Rhizocorallium in the upper Austin Chalk, Ellis County, Texas. Texas J. of Sci. 23, 207–214 (1980).
Google Scholar 
Dawson, W. C. & Reaser, D. F. Ichnology and paleoenvironments of the middle and upper Austin Chalk (Upper Cretaceous), northeastern Texas. Trans. Am. Assoc. Pet. Geol. Southwest Sec. 1985, 47–67 (1985).
Google Scholar 
Dawson, W. C. & Reaser, D. F. Trace fossils and paleoenvironments of lower and middle Austin Chalk (Upper Cretaceous), north-central Texas. Trans. Gulf Coast Assoc. Geol. Soc. 40, 161–173 (1990).
Google Scholar 
Dawson, W. C. & Reaser, D. F. Ichnology and Paleosubstrates of Austin Chalk (Cretaceous) Outcrops: Southern Dallas and Ellis Counties, Texas. Am. Assoc. Pet. Geol. Search Discovery Article #91004 (1991).Fürsich, F. T., Kennedy, W. J. & Palmer, T. J. Trace fossils at a regional discontinuity surface: The Austin/Taylor (Upper Cretaceous) contact in central Texas. J. Paleontol. 55, 537–551 (1981).
Google Scholar 
Morgan, R. F. A new ichnospecies of Gyrolithes from the Austin Chalk, Upper Cretaceous, Texas, USA. Ichnos 26, 1–7 (2018).Article 

Google Scholar 
Cooper, J. R., Godet, A. & Pope, M. C. Tectonic and eustatic impact on depositional features in the upper Cretaceous Austin Chalk Group of south-central Texas, USA. Sediment. Geol. 401, 105632 (2020).Article 

Google Scholar 
Loucks, R. G. et al. Geologic characterization of the type cored section for the Upper Cretaceous Austin Chalk Group in southern Texas: A combination fractured and unconventional reservoir. Am. Assoc. Pet. Geol. Bull. 104, 2209–2245 (2020).
Google Scholar 
Loucks, R. G., Reed, R. M., Ko, L. T., Zahm, C. K. & Larson, T. E. Micropetrographic characterization of a siliciclastic-rich chalk; Upper Cretaceous Austin Chalk Group along the onshore northern Gulf of Mexico, USA. Sediment. Geol. 412, 105821 (2021).CAS 
Article 

Google Scholar 
Bottjer, D. J. Paleoecology, Ichnology, and Depositional Environments of Upper Cretaceous Chalks (Annona Formation; chalk Member of Saratoga Formation), Southwestern Arkansas. PhD Dissertation, Indiana University, 424 (1978).Bottjer, D. J. Ichnology and depositional environments of Upper Cretaceous chalks, southwestern Arkansas (Annona Formation; chalk member, Saratoga Formation). Am. Assoc. Pet. Geol. Bull. 63, 422 (1979).
Google Scholar 
Bottjer, D. J. Trace fossils and paleoenvironments of two Arkansas Upper Cretaceous discontinuity surfaces. J. Paleontol. 59, 282–298 (1985).
Google Scholar 
Bottjer, D. J. Campanian-Maastrichtian chalks of southwestern Arkansas: Petrology, paleoenvironments and comparison with other North American and European chalks. Cretac. Res. 7, 161–196 (1986).Article 

Google Scholar 
Bayet-Goll, A., Neto de Carvalho, C., Monaco, P. & Sharafi, M. Sequence stratigraphic and sedimentologic significance of biogenic structures from chalky limestones of the Turonian-Campanian Abderaz Formation, Kopet-Dagh, Iran. In Cretaceous Period: Biotic Diversity and Biogeography (eds Khosla, A. & Lucas, S. G.) 19–43 (New Mex. Mus. Nat. His. Sci. Bull. 71, 2016).
Google Scholar 
Locklair, R. E. & Savrda, C. E. Ichnology of rhythmically bedded Demopolis Chalk (Upper Cretaceous, Alabama): Implications for paleoenvironment, depositional cycle origins, and tracemaker behavior. Palaios 13, 423–438 (1998).ADS 
Article 

Google Scholar 
Locklair, R. E. & Savrda, C. E. Ichnofossil tiering analysis of a rhythmically bedded chalk-marl sequence in the Upper Cretaceous of Alabama. Lethaia 31, 311–322 (1998).Article 

Google Scholar 
Kennedy, W. J. Trace fossils in the chalk environment. In Trace Fossils (eds Crimes, T. P. & Harper, J. C.) 263–282 (Geological Journal Special Issue 3, 1970).
Google Scholar 
Mortimore, R. N. & Pomerol, B. Stratigraphy and eustatic implications of trace fossil events in the Upper Cretaceous Chalk of northern Europe. Palaios 6, 216–231 (1991).ADS 
Article 

Google Scholar 
Foster, C. B. III. Geology of the Moscow Landing Section, Tombigbee River, Western Alabama, with Focus on Ichnologic Aspects of the Lower Paleocene Clayton Formation. M.Sc. Dissertation, Auburn University, 88 (2019).Gabdullin, R. R. Rhythmicity of the Upper Cretaceous Deposits of the East European Craton, Northwestern Caucasus and Southwestern Crimea: Structure, Classification, Formation Models (Mosk. Gos. Univ., 2002).
Google Scholar 
Baraboshkin, E. Y. & Zibrov, I. A. Characteristics of the Middle Cenomanian Rhythmic Sequence from Mount Selbukhra in Southwest Crimea. Moscow Univ. Geol. Bull. 67, 176–184 (2012).Article 

Google Scholar 
Blinkenberg, K. H., Lauridsen, B. W., Knaust, D. & Stemmerik, L. New ichnofabrics of the Cenomanian-Danian Chalk Group. J. Sediment. Res. 90, 701–712 (2020).ADS 
Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Trace fossils and ichnofabric in the Kjolby Gaard Marl, uppermost Cretaceous, Denmark. Bull. Geol. Soc. Denmark 31, 107–119 (1983).Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Cretaceous chalk ichnofacies in northern Europe. Geobios 8, 201–204 (1984).Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Analysis of composite ichnofabrics; An example in Uppermost Cretaceous chalk of Denmark. Palaios 6, 232–249 (1991).ADS 
Article 

Google Scholar 
Surlyk, F. et al. The cyclic Rørdal Member—A new lithostratigraphic unit of chronostratigraphic and palaeoclimatic importance in the upper Maastrichtian of Denmark. Bull. Geol. Soc. Denmark 58, 89–98 (2010).Article 

Google Scholar 
Lauridsen, B. W., Surlyk, F. & Bromley, R. G. Trace fossils of a cyclic chalk marl succession; the upper Maastrichtian Rørdal Member, Denamrk. Cretac. Res. 32, 194–211 (2011).Article 

Google Scholar 
Frey, R. W. Trace fossils of Fort Hays Limestone Member of Niobrara Chalk (Upper Cretaceous), west-central Kansas. Univ. Kansas Paleontol. Contrib. 53, 52 (1970).
Google Scholar 
Hattin, D. E. Stratigraphy and depositional environment of Smoky Hill Chalk Member, Niobrara Chalk (Upper Cretaceous) of the type area western Kansas. Kansas Geol. Surv. Bull. 225, 1–108 (1982).
Google Scholar 
Savrda, C. E. Ichnocoenoses in the Niobrara Formation: Implications for benthic oxygenation histories. In Stratigraphy and Paleoenvironments of the Cretaceous Western Interior Seaway, USA (eds Dean, W. E. & Arthur, M. A.) 137–151 (SEPM Society for Sedimentary Geology 6, 1998).Chapter 

Google Scholar 
Hattin, D. E. Widespread, synchronously deposited, burrow-mottled limestone beds in Greenhorn Limestone (Upper Cretaceous) of Kansas and southeastern Colorado. Am. Assoc. Pet. Geol. Bull. 55, 412–431 (1971).
Google Scholar 
Hattin, D. E. Stratigraphy and depositional environment of Greenhorn Limestone (Upper Cretaceous) of Kansas. Kansas Geol. Surv. Bull. 209, 128 (1975).
Google Scholar 
Savrda, C. E. Ichnology of the Bridge Creek Limestone: Evidence for temporal and spatial variations in paleo-oxygenation in the Western Interior Seaway. In Stratigraphy and Paleoenvironments of the Cretaceous Western Interior Seaway, USA (eds Dean, W. E. & Arthur, M. A.) 127–136 (SEPM Society for Sedimentary Geology 6, 1998).Chapter 

Google Scholar 
Rasmussen, S. L. & Surlyk, F. Facies and ichnology of an Upper Cretaceous chalk contourite drift complex, eastern Denmark, and the validity of contourite facies models. J. Geol. Soc. Lond. 169, 435–447 (2012).Article 

Google Scholar 
Surlyk, F. et al. Upper Campanian-Maastrichtian holostratigraphy of the eastern Danish Basin. Cretac. Res. 46, 232–256 (2013).Article 

Google Scholar 
Boussaha, M., Thibault, N., Anderskouv, K., Moreau, J. & Stemmerik, L. Controls on upper Campanian-Maastrichtian chalk deposition in the eastern Danish Basin. Sedimentology 64, 1998–2030 (2017).Article 

Google Scholar 
Reolid, J. & Betzler, C. The ichnology of carbonate drifts. Sedimentology 66, 1427–1448 (2019).Article 

Google Scholar 
Nygaard, E. Bathichnus and Its Significance in the Trace Fossil Association of Upper Cretaceous Chalk, Mors, Denmark 107–113 (Danm. Geol. Unders. Årbog, 1983).
Google Scholar 
Scholle, P. A., Albrechtsen, T. & Tirsgaard, H. Formation and diagenesis of bedding cycles in uppermost Cretaceous chalks of the Dan Field, Danish North Sea. Sedimentology 45, 223–243 (1998).ADS 
CAS 
Article 

Google Scholar 
Damholt, T. & Surlyk, F. Laminated–bioturbated cycles in Maastrichtian chalk of the North Sea: Oxygenation fluctuations within the Milankovitch frequency band. Sedimentology 51, 1323–1342 (2004).ADS 
Article 

Google Scholar 
Anderskouv, K. & Surlyk, F. Upper Cretaceous chalk facies and depositional history recorded in the Mona-1 core, Mona Ridge, Danish North Sea. Geol. Surv. Denmark Greenland Bull. 25, 1–60 (2011).Article 

Google Scholar 
Maliva, R. G. & Dickson, J. A. D. Microfacies and diagenetic controls of porosity in Cretaceous/Tertiary chalks, Eldfisk Field, Norwegian North Sea. Am. Assoc. Pet. Geol. Bull. 76, 1825–1838 (1992).
Google Scholar 
Knaust, D., Dorador, J. & Rodríguez-Tovar, F. J. Burrowed matrix powering dual porosity systems—A case study from the Maastrichtian chalk of the Gullfaks Field Norwegian North Sea. Mar. Petrol. Geol. 113, 104158 (2020).Article 

Google Scholar 
Phillips, C. & McIlroy, D. Ichnofabrics and biologically mediated changes in clay mineral assemblages from a deep-water, fine-grained, calcareous sedimentary succession: An example from the Upper Cretaceous Wyandot Formation, offshore Nova Scotia. Bull. Can. Petrol. Geol. 58, 203–218 (2010).Article 

Google Scholar 
Rodríguez-Tovar, F. J. & Hernández-Molina, F. J. Ichnological analysis of contourites: Past, present and future. Earth-Sci. Rev. 182, 28–41 (2018).ADS 
Article 

Google Scholar 
Miguez-Salas, O. & Rodríguez-Tovar, F. J. Ichnofacies distribution in the Eocene-Early Miocene Petra Tou Romiou outcrop, Cyprus: Sea level dynamics and palaeoenvironmental implications in a contourite environment. Int. J. Earth Sci. 108, 2531–2544 (2019).CAS 
Article 

Google Scholar 
Nelson, C. S. Bioturbation in middle bathyal, Cenozoic nannofossil oozes and chalks, southwest Pacific. In Initial Reports of the Deep Sea Drilling Project 90 (eds Kennett, J. P., von der Borch, C. C. et al.) 1189–1200 (Washington U.S. Government Printing Office, 1986).
Google Scholar 
Fütterer, D. K. Bioturbation and trace fossils in deep sea sediments of the Walvis Ridge, southeastern Atlantic, Leg 74. In Initial Reports of the Deep Sea Drilling Project 74 (eds Moore, T. C., Rabinowitz, P. D. et al.) 543–555 (Government Printing Office, 1984).
Google Scholar 
Wetzel, A. Ichnofabrics in Eocene to Maestrichtian sediments from Deep Sea Drilling Project Site 605, off the New Jersey coast. In Initial Reports of the Deep Sea Drilling Project 93 (eds. Hinte, J. E., Wise Jr., S. W. et al.) 825–835 (1987).Droser, M. L. & Bottjer, D. J. Trace fossils and ichnofabrics in Leg 119 cores. In Proceedings of the Ocean Drilling Program, Scientific Results 119 (eds. Barron, J., Larsen, B. et al.) 635–641 (1991).Desai, B. G. Ichnofabric analysis of bathyal chalks: The Miocene Inglis Formation of the Andaman and Nicobar Islands, India. J. Palaeogeogr. 10, 1–15 (2021).Article 

Google Scholar 
Warme, J. E., Kennedy, W. J. & Scheidermann, N. Biogenic sedimentary structures (trace fossils) in Leg 15 cores. In Initial Reports of the Deep Sea Drilling Project 15 (eds. Edgar, N. T., Saunders, J. B. et al.) 813–831 (1973).Maurrasse, F. Sedimentary structures of Caribbean Leg 15 sediments. In Initial Reports of the Deep-Sea Drilling Project 15 (eds. Edgar, T. et al.) (1974).Erba, E. & Premoli-Silva, I. Orbitally driven cycles in trace-fossil distribution from the Piobbico core (late Albian, central Italy). In Orbital Forcing and Cyclic Sequences, IAS Spec. Publ. 19 (eds De Boer, P. L. & Smith, D. G.) 211–225 (Blackwell Scientific, 1994).
Google Scholar 
Chamberlain, C. K. Trace fossils in DSDP cores of the Pacific. J. Paleontol. 49, 1074–1096 (1975).
Google Scholar 
Ekdale, A. A. Trace fossils in Deep Sea Drilling Project Leg 58 cores. In Initial Reports of the Deep Sea Drilling Project 58 (eds. de Vries Klein, G., Kobyashi, K. et al.) 601–605 (1980).Ekdale, A. A. Geologic history of the abyssal benthos: Evidence from trace fossils in Deep-Sea Drilling Project cores. PhD Dissertation, Rice University, 154 (1974).Ekdale, A. A. Abyssal trace fossils in worldwide Deep Sea Drilling Project cores. In Trace Fossils 2 (eds. Crimes, T. P. & Harper, J. C.) 163–182 (Geol. J., Spec. Iss. 9, 1977).Ekdale, A. A. & Berger, W. H. Deep-sea ichnofacies: Modern organism traces on and in pelagic carbonates of the western equatorial Pacific. Palaeogeogr. Palaeoclimatol. Palaeoecol. 23, 263–278 (1978).Article 

Google Scholar 
Ekdale, A. A., Muller, L. N. & Novak, M. T. Quantitative ichnology of modern pelagic deposits in the abyssal Atlantic. Palaeogeogr. Palaeoclimatol. Palaeoecol. 45, 189–223 (1984).CAS 
Article 

Google Scholar 
Savrda, C. E. Limited ichnologic fidelity and temporal resolution in pelagic sediments: Paleoenvironmental and paleoecologic implications. Palaios 29, 210–217 (2014).ADS 
Article 

Google Scholar 
Bromley, R. G. & Ekdale, A. A. Composite ichnofabrics and tiering of burrows. Geol. Mag. 123, 59–65 (1986).ADS 
Article 

Google Scholar 
Griffin, J. N. et al. Spatial heterogeneity increases the importance of species richness for an ecosystem process. Oikos 118, 1335–1342 (2009).Article 

Google Scholar 
Valentine, J. W. Overview of marine biodiversity. In Marine Macroecology (eds Witman, J. D. & Roy, K.) 3–28 (University of Chicago Press, 2009).Chapter 

Google Scholar 
Schlacher, T. A. et al. Soft-sediment benthic community structure in a coral reef lagoon—The prominence of spatial heterogeneity and “spot endemism”. Mar. Ecol. Prog. Ser. 174, 159–174 (1998).ADS 
Article 

Google Scholar 
Hummel, H. et al. Geographic patterns of biodiversity in European coastal marine benthos. J. Mar. Biol. Assoc. U.K. 97, 507–523 (2017).Article 

Google Scholar 
Harborne, A. R., Mumby, P. J., Żychaluk, K., Hedley, J. D. & Blackwell, P. G. Modeling the beta diversity of coral reefs. Ecology 87, 2871–2881 (2006).PubMed 
Article 

Google Scholar 
Christia, C., Giordani, G. & Papastergiadou, E. Environmental variability and macrophyte assemblages in coastal lagoon types of Western Greece (Mediterranean Sea). Water 10, 151 (2018).Article 
CAS 

Google Scholar 
Dorador, J., Rodríguez-Tovar, F. J., IODP Expedition 339 Scientists. Digital image treatment applied to ichnological analysis of marine core sediments. Facies 60, 39–44 (2014).Article 

Google Scholar 
Dorador, J. & Rodríguez-Tovar, F. J. High-resolution image treatment in ichnological core analysis: Initial steps, advances and prospects. Earth-Sci. Rev. 177, 226–237 (2018).ADS 
Article 

Google Scholar 
Taylor, A. M. & Goldring, R. Description and analysis of bioturbation and ichnofabric. J. Geol. Soc. 150, 141–148 (1993).ADS 
Article 

Google Scholar 
Cao, Y. M., Curran, A. H. & Glumac, B. Testing the use of photoshop and imageJ for evaluating ichnofabrics. 2015 GSA Annual Meeting in Baltimore, Maryland, USA, Paper No. 128-11 (The Geol. Soc. of Am., 2015). More

Zabalza, A., Orcaray, L., Fernandez-Escalada, M., Zulet-Gonzalez, A. & Royuela, M. The pattern of shikimate pathway and phenylpropanoids after inhibition by glyphosate or quinate feeding in pea roots. Pestic Biochem. Physiol. 141, 96–102. https://doi.org/10.1016/j.pestbp.2016.12.005 (2017).CAS 
Article 
PubMed 

Google Scholar 
Amrhein, N., Deus, B., Gehrke, P. & Steinrucken, H. C. The site of the inhibition of the shikimate pathway by glyphosate: II. Interference of glyphosate with chorismate formation in vivo and in vitro. Plant Physiol. 66, 830–834. https://doi.org/10.1104/pp.66.5.830 (1980).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Landrigan, P. J. & Belpoggi, F. The need for independent research on the health effects of glyphosate-based herbicides. Environ. Health 17, 51. https://doi.org/10.1186/s12940-018-0392-z (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tu, M. & Randall, J. Adjuvants. Tu, M. et al. Weed Control Methods Handbook the Nature Conservancy. 1–24. (TNC, 2003).Brausch, J. M. & Smith, P. N. Toxicity of three polyethoxylated tallowamine surfactant formulations to laboratory and field collected fairy shrimp, Thamnocephalus platyurus. Arch. Environ. Contam. Toxicol. 52, 217–221. https://doi.org/10.1007/s00244-006-0151-y (2007).CAS 
Article 
PubMed 

Google Scholar 
Brausch, J. M., Beall, B. & Smith, P. N. Acute and sub-lethal toxicity of three POEA surfactant formulations to Daphnia magna. Bull. Environ. Contam. Toxicol. 78, 510–514. https://doi.org/10.1007/s00128-007-9091-0 (2007).CAS 
Article 
PubMed 

Google Scholar 
Tsui, M. T. & Chu, L. M. Aquatic toxicity of glyphosate-based formulations: Comparison between different organisms and the effects of environmental factors. Chemosphere 52, 1189–1197. https://doi.org/10.1016/S0045-6535(03)00306-0 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Adam, A., Marzuki, A., Abdul Rahman, H. & Abdul Aziz, M. The oral and intratracheal toxicities of ROUNDUP and its components to rats. Vet. Hum. Toxicol. 39, 147–151 (1997).CAS 
PubMed 

Google Scholar 
Howe, C. M. et al. Toxicity of glyphosate-based pesticides to four North American frog species. Environ. Toxicol. Chem. 23, 1928–1938. https://doi.org/10.1897/03-71 (2004).CAS 
Article 
PubMed 

Google Scholar 
Mesnage, R., Benbrook, C. & Antoniou, M. N. Insight into the confusion over surfactant co-formulants in glyphosate-based herbicides. Food Chem. Toxicol. 128, 137–145. https://doi.org/10.1016/j.fct.2019.03.053 (2019).CAS 
Article 
PubMed 

Google Scholar 
Mesnage, R., Bernay, B. & Seralini, G. E. Ethoxylated adjuvants of glyphosate-based herbicides are active principles of human cell toxicity. Toxicology 313, 122–128. https://doi.org/10.1016/j.tox.2012.09.006 (2013).CAS 
Article 
PubMed 

Google Scholar 
Chlopecka, M., Mendel, M., Dziekan, N. & Karlik, W. The effect of glyphosate-based herbicide Roundup and its co-formulant, POEA, on the motoric activity of rat intestine—In vitro study. Environ. Toxicol. Pharmacol. 49, 156–162. https://doi.org/10.1016/j.etap.2016.12.010 (2017).CAS 
Article 
PubMed 

Google Scholar 
Authority, E. F. S. Request for the evaluation of the toxicological assessment of the co-formulant POE-tallowamine. EFSA J. 13, 4303 (2015).
Google Scholar 
Bolognesi, C. et al. Genotoxic activity of glyphosate and its technical formulation Roundup. J. Agric. Food Chem. 45, 1957–1962 (1997).CAS 
Article 

Google Scholar 
Hao, Y. et al. Roundup((R)) confers cytotoxicity through DNA damage and mitochondria-associated apoptosis induction. Environ. Pollut. 252, 917–923. https://doi.org/10.1016/j.envpol.2019.05.128 (2019).CAS 
Article 
PubMed 

Google Scholar 
Luo, L. et al. In vitro cytotoxicity assessment of roundup (glyphosate) in L-02 hepatocytes. J. Environ. Sci. Health B 52, 410–417. https://doi.org/10.1080/03601234.2017.1293449 (2017).CAS 
Article 
PubMed 

Google Scholar 
Young, F., Ho, D., Glynn, D. & Edwards, V. Endocrine disruption and cytotoxicity of glyphosate and roundup in human JAr cells in vitro. Synthesis 14, 17 (2015).
Google Scholar 
Weinhold, B. Mystery in a bottle: Will the EPA require public disclosure of inert pesticide ingredients?. Environ. Health Perspect. 118, A168-171. https://doi.org/10.1289/ehp.118-a168 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Richmond, M. E. Glyphosate: A review of its global use, environmental impact, and potential health effects on humans and other species. J. Environ. Stud. Sci. 8, 416–434 (2018).Article 

Google Scholar 
Cole, R. D., Anderson, G. L. & Williams, P. L. The nematode Caenorhabditis elegans as a model of organophosphate-induced mammalian neurotoxicity. Toxicol. Appl. Pharmacol. 194, 248–256. https://doi.org/10.1016/j.taap.2003.09.013 (2004).CAS 
Article 
PubMed 

Google Scholar 
Lai, C. H., Chou, C. Y., Ch’ang, L. Y., Liu, C. S. & Lin, W. Identification of novel human genes evolutionarily conserved in Caenorhabditis elegans by comparative proteomics. Genome Res. 10, 703–713. https://doi.org/10.1101/gr.10.5.703 (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Negga, R. et al. Exposure to glyphosate- and/or Mn/Zn-ethylene-bis-dithiocarbamate-containing pesticides leads to degeneration of gamma-aminobutyric acid and dopamine neurons in Caenorhabditis elegans. Neurotox. Res. 21, 281–290. https://doi.org/10.1007/s12640-011-9274-7 (2012).CAS 
Article 
PubMed 

Google Scholar 
Negga, R. et al. Exposure to Mn/Zn ethylene-bis-dithiocarbamate and glyphosate pesticides leads to neurodegeneration in Caenorhabditis elegans. Neurotoxicology 32, 331–341. https://doi.org/10.1016/j.neuro.2011.02.002 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schuske, K., Beg, A. A. & Jorgensen, E. M. The GABA nervous system in C. elegans. Trends Neurosci. 27, 407–414. https://doi.org/10.1016/j.tins.2004.05.005 (2004).CAS 
Article 
PubMed 

Google Scholar 
McIntire, S. L., Jorgensen, E. & Horvitz, H. R. Genes required for GABA function in Caenorhabditis elegans. Nature 364, 334–337. https://doi.org/10.1038/364334a0 (1993).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Eastman, C., Horvitz, H. R. & Jin, Y. Coordinated transcriptional regulation of the unc-25 glutamic acid decarboxylase and the unc-47 GABA vesicular transporter by the Caenorhabditis elegans UNC-30 homeodomain protein. J. Neurosci. 19, 6225–6234 (1999).CAS 
Article 

Google Scholar 
Bamber, B. A., Beg, A. A., Twyman, R. E. & Jorgensen, E. M. The Caenorhabditis elegans unc-49 locus encodes multiple subunits of a heteromultimeric GABA receptor. J. Neurosci. 19, 5348–5359 (1999).CAS 
Article 

Google Scholar 
Risley, M. G., Kelly, S. P., Jia, K., Grill, B. & Dawson-Scully, K. Modulating behavior in C. elegans using electroshock and antiepileptic drugs. PLoS ONE 11, e0163786. https://doi.org/10.1371/journal.pone.0163786 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pandey, R. et al. Baccoside A suppresses epileptic-like seizure/convulsion in Caenorhabditis elegans. Seizure 19, 439–442. https://doi.org/10.1016/j.seizure.2010.06.005 (2010).Article 
PubMed 

Google Scholar 
Risley, M. G., Kelly, S. P. & Dawson-Scully, K. Electroshock induced seizures in adult C. elegans. Bio-Protoc. 7, 163786 (2017).Article 

Google Scholar 
Risley, M. G., Kelly, S. P., Minnerly, J., Jia, K. & Dawson-Scully, K. egl-4 modulates electroconvulsive seizure duration in C. elegans. Invert. Neurosci. 18, 8. https://doi.org/10.1007/s10158-018-0211-9 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McVey, K. A. et al. Exposure of C. elegans eggs to a glyphosate-containing herbicide leads to abnormal neuronal morphology. Neurotoxicol. Teratol. 55, 23–31. https://doi.org/10.1016/j.ntt.2016.03.002 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Burchfield, S. L. et al. Acute exposure to a glyphosate-containing herbicide formulation inhibits Complex II and increases hydrogen peroxide in the model organism Caenorhabditis elegans. Environ. Toxicol. Pharmacol. 66, 36–42. https://doi.org/10.1016/j.etap.2018.12.019 (2019).CAS 
Article 
PubMed 

Google Scholar 
Weisskopf, M. G., Moisan, F., Tzourio, C., Rathouz, P. J. & Elbaz, A. Pesticide exposure and depression among agricultural workers in France. Am. J. Epidemiol. 178, 1051–1058. https://doi.org/10.1093/aje/kwt089 (2013).Article 
PubMed 

Google Scholar 
Kamel, F. et al. Pesticide exposure and self-reported Parkinson’s disease in the agricultural health study. Am. J. Epidemiol. 165, 364–374. https://doi.org/10.1093/aje/kwk024 (2007).CAS 
Article 
PubMed 

Google Scholar 
Tanner, C. M. Advances in environmental epidemiology. Mov. Disord. 25(Suppl 1), S58-62. https://doi.org/10.1002/mds.22721 (2010).Article 
PubMed 

Google Scholar 
Dick, F. D. Parkinson’s disease and pesticide exposures. Br. Med. Bull. 79–80, 219–231. https://doi.org/10.1093/bmb/ldl018 (2006).CAS 
Article 
PubMed 

Google Scholar 
Brown, T. P., Rumsby, P. C., Capleton, A. C., Rushton, L. & Levy, L. S. Pesticides and Parkinson’s disease–Is there a link?. Environ. Health Perspect. 114, 156–164. https://doi.org/10.1289/ehp.8095 (2006).Article 
PubMed 

Google Scholar 
Firestone, J. A. et al. Pesticides and risk of Parkinson disease: A population-based case-control study. Arch. Neurol. 62, 91–95. https://doi.org/10.1001/archneur.62.1.91 (2005).Article 
PubMed 

Google Scholar 
Martinez, M. A. et al. Neurotransmitter changes in rat brain regions following glyphosate exposure. Environ. Res. 161, 212–219. https://doi.org/10.1016/j.envres.2017.10.051 (2018).CAS 
Article 
PubMed 

Google Scholar 
Kalueff, A. V. & Nutt, D. J. Role of GABA in anxiety and depression. Depress. Anxiety 24, 495–517. https://doi.org/10.1002/da.20262 (2007).CAS 
Article 
PubMed 

Google Scholar 
Mohler, H. The GABA system in anxiety and depression and its therapeutic potential. Neuropharmacology 62, 42–53. https://doi.org/10.1016/j.neuropharm.2011.08.040 (2012).CAS 
Article 
PubMed 

Google Scholar 
Brambilla, P., Perez, J., Barale, F., Schettini, G. & Soares, J. C. GABAergic dysfunction in mood disorders. Mol. Psychiatry 8, 721–737. https://doi.org/10.1038/sj.mp.4001362 (2003) (715).CAS 
Article 
PubMed 

Google Scholar 
Xia, G. et al. Reciprocal control of obesity and anxiety-depressive disorder via a GABA and serotonin neural circuit. Mol. Psychiatry 26, 2837–2853. https://doi.org/10.1038/s41380-021-01053-w (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martinez, A. & Al-Ahmad, A. J. Effects of glyphosate and aminomethylphosphonic acid on an isogeneic model of the human blood-brain barrier. Toxicol. Lett. 304, 39–49. https://doi.org/10.1016/j.toxlet.2018.12.013 (2019).CAS 
Article 
PubMed 

Google Scholar 
Goetz, T., Arslan, A., Wisden, W. & Wulff, P. GABA(A) receptors: Structure and function in the basal ganglia. Prog. Brain Res. 160, 21–41. https://doi.org/10.1016/S0079-6123(06)60003-4 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Shaw, W. Elevated urinary glyphosate and clostridia metabolites with altered dopamine metabolism in triplets with autistic spectrum disorder or suspected seizure disorder: A case study. Integr. Med. (Encinitas) 16, 50–57 (2017).
Google Scholar 
Gaupp-Berghausen, M., Hofer, M., Rewald, B. & Zaller, J. G. Glyphosate-based herbicides reduce the activity and reproduction of earthworms and lead to increased soil nutrient concentrations. Sci. Rep. 5, 12886. https://doi.org/10.1038/srep12886 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kanabar, M. et al. Roundup negatively impacts the behavior and nerve function of the Madagascar hissing cockroach (Gromphadorhina portentosa). Environ. Sci. Pollut. Res. Int. https://doi.org/10.1007/s11356-021-13021-6 (2021).Article 
PubMed 

Google Scholar 
Loscher, W., Fassbender, C. P. & Nolting, B. The role of technical, biological and pharmacological factors in the laboratory evaluation of anticonvulsant drugs. II. Maximal electroshock seizure models. Epilepsy Res. 8, 79–94. https://doi.org/10.1016/0920-1211(91)90075-q (1991).CAS 
Article 
PubMed 

Google Scholar 
Castel-Branco, M. M., Alves, G. L., Figueiredo, I. V., Falcao, A. C. & Caramona, M. M. The maximal electroshock seizure (MES) model in the preclinical assessment of potential new antiepileptic drugs. Methods Find. Exp. Clin. Pharmacol. 31, 101–106. https://doi.org/10.1358/mf.2009.31.2.1338414 (2009).CAS 
Article 
PubMed 

Google Scholar 
Luszczki, J. J. et al. Anticonvulsant and acute neurotoxic effects of imperatorin, osthole and valproate in the maximal electroshock seizure and chimney tests in mice: A comparative study. Epilepsy Res. 85, 293–299. https://doi.org/10.1016/j.eplepsyres.2009.03.027 (2009).CAS 
Article 
PubMed 

Google Scholar 
Suthakaran, N. et al. O-GlcNAc transferase OGT-1 and the ubiquitin ligase EEL-1 modulate seizure susceptibility in C. elegans. PLoS ONE 16, e0260072. https://doi.org/10.1371/journal.pone.0260072 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hedberg, D. & Wallin, M. Effects of Roundup and glyphosate formulations on intracellular transport, microtubules and actin filaments in Xenopus laevis melanophores. Toxicol. In Vitro 24, 795–802. https://doi.org/10.1016/j.tiv.2009.12.020 (2010).CAS 
Article 
PubMed 

Google Scholar  More

Garber, P. A. Foraging strategies among living primates. Annu. Rev. Anthropol. 16, 339–364 (1987).Article 

Google Scholar 
Stephens, D. W. & Krebs, J. K. Foraging Theory (Princeton University Press, 1987).Book 

Google Scholar 
Felton, A. M. et al. Nutritional ecology of Ateles chamek in lowland Bolivia: How macronutrient balancing influences food choices. Int. J. Primatol. 30, 675–696 (2009).Article 

Google Scholar 
Marshall, A. J. & Wrangham, R. W. Evolutionary consequences of fallback foods. Int. J. Primatol. 28, 1219–1235 (2007).Article 

Google Scholar 
Rothman, J. M., Raubenheimer, D., Bryer, M. A. H., Takahashi, M. & Gilbert, C. C. Nutritional contributions of insects to primate diets: Implications for primate evolution. J. Hum. Evol. 71, 59–69 (2014).PubMed 
Article 

Google Scholar 
Felton, A. M. et al. Protein content of diets dictates the daily energy intake of a free-ranging primate. Behav. Ecol. 20, 685–690 (2009).Article 

Google Scholar 
Clare, E. L., Symondson, W. O. C. & Fenton, M. B. An inordinate fondness for beetles? Variation in seasonal dietary preferences of night-roosting big brown bats (Eptesicus fuscus). Mol. Ecol. 23, 3633–3647 (2014).PubMed 
Article 

Google Scholar 
Stevenson, P. R., Quinones, M. J. & Ahumada, J. A. Influence of fruit availability on ecological overlap among four neotropical primates at Tinigua National Park, Colombia. Biotropica 32, 533–544 (2000).Article 

Google Scholar 
Chapman, C. Patterns of foraging and range use by three species of neotropical primates. Primates 29, 177–194 (1988).Article 

Google Scholar 
Felton, A. M., Felton, A., Lindenmayer, D. B. & Foley, W. J. Nutritional goals of wild primates. Funct. Ecol. 23, 70–78 (2009).Article 

Google Scholar 
Kay, R. On the use of anatomical features to infer foraging behavior in extinct primates. In Adaptations for Foraging in Nonhuman Primates (eds Rodman, P. & Cant, J.) 21–53 (Columbia University Press, 1984).Chapter 

Google Scholar 
Bravo, S. P. Seed dispersal and ingestion of insect-infested seeds by black howler monkeys in flooded forests of the Parana River, Argentina: Insect-infested seed ingestion and dispersal. Biotropica 40, 471–476 (2008).Article 

Google Scholar 
Deluycker, A. M. Insect prey foraging strategies in Callicebus oenanthe in Northern Peru: Insect foraging in Callicebus oenanthe. Am. J. Primatol. 74, 450–461 (2012).PubMed 
Article 

Google Scholar 
Link, A. Insect-eating by spider monkeys. Neotropical Primates 11, 104–107 (2003).ADS 

Google Scholar 
MacKinnon, K. C. Food choice by juvenile capuchin monkeys (Cebus capucinus) in a tropical dry forest. In New Perspectives in the Study of Mesoamerican Primates (eds Estrada, A. et al.) 349–365 (Kluwer Academic Publishers, 2006). https://doi.org/10.1007/0-387-25872-8_17.Chapter 

Google Scholar 
Fonseca, M. L., Cruz, D. M., Acosta Rojas, D. C., Páez Crespo, J. & Stevenson, P. R. Influence of arthropod and fruit abundance on the dietary composition of highland Colombian woolly monkeys (Lagothrix lagotricha lugens). Folia Primatol. (Basel) 90, 240–257 (2019).Article 

Google Scholar 
Vargas, S. A. et al. Population density and ecological traits of highland woolly monkeys at Cueva de los Guacharos National Park, Colombia. In High Altitude Primates (eds Grow, N. B. et al.) 85–102 (Springer, 2014). https://doi.org/10.1007/978-1-4614-8175-1_5.Chapter 

Google Scholar 
Bryer, M. A. H., Chapman, C. A., Raubenheimer, D., Lambert, J. E. & Rothman, J. M. Macronutrient and energy contributions of insects to the diet of a frugivorous monkey (Cercopithecus ascanius). Int. J. Primatol. 36, 839–854 (2015).Article 

Google Scholar 
Gómez-Posada, C., Rey-Goyeneche, J. & Tenorio, E. A. Ranging responses to fruit and arthropod availability by a tufted capuchin group (Sapajus apella) in the Colombian Amazon. In Movement Ecology of Neotropical Forest Mammals (eds Reyna-Hurtado, R. & Chapman, C. A.) 195–215 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-030-03463-4_12.Chapter 

Google Scholar 
Mallott, E. K., Garber, P. A. & Malhi, R. S. Integrating feeding behavior, ecological data, and DNA barcoding to identify developmental differences in invertebrate foraging strategies in wild white-faced capuchins (Cebus capucinus): Mallott et al. Am. J. Phys. Anthropol. 162, 241–254 (2017).PubMed 
Article 

Google Scholar 
Defler, T. R. & Defler, S. B. Diet of a group of Lagothrix lagothricha lagothricha in southeastern Colombia. Int. J. Primatol. 17, 161–190 (1996).Article 

Google Scholar 
Di Fiore, A. Diet and feeding ecology of woolly monkeys in a western Amazonian rain forest. Int. J. Primatol. 25, 767–801 (2004).Article 

Google Scholar 
Stevenson, P. R., Quinones, M. J. & Ahumada, J. A. Ecological strategies of woolly monkeys (Lagothrix lagotricha) at Tinigua National Park, Colombia. Am. J. Primatol. 32, 123–140 (1994).PubMed 
Article 

Google Scholar 
Izawa, K. Foods and feeding behavior of monkeys in the upper Amazon basin. Primates 16, 295–316 (1975).Article 

Google Scholar 
Peres, C. A. Diet and feeding ecology of gray woolly monkeys (Lagothrix lagotricha cana) in central Amazonia: Comparisons with other atelines. Int. J. Primatol. 15, 333–372 (1994).Article 

Google Scholar 
Stevenson, P. R. Activity and ranging patterns of Colombian woolly monkeys in north-western Amazonia. Primates 47, 239–247 (2006).PubMed 
Article 

Google Scholar 
Milton, K. & Nessimian, J. L. Evidence for insectivory in two primate species (Callicebus torquatus lugens and Lagothrix lagothricha lagothricha) from northwestern Amazonia. Am. J. Primatol. 6, 367–371 (1984).PubMed 
Article 

Google Scholar 
Soini, P. A synecological study of a primate community in the Pacaya-Samiria National Reservee, Peru. Primate Conserv. 7, 63–71 (1986).
Google Scholar 
Pickett, S. B., Bergey, C. M. & Di Fiore, A. A metagenomic study of primate insect diet diversity: A metagenomic study of primate diet. Am. J. Primatol. 74, 622–631 (2012).CAS 
PubMed 
Article 

Google Scholar 
Estupiñan, L. & Muñoz, D. Estudio ecológico comparativo de la artropofauna presente en los receptáculos axilares de dos bromeliáceas epífitas en diferentes bosques andinos. In Estudios ecológicos del páramos y del bosque altoandino Cordillera Oriental de Colombia (eds Mora, L. & Sturm, H.) 679–696 (Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 1995).
Google Scholar 
Solé, R. V. & Montoya, M. Complexity and fragility in ecological networks. Proc. R. Soc. Lond. B Biol. Sci. 268, 2039–2045 (2001).Article 

Google Scholar 
Symondson, W. O. C. Molecular identification of prey in predator diets. Mol. Ecol. 15, 3790–3798 (2002).
Google Scholar 
Gunst, N., Boinski, S. & Fragaszy, D. M. Development of skilled detection and extraction of embedded prey by wild brown capuchin monkeys (Cebus apella apella). J. Comp. Psychol. 124, 194–204 (2010).PubMed 
Article 

Google Scholar 
Panger, M. A. et al. Cross-site differences in foraging behavior of white-faced capuchins (Cebus capucinus). Am. J. Phys. Anthropol. 119, 52–66 (2002).PubMed 
Article 

Google Scholar 
Agostini, I. & Visalberghi, E. Social influences on the acquisition of sex-typical foraging patterns by juveniles in a group of wild tufted capuchin monkeys (Cebus nigritus). Am. J. Primatol. 65, 335–351 (2005).PubMed 
Article 

Google Scholar 
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 
Article 

Google Scholar 
Creer, S. et al. The ecologist’s field guide to sequence-based identification of biodiversity. Methods Ecol. Evol. 7, 1008–1018 (2016).Article 

Google Scholar 
Clare, E. L., Fraser, E. E., Braid, H. E., Fenton, M. B. & Hebert, P. D. N. Species on the menu of a generalist predator, the eastern red bat (Lasiurus borealis): Using a molecular approach to detect arthropod prey. Mol. Ecol. 18, 2532–2542 (2009).PubMed 
Article 

Google Scholar 
Thuo, D. et al. Food from faeces: Evaluating the efficacy of scat DNA metabarcoding in dietary analyses. PLoS One 14, e0225805 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Siegenthaler, A., Wangensteen, O. S., Benvenuto, C., Campos, J. & Mariani, S. DNA metabarcoding unveils multiscale trophic variation in a widespread coastal opportunist. Mol. Ecol. 28, 232–249 (2019).CAS 
PubMed 
Article 

Google Scholar 
De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).PubMed 
Article 
CAS 

Google Scholar 
Esnaola, A., Arrizabalaga-Escudero, A., González-Esteban, J., Elosegi, A. & Aihartza, J. Determining diet from faeces: Selection of metabarcoding primers for the insectivore Pyrenean desman (Galemys pyrenaicus). PLoS One 13, e0208986 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Mallott, E. K. & Amato, K. R. The microbial reproductive ecology of white-faced capuchins (Cebus capucinus). Am. J. Primatol. 80, e22896 (2018).PubMed 
Article 

Google Scholar 
Wray, A. K. et al. Predator preferences shape the diets of arthropodivorous bats more than quantitative local prey abundance. Mol. Ecol. 30, 855–873 (2021).PubMed 
Article 

Google Scholar 
Quiroga-González, C. et al. Monitoring the variation in the gut microbiota of captive woolly monkeys related to changes in diet during a reintroduction process. Sci. Rep. 11, 6522 (2021).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Prada, C. M. & Stevenson, P. R. Plant composition associated with environmental gradients in tropical montane forests (Cueva de Los Guacharos National Park, Huila, Colombia). Biotropica 48, 568–576 (2016).Article 

Google Scholar 
García-Toro, C., Link, A., Páez Crespo, J. & Stevenson, P. R. Home range and daily traveled distances of highland Colombian woolly monkeys (Lagothrix lagothricha lugens): Comparing spatial data from GPS collars and direct follows. In Movement Ecology of Neotropical Forest Mammals (eds Reyna-Hurtado, R. & Chapman, C. A.) 173–193 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-030-03463-4_3.Chapter 

Google Scholar 
Baulu, J. & Redmond, D. E. Some sampling considerations in the quantitation of monkey behavior under field and captive conditions. Primates 19, 391–399 (1978).Article 

Google Scholar 
Julliot, C. Seed dispersal by red howling monkeys (Alouatta seniculus) in the tropical rain forest of French Guiana. Int. J. Primatol. 17, 239–258 (1996).Article 

Google Scholar 
Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211 (1984).Article 

Google Scholar 
Russo, L., Stehouwer, R., Heberling, J. M. & Shea, K. The composite insect trap: An innovative combination trap for biologically diverse sampling. PLoS One 6, e21079 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ohmart, C. P., Stewart, L. G. & Thomas, J. R. Phytophagous insect communities in the canopies of three Eucalyptus forest types in south-eastern Australia. Austral Ecol. 8, 395–403 (1983).Article 

Google Scholar 
Erwin, T. L. Tropical forests: Their richness in Coleoptera and other arthropod species. Coleopt. Bull. 36, 74–75 (1982).
Google Scholar 
Schowalter, T. D., Webb, J. W. & Crossley, D. A. Communtiy structure and nutrient content of canopy arthropods in clearcut and uncut forest ecosystems. Ecology 62, 1010–1019 (1981).Article 

Google Scholar 
Stevenson, P. R. Phenological patterns of woody vegetation at Tinigua Park, Colombia: Methodological comparisons with emphasis on fruit production. Caldasia 26, 125–150 (2004).
Google Scholar 
Vargas, I. & Stevenson, P. R. Patrones fenológicos en la Estación Biológica Mosiro Itajura-Caparú: Producción de frutos estimada a partir de transectos fenológicos y trampas de frutos. In Estación Biológica Mosiro Itajura-Caparú: Biodiversidad en el territorio Yagojé-Apaporis (eds Alarcón-Nieto, G. & Palacios, E.) 99–104 (Conservación Internacional Colombia, 2009).
Google Scholar 
Bautista, S. Patrones de productividad de frutos y dispersión de semillas en diferentes bosques de Colombia, y su relación con la biomasa de primates (2019).King, R. A., Read, D. S., Traugott, M. & Symondson, W. O. C. Invited Review: Molecular analysis of predation: A review of best practice for DNA-based approaches: Optimizing molecular analysis of predation. Mol. Ecol. 17, 947–963 (2008).CAS 
PubMed 
Article 

Google Scholar 
Mata, V. A. et al. How much is enough? Effects of technical and biological replication on metabarcoding dietary analysis. Mol. Ecol. 28, 165–175 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zeale, M. R. K., Butlin, R. K., Barker, G. L. A., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces: DNA barcoding. Mol. Ecol. Resour. 11, 236–244 (2011).CAS 
PubMed 
Article 

Google Scholar 
Jusino, M. A. et al. An improved method for utilizing high-throughput amplicon sequencing to determine the diets of insectivorous animals. Mol. Ecol. Resour. 19, 176–190 (2019).CAS 
PubMed 
Article 

Google Scholar 
Aldasoro, M. et al. Gaining ecological insight on dietary allocation among horseshoe bats through molecular primer combination. PLoS One 14, e0220081 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, 2018).Book 

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

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 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Ratnasingham & Hebert. bold: The barcode of life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Phillips, C. A. & McGrew, W. C. Identifying species in chimpanzee (Pan troglodytes) feces: A methodological lost cause?. Int. J. Primatol. 34, 792–807 (2013).Article 

Google Scholar 
Liu, M., Clarke, L. J., Baker, S. C., Jordan, G. J. & Burridge, C. P. A practical guide to DNA metabarcoding for entomological ecologists. Ecol. Entomol. 45, 373–385 (2020).Article 

Google Scholar 
Porter, T. M. & Hajibabaei, M. Over 2.5 million COI sequences in GenBank and growing. PLoS One 13, e0200177 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Valentini, A., Pompanon, F. & Taberlet, P. DNA barcoding for ecologists. Trends Ecol. Evol. 24, 110–117 (2009).PubMed 
Article 

Google Scholar 
Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10, 20140562 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hajibabaei, M. et al. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 6, 959–964 (2006).CAS 
Article 

Google Scholar 
Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).CAS 
Article 

Google Scholar 
Piñol, J., Senar, M. A. & Symondson, W. O. C. The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol. Ecol. 28, 407–419 (2019).PubMed 
Article 
CAS 

Google Scholar 
R Studio Team. R Studio: Integrated Development of R (Rstudio, PBC, 2020).Hijmans, R. & van Etten, J. raster: Geographic analysis and modeling with raster data (2012).Wickham, H. ggplot2: Elegant graphics for data analysis (2016).Di Fiore, A. & Rodman, P. S. Time allocation patterns of lowland woolly monkeys (Lagothrix lagotricha poeppigii) in a neotropical Terra Firma Forest. Int. J. Primatol. 22, 449–480 (2001).Article 

Google Scholar 
Dew, J. L. Foraging, food choice, and food processing by sympatric ripe-fruit specialists: Lagothrix lagotricha poeppigii and Ateles belzebuth belzebuth. Int. J. Primatol. 26, 1107–1135 (2005).Article 

Google Scholar 
Deblauwe, I. & Janssens, G. P. J. New insights in insect prey choice by chimpanzees and gorillas in Southeast Cameroon: The role of nutritional value. Am. J. Phys. Anthropol. 135, 42–55 (2008).PubMed 
Article 

Google Scholar 
de Carvalho Jr, O., Ferrari, S. F. & Strier, K. B. Diet of a muriqui group (Brachyteles arachnoides) in continuous primary forest. Primates 45, 201–204 (2004).Article 

Google Scholar 
Talebi, M., Bastos, A. & Lee, P. C. Diet of southern muriquis in continuous Brazilian Atlantic forest. Int. J. Primatol. 26, 1175–1187 (2005).Article 

Google Scholar 
Kowalzik, B. K., Pavelka, M. S. M., Kutz, S. J. & Behie, A. Parasites, primates, and ant-plants: Clues to the life cycle of Controrchis spp. in black howler monkeys (Alouatta pigra) in Southern Belize. J. Wildl. Dis. 46, 1330–1334 (2010).PubMed 
Article 

Google Scholar 
Tebbich, S., Taborsky, M., Fessl, B., Dvorak, M. & Winkler, H. Feeding behavior of four arboreal Darwin’s finches: Adaptations to spatial and seasonal variability. Condor 106, 95–105 (2004).Article 

Google Scholar 
Páez Crespo, J. Comportamiento y caracterización genética de churucos de montaña (Lagothrix lagothricha lugens): Inferencias en la filopatría de machos (Universidad de los Andes, 2016).
Google Scholar 
Blüthgen, N., Verhaagh, M., Goitía, W. & Blüthgen, N. Ant nests in tank bromeliads—An example of non-specific interaction. Insectes Soc. 47, 313–316 (2000).Article 

Google Scholar 
Huxley, C. Symbiosos between ants and epiphytes. Biol. Rev. 55, 321–340 (1980).Article 

Google Scholar 
Brehm, G., Pitkin, L. M., Hilt, N. & Fiedler, K. Montane Andean rain forests are a global diversity hotspot of geometrid moths: Hotspot of geometrid moths. J. Biogeogr. 32, 1621–1627 (2005).Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Campuzano, E. F., Ibarra-Núñez, G., Machkour-M’Rabet, S., Morón-Ríos, A. & Jiménez, M. L. Diversity and seasonal variation of ground and understory spiders from a tropical mountain cloud forest. Insect Sci. 27, 826–844 (2020).PubMed 
Article 

Google Scholar 
Miller, J. S. & Thiaucourt, P. Diversity of prominent moths (Lepidoptera: Noctuoidea: Notodontidae) in the cloud forests of northeastern Ecuador, with descriptions of 27 new species. Ann. Entomol. Soc. Am. 104, 1033–1077 (2011).Article 

Google Scholar 
Lambert, J. E. Primate digestion: Interactions among anatomy, physiology, and feeding ecology. Evol. Anthropol. 7, 8–20 (1998).Article 

Google Scholar 
Janiak, M. C. No evidence of copy number variation in acidic mammalian chitinase genes (CHIA) in new world and old world monkeys. Int. J. Primatol. 39, 269–284 (2018).Article 

Google Scholar 
Remis, M. J. & Dierenfeld, E. S. Digesta passage, digestibility and behavior in captive gorillas under two dietary regimens. Int. J. Primatol. 25, 825–845 (2004).Article 

Google Scholar 
Wolda, H. Seasonality of tropical insects. J. Anim. Ecol. 49, 277 (1980).Article 

Google Scholar 
Yanoviak, S. P., Walker, H. & Nadkarni, N. M. Arthropod assemblages in vegetative vs. humic portions of epiphyte mats in a neotropical cloud forest. Pedobiologia 48, 51–58 (2004).Article 

Google Scholar 
Augspurger, C. K. Seedling survival of tropical tree species: Interactions of dispersal distance, light-gaps, and pathogens. Ecology 65, 1705–1712 (1984).Article 

Google Scholar 
Richards, L. A. & Windsor, D. M. Seasonal variation of arthropod abundance in gaps and the understorey of a lowland moist forest in Panama. J. Trop. Ecol. 23, 169–176 (2007).Article 

Google Scholar 
Tercel, M. P. T. G., Symondson, W. O. C. & Cuff, J. P. The problem of omnivory: A synthesis on omnivory and DNA metabarcoding. Mol. Ecol. 30, 2199–2206 (2021).PubMed 
Article 

Google Scholar  More

Study systemWe conducted our research at the Jornada Caves, New Mexico, USA from 8 to 29 June 2018. This remote cave site on private land in the Chihuahuan Desert occupies an elevated volcanic plateau at approximately 1500 m altitude, with the remains of collapsed lava tubes forming a deep canyon with cave and arch features. The site was chosen because of the presence of a population of Swainson’s Hawks (Buteo swainsoni) that predates the population of Mexican Free-tailed Bats (Tadarida brasiliensis) that emerge from the caves en masse daily throughout the summer39. The bats migrate to the site during their breeding season from May to September40, and use the caves as a day roost before flying to their feeding grounds at dusk. The population consists of a maternal colony of approximately 700,000 to 900,000 bats which inhabit two connected caves named North and South. The largest and most reliable emergence was from the South cave, occurring every evening without exception. Emergence from the North cave was less reliable, with no bats emerging at all on some nights during the first week of observations. The numbers of bats were topped up in the second week by new arrivals, and emergence from the North cave was reliable thereafter. Emergence began at a variable time between approximately 18:30 and 20:00 MDT and lasted from 10 to 25 min depending on the number of bats emerging. Sunset was between 20:16 and 20:21 MDT, so the bats usually emerged in broad daylight. During the third week of observations, a substantial second emergence usually occurred at each cave, beginning around 0.5 h after the end of the first emergence, when fewer hawks were present. No ethical issues were identified by the Animal Welfare and Ethical Review Board of the University of Oxford’s Department of Zoology. We attended only as observers, and never entered the caves, so the risk of causing disturbance as the bats emerged was low41.Video observationsWe recorded video of the hawks attacking the bats every evening from 8 to 29 June 2018, except for one evening that had to be missed due to bad weather. We used three pairs of high-definition video cameras (Lumix DMC-FZ1000/2500, Panasonic Corporation, Osaka, Japan) to enable reconstruction of the three-dimensional flight trajectories of the hawks and bats, setting the camera lens to its widest zoom setting. We recorded 25 Hz video at 3840 × 2160 pixels for the first three days and 50 Hz video at 1920 × 1080 pixels for the remainder of the study (Movie S1). This higher frame rate proved necessary to facilitate tracking of the bats’ erratic movements but was traded off against lower spatial resolution. Each camera pair was set in widely spaced stereo configuration to enable three-dimensional reconstruction of the attacks, with a baseline distance of 16 to 27 m. The cameras were mounted on tripods which were adjusted to the same height using an optical level kit (GOL20D/BT160/GR500, Robert Bosch GmbH, Gerlingen, Germany). We used the same optical level kit to measure the baseline distance between the cameras.We set up two camera pairs facing approximately north and south across the South cave for the duration of the study. As the swarm’s overall flight direction was variable and influenced by the wind, we positioned the north- and south-facing camera pairs to allow them to be panned from northeast to northwest and from southeast to southwest, respectively. This enabled us to cover most flight directions, except due east (where the bats rarely flew) and due west (which was subject to glare). We set up a third camera pair to view the emergence that occurred from the North cave from the second week onward. When leaving the North cave, the bats usually flew along the lava tube and beneath a rock arch before climbing out of the canyon. We therefore positioned the cameras close to where the swarm began climbing out above the canyon rim, aiming to capture attacks as the hawks swooped low over the canyon.The hawks consistently appeared within a few minutes of the start of emergence, which enabled us to observe the general direction in which the bat swarm was emerging, and to reorient the cameras to view the swarm before the attacks began. As soon as the bats began emerging, the cameras were turned on and left to record. To begin with, all fieldworkers retreated into make-shift hides, but these were gradually phased out for reasons of practicality. The birds quickly became habituated to our presence, venturing close to the cave even when fieldworkers were present. Each attack began with the hawk approaching the swarm in level flight or stooping in from above. This was followed by fast flight through the stream of bats, with one or more attempts made to grab a bat using a pitch-up, pitch-down, or rolling grab manoeuvre with the legs and talons extended (Movie S1). If the first attack was unsuccessful, then the hawks would usually perform further short-range swoops through the stream until they made a catch. Once a bat was caught, the hawk would drift away from the swarm, to consume its prey on the wing.VideogrammetryWe synchronized the videos using the DLTdv5 video tracking toolbox42 in MATLAB R2020a (MathWorks Inc., Natick, MA). To do so, we matched the complex motions involved in the hawks’ attack manoeuvres visually between videos, and applied the relevant frame offset to synchronize them to the nearest frame. To verify the accuracy of this method, we compared the position of the hawk’s wings between the two videos for the three pairs of frames used for synchronization, and again for the three pairs of frames recorded 50 frames later (Fig. S3). This comparison shows that the frame synchronization remains stable as expected over this 1 s time interval, for the randomly selected flight displayed in Fig. S3. Nevertheless, because the cameras’ shutters were not electronically synchronized, this post hoc procedure can only guarantee synchronization of the frames to within ±0.01 s at the 50 Hz frame rate (see Fig. S3). To assess the sensitivity of our trajectory reconstructions to this remaining synchronization error, we compared the flight trajectories that we had already reconstructed with those that would have been reconstructed had the videos been shifted ±1 frame (Fig. S4). This comparison shows that the displacement of the trajectories resulting from a synchronization error of ±1 frame is small in comparison to their path length, and that their shape remains approximately the same, even for the two stooping flight trajectories plotted in Fig. S4.We used the DLTdv5 toolbox to identify the pixel coordinates of the hawk in both videos within a pair, manually tracking the visual centre of the subject’s body from the point at which it appeared in both cameras up to the point of interception. We used the same method to track the bat that the hawk caught or attempted to catch during the terminal attack sequences that we recorded at close range. The bats were too distant to be tracked individually in recordings of the hawks’ long-range approaches, but the point of actual or attempted capture was nevertheless obvious from the hawks’ flight behaviour. We aimed to reconstruct all attack trajectories that were captured by both cameras within a pair. We were able to reconstruct n = 62 terminal attack trajectories, drawn from n = 50 separate attack flights (i.e. n = 12 of these comprised follow-on attack passes, up to a maximum of four consecutive passes made in cases where the first attack pass was unsuccessful; see Supporting Data and Code for details). We were also able to reconstruct n = 26 long-range approaches. Hence, as the population of hawks peaked at approximately 20 birds, there will have been repeated sampling within individuals in both cases.We calibrated the cameras by matching 15 points across both frames, including background features and points on the hawk, which we selected with the objective of covering as much of the capture volume as possible. The image coordinates of these calibration points were exported from the DLTdv5 toolbox into custom-written software in MATLAB, which solved the camera collinearity equations43 using a nonlinear least squares bundle adjustment implemented using the MATLAB Optimization Toolbox R2020a (see Supporting Data and Code). The bundle adjustment routine identifies jointly optimal estimates of the camera calibration parameters and unknown spatial coordinates of the calibration points, by minimizing the sum of the squared reprojection error of the associated image points. The reprojection error of an image point matched across camera views is defined as the difference between its measured image coordinates and those expected under the camera calibration model given its estimated spatial coordinates. This nonlinear approach enabled us to self-calibrate the cameras using identified features of the environment, whilst also incorporating prior knowledge of the intrinsic and extrinsic camera parameters. This in turn avoided the need to move a known calibration object through the very large imaging volume.We set the calibrated baseline distance between the cameras equal to the measurement that we made of this in the field using the optical level. We fixed the focal length of each camera at 1468.9 pixels for the 1920 × 1080 recordings and at 3918.5 pixels for the 3840 × 2160 recordings. These values were estimated using the Camera Calibrator toolbox in MATLAB, from a set of 20 calibration images of a checkerboard pattern held in front of the camera. Lens distortions were found to be minimal, and we therefore assumed a central perspective projection43 in which we assumed no lens distortion and no principal point offset with respect to the camera sensor. The resulting stereo camera calibration was used to solve for the spatial coordinates of the tracked hawk and bat in MATLAB. This is a least squares solution, in the sense that it minimizes the sum of the squared reprojection error for each image point matched across stereo video frames. We therefore report the root mean square (RMS) reprojection error as a check on the accuracy of the calibrations and reconstructions.For the terminal attack trajectories filmed at close range, the mean RMS reprojection error of the 16 calibrations was 0.73 ± 0.35 pixels, whilst for the reconstructed flight trajectories it was 1.22 ± 1.18 pixels for the hawks and 1.87 ± 2.39 pixels for the bats over all n = 62 flights (mean ± SD). For the long-range approaches filmed at a distance, the RMS reprojection error of the 18 calibrations was 0.53 ± 0.61 pixels, whilst for the reconstructed flight trajectories it was 1.08 ± 1.07 pixels for the hawks over all n = 28 flights (mean ± SD). The sub-pixel reprojection error that we achieved in the calibrations is appropriate to the method. The higher reprojection error of the reconstructions is also to be expected, because whereas the bundle adjustment optimizes the camera calibration parameters jointly with the estimated spatial coordinates of the calibration points, the calibration is held fixed in the reconstructions. In addition, any spatiotemporal error in the matching of points across camera frames will manifest itself as reprojection error in the reconstructions.The foregoing calibration reconstructs the spatial coordinates of the matched image points in a Cartesian coordinate system aligned with the sensor axes of one of the cameras. To aid visualization and interpretation of the flight trajectories, we therefore transformed the spatial coordinates of the hawks and bats into an Earth axis system in which the z axis was vertical. To do so, we filmed and reconstructed the ballistic trajectory of a small rock thrown high into the air through the volume of stereo overlap. We identified the image coordinates of the peak of its parabolic path, together with the image coordinates of two flanking points located ±20 or 25 frames to either side. We took the line dropped from the peak of the parabola perpendicular to the line connecting the two flanking points to define the direction of gravitational acceleration. We then used this to identify the rotation needed to transform the spatial coordinates of the hawks and bats into Earth axes with the z axis as vertical. Finally, we made use of the fact that the two cameras in each pair were fixed at the same height to verify the transformation to Earth axes. For the 16 calibrations used to reconstruct the terminal attack trajectories, the inclination of the baseline between the cameras in Earth axes had a median absolute value of just 1.2˚ (1st, 3rd quartiles: 0.8˚, 2.2˚), providing independent validation of the calibration method that we used.Trajectory analysisAll trajectory analysis was done using custom-written software in MATLAB R2020a (see Supporting Data and Code). We used piecewise cubic Hermite interpolation of the reconstructed trajectories to estimate the spatial coordinates of the hawk or bat for any occasional frames in which this was obscured. We then smoothed the trajectories using quintic spline fitting. For the long-range approaches, we used a spline tolerance designed to remove an RMS spatial position error of 0.5 m, corresponding approximately to the wing length of a hawk. For the terminal attack trajectories, we used a tolerance designed to remove an RMS position error of 0.12 m, corresponding approximately to the wing length of a bat. These values were chosen as representative estimates of the accuracy with which it was possible to match points across frames at long and close range, respectively. Finally, we differentiated and evaluated the splines analytically to estimate the velocity and acceleration of the bird and bat at an up-sampled frequency of 2 kHz. This ensured a suitably small integration step size for the subsequent numerical simulations. On average, the hawks flew faster than the bats (Fig. S5A), so were tracked over longer distances (Fig. S5B), but with considerable overlap in their respective distributions.We simulated the hawk’s attack trajectory in the Earth axes using a guidance law of the form:$${{{{{bf{a}}}}}}(t){{{{{boldsymbol{=}}}}}}N{{{{{boldsymbol{omega }}}}}}(t-tau )times {{{{{bf{v}}}}}}(t){{{{{boldsymbol{-}}}}}}K{{{{{boldsymbol{delta }}}}}}(t-tau )times {{{{{bf{v}}}}}}(t)$$
(1)
where a is the hawk’s commanded centripetal acceleration, v is its velocity, ω is the angular velocity of the line-of-sight r from the hawk to its target, and δ is the deviation angle between r and v, written in vector form with δ mutually perpendicular to r and v. Here, t is time, τ is a fixed time delay, and N and K are guidance constants. With K = 0, Eq. 1 describes proportional navigation (PN), whereas with N = 0, Eq. 1 describes pure proportional pursuit (PP). In the case that K ≠ 0 and N ≠ 0, Eq. 1 describes mixed PN + PP guidance. Dividing through by the hawk’s speed (v=left|{{{{{bf{v}}}}}}right|) converts the commanded centripetal acceleration to the commanded angular velocity. It can therefore be seen that Eq. 1 generalizes, in vector form, the PN + PP guidance law that is written as (dot{gamma }(t)=Ndot{lambda }(t-tau )-Kdelta (t-tau )) in the main text, where the magnitudes of the scalar turn rate, scalar line-of-sight rate, and scalar deviation angle are given respectively as (left|dot{gamma }right vert=left|{{{{{bf{a}}}}}}right|/left|{{{{{bf{v}}}}}}right|), (left|dot{lambda }right vert=left|{{{{{boldsymbol{omega }}}}}}right|), and (left|deltaright vert=left|{{{{{boldsymbol{delta }}}}}}right|).Our simulations make use of the kinematic equations:$${{{{{bf{r}}}}}}={hat{{{{{{bf{x}}}}}}}}_{{{{{{rm{T}}}}}}}-{{{{{bf{x}}}}}}$$
(2)
$${{{{{boldsymbol{omega }}}}}}=frac{{{{{{bf{r}}}}}},times left({hat{{{{{{bf{v}}}}}}}}_{{{{{{rm{T}}}}}}}-{{{{{bf{v}}}}}}right)}{{left|{{{{{bf{r}}}}}}right|}^{{{{{{bf{2}}}}}}}}$$
(3)
$${{{{{boldsymbol{delta }}}}}}=left({{{cos }}}^{-1}frac{{{{{{bf{r}}}}}},cdot, {{{{{bf{v}}}}}}}{left|{{{{{bf{r}}}}}}right|,left|{{{{{bf{v}}}}}}right|}right)left(frac{{{{{{bf{r}}}}}},times {{{{{bf{v}}}}}}}{left|{{{{{bf{r}}}}}},times {{{{{bf{v}}}}}}right|}right)$$
(4)
where x is the simulated position of the hawk, and where ({hat{{{{{{bf{x}}}}}}}}_{{{{{{rm{T}}}}}}}) and ({hat{{{{{{bf{v}}}}}}}}_{{{{{{rm{T}}}}}}}) are the measured position and velocity of the target with respect to the Earth axes. Our simulations are implemented in discrete time by coupling the guidance law (Eq. 1) with the kinematic equations (Eqs. 2–4) using the difference equations:$${{{{{{bf{x}}}}}}}_{n+1}={{{{{{bf{x}}}}}}}_{n}+Delta t,{{{{{{bf{v}}}}}}}_{n}.$$
(5)
$${{{{{{bf{v}}}}}}}_{n+1}={hat{v}}_{n+1},frac{{{{{{{bf{v}}}}}}}_{n}+Delta t,{{{{{{bf{a}}}}}}}_{n}}{left|{{{{{{bf{v}}}}}}}_{n}+Delta t,{{{{{{bf{a}}}}}}}_{n}right|}$$
(6)
where the subscript notation indicates the values of the variables at successive time steps, such that ({t}_{n+1}={t}_{n}+Delta t), and where (hat{v}) is the hawk’s measured groundspeed. The simulations were initiated given the hawk’s measured initial position ({{{{{{bf{x}}}}}}}_{0}={hat{{{{{{bf{x}}}}}}}}_{0}) and velocity ({{{{{{bf{v}}}}}}}_{0}={hat{{{{{{bf{v}}}}}}}}_{0}), and were used to predict the trajectory that it would follow under the guidance law (Eq. 1) parameterized by the guidance constants N and K, and time delay τ. Note that Eq. 6 matches the hawk’s simulated groundspeed (v=left|{{{{{bf{v}}}}}}right|) to its measured groundspeed (hat{v}) at all times, such that the guidance law is only used to command turning. We verified that the step size of our simulations ((Delta t=5times {10}^{-4}) s) was small enough to guarantee the numerical accuracy of the fitted guidance parameters and prediction error to the level of precision at which they are reported in the Results.We defined the prediction error η of each simulation as the mean absolute distance between the measured and simulated flight trajectories:$$eta=frac{1}{k}mathop{sum }limits_{n=1}^{k}left|{{{{{{bf{x}}}}}}}_{n}-{hat{{{{{{bf{x}}}}}}}}_{n}right|$$
(7)
where (hat{{{{{{bf{x}}}}}}}) is the hawk’s simulated position, and k is the number of time steps in the simulation. We fitted the guidance constants K and/or N under the various combinations of guidance law (i.e. PN, PP or PN + PP) and target definition (i.e. measured bat position, final bat position, final hawk position) for delays of 0 ≤ τ ≤ 0.1 s at 0.02 s spacing corresponding to the inter-frame interval. In each case, we used a Nelder–Mead simplex algorithm in MATLAB to find the value of K and/or N that minimised the prediction error η for each flight at the given time delay τ. To ensure that we fitted the same section of flight for all time delays 0 ≤ τ ≤ 0.1 s, we began each simulation from 0.1 s after the first point on the trajectory, and ended the simulation at the time of intercept or near-miss. However, as we found the best-fitting delay to be τ = 0, we subsequently re-fitted the simulations with no delay to begin from the first point on the trajectory and report these simulations in the Results. For the terminal attack trajectories, we took the first point on the trajectory to be the earliest point from which it was possible to track the bat that the hawk caught or attempted to catch, and took the time of intercept or near-miss to be the time at which the measured distance between the hawk and bat was minimal. For the long-range approaches, we tested a range of alternative start points from 1.0 s up to a maximum of 20.0 s before the observed grab manoeuvre, in 0.2 s intervals, to accommodate the fact that the hawk could sometimes be tracked for longer than it appeared to be engaged in directed attack behaviour.Statistical analysisAll statistics were computed using MATLAB R2020a. As the hawks could not be individually identified, we were unable to control for repeated measures from the same individual, and therefore treated each attack trajectory as an independent sample. Because the distributions of the model parameters and errors are skewed (Fig. 2), we report their median, denoted using tilde notation, together with a bias-corrected and accelerated bootstrap 95% confidence interval (CI) computed using 100,000 resamples44. For robustness, we use two-tailed sign tests to compare their distributions between different guidance models and target definitions. We state sample proportions together with a 95% confidence interval (CI) computed using the Clopper–Pearson method. We used a two-tailed Fisher’s exact test to compare the odds of success in attacks on lone bats versus attacks on the swarm. Following our previous observational study18, bats classified as lone bats were judged to be flying >5 body lengths from their nearest neighbours and/or appeared to be flying in a different direction to the coordinated members of the swarm (Table S3).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Ballantyne, G., Baldock, K. C. R., Rendell, L. & Willmer, P. G. Pollinator importance networks illustrate the crucial value of bees in a highly speciose plant community. Sci. Rep. 7, 8389 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Smith, M. R., Singh, G. M., Mozaffarian, D. & Myers, S. S. Effect of decreases of animal pollinators on human nutrition and global health: A modeling analysis. Lancet 386, 1964–1972 (2015).PubMed 
Article 

Google Scholar 
Jung, C. & Cho, S. Relationship between honey bee population and honey production in Korea: A historical trend analysis. J. Apic. 30(1), 7–12 (2015).
Google Scholar 
Abrol, D. P. Asiatic Honey Bee Apis cerana: Biodiversity Conservation and Agricultural Production (Springer, 2013).Book 

Google Scholar 
Chandel, Y. S., Kumar, A. & Srivastva, S. Comparative performance of Apis mellifera L. vis a vis Apis cerana Fab. on toria (Brassica campestris var Toria) in mid-hill zone of Himachal Pradesh, India. Indian J. Agric. Res. 34, 264–267 (2000).
Google Scholar 
Feng, M., Ramadan, H., Han, B., Yu, F. & Li, J. Hemolymph proteome changes during worker brood development match the biological divergences between western honey bees (Apis mellifera) and eastern honey bees (Apis cerana). BMC Genomics 15, 563–576 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Peng, Y. S., Fang, Y., Xu, S. & Ge, L. The resistance mechanism of the Asian honey bee, Apis cerana Fabr., to an ectoparasitic mite, Varroa jacobsoni Oudemans. J. Invertebr. Pathol. 49, 54–60 (1987).Article 

Google Scholar 
McClenaghan, et al. Behavioral responses of honey bees, Apis cerana and Apis mellifera, to Vespa mandarinia marking and alarm pheromones. J. Apic. Res. 58(1), 141–148 (2018).Article 

Google Scholar 
Lin, Z. et al. Go east for better honey bee health: Apis cerana is faster at hygienic behavior than A. mellifera. PLoS ONE 11(9), e0162647 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Verma, L. R. & Dutta, P. C. Foraging behaviour of Apis cerana indicia and Apis mellifera in pollinating apple flowers. J. Apic. Res. 25, 197–201 (1986).Article 

Google Scholar 
Wang, Z. & Tan, K. Comparative analysis of olfactory learning of Apis cerana and Apis mellifera. Apidologie 45(1), 45–52 (2014).ADS 
Article 

Google Scholar 
Beekman, M. & Ratnieks, F. L. W. Long-range foraging by the honey-bee Apis mellifera L.. Funct. Ecol. 14, 490–496 (2000).Article 

Google Scholar 
Dyer, F. C. & Seeley, T. D. Dance dialects and foraging range in three Asian honey bee species. Behav. Ecol. Sociobiol. 28, 227–233 (1991).Article 

Google Scholar 
Koetz, A. H. Ecology, behaviour and control of Apis cerana with a focus on relevance to the Australian incursion. Insects 4(4), 558–592 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Joshi, N. C. & Joshi, P. C. Foraging behavior of Apis spp. On apple flowers in a subtropical environment. N. Y. Sci. J. 3(3), 71–76 (2010).
Google Scholar 
Atwal, A. S. & Sharma, O. P. The dominance of Apis mellifera over Apis indica. Am. Bee J. 111, 343 (1971).
Google Scholar 
Kato, M., Shibata, A., Yasui, T. & Nagamasu, H. Impact of introduced honey bees, Apis mellifera, upon native bee communities in the Bonin (Ogasawara) Islands. Res. Popul. Ecol. 41, 217–228 (1999).Article 

Google Scholar 
Thorp, D. W., Wenner, A. M. & Barthell, J. F. Pollen and nectar resource overlap among bees on Santa Cruz Island. MBC Appl. Environ. Sci. 2020, 261–267 (2000).
Google Scholar 
Yang, G. Harm of introducing the western honey bee Apis mellifera L. to the Chinese honey bee Apis cerana F. and its ecological impact. Acta Entomol. Sin. 48, 401–406 (2005) ((in Chinese)).
Google Scholar 
Dubois, T., Pasquaretta, C., Barron, A. B., Gautrais, J. & Lihoreau, M. A model of resource partitioning between foraging bees based on learning. PLoS Comput. Biol. 17(7), e1009260 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Louveaux, J., Maurizio, A. & Vorwohl, G. Methods of melissopalynology. Bee World 59, 139–153 (1978).Article 

Google Scholar 
Hawkins, J., de Vere, N., Griffith, A. & Ford, C. R. Using DNA metabarcoding to Identify the floral composition of honey: A new tool for investigating honey bee foraging preferences. PLoS ONE 10(8), e0134735 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Smart, M. D. et al. A Comparison of honey bee-collected pollen from working agricultural lands using light microscopy and its metabarcoding. Environ. Entomol. 46(1), 38–49 (2016).
Google Scholar 
Keller, A. et al. Evaluating multiplexed next-generation sequencing as a method in palynology for mixed pollen samples. Plant Biol. 17, 558–566 (2015).CAS 
PubMed 
Article 

Google Scholar 
Richardson, R. T. et al. Rank-based characterization of pollen assemblages collected by honey bees using a multi-locus metabarcoding approach. Appl. Plant Sci. 3, 1500043 (2015).Article 

Google Scholar 
Sickel, W. et al. Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach. BMC Ecol. 15, 20 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kress, W. J. & Erickson, D. L. DNA barcodes: Genes, genomics, and bioinformatics. PNAS 105, 2761–2762 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hollingsworth, P. M., Graham, S. W. & Little, D. P. Choosing and using a plant DNA barcode. PLoS ONE 6, e19254 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, S. et al. Validation of the ITS2 region as a novel DNA barcode for identifying medicinal plant species. PLoS ONE 5, e8613 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
CBOL Plant Working Group. A DNA barcode for land plants. PNAS 106, 12794–12797 (2009).ADS 
PubMed Central 
Article 

Google Scholar 
Pornon, A. et al. Using metabarcoding to reveal and quantify plant-pollinator interactions. Sci. Rep. 6, 27282 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bell, K. L. et al. Quantitative and qualitative assessment of pollen DNA metabarcoding using constructed species mixtures. Mol. Ecol. 28(2), 431–455 (2018).PubMed 
Article 
CAS 

Google Scholar 
Baksay, S. et al. Experimental quantificarion of pollen with DNA metabarcoding using ITS1 and trnL. Sci. Rep. 10, 4202 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ghosh, S. & Jung, C. Nutritional value of bee-collected pollens of hardy kiwi, Actinidia arguta (Actinidiaceae) and oak, Quercus sp. (Fagaceae). J. Asia Pac. Entomol. 20(1), 245–251 (2017).Article 

Google Scholar 
Brunet, J., Thairu, M. W., Henss, J. M., Link, R. I. & Kluevert, J. A. The effects of flower, floral display, and reward sizes on bumblebee foraging behaviour when pollen is the reward and plants are dichogamous. Int. J. Plant Sci. 176(9), 811–819 (2015).Article 

Google Scholar 
de Vere, N. et al. Using DNA metabarcoding to investigate honey bee foraging reveals limited flower use despite high floral availability. Sci. Rep. 7(1), 42838 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barth, O. M. In O polen no mel brasileiro. Rio de Janeiro, Luxor 151 (1989).Lo, T., Gloag, R. S., Anderson, D. L. & Oldroyd, B. P. A molecular phylogeny of the genus Apis suggests that the giant honey bee of southern India, A. indica Fabricius, are valid species. Syst. Entomol. 35, 226–223 (2010).Article 

Google Scholar 
Pirk, C. W. W., Sole, C. L. & Crewe, R. M. Pheromones. In Honey Bees of Asia (eds Hepburn, H. R. & Radloff, S. E.) 207–214 (Springer, Berlin, 2011).Chapter 

Google Scholar 
Theisen-Jones, H. & Bienefeld, K. The Asian honey bee (Apis cerana) is significantly in decline. Bee World 93, 90–97 (2016).Article 

Google Scholar 
Sakagami, S. F. Some interspecific relations between Japanese and European honey bees. J. Anim. Ecol. 28, 51–68 (1959).Article 

Google Scholar 
Thomson, D. Competitive interactions between the invasive European honey bee and native bumble bees. Ecology 85, 458–470 (2004).Article 

Google Scholar 
Brosi, B. J. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. PNAS 110(32), 13044–13048 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Iwasaki, J. M. et al. Floral usage partitioning and competition between social (Apis mellifera, Bombus terrestris) and solitary bees in New Zealand: Niche partitioning via floral preferences?. Austral Ecol. 43(8), 937–948 (2018).Article 

Google Scholar 
Rodrigues, C. S., Ferasso, D. C., Mossi, A. J. & Coelho, G. C. Pollen resources partitioning of stingless bees (Hymenoptera: Apidae) from the southern Atlantic forest Acta Scientiarum. Biol. Sci. 42, e48714 (2020).
Google Scholar 
Lucas, A. et al. Floral resource partitioning by individuals within generalised hoverfly pollination networks revealed by DNA metabarcoding. Sci. Rep. 8(1), 5133 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lucek, K. et al. Metabarcoding of honey to assess differences in plant-pollinator interactions between urban and non-urban sites. Apidologie 50, 317–329 (2019).Article 

Google Scholar 
Tatsuno, M. & Osawa, N. Flower visitation patterns of the coexisting honey bees Apis cerana japonica and Apis mellifera (Hymentoptera: Apidae). Entomol. Sci. https://doi.org/10.1111/ens.12206 (2016).Article 

Google Scholar 
Kuang, B. Y. & Kuang, H. O. Biology of the Honey bee (Yunnan Science and Technology Press, 2002) ([In Chines]).
Google Scholar 
Ghorab, A. et al. Sensorial, melissopalynological and physico-chemical characteristics of honey from Babors Kabylia’s region (Algeria). Foods 10, 225 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sasaki, M. In Bee’s eye view of flowering plants: Nectar- and pollen-source plants and related honey bee products. Kaiyusha, Tokyo, Japan (2010).Simpson, M. G. Diversity and classification of flowering plant: Eudicots. Plant Syst. 2010, 275–448 (2010).Article 

Google Scholar 
Wilms, W. & Wiechers, B. Floral resource partitioning between native Melipona bees and the introduced Africanized honey bee in the Brazilian Atlantic rain forest. Apidologie 28, 339–355 (1997).Article 

Google Scholar 
Klein, S. et al. Honey bees increase their foraging performance and frequency of pollen trips through experience. Sci. Rep. 9, 6778 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bovo, S. et al. Shotgun metagenomics of honey DNA: Evaluation of a methodological approach to describe a multikingdom honey bee derived environmental DNA signature. PLoS ONE 13(10), e0205575 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Prosser, S. W. J. & Hebert, P. D. N. Rapid identification of the botanical and entomological sources of honey using DNA metabarcoding. Food Chem. 214, 183–191 (2017).CAS 
PubMed 
Article 

Google Scholar 
Dhaliwai, H. S. & Sharma, P. L. Foraging range of the Indian honey bee. J. Apic. Res. 13, 137–141 (1974).Article 

Google Scholar 
ESRI. ArcGis Pro (Version 10.6); ESRI Inc.: Redlands, CA, USA (2020).Palmieri, L., Bozza, E. & Giongo, L. Soft fruit traceability in food matrices using real-time PCR. Nutrients 1, 316–328 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kress, W. J. et al. Plant DNA barcodes and a community phylogeny of a tropical forest dynamics plot in Panama. PNAS 106, 18621–18626 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kress, J. W. & Erickson, L. D. A two-locus global DNA barcode for land plants: The coding rbcL gene complements the non-coding trnH-psbA spacer region. PLoS ONE 2(6), 1–10 (2007).
Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Joshi, N. A. & Fass, J. N. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33). https://github.com/najoshi/sickle (2011).Boyer, F. et al. OBITools: A Unix-inspired software package for DNA metabarcoding. Mol. Ecol. Res. 16, 176–182 (2016).CAS 
Article 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. https://www.gbif.org/tool/81287/r-a-language-andenvironment-for-statistical-computing (2022).Bosch, J., Retana, J. & Cerdá, X. Flowering phenology, floral traits and pollinator composition in a herbaceous Mediterranean plant community. Oecologia 109, 583–591 (1997).ADS 
PubMed 
Article 

Google Scholar 
Endress, P. K. Flower structure and trends of evolution in eudicots and their major subclades. Ann. Missouri Bot. Gard. 97(4), 541–583 (2010).Article 

Google Scholar 
Gómez, J. M., Torices, R., Lorite, J., Klingenberg, C. P. & Perfectti, F. The role of pollinators in the evolution of corolla shape variation, disparity and integration in a highly diversified plant family with a conserved floral bauplan. Ann. Bot. 117, 899–904 (2016).Article 

Google Scholar 
Watts, S., Dormann, C. F., González, M. M. & Ollerton, J. The influence of floral traits on specialization and modularity of plant-pollinator networks in a biodiversity hotspot in the Peruvian Andes. Ann. Bot. 118, 415–429 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, C. B. Coloured Flora of Korea. Hyangmunsa, Seoul, Republic of Korea (2003). More

DataWe used temperature data, rainfall data, and data on the incidence of malaria collected from 1901 to 2015 for 43 African countries to construct networks to determine the relationships between transmission of malaria and climate change elements, especially temperature and rainfall. Data resolution is given by the latitude and longitude of the capital city for every country in Africa. Temperature and rainfall data are provided in terms of monthly averages in the country wise. The nodes in the network represent the country, and the edges in the network represent the relationship between countries. We collected malaria data from Harvard Dataverse35 and the world malaria report from the WHO31. Data for temperature and rainfall were obtained from the Climate Change Knowledge Portal of the World Bank Group36.Network generation and analysisThe networks were constructed by using the threshold method where the network depends on the mean, standard deviation, and the real number ((n)) used to control the features of the network. Therefore, data for temperature, rainfall, and the incidence of malaria were divided into six groups mostly comprising ranges of 20 years (1900–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000) as well as the period from 2001 to 2015. The missing data in Malaria incidence data are filled by the average amount of malaria incidence collected per year.In Table S1, a malaria report from the World Health Organization shows that the rate of death is directly proportional to the incidence of malaria35. The death toll in Africa from malaria is about 98% of world deaths from malaria. Such deaths in African regions decrease thanks to efforts the WHO, governments, and the private sector have been conducting to prevent them. Weather and climate are among the factors that drive increases in malaria infections in different areas.We consider networks based on the threshold method (see the “Methods and Materials” section below). First, we fill the missing malaria incidence data, and we calculate normalized Pearson correlation coefficients of three-time series between two countries. Then, we obtain a correlation matrix for the countries. We estimate the average value of the correlation coefficients from the time intervals 1901–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000, and 2001–2015 for three time series: temperature, rainfall, and incidence of malaria. We summarize the averages and standard deviations of the correlation coefficients, as shown in Table S2. The mean values from the correlation in temperature are high, compared to those for rainfall and the incidence of malaria. The standard deviations in temperature and rainfall are large, but the standard deviation for the incidence of malaria is small.We chose an ad hoc threshold value of the correlation coefficients to generate sparse networks. The characteristic values for (n) of the threshold are given in Table S3. We consider three types of thresholds in order to observe changes in the networks according to the threshold.Let us define the normalized variance of each time series. We considered time series (T_{i} left( t right)), (M_{i} left( t right)), and (R_{i} left( t right)) in country (i) for temperature, the incidence of malaria, and rainfall, respectively. We define normalized variance as$$r_{ij} = frac{{x_{i} left( t right)x_{j} left( t right) – x_{i} left( t right)x_{j} left( t right)}}{{sigma_{i} sigma_{j} }}$$
(1)
where (x_{i} left( t right)) = (T_{i} left( t right)), (M_{i} left( t right)), (R_{i} left( t right)). We obtained a Pearson correlation matrix for each time series as follows:$$R_{S} = left[ {begin{array}{*{20}c} {r_{11} } & cdots & {r_{1N} } \ vdots & {r_{ij} } & vdots \ {r_{N1} } & cdots & {r_{NN} } \ end{array} } right]$$
(2)
where (S = T, M, R).We calculated the average value, (overline{r }), and the standard deviation, (sigma), for the correlation coefficients of the matrix. We applied the threshold method to generate a sparse network from the correlation matrix. Two countries are connected in the correlation network if and only if the value of the correlation coefficient is greater than, or equal to, the threshold value:$$r_{{ij}} = left{ {begin{array}{*{20}c} 1 & {{text{if}};r_{{ij}} ge bar{r}{text{ + n}}sigma } \ 0 & {{text{otherwise}}} \ end{array} } right.$$
(3)
where (r_{ij}) is the correlation coefficient between two countries, and (n) is an element of real numbers ((n in {mathbb{R}})). The value of (n) determines whether the network is sparsely or densely connected.We use Python programming language, packages, numpy for mathematical functions and random number generator, pandas for data analysis and manipulations, networkx for creation, manipulation, and studying the structure of the complex network, matplotlib for visualization and plotting graph and base map for map projection and visualization of geographic information. More