Ecology

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

Fodrie, F. J. et al. Measuring individuality in habitat use across complex landscapes: Approaches, constraints, and implications for assessing resource specialization. Oecologia 178, 75–87 (2015).ADS 
PubMed 
Article 

Google Scholar 
Bacheler, N. M., Michelot, T., Cheshire, R. T. & Shertzer, K. W. Fine-scale movement patterns and behavioral states of gray triggerfish Balistes capriscus determined from acoustic telemetry and hidden Markov models. Fish. Res. 215, 76–89 (2019).Article 

Google Scholar 
Furey, N. B., Dance, M. A. & Rooker, J. R. Fine-scale movements and habitat use of juvenile southern flounder Paralichthys lethostigma in an estuarine seascape. J. Fish Biol. 82, 1469–1483 (2013).CAS 
PubMed 
Article 

Google Scholar 
Froehlich, C. Y. M., Garcia, A. & Kline, R. J. Daily movement patterns of red snapper (Lutjanus campechanus) on a large artificial reef. Fish. Res. 209, 49–57 (2019).Article 

Google Scholar 
Williams-Grove, L. J. & Szedlmayer, S. T. Acoustic positioning and movement patterns of red snapper, Lutjanus campechanus, around artificial reefs in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 553, 233–251 (2016).ADS 
Article 

Google Scholar 
Secor, D. H., Zhang, F., O’Brien, M. H. P. & Li, M. Ocean destratification and fish evacuation caused by a Mid-Atlantic tropical storm. ICES J. Mar. Sci. 76, 573–584 (2019).Article 

Google Scholar 
Bacheler, N. M., Shertzer, K. W., Cheshire, R. T. & MacMahan, J. H. Tropical storms influence the movement behavior of a demersal oceanic fish species. Sci. Rep. 9, 1–13 (2019).CAS 
Article 

Google Scholar 
Lowerre-Barbieri, S. K., Walters, S., Bickford, J., Cooper, W. & Muller, R. Site fidelity and reproductive timing at a spotted seatrout spawning aggregation site: Individual versus population scale behavior. Mar. Ecol. Prog. Ser. 481, 181–197 (2013).ADS 
Article 

Google Scholar 
Espinoza, M., Farrugia, T. J., Webber, D. M., Smith, F. & Lowe, C. G. Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals. Fish. Res. 108, 364–371 (2011).Article 

Google Scholar 
Roy, R. et al. Testing the VEMCO positioning system: Spatial distribution of the probability of location and the positioning error in a reservoir. Anim. Biotelemetry 2, 1 (2014).CAS 
Article 

Google Scholar 
Guzzo, M. M. et al. Field testing a novel high residence positioning system for monitoring the fine-scale movements of aquatic organisms. Methods Ecol. Evol. 9, 1478–1488 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Smedbol, S., Smith, F., Webber, D., Vallée, R. & King, T. Using underwater coded acoustic telemetry for fine scale positioning of aquatic animals. In 20th Symposium of the International Society on Biotelemetry Proceedings, 9–11 (2014).Dean, M. J., Hoffman, W. S., Zemeckis, D. R. & Armstrong, M. P. Fine-scale diel and gender-based patterns in behaviour of Atlantic cod (Gadus morhua) on a spawning ground in the western Gulf of Maine. ICES J. Mar. Sci. 71, 1474–1489 (2014).Article 

Google Scholar 
Tarnecki, J. H. & Patterson, W. F. A mini ROV-based method for recovering marine instruments at depth. PLoS One 15, 1–9 (2020).
Google Scholar 
Ellis, R. D. et al. Acoustic telemetry array evolution: From species- and project-specific designs to large-scale, multispecies, cooperative networks. Fish. Res. 209, 186–195 (2019).Article 

Google Scholar 
Friess, C. et al. Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network. Mar. Ecol. Prog. Ser. 663, 157–177 (2021).ADS 
Article 

Google Scholar 
Walters, C. J. & Juanes, F. Recruitment limitation as a consequence of natural selection for use of restricted feeding habitats and predation risk taking by juvenile fishes. Can. J. Fish. Aquat. Sci. 50, 2058–2070 (1993).Article 

Google Scholar 
Ahrens, R. N. M., Walters, C. J. & Christensen, V. Foraging arena theory. Fish Fish. 13, 41–59 (2012).Article 

Google Scholar 
Schwartzkopf, B. D., Langland, T. A. & Cowan, J. H. Habitat selection important for red snapper feeding ecology in the northwestern Gulf of Mexico. Mar. Coast. Fish. 9, 373–387 (2017).Article 

Google Scholar 
Wells, R. J. D., Cowan, J. H. Jr. & Fry, B. Feeding ecology of red snapper Lutjanus campechanus in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 361, 213–225 (2008).ADS 
Article 

Google Scholar 
Goldman, S. F., Glasgow, D. M. & Falk, M. M. Feeding habits of 2 reef-associated fishes, red porgy (Pagrus pagrus) and gray triggerfish (Balistes capriscus), off the Southeastern United States. Fish. Bull. 114, 317–329 (2016).Article 

Google Scholar 
Villegas-Ríos, D., Réale, D., Freitas, C., Moland, E. & Olsen, E. M. Personalities influence spatial responses to environmental fluctuations in wild fish. J. Anim. Ecol. 87, 1309–1319 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Rooker, J. R. et al. Seascape connectivity and the influence of predation risk on the movement of fishes inhabiting a back-reef ecosystem. Ecosphere 9, e02200 (2018).Article 

Google Scholar 
Forman, R. T. T. & Godron, M. Patches and structural components for a landscape ecology. Bioscience 31, 733–740 (1981).Article 

Google Scholar 
Dahl, K. A. & Patterson, W. F. Movement, home range, and depredation of invasive lionfish revealed by fine-scale acoustic telemetry in the northern Gulf of Mexico. Mar. Biol. 167, 1–22 (2020).Article 
CAS 

Google Scholar 
Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Moulton, D. L. et al. Habitat partitioning and seasonal movement of red drum and spotted seatrout. Estuaries Coasts 40, 905–916 (2017).Article 

Google Scholar 
Hammerschlag, N., Luo, J., Irschick, D. J. & Ault, J. S. A Comparison of spatial and movement patterns between sympatric predators: bull sharks (Carcharhinus leucas) and Atlantic tarpon (Megalops atlanticus). PLoS ONE 7, e45958 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Novak, A. J. et al. Scale of biotelemetry data influences ecological interpretations of space and habitat use in yellowtail snapper. Mar. Coast. Fish. 12, 364–377 (2020).Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Werner, E. E. & Gilliam, J. F. The ontogenetic niche and species interactions in size-structured populations. Annu. Rev. Ecol. Syst. 15, 393–425 (1984).Article 

Google Scholar 
Reale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).Article 

Google Scholar 
Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
Article 

Google Scholar 
Huntingford, F. A. The relationship between anti-predator behavior and aggression among conspecifics in the three-spined stickleback, Gasterosteus aculeatus. Anim. Behav. 24, 245–260 (1976).Article 

Google Scholar 
Wilson, D. S., Clark, A. B., Coleman, K. & Dearstyne, T. Shyness and boldness in humans and other animals. Trends Ecol. Evol. 9, 442–446 (1994).Article 

Google Scholar 
Harrison, P. M. et al. Personality-dependent spatial ecology occurs independently from dispersal in wild burbot (Lota lota). Behav. Ecol. 26, 483–492 (2015).Article 

Google Scholar 
Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).CAS 
PubMed 
Article 

Google Scholar 
Hussey, N. E. et al. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642–1255642 (2015).PubMed 
Article 
CAS 

Google Scholar 
Lowerre-Barbieri, S. K., Kays, R., Thorson, J. T. & Wikelski, M. The ocean’s movescape: Fisheries management in the bio-logging decade (2018–2028). ICES J. Mar. Sci. 76, 477–488 (2019).Article 

Google Scholar 
National Marine Fisheries Service. Fisheries Economics of the United State 2016. NOAA Tech. Memo. NMFS-F/SPO-187a. https://www.fisheries.noaa.gov/resource/document/fisheries-economics-united-states-report-2016 (2018). Accessed 08 January 2018.Patterson, W. F. III, Tarnecki, J., Addis, D. T. & Barbieri, L. R. Reef fish community structure at natural versus artificial reefs in the northern Gulf of Mexico. In Proc. 66th Gulf Caribb. Fish. Inst. 4–8 (2014).Streich, M. K. et al. Effects of a new artificial reef complex on red snapper and the associated fish community: An evaluation using a before–after control–impact approach. Mar. Coast. Fish. 9, 404–418 (2017).Article 

Google Scholar 
Dance, M. A., Patterson, W. F. III. & Addis, D. T. Fish community and trophic structure at artificial reef sites in the northeastern Gulf of Mexico. Bull. Mar. Sci. 87, 301–324 (2011).Article 

Google Scholar 
Cowan, J. H. Red snapper in the Gulf of Mexico and the U.S. South Atlantic: data, doubt, and debate. Fisheries 36, 319–331 (2011).Article 

Google Scholar 
Addis, D. T., Patterson, W. F. III. & Dance, M. A. The potential for unreported artificial reefs to serve as refuges from fishing mortality for reef fishes. N. Am. J. Fish. Manag. 36, 131–139 (2016).Article 

Google Scholar 
McCawley, J. R., Cowan, J. H. Jr. & Shipp, R. L. Feeding periodicity and prey habitat preference of red snapper, Lutjanus campechanus (Poey, 1860), on Alabama artificial reefs. Gulf Mex. Sci. 24, 14–27 (2006).
Google Scholar 
Glenn, H. D., Cowan, J. H. Jr. & Powers, J. E. A comparison of red snapper reproductive potential in the northwestern Gulf of Mexico: Natural versus artificial habitats. Mar. Coast. Fish. 9, 139–148 (2017).Article 

Google Scholar 
Kulaw, D. H., Cowan, J. H. Jr. & Jackson, M. W. Temporal and spatial comparisons of the reproductive biology of northern Gulf of Mexico (USA) red snapper (Lutjanus campechanus) collected a decade apart. PLoS One 12, e0172360 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vose, F. E. & Nelson, W. G. Gray triggerfish (Balistes capriscus Gmelin) feeding from artificial and natural substrate in shallow Atlantic waters of Florida. Bull. Mar. Sci. 55, 1316–1323 (1994).
Google Scholar 
Herbig, J. L. & Szedlmayer, S. T. Movement patterns of gray triggerfish, Balistes capriscus, around artificial reefs in the northern Gulf of Mexico. Fish. Manag. Ecol. 23, 418–427 (2016).Article 

Google Scholar 
Szedlmayer, S. T. & Schroepfer, R. L. Long-term residence of red snapper on artificial reefs in the northeastern Gulf of Mexico. Trans. Am. Fish. Soc. 134, 315–325 (2005).Article 

Google Scholar 
Watterson, J. C. III., Patterson, W. F. I. I. I., Shipp, R. L. & Cowan, J. H. Jr. Movement of red snapper, Lutjanus campechanus, in the north central Gulf of Mexico: Potential effects of hurricanes. Gulf Mex. Sci. 16, 92–104 (1998).
Google Scholar 
Ingram, G. W. Jr. & Patterson, W. F. I. I. I. Movement patterns of red snapper (Lutjanus campechanus), greater amberjack (Seriola dumerili), and gray triggerfish (Balistes capriscus) in the Gulf of Mexico and the utility of marine reserves as management tools. Proc. Gulf Caribb. Fish. Inst. 52, 686–699 (2001).
Google Scholar 
Strelcheck, A. J., Cowan, J. H. Jr. & Patterson, W. F. III. Site fidelity, movement, and growth of red snapper Lutjanus campechanus: implications for artificial reef management. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 147–162 (2007).Addis, D. T., Patterson, W. F. I. I. I., Dance, M. A. & Ingram, G. W. Jr. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

Google Scholar 
Topping, D. T. & Szedlmayer, S. T. Site fidelity, residence time and movements of red snapper Lutjanus campechanus estimated with long-term acoustic monitoring. Mar. Ecol. Prog. Ser. 437, 183–200 (2011).ADS 
Article 

Google Scholar 
Everett, A. G., Szedlmayer, S. T. & Gallaway, B. J. Movement patterns of red snapper Lutjanus campechanus based on acoustic telemetry around oil and gas platforms in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 649, 155–173 (2020).Article 

Google Scholar 
Tarnecki, J. H. & Patterson, W. F. I. I. I. Changes in red snapper diet and trophic ecology following the Deepwater Horizon Oil Spill. Mar. Coast. Fish. 7, 135–147 (2015).Article 

Google Scholar 
McCawley, J. R. & Cowan, J. H. Jr. Seasonal and size specific diet and prey demand of Red Snapper on Alabama artificial reefs. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III., Cowan, J. H. Jr., Fitzhugh, G. R. & Nieland, D. L.), 77–104 (2007).Piraino, M. N. & Szedlmayer, S. T. Fine-scale movements and home ranges of red snapper around artificial reefs in the northern Gulf of Mexico. Trans. Am. Fish. Soc. 143, 988–998 (2014).Article 

Google Scholar 
Williams-Grove, L. J. & Szedlmayer, S. T. Depth preferences and three-dimensional movements of red snapper, Lutjanus campechanus, on an artificial reef in the northern Gulf of Mexico. Fish. Res. 190, 61–70 (2017).Article 

Google Scholar 
Topping, D. T. & Szedlmayer, S. T. Home range and movement patterns of red snapper (Lutjanus campechanus) on artificial reefs. Fish. Res. 112, 77–84 (2011).Article 

Google Scholar 
Baker, M. S. J. & Wilson, C. A. Use of bomb radiocarbon to validate otolith section ages of red snapper Lutjanus campechanus from the northern Gulf of Mexico. Limnol. Oceanogr. 46, 1819–1824 (2001).ADS 
Article 

Google Scholar 
Allman, R. J., Fioramonti, C. L., Patterson, W. F. III. & Pacicco, A. E. Validation of annual growth-zone formation in gray triggerfish Balistes capriscus dorsal spines, fin rays, and vertebrae. Gulf Mex. Sci. 33, 68–76 (2016).
Google Scholar 
Frazer, T. K., Lindberg, W. J. & Stanton, G. R. Predation on sand dollars by gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Bull. Mar. Sci. 48, 159–164 (1991).
Google Scholar 
Delorenzo, D. M., Bethea, D. M. & Carlson, J. K. An assessment of the diet and trophic level of Atlantic sharpnose shark Rhizoprionodon terraenovae. J. Fish Biol. 86, 385–391 (2015).CAS 
PubMed 
Article 

Google Scholar 
Aines, A. C., Carlson, J. K., Boustany, A., Mathers, A. & Kohler, N. E. Feeding habits of the tiger shark, Galeocerdo cuvier, in the northwest Atlantic Ocean and Gulf of Mexico. Environ. Biol. Fish. 101, 403–415 (2018).Article 

Google Scholar 
Castro, J. I. The Sharks of North America (Oxford University Press, 2011).
Google Scholar 
Springer, S. A collection of fishes from the stomachs of sharks taken off Salerno, Florida. Copeia 3, 174–175 (1946).Article 

Google Scholar 
Bohaboy, E. C., Guttridge, T. L., Hammerschlag, N., Van Zinnicq Bergmann, M. P. M. & Patterson, W. F. III. Application of three-dimensional acoustic telemetry to assess the effects of rapid recompression on reef fish discard mortality. ICES J. Mar. Sci. 77, 83–96 (2020).Article 

Google Scholar 
Drymon, J. M., Powers, S. P., Dindo, J., Dzwonkowski, B. & Henwood, T. Distributions of sharks across a continental shelf in the northern Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 2, 440–450 (2010).Article 

Google Scholar 
Ajemian, M. J. et al. Movement patterns and habitat use of tiger sharks (Galeocerdo cuvier) across ontogeny in the Gulf of Mexico. PLoS One 15, 1–24 (2020).
Google Scholar 
Ouzts, A. C. & Szedlmayer, S. T. Diel feeding patterns of Red Snapper on artificial reefs in the north-central Gulf of Mexico. Trans. Am. Fish. Soc. 132, 1186–1193 (2003).Article 

Google Scholar 
White, D. B. & Palmer, S. M. Age, growth, and reproduction of the red snapper, Lutjanus campechanus, from the Atlantic waters of the Southeastern US. Bull. Mar. Sci. 75, 335–360 (2004).
Google Scholar 
Fitzhugh, G. R., Lyon, H. M. & Barnett, B. K. Reproductive parameters of gray triggerfish (Balistes capriscus) from the Gulf of Mexico: Sex ratio, maturity and spawning fraction. SEDAR43-WP-03. (2015). http://sedarweb.org/sedar-82-rd14-sedar43-wp-03reproductive-parameters-gray-triggerfish-balistes-capriscus-gulf-mexico. Accessed 12 April 2021.Kelly-Stormer, A. et al. Gray Triggerfish reproductive biology, age, and growth off the Atlantic coast of the Southeastern USA. Trans. Am. Fish. Soc. 146, 523–538 (2017).Article 

Google Scholar 
Porch, C. E., Fitzhugh, G. R., Lang, E. T., Lyon, H. M. & Linton, B. C. Estimating the dependence of spawning frequency on size and age in Gulf of Mexico red snapper. Mar. Coast. Fish. 7, 233–245 (2015).Article 

Google Scholar 
Lang, E. T. & Fitzhugh, G. R. Oogenesis and fecundity type of gray triggerfish in the Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 7, 338–348 (2015).Article 

Google Scholar 
Woods, M. K. et al. Size and age at maturity of female red snapper Lutjanus campechanus in the Northern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 54, 526–537 (2003).
Google Scholar 
Simmons, C. M. & Szedlmayer, S. T. Territoriality, reproductive behavior, and parental care in gray triggerfish, Balistes capriscus, from the Northern Gulf of Mexico. Bull. Mar. Sci. 88, 197–209 (2012).Article 

Google Scholar 
Mackichan, C. A. & Szedlmayer, S. T. Reproductive behavior of the gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 59, 213–218 (2007).
Google Scholar 
Diamond, S. L. et al. Movers and stayers: Individual variability in site fidelity and movements of red snapper off Texas. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 163–187 (2007).Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).ADS 
PubMed 
Article 

Google Scholar 
Smith, F. Understanding HPE in the VEMCO Positioning System (VPS). (2013).US Department of Defense. Global Positioning System Standard Positioning Service Performance Standard. http://www.gps.gov/technical/ps/2008-SPS-performance-standard.pdf (2008). Accessed 08 July 2020.Heupel, M. R., Reiss, K. L., Yeiser, B. G. & Simpfendorfer, C. A. Effects of biofouling on performance of moored data logging acoustic receivers. Limnol. Oceanogr. Methods 6, 327–335 (2008).Article 

Google Scholar 
National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42012—Orange Beach. http://www.ndbc.noaa.gov/station_page.php?station=42012 (2017). Accessed 07 November 2017.National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42040- Luke Offshore Test Platform. https://www.ndbc.noaa.gov/station_page.php?station=42040 (2019). Accessed 07 January 2019.Lazaridis, E. R Package ‘lunar’: lunar phase & distance, seasons and other environmental factors. https://cran.r-project.org/web/packages/lunar/lunar.pdf (2015). Accessed 12 August 2019.Thieurmel, B. & Elmarhraoui, A. R Package ‘suncalc’: compute sun position, sunlight phases, moon position and lunar phase. https://cran.r-project.org/web/packages/suncalc/suncalc.pdf (2019). Accessed 22 June 2019.National Geophysical Data Center. U.S. Coastal Relief Model—Central Gulf of Mexico. https://doi.org/10.7289/V54Q7RW0 (2001).Cox, D. R. & Oakes, D. Analysis of Survival Data (Chapman and Hall, 1984).Benhamou, S. Dynamic approach to space and habitat use based on biased random bridges. PLoS One 6, e14592 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Horne, J. S., Garton, E. O., Krone, S. M. & Lewis, J. S. Analyzing animal movements using Brownian bridges. Ecology 88, 2354–2363 (2007).PubMed 
Article 

Google Scholar 
Tracey, J. A. et al. Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation. PLoS One 9, e101205 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tracey, J. A. et al. R Package ‘mkde’: 2D and 3D movement-based kernel density estimates (MKDEs). https://CRAN.R-project.org/package=mkde (2014). Accessed 17 June 2019.Worton, B. J. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989).Article 

Google Scholar 
Wood, S. N. Package ‘mgcv’: Mixed GAM computation vehicle with automatic smoothness estimation. https://doi.org/10.1201/9781315370279 (2019). More

Wilson, E. O. Sociobiology: The New Synthesis (Harvard Univ. Press, 1975).Diamond, J. M. & Ordunio, D. Guns, Germs, and Steel (Books on Tape, 1999).Couzin, I. D. et al. Self-organization and collective behavior in vertebrates. Adv. Study Behav. 32, 1–75 (2003).
Google Scholar 
Keller, L. Adaptation and the genetics of social behaviour. Philos. Trans. R. Soc. Lond. B 364, 3209–3216 (2009).
Google Scholar 
Kay, T., Keller, L. & Lehmann, L. The evolution of altruism and the serial rediscovery of the role of relatedness. Proc. Natl Acad. Sci. USA 117, 28894–28898 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).CAS 
PubMed 

Google Scholar 
Johnson, K. V. A. & Foster, K. R. Why does the microbiome affect behaviour? Nat. Rev. Microbiol. 16, 647–655 (2018).CAS 
PubMed 

Google Scholar 
Sherwin, E., Bordenstein, S. R., Quinn, J. L., Dinan, T. G. & Cryan, J. F. Microbiota and the social brain. Science 366, eaar2016 (2019).CAS 
PubMed 

Google Scholar 
Desbonnet, L., Clarke, G., Shanahan, F., Dinan, T. G. & Cryan, J. F. Microbiota is essential for social development in the mouse. Mol. Psychiatry 19, 146–148 (2014).CAS 
PubMed 

Google Scholar 
Sharon, G. et al. Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell 177, 1600–1618 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, M. et al. A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children. Sci. Adv. 6, eaba3760 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, W.-L. et al. Microbiota regulate social behaviour via stress response neurons in the brain. Nature 595, 409–414 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vuong, H. E., Yano, J. M., Fung, T. C. & Hsiao, E. Y. The microbiome and host behavior. Annu. Rev. Neurosci. 40, 21–49 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, A. E. Simple animal models for microbiome research. Nat. Rev. Microbiol. 17, 764–775 (2019).CAS 
PubMed 

Google Scholar 
Schretter, C. E. Links between the gut microbiota, metabolism, and host behavior. Gut Microbes 11, 245–248 (2020).PubMed 

Google Scholar 
Liberti, J. & Engel, P. The gut microbiota–brain axis of insects. Curr. Opin. Insect Sci. 39, 6–13 (2020).PubMed 

Google Scholar 
O’Donnell, M. P., Fox, B. W., Chao, P.-H., Schroeder, F. C. & Sengupta, P. A neurotransmitter produced by gut bacteria modulates host sensory behaviour. Nature 583, 415–420 (2020).PubMed 
PubMed Central 

Google Scholar 
Wilson, E. O. The Insect Societies (Harvard Univ. Press, 1971).Hölldobler, B. & Wilson, E. O. The Ants (Harvard Univ. Press, 1990).Teseo, S. et al. The scent of symbiosis: gut bacteria may affect social interactions in leaf-cutting ants. Anim. Behav. 150, 239–254 (2019).
Google Scholar 
Vernier, C. L. et al. The gut microbiome defines social group membership in honey bee colonies. Sci. Adv. 6, eabd3431 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, L. et al. Gut microbiome drives individual memory variation in bumblebees. Nat. Commun. 12, 6588 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Choi, S. H. et al. Individual variations lead to universal and cross-species patterns of social behavior. Proc. Natl Acad. Sci. USA 117, 31754–31759 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Geffre, A. C. et al. Honey bee virus causes context-dependent changes in host social behavior. Proc. Natl Acad. Sci. USA 117, 10406–10413 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76 (2018).CAS 
PubMed 

Google Scholar 
Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).PubMed 
PubMed Central 

Google Scholar 
Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. Proc. Natl Acad. Sci. USA 114, 4775–4780 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 15, e2003467 (2017).PubMed 
PubMed Central 

Google Scholar 
Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814 (2020).PubMed 

Google Scholar 
Mersch, D. P., Crespi, A. & Keller, L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340, 1090–1093 (2013).CAS 
PubMed 

Google Scholar 
Stroeymeyt, N. et al. Social network plasticity decreases disease transmission in a eusocial insect. Science 362, 941–945 (2018).CAS 
PubMed 

Google Scholar 
Kao, A. B. & Couzin, I. D. Modular structure within groups causes information loss but can improve decision accuracy. Philos. Trans. R. Soc. Lond. B 374, 20180378 (2019).
Google Scholar 
de Groot, A. P. Protein and amino acid requirements of the honeybee (Apis mellifica L.). Physiol. Comp. Oecol. 3, 197–285 (1953).
Google Scholar 
Billard, J.-M. d-Amino acids in brain neurotransmission and synaptic plasticity. Amino Acids 43, 1851–1860 (2012).CAS 
PubMed 

Google Scholar 
Marcaggi, P. & Attwell, D. Role of glial amino acid transporters in synaptic transmission and brain energetics. Glia 47, 217–225 (2004).PubMed 

Google Scholar 
Gage, S. L., Calle, S., Jacobson, N., Carroll, M. & DeGrandi-Hoffman, G. Pollen alters amino acid levels in the honey bee brain and this relationship changes with age and parasitic stress. Front. Neurosci. 14, 231 (2020).PubMed 
PubMed Central 

Google Scholar 
Kawase, T. et al. Gut microbiota of mice putatively modifies amino acid metabolism in the host brain. Br. J. Nutr. 117, 775–783 (2017).CAS 
PubMed 

Google Scholar 
Socha, E., Koba, M. & Koslinski, P. Amino acid profiling as a method of discovering biomarkers for diagnosis of neurodegenerative diseases. Amino Acids 51, 367–371 (2019).CAS 
PubMed 

Google Scholar 
Tarlungeanu, D. C. et al. Impaired amino acid transport at the blood brain barrier is a cause of autism spectrum disorder. Cell 167, 1481–1494 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Maynard, T. M. & Manzini, M. C. Balancing act: maintaining amino acid levels in the autistic brain. Neuron 93, 476–479 (2017).CAS 
PubMed 

Google Scholar 
Kurochkin, I. et al. Metabolome signature of autism in the human prefrontal cortex. Commun. Biol. 2, 234 (2019).PubMed 
PubMed Central 

Google Scholar 
van der Velpen, V. et al. Systemic and central nervous system metabolic alterations in Alzheimer’s disease. Alzheimer’s Res. Ther. 11, 93 (2019).
Google Scholar 
Aldana, B. I. et al. Glutamate–glutamine homeostasis is perturbed in neurons and astrocytes derived from patient iPSC models of frontotemporal dementia. Mol. Brain 13, 125 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Galizia, C. G., Eisenhardt, D. & Giurfa M. (eds) Honeybee Neurobiology and Behavior: A Tribute to Randolf Menzel (Springer Science & Business Media, 2011).Menzel, R. The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13, 758–768 (2012).CAS 
PubMed 

Google Scholar 
Ellegaard, K. M. & Engel, P. Genomic diversity landscape of the honey bee gut microbiota. Nat. Commun. 10, 446 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bruno, F., Angilica, A., Cosco, F., Luchi, M. L. & Muzzupappa, M. Mixed prototyping environment with different video tracking techniques. In IMProVe 2011 International Conference on Innovative Methods in Product Design (eds Concheri, G. et al.) 105–113 (Libreria Internazionale Cortina Padova, 2011).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Anderson, K. E., Rodrigues, P. A. P., Mott, B. M., Maes, P. & Corby-Harris, V. Ecological succession in the honey bee gut: shift in Lactobacillus strain dominance during early adult development. Microb. Ecol. 71, 1008–1019 (2016).CAS 
PubMed 

Google Scholar 
Almasri, H., Liberti, J., Brunet, J. L., Engel, P. & Belzunces, L. P. Mild chronic exposure to pesticides alters physiological markers of honey bee health without perturbing the core gut microbiota. Sci. Rep. 12, 4281 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).Gallup, J. M. in PCR Troubleshooting and Optimization: The Essential Guide (eds Kennedy, S. & Oswald, N.) 23–65 (Caister Academic Press, 2011).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
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 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).
Google Scholar 
Patassini, S. et al. Identification of elevated urea as a severe, ubiquitous metabolic defect in the brain of patients with Huntington’s disease. Biochem. Biophys. Res. Commun. 468, 161–166 (2015).CAS 
PubMed 

Google Scholar 
Gonzalez-Riano, C., Garcia, A. & Barbas, C. Metabolomics studies in brain tissue: a review. J. Pharm. Biomed. Anal. 130, 141–168 (2016).CAS 
PubMed 

Google Scholar 
Belle, J. E. L., Harris, N. G., Williams, S. R. & Bhakoo, K. K. A comparison of cell and tissue extraction techniques using high-resolution 1H-NMR spectroscopy. NMR Biomed. 15, 37–44 (2002).PubMed 

Google Scholar 
Wanichthanarak, K., Jeamsripong, S., Pornputtapong, N. & Khoomrung, S. Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data. Comput. Struct. Biotechnol. J. 17, 611–618 (2019).PubMed 
PubMed Central 

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

Google Scholar 
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
PubMed 

Google Scholar 
Wallberg, A. et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genomics 20, 275 (2019).PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).CAS 
PubMed 

Google Scholar 
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).PubMed 
PubMed Central 

Google Scholar 
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).CAS 
PubMed 

Google Scholar 
Reijnders, M. J. & Waterhouse, R. M. Summary visualisations of gene ontology terms with GO-Figure! Front. Bioinform. 1, 638255 (2021).
Google Scholar  More

Flombaum, P. et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. PNAS 110, 9824–9829 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Honda, D. & Yokota, A. Detection of seven major evolutionary lineages in cyanobacteria based on the 165 rRNA gene sequence analysis with new sequences of five marine Synechococcus strains. J Mol Evol 48, 723–739 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Robertson, B. R., Tezuka, N. & Watanabe, M. M. Phylogenetic analyses of Synechococcus strains (cyanobacteria) using sequences of 16S rDNA and part of the phycocyanin operon reveal multiple evolutionary lines and reflect phycobilin content. Int. J. Syst. Evol. Microbiol. 51, 861–871 (2001).CAS 
PubMed 
Article 

Google Scholar 
Stomp, M. et al. Adaptive divergence in pigment composition promotes phytoplankton biodiversity. Nature 432, 104–107 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Albrecht, M., Pröschold, T. & Schumann, R. Identification of Cyanobacteria in a eutrophic coastal lagoon on the Southern Baltic Coast. Front. Microbiol. 8, 1–16 (2017).Article 

Google Scholar 
Bertos-Fortis, M. et al. Unscrambling cyanobacteria community dynamics related to environmental factors. Front. Microbiol. 7, 625 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hunter-Cevera, K. R. et al. Seasons of syn. Limnol. Oceanogr. 65, 1–18 (2019).
Google Scholar 
Kuosa, H. Picoplanktonic algae in the northern Baltic Sea: Seasonal dynamics and flagellate grazing. Mar. Ecol. Prog. Ser. 73, 269–276 (1991).ADS 
Article 

Google Scholar 
Sathicq, M. B., Unrein, F. & Gómez, N. Recurrent pattern of picophytoplankton dynamics in estuaries around the world: The case of Río de la Plata. Mar. Environ. Res. 161, 105136 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rajaneesh, K. M. & Mitbavkar, S. Factors controlling the temporal and spatial variations in Synechococcus abundance in a monsoonal estuary. Mar. Environ. Res. 92, 133–143 (2013).Article 
CAS 

Google Scholar 
Crosbie, N. D., Pöckl, M. & Weisse, T. Dispersal and phylogenetic diversity of nonmarine picocyanobacteria, inferred from 16S rRNA gene and cpcBA-intergenic spacer sequence analyses. Appl. Environ. Microbiol. 69, 5716–5721 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ernst, A., Becker, S., Wollenzien, U. I. A. & Postius, C. Ecosystem-dependent adaptive radiations ofpicocyanobacteria inferred from 16S rRNA and ITS-1 sequence analysis. Microbiology 149, 217–228 (2003).CAS 
PubMed 
Article 

Google Scholar 
Sánchez-Baracaldo, P., Handley, B. A. & Hayest, P. K. Picocyanobacterial community structure of freshwater lakes and the Baltic Sea revealed by phylogenetic analyses and clade-specific quantitative PCR. Microbiology 154, 3347–3357 (2008).PubMed 
Article 
CAS 

Google Scholar 
Hu, Y. O. O., Karlson, B., Charvet, S. & Andersson, A. F. Diversity of pico- to mesoplankton along the 2000 km salinity gradient of the Baltic Sea. Front. Microbiol. 7, 679 (2016).PubMed 
PubMed Central 

Google Scholar 
Larsson, J. et al. Picocyanobacteria containing a novel pigment gene cluster dominate the brackish water Baltic Sea. ISME J. 8, 1892–1903 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Celepli, N. et al. Meta-omic analyses of Baltic Sea cyanobacteria: Diversity, community structure and salt acclimation. Environ. Microbiol. 19, 673–686 (2017).CAS 
PubMed 
Article 

Google Scholar 
Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. 13, 116–120 (2020).ADS 
CAS 
Article 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 
Article 

Google Scholar 
Cabré, A., Marinov, I. & Leung, S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 earth system models. Clim. Dyn. 45, 1253–1280 (2015).Article 

Google Scholar 
Wang, T., Chen, X., Qin, S. & Li, J. Phylogenetic and phenogenetic diversity of Synechococcus along a yellow sea section reveal its environmental dependent distribution and co-occurrence microbial pattern. J. Mar. Sci. Eng. 9, 1018 (2021).Article 

Google Scholar 
Tai, V. & Palenik, B. Temporal variation of Synechococcus clades at a coastal Pacific Ocean monitoring site. ISME J. 3, 903–915 (2009).CAS 
PubMed 
Article 

Google Scholar 
Ahlgren, N. A. & Rocap, G. Diversity and distribution of marine Synechococcus: Multiple gene phylogenies for consensus classification and development of qPCR assays for sensitive measurement of clades in the ocean. Front. Microbiol. 3, 1–24 (2012).Article 
CAS 

Google Scholar 
Rajaneesh, K. M., Mitbavkar, S., Anil, A. C. & Sawant, S. S. Synechococcus as an indicator of trophic status in the Cochin backwaters, west coast of India. Ecol. Indic. 55, 118–130 (2015).Article 

Google Scholar 
Campbell, L. & Carpenter, E. J. Characterization of phycoerythrin-containing Synechococcus spp. populations by immunofluorescence. J. Plankton Res. 9, 1167–1181 (1987).Article 

Google Scholar 
Stomp, M. et al. Colourful coexistence of red and green picocyanobacteria in lakes and seas. Ecol. Lett. 10, 290–298 (2007).PubMed 
Article 

Google Scholar 
Callieri, C. & Stockner, J. G. Freshwater autotrophic picoplankton: A review. J. Limnol. 61, 1–14 (2002).Article 

Google Scholar 
Liu, H., Jing, H., Wong, T. H. C. & Chen, B. Co-occurrence of phycocyanin- and phycoerythrin-rich Synechococcus in subtropical estuarine and coastal waters of Hong Kong. Environ. Microbiol. Rep. 6, 90–99 (2014).PubMed 
Article 
CAS 

Google Scholar 
Haverkamp, T. et al. Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from their ITS and phycobiliprotein operons. Environ. Microbiol. 10, 174–188 (2008).CAS 
PubMed 

Google Scholar 
Otero-Ferrer, J. L. et al. Factors controlling the community structure of picoplankton in contrasting marine environments. Biogeosciences 15, 6199–6220 (2018).ADS 
CAS 
Article 

Google Scholar 
Ploug, H. et al. Carbon, nitrogen and O2 fluxes associated with the cyanobacterium Nodularia spumigena in the Baltic Sea. ISME J. 5, 1549–1558 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ohlendieck, U., Stuhr, A. & Siegmund, H. Nitrogen fixation by diazotrophic cyanobacteria in the Baltic Sea and transfer of the newly fixed nitrogen to picoplankton organisms. J. Mar. Syst. 25, 213–219 (2000).Article 

Google Scholar 
Klawonn, I. et al. Untangling hidden nutrient dynamics: Rapid ammonium cycling and single-cell ammonium assimilation in marine plankton communities. ISME J. 13, 1960–1974 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lin, Y., Wang, L., Xu, K., Huang, H. & Ren, H. Algae biofilm reduces microbe-derived dissolved organic nitrogen discharges: Performance and mechanisms. Environ. Sci. Technol. 55, 6227–6238 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Berthelot, H., Bonnet, S., Camps, M., Grosso, O. & Moutin, T. Assessment of the dinitrogen released as ammonium and dissolved organic nitrogen by unicellular and filamentous marine diazotrophic cyanobacteria grown in culture. Front. Mar. Sci. https://doi.org/10.3389/fmars.2015.00080 (2015).Article 

Google Scholar 
Loick-Wilde, N. et al. De novo amino acid synthesis and turnover during N2 fixation. Limnol. Ocean. 63, 1076–1092 (2018).CAS 
Article 

Google Scholar 
Glibert, P. M. & Bronk, D. A. Release of dissolved organic nitrogen by marine diazotrophic cyanobacteria Trichodesmium spp.. Appl. Environ. Microbiol. 60, 3996–4000 (1994).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kuo, J. et al. Picoplankton dynamics and picoeukaryote diversity in a hyper-eutrophic subtropical lagoon. J. Environ. Sci. Heal. 4, 521–523 (2014).
Google Scholar 
Grébert, T. et al. Light color acclimation is a key process in the global ocean distribution of Synechococcus cyanobacteria. PNAS 115, E2010–E2019 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Urbach, E., Scanlan, D., Distel, D., Waterbury, J. & Chisholm, S. Rapid diversification of marine picophytoplankton with dissimilar light-harvesting structures inferred from sequences of Prochlorococcus and Synechococcus (cyanobacteria). J. Mol. Biol. 46, 188–201 (1998).ADS 
CAS 

Google Scholar 
Farrant, G. K. et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. PNAS 113, E3365–E3374 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rocap, G., Distel, D. L., Waterbury, J. B. & Chisholm, S. W. Resolution of Prochlorococcus and Synechococcus ecotypes by using 16S–23S ribosomal DNA internal transcribed spacer sequences. Appl. Environ. Microbiol. 68, 1180–1191 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mazard, S., Ostrowski, M., Partensky, F. & Scanlan, D. J. Multi-locus sequence analysis, taxonomic resolution and biogeography of marine Synechococcus. Environ. Microbiol. 14, 372–386 (2012).CAS 
PubMed 
Article 

Google Scholar 
Huang, S. et al. Novel lineages of Prochlorococcus and Synechococcus in the global oceans. ISME J. 6, 285–297 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Choi, D. H. & Noh, J. H. Phylogenetic diversity of Synechococcus strains isolated from the East China Sea and the East Sea. FEMS Microbiol. Ecol. 69, 439–448 (2009).CAS 
PubMed 
Article 

Google Scholar 
Lee, M. D. et al. Marine Synechococcus isolates representing globally abundant genomic lineages demonstrate a unique evolutionary path of genome reduction without a decrease in GC content. Environ. Microbiol. 21, 1677–1686 (2019).CAS 
PubMed 
Article 

Google Scholar 
Paerl, R., Foster, R., Jenkins, B., Montoya, J. & Zehr, J. Phylogenetic diversity of cyanobacterial narB genes from various marine habitats. Environ. Microbiol. 10, 3377–3387 (2008).CAS 
PubMed 
Article 

Google Scholar 
Fuller, N. et al. Clade-specific 16S ribosomal DNA oligonucleotides reveal the predominance of a single marine Synechococcus clade throughout a stratified water column in the Red sea. Appl. Environ. Microbiol. 69, 2430–2443 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scanlan, D. J. et al. Ecological genomics of marine Picocyanobacteria. Microbiol. Mol. Biol. Rev. 73, 249–299 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mazard, S., Wilson, W. H. & Scanlan, D. J. Dissecting the physiological response to phosphorus stress in marine Synechococcus isolates (cyanophyceae). J. Phycol. 48, 94–105 (2012).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. Synechococcus bloom in the Pearl River Estuary and adjacent coastal area –With special focus on flooding during wet seasons. Sci. Total Environ. 692, 769–783 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zwirglmaier, K. et al. Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes. Environ. Microbiol. 10, 147–161 (2008).PubMed 

Google Scholar 
Sohm, J. A. et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 10, 333–345 (2016).CAS 
PubMed 
Article 

Google Scholar 
Bunse, C. et al. High frequency multi-year variability in Baltic Sea microbial plankton stocks and activities. Front. Microbiol. 10, 1–18 (2019).Article 

Google Scholar 
Alegria Zufia, J., Farnelid, H. & Legrand, C. Seasonality of coastal picophytoplankton growth, nutrient limitation and biomass contribution. Front. Microbiol. 12, 1–13 (2021).Article 

Google Scholar 
Granéli, E., Wallström, K., Larsson, U., Granéli, W. & Elmgren, R. Nutrient limitation of primary production in the Baltic Sea Area. Ambio 19, 142–151 (1990).
Google Scholar 
Mazur-Marzec, H. et al. Occurrence of cyanobacteria and cyanotoxin in the Southern Baltic Proper. Filamentous cyanobacteria versus single-celled picocyanobacteria. Hydrobiologia 701, 235–252 (2013).CAS 
Article 

Google Scholar 
Stal, L. et al. BASIC: Baltic Sea cyanobacteria. An investigation of the structure and dynamics of water blooms of cyanobacteria in the Baltic Sea—Responses to a changing environment. Cont. Shelf Res. 23, 1695–1714 (2003).ADS 
Article 

Google Scholar 
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hugerth, L. W. et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biol. 16, 1–18 (2015).Article 
CAS 

Google Scholar 
Walve, J. & Larsson, U. Seasonal changes in Baltic Sea seston stoichiometry: The influence of diazotrophic cyanobacteria. Mar. Ecol. Prog. Ser. 407, 13–25 (2010).ADS 
CAS 
Article 

Google Scholar 
Huber, P. et al. Primer design for an accurate view of picocyanobacterial community structure by using high-throughput sequencing. Appl. Environ. Microbiol. 85, 1–17 (2019).Article 

Google Scholar 
Jiang, T. et al. Temporal and spatial variations of abundance of phycocyanin- and phycoerythrin-rich Synechococcus in Pearl River Estuary and adjacent coastal area. J. Ocean Univ. China 15, 897–904 (2016).ADS 
CAS 
Article 

Google Scholar 
Li, S. et al. Unexpected predominance of photosynthetic picoeukaryotes in shallow eutrophic lakes. J. Plankton Res. 38, 830–842 (2016).CAS 
Article 

Google Scholar 
Collos, Y. et al. Oligotrophication and emergence of picocyanobacteria and a toxic dinoflagellate in Thau lagoon, southern France. J. Sea Res. 61, 68–75 (2009).ADS 
Article 

Google Scholar 
Bec, B., Husseini-Ratrema, J., Collos, Y., Souchu, P. & Vaquer, A. Phytoplankton seasonal dynamics in a Mediterranean coastal lagoon: Emphasis on the picoeukaryote community. J. Plankton Res. 27, 881–894 (2005).CAS 
Article 

Google Scholar 
Hunter-Cevera, K. R. et al. Physiological and ecological drivers of early spring blooms of coastal phytoplankter. Science 354, 326–329 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Albertano, P., Di Somma, D. & Capucci, E. Cyanobacterial picoplankton from the central Baltic Sea: Cell size classification by image analyzed fluorescence microscopy. J. Plankton Res. 19, 1405–1416 (1997).Article 

Google Scholar 
Paulsen, M. L. et al. Synechococcus in the Atlantic gateway to the Arctic Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00191 (2016).Article 

Google Scholar 
Felföldi, T. et al. Diversity and seasonal dynamics of the photoautotrophic picoplankton in Lake Balaton (Hungary). Aquat. Microb. Ecol. 63, 273–287 (2011).Article 

Google Scholar 
Grinienė, E., Šulčius, S. & Kuosa, H. Size-selective microzooplankton grazing on the phytoplankton in the Curonian Lagoon (SE Baltic Sea). Oceanologia 58, 292–301 (2016).Article 

Google Scholar 
Tsai, A. Y., Gong, G. C., Huang, Y. W. & Chao, C. F. Estimates of bacterioplankton and Synechococcus spp. mortality from nanoflagellate grazing and viral lysis in the subtropical Danshui River estuary. Estuar. Coast. Shelf Sci. 153, 54–61 (2015).ADS 
Article 

Google Scholar 
Camacho, A., Miracle, M. R. & Vicente, E. Which factors determine the abundance and distribution of picocyanobacteria in inland waters? A comparison among different types of lakes and ponds. Arch. Hydrobiol. 157(321), 338 (2003).
Google Scholar 
Berry, D. L. et al. Shifts in cyanobacterial strain dominance during the onset of harmful algal blooms in Florida Bay, USA. Microb. Ecol. 70, 361–371 (2015).PubMed 
Article 

Google Scholar 
Zborowsky, S. & Lindell, D. Resistance in marine cyanobacteria differs against specialist and generalist cyanophages. PNAS 116, 16899–16908 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wall, C. C., Rodgers, B. S., Gobler, C. J. & Peterson, B. J. Responses of loggerhead sponges Spechiospongia vesparium during harmful cyanobacterial blooms in a sub-tropical lagoon. Mar. Ecol. Prog. Ser. 451, 31–43 (2012).ADS 
Article 

Google Scholar 
Glibert, P. M. et al. Pluses and minuses of ammonium and nitrate uptake and assimilation by phytoplankton and implications for productivity and community composition, with emphasis on nitrogen-enriched conditions. Limnol. Oceanogr. 61, 165–197 (2016).ADS 
Article 

Google Scholar 
Herbert, R. A. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol. Rev. 23, 563–590 (1999).CAS 
PubMed 
Article 

Google Scholar 
Cai, J., Hodoki, Y. & Nakano, S. I. Phylogenetic diversity of the picocyanobacterial community from a novel winter bloom in Lake Biwa. Limnology 22, 161–167 (2021).Article 

Google Scholar 
Guyet, U. et al. Synergic effects of temperature and irradiance on the physiology of the marine Synechococcus strain WH7803. Front. Microbiol. 11, 1707 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Meier, H. E. M. et al. Ensemble modeling of the Baltic Sea ecosystem to provide scenarios for management. Ambio 43, 37–48 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Neumann, T. et al. Extremes of temperature, oxygen and blooms in the baltic sea in a changing climate. Ambio 41, 574–585 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Andersson, A. et al. Projected future climate change and Baltic Sea ecosystem management. Ambio 44, 345–356 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schmidt, K. et al. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Glob. Change Biol. 26, 5574–5587 (2020).ADS 
Article 

Google Scholar 
Legrand, C. et al. Interannual variability of phyto-bacterioplankton biomass and production in coastal and offshore waters of the Baltic Sea. Ambio 44, 427–438 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Capuzzo, E. et al. A decline in primary production in the North Sea over 25 years, associated with reductions in zooplankton abundance and fish stock recruitment. Glob. Change Biol. 24, e352–e364 (2017).Article 

Google Scholar 
Valderrama, J. C. Methods of nutrient analysis. In Manual on Harmful Marine Microalgae (eds Hallagraeff, G. M. et al.) 251–268 (IOC Manuals and Guides, 1995).
Google Scholar 
Jespersen, A. M. & Christoffersen, K. Measurements of chlorophyll-a from phytoplankton using ethanol as extraction solvent. Arch. Hydrobiol. 109, 445–454 (1987).CAS 

Google Scholar 
Edler, L. Recommendations on methods for marine biological studies in the Baltic Sea. Phytoplankton and chlorophyll (Baltic Marine Biologists BMB (Sweden), 1979).HELCOM Phytoplankton Expert Group. Phytoplankton biovolume and carbon content. https://www.ices.dk/data/Documents/ENV/PEG_BVOL.zip (2013).Mostböck, S. FCSalyzer (2021).Gregory Caporaso, J. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods https://doi.org/10.1038/nmeth.f.303 (2010).Article 
PubMed 

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

Google Scholar 
Katoh, K., Misawa, K., Kuma, K. I. & Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Crosbie, N. D., Pöckl, M. & Weisse, T. Rapid establishment of clonal isolates of freshwater autotrophic picoplankton by single-cell and single-colony sorting. J. Microbiol. Methods 55, 361–370 (2003).CAS 
PubMed 
Article 

Google Scholar 
Silva, C. S. P., Genuário, D. B., Vaz, M. G. M. V. & Fiore, M. F. Phylogeny of culturable cyanobacteria from Brazilian mangroves. Syst. Appl. Microbiol. 37, 100–112 (2014).CAS 
PubMed 
Article 

Google Scholar 
Marsan, D., Wommack, K. E. & Ravel, J. Draft genome sequence of Synechococcus sp. strain CB0101, isolated from the Chesapeake Bay estuary. Genome Announc. 2, e01111 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. R version 3.5.1. https://www.r-project.org/ (2019).Oksanen, J. et al. Package ‘vegan’ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, New York, 2016) (ISBN 978-3-319-24277-4).MATH 
Book 

Google Scholar 
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 
PubMed 
Article 

Google Scholar 
Gill, A. E. Atmosphere-Ocean Dynamics (Academic Press, USA, 1982).
Google Scholar 
Li, X., Wang, Y., Li, J. & Lei, B. Physical and socioeconomic driving forces of land-use and land-cover changes: A Case Study of Wuhan City, China. Discret Dyn. Nat. Soc. 2016 (2016).Paliy, O. & Shankar, V. Application of multivariate statistical techniques in microbial ecology. Mol. Ecol. 25, 1032–1057 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Lu, N. et al. Biophysical and economic constraints on China’s natural climate solutions. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01432-3 (2022). More

Appel, M., Lahn, F., Buytaert, W. & Pebesma, E. Open and scalable analytics of large earth observation datasets: From scenes to multidimensional arrays using SCIDB and GDAL. ISPRS J. Photogramm. Remote Sens. 138, 47–56 (2018).ADS 
Article 

Google Scholar 
Audebert, N., Saux, B. L. & Lefvre, S. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote Sens. 140, 20–32 (2018).ADS 
Article 

Google Scholar 
Ball J. E., Anderson D. T., & Chan C. S. Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community. J. Appl. Remote Sens. https://doi.org/10.1117/1.JRS.11.042609 (2017).Proceedings of the Royal Society B: Biological Sciences. Vol. 282. 20141657 (2015).Velázquez, E., Paine, C. T., May, F. & Wiegand, T. Linking trait similarity to interspecific spatial associations in a moist tropical forest. J. Veg. Sci. 26, 1068–1079 (2015).Article 

Google Scholar 
Ben-Said, M. Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review. Ecol. Process. 10, 1–23 (2021).Article 

Google Scholar 
Watt, A. S. Pattern and process in the plant community. J. Ecol. 35, 1–22 (1947).Article 

Google Scholar 
Pielou, E.C. Mathematical Ecology; Number 574.50151 P613 1977. (Wiley, 1977).Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Brown, C., Law, R., Illian, J. B. & Burslem, D. F. Linking ecological processes with spatial and non-spatial patterns in plant communities. J. Ecol. 99, 1402–1414 (2011).Article 

Google Scholar 
Detto, M. & Muller-Landau, H. C. Fitting ecological process models to spatial patterns using scalewise variances and moment equations. Am. Nat. 181, E68–E82 (2013).Article 

Google Scholar 
May, F., Huth, A., & Wiegand, T. Moving beyond abundance distributions: neutral theory and spatial patterns in a tropical forest. Proceedings. Biological sciences 282(1802), 20141657. https://doi.org/10.1098/rspb.2014.1657 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Kerr, J. T. & Ostrovsky, M. From space to species: Ecological applications for remote sensing. Trends Ecol. Evol. 18, 299–305 (2003).Article 

Google Scholar 
Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P. & Saatchi, S. Measuring and modelling biodiversity from space. Prog. Phys. Geogr. 32, 203–221 (2008).Article 

Google Scholar 
He, J., Zhang, L., Wang, Q. & Li, Z. Using diffusion geometric coordinates for hyperspectral imagery representation. IEEE Geosci. Remote Sens. Lett. 6(4), 767–771 (2009).ADS 
Article 

Google Scholar 
Lechner, A.M., Foody, G.M., & Boyd, D.S. Applications in remote sensing to forest ecology and management. One Earth 2.5, 405–412 (2020).Arévalo, P., Olofsson, P. & Woodcock, C. E. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sens. Environ. 238, 111051 (2020).ADS 
Article 

Google Scholar 
Gillespie, T.W. et al. Measuring and modelling biodiversity from space. Prog. Phys. Geogr. 32.2, 203–221 (2008).Lausch, A., Erasmi, S., King, D. J., Magdon, P. & Heurich, M. Understanding forest health with remote sensing-part II—A review of approaches and data models. Remote Sens. 9(2), 129 (2017).ADS 
Article 

Google Scholar 
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., et al. Monitoring vegetation systems in the Great Plains with ERTS. in NASA Special Publication. Vol. 351. 309 (1974).Chen, J. M. & Black, T. Defining leaf area index for non-flat leaves. Plant Cell Environ. 15, 421–429 (1992).Article 

Google Scholar 
Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).Article 

Google Scholar 
Zhao, S. et al. Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient. Int. J. Appl. Earth Obs. Geoinf. 12, 351–358 (2010).ADS 

Google Scholar 
Gao, B. C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266 (1996).ADS 
Article 

Google Scholar 
Wan, Z. & Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens. 34, 892–905 (1996).ADS 
Article 

Google Scholar 
Xu, H., Wang, Y., Guan, H., Shi, T. & Hu, X. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sensing 11, 2345 (2019).ADS 
Article 

Google Scholar 
List of Top 10 Sources of Free Remote Sensing Data (2017).USGS Earth Explorer: Download Free Landsat Imagery (2021).Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote. Sens. 65, 2–16 (2010).ADS 
Article 

Google Scholar 
Li, M., Zang, S., Zhang, B., Li, S. & Wu, C. A review of remote sensing image classification techniques: The role of spatio-contextual information. Eur. J. Remote Sens. 47, 389–411 (2014).Article 

Google Scholar 
Gómez-Chova, L., Tuia, D., Moser, G. & Camps-Valls, G. Multimodal classification of remote sensing images: A review and future directions. Proc. IEEE 103, 1560–1584 (2015).Article 

Google Scholar 
Alajlan, N., Bazi, Y., Melgani, F. & Yager, R. R. Fusion of supervised and unsupervised learning for improved classification of hyperspectral images. Inf. Sci. 217, 39–55 (2012).Article 

Google Scholar 
Csillik, O. Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sens. 9, 243 (2017).ADS 
Article 

Google Scholar 
Thanh Noi, P. & Kappas, M. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18, 18 (2018).ADS 
Article 

Google Scholar 
Jiang, S., Zhao, H., Wu, W., & Tan, Q. A novel framework for remote sensing image scene classification. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2018, 42 (2018).Baddeley, A. Spatial Point Process Modelling and Its Applications. Vol. 20. (Publicacions de la Universitat Jaume I, 2004).Vasudevan, K., Eckel, S., Fleischer, F., Schmidt, V. & Cook, F. Statistical analysis of spatial point patterns on deep seismic reflection data: A preliminary test. Geophys. J. Int. 171, 823–840 (2007).ADS 
Article 

Google Scholar 
Cheng, Y. & Luo, J. Statistical analysis of metastable pitting events on carbon steel. Br. Corros. J. 35, 125–130 (2000).CAS 
Article 

Google Scholar 
Velázquez, E., Martínez, I., Getzin, S., Moloney, K. A. & Wiegand, T. An evaluation of the state of spatial point pattern analysis in ecology. Ecography 39, 1042–1055 (2016).Article 

Google Scholar 
Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).Article 

Google Scholar 
Stoyan, D., & Penttinen, A. Recent applications of point process methods in forestry statistics. Stat. Sci. 2000, 61–78 (2000).Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. Statistical Analysis and Modelling of Spatial Point Patterns. Vol. 70. (Wiley, 2008).Brodrick, P. G., Davies, A. B. & Asner, G. P. Uncovering ecological patterns with convolutional neural networks. Trends Ecol. Evol. 34, 734–745 (2019).Article 

Google Scholar 
Liu, S., Luo, H., Tu, Y., He, Z., & Li, J. Wide contextual residual network with active learning for remote sensing image classification. in IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium. 7145–7148 (IEEE, 2018).Lee, H. & Kwon, H. Going deeper with contextual CNN for hyperspectral image classification. IEEE Trans. Image Process. 26, 4843–4855 (2017).ADS 
MathSciNet 
Article 

Google Scholar 
Cheng, G., Xie, X., Han, J., Guo, L. & Xia, G. S. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 3735–3756 (2020).ADS 
Article 

Google Scholar 
Lewy, D., & Mandziuk, J. An overview of mixing augmentation methods and augmentation strategies. arXiv preprint arXiv:2107.09887 (2021).Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q.V. Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018).Naveed, H. Survey: Image mixing and deleting for data augmentation. arXiv preprint arXiv:2106.07085 (2021).Freeman, I., Roese-Koerner, L. & Kummert, A. Effnet: An efficient structure for convolutional neural networks. 25th IEEE international conference on image processing (ICIP). IEEE 2018, 6–10 (2018).
Google Scholar 
LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).Article 

Google Scholar 
Raeisi, M., Bonneu, F. & Gabriel, E. A spatio-temporal multi-scale model for Geyer saturation point process: Application to forest fire occurrences. Spatial Stat. 41, 100492 (2021).MathSciNet 
Article 

Google Scholar 
Baddeley, A. Analysing spatial point patterns in R. in Workshop Notes Version. Vol. 3 (2008). More