Philippa Kaur

Oliveira, R. F. Social plasticity in fish: Integrating mechanisms and function. J. Fish Biol. 81, 2127–2150 (2012).CAS 
PubMed 

Google Scholar 
Oliveira, R. F. Mind the fish: Zebrafish as a model in cognitive social neuroscience. Front. Neural Circuits 7, 1–15 (2013).
Google Scholar 
Hofmann, H. A. et al. An evolutionary framework for studying mechanisms of social behavior. Trends Ecol. Evol. 29, 581–589 (2014).PubMed 

Google Scholar 
Maruska, K., Soares, M., Lima-Maximino, M., de Siqueira-Silva, D. H. & Maximino, C. Social plasticity in the fish brain: Neuroscientific and ethological aspects. Brain Res. 1711, 156–172 (2019).CAS 
PubMed 

Google Scholar 
O’Connell, L. A. & Hofmann, H. A. The Vertebrate mesolimbic reward system and social behavior network: A comparative synthesis. J. Comp. Neurol. 519, 3599–3639 (2011).PubMed 

Google Scholar 
Teles, M. C., Almeida, O., Lopes, J. S. & Oliveira, R. F. Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish. Proc. R. Soc. B Biol. Sci. 282, 20151099 (2015).
Google Scholar 
Rittschof, C. C. et al. Neuromolecular responses to social challenge: Common mechanisms across mouse, stickleback fish, and honey bee. Proc. Natl. Acad. Sci. U.S.A. 111, 17929–17934 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kasper, C., Colombo, M., Aubin-horth, N. & Taborsky, B. Physiology & behavior brain activation patterns following a cooperation opportunity in a highly social cichlid fish. Physiol. Behav. 195, 37–47 (2018).CAS 
PubMed 

Google Scholar 
Filby, A. L., Paull, G. C., Bartlett, E. J., Van Look, K. J. W. & Tyler, C. R. Physiological and health consequences of social status in zebrafish (Danio rerio). Physiol. Behav. 101, 576–587 (2010).CAS 
PubMed 

Google Scholar 
Munchrath, L. A. & Hofmann, H. A. Distribution of sex steroid hormone receptors in the brain of an African cichlid fish, Astatotilapia burtoni. J. Comp. Neurol. 518, 3302–3326 (2010).CAS 
PubMed 

Google Scholar 
Robinson, G. E., Fernald, R. D. & Clayton, D. F. Genes and social behavior. Science 322, 896–900 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Barron, A. B. & Robinson, G. E. The utility of behavioral models and modules in molecular analyses of social behavior. Genes Brain Behav. 7, 257–265 (2008).PubMed 

Google Scholar 
Qiu, Y.-Q. KEGG pathway database. In Encyclopedia of Systems Biology (ed. Dubitzky, W.) 1068–1069 (Springer, 2013).
Google Scholar 
Bloch, G. & Grozinger, C. M. Social molecular pathways and the evolution of bee societies. Philos. Trans. R. Soc. B Biol. Sci. 366, 2155–2170 (2011).
Google Scholar 
Waldie, P. A., Blomberg, S. P., Cheney, K. L., Goldizen, A. W. & Grutter, A. S. Long-term effects of the cleaner fish Labroides dimidiatus on coral reef fish communities. PLoS ONE 6, e21201 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Grutter, A. S. Cleaner fish really do clean. Nature. 398, 672–673. https://doi.org/10.1038/19443 (1999).CAS 
Article 

Google Scholar 
Soares, M., Oliveira, R. F., Ros, A. F. H., Grutter, A. S. & Bshary, R. Tactile stimulation lowers stress in fish. Nat. Commun. 2, 534–535 (2011).PubMed 

Google Scholar 
Soares, M., Gerlai, R. & Maximino, C. The integration of sociality, monoamines and stress neuroendocrinology in fish models: Applications in the neurosciences. J. Fish Biol. 93, 170–191 (2018).PubMed 

Google Scholar 
Grutter, A. Parasite removal rates by the cleaner wrasse Labroides dimidiatus. Mar. Ecol. Prog. Ser. 130, 61–70 (1996).
Google Scholar 
Grutter, A. S. Effect of the removal of cleaner fish on the abundance and species composition of reef fish. Oecologia 111, 137–143 (1997).PubMed 

Google Scholar 
Tebbich, S., Bshary, R. & Grutter, A. Cleaner fish Labroides dimidiatus recognise familiar clients. Anim. Cogn. 5, 139–145 (2002).CAS 
PubMed 

Google Scholar 
Pinto, A., Oates, J., Grutter, A. & Bshary, R. Cleaner wrasses Labroides dimidiatus are more cooperative in the presence of an audience. Curr. Biol. 21, 1140–1144 (2011).CAS 
PubMed 

Google Scholar 
Soares, M. The neurobiology of mutualistic behavior: The cleanerfish swims into the spotlight. Front. Behav. Neurosci. 11, 1–12 (2017).
Google Scholar 
Soares, M. C., Bshary, R., Mendonça, R., Grutter, A. S. & Oliveira, R. F. Arginine vasotocin regulation of interspecific cooperative behaviour in a cleaner fish. PLoS ONE 7, 39583 (2012).
Google Scholar 
Paula, J. R., Messias, J., Grutter, A., Bshary, R. & Soares, M. The role of serotonin in the modulation of cooperative behavior. Behav. Ecol. 26, 1005–1012 (2015).
Google Scholar 
Schunter, C., Jarrold, M. D., Munday, P. L. & Ravasi, T. Diel CO2 fluctuations alter the molecular response of coral reef fishes to ocean acidification conditions. Mol. Ecol. 30, 5150–5118 (2021).
Google Scholar 
Soares, M. C., Santos, T. P. & Messias, J. P. M. Dopamine disruption increases cleanerfish cooperative investment in novel client partners. R. Soc. Open Sci. 4, 1–7 (2017).
Google Scholar 
Paula, J. R. et al. Neurobiological and behavioural responses of cleaning mutualisms to ocean warming and acidification. Sci. Rep. 9, 1–10 (2019).
Google Scholar 
Cardoso, S. C. et al. Arginine vasotocin modulates associative learning in a mutualistic cleaner fish. Behav. Ecol. Sociobiol. 69, 1173–1181 (2015).
Google Scholar 
Cardoso, S. C. et al. Forebrain neuropeptide regulation of pair association and behavior in cooperating cleaner fish. Physiol. Behav. 145, 1–7 (2015).CAS 
PubMed 

Google Scholar 
O’Connell, L. A., Fontenot, M. R. & Hofmann, H. A. Characterization of the dopaminergic system in the brain of an African cichlid fish, Astatotilapia burtoni. J. Comp. Neurol. 519, 75–92 (2011).PubMed 

Google Scholar 
Vernier, P. The Brains of Teleost Fishes. Evolution of Nervous Systems 2nd edn, 1–4 (Elsevier, 2016).
Google Scholar 
Weitekamp, C. A. & Hofmann, H. A. Neuromolecular correlates of cooperation and conflict during territory defense in a cichlid fish. Horm. Behav. 89, 145–156 (2017).CAS 
PubMed 

Google Scholar 
Messias, J., Santos, T. P., Pinto, M. & Soares, M. C. Stimulation of dopamine D1 receptor improves learning capacity in cooperating cleaner fish. Proc. R. Soc. B Biol. Sci. 283, 20152272 (2016).
Google Scholar 
Bshary, R. & Grutter, A. S. Punishment and partner switching cause cooperative behaviour in a cleaning mutualism. Biol. Lett. 1, 396–399 (2005).PubMed 
PubMed Central 

Google Scholar 
Bajaffer, A., Mineta, K. & Gojobori, T. Evolution of memory system-related genes. FEBS Open Bio 11, 3201–3210 (2021).PubMed 
PubMed Central 

Google Scholar 
Soares, M., Cardoso, S. C., Grutter, A. S., Oliveira, R. F. & Bshary, R. Cortisol mediates cleaner wrasse switch from cooperation to cheating and tactical deception. Horm. Behav. 66, 346–350 (2014).CAS 
PubMed 

Google Scholar 
de Abreu, M. S., Messias, J., Thörnqvist, P. O., Winberg, S. & Soares, M. C. The variable monoaminergic outcomes of cleaner fish brains when facing different social and mutualistic contexts. PeerJ 2018, 1–17 (2018).
Google Scholar 
Terry, W. S. Classical conditioning. In Learning and Memory (ed. Terry, W. S.) 76–112 (Psychology Press, 2021).
Google Scholar 
Dunn, A. R. et al. Synaptic vesicle glycoprotein 2C (SV2C) modulates dopamine release and is disrupted in Parkinson disease. Proc. Natl. Acad. Sci. U.S.A. 114, E2253–E2262 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Studzinski, A. L. M., Barros, D. M. & Marins, L. F. Growth hormone (GH) increases cognition and expression of ionotropic glutamate receptors (AMPA and NMDA) in transgenic zebrafish (Danio rerio). Behav. Brain Res. 294, 36–42 (2015).CAS 
PubMed 

Google Scholar 
von Trotha, J. W., Vernier, P. & Bally-Cuif, L. Emotions and motivated behavior converge on an amygdala-like structure in the zebrafish. Eur. J. Neurosci. 40, 3302–3315 (2014).
Google Scholar 
Hoppmann, V., Wu, J. J., Søviknes, A. M., Helvik, J. V. & Becker, T. S. Expression of the eight AMPA receptor subunit genes in the developing central nervous system and sensory organs of zebrafish. Dev. Dyn. 237, 788–799 (2008).CAS 
PubMed 

Google Scholar 
Weld, M. M., Kar, S., Maler, L. & Quirion, R. The distribution of excitatory amino acid binding sites in the brain of an electric fish, Apteronotus leptorhynchus. J. Chem. Neuroanat. 4, 39–61 (1991).
Google Scholar 
Zoicas, I. & Kornhuber, J. The role of metabotropic glutamate receptors in social behavior in Rodents. Int. J. Mol. Sci. 20, 1412 (2019).CAS 
PubMed Central 

Google Scholar 
Borroni, A. M., Fichtenholtz, H., Woodside, B. L. & Teyler, T. J. Role of voltage-dependent calcium channel long-term potentiation (LTP) and NMDA LTP in spatial memory. J. Neurosci. 20, 9272–9276 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Oliveira, R. F. Social plasticity in fish: Integrating mechanisms. J. Fish Biol. 81, 2127–2150 (2012).CAS 
PubMed 

Google Scholar 
O’Connell, L. A., Ding, J. H. & Hofmann, H. A. Sex differences and similarities in the neuroendocrine regulation of social behavior in an African cichlid fish. Horm. Behav. 64, 468–476 (2013).PubMed 

Google Scholar 
Soares, M., Bshary, R., Cardoso, S. C. & Côté, I. M. The meaning of jolts by fish clients of cleaning gobies. Ethology 114, 209–214 (2008).
Google Scholar 
Grutter, A. S. & Bshary, R. Cleaner wrasse prefer client mucus: Support for partner control mechanisms in cleaning interactions. Proc. R. Soc. B Biol. Sci. 270, S242–S244. https://doi.org/10.1098/rsbl.2003.0077 (2003).Article 

Google Scholar 
Soares, M. et al. Hormonal mechanisms of cooperative behaviour. Philos. Trans. R. Soc. B Biol. Sci. 365, 2737–2750 (2010).
Google Scholar 
Alberini, C. M. Transcription factors in long-term memory and synaptic plasticity. Physiol. Rev. 89, 121–145 (2009).CAS 
PubMed 

Google Scholar 
Dou, Y. et al. Memory function in feeding habit transformation of mandarin fish (Siniperca chuatsi). Int. J. Mol. Sci. 19, 1254 (2018).PubMed Central 

Google Scholar 
Blanton, M. L. & Specker, J. L. The hypothalamic-pituitary-thyroid (HPT) axis in fish and its role in fish development and reproduction. Crit. Rev. Toxicol. 37, 97–115 (2007).CAS 
PubMed 

Google Scholar 
Kawauchi, H., Sower, S. A. & Moriyama, S. Chapter 5. The neuroendocrine regulation of prolactin and somatolactin secretion in fish. In Fish Physiology Vol. 28 (eds Kawauchi, H. et al.) 197–234 (Elsevier Inc., 2009).
Google Scholar 
Helmreich, D. L., Parfitt, D. B., Lu, X. Y., Akil, H. & Watson, S. J. Relation between the hypothalamic-pituitary-thyroid (HPT) axis and the hypothalamic-pituitary-adrenal (HPA) axis during repeated stress. Neuroendocrinology 81, 183–192 (2005).CAS 
PubMed 

Google Scholar 
Jönsson, E. & Björnsson, B. Physiological functions of growth hormone in fish with special reference to its influence on behaviour. Fish. Sci. 68, 742–748 (2002).
Google Scholar 
Zoeller, R. T., Tan, S. W. & Tyl, R. W. General background on the hypothalamic-pituitary-thyroid (HPT) axis. Crit. Rev. Toxicol. 37, 11–53 (2007).CAS 
PubMed 

Google Scholar 
Björnsson, B. et al. Growth hormone endocrinology of salmonids: Regulatory mechanisms and mode of action. Fish Physiol. Biochem. 27, 227–242 (2002).
Google Scholar 
Trainor, B. C. & Hofmann, H. A. Somatostatin regulates aggressive behavior in an African cichlid fish. Endocrinology 147, 5119–5125 (2006).CAS 
PubMed 

Google Scholar 
Doyon, C., Gilmour, K. M., Trudeau, V. L. & Moon, T. W. Corticotropin-releasing factor and neuropeptide Y mRNA levels are elevated in the preoptic area of socially subordinate rainbow trout. Gen. Comp. Endocrinol. 133, 260–271 (2003).CAS 
PubMed 

Google Scholar 
du Sert, N. P. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411 (2020).
Google Scholar 
Triki, Z. & Bshary, R. Sex differences in the cognitive abilities of a sex-changing fish species Labroides dimidiatus. R. Soc. Open Sci. 8, 210239 (2021).PubMed 
PubMed Central 

Google Scholar 
Grutter, A. S. Cleaner fish use tactile dancing behavior as a preconflict management strategy. Curr. Biol. 14, 1080–1083 (2004).CAS 
PubMed 

Google Scholar 
Friard, O. & Gamba, M. BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).
Google Scholar 
Andrews, S. Babraham Bioinformatics—FastQC: A Quality Control Tool for High Throughput Sequence Data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).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 
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).CAS 
PubMed 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).CAS 
PubMed 

Google Scholar 
Götz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36, 3420–3435 (2008).PubMed 
PubMed Central 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2021). More

Saponari, M., Boscia, D., Nigro, F. & Martelli, G. P. Identification of DNA sequences related to Xylella fastidiosa in oleander, almond and olive trees exhibiting leaf scorch symptoms in Apulia (Southern Italy). J. Plant Pathol. 95, 668 (2013).
Google Scholar 
Janse, J. D. & Obradovic, A. Xylella fastidiosa: Its biology, diagnosis, control and risks. J. Plant Pathol. 92, 35–48 (2010).
Google Scholar 
EPPO EPPO Global Database (available online). https://gd.eppo.int (2022)Article 

Google Scholar 
Bragard, C. et al. Update of the scientific opinion on the risks to plant health posed by Xylella fastidiosa in the EU territory. EFSA J. 17, 5665 (2019).
Google Scholar 
Nunney, L., Ortiz, B., Russell, S. A., Sánchez, R. R. & Stouthamer, R. The complex biogeography of the plant pathogen Xylella fastidiosa: Genetic evidence of introductions and subspecific introgression in central America. PLoS ONE 9, e112463 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Sicard, A. et al. Introduction and adaptation of an emerging pathogen to olive trees in Italy. Microb. Genom. 7, 000735 (2021).CAS 
PubMed Central 

Google Scholar 
Cornara, D. et al. Transmission of Xylella fastidiosa by naturally infected Philaenus spumarius (Hemiptera, Aphrophoridae) to different host plants. J. Appl. Entomol. 141, 80–87 (2017).Article 

Google Scholar 
Cornara, D. et al. Spittlebugs as vectors of Xylella fastidiosa in olive orchards in Italy. J. Pest Sci. 2004, 521–530 (2017).Article 

Google Scholar 
Bodino, N. et al. Phenology, seasonal abundance and stage-structure of spittlebug (Hemiptera: Aphrophoridae) populations in olive groves in Italy. Sci. Rep. 9, 17725 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Di Serio, F. et al. Collection of data and information on biology and control of vectors of Xylella fastidiosa. EFSA Support. Publ. 16, 2 (2019).
Google Scholar 
Bayram, A., Salerno, G., Onofri, A. & Conti, E. Lethal and sublethal effects of preimaginal treatments with two pyrethroids on the life history of the egg parasitoid Telenomus busseolae. Biocontrol 55, 697–710 (2010).CAS 
Article 

Google Scholar 
Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in Apulia: Where we stand. Phytopathology 109, 175–186 (2019).CAS 
PubMed 
Article 

Google Scholar 
Virant-Doberlet, M. & Cokl, A. Vibrational communication in insects. Neotrop. Entomol. 33, 121–134 (2004).Article 

Google Scholar 
Avosani, S. et al. Vibrational communication and mating behavior of the meadow spittlebug Philaenus spumarius. Entomol. Gen. 40, 307–321 (2020).Article 

Google Scholar 
Polajnar, J., Eriksson, A., Virant-Doberlet, M. & Mazzoni, V. Mating disruption of a grapevine pest using mechanical vibrations: From laboratory to the field. J. Pest Sci. 2004(89), 909–921 (2016).Article 

Google Scholar 
Boullis, A. & Verheggen, F. J. Chemical ecology of aphids (Hemiptera: Aphididae). In Biology and Ecology of Aphids (ed. Vilcinskas, A.) 181–208 (CRC Press, 2016). https://doi.org/10.1201/b19967-11.Chapter 

Google Scholar 
Ganassi, S. et al. Evidence of a female-produced sex pheromone in the European pear psylla Cacopsylla pyri. Bull. Insectol. 71, 57–64 (2018).
Google Scholar 
Tabata, J. & Ichiki, R. T. Sex pheromone of the cotton mealybug, Phenacoccus solenopsis, with an unusual cyclobutane structure. J. Chem. Ecol. 42, 1193–1200 (2016).CAS 
PubMed 
Article 

Google Scholar 
Millar, J. G. Pheromones of true bugs. Top. Curr. Chem. 240, 37–84 (2000).Article 
CAS 

Google Scholar 
Khrimian, A. et al. Discovery of the aggregation pheromone of the brown marmorated stink bug (Halyomorpha halys) through the creation of stereoisomeric libraries of 1-Bisabolen-3-ols. J. Nat. Prod. 77, 1708–1717 (2014).CAS 
PubMed 
Article 

Google Scholar 
Borges, M., Blassioli-Moraes, M. C., Laumann, R. A. & Čokl, A. Suggestions for neotropic stink bug pest status and control. In Stink Bugs: Biorational Control Based on Communication Processes (eds Cokl, A. & Borges, M.) 246–254 (CRC Press, 2017). https://doi.org/10.1201/9781315120713.Chapter 

Google Scholar 
Ranieri, E., Ruschioni, S., Riolo, P., Isidoro, N. & Romani, R. Fine structure of antennal sensilla of the spittlebug Philaenus spumarius L. (Insecta: Hemiptera: Aphrophoridae). I. Chemoreceptors and thermo-/hygroreceptors. Arthropod Struct. Dev. 45, 432–439 (2016).PubMed 
Article 

Google Scholar 
Germinara, G. S. et al. Antennal olfactory responses of adult meadow spittlebug, Philaenus spumarius, to volatile organic compounds (VOCs). PLoS ONE 12, e0190454 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ganassi, S. et al. Electrophysiological and behavioural response of Philaenus spumarius to essential oils and aromatic plants. Sci. Rep. 10, 3114 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Nault, L. R., Wood, T. K. & Goff, A. M. Treehopper (Membracidae) alarm pheromones. Nature 249, 387–388 (1974).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Chen, X. & Liang, A. P. Identification of a self-regulatory pheromone system that controls nymph aggregation behavior of rice spittlebug Callitettix versicolor. Front. Zool. 12, 10 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Liang, A. P. A new structure on the frons of male adults of the Asian rice spittlebug Callitettix versicolor (Hemiptera: Auchenorrhyncha: Cercopidae). Zootaxa 4801, 591–599 (2020).Article 

Google Scholar 
Cocroft, R. B. & Rodríguez, R. L. The behavioral ecology of insect vibrational communication. Bioscience 55, 323–334 (2005).Article 

Google Scholar 
Mazzoni, V. et al. Mating disruption by vibrational signals: state of the field and perspectives. In Biotremology: Studying Vibrational Behavior (eds Hill, P. S. M. et al.) 331–354 (Springer, Cham, 2019). https://doi.org/10.1007/978-3-030-22293-2_17.Chapter 

Google Scholar 
Bachmann, G. E. et al. Male sexual behavior and pheromone emission is enhanced by exposure to guava fruit volatiles in Anastrepha fraterculus. PLoS ONE 10, e0124250 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Frati, F., Salerno, G., Conti, E. & Bin, F. Role of the plant–conspecific complex in host location and intra-specific communication of Lygus rugulipennis. Physiol. Entomol. 33, 129–137 (2008).Article 

Google Scholar 
Frati, F. et al. Vicia faba–Lygus rugulipennis interactions: Induced plant volatiles and sex pheromone enhancement. J. Chem. Ecol. 35, 201–208 (2009).CAS 
PubMed 
Article 

Google Scholar 
Lubanga, U. K., Guédot, C., Percy, D. M. & Steinbauer, M. J. Semiochemical and vibrational cues and signals mediating mate finding and courtship in Psylloidea (Hemiptera): A synthesis. Insects 5, 577–595 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Borges, M. & Blassioli-Moraes, M. C. The semiochemistry of Pentatomidae. In Stink Bugs: Biorational Control Based on Communication Processes 95–124 (CRC Press, 2017). https://doi.org/10.1201/9781315120713.Chapter 

Google Scholar 
Yin, L. & Maschwitz, U. Sexual pheromone in the green house whitefly Trialeurodes vaporariorum Westw. Zeitschrift für Angew. Entomol. 95, 439–446 (1983).Article 

Google Scholar 
Dawson, G. W. et al. Identification of an aphid sex pheromone. Nature 325, 614–616 (1987).CAS 
Article 
ADS 

Google Scholar 
Zanardi, O. Z. et al. Putative sex pheromone of the Asian citrus psyllid, Diaphorina citri, breaks down into an attractant. Sci. Rep. 8, 455 (2018).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Sevarika, M., di Giulio, A., Rondoni, G., Conti, E. & Romani, R. Morpho-functional analysis of the head glands in three Auchenorrhynca species and their possible biological significance. bioRxiv 03.03.482260 (2022).Mazzoni, V. et al. Use of substrate-borne vibrational signals to attract the brown marmorated stink bug Halyomorpha halys. J. Pest Sci. 2004, 1219–1229 (2017).Article 

Google Scholar 
Avosani, S., Franceschi, P., Ciolli, M., Verrastro, V. & Mazzoni, V. Vibrational playbacks and microscopy to study the signalling behaviour and female physiology of Philaenus spumarius. J. Appl. Entomol. https://doi.org/10.1111/jen.12874 (2021).Article 

Google Scholar 
Stewart, A. J. A. & Lees, D. R. Genetic control of colour polymorphism in spittlebugs (Philaenus spumarius) differs between isolated populations. Heredity (Edinb). 59, 445–448 (1987).Article 

Google Scholar 
Stewart, A. J. A. The colour/pattern polymorphism of Philaenus spumarius (L.) (Homoptera: Cercopidae) in England and Wales. Philos. Trans. R. Soc. B Biol. Sci. 351, 69–89 (1996).Article 
ADS 

Google Scholar 
Moyal, P. et al. Origin and taxonomic status of the Palearctic population of the stem borer Sesamia nonagrioides (Lefèbvre) (Lepidoptera: Noctuidae). Biol. J. Linn. Soc. 103, 904–922 (2011).Article 

Google Scholar 
Glaser, N. et al. Differential expression of the chemosensory transcriptome in two populations of the stemborer Sesamia nonagrioides. Insect Biochem. Mol. Biol. 65, 28–34 (2015).CAS 
PubMed 
Article 

Google Scholar 
Bodino, N. et al. Spittlebugs of mediterranean olive groves: host-plant exploitation throughout the year. Insects 11, 130 (2020).PubMed Central 
Article 

Google Scholar 
Cook, S. M., Khan, Z. R. & Pickett, J. A. The use of push-pull strategies in integrated pest management. Annu. Rev. Entomol. 52, 375–400 (2007).CAS 
PubMed 
Article 

Google Scholar 
Molinatto, G. et al. Biology and prevalence in Northern Italy of Verrallia aucta (Diptera, Pipunculidae), a parasitoid of Philaenus spumarius (Hemiptera, Aphrophoridae), the main vector of Xylella fastidiosa in Europe. Insects 11, 607 (2020).PubMed Central 
Article 

Google Scholar 
Mesmin, X. et al. Ooctonus vulgatus (Hymenoptera, Mymaridae), a potential biocontrol agent to reduce populations of Philaenus spumarius (Hemiptera, Aphrophoridae) the main vector of Xylella fastidiosa in Europe. PeerJ 2020, e8591 (2020).Article 

Google Scholar 
Conti, E., Jones, W. A., Bin, F. & Vinson, S. B. Physical and chemical factors involved in host recognition behavior of Anaphes iole Girault, an egg parasitoid of Lygus hesperus knight (Hymenoptera: Mymaridae; Heteroptera: Miridae). Biol. Control 7, 10–16 (1996).Article 

Google Scholar 
Conti, E., Jones, W. A., Bin, F. & Vinson, S. B. Oviposition behavior of Anaphes iole, an egg parasitoid of Lygus hesperus (Hymenoptera: Mymaridae; Heteroptera: Miridae). Ann. Entomol. Soc. Am. 90, 91–101 (1997).Article 

Google Scholar 
Chiappini, E. et al. Role of volatile semiochemicals in host location by the egg parasitoid Anagrus breviphragma. Entomol. Exp. Appl. 144, 311–316 (2012).CAS 
Article 

Google Scholar 
Conti, E. et al. Biological control of invasive stink bugs: review of global state and future prospects. Entomol. Exp. Appl. 169, 28–51 (2021).Article 

Google Scholar 
Rondoni, G. et al. Native egg parasitoids recorded from the invasive Halyomorpha halys successfully exploit volatiles emitted by the plant–herbivore complex. J. Pest Sci. 2004, 1087–1095 (2017).Article 

Google Scholar 
Rondoni, G., Ielo, F., Ricci, C. & Conti, E. Behavioural and physiological responses to prey-related cues reflect higher competitiveness of invasive vs native ladybirds. Sci. Rep. 7, 3716 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Colazza, S. et al. Xbug, a video tracking and motion analysis system for LINUX. in XII International Entomophagous Insects Workshop. Pacific Grove, California (1999).De Cristofaro, A. et al. Electrophysiological responses of Cydia pomonella to codlemone and pear ester ethyl (E, Z)-2,4-decadienoate: Peripheral interactions in their perception and evidences for cells responding to both compounds. Bull. Insectol. 57, 137–144 (2004).
Google Scholar 
Raguso, R. A. & Light, D. M. Electroantennogram responses of male Sphinx perelegans hawkmoths to floral and ‘green-leaf volatiles’. Entomol. Exp. Appl. 86, 287–293 (1998).CAS 
Article 

Google Scholar 
Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-PLUS (Springer, 2000). https://doi.org/10.1007/b98882.Book 
MATH 

Google Scholar 
Rondoni, G., Onofri, A. & Ricci, C. Differential susceptibility in a specialised aphidophagous ladybird, Platynaspis luteorubra (Coleoptera: Coccinellidae), facing intraguild predation by exotic and native generalist predators. Biocontrol Sci. Technol. 22, 1334–1350 (2012).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Verlag, 2009). https://doi.org/10.18637/jss.v032.b01.Book 
MATH 

Google Scholar 
Bertoldi, V., Rondoni, G., Brodeur, J. & Conti, E. An egg parasitoid efficiently exploits cues from a coevolved host but not those from a novel host. Front. Physiol. 10, 746 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Suh, E., Choe, D.-H., Saveer, A. M. & Zwiebel, L. J. Suboptimal larval habitats modulate oviposition of the malaria vector mosquito Anopheles coluzzii. PLoS ONE 11, e0149800 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org (2020).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., R Core Team. nlme: Linear and Nonlinear Mixed Effects Models (2020). R package version 3.1–148, https://CRAN.R-project.org/package=nlme.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002). https://doi.org/10.1007/978-0-387-21706-2.Book 
MATH 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar 
Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means (2019). R package version 1.3.2. Available online at: https://CRAN.R-project.org/package=emmeans. More

Béné, C. & Heck, S. Fish and food security in Africa. NAGA WorldFish Center Q. 28, 8–13 (2005).
Google Scholar 
Funge-Smith, S. J. Review of the state of world fishery resources: inland fisheries. FAO Fisheries and Aquaculture Circular (2018).Funge-Smith, S. & Bennett, A. A fresh look at inland fisheries and their role in food security and livelihoods. Fish Fish. (Oxf.) 20, 1176–1195 (2019).Article 

Google Scholar 
De Graaf, G. & Garibaldi, L. The value of African fisheries. FAO fisheries and aquaculture circular, I (2015).FAO. FAO yearbook. Fishery and Aquaculture Statistics 2018/FAO annuaire. Statistiques des pêches et de l’aquaculture 2018/FAO anuario. Estadísticas de pesca y acuicultura 2018 (2020).Olaosebikan, B. D. & Bankole, N. O. An analysis of Nigerian freshwater fishes: those under threat and conservation options, In Proceedings of the 19th annual conference of the fisheries society of Nigeria (FISON), 29 Nov – 03 Dec 2004. 754–762.Marshall, B. E. Inland fisheries of tropical Africa. In Freshwater Fisheries Ecology (ed. Graig, J. F.) 349 (Wiley, Chichester, 2016).
Google Scholar 
Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. (Camb.) 81, 163–182 (2006).Article 

Google Scholar 
United Nations-Department of Economic and Social Affairs-Population Division. World population prospects 2019: Highlights (st/esa/ser. A/423). (2019).FAO, IFAD, UNICEF, WFP & WHO. The state of food security and nutrition in the world 2019: safeguarding against economic slowdowns and downturns 2019. (Rome, Italy: FAO, http://www.fao.org/3/ca5162en/ca5162en.pdf 2019).Carvalho, G. R. & Hauser, L. Molecular genetics and the stock concept in fisheries. In Molecular Genetics in Fisheries (eds Carvalho, G. R. & Pitcher, T. J.) 55–79 (Springer, Berlin, 1995).Chapter 

Google Scholar 
Abban, E. K. Considerations for the conservation of African fish genetic resources for their sustainable exploitation. In Towards Policies for Conservation and Sustainable Use of Aquatic Genetic Resources. ICLARM Conf. Proc. 59, 277p. (eds R.S.V. Pullin, D.M. Bartley, & J. Kooiman) 95–100 (International Center for Living Aquatic Resources Management (ICLARM) and FAO).FAO. Fishery Statistical Collections: Global Capture Production 1950–2018. http://www.fao.org/fishery/statistics/global-capture-production/query/en (2020).Chan, C. Y. et al. Prospects and challenges of fish for food security in Africa. Glob. Food Sec. 20, 17–25 (2019).Article 

Google Scholar 
Olopade, O. A., Taiwo, I. O. & Dienye, H. E. Management of Overfishing in the Inland Capture Fisheries in Nigeria. LimnoFish 3, 189–194 (2017).Article 

Google Scholar 
Gbaguidi, A. S. & Pfeiffer, V. Stastistiques des peches continentals, Annees 1987–1995. Cotonou, Benin: GTZ-GmbH, Benin Direction des Pêches (1996).Monentcham, S.-E., Kouam, J., Pouomogne, V. & Kestemont, P. Biology and prospect for aquaculture of African bonytongue, Heterotis niloticus (Cuvier, 1829): A review. Aquaculture 289, 191–198 (2009).Article 

Google Scholar 
FAO. The State of the World’s Aquatic Genetic Resources for Food and Agriculture. (Rome, 2019).Mustapha, M. K. Heterotis niloticus (Cuvier, 1829) a threatened fish species in Oyun reservoir, Offa, Nigeria; the need for its conservation. Asian J. Exp. Biol. Sci. 1, 1–7 (2010).
Google Scholar 
Hurtado, L. A., Carrera, E., Adite, A. & Winemiller, K. O. Genetic differentiation of a primitive teleost, the African bonytongue Heterotis niloticus, among river basins and within a floodplain river system in Benin, West Africa. J. Fish Biol. 83, 682–690 (2013).CAS 
Article 

Google Scholar 
Hauber, M. E., Bierbach, D. & Linsenmair, K. E. A description of teleost fish diversity in floodplain pools (‘Whedos’) and the Middle-Niger at Malanville (north-eastern Benin). J. Appl. Ichthyol. 27, 1095–1099 (2011).Article 

Google Scholar 
Carrera, E., Renshaw, M. A., Winemiller, K. O. & Hurtado, L. A. Isolation and characterization of nuclear-encoded microsatellite DNA primers for the African bonytongue, Heterotis niloticus. Conserv. Genet. Resour. 3, 537–539 (2011).Article 

Google Scholar 
Lischer, H. E. L. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2012).CAS 
Article 

Google Scholar 
Raymond, M. & Rousset, F. GENEPOP (version-1.2)—Population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).Article 

Google Scholar 
Rousset, F. Genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).Article 

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

Google Scholar 
Peakall, R. & Smouse, P. E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. https://doi.org/10.1111/j.1471-8286.2005.01155.x (2006).Article 

Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).CAS 
Article 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

Google Scholar 
Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).CAS 
Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
PubMed 

Google Scholar 
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
Article 

Google Scholar 
Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
Article 

Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).Article 

Google Scholar 
Miller, J. M., Cullingham, C. I. & Peery, R. M. The influence of a priori grouping on inference of genetic clusters: simulation study and literature review of the DAPC method. Heredity https://doi.org/10.1038/s41437-020-0348-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
Article 

Google Scholar 
Mantel, N. The detection of disease clustering and a Generalized Regression Approach. Cancer Res. 27, 209–220 (1967).CAS 
PubMed 

Google Scholar 
Allendorf, F. W., Ryman, N. & Utter, F. M. Genetics and fishery management: Past, present, and future. In Population Genetics and Fishery Management (eds Ryman, N. & Utter, F.) 1–19 (Washington Sea Grant Publications/University of Washington Press, 1987).
Google Scholar 
Otobo, F. O. The commercial fishery of the middle River Niger, Nigeria. In Symposium on River and Floodplain Fisheries in Africa, Bujumbura, Burundi, 21–23 November 1977, Review and Experience Papers Vol. CIFA TECHNICAL PAPER No. 5 (ed R. L. Welcomme) (Committe for Inland Fisheries of Africa, FAO, 1978).Lelek, A. & El-Zarka, A. Ecological comparison of the preimpoundment and postimpoundment fish faunas of the River Niger and Kainji Lake, Nigeria. Geophys. Monogr. Ser. 17, 655–660 (1973).ADS 

Google Scholar 
Morin, P. A., Manaster, C., Mesnick, S. L. & Holland, R. Normalization and binning of historical and multi-source microsatellite data: Overcoming the problems of allele size shift with allelogram. Mol. Ecol. Resour. 9, 1451–1455 (2009).Article 

Google Scholar 
Pruett, C. L. & Winker, K. The effects of sample size on population genetic diversity estimates in song sparrows Melospiza melodia. J. Avian Biol. 39, 252–256. https://doi.org/10.1111/j.2008.0908-8857.0409 (2008).Article 

Google Scholar 
Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE 7, e45170 (2012).ADS 
CAS 
Article 

Google Scholar 
Macedo, D. et al. Population genetics and historical demographic inferences of the blue crab Callinectes sapidus in the US based on microsatellites. PeerJ 7, e7780 (2019).Article 

Google Scholar 
Latch, E. K., Dharmarajan, G., Glaubitz, J. C. & Rhodes, O. E. Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv. Genet. 7, 295–302 (2006).Article 

Google Scholar 
Lind, C. E. et al. Genetic diversity of Nile tilapia (Oreochromis niloticus) throughout West Africa. Sci. Rep. 9, 1–12 (2019).ADS 

Google Scholar 
Araripe, J., do Rêgo, P. S., Queiroz, H., Sampaio, I. & Schneider, H. Dispersal capacity and genetic structure of Arapaima gigas on different geographic scales using microsatellite markers. PLoS ONE 8, e54470 (2013).ADS 
CAS 
Article 

Google Scholar 
Hilton, E. J. & Lavoué, S. A review of the systematic biology of fossil and living bony-tongue fishes, Osteoglossomorpha (Actinopterygii: Teleostei). Neotrop. Ichthyol. 16 (2018).DeWoody, J. A. & Avise, J. C. Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals. J. Fish Biol. 56, 461–473 (2000).CAS 
Article 

Google Scholar 
Abiodun, J. A. Fisheries Statistical Bulletin Kainji Lake, Nigeria, 2001. 25p (2002).Yem, I. Y., Sani, A. O., Bankole, N. O., Onimisi, H. U. & Musa, Y. M. Over fishing as a factor responsible for declined in fish species diversity of Kainji, Nigeria. In 21st Annual Conference of the Fisheries Society of Nigeria (FISON). 79–85.Mshelia, M. B. et al. Responsible fisheries enhancing poverty alleviation of fishing communities of Lake Kainji. In 19th Annual Conference of the Fisheries Society of Nigeria (FISON) 597–604.Adelakun, K. M. & Kehinde, A. S. Heavy metals bioaccumulations in Chrysichthys nigrodigitatus (Silver catfish) from River Oli, Kainji Lake National Park, Nigeria. Egypt. J. Aquat. Biol. Fish. 23, 253–259 (2019).Article 

Google Scholar 
Ikomi, R. B. & Arimoro, F. O. Effects of recreational activities on the littoral macroinvertebrates of Ethiope River, Niger Delta, Nigeria. J. Aquat. Sci. 29, 155–170 (2014).
Google Scholar 
Ushurhe, O., Origho, T. & Ewhuwhe-Ezo, J. Determinant of water quality and suitability of River Ethiope for fish survival in Southern Nigeria. Can. J. Agr. Crop. 1, 11–18 (2016).
Google Scholar 
Arojojoye, O. A., Oyagbemi, A. A. & Afolabi, J. M. Toxicological assessment of heavy metal bioaccumulation and oxidative stress biomarkers in Clarias gariepinus from Igbokoda River of South Western Nigeria. Bull. Environ. Contam. Toxicol. 100, 765–771 (2018).CAS 
Article 

Google Scholar 
Arojojoye, O. A. et al. Assessment of water quality of selected rivers in the Niger Delta region of Nigeria using biomarkers in Clarias gariepinus. Environ. Sci. Pollut. Res. 28, 22936–22943 (2021).CAS 
Article 

Google Scholar 
Soyinka, O. O. & Ebigbo, C. H. Species diversity and growth pattern of the fish fauna of Epe Lagoon, Nigeria. J. Fish. Aquat. Sci. 7, 392–401 (2012).
Google Scholar 
Akinsanya, B., Ayanda, I. O., Fadipe, A. O., Onwuka, B. & Saliu, J. K. Heavy metals, parasitologic and oxidative stress biomarker investigations in Heterotis niloticus from Lekki Lagoon, Lagos, Nigeria. Toxicol. Rep. 7, 1075–1082 (2020).CAS 
Article 

Google Scholar 
Akinsanya, B., Ayanda, I. O., Onwuka, B. & Saliu, J. K. Bioaccumulation of BTEX and PAHs in Heterotis niloticus (Actinopterygii) from the Epe Lagoon, Lagos, Nigeria. Heliyon 6, e03272. https://doi.org/10.1016/j.heliyon.2020.e03272 (2020).Article 
PubMed 
PubMed Central 

Google Scholar  More

New wave: Petar Puškarić used yeast isolated from the Adriatic Sea to make a beer that he named Morski Kukumar (Sea Cucumber).Credit: Marin Ordulj

Petar Puškarić is an engineer, ecologist and head of beer production at LAB Split, a craft brewery in Split, Croatia. He graduated with a master’s degree from the department of marine studies at the University of Split last year, after successfully making a beer from Candida famata, a yeast that can be isolated from sea water. He now hopes to brew this sea-yeast beer commercially. He speaks to Nature about some of the challenges in going from dissertation to commercialization.How did your marine-yeast beer come about?I’ve had an interest in brewing beer for a long time, and started brewing as a hobby when I was a student. During a marine-microbiology lecture as part of my undergraduate degree in ecology, my mentor Marin Ordulj and I started to talk about marine yeasts, and one question led to another. We wondered whether sea yeast could ferment beer.We researched the literature and could not find anyone who had made a beer with a yeast isolated from the sea. Perhaps we could become the first to do so? The idea stayed with me for a few years as I continued my degree and moved on to my master’s course. When I came to choose my dissertation topic, I decided it was time to put the idea to the test. I discussed things with Marin, and he agreed to help me plan an experiment. By then, I was working part-time at the LAB Split brewery, so I had some brewing experience to bring to our investigations.Our first task was to isolate yeasts from the sea. We then tested the fermentation abilities of the isolated yeasts and grew cultures from the most promising samples. Finally, we used those cultures to brew beer.How did you manage your time between brewing and your degree?I wasn’t overorganized, but I always made sure to be disciplined and to do whatever was needed as tasks came along. I kept active outside work as well, continuing to play as a mandolinist in an orchestra, for example.I didn’t think too strictly about my career, and made time to do the things I enjoyed. I’d recommend that other students also try to enjoy life and spend as much time as possible with friends. After all, life is not just about building a career. I was lucky in proposing a graduate topic that I found interesting and that my mentor liked: that helped me through the duller and more difficult moments.What was the hardest part of the process?The biggest problem was created by marine bacteria, which would outgrow the yeast colonies and thus make the isolation of yeast more difficult. We tackled this problem by using selective nutrient media, which inhibit the growth of bacteria. Eventually, this resulted in pure yeast cultures.What did the beer taste like?The first beer tasting after all that research, thinking and anticipation was really exciting. We noted clove and fruit aromas and a slightly sour tone. It didn’t carry the taste of the sea; the flavour was closest to that of sour beer.What impact do you hope this work will have?The beer is an exciting product of my graduate work, but I also hope that my thesis will encourage others to explore in more detail the yeasts in the Adriatic Sea, and to realize their potential in ecology, medicine and nutrition. Split is on the Adriatic coast and I like the idea that we’re contributing in some small way to protecting that coastline.Sea Cucumber, as we’ve named the beer, might not help much directly in that regard, but I do hope that it could raise awareness about how many useful things there are in the sea.Are you planning on taking the sea yeast further in your career?Any experience in microbiology helps in the food industry. Sea yeast might turn out to be useful in brewing, but we have to consider the finances and infrastructure we’d need to support its use commercially. For now, we’re concentrating on brewing more standard beers. In the future, I hope to brew some of my own recipes, whether Sea Cucumber or something else. I would definitely like to combine brewing with the search for new yeasts that can be used not only in beer making, but in other industries as well. More

Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proc. R. Soc. B: Biol. Sci. 276, 3019–3025 (2009).Article 

Google Scholar 
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492 (2018).CAS 
PubMed 
Article 

Google Scholar 
Drury, C. & Lirman, D. Genotype by environment interactions in coral bleaching. Proc. R. Soc. B Biol. Sci., https://doi.org/10.1098/rspb.2021.0177 (2021).Kenkel, C. D., Almanza, A. T. & Matz, M. V. Fine-scale environmental specialization of reef-building corals might be limiting reef recovery in the Florida Keys. Ecology 96, 3197–3212 (2015).PubMed 
Article 

Google Scholar 
Howells, E. J., Abrego, D., Meyer, E., Kirk, N. L. & Burt, J. A. Host adaptation and unexpected symbiont partners enable reef‐building corals to tolerate extreme temperatures. Glob. Change Biol. 22, 2702–2714 (2016).Article 

Google Scholar 
Thomas, L. et al. Mechanisms of thermal tolerance in reef-building corals across a fine-grained environmental mosaic: lessons from Ofu, American Samoa. Front. Mar. Sci., https://doi.org/10.3389/fmars.2017.00434 (2018).Thomas, L., López, E. H., Morikawa, M. K. & Palumbi, S. R. Transcriptomic resilience, symbiont shuffling, and vulnerability to recurrent bleaching in reef‐building corals. Mol. Ecol. 28, 3371–3382 (2019).PubMed 
Article 

Google Scholar 
Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. USA 110, 1387–1392 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, e33353 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Glob. Chang. Biol. 26, 3473–3481 (2020).Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, e1701413 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Quigley, K. M., Bay, L. K. & van Oppen, M. J. Genome‐wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol. Ecol. 29, 2176–2188 (2020).Howells, E. J. et al. Enhancing the heat tolerance of reef-building corals to future warming. Sci. Adv. 7, eabg6070 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580 (2018).CAS 
PubMed 
Article 

Google Scholar 
Rowan, R. Coral bleaching: thermal adaptation in reef coral symbionts. Nature 430, 742 (2004).CAS 
PubMed 
Article 

Google Scholar 
Sampayo, E. M., Ridgway, T., Bongaerts, P. & Hoegh-Guldberg, O. Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc. Natl Acad. Sci. USA 105, 10444–10449 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maire, J. et al. Intracellular bacteria are common and taxonomically diverse in cultured and in hospite algal endosymbionts of coral reefs. ISME J., 15, 2028–2042 (2021).Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Oppen, M. J. & Blackall, L. L. Coral microbiome dynamics, functions and design in a changing world. Nat. Rev. Microbiol. 17, 557–567 (2019).PubMed 
Article 
CAS 

Google Scholar 
Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching. Science 369 (2020).Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015).CAS 
PubMed 
Article 

Google Scholar 
Jin, Y. K. et al. Genetic markers for antioxidant capacity in a reef-building coral. Sci. Adv. 2, e1500842 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cooke, I. et al. Genomic signatures in the coral holobiont reveal host adaptations driven by Holocene climate change and reef specific symbionts. Sci. Adv. 6, eabc6318 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Bay, R. A. & Palumbi, S. R. Multilocus adaptation associated with heat resistance in reef-building corals. Curr. Biol. 24, 2952–2956 (2014).CAS 
PubMed 
Article 

Google Scholar 
Drury, C. Resilience in reef-building corals: the ecological and evolutionary importance of the host response to thermal stress. Mol. Ecol. 00, 1–18 (2019).CAS 

Google Scholar 
Quigley, K. M., Willis, B. L. & Bay, L. K. Heritability of the Symbiodinium community in vertically-and horizontally-transmitting broadcast spawning corals. Sci. Rep. 7, 8219 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Van Hooidonk, R., Maynard, J. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change 3, 508 (2013).Article 
CAS 

Google Scholar 
Quigley, K. M., Warner, P. A., Bay, L. K. & Willis, B. L. Unexpected mixed-mode transmission and moderate genetic regulation of Symbiodinium communities in a brooding coral. Heredity, 121, 524–536 (2018).Cunning, R., Ritson-Williams, R. & Gates, R. D. Patterns of bleaching and recovery of Montipora capitata in Kāne’ohe Bay, Hawai’i, USA. Mar. Ecol. Prog. Ser. 551, 131–139 (2016).CAS 
Article 

Google Scholar 
Dilworth, J., Caruso, C., Kahkejian, V. A., Baker, A. C. & Drury, C. Host genotype and stable differences in algal symbiont communities explain patterns of thermal stress response of Montipora capitata following thermal pre-exposure and across multiple bleaching events. Coral Reefs, https://doi.org/10.1007/s00338-020-02024-3 (2020).Rocha de Souza, M. et al. Community composition of coral-associated Symbiodiniaceae is driven by fine-scale environmental gradients. bioRxiv https://doi.org/10.1101/2021.11.10.468165 (2021).Innis, T., Cunning, R., Ritson-Williams, R., Wall, C. & Gates, R. Coral color and depth drive symbiosis ecology of Montipora capitata in Kāne’ohe Bay, O’ahu, Hawai’i. Coral Reefs 37, 423–430 (2018).Article 

Google Scholar 
Shore-Maggio, A., Runyon, C. M., Ushijima, B., Aeby, G. S. & Callahan, S. M. Differences in bacterial community structure in two color morphs of the Hawaiian reef coral Montipora capitata. Appl. Environ. Microbiol. 81, 7312–7318 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roach, T. N., Dilworth, J., Jones, A. D., Quinn, R. A. & Drury, C. Metabolomic signatures of coral bleaching history. Nat. Ecol. Evol., 5, 495–503 (2021).Baird, A. H., Guest, J. R. & Willis, B. L. Systematic and biogeographical patterns in the reproductive biology of scleractinian corals. Annu. Rev. Ecol., Evolution, Syst. 40, 551–571 (2009).Article 

Google Scholar 
Caruso, C. et al. Genetic patterns in Montipora capitata across an environmental mosaic in Kāne’ohe Bay. bioRxiv https://doi.org/10.1101/2021.10.07.463582 (2021).Rose, N. H., Bay, R. A., Morikawa, M. K. & Palumbi, S. R. Polygenic evolution drives species divergence and climate adaptation in corals. Evolution 72, 82–94 (2017).PubMed 
Article 

Google Scholar 
Rose, N. H. et al. Genomic analysis of distinct bleaching tolerances among cryptic coral species. Proc. R. Soc. B 288, 20210678 (2021).PubMed 
Article 

Google Scholar 
Forsman, Z. H. et al. Ecomorph or endangered coral? DNA and microstructure reveal Hawaiian species complexes: Montipora dilatata/flabellata/turgescens & M. patula/verrilli. PLoS ONE 5, e15021 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dixon, G., Abbott, E. & Matz, M. Meta‐analysis of the coral environmental stress response: Acropora corals show opposing responses depending on stress intensity. Mol. Ecol. 29, 2855–2870 (2020).CAS 
PubMed 
Article 

Google Scholar 
Lim, S., Kim, D. G. & Kim, S. ERK-dependent phosphorylation of the linker and substrate-binding domain of HSP70 increases folding activity and cell proliferation. Exp. Mol. Med. 51, 1–14 (2019).CAS 
PubMed 
Article 

Google Scholar 
Yancey, P. H. et al. Betaines and dimethylsulfoniopropionate as major osmolytes in cnidaria with endosymbiotic dinoflagellates. Physiol. Biochem. Zool. 83, 167–173 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hill, R., Li, C., Jones, A., Gunn, J. & Frade, P. Abundant betaines in reef-building corals and ecological indicators of a photoprotective role. Coral Reefs 29, 869–880 (2010).Article 

Google Scholar 
Ngugi, D. K., Ziegler, M., Duarte, C. M. & Voolstra, C. R. Genomic blueprint of glycine betaine metabolism in coral metaorganisms and their contribution to reef nitrogen budgets. iScience 23, 101120 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, A. et al. Metabolome shift associated with thermal stress in coral holobionts. bioRxiv https://doi.org/10.1101/2020.06.04.134619 (2021).Sakamoto, A. & Murata, N. The role of glycine betaine in the protection of plants from stress: clues from transgenic plants. Plant, Cell Environ. 25, 163–171 (2002).CAS 
Article 

Google Scholar 
Burg, M. B. & Ferraris, J. D. Intracellular organic osmolytes: function and regulation. J. Biol. Chem. 283, 7309–7313 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, T. H. & Murata, N. Glycinebetaine protects plants against abiotic stress: mechanisms and biotechnological applications. Plant Cell Environ. 34, 1–20 (2011).PubMed 
Article 
CAS 

Google Scholar 
Petronini, P., De Angelis, E., Borghetti, A. & Wheeler, K. Effect of betaine on HSP70 expression and cell survival during adaptation to osmotic stress. Biochem. J. 293, 553–558 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Padilla-Gamiño, J. L., Pochon, X., Bird, C., Concepcion, G. T. & Gates, R. D. From parent to gamete: vertical transmission of Symbiodinium (Dinophyceae) ITS2 sequence assemblages in the reef building coral Montipora capitata. PLoS ONE 7, e38440 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cunning, R. & Baker, A. C. Thermotolerant coral symbionts modulate heat stress‐responsive genes in their hosts. Mol. Ecol. 29, 2940–2950 (2020).CAS 
PubMed 
Article 

Google Scholar 
Buerger, P. et al. Heat-evolved microalgal symbionts increase coral bleaching tolerance. Sci. Adv. 6, eaba2498 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mayfield, A. B. & Gates, R. D. Osmoregulation in anthozoan–dinoflagellate symbiosis. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 147, 1–10 (2007).PubMed 
Article 
CAS 

Google Scholar 
Chan, W. Y., Peplow, L. M., Menéndez, P., Hoffmann, A. A. & van Oppen, M. J. Interspecific hybridization may provide novel opportunities for coral reef restoration. Front. Mar. Sci. 5, 160 (2018).Article 

Google Scholar 
Rose, N. H., Seneca, F. O. & Palumbi, S. R. Gene networks in the wild: identifying transcriptional modules that mediate coral resistance to experimental heat stress. Genome Biol. Evolution 8, 243–252 (2016).CAS 
Article 

Google Scholar 
Ruiz-Jones, L. J. & Palumbi, S. R. Tidal heat pulses on a reef trigger a fine-tuned transcriptional response in corals to maintain homeostasis. Sci. Adv. 3, e1601298 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Chakravarti, L. J., Beltran, V. H. & van Oppen, M. J. Rapid thermal adaptation in photosymbionts of reef‐building corals. Glob. Change Biol. 23, 4675–4688 (2017).Article 

Google Scholar 
Little, A. F., Van Oppen, M. J. & Willis, B. L. Flexibility in algal endosymbioses shapes growth in reef corals. Science 304, 1492–1494 (2004).CAS 
PubMed 
Article 

Google Scholar 
Quigley, K., Randall, C., van Oppen, M. & Bay, L. Assessing the role of historical temperature regime and algal symbionts on the heat tolerance of coral juveniles. Biol. Open 9, bio047316 (2020).Matsuda, S. et al. Coral bleaching susceptibility is predictive of subsequent mortality within but not between coral species. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2020.00178 (2020).Ritson-Williams, R. & Gates, R. D. Coral community resilience to successive years of bleaching in Kane ‘ohe Bay, Hawai ‘i. Coral Reefs. 39, 757–769 (2020).Hancock, J. et al. Coral husbandry for ocean futures: leveraging abiotic factors to increase survivorship, growth and resilience in juvenile Montipora capitata. Mar. Ecol. Prog. Ser., https://doi.org/10.3354/meps13534 (2020).Falconer, D. S. Introduction To Quantitative Genetics (Pearson, 1960).Cunning, R. & Baker, A. C. Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 259–262 (2012).Article 

Google Scholar 
Cunning, R., Gillette, P., Capo, T., Galvez, K. & Baker, A. Growth tradeoffs associated with thermotolerant symbionts in the coral Pocillopora damicornis are lost in warmer oceans. Coral Reefs 34, 155–160 (2015).Article 

Google Scholar 
Alonge, M. et al. RaGOO: fast and accurate reference-guided scaffolding of draft genomes. Genome Biol. 20, 1–17 (2019).Article 

Google Scholar 
Shumaker, A. et al. Genome analysis of the rice coral Montipora capitata. Sci. Rep. 9, 2571 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 356 (2014).Article 

Google Scholar 
Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing data. Genetics 195, 693–702 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 1.17-2. R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2010).
Google Scholar 
Wright, R. M., Aglyamova, G. V., Meyer, E. & Matz, M. V. Gene expression associated with white syndromes in a reef building coral, Acropora hyacinthus. BMC Genomics 16, 371 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hivert, V., Leblois, R., Petit, E. J., Gautier, M. & Vitalis, R. Measuring genetic differentiation from Pool-seq data. Genetics 210, 315–330 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pluskal, T., Castillo, S., Villar-Briones, A. & Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinforma. 11, 1–11 (2010).Article 
CAS 

Google Scholar 
Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nothias, L.-F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dührkop, K. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. methods 16, 299–302 (2019).PubMed 
Article 
CAS 

Google Scholar 
Ludwig, M., Fleischauer, M., Dührkop, K., Hoffmann, M. A. & Böcker, S. in Computational Methods and Data Analysis for Metabolomics 185–207 (Springer, 2020).Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 9, 1–13 (2008).Article 
CAS 

Google Scholar 
Pedersen, H. K. et al. A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links. Nat. Protoc. 13, 2781–2800 (2018).CAS 
PubMed 
Article 

Google Scholar 
Pei, G., Chen, L. & Zhang, W. in Methods in enzymology 585 135–158 (Elsevier, 2017).Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric. Ecosyst. Environ. 84, 1–20 (2001).
Google Scholar 
Wilcove, D. S. & Chen, L. Y. Management costs for endangered species. Conserv. Biol. 12, 1405–1407 (1998).
Google Scholar 
Singer, M. C., Wee, B., Hawkins, S. & Butcher, M. Rapid natural and anthropogenic diet evolution: three examples from checkerspot butterflies in The Evolutionary Biology of Herbivorous Insects: Speciation, Specialization and Radiation (ed. Tilmon, K. J.). 311–324. (University of California Press, 2008).Ruesink, J. L., Parker, I. M., Groom, M. J. & Kareiva, P. M. Reducing the risks of nonindigenous species introductions. Bioscience 45, 465–477 (1995).
Google Scholar 
Rhymer, J. M. & Simberloff, D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).
Google Scholar 
Vitousek, P. M., D’Antonio, C. M., Loope, L. L. & Westbrooks, R. Biological invasions as global environmental change. Am. Sci. 84, 468–478 (1996).ADS 

Google Scholar 
Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife-threats to biodiversity and human health. Science 287, 443–449 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Lockwood, J. L., Cassey, P. & Blackburn, T. The role of propagule pressure in explaining species invasions. Trends Ecol. Evol. 20, 223–228 (2005).PubMed 

Google Scholar 
Blackburn, T. M. & Jeschke, J. M. Invasion success and threat status: two sides of a different coin?. Ecography 32, 83–88 (2009).
Google Scholar 
Facon, B. et al. A general eco-evolutionary framework for understanding bioinvasions. Trends Ecol. Evol. 21, 130–135 (2006).PubMed 

Google Scholar 
Ellstrand, N. C. & Schierenbeck, K. A. Hybridization as a stimulus for the evolution of invasiveness in plants?. Proc. Natl. Acad. Sci. USA 97, 7043–7050 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Verhoeven, K. J. F., Macel, M., Wolfe, L. M. & Biere, A. Population admixture, biological invasions and the balance between local adaptation and inbreeding depression. Proc. R. Soc. B-Biol. Sci. 278, 2–8 (2011).
Google Scholar 
Brevik, K., Lindström, L., McKay, S. D. & Chen, Y. H. Transgenerational effects of insecticides-implications for rapid pest evolution in agroecosystems. Curr. Opin. Insect Sci. 26, 34–40 (2018).PubMed 

Google Scholar 
Kirk, W. D. J. & Terry, L. I. The spread of the western flower thrips Frankliniella occidentalis (Pergande). Agr. Forest. Entomol. 5, 301–310 (2003).
Google Scholar 
Piiroinen, S., Lyytinen, A. & Lindström, L. Stress for invasion success? Temperature stress of preceding generations modifies the response to insecticide stress in an invasive pest insect. Evol. Appl. 6, 313–323 (2013).PubMed 

Google Scholar 
Margus, A. et al. Sublethal pyrethroid insecticide exposure carries positive fitness effects over generations in a pest insect. Sci. Rep. 9, 1–10 (2019).CAS 

Google Scholar 
Vais, H., Williamson, M. S., Devonshire, A. L. & Usherwood, P. N. R. The molecular interactions of pyrethroid insecticides with insect and mammalian sodium channels. Pest Manag. Sci. 57, 877–888 (2001).CAS 
PubMed 

Google Scholar 
Smith, L. B., Kasai, S. & Scott, J. G. Voltage-sensitive sodium channel mutations S989P+ V1016G in Aedes aegypti confer variable resistance to pyrethroids, DDT and oxadiazines. Pest Manag. Sci. 74, 737–745 (2018).CAS 
PubMed 

Google Scholar 
Guerrero, F. D., Jamroz, R. C., Kammlah, D. & Kunz, S. E. Toxicological and molecular characterization of pyrethroid-resistant horn flies, Haematobia irritans: Identification of kdr and super-kdr point mutations. Insect Biochem. Mol. 27, 745–755 (1997).CAS 

Google Scholar 
Morin, S. et al. Mutations in the Bemisia tabaci para sodium channel gene associated with resistance to a pyrethroid plus organophosphate mixture. Insect Biochem. Mol. 32, 1781–1791 (2002).CAS 

Google Scholar 
Kasai, S. et al. First detection of a putative knockdown resistance gene in major mosquito vector, Aedes albopictus. Jpn. J. Infect. Dis. 64, 217–221 (2011).CAS 
PubMed 

Google Scholar 
Brito, L. P. et al. Assessing the effects of Aedes aegypti kdr mutations on pyrethroid resistance and its fitness cost. PLoS ONE 8, e60678 (2013).ADS 
MathSciNet 

Google Scholar 
De Barro, P. J., Liu, S. S., Boykin, L. M. & Dinsdale, A. B. Bemisia tabaci: A statement of species status. Annu. Rev. Entomol. 56, 1–19 (2011).PubMed 

Google Scholar 
Perring, T. M. The Bemisia tabaci species complex. Crop Prot. 20, 725–737 (2001).
Google Scholar 
Navas-Castillo, J., Fiallo-Olivé, E. & Sánchez-Campos, S. Emerging virus diseases transmitted by whiteflies. Annu. Rev. Phytopathol. 49, 219–248 (2011).CAS 
PubMed 

Google Scholar 
Mugerwa, H. et al. African ancestry of new world, Bemisia tabaci-whitefly species. Sci. Rep. 8, 2734 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kanakala, S. & Ghanim, M. Global genetic diversity and geographical distribution of Bemisia tabaci and its bacterial endosymbionts. PLoS ONE 14, e0213946 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hu, J. et al. New putative cryptic species detection and genetic network analysis of Bemisia tabaci (Hemiptera: Aleyrodidae) in China based on mitochondrial COI sequences. Mitochondr. DNA Part DNA Mapp. Seq. Anal. 29, 474–484 (2018).Vyskocilova, S., Tay, W. T., van Brunschot, S., Seal, S. & Colvin, J. An integrative approach to discovering cryptic species within the Bemisia tabaci whitefly species complex. Sci. Rep. 8, 10886 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cheek, S. & Macdonald, O. Statutory controls to prevent the establishment of Bemisia tabaci in the United Kingdom. Pestic. Sci. 42, 135–137 (1994).CAS 

Google Scholar 
Horowitz, A. R. et al. Biotype Q of Bemisia tabaci identified in Israel. Phytoparasitica 31, 94–98 (2003).
Google Scholar 
Basit, M. Status of insecticide resistance in Bemisia tabaci: Resistance, cross-resistance, stability of resistance, genetics and fitness costs. Phytoparasitica 47, 207–225 (2019).CAS 

Google Scholar 
Horowitz, A. R., Kontsedalov, S., Khasdan, V. & Ishaaya, I. Biotypes B and Q of Bemisia tabaci and their relevance to neonicotinoid and pyriproxyfen resistance. Arch. Insect Biochem. Physiol. 58, 216–225 (2005).CAS 
PubMed 

Google Scholar 
Horowitz, A. R., Ghanim, M., Roditakis, E., Nauen, R. & Ishaaya, I. Insecticide resistance and its management in Bemisia tabaci species. J. Pest. Sci. 93, 893–910 (2020).
Google Scholar 
Delatte, H. et al. A new silverleaf-inducing biotype Ms of Bemisia tabaci (Hemiptera: Aleyrodidae) indigenous to the islands of the south-west Indian Ocean. B. Entomol. Res. 95, 29–35 (2005).CAS 

Google Scholar 
Peterschmitt, M. et al. First report of tomato yellow leaf curl virus in Réunion Island. Plant Dis. 83, 303 (1999).CAS 
PubMed 

Google Scholar 
Delatte, H., Lett, J.-M., Lefeuvre, P., Reynaud, B. & Peterschmitt, M. An insular environment before and after TYLCV introduction in Tomato Yellow Leaf Curl Virus Disease: Management, Molecular Biology, Breeding for Resistance (ed. Czosnek, H.). 13–23. (Springer, 2007).Delatte, H. et al. Microsatellites reveal extensive geographical, ecological and genetic contacts between invasive and indigenous whitefly biotypes in an insular environment. Genet. Res. 87, 109–124 (2006).CAS 
PubMed 

Google Scholar 
Delatte, H. et al. Genetic diversity, geographical range and origin of Bemisia tabaci (Hemiptera: Aleyrodidae) Indian Ocean Ms. B. Entomol. Res. 101, 487–497 (2011).CAS 

Google Scholar 
Thierry, M. et al. Mitochondrial, nuclear, and endosymbiotic diversity of two recently introduced populations of the invasive Bemisia tabaci MED species in La Réunion. Insect. Conserv. Divers. 8, 71–80 (2015).
Google Scholar 
Tsagkarakou, A. et al. Molecular diagnostics for detecting pyrethroid and organophosphate resistance mutations in the Q biotype of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae). Pestic. Biochem. Phys. 94, 49–54 (2009).CAS 

Google Scholar 
Delatte, H. et al. Differential invasion success among biotypes: case of Bemisia tabaci. Biol. Invasions 11, 1059–1070 (2009).
Google Scholar 
Chu, D., Tao, Y.-L., Zhang, Y.-J., Wan, F.-H. & Brown, J. K. Effects of host, temperature and relative humidity on competitive displacement of two invasive Bemisia tabaci biotypes [Q and B]. Insect Sci. 19, 595–603 (2012).
Google Scholar 
Chu, D., Wan, F. H., Zhang, Y. J. & Brown, J. K. Change in the biotype composition of Bemisia tabaci in Shandong Province of China from 2005 to 2008. Environ. Entomol. 39, 1028–1036 (2010).PubMed 

Google Scholar 
Pascual, S. & Callejas, C. Intra- and interspecific competition between biotypes B and Q of Bemisia tabaci (Hemiptera: Aleyrodidae) from Spain. B. Entomol. Res. 94, 369–375 (2004).CAS 

Google Scholar 
Pan, H. et al. Insecticides promote viral outbreaks by altering herbivore competition. Ecol. Appl. 25, 1585–1595 (2015).PubMed 

Google Scholar 
Shatters, R. G. et al. Population genetics of Bemisia tabaci biotypes B and Q from the Mediterranean and the U.S. inferred using microsatellite markers. in Fourth International Bemisia Workshop International Whitefly Genomics Workshop (3–8 December 2006). (Duck Key: USDA/ARS US Horticultural Research Laboratory, 2006).McKenzie, C. L. & Osborne, L. S. Bemisia tabaci MED (Q biotype) (Hemiptera: Aleyrodidae) in Florida is on the move to residential landscapes and may impact open-field agriculture. Fla. Entomol. 100, 481–484 (2017).
Google Scholar 
Guo, X.-J. et al. Diversity and genetic differentiation of the whitefly Bemisia tabaci species complex in China based on mtCOI and cDNA-AFLP analysis. J. Integr. Agr. 11, 206–214 (2012).CAS 

Google Scholar 
Prabhaker, N., Castle, S., Henneberry, T. J. & Toscano, N. C. Assessment of cross-resistance potential to neonicotinoid insecticides in Bemisia tabaci (Hemiptera: Aleyrodidae). B. Entomol. Res. 95, 535–543 (2005).CAS 

Google Scholar 
Taquet, A. et al. Insecticide resistance and fitness cost in Bemisia tabaci (Hemiptera: Aleyrodidae) invasive and resident species in La Réunion Island. Pest Manag. Sci. 76, 1235–1244 (2020).CAS 
PubMed 

Google Scholar 
Elfekih, S. et al. Genome-wide analyses of the Bemisia tabaci species complex reveal contrasting patterns of admixture and complex demographic histories. PLoS ONE 13, e0190555 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thierry, M. et al. Symbiont diversity and non-random hybridization among indigenous (Ms) and invasive (B) biotypes of Bemisia tabaci. Mol. Ecol. 20, 2172–2187 (2011).CAS 
PubMed 

Google Scholar 
Gauthier, N. et al. Genetic structure of Bemisia tabaci Med populations from home-range countries, inferred by nuclear and cytoplasmic markers: impact on the distribution of the insecticide resistance genes. Pest Manag. Sci. 70, 1477–1491 (2014).CAS 
PubMed 

Google Scholar 
Alon, M. et al. Multiple origins of pyrethroid resistance in sympatric biotypes of Bemisia tabaci (Hemiptera: Aleyrodidae). Insect Biochem. Mol. 36, 71–79 (2006).CAS 

Google Scholar 
Vassiliou, V. et al. Insecticide resistance in Bemisia tabaci from Cyprus. Insect Sci. 18, 30–39 (2011).CAS 

Google Scholar 
Gnankiné, O., Hema, O., Namountougou, M., Mouton, L. & Vavre, F. Impact of pest management practices on the frequency of insecticide resistance alleles in Bemisia tabaci (Hemiptera: Aleyrodidae) populations in three countries of West Africa. Crop Prot. 104, 86–91 (2018).
Google Scholar 
Cahill, M., Byrne, F. J., Gorman, K., Denholm, I. & Devonshire, A. L. Pyrethroid and organophosphate resistance in the tobacco whitefly Bemisia tabaci (Homoptera: Aleyrodidae). B. Entomol. Res. 85, 181–187 (1995).CAS 

Google Scholar 
Weill, M. et al. Insecticide resistance: A silent base prediction. Curr. Biol. 14, 552–553 (2004).
Google Scholar 
Bouvier, J.-C. et al. Deltamethrin resistance in the codling moth (Lepidoptera: Tortricidae): Inheritance and number of genes involved. Heredity (Edinb) 87, 456–462 (2001).CAS 

Google Scholar 
Calvert, L. A. et al. Morphological and mitochondrial DNA marker analyses of whiteflies (Homoptera: Aleyrodidae) colonizing cassava and beans in Colombia. Ann. Entomol. Soc. Am. 94, 512–519 (2001).CAS 

Google Scholar 
Tocko-Marabena, B. K. et al. Genetic diversity of Bemisia tabaci species colonizing cassava in Central African Republic characterized by analysis of cytochrome c oxidase subunit I. PLoS ONE 12, e0182749 (2017).PubMed 
PubMed Central 

Google Scholar 
Ally, H. M. et al. What has changed in the outbreaking populations of the severe crop pest whitefly species in cassava in two decades?. Sci. Rep. 9, 1–13 (2019).CAS 

Google Scholar 
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Raymond, M. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).
Google Scholar 
Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).
Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 

Google Scholar 
Earl, D. A. & VonHoldt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.r-project.org/ (2020).Jombart, T. & Ahmed, I. Adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).PubMed 

Google Scholar 
Vähä, J.-P. & Primmer, C. R. Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci. Mol. Ecol. 15, 63–72 (2006).PubMed 

Google Scholar  More

Our online sampling methods largely follow protocols detailed in3,4, though we limited our online searches to online shops and did not extend to social media. Large portions of code are directly re-used from those papers, although we provide modified code with this paper additionally. For keyword searches and data review we used R v.4.1.149 via RStudio v.1.4.110350, and made wide use of functions supplied by the anytime v.0.3.951, assertthat v.0.2.152, dplyr v.1.0.753, glue v.1.4.254, lazyeval v.0.2.255, lubridate v.1.7.1056, magrittr v.2.0.157, 17urr v.0.3.458, reshape2 v.1.4.459, stringr v.1.4.060, and tidyr v.1.1.361 other specific package uses are listed during the methods description. We used the grateful v.0.0.362 package to generate citations for all R packages. Code and data used to produce figures and summary data are also available at: 10.5281/zenodo.5758541.Website sampling and searchWe searched for contemporary arachnid selling websites using the Google search engine and targeted nine languages (English, French, Spanish, German, Portuguese, Japanese, Czech, Polish, Russian). Terms were created to be inclusive, so only spiders and scorpions were on the initial search string as specialist groups may exist for either, but are unlikely for smaller arachnid groups, which were often listed under “other” in online shops. Terms were selected to be encompassing so that any sites listing variants of “spider” or mentioning arachnid in the chosen language were selected. Whilst some groups such as tarantulas are more popular as pets such sites will not omit translations of spider and should also be captured in the search, hence Terraristika (as was shown in previous analysis of amphibians and reptiles) listed the greatest number of species, despite not being a specialist site. We used the localised versions of each of these languages with the following Boolean search strings:

English: (Spider OR scorpion OR arachnid) AND for sale

French: (Araignée OR scorpion OR arachnide) AND à vendre

Spanish: (Araña OR escorpión OR arácnido) AND en venta

German: (Arachnoid OR Spinne OR Skorpion OR Spinnentier) AND zum Verkauf

Portuguese: (Aranha OR escorpião OR aracnídeo) AND à venda

Japanese: (クモ OR サソリ OR クモ型類) AND (中村彰宏 OR 販売)

Czech: (Pavouk OR Štír OR pavoukovec) AND prodej

Polish: (Pająk OR Skorpion OR pajęczak) AND sprzedaż

Russian: Продажа пауков OR скорпионов

We undertook these searches in a private window in the Firefox v.92.0.1 browser63 to limit the impacts of search history. These keywords were used to identify sites which may be selling arachnids, which could then be checked before a comprehensive scrape.For each language, we downloaded the first 15 pages of results between 2021-06-06 and 2021-07-07 (or fewer in the result that the search returned fewer than 15 pages: German 8 pages and Spanish 14 pages). This resulted in ~1270 sites that could potentially be selling arachnids. After removing duplicates and simplifying the URLs (so all ended in.com,.org,. co.uk etc.; Code S1), we reviewed each site for the following criteria (2021-07-31 to 2021-08-02): whether they sell arachnids, the type of site (trade or classified ads), the order arachnids were listed in (e.g., date or alphabetical), the best search method for gather species appearances (see below for hierarchical search methods), a refined target URL listing species inventory, the number of pages within the website potentially required to cycle through, and if the search method required a crawl, whether the site explicitly forbade crawling data collection and whether we could limit the crawl’s scope with a filter on downstream URLs. Finally, we assigned all suitable sites with a unique ID. We have made a censored version of the website review results available in Data S1. In addition to the systematic search for arachnid trade, we added 43 websites discovered ad hoc from links on previously discovered sites (many listed online shops), those listed in other journal articles on invertebrate trade (i.e.,6) or from discussion with informed colleagues (between 2021-08-07 and 2021-09-15). After reviewing these ad hoc sites (2021-08-07 to 2021-09-15), we had a combined total of 111 sites to attempt to search for the appearance of arachnid species.Our searches of websites took one of five forms (Code S2), designed to minimise server load and limit the number of irrelevant pages searched, while ensuring we captured the pages listing species. We prioritised using the lowest/simplest search method possible for each site.Single page or PDFFor websites that listed their entire arachnid stock on a single page, we retrieved that single page using the downloader v.0.4 package64. In cases where the inventory was listed in a PDF, we manually downloaded the PDF and used pdftools v.3.0.165 to assess the text.CycleSome websites had large stocklists split across multiple pages that could be accessed sequentially. In these cases, we used the downloader v.0.4 package64 to retrieve each page, as we cycled from page 1 to the terminal page identified during the website review stage. Two sites required a slight modification to the page cycling process: as the sequential pages were not defined by pages, but by the number of adverts displayed. In these instances, we cycled through all adverts 20 adverts at a time (i.e., matching the default number displayed at a time by the site). For all cycling we implemented a 10 s cooldown between requests to limit server load.Level 1 crawlFor websites that split their stock between multiple pages, but with no sequential ordering, we used a level 1 crawl, via the Rcrawler v.0.1.9.1 package66 to access them all. For example, where a site had an “arachnid for sale” page, but full species names existed only in linked pages (e.g., “tarantulas”, “scorpions”).Cycle and level 1 crawlSome websites required a combined approach, where stock was split sequentially across pages, and the species identities (i.e., scientific names) required accessing the pages linked to from the sequential pages. In these cases, we ran the initial sequential sampling followed by a level 1 crawl.Level 2 crawlWhere level 1 crawls were insufficient to cover all species sold on a site, we used a level 2 crawl to reach all pages listing species. This tended to be the case on websites with multiple categories to classify and split their stock (e.g., “arachnid”—“spider”—“tarantula”).For all crawls, we used a cooldown of 20 s between requests to limit server load, and where possible we limited the scope of the crawl (i.e., linked pages to be retrieved) using a key phrase common to all stock listing pages (e.g., “/category=arachnid/”).In addition to the sampling of contemporary sites, we explored the archived pages available for https://www.terraristik.com via the Internet Archive (2002–201967). Terraristika had been previously shown as a major contributor to traded species lists4, and the website’s age and accessibility via the internet archive meant it was one of the few websites where temporal sampling was feasible. We used pages retrieved via the Internet Archive’s Wayback machine API68, via code created for3,4. The code used was based on the wayback v.0.4.0 package69, but additionally made httr v.1.4.270, jsonlite v.1.7.271, downloader v.0.464, lubridate v.1.7.1056, and tibble v.3.1.3 packages72 (Code S3).Keyword generationWe relied on multiple sources to build a list of arachnid species (spiders, scorpions and uropygi). For spiders we used the WSC (ref. 18; https://wsc.nmbe.ch/dataresources; accessed 2021-09-18). We filtered the WSC dataset to remove subspecies, then used a combination of rvest v.1.0.173, dplyr v.1.0.753, and stringr v.1.4.0 packages60 (see Code S4) to query the online version of the WSC database to retrieve all synonyms for each species. Where the synonyms were listed with an abbreviated genus, we replace the abbreviation with the first instance of a genus that matched the first letter of the abbreviation.We combined the WSC data with a list manually retrieved from the Scorpion Files74 (https://www.ntnu.no/ub/scorpion-files/index.php; accessed 2021-09-19). For the uropygi species, we combined species listings from Integrated Taxonomic Information System (ITIS75; https://www.itis.gov/servlet/SingleRpt/RefRpt?search_type=source&search_id=source_id&search_id_value=1209 and https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&anchorLocation=SubordinateTaxa&credibilitySort=TWG%20standards%20met&rankName=ALL&search_value=82710&print_version=SCR&source=from_print#SubordinateTaxa; accessed 2021-09-19) and the Western Australian Museum76 (http://www.museum.wa.gov.au/catalogues-beta/browse/uropygi; accessed 2021-09-19). We were unable to source reliable data on all scorpion and uropygi synonyms; therefore, we used all names listed from all sources, but made note of those names considered nomen dubium. Our final keyword list contained 52,111 species, 94,184 different species names, with mean of 1.81 SE ± 0.01 terms per species (range 1–61). For summaries of total species, we relied on the species classed as accepted by the species databases (WSC, Scorpion Files, ITIS and the Western Australian Museum).Keyword searchWe successfully retrieved 3020 pages from 103 websites (mean = 28.78 SE ± 11.42, range: 1–1077), and used a further 4668 previously archived pages. To prepare each of the retrieved web pages for keyword searching, we removed all double spaces, html elements, and non-alpha-numeric characters, replacing them with single spaces (Code S5). For this process we used rvest v.1.0.173, XML v.3.99.0.877, and xml2 v.1.3.278 packages. This process increased the chances that genus and species epithets would appear in a compatible format when compared to our keyword list. The process was not able to repair abbreviated genera, or aid detection where genus and species epithet were not reported side-by-side.Due to the large number of species we were forced to adapt previous searching methods, instead implementing a hierarchical genus-species search (Code S6). We searched each retrieved page for any mention of genera, then only searched for species that were contained within that genus. We did not differentiate whether the genus was currently accepted or old, so if a species had ever belonged to a genus it was included in the second stage of the search. The specifics of the keyword search used case-insensitive fixed string matching (via the stringr v.1.4.0 package60). While collation string matching would have helped detect species with differently coded ligatures or diacritic marks, the occurrence of ligature and diacritic marks are infrequent in scientific names and did not warrant the considerably increased computational costs.Via the keyword search we recorded all instances of genus matches, species matches, the website ID, and the page number. We also collected the words surrounding a genus match (3 prior and four after) as a means of exploring common terms that may be used to describe the genera.We provide the compiled outputs from searching contemporary and historic pages in Data S2–S4. Prior to combining these two datasets into a final list of traded species, and summarising the overall patterns, we cleaned out instances of spurious genera and species detections. Predominantly this included short genera names that could appear at the start of longer words (e.g., terms such as: “rufus”, “Dia”, “Diana”, “Mala”, “Inca”, “Pero”, “May”, “Janus”, “Yukon”, “Lucia”, “Zora”, “Beata”, “Neon”, “Prima”, “Meta”, “Patri”, “Enna”, “Maso”, “Mica”, “Perro”; we already implemented a filter that required genera to be preceded by a space and thus these were not part of the species name). We are confident these genera should be excluded, as none had species detected within them.Trade database and third-party dataWe downloaded United States Fish and Wildlife Service’s LEMIS data compiled by79,80 from https://doi.org/10.5281/zenodo.3565869 (Data S5). We filtered the LEMIS data to records where the class was listed as Arachnida (Code S6).We downloaded the Gross imports data from the CITES trade database from the website and filtered to Class Arachnid, years 1975–202181 (accessed 2021-09-15; Data S6), and downloaded the CITES appendices filtered to arachnids82 (Data S7).We downloaded the IUCN Redlist assessments for arachnids from https://www.iucnredlist.org83 (accessed 2021-09-15; Data S8).Species summary and visualisationWe compiled all sources of trade data (online, LEMIS, CITES) into a single dataset detailing which genera/species had been detected in each source (Data S9 and Code S7). We used two criteria to determine detection, whether there was an exact match with an accepted genus/species or whether there was a match to any historically used genera/species name. Because of splits in genera, the “ANY genera” matching is likely overly generous. For broad summaries we rely on the “ANY species” name matching.We used cowplot v.1.1.184, ggplot2 v.3.3.585, ggpubr v.0.4.086, ggtext v.0.1.187, scales v.1.1.188, scico v.1.2.089, and UpSetR v.1.4.090 to generate summary visuals (Code S8; Code S9). We added additional details to the upset plot and modified the position of plot labels using Affinity Designer v.1.10.391. We also used Affinity Designer to create the Uropygid silhouette for Fig. 1. We obtained public domain licensed spider and scorpion silhouettes from http://phylopic.org/ (https://phylopic.org/image/d7a80fdc0-311f-4bc5-b4fc-1a45f4206d27/; http://phylopic.org/image/4133ae32-753e-49eb-bd31-50c67634aca1/).Descriptions and coloursWe explored the lag time between species descriptions, and their detection in LEMIS or online trade (Code S10). We relied on the description dates provided alongside the lists of species names. Unlike the broader summaries, we restricted explorations of lag times to species detected only via exact matches (operating under the assumption that newly described species traded swiftly after description would be using the modern accepted name). We distinguished between those species detected only in the complementary data, as the earliest trade date was not known; therefore, our summaries of lag time are based on those species detected in a particular year either via LEMIS or temporal online trade.Following a visual inspection of sites where we often noticed listings with either colour or localities (e.g., “Chilobrachys spp. “Electric Blue” 0.1.3. Chilobrachys sp. “Kaeng Krachan” 0.1.0. Chilobrachys spp. “Prachuap Khiri Khan”: Data S9). We explored the words that surrounded detected genera. After using the forcats v.0.5.192, stringr v.1.4.060, and tidytext v.0.3.193 package to compile common terms and remove English stop words, we determine colour was frequently mentioned (Code S11). To filter out non-colour words, we used wikipedia’s list of colours (https://en.wikipedia.org/wiki/List_of_colors:_N%E2%80%93Z). Once cleaned, we further removed terms that are ambiguously colour related (e.g., “space”, “racing”, “photo”, “boy”, “bean”, “blaze”, “jungle”, “mountain”, “dune”, “web”, “colour”, “rainforest”, “tree”, “sea”). We then summarised this data as the counts of instances where a genus appeared alongside a given colour term (n.b., counts are therefore impacted by any underlying imbalances in how many times a site mentioned a genus). We plotted all colours using the same hex codes listed on the wikipedia page, with the exception of “cobalt”, “grey”, “metallic”, “slate”, “electric”, “dark”, “sheen”, and “chocolate” that required manual linking to a hex code.Summary of trade numbersWe summarised LEMIS data using a number of filters (Code S12). Following3,4,94, we limited our summaries to items that feasibly can be considered to represent whole individuals (LEMIS code = Dead animal BOD, live eggs (EGL), dead specimen (DEA), live specimen (LIV), specimen (SPE), whole skin (SKI), entire animal trophy (TRO)). We describe the portion of trade that is prevented (i.e., seized, where disposition == “S”). We classed non-commercial trade as anything listed as for Biomedical research (M), Scientific (S), or Reintroduction/introduction into the wild (Y). For captive vs. wild summaries, we treated all Animals bred in captivity (C and F), Commercially bred (D), and Specimens originating from a ranching operation (R) as originating from captivity. We only included animals listed as Specimens taken from the wild (W) in wild counts. The few instances that fell outside of our defined captive vs. wild categorisation are treated as other. For summaries of wild capture per genus, we relied entirely on LEMIS’s listings of genera, making no effort to determine synonymisations. We did filter out those listed only as “Non-CITES entry” or NA. We used the countrycode v.1.3.095 package to help plot the LEMIS countries of origin. Taxonomy represents an ongoing challenge, we were limited to recognising the species listed in the aforementioned databases, generating synonym lists from these sources, and attempting to reconcile these lists. Rapid rates of species description means that compiling comprehensive lists can be challenging, and species may be traded under junior synonyms or old names, and newer descriptions may not have been added to sites96. We were also limited to platforms that advertised using text not images, as images can be challenging to identify accurately.MappingMapping species is challenging due to the lack of standardised data on species distributions. Spider distributions were mapped based on the data in the World Spider Catalogue (Data S12). Firstly, the localities associated with each species were collated into four spreadsheets based on the data provided in the WSC (WSC18; https://wsc.nmbe.ch/dataresources; accessed 2021-09-18), these listed (1) country, (2) region, (3) “to” (where the range was given as one country to another) and (4) Island.Before processing any “introduced” localities were removed, the four sheets were then checked for any simple spelling errors (in islands file) or mislistings (i.e., regions in the islands file). Country data were cross-referenced with the names of country provided by Thematic Mapper to standardise them (https://thematicmapping.org/; Data S11). This was done by uploading data into Arcmap and using joins and connects to connect it to the standard country name file, and any which could not be paired were corrected to ensure all could be successfully digitised.Regions were digitised based on accepted names of different regions and included 33 different regions (see supplements) for each of these the standard accepted area within each of these regions was searched online to determine the accepted boundaries. These were then selected from the Thematic mapper, exported and labelled with the corresponding region. Once this was completed for all 33 regions they were merged and exported to a geodatabase. The spreadsheet listing regional preferences of each species was also uploaded to Arcmap 10.3, then exported into the geodatabase, then connected to a regional map using joins and relates to connect the regional preferences from the spreadsheet to the shapefiles. The new dbf was then exported to provide a listing of each species and each country in the region it was connected to, and then copied into the same csv as the corrected country listings.For preferences listed as “to” we first separated each country listed in the “to” listings into a separate column, then developed a list of species and each of the countries listed in the “to” list (which was frequently between 5–6). These were then corrected to the standard names from thematic mapper for both countries and the regions used in the previous section. We then merged the countries and regions file and added fields of geometry in ArcMap to provide a centroid for each designated area. This table was then exported and joined and connected to the species in the “to” file. This data was then converted to point form and turned to a point file, then a minimum convex polygon (convex hulls) developed for each species to connect the regions between all those listed. These species specific minimum convex polygons were then intersected with the countries from Thematic mapper, and then dissolve was used to form a shapefile that just listed species and all the countries between those ranges. This was then exported and merged with the listings from countries and regions.The islands file included both independent islands (which needed names corrected, or archipelago names given) and those that fall within a national designation. For those islands we replaced the island name with that of the country, as listings of species may be particularly poor, and tiny non-independent islands are not visible in the global-scale analysis.This forth database table was then merged with the former three, and remove duplicates used to remove any duplicate entries, as species often had individual countries listed in additions to regions or “to”. This was then uploaded into Arcmap and exported to a geodatabase file then connected to the original Thematic mapper file and exported to the geodatabase to yield 134,187 connections between species and countries. This was then connected to our main analysis to include the trade status, and CITES and IUCN Redlist status for each species for further analysis.Scorpion data was considerably messier than that on the world spider catalogue. Firstly, we downloaded all scorpion data from iNaturalist and GBIF97,98 (search; scorpions), removed duplicates, then cross-referenced these with the thematic mapper file within Quantum GIS. Species listed in regions where they were clearly not native (i.e., a species listed in the UK when the rest of that species or genus were in Australia) were removed, and all extinct species were excluded.In addition, all the “update files” were downloaded from the “Scorpion files”, the PDFs collated then using smallpdf tools the tables were extracted into excel form and cleaned to include just species and country listing. This was added to the countries listed for species within99 and100 though this was restricted to a subset of species. The data were all collated into an excel file with the species name, and country listing. This was then added to all the data from https://scorpiones.pl/maps/. These maps have a good coverage of species countries, but are apparently no longer being updated (Jan Ove Rein pers comm 2021) hence the need for further data to provide complete and updated and comprehensive coverage for all species. Country names were then standardised based on the Thematic Mapper standards (Data S13 and Data S11). Species names were then cross-referenced to those listed in the Scorpion files, any not matching were checked as synonyms and converted to the accepted name (though the only collated data for Scorpion synonyms was on French-language Wikipedia, i.e., see https://fr.wikipedia.org/wiki/Bothriurus). Once all country and species names were corrected this provided a listing of 4059 species-country associations. These were then associated with country files in the same way as spiders. We plotted spider and scorpion species/genera, as well as LEMIS origins, using ggplot285, combining Thematic world border data (https://thematicmapping.org/) with summaries of species/genera/and trade levels. Species listed in a single-country (and thus more likely to be country endemic) were also counted using summary statistics, so that species most vulnerable to trade could be noted separately.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

PreliminariesTo unify all the five classes of two-by-two games, Yamamoto et al.35 introduced the weightlifting game. In this game, each player either cooperates or defects in carrying a weight. Players who carry the weight pay a cost, (cge 0). The weight is successfully lifted with probability ({p}_{i}), where (i=mathrm{0,1},2) is the total number of cooperators and ({p}_{i}) increases with the number of cooperators (i). If the cooperators succeed, both players receive a benefit (b >0). However, in case of failure, both players gain nothing. The pay-off of the cooperators is (b{p}_{i}-c), and the pay-off of the defectors is (b{p}_{i}) (Table 2). In terms of the parameters (Delta {p}_{1}={p}_{1}-{p}_{0}) and (Delta {p}_{2}={p}_{2}-{p}_{1}), which represents the increase in the probability of success due to an additional cooperator, the following inequalities are obtained for the pay-offs (R, T, S), and (P) (Table 1):

(i)

(Delta {p}_{1} >c/b) for (S >P),

(ii)

(Delta {p}_{2} >c/b) for (R >T), and

(iii)

(Delta {p}_{1}+Delta {p}_{2} >c/b) for (R >P).

Table 2 Pay-off table of two-person weightlifting game.Full size tablePD satisfies only (iii), CH satisfies (i) and (iii), SH satisfies (ii) and (iii), DT satisfies none of the three conditions, and CT satisfies all three. In 2021, Chiba et al.1 studied the evolution of cooperation in society by incorporating environmental value in the weightlifting game. They found that the evolution of cooperation seems to follow a DT to DT trajectory, which can explain the rise and fall of human societies.The ({varvec{n}})-player weightlifting gameIn this study, we generalize the weightlifting game to (n)-players. Suppose (n) self-interested and rational individuals selected from a population of infinite size. The (n) players are asked to lift a weight. Each individual (or player) can decide to either carry the weight (cooperate, (C)) or not carry/pretend to carry the weight (defect, (D)). Players who decide to carry the weight can either succeed or fail. The probability of successful weightlifting is denoted by ({p}_{i}), (i=mathrm{0,1},dots ,n), where (i) indicates the number of cooperators (henceforth, (i) always represents the number of cooperators). The probability of success increases with the number of individuals cooperating, and it may remain less than unity even if all (n) individuals cooperate. Players who decide to carry the weight pay a cost, (cge 0), regardless of the outcome, while those who defect need not pay anything. If the cooperators succeed, all (n) individuals receive a benefit (bge 0). There is no penalty for failure. We use the expected gains/losses of the players as the pay-off. If there are (i-1) cooperative players, then the pay-off of (j) is ({B}_{C}left(iright)=b{p}_{i}-c) when (j) cooperates and ({B}_{D}left(i-1right)=b{p}_{i-1}) when (j) defects. The number of cooperators differs by one, since in ({B}_{C}left(iright)), there is an additional cooperator, which is (j) him- or herself. To decide whether to cooperate or defect, all players weigh their expected gain and rationally choose the option with the highest expected gain. The graphical outline of this game is illustrated in Fig. 1 (see also Supplementary Figure S1 for the flow of the game). The pay-off table for a four-player game is shown as an example in Table 3. Here, player (1) is the innermost row (strategies are listed in the second column of the table), player (2) is the innermost column (strategies are listed in the second row of the table), and the succeeding players take the succeeding rows or columns (we enter the first player as a row player and the following player as a column player and continue in this order). Each cell represents players’ pay-offs, with the first component being the pay-off for the first player, the second for the second player, and so on. For instance, consider the entry in the first row and third column, where players (1, 2) and (3) cooperate but player (4) defects. The pay-offs of players (1) to (3) are ({B}_{C}(3)), while the pay-off of player (4) is ({B}_{D}left(3right)). In the above example, there are as many row players as column players because the number of players is even. However, we can have one more player in the rows than in the columns if there is an odd number of players.Figure 1A schematic diagram of the n-player weightlifting game. In this game, players decide whether to cooperate or defect in carrying the weight. Cooperators need to pay a cost. The weightlifting can either succeed or fail. In case of success, all players receive a benefit. In case of failure, all players receive nothing. The player’s pay-off depends on the benefit, cost and probability of success. Each player decides whether to cooperate or defect so as to maximize the expected gain.Full size imageTable 3 Pay-off table of four-player weightlifting game.Full size tableNash equilibrium and pareto optimal strategiesHere we present the Nash equilibrium and Pareto optimal strategies of the (n)-player weightlifting game in terms of the cost-to-benefit ratio (c/b) and probability of success ({p}_{i}). The Nash equilibrium consists of the best responses of each player. Players have no incentive to deviate from this strategy profile since deviation will not increase an individual’s pay-off if the other players maintain the same strategy. If ({B}_{C}(i)ge {B}_{D}(i-1)), the best response of player (j) is to cooperate, but if ({B}_{C}(i)le {B}_{D}(i-1)), the best response is to defect.We have (Delta {p}_{i}={p}_{i}-{p}_{i-1}ge 0) for the increase in the probability of success because the probability ({p}_{i}) increases with the number of cooperators (i). It is convenient to divide cases depending on whether (Delta {p}_{i} >c/b) or (Delta {p}_{i} More