Philippa Kaur

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

Díaz, S. et al. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/zenodo.3553579 (2019).Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).ADS 
Article 

Google Scholar 
Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).Article 

Google Scholar 
HyspIRI Mission Concept Team. HyspIRI Final Report. https://hyspiri.jpl.nasa.gov/downloads/reports_whitepapers/HyspIRI_FINAL_Report_1October2018_20181005a.pdf. Jet Propulsion Laboratories, California Institute of Technology, Pasadena, CA, USA (2018).Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).ADS 
CAS 
Article 

Google Scholar 
Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).Article 

Google Scholar 
Cawse-Nicholson, K. et al. NASA’s surface biology and geology designated observable: a perspective on surface imaging algorithms. Remote Sens. Environ. 257, 112349 (2021).ADS 
Article 

Google Scholar 
Stavros, E. N. et al. ISS Observations Offer Insights Into Plant Function. Nature Ecology and Evolution 1, https://doi.org/10.1038/s41559-017-0194 (2017).Rast, M., Nieke, J., Adams, J., Isola, C. & Gascon, F. Copernicus Hyperspectral Imaging Mission for the Environment (Chime). IEEE International Geoscience and Remote Sensing Symposium IGARSS, 108–111, https://doi.org/10.1109/IGARSS47720.2021.9553319 (2021).Cogliati, S. et al. The PRISMA imaging spectroscopy mission: overview and first performance analysis. Remote Sens. Environ. 262, 112499 (2021).ADS 
Article 

Google Scholar 
Asner, G. P. et al. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355, 385–389 (2017).ADS 
CAS 
Article 

Google Scholar 
Meireles, J. E. et al. Leaf reflectance spectra capture the evolutionary history of seed plants. N. Phytologist 228, 485–493 (2020).Article 

Google Scholar 
Schweiger, A. K. et al. Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat. Ecol. Evolution https://doi.org/10.1038/s41559-018-0551-1 (2018).Article 

Google Scholar 
Cavender-Bares, J. et al. Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am. J. Bot. 104, 966–969 (2017).Article 

Google Scholar 
Laliberté, E., Schweiger, A. K. & Legendre, P. Partitioning plant spectral diversity into alpha and beta components. Ecol. Lett. 23, 370–380 (2020).Article 

Google Scholar 
Rocchini, D. et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol. Inform. 5, 318–329 (2010).Article 

Google Scholar 
Gholizadeh, H. et al. Detecting prairie biodiversity with airborne remote sensing. Remote Sens. Environ. 221, 38–49 (2019).ADS 
Article 

Google Scholar 
Wang, R. et al. Influence of species richness, evenness, and composition on optical diversity: a simulation study. Remote Sens. Environ. 211, 218–228 (2018).ADS 
Article 

Google Scholar 
Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high‐fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).Article 

Google Scholar 
Draper, F. C. et al. Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities. J. Ecol. 107, 696–710 (2019).Article 

Google Scholar 
Wang, R., Gamon, J. A., Cavender‐Bares, J., Townsend, P. A. & Zygielbaum, A. I. The spatial sensitivity of the spectral diversity–biodiversity relationship: an experimental test in a prairie grassland. Ecol. Appl. 28, 541–556 (2018).Article 

Google Scholar 
Rossi, C. et al. Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species-rich grasslands. Remote Sens. Ecol. Conserv. https://doi.org/10.1002/rse2.244 (2021).Article 

Google Scholar 
Finderup Nielsen, T., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 

Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evolution 14, 450–453 (1999).CAS 
Article 

Google Scholar 
Anderson, M. J. et al. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).ADS 
Article 

Google Scholar 
Rocchini, D. et al. Measuring β‐diversity by remote sensing: a challenge for biodiversity monitoring. Methods Ecol. Evolution 9, 1787–1798 (2018).Article 

Google Scholar 
Chadwick, K. D. & Asner, G. P. Landscape evolution and nutrient rejuvenation reflected in Amazon forest canopy chemistry. Ecol. Lett. 21, 978–988 (2018).Article 

Google Scholar 
Felsenstein, J. Phylogenies and the comparative method. American Naturalist, 1-15, https://doi.org/10.1086/284325 (1985).Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).ADS 
Article 

Google Scholar 
Schimel, D. S., Asner, G. P. & Moorcroft, P. Observing changing ecological diversity in the Anthropocene. Front. Ecol. Environ. 11, 129–137 (2013).Article 

Google Scholar 
NEON (National Ecological Observatory Network). Spectrometer orthorectified surface directional reflectance—mosaic, RELEASE-2021 (DP3.30006.001). https://doi.org/10.48443/qeae-3×15. Dataset accessed from https://data.neonscience.org on March (2021).Richter, R. & Schläpfer, D. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction. Int. J. Remote Sens. 23, 2631–2649 (2002).Article 

Google Scholar 
Asner, G. P. & Martin, R. E. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front. Ecol. Environ. 7, 269–276 (2009).Article 

Google Scholar 
Rüfenacht, D., Fredembach, C. & Süsstrunk, S. Automatic and accurate shadow detection using near-infrared information. IEEE Trans. pattern Anal. Mach. Intell. 36, 1672–1678 (2013).Article 

Google Scholar 
NEON (National Ecological Observatory Network). High-resolution orthorectified camera imagery mosaic, RELEASE-2021 (DP3.30010.001). https://doi.org/10.48443/4e85-cr14. Dataset accessed from https://data.neonscience.org on March 3 (2021).Feilhauer, H., Asner, G. P., Martin, R. E. & Schmidtlein, S. Brightness-normalized partial least squares regression for hyperspectral data. J. Quant. Spectrosc. Radiat. Transf. 111, 1947–1957 (2010).ADS 
CAS 
Article 

Google Scholar 
NEON (National Ecological Observatory Network). Plant presence and percent cover, RELEASE-2021 (DP1.10058.001). https://doi.org/10.48443/abge-r811. Dataset accessed from https://data.neonscience.org on March 3 (2021).NEON (National Ecological Observatory Network). Woody plant vegetation structure, RELEASE-2021 (DP1.10098.001). https://doi.org/10.48443/e3qn-xw47. Dataset accessed from https://data.neonscience.org on March 3 (2021).Schweiger, A. K. NEON_crown_area (1.0.0). https://doi.org/10.5281/zenodo.6383923 (2022).R Foundation for Statistical Computing. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2019).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Jin, Y. & Qian, H. V. PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).Article 

Google Scholar 
Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).Article 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
Article 

Google Scholar 
NEON (National Ecological Observatory Network). Plant foliar traits, RELEASE-2021 (DP1.10026.001). https://doi.org/10.48443/za0d-wn97. Dataset accessed from https://data.neonscience.org on March 3 (2021).Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).Article 

Google Scholar 
Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. nlme: Linear and nonlinear mixed effects models. R package version 3.1-152 (2021).NEON (National Ecological Observatory Network). LAI—spectrometer—mosaic, RELEASE-2021 (DP3.30012.001). https://doi.org/10.48443/h2rb-pj34. Dataset accessed from https://data.neonscience.org on March 3 (2021). More

Duarte C, Middelburg JJ, Caraco N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences. 2005;2:1–8.CAS 
Article 

Google Scholar 
Kloareg B, Quatrano RS. Structure of the cell walls of marine algae and ecophysiological functions of the matrix polysaccharides. Ocean Mar Biol Annu Rev. 1988;26:259–315.
Google Scholar 
Fletcher HR, Biller P, Ross AB, Adams JMM. The seasonal variation of fucoidan within three species of brown macroalgae. Algal Res. 2017;22:79–86.Article 

Google Scholar 
Deniaud-Bouët E, Hardouin K, Potin P, Kloareg B, Hervé C. A review about brown algal cell walls and fucose-containing sulfated polysaccharides: Cell wall context, biomedical properties and key research challenges. Carbohydr Polym. 2017;175:395–408.PubMed 
Article 
CAS 

Google Scholar 
Haug A, Larsen B, Smidsrød O. Uronic acid sequence in alginate from different sources. Carbohydr Res. 1974;32:217–225.CAS 
Article 

Google Scholar 
Bruhn A, Janicek T, Manns D, Nielsen MM, Balsby TJS, Meyer AS, et al. Crude fucoidan content in two North Atlantic kelp species, Saccharina latissima and Laminaria digitata—seasonal variation and impact of environmental factors. J Appl Phycol. 2017;29:3121–3137.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ponce NMA, Stortz CA. A comprehensive and comparative analysis of the fucoidan compositional data across the Phaeophyceae. Front Plant Sci. 2020;11:556312.PubMed 
PubMed Central 
Article 

Google Scholar 
Fleurence J. The enzymatic degradation of algal cell walls: A useful approach for improving protein accessibility? J Appl Phycol. 1999;11:313–314.CAS 
Article 

Google Scholar 
Verhaeghe EF, Fraysse A, Guerquin-Kern JL, Wu TD, Devès G, Mioskowski C, et al. Microchemical imaging of iodine distribution in the brown alga Laminaria digitata suggests a new mechanism for its accumulation. J Biol Inorg Chem. 2008;13:257–269.CAS 
PubMed 
Article 

Google Scholar 
Schiener P, Black KD, Stanley MS, Green DH. The seasonal variation in the chemical composition of the kelp species Laminaria digitata, Laminaria hyperborea, Saccharina latissima and Alaria esculenta. J Appl Phycol. 2015;27:363–373.CAS 
Article 

Google Scholar 
Deniaud-Bouët E, Kervarec N, Michel G, Tonon T, Kloareg B, Hervé C. Chemical and enzymatic fractionation of cell walls from Fucales: Insights into the structure of the extracellular matrix of brown algae. Ann Bot. 2014;114:1203–1216.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Michel G, Tonon T, Scornet D, Cock JM, Kloareg B. Central and storage carbon metabolism of the brown alga Ectocarpus siliculosus: Insights into the origin and evolution of storage carbohydrates in Eukaryotes. N. Phytol. 2010;188:67–81.CAS 
Article 

Google Scholar 
Mann K. Ecology of coastal waters—A systems approach, Berkeley: University of California Press; 1982.Egan S, Harder T, Burke C, Steinberg P, Kjelleberg S, Thomas T. The seaweed holobiont: Understanding seaweed-bacteria interactions. FEMS Microbiol Rev. 2013;37:462–476.CAS 
PubMed 
Article 

Google Scholar 
Kirchman DL. The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 2002;39:91–100.CAS 
PubMed 

Google Scholar 
Thomas F, Hehemann JH, Rebuffet E, Czjzek M, Michel G. Environmental and gut Bacteroidetes: The food connection. Front Microbiol. 2011;2:93.PubMed 
PubMed Central 
Article 

Google Scholar 
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–611.CAS 
PubMed 
Article 

Google Scholar 
Wietz M, Wemheuer B, Simon H, Giebel HA, Seibt MA, Daniel R, et al. Bacterial community dynamics during polysaccharide degradation at contrasting sites in the Southern and Atlantic Oceans. Environ Microbiol. 2015;17:3822–3831.CAS 
PubMed 
Article 

Google Scholar 
Arnosti C, Wietz M, Brinkhoff T, Hehemann J-H, Probant D, Zeugner L, et al. The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Ann Rev Mar Sci. 2020;13:9.1–9.28.
Google Scholar 
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:490–495.Article 
CAS 

Google Scholar 
Barbeyron T, Brillet-Guéguen L, Carré W, Carrière C, Caron C, Czjzek M, et al. Matching the diversity of sulfated biomolecules: Creation of a classification database for sulfatases reflecting their substrate specificity. PLoS One. 2016;11:1–33.Article 
CAS 

Google Scholar 
Tang K, Lin Y, Han Y, Jiao N. Characterization of potential polysaccharide utilization systems in the marine Bacteroidetes Gramella flava JLT2011 using a multi-omics approach. Front Microbiol. 2017;8:220.PubMed 
PubMed Central 

Google Scholar 
Zhu Y, Chen P, Bao Y, Men Y, Zeng Y, Yang J, et al. Complete genome sequence and transcriptomic analysis of a novel marine strain Bacillus weihaiensis reveals the mechanism of brown algae degradation. Sci Rep. 2016;6:38248.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas F, Bordron P, Eveillard D, Michel G. Gene expression analysis of Zobellia galactanivorans during the degradation of algal polysaccharides reveals both substrate-specific and shared transcriptome-wide responses. Front Microbiol. 2017;8:1808.PubMed 
PubMed Central 
Article 

Google Scholar 
Ficko-Blean E, Préchoux A, Thomas F, Rochat T, Larocque R, Zhu Y, et al. Carrageenan catabolism is encoded by a complex regulon in marine heterotrophic bacteria. Nat Commun. 2017;8:1685.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Koch H, Dürwald A, Schweder T, Noriega-Ortega B, Vidal-Melgosa S, Hehemann JH, et al. Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J. 2019;13:92–103.CAS 
PubMed 
Article 

Google Scholar 
Bunse C, Koch H, Breider S, Simon M, Wietz M. Sweet spheres: succession and CAZyme expression of marine bacterial communities colonizing a mix of alginate and pectin particles. Environ Microbiol. 2021;23:3130–3148.CAS 
PubMed 
Article 

Google Scholar 
Hehemann JH, Arevalo P, Datta MS, Yu X, Corzett CH, Henschel A, et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat Commun. 2016;7:12860.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gralka M, Szabo R, Stocker R, Cordero OX. Trophic interactions and the drivers of microbial community assembly. Curr Biol. 2020;30:R1176–R1188.CAS 
PubMed 
Article 

Google Scholar 
Jiménez DJ, Dini-Andreote F, DeAngelis KM, Singer SW, Salles JF, van Elsas JD. Ecological insights into the dynamics of plant biomass-degrading microbial consortia. Trends Microbiol. 2017;25:788–796.PubMed 
Article 
CAS 

Google Scholar 
Kang S, Kim JK. Reuse of red seaweed waste by a novel bacterium, Bacillus sp. SYR4 isolated from a sandbar. World J Microbiol Biotechnol. 2015;31:209–217.PubMed 
Article 

Google Scholar 
Jonnadula R, Verma P, Shouche YS, Ghadi SC. Characterization of Microbulbifer strain CMC-5, a new biochemical variant of Microbulbifer elongatus type strain DSM6810T isolated from decomposing seaweeds. Curr Microbiol. 2009;59:600–607.CAS 
PubMed 
Article 

Google Scholar 
Martin M, Barbeyron T, Martin R, Portetelle D, Michel G, Vandenbol M. The cultivable surface microbiota of the brown alga Ascophyllum nodosum is enriched in macroalgal-polysaccharide-degrading bacteria. Front Microbiol. 2015;6:1487.PubMed 
PubMed Central 
Article 

Google Scholar 
Dogs M, Wemheuer B, Wolter L, Bergen N, Daniel R, Simon M, et al. Rhodobacteraceae on the marine brown alga Fucus spiralis are abundant and show physiological adaptation to an epiphytic lifestyle. Syst Appl Microbiol. 2017;40:370–382.CAS 
PubMed 
Article 

Google Scholar 
Brunet M, le Duff N, Fuchs B, Amann R, Barbeyron T, Thomas F. Specific detection and quantification of the marine flavobacterial genus Zobellia on macroalgae using novel qPCR and CARD-FISH assays. Syst Appl Microbiol. 2021;44:126269.CAS 
PubMed 
Article 

Google Scholar 
Barbeyron T, L’Haridon S, Corre E, Kloareg B, Potin P. Zobellia galactanovorans gen. nov., sp. nov., a marine species of Flavobacteriaceae isolated from a red alga, and classification of [Cytophaga] uliginosa (ZoBell and Upham 1944) Reichenbach 1989 as Zobellia uliginosa gen. nov., comb. nov. Int J Syst Evol Microbiol. 2001;51:985–997.CAS 
PubMed 
Article 

Google Scholar 
Barbeyron T, Thiébaud M, Le Duff N, Martin M, Corre E, Tanguy G, et al. Zobellia roscoffensis sp. nov. and Zobellia nedashkovskayae sp. nov., two flavobacteria from the epiphytic microbiota of the brown alga Ascophyllum nodosum, and emended description of the genus Zobellia. Int J Syst Evol Microbiol. 2021;71:004913.Nedashkovskaya OI, Suzuki M, Vancanneyt M, Cleenwerck I, Lysenko AM, Mikhailov VV, et al. Zobellia amurskyensis sp. nov., Zobellia laminariae sp. nov. and Zobellia russellii sp. nov., novel marine bacteria of the family Flavobacteriaceae. Int J Syst Evol Microbiol. 2004;54:1643–1648.CAS 
PubMed 
Article 

Google Scholar 
Nedashkovskaya O, Otstavnykh N, Zhukova N, Guzev K, Chausova V, Tekutyeva L, et al. Zobellia barbeyronii sp. nov., a new member of the family Flavobacteriaceae, isolated from seaweed, and emended description of the species Z. amurskyensis, Z. laminariae, Z. russellii and Z. uliginosa. Diversity. 2021;13:520.CAS 
Article 

Google Scholar 
Chernysheva N, Bystritskaya E, Stenkova A, Golovkin I. Comparative genomics and CAZyme genome repertoires of marine Zobellia amurskyensis KMM 3526T and Zobellia laminariae KMM 3676T. Mar Drugs. 2019;17:661.CAS 
PubMed Central 
Article 

Google Scholar 
Chernysheva N, Bystritskaya E, Likhatskaya G, Nedashkovskaya O, Isaeva M. Genome-wide analysis of PL7 alginate lyases in the genus Zobellia. Molecules. 2021;26:2387.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barbeyron T, Thomas F, Barbe V, Teeling H, Schenowitz C, Dossat C, et al. Habitat and taxon as driving forces of carbohydrate catabolism in marine heterotrophic bacteria: Example of the model algae-associated bacterium Zobellia galactanivorans DsijT. Environ Microbiol. 2016;18:4610–4627.CAS 
PubMed 
Article 

Google Scholar 
Potin P, Sanseau A, Le Gall Y, Rochas C, Kloareg B. Purification and characterization of a new k‐carrageenase from a marine Cytophaga‐like bacterium. Eur J Biochem. 1991;201:241–247.CAS 
PubMed 
Article 

Google Scholar 
Lami R, Grimaud R, Sanchez-Brosseau S, Six C, Thomas F, West NJ, et al. Marine bacterial models for experimental biology. In: Boutet A, Schierwater B, editors. Handbook of Marine Model Organisms in Experimental Biology. London: Taylor & Francis Ltd; 2021.Dudek M, Dieudonné A, Jouanneau D, Rochat T, Michel G, Sarels B, et al. Regulation of alginate catabolism involves a GntR family repressor in the marine flavobacterium Zobellia galactanivorans DsijT. Nucleic Acids Res. 2020;48:7786–7800.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas F, Lundqvist LCE, Jam M, Jeudy A, Barbeyron T, Sandström C, et al. Comparative characterization of two marine alginate lyases from Zobellia galactanivorans reveals distinct modes of action and exquisite adaptation to their natural substrate. J Biol Chem. 2013;288:23021–23037.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas F, Barbeyron T, Tonon T, Génicot S, Czjzek M, Michel G. Characterization of the first alginolytic operons in a marine bacterium: from their emergence in marine Flavobacteriia to their independent transfers to marine Proteobacteria and human gut Bacteroides. Environ Microbiol. 2012;14:2379–94.CAS 
PubMed 
Article 

Google Scholar 
Jam M, Flament D, Allouch J, Potin P, Thion L, Kloareg B, et al. The endo-β-agarases AgaA and AgaB from the marine bacterium Zobellia galactanivorans: Two paralogue enzymes with different molecular organizations and catalytic behaviours. Biochem J. 2005;385:703–713.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hehemann JH, Correc G, Thomas F, Bernard T, Barbeyron T, Jam M, et al. Biochemical and structural characterization of the complex agarolytic enzyme system from the marine bacterium Zobellia galactanivorans. J Biol Chem. 2012;287:30571–30584.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Labourel A, Jam M, Jeudy A, Hehemann JH, Czjzek M, Michel G. The β-glucanase ZgLamA from Zobellia galactanivorans evolved a bent active site adapted for efficient degradation of algal laminarin. J Biol Chem. 2014;289:2027–2042.CAS 
PubMed 
Article 

Google Scholar 
Labourel A, Jam M, Legentil L, Sylla B, Hehemann JH, Ferrières V, et al. Structural and biochemical characterization of the laminarinase ZgLamCGH16 from Zobellia galactanivorans suggests preferred recognition of branched laminarin. Acta Crystallogr. 2015;D71:173–184.
Google Scholar 
Dorival J, Ruppert S, Gunnoo M, Orłowski A, Chapelais-Baron M, Dabin J, et al. The laterally-acquired GH5 ZgEngAGH5_4 from the marine bacterium Zobellia galactanivorans is dedicated to hemicellulose hydrolysis. Biochem J. 2018;475:3609–3628.PubMed 
Article 

Google Scholar 
Groisillier A, Labourel A, Michel G, Tonon T. The mannitol utilization system of the marine bacterium Zobellia galactanivorans. Appl Environ Microbiol. 2015;81:1799–1812.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fournier JB, Rebuffet E, Delage L, Grijol R, Meslet-Cladière L, Rzonca J, et al. The vanadium iodoperoxidase from the marine Flavobacteriaceae species Zobellia galactanivorans reveals novel molecular and evolutionary features of halide specificity in the vanadium haloperoxidase enzyme family. Appl Environ Microbiol. 2014;80:7561–7573.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Grigorian E, Groisillier A, Thomas F, Leblanc C, Delage L. Functional characterization of a L-2-haloacid dehalogenase from Zobellia galactanivorans DsijT suggests a role in haloacetic acid catabolism and a wide distribution in marine environments. Front Microbiol. 2021;12:725997.PubMed 
PubMed Central 
Article 

Google Scholar 
Zhu Y, Thomas F, Larocque R, Li N, Duffieux D, Cladière L, et al. Genetic analyses unravel the crucial role of a horizontally acquired alginate lyase for brown algal biomass degradation by Zobellia galactanivorans. Environ Microbiol. 2017;19:2164–2181.CAS 
PubMed 
Article 

Google Scholar 
Zablackis E, Perez J. A partially pyruvated carrageenan from hawaiian Grateloupia filicina (Cryptonemiales, Rhodophyta). Bot Mar. 1990;33:273–276.CAS 
Article 

Google Scholar 
Filisetti-Cozzi T, Carpita N. Measurement of uronic acids without interference from neutral sugars. Anal Biochem. 1991;197:15162.Article 

Google Scholar 
Blumenkrantz N, Asboe-Hansen G. New method for quantitative determination of uronic acids. Anal Biochem. 1973;54:484–489.CAS 
PubMed 
Article 

Google Scholar 
Cumashi A, Ushakova NA, Preobrazhenskaya ME, D’Incecco A, Piccoli A, Totani L, et al. A comparative study of the anti-inflammatory, anticoagulant, antiangiogenic, and antiadhesive activities of nine different fucoidans from brown seaweeds. Glycobiology. 2007;17:541–552.CAS 
PubMed 
Article 

Google Scholar 
Jung SY, Oh TK, Yoon JH. Tenacibaculum aestuarii sp. nov., isolated from a tidal flat sediment in Korea. Int J Syst Evol Microbiol. 2006;56:1577–1581.CAS 
PubMed 
Article 

Google Scholar 
ZoBell C. Studies on marine bacteria. I. The cultural requirements of heterotrophic aerobes. J Mar Res. 1941;4:75.
Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 
Article 

Google Scholar 
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–419.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vallenet D, Calteau A, Dubois M, Amours P, Bazin A, Beuvin M, et al. MicroScope: An integrated platform for the annotation and exploration of microbial gene functions through genomic, pangenomic and metabolic comparative analysis. Nucleic Acids Res. 2020;48:D579–D589.CAS 
PubMed 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–359.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–2079.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thomas F, Barbeyron T, Michel G. Evaluation of reference genes for real-time quantitative PCR in the marine flavobacterium Zobellia galactanivorans. J Microbiol Methods. 2011;84:61–6.CAS 
PubMed 
Article 

Google Scholar 
Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29:24–26.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. 2018. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.Lex A, Gehlenborg N, Strobelt H. UpSet: Visualization of intersecting sets. IEEE Trans Vis Comput Graph. 2014;20:1983–1992.PubMed 
PubMed Central 
Article 

Google Scholar 
Krassowski M. krassowski/complex-upset. 2020. https://doi.org/10.5281/zenodo.3700590.Murtagh F, Legendre P. Ward’s hierarchical clustering method: clustering criterion and agglomerative algorithm. J Classif. 2014;31:274–295.Article 

Google Scholar 
Wickham H Use R! ggplot2: Elegant graphics for data analysis. 2nd ed. London: Springer; 2016.Kidby DK, Davidson DJ. Ferricyanide estimation of sugars in the nanomole range. Anal Biochem. 1973;55:321–325.CAS 
PubMed 
Article 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. DbCAN2: A meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen X, Hu Y, Yang B, Gong X, Zhang N, Niu L, et al. Structure of lpg0406, a carboxymuconolactone decarboxylase family protein possibly involved in antioxidative response from Legionella pneumophila. Protein Sci. 2015;24:2070–2075.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Enke TN, Datta MS, Schwartzman J, Cermak N, Schmitz D, Barrere J, et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr Biol. 2019;29:1528–1535.e6.CAS 
PubMed 
Article 

Google Scholar 
Pollak S, Gralka M, Sato Y, Schwartzman J, Lu L, Cordero OX. Public good exploitation in natural bacterioplankton communities. Sci Adv. 2021;7:eabi4717.Pontrelli S, Szabo R, Pollak S, Schwartzman J, Ledezma D, Cordero OX, et al. Metabolic cross-feeding structures the assembly of polysaccharide degrading communities. Sci Adv. 2022;8:eabk3076.Holdt SL, Kraan S. Bioactive compounds in seaweed: Functional food applications and legislation. J Appl Phycol. 2011;23:543–597.CAS 
Article 

Google Scholar 
Kawamura-Konishi Y, Watanabe N, Saito M, Nakajima N, Sakaki T, Katayama T, et al. Isolation of a new phlorotannin, a potent inhibitor of carbohydrate-hydrolyzing enzymes, from the brown alga Sargassum patens. J Agric Food Chem. 2012;60:5565–5570.CAS 
PubMed 
Article 

Google Scholar 
Garbary DJ, Brown NE, MacDonell HJ, Toxopeux J. Ascophyllum and its symbionts — A complex symbiotic community on North Atlantic shores. Algal and Cyanobacteria Symbioses. 2017:547–572.Pluvinage B, Grondin JM, Amundsen C, Klassen L, Moote PE, Xiao Y, et al. Molecular basis of an agarose metabolic pathway acquired by a human intestinal symbiont. Nat Commun. 2018;9:1043.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Reintjes G, Arnosti C, Fuchs BM, Amann R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 2017;11:1640–1650.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hollants J, Leliaert F, de Clerck O, Willems A. What we can learn from sushi: A review on seaweed-bacterial associations. FEMS Microbiol Ecol. 2013;83:1–16.CAS 
PubMed 
Article 

Google Scholar 
Thomas F, Le Duff N, Wu TD, Cébron A, Uroz S, Riera P, et al. Isotopic tracing reveals single-cell assimilation of a macroalgal polysaccharide by a few marine Flavobacteria and Gammaproteobacteria. ISME J. 2021;15:3062–3075.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Datta MS, Sliwerska E, Gore J, Polz MF, Cordero OX. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat Commun. 2016;7:11965.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Enke TN, Leventhal GE, Metzger M, Saavedra JT, Cordero OX. Microscale ecology regulates particulate organic matter turnover in model marine microbial communities. Nat Commun. 2018;9:2743.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sichert A, Cordero OX. Polysaccharide-bacteria Interactions from the lens of evolutionary ecology. Front Microbiol. 2021;12:705082.PubMed 
PubMed Central 
Article 

Google Scholar 
Sichert A, Corzett CH, Schechter M, Unfried F, Markert S, Becher D, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5:1026–1039.CAS 
PubMed 
Article 

Google Scholar 
Reisky L, Préchoux A, Zühlke MK, Bäumgen M, Robb CS, Gerlach N, et al. A marine bacterial enzymatic cascade degrades the algal polysaccharide ulvan. Nat Chem Biol. 2019;15:803–812.CAS 
PubMed 
Article 

Google Scholar 
Mabeau S, Kloareg B, Joseleau J-P. Fractionation and analysis of fucans from brown algae. Phytochemistry. 1990;29:2441–2445.CAS 
Article 

Google Scholar 
Küpper FC, Kloareg B, Guern J, Potin P. Oligoguluronates elicit an oxidative burst in the brown algal kelp Laminaria digitata. Plant Physiol. 2001;125:278–291.PubMed 
PubMed Central 
Article 

Google Scholar 
Küpper FC, Müller DG, Peters AF, Kloareg B, Potin P. Oligoalginate recognition and oxidative burst play a key role in natural and induced resistance of sporophytes of Laminariales. J Chem Ecol. 2002;28:2057–2081.PubMed 
Article 

Google Scholar 
Leonard S, Hommais F, Nasser W, Reverchon S. Plant–phytopathogen interactions: bacterial responses to environmental and plant stimuli. Environ Microbiol. 2017;19:1689–1716.PubMed 
Article 

Google Scholar 
Sato K, Naito M, Yukitake H, Hirakawa H, Shoji M, McBride MJ, et al. A protein secretion system linked to bacteroidete gliding motility and pathogenesis. PNAS. 2010;107:276–281.CAS 
PubMed 
Article 

Google Scholar 
Eckroat TJ, Greguske C, Hunnicutt DW. The type 9 secretion system is required for Flavobacterium johnsoniae biofilm formation. Front Microbiol. 2021;12:660887.PubMed 
PubMed Central 
Article 

Google Scholar 
Xie S, Tan Y, Song W, Zhang W, Qi Q, Lu X. N-glycosylation of a cargo protein C-terminal domain recognized by the type IX secretion system in Cytophaga hutchinsonii affects protein secretion and localization. Appl Environ Microbiol. 2022;88:e0160621.PubMed 
Article 

Google Scholar  More

Jones, J. T. et al. Top 10 plant-parasitic nematodes in molecular plant pathology. Mol. Plant Pathol. 14, 946–961. https://doi.org/10.1111/mpp.12057 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Collange, B., Navarrete, M., Peyre, G., Mateille, T. & Tchamitchian, M. Root-knot nematode (Meloidogyne) management in vegetable crop production: The challenge of an agronomic system analysis. Crop Prot. 30, 1251–1262. https://doi.org/10.1016/j.cropro.2011.04.016 (2011).Article 

Google Scholar 
Nyaku, S. T., Affokpon, A., Danquah, A. & Brentu, F. C. in Nematology–concepts, diagnosis and control (eds Mohammad Manjur Shah & Mohammad Mahamood) 153–182 (IntechOpen, 2017).Desaeger, J., Wram, C. & Zasada, I. New reduced-risk agricultural nematicides-rationale and review. J. Nematol. 52, 1 (2020).Article 

Google Scholar 
Dong, L. & Zhang, K. Microbial control of plant-parasitic nematodes: a five-party interaction. Plant Soil 288, 31–45. https://doi.org/10.1007/s11104-006-9009-3 (2006).CAS 
Article 

Google Scholar 
Singh, S., Singh, B. & Singh, A. Nematodes: A threat to sustainability of agriculture. Procedia Environ. Sci. 29, 215–216. https://doi.org/10.1016/j.proenv.2015.07.270 (2015).Article 

Google Scholar 
Oka, Y. Mechanisms of nematode suppression by organic soil amendments—A review. Appl. Soil Ecol. 44, 101–115. https://doi.org/10.1016/j.apsoil.2009.11.003 (2010).Article 

Google Scholar 
Yue, X., Li, F. & Wang, B. Activity of four nematicides against Meloidogyne incognita race 2 on tomato plants. J. Phytopathol. 168, 399–404. https://doi.org/10.1111/jph.12904 (2020).CAS 
Article 

Google Scholar 
Huang, W.-K. et al. Mutations in Acetylcholinesterase2 (ace 2) increase the insensitivity of acetylcholinesterase to fosthiazate in the root-knot nematode Meloidogyne incognita. Sci. Rep. 6, 1–9. https://doi.org/10.1038/srep38102 (2016).CAS 
Article 

Google Scholar 
Yoon, Y., Kim, E.-S., Hwang, Y.-S. & Choi, C.-Y. Avermectin: Biochemical and molecular basis of its biosynthesis and regulation. Appl. Microbiol. Biotechnol. 63, 626–634. https://doi.org/10.1007/s00253-003-1491-4 (2004).CAS 
Article 
PubMed 

Google Scholar 
Wolstenholme, A. J. & Rogers, A. Glutamate-gated chloride channels and the mode of action of the avermectin/milbemycin anthelmintics. Parasitology 131, S85–S95. https://doi.org/10.1017/S0031182005008218 (2005).CAS 
Article 
PubMed 

Google Scholar 
Haydock, P., Woods, S., Grove, I. & Hare, M. in Plant nematology (eds Roland N Perry & Maurice Moens) 459–479 (CABI, 2013).Forghani, F. & Hajihassani, A. Recent advances in the development of environmentally benign treatments to control root-knot nematodes. Front. Plant Sci. 11, 1. https://doi.org/10.3389/fpls.2020.01125 (2020).Article 

Google Scholar 
Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. 63, 541–556. https://doi.org/10.1146/annurev.micro.62.081307.162918 (2009).CAS 
Article 
PubMed 

Google Scholar 
Mhatre, P. H. et al. Plant growth promoting rhizobacteria (PGPR): a potential alternative tool for nematodes bio-control. Biocatal. Agr. Biotechnol. 17, 119–128. https://doi.org/10.1016/j.bcab.2018.11.009 (2019).Article 

Google Scholar 
Eissa, M. F. & Abd-Elgawad, M. M. in Biocontrol agents of phytonematodes (eds Tarique Hassan Askary & Paulo Roberto Martinelli) 217–243 (CABI, 2015).Luo, T., Hou, S., Yang, L., Qi, G. & Zhao, X. Nematodes avoid and are killed by Bacillus mycoides-produced styrene. J. Invertebr. Pathol. 159, 129–136. https://doi.org/10.1016/j.jip.2018.09.006 (2018).CAS 
Article 
PubMed 

Google Scholar 
Siddiqui, I. & Shaukat, S. Systemic resistance in tomato induced by biocontrol bacteria against the root-knot nematode, Meloidogyne javanica is independent of salicylic acid production. J. Phytopathol. 152, 48–54. https://doi.org/10.1046/j.1439-0434.2003.00800.x (2004).Article 

Google Scholar 
Li, W. et al. Broad spectrum anti-biotic activity and disease suppression by the potential biocontrol agent Burkholderia ambifaria BC-F. Crop Protect. 21, 129–135. https://doi.org/10.1016/S0261-2194(01)00074-6 (2002).Article 

Google Scholar 
Khanna, K. et al. Role of plant growth promoting Bacteria (PGPRs) as biocontrol agents of Meloidogyne incognita through improved plant defense of Lycopersicon esculentum. Plant. Soil 436, 325–345. https://doi.org/10.1007/s11104-019-03932-2 (2019).CAS 
Article 

Google Scholar 
Subedi, P., Gattoni, K., Liu, W., Lawrence, K. S. & Park, S.-W. Current utility of plant growth-promoting rhizobacteria as biological control agents towards plant-parasitic nematodes. Plants 9, 1167. https://doi.org/10.3390/plants9091167 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Oka, Y. et al. New strategies for the control of plant-parasitic nematodes. Pest Manag. Sci. 56, 983–988. https://doi.org/10.1002/1526-4998(200011)56:11%3c983::AID-PS233%3e3.0.CO;2-X (2000).CAS 
Article 

Google Scholar 
Ralmi, N. H. A. A., Khandaker, M. M. & Mat, N. Occurrence and control of root knot nematode in crops: A review. Aust. J. Crop Sci. 11, 1649 (2016).Article 

Google Scholar 
Topalović, O. & Heuer, H. Plant-nematode interactions assisted by microbes in the rhizosphere. Curr. Issues Mol. Biol. 30, 75–88 (2019).Article 

Google Scholar 
Olanrewaju, O. S., Ayangbenro, A. S., Glick, B. R. & Babalola, O. O. Plant health: Feedback effect of root exudates-rhizobiome interactions. Appl. Microbiol. Biotechnol. 103, 1155–1166. https://doi.org/10.1007/s00253-018-9556-6 (2019).CAS 
Article 
PubMed 

Google Scholar 
Handley, K. M. et al. Biostimulation induces syntrophic interactions that impact C, S and N cycling in a sediment microbial community. ISME J. 7, 800–816. https://doi.org/10.1038/ismej.2012.148 (2013).CAS 
Article 
PubMed 

Google Scholar 
Tang, Y. et al. Changes in nitrogen-cycling microbial communities with depth in temperate and subtropical forest soils. Appl. Soil Ecol. 124, 218–228. https://doi.org/10.1016/j.apsoil.2017.10.029 (2018).ADS 
Article 

Google Scholar 
Babić, K. H. et al. Influence of different Sinorhizobium meliloti inocula on abundance of genes involved in nitrogen transformations in the rhizosphere of alfalfa (Medicago sativa L.). Environ. Microbiol. 10, 2922–2930 (2008).Article 

Google Scholar 
Ke, X. et al. Effect of inoculation with nitrogen-fixing bacterium Pseudomonas stutzeri A1501 on maize plant growth and the microbiome indigenous to the rhizosphere. Syst. Appl. Microbiol. 42, 248–260. https://doi.org/10.1016/j.syapm.2018.10.010 (2019).CAS 
Article 
PubMed 

Google Scholar 
Hogan, G. et al. Microbiome analysis as a platform R&D tool for parasitic nematode disease management. ISME J. 13, 2664–2680. https://doi.org/10.1038/s41396-019-0462-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, Y. et al. Draft genome sequence of Stenotrophomonas maltophilia strain B418, a promising agent for biocontrol of plant pathogens and root-knot nematode. Genome Announc. 3, e00015-00015. https://doi.org/10.1128/genomeA.00015-15 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Isolation and identification of nematicidal active substance from Burkholderia vietnamiensis B418. Plant Prot. 40, 65–69 (2014).
Google Scholar 
Li, S., Li, J., Xu, W., Chen, K. & Yang, H. Field efficacy test of biocontrol agent YKT41 and B418 against eggplant root-knot nematode disease. Shandong Sci. 24, 10–13 (2011).CAS 

Google Scholar 
Wang, Y., Wang, Z., Liu, B., Pan, M. & Li, J. Field trial of Burkholderia vietnamiensis and its composite microbial flora on cucumber root-knot nematode. Shandong Sci. 31, 39. https://doi.org/10.3976/j.issn.1002-4026.2018.01.007 (2018).Article 

Google Scholar 
Saad, A.-F.S., Massoud, M. A., Ibrahim, H. S. & Khalil, M. S. Management study for the root-knot nematodes, Meloidogyne incognita on tomatoes using fosthiazate and arbiscular mycorrhiza fungus. J. Adv. Agric. Res. 16, 137–147 (2011).
Google Scholar 
Huang, W.-K. et al. Efficacy evaluation of fungus Syncephalastrum racemosum and nematicide avermectin against the root-knot nematode Meloidogyne incognita on cucumber. PLoS ONE 9, e89717. https://doi.org/10.1371/journal.pone.0089717 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jayakumar, J. & Ramakrishnan, S. Evaluation of avermectin and its combination with nematicide and bioagents against root knot nematode, Meloidogyne incognita in tomato. J. Biol. Control 23, 317–319 (2009).
Google Scholar 
Moosavi, M. & Zare, R. in Biocontrol Agents of Phytonematodes (eds Tarique Hassan Askary & Paulo Roberto Martinelli) 423–445 (CABI, 2015).Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486. https://doi.org/10.1016/j.tplants.2012.04.001 (2012).CAS 
Article 
PubMed 

Google Scholar 
Reinhold-Hurek, B., Bünger, W., Burbano, C. S., Sabale, M. & Hurek, T. Roots shaping their microbiome: Global hotspots for microbial activity. Annu. Rev. Phytopathol. 53, 403–424. https://doi.org/10.1146/annurev-phyto-082712-102342 (2015).CAS 
Article 
PubMed 

Google Scholar 
Ahemad, M. & Kibret, M. Mechanisms and applications of plant growth promoting rhizobacteria: Current perspective. J. King Saud Univ.-Sci. 26, 1–20. https://doi.org/10.1016/j.jksus.2013.05.001 (2014).Article 

Google Scholar 
Ciccillo, F. et al. Effects of two different application methods of Burkholderia ambifaria MCI 7 on plant growth and rhizospheric bacterial diversity. Environ. Microbiol. 4, 238–245. https://doi.org/10.1046/j.1462-2920.2002.00291.x (2002).Article 
PubMed 

Google Scholar 
Jo, H. et al. Response of soil bacterial community and pepper plant growth to application of Bacillus thuringiensis KNU-07. Agronomy 10, 551. https://doi.org/10.3390/agronomy10040551 (2020).CAS 
Article 

Google Scholar 
Wang, J. et al. Traits-based integration of multi-species inoculants facilitates shifts of indigenous soil bacterial community. Front. Microbiol. 9, 1692. https://doi.org/10.3389/fmicb.2018.01692 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Welbaum, G. E., Sturz, A. V., Dong, Z. & Nowak, J. Managing soil microorganisms to improve productivity of agro-ecosystems. Crit. Rev. Plant Sci. 23, 175–193. https://doi.org/10.1080/07352680490433295 (2004).CAS 
Article 

Google Scholar 
Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: Significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).CAS 
Article 
PubMed 

Google Scholar 
Li, J. et al. Trichoderma harzianum inoculation reduces the incidence of clubroot disease in Chinese cabbage by regulating the rhizosphere microbial community. Microorganisms 8, 1325. https://doi.org/10.3390/microorganisms8091325 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Song, L. et al. Regular biochar and bacteria-inoculated biochar alter the composition of the microbial community in the soil of a Chinese fir plantation. Forests 11, 951. https://doi.org/10.3390/f11090951 (2020).Article 

Google Scholar 
Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100. https://doi.org/10.1126/science.1203980 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Palaniyandi, S. A., Yang, S. H., Zhang, L. & Suh, J.-W. Effects of actinobacteria on plant disease suppression and growth promotion. Appl. Microbiol. Biotechnol. 97, 9621–9636. https://doi.org/10.1007/s00253-013-5206-1 (2013).CAS 
Article 
PubMed 

Google Scholar 
Zhou, D. et al. Rhizosphere microbiomes from root knot nematode non-infested plants suppress nematode infection. Microbial Ecol. 78, 470–481. https://doi.org/10.1007/s00248-019-01319-5 (2019).CAS 
Article 

Google Scholar 
Zou, Y. et al. Metagenomic insights into the effect of oxytetracycline on microbial structures, functions and functional genes in sediment denitrification. Ecotox. Environ. Safe. 161, 85–91. https://doi.org/10.1016/j.ecoenv.2018.05.045 (2018).CAS 
Article 

Google Scholar 
Kong, Z. et al. Seasonal dynamics of the bacterioplankton community in a large, shallow, highly dynamic freshwater lake. Can. J. Microbiol. 64, 786–797. https://doi.org/10.1139/cjm-2018-0126 (2018).CAS 
Article 
PubMed 

Google Scholar 
Bach, E. M., Williams, R. J., Hargreaves, S. K., Yang, F. & Hofmockel, K. S. Greatest soil microbial diversity found in micro-habitats. Soil Biol. Biochem. 118, 217–226. https://doi.org/10.1016/j.soilbio.2017.12.018 (2018).CAS 
Article 

Google Scholar 
Wang, W. et al. Predatory Myxococcales are widely distributed in and closely correlated with the bacterial community structure of agricultural land. Appl. Soil Ecol. 146, 103365. https://doi.org/10.1016/j.apsoil.2019.103365 (2020).Article 

Google Scholar 
Schmidt, J. E., Kent, A. D., Brisson, V. L. & Gaudin, A. C. Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome 7, 1–18. https://doi.org/10.1186/s40168-019-0756-9 (2019).Article 

Google Scholar 
Hu, W., Strom, N., Haarith, D., Chen, S. & Bushley, K. E. Mycobiome of cysts of the soybean cyst nematode under long term crop rotation. Front. Microbiol. 9, 386. https://doi.org/10.3389/fmicb.2018.00386 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, W.-H. & Liu, Q.-Z. Changes in fungal community and diversity in strawberry rhizosphere soil after 12 years in the greenhouse. J. Integ. Agric. 18, 677–687. https://doi.org/10.1016/S2095-3119(18)62003-9 (2019).Article 

Google Scholar 
Qiu, W. et al. Organic fertilization assembles fungal communities of wheat rhizosphere soil and suppresses the population growth of Heterodera avenae in the field. Front. Plant Sci. 11, 1225. https://doi.org/10.3389/fpls.2020.01225 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Schardl, C. L., Leuchtmann, A. & Spiering, M. J. Symbioses of grasses with seedborne fungal endophytes. Annu. Rev. Plant Biol. 55, 315–340. https://doi.org/10.1146/annurev.arplant.55.031903.141735 (2004).CAS 
Article 
PubMed 

Google Scholar 
Edgington, S., Thompson, E., Moore, D., Hughes, K. A. & Bridge, P. Investigating the insecticidal potential of Geomyces (Myxotrichaceae: Helotiales) and Mortierella (Mortierellacea: Mortierellales) isolated from Antarctica. Springerplus 3, 1–8. https://doi.org/10.1186/2193-1801-3-289 (2014).Article 

Google Scholar 
Yi, X. et al. Comparison of the abundance and community structure of N-Cycling bacteria in paddy rhizosphere soil under different rice cultivation patterns. Int. J. Mol. Sci. 19, 3772. https://doi.org/10.3390/ijms19123772 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Duval, S. et al. Electron transfer precedes ATP hydrolysis during nitrogenase catalysis. Proc. Natl. Acad. Sci. USA 110, 16414–16419. https://doi.org/10.1073/pnas.1311218110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pham, V. T. et al. The plant growth-promoting effect of the nitrogen-fixing endophyte Pseudomonas stutzeri A15. Arch. Microbiol. 199, 513–517. https://doi.org/10.1007/s00203-016-1332-3 (2017).CAS 
Article 
PubMed 

Google Scholar 
Ouyang, Y., Evans, S. E., Friesen, M. L. & Tiemann, L. K. Effect of nitrogen fertilization on the abundance of nitrogen cycling genes in agricultural soils: a meta-analysis of field studies. Soil Biol. Biochem. 127, 71–78. https://doi.org/10.1016/j.soilbio.2018.08.024 (2018).CAS 
Article 

Google Scholar 
Dynarski, K. A. & Houlton, B. Z. Nutrient limitation of terrestrial free-living nitrogen fixation. New Phytol. 217, 1050–1061. https://doi.org/10.1111/nph.14905 (2018).CAS 
Article 
PubMed 

Google Scholar 
Kastl, E.-M., Schloter-Hai, B., Buegger, F. & Schloter, M. Impact of fertilization on the abundance of nitrifiers and denitrifiers at the root–soil interface of plants with different uptake strategies for nitrogen. Biol. Fert. Soils 51, 57–64. https://doi.org/10.1007/s00374-014-0948-1 (2015).CAS 
Article 

Google Scholar 
Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95. https://doi.org/10.1038/nature11336 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Southey, J. in Laboratory methods for work with plants and soil nematodes (ed JF Southey) 42–44 (HMSO, 1986).Ladner, D. C., Tchounwou, P. B. & Lawrence, G. W. Evaluation of the effect of ecologic on root knot nematode, Meloidogyne incognita, and tomato plant, Lycopersicon esculenum. Int. J. Environ. Res. Public Health 5, 104–110. https://doi.org/10.3390/ijerph5020104 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Niu, D.-D. et al. Application of PSX biocontrol preparation confers root-knot nematode management and increased fruit quality in tomato under field conditions. Biocontrol Sci. Technol. 26, 174–180. https://doi.org/10.1080/09583157.2015.1085489.18 (2016).Article 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucl. Acids Res. 41, e1–e1. https://doi.org/10.1093/nar/gks808 (2013).CAS 
Article 
PubMed 

Google Scholar 
Buee, M. et al. 454 Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. New Phytol. 184, 449–456. https://doi.org/10.1111/j.1469-8137.2009.03003.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Rösch, C., Mergel, A. & Bothe, H. Biodiversity of denitrifying and dinitrogen-fixing bacteria in an acid forest soil. Appl. Environ. Microbiol. 68, 3818–3829. https://doi.org/10.1128/AEM.68.8.3818-3829.2002 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Throbäck, I. N., Enwall, K., Jarvis, Å. & Hallin, S. Reassessing PCR primers targeting nirS, nirK and nosZ genes for community surveys of denitrifying bacteria with DGGE. FEMS Microbiol. Ecol. 49, 401–417. https://doi.org/10.1016/j.femsec.2004.04.011 (2004).CAS 
Article 
PubMed 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200. https://doi.org/10.1093/bioinformatics/btr381 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl. Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).CAS 
Article 
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, 1–21. https://doi.org/10.1186/s13059-014-0550-8 (2014).CAS 
Article 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Abbasi, N., Bahramloo, R. & Movahedan, M. Strategic planning for remediation and optimization of irrigation and drainage networks: a case study of Iran. J. Agric. Agric. Sci. Proc. 4, 211–221 (2015).
Google Scholar 
Abdelhaleem, F., Basiouny, M. & Mahmoud, A. Application of remote sensing and geographic information systems in irrigation water management under water scarcity conditions in Fayoum, Egypt. J. Environ. Manag. 299, 113683 (2021).Article 

Google Scholar 
Akbari-Alashti, H., Bozorg-Haddad, O., Fallah-Mehdipour, E. & Mariño, M. A. Multi-reservoir real-time operation rules: a new genetic programming approach. Proc. Instit. Civil Eng. Water Manag. 167(10), 561–576 (2014).Article 

Google Scholar 
Akhmouch, A. & Correia, F. N. The 12 OECD principles on water governance: when science meets policy. J. Utilities Policy. 43, 14–20 (2016).Article 

Google Scholar 
Amblard, L. & Mann, C. Understanding collective action for the achievement of EU water policy objectives in agricultural landscapes: insights from the institutional design principles and integrated landscape management approaches. J. Environ. Sci. Policy. 125, 76–86 (2021).Article 

Google Scholar 
Babaeian, F., Delavar, M., Morid, S. & Srinivasan, R. Robust climate change adaptation pathways in agricultural water management. J. Agric. Water Manag. 252, 106904 (2021).Article 

Google Scholar 
Barbosa, M. C., Alam, K. & Mushtaq, S. Water policy implementation in the state of São Paulo, Brazil: key challenges and opportunities. J. Environ. Sci. Policy. 60, 11–18 (2016).Article 

Google Scholar 
Barrett, S. M. Implementation studies: time for a revival? Personal reflections on 20 years of implementation studies. J. Public Admin. 82(2), 249–269 (2004).MathSciNet 
Article 

Google Scholar 
Baumgartner, R. J. & Korhonen, J. Strategic thinking for sustainable development. J. Sustain. Dev. 18(2), 71–75 (2010).Article 

Google Scholar 
Biswas, S. Measuring performance of healthcare supply chains in India: a comparative analysis of multi-criteria decision making methods. J. Decis. Making Appl. Manag. Eng. 3(2), 162–189 (2020).Article 

Google Scholar 
Biswas, S., Majumder, S., Pamucar, D. & Suman, D. An extended LBWA framework in picture fuzzy environment using actual score measures application in social enterprise systems. J. Enterp. Inform. Syst. (IJEIS) 17(4), 37–68 (2021).Article 

Google Scholar 
Biswas, S., Pamucar, D., Chowdhury, P. & Kar, S. A new decision support framework with picture fuzzy information: comparison of video conferencing platforms for higher education in India. J. Disc. Dyn. Nat. Soc. (2021).Bozorg-Haddad, O., Moradi-Jalal, M., Mirmomeni, M., Kholghi, M. K. H. & Mariño, M. A. Optimal cultivation rules in multi-crop irrigation areas. J. Irrig. Drain. 58(1), 38–49 (2009).Article 

Google Scholar 
Bozorg-Haddad, O., Loáiciga, H. A. & Zolghadr-Asli, B. A handbook on multi-attribute decision-making methods chapter (Wiley, 2021).MATH 
Book 

Google Scholar 
Buckley, J. J. Fuzzy hierarchical analysis. J. Fuzzy Sets Syst. 17(3), 233–247 (1985).MathSciNet 
MATH 
Article 

Google Scholar 
Chang, H. H. & Huang, W. C. Application of a quantification SWOT analytical method. J. Math. Comput. Model. 43, 158–169 (2006).MathSciNet 
MATH 
Article 

Google Scholar 
Chen, C. T. Extension of the TOPSIS for group decision-making under fuzzy environment. J. Fuzzy Sets Syst. 114(1), 1–9 (2000).MATH 
Article 

Google Scholar 
Conrad, C., Usman, M., Morper-Bush, L. & Schönbrodt-Stitt, S. Remote sensing-based assessments of land use, soil and vegetation status, crop production and water use in irrigation systems of the Aral Sea Basin. J. Water Sec. 11, 100078 (2020).Article 

Google Scholar 
David, F. R. Strategic management: concepts and cases (Prentice Hall, 2011).
Google Scholar 
Fallah-Mehdipour, E., Bozorg-Haddad, O., Beygi, S. & Mariño, M. A. Effect of utility function curvature of Young’s bargaining method on the design of WDNs. J. Water Resour. Manag. 25(9), 2197–2218 (2011).Article 

Google Scholar 
Fanghua, H. & Guanchun, C. Fuzzy multi-criteria group decision-making model based on weighted borda scoring method for watershed ecological risk management: a case study of three Gorges reservoir area of China. J. Water Resour. Manag. 24(10), 2139–2165 (2010).Article 

Google Scholar 
Gallego-Ayala, J. & Juızo, D. Strategic implementation of integrated water resources management in Mozambique: an A’WOT analysis. J. PhysChem. Earth. 36(14–15), 1103–1111 (2011).ADS 
Article 

Google Scholar 
Gao, C. Y. & Peng, D. H. Consolidating SWOT analysis with nonhomogeneous uncertain preference information. J. Knowl. Based Syst. 24, 796–808 (2011).Article 

Google Scholar 
Gosling, S. N. & Arnell, N. W. A global assessment of the impact of climate change on water scarcity. J. Clim. Change. 134, 371–385 (2016).ADS 
Article 

Google Scholar 
Gurel, M. & Tat, M. SWOT analysis: a theoretical review. J. Int. Soc. Res. 10(51), 994–1006 (2017).Article 

Google Scholar 
Hamdy, A., & Trisorio-Liuzzi, G. Water management strategies to combat drought in the semiarid regions. Water management for drought mitigation in the Mediterranean at the regional conference on arab water, Cairo, Egypt (2004).Hartmann, T. & Spit, T. Frontiers of land and water governance in urban regions. J. Water Int. 39(6), 791–797 (2014).Article 

Google Scholar 
He, L., Bao, J., Daccache, A., Wang, S. & Guo, P. Optimize the spatial distribution of crop water consumption based on a cellular automata model: a case study of the middle Heihe River basin, China. J. Sci. Total Environ. 720, 137569 (2020).ADS 
CAS 
Article 

Google Scholar 
Hwang, C.L. & Yoon, K. Methods for multiple attribute decision making. In: Multiple attribute decision making: lecture notes in economics and mathematical systems, Springer, Heidelberg, Germany, vol 186 (1981).Hwang, F. P., Chen, S. J. & Hwang, C. L. Fuzzy multiple attribute decision making: methods and applications (Springer, 1992).MATH 

Google Scholar 
Islam, M. S., Sadiq, R. & Rodriguez, M. J. Evaluating water quality failure potential in water distribution systems: a fuzzy-TOPSIS-OWA-based methodology. J. Water Resour. Manag. 27(7), 2195–2216 (2013).Article 

Google Scholar 
Karabasevic, D., Zavadskas, E. K., Turskis, Z. & Stanujkic, D. The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. J. Inform. 27(1), 49–65 (2016).Article 

Google Scholar 
Keršuliene, V., Zavadskas, E. K. & Turskis, Z. Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (Swara). J. Bus. Econ. Manag. 11(2), 243–258 (2010).Article 

Google Scholar 
Kim, S. et al. Developing spatial agricultural drought risk index with controllable geo-spatial indicators: a case study for South Korea and Kazakhstan. J. Disast. Risk Reduct. 54, 102056 (2021).Article 

Google Scholar 
Kousar, S., Zafar, A., Kausar, N., Pamucar, D. & Kattel, P. Fruit production planning in semiarid zones: a novel triangular intuitionistic fuzzy linear programming approach. J. Math. Prob. Eng. (2022).Lautze, J., de Silva, S., Giordano, M. & Sanford, L. Putting the cart before the horse: Water governance and IWRM. J. Nat. Resour. Forum Unit. Nat. Develop. 35(1), 1–8 (2011).Lee, K. L. & Lin, S. C. A fuzzy quantified SWOT procedure for environmental evaluation of an international distribution center. J. Inform. Sci. 178, 531–549 (2008).Article 

Google Scholar 
Loucks, D. P. Sustainable water resources management. Water International. Taylor & Francis, Milton Park (2000).Malczeweski, J. GIS and multicriteria decision analysis (Wiley, 1999).
Google Scholar 
Meza, I. et al. Drought risk for agricultural systems in South Africa: drivers, spatial patterns, and implications for drought risk management. J. Sci. Total Environ. 799, 149505 (2021).ADS 
CAS 
Article 

Google Scholar 
OECD. OECD principles on water governance. OECD Publishing (2015).Pahl-Wostl, C., Holtz, G., Kastens, B. & Knieper, C. Analyzing complex water governance regimes: the management and transition framework. J. Environ. Sci. Policy. 13(7), 571–581 (2010).Article 

Google Scholar 
Pahl-Wostl, C. et al. Environmental flows and water governance: managing sustainable water uses. J. Curr. Opin. Environm. Sustain. 5(3), 341–351 (2013).Article 

Google Scholar 
Pamucar, D., Torkayesh, A.E. & Biswas, S. Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach. J. Ann. Oper. Res. doi:https://doi.org/10.1007/s10479-022-04529-2(2022).Panchal, D., Chatterjee, P., Pamucar, D. & Yazdani, M. A novel fuzzy-based structured framework for sustainable operation and environmental friendly production in coal-fired power industry. J. Intell. Syst. doi: https://doi.org/10.1002/int.22507(2021).Peldschus, F., Zavadskas, E. K., Turskis, Z. & Tamosaitiene, J. Sustainable assessment of construction site by applying game theory. J. Eng. Econ. 21(3), 223–237 (2010).
Google Scholar 
Pérez-Blanco, C. & Gómez, C. Drought management plans and water availability in agriculture: a risk assessment model for a Southern European basin. J. Weather Clim. Extrem. 4, 11–18 (2014).Article 

Google Scholar 
Portoghese, I., Giannoccaro, G., Giordano, R. & Pagano, A. Modeling the impact of volumetric water pricing in irrigation districts with conjunctive use of water of surface and groundwater resources. J. Agric. Water Manag. 244, 106561 (2020).Article 

Google Scholar 
Rani, P., Mishra, A. R., Saha, A., Hezam, I. M. & Pamucar, D. Fermatean fuzzy Heronian mean operators and MEREC-based additive ratio assessment method: an application to food waste treatment technology selection. J. Intell. Syst. 37(3), 2612–2647 (2021).Article 

Google Scholar 
Rogers, P., & Hall, A.W. Effective water governance. J. Tech. Comm. Background Papers.7, Global Water Partnership (GWP) (2003).Rouillard, J. & Rinaudo, J. From State to user-based water allocations: an empirical analysis of institutions developed by agricultural user associations in France. J. Agric. Water Manag. 239, 106269 (2020).Article 

Google Scholar 
Ruzgys, A., Volvačiovas, R., Ignatavičius, Č & Turskis, Z. Integrated evaluation of external wall insulation in residential buildings using SWARA-TODIM MCDM method. J. Civil Eng. Manag. 20(1), 103–110 (2014).Article 

Google Scholar 
Saaty, T. L. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281 (1977).MathSciNet 
MATH 
Article 

Google Scholar 
Saaty, T. L. The analytic hierarchy process (McGraw-Hill, 1980).MATH 

Google Scholar 
Saaty, T. L. The analytic hierarchy process: planning, priority setting, resource allocation (RWS Publication, 1996).MATH 

Google Scholar 
Saaty, T. L. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 83–98 (2008).
Google Scholar 
Soltanjalili, M., Bozorg-Haddad, O. & Mariño, M. A. Effect of breakage level one in design of water distribution networks. J. Water Resour. Manag. 25(1), 311–337 (2011).Article 

Google Scholar 
Srdjevic, Z., Bajcetic, R. & Srdjevic, B. Identifying the criteria set for multi criteria decision making based on SWOT/PESTLE analysis: a case study of reconstructing a water intake structure. J. Water Resour. Manag. 26(12), 3379–3393 (2012).Article 

Google Scholar 
Stewart, R. A., Mohamed, S. & Daet, R. Strategic implementation of IT/IS projects in construction: a case study. J. Autom. Const. 11, 681–694 (2002).Article 

Google Scholar 
Thaler, T., Nordbeck, R. & Seher, W. Cooperation in flood risk management: understanding the role of strategic planning in two Austrian policy instruments. J. Environ. Sci. Policy. 114, 170–177 (2020).Article 

Google Scholar 
Thomson, J. et al. Spatial conservation action planning in heterogeneous landscapes. J. Biol. Conser. 250, 108735 (2020).Article 

Google Scholar 
Tortajada, C. Water governance: some critical issues. J. Water Resour. Develop. 26(2), 297–307 (2010).Article 

Google Scholar 
Tropp, H. Water governance: trends and needs for new capacity development. J. Water Policy. 9(2), 19–30 (2007).Article 

Google Scholar 
Van Laarhoven, P. J. & Pedrycz, W. A fuzzy extension of Saaty’s priority theory. J. Fuzzy Sets Syst. 11(1–3), 229–241 (1983).MathSciNet 
MATH 
Article 

Google Scholar 
Venot, J., Reddy, V. R. & Umapathy, D. Coping with drought in irrigated South India: Farmers’ adjustments in Nagarjuna Sagar. J. Agric. Water Manag. 97(10), 1434–1442 (2010).Article 

Google Scholar 
Vermillion, D.L. Irrigation sector reform in Asia: from patronage under participation to empowerment with partnership. In Asian Irrigation in Transition. New Delhi: Sage publications. https://www.cabdirect.org/cabdirect/abstract/20073076323(2003).Yazdani, M., Wen, Z., Liao, H., Banaitis, A. & Turskis, Z. A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. J. Civil Eng. Manag. 25(8), 858–874 (2019).Article 

Google Scholar 
Yuksel, I. & Dagdeviren, M. Using the analytic network process (ANP) in a SWOT analysis: a case study for a textile firm. J. Inform. Sci. 177, 3364–3382 (2007).MATH 
Article 

Google Scholar 
Zadeh, L. A. Fuzzy sets. J. Inform. Control. 8(3), 338–353 (1965).MATH 
Article 

Google Scholar 
Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A. & Nor, K. M. Development of TOPSIS method to solve complicated decision-making problems: an overview on developments from 2000 to 2015. J. Inform. Technol. Dec. Making. 15(03), 645–682 (2016).Article 

Google Scholar 
Zuo, Q., Wu, Q., Yu, L., Li, Y. & Fan, Y. Optimization of uncertain agricultural management considering the framework of water, energy and food. J. Agric. Water Manag. 253, 106907 (2021).Article 

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

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