Philippa Kaur

Connor, E. F. & Taverner, M. P. The evolution and adaptive significance of the leaf-mining habit. Oikos 79, 6–25. https://doi.org/10.2307/3546085 (1997).Article 

Google Scholar 
Hespenheide, H. A. Bionomics of leaf-mining insects. Annu. Rev. Entomol. 36, 535–560. https://doi.org/10.1146/annurev.en.36.010191.002535 (1991).Article 

Google Scholar 
Kato, M. Structure, organization, and response of a species-rich parasitoid community to host leafminer population dynamics. Oecologia 97, 17–25 (1994).ADS 
Article 

Google Scholar 
López, R., Carmona, D., Vincini, A. M., Monterubbianesi, G. & Caldiz, D. Population dynamics and damage caused by the leafminer Liriomyza huidobrensis Blanchard (Diptera: Agromyzidae), on seven potato processing varieties grown in temperate environment. Neotrop. Entomol. 39, 108–114. https://doi.org/10.1590/S1519-566X2010000100015 (2010).Article 
PubMed 

Google Scholar 
Lopez-Vaamonde, C., Godfray, H. C. J. & Cook, J. M. Evolutionary dynamics of host-plant use in a genus of leaf-mining moths. Evolution 57, 1804–1821. https://doi.org/10.1111/j.0014-3820.2003.tb00588.x (2003).Article 
PubMed 

Google Scholar 
Lopez-Vaamonde, C. et al. Fossil-calibrated molecular phylogenies reveal that leaf-mining moths radiated millions of years after their host plants. J. Evol. Biol. 19, 1314–1326. https://doi.org/10.1111/j.1420-9101.2005.01070.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Scheffer, S. J., Lewis, M. L., Hébert, J. B. & Jacobsen, F. Diversity and host plant-use in North American Phytomyza Holly Leafminers (Diptera: Agromyzidae): Colonization, divergence, and specificity in a host-associated radiation. Ann. Entomol. Soc. Am. 114, 59–69. https://doi.org/10.1093/aesa/saaa034 (2021).CAS 
Article 

Google Scholar 
Tooker, J. F. & Giron, D. The evolution of endophagy in herbivorous insects. Front. Plant Sci. 11, 581816. https://doi.org/10.3389/fpls.2020.581816 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hawkins, B. A. Pattern and Process in Host-Parasitoid Interactions (Cambridge University Press, 1994).Book 

Google Scholar 
Novotny, V. & Basset, Y. Host specificity of insect herbivores in tropical forests. Proc. R. Soc. B Biol. Sci. 272, 1083–1090. https://doi.org/10.1098/rspb.2004.3023 (2005).Article 

Google Scholar 
Lewis, O. T. et al. Structure of a diverse tropical forest insect-parasitoid community. J. Anim. Ecol. 71, 855–873. https://doi.org/10.1046/j.1365-2656.2002.00651.x (2002).Article 

Google Scholar 
Hirao, T. & Murakami, M. Quantitative food webs of lepidopteran leafminers and their parasitoids in a Japanese deciduous forest. Ecol. Res. 23, 159–168. https://doi.org/10.1007/s11284-007-0351-6 (2008).Article 

Google Scholar 
Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977. https://doi.org/10.1126/science.1214915 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Leppänen, S. A., Altenhofer, E., Liston, A. D. & Nyman, T. Phylogenetics and evolution of host-plant use in leaf-mining sawflies (Hymenoptera: Tenthredinidae: Heterarthrinae). Mol. Phylogenet. Evol. 64, 331–341. https://doi.org/10.1016/j.ympev.2012.04.005 (2012).Article 
PubMed 

Google Scholar 
Doorenweerd, C., Van Nieukerken, E. J. & Menken, S. B. J. A global phylogeny of leafmining Ectoedemia moths (Lepidoptera: Nepticulidae): Exploring host plant family shifts and allopatry as drivers of speciation. PLoS ONE 10, 1–20. https://doi.org/10.1371/journal.pone.0119586 (2015).CAS 
Article 

Google Scholar 
Nakadai, R. & Kawakita, A. Phylogenetic test of speciation by host shift in leaf cone moths (Caloptilia) feeding on maples (Acer). Ecol. Evol. 6, 4958–4970. https://doi.org/10.1002/ece3.2266 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Opler, P. A. Fossil lepidopterous leaf mines demonstrate the age of some insect-plant relationships. Science 179, 1321–1323. https://doi.org/10.1126/science.179.4080.1321 (1973).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Labandeira, C. C., Dilcher, D. L., Davis, D. R. & Wagner, D. L. Ninety-seven million years of angiosperm-insect association: Paleobiological insights into the meaning of coevolution. Proc. Natl. Acad. Sci. U. S. A. 91, 12278–12282. https://doi.org/10.1073/pnas.91.25.12278 (1994).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Winkler, I. S., Labandeira, C. C., Wappler, T. & Wilf, P. Distinguishing Agromyzidae (Diptera) leaf mines in the fossil record: New taxa from the Paleogene of North America and Germany and their evolutionary implications. J. Paleontol. 84, 935–954. https://doi.org/10.1666/09-163.1 (2010).Article 

Google Scholar 
van Nieukerken, E. J., Doorenweerd, C., Hoare, R. J. B. & Davis, D. R. Revised classification and catalogue of global Nepticulidae and Opostegidae (Lepidoptera, Nepticuloidea). Zookeys 2016, 65–246. https://doi.org/10.3897/zookeys.628.9799 (2016).Article 

Google Scholar 
Maccracken, S. A., Sohn, J.-C., Miller, I. M. & Labandeira, C. C. A new Late Cretaceous leaf mine Leucopteropsa spiralae gen. et sp. nov. (Lepidoptera: Lyonetiidae) represents the first confirmed fossil evidence of the Cemiostominae. J. Syst. Palaeontol. 19, 131–144. https://doi.org/10.1080/14772019.2021.1881177 (2021).Article 

Google Scholar 
Wilf, P., Labandeira, C. C., Johnson, K. R. & Ellis, B. Decoupled plant and insect diversity after the end-Cretaceous extinction. Science 313, 1112–1115. https://doi.org/10.1126/science.1129569 (2006)ADS 
CAS 
Article 
PubMed 

Google Scholar 
Donovan, M. P., Wilf, P., Labandeira, C. C., Johnson, K. R. & Peppe, D. J. Novel insect leaf-mining after the end-Cretaceous extinction and the demise of Cretaceous leaf miners, Great Plains, USA. PLoS ONE 9, e103542. https://doi.org/10.1371/journal.pone.0103542 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Donovan, M. P., Iglesias, A., Wilf, P., Labandeira, C. C. & Cúneo, N. R. Rapid recovery of Patagonian plant–insect associations after the end-Cretaceous extinction. Nat. Ecol. Evol. 1, 0012. https://doi.org/10.1038/s41559-016-0012 (2017).Article 

Google Scholar 
Donovan, M. P., Wilf, P., Iglesias, A., Cúneo, N. R. & Labandeira, C. C. Persistent biotic interactions of a Gondwanan conifer from Cretaceous Patagonia to modern Malesia. Commun. Biol. 3, 708. https://doi.org/10.1038/s42003-020-01428-9 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Labandeira, C. C. The four phases of plant-arthropod associations in deep time. Geol. Acta 4, 409–438. https://doi.org/10.1344/105.000000344 (2006).Article 

Google Scholar 
Labandeira, C. C. Silurian to Triassic plant and hexapod clades and their associations: new data, a review, and interpretations. Arthropod Syst. Phylogen. 64, 53–94 (2006).
Google Scholar 
Wakita, K., Nakagawa, T., Sakata, M., Tanaka, N. & Oyama, N. Phanerozoic accretionary history of Japan and the western Pacific margin. Geol. Mag. https://doi.org/10.1017/s0016756818000742 (2018).Article 

Google Scholar 
Katayama, M. Stratigraphical study on the Mine Series. J. Geol. Soc. Jpn. 46, 127–141. https://doi.org/10.5575/geosoc.46.127 (1939).Article 

Google Scholar 
Maeda, H. & Oyama, N. Stratigraphy and fossil assemblages of the Triassic Mine Group and Jurassic Toyora Group in western Yamaguchi Prefecture. J. Geol. Soc. Japan 125, 585–594. https://doi.org/10.5575/geosoc.2019.0020 (2019).Article 

Google Scholar 
Aizawa, J. Fossil insect-bearing strata of the Triassic Mine Group, Yamaguchi Prefecture. Bull. Kitakyushu Mus. Nat. Hist. Hum. Hist. Ser. A 10, 91–98 (1991).
Google Scholar 
Oyama, N. & Maeda, H. Madygella humioi sp. nov. from the Upper Triassic Mine Group, Southwest Japan: The oldest record of a sawfly (Hymenoptera: Symphyta) in East Asia. Paleontol. Res. 24, 64–71 (2020).Article 

Google Scholar 
Fujiyama, I. Mesozoic insect fauna of East Asia part 1. Introduction and upper Triassic faunas. Bull. Natl. Sci. Mus. 16, 331–386 (1973).
Google Scholar 
Fujiyama, I. Late Triassic insects from Mine, Yamaguchi, Japan, Part 1. Odonata. Bull. Natl. Sci. Mus. Tokyo Ser. C 17, 49–56 (1991).
Google Scholar 
Ueda, K. A Triassic fossil of scorpion fly from Mine, Japan. Bull. Kitakyushu Mus. Nat. Hist. Hum. Hist. Ser. Ser. A 10, 99–103 (1991).
Google Scholar 
Takahashi, F., Ishida, H., Nohara, M., Doi, E. & Taniguchi, S. Occurrence of insect fossils from the Late Triassic Mine Group. Bull. Mine City Mus. Yamaguchi Prefect. Jpn. 13, 1–27 (1997).CAS 

Google Scholar 
Kametaka, M. Provenance of the Upper Triassic mine group Southwest Japan. J. Geol. Soc. Jpn. 105, 651–667 (1999).CAS 
Article 

Google Scholar 
Takahashi, E. & Mikami, T. Triassic. In Geology of Yamaguchi Prefecture (ed. Yamaguchi Museum) 93–108 (Yamaguchi Museum, 1975).Kiminami, K. Atsu Group and Mine Group. In Monograph on Geology of Japan 6, Chugoku Region (ed. Geological Society of Japan) 85–88 (Asakura Publishing Co., Ltd., 2009).Naito, G. Plant Fossils from the Mine Group (Mine City Education Comittee, 2000).
Google Scholar 
Kimura, T. Geographical distribution of Palaeozoic and Mesozoic plants in East and Southeast Asia. Hist. Biogeogr. Plate Tecton. Evol. Jpn. East Asia 1982, 135–200 (1987).
Google Scholar 
Kimura, T., Naito, G. & Ohana, T. Baiera cf. furcata (Lindley and Hutton) Braun from the Carnic Momonoki Formation, Japan. Bull. Natl. Sci. Mus. 9, 91–114 (1983).
Google Scholar 
Katagiri, T. Pallaviciniites oishii (comb. Nov.), a thalloid liverwort from the Late Triassic of Japan. Bryologist 118, 245–251. https://doi.org/10.1639/0007-2745-118.3.245 (2015).Article 

Google Scholar 
Kustatscher, E. et al. Flora of the Late Triassic. In The Late Triassic World, Topics in Geobiology, Vol. 46 (ed. Tanner, L. H.) 545–622 (Springer, 2018). https://doi.org/10.1007/978-3-319-68009-5_13.Oyama, N., Yukawa, H. & Maeda, H. Mesozoic insect fossils of Japan: Significance of the Upper Triassic insect fauna of the Mine Group, Yamaguchi Pref. Bull. Mine City Mus. Yamaguchi Prefect. Jpn. 33, 1–13 (2020).
Google Scholar 
Shcherbakov, D. E., Lukashevich, E. D. & Blagoderov, V. Triassic Diptera and initial radiation of the order. Int. J. Dipterol. Res. 6, 75–115 (1995).
Google Scholar 
Krzemiński, W. & Krzemińska, E. Triassic Diptera: Descriptions, revisions and phylogenetic relations. Acta Zool. Cracov. 46, 153–184 (2003).
Google Scholar 
Blagoderov, V., Grimaldi, D. A. & Fraser, N. C. How time flies for flies: Diverse Diptera from the Triassic of Virginia and early radiation of the order. Am. Mus. Novit. 3572, 1–39. https://doi.org/10.1206/0003-0082(2007)509[1:HTFFFD]2.0.CO;2 (2007).Article 

Google Scholar 
Lukashevich, E. D., Przhiboro, A. A., Marchal-Papier, F. & Grauvogel-Stamm, L. The oldest occurrence of immature Diptera (Insecta), Middle Triassic France. Ann. la Société Entomol. Fr. 46, 4–22. https://doi.org/10.1080/00379271.2010.10697636 (2010).Article 

Google Scholar 
Schmidt, A. R. et al. Arthropods in amber from the Triassic Period. Proc. Natl. Acad. Sci. 109, 14796–14801. https://doi.org/10.1073/pnas.1208464109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lara, M. B. & Lukashevich, E. D. The first Triassic dipteran (Insecta) from South America, with review of Hennigmatidae. Zootaxa 3710, 81–92. https://doi.org/10.11646/zootaxa.3710.1.6 (2013).Article 
PubMed 

Google Scholar 
Kimura, T. & Ohana, T. Some fossil ferns from the Middle Carnic Momonoki Formation, Yamaguchi prefecture, Japan. Bull. Natl. Sci. Mus. Ser. C Geol. Paleontol. 6, 73–92 (1980).
Google Scholar 
Hering, E. M. Biology of the Leaf Miners https://doi.org/10.1007/978-94-015-7196-8. (Springer, 1951).Book 

Google Scholar 
Kirichenko, N. et al. Systematics of Phyllocnistis leaf-mining moths (Lepidoptera, Gracillariidae) feeding on dogwood (Cornus spp.) in Northeast Asia, with the description of three new species. Zookeys 2018, 79–118. https://doi.org/10.3897/zookeys.736.20739 (2018).Article 

Google Scholar 
Cerdeña, J. et al. Phyllocnistis furcata sp. nov.: A new species of leaf-miner associated with Baccharis (Asteraceae) from Southern Peru (Lepidoptera, Gracillariidae). Zookeys 2020, 121–145. https://doi.org/10.3897/zookeys.996.53958 (2020).Article 

Google Scholar 
Elb, P. M., Melo-de-Pinna, G. F. & de Menezes, N. L. Morphology and anatomy of leaf miners in two species of Commelinaceae (Commelina diffusa Burm. F. and Floscopa glabrata (Kunth) Hassk). Acta Bot. Brasilica 24, 283–287. https://doi.org/10.1590/S0102-33062010000100030 (2010).Article 

Google Scholar 
Vasco, A., Moran, R. C. & Ambrose, B. A. The evolution, morphology, and development of fern leaves. Front. Plant Sci. 4, 1–16. https://doi.org/10.3389/fpls.2013.00345 (2013).Article 

Google Scholar 
Eiseman, C. Leafminers of North America. (Charley Eiseman, 2019).Yang, J., Wang, X., Duffy, K. & Dai, X. A preliminary world checklist of fern-mining insects. Biodivers. Data J. 9, e62839. https://doi.org/10.3897/BDJ.9.e62839 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ding, Q., Labandeira, C. C. & Ren, D. Biology of a leaf miner (Coleoptera) on Liaoningocladus boii (Coniferales) from the Early Cretaceous of northeastern China and the leaf-mining biology of possible insect culprit clades. Arthropod Syst. Phylogen. 72, 281–308 (2014).
Google Scholar 
Boucher, S. Revision of the Canadian species of Amauromyza Hendel (Diptera: Agromyzidae). Can. Entomol. 144, 733–757. https://doi.org/10.4039/tce.2012.80 (2012).Article 

Google Scholar 
Scheirs, J., Vandevyvere, I. & De Bruyn, L. Influence of monocotyl leaf anatomy on the feeding pattern of a grass-mining agromyzid (Diptera). Ann. Entomol. Soc. Am. 90, 646–654 (1997).Article 

Google Scholar 
Boucher, S. Leaf-miner flies (Diptera: Agromyzidae). In Encyclopedia of Entomology (ed. Capinera J. L.) 2163–2169 (Springer, 2008). https://doi.org/10.1007/978-1-4020-6359-6.Eiseman, C. S. New rearing records for muscoid leafminers (Diptera: Anthomyiidae, Scathophagidae) in the United States. Proc. Entomol. Soc. Wash. 120, 25–50. https://doi.org/10.4289/0013-8797.120.1.25 (2018).Article 

Google Scholar 
Meikle, A. A. The insects associated with bracken. Agric. Prog. 14, 58–61 (1937).
Google Scholar 
Lawton, J. H. The structure of the arthropod community on bracken. Bot. J. Linn. Soc. 73, 187–216. https://doi.org/10.1111/j.1095-8339.1976.tb02022.x (1976).Article 

Google Scholar 
Lawton, J. H., MacGarvin, M. & Heads, P. A. Effects of altitude on the abundance and species richness of insect herbivores on bracken. J. Anim. Ecol. 56, 147–160. https://doi.org/10.2307/4805 (1987).Article 

Google Scholar 
Cooper-Driver, Gi. A. Insect-fern associations. Entomol. Exp. Appl. 24, 310–316. https://doi.org/10.1111/j.1570-7458.1978.tb02787.x (1978).Article 

Google Scholar 
Eiseman, C. S. Further Nearctic rearing records for phytophagous muscoid flies (Diptera: Anthomyiidae, Scathophagidae). Proc. Entomol. Soc. Washingt. 122, 595–603. https://doi.org/10.4289/0013-8797.122.3.595 (2020).Article 

Google Scholar 
Santos, M. G. & Maia, V. C. A synopsis of fern galls in Brazil. Biota Neotrop. 18, e20180513. https://doi.org/10.1590/1676-0611-BN-2018-0513 (2018).Article 

Google Scholar 
Peters, R. S. et al. Evolutionary history of the Hymenoptera. Curr. Biol. 27, 1013–1018. https://doi.org/10.1016/j.cub.2017.01.027 (2017). CAS 
Article 
PubMed 

Google Scholar 
Ronquist, F. et al. A total-evidence approach to dating with fossils, applied to the early radiation of the Hymenoptera. Syst. Biol. 61, 973–999. https://doi.org/10.1093/sysbio/sys058 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Needham, J. G., Frost, S. W. & Tothill, B. H. Leaf-Mining Insects (Waverly Press, 1928).
Google Scholar 
Smith, D. R., Eiseman, C. S., Charney, N. D. & Record, S. A new Nearctic Scolioneura (Hymenoptera, Tenthredinidae) mining leaves of Vaccinium (Ericaceae). J. Hymenopt. Res. 43, 1–8. https://doi.org/10.3897/JHR.43.4546 (2015).Article 

Google Scholar 
Zheng, D. et al. Middle-Late Triassic insect radiation revealed by diverse fossils and isotopic ages from China. Sci. Adv. 4, eaat1380. https://doi.org/10.1126/sciadv.aat1380 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, S. Q. et al. Evolutionary history of Coleoptera revealed by extensive sampling of genes and species. Nat. Commun. 9, 1–11. https://doi.org/10.1038/s41467-017-02644-4 (2018).ADS 
CAS 
Article 

Google Scholar 
McKenna, D. D. et al. The evolution and genomic basis of beetle diversity. Proc. Natl. Acad. Sci. 116, 24729–24737. https://doi.org/10.1073/pnas.1909655116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gimmel, M. L. & Ferro, M. L. General overview of saproxylic Coleoptera. In Saproxylic Insects, Zoological Monographs, Vol. 1 (ed. Ulyshen, M. D.) 51–128 (Springer, 2018). https://doi.org/10.1007/978-3-319-75937-1_2.Labandeira, C. C., Anderson, J. M. & Anderson, H. M. Expansion of arthropod herbivory in Late Triassic South Africa: The Molteno Biota, Aasvoëlberg 411 site and developmental biology of a gall. In The Late Triassic World, Topics in Geobiology Vol. 46 (ed. Tanner, L. H.) 623–719 (Springer International Publishing AG, 2018).Chapter 

Google Scholar 
Fiebrig, K. Eine Schaum bildende Käferlarve Pachyschelus spec. (Bupr. Sap.) Die Ausscheidung von Kautschuk aus der Nahrung und dessen Verwertung zu Schutzzwecken (auch bei Rhynchoten). Z. f. Wiss. Insektenbiol. 4, 333–339 (1908).
Google Scholar 
Bruch, C. Metamórfosis de Pachyschelus undularius (Burm.). Physis 3, 30–36 (1917).
Google Scholar 
Hering, E. M. Neotropische Buprestiden-Minen. Arb. Physiol. Angew. Entomol. 9, 241–249 (1942).
Google Scholar 
Kogan, M. Contribuição ao conhecimento da sistemática e biologia de buprestídeos minadores do gênero Pachyschelus Solier, 1833: (Coleoptera, Buprestidae). Mem. Inst. Oswaldo Cruz 61, 429–457 (1963).CAS 
Article 

Google Scholar 
Kawahara, A. Y. et al. Phylogenomics reveals the evolutionary timing and pattern of butterflies and moths. Proc. Natl. Acad. Sci. 116, 22657–22663. https://doi.org/10.1073/pnas.1907847116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van Eldijk, T. J. B. et al. A Triassic-Jurassic window into the evolution of lepidoptera. Sci. Adv. 4, e1701568. https://doi.org/10.1126/sciadv.1701568 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sohn, J. C., Labandeira, C. C., Davis, D. & Mitter, C. An annotated catalog of fossil and subfossil Lepidoptera (Insecta: Holometabola) of the world. Zootaxa. https://doi.org/10.11646/zootaxa.3286.1.1 (2012).Doorenweerd, C., Van Nieukerken, E. J., Sohn, J. C. & Labandeira, C. C. A revised checklist of Nepticulidae fossils (Lepidoptera) indicates an Early Cretaceous origin. Zootaxa 3963, 295–334. https://doi.org/10.11646/zootaxa.3963.3.2 (2015).Article 
PubMed 

Google Scholar 
Kawahara, A. Y. et al. A molecular phylogeny and revised higher-level classification for the leaf-mining moth family Gracillariidae and its implications for larval host-use evolution. Syst. Entomol. 42, 60–81. https://doi.org/10.1111/syen.12210 (2017).Article 

Google Scholar 
Mazumdar, J. Phytoliths of pteridophytes. S. Afr. J. Bot. 77, 10–19. https://doi.org/10.1016/j.sajb.2010.07.020 (2011).Article 

Google Scholar 
Trembath-Reichert, E., Wilson, J. P., McGlynn, S. E. & Fischer, W. W. Four hundred million years of silica biomineralization in land plants. Proc. Natl. Acad. Sci. U. S. A. 112, 5449–5454 https://doi.org/10.1073/pnas.1500289112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hunt, J. W., Dean, A. P., Webster, R. E., Johnson, G. N. & Ennos, A. R. A novel mechanism by which silica defends grasses against herbivory. Ann. Bot. 102, 653–656. https://doi.org/10.1093/aob/mcn130 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Reynolds, O. L., Keeping, M. G. & Meyer, J. H. Silicon-augmented resistance of plants to herbivorous insects: A review. Ann. Appl. Biol. 155, 171–186. https://doi.org/10.1111/j.1744-7348.2009.00348.x (2009).CAS 
Article 

Google Scholar 
Edwards, N. P. et al. Leaf metallome preserved over 50 million years. Metallomics 6, 774–782. https://doi.org/10.1039/C3MT00242J (2014).CAS 
Article 
PubMed 

Google Scholar 
Müller, A. H. Über Hyponome fossiler und rezenter Insekten, erster Beitrag. Freib. Forschungsh. C 366, 7–27 (1982).
Google Scholar 
Beck, A. L. & Labandeira, C. C. Early Permian insect folivory on a gigantopterid-dominated riparian flora from north-central Texas. Palaeogeogr. Palaeoclimatol. Palaeoecol. 142, 139–173. https://doi.org/10.1016/S0031-0182(98)00060-1 (1998).Article 

Google Scholar 
Jarzembowski, E. A. The oldest plant-insect interaction in Croatia: Carboniferous evidence. Geol. Croat. 65(3), 387–392. https://doi.org/10.4154/GC.2012.28 (2002).Article 

Google Scholar 
Donovan, M. P. & Lucas, S. G. Insect herbivory on the Late Pennsylvanian Kinney Brick Quarry Flora, New Mexico, USA. Kinney Brick Quarry Lagerstätte. N. M. Mus. Nat. Hist. Sci. Bull. 84, 193–207 (2021).Potonié, R. Ueber das Rothliegende des Thüringer Waldes. Theil II: Die Flora des Rothliegenden von Thüringen. Abh. Preuss. Geol. Landesanst. 9, 1–298 (1893).
Google Scholar 
Potonié, R. Mitteilungen über mazerierte kohlige Pflanzenfossilien. Z. Bot. 13, 79–88 (1921).Adami-Rodrigues, K. A., Iannuzzi, R. & Pinto, I. D. Permian plant-insect interactions from a Gondwana flora of southern Brazil. Foss. Strat. 51, 106–126 (2004).
Google Scholar 
Krassilov, V. A. & Karasev, E. First evidence of plant–arthropod interaction at the Permian–Triassic boundary in the Volga Basin European Russia. Alavesia 2, 247–252 (2008).
Google Scholar 
Labandeira, C. C., Wilf, P., Johnson, K. & Marsh, F. Guide to insect (and other) damage types on compressed plant fossils. Version 3.0. Smithson. Institution, Washington, DC 25 (2007).Scott, A. C., Anderson, J. M. & Anderson, H. M. Evidence of plant-insect interactions in the Upper Triassic Molteno formation of South Africa. J. Geol. Soc. London. 161, 401–410. https://doi.org/10.1144/0016-764903-118 (2004).Article 

Google Scholar 
Tillyard, R. J. Mesozoic Insects of Queensland No. 9. Orthoptera, and Additions to the Protorthoptera, Odonata, Hemiptera, and Planipennia. Proc. Linn. Soc. N. S. W. 47, 447–470 (1922).
Google Scholar 
Rozefelds, A. C. & Sobbe, I. Problematic insect leaf mines from the Upper Triassic Ipswich Coal Measures of Southeastern Queensland Australia. Alcheringa 11, 51–57 (1987).Article 

Google Scholar 
Wappler, T., Kustatscher, E. & Dellantonio, E. Plant-insect interactions from Middle Triassic (late Ladinian) of Monte Agnello (Dolomites, N-Italy)-Initial pattern and response to abiotic environmental pertubations. PeerJ 2015, e921. https://doi.org/10.7717/peerj.921 (2015).Article 

Google Scholar 
Meller, B., Ponomarenko, A. G., Vasilenko, D. V., Fischer, T. C. & Aschauer, B. First beetle elytra, abdomen (Coleoptera) and a mine trace from Lunz (Carnian, Late Triassic, Lunz-am-See, Austria) and their taphonomical and evolutionary aspects. Palaeontology 54, 97–110. https://doi.org/10.1111/j.1475-4983.2010.01009.x (2011).Article 

Google Scholar 
Vassilenko, D. V. Traces of plant-arthropod interactions from Madygen (Triassic, Kyrgyzstan): Preliminary data. Sovremennaya paleontologia: klassicheskie i noveishie metody 9–16 (2009).Zherikhin, V. V. Insect Trace Fossils. In History of Insects (ed. Rasnitsyn A. P., Quicke, D. L.) 303–324 (Kluwer Academic Publishers, 2010).Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).CAS 
Article 
PubMed 

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

Google Scholar  More

Ethics oversightThis study was carried out within the frame of our housing and breeding permit (311.4-si) granted by the Landratsamt Starnberg, Germany. Attachment of backpacks was approved by the Regierung von Oberbayern, Germany (ROB-55-2-2532. Vet_02-17-211).Study populationsWe used four zebra finch populations that are genetically differentiated due to founder effects and selection (see Supplementary Fig. 1 & Fig. 2): two domesticated populations (D1 and D2) that have been maintained in captivity in Europe for about 150 years and two populations (W1 and W2) that have been taken from the wild about 10–30 years ago (see Supplementary Fig. 1). We ran all experiments in two independent replicates. We used individuals from populations D1 and W1 for replicate 1 and individuals from D2 and W2 for replicate 2.Breeding experiment Generation 1We created four groups of 36 individuals (9 males and 9 females from both a domesticated and a wild-derived population, two groups within each replicate) and put each group separately in an indoor aviary (5 m × 2.0 m × 2.5 m). All individuals had been reared normally by their genetic parents in similar breeding aviaries, were inexperienced (never mated before) and unfamiliar to all opposite-sex individuals. In replicate 1 (W1 – D1, starting December 2016), birds were 142 ± 32 days old at the start of the experiment (range: 101–191 days); in replicate 2 (W2 – D2, starting March 2017), birds were 241 ± 47 days old (range: 151–306 days). In each aviary, we provided nest material and nest boxes to stimulate breeding and observed pair-bonding behaviour for ca. 60 h spread over 14 days. Two observers recorded all instances of allopreening, sitting in bodily contact, and visiting a nest box together, which reflects pair bonding64.In total, we observed 3166 instances of heterosexual association among the 4 × 36 individuals (Supplementary Table 3). We defined a pair-bond between two opposite-sex individuals if they were recorded in pair-bonding behaviour at least five times (mean: 22 ± 14 SD, range: 5 – 73). This cut-off was chosen (blind to the outcome of data analysis) based on the frequency distribution showing a clear deviation from a random, zero-truncated Poisson distribution (Supplementary Fig. 8). Using this definition, we identified a total of 60 pairs (30 in each replicate). Of all females, 48 and 6 had a pair-bond with one and two males, respectively (18 females remained unpaired). Conversely, 34, 10, and 2 males had a pair-bond with one, two, and three females, respectively (26 males remained unpaired).Cross-fostering for Generation 2 experimentsAfter the breeding experiment of Generation 1, in 2017, we established two different cultural lineages within each genetic population by cross-fostering eggs, either within or between populations (Fig. 3). For this purpose, we used 16 aviaries (four per population), each containing 8 males and 8 females of the same population (Generation 1). Individuals were allowed to freely form pairs and breed. We reciprocally exchanged eggs shortly after laying between two aviaries per population (within-population cross-fostering) and between pairs of aviaries from different populations (between-population cross-fostering). This resulted in four cultural lineages per replicate (DD, DW, WD, and WW; Fig. 3). Each lineage was maintained in two separate breeding aviaries to ensure the availability of unfamiliar opposite-sex Generation 2 individuals from the same line. Offspring remained with their foster parents until they reached sexual maturity, when the following experiment started.Social experiment Generation 2Between December 2017 and March 2018, we put four groups of individuals (two groups for each replicate) in indoor aviaries (same as in Generation 1 experiment). Each group consisted of 10 males and 10 females from each of the cross-fostered groups DD, WW, DW and WD, i.e., a total of 80 birds per aviary, except that one aviary of replicate 2 only consisted of 63 individuals (7DD, 8WW, 8DW and 8WD) due to a shortage of birds. In replicate 1 (W1 – D1, starting December 2017), birds were 170 ± 25 days old at the start of the experiment (range: 105–199 days); in replicate 2 (W2 – D2, starting January 2018), birds were 200 ± 29 days old (range: 120–241 days). We recorded the position of individuals using an automated barcode-based tracking system31. We fitted each individual with a unique machine-readable barcode (Supplementary Fig. 4a) and placed eight cameras (8-megapixel Camera Module V2; RS Components Ltd and Allied Electronics Inc.), each connected to a Raspberry Pi (Raspberry Pi 3 Model Bs; Raspberry Pi Foundation) in each aviary. For 30 consecutive days, the cameras recorded individuals at six perches and at two feeders (Supplementary Fig. 4b, c). Between 05:30 and 20:00, when lights were switched on, each camera took a picture every two seconds.Each day, pictures stored on the Raspberry Pis were downloaded to a central server and processed using customised scripts. The customised software used the PinPoint library in Python65 to identify each barcode in each picture, allowing us to simultaneously track the position and orientation of each individual (Supplementary Fig. 4b) for the duration of the experiment. The tracking system generated 118 million observations across all four aviaries (Supplementary Fig. 4c). From these data, we extracted the average distance between the male and the female (in mm) for each male-female dyad, either daily or across the entire 30-day period (for comparison, such distance data were also extracted for all male-male and all female-female dyads). We used this dataset to identify the nearest opposite-sex individual for each of 151 males and females (55% of these 151 associations were reciprocal). Out of 151 nearest males to females, 74 (49%) paired with that female in the following breeding experiment (see below) and this proportion strongly increased as the average distance between partners decreased (Supplementary Fig. 9).Breeding experiment Generation 2Immediately after the social experiment, we moved each group into a separate semi-outdoor aviary (5 m × 2.5 m × 2.5 m) and provided nest material and nest boxes. During the next 2 months, three observers scored heterosexual associations to identify pair bonds as described for ‘breeding experiment Generation 1’ (ca 300 h per replicate). In total, we observed 6072 associations involving 284 individuals (Supplementary Table 3). Consistent with the previous experiment, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 13 SD range: 5 – 61; Supplementary Fig. 8). Using this definition, we identified 147 pairs (79 pairs in replicate 1 and 68 in replicate 2). Of all males, 97, 22 and 2 had a pair-bond with 1, 2 and 3 females, respectively (27 males remained unpaired). Conversely, 99, 21 and 2 females had a pair-bond with 1, 2 and 3 males (26 females remained unpaired).Breeding experiment Generation 3Between April and December 2018, we housed the four cultural lineages (DD, WW, DW and WD) separately again. We placed 8 males and 8 females in each of 16 breeding aviaries (four per lineage) and allowed them to freely form pairs and breed. The offspring belong to four lineages (Fig. 3): two lineages with individuals that were raised by parents that had not been cross-fostered between the domestic and wild-derived population (DDD and WWW) and two lineages with individuals from the same genetic background, but where their parents had been cross-fostered and raised by the other population (DDW and WWD).Between December 2018 and February 2019, we put four groups of 36 birds (two per replicate, i.e., 2 with 18 DDD and 18 DDW individuals and 2 with 18 WWW and 18 WWD individuals; 9 males and 9 females per lineage; Supplementary Table 3) in an outdoor aviary (same as above). In replicate 1 (W1 – D1, starting December 2018), birds were 172 ± 44 days old at the start of the experiment (range: 131–195 days); in replicate 2 (W2 – D2, starting January 2019), birds were 191 ± 40 days old (range: 122–230 days). During 14 days, two observers recorded all pair-bond behaviours as described under ‘breeding experiment Generation 1’. In total, we observed 3378 instances of pair-bond behaviour involving 137 individuals (Supplementary Table 3). As above, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 11 SD, range: 5 – 47; Supplementary Fig. 8). We identified 82 pair bonds (37 in replicate 1 and 45 in replicate 2). Of all males, 34, 16, 4 and 1 had a pair-bond with 1, 2, 3 and 4 females, respectively (17 males remained unpaired). Conversely, 42, 16, 1 and 1 females had a pair-bond with 1, 2, 3 and 5 males, respectively (12 females remained unpaired).Morphological measurementsAfter birds had reached sexual maturity ( >100 days of age), we measured body mass (to the nearest 0.1 g), tarsus length (to the nearest 0.1 mm), and wing length (to the nearest 0.5 mm) of all individuals (all measured by WF). We included these three variables in a principal component analysis (PCA) and used the first principal component (PC1, 67% of variation explained) as a measure of body size.Song recording and analysis approachWe recorded the songs of the parental males from Generation 1 (16 aviaries x 8 males = 128 males, of which 122 were successfully recorded between November and December in 2017) and of their offspring (Generation 2; 146 out of 152 males were successfully recorded between March and May 2018). To elicit courtship song, each male was placed together with an unfamiliar female in a metal wire cage (50 cm × 30 cm × 40 cm) equipped with three perches and containing food and water. The cage was placed within one of two identical sound-attenuated chambers. We mounted a Behringer condenser microphone (TC20, Earthworks, USA) at a 45° angle between the ceiling and the side wall of the chamber, such that the distance to each perch was approximately 35 cm. The microphone was connected to a PR8E amplifier (SM Pro Audio, Melbourne, Australia) from which we recorded directly through a M-Audio Delta 44 sound card (AVID Technology GmbH, Hallbergmoos, Germany) onto the hard drive of a computer.Previous studies that quantified differentiation of songs between zebra finch populations using specific song parameters (e.g., duration and frequency measures) largely failed to detect prominent differences12,49,50. We, therefore, used the following two approaches (Sound Analysis Pro and Machine Learning) to quantify the extent to which a given male’s song resembled the songs of other males.Song similarity analysis with SAPUsing Sound Analysis Pro (SAP) version 2011.10427, we quantified song similarity (ranging from 0 to 100) by direct pairwise comparison of song motifs (the main part of a male’s song that is stereotypically repeated and about 0.8 s long, excluding introductory syllables). Pair-wise comparisons of two males (based on one representative motif recording per male) revealed higher within-population similarity than between-population similarity (Supplementary Table 2, data from Generation 1). Further, for offspring that were cross-fostered between populations (N = 73 males from Generation 2) song similarity to their foster father was higher than song similarity to their genetic father (80 versus 68, paired t-test: p  More

Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).CAS 
PubMed 

Google Scholar 
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).PubMed 
PubMed Central 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).CAS 
PubMed 

Google Scholar 
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).CAS 
PubMed 

Google Scholar 
Feeley, K. J., Bravo-Avila, C., Fadrique, B., Perez, T. M. & Zuleta, D. Climate-driven changes in the composition of New World plant communities. Nat. Clim. Change 10, 965–970 (2020).CAS 

Google Scholar 
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).PubMed 

Google Scholar 
Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).CAS 
PubMed 

Google Scholar 
Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).CAS 
PubMed 

Google Scholar 
van der Putten, W. H., Macel, M. & Visser, M. E. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Proc. R. Soc. B 365, 2025–2034 (2010).
Google Scholar 
Gaüzère, P., Iversen, L. L., Barnagaud, J.-Y., Svenning, J.-C. & Blonder, B. Empirical predictability of community responses to climate change. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00186 (2018).Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).CAS 
PubMed 

Google Scholar 
Bennett, J. A. et al. Plant–soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science 355, 181–184 (2017).CAS 
PubMed 

Google Scholar 
Teste, F. P. et al. Plant–soil feedback and the maintenance of diversity in Mediterranean-climate shrublands. Science 355, 173–176 (2017).CAS 
PubMed 

Google Scholar 
Kardol, P., Bezemer, T. M. & van der Putten, W. H. Temporal variation in plant–soil feedback controls succession. Ecol. Lett. 9, 1080–1088 (2006).PubMed 

Google Scholar 
van der Putten, W. H., van Dijk, C. & Peters, B. A. M. Plant-specific soil-borne diseases contribute to succession in foredune vegetation. Nature 362, 53–56 (1993).
Google Scholar 
Bever, J. D. Feedback between plants and their soil communities in an old field community. Ecology 75, 1965–1977 (1994).
Google Scholar 
Bever, J. D., Westover, K. M. & Antonovics, J. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. J. Ecol. 85, 561–573 (1997).
Google Scholar 
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Google Scholar 
Bever, J. D. Soil community feedback and the coexistence of competitors: conceptual frameworks and empirical tests. New Phytol. 157, 465–473 (2003).PubMed 

Google Scholar 
Revilla, T. A., Veen, G. F., Eppinga, M. B. & Weissig, F. J. Plant–soil feedbacks and the coexistence of competing plants. Theor. Ecol. 6, 99–113 (2013).
Google Scholar 
Molofsky, J. & Bever, J. D. A novel theory to explain species diversity in landscapes: positive frequency dependence and habitat suitability. Proc. R. Soc. B 269, 2389–2393 (2002).PubMed 
PubMed Central 

Google Scholar 
Ke, P. J. & Wan, J. Effects of soil microbes on plant competition: a perspective from modern coexistence theory. Ecol. Monogr. 90, e01391 (2020).
Google Scholar 
Mack, K. M. L. & Bever, J. D. Coexistence and relative abundance in plant communities are determined by feedbacks when the scale of feedback and dispersal is local. J. Ecol. 102, 1195–1201 (2014).PubMed 
PubMed Central 

Google Scholar 
Bauer, J. T., Mack, K. M. L. & Bever, J. D. Plant–soil feedbacks as drivers of succession: evidence from remnant and restored tallgrass prairies. Ecosphere 6, art158 (2015).
Google Scholar 
Kulmatiski, A., Beard, K. H., Grenzer, J., Forero, L. & Heavilin, J. Using plant–soil feedbacks to predict plant biomass in diverse communities. Ecology 97, 2064–2073 (2016).PubMed 

Google Scholar 
Reinhart, K. O. et al. Globally, plant–soil feedbacks are weak predictors of plant abundance. Ecol. Evol. 11, 1756–1768 (2021).PubMed 
PubMed Central 

Google Scholar 
Casper, B. B. & Castelli, J. P. Evaluating plant–soil feedback together with competition in a serpentine grassland. Ecol. Lett. 10, 394–400 (2007).PubMed 

Google Scholar 
Shannon, S., Flory, S. L. & Reynolds, H. Competitive context alters plant–soil feedback in an experimental woodland community. Oecologia 169, 235–243 (2012).PubMed 

Google Scholar 
Lekberg, Y. et al. Relative importance of competition and plant–soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).PubMed 

Google Scholar 
Kostenko, O., van de Voorde, T. F. J., Mulder, P. P. J., van der Putten, W. H. & Bezemer, M. T. Legacy effects of aboveground–belowground interactions. Ecol. Lett. 15, 813–821 (2012).PubMed 

Google Scholar 
Bezemer, M. T. et al. Above- and below-ground herbivory effects on below-ground plant–fungus interactions and plant–soil feedback responses. J. Ecol. 101, 325–333 (2013).
Google Scholar 
Classen, A. T. et al. Direct and indirect effects of climate change on soil microbial and soil microbial–plant interactions: what lies ahead? Ecosphere 6, art130 (2015).
Google Scholar 
McCarthy-Neumann, S. & Kobe, R. K. Site soil-fertility and light availability influence plant–soil feedback. Front. Ecol. Evol. 7, 383 (2019).
Google Scholar 
Smith-Ramesh, L. M. & Reynolds, H. L. The next frontier of plant–soil feedback research: unraveling context dependence across biotic and abiotic gradients. J. Veg. Sci. 28, 484–494 (2017).
Google Scholar 
Crawford, K. M. et al. When and where plant–soil feedback may promote plant coexistence: a meta-analysis. Ecol. Lett. 22, 1274–1284 (2019).PubMed 

Google Scholar 
de Long, J. R., Fry, E. L., Veen, G. F. & Kardol, P. Why are plant–soil feedbacks so unpredictable, and what to do about it? Funct. Ecol. 33, 118–128 (2019).
Google Scholar 
Beals, K. K. et al. Predicting plant–soil feedback in the field: meta-analysis reveals that competition and environmental stress differentially influence PSF. Front. Ecol. Evol. 8, 191 (2020).
Google Scholar 
van der Putten, W. H., Bradford, M. A., Brinkman, P. E., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).
Google Scholar 
Pugnaire, F. I. et al. Climate change effects on plant–soil feedbacks and consequences for biodiversity and functioning of terrestrial ecosystems. Sci. Adv. 5, eaaz1834 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
Google Scholar 
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).PubMed 
PubMed Central 

Google Scholar 
Fierer, N., Schimel, J. P. & Holden, P. A. Influence of drying–rewetting frequency on soil bacterial community structure. Microb. Ecol. 45, 63–71 (2003).CAS 
PubMed 

Google Scholar 
Drenovsky, R. E., Vo, D., Graham, K. J. & Scow, K. M. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
PubMed 

Google Scholar 
Brockett, B. F., Prescott, C. E. & Grayston, S. J. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol. Biochem. 44, 9–20 (2012).CAS 

Google Scholar 
Manzoni, S., Schimel, J. P. & Porporato, A. Responses of soil microbial communities to water stress: results from a meta-analysis. Ecology 93, 930–938 (2012).PubMed 

Google Scholar 
de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).PubMed 
PubMed Central 

Google Scholar 
de Oliveira, T. B. et al. Fungal communities differentially respond to warming and drought in tropical grassland soil. Mol. Ecol. 29, 1550–1559 (2020).PubMed 

Google Scholar 
Eastburn, D. M., McElrone, A. J. & Bilgin, D. D. Influence of atmospheric and climatic change on plant–pathogen interactions. Plant Pathol. 60, 54–69 (2011).
Google Scholar 
Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E. & Mittler, R. Abiotic and biotic stress combinations. New Phytol. 203, 32–43 (2014).PubMed 

Google Scholar 
Cavagnaro, T. R. Soil moisture legacy effects: impacts on soil nutrients, plants and mycorrhizal responsiveness. Soil Biol. Biochem. 95, 173–179 (2016).CAS 

Google Scholar 
Crawford, K. M. & Hawkes, C. V. Soil precipitation legacies influence intraspecific plant–soil feedback. Ecology 101, e03142 (2020).PubMed 

Google Scholar 
Fry, E. L. et al. Drought neutralises plant–soil feedback of two mesic grassland forbs. Oecologia 186, 1113–1125 (2018).PubMed 
PubMed Central 

Google Scholar 
Snyder, A. E. & Harmon-Threatt, A. N. Reduced water-availability lowers the strength of negative plant–soil feedbacks of two Asclepias species. Oecologia 190, 425–432 (2019).PubMed 

Google Scholar 
Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant–soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).PubMed 

Google Scholar 
Brinkman, P. E., van der Putten, W. H., Bakker, E.-J. & Verhoeven, K. J. Plant–soil feedback: experimental approaches, statistical analyses and ecological interpretations. J. Ecol. 98, 1063–1073 (2010).
Google Scholar 
Bever, J. D. Negative feedback within a mutualism: host-specific growth of mycorrhizal fungi reduces plant benefit. Proc. R. Soc. B 269, 2595–2601 (2002).PubMed 
PubMed Central 

Google Scholar 
Castelli, J. P. & Casper, B. B. Intraspecific AM fungal variation contributes to plant–fungal feedback in a serpentine grassland. Ecology 84, 323–336 (2003).
Google Scholar 
Mangan, S. A., Herre, E. A. & Bever, J. D. Specificity between neotropical tree seedlings and their fungal mutualists leads to plant–soil feedback. Ecology 91, 2594–2603 (2010).PubMed 

Google Scholar 
Bever, J. D., Mangan, S. A. & Alexander, H. M. Maintenance of plant species diversity by pathogens. Annu. Rev. Ecol. Evol. Syst. 46, 305–325 (2015).
Google Scholar 
Gilbert, G. S. & Parker, I. M. The evolutionary ecology of plant disease: a phylogenetic perspective. Annu. Rev. Phytopathol. 54, 549–578 (2016).CAS 
PubMed 

Google Scholar 
Milici, V. R., Dalui, D., Mickley, J. G. & Bagchi, R. Responses of plant–pathogen interactions to precipitation: implications for tropical tree richness in a changing world. J. Ecol. 108, 1800–1809 (2020).
Google Scholar 
Kaisermann, A., de Vries, F. T., Griffiths, R. I. & Bardgett, R. D. Legacy effects of drought on plant–soil feedbacks and plant–plant interactions. New Phytol. 215, 1413–1424 (2017).CAS 
PubMed 

Google Scholar 
Revillini, D., Gehring, C. A. & Johnson, N. C. The role of locally adapted mycorrhizas and rhizobacteria in plant–soil feedback systems. Funct. Ecol. 30, 1086–1098 (2016).
Google Scholar 
Ji, B. & Bever, J. D. Plant preferential allocation and fungal reward decline with soil phosphorus: implications for mycorrhizal mutualism. Ecosphere 7, e01256 (2016).
Google Scholar 
Rubin, R. L., van Groenigen, K. J. & Hungate, B. A. Plant growth promoting rhizobacteria are more effective under drought: a meta-analysis. Plant Soil 416, 309–323 (2017).CAS 

Google Scholar 
Brinkman, E. P., Duyts, H., Karssen, G., van der Stoel, C. D. & van der Putten, W. H. Plant-feeding nematodes in coastal sand dunes: occurrence, host specificity and effects on plant growth. Plant Soil 397, 17–30 (2015).CAS 

Google Scholar 
Hoeksema, J. D. et al. A meta-analysis of context-dependency in plant response to inoculation with mycorrhizal fungi. Ecol. Lett. 13, 394–407 (2010).PubMed 

Google Scholar 
Chase, J. M. Community assembly: when should history matter? Oecologia 136, 489–498 (2003).PubMed 

Google Scholar 
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).
Google Scholar 
Reinhart, K. O. & Rinella, M. J. A common soil handling technique can generate incorrect estimates of soil biota effects on plants. New Phytol. 210, 786–789 (2016).PubMed 

Google Scholar 
Mehlich, A. Mehlich-3 soil test extractant: a modification of Mehlich-2 extractant. Commun. Soil Sci. Plant Anal. 15, 1409–1416 (1984).CAS 

Google Scholar 
Rhoades, J. D. in Methods of Soil Analysis: Part 2 (eds Page, A. L. et al.) Ch. 10 (American Society of Agronomy and Soil Science Society of America, 1982).Schofield, R. K. & Taylor, A. W. The measurement of soil pH. Soil Sci. Soc. Am. Proc. 19, 164–167 (1955).CAS 

Google Scholar 
Keeney, D. R. in Methods of Soil Analysis: Part 2 (eds Page, A. L. et al.) Ch. 35 (American Society of Agronomy and Soil Science Society of America, 1982).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pauvert, C. et al. Bioinformatics matters: the accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. Fungal Ecol. 41, 23–33 (2019).
Google Scholar 
Abarenkov, K. et al UNITE QIIME Release for Fungi. Version 04.02.2020 (UNITE Community, 2020).Francioli, D., van Ruijven, J., Bakker, L. & Mommer, L. Drivers of total and pathogenic soil-borne fungal communities in grassland plant species. Fungal Ecol. 48, 100987 (2020).
Google Scholar 
Nhu, H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Google Scholar 
Brooks, M. B. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Lou, J. Entropy and diversity. Oikos 113, 363–375 (2006).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R version 2.5–7 https://CRAN.R-project.org/package=vegan (2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2020).
Google Scholar 
Wilensky, U. NetLogo http://ccl.northwestern.edu/netlogo (1999).Salecker, J., Sciaini, M., Meyer, K. M. & Wiegand, K. The NLRX R package: a next-generation framework for reproducible NetLogo model analyses. Methods Ecol. Evol. 10, 1854–1863 (2019).
Google Scholar 
Wickham et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).
Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Lázaro-Lobo, A. & Ervin, G. N. A global examination on the differential impacts of roadsides on native versus exotic and weedy plant species. Glob. Ecol. Conserv. 17(e00555), 1–13 (2019).
Google Scholar 
Christen, D. C. & Matlack, G. R. The habitat and conduit functions of roads in the spread of three invasive plant species. Biol. Invasions 11(2), 453–465 (2009).Article 

Google Scholar 
Mortensen, D. A., Rauschert, E. S., Nord, A. N. & Jones, B. P. Forest roads facilitate the spread of invasive plants. Invasive Plant Sci. Manag. 2(3), 191–199 (2009).Article 

Google Scholar 
Lemke, A., Kowarik, I. & von der Lippe, M. How traffic facilitates population expansion of invasive species along roads: The case of common ragweed in Germany. J. Appl. Ecol. 56(2), 413–422 (2019).Article 

Google Scholar 
Rauschert, E. S., Mortensen, D. A. & Bloser, S. M. Human-mediated dispersal via rural road maintenance can move invasive propagules. Biol. Invasions 19(7), 2047–2058 (2017).Article 

Google Scholar 
Meunier, G. & Lavoie, C. Roads as corridors for invasive plant species: New evidence from smooth bedstraw (Galium mollugo). Invasive Plant Sci. Manag. 5(1), 92–100 (2012).Article 

Google Scholar 
Mohit, S., Johnson, T. B. & Arnott, S. E. Recreational watercraft decontamination: Can current recommendations reduce aquatic invasive species spread?. Manag. Biol. Invasions 12(1), 148–164 (2021).Article 

Google Scholar 
Ferguson, L., Duncan, C. L., & Snodgrass, K. Backcountry road maintenance and weed management. United States: U.S. Department of Agriculture, Forest Service, Technology & Development Program. 22pp (2003). At https://www.google.com/books/edition/Backcountry_Road_Maintenance_and_Weed_Ma/y2amRwT1rIsC?hl=en&gbpv=0.Lelong, B., Lavoie, C., Jodoin, C. & Belzile, F. Expansion pathways of the exotic common reed (Phragmites australis): A historical and genetic analysis. Divers. Distrib. 13, 430–437 (2007).Article 

Google Scholar 
Joly, M. et al. Paving the way for invasive species: Road type and the spread of common ragweed (Ambrosia artemisiifolia). Environ. Manag. 48(3), 514–522 (2011).ADS 
Article 

Google Scholar 
Thompson, D. Q., Stuckey, R. L. & Thompson, E. B. Spread, impact, and control of purple loosestrife (Lythrum salicaria) in North American wetlands. U. S. Fish and Wildlife Service (1987). At http://stoppinginvasives.com/dotAsset/670d2f92-cd0c-41ab-9955-7204f1a9a192.pdf.Stuckey, R. L. Distributional history of Lythrum salicaria (purple loosestrife) in North America. Bartonia 47, 3–20 (1980).
Google Scholar 
Blossey, B., Skinner, L. C. & Taylor, J. Impact and management of purple loosestrife (Lythrum salicaria) in North America. Biodivers. Conserv. 10(10), 1787–1807 (2001).Article 

Google Scholar 
Wilcox, D. A. Migration and control of purple loosestrife (Lythrum salicaria L.) along highway corridors. Environ. Manag. 13(3), 365–370 (1989).ADS 
Article 

Google Scholar 
St. Louis, E., Stastny, M. & Sargent, R. D. The impacts of biological control on the performance of Lythrum salicaria 20 years post-release. Biol. Control. 140, 104–123 (2020).Article 

Google Scholar 
NYSDOT Environmental Science Bureau. Environmental Handbook for Transportation Operations: A Summary of the Environmental Requirements and Best Practices for Maintaining the Constructing Highways and Transportation Systems. Prepared by NYSDOT Environmental Science Bureau, (2011) At https://www.dot.ny.gov/divisions/engineering/environmental-analysis/repository/oprhbook.pdf.Blossey, B., Schroeder, D., Hight, S. D. & Malecki, R. A. Host specificity and environmental impact of two leaf beetles (Galerucella calmariensis and G. pusilla) for biological control of purple loosestrife (Lythrum salicaria). Weed Sci. 42, 134–140 (1994).Article 

Google Scholar 
Blossey, B. Before, during and after: The need for long-term monitoring in invasive plant species management. Biol. Invasions 1, 301–311 (1999).Article 

Google Scholar 
Blossey, B. & Hunt, T. R. Mass rearing methods for Galerucella calmariensis and G. pusilla (Coleoptera: Chrysomelidae), biological control agents of Lythrum salicaria (Lythraceae). J. Econ. Entomol. 92(2), 325–334 (1999).CAS 
Article 

Google Scholar 
Grevstad, F. S. Ten-year impacts of the biological control agents Galerucella pusilla and G. calmariensis (Coleoptera: Chrysomelidae) on purple loosestrife (Lythrum salicaria) in Central New York State. Biol. Control 39(1), 1–8 (2006).Article 

Google Scholar 
Boag, A. E. & Eckert, C. G. The effect of host abundance on the distribution and impact of biocontrol agents on purple loosestrife (Lythrum salicaria, Lythraceae). Écoscience 20(1), 90–99 (2013).Article 

Google Scholar 
Lakoba, V. T., Brooks, R. K., Haak, D. C. & Barney, J. N. An Analysis of US State regulated weed lists: A discordance between biology and policy. Bioscience 70(9), 804–813 (2020).Article 

Google Scholar 
Welling, C. H. & Becker, R. L. Seed bank dynamics of Lythrum salicaria L.: Implications for control of this species in North America. Aquat. Bot. 38, 303–309 (1990).Article 

Google Scholar 
Brown, B. J. & Wickstrom, C. E. Adventitious root production and survival of purple loosestrife (Lythrum salicaria) shoot sections. Ohio J. Sci. 97, 2–4 (1997).
Google Scholar 
Farnsworth, E. J. & Ellis, D. R. Is purple loosestrife (Lythrum salicaria) an invasive threat to freshwater wetlands? Conflicting evidence from several ecological metrics. Wetlands 21(2), 199–209 (2001).Article 

Google Scholar 
Mahaney, W. M., Smemo, K. A. & Yavitt, J. B. Impacts of Lythrum salicaria invasion on plant community and soil properties in two wetlands in central New York, USA. Botany 84(3), 477–484 (2006).
Google Scholar 
Treberg, M. A. & Husband, B. C. Relationship between the abundance of Lythrum salicaria (purple loosestrife) and plant species richness along the Bar River Canada. Wetlands 19(1), 118–125 (1999).Article 

Google Scholar 
Hager, H. & Vinebrooke, R. E. Positive relationships between invasive purple loosestrife (Lythrum salicaria) and plant species diversity and abundance in Minnesota wetlands. Can. J. Bot. 82(6), 763–773 (2004).Article 

Google Scholar 
Lavoie, C. Should we care about purple loosestrife? The history of an invasive plant in North America. Biol. Invasions 12(7), 1967–1999 (2010).Article 

Google Scholar 
Fickbohm, S. S. & Zhu, W. X. Exotic purple loosestrife invasion of native cattail freshwater wetlands: Effects on organic matter distribution and soil nitrogen cycling. Appl. Soil. Ecol. 32(1), 123–131 (2006).Article 

Google Scholar 
Ramula, S. Annual mowing has the potential to reduce the invasion of herbaceous Lupinus polyphyllus. Biol. Invasions 22(10), 3163–3173 (2020).Article 

Google Scholar 
Milakovic, I., Fiedler, K. & Karrer, G. Management of roadside populations of invasive Ambrosia artemisiifolia by mowing. Weed Res. 54(3), 256–264 (2014).Article 

Google Scholar 
Vitalos, M. & Karrer, G. Dispersal of Ambrosia artemisiifolia seeds along roads: The contribution of traffic and mowing machines. Neobiota 8, 53–60 (2009).
Google Scholar 
Forman, R. T. & Alexander, L. E. Roads and their major ecological effects. Annu. Rev. Ecol. Syst. 29(1), 207–231 (1998).Article 

Google Scholar 
Milt, A. W. et al. Minimizing opportunity costs to aquatic connectivity restoration while controlling an invasive species. Conserv. Biol. 32(4), 894–904 (2018).Article 

Google Scholar 
RStudio Team. RStudio: Integrated Development Environment for R. RStudio, PBC. (2021). URL http://www.rstudio.com/.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2021). https://www.R-project.org/.U. S. Fish and Wildlife Service. National Wetlands Inventory. http://www.fws.gov/wetlands/ (2020).Yakimowski, S. B., Hager, H. A. & Eckert, C. G. Limits and effects of invasion by the nonindigenous wetland plant Lythrum salicaria (purple loosestrife): A seed bank analysis. Biol. Invasions 7, 687–698 (2005).Article 

Google Scholar 
Thomas, S. M. & Moloney, K. A. Combining the effects of surrounding land-use and propagule pressure to predict the distribution of an invasive plant. Biol. Invasions 17, 477–495 (2015).Article 

Google Scholar 
Barbier, E. B., Knowler, D., Gwatipedza, J., Reichard, S. H. & Hodges, A. R. Implementing policies to control invasive plant species. Bioscience 63(2), 132–138 (2013).Article 

Google Scholar 
Blossey, B. Measuring and Evaluating Ecological Outcomes of Biological Control Introductions. In Integrating Biological Control into Conservation Practice (eds Van Driesche, R. et al.) 161–188 (Wiley, 2016).Chapter 

Google Scholar 
Rowell, N. Warren County Purple Loosestrife Management Program Final Report. (2015). At https://www.warrenswcd.org/reports.html.Vanneste, T. et al. Plant diversity in hedgerows and road verges across Europe. J. Appl. Ecol. 57(7), 1244–1257 (2020).Article 

Google Scholar 
Auffret, A. G. & Lindgren, E. Roadside diversity in relation to age and surrounding source habitat: Evidence for long time lags in valuable green infrastructure. Ecol. Solut. Evid. 1(1), e12005 (2020).Article 

Google Scholar 
Mccleery, R. A., Holdorf, A. R., Hubbard, L. L. & Peer, B. D. Maximizing the wildlife conservation value of road right-of-ways in an agriculturally dominated lands. Plos one 10(3), e0120375 (2015).Article 

Google Scholar 
New York Invasive Species Information (NYISI). Purple Loosestrife. (2019). at http://nyis.info/invasive_species/purple-loosestrife.Rogers, J. Controlling purple loosestrife (Lythrum Salicaria) along roadsides in St. Lawrence County: Monitoring and biological controls. Adirondack J. Environ. Stud. 23(1), 5 (2019).
Google Scholar 
New York State Department of Transportation. Clear Zones. (2021). At https://www.dot.ny.gov/divisions/engineering/environmental-analysis/landscape/trees/rs-lsf-plant-photos.ESRI. ArcGIS Pro: Version 2.9: Environmental System Research Institute. (2021). At https://pro.arcgis.com/en/pro-app/latest/get-started/get-started.htm.IBM Corp. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp. Released 2017. More

Base run simulationFigure 6 shows the results of the base run simulation. In this scenario, strong vegetation declines over time, while the empty area and flammable vegetation have increasing trends. As such, more fuel would be available for burning, and the wildfire can burn broader areas. Panel (a) shows an oscillatory trend for the burn rate with an average upward trend (To make sure the oscillatory behavior of the model does not fade, Appendix 4 shows the simulation result for 100 years). The observed pattern in the burn rate can be traced back to the patterns of human ignition (Panel b), and the growing trend of vulnerable properties (Panel c). In addition, the results show the long-term declining trend of strong vegetation in our base line simulation (Panel d); over time, stronger vegetation is replaced by flammable vegetation which can lead to more fire. This change in vegetation composition effectively increases the average burn rate. Over time, with more flammable vegetation and with the expansion of vulnerable properties, the likelihood of human-made ignition increases.Figure 6Base run simulation for a 20-year run of the model.Full size imageCoupling effectsFigure 7 shows how the relation between perceived fire risk and the burn rate influences the system. The black line is the base run simulation for comparison. The blue dashed line depicts the condition in which risk perception changes extremely slowly, and the human system is almost disconnected from the natural system. In this situation, if humans underestimate the fire potential, the system burns down nature, resulting in a catastrophic environmental outcome as depicted in panel (a). Panel (a) shows that the burn rate overshoots in the short term but relatively declines due to less remaining natural resources to burn.Figure 7Coupling effect analysis for 20 years. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanel (b) displays the total burn rate throughout the study time to cast further insight into the burn rate sensitivity to perceived risk. The overall burn rate does not significantly change when the risk perception changes from 0.5 to 2, indicating the difference among burn rates in panel (a) is more about the fluctuation timing, but not the size. However, an additional rise in the sense of risk greatly raises the overall burn rate, as seen in panel (a).In the case of prolonged change in risk perception, human ignition continues to increase (panel c) as the perceived risk changes slowly. Furthermore, vulnerable properties are being built faster than their demolition (panel d). A slighter delay in perception leads to a higher frequency of oscillation as depicted in the graphs by the red dashed lines and a longer delay in a lower frequency oscillation, as shown by the purple graphs. Overall, the results are not much different from the base run. We are losing forests (panel e) and have periodic burn rates of increasing magnitude over time.Policy experimentsHere we examine the impact of implementing four proposed policies introduced in Table 2. To prevent the initial condition and transition periods affecting our comparison of proposed policies, we imposed each policy at the fifth year and compared the total burn rates between 10 and 20 years. Figure 8 shows the effect of these policies on different variables. Figure 8Policy implementation. Note: P1: limits vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanels (a) and (b) show the burn rate over time and cumulative, respectively. All four policies reduce the burn-rate magnitude compared to the base run. P3 is more effective in early burning-rate reduction compared to other policies, but they ultimately result in similar behavior. It is worth noticing that P1 has the most effect on long-run fluctuation reduction, although its total effect in the time span is less than P3. It seems that firefighting is more effective in the short run, but it fails to dampen the fluctuation and instead limits its growth. This is partly because of the increase in human ignition and settlement due to the success of firefighting in the short run. As a result, people perceive less fire danger and continue to engage in high-risk activities and expand housing in the WUI. The result is further fluctuation in the burn rate even when P3 is implemented. On the other hand, the WUI expansion limitation policy can effectively reduce the burn-rate fluctuation in a timely manner. Implementing P4 causes a reduction in strong vegetation, which leads to flammable vegetation increase. As flammable vegetation is the main fuel for wildfire, this policy cause increase in fuel availability and an increase in the burning rate.Change in human ignition is provided in panel (c). Different levels of human-made ignition are observable, and the reason is that people adjust their high-risk behavior with burn rate, and not with the number of fires. In the firefighting policy, as for a given level of ignition, the burn rate declines, we observe more risky behavior and more human-made ignition. It is interesting to note that, as panel (c) shows, we end up with more WUI under policies 2, 3, and 4. In fact, the reason is that the firefighting, prescribed burning and clear cutting only affect natural sector of the model, decrease burn rate, which decreases risk perception and in turn result in more WUI development. On the other hand, P1 directly targets WUIs.Panel (e) displays the change in strong vegetation, which shows that P4 causes the most reduction in forest tree cover as it directly removes strong vegetation. P2 also causes a decrease in strong vegetation compared to the base run. The reason is that burning flammable vegetation damages young trees and prevents them from developing into solid vegetation. On the other hand, P3 has the least effect on strong vegetation by slowing the damage to young trees and confining the fire. Panel (f) shows the flammable vegetation dynamic after imposing each policy. P3 and P2 reduce flammable vegetation more than P1. However, there is an important difference in how these policies cause the reduction in flammable vegetation. In comparing panels (a) and (b), we see that while P3 causes further increases in the strong vegetation, P2 causes an increase in the empty area. P4 is the only policy that increases flammable vegetation by removing the strong vegetation and providing an empty area to be filled with young vegetation.Overall, it looks like each policy has some marginal effect on containing wildfire, though the magnitudes of effect are not considerable.Replication of United States dataFor model validation, we investigate its ability to fit a single case, United States’ wildfires from 1996 to 2015. We utilize the United States Department of Agriculture’s wildfire database for the conterminous United States (Short, 2017). The results are shown in Fig. 9. In this figure, simulation of burning rate and human ignition (continuous lines, in black) closely follows the real-world data (dotted lines, in red), and the model fairly replicates the historical trends.Figure 9Burning rate and human ignition per unit of forest area. The black line represents the model result, and the red dotted line represents the historical wildfire activity in the conterminous United States.Full size imageCombination policy implementation analysisTo better understand the impacts of our policies, we run different pairs of policies simultaneously. The results illustrate the nonlinear incremental impacts between policies. Simply put, it appears that the impact of several policies is enforced when combined synergistically. In other words, applying several policies might have a greater overall impact than the sum of the policies’ individual effects and suggests that policymakers should avoid searching for a panacea and adopt a broad range of approaches thoughtfully.The results of multiple policy implementations along with single ones are presented in Fig. 10. For example, P1 and P2 each reduce the total burn rate by 4.9% and 4.5%, respectively. While the summation of these effects is 9.4%, simultaneously implementing P1 and P2 lead to a 13.6% burn-rate reduction—P1 controls the human ignition, and P2 reduces the flammable vegetation stock—together, the burn rate is more affected than if implemented separately. The case is more interesting when P1 and P3 are imposed together. The result is a 38% burn-rate reduction compared to 13.9%, which is the sum of solely implementing each policy. The synergic effect happens because P3 lets the flammable vegetation (mainly young trees) age and become strong vegetation. Furthermore, the P1 also prevents human ignition from growing as fast as a single P3 implementation.Figure 10The nonlinear effect of policies. The benefits of implementing multiple policies differ from the sum of the effect of policies. The figure shows the percent of burn rate reduction. Note: P1: limit vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting.Full size imageAn interesting case happens when P2 and P3 are implemented together. The synergic effect is less than the sum of separate implementation, mainly because both policies affect the vegetation dynamic and not the human factor in the wildfire. P2 and P3 both cause a lower initial burn rate, but due to the reduction in perceived risk of wildfire and expansion of WUI, this effect quickly disappears. This is another evidence for the importance of considering the problem as an interconnected natural and human system, where effective policies should address both sides.Finally, an interesting result emerges when all policies impose together. Surprisingly, imposing all policies together does not have the most impact on the total burn rate (32.5%), which is less than the P1 and P3 effect (38.0%). The reason relates mainly to the fact P2 and P4 both cause increase in flammable vegetation after empty area filled, which lead to more burning rate after a delay.Sensitivity analysisWe conducted a series of sensitivity analysis to check the model’s robustness to our assumptions. Specifically, we conducted a Monte-Carlo analysis and changed several parameter values to determine the range of outcomes. The results are reported in Appendix 2. In summary, the focus was on parameters that can take on substantially different values from those assumed in the model, including parameters used for risk perception formulation, its effect on human behavior, such as time to perceive risk and time to change behavior, in addition to fractional burning rate per ignition, average s burning, initial flammable vegetation, initial strong vegetation, human ignition multiplier, and initial vulnerable property. As described in the Appendix, for most of these variables, we changed the corresponding variable up to double its base run value. Moreover, we test different values for initial strong vegetation and initial flammable vegetation changing them between zero and their base run values. Each sensitivity test is the outcome of 2000 simulation runs using a uniformly distributed random distribution of the parameters within the specified intervals. The results are qualitatively robust, and their variability is within reasonable limits (See Figure A1). More

Seigel, R. A. & Ford, N. B. Reproductive ecology in Snakes: Ecology and Evolutionary Biology (eds. Seigel, R. A., Collins, J. T. &. Novak, S. S.). 210–252. (MacMillan Publishing, 1987).Kremen, C., Merenlender, A. M. & Murphy, D. D. Ecological monitoring: A vital need for integrated conservation and development programs in the tropics. Conserv. Biol. 8, 388–397 (1994).
Google Scholar 
Shine, R. & Bonnet, X. Snakes: A new ‘model organism’ in ecological research?. Trends Ecol. Evol. 15, 221–222 (2000).CAS 
PubMed 

Google Scholar 
Vilela, B., Villalobos, F., Rodríguez, M. Á. & Terribile, L. C. Body size, extinction risk and knowledge bias in New World snakes. PLoS ONE 9, e113429 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mathies, T. Reproductive cycles of tropical snakes. in Reproductive Biology and Phylogeny of Snakes (eds. Sever, D. & Aldridge, R.). 523–562. (CRC Press, 2016).Shine, R., Harlow, P. S. & Keogh, J. S. The allometry of life-history traits: Insights from a study of giant snakes (Python reticulatus). J. Zool. 244, 405–414 (1998).
Google Scholar 
Natusch, D. J., Lyons, J. A., Riyanto, A., Khadiejah, S. & Shine, R. Detailed biological data are informative, but robust trends are needed for informing sustainability of wildlife harvesting: A case study of reptile offtake in Southeast Asia. Biol. Conserv. 233, 83–92 (2019).
Google Scholar 
Freeman, A. & Freeman, A. Habitat use in a large rainforest python (Morelia kinghorni) in the wet tropics of north Queensland, Australia. Herpetol. Conserv. Biol. 4, 252–260 (2009).
Google Scholar 
Smith, S. N., Jones, M. D., Marshall, B. M. & Strine, C. T. Native Burmese pythons exhibit site fidelity and preference for aquatic habitats in an agricultural mosaic. Sci. Rep. 11, 7014 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kramer, D. L. & Chapman, M. R. Implications of fish home range size and relocation for marine reserve function. Environ. Biol. Fishes 55, 65–79 (1999).
Google Scholar 
Spong, G. Space use in lions, Panthera leo, in the Selous Game Reserve: Social and ecological factors. Behav. Ecol. Sociobiol. 52, 303–307 (2002).
Google Scholar 
Webb, J. K. & Shine, R. A field study of spatial ecology and movements of a threatened snake species, Hoplocephalus bungaroides. Biol. Conserv. 82, 203–217 (1997).
Google Scholar 
Fearn, S. & Sambono, J. A reliable size record for the scrub python Morelia amethistina (Serpentes: Pythonidae) in north east Queensland. Herpetofauna 30, 2–6 (2000).
Google Scholar 
Grow, D., Wheeler, S. & Clark, B. Reproduction of the Amethystine python Python amethystinus kinghorni at the Oklahoma City Zoo. Int. Zoo Year. 27, 241–244 (1988).
Google Scholar 
Feldman, A. & Meiri, S. Length–mass allometry in snakes. Biol. J. Linn. Soc. 108, 161–172 (2013).
Google Scholar 
Harvey, M. B., Barker, D. G., Ammerman, L. K. & Chippindale, P. T. Systematics of pythons of the Morelia amethistina complex (Serpentes: Boidae) with the description of three new species. Herpetol. Monogr. 14, 139–185 (2000).
Google Scholar 
Fearn, S., Schwarzkopf, L. & Shine, R. Giant snakes in tropical forests: A field study of the Australian scrub python, Morelia kinghorni. Wildl. Res. 32, 193–201 (2005).
Google Scholar 
Natusch, D. J. D., Lyons, J. A. & Shine, R. Rainforest pythons flexibly adjust foraging ecology to exploit seasonal concentrations of prey. J. Zool. 313, 114–123 (2021).
Google Scholar 
Martin, R. W. Field observation of predation on Bennett’s tree-kangaroo (Dendrolagus bennettianus) by an amethystine python (Morelia amethistina). Herpetol. Rev. 26, 74–75 (1995).
Google Scholar 
Natusch, D., Lyons, J., Mears, L. A. & Shine, R. Biting off more than you can chew: Attempted predation on a human by a giant snake (Simalia amethistina). Austral. Ecol. 46, 159–162 (2021).
Google Scholar 
Neldner, V. J. & Clarkson, J. R. Vegetation of Cape York Peninsula. (Department of Environment and Heritage, 1995).Bureau of Meteorology. Climate Data Online. http://www.bom.gov.au/climate/data/. Accessed 17 July 2020 (2020).Whitaker, P. B. & Shine, R. A radiotelemetric study of movements and shelter-site selection by free-ranging brownsnakes (Pseudonaja textilis, Elapidae). Herpetol. Monogr. 17, 130–144 (2003).
Google Scholar 
Harris, S. et al. Home-range analysis using radio-tracking data–A review of problems and techniques particularly as applied to the study of mammals. Mamm. Rev. 20, 97–123 (1990).
Google Scholar 
Fearn, S. & Sambono, J. Some ambush predation postures of the Scrub Python Morelia amethistina (Serpentes: Pythonidae) in north east Queensland. Herpetofauna 30, 39–44 (2000).
Google Scholar 
Caswell, H. Theory and models in ecology: A different perspective. Ecol. Model. 43, 33–44 (1988).
Google Scholar 
Silva, I., Crane, M., Marshall, B. M. & Strine, C. T. Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian bridge movement models. Move. Ecol. 8, 43 (2020).
Google Scholar 
Row, J. R. & Blouin-Demers, G. Kernels are not accurate estimators of home-range size for herpetofauna. Copeia 2006, 797–802 (2006).
Google Scholar 
Newman, P., Dwyer, R. G., Belbin, L. & Campbell, H. A. ZoaTrack—An online tool to analyse and share animal location data: User engagement and future perspectives. Aust. Zool. 41, 12–18. https://zoatrack.org/toolkit/doi (2020).Pearson, D. J. & Shine, R. Expulsion of interperitoneally-implanted radiotransmitters by Australian pythons. Herpetol. Rev. 33, 261–263 (2002).
Google Scholar 
Hale, V. L. et al. Radio transmitter implantation and movement in the wild timber rattlesnake (Crotalus horridus). J. Wildl. Dis. 53, 591–595 (2017).PubMed 

Google Scholar 
Martin, A. E., Jørgensen, D. & Gates, C. C. Costs and benefits of straight versus tortuous migration paths for Prairie Rattlesnakes (Crotalus viridis viridis) in seminatural and human-dominated landscapes. Can. J. Zool. 95, 921–928 (2017).
Google Scholar 
Glaudas, X., Rice, S. E., Clark, R. W. & Alexander, G. J. Male energy reserves, mate-searching activities, and reproductive success: Alternative resource use strategies in a presumed capital breeder. Oecologia 194, 415–425 (2020).ADS 
PubMed 

Google Scholar 
Glaudas, X., Rice, S. E., Clark, R. W. & Alexander, G. J. The intensity of sexual selection, body size and reproductive success in a mating system with male–male combat: is bigger better?. Oikos 129, 998–1011 (2020).
Google Scholar 
Gannon, V. P. J. & Secoy, D. M. Seasonal and daily activity patterns in a Canadian population of the prairie rattlesnake, Crotalus viridus viridis. Can. J. Zool. 63, 86–91 (1985).
Google Scholar 
Heard, G. W., Black, D. & Robertson, P. Habitat use by the inland carpet python (Morelia spilota metcalfei: Pythonidae): Seasonal relationships with habitat structure and prey distribution in a rural landscape. Austral. Ecol. 29, 446–460 (2004).
Google Scholar 
Madsen, T. & Shine, R. Seasonal migration of predators and prey—A study of pythons and rats in tropical Australia. Ecology 77, 149–156 (1996).
Google Scholar 
Graves, B. M. & Duvall, D. Reproduction, rookery use, and thermoregulation in free-ranging, pregnant Crotalus v. viridis. J. Herpetol. 27, 33–41 (1993).
Google Scholar 
Chiaraviglio, M. The effects of reproductive condition on thermoregulation in the Argentina boa constrictor (Boa constrictor occidentalis) (Boidae). Herpetol. Monogr. 20, 172–177 (2006).
Google Scholar 
Smith, C. F., Schuett, G. W., Earley, R. L. & Schwenk, K. The spatial and reproductive ecology of the copperhead (Agkistrodon contortrix) at the northeastern extreme of its range. Herpetol. Monogr. 23, 45–73 (2009).
Google Scholar 
Shine, R. & Fitzgerald, M. Large snakes in a mosaic rural landscape: The ecology of carpet pythons Morelia spilota (Serpentes: Pythonidae) in coastal eastern Australia. Biol. Conserv. 76, 113–122 (1996).
Google Scholar 
Heard, G. W. et al. Canid predation: A potentially significant threat to relic populations of the Inland Carpet Python ‘Morelia spilota metcalfei’ (Pythonidae) in Victoria. Vic. Nat. 123, 68–74 (2006).
Google Scholar 
Downes, S. & Shine, R. Sedentary snakes and gullible geckos: Predator–prey coevolution in nocturnal rock-dwelling reptiles. Anim. Behav. 55, 1373–1385 (1998).CAS 
PubMed 

Google Scholar 
Miller, A. K., Maritz, B., McKay, S., Glaudas, X. & Alexander, G. J. An ambusher’s arsenal: chemical crypsis in the puff adder (Bitis arietans). Proc. R. Soc. B 282, 20152182 (2015).PubMed 
PubMed Central 

Google Scholar 
Maritz, B. & Alexander, G. J. Dwarfs on the move: Spatial ecology of the world’s smallest viper, Bitis schneideri. Copeia 2012, 115–120 (2012).
Google Scholar 
Stirrat, S. C. Seasonal changes in home-range area and habitat use by the agile wallaby (Macropus agilis). Wildl. Res. 30, 593–600 (2003).
Google Scholar 
Ayers, D. Y. & Shine, R. Thermal influences on foraging ability: Body size, posture and cooling rate of an ambush predator, the python Morelia spilota. Funct. Ecol. 11, 342–347 (1997).
Google Scholar 
Pearson, D., Shine, R. & Williams, A. Spatial ecology of a threatened python (Morelia spilota imbricata) and the effects of anthropogenic habitat change. Austral. Ecol. 30, 261–274 (2005).
Google Scholar 
Freeman, A. A study in power and grace: The amethystine python. Wildl. Aust. 53, 27–29 (2016).
Google Scholar 
Silva, I., Crane, M., Suwanwaree, P., Strine, C. & Goode, M. Using dynamic Brownian bridge movement models to identify home range size and movement patterns in king cobras. PLoS ONE 13, e0203449 (2018).PubMed 
PubMed Central 

Google Scholar 
Marshall, B. M. et al. Space fit for a king: Spatial ecology of king cobras (Ophiophagus hannah) in Sakaerat Biosphere Reserve, Northeastern Thailand. Amphibia-Reptilia 40, 163–178 (2019).
Google Scholar 
Udyawer, V., Simpfendorfer, C. A., Heupel, M. R. & Clark, T. D. Temporal and spatial activity-associated energy partitioning in free-swimming sea snakes. Funct. Ecol. 31, 1739–1749 (2017).
Google Scholar 
Smaniotto, N. P., Moreira, L. F., Rivas, J. A. & Strüssmann, C. Home range size, movement, and habitat use of yellow anacondas (Eunectes notaeus). Salamandra 56, 159–167 (2020).
Google Scholar 
Low, M. R. Rescue, rehabilitation and release of reticulated pythons in Singapore. in Global Reintroduction Perspectives: 2018. Case Studies from Around the Globe (ed. Soorae, P. S.) 78–81 (IUCN/SSC Reintroduction Specialist Group, 2018).Alexander, G. J. & Maritz, B. Sampling interval affects the estimation of movement parameters in four species of African snakes. J. Zool. 297, 309–318 (2015).
Google Scholar 
Smith, B. J. et al. Betrayal: Radio-tagged Burmese pythons reveal locations of conspecifics in Everglades National Park. Biol. Invasions 18, 3239–3250 (2016).
Google Scholar  More

Marine Ecology Research Group and Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New ZealandMads S. Thomsen, Luca Mondardini, David R. Schiel & Alfonso SicilianoDepartment of Bioscience, Aarhus University, 4000, Roskilde, DenmarkMads S. ThomsenSmithsonian Tropical Research Institute, Apartado, 0843-03092, Balboa, Ancon, Republic of PanamaAndrew H. Altieri, Viktoria M. M. Frühling, Seamus B. Harrison & Gerhard ZotzEnvironmental Engineering Sciences, University of Florida, Gainesville, FL, USAAndrew H. Altieri & Christine AngeliniDepartment of Biological Sciences, Macquarie University, Sydney, NSW, AustraliaMelanie J. Bishop & Semonn OleksynDipartimento di Biologia, Università di Pisa, CoNISMa, Via Derna 1, 56126, Pisa, ItalyFabio Bulleri & Joachim LangeneckMarine Sciences, University of Georgia, Athens, GA, USARoxanne FarhanCentre for Marine Science and Innovation, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamSydney Institute of Marine Science, Chowder Bay Road, Mosman, 2088, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamCoastal Ecology Lab, MOE Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, 2005 Songhu Road, 200438, Shanghai, ChinaQiang HeInstitute for Biology and Environmental Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, GermanyMoritz Klinghardt, Tristan Schneider & Gerhard ZotzSchool of Biological Sciences and UWA Oceans Institute, University of Western Australia, Perth, WA, AustraliaYannick Mulders & Thomas WernbergDepartment of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, NC, USAAaron P. RamusNicholas School of the Environment, Duke University, 135 Duke Marine Lab Road, Beaufort, NC, USABrian R. Silliman & Stacy ZhangMarine Biological Association of the United Kingdom, The Laboratory, Citadel Hill, Plymouth, PL1 2PB, UKDan A. SmaleCawthron Institute, Nelson, New ZealandPaul M. South More

Cardoso, Y. P. et al. A continental-wide molecular approach unraveling mtDNA diversity and geographic distribution of the Neotropical genus Hoplias. PLoS ONE 13(8), e0202024. https://doi.org/10.1371/journal.pone.0202024 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bertollo, L. A. C., Born, G. G., Dergam, J. A., Fenocchio, A. S. & Moreira-Filho, O. A biodiversity approach in the Neotropical Erythrinidae fish, Hoplias malabaricus: Karyotypic survey, geographic distribution of karyomorphs and cytotaxonomic considerations. Chrom. Res. 8(7), 603–613 (2000).CAS 
Article 

Google Scholar 
Oyakawa, O. T. Family Erythrinidae (Trahiras). in Check list of the freshwater fishes of South and Central America (Reis, R. E., Kullander, S. O. & Ferraris, C.). Edipucrs 238–240 (Porto Alegre, 2003).Dagosta, F. C. P. & de Pinna, M. C. C. The fishes of the Amazon: distribution and biogeographical patterns, with a comprehensive list of species. Bull. Am. Museum Nat. Hist. 431, 1–163 (2019).
Google Scholar 
Da Rosa, R., Vicari, M. R., Dias, A. L. & Giuliano-Caetano, L. New insights into the biogeographic and Karyotypic Evolution of Hoplias Malabaricus. Zebrafish 11(3), 198–206. https://doi.org/10.1089/zeb.2013.0953 (2014).CAS 
Article 
PubMed 

Google Scholar 
Santos, U. et al. Molecular and karyotypic phylogeography in the neotropical Hoplias malabaricus (Erythrinidae) fish in eastern Brazil. J. Fish Biol. 75(9), 2326–2343. https://doi.org/10.1111/j.1095-8649.2009.02489.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Blanco, D. R., Lui, R. L., Bertollo, L. A. C., Diniz, D. & Filho, O. M. Characterization of invasive fish species in a river transposition region: Evolutionary chromosome studies in the genus Hoplias (Characiformes, Erythrinidae). Rev. Fish Biol. Fish. 20(1), 1–8. https://doi.org/10.1007/s11160-009-9116-3 (2010).Article 

Google Scholar 
Jacobina, U. P. et al. DNA barcode sheds light on systematics and evolution of neotropical freshwater trahiras. Genetica 146, 505. https://doi.org/10.1007/s10709-018-0043-x (2018).CAS 
Article 
PubMed 

Google Scholar 
Marques, D. F., Santos, F. A., da Silva, S. S., Sampaio, I. & Rodrigues, L. R. R. Cytogenetic and DNA barcoding reveals high divergence within the trahira, Hoplias malabaricus (Characiformes: Erythrinidae) from the lower Amazon River. Neotrop. Ichthyol. 11(2), 459–466. https://doi.org/10.1590/S1679-62252013000200015 (2013).Article 

Google Scholar 
Paz, F. P. C., Batista, J. S. & Porto, J. I. R. DNA barcodes of rosy tetras and allied species (Characiformes: Characidae: Hyphessobrycon) from the Brazilian Amazon Basin. PLoS ONE 9(5), e98603. https://doi.org/10.1371/journal.pone.0098603 (2014).ADS 
CAS 
Article 

Google Scholar 
Guimarães, K. L. A., de Sousa, M. P. A., Ribeiro, F. R. V., Porto, J. I. R. & Rodrigues, L. R. R. DNA barcoding of fish fauna from low order streams of Tapajós River basin. PLoS ONE 13(12), e0209430. https://doi.org/10.1371/journal.pone.0209430 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Machado, V. N. et al. One thousand DNA barcodes of piranhas and pacus reveal geographic structure and unrecognized diversity in the Amazon. Sci. Rep. 8, 8387. https://doi.org/10.1038/s41598-018-26550-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hebert, P. D. N., Cywinska, A., Ball, S. L. & Dewaard, J. R. Biological identifications through DNA barcodes. Philos. Trans. R. Soc. B 270(1512), 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).CAS 
Article 

Google Scholar 
Pugedo, M. L., de Andrade Neto, F. R., Pessali, T. C., Birindelli, J. L. O. & Carvalho, D. C. Integrative taxonomy supports new candidate fish species in a poorly studied neotropical region: the Jequitinhonha River Basin. Genetica 144(3), 1–9. https://doi.org/10.1007/s10709-016-9903-4 (2016).Article 

Google Scholar 
Rosso, J. J. et al. Integrative taxonomy reveals a new species of the Hoplias malabaricus species complex (Teleostei: Erythrinidae). Ichthyol. Explor. Freshw. 1, 1–18. https://doi.org/10.23788/IEF-1076 (2018).Article 

Google Scholar 
Azpelicueta, M. M., Benítez, M., Aichino, D. & Mendez, C. M. D. A new species of the genus Hoplias (Characiformes, Erythrinidae), a tararira from the lower Paraná River, in Missiones, Argentina. Acta Zool. Lilloana 59(1–2), 71–82 (2015).
Google Scholar 
Rosso, J. J. et al. A new species of the Hoplias malabaricus species complex (Characiformes: Erythrinidae) from the La Plata River basin. Cybium 40(3), 199–208 (2016).
Google Scholar 
Cardoso, Y. P. & Montoya-Burgos, J. I. Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region. Mol. Ecol. 18(5), 947–964. https://doi.org/10.1111/j.1365-294X.2008.04068.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Hoorn, C., Wesselingh, F. P., Hovikoski, J. & Guerrero, J. The development of the Amazonian mega-wetland (Miocene; Brazil, Colombia, Peru, Bolivia). Amazon. Landsc. Species Evol. https://doi.org/10.1002/9781444306408.ch8 (2010).Article 

Google Scholar 
Albert, J. S. & Reis, R. E. Introduction to neotropical freshwaters. In Historical Biogeography of Neotropical Freshwater Fishes (eds Albert, J. S. & Reis, R. E.) 3–19 (University of California Press, 2011).
Google Scholar 
Leys, M., Keller, I., Räsänen, K., Gattolliat, J.-L. & Robinson, C. T. Distribution and population genetic variation of cryptic species of the Alpine mayfly Baetis alpinus (Ephemeroptera: Baetidae) in the Central Alps. BMC Evol. Biol. https://doi.org/10.1186/s12862-016-0643-y (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Aljanabi, S. M. & Martinez, I. Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Res. 25(22), 4692–4693 (1997).CAS 
Article 

Google Scholar 
Vitorino, C. A., Oliveira, R. C. C., Margarido, V. P. & Venere, P. C. Genetic diversity of Arapaima gigas (Schinz, 1822) (Osteoglossiformes: Arapaimidae) in the Araguaia-Tocantins basin estimated by ISSR marker. Neotrop. Ichthyol. 13, 557–568. https://doi.org/10.1590/1982-0224-20150037 (2015).Article 

Google Scholar 
Ward, R. D., Zemlak, T. S., Innes, B. H., Last, P. R. & Hebert, P. D. N. DNA barcoding Australia’s fish species. Philos. Trans. R. Soc. B 359, 1847–1857. https://doi.org/10.1098/srtb.2005.1716 (2005).Article 

Google Scholar 
Dunn, I. S. & Blattner, F. R. Sharons 36 to 40: Multienzyme, high capacity, recombination deficient replacement vectors with polylinkers and polystuffers. Nucleic Acids Res. 15, 2677–2698 (1987).CAS 
Article 

Google Scholar 
Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680 (1994).CAS 
Article 

Google Scholar 
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552. https://doi.org/10.1093/oxfordjournals.molbev.a026334 (2000).CAS 
Article 

Google Scholar 
Ratnasingham, S. & Hebert, P. D. N. DNA-Based registry for all animal species: The Barcode Index Number (BIN) system. PLoS ONE 8(7), e66213. https://doi.org/10.1371/journal.pone.0066213 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pons, J. et al. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Syst. Biol. 55(4), 595–609. https://doi.org/10.1080/10635150600852011 (2006).Article 
PubMed 

Google Scholar 
Fujisawa, T. & Barraclough, T. G. Delimiting species using single-locus data and the generalized mixed yule coalescent approach: A revised method and evaluation on simulated data sets. Syst. Biol. 62(5), 707–724. https://doi.org/10.1093/sysbio/syt033 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, automatic barcode gap discovery for primary species delimitation. Mol. Ecol. 21(8), 1864–1877. https://doi.org/10.1111/j.1365-294X.2011.05239.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Drummond, A. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214. https://doi.org/10.1186/1471-2148-7-214 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Posada, D. jModelTest: Phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256. https://doi.org/10.1093/molbev/msn083 (2008).CAS 
Article 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2017).Ezard, T., Fujisawa, T. & Barraclough, T. splits: Species Limits by Threshold Statistics. R package version 1.0–19/r52. https://R-Forge.R-project.org/projects/splits/ (2017).Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).Article 

Google Scholar 
Bermingham, E., McCafferty, S. S. & Martin, A. P. Fish biogeography and molecular clocks: Perspectives from the Panamanian Isthmus. In Molecular Systematics of Fishes (eds Kocher, T. D. & Stepien, C. A.) 113–128 (Academic Press, 1997).Chapter 

Google Scholar 
Thomaz, A. T., Malabarba, L. R., Bonatto, S. L. & Knowles, L. L. Testing the effect of palaeodrainages versus habitat stability on genetic divergence in riverine systems: Study of a Neotropical fish of the Brazilian coastal Atlantic Forest. J. Biogeogr. 42, 2389–2401. https://doi.org/10.1111/jbi.12597 (2015).Article 

Google Scholar 
Kimura, M. A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
Article 

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

Google Scholar 
Guillot, G., Renaud, S., Ledevin, R., Michaux, J. & Claude, J. A unifying model for the analysis of phenotypic, genetic and geograhic data. Syst. Biol. 61(6), 897–911. https://doi.org/10.1093/sysbio/sys038 (2012).Article 
PubMed 

Google Scholar 
Excoffier, L., Laval, G. & Schneider, S. Arlequin: A Software for Population Data Analysis. Version 3.1. http://cmpg.unibe.ch/software/arlequin3 (2007).Wright, S. Evolution and the genetics of populations: Variability within and among natural populations. Univ. Chicago 4, 580 (1978).
Google Scholar 
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large datasets. Mol. Biol. Evol. 34, 3299–3302. https://doi.org/10.1093/molbev/msx248 (2017).CAS 
Article 
PubMed 

Google Scholar 
Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16(1), 37–48 (1999).CAS 
Article 

Google Scholar 
Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116. https://doi.org/10.1111/2041-210X.12410 (2015).Article 

Google Scholar 
Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
Article 

Google Scholar 
Fu, Y. X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).CAS 
Article 

Google Scholar 
Austin, M. P. Continuum concept, ordination methods, and niche theory. Annu. Rev. Ecol. Syst. 16(1), 39–61. https://doi.org/10.1146/annurev.es.16.110185.000351 (1985).MathSciNet 
Article 

Google Scholar 
Graham, A., Atkinson, P. & Danson, F. Spatial analysis for epidemiology. Acta Trop. 91(3), 219–225. https://doi.org/10.1016/j.actatropica.2004.05.001 (2004).CAS 
Article 
PubMed 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Guimarães, K. L. A., Rosso, J. J., Souza, M. F. B., de Astarloa, J. M. D. & Rodrigues, L. R. R. Integrative taxonomy reveals disjunct distribution and first record of Hoplias misionera (Characiformes: Erythrinidae) in the Amazon River basin: Morphological, DNA barcoding and cytogenetic considerations. Neotrop. Ichthyol. 19(2), e200110. https://doi.org/10.1590/1982-0224-2020-0110 (2021).Article 

Google Scholar 
Queiroz, L. J. et al. Evolutionary units delimitation and continental multilocus phylogeny of the hyperdiverse catfish genus Hypostomus. Mol. Phylogenet. Evol. 145, 106711. https://doi.org/10.1016/j.ympev.2019.106711 (2020).Article 

Google Scholar 
Phillips, J. D., Gillis, D. J. & Hanner, R. H. Incomplete estimates of genetic diversity within species: Implications for DNA barcoding. Ecol. Evol. https://doi.org/10.1002/ece3.4757 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Blaxter, M. L. The promise of a DNA taxonomy. Philos. Trans. R. Soc. B. 359(1444), 669–679. https://doi.org/10.1098/rstb.2003.1447 (2004).CAS 
Article 

Google Scholar 
Nwani, C. D. et al. DNA barcoding discriminates freshwater fishes from southeastern Nigeria and provides river system-level phylogeographic resolution within some species. Mitochondrial DNA 22(1), 43–51. https://doi.org/10.3109/19401736.2010.536537 (2011).CAS 
Article 
PubMed 

Google Scholar 
Aguirre, W. E., Shervette, V. R., Navarrete, R., Calle, P. & Agorastos, S. Morphological and genetic divergence of Hoplias microlepis (Characiformes: Erythrinidae) in rivers and artificial impoundments of Western Ecuador. Copeia 2013(2), 312–323. https://doi.org/10.1643/ci-12-083 (2013).Article 

Google Scholar 
Pires, W. M. M., Barros, M. C. & Fraga, E. C. DNA Barcoding unveils cryptic lineages of Hoplias malabaricus from Northeastern Brazil. Braz. J. Biol. 81(4), 917–927. https://doi.org/10.1590/1519-6984.231598 (2020).Article 

Google Scholar 
Souza, F. H. S. et al. interspecific genetic differences and historical demography in South American Arowanas (Osteoglossiformes, Osteoglossidae, Osteoglossum). Genes 10(9), 693. https://doi.org/10.3390/genes10090693 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Torati, L. S. et al. Genetic diversity and structure in Arapaima gigas populations from Amazon and Araguaia-Tocantins river basins. BMC Genet. https://doi.org/10.1186/s12863-018-0711-y (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Lovejoy, N. R. & Araujo, M. L. G. Molecular systematics, biogeography and population structure of Neotropical freshwater needlefishes of the genus Potamorrhaphis. Mol. Ecol. 9(3), 259–268. https://doi.org/10.1046/j.1365-294x.2000.00845.x (2000).CAS 
Article 
PubMed 

Google Scholar 
Mabesoone, J. M. Sedimentary Basins of Northeast Brazil (Federal University of Pernambuco, 1994).
Google Scholar 
Haffer, J. & Prance, G. T. Impulsos climáticos da evolução na Amazônia durante o Cenozóico: Sobre a teoria dos Refúgios da diferenciação biótica. Estudos Avançados USP 46, 175–208. https://doi.org/10.1590/S0103-40142002000300014 (2002).Article 

Google Scholar 
Riker, S. R. L., Lima, F. J. C., Motta, M. B. Evidências de glaciação Pleistocênica na Amazônia Brasileira. Anais do 14° Simpósio de Geologia da Amazônia, Sociedade Brasileira de Geologia 15–18 (2015).Albert, J. S., Val, P. & Hoorn, C. The changing course of the Amazon River in the Neogene: Center stage for Neotropical diversification. Neotrop. Ichthyol. 16(3), e180033. https://doi.org/10.1590/1982-0224-20180033 (2018).Article 

Google Scholar 
Lundberg, J. G. et al. The stage for Neotropical fish diversification: a history of tropical South American rivers. (eds. Malabarba, L. R., Reis, R. E., Vari, R. P., Lucena, Z. M., Lucena, C. A. S. Phylogeny and classification of Neotropical fishes). Edipucrs 13–48 (1998).Hubert, N. & Renno, J. F. Historical biogeography of South American freshwater fishes. J. Biogeogr. 33(8), 1414–1436. https://doi.org/10.1111/j.1365-2699.2006.01518.x (2006).Article 

Google Scholar 
Farias, I. P. & Hrbek, T. Patterns of diversification in the discus fishes (Symphysodon spp. Cichlidae) of the Amazon basin. Mol. Phylogenet. Evol. 49, 32–43. https://doi.org/10.1016/j.ympev.2008.05.033 (2008).CAS 
Article 
PubMed 

Google Scholar 
Tagliacollo, V. A., Bernt, M. J., Craig, J. M., Oliveira, C. & Albert, J. S. Model-based total evidence phylogeny of Neotropical electric knifefishes (Teleostei, Gymnoti-formes). Mol. Phylogenet. Evol. 95, 20–33. https://doi.org/10.1016/j.ympev.2015.11.007 (2015).Article 
PubMed 

Google Scholar 
Hutchinson, G. E. Concluding remarks. Cold Spring Harbor Symposium. Quant. Biol. 22, 415–427 (1957).Article 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Inte-grating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

Google Scholar 
McNyset, K. M. Ecological niche conservatism in North American freshwater fishes. Biol. J. Lin. Soc. 96, 282–295 (2009).Article 

Google Scholar 
Silva, W. C., Marceniuk, A. P., Sales, J. B. L. & Araripe, J. Early pleistocene lineages of Bagre bagre (Linnaeus, 1766) (Siluriformes: Ariidae), from the Atlantic coast of South America, with insights into the demography and biogeography of the species. Neotrop. Ichthyol. https://doi.org/10.1590/1982-0224-20150184 (2016).Article 

Google Scholar 
Lemopoulos, A. & Covain, R. Biogeography of the freshwater fishes of the Guianas using a partitioned parsimony analysis of endemicity with reappraisal of ecoregional boundaries. Cladistics 35(2019), 106–124. https://doi.org/10.1111/cla.12341 (2018).Article 
PubMed 

Google Scholar 
Hoorn, C. Marine incursions and the influence of Andean tectonics on the Miocene depositional history of northwestern Amazonia: Results of a palynostratigraphic study. Palaeogeogr. Palaeoclimatol. Palaeoecol. 105, 267–309. https://doi.org/10.1016/0031-0182(93)90087-Y (1993).Article 

Google Scholar 
Hoorn, C., Guerreiro, J. & Sarmiento, G. Andean tectonics as a cause for changing drainage patterns in Miocene Northern South America. Geology 23(3), 237–240. https://doi.org/10.1130/0091-7613(1995)023%3c0237:ATAACF%3e2.3.CO;2 (1995).ADS 
Article 

Google Scholar 
Ribeiro, A. C. Tectonic history and the biogeography of the freshwater fishes from the coastal drainages of eastern Brazil: An example of faunal evolution associated with a divergent continental margin. Neotrop. Ichthyol. 4(2), 225–246. https://doi.org/10.1590/S1679-62252006000200009 (2006).Article 

Google Scholar 
Lovejoy, N. R., Albert, J. S. & Crampton, W. G. R. Miocene marine incursions and marine/freshwater transitions: Evidence from Neotropical fishes. J. S. Am. Earth Sci. 21(1–2), 5–13. https://doi.org/10.1016/j.jsames.2005.07.009 (2006).Article 

Google Scholar  More