Philippa Kaur

Haas, M., Schreiber, M. & Mascher, M. Domestication and crop evolution of wheat and barley: Genes, genomics, and future directions. J. Integr. Plant Biol. 61(3), 204–225 (2019).PubMed 
Article 

Google Scholar 
Hebelstrup, K. H. Differences in nutritional quality between wild and domesticated forms of barley and emmer wheat. Plant Sci. 256, 1–4 (2017).CAS 
PubMed 
Article 

Google Scholar 
Borisjuk, N. et al. Genetic modification for wheat improvement: From transgenesis to genome editing. Biomed. Res. Int. 2019, 6216304 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Maccaferri, M. et al. Durum wheat genome highlights past domestication signatures and future improvement targets. Nat. Genet. 51(5), 885–895 (2019).CAS 
PubMed 
Article 

Google Scholar 
Brenchley, R. et al. Analysis of the bread wheat genome using whole-genome shotgun sequencing. Nature 491(7426), 705–710 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Zimin, A. V. et al. The first near-complete assembly of the hexaploid bread wheat genome, Triticum aestivum. Gigascience 6(11), 1–7 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Avni, R. et al. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science 357(6346), 93–97 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Luo, M. C. et al. Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature 551(7681), 498–502 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Jia, J. et al. Aegilops tauschii draft genome sequence reveals a gene repertoire for wheat adaptation. Nature 496(7443), 91–95 (2013).CAS 
PubMed 
Article 

Google Scholar 
Peng, J. et al. Domestication quantitative trait loci in Triticum dicoccoides, the progenitor of wheat. Proc. Natl. Acad. Sci. U. S. A. 100(5), 2489–2494 (2003).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Allen, A. M. et al. Discovery and development of exome-based, co-dominant single nucleotide polymorphism markers in hexaploid wheat (Triticum aestivum L.). Plant Biotechnol. J. 11(3), 279–295 (2013).CAS 
PubMed 
Article 

Google Scholar 
Merchuk-Ovnat, L., Fahima, T., Krugman, T. & Saranga, Y. Ancestral QTL alleles from wild emmer wheat improve grain yield, biomass and photosynthesis across enviroinments in modern wheat. Plant Sci. 251, 23–34 (2018).Article 
CAS 

Google Scholar 
Bhalla, P. L., Sharma, A. & Singh, M. B. Enabling molecular technologies for trait improvement in wheat. Methods Mol. Biol. 1679, 3–24 (2017).CAS 
PubMed 
Article 

Google Scholar 
Hong, J., Yang, L., Zhang, D., & Shi, J. Plant metabolomics: An indispensable system biology tool for plant science. Int. J. Mol. Sci. 17(6), 1–16 (2016).ADS 

Google Scholar 
Batyrshina, Z. S., Yaakov, B., Shavit, R., Singh, A. & Tzin, V. Comparative transcriptomic and metabolic analysis of wild and domesticated wheat genotypes reveals differences in chemical and physical defense responses against aphids. BMC Plant Biol. 20(1), 19 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zorb, C., Langenkamper, G., Betsche, T., Niehaus, K. & Barsch, A. Metabolite profiling of wheat grains (Triticum aestivum L.) from organic and conventional agriculture. J. Agric. Food Chem. 54(21), 8301–8306 (2006).PubMed 
Article 
CAS 

Google Scholar 
Matthews, S. B. et al. Metabolite profiling of a diverse collection of wheat lines using ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry. PLoS ONE 7(8), e44179 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
de Leonardis, A. M. et al. Effects of heat stress on metabolite accumulation and composition, and nutritional properties of durum wheat grain. Int. J. Mol. Sci. 16(12), 30382–30404 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Allwood, J. W. et al. Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions. Phytochemistry 115, 99–111 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Ullah, N., Yuce, M., Neslihan Ozturk Gokce, Z. & Budak, H. Comparative metabolite profiling of drought stress in roots and leaves of seven Triticeae species. BMC Genom. 18(1), 969 (2017).Article 
CAS 

Google Scholar 
Lannucci, A., Fragasso, M., Beleggia, R., Nigro, F. & Papa, R. Evolution of the crop rhizosphere: Impact of domestication on root exudates in tetraploid wheat (Triticum turgidum L.). Front Plant Sci. 8, 2124 (2017).Article 

Google Scholar 
Beleggia, R. et al. Evolutionary metabolomics reveals domestication-associated changes in tetraploid wheat kernels. Mol. Biol. Evol. 33(7), 1740–1753 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poudel, R., Bhinderwala, F., Morton, M., Powers, R. & Rose, D. J. Metabolic profiling of historical and modern wheat cultivars using proton nuclear magnetic resonance spectroscopy. Sci. Rep. 11(1), 3080 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Hanhineva, K. et al. Non-targeted analysis of spatial metabolite composition in strawberry (Fragariaxananassa) flowers. Phytochemistry 69(13), 2463–2481 (2008).CAS 
PubMed 
Article 

Google Scholar 
Ben-Abu, Y. & Itsko, M. “Changes in “natural antibiotic” metabolite composition during tetraploid wheat domestication. Sci. Rep. 11(1), 20340. https://doi.org/10.1038/s41598-021-98764-5 (2021).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Salamini, F., Ozkan, H., Brandolini, A., Schäfer-Pregl, R. & Martin, W. Genetics and geography of wild cereal domestication in the near east. Nat. Rev. Genet. 3(6), 429–441. https://doi.org/10.1038/nrg817 (2002).CAS 
Article 
PubMed 

Google Scholar 
Zörb, C., Langenkämper, G., Betsche, T., Niehaus, K. & Barsch, A. Metabolite profiling of wheat grains (Triticum aestivum L.) from organic and conventional agriculture. J. Agric. Food Chem. 54(21), 8301–8306 (2006).PubMed 
Article 
CAS 

Google Scholar 
Ben-Abu, Y., Beiles, A., Flom, D. & Nevo, E. Adaptive evolution of benzoxazinoids in wild emmer wheat, Triticum dicoccoides, at “Evolution Canyon”, Mount Carmel, Israel. PLoS ONE. 13(2), e0190424 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ben-Abu, Y., et al., Durum wheat evolution—a genomic analysis. In Proceedings of the International Symposium on Genetics and Breeding of Durum Wheat, Vol. 110 29–44 (2014).Zaynab, M. et al. Role of secondary metabolites in plant defense against pathogens. Microb. Pathog. 124, 198–202 (2018).CAS 
PubMed 
Article 

Google Scholar 
de Bruijn, W. J. C., Gruppen, H. & Vincken, J. P. Structure and biosynthesis of benzoxazinoids: Plant defence metabolites with potential as antimicrobial scaffolds. Phytochemistry 155, 233–243 (2018).PubMed 
Article 
CAS 

Google Scholar 
Arbona, V. & Gomez-Cadenas, A. Metabolomics of Disease resistance in crops. Mol. Biol. 19, 13–30 (2016).
Google Scholar 
Okada, K., Abe, H. & Arimura, G. Jasmonates induce both defense responses and communication in monocotyledonous and dicotyledonous plants. Plant Cell Physiol. 56(1), 16–27 (2015).CAS 
PubMed 
Article 

Google Scholar 
Belz, R. G. Allelopathy in crop/weed interactions–an update. Pest. Manag. Sci. 63(4), 308–326 (2007).CAS 
PubMed 
Article 

Google Scholar 
Mondal, S. et al. Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect pest resistance and nutrition through conventional and modern breeding approaches. Front. Plant Sci. 7, 991 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Huang, L. et al. Evolution and adaptation of wild emmer wheat populations to biotic and abiotic stresses. Annu. Rev. Phytopathol. 54, 279–301 (2016).CAS 
PubMed 
Article 

Google Scholar 
Ben-David, R., Dinoor, A., Peleg, Z. & Fahima, T. Reciprocal hosts’ responses to powdery mildew isolates originating from domesticated wheats and their wild progenitor. Front. Plant Sci. 9, 75 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Yahiaoui, N., Brunner, S. & Keller, B. Rapid generation of new powdery mildew resistance genes after wheat domestication. Plant J. 47(1), 85–98 (2006).CAS 
PubMed 
Article 

Google Scholar 
Parween, T., Jan, S., Mahmooduzzafar, S., Fatma, T. & Siddiqui, Z. H. Selective effect of pesticides on plant—a review. Crit. Rev. Food Sci. Nutr. 56(1), 160–179 (2016).CAS 
PubMed 
Article 

Google Scholar 
Mou, Y., et al. Genome-wide identification and characterization of the OPR gene family in wheat (Triticum aestivum L). Int. J. Mol. Sci. 20(8), 85–97 (2019).Article 
CAS 

Google Scholar 
Kage, U., Karre, S., Kushalappa, A. C. & McCartney, C. Identification and characterization of a fusarium head blight resistance gene TaACT in wheat QTL-2DL. Plant Biotechnol. J. 15(4), 447–457 (2017).CAS 
PubMed 
Article 

Google Scholar 
Dutartre, L., Hilliou, F. & Feyereisen, R. Phylogenomics of the benzoxazinoid biosynthetic pathway of Poaceae: Gene duplications and origin of the Bx cluster. BMC Evol. Biol. 12, 64 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gill, S. S. & Tuteja, N. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol. Biochem. 48(12), 909–930 (2010).CAS 
PubMed 
Article 

Google Scholar 
Dhokane, D., Karre, S., Kushalappa, A. C. & McCartney, C. Integrated metabolo-transcriptomics reveals fusarium head blight candidate resistance genes in wheat QTL-Fhb2. PLoS ONE 11(5), e0155851 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kage, U., Yogendra, K. N. & Kushalappa, A. C. TaWRKY70 transcription factor in wheat QTL-2DL regulates downstream metabolite biosynthetic genes to resist Fusarium graminearum infection spread within spike. Sci. Rep. 7, 42596 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Masisi, K., Beta, T. & Moghadasian, M. H. Antioxidant properties of diverse cereal grains: A review on in vitro and in vivo studies. Food Chem. 196, 90–97 (2016).CAS 
PubMed 
Article 

Google Scholar 
Sova, M. Antioxidant and antimicrobial activities of cinnamic acid derivatives. Mini Rev. Med. Chem. 12(8), 749–767 (2012).CAS 
PubMed 
Article 

Google Scholar 
Perez-Vizcaino, F. & Fraga, C. G. Research trends in flavonoids and health. Arch Biochem. Biophys. 646, 107–112 (2018).CAS 
PubMed 
Article 

Google Scholar 
Kong, L., Guo, H. & Sun, M. Signal transduction during wheat grain development. Planta 241(4), 789–801 (2015).CAS 
PubMed 
Article 

Google Scholar 
Nadolska-Orczyk, A., Rajchel, I. K., Orczyk, W. & Gasparis, S. Major genes determining yield-related traits in wheat and barley. Theor Appl Genet 130(6), 1081–1098 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, W. & Yang, B. Translational genomics of grain size regulation in wheat. Theor. Appl. Genet. 130(9), 1765–1771 (2017).CAS 
PubMed 
Article 

Google Scholar 
Qi, P. F. et al. Transcriptional reference map of hormone responses in wheat spikes. BMC Genom. 20(1), 390 (2019).Article 
CAS 

Google Scholar 
Hill, C. B. & Li, C. Genetic architecture of flowering phenology in cereals and opportunities for crop improvement. Front .Plant Sci. 7, 1906 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Jiang, Y., Schmidt, R. H., Zhao, Y. & Reif, J. C. A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat. Nat. Genet. 49(12), 1741–1746 (2017).CAS 
PubMed 
Article 

Google Scholar  More

Brown, W. L. Jr. & Wilson, E. O. Character displacement. Syst. Biol. 5, 49–64 (1956).
Google Scholar 
Stuart, Y. E. & Losos, J. B. Ecological character displacement: glass half full or half empty? Trends Ecol. Evol. 28, 402–408 (2013).PubMed 
Article 

Google Scholar 
Schluter, D. & McPhail, J. D. Ecological character displacement and speciation in sticklebacks. Am. Nat. 140, 85–108 (1992).CAS 
PubMed 
Article 

Google Scholar 
Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt. Nature 371, 65–66 (1994).ADS 
Article 

Google Scholar 
Ghoul, M. & Mitri, S. The ecology and evolution of microbial competition. Trends Microbiol. 24, 833–845 (2016).CAS 
PubMed 
Article 

Google Scholar 
Pfennig, D. W., Rice, A. M. & Martin, R. A. Ecological opportunity and phenotypic plasticity interact to promote character displacement and species coexistence. Ecology 87, 769–779 (2006).PubMed 
Article 

Google Scholar 
Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
Day, T. & Young, K. A. Competitive and facilitative evolutionary diversification. Bioscience 54, 101–109 (2004).Article 

Google Scholar 
Stachowicz, J. J. Mutualism, facilitation, and the structure of ecological communities. Bioscience 51, 235–246 (2001).Article 

Google Scholar 
Stuart, Y. E., Inkpen, S. A., Hopkins, R. & Bolnick, D. I. Character displacement is a pattern: so, what causes it? Biol. J. Linn. Soc. 121, 711–715 (2017).Article 

Google Scholar 
Brockhurst, M. A., Hochberg, M. E., Bell, T. & Buckling, A. Character displacement promotes cooperation in bacterial biofilms. Curr. Biol. 16, 2030–2034 (2006).CAS 
PubMed 
Article 

Google Scholar 
Ellis, C. N., Traverse, C. C., Mayo-Smith, L., Buskirk, S. W. & Cooper, V. S. Character displacement and the evolution of niche complementarity in a model biofilm community. Evolution 69, 283–293 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Rainey, P. B., Buckling, A., Kassen, R. & Travisano, M. The emergence and maintenance of diversity: insights from experimental bacterial populations. Trends Ecol. Evol. 15, 243–247 (2000).CAS 
PubMed 
Article 

Google Scholar 
Turner, P. E., Souza, V. & Lenski, R. E. Tests of ecological mechanisms promoting the stable coexistence of two bacterial genotypes. Ecology 77, 2119–2129 (1996).Article 

Google Scholar 
Xavier, J. B. & Foster, K. R. Cooperation and conflict in microbial biofilms. Proc. Natl. Acad. Sci. USA 104, 876–881 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Westeberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Evol. Syst. 20, 249–278 (1989).Article 

Google Scholar 
Turcotte, M. M. & Levine, J. M. Phenotypic plasticity and species coexistence. Trends Ecol. Evol. 31, 803–813 (2016).PubMed 
Article 

Google Scholar 
Pfennig, D. W. & Pfennig, K. S. Development and evolution of character displacement. Ann NY Acad Sci. 1256, 89–107 (2012).ADS 
PubMed 
MATH 
Article 

Google Scholar 
Finkel, O. M., Castrillo, G., Herrera Paredes, S., Salas Gonzalez, I. & Dangl, J. L. Understanding and exploiting plant beneficial microbes. Curr. Opin. Plant Biol. 38, 155–163 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Muller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A. The plant microbiota: systems-level insights and perspectives. Annu. Rev. Genet. 50, 211–234 (2016).CAS 
PubMed 
Article 

Google Scholar 
Schlaeppi, K. & Bulgarelli, D. The plant microbiome at work. Mol. Plant Microbe Interact. 28, 212–217 (2015).CAS 
PubMed 
Article 

Google Scholar 
Leveau, J. H. & Lindow, S. E. Appetite of an epiphyte: quantitative monitoring of bacterial sugar consumption in the phyllosphere. Proc. Natl. Acad. Sci. USA 98, 3446–3453 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lindow, S. E. & Leveau, J. H. Phyllosphere microbiology. Curr. Opin. Biotechnol. 13, 238–243 (2002).CAS 
PubMed 
Article 

Google Scholar 
Meyer, K. M. & Leveau, J. H. Microbiology of the phyllosphere: a playground for testing ecological concepts. Oecologia 168, 621–629 (2012).ADS 
PubMed 
Article 

Google Scholar 
Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl. Acad. Sci. USA 106, 16428–16433 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).CAS 
PubMed 
Article 

Google Scholar 
Carlstrom, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Müller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe. 22, 142–155 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bodenhausen, N., Horton, M. W. & Bergelson, J. Bacterial communities associated with the leaves and the roots of Arabidopsis thaliana. PLoS ONE 8, e56329 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Horton, M. W. et al. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5, 5320 (2014).ADS 
PubMed 
Article 

Google Scholar 
Roman-Reyna, V. et al. Characterization of the leaf microbiome from whole-genome sequencing data of the 3000 rice genomes project. Rice (NY) 13, 72 (2020).Article 

Google Scholar 
Zarraonaindia, I. et al. The soil microbiome influences grapevine-associated microbiota. mBio. 6, e02527–14 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Laforest-Lapointe, I. & Whitaker, B. K. Decrypting the phyllosphere microbiota: progress and challenges. Am. J. Bot. 106, 171–173 (2019).PubMed 

Google Scholar 
Baldotto, L. E. B. & Olivares, F. L. Phylloepiphytic interaction between bacteria and different plant species in a tropical agricultural system. Can. J. Microbiol. 54, 918–931 (2008).CAS 
PubMed 
Article 

Google Scholar 
Lindow, S. E. & Brandl, M. T. Microbiology of the phyllosphere. Appl. Environ. Microbiol. 69, 1875–1883 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Monier, J. M. & Lindow, S. E. Differential survival of solitary and aggregated bacterial cells promotes aggregate formation on leaf surfaces. Proc. Natl. Acad. Sci. USA 100, 15977–15982 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Monier, J. M. & Lindow, S. E. Frequency, size, and localization of bacterial aggregates on bean leaf surfaces. Appl. Environ. Microbiol. 70, 346–355 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morris, C. E., Monier, J. M. & Jacques, M. A. A technique To quantify the population size and composition of the biofilm component in communities of bacteria in the phyllosphere. Appl. Environ. Microbiol. 64, 4789–4795 (1998).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Remus-Emsermann, M. N. P. et al. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ. Microbiol. 16, 2329–2340 (2014).CAS 
PubMed 
Article 

Google Scholar 
Remus-Emsermann, M. N. P. & Schlechter, R. O. Phyllosphere microbiology: at the interface between microbial individuals and the plant host. New Phytol. 218, 1327–1333 (2018).PubMed 
Article 

Google Scholar 
Gourion, B., Rossignol, M. & Vorholt, J. A. A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth. Proc. Natl. Acad. Sci. USA 103, 13186–13191 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jacobs, J. L., Carroll, T. L. & Sundin, G. W. The role of pigmentation, ultraviolet radiation tolerance, and leaf colonization strategies in the epiphytic survival of phyllosphere bacteria. Microb. Ecol. 49, 104–113 (2005).CAS 
PubMed 
Article 

Google Scholar 
Müller, D. B., Schubert, O. T., Rost, H., Aebersold, R. & Vorholt, J. A. Systems-level proteomics of two ubiquitous leaf commensals reveals complementary adaptive traits for phyllosphere colonization. Mol. Cell. Proteom. 15, 3256–3269 (2016).Article 
CAS 

Google Scholar 
Ochsner, A. M. et al. Use of rare-earth elements in the phyllosphere colonizer Methylobacterium extorquens PA1. Mol. Microbiol. 111, 1152–1166 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Helmann, T. C., Deutschbauer, A. M. & Lindow, S. E. Genome-wide identification of Pseudomonas syringae genes required for fitness during colonization of the leaf surface and apoplast. Proc. Natl. Acad. Sci. USA 116, 18900–18910 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nobori, T. et al. Transcriptome landscape of a bacterial pathogen under plant immunity. Proc. Natl. Acad. Sci. USA 115, E3055–E3064 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pulawska, J. et al. Transcriptome analysis of Xanthomonas fragariae in strawberry leaves. Sci. Rep. 10, 20582 (2020).Knief, C. et al. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J. 6, 1378–1390 (2012).CAS 
PubMed 
Article 

Google Scholar 
Innerebner, G., Knief, C. & Vorholt, J. A. Protection of Arabidopsis thaliana against leaf-pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl. Environ. Microbiol. 77, 3202–3210 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vogel, C., Innerebner, G., Zingg, J., Guder, J. & Vorholt, J. A. Forward genetic in planta screen for identification of plant-protective traits of Sphingomonas sp Strain Fr1 against Pseudomonas syringae DC3000. Appl. Environ. Microbiol. 78, 5529–5535 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ryffel, F. et al. Metabolic footprint of epiphytic bacteria on Arabidopsis thaliana leaves. ISME J. 10, 632–643 (2016).CAS 
PubMed 
Article 

Google Scholar 
Vogel, C. M., Potthoff, D. B., Schafer, M., Barandun, N. & Vorholt, J. A. Protective role of the Arabidopsis leaf microbiota against a bacterial pathogen. Nat. Microbiol. 6, 1537–1548 (2021).CAS 
PubMed 
Article 

Google Scholar 
Pfeilmeier, S. et al. The plant NADPH oxidase RBOHD is required for microbiota homeostasis in leaves. Nat. Microbiol. 6, 852–864 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maier, B. A. et al. A general non-self response as part of plant immunity. Nat. Plants 7, 696–705 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Breton, C., Snajdrova, L., Jeanneau, C., Koca, J. & Imberty, A. Structures and mechanisms of glycosyltransferases. Glycobiology 16, 29r–37r (2006).CAS 
PubMed 
Article 

Google Scholar 
Tao, F., Swarup, S. & Zhang, L. H. Quorum sensing modulation of a putative glycosyltransferase gene cluster essential for Xanthomonas campestris biofilm formation. Environ. Microbiol. 12, 3159–3170 (2010).CAS 
PubMed 
Article 

Google Scholar 
Zhou, M. X., Zhu, F., Dong, S. L., Pritchard, D. G. & Wu, H. A novel glucosyltransferase is required for glycosylation of a serine-rich adhesin and biofilm formation by Streptococcus parasanguinis. J. Biol. Chem. 285, 12140–12148 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Becker, A. et al. Regulation of succinoglycan and galactoglucan biosynthesis in Sinorhizobium meliloti. J. Mol. Microbiol. Biotechnol. 4, 187–190 (2002).CAS 
PubMed 

Google Scholar 
Halder, U., Banerjee, A. & Bandopadhyay, R. Structural and functional properties, biosynthesis, and patenting trends of bacterial succinoglycan: a review. Indian J. Microbiol. 57, 278–284 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Niehaus, K. & Becker, A. The role of microbial surface polysaccharides in the Rhizobium-legume interaction. Sub-Cell. Biochem. 29, 73–116 (1998).CAS 
Article 

Google Scholar 
Ellis, H. R. Mechanism for sulfur acquisition by the alkanesulfonate monooxygenase system. Bioorg. Chem. 39, 178–184 (2011).CAS 
PubMed 
Article 

Google Scholar 
Marco, M. L., Legac, J. & Lindow, S. E. Pseudomonas syringae genes induced during colonization of leaf surfaces. Environ. Microbiol. 7, 1379–1391 (2005).CAS 
PubMed 
Article 

Google Scholar 
Yu, X. L. et al. Transcriptional responses of Pseudomonas syringae to growth in epiphytic versus apoplastic leaf sites. Proc. Natl. Acad. Sci. USA 110, E425–E434 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cai, S. J. & Inouye, M. EnvZ-OmpR interaction and osmoregulation in Escherichia coli. J. Biol. Chem. 277, 24155–24161 (2002).CAS 
PubMed 
Article 

Google Scholar 
Freeman, B. C. et al. Physiological and transcriptional responses to osmotic stress of two Pseudomonas syringae strains that differ in epiphytic fitness and osmotolerance. J. Bacteriol. 195, 4742–4752 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scheublin, T. R. et al. Transcriptional profiling of gram-positive Arthrobacter in the phyllosphere: induction of pollutant degradation genes by natural plant phenolic compounds. Environ. Microbiol. 16, 2212–2225 (2014).CAS 
PubMed 
Article 

Google Scholar 
Felix, G., Duran, J. D., Volko, S. & Boller, T. Plants have a sensitive perception system for the most conserved domain of bacterial flagellin. Plant J. 18, 265–276 (1999).CAS 
PubMed 
Article 

Google Scholar 
Macho, A. P. & Zipfel, C. Plant PRRs and the activation of innate immune signaling. Mol. Cell 54, 263–272 (2014).CAS 
PubMed 
Article 

Google Scholar 
Hopsu-Havu, V. K. & Glenner, G. G. A new dipeptide naphthylamidase hydrolyzing glycyl-prolyl-beta-naphthylamide. Histochemie 7, 197–201 (1966).CAS 
PubMed 
Article 

Google Scholar 
Kavi Kishor, P. B., Hima Kumari, P., Sunita, M. S. & Sreenivasulu, N. Role of proline in cell wall synthesis and plant development and its implications in plant ontogeny. Front. Plant Sci. 6, 544 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Chipperfield, J. R. & Ratledge, C. Salicylic acid is not a bacterial siderophore: a theoretical study. Biometals 13, 165–168 (2000).CAS 
PubMed 
Article 

Google Scholar 
Visca, P., Ciervo, A., Sanfilippo, V. & Orsi, N. Iron-regulated salicylate synthesis by Pseudomonas Spp. J. Gen. Microbiol. 139, 1995–2001 (1993).CAS 
PubMed 
Article 

Google Scholar 
Seifert, G. J., Barber, C., Wells, B., Dolan, L. & Roberts, K. Galactose biosynthesis in Arabidopsis: genetic evidence for substrate channeling from UDP-D-galactose into cell wall polymers. Curr. Biol. 12, 1840–1845 (2002).CAS 
PubMed 
Article 

Google Scholar 
Zablackis, E., Huang, J., Muller, B., Darvill, A. G. & Albersheim, P. Characterization of the cell-wall polysaccharides of Arabidopsis thaliana leaves. Plant Physiol. 107, 1129–1138 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Santos-Beneit, F. The Pho regulon: a huge regulatory network in bacteria. Front. Microbiol. 6, 402 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Mortimer, J. C. et al. An unusual xylan in Arabidopsis primary cell walls is synthesised by GUX3, IRX9L, IRX10L and IRX14. Plant J. 83, 413–426 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Honer Zu Bentrup, K., Miczak, A., Swenson, D. L. & Russell, D. G. Characterization of activity and expression of isocitrate lyase in Mycobacterium avium and Mycobacterium tuberculosis. J. Bacteriol. 181, 7161–7167 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reinscheid, D. J., Eikmanns, B. J. & Sahm, H. Characterization of the isocitrate lyase gene from Corynebacterium glutamicum and biochemical analysis of the enzyme. J. Bacteriol. 176, 3474–3483 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Groisman, E. A., Chiao, E., Lipps, C. J. & Heffron, F. Salmonella typhimurium phoP virulence gene is a transcriptional regulator. Proc. Natl. Acad. Sci. USA 86, 7077–7081 (1989).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lamarche, M. G., Wanner, B. L., Crepin, S. & Harel, J. The phosphate regulon and bacterial virulence: a regulatory network connecting phosphate homeostasis and pathogenesis. FEMS Microbiol. Rev. 32, 461–473 (2008).CAS 
PubMed 
Article 

Google Scholar 
Jameson, G. N., Cosper, M. M., Hernandez, H. L., Johnson, M. K. & Huynh, B. H. Role of the [2Fe-2S] cluster in recombinant Escherichia coli biotin synthase. Biochemistry 43, 2022–2031 (2004).Sirithanakorn, C. & Cronan, J. E. Biotin, a universal and essential cofactor: synthesis, ligation and regulation. FEMS Microbiol. Rev. 45, fuab003 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Choi-Rhee, E. & Cronan, J. E. Biotin synthase is catalytic in vivo, but catalysis engenders destruction of the protein. Chem. Biol. 12, 461–468 (2005).CAS 
PubMed 
Article 

Google Scholar 
Wilmes, P. et al. Community proteogenomics highlights microbial strain-variant protein expression within activated sludge performing enhanced biological phosphorus removal. ISME J. 2, 853–864 (2008).CAS 
PubMed 
Article 

Google Scholar 
Beier, S., Rivers, A. R., Moran, M. A. & Obernosterer, I. Phenotypic plasticity in heterotrophic marine microbial communities in continuous cultures. ISME J. 9, 1141–1151 (2015).PubMed 
Article 

Google Scholar 
Kim, H. et al. High population of Sphingomonas species on plant surface. J. Appl. Microbiol. 85, 731–736 (1998).Article 

Google Scholar 
Singh, P., Santoni, S., Weber, A., This, P. & Peros, J. P. Understanding the phyllosphere microbiome assemblage in grape species (Vitaceae) with amplicon sequence data structures. Sci. Rep. 9, 14294 (2019).Kosma, D. K. et al. The impact of water deficiency on leaf cuticle lipids of Arabidopsis. Plant Physiol. 151, 1918–1929 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Piffeteau, A. & Gaudry, M. Biotin uptake: influx, efflux and countertransport in Escherichia coli K12. Biochim. Biophys. Acta 816, 77–82 (1985).CAS 
PubMed 
Article 

Google Scholar 
D’Souza, G. et al. Less is more: selective advantages can explain the prevalent loss of biosynthetic genes in bacteria. Evolution 68, 2559–2570 (2014).PubMed 
Article 
CAS 

Google Scholar 
Hassani, M. A., Duran, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 58 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Mas, A., Jamshidi, S., Lagadeuc, Y., Eveillard, D. & Vandenkoornhuyse, P. Beyond the black queen hypothesis. ISME J. 10, 2085–2091 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Morris, B. E., Henneberger, R., Huber, H. & Moissl-Eichinger, C. Microbial syntrophy: interaction for the common good. FEMS Microbiol. Rev. 37, 384–406 (2013).CAS 
PubMed 
Article 

Google Scholar 
Pacheco, A. R., Moel, M. & Segre, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pande, S. et al. Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria. ISME J. 8, 953–962 (2014).CAS 
PubMed 
Article 

Google Scholar 
Joyner, D. C. & Lindow, S. E. Heterogeneity of iron bioavailability on plants assessed with a whole-cell GFP-based bacterial biosensor. Microbiol. 146, 2435–2445 (2000).CAS 
Article 

Google Scholar 
Remus-Emsermann, M. N., de Oliveira, S., Schreiber, L. & Leveau, J. H. Quantification of lateral heterogeneity in carbohydrate permeability of isolated plant leaf cuticles. Front. Microbiol. 2, 197 (2011).PubMed 
PubMed Central 

Google Scholar 
Remus-Emsermann, M. N. P., Tecon, R., Kowalchuk, G. A. & Leveau, J. H. J. Variation in local carrying capacity and the individual fate of bacterial colonizers in the phyllosphere. ISME J. 6, 756–765 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Peredo, E. L. & Simmons, S. L. Leaf-FISH: microscale imaging of bacterial taxa on phyllosphere. Front. Microbiol. 8, 2669 (2018).Dar, D., Dar, N., Cai, L. & Newman, D. K. Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution. Science 373, eabi4882 (2021).CAS 
PubMed 
Article 

Google Scholar 
Ledermann, R., Strebel, S., Kampik, C. & Fischer, H. M. Versatile vectors for efficient mutagenesis of Bradyrhizobium diazoefficiens and other alphaproteobacteria. Appl. Environ. Microbiol. 82, 2791–2799 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roux, M. et al. The Arabidopsis leucine-rich repeat receptor-like kinases BAK1/SERK3 and BKK1/SERK4 are required for innate immunity to hemibiotrophic and biotrophic pathogens. Plant Cell 23, 2440–2455 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Staswick, P. E., Tiryaki, I. & Rowe, M. L. Jasmonate response locus JAR1 and several related Arabidopsis genes encode enzymes of the firefly luciferase superfamily that show activity on jasmonic, salicylic, and indole-3-acetic acids in an assay for adenylation. Plant Cell 14, 1405–1415 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Torres, M. A., Dangl, J. L. & Jones, J. D. Arabidopsis gp91phox homologues AtrbohD and AtrbohF are required for accumulation of reactive oxygen intermediates in the plant defense response. Proc. Natl. Acad. Sci. USA 99, 517–522 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cao, H., Glazebrook, J., Clarke, J. D., Volko, S. & Dong, X. The Arabidopsis NPR1 gene that controls systemic acquired resistance encodes a novel protein containing ankyrin repeats. Cell 88, 57–63 (1997).CAS 
PubMed 
Article 

Google Scholar 
Schlesier, B., Breton, F. & Mock, H. P. A hydroponic culture system for growing Arabidopsis thaliana plantlets under sterile conditions. Plant Mol. Biol. Rep. 21, 449–456 (2003).CAS 
Article 

Google Scholar 
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
Hemmerle, L., Ochsner, A. M., Vonderach, T., Hattendorf, B. & Vorholt, J. A. Mass spectrometry-based approaches to study lanthanides and lanthanide-dependent proteins in the phyllosphere. Methods Enzymol. 650, 215–236 (2021).CAS 
PubMed 
Article 

Google Scholar 
Uhrig, R. G. et al. Diurnal dynamics of the Arabidopsis rosette proteome and phosphoproteome. Plant Cell Environ. 44, 821–841 (2021).CAS 
PubMed 
Article 

Google Scholar 
Davis, J. J. et al. The PATRIC bioinformatics resource center: expanding data and analysis capabilities. Nucleic Acids Res. 48, D606–D612 (2020).CAS 
PubMed 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293 (2016).CAS 
PubMed 
Article 

Google Scholar 
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).CAS 
PubMed 
Article 

Google Scholar  More

Sabo, J. et al. Riparian zones increase regional species richness by harbouring different, not more, species. Ecology 86, 56–62 (2005).Article 

Google Scholar 
Lind, L., Hasselquist, E. & Laudon, H. Towards ecologically functional riparian zones: A meta-analysis to develop guidelines for protecting ecosystem functions and biodiversity in agricultural landscapes. J. Environ. Manage. 249, 109391–109391 (2019).PubMed 
Article 

Google Scholar 
Merritt, D., Nilsson, C. & Jansson, R. Consequences of propagule dispersal and river fragmentation for riparian plant community diversity and turnover. Ecol. Monogr. 80, 609–626 (2010).Article 

Google Scholar 
Jansson, R., Nilsson, C. & Renöfält, B. Fragmentation of riparian floras in rivers with multiple dams. Ecology 81, 899–903 (2000).Article 

Google Scholar 
Mari, L. et al. Metapopulation persistence and species spread in river networks. Ecol. Lett. 17, 426–434 (2014).ADS 
PubMed 
Article 

Google Scholar 
Blöschl, G. et al. Changing climate both increases and decreases European river floods. Nature 573, 108–111. https://doi.org/10.1038/s41586-019-1495-6 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Tabari, H. Climate change impact on flood and extreme precipitation increases with water availability. Sci. Rep. 10, 13768. https://doi.org/10.1038/s41598-020-70816-2 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wobus, C. et al. Climate change impacts on flood risk and asset damages within mapped 100-year floodplains of the contiguous United States. Nat. Hazards Earth Syst. Sci. 17, 2199–2211 (2017).ADS 
Article 

Google Scholar 
Meyer, J. L. et al. The contribution of headwater streams to biodiversity in river networks1. J. Am. Water Resour. Assoc. 43, 86–103. https://doi.org/10.1111/j.1752-1688.2007.00008.x (2007).ADS 
Article 

Google Scholar 
Van Looy, K. & Piffady, J. Metapopulation modelling of riparian tree species persistence in river networks under climate change. J. Environ. Manage. 202, 437–446 (2017).PubMed 
Article 

Google Scholar 
Sochor, M. et al. Can gene flow among populations counteract the habitat loss of extremely fragile biotopes? An example from the population genetic structure in Salix daphnoides. Tree Genet. Genomes 9, 1193–1205 (2013).Article 

Google Scholar 
Garssen, A. G. et al. Effects of increased flooding on riparian vegetation: Field experiments simulating climate change along five European lowland streams. Glob. Change Biol. 23, 3052–3063. https://doi.org/10.1111/gcb.13687 (2017).ADS 
Article 

Google Scholar 
Ellenberg, H. Vegetation Mitteleuropas mit den Alpen in Ökologischer, Dynamischer und historischer Sicht. 6., vollst. neu bearb. und stark erw. Aufl edn, (Ulmer, 2010).Hanski, I. Metapopulation Biology: Ecology, Genetics, and Evolution (Academic Press, New York, 1997).MATH 

Google Scholar 
Wubs, E. R. J. et al. Going against the flow: A case for upstream dispersal and detection of uncommon dispersal events. Freshw. Biol. 61, 580–595 (2016).CAS 
Article 

Google Scholar 
Chen, F.-Q. & Xie, Z.-Q. Reproductive allocation, seed dispersal and germination of Myricaria laxiflora, an endangered species in the Three Gorges Reservoir area. Plant Ecol. 191, 67–75 (2007).Article 

Google Scholar 
Bonn, S. Ausbreitungsbiologie der Pflanzen Mitteleuropas: Grundlagen und kulturhistorische Aspekte. (Quelle und Meyer Verlag, 1998).Müller-Schneider, P. Verbreitungsbiologie der Blütenpflanzen Graubündens: Diasporology of the Spermatophytes of the Grisons. Vol. 85. (Switzerland) (1986).Aradottir, A., Svavarsdottir, K. & Bau, A. Clonal variability of native willows (Salix pylicifofia and Salix lanata) in Iceland and implications for use in restoration. Icel. Agric. Sci. 20, 61–72 (2007).
Google Scholar 
Egelund, B., Pertoldi, C. & Barfod, A. S. Isolation and reduced gene flow among Faroese populations of tea-leaved willow (Salix phylicifolia, Salicaceae). N. J. Bot. J. Bot. Soc. B. Isles 2, 9–15 (2012).
Google Scholar 
Van Puyvelde, K. & Triest, L. ISSRs indicate isolation by distance and spatial structuring in Salix alba populations along Alpine upstream rivers (Alto Adige and Upper Rhine). Belg. J. Bot. 140, 100–108 (2007).
Google Scholar 
Ngeve, M. N., Van der Stocken, T., Sierens, T., Koedam, N. & Triest, L. Bidirectional gene flow on a mangrove river landscape and between-catchment dispersal of Rhizophora racemosa (Rhizophoraceae). Hydrobiologia 790, 93–108. https://doi.org/10.1007/s10750-016-3021-2 (2017).Article 

Google Scholar 
Werth, S., Schoedl, M. & Scheidegger, C. Dams and canyons disrupt gene flow among populations of a threatened riparian plant. Freshw. Biol. 59, 2502–2515 (2014).Article 

Google Scholar 
Pollux, B. J. A., Luteijn, A., Van-Groenendael, J. M., Ouborg, N. J. & Ouborg, N. J. Gene flow and genetic structure of the aquatic macrophyte Sparganium emersum in a linear unidirectional river. Freshw. Biol. 54, 64–76 (2009).Article 

Google Scholar 
Davis, C., Epps, C., Flitcroft, R. & Banks, M. Refining and defining riverscape genetics: How rivers influence population genetic structure. Wiley Interdiscip. Rev. Water 5, e1269 (2018).Article 

Google Scholar 
Vega-Retter, C. et al. Dammed river: Short- and long-term consequences for fish species inhabiting a river in a Mediterranean climate in central Chile. Aquat. Conserv.Mar. Freshw. Ecosyst. 30, 2254–2268. https://doi.org/10.1002/aqc.3425 (2020).Article 

Google Scholar 
Rannala, B. & Mountain, J. L. Detecting immigration by using multilocus genotypes. Proc. Natl. Acad. Sci. U.S.A. 94, 9197–9201 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Altermatt, F., Alther, R. & Mächler, E. Spatial patterns of genetic diversity, community composition and occurrence of native and non-native amphipods in naturally replicated tributary streams. BMC Ecol. 16, 23. https://doi.org/10.1186/s12898-016-0079-7 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Paz-Vinas, I. et al. Systematic conservation planning for intraspecific genetic diversity. Proc. R. Soc. Lond. B: Biol. Sci. 285, 20172746. https://doi.org/10.1098/rspb.2017.2746 (2018).Article 

Google Scholar 
Sitzia, T., Kudrnovsky, H., Müller, N. & Michielon, B. Biological flora of Central Europe Myricaria germanica (L.) Desv. Perspect. Plant Ecol. Evol. Syst. 52, 125629. https://doi.org/10.1016/j.ppees.2021.125629 (2021).Article 

Google Scholar 
Egger, G., Steineder, R. & Angermann, K. Verbreitung und Erhaltungszustand des FFH-Lebensraumtyps 3230 “Alpine Flüsse mit Ufergehölzen von Myricaria germanica” an der Isel und deren Zubringern (Osttirol, Österreich). Carinthia II 204, 391–432 (2014).
Google Scholar 
Schletterer, M., Gewolf, S., Egger, G. & Fink, S. Forschungsprojekt Tamariske: Genetische Untersuchung von Populationen an der Isel – Dokumentation der Beprobungen 2018. 32 (Innbruck, 2019).Scheidegger, C. & Wiedmer, A. Genetische Untersuchung zur Deutschen Tamariske in Tirol. (Eidg. Forschungsanstalt WSL, Birmensdorf, 2014).Hedrick, P., Lacy, R., Allendorf, F. & Soule, M. Directions in conservation biology: Comments on caughley. Conserv. Biol. 10, 1312–1320 (1996).Article 

Google Scholar 
Sampson, J., Byrne, M., Gibson, N. & Yates, C. Limiting inbreeding in disjunct and isolated populations of a woody shrub. Ecol. Evol. 6, 5867–5880 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Kudrnovsky, H. & Stöhr, O. Myricaria germanica (L.) Desv. historisch und aktuell in Österreich: Ein dramatischer Rückgang einer Indikatorart von europäischem Interesse. STAPFIA Rep. 99, 13–34 (2013).
Google Scholar 
Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 global biodiversity framework must be improved. Biol. Conserv. 248, 108654. https://doi.org/10.1016/j.biocon.2020.108654 (2020).Article 

Google Scholar 
Auffret, A. G., Plue, J. & Cousins, S. A. O. The spatial and temporal components of functional connectivity in fragmented landscapes. Ambio 44, 51–59. https://doi.org/10.1007/s13280-014-0588-6 (2015).Article 
PubMed Central 

Google Scholar 
Herrmann, J. et al. Connectivity from a different perspective: Comparing seed dispersal kernels in connected vs. unfragmented landscapes. Ecology 97, 1274–1282 (2016).PubMed 
Article 

Google Scholar 
Mortelliti, A., Amori, G. & Boitani, L. The role of habitat quality in fragmented landscapes: A conceptual overview and prospectus for future research. Oecologia 163, 535–547 (2010).ADS 
PubMed 
Article 

Google Scholar 
Mosner, E., Liepelt, S., Ziegenhagen, B. & Leyer, I. Floodplain willows in fragmented river landscapes: Understanding spatio-temporal genetic patterns as a basis for restoration plantings. Biol. Conserv. 153, 211–218 (2012).Article 

Google Scholar 
Chambers, J., MacMahon, J. & Brown, R. Alpine seedling establishment: The influence of disturbance type. Ecology 71, 1323–1341 (1990).Article 

Google Scholar 
Bill, H.-C. Besiedlungsdynamik und Populationsbiologie charakteristischer Pionierpflanzenarten nordalpiner Wildflüsse PhD thesis, Philipps-Universität Marburg, (2000).Lite, S. J., Bagstad, K. J. & Stromberg, J. C. Riparian plant species richness along lateral and longitudinal gradients of water stress and flood disturbance, San Pedro River, Arizona, USA. J. Arid Environ. 63, 785–813. https://doi.org/10.1016/j.jaridenv.2005.03.026 (2005).ADS 
Article 

Google Scholar 
Andersson, E., Nilsson, C. & Johansson, M. E. Plant dispersal in boreal rivers and its relation to the diversity of riparian flora. J. Biogeogr. 27, 1095–1106 (2000).Article 

Google Scholar 
Aguiar, F. et al. The abundance and distribution of guilds of riparian woody plants change in response to land use and flow regulation. J. Appl. Ecol. 55, 2227–2240 (2018).Article 

Google Scholar 
Leyer, I. Dispersal, diversity and distribution patterns in pioneer vegetation: The role of river-floodplain connectivity. J. Veg. Sci. 17, 407–416 (2006).Article 

Google Scholar 
Crookes, S. & Shaw, P. W. Isolation by distance and non-identical patterns of gene flow within two river populations of the freshwater fish Rutilus rutilus (L. 1758). Conserv. Genet. 17, 861–874. https://doi.org/10.1007/s10592-016-0828-3 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Werth, S. & Scheidegger, C. Gene flow within and between catchments in the threatened riparian plant Myricaria germanica. PLoS ONE 9, e99400 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jacquemyn, H., Honnay, O., Van Looy, K. & Breyne, P. Spatiotemporal structure of genetic variation of a spreading plant metapopulation on dynamic riverbanks along the Meuse River. Heredity 96, 471–478. https://doi.org/10.1038/sj.hdy.6800825 (2006).CAS 
Article 
PubMed 

Google Scholar 
Mayer, C., Schiegg, K. & Pasinelli, G. Patchy population structure in a short-distance migrant: evidence from genetic and demographic data. Mol. Ecol. 18, 2353–2364 (2009).CAS 
PubMed 
Article 

Google Scholar 
Benda, L. E. E. et al. The network dynamics hypothesis: How Channel networks structure riverine habitats. Bioscience 54, 413–427 (2004).Article 

Google Scholar 
Miettinen, A. et al. A large wild salmon stock shows genetic and life history differentiation within, but not between, rivers. Conserv. Genet. 22, 35–51. https://doi.org/10.1007/s10592-020-01317-y (2021).CAS 
Article 

Google Scholar 
Fink, S., Lanz, T., Stecher, R. & Scheidegger, C. Colonization potential of an endangered riparian shrub species. Biodivers. Conserv. 26, 2099–2114. https://doi.org/10.1007/s10531-017-1347-3 (2017).Article 

Google Scholar 
Merritt, D. & Wohl, E. Plant dispersal along rivers fragmented by dams. River Res. Appl. 22, 1–26 (2006).Article 

Google Scholar 
Sitzia, T., Michielon, B., Iacopino, S. & Kotze, D. J. Population dynamics of the endangered shrub Myricaria germanica in a regulated Alpine river is influenced by active channel width and distance to check dams. Ecol. Eng. 95, 828–838 (2016).Article 

Google Scholar 
Wöllner, R., Scheidegger, C. & Fink, S. Gene flow in a highly dynamic habitat and a single founder event: Proof from a plant population on a relocated river site. Glob. Ecol. Conserv. 28, e01686. https://doi.org/10.1016/j.gecco.2021.e01686 (2021).McLaughlin, B. et al. Hydrologic refugia, plants, and climate change. Glob. Change Biol. 23, 2941–2961 (2017).ADS 
Article 

Google Scholar 
Chiu, M. C. et al. Branching networks can have opposing influences on genetic variation in riverine metapopulations. bioRxiv https://doi.org/10.1101/550194 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Catford, J. & Jansson, R. Drowned, buried and carried away: Effects of plant traits on the distribution of native and alien species in riparian ecosystems. New Phytol. 204, 19–36 (2014).PubMed 
Article 

Google Scholar 
Schletterer, M. & Scheiber, T. Wiederansiedlung der deutschen tamariske (Myricaria germanica (L.) DESV.) an der Leutascher Ache (Nordtirol, Österreich). B. Naturwiss. Med. Ver. Innsbr. 95, 53–65 (2008).
Google Scholar 
Riehl, S. & Zehm, A. in ANLiegen Natur Vol. 40, 17–20 (ANL Bayern, Laufen, 2017).Egger, G., Angermann, K. & Gruber, A. Wiederansiedlung der Deutschen Tamariske (Myricaria germanica (L.) Desv.) in Kärnten. Carinthia II 393–418 (2010).Kudrnovsky, H. Alpine rivers and their ligneous vegetation with Myricaria germanica and riverine landscape diversity in the Eastern Alps: Proposing the Isel river system for the Natura 2000 network. Eco. Mont 5, 5–18 (2013).
Google Scholar 
Lener, F. P. Etablierung und Entwicklung der Deutschen Tamariske (Myricaria germanica) an der oberen Drau in Kärnten Master thesis (University of Vienna, Vienna, 2011).
Google Scholar 
Schiechtl, H. M. in Alpenländ. Bienenzeitung Vol. 4 125–131 (1957).Bill, H.-C., Poschlod, P., Reich, M. & Plachter, H. Experiments and observations on seed dispersal by running water in an Alpine floodplain. Bull. Geobot. Inst. ETH 65, 13–28 (1999).
Google Scholar 
Nilsson, C., Brown, R., Jansson, R. & Merritt, D. The role of hydrochory in structuring riparian and wetland vegetation. Biol. Rev. 85, 837–858 (2010).PubMed 

Google Scholar 
Lener, F. P., Egger, G. & Karrer, G. Sprossaufbau und entwicklung der deutschen tamariske (Myricaria germanica) an der Oberen Drau (Kärnten, Österreich). Carinthia II(203), 515–552 (2013).
Google Scholar 
Werth, S. & Scheidegger, C. Isolation and characterization of 22 nuclear and 5 chloroplast microsatellite loci in the threatened riparian plant Myricaria germanica (Tamaricaceae, Caryophyllales). Conserv. Genet. Resour. 3, 445–448 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comp., (2016).Excoffier, L., Laval, G. & Schneider, S. Arlequin ver 3.0: An integrated software package for population genetics data analysis. Evol. Bioinform. Online 1, 47–50 (2005).CAS 
Article 

Google Scholar 
Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Luikart, G., Allendorf, F. W., Cornuet, J. M. & Sherwin, W. B. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. 89, 238–247. https://doi.org/10.1093/jhered/89.3.238 (1998).CAS 
Article 
PubMed 

Google Scholar 
Falush, D., Stephens, M. & Pritchard, J. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 7, 574–578 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Res. 4, 359–361 (2012).Article 

Google Scholar 
Smouse, P. E., Peakall, R., GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Piry, S. et al. GENECLASS2: A software for genetic assignment and first-generation migrant detection. J. Hered. 95, 536–539. https://doi.org/10.1093/jhered/esh074 (2004).CAS 
Article 
PubMed 

Google Scholar 
Paetkau, D., Slade, R., Burden, M. & Estoup, A. Genetic assignment methods for the direct, real-time estimation of migration rate: A simulation-based exploration of accuracy and power. Mol. Ecol. 13, 55–65. https://doi.org/10.1046/j.1365-294X.2004.02008.x (2004).CAS 
Article 
PubMed 

Google Scholar 
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).PubMed 
PubMed Central 
Article 

Google Scholar 
Rannala, B. (ed University of California Davis) 1–12 (2007).Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meirmans, P. G. Nonconvergence in Bayesian estimation of migration rates. Mol. Ecol. Resour. 14, 726–733. https://doi.org/10.1111/1755-0998.12216 (2014).Article 
PubMed 

Google Scholar 
Greenland, S. et al. Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. Eur. J. Epidemiol. 31, 337–350. https://doi.org/10.1007/s10654-016-0149-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar  More

Stirling, I., Calvert, W. & Cleator, H. Underwater vocalizations as a tool for studying the distribution and relative abundance of wintering pinnipeds in the High Arctic. Arctic https://doi.org/10.14430/arctic2275 (1983).Article 

Google Scholar 
Sirovic, A. et al. Seasonality of blue and fin whale calls and the influence of sea ice in the Western Antarctic Peninsula. Deep Sea Res. II 51, 2327–2344 (2004).Article 
ADS 

Google Scholar 
Jones, J. M. et al. Ringed, bearded, and ribbon seal vocalizations north of barrow, Alaska: Seasonal presence and relationship with sea ice. Arctic 67, 203–222 (2014).Article 

Google Scholar 
Clark, C. W. et al. A year in the acoustic world of bowhead whales in the Bering, Chukchi and Beaufort seas. Prog. Oceanogr. 136, 223–240 (2015).Article 
ADS 

Google Scholar 
Marques, T. A., Munger, L., Thomas, L., Wiggins, S. & Hildebrand, J. A. Estimating North Pacific right whale Eubalaena japonica density using passive acoustic cue counting. Endangered Species Res. 13, 163–172 (2011).Article 

Google Scholar 
Hildebrand, J. A. et al. Passive acoustic monitoring of beaked whale densities in the Gulf of Mexico. Sci. Rep. 5, 16343 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
von Benda-Beckmann, A. M., Thomas, L., Tyack, P. L. & Ainslie, M. A. Modelling the broadband propagation of marine mammal echolocation clicks for click-based population density estimates. J. Acoust. Soc. Am. 143, 954–967 (2018).Article 
ADS 

Google Scholar 
Hildebrand, J. A. et al. Assessing seasonality and density from passive acoustic monitoring of signals presumed to be from pygmy and dwarf sperm whales in the Gulf of Mexico. Front. Mar. Sci. 6, 66 (2019).Article 

Google Scholar 
Marques, T. A. et al. Estimating animal population density using passive acoustics. Biol. Rev. 88, 287–309 (2013).PubMed 
Article 

Google Scholar 
Helble, T. A., D’Spain, G. L., Campbell, G. S. & Hildebrand, J. A. Calibrating passive acoustic monitoring: Correcting humpback call detections for site-specific and time-dependent environmental characterisitcs. J. Acoust. Soc. Amer. 134, EL400–EL406 (2013).Article 
ADS 

Google Scholar 
Frasier, K. E. et al. Delphinid echolocation click detection probability on near-seafloor sensors. J. Acoust. Soc. Am. 140, 1918–1930 (2016).PubMed 
Article 
ADS 

Google Scholar 
Helble, T. A. et al. Site specific probability of passive acoustic detection of humpback whale calls from single fixed hydrophones. J. Acoust. Soc. Am. 134, 2556–2570 (2013).PubMed 
Article 
ADS 

Google Scholar 
Larsen Tempel, J. T., Wise, S., Osborne, T. Q., Sparks, K. & Atkinson, S. “Life without ice: Perceptions of environmental impacts on marine resources and subsistence users of St. Lawrence Island. Ocean Coast. Manag. 212, 105819 (2021).Article 

Google Scholar 
Clark, C. W. & Johnson, J. H. The sounds of the bowhead whale, Balaena mysticetus, during the spring migrations of 1979 and 1980. Can. J. Zool. 62, 1436–1441 (1984).Article 

Google Scholar 
Ashjian, C. J., Braund, S. R., Campbell, R. G., George, J. C., Kruse, J., Maslowski, W., Moore, S. E., Nicolson, C. R., Okkonen, S. R., & Sherr, B. F. Climate variability, oceanography, bowhead whale distribution, and Iñupiat subsistence whaling near Barrow, Alaska, Arctic 179–194 (2010).Moore, S. E. & Clarke, J. T. Bowhead whale fall distribution and relative abundance in relation to oil and gas lease areas in the northeastern Chukchi Sea. Polar Rec. 29, 209–214 (1993).Article 

Google Scholar 
Reeves, R., Rosa, C., George, J. C., Sheffield, G. & Moore, M. “Implications of Arctic industrial growth and strategies to mitigate future vessel and fishing gear impacts on bowhead whales. Mar. Policy 36, 454–462 (2012).Article 

Google Scholar 
Blackwell, S. B., Richardson, W., Greene Jr, C., & Streever, B. Bowhead whale (Balaena mysticetus) migration and calling behaviour in the Alaskan Beaufort Sea, Autumn 2001–04: An acoustic localization study, Arctic 255–270 (2007).Mathias, D., Thode, A., Blackwell, S. B., & Greene, C. Computer-aided classification of bowhead whale call categories for mitigation monitoring. In New Trends for Environmental Monitoring Using Passive Systems, Hyeres, French Riviera 1–6 (2008).Delarue, J., Laurinolli, M. & Martin, B. Bowhead whale (Balaena mysticetus) songs in the Chukchi Sea between October 2007 and May 2008. J. Acoust. Soc. Am. 126, 3319–3328 (2009).PubMed 
Article 
ADS 

Google Scholar 
Moore, S. E., Stafford, K. M. & Munger, L. M. Acoustic and visual surveys for bowhead whales in the western Beaufort and far northeastern Chukchi seas. Deep Sea Res. Part II 57, 153–157 (2010).Article 
ADS 

Google Scholar 
Ballard, M. S. et al. Temporal and spatial dependence of a yearlong record of sound propagation from the Canada Basin to the Chukchi Shelf. J. Acoust. Soc. Am. 148, 1663–1680 (2020).PubMed 
Article 
ADS 

Google Scholar 
Duda, T. F., Zhang, W. G. & Lin, Y.-T. Effects of Pacific Summer Water layer variations and ice cover on Beaufort Sea underwater sound ducting. J. Acoust. Soc. Am. 149, 2117–2136 (2021).PubMed 
Article 
ADS 

Google Scholar 
Diachok, O. I. & Winokur, R. S. Spatial variability of underwater ambient noise at the Arctic ice-water boundary. J. Acoust. Soc. Am. 55, 750–753 (1974).Article 
ADS 

Google Scholar 
Diachok, O. I. Effects of sea-ice ridges on sound propagation in the Arctic Ocean. J. Acoust. Soc. Am. 59, 1110–1120 (1976).Article 
ADS 

Google Scholar 
Yang, T. & Votaw, C. W. Under ice reflectivities at frequencies below 1 kHz. J. Acoust. Soc. Am. 70, 841–851 (1981).Article 
ADS 

Google Scholar 
Milne, A. R. & Ganton, J. H. Ambient Noise under Arctic-Sea Ice. J. Acoust. Soc. Am. 36, 855–865 (1964).Article 
ADS 

Google Scholar 
Brown, J. R. & Milne, A. R. Reverberation under Arctic Sea-Ice. J. Acoust. Soc. Am. 42, 78–82 (1967).Article 
ADS 

Google Scholar 
Duckworth, G., LePage, K. & Farrell, T. Low-frequency long-range propagation and reverberation in the central Arctic: Analysis of experimental results. J. Acoust. Soc. Am. 110, 747–760 (2001).Article 
ADS 

Google Scholar 
Jensen, F. B. & Kuperman, W. A. Optimum frequency of propagation in shallow water environments. J. Acoust. Soc. Am. 73, 813–819 (1983).Article 
ADS 

Google Scholar 
Keen, K. A., Thayre, B. J., Hildebrand, J. A. & Wiggins, S. M. Seismic airgun sound propagation in Arctic Ocean waveguides. Deep Sea Res. I 141, 24–32 (2018).Article 

Google Scholar 
Greene, C. R. & Buck, B. M. Arctic ocean ambient noise. J. Acoust. Soc. Am. 36, 1218–1220 (1964).Article 
ADS 

Google Scholar 
Roth, E. H., Hildebrand, J. A., Wiggins, S. M. & Ross, D. Underwater ambient noise on the Chukchi Sea continental slope from 2006–2009. J. Acoust. Soc. Am. 131, 104–110 (2012).PubMed 
Article 
ADS 

Google Scholar 
Kinda, G. B., Simard, Y., Gervaise, C., Mars, J. I. & Fortier, L. Arctic underwater noise transients from sea ice deformation: Characteristics, annual time series, and forcing in Beaufort Sea. J. Acoust. Soc. Am. 138, 2034–2045 (2015).PubMed 
Article 
ADS 

Google Scholar 
Hildebrand, J. A., Frasier, K. E., Baumann-Pickering, S. & Wiggins, S. M. An empirical model for wind-generated ocean noise. J. Acoust. Soc. Am. 149, 4516–4533 (2021).PubMed 
Article 
ADS 

Google Scholar 
Farmer, D. M. & Xie, Y. The sound of cracking sea ice. J. Acoust. Soc. Am. 84, S123–S123 (1988).Article 
ADS 

Google Scholar 
Bassett, C., Thomson, J., Dahl, P. H. & Polagye, B. Flow-noise and turbulence in two tidal channels. J. Acoust. Soc. Am. 135(4), 1764–1774 (2014).PubMed 
Article 
ADS 

Google Scholar 
Chapman, R. P. & Harris, J. Surface backscattering strengths measured with explosive sound sources. J. Acoust. Soc. Am. 34, 1592–1597 (1962).Article 
ADS 

Google Scholar 
Gauss, R. C., Fialkowski, J. M., & Wurmser, D. A low- and mid-frequency bistatic scattering model for the ocean surface. In Proceedings of OCEANS 2005 MTS/IEEE, Vol. 2 1738–1744 (2005)Jin, G., Lynch, J. F., Pawlowicz, R. & Worcester, P. Acoustic scattering losses in the Greenland Sea marginal ice zone during the 1988–89 tomography experiment. J. Acoust. Soc. Am. 96, 3045–3053 (1994).Article 
ADS 

Google Scholar 
Citta, J. J. et al. Ecological characteristics of core-use areas used by Bering-Chukchi-Beaufort (BCB) bowhead whales, 2006–2012. Prog. Oceanogr. 136, 201–222 (2015).Article 
ADS 

Google Scholar 
Thode, A. M., Blackwell, S. B., Conrad, A. S., Kim, K. H. & Macrander, A. M. Decadal-scale frequency shift of migrating bowhead whale calls in the shallow Beaufort Sea. J. Acoust. Soc. Am. 142, 1482–1502 (2017).PubMed 
Article 
ADS 

Google Scholar 
Wiggins, S. M., & Hildebrand, J. A. High-frequency acoustic recording package (HARP) for broad-band, long-term marine mammal monitoring. In International Symposium on Underwater Technology 2007 and International Workshop on Scientific use of Submarine Cables & Related Technologies 2007. Institute of Electrical and Electronics Engineers, Tokyo, Japan 551–557 (2007).Marques, T. A., Thomas, L., Ward, J., DiMarzio, N. & Tyack, P. L. Estimating cetacean population density using fixed passive acoustic sensors: An example with Blainville’s beaked whales. J. Acoust. Soc. Am. 125, 1982–1994 (2009).PubMed 
Article 
ADS 

Google Scholar 
Küsel, E. T. et al. Cetacean population density estimation from single fixed sensors using passive acoustics. J. Acoust. Soc. Am. 129, 3610–3622 (2011).PubMed 
Article 
ADS 

Google Scholar 
Cummings, W. C. & Holliday, D. V. Sounds and source levels from bowhead whales off Pt. Barrow, Alaska. J. Acoust. Soc. Am. 82, 814–821 (1987).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Thode, A. M. et al. Source level and calling depth distributions of migrating bowhead whale calls in the shallow Beaufort Sea. J. Acoust. Soc. Am. 140, 4288 (2016).PubMed 
Article 
ADS 

Google Scholar 
Blackwell, S. B. et al. Directionality of bowhead whale calls measured with multiple sensors. Mar. Mammal Sci. 28, 200–212 (2012).Article 

Google Scholar 
Markus, T., Stroeve, J. C. & Miller, J. Recent changes in Arctic sea ice melt onset, freezeup, and melt season length. J. Geophys. Res. Oceans https://doi.org/10.1029/2009JC005436 (2009).Article 

Google Scholar 
Kwok, R. & Cunningham, G. Variability of Arctic sea ice thickness and volume from CryoSat-2. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 373, 20140157 (2015).Article 
ADS 

Google Scholar 
Krishfield, R., Toole, J., Proshutinsky, A. & Timmermans, M.-L. Automated ice-tethered profilers for seawater observations under pack ice in all seasons. J. Atmos. Oceanic Tech. 25, 2091–2105 (2008).Article 
ADS 

Google Scholar 
Gong, D. & Pickart, R. S. Summertime circulation in the eastern Chukchi Sea. Deep Sea Res. Part II 118, 18–31 (2015).Article 

Google Scholar 
Meng, X., Li, G., Han, G. & Kan, G. Sound velocity and related properties of seafloor sediments in the Bering Sea and Chukchi Sea. Acta Oceanol. Sin. 34, 75–80 (2015).CAS 
Article 

Google Scholar 
Toole, J. M., Krishfield, R. A., Timmermans, M.-L. & Proshutinsky, A. The ice-tethered profiler: Argo of the Arctic. Oceanography 24, 126–135 (2011).Article 

Google Scholar 
Millero, F. J., Feistel, R., Wright, D. G. & McDougall, T. J. The composition of Standard Seawater and the definition of the Reference-Composition Salinity Scale. Deep Sea Res. Part I 55, 50–72 (2008).Article 

Google Scholar 
Kutschale, H. Long-range sound transmission in the Arctic Ocean. J. Geophys. Res. 66, 2189–2198 (1961).Article 
ADS 

Google Scholar 
Jakobsson, M. et al. The international bathymetric chart of the Arctic Ocean (IBCAO) version 3.0. Geophys. Res. Lett. https://doi.org/10.1029/2012GL052219 (2012).Article 

Google Scholar 
Warner, G. A., Dosso, S. E., Dettmer, J. & Hannay, D. E. Bayesian environmental inversion of airgun modal dispersion using a single hydrophone in the Chukchi Sea. J. Acoust. Soc. Am. 137, 3009–3023 (2015).PubMed 
Article 
ADS 

Google Scholar 
Pang, X. et al. Comparison between AMSR2 sea ice concentration products and pseudo-ship observations of the Arctic and Antarctic sea ice edge on cloud-free days. Remote Sens. 10, 317 (2018).Article 
ADS 

Google Scholar 
Kahru, M. Windows image manager: Image display and analysis program for Microsoft Windows with special features for satellite images (2001).Duncan, A. J. & Maggi, A. L. A consistent, user friendly interface for running a variety of underwater acoustic propagation codes. Proc. Acoust. 2006, 471–477 (2006).
Google Scholar 
Collins, M. D. A split-step Padé solution for the parabolic equation method. J. Acoust. Soc. Am. 93, 1736–1742 (1993).Article 
ADS 

Google Scholar 
Alexander, P., Duncan, A., Bose, N., & Smith, D. Modelling acoustic transmission loss due to sea ice cover. Acoust. Aust. 41 (2013).Goff, J. A. Quantitative analysis of sea ice draft: 1. Methods for stochastic modeling. J. Geophys. Res. Oceans 100, 6993–7004 (1995).Article 
ADS 

Google Scholar 
Gavrilov, A. N. & Mikhalevsky, P. N. Low-frequency acoustic propagation loss in the Arctic Ocean: Results of the Arctic climate observations using underwater sound experiment. J. Acoust. Soc. Am. 119, 3694–3706 (2006).Article 
ADS 

Google Scholar  More

Giesecke, T. et al. Towards mapping the late Quaternary vegetation change of Europe. Veg. Hist. Archaeobot. 23, 75–86. https://doi.org/10.1007/s00334-012-0390-y (2013).Article 

Google Scholar 
Fyfe, R. M., Woodbridge, J. & Roberts, N. From forest to farmland: pollen-inferred land cover change across Europe using the pseudobiomization approach. Glob. Chang. Biol. 21, 1197–1212. https://doi.org/10.1111/gcb.12776 (2015).Article 
PubMed 

Google Scholar 
Gilliam, F. S. Forest ecosystems of temperate climatic regions: From ancient use to climate change. New Phytol. 212, 871–887. https://doi.org/10.1111/nph.14255 (2016).Article 
PubMed 

Google Scholar 
Jamrichová, E. et al. Human impact on open temperate woodlands during the middle Holocene in Central Europe. Rev. Palaeobot. Palynol. 245, 55–68. https://doi.org/10.1016/j.revpalbo.2017.06.002 (2017).Article 

Google Scholar 
Kaplan, J. O., Krumhardt, K. M. & Zimmermann, N. The prehistoric and preindustrial deforestation of Europe. Quatern. Sci. Rev. 28, 3016–3034. https://doi.org/10.1016/j.quascirev.2009.09.028 (2009).Article 

Google Scholar 
Kalis, A. J., Merkt, J. & Wunderlich, J. Environmental changes during the Holocene climatic optimum in central Europe—human impact and natural causes. Quatern. Sci. Rev. 22, 33–79. https://doi.org/10.1016/S0277-3791(02)00181-6 (2003).Article 

Google Scholar 
Molinari, C. et al. Exploring potential drivers of European biomass burning over the Holocene: A data-model analysis. Glob. Ecol. Biogeogr. 22, 1248–1260. https://doi.org/10.1111/geb.12090 (2013).Article 

Google Scholar 
Roberts, N. et al. Europe’s lost forests: A pollen-based synthesis for the last 11,000 years. Sci. Rep. 8, 716. https://doi.org/10.1038/s41598-017-18646-7 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl. Acad. Sci. USA 118. https://doi.org/10.1073/pnas.2023483118 (2021).Ellis, E. C. Anthropogenic transformation of the terrestrial biosphere. Philos. Trans. A Math. Phys. Eng. Sci. 369, 1010–1035. https://doi.org/10.1098/rsta.2010.0331 (2011).Article 
PubMed 

Google Scholar 
Drake, B. L. Changes in North Atlantic Oscillation drove Population Migrations and the Collapse of the Western Roman Empire. Sci. Rep. 7, 1227. https://doi.org/10.1038/s41598-017-01289-z (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Enters, D., Dörfler, W. & Zolitschka, B. Historical soil erosion and land-use change during the last two millennia recorded in lake sediments of Frickenhauser See, northern Bavaria, central Germany. The Holocene 18, 243–254. https://doi.org/10.1177/0959683607086762 (2008).Article 

Google Scholar 
Haldon, J. et al. History meets palaeoscience: Consilience and collaboration in studying past societal responses to environmental change. Proc. Natl. Acad. Sci. USA 115, 3210–3218. https://doi.org/10.1073/pnas.1716912115 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yeloff, D. & van Geel, B. Abandonment of farmland and vegetation succession following the Eurasian plague pandemic of ad 1347?52. J. Biogeogr. 34, 575–582. https://doi.org/10.1111/j.1365-2699.2006.01674.x (2007).Article 

Google Scholar 
Alt, K. W. et al. Lombards on the Move—An Integrative Study of the Migration Period Cemetery at Szólád Hungary. PLoS ONE 9, e110793. https://doi.org/10.1371/journal.pone.0110793 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pohl, W. in Ethnicity as a Political Resource Conceptualizations across Disciplines, Regions, and Periods (ed Resource« University of Cologne Forum »Ethnicity as a Political) 201–208 (Transcript Verlag, 2015).Dreibrodt, S. & Wiethold, J. Lake Belau and its catchment (northern Germany): A key archive of environmental history in northern central Europe since the onset of agriculture. The Holocene 25, 296–322. https://doi.org/10.1177/0959683614558648 (2014).Article 

Google Scholar 
Dreßler, M. et al. Environmental changes and the Migration Period in northern Germany as reflected in the sediments of Lake Dudinghausen. Quatern. Res. 66, 25–37. https://doi.org/10.1016/j.yqres.2006.02.007 (2017).CAS 
Article 

Google Scholar 
Leuschner, C. & Ellenberg, H. in Ecology of Central European Forests: Vegetation Ecology of Central Europe, Volume I (eds Christoph Leuschner & Heinz Ellenberg) 31–116 (Springer International Publishing, 2017).Pędziszewska, A. et al. in The Migration Period between the Oder and the Vistula (2 vols) (eds A. Bursche, H. John, & A. Zapolska) 137–198 (Brill, 2020).Mączyńska, M. in The Migration Period between the Oder and the Vistula (2 vols) (eds A. Bursche, H. John, & A. Zapolska) 201–224 (Brill, 2020).Lamentowicz, M. et al. Reconstructing climate change and ombrotrophic bog development during the last 4000years in northern Poland using biotic proxies, stable isotopes and trait-based approach. Palaeogeogr. Palaeoclimatol. Palaeoecol. 418, 261–277. https://doi.org/10.1016/j.palaeo.2014.11.015 (2015).Article 

Google Scholar 
Makohonienko, M. in Late Glacial and Holocene history of vegetation in Poland based on isopollen maps (eds M. Ralska-Jasiewiczowa et al.) 411–416 (W. Szafer Institute of Botany, Polish Academy of Sciences, 2004).Ralska-Jasiewiczowa, M., Nalepka, D. & Goslar, T. Some problems of forest transformation at the transition to the oligocratic/ Homo sapiens phase of the Holocene interglacial in northern lowlands of central Europe. Veg. Hist. Archaeobot. 12, 233–247. https://doi.org/10.1007/s00334-003-0021-8 (2003).Article 

Google Scholar 
Moździoch, M. in The Past Societies. Polish lands from the first evidence of human presence to the Early Middle Ages Vol. 5: 500–1000 AD (eds P. Urbańczyk & M. Trzeciecki) 123–167 (The Institute of Archaeology and Ethnology, Polish Academy of Sciences, 2016).Wołoszyn, M. in The Migration Period between the Oder and the Vistula (2 vols) (eds A. Bursche, H. John, & A. Zapolska) 84–136 (Brill, 2020).Karczewski, M. Archeologia środowiska zachodniobałtyjskiego kręgu kulturowego na pojezierzach. (Bogucki Wydawnictwo Naukowe, 2011).Nowakiewicz, T. in The Past Societies. Polish lands from the first evidence of human presence to the Early Middle Ages (eds P. Urbańczyk & M. Trzeciecki) 170–217 (The Institute of Archaeology and Ethnology, Polish Academy of Sciences, 2016).Okulicz-Kozaryn, Ł. Dzieje Prusów (Wydawnictwo Monografie FNP, 1997).Okulicz, J. Osadnictwo ziem pruskich od czasów najdawniejszych do XIII wieku. Dzieje Warmii i Mazur w zarysie (Polskie Wydawnictwo Naukowe, 1981).Ralska-Jasiewiczowa, M. Correlation between the Holocene history of the Carpinus betulus and prehistoric settlement in North Poland. Acta Soc. Bot. Pol. 33, 461–468 (1964).Article 

Google Scholar 
Noryśkiewicz, A. M. Historia roślinności i osadnictwa ziemi chełmińskiej w późnym holocenie. Studium palinologiczne. (Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, 2013).Ralska-Jasiewiczowa, M. L., M. et al. Late Glacial and Holocene history of vegetation in Poland based on isopollen maps. (W. Szafer Institute of Botany, Polish Academy of Sciences, 2004).Brown, A., Poska, A. & Pluskowski, A. The environmental impact of cultural change: Palynological and quantitative land cover reconstructions for the last two millennia in northern Poland. Quatern. Int. 522, 38–54. https://doi.org/10.1016/j.quaint.2019.05.014 (2019).Article 

Google Scholar 
Wacnik, A., Goslar, T. & Czernik, J. Vegetation changes caused by agricultural societies in the Great Mazurian Lake District. Acta Palaeobotanica 52, 59–104 (2012).
Google Scholar 
Pędziszewska, A. et al. Holocene environmental changes reflected by pollen, diatoms, and geochemistry of annually laminated sediments of Lake Suminko in the Kashubian Lake District (N Poland). Rev. Palaeobot. Palynol. 216, 55–75. https://doi.org/10.1016/j.revpalbo.2015.01.008 (2015).Article 

Google Scholar 
Słowiński, M. et al. The role of Medieval road operation on cultural landscape transformation. Sci. Rep. 11, 20876. https://doi.org/10.1038/s41598-021-00090-3 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gałka, M., Tobolski, K., Zawisza, E. & Goslar, T. Postglacial history of vegetation, human activity and lake-level changes at Jezioro Linówek in northeast Poland, based on multi-proxy data. Veg. Hist. Archaeobotany 23, 123–152. https://doi.org/10.1007/s00334-013-0401-7 (2013).Article 

Google Scholar 
Marks, L. Timing of the Late Vistulian (Weichselian) glacial phases in Poland. Quatern. Sci. Rev. 44, 81–88. https://doi.org/10.1016/j.quascirev.2010.08.008 (2012).Article 

Google Scholar 
Woś, A. Klimat Polski. (Wydawnictwo Naukowe PWN, 1999).Matuszkiewicz, W. et al. Potential natural vegetation of Poland. General map 1:300 000. (IGiPZ PAN, 1995).Zając, A. & Zając, M. Atlas rozmieszczenia roślin naczyniowych w Polsce. Distribution Atlas of Vascular Plants in Poland. (Nakładem Pracowni Chorologii Komputerowej Instytutu Botaniki UJ, 2001).Matuszkiewicz, J. M. & Solon, J. Przestrzenne zróżnicowanie i cechy charakterystyczne krajobrazów Polski w ujęciu geobotanicznym. Problemy Ekologii Krajobrazu XL, 85–101 (2015).Broda, J. Historia leśnictwa w Polsce. (Wydaw. Akademii Rolniczej im. Augusta Cieszkowskiego, 2000).Rozkrut, D. et al. Statistical Yearbook of Forestry. (Główny Urząd Statystyczny, 2020).Lamentowicz, M. et al. Climate and human induced hydrological change since AD 800 in an ombrotrophic mire in Pomerania (N Poland) tracked by testate amoebae, macro-fossils, pollen and tree rings of pine. Boreas 38, 214–229. https://doi.org/10.1111/j.1502-3885.2008.00047.x (2009).Article 

Google Scholar 
Lamentowicz, M. et al. How Joannites’ economy eradicated primeval forest and created anthroecosystems in medieval Central Europe. Sci. Rep. 10, 18775. https://doi.org/10.1038/s41598-020-75692-4 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Czerwiński, S. et al. Environmental implications of past socioeconomic events in Greater Poland during the last 1200 years. Synthesis of paleoecological and historical data. Quatern. Sci. Rev. 259. https://doi.org/10.1016/j.quascirev.2021.106902 (2021).Ralska-Jasiewiczowa, M., van Geel, B. & Demsk, D. in Lake Gościąż, central Poland: a monographic study. Part 1 (eds M. Ralska-Jasiewiczowa, T. Goslar, T. Madeyska, & L. Starkel) (W. Szafer Institute of Botany, Polish Academy of Sciences, 1998).Lamentowicz, M. et al. Multiproxy study of anthropogenic and climatic changes in the last two millennia from a small mire in central Poland. Hydrobiologia 631, 213–230. https://doi.org/10.1007/s10750-009-9812-y (2009).Article 

Google Scholar 
Pędziszewska, A. & Latałowa, M. Stand-scale reconstruction of late Holocene forest succession on the Gdańsk Upland (N. Poland) based on integrated palynological and macrofossil data from paired sites. Veget. History Archaeobot. 25, 239–254. https://doi.org/10.1007/s00334-015-0546-7 (2016).Lamentowicz, M., Gałka, M., Pawlyta, J., Lamentowicz, Ł. G., Tomasz & Miotk-Szpiganowicz, G. Climate change and human impact in the southern Baltic during the last millennium reconstructed from an ombrotrophic bog archive. Studia Quaternaria 28, 3–16 (2011).Cywa, K. Trees and shrubs used in medieval Poland for making everyday objects. Veg. Hist. Archaeobotany 27, 111–136. https://doi.org/10.1007/s00334-017-0644-9 (2018).Article 

Google Scholar 
Dzieduszycki, W. Wykorzystywanie surowca drzewnego we wczesnośredniowiecznej i średniowiecznej Kruszwicy. Kwartalnik Historii Kultury Materialnej, 35–54 (1976).Kara, M. & Przybył, M. Wczesnośredniowieczne grodzisko wklęsłe w Bninie koło Poznania w świetle dotychczasowych ustaleń dendrochronologicznych. Folia Prahistorica Posnaniensia 10, 255–268 (2003).Article 

Google Scholar 
Gałka, M. et al. Unveiling exceptional Baltic bog ecohydrology, autogenic succession and climate change during the last 2000 years in CE Europe using replicate cores, multi-proxy data and functional traits of testate amoebae. Quatern. Sci. Rev. 156, 90–106. https://doi.org/10.1016/j.quascirev.2016.11.034 (2017).Article 

Google Scholar 
Kinder, M. et al. Holocene history of human impacts inferred from annually laminated sediments in Lake Szurpiły, northeast Poland. J. Paleolimnol. 61, 419–435. https://doi.org/10.1007/s10933-019-00068-2 (2019).Article 

Google Scholar 
Marcisz, K., Kołaczek, P., Gałka, M., Diaconu, A.-C. & Lamentowicz, M. Exceptional hydrological stability of a Sphagnum-dominated peatland over the late Holocene. Quatern. Sci. Rev. 231, 106180. https://doi.org/10.1016/j.quascirev.2020.106180 (2020).Article 

Google Scholar 
Wacnik, A. et al. Determining the responses of vegetation to natural processes and human impacts in north-eastern Poland during the last millennium: Combined pollen, geochemical and historical data. Veg. Hist. Archaeobotany 25, 479–498. https://doi.org/10.1007/s00334-016-0565-z (2016).Article 

Google Scholar 
Szal, M., Kupryjanowicz, M., Tylmann, W. & Piotrowska, N. Was it ‘terra desolata’? Conquering and colonizing the medieval Prussian wilderness in the context of climate change. The Holocene 27, 465–480. https://doi.org/10.1177/0959683616660167 (2016).Article 

Google Scholar 
Szal, M., Kupryjanowicz, M., Wyczółkowski, M. & Tylmann, W. The Iron Age in the Mrągowo Lake District, Masuria, NE Poland: the Salęt settlement microregion as an example of long-lasting human impact on vegetation. Veg. Hist. Archaeobotany 23, 419–437. https://doi.org/10.1007/s00334-014-0465-z (2014).Article 

Google Scholar 
Brown, A. et al. The ecological impact of conquest and colonization on a medieval frontier landscape: Combined Palynological and geochemical analysis of lake sediments from Radzyń Chełminski Northern Poland. Geoarchaeology 30, 511–527. https://doi.org/10.1002/gea.21525 (2015).Article 

Google Scholar 
Williams, J. W. et al. The Neotoma Paleoecology Database, a multiproxy, international, community-curated data resource. Quatern. Res. 89, 156–177. https://doi.org/10.1017/qua.2017.105 (2018).Article 

Google Scholar 
Marcisz, K. et al. Long-term hydrological dynamics and fire history over the last 2000 years in CE Europe reconstructed from a high-resolution peat archive. Quatern. Sci. Rev. 112, 138–152. https://doi.org/10.1016/j.quascirev.2015.01.019 (2015).Article 

Google Scholar 
Milecka, K., Gałka, M. & Lamentowicz, M. Regionalna i lokalna sukcesja roślinności w Dolinie Stążki na podstawie analizy pyłkowej. Stud. Limnol. Telmatol. 6, 61–69 (2012).
Google Scholar 
Lamentowicz, M. et al. A 1300-year multi-proxy, high-resolution record from a rich fen in northern Poland: reconstructing hydrology, land use and climate change. J. Quat. Sci. 28, 582–594. https://doi.org/10.1002/jqs.2650 (2013).Article 

Google Scholar 
Lamentowicz, M. et al. Always on the tipping point—A search for signals of past societies and related peatland ecosystem critical transitions during the last 6500 years in N Poland. Quat. Sci. Rev. 225. https://doi.org/10.1016/j.quascirev.2019.105954 (2019).Wacnik, A., Kupryjanowicz, M., Mueller-Bieniek, A., Karczewski, M. & Cywa, K. The environmental and cultural contexts of the late Iron Age and medieval settlement in the Mazurian Lake District, NE Poland: combined palaeobotanical and archaeological data. Veg. Hist. Archaeobotany 23, 439–459. https://doi.org/10.1007/s00334-014-0458-y (2014).Article 

Google Scholar 
Gałka, M. et al. Palaeoenvironmental changes in Central Europe (NE Poland) during the last 6200 years reconstructed from a high-resolution multi-proxy peat archive. The Holocene 25, 421–434. https://doi.org/10.1177/0959683614561887 (2014).Article 

Google Scholar 
Latałowa, M., Zimny, M., Jędrzejewska, B. & Samojlik, T. in Europe’s Changing Woods and Forests: From Wildwood to Managed Landscapes (eds K.J. Kirby & C. Watkins) Ch. 17, 243–263 (CAB International, 2015).Słowiński, M. et al. Paleoecological and historical data as an important tool in ecosystem management. J. Environ. Manage. 236, 755–768. https://doi.org/10.1016/j.jenvman.2019.02.002 (2019).Article 
PubMed 

Google Scholar 
Żarczyński, M., Wacnik, A. & Tylmann, W. Tracing lake mixing and oxygenation regime using the Fe/Mn ratio in varved sediments: 2000 year-long record of human-induced changes from Lake Żabińskie (NE Poland). Sci. Total Environ. 657, 585–596. https://doi.org/10.1016/j.scitotenv.2018.12.078 (2019).CAS 
Article 
PubMed 

Google Scholar 
Wirth, C., Messier, C., Bergeron, Y., Frank, D. & Fankhänel, A. Old-Growth Forest Definitions: a Pragmatic View. 11–33 (Springer Berlin Heidelberg, 2009).Kołaczek, P. M., K. et al. in 20th Congress of the International Union for Quaternary Research (INQUA) (Dublin, Ireland, 2019).Szmoniewski, B. S. in The Past Societies. Polish lands from the first evidence of human presence to the Early Middle Ages. Vol. 5: 500–1000 AD (eds P. Urbańczyk & M. Trzeciecki) 21–74 (The Institute of Archaeology and Ethnology, Polish Academy of Sciences, 2016).Moździoch, M., Chudziak, W. & Poleski, J. Atlas grodzisk wczesnośredniowiecznych z obszaru Polski, 2015).Trzeciecki, M. in The Past Societies. Polish lands from the first evidence of human presence to the Early Middle Ages. Vol. 5: 500–1000 AD (eds P. Urbańczyk & M. Trzeciecki) 277–341 (The Institute of Archaeology and Ethnology, Polish Academy of Sciences, 2016).Faliński, J. B. & Pawlaczyk, P. in Grab zwyczajny – Carpinus betulus L. Nasze drzewa leśne, monografie popularnonaukowe Vol. 9 (ed W. Bugała) 157–264 (Polska Akademia Nauk, Instytut Dendrologii, „Sorus”,, 1993).Sikkema, R., Caudullo, G. & de Rigo, D. in European Atlas of Forest Tree Species (eds J. San-Miguel-Ayanz et al.) (Publ. Off. EU, 2016).Hensel, W. Słowiańszczyzna Wczesnośredniowieczna. Zarys kultury materialnej. (Państwowe Wydawnictwo Naukowe, 1987).Jørgensen, D. Pigs and Pollards: Medieval insights for UK wood pasture restoration. Sustainability 5, 387–399. https://doi.org/10.3390/su5020387 (2013).Article 

Google Scholar 
Plieninger, T. et al. Wood-pastures of Europe: Geographic coverage, social–ecological values, conservation management, and policy implications. Biol. Cons. 190, 70–79. https://doi.org/10.1016/j.biocon.2015.05.014 (2015).Article 

Google Scholar 
Watkins, A. Cattle grazing in the forest of arden in the later middle ages. Agric. Hist. Rev. 37, 12–25 (1989).
Google Scholar 
Ładowski, S. Dykcyonarz służący do poznania historyi naturalney y rożnych osobliwszych starożytności, ktore ciekawi w gabinetach znayduią Vol. 2 (1783).Tobolski, K. in Wstęp do paleoekologii Lednickiego Parku Krajobrazowego (ed K. Tobolski) (Wydawnictwo Naukowe Uniwersytetu im. Adama Mickiewicza w Poznaniu, 1991).Litt, T. & Tobolski, K. in Wstęp do paleoekologii Lednickiego Parku Krajobrazowego (ed K. Tobolski) (1991).Makohonienko, M. Przyrodnicza historia Gniezna. (Homini, 2000).Makohonienko, M. in Wstęp do paleoekologii Lednickiego Parku Krajobrazowego (ed K. Tobolski) (Wydawnictwo Naukowe Uniwersytetu im. Adama Mickiewicza w Poznaniu, 1991).Filbrandt, A. in Wstęp do paleoekologii Lednickiego Parku Krajobrazowego (ed K. Tobolski) (Wydawnictwo Naukowe Uniwersytetu im. Adama Mickiewicza w Poznaniu, 1991).Pidek, I. A. Carpinus betulus pollen accumulation rates in Roztocze (SE Poland) in relation to presence of Carpinus in Ferdynandovian pollen diagrams. Ecol. Quest. 26, 95–101 (2017).
Google Scholar 
Wiśniewski, J. in Studia I Materiały Do Dziejów Suwalszczyzny (ed J. Antoniewicz) 51–138 (Prace Białostockiego Towarzystwa Naukowego Nr 4, Białostockie Towarzystwo Naukowe,, 1965).Biskup, M. et al. Państwo zakonu krzyżackiego w Prusach. Władza i społeczeństwo. (Państwowe Wydawnictwo Naukowe PWN, 2008).Pluskowski, A. The archaeology of the Prussian Crusade: Holy War and colonisation. (2012).Marcisz, K. et al. Seasonal changes in Sphagnum peatland testate amoeba communities along a hydrological gradient. Eur. J. Protistol. 50, 445–455. https://doi.org/10.1016/j.ejop.2014.07.001 (2014).Article 
PubMed 

Google Scholar 
Marcisz, K. et al. Fire activity and hydrological dynamics in the past 5700 years reconstructed from Sphagnum peatlands along the oceanic–continental climatic gradient in northern Poland. Quatern. Sci. Rev. 177, 145–157. https://doi.org/10.1016/j.quascirev.2017.10.018 (2017).Article 

Google Scholar 
Boratyńska, K. in Biology and Ecology of Norway Spruce (eds Mark G. Tjoelker, Adam Boratyński, & Władysław Bugała) 23–36 (Springer Netherlands, 2007).Jaroszewicz, B. et al. Białowieża forest—a relic of the high naturalness of European forests. Forests 10, 849. https://doi.org/10.3390/f10100849 (2019).Article 

Google Scholar 
Zimny, M., Latałowa, M. & Pędziszewska, A. The Late-Holocene history of forests in the Strict Reserve of Białowieża National Park. 29–59 (Białowieski Park Narodowy, 2017).Blaauw, M., Christen, J. A., Bennett, K. D. & Reimer, P. J. Double the dates and go for Bayes—Impacts of model choice, dating density and quality on chronologies. Quatern. Sci. Rev. 188, 58–66. https://doi.org/10.1016/j.quascirev.2018.03.032 (2018).Article 

Google Scholar 
Lisitsyna, O. V., Giesecke, T. & Hicks, S. Exploring pollen percentage threshold values as an indication for the regional presence of major European trees. Rev. Palaeobot. Palynol. 166, 311–324. https://doi.org/10.1016/j.revpalbo.2011.06.004 (2011).Article 

Google Scholar 
Huntley, B. & Birks, H. J. B. An Atlas of past and present pollen maps for Europe: 0–13000 years ago. (Cambridge University Press, 1983). More

Permits and ethics approvalAnimal collections and all experimental procedures were conducted with ethical approval from the University of Exeter (2013/247), James Cook University (A2361) and Lizard Island Research Station, permission from the Great Barrier Reef Marine Park Authority (GBRMPA) (G17-39752.1) and under licence from the Australian Government Department of Fisheries (170251).Study speciesThe spiny chromis (Acanthochromis polyacanthus) is a damselfish that exhibits bi-parental care of eggs and juveniles at nests within shallow reef habitat in the tropical Western Pacific39 (Fig. 4). Spiny chromis are planktivores on the Great Barrier Reef, importing nutrients from the plankton to the reef40. As such, their preferred habitat is the reef edge (within ~7 m). Most pairs raise one clutch per season—in our study, second clutches occurred in only 7% of the population—so we tracked first clutches from adult pairs. Adults enhance their reproductive success by fanning eggs to oxygenate them, and chasing away potential predators from eggs and juveniles16,41. Adults also allow their offspring to eat some of their body mucus in a behaviour known as ‘glancing’ that has potential nutritional and/or immunological benefits42.Fig. 4: Spiny chromis (Acanthochromis polyacanthus) nest at the edge of coral reef habitat.Parents, juveniles and a predator (peacock grouper, Cephalopholis argus) can be seen in this photo. Photo credit: S Nedelec.Full size imageField studyWe conducted the field study at Lizard Island Research Station (LIRS) (14° 40′ S, 145° 28′ E), Great Barrier Reef, Australia over an entire spiny chromis breeding season (90 days from 23 October 2017 to 20 January 2018).Sites and nestsWe selected six coral reef edge sites (113–218 m in length) within the lagoon on the south of Lizard Island (Fig. 5). Water temperature ranged from ~26 °C at the start to ~29 °C at the end of the season. The reefs were composed mainly of a mixture of live and dead coral. Prevailing currents were wind-driven from south to north. Three of the sites were exposed to ‘busy boating’ while boating was limited at the other three. Treatments were allocated to sites partly randomly and partly allowing for ease and safety of motorboat access. Nest positions within sites did not differ between treatments in: depth of bottom next to reef (1–5 m at mid-tide with a tidal range of ~2 m, N = 67 measurements at a range of tidal heights, mean ± SE depth = 2.3 ± 0.1 m; LMM, sound treatment: Χ21 = 0.82, p = 0.365, random effect of site: variance = 0.14, standard deviation=0.38); nest height above the sand (range = 0–4 m, mean ± SE = 0.6 ± 0.1 m; sound treatment: Χ21 = 0.52, p = 0.470, site: variance=0.33, standard deviation = 0.57); or distance from the edge of the reef (range = 0–6.6 m, mean ± SE = 1.2 ± 0.2 m; t-test: t33,32 = 1.61, p = 0.112; a linear model did not fit the data). A small number of broods (mean ± SE per site = 4.7 ± 1.0) were already present at each site at the start of the season; spiny chromis occasionally breed outside the main breeding season. These were marked and ignored for the purpose of the experiment. The mean ± SD number of nesting pairs identified at each site at the start of the season was 9.5 ± 2.0 for limited-boating sites and 10.7 ± 3.2 for busy-boating sites. Nesting pairs did not always form clear, stable pairs with obvious territories and so pairs without broods were not tracked through the experiment. The mean ± SD total number of adults at each site at the start of the experiment was 185 ± 92 for limited-boating sites and 119 ± 20 for busy-boating sites.Fig. 5: Map of the experimental sites.Red sites were ‘busy-boating’ areas while blue sites were ‘limited-boating’ areas.Full size imageMotorboat traffic exposure and protectionThere is a navigable channel through the lagoon where the experiment was conducted. Fishing boats, tourist boats and research station boats pass through the channel, but the main source of traffic is research station boats. We chose six sites along the navigable route and randomly allocated these to treatments. Following random allocation, two sites were switched for safety reasons for motorboat drivers (Fig. 5). We experimentally elevated motorboat noise at three of the six sites (busy-boating treatment) to mimic typical traffic around a port, harbour or regularly visited reef. At these sites, we drove eight different 5 m aluminium motorboats with 40 hp Suzuki four-stroke outboard engines repeatedly along the length of the site within 10–30 m of the edge of the reef. Busy-boating sites received an average of 180 motorboat passes each day during 3–6 ‘exposure periods’ lasting 15–20 min each; this totalled 1.25–1.5 h per day of traffic noise at each busy-boating site. The other three sites were protected from motorboat traffic (limited-boating treatment), by marking these reefs on the research station map as areas to avoid by at least 100 m and monitoring activity in the lagoon daily. When experimenters needed to access protected sites, speed was reduced to that where no wake was created (roughly ¼ throttle) within 100 m and boats were anchored 20 m from the reef. See Supplementary Information for further details of motorboat traffic exposure and protection.Acoustic recordings and analysisWe made acoustic recordings of both pressure and particle motion at three locations within each site using an accelerometer with integrated hydrophone (M20-040 manufactured and calibrated by Geospectrum Techologies Inc. Dartmouth, Canada; sensitivity follows a curve from 0 to 5000 Hz) and a digital recorder (Zoom F4, Zoom Corporation, Tokyo, Japan; calibrated using pure sine waves measured with an oscilloscope). See Fig. 6 and Supplementary Information for further details of acoustic recordings, analysis and results.Fig. 6: Pressure power spectral density level (Lp,f (re 1 µPa2 Hz−1)) and particle acceleration power spectral density level (La,f (re 1 (µm s−2)2 Hz−1)) plots showing the mean (solid line) with 5% and 95% exceedance levels (coloured band around solid line) for busy-boating and limited-boating or no-boating treatments.A Lp,f in the wild study (average from three nests per site, ‘busy boating’ includes three passes of a motorboat at 10–250 m per recording location, recordings of limited boating were three minutes in duration per nest). B La,f in the wild study (same recording design as for pressure). C Lp,f in the parental tanks in the captive study (27 locations in a 3 × 3 × 3 grid within the tank, 1-min sample of motorboat playback and ambient reef sound playback for each case). D La,f in the parental tanks in the captive study (same recording design as for pressure), E Lp,f in the juvenile tanks in the captive study from the centre of the rearing tank (these tanks were too small to accommodate the particle motion sensor).Full size imageBreeding and reproductive successEach site was checked by snorkellers every other day to monitor breeding by spiny chromis pairs. Nests with new hatchlings were marked with flagging tape and continued to be checked every other day throughout the season to monitor reproductive success. The day in the season that broods hatched was used to test for an effect of motorboat exposure on the timing of breeding using a linear mixed-effects model (fitted in R) with site as a random effect. The proportion of nests retaining offspring at the end of the season in the two treatments was compared using a Chi-squared test.Brood sizeWe counted the number of offspring in broods within four days after hatching at a subset of 59 nests; 32 in the three busy-boating sites (N = 9, 11, 12) and 27 in the three limited-boating sites (N = 5, 10, 12). Average clutch size at hatching in the wild was 126 ± 16 (mean ± SE). Some of these nests were part of the predator presence observations (details below), some were part of the size monitoring (details below) and some were independent. We counted the number of offspring in three photos and used the highest number for analyses. We tested for an effect of motorboat treatment on brood size at hatching using a Welch’s t-test.Predator presence around nestsWe determined baseline predatory threat (counts of heterospecific piscivores, potential predators of juveniles) using video camera (GoPro 5) deployments. Thirty-three nests (not studied for size) were videoed once or twice between 1 and 11 days post-hatching; 18 in the three busy-boating sites (N = 7, 6, 5) and 15 in the three limited-boating sites (N = 6, 5, 4). A total of 55 videos were analysed. A camera stand was placed at each nest on the first or second day post-hatching and remained in place 2–3 m from the nest. For each survey, after GoPro cameras were attached to camera stands, several minutes settling time was allowed (mean ± sd: 827 ± 35 s), followed by a 30-s recording of predator presence in the absence of any motorboats. All nests that were videoed were at least 10 m away from one another (parents spend most of their time within 2 m of the nest). Videos were randomly named and analysed by KEC without sound (to remain ‘blind’ to treatment) using BORIS 7.6.143. Spiny chromis offspring are small and vulnerable to any piscivore on the reef and parents defend their offspring by chasing potential predators. The number of heterospecific piscivores at each nest was surveyed (conspecifics are known to cannibalise offspring, but this is very rare16). A negative binomial regression parameterised such that the variance is a quadratic function of the mean was fitted using glmmTMB in R with piscivore counts as the response variable, motorboat treatment and days into the season as potentially interacting fixed factors, and nest and site as random effects.Juvenile survivalIt was not possible to observe the eggs as they were laid in caves, so we studied juveniles from when they could be observed above the substrate (shortly after hatching – this species completes the larval stage inside the egg and hatches at the juvenile stage16). We also counted the number of surviving offspring at the subset of 59 nests every 4–8 days. When all juveniles from a nest could be captured in a frame, we used three photos per time point and used the highest number. As juveniles aged, they used more space and could not be captured in a single photo; then, they were counted by snorkellers with experience in fish surveys (SLN and IKD). The reliability of snorkellers’ counts was tested against one another and did not differ when there were 20 juveniles based on counts of 43 nests. Usually, however, when there were >20 juveniles at a nest, they were at an earlier developmental stage and counts could be taken from photos. Survival of juveniles was recorded as number of days from hatching until they were no longer seen at the nest. Where all offspring from a nest were apparently lost to predation (mean survival time = 21 days), the nest continued to be monitored every other day for the remainder of the season to ensure offspring had not temporarily disappeared and to check for second clutches. A Cox proportional-hazards survival model was fitted in R with motorboat treatment and initial hatching count as fixed effects, and nest and site as random effects. We discounted three nests where counts increased due to assumed experimenter error or immigration. The package Coxme was used in R to test for the interaction between treatment and start count (hatch day was not included in the model as there was no indication of an effect of treatment or site on hatch day), with nest and site as random effects. The package coxph was used to create Fig. 1B, which does not account for the random effects, but is used for illustrative purposes.Juvenile sizeWe monitored juvenile size at a subset of 22 nests; 11 in the three busy-boating sites (N = 3, 4, 4) and 11 in the three limited-boating sites (N = 3, 4, 4). Up to 10 juveniles (depending on catch success) were caught by snorkellers or SCUBA divers using hand nets from each nest each week. A total of 275 juveniles between 1 and 53 days post-hatching were sampled. Juveniles were transported to the field station in bags of fresh seawater. We measured standard length either under the microscope at 10× magnification or with a Vernier caliper, depending on fish size. All nests within a site were sampled on the same day and each site was visited for juvenile size sampling each week. Data were log-transformed and analysed using a linear mixed-effects model (fitted in R), with age and motorboat treatment as fixed effects, and nest and site as random effects.Laboratory studyWe conducted the laboratory study in the Marine and Aquaculture Research Facilities Unit (MARFU) at James Cook University, Townsville, Australia from March to July 2018. Spiny chromis adults were caught with fine monofilament barrier nets and hand nets from the section of reef on the lagoon side of Palfrey Island within the lagoon around Lizard Island in the northern Great Barrier Reef (14° 41′ S, 145° 27′ E) during November 2016. All adults would have experienced equivalent prior noise exposure. The mean ± SE standard length of adults was 10.7 ± 0.1 cm. Spiny chromis were randomly allocated to treatments and were housed in 30 male–female pairs, and maintained at a mean ± SE temperature of 27.7 ± 0.1 °C in the presence of either a busy-boating treatment (playback of four of the five recorded motorboats in a pattern matching exposures in the field) or a no-boating treatment (playback of ambient reef sound). We kept most of each brood with the parents to measure survival (cannibalism can rarely occur in this species under stress16) and isolated 50 individuals per brood as a single group in a separate tank (where parents could not compete with offspring for food) with the same playback treatment to measure size. See Supplementary Information for further details of tank setup and conditions, acoustic exposure regime, playback construction, acoustic recording analysis and results for the tanks.BreedingWe checked all adult tanks daily after lights were switched on, but before playbacks began, for the presence of a newly laid clutch. The number of days since the start of the treatment that clutches were laid was used to test for an effect of motorboat noise exposure on the timing of breeding using a two-sample Welch’s t-test.Clutch size and brood sizeWe photographed newly laid clutches and estimated clutch size by counting the number of eggs in a square on an overlaid grid and counting the number of grid squares containing eggs. All adult tanks were checked daily after lights were switched on, but before playbacks began, for the presence of a newly hatched brood. Clutch size and brood size were compared between treatments using two-sample Welch’s t-tests.Egg characteristics and embryonic developmentWe monitored clutch-level and individual-level egg and embryo characteristics at days 1 and 10 during the egg phase of the first clutch laid by each breeding pair. Measures taken were: (1) egg area, (2) yolk sac area, (3) dorsal spine length (day 10 embryos only), and (4) dry weight (at 10 days). Egg area, yolk sac area and spine length were obtained by measuring 10 randomly sampled individuals per clutch under a light microscope (Olympus SZXY). For dry weights, fish were dried in an oven for >24 h at 60 °C and weighed on a Mettler microbalance with ±0.001 mg accuracy. The number of days between laying and hatching was used as the embryonic developmental time. Linear mixed-effects models (fitted in R) were used, with clutch as a random effect and motorboat treatment as a fixed effect.Parental care of embryosWe filmed parental activity (distance moved by both parents) and time spent fanning eggs at day 10 of the egg phase during periods of playback. Two cameras were used: a Logitech HD Webcam C615 camera positioned 45 cm above the tank looking down with the entire tank in the field of view (‘top camera’), and a GoPro HERO 5 positioned inside the tank in front and looking into the nest (‘side camera’). Following a 30-minute settling time, minimising the disturbance to the fish, baseline behaviour was observed for five minutes. Then, in ‘busy-boating’ tanks, motorboat noise was played for a further five minutes, while in ‘no-boating’ tanks, a different ambient track was played for five minutes.Two key nest-caring parental behaviours were identified for analysis:

(1)

Activity – the distance travelled by parents. Average distance travelled within each breeding pair was calculated from the total distance travelled by both the male and female. Activity was observed from the top camera. Distance was calibrated by measuring a known distance on the bottom of the tank, present in all videos. The distance travelled was calculated by marking the position of the fish every second using the manual tracking feature of ImageJ version 1.52d (https://imagej.nih.gov/ij/download.html).

(2)

Fanning – the amount of time parents spent fanning the clutch of eggs. Fanning was observed from the side camera and analysed using Solomon Coder software (https://solomoncoder.com/download.php).

T-tests were used to test for effects of treatment on parental care behaviour.Juvenile survivalWe counted the number of juveniles that survived with their parents at day 21 post-hatching (maximum count from three photos for each tank). Survival was measured at day 21 because that was the mean survival time in the field; also, by day 42 post-hatching, most parents had produced a second clutch which confounded observations of survival. The fish removed at hatching were included in the final count by modelling their survival as equal to that of the rest of the brood. Survival was converted to a percentage from the number of eggs laid in the clutch. This measure of survival is conservative compared with that expected in the field because the only potential predators of the juveniles in the tanks were their parents. Percentage survival rates were non-normally distributed and so were compared between treatments using a Wilcoxon signed-ranks test.Juvenile sizeWe measured the standard lengths (from photos using ImageJ) and dry weights of ten juveniles per clutch at day 21 post-hatching and of eight juveniles per clutch at day 42 post-hatching, following humane sacrifice; fish were dried in an oven for >24 h at 60 °C and weighed on a Mettler microbalance with 0.001 mg accuracy. We measured length and weight from juveniles that were isolated from the parents to avoid the possibility that the parents could compete with the juveniles for food. Dry weights at day 21 and 42 were compared between treatments using an LMM with clutch as a random effect.General statistical approachesFor measures of parental care, or at the level of clutch, t-tests or Wilcoxon signed ranks tests were used. Where we measured several individuals from within multiple sites, clutches or broods, LMMs or GLMMs were used to control for the random effects of site, clutch or brood, provided models fit the data satisfactorily. Plots of residuals vs fitted values were examined to check model fit and where models did not fit the data, standard tests such as t-test/Wilcoxon signed ranks were used in their place. Any effects among nests (such as slight variations in water flow) were therefore controlled for by the LMM or GLMM statistical models. The variance attributed to, and standard deviation of the variance for, random effects are presented as part of the full output from models. We used the same approach for model selection as in13. To establish the best-fitting model, terms were eliminated one by one from a maximal model. Simplified models were compared with more complex ones using maximum likelihood ratio tests that employ chi-square statistics to establish whether a simpler model performed significantly worse at explaining the data than a more complex model. If the simpler model was not significantly worse when a term was removed, the simpler model was deemed better and thus the removed term was dropped. If the simpler model was significantly worse, the term was maintained in the model44. The degrees of freedom from maximum likelihood tests presented in the Results of the main paper are the difference between the degrees of freedom of the simpler and the more complex models. All potential interactions of fixed effects were examined and are only presented where their exclusion from the model made the model significantly worse at explaining the data at the significance level p  More

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

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

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

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

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

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

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

Czech: (Pavouk OR Štír OR pavoukovec) AND prodej

Polish: (Pająk OR Skorpion OR pajęczak) AND sprzedaż

Russian: Продажа пауков OR скорпионов

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

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

(i)

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

(ii)

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

(iii)

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

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