Ecology

Darwin, C. On the origin of species by means of natural selection, or, The preservation of favoured races in the struggle for life. (1859).Wallace, A. R. The Malay Archipelago: The Land of the Orang-utan and the Bird of Paradise; a Narrative of Travel, with Studies of Man and Nature (Courier Corporation, 1962).Mayr, E. Systematics and the Origin of Species from the Viewpoint of a Zoologist (Columbia Uni. Press, 1942).Emerson, B. C. Speciation on islands: what are we learning? Biol. J. Linn. Soc. Lond. 95, 47–52 (2008).
Google Scholar 
Lomolino, M. V., Riddle, B. R., Whittaker, R. J., Brown, J. H. & Lomolino, M. V. Biogeography (Sunderland, Mass: Sinauer Associates, 2017).Baeckens, S. & Van Damme, R. The island syndrome. Curr. Biol. 30, R338–R339 (2020).CAS 

Google Scholar 
Burns, K. C. Evolution in Isolation: The Search for an Island Syndrome in Plants (Cambridge University Press, 2019).Blaschke, J. D. & Sanders, R. W. Preliminary insights into the phylogeny and speciation of scalesia (asteraceae), galápagos islands. J. Bot. Res. Inst. Tex. 3, 177–191 (2009).
Google Scholar 
Fernández-Mazuecos, M. et al. The radiation of Darwin’s giant daisies in the Galápagos Islands. Curr. Biol. 30, 4989–4998.e7 (2020).
Google Scholar 
Crawford, D. J. et al. Genetic diversity in Asteraceae endemic to oceanic islands: Baker’s Law and polyploidy. Syst. Evol. Biogeogr. Compos 139, 151 (2009).
Google Scholar 
Eliasson, U. Studies in Galápagos plants. XIV. The genus Scalesia Arn. Opera Bot. 36, 1–117 (1974).
Google Scholar 
Itow, S. Phytogeography and ecology of Scalesia (compositae) endemic to the Galapagos islands! Pac. Sci. 49, 17–30 (1995).
Google Scholar 
Stöcklin, J. Darwin and the plants of the Galápagos-Islands. Bauhinia 21, 33–48 (2009).
Google Scholar 
Ono, M. Chromosome number of Scalesia (Compositae), an endemic genus of the Galapagos Islands. J. Jpn. Bot. 42, 353–360 (1967).
Google Scholar 
Eliasson, U. Studies in Galapagos plants. XIV. The genus Scalesia Arn. Opera Bot. 36, 1–117 (1974).
Google Scholar 
Meudt, H. M. et al. Polyploidy on islands: its emergence and importance for diversification. Front. Plant Sci. 12, 637214 (2021).PubMed Central 

Google Scholar 
Spring, O., Heil, N. & Vogler, B. Sesquiterpene lactones and flavanones in Scalesia species. Phytochemistry 46, 1369–1373 (1997).CAS 

Google Scholar 
Schilling, E. E., Panero, J. L. & Eliasson, U. H. Evidence from chloroplast DNA restriction site analysis on the relationships of Scalesia (Asteraceae: Heliantheae). Am. J. Bot. 81, 248–254 (1994).
Google Scholar 
Peona, V., Weissensteiner, M. H. & Suh, A. How complete are ‘complete’ genome assemblies?-An avian perspective. Mol. Ecol. Resour. 18, 1188–1195 (2018).CAS 

Google Scholar 
Badouin, H. et al. The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution. Nature 546, 148–152 (2017).ADS 
CAS 

Google Scholar 
Reyes-Chin-Wo, S. et al. Genome assembly with in vitro proximity ligation data and whole-genome triplication in lettuce. Nat. Commun. 8, 14953 (2017).ADS 
CAS 
PubMed Central 

Google Scholar 
Bellinger, M. R., Datlof, E., Selph, K. E., Gallaher, T. J. & Knope, M. L. A genome for Bidens hawaiensis: a member of a hexaploid Hawaiian plant adaptive radiation. J. Hered. https://doi.org/10.1093/jhered/esab077 (2022).Edger, P. P., McKain, M. R., Bird, K. A. & VanBuren, R. Subgenome assignment in allopolyploids: challenges and future directions. Curr. Opin. Plant Biol. 42, 76–80 (2018).CAS 

Google Scholar 
Session, A. M. et al. Genome evolution in the allotetraploid frog Xenopus laevis. Nature 538, 336–343 (2016).ADS 
CAS 
PubMed Central 

Google Scholar 
Mitros, T. et al. Genome biology of the paleotetraploid perennial biomass crop Miscanthus. Nat. Commun. 11, 5442 (2020).ADS 
CAS 
PubMed Central 

Google Scholar 
Funk, V. A. Systematics, Evolution, and Biogeography of Compositae (International Association for Plant Taxonomy, 2009).Julca, I. et al. Genomic evidence for recurrent genetic admixture during the domestication of Mediterranean olive trees (Olea europaea L.). BMC Biol 18, 148 (2020).PubMed Central 

Google Scholar 
te Beest, M. et al. The more the better? The role of polyploidy in facilitating plant invasions. Ann Bot. 109, 19–45 (2012).
Google Scholar 
Mandel, J. R. et al. A fully resolved backbone phylogeny reveals numerous dispersals and explosive diversifications throughout the history of Asteraceae. Proc. Natl Acad. Sci. USA 116, 14083–14088 (2019).CAS 
PubMed Central 

Google Scholar 
Whittaker, R. J., School of Geography Robert J Whittaker & Fernandez-Palacios, J. M. Island Biogeography: Ecology, Evolution, and Conservation (OUP Oxford, 2007).Diop, S. I. et al. A pseudomolecule-scale genome assembly of the liverwort Marchantia polymorpha. Plant J. 101, 1378–1396 (2020).CAS 

Google Scholar 
Li, F.-W. et al. Anthoceros genomes illuminate the origin of land plants and the unique biology of hornworts. Nat. Plants 6, 259–272 (2020).CAS 
PubMed Central 

Google Scholar 
Lang, D. et al. ThePhyscomitrella patenschromosome-scale assembly reveals moss genome structure and evolution. Plant J. 93, 515–533 (2018).CAS 

Google Scholar 
Bird, K. A., VanBuren, R., Puzey, J. R. & Edger, P. P. The causes and consequences of subgenome dominance in hybrids and recent polyploids. N. Phytol. 220, 87–93 (2018).
Google Scholar 
Freeling, M., Scanlon, M. J. & Fowler, J. E. Fractionation and subfunctionalization following genome duplications: mechanisms that drive gene content and their consequences. Curr. Opin. Genet. Dev. 35, 110–118 (2015).CAS 

Google Scholar 
Wolfe, K. H. Yesterday’s polyploids and the mystery of diploidization. Nat. Rev. Genet. 2, 333–341 (2001).CAS 

Google Scholar 
Bird, K. A. et al. Replaying the evolutionary tape to investigate subgenome dominance in allopolyploid Brassica napus. N. Phytol. 230, 354–371 (2021).CAS 

Google Scholar 
Alger, E. I. & Edger, P. P. One subgenome to rule them all: underlying mechanisms of subgenome dominance. Curr. Opin. Plant Biol. 54, 108–113 (2020).CAS 

Google Scholar 
Renny-Byfield, S., Gong, L., Gallagher, J. P. & Wendel, J. F. Persistence of subgenomes in paleopolyploid cotton after 60 my of evolution. Mol. Biol. Evol. 32, 1063–1071 (2015).CAS 

Google Scholar 
Douglas, G. M. et al. Hybrid origins and the earliest stages of diploidization in the highly successful recent polyploid Capsella bursa-pastoris. Proc. Natl Acad. Sci. USA 112, 2806–2811 (2015).ADS 
CAS 
PubMed Central 

Google Scholar 
Barrier, M., Baldwin, B. G., Robichaux, R. H. & Purugganan, M. D. Interspecific hybrid ancestry of a plant adaptive radiation: allopolyploidy of the Hawaiian silversword alliance (Asteraceae) inferred from floral homeotic gene duplications. Mol. Biol. Evol. 16, 1105–1113 (1999).CAS 

Google Scholar 
Catchen, J. M., Conery, J. S. & Postlethwait, J. H. Automated identification of conserved synteny after whole-genome duplication. Genome Res. 19, 1497–1505 (2009).CAS 
PubMed Central 

Google Scholar 
Őszi, E. et al. E2FB interacts with RETINOBLASTOMA RELATED and regulates cell proliferation during leaf development. Plant Physiol. 182, 518–533 (2020).
Google Scholar 
Berckmans, B. et al. Light-dependent regulation of DEL1 is determined by the antagonistic action of E2Fb and E2Fc. Plant Physiol. 157, 1440–1451 (2011).CAS 
PubMed Central 

Google Scholar 
Kojima, S. et al. Asymmetric leaves2 and Elongator, a histone acetyltransferase complex, mediate the establishment of polarity in leaves of Arabidopsis thaliana. Plant Cell Physiol. 52, 1259–1273 (2011).CAS 

Google Scholar 
Husbands, A. Y., Benkovics, A. H., Nogueira, F. T. S., Lodha, M. & Timmermans, M. C. P. The ASYMMETRIC LEAVES complex employs multiple modes of regulation to affect adaxial-abaxial patterning and leaf complexity. Plant Cell 27, 3321–3335 (2016).
Google Scholar 
Crane, R. A. et al. Negative regulation of age-related developmental leaf senescence by the IAOx pathway, PEN1, and PEN3. Front. Plant Sci. 10, 1202 (2019).PubMed Central 

Google Scholar 
Fu, M. et al. AtWDS1 negatively regulates age-dependent and dark-induced leaf senescence in Arabidopsis. Plant Sci. 285, 44–54 (2019).CAS 

Google Scholar 
Zhang, B., Jia, J., Yang, M., Yan, C. & Han, Y. Overexpression of a LAM domain containing RNA-binding protein LARP1c induces precocious leaf senescence in Arabidopsis. Mol. Cells 34, 367–374 (2012).PubMed Central 

Google Scholar 
Ma, Z., Wu, W., Huang, W. & Huang, J. Down-regulation of specific plastid ribosomal proteins suppresses thf1 leaf variegation, implying a role of THF1 in plastid gene expression. Photosynth. Res. 126, 301–310 (2015).CAS 

Google Scholar 
Wang, Z. et al. Two chloroplast proteins suppress drought resistance by affecting ROS production in guard cells. Plant Physiol. 172, 2491–2503 (2016).CAS 
PubMed Central 

Google Scholar 
Meurer, J. et al. PALE CRESS binds to plastid RNAs and facilitates the biogenesis of the 50S ribosomal subunit. Plant J. 92, 400–413 (2017).CAS 

Google Scholar 
Holding, D. The chloroplast and leaf developmental mutant, pale cress, exhibits light-conditional severity and symptoms characteristic of its ABA deficiency. Ann. Bot. 86, 953–962 (2000).CAS 

Google Scholar 
Meurer, J., Grevelding, C., Westhoff, P. & Reiss, B. The PAC protein affects the maturation of specific chloroplast mRNAs in Arabidopsis thaliana. Mol. Gen. Genet. MGG 258, 342–351 (1998).CAS 

Google Scholar 
Lawesson, J. E. Stand-level dieback and regeneration of forests in the Galápagos Islands. Temporal and Spatial Patterns of Vegetation Dynamics 87–93. https://doi.org/10.1007/978-94-009-2275-4_10 (1988).Endo, M., Kudo, D., Koto, T., Shimizu, H. & Araki, T. Light-dependent destabilization of PHL in Arabidopsis. Plant Signal. Behav. 9, e28118 (2014).PubMed Central 

Google Scholar 
Endo, M., Tanigawa, Y., Murakami, T., Araki, T. & Nagatani, A. PHYTOCHROME-DEPENDENT LATE-FLOWERING accelerates flowering through physical interactions with phytochrome B and CONSTANS. Proc. Natl Acad. Sci. USA 110, 18017–18022 (2013).ADS 
CAS 
PubMed Central 

Google Scholar 
Li, G. et al. Coordinated transcriptional regulation underlying the circadian clock in Arabidopsis. Nat. Cell Biol. 13, 616–622 (2011).CAS 

Google Scholar 
Basset, G. J. C. et al. Folate synthesis in plants: the last step of the p-aminobenzoate branch is catalyzed by a plastidial aminodeoxychorismate lyase. Plant J. 40, 453–461 (2004).CAS 

Google Scholar 
Smeekens, S. Faculty Opinions recommendation of Large-scale analysis of mRNA translation states during sucrose starvation in arabidopsis cells identifies cell proliferation and chromatin structure as targets of translational control. Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature. https://doi.org/10.3410/f.1032260.373846 (2006).Oravecz, A. et al. CONSTITUTIVELY PHOTOMORPHOGENIC1 is required for the UV-B response in Arabidopsis. Plant Cell 18, 1975–1990 (2006).CAS 
PubMed Central 

Google Scholar 
Dal Bosco, C. et al. Inactivation of the chloroplast ATP synthase gamma subunit results in high non-photochemical fluorescence quenching and altered nuclear gene expression in Arabidopsis thaliana. J. Biol. Chem. 279, 1060–1069 (2004).CAS 

Google Scholar 
Tan, Y.-F., O’Toole, N., Taylor, N. L. & Millar, A. H. Divalent metal ions in plant mitochondria and their role in interactions with proteins and oxidative stress-induced damage to respiratory function. Plant Physiol. 152, 747–761 (2010).CAS 
PubMed Central 

Google Scholar 
Kim, J. Y. et al. Functional characterization of a glycine-rich RNA-binding protein 2 in Arabidopsis thaliana under abiotic stress conditions. Plant J. 50, 439–451 (2007).CAS 

Google Scholar 
ten Hove, C. A. et al. Probing the roles of LRR RLK genes in Arabidopsis thaliana roots using a custom T-DNA insertion set. Plant Mol. Biol. 76, 69–83 (2011).PubMed Central 

Google Scholar 
Jakoby, M. J. et al. Transcriptional profiling of mature Arabidopsis trichomes reveals that NOECK encodes the MIXTA-like transcriptional regulator MYB106. Plant Physiol. 148, 1583–1602 (2008).CAS 
PubMed Central 

Google Scholar 
Fox, A. R. et al. Plasma membrane aquaporins interact with the endoplasmic reticulum resident VAP27 proteins at ER-PM contact sites and endocytic structures. N. Phytol. 228, 973–988 (2020).CAS 

Google Scholar 
Wang, P. et al. Plant AtEH/Pan1 proteins drive autophagosome formation at ER-PM contact sites with actin and endocytic machinery. Nat. Commun. 10, 5132 (2019).ADS 
PubMed Central 

Google Scholar 
Bittner, A., Hause, B. & Baier, M. Cold-priming causes oxylipin dampening during the early cold and light response of Arabidopsis thaliana. J. Exp. Bot. https://doi.org/10.1093/jxb/erab314 (2021).Kuki, Y., Ohno, R., Yoshida, K. & Takumi, S. Heterologous expression of wheat WRKY transcription factor genes transcriptionally activated in hybrid necrosis strains alters abiotic and biotic stress tolerance in transgenic Arabidopsis. Plant Physiol. Biochem. 150, 71–79 (2020).CAS 

Google Scholar 
Czarnocka, W. et al. FMO1 is involved in excess light stress-induced signal transduction and cell death signaling. Cells 9, 2163 (2020).Kleine, T., Kindgren, P., Benedict, C., Hendrickson, L. & Strand, A. Genome-wide gene expression analysis reveals a critical role for CRYPTOCHROME1 in the response of Arabidopsis to high irradiance. Plant Physiol. 144, 1391–1406 (2007).CAS 
PubMed Central 

Google Scholar 
Castells, E. et al. The conserved factor DE-ETIOLATED 1 cooperates with CUL4-DDB1DDB2 to maintain genome integrity upon UV stress. EMBO J. 30, 1162–1172 (2011).CAS 
PubMed Central 

Google Scholar 
Lahari, T., Lazaro, J., Marcus, J. M. & Schroeder, D. F. RAD7 homologues contribute to Arabidopsis UV tolerance. Plant Sci. 277, 267–277 (2018).CAS 

Google Scholar 
Kim, A. et al. Non-intrinsic ATP-binding cassette proteins ABCI19, ABCI20 and ABCI21 modulate cytokinin response at the endoplasmic reticulum in Arabidopsis thaliana. Plant Cell Rep. 39, 473–487 (2020).CAS 
PubMed Central 

Google Scholar 
Chen, D., Molitor, A., Liu, C. & Shen, W.-H. The Arabidopsis PRC1-like ring-finger proteins are necessary for repression of embryonic traits during vegetative growth. Cell Res. 20, 1332–1344 (2010).CAS 

Google Scholar 
Shen, L. et al. The putative PRC1 RING-finger protein AtRING1A regulates flowering through repressing MADS AFFECTING FLOWERING genes in Arabidopsis. Development 141, 1303–1312 (2014).CAS 

Google Scholar 
Li, J., Wang, Z., Hu, Y., Cao, Y. & Ma, L. Polycomb group proteins RING1A and RING1B regulate the vegetative phase transition in Arabidopsis. Front. Plant Sci. 8, 867 (2017).PubMed Central 

Google Scholar 
An, Z. et al. The histone methylation readers MRG1/MRG2 and the histone chaperones NRP1/NRP2 associate in fine-tuning Arabidopsis flowering time. Plant J. 103, 1010–1024 (2020).CAS 

Google Scholar 
Gómez-Zambrano, Á. et al. Arabidopsis SWC4 binds DNA and recruits the SWR1 complex to modulate histone H2A.Z deposition at key regulatory genes. Mol. Plant 11, 815–832 (2018).
Google Scholar 
Glass, M., Barkwill, S., Unda, F. & Mansfield, S. D. Endo-β−1,4-glucanases impact plant cell wall development by influencing cellulose crystallization. J. Integr. Plant Biol. 57, 396–410 (2015).CAS 

Google Scholar 
Markakis, M. N. et al. Identification of genes involved in the ACC-mediated control of root cell elongation in Arabidopsis thaliana. BMC Plant Biol. 12, 1–11 (2012).Noutoshi, Y. et al. Loss of necrotic spotted lesions 1 associates with cell death and defense responses in Arabidopsis thaliana. Plant Mol. Biol. 62, 29–42 (2006).CAS 

Google Scholar 
Fukunaga, S. et al. Dysfunction of Arabidopsis MACPF domain protein activates programmed cell death via tryptophan metabolism in MAMP-triggered immunity. Plant J. 89, 381–393 (2017).CAS 

Google Scholar 
Singh, S., Kailasam, S., Lo, J. & Yeh, K. Histone H3 lysine4 trimethylation‐regulated GRF11 expression is essential for the iron‐deficiency response in Arabidopsis thaliana. N. Phytologist 230, 244–258 (2021).CAS 

Google Scholar 
Fal, K. et al. Phyllotactic regularity requires the Paf1 complex in Arabidopsis. Development https://doi.org/10.1242/dev.154369 (2017).He, Y. PAF1-complex-mediated histone methylation of FLOWERING LOCUS C chromatin is required for the vernalization-responsive, winter-annual habit in Arabidopsis. Genes Dev. 18, 2774–2784 (2004).CAS 
PubMed Central 

Google Scholar 
Hoson, T. et al. Growth stimulation in inflorescences of an Arabidopsis tubulin mutant under microgravity conditions in space. Plant Biol. 16, 91–96 (2014).
Google Scholar 
Xiong, X., Xu, D., Yang, Z., Huang, H. & Cui, X. A single amino-acid substitution at lysine 40 of an Arabidopsis thaliana α-tubulin causes extensive cell proliferation and expansion defects. J. Integr. Plant Biol. 55, 209–220 (2013).CAS 

Google Scholar 
Whitewoods, C. D. et al. CLAVATA was a genetic novelty for the morphological innovation of 3D growth in land plants. Curr. Biol. 30, 2645–2648 (2020).CAS 
PubMed Central 

Google Scholar 
Galbraith, D. W. et al. Rapid flow cytometric analysis of the cell cycle in intact plant tissues. Science 220, 1049–1051 (1983).ADS 
CAS 

Google Scholar 
Dolezel, J., Sgorbati, S. & Lucretti, S. Comparison of three DNA fluorochromes for flow cytometric estimation of nuclear DNA content in plants. Physiologia Plant. 85, 625–631 (1992).CAS 

Google Scholar 
Loureiro, J., Rodriguez, E., Dolezel, J. & Santos, C. Two new nuclear isolation buffers for plant DNA flow cytometry: a test with 37 species. Ann. Bot. 100, 875–888 (2007).CAS 
PubMed Central 

Google Scholar 
Suda, J. et al. Genome size variation and species relationships in Hieracium sub-genus Pilosella (Asteraceae) as inferred by flow cytometry. Ann. Bot. 100, 1323–1335 (2007).PubMed Central 

Google Scholar 
Greilhuber, J., Dolezel, J., Lysák, M. A. & Bennett, M. D. The origin, evolution and proposed stabilization of the terms ‘genome size’ and ‘C-value’ to describe nuclear DNA contents. Ann. Bot. 95, 255–260 (2005).CAS 
PubMed Central 

Google Scholar 
Dolezel, J., Bartos, J., Voglmayr, H. & Greilhuber, J. Nuclear DNA content and genome size of trout and human. Cytom. Part A: J. Int. Soc. Anal. Cytol. 51, 127–128 (2003).CAS 

Google Scholar 
Putnam, N. H. et al. Chromosome-scale shotgun assembly using an in vitro method for long-range linkage. Genome Res. 26, 342–350 (2016).CAS 
PubMed Central 

Google Scholar 
Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).CAS 

Google Scholar 
Laetsch, D. R. & Blaxter, M. L. BlobTools: Interrogation of genome assemblies. F1000Res. 6, 1287 (2017).
Google Scholar 
Guan, D. et al. Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics 36, 2896–2898 (2020).CAS 
PubMed Central 

Google Scholar 
Bradnam, K. R. et al. Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species. Gigascience 2, 10 (2013).PubMed Central 

Google Scholar 
Smit, A., Hubley, R. & Green, P. RepeatMasker 4.0 (Institute for Systems Biology, 2013).Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. USA 117, 9451–9457 (2020).CAS 
PubMed Central 

Google Scholar 
Tardaguila, M. et al. Corrigendum: SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Res. 28, 1096 (2018).CAS 
PubMed Central 

Google Scholar 
Holt, C. & Yandell, M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinforma. 12, 491 (2011).
Google Scholar 
Moore, B., Holt, C., Alvarado, A. S. & Yandell, M. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome 18, 188–196 (2008).Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).CAS 

Google Scholar 
Korf, I. Gene finding in novel genomes. BMC Bioinforma. 5, 59 (2004).
Google Scholar 
Keller, O., Kollmar, M., Stanke, M. & Waack, S. A novel hybrid gene prediction method employing protein multiple sequence alignments. Bioinformatics 27, 757–763 (2011).CAS 

Google Scholar 
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
Google Scholar 
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).CAS 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227–245 (2019).CAS 

Google Scholar 
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Schubert, M., Lindgreen, S. & Orlando, L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. Notes 9, 88 (2016).PubMed Central 

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

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

Google Scholar 
Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
PubMed Central 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Mandel, J. R. et al. A target enrichment method for gathering phylogenetic information from hundreds of loci: an example from the Compositae. Appl. Plant Sci. 2, 1300085 (2014).Faircloth, B. C. PHYLUCE is a software package for the analysis of conserved genomic loci. Bioinformatics 32, 786–788 (2016).CAS 

Google Scholar 
Faircloth, B. C. et al. Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales. Syst. Biol. 61, 717–726 (2012).
Google Scholar 
Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).PubMed Central 

Google Scholar 
Delcher, A. L. et al. Alignment of whole genomes. Nucleic Acids Res. 27, 2369–2376 (1999).CAS 
PubMed Central 

Google Scholar 
Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).PubMed Central 

Google Scholar 
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).CAS 
PubMed Central 

Google Scholar 
Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R. & Kievit, R. A. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res. 4, 63 (2019).PubMed Central 

Google Scholar 
Hao, Z. et al. RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput Sci. 6, e251 (2020).PubMed Central 

Google Scholar 
Laforest, M. et al. A chromosome-scale draft sequence of the Canada fleabane genome. Pest Manag. Sci. 76, 2158–2169 (2020).CAS 

Google Scholar 
Liu, B. et al. Mikania micrantha genome provides insights into the molecular mechanism of rapid growth. Nat. Commun. 11, 340 (2020).ADS 
CAS 
PubMed Central 

Google Scholar 
Emms, D. M. & Kelly, S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 16, 157 (2015).PubMed Central 

Google Scholar 
Cerca, J. et al. The Tetragnatha kauaiensis genome sheds light on the origins of genomic novelty in spiders. Genome Biol. Evol. 13, evab262 (2021).Laetsch, D. R. & Blaxter, M. L. KinFin: software for taxon-aware analysis of clustered protein sequences. G3 7, 3349–3357 (2017).CAS 
PubMed Central 

Google Scholar 
Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).CAS 
PubMed Central 

Google Scholar 
Sanderson, M. J. r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19, 301–302 (2003).CAS 

Google Scholar 
Lovell, J. T. et al. Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass. Nature 590, 438–444 (2021).ADS 
CAS 
PubMed Central 

Google Scholar 
Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinforma. 9, 18 (2008).
Google Scholar 
Steinbiss, S., Willhoeft, U., Gremme, G. & Kurtz, S. Fine-grained annotation and classification of de novo predicted LTR retrotransposons. Nucleic Acids Res. 37, 7002–7013 (2009).CAS 
PubMed Central 

Google Scholar 
Eddy, S. HMMER user’s guide. Dep. Genet., Wash. Univ. Sch. Med. 2, 13 (1992).
Google Scholar 
Llorens, C. et al. The Gypsy Database (GyDB) of mobile genetic elements: release 2.0. Nucleic Acids Res. 39, D70–D74 (2011).CAS 

Google Scholar 
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).CAS 

Google Scholar 
Talavera, G. & Castresana, J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 56, 564–577 (2007).CAS 

Google Scholar 
De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269–1271 (2006).
Google Scholar 
Mendes, F. K., Vanderpool, D., Fulton, B. & Hahn, M. W. CAFE 5 models variation in evolutionary rates among gene families. Bioinformatics https://doi.org/10.1093/bioinformatics/btaa1022 (2020).Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).CAS 
PubMed Central 

Google Scholar 
Alexa, A. & Rahnenfuhrer, J. topGO: enrichment analysis for gene ontology. R. Package Version 2, 2010 (2010).
Google Scholar 
Alexa, A. & Rahnenführer, J. Gene set enrichment analysis with topGO. Bioconductor Improv 27, 1–26 (2009).
Google Scholar 
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).ADS 
CAS 
PubMed Central 

Google Scholar 
Löytynoja, A. Phylogeny-aware alignment with PRANK. Methods Mol. Biol. 1079, 155–170 (2014).
Google Scholar 
Wu, M., Chatterji, S. & Eisen, J. A. Accounting for alignment uncertainty in phylogenomics. PLoS ONE 7, e30288 (2012).ADS 
CAS 
PubMed Central 

Google Scholar 
Smith, M. D. et al. Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015).CAS 
PubMed Central 

Google Scholar 
Pond, S. L. K., Frost, S. D. W. & Muse, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics 21, 676–679 (2005).CAS 

Google Scholar 
Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).CAS 

Google Scholar  More

War, A. R. et al. Mechanisms of plant defense against insect herbivores. Plant Signal. Behav. 7, 1306–1320 (2012).Article 

Google Scholar 
Pentzold, S., Zagrobelny, M., Roelsgaard, P. S., Møller, B. L. & Bak, S. The multiple strategies of an insect herbivore to overcome plant cyanogenic glucoside defence. PLoS ONE 9, e91337. https://doi.org/10.1371/journal.pone.0091337 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Abdalsamee, M. K., Giampa, M., Niehaus, K. & Müller, C. Rapid incorporation of glucosinolates as a strategy used by a herbivore to prevent activation by myrosinases. Insect Biochem. Mol. Biol. 52, 115–123. https://doi.org/10.1016/j.ibmb.2014.07.002 (2014).CAS 
Article 
PubMed 

Google Scholar 
Winde, I. & Wittstock, U. Insect herbivore counteradaptations to the plant glucosinolate-myrosinase system. Phytochemistry 72, 1566–1575. https://doi.org/10.1016/j.phytochem.2011.01.016 (2011).CAS 
Article 
PubMed 

Google Scholar 
Sporer, T., Körnig, J. & Beran, F. Ontogenetic differences in the chemical defence of flea beetles influence their predation risk. Funct Ecol. 34, 1370–1379. https://doi.org/10.1111/1365-2435.13548 (2020).Article 

Google Scholar 
Hammer, T. J. & Moran, N. A. Links between metamorphosis and symbiosis in holometabolous insects. Philos. Trans. R. Soc. B-Biol. Sci. 374, 20190068. https://doi.org/10.1098/rstb.2019.0068 (2019).CAS 
Article 

Google Scholar 
Wäckers, F. L., Romeis, J. & van Rijn, P. Nectar and pollen feeding by insect herbivores and implications for multitrophic interactions. Annu. Rev. Entomol. 52, 301–323. https://doi.org/10.1146/annurev.ento.52.110405.091352 (2007).CAS 
Article 
PubMed 

Google Scholar 
Altermatt, F. & Pearse, I. S. Similarity and specialization of the larval versus adult diet of european butterflies and moths. Am. Nat. 178, 372–382. https://doi.org/10.1086/661248 (2011).Article 
PubMed 

Google Scholar 
Hammer, T. J., McMillan, W. O. & Fierer, N. Metamorphosis of a butterfly-associated bacterial community. PLoS ONE 9, e86995. https://doi.org/10.1371/journal.pone.0086995 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Shukla, S. P., Sanders, J. G., Byrne, M. J. & Pierce, N. E. Gut microbiota of dung beetles correspond to dietary specializations of adults and larvae. Mol. Ecol. 25, 6092–6106. https://doi.org/10.1111/mec.13901 (2016).CAS 
Article 
PubMed 

Google Scholar 
Blažević, I. et al. Glucosinolate structural diversity, identification, chemical synthesis and metabolism in plants. Phytochemistry 169, 112100. https://doi.org/10.1016/j.phytochem.2019.112100 (2020).CAS 
Article 
PubMed 

Google Scholar 
Halkier, B. A. & Gershenzon, J. Biology and biochemistry of glucosinolates. Annu. Rev. Plant Biol. 57, 303–333. https://doi.org/10.1146/annurev.arplant.57.032905.105228 (2006).CAS 
Article 
PubMed 

Google Scholar 
Wittstock, U., Kurzbach, E., Herfurth, A. M. & Stauber, E. J. Glucosinolate breakdown. Adv. Botanical Res. – Glucosinolates 80, 125–169. https://doi.org/10.1016/bs.abr.2016.06.006 (2016).CAS 
Article 

Google Scholar 
Jeschke, V., Gershenzon, J. & Vassão, D. G. in Glucosinolates Vol. 80 Advances in Botanical Research (ed S. Kopriva), 199–245 (2016).Sun, R. et al. Tritrophic metabolism of plant chemical defenses and its effects on herbivore and predator performance. eLife 9, e51029, doi:https://doi.org/10.7554/eLife.51029 (2019).Malka, O. et al. Glucosinolate desulfation by the phloem-feeding insect Bemisia tabaci. J. Chem. Ecol. 42, 230–235. https://doi.org/10.1007/s10886-016-0675-1 (2016).CAS 
Article 
PubMed 

Google Scholar 
Schramm, K., Vassão, D. G., Reichelt, M., Gershenzon, J. & Wittstock, U. Metabolism of glucosinolate-derived isothiocyanates to glutathione conjugates in generalist lepidopteran herbivores. Insect Biochem. Mol. Biol. 42, 174–182. https://doi.org/10.1016/j.ibmb.2011.12.002 (2012).CAS 
Article 
PubMed 

Google Scholar 
Beran, F. et al. Phyllotreta striolata flea beetles use host plant defense compounds to create their own glucosinolate-myrosinase system. Proc. Natl. Acad. Sci. USA 111, 7349–7354. https://doi.org/10.1073/pnas.1321781111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Beran, F. et al. One pathway is not enough: The cabbage stem flea beetle Psylliodes chrysocephala uses multiple strategies to overcome the glucosinolate-myrosinase defense in its host plants. Front. Plant Sci. 9, 1754. https://doi.org/10.3389/fpls.2018.01754 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Müller, C. et al. Sequestration of host plant glucosinolates in the defensive hemolymph of the sawfly Athalia rosae. J. Chem. Ecol. 27, 2505–2516 (2001).Article 

Google Scholar 
Ratzka, A., Vogel, H., Kliebenstein, D. J., Mitchell-Olds, T. & Kroymann, J. Disarming the mustard oil bomb. Proc. Natl. Acad. Sci. USA. 99, 11223–11228 (2002).ADS 
CAS 
Article 

Google Scholar 
Wittstock, U. et al. Successful herbivore attack due to metabolic diversion of a plant chemical defense. Proc. Natl. Acad. Sci. USA. 101, 4859–4864 (2004).ADS 
CAS 
Article 

Google Scholar 
Falk, K. L. & Gershenzon, J. The desert locust, Schistocerca gregaria, detoxifies the glucosinolates of Schouwia purpurea by desulfation. J. Chem. Ecol. 33, 1542–1555. https://doi.org/10.1007/s10886-007-9331-0 (2007).CAS 
Article 
PubMed 

Google Scholar 
Vanhaelen, N., Haubruge, E., Lognay, G. & Francis, F. Hoverfly glutathione S-transferases and effect of Brassicaceae secondary metabolites. Pestic. Biochem. Phys. 71, 170–177 (2001).CAS 
Article 

Google Scholar 
Friedrichs, J. et al. Novel glucosinolate metabolism in larvae of the leaf beetle Phaedon cochleariae. Insect Biochem. Mol. Biol. 124, 103431. https://doi.org/10.1016/j.ibmb.2020.103431 (2020).CAS 
Article 
PubMed 

Google Scholar 
Reifenrath, K., Riederer, M. & Müller, C. Leaf surface wax layers of Brassicaceae lack feeding stimulants for Phaedon cochleariae. Entomol. Exp. Appl. 115, 41–50 (2005).CAS 
Article 

Google Scholar 
Cataldi, T. R. I., Lelario, F., Orlando, D. & Bufo, S. A. Collision-induced dissociation of the A+2 isotope ion facilitates glucosinolates structure elucidation by electrospray Ionization-Tandem Mass Spectrometry with a linear Quadrupole Ion Trap. Anal. Chem. 82, 5686–5696. https://doi.org/10.1021/ac100703w (2010).CAS 
Article 
PubMed 

Google Scholar 
Cataldi, T. R. I., Rubino, A., Lelario, F. & Bufo, S. A. Naturally occuring glucosinolates in plant extracts of rocket salad (Eruca sativa L.) identified by liquid chromatography coupled with negative ion electrospray ionization and quadrupole ion-trap mass spectrometry. Rapid Commun. Mass Spectrom. 21, 2374–2388, doi:https://doi.org/10.1002/rcm.3101 (2007).Yang, Z. L., Kunert, G., Sporer, T., Kornig, J. & Beran, F. Glucosinolate abundance and composition in Brassicaceae influence sequestration in a specialist flea beetle. J. Chem. Ecol. 46, 186–197. https://doi.org/10.1007/s10886-020-01144-y (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Smirnoff, N. Ascorbic acid metabolism and functions: a comparison of plants and mammals. Free Radical Biol. and Medic. 122, 116–129. https://doi.org/10.1016/j.freeradbiomed.2018.03.033 (2018).CAS 
Article 

Google Scholar 
Agerbirk, N., De Vos, M., Kim, J. H. & Jander, G. Indole glucosinolate breakdown and its biological effects. Phytochem. Rev. 8, 101–120. https://doi.org/10.1007/s11101-008-9098-0 (2009).CAS 
Article 

Google Scholar 
Goggin, F. L., Avila, C. A. & Lorence, A. Vitamin C content in plants is modified by insects and influences susceptibility to herbivory. BioEssays 32, 777–790. https://doi.org/10.1002/bies.200900187 (2010).CAS 
Article 
PubMed 

Google Scholar 
Kim, J. H., Lee, B. W., Schroeder, F. C. & Jander, G. Identification of indole glucosinolate breakdown products with antifeedant effects on Myzus persicae (green peach aphid). Plant J. 54, 1015–1026 (2008).CAS 
Article 

Google Scholar 
Liu, T. T. & Yang, T. S. Stability and antimicrobial activity of allyl isothiocyanate during long-term storage in an oil-in-water emulsion. J. Food Sci. 75, C445–C451. https://doi.org/10.1111/j.1750-3841.2010.01645.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Luang-In, V. & Rossiter, J. T. Stability studies of isothiocyanates and nitriles in aqueous media. Songklanakarin J. Sci. Technol. 37, 625–630 (2015).CAS 

Google Scholar 
Tsao, R., Yu, Q., Friesen, I., Potter, J. & Chiba, M. Factors affecting the dissolution and degradation of oriental mustard-derived sinigrin and allyl isothiocyanate in aqueous media. J. Agric. Food Chem. 48, 1898–1902. https://doi.org/10.1021/jf9906578 (2000).CAS 
Article 
PubMed 

Google Scholar 
Brodbeck, B. & Strong, D. in Insect Outbreaks (eds P. Barbosa & J. C. Schultz) Ch. 14, 347–363 (Academic Press, INC., 1987).Kumar, V. et al. Differential distribution of amino acids in plants. Amino Acids 49, 821–869. https://doi.org/10.1007/s00726-017-2401-x (2017).CAS 
Article 
PubMed 

Google Scholar 
Millar, K. A., Gallagher, E., Burke, R., McCarthy, S. & Barry-Ryan, C. Proximate composition and anti-nutritional factors of fava-bean (Vicia faba), green-pea and yellow-pea (Pisum sativum) flour. J. Food Compos. Anal. 82, doi:https://doi.org/10.1016/j.jfca.2019.103233 (2019).Miller, R. W., McGrew, C., Wolff, I. A., Jones, Q. & Vanetten, C. H. Seed meal amino acids – amino acid composition of seed meals from 41 species of Cruciferae. J. Agric. Food Chem. 10, 426-430. https://doi.org/10.1021/jf60123a023 (1962).Article 

Google Scholar 
Fischer, W. N. et al. Low and high affinity amino acid H+-cotransporters for cellular import of neutral and charged amino acids. Plant J. 29, 717–731. https://doi.org/10.1046/j.1365-313X.2002.01248.x (2002).CAS 
Article 
PubMed 

Google Scholar 
Lea, P. J., Sodek, L., Parry, M. A. J., Shewry, R. & Halford, N. G. Asparagine in plants. Ann. Appl. Biol. 150, 1–26. https://doi.org/10.1111/j.1744-7348.2006.00104.x (2007).CAS 
Article 

Google Scholar 
Leroy, P. D. et al. Aphid-host plant interactions: does aphid honeydew exactly reflect the host plant amino acid composition? Arthropod-Plant Inte. 5, 193–199. https://doi.org/10.1007/s11829-011-9128-5 (2011).Article 

Google Scholar 
Shukla, S. P. & Beran, F. Gut microbiota degrades toxic isothiocyanates in a flea beetle pest. Mol. Ecol. 29, 4692–4705. https://doi.org/10.1111/mec.15657 (2020).CAS 
Article 
PubMed 

Google Scholar 
Angelino, D. et al. Myrosinase-dependent and -independent formation and control of isothiocyanate products of glucosinolate hydrolysis. Front. Plant Sci. 6, 831. https://doi.org/10.3389/fpls.2015.00831 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Liou, C. S. et al. A metabolic pathway for activation of dietary glucosinolates by a human gut symbiont. Cell 180, 717–729. https://doi.org/10.1016/j.cell.2020.01.023 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, X. J. et al. Dietary broccoli alters rat cecal microbiota to improve glucoraphanin hydrolysis to bioactive isothiocyanates. Nutrients 9, 262. https://doi.org/10.3390/nu9030262 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Sikorska-Zimny, K. & Beneduce, L. The metabolism of glucosinolates by gut microbiota. Nutrients 13, 2750. https://doi.org/10.3390/nu13082750 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Müller, C., Vogel, H. & Heckel, D. G. Transcriptional responses to short-term and long-term host plant experience and parasite load in an oligophagous beetle. Mol. Ecol. 26, 6370–6383. https://doi.org/10.1111/mec.14349 (2017).CAS 
Article 
PubMed 

Google Scholar 
Rueckert, S., Betts, E. L. & Tsaousis, A. D. The symbiotic spectrum: where do the gregarines fit? Trends Parasitol. 35, 687–694. https://doi.org/10.1016/j.pt.2019.06.013 (2019).Article 
PubMed 

Google Scholar 
Kühnle, A. & Müller, C. Responses of an oligophagous beetle species to rearing for several generations on alternative host plant species. Ecol. Entomol. 36, 125–134. https://doi.org/10.1111/j.1365-2311.2010.01256.x (2011).Article 

Google Scholar 
Sporer, T. et al. Hijacking the mustard-oil bomb: How a glucosinolate-sequestering flea beetle copes with plant myrosinases. Front. Plant Sci. 12, 645030. https://doi.org/10.3389/fpls.2021.645030 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Kallenbach, M. et al. A robust, simple, high-throughput technique for time-resolved plant volatile analysis in field experiments. Plant J. 78, 1060–1072. https://doi.org/10.1111/tpj.12523 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kallenbach, M., Veit, D., Eilers, E. J. & Schuman, M. C. Application of silicone tubing for robust, simple, high-throughput, and time-resolved analysis of plant volatiles in field experiments. Bioprotocol 5, e1391 (2015).
Google Scholar 
Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J. & Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminf. 8, 3. https://doi.org/10.1186/s13321-016-0115-9 (2016).CAS 
Article 

Google Scholar 
Kováts, E. Characterization of organic compounds by gas chromatography. Part 1. Retention indices of aliphatic halides, alcohols, aldehydes and ketones. Helv. Chim. Acta 41, 1915–1932, doi:https://doi.org/10.1002/hlca.19580410703 (1958).El-Sayed, A. M. The Pherobase: Database of Pheromones and Semiochemicals. (2012).McDanell, R., McLean, A. E. M., Hanley, A. B., Heaney, R. K. & Fenwick, G. R. Chemical and biological properties of indole glucosinolates (glucobrassicins): a review. Food Chem. Toxicol. 26, 59–70. https://doi.org/10.1016/0278-6915(88)90042-7 (1988).CAS 
Article 
PubMed 

Google Scholar 
Weber, G., Oswald, S. & Zöllner, U. Suitability of rapae cultivars with a different glucosinolate content for Brevicoryne brassicae (L) and Myzus persicae (Sulzer) (Hemiptera, Aphididae). Z. Pflanzenk. Pflanzenschutz 93, 113–124 (1986).CAS 

Google Scholar 
Wadleigh, R. W. & Yu, S. J. Detoxification of isothiocynante allelochemicals by glutathione transferase in three lepidopterous species. J. Chem. Ecol. 14, 1279–1288. https://doi.org/10.1007/bf01019352 (1988).CAS 
Article 
PubMed 

Google Scholar 
Francis, F., Lognay, G., Wathelet, J. P. & Haubruge, E. Effects of allelochemicals from first (Brassicaceae) and second (Myzus persicae and Brevicoryne brassicae) trophic levels on Adalia bipunctata. J. Chem. Ecol. 27, 243–256. https://doi.org/10.1023/A:1005672220342 (2001).CAS 
Article 
PubMed 

Google Scholar 
Aliabadi, A., Renwick, J. A. A. & Whitman, D. W. Sequestration of glucosinolates by harlequin bug Murgantia histrionica. J. Chem. Ecol. 28, 1749–1762. https://doi.org/10.1023/a:1020505016637 (2002).CAS 
Article 
PubMed 

Google Scholar 
Bridges, M. et al. Spatial organization of the glucosinolate-myrosinase system in brassica specialist aphids is similar to that of the host plant. Proc. R. Soc. B-Biol. Sci. 269, 187–191. https://doi.org/10.1098/rspb.2001.1861 (2002).CAS 
Article 

Google Scholar 
Müller, C., Agerbirk, N. & Olsen, C. E. Lack of sequestration of host plant glucosinolates in Pieris rapae and P. brassicae. Chemoecology 13, 47–54, doi: https://doi.org/10.1007/s000490300005 (2003).Francis, F., Vanhaelen, N. & Haubruge, E. Glutathione S-transferases in the adaptation to plant secondary metabolites in the Myzus persicae aphid. Arch. Insect Biochem. Physiol. 58, 166–174. https://doi.org/10.1002/arch.20049 (2005).CAS 
Article 
PubMed 

Google Scholar 
Müller, C. & Wittstock, U. Uptake and turn-over of glucosinolates sequestered in the sawfly Athalia rosae. Insect Biochem. Mol. Biol. 35, 1189–1198. https://doi.org/10.1016/j.ibmb.2005.06.001 (2005).CAS 
Article 
PubMed 

Google Scholar 
Agerbirk, N., Müller, C., Olsen, C. E. & Chew, F. S. A common pathway for metabolism of 4-hydroxybenzylglucosinolate in Pieris and Anthocaris (Lepidoptera: Pieridae). Biochem. Syst. Ecol. 34, 189–198. https://doi.org/10.1016/j.bse.2005.09.005 (2006).CAS 
Article 

Google Scholar 
Vergara, F. et al. Glycine conjugates in a lepidopteran insect herbivore: the metabolism of benzylglucosinolate in the cabbage white butterfly Pieris rapae. ChemBioChem 7, 1982–1989. https://doi.org/10.1002/cbic.200600280 (2006).Article 
PubMed 

Google Scholar 
Agerbirk, N., Olsen, C. E., Topbjerg, H. B. & Sørensen, J. C. Host plant-dependent metabolism of 4-hydroxybenzylglucosinolate in Pieris rapae: Substrate specificity and effects of genetic modification and plant nitrile hydratase. Insect Biochem. Mol. Biol. 37, 1119–1130. https://doi.org/10.1016/j.ibmb.2007.06.009 (2007).CAS 
Article 
PubMed 

Google Scholar 
Kazana, E. et al. The cabbage aphid: a walking mustard oil bomb. Proc. R. Soc. B-Biol. Sci. 274, 2271–2277 (2007).CAS 
Article 

Google Scholar 
Agerbirk, N., Olsen, C. E., Poulsen, E., Jacobsen, N. & Hansen, P. R. Complex metabolism of aromatic glucosinolates in Pieris rapae caterpillars involving nitrile formation, hydroxylation, demethylation, sulfation, and host plant dependent carboxylic acid formation. Insect Biochem. Mol. Biol. 40, 126–137. https://doi.org/10.1016/j.ibmb.2010.01.003 (2010).CAS 
Article 
PubMed 

Google Scholar 
Opitz, S. E. W., Jensen, S. R. & Muller, C. Sequestration of glucosinolates and iridoid glucosides in sawfly species of the genus Athalia and their role in defense against ants. J. Chem. Ecol. 36, 148–157. https://doi.org/10.1007/s10886-010-9740-3 (2010).CAS 
Article 
PubMed 

Google Scholar 
Opitz, S. E. W., Mix, A., Winde, I. B. & Müller, C. Desulfation followed by sulfation: metabolism of benzylglucosinolate in Athalia rosae (Hymenoptera: Tenthredinidae). ChemBioChem 12, 1252–1257. https://doi.org/10.1002/cbic.201100053 (2011).CAS 
Article 
PubMed 

Google Scholar 
Elbaz, M. et al. Asymmetric adaptation to indolic and aliphatic glucosinolates in the B and Q sibling species of Bemisia tabaci (Hemiptera: Aleyrodidae). Mol. Ecol. 21, 4533–4546. https://doi.org/10.1111/j.1365-294X.2012.05713.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Opitz, S. E. W. et al. Host shifts from Lamiales to Brassicaceae in the sawfly genus Athalia. PLoS ONE 7, e33649. https://doi.org/10.1371/journal.pone.0033649 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stauber, E. J. et al. Turning the “Mustard oil bomb” into a “Cyanide bomb”: aromatic glucosinolate metabolism in a specialist insect herbivore. PLoS ONE 7, e35545. https://doi.org/10.1371/journal.pone.0035545 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gloss, A. D. et al. Evolution in an ancient detoxification pathway is coupled with a transition to herbivory in the Drosophilidae. Mol. Biol. Evol. 31, 2441–2456. https://doi.org/10.1093/molbev/msu201 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Goodey, N. A., Florance, H. V., Smirnoff, N. & Hodgson, D. J. Aphids pick their poison: selective sequestration of plant chemicals affects host plant use in a specialist herbivore. J. Chem. Ecol. 41, 956–964. https://doi.org/10.1007/s10886-015-0634-2 (2015).CAS 
Article 
PubMed 

Google Scholar 
Jeschke, V. et al. How glucosinolates affect generalist lepidopteran larvae: growth, development and glucosinolate metabolism. Front. Plant Sci. 8, doi:https://doi.org/10.3389/fpls.2017.01995 (2017).Steiner, A. M., Busching, C., Vogel, H. & Wittstock, U. Molecular identification and characterization of rhodaneses from the insect herbivore Pieris rapae. Sci. Rep. 8, 10819. https://doi.org/10.1038/s41598-018-29148-5 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ahn, S. J. et al. Identification and evolution of glucosinolate sulfatases in a specialist flea beetle. Sci. Rep. 9, 15725. https://doi.org/10.1038/s41598-019-51749-x (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Malka, O. et al. Glucosylation prevents plant defense activation in phloem-feeding insects. Nat. Chem. Biol. 16, 1420–1426. https://doi.org/10.1038/s41589-020-00658-6 (2020).CAS 
Article 
PubMed 

Google Scholar 
Sun, R. et al. Detoxification of plant defensive glucosinolates by an herbivorous caterpillar is beneficial to its endoparasitic wasp. Mol. Ecol. 29, 4014–4031. https://doi.org/10.1111/mec.15613 (2020).CAS 
Article 
PubMed 

Google Scholar 
Manivannan, A. et al. Identification of a sulfatase that detoxifies glucosinolates in the phloem-feeding insect Bemisia tabaci and prefers indolic glucosinolates. Front. Plant Sci. 12, 671286. https://doi.org/10.3389/fpls.2021.671286 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Yang, Z. L. et al. Sugar transporters enable a leaf beetle to accumulate plant defense compounds. Nat. Commun. 12, 2658. https://doi.org/10.1038/s41467-021-22982-8 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

IPCC Climate Change 2007: Synthesis Report (eds Pachauri, R. K. & Reisinger, A.) (IPCC, 2007).Boyd, J. & Banzhaf, S. Ecol. Econ. 63, 616–626 (2007).Article 

Google Scholar 
Ruhl, J. B. et al. Front. Ecol. Environ. 19, 519–525 (2021).Article 

Google Scholar 
Carleton, T. & Greenstone, M. Updating the United States Government’s Social Cost of Carbon Working Paper 2021-04 (Univ. Chicago, Becker Friedman Institute for Economics, 2021).Mandle, L. et al. Nat. Sustain. 4, 161–169 (2021).Article 

Google Scholar 
Druckenmiller, H. Estimating an Economic and Social Value of Forests: Evidence from Tree Mortality in the American West (Univ. California Berkeley, 2021).Burkett, V. R. et al. Ecol. Complexity 2, 357–394 (2005).Article 

Google Scholar 
Hanley, N. & Czajkowski, M. Rev. Environ. Econ. Policy 13, 248–266 (2019).Article 

Google Scholar 
Mendelsohn, R. Rev. Environ. Econ. Policy 13, 267–282 (2019).Article 

Google Scholar 
Fenichel, E. P. et al. Proc. Natl Acad. Sci. USA 113, 2382–2387 (2016).CAS 
Article 

Google Scholar 
Martin-Ortega, J. et al. Ecosyst. Serv. 50, 101327 (2021).Article 

Google Scholar 
Borrelli, P. et al. Proc. Natl Acad. Sci. USA 117, 21994–22001 (2020).CAS 
Article 

Google Scholar 
Tropek, R. et al. Science 344, 981–981 (2014).CAS 
Article 

Google Scholar 
Vardon, M., Burnett, P. & Dovers, S. Ecol. Econ. 124, 145–152 (2016).Article 

Google Scholar 
Bastien-Olvera, B. A. & Moore, F. C. Nat. Sustain. 4, 101–108 (2021).Article 

Google Scholar 
Beland, M. et al. For. Ecol. Manage. 450, 117484 (2019).Article 

Google Scholar 
Vargas, L., Willemen, L. & Hein, L. Environ. Manage. 63, 1–15 (2019).Article 

Google Scholar 
Hallgren, W. et al. Environ. Model. Softw. 76, 182–186 (2016).Article 

Google Scholar 
Rolf, E. et al. Nat. Commun. 12, 4392 (2021).CAS 
Article 

Google Scholar 
Chernozhukov, V. et al. NBER Working Paper 24678 (National Bureau of Economic Research, 2018). More

Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
PubMed 

Google Scholar 
FAO. The State of World Fisheries and Aquaculture 2018—Meeting the Sustainable Development Goals. (2018).Teh, L. C. L. & Sumaila, U. R. Contribution of marine fisheries to worldwide employment. Fish Fish. 14, 77–88 (2013).
Google Scholar 
Halpern, B. S., Selkoe, K. A., Micheli, F. & Kappel, C. V. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conserv. Biol. 21, 1301–1315 (2007).PubMed 

Google Scholar 
Kaiser, M. J., Collie, J. S., Hall, S. J., Jennings, S. & Poiner, I. R. Modification of marine habitats by trawling activities: Prognosis and solutions. Fish Fish. 3, 114–136 (2002).
Google Scholar 
Hiddink, J. G. et al. Selection of indicators for assessing and managing the impacts of bottom trawling on seabed habitats. J. Appl. Ecol. 57, 1199–1209 (2020).
Google Scholar 
Funes, M., Marinao, C. & Galván, D. E. Does trawl fisheries affect the diet of fishes? A stable isotope analysis approach. Isotop. Environ. Health Stud. 10, 1–17 (2019).
Google Scholar 
Preciado, I. et al. Small-scale spatial variations of trawling impact on food web structure. Ecol. Ind. 98, 442–452 (2019).
Google Scholar 
Su, L. et al. Decadal-scale variation in mean trophic level in Beibu Gulf based on bottom-trawl survey data. Mar. Coast. Fish. 13, 174–182 (2021).
Google Scholar 
Jennings, S., van Hal, R., Hiddink, J. G. & Maxwell, T. A. D. Fishing effects on energy use by North Sea fishes. J. Sea Res. 60, 74–88 (2008).ADS 

Google Scholar 
de Ruiter, P. C., Neutel, A.-M. & Moore, J. C. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science 269, 1257–1260 (1995).ADS 
PubMed 

Google Scholar 
Bascompte, J. Disentangling the web of life. Science 325, 416–419 (2009).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Wootton, K. L. Omnivory and stability in freshwater habitats: Does theory match reality?. Freshw. Biol. 62, 821–832 (2017).
Google Scholar 
Borrelli, J. J. & Ginzburg, L. R. Why there are so few trophic levels: Selection against instability explains the pattern. Food Webs 1, 10–17 (2014).
Google Scholar 
Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl. Acad. Sci. USA 108, 3648–52 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Márquez-Velásquez, V., Raimundo, R. L. G., de Souza Rosa, R. & Navia, A. F. The use of ecological networks as tools for understanding and conserving marine biodiversity. In Marine Coastal Ecosystems Modelling and Conservation: Latin American Experiences, pp 179–202 (eds Ortiz, M. & Jordán, F.) (Springer, 2021). https://doi.org/10.1007/978-3-030-58211-1_9.Chapter 

Google Scholar 
Neutel, A.-M. & Thorne, M. A. S. Interaction strengths in balanced carbon cycles and the absence of a relation between ecosystem complexity and stability. Ecol. Lett. 17, 651–661 (2014).PubMed 
PubMed Central 

Google Scholar 
Neutel, A.-M. & Thorne, M. A. S. Beyond connectedness: Why pairwise metrics cannot capture community stability. Ecol. Evol. 6, 7199–7206 (2016).PubMed 
PubMed Central 

Google Scholar 
Saravia, L. A., Marina, T. I., Kristensen, N. P., De Troch, M. & Momo, F. R. Ecological network assembly: How the regional metaweb influences local food webs. J. Anim. Ecol. 3, 25 (2021).
Google Scholar 
Góngora, M. E., GonzalezZevallos, D., Pettovello, A. & Mendia, L. Caracterizacion de las principales pesquerias del golfo San Jorge Patagonia, Argentina. Latin Am. J. Aquat. Res. 40, 1–11 (2012).
Google Scholar 
Yorio, P. Marine protected areas, spatial scales, and governance: Implications for the conservation of breeding seabirds. Conserv. Lett. 2, 171–178 (2009).
Google Scholar 
Rincón-Díaz, M. P., Bovcon, N. D., Cochia, P. D., Góngora, M. E. & Galván, D. E. Fish functional diversity as an indicator of resilience to industrial fishing in Patagonia Argentina. J. Fish Biol. 99, 1650–1667 (2021).PubMed 

Google Scholar 
González-Zevallos, D. & Yorio, P. Consumption of discards and interactions between Black-browed Albatrosses (Thalassarche melanophrys) and Kelp Gulls (Larus dominicanus) at trawl fisheries in Golfo San Jorge, Argentina. J. Ornithol. 152, 827–838 (2011).
Google Scholar 
Vinuesa, J. H. & Varisco, M. Trophic ecology of the lobster krill Munida gregaria in San Jorge Gulf, Argentina. Investig. Mar. 35, 25–34 (2007).
Google Scholar 
Belleggia, M. et al. Trophic ecology of yellownose skate Zearaja chilensis, a top predator in the south-western Atlantic Ocean. J. Fish Biol. 88, 1070–1087 (2016).CAS 
PubMed 

Google Scholar 
Pasti, A. T. et al. The diet of Mustelus schmitti in areas with and without commercial bottom trawling (Central Patagonia, Southwestern Atlantic): Is it evidence of trophic interaction with the Patagonian shrimp fishery?. Food Webs 29, e00214 (2021).
Google Scholar 
Yorio, P., Bertellotti, M., Gandini, P. & Frere, E. Kelp gulls Larus dominicanus breeding on the argentine coast: Population status and relationship with coastal management and conservation. Mar. Ornithol. 26, 11–18 (1998).
Google Scholar 
Dans, S. et al. El golfo san jorge como área prioritaria de investigación, manejo y conservación en el marco de la iniciativa pampa azul. Rev. Cie. Investig. 71, 21–43 (2021).
Google Scholar 
de la Garza, J. M., Ferníndez, M. & Ravalli, C. Langostino patagónico (Pleoticus muelleri). Inf. Campa 20, 20 (2013).
Google Scholar 
Varisco, M. & La Vinuesa, J. H. Alimentación de Munida gregaria (Fabricius, 1793) (Crustacea:Anomura:Galatheidae) en fondos de pesca del Golfo San Jorge, Argentina. Rev. Biol. Mar. Oceanogr. 42, 221–229 (2007).
Google Scholar 
Tschopp, A., Cristiani, F., García, N. A., Crespo, E. A. & Coscarella, M. A. Trophic niche partitioning of five skate species of genus Bathyraja in northern and central Patagonia, Argentina. J. Fish. Biol. 97, 656–667 (2020).PubMed 

Google Scholar 
Kasinsky, T., Yorio, P., Dell’Arciprete, P., Marinao, C. & Suárez, N. Geographical differences in sex-specific foraging behaviour and diet during the breeding season in the opportunistic Kelp Gull (Larus dominicanus). Mar. Biol. 168, 14 (2021).CAS 

Google Scholar 
González-Zevallos, D. & Yorio, P. Seabird use of discards and incidental captures at the Argentine hake trawl fishery in the Golfo San Jorge, Argentina. Mar. Ecol. Progress Ser. 316, 175–183 (2006).ADS 

Google Scholar 
Crespo, E. A. et al. Direct and indirect effects of the Highseas fisheries on the marine mammal populations in the northern and central Patagonian coast. J. Northw. Atl. Fish. Sci. 22, 189–207 (1997).
Google Scholar 
Gandini, P. A., Frere, E., Pettovello, A. D. & Cedrola, P. V. Interaction between Magellanic Penguins and Shrimp Fisheries in Patagonia, Argentina. Condor 101, 783–789 (1999).
Google Scholar 
Fu, C. et al. Making ecological indicators management ready: Assessing the specificity, sensitivity, and threshold response of ecological indicators. Ecol. Ind. 105, 16–28 (2019).
Google Scholar 
Olivier, P. et al. Exploring the temporal variability of a food web using long-term biomonitoring data. Ecography 42, 2107–2121 (2019).
Google Scholar 
Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407 (2002).MATH 

Google Scholar 
Gellner, G. & McCann, K. Reconciling the omnivory-stability debate. Am. Nat. 179, 22–37 (2012).PubMed 

Google Scholar 
Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 26113 (2004).ADS 
CAS 

Google Scholar 
Reichardt, J. & Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 74, 16110 (2006).ADS 
MathSciNet 

Google Scholar 
Allesina, S. & Pascual, M. Network structure, predator-prey modules, and stability in large food webs. Theor. Ecol. 1, 55–64 (2008).
Google Scholar 
Strona, G., Nappo, D., Boccacci, F., Fattorini, S. & San-Miguel-Ayanz, J. A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals. Nat. Commun. 5, 4114 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Scholz, F. W. & Stephens, M. A. K-sample Anderson–Darling tests. J. Am. Stat. Assoc. 82, 918–924 (1987).MathSciNet 

Google Scholar 
Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Front. Psychol. 4, 863 (2013).PubMed 
PubMed Central 

Google Scholar 
Saravia, L. A. Multiweb: An R Package for Multiple Interaction Ecological Networks (Zenodo, 2019). https://doi.org/10.5281/zenodo.3370397.Book 

Google Scholar 
Kortsch, S. et al. Disentangling temporal food web dynamics facilitates understanding of ecosystem functioning. J. Anim. Ecol. 20, 20 (2021).
Google Scholar 
Marina, T. I. et al. Architecture of marine food webs: To be or not be a “small-world’’. PLoS One 13, 1–13 (2018).
Google Scholar 
Panel, E. P. A. Ecosystem-based Fishery Management: A Report to Congress by the Ecosystem Principles Advisory Panel. https://repository.library.noaa.gov/view/noaa/23730 (1998)Armoškaitė, A. et al. Establishing the links between marine ecosystem components, functions and services: An ecosystem service assessment tool. Ocean Coast. Manage. 193, 105229 (2020).
Google Scholar 
Navia, A. F., Cruz-Escalona, V. H., Giraldo, A. & Barausse, A. The structure of a marine tropical food web, and its implications for ecosystem-based fisheries management. Ecol. Model. 328, 23–33 (2016).
Google Scholar 
Agnetta, D. et al. Benthic-pelagic coupling mediates interactions in Mediterranean mixed fisheries: An ecosystem modeling approach. PLoS One 14, e0210659 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Baum, J. K. et al. Collapse and conservation of shark populations in the Northwest Atlantic. Sciencehttps://doi.org/10.1126/science.1079777 (2003).Article 
PubMed 

Google Scholar 
Bearzi, G. et al. Overfishing and the disappearance of short-beaked common dolphins from western Greece. Endang. Species Res. 5, 1–12 (2008).
Google Scholar 
Lotze, H. K., Coll, M., Magera, A. M., Ward-Paige, C. & Airoldi, L. Recovery of marine animal populations and ecosystems. Trends Ecol. Evol. 26, 595–605 (2011).PubMed 

Google Scholar 
Reyes, L. M. Cetaceans of Central Patagonia, Argentina. Aquat. Mammals 32, 20–30 (2006).
Google Scholar 
Lisnizer, N., Garcia-Borboroglu, P. & Yorio, P. Spatial and temporal variation in population trends of Kelp Gulls in northern Patagonia, Argentina. Emu Austral Ornithol. 111, 259–267 (2011).
Google Scholar 
Yorio, P. et al. Population trends of Imperial Cormorants (Leucocarbo atriceps) in northern coastal Argentine Patagonia over 26 years. Emu Austral Ornithol. 120, 114–122 (2020).
Google Scholar 
Irigoyen, A. & Trobbiani, G. Depletion of trophy large-sized sharks populations of the Argentinean coast, south-western Atlantic: Insights from fishers’ knowledge. Neotrop. Ichthyol. 14, 20 (2016).
Google Scholar 
Vasas, V., Lancelot, C., Rousseau, V. & Jordán, F. Eutrophication and overfishing in temperate nearshore pelagic food webs: A network perspective. Mar. Ecol. Prog. Ser. 336, 1–14 (2007).ADS 
CAS 

Google Scholar 
Gilarranz, L. J., Mora, C. & Bascompte, J. Anthropogenic effects are associated with a lower persistence of marine food webs. Nat. Commun. 7, 10737 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. 3, 345–354 (2019).PubMed 

Google Scholar 
May, R. M. Stability and Complexity in Model Ecosystems Vol. 6 (Princeton University Press, 1974).
Google Scholar 
McCann, K. S. The diversity-stability debate. Nature 405, 228–233 (2000).CAS 
PubMed 

Google Scholar 
van Altena, C., Hemerik, L. & de Ruiter, P. C. Food web stability and weighted connectance: The complexity-stability debate revisited. Theor. Ecol. 9, 49–58 (2016).
Google Scholar 
Dougoud, M., Vinckenbosch, L., Rohr, R. P., Bersier, L.-F. & Mazza, C. The feasibility of equilibria in large ecosystems: A primary but neglected concept in the complexity-stability debate. PLoS Comput. Biol. 14, e1005988 (2018).PubMed 
PubMed Central 

Google Scholar 
McCann, K. & Hastings, A. Re-evaluating the omnivory-stability relationship in food webs. Proc. R. Soc. Lond. B 264, 1249–1254 (1997).ADS 

Google Scholar 
Pimm, S. L. & Lawton, J. H. On feeding on more than one trophic level. Nature 275, 542–544 (1978).ADS 

Google Scholar 
Link, J. Does food web theory work for marine ecosystems?. Mar. Ecol. Prog. Ser. 230, 1–9 (2002).ADS 

Google Scholar 
Bieg, C. et al. Linking humans to food webs: A framework for the classification of global fisheries. Front. Ecol. Environ. 16, 412–420 (2018).
Google Scholar 
Shephard, S. et al. Scavenging on trawled seabeds can modify trophic size structure of bottom-dwelling fish. ICES J. Mar. Sci. 71, 398–405 (2014).
Google Scholar 
Gilarranz, L. J., Rayfield, B., Liñán-Cembrano, G., Bascompte, J. & Gonzalez, A. Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science 357, 199–201 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E. & Fontaine, C. Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities. Ecol. Lett. 24, 2364–2377 (2021).PubMed 

Google Scholar 
Shanafelt, D. W. & Loreau, M. Stability trophic cascades in food chains. R. Soc. Open Sci. 5, 180995 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Barbier, M. & Loreau, M. Pyramids and cascades: A synthesis of food chain functioning and stability. Ecol. Lett. 22, 405–419 (2019).PubMed 

Google Scholar 
Sánchez, M. F. et al. Caracterización ecológica del Golfo San Jorge (Argentina) mediante modelación ecotrófica multiespecífica. 30 https://www.inidep.edu.ar/wordpress/?page_id=1959 (2009)Gaitán, E. N. Tramas Tróficas en Sistemas Frontales del Mar Argentino: Estructura, Dinámica y Complejidad Analizada Mediante Isótopos Estables (Universidad Nacional de Mar del Plata, Facultad de Ciencias Exactas y Naturales, 2012).
Google Scholar 
Pinnegar, J. K. & Polunin, N. V. C. Differential fractionation of 13C and 15N among fish tissues: Implications for the study of trophic interactions. Funct. Ecol. 13, 225–231 (1999).
Google Scholar 
Philippsen, J. S. & Benedito, E. Discrimination factor in the trophic ecology of fishes: A review about sources of variation and methods to obtain it. Oecol. Aust. 17, 205–2016 (2013).
Google Scholar 
Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).PubMed 

Google Scholar 
Lefebvre, S. & Dubois, S. The stony road to understand isotopic enrichment and turnover rates: Insight into the metabolic part. Vie Milieu-life Environ. 66, 305–314 (2016).
Google Scholar 
Funes, M., Irigoyen, A. J., Trobbiani, G. A. & Galván, D. E. Stable isotopes reveal different dependencies on benthic and pelagic pathways between Munida gregaria ecotypes. Food Webs 17, e00101 (2018).
Google Scholar 
Santos, B. & Villarino, M. F. Evaluación del Estado de Explotación del Efectivo sur de 41 S de la Merluza (Merluccius hubbsi) y Estimación de la Captura Biológicamente Aceptable Para 2014. Informe Técnico Oficial INIDEP. 1–30 (2013).Belleggia, M., Giberto, D. & Bremec, C. Adaptation of diet in a changed environment: Increased consumption of lobster krill Munida gregaria (Fabricius, 1793) by Argentine hake. Mar. Ecol. 38, e12445 (2017).ADS 

Google Scholar 
Diez, M. J., Cabreira, A. G., Madirolas, A. & Lovrich, G. A. Hydroacoustical evidence of the expansion of pelagic swarms of Munida gregaria (Decapoda, Munididae) in the Beagle Channel and the Argentine Patagonian Shelf, and its relationship with habitat features. J. Sea Res. 114, 1–12 (2016).ADS 

Google Scholar 
Ravalli, C., De La Garza, J. & Greco, L. L. Distribución de los morfotipos gregaria y subrugosa de la langostilla Munida gregaria (Decapoda, Galatheidae) en el Golfo San Jorge en la campaña de verano AE-01/2011. Integración de resultados con las campañas 2009 y 2010. Rev. Invest. Desarr. Pesq. 22, 29–41 (2013).
Google Scholar 
Belleggia, M. et al. Are hakes truly opportunistic feeders? A case of prey selection by the Argentine hake Merluccius hubbsi off southwestern Atlantic. Fish. Res. 214, 166–174 (2019).
Google Scholar 
Roux, A., Piñero, R., Moriondo, P. & Fernández, M. Diet of the red shrimp Pleoticus muelleri (Bate, 1888) in Patagonian fishing grounds, Argentine. Rev. Biol. Mar. Oceanogr. 44, 25 (2009).
Google Scholar 
de la Garza, J. et al. An Overview of the Argentine Red Shrimp (Pleoticus muelleri, Decapoda, Solenoceridae) Fishery in Argentina: Biology, Fishing, Management and Ecological Interactions (Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), 2017).
Google Scholar 
Sánchez, M. F. & Prenski, L. B. Ecología trófica de peces demersales en el Golfo San Jorge. Trophic Ecol. Demersal Fish San Jorge Gulf 10, 57–71 (1996).
Google Scholar 
Copello, S., Quintana, F. & Pérez, F. Diet of the southern giant petrel in Patagonia: Fishery-related items and natural prey. Endang. Species Res. 6, 15–23 (2008).
Google Scholar 
Alonso, R. B. et al. The opportunistic sense: The diet of Argentine hake Merluccius hubbsi reflects changes in prey availability. Region. Stud. Mar. Sci. 27, 100540 (2019).
Google Scholar 
Marón, C. F. et al. Increased wounding of southern right whale (Eubalaena australis) calves by kelp gulls (Larus dominicanus) at Península Valdés, Argentina. PLoS One 10, e0139291 (2015).PubMed 
PubMed Central 

Google Scholar 
Fazio, A., Argüelles, M. B. & Bertellotti, M. Change in southern right whale breathing behavior in response to gull attacks. Mar. Biol. 162, 267–273 (2015).
Google Scholar 
Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Kéfi, S. et al. Network structure beyond food webs: Mapping non-trophic and trophic interactions on Chilean rocky shores. Ecology 96, 291–303 (2015).
Google Scholar 
Mougi, A. The roles of amensalistic and commensalistic interactions in large ecological network stability. Sci. Rep. 6, 29929 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mougi, A. & Kondoh, M. Diversity of interaction types and ecological community stability. Science 337, 349–351 (2012).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Kéfi, S., Miele, V., Wieters, E. A., Navarrete, S. A. & Berlow, E. L. How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience. PLoS Biol. 14, e1002527 (2016).PubMed 
PubMed Central 

Google Scholar  More

May, R. M. Biological populations with nonoverlapping generations: stable points, stable cycles, and chaos. Science 186, 645–647 (1974).CAS 
PubMed 
Article 

Google Scholar 
Beddington, J. R., Free, C. A. & Lawton, J. H. Dynamic complexity in predator–prey models framed in difference equations. Nature 255, 58–60 (1975).Article 

Google Scholar 
Hastings, A., Hom, C. L., Ellner, S., Turchin, P. & Godfray, H. C. J. Chaos in ecology: is Mother Nature a strange attractor? Annu. Rev. Ecol. Syst. 24, 1–33 (1993).Article 

Google Scholar 
Cressie, N. & Wikle, C. K. Statistics for Spatio-Temporal Data (John Wiley & Sons, 2011).The State of World Fisheries and Aquaculture 2020 (FAO, 2020).Hastings, A. & Powell, T. Chaos in a three-species food chain. Ecology 72, 896–903 (1991).Article 

Google Scholar 
Huisman, J. & Weissing, F. J. Biodiversity of plankton by species oscillations and chaos. Nature 402, 407–410 (1999).Article 

Google Scholar 
Doebeli, M. & Ispolatov, I. Chaos and unpredictability in evolution. Evolution 68, 1365–1373 (2014).PubMed 
Article 

Google Scholar 
Pearce, M. T., Agarwala, A. & Fisher, D. S. Stabilization of extensive fine-scale diversity by ecologically driven spatiotemporal chaos. Proc. Natl Acad. Sci. USA 117, 14572–14583 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Costantino, R. F., Desharnais, R. A., Cushing, J. M. & Dennis, B. Chaotic dynamics in an insect population. Science 275, 389–391 (1997).CAS 
PubMed 
Article 

Google Scholar 
Becks, L., Hilker, F. M., Malchow, H., Jürgens, K. & Arndt, H. Experimental demonstration of chaos in a microbial food web. Nature 435, 1226–1229 (2005).CAS 
PubMed 
Article 

Google Scholar 
Benincá, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).PubMed 
Article 
CAS 

Google Scholar 
Tilman, D. & Wedin, D. Oscillations and chaos in the dynamics of a perennial grass. Nature 353, 653–655 (1991).Article 

Google Scholar 
Turchin, P. & Ellner, S. P. Living on the edge of chaos: population dynamics of fennoscandian voles. Ecology 81, 3099–3116 (2000).Article 

Google Scholar 
Ferrari, M. J. et al. The dynamics of measles in sub-Saharan Africa. Nature 451, 679–684 (2008).CAS 
PubMed 
Article 

Google Scholar 
Benincà, E., Ballantine, B., Ellner, S. P. & Huisman, J. Species fluctuations sustained by a cyclic succession at the edge of chaos. Proc. Natl Acad. Sci. USA 112, 6389–6394 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hassell, M. P., Lawton, J. H. & May, R. M. Patterns of dynamical behaviour in single-species populations. J. Anim. Ecol. 45, 471–486 (1976).Article 

Google Scholar 
Sibly, R. M., Barker, D., Hone, J. & Pagel, M. On the stability of populations of mammals, birds, fish and insects. Ecol. Lett. 10, 970–976 (2007).PubMed 
Article 

Google Scholar 
Shelton, A. O. & Mangel, M. Fluctuations of fish populations and the magnifying effects of fishing. Proc. Natl Acad. Sci USA. 108, 7075–7080 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salvidio, S. Stability and annual return rates in amphibian populations. Amphib. Reptil. 32, 119–124 (2011).Article 

Google Scholar 
Snell, T. W. & Serra, M. Dynamics of natural rotifer populations. Hydrobiologia 368, 29–35 (1998).Article 

Google Scholar 
Gross, T., Ebenhöh, W. & Feudel, U. Long food chains are in general chaotic. Oikos 109, 135–144 (2005).Article 

Google Scholar 
Ispolatov, I., Madhok, V., Allende, S. & Doebeli, M. Chaos in high-dimensional dissipative dynamical systems. Sci. Rep. 5, 12506 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clark, T. J. & Luis, A. D. Nonlinear population dynamics are ubiquitous in animals. Nat. Ecol. Evol. 4, 75–81 (2020).CAS 
PubMed 
Article 

Google Scholar 
Sivakumar, B., Berndtsson, R., Olsson, J. & Jinno, K. Evidence of chaos in the rainfall-runoff process. Hydrol. Sci. J. 46, 131–145 (2001).CAS 
Article 

Google Scholar 
Hanski, I., Turchin, P., Korpimäki, E. & Henttonen, H. Population oscillations of boreal rodents: regulation by mustelid predators leads to chaos. Nature 364, 232–235 (1993).CAS 
PubMed 
Article 

Google Scholar 
Turchin, P. & Taylor, A. D. Complex dynamics in ecological time series. Ecology 73, 289–305 (1992).Article 

Google Scholar 
Munch, S. B., Brias, A., Sugihara, G. & Rogers, T. L. Frequently asked questions about nonlinear dynamics and empirical dynamic modelling. ICES J. Mar. Sci. 77, 1463–1479 (2020).Article 

Google Scholar 
Sugihara, G. & May, R. M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990).CAS 
PubMed 
Article 

Google Scholar 
Ellner, S. P. & Turchin, P. Chaos in a noisy world: new methods and evidence from time-series analysis. Am. Nat. 145, 343–375 (1995).Article 

Google Scholar 
Nychka, D., Ellner, S., Gallant, A. R. & McCaffrey, D. Finding chaos in noisy systems. J. R. Stat. Soc. B 54, 399–426 (1992).
Google Scholar 
Webber, C. L. & Zbilut, J. P. Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol. 76, 965–973 (1994).PubMed 
Article 

Google Scholar 
Bandt, C. & Pompe, B. Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88, 174102 (2002).PubMed 
Article 
CAS 

Google Scholar 
Luque, B., Lacasa, L., Ballesteros, F. & Luque, J. Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80, 46103 (2009).CAS 
Article 

Google Scholar 
Toker, D., Sommer, F. T. & D’Esposito, M. A simple method for detecting chaos in nature. Commun. Biol. 3, 11 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pikovsky, A. & Politi, A. Lyapunov Exponents: A Tool to Explore Complex Dynamics (Cambridge Univ. Press, 2016).Rosenstein, M. T., Collins, J. J. & De Luca, C. J. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65, 117–134 (1993).Article 

Google Scholar 
Dämmig, M. & Mitschke, F. Estimation of Lyapunov exponents from time series: the stochastic case. Phys. Lett. A 178, 385–394 (1993).Article 

Google Scholar 
Prendergast, J., Bazeley-White, E., Smith, O., Lawton, J. & Inchausti, P. The Global Population Dynamics Database (KNB, 2010); https://doi.org/10.5063/F1BZ63Z8Thibaut, L. M. & Connolly, S. R. Hierarchical modeling strengthens evidence for density dependence in observational time series of population dynamics. Ecology 101, e02893 (2020).PubMed 
Article 

Google Scholar 
Knape, J. & de Valpine, P. Are patterns of density dependence in the Global Population Dynamics Database driven by uncertainty about population abundance? Ecol. Lett. 15, 17–23 (2012).PubMed 
Article 

Google Scholar 
Takens, F. in Dynamical Systems and Turbulence (eds Rand, D. A. & Young, L. S.) 366–381 (Springer, 1981).Sugihara, G. Nonlinear forecasting for the classification of natural time series. Philos. Trans. R. Soc. A 348, 477–495 (1994).
Google Scholar 
Loh, J. et al. The Living Planet Index: using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. B 360, 289–295 (2005).Article 

Google Scholar 
Kendall, B. E. Cycles chaos, and noise in predator–prey dynamics. Chaos Solitons Fractals 12, 321–332 (2001).Article 

Google Scholar 
Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).CAS 
PubMed 
Article 

Google Scholar 
Anderson, D. M. & Gillooly, J. F. Allometric scaling of Lyapunov exponents in chaotic populations. Popul. Ecol. 62, 364–369 (2020).Article 

Google Scholar 
Graham, D. W. et al. Experimental demonstration of chaotic instability in biological nitrification. ISME J. 1, 385–393 (2007).CAS 
PubMed 
Article 

Google Scholar 
Turchin, P. Nonlinear time-series modeling of vole population fluctuations. Res. Popul. Ecol. 38, 121–132 (1996).Article 

Google Scholar 
Becks, L. & Arndt, H. Different types of synchrony in chaotic and cyclic communities. Nat. Commun. 4, 1359 (2013).PubMed 
Article 
CAS 

Google Scholar 
Becks, L. & Arndt, H. Transitions from stable equilibria to chaos, and back, in an experimental food web. Ecology 89, 3222–3226 (2008).PubMed 
Article 

Google Scholar 
Rezende, E. L., Albert, E. M., Fortuna, M. A. & Bascompte, J. Compartments in a marine food web associated with phylogeny, body mass, and habitat structure. Ecol. Lett. 12, 779–788 (2009).PubMed 
Article 

Google Scholar 
Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).CAS 
PubMed 
Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2020-2 (IUCN, 2020); https://www.iucnredlist.orgFreckleton, R. P. & Watkinson, A. R. Are weed population dynamics chaotic? J. Appl. Ecol. 39, 699–707 (2002).Article 

Google Scholar 
May, R. M. Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976).CAS 
PubMed 
Article 

Google Scholar 
Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the ocean? PLoS Biol. 9, e1001127 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: a global meta-analysis. Fish Fish. 19, 964–973 (2018).Article 

Google Scholar 
Boettiger, C., Harte, T., Chamberlain, S. & Ram, K. rgpdd: R Interface to the Global Population Dynamics Database. https://docs.ropensci.org/rgpdd, https://github.com/ropensci/rgpdd (2019).Brook, B. W., Traill, L. W. & Bradshaw, C. J. A. Minimum viable population sizes and global extinction risk are unrelated. Ecol. Lett. 9, 375–382 (2006).PubMed 
Article 

Google Scholar 
Baars, J. W. M. Autecological investigations of marine diatoms, 2. Generation times of 50 species. Hydrobiol. Bull. 15, 137–151 (1981).Article 

Google Scholar 
Lavigne, A. S., Sunesen, I. & Sar, E. A. Morphological, taxonomic and nomenclatural analysis of species of Odontella, Trieres and Zygoceros (Triceratiaceae, Bacillariophyta) from Anegada Bay (Province of Buenos Aires, Argentina). Diatom Res. 30, 307–331 (2015).Article 

Google Scholar 
Anderson, D. M. & Gillooly, J. F. Physiological constraints on long-term population cycles: a broad-scale view. Evol. Ecol. Res. 18, 693–707 (2017).
Google Scholar 
Janes, M. J. Oviposition studies on the chinch bug, Blissus leucopterus (Say). Ann. Entomol. Soc. Am. 28, 109–120 (1935).Article 

Google Scholar 
Cook, L. M. Food-plant specialization in the moth Panaxia dominula L. Evolution 15, 478–485 (1961).Article 

Google Scholar 
Casey, T. M. Flight energetics of sphinx moths: power input during hovering flight. J. Exp. Biol. 64, 529–543 (1976).CAS 
PubMed 
Article 

Google Scholar 
Kobayashi, A., Tanaka, Y. & Shimada, M. Genetic variation of sex allocation in the parasitoid wasp Heterospilus prosopidis. Evolution 57, 2659–2664 (2003).PubMed 
Article 

Google Scholar 
Hozumi, N. & Miyatake, T. Body-size dependent difference in death-feigning behavior of adult Callosobruchus chinensis. J. Insect Behav. 18, 557–566 (2005).Article 

Google Scholar 
Huntley, M. E. & Lopez, M. D. G. Temperature-dependent production of marine copepods: a global synthesis. Am. Nat. 140, 201–242 (1992).CAS 
PubMed 
Article 

Google Scholar 
Cohen, R. E. & Lough, R. G. Length–weight relationships for several copepods dominant in the Georges Bank–Gulf of Maine area. J. Northwest Atl. Fish. Sci. 2, 47–52 (1981).Article 

Google Scholar 
World Register of Marine Species (WoRMS, accessed 1 November 2020); https://doi.org/10.14284/170Nakamura, Y. Growth and grazing of a large heterotrophic dinoflagellate, Noctiluca scintillans, in laboratory cultures. J. Plankton Res. 20, 1711–1720 (1998).Article 

Google Scholar 
Boulding, E. G. & Platt, T. Variation in photosynthetic rates among individual cells of a marine dinoflagellate. Mar. Ecol. Prog. Ser. 29, 199–203 (1986).CAS 
Article 

Google Scholar 
Rimet, F. et al. The Observatory on LAkes (OLA) database: sixty years of environmental data accessible to the public. J. Limnol. https://doi.org/10.4081/jlimnol.2020.1944 (2020).Rudstam, L. Zooplankton Survey of Oneida Lake, New York, 1964 to Present (KNB, 2020); https://knb.ecoinformatics.org/view/kgordon.17.99https://knb.ecoinformatics.org/knb/metacat/kgordon.17.67/defaultDumont, H. J., Van de Velde, I. & Dumont, S. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecologia 19, 75–97 (1975).PubMed 
Article 

Google Scholar 
Geller, W. & Müller, H. Seasonal variability in the relationship between body length and individual dry weight as related to food abundance and clutch size in two coexisting Daphnia species. J. Plankton Res. 7, 1–18 (1985).Article 

Google Scholar 
Branstrator, D. K. Contrasting life histories of the predatory cladocerans Leptodora kindtii and Bythotrephes longimanus. J. Plankton Res. 27, 569–585 (2005).Article 

Google Scholar 
Rosen, R. A. Length–dry weight relationships of some freshwater zooplankton. J. Freshw. Ecol. 1, 225–229 (1981).Article 

Google Scholar 
Peters, R. H. & Downing, J. A. Empirical analysis of zooplankton filtering and feeding rates. Limnol. Oceanogr. 29, 763–784 (1984).Article 

Google Scholar 
Eckmann, J. P., Kamphorst, S. O. & Ruelle, D. Recurrence plots of dynamical systems. Europhys. Lett. 4, 973–977 (1987).Article 

Google Scholar 
Luque, B., Lacasa, L., Ballesteros, F. J. & Robledo, A. Analytical properties of horizontal visibility graphs in the Feigenbaum scenario. Chaos 22, 013109 (2012).PubMed 
Article 

Google Scholar 
McCaffrey, D. F., Ellner, S., Gallant, A. R. & Nychka, D. W. Estimating the Lyapunov exponent of a chaotic system with nonparametric regression. J. Am. Stat. Assoc. 87, 682–695 (1992).Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Ricker, W. E. Stock and recruitment. J. Fish. Board Can. 11, 559–623 (1954).Article 

Google Scholar  More

The data set consists of tree-ring width measurements in Decadal/Tuscon RWL format24, COFECHA25 listings for every RWL file, online-only Tables 1 and 2 with the description for every living-tree and historical chronology. In each RWL file the measurements for each tree denoted by a number are usually represented by several cores denoted by the letters a,b,c, etc., e.g. T15S1a and T15S1b are two cores for the first tree at the site T15S, T15S15a and T15S15b are two cores for the 15th tree at the site. The historical chronologies usually contain several codes referring to different sources of materials, but the numbering is the same – numbers denote different beams from each source and letters a-d denote the measurements along different radii from each beam.Missing values in RWL files are denoted either by zeroes in the case of missing rings or by −888 in the case of missing core segments. The description of each site contains the information on the location, geographical coordinates, number of trees and samples, information on series intercorrelation, average mean sensitivity, quality of the cross-dating, and related publications (online-only Tables 1, 2). Some sites also have descriptions of vegetation and soils. The RWL files of the measurements and the related COFECHA quality control listings are publicly available in ITRDB. The ITRDB codes and links are provided in the online-only Tables 1 and 2. The whole data set is also available as a standalone set of files26 in Figshare repository, where RWL files are named as the site code plus ‘.rwl’ extension, the COFECHA listings are named as the site code plus ‘COF.txt’. For example, the site T15S is represented by the files ‘T15S.rwl’ and ‘T15SCOF.txt’. Supplementary Tables 1 and 2 represent printable versions of Online-only Tables 1 and 2, respectively.Below we describe the sources of material for each historical chronology.KirillovMaterials for the Kirillov chronology were collected over many years from archaeological excavations in the town of Kirillov, Vologda region. They include wood samples obtained from architectural buildings and various small archaeological excavations in the vicinity of the Kirillo-Belozersky monastery (59.86°N, 38.37°E). During restoration work in 1969, 1971, 1985, and 1987, samples of wooden ties and piles of foundations from brick defensive walls and monastery buildings were collected. The archaeological part of the collection also contains samples from wooden log cabins, wells, and log heaps (remnants of buildings demolished during renovation) and discovered during rescue excavations in 1994, 1998–2000, 2007, 2008, 2011, 2015, 2016, and 2018. The samples were processed in the Laboratory of Natural Science Methods in Archaeology, Institute of Archaeology RAS. Unfortunately, most of the original material has not been archived after the measurements were made. The Kirillov chronology was calendar dated with living trees from the Vologda region (sites KOV and SHBO) and materials from the Museum of Wooden Architecture of the Vologda Region “Semyonkovo”27.VologdaThe collection consists of materials from wooden buildings in the city of Vologda (59.22°N, 39.89°E). The data was assembled by D. Kats in the 1990s and later archived at the Institute of Plant and Animal Ecology in Ekaterinburg. In 2009 the collection was transferred again, and now resides at the Institute of Geography RAS, where ring-widths were measured a second time. The data set includes the samples from 19th century wooden houses on Gogol Street, numbers 3 and 5 (codes AU and AV), from Gertsen Street number 58 (code BA), from the Spaso-Prilutskiy Monastery in the northern outskirts of Vologda (code BB), and from samples of unknown origin from the 18th century (code M). The Vologda chronology was calendar dated with the Kirillov chronology.NovgorodMaterials in the Novgorod chronology are derived from archaeological excavations in the city of Velikiy Novgorod (58.52°N, 31.27°E), in addition to samples from wooden buildings of the Novgorod Region. The latter include materials from building transferred to the Museum of Wooden Architecture “Vitoslavlitsy” from the Novgorod region. These include the Chapel of Magdalena (code N04A), the Church of St. Nicolay from the village of Visokiy Ostrov (code N09A), and a church from the village of Tukholi (code N11A). Archaeological materials come from the city of Novgorod, from the excavation of Yaroslavovo Dvorische (archaeologist A.V. Andrienko, code N02A28), as well as excavations on Telegina-Redyatina Street (code ‘tere’), Posolskaya Street (code ‘posol’), Znamenskaya Street (code ‘znam’), Troitskaya Street (codes ‘35a-1-b1’ and ‘16a-1-v2’), and B. Konyushennaya Street (code ‘kon’), which were directed by archaeologist O.I. Oleynikov. The Novgorod chronology was calendar dated using the russ1 chronology from the ITRDB (with a correction for the known error of 1 year29), and by crossdating with the Kirillov and Vologda chronologies.ArkhangelskThe Arkhangelsk chronology includes samples from houses and churches from the northwestern part of the Arkhangelsk region (63.4–64.7°N, 37.4–43.4°E). These include wooden houses from the town of Pinega, Kudrina Street 45 and 55 (codes I15A and I14A, 64.70°N, 43.39°E), the house of the Bazheniny family in the village of Vavchuga, Kholmogorskoye district (code I21A, 64.23°N, 41.92°E), the Church of Introduction in the village of Vorzogory (code I02A, 63.89°N, 37.67°E), the Church of Vladimir in the village of Medvedevskaya (code I04A, 63.81°N, 38.32°E), and from the the Ensemble of the Church in the village of Piyala (codes I08A, I09A, P, 63.43°N, 39.08°E), all located in the Onezhskiy District. The chronology was calendar dated using a living pine tree-ring series (code I24S, 64.11°N, 38.03°E) in addition to crossdating with the Solovki chronology30.KareliaThis chronology includes materials from eight churches in the Republic of Karelia, all located along the shores of Onega Lake (60.80–62.72°N, 33.06–35.27°E)31. Most of these measurements are of lower precision than of the other data in this study (0.05 mm versus 0.001 mm) however, they are vital to the dendrochronological dating in the region. The Karelia chronology was calendar dated using the Solovki and Arkhangelsk chronologies.Zapadnaya Dvina (ZD1, ZD2)Tree-ring chronologies ZD1 and ZD2 were constructed with subfossil oak trees sampled in the alluvial deposits of the Zapadnaya Dvina River and its tributary, the Velesa River. The sample sites include reaches of both rivers upstream of their confluence (56.06°N, 31.97°E). Subfossil oak tree trunks were discovered in the riverbed as well as in riverbank alluvial deposits and oxbow lakes. The ZD1 and ZD2 chronologies do not overlap with the living oak tree-ring series from the region, but were crossdated with chronologies from Belarus and from the Baltic region. ZD1 (CE 572–1382) was calendar dated with oak samples from the Church of the Saviour’s Transfiguration in Polotsk (Belarus) which spans CE 869-112232; it also crossdates with subfossil oak series from Smarhon, Belarus33 and the Baltic 1 chronology34. A detailed report was previously published elsewhere14. The calendar age of the ZD2 chronology (CE 1346–1762) was established by comparison with the 2021BLT3 chronology35.KostromaMaterials for the Kostroma chronology come from archaeological excavations in the City of Kostroma and from the wooden buildings from the surrounding Kostroma Region. They include materials from a church in the Andreevskoye village (code K2A, 58.16°N, 41.30°E), two buildings from the Museum of Wooden Architecture in the Kostroma region, which include the house of Skobyolkin (code K13A), and the Church of Ilijah the Prophet (code K14A). The other materials come from the ‘Melochniye Ryady’ excavations in the center of Kostroma, (archaeologist A.Lazarev, code K09A). The chronology was calendar dated using the Kirillov and Vologda chronologies.SmolenskSeven beams of pine come from archaeological excavations at Pobedy Square in the city of Smolensk (54.78°N, 32.05°E)36. They were crossdated using the chronology from the Dannenshtern House in Riga37. The material of the Dannenshtern House likely comes from near the headwaters of the Kasplya tributary of the Daugava River (Zapadnaya Dvina River) located near Smolensk.SolovkiThe Solovki chronology consists of measurements from living trees (pines PDB and spruce PDEL; 65.12°N, 35.57°E), beams in a church on Malaya Muksalma Island (code MMCH; 65.01°N, 36.00°E), a building built for resin extraction (code SMOL), a barn (code SOLAM), and from a monastery outbuilding (or skit) on Sekirnaya Hill (code SLKL; 64.08°N, 35.57°E). Also included in the chronology are series from a satellite monastery building on Bolshaya Muksalma Island (code BMSK; 65.03°N, 35.90°E), series from a bathhouse nearby (code BMBN), samples from the Church of Andrew the First-Called on Zayatskiy Island (code B24A; 64.97°N, 35.65°E), series from a 19th century building (code SOLIZ), along with archaeological materials from the monastery (codes B27A, B28A), and a barn on Anzer Island (codes B39A, B38A; 65.19°N, 35.98°E). The earliest part of the chronology consists of ring-width series from beams from the 16th century Spaso-Preobrazhenskiy Cathedral (code SP; 65.02°N, 35.71°E). More

van den Hoogen J, Geisen S, Routh D. Soil nematode abundance and functional group composition at a global scale. Nature 2019;572:194–98.PubMed 

Google Scholar 
Yeates GW, Bongers T, Degoede RGM, Freckman DW, Georgieva SS. Feeding habits in soil nematode families and genera – an outline for soil ecologists. J Nematol. 1993;25:315–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nicol JM, Turner SJ, Coyne DL, Nijs Ld, Hockland S, Maafi ZT. Current nematode threats to world agriculture. In: Jones J, Gheysen G, Fenoll C, editors. Genomics and Molecular Genetics of Plant-Nematode Interactions. Dordrecht: Springer; 2011. p. 21–43.Decraemer W, Hunt D. Structure and Classification. In: R. N. Perry, M. Moens, Eds. Plant Nematology. CABI, Wallingford, Oxfordshire, UK and Boston, USA, 2005, pp. 26–27.Fleming TR, Maule AG, Fleming CC. Chemosensory responses of plant parasitic nematodes to selected phytochemicals reveal long-term habituation traits. J Nematol. 2017;49:462–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Murungi LK, Kirwa H, Coyne D, Teal PEA, Beck JJ, Torto B. Identification of key root volatiles signaling preference of tomato over spinach by the root knot nematode Meloidogyne incognita. J AgricFood Chem. 2018;66:7328–36.CAS 

Google Scholar 
Wang CL, Masler EP, Rogers ST. Responses of Heterodera glycines and Meloidogyne incognita infective juveniles to root tissues, root exudates, and root extracts from three plant species. Plant Dis. 2018;102:1733–40.CAS 
PubMed 

Google Scholar 
Sikder MM, Vestergård M. Impacts of root metabolites on soil nematodes. Front Plant Sci. 2020;10:1792.PubMed 
PubMed Central 

Google Scholar 
van Dam NM, Tytgat TOG, Kirkegaard JA. Root and shoot glucosinolates: A comparison of their diversity, function and interactions in natural and managed ecosystems. Phytochem Rev. 2009;8:171–86.CAS 

Google Scholar 
Bressan M, Roncato MA, Bellvert F, et al. Exogenous glucosinolate produced by Arabidopsis thaliana has an impact on microbes in the rhizosphere and plant roots. ISME J. 2009;3:1243–57.CAS 
PubMed 

Google Scholar 
Mucha S, Heinzlmeir S, Kriechbaumer V, Strickland B, Kirchhelle C, Choudhary M, et al. The formation of a camalexin biosynthetic metabolon. Plant Cell. 2019;31:2697–710.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kettles GJ, Drurey C, Schoonbeek HJ, Maule AJ, Hogenhout SA. Resistance of Arabidopsis thaliana to the green peach aphid, Myzus persicae, involves camalexin and is regulated by microRNAs. N. Phytol. 2013;198:1178–90.CAS 

Google Scholar 
Tsuji J, Jackson EP, Gage DA, Hammerschmidt R, Somerville SC. Phytoalexin accumulation in Arabidopsis thaliana during the hypersensitive reaction to Pseudomonas syringae pv. syringae. Plant Physiol. 1992;98:1304–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Thomma BPHJ, Nelissen I, Eggermont K, Broekaert WF. Deficiency in phytoalexin production causes enhanced susceptibility of Arabidopsis thaliana to the fungus Alternaria brassicicola. Plant J 1999;19:163–71.CAS 
PubMed 

Google Scholar 
Teixeira MA, Wei LH, Kaloshian I. Root-knot nematodes induce pattern-triggered immunity in Arabidopsis thaliana roots. N Phytol. 2016;211:276–87.CAS 

Google Scholar 
Shah SJ, Anjam MS, Mendy B, Anwer MA, Habash SS, Lozano-Torres JL, et al. Damage-associated responses of the host contribute to defence against cyst nematodes but not root-knot nematodes. J Exp Bot. 2017;68:5949–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ali MA, Wieczorek K, Kreil DP, Bohlmann H. The beet cyst nematode Heterodera schachtii modulates the expression of WRKY transcription factors in syncytia to favour its development in Arabidopsis roots. PLoS One. 2014;9:e102360.PubMed 
PubMed Central 

Google Scholar 
Lazzeri L, Curto G, Leoni O, Dallavalle E. Effects of glucosinolates and their enzymatic hydrolysis products via myrosinase on the root-knot nematode Meloidogyne incognita (Kofoid et White) Chitw. J Agric Food Chem. 2004;52:6703–07.CAS 
PubMed 

Google Scholar 
Avato P, D’Addabbo T, Leonetti P, Argentieri MP. Nematicidal potential of Brassicaceae. Phytochem Rev. 2013;12:791–802.CAS 

Google Scholar 
Mathesius U. Flavonoid functions in plants and their interactions with other organisms. Plants (Basel) 2018;7:30.
Google Scholar 
Weston LA, Mathesius U. Flavonoids: Their structure, biosynthesis and role in the rhizosphere, including allelopathy. J Chem Ecol. 2013;39:283–97.CAS 
PubMed 

Google Scholar 
Badri DV, Loyola-Vargas VM, Broeckling CD, De-la-Pena C, Jasinski M, Santelia D, et al. Altered profile of secondary metabolites in the root exudates of Arabidopsis ATP-binding cassette transporter mutants. Plant Physiol. 2008;146:762–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Cesco S, Neumann G, Tomasi N, Pinton R, Weisskopf L. Release of plant-borne flavonoids into the rhizosphere and their role in plant nutrition. Plant Soil. 2010;329:1–25.CAS 

Google Scholar 
Drewnowski A, Gomez-Carneros C. Bitter taste, phytonutrients, and the consumer: A review. Am J Clin Nutr. 2000;72:1424–35.CAS 
PubMed 

Google Scholar 
Chin S, Behm CA, Mathesius U. Functions of flavonoids in plant-nematode interactions. Plants (Basel) 2018;7:1–17.
Google Scholar 
Kaplan DT, Keen NT, Thomason IJ. Association of glyceollin with the incompatible response of soybean roots to Meloidogyne incognita. Physiol Plant Pathol. 1980;16:309–18.CAS 

Google Scholar 
Aoudia H, Ntalli N, Aissani N, Yahiaoui-Zaidi R, Caboni P. Nematotoxic phenolic compounds from Melia azedarach against Meloidogyne incognita. J AgricFood Chem. 2012;60:11675–80.CAS 

Google Scholar 
Kennedy MJ, Niblack TL, Krishnan HB. Infection by Heterodera glycines elevates isoflavonoid production and influences soybean nodulation. J Nematol. 1999;31:341–47.CAS 
PubMed 
PubMed Central 

Google Scholar 
Collingborn FMB, Gowen SR, Mueller-Harvey I. Investigations into the biochemical basis for nematode resistance in roots of three Musa cultivars in response to Radopholus similis infection. J Agric Food Chem. 2000;48:5297–301.CAS 
PubMed 

Google Scholar 
Cook R, Tiller SA, Mizen KA, Edwards R. Isoflavonoid metabolism in resistant and susceptible cultivars of white clover infected with the stem nematode Ditylenchus dipsaci. J Plant Physiol. 1995;146:348–54.CAS 

Google Scholar 
Kirwa HK, Murungi LK, Beck JJ, Torto B. Elicitation of differential responses in the root-knot nematode Meloidogyne incognita to tomato root exudate cytokinin, flavonoids, and alkaloids. J AgricFood Chem. 2018;66:11291–300.CAS 

Google Scholar 
Wuyts N, Swennen R, De, Waele D. Effects of plant phenylpropanoid pathway products and selected terpenoids and alkaloids on the behaviour of the plant-parasitic nematodes Radopholus similis. Pratylenchus penetrans Meloidogyne Incogn Nematol. 2006;8:89–101.CAS 

Google Scholar 
Hartwig UA, Joseph CM, Phillips DA. Flavonoids released naturally from alfalfa seeds enhance growth rate of Rhizobium meliloti. Plant Physiol. 1991;95:797–803.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hassan S, Mathesius U. The role of flavonoids in root-rhizosphere signalling: Opportunities and challenges for improving plant-microbe interactions. J Exp Bot. 2012;63:3429–44.CAS 
PubMed 

Google Scholar 
Kudjordjie EN, Sapkota R, Nicolaisen M. Arabidopsis assemble distinct root-associated microbiomes through the synthesis of an array of defense metabolites. PLoS One. 2021;10:e0259171.
Google Scholar 
Rønn R, Vestergård M, Ekelund F. Interactions between bacteria, protozoa and nematodes in soil. Acta Protozool. 2012;51:223–35.
Google Scholar 
Thakur MP, Geisen S. Trophic regulations of the soil microbiome. Trends Microbiol. 2019;27:771–80.CAS 
PubMed 

Google Scholar 
Elhady A, Gine A, Topalovic O, Jacquiod S, Sorensen SJ, Sorribas FJ, et al. Microbiomes associated with infective stages of root-knot and lesion nematodes in soil. PLoS One. 2017;12:e0177145.PubMed 
PubMed Central 

Google Scholar 
Toju H, Tanaka Y. Consortia of anti-nematode fungi and bacteria in the rhizosphere of soybean plants attacked by root-knot nematodes. R Soc Open Sci. 2019;6:181693.CAS 
PubMed 
PubMed Central 

Google Scholar 
Topalović O, Bredenbruch S, Schleker ASS, Heuer H. Microbes attaching to endoparasitic phytonematodes in soil trigger plant defense upon root penetration by the nematode. Front Plant Sci 2020;11:138.PubMed 
PubMed Central 

Google Scholar 
Schaad NW, Walker JT. The use of density-gradient centrifugation for the purification of eggs of Meloidogyne spp. J Nematol. 1975;7:203–04.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hooper DJ, Hallmann J, Subbotin SA. Methods for extraction, processing and detection of plant and soil nematodes. In: Luc M, Sikora RA, Bridge J, editors. Plant parasitic nematodes in subtropical and tropical agriculture. Second ed. Wallingford, UK: CABI Publishing; 2005. p. 53.Topalovic O, Elhady A, Hallmann J, Richert-Poggeler KR, Heuer H. Bacteria isolated from the cuticle of plant-parasitic nematodes attached to and antagonized the root-knot nematode Meloidogyne hapla. Sci Rep. 2019;9:11477.PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.Porazinska DL, Giblin-Davis RM, Faller L, Farmerie W, Kanzaki N, Morris K, et al. Evaluating high-throughput sequencing as a method for metagenomic analysis of nematode diversity. Mol Ecol Resour. 2009;9:1439–50.CAS 
PubMed 

Google Scholar 
Sapkota R, Nicolaisen M. High-throughput sequencing of nematode communities from total soil DNA extractions. BMC Ecol. 2015;15:3.PubMed 
PubMed Central 

Google Scholar 
Sikder MM, Vestergård M, Sapkota R, Kyndt T, Nicolaisen M. Evaluation of metabarcoding primers for analysis of soil nematode communities. Diversity (Basel) 2020;12:388.CAS 

Google Scholar 
Ihrmark K, Bodeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, et al. New primers to amplify the fungal ITS2 region – evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol. 2012;82:666–77.CAS 
PubMed 

Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 

Google Scholar 
Sapkota R, Skantar AM, Nicolaisen M. A TaqMan real-time PCR assay for detection of Meloidogyne hapla in root galls and in soil. Nematol. 2016;18:147–54.CAS 

Google Scholar 
Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: A versatile open source tool for metagenomics. Peer J. 2016;4:e2584.PubMed 
PubMed Central 

Google Scholar 
Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol Evol. 2013;4:914–19.
Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D6.CAS 
PubMed 

Google Scholar 
Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–D8.CAS 
PubMed 

Google Scholar 
UNITE. UNITE QIIME release for Fungi [Internet]. UNITE Community. 2020.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–36.CAS 
PubMed 
PubMed Central 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J, Blanchet FG, Kindt R, Friendly M, Legendre P, McGlinn D, et al. Vegan: Community Ecology Package. Ordination methods, diversity analysis and other functions for community and vegetation ecologists. R Package Version 2.5-5 ed: The Comprehensive R Archive Network; 2019.Love MI, Huber W, Anders S. Moderated estimation of fold change anddispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 

Google Scholar 
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics 2014;30:3123–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kudjordjie EN, Sapkota R, Steffensen SK, Fomsgaard IS, Nicolaisen M. Maize synthesized benzoxazinoids affect the host associated microbiome. Microbiome 2019;7:59.PubMed 
PubMed Central 

Google Scholar 
McCarthy DJ, Chen YS, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40:4288–97.CAS 
PubMed 
PubMed Central 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139–40.CAS 
PubMed 

Google Scholar 
Frerigmann H, Gigolashvili T. MYB34, MYB51, and MYB122 distinctly regulate indolic glucosinolate biosynthesis in Arabidopsis thaliana. Mol Plant. 2014;7:814–28.CAS 
PubMed 

Google Scholar 
Schulz E, Tohge T, Zuther E, Fernie AR, Hincha DK. Flavonoids are determinants of freezing tolerance and cold acclimation in Arabidopsis thaliana. Sci Rep. 2016;6:34027.Borevitz JO, Xia Y, Blount J, Dixon RA, Lamb C. Activation tagging identifies a conserved MYB regulator of phenylpropanoid biosynthesis. Plant Cell. 2000;12:2383–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Du SS, Zhang HM, Bai CQ, Wang CF, Liu QZ, Liu ZL, et al. Nematocidal flavone-C-glycosides against the root-knot nematode (Meloidogyne incognita) from Arisaema erubescens tubers. Molecules 2011;16:5079–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhou DM, Feng H, Schuelke T, De Santiago A, Zhang QM, Zhang JF, et al. Rhizosphere microbiomes from root knot nematode non-infested plants suppress nematode Infection. Micro Ecol. 2019;78:470–81.CAS 

Google Scholar 
Topalović O, Vestergård M. Can microorganisms assist the survival and parasitism of plant-parasitic nematodes? Trends Parasitol. 2021;37:947–58.PubMed 

Google Scholar 
De Mesel I, Derycke S, Moens T, Van der Gucht K, Vincx M, Swings J. Top-down impact of bacterivorous nematodes on the bacterial community structure: a microcosm study. Environ Microbiol. 2004;6:733–44.PubMed 

Google Scholar 
Adam M, Westphal A, Hallmann J, Heuer H. Specific microbial attachment to root knot nematodes in suppressive soil. Appl Environ Microbiol. 2014;80:2679–86.PubMed 
PubMed Central 

Google Scholar 
Ramyabharathi S, Sankari Meena K, Rajendran L, Karthikeyan G, Jonathan EI, Raguchander T. Biocontrol of wilt-nematode complex infecting gerbera by Bacillus subtilis under protected cultivation. Egypt J Biol Pest Co. 2018;28:21.
Google Scholar 
Jamal Q, Cho JY, Moon JH, Munir S, Anees M, Kim KY. Identification for the first time of cyclo (D-Pro-L-Leu) produced by Bacillus amyloliquefaciens y1 as a nematocide for control of Meloidogyne incognita. Molecules 2017;22:1839.PubMed Central 

Google Scholar 
Moosavi MR, Zare R. Fungi as biological control agents of plant-parasitic nematodes. In: Mérillon J-M, Ramawat KG, editors. Plant Defence: Biological Control. Progress in Biological Control 22. 2nd Edition ed. Switzerland: Springer; 2020. p. 333–84.Ashrafi S, Stadler M, Dababat AA, Richert-Poggeler KR, Finckh MR, Maier W. Monocillium gamsii sp nov and Monocillium bulbillosum: two nematode-associated fungi parasitising the eggs of Heterodera filipjevi. Mycokeys. 2017;27:21–38.
Google Scholar 
Nuaima RH, Ashrafi S, Maier W, Heuer H. Fungi isolated from cysts of the beet cyst nematode parasitized its eggs and counterbalanced root damages. J Pest Sci. 2021;94:563–72.
Google Scholar 
Iqbal M, Dubey M, McEwan K, Menzel U, Franko MA, Viketoft M, et al. Evaluation of Clonostachys rosea for control of plant parasitic nematodes in soil and in roots of carrot and wheat. Phytopathology 2018;108:52–59.CAS 
PubMed 

Google Scholar 
DiLegge MJ, Manter DK, Vivanco JM. A novel approach to determine generalist nematophagous microbes reveals Mortierella globalpina as a new biocontrol agent against Meloidogyne spp. nematodes. Sci Rep. 2019;9:7521.PubMed 
PubMed Central 

Google Scholar 
Goswami J, Pandey RK, Tewari JP, Goswami BK. Management of root knot nematode on tomato through application of fungal antagonists, Acremonium strictum and Trichoderma harzianum. J Environ Sci Health. 2008;43:237–40.CAS 

Google Scholar 
Chen Q, Peng D. Nematode chitin and application. In: Yang Q, Fukamizo T, editors. Targeting Chitin-containing Organisms. Advances in Experimental Medicine and Biology. 1142. Singapore: Springer; 2019. pp. 209–219.Zhou WQ, Verma VC, Wheeler TA, Woodward JE, Starr JL, Sword GA. Tapping into the cotton fungal phytobiome for novel nematode biological control tools. Phytobiomes J 2020;4:19–26.
Google Scholar 
Alcazar R, von Reth M, Bautor J, Chae E, Weigel D, Koornneef M, et al. Analysis of a plant complex resistance gene locus underlying immune-related hybrid incompatibility and its occurrence in nature. PLoS Genet. 2014;10:e1004848.PubMed 
PubMed Central 

Google Scholar 
Mikkelsen MD, Hansen CH, Wittstock U, Halkier BA. Cytochrome P450CYP79B2 from Arabidopsis catalyzes the conversion of tryptophan to indole-3-acetaldoxime, a precursor of indole glucosinolates and indole-3-acetic acid. J Biol Chem. 2000;275:33712–17.CAS 
PubMed 

Google Scholar 
Hull AK, Vij R, Celenza JL. Arabidopsis cytochrome P450s that catalyze the first step of tryptophan-dependent indole-3-acetic acid biosynthesis. Proc Natl Acad Sci USA. 2000;97:2379–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhao YD, Hull AK, Gupta NR, Goss KA, Alonso J, Ecker JR, et al. Trp-dependent auxin biosynthesis in Arabidopsis: involvement of cytochrome P450s CYP79B2 and CYP79B3. GenesDev. 2002;16:3100–12.CAS 

Google Scholar 
Schlaeppi K, Bodenhausen N, Buchala A, Mauch F, Reymond P. The glutathione-deficient mutant pad2-1 accumulates lower amounts of glucosinolates and is more susceptible to the insect herbivore Spodoptera littoralis. Plant J. 2008;55:774–86.CAS 
PubMed 

Google Scholar 
Schuhegger R, Nafisi M, Mansourova M, Petersen BL, et al. CYP71B15 (PAD3) catalyzes the final step in camalexin biosynthesis. Plant Physiol. 2006;141:1248–54.CAS 
PubMed 
PubMed Central 

Google Scholar 
Glawischnig E. The role of cytochrome P450 enzymes in the biosynthesis of camalexin. Biochem Soc Trans. 2006;34:1206–8.CAS 
PubMed 

Google Scholar 
Haughn GW, Davin L, Giblin M, Underhill EW. Biochemical genetics of plant secondary metabolites in Arabidopsis thaliana: The glucosinolates. Plant Physiol. 1991;97:217–26.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kroymann J, Textor S, Tokuhisa JG, Falk KL, Bartram S, Gershenzon J, et al. A gene controlling variation in Arabidopsis glucosinolate composition is part of the methionine chain elongation pathway. Plant Physiol. 2001;127:1077–88.CAS 
PubMed 
PubMed Central 

Google Scholar 
Textor S, de Kraker JW, Hause B, Gershenzon J, Tokuhisa JG. MAM3 catalyzes the formation of all aliphatic glucosinolate chain lengths in Arabidopsis. Plant Physiol. 2007;144:60–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Barth C, Jander G. Arabidopsis myrosinases TGG1 and TGG2 have redundant function in glucosinolate breakdown and insect defense. Plant J. 2006;46:549–62.CAS 
PubMed 

Google Scholar 
Dong XY, Braun EL, Grotewold E. Functional conservation of plant secondary metabolic enzymes revealed by complementation of Arabidopsis flavonoid mutants with maize genes. Plant Physiol. 2001;127:46–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
Peer WA, Brown DE, Tague BW, Muday GK, Taiz L, Murphy AS. Flavonoid accumulation patterns of transparent testa mutants of Arabidopsis. Plant Physiol. 2001;126:536–48.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gonzalez A, Brown M, Hatlestad G, Akhavan N, Smith T, Hembd A, et al. TTG2 controls the developmental regulation of seed coat tannins in Arabidopsis by regulating vacuolar transport steps in the proanthocyanidin pathway. Dev Biol. 2016;419:54–63.CAS 
PubMed 

Google Scholar 
Walker AR, Davison PA, Bolognesi-Winfield AC, James CM, Srinivasan N, Blundell TL, et al. The TRANSPARENT TESTA GLABRA1 locus, which regulates trichome differentiation and anthocyanin biosynthesis in Arabidopsis, encodes a WD40 repeat protein. Plant Cell. 1999;11:1337–49.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).PubMed 

Google Scholar 
IPBES. Global Assessment Report on Biodiversity and Ecosystem Service. Debating Nature’s Value (IPBES, 2019).Harrison, S. Plant community diversity will decline more than increase under climatic warming. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190106 (2020).
Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science (80-.). 1327, eaax3100 (2019).Chapin, F. S. et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).CAS 
PubMed 

Google Scholar 
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).PubMed 

Google Scholar 
Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science (80-.). 348, 336–340 (2015).CAS 

Google Scholar 
Díaz, S., Fargione, J., Chapin, F. S. & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol. 4, e277 (2006).PubMed 
PubMed Central 

Google Scholar 
Pennekamp, F. et al. Biodiversity increases and decreases ecosystem stability. Nature 563, 109–112 (2018).CAS 
PubMed 

Google Scholar 
Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Natl Acad. Sci. USA 117, 24345–24351 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Global evidence of positive biodiversity effects on spatial ecosystem stability in natural grasslands. Nat. Commun. 10, 1–9 (2019).
Google Scholar 
Poorter, L. et al. Diversity enhances carbon storage in tropical forests. Glob. Ecol. Biogeogr. 24, 1314–1328 (2015).
Google Scholar 
Schnabel, F. et al. Drivers of productivity and its temporal stability in a tropical tree diversity experiment. Glob. Chang. Biol. 25, 4257–4272 (2019).PubMed 

Google Scholar 
Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl Acad. Sci. USA 111, 13697–13702 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mori, A. S. Advancing nature-based approaches to address the biodiversity and climate emergency. Ecol. Lett. 23, 1729–1732 (2020).PubMed 

Google Scholar 
Mazzochini, G. G. et al. Plant phylogenetic diversity stabilizes large-scale ecosystem productivity. Glob. Ecol. Biogeogr. 28, 1430–1439 (2019).
Google Scholar 
Manhães, A. P., Mazzochini, G. G., Oliveira-Filho, A. T., Ganade, G. & Carvalho, A. R. Spatial associations of ecosystem services and biodiversity as a baseline for systematic conservation planning. Divers. Distrib. 22, 932–943 (2016).
Google Scholar 
García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity–ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).PubMed 
PubMed Central 

Google Scholar 
De Keersmaecker, W. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015).
Google Scholar 
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).CAS 
PubMed 

Google Scholar 
Linscheid, N. et al. Towards a global understanding of vegetation-climate dynamics at multiple timescales. Biogeosciences 17, 945–962 (2020).
Google Scholar 
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science (80-.). 300, 1560–1563 (2003).CAS 

Google Scholar 
Quetin, G. R. & Swann, A. L. S. Empirically derived sensitivity of vegetation to climate across global gradients of temperature and precipitation. J. Clim. 30, 5835–5849 (2017).
Google Scholar 
Cavender-bares, J. et al. The role of diversification in community assembly of the oaks (Quercus L.) across the continental U. S. Am. J. Bot. 105, 565–586 (2018).PubMed 

Google Scholar 
Woodward, F. I., Lomas, M. R. & Kelly, C. K. Global climate and the distribution of plant biomes. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 1465–1476 (2004).CAS 

Google Scholar 
Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).PubMed 

Google Scholar 
Cavender-Bares, J., Ackerly, D. D., Hobbie, S. E. & Townsend, P. A. Evolutionary legacy effects on ecosystems: biogeographic origins, plant traits, and implications for management in the era of global change. Annu. Rev. Ecol. Evol. Syst. 47, 433–462 (2016).
Google Scholar 
Harrison, S., Spasojevic, M. J. & Li, D. Climate and plant community diversity in space and time. Proc. Natl Acad. Sci. USA 117, 4464–4470 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Šímová, I. et al. Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species. J. Biogeogr. 45, 895–916 (2018).
Google Scholar 
Lamanna, C. et al. Functional trait space and the latitudinal diversity gradient. Proc. Natl Acad. Sci. USA 111, 13745–13750 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Craven, D. et al. A cross-scale assessment of productivity–diversity relationships. Glob. Ecol. Biogeogr. 29, 1940–1955 (2020).
Google Scholar 
White, H. J. et al. Ecosystem stability at the landscape scale is primarily associated with climatic history. Funct. Ecol. 1–13 https://doi.org/10.1111/1365-2435.13957 (2021).Enquist, B. J. et al. Scaling from Traits to Ecosystems: Developing a General Trait Driver Theory via Integrating Trait-Based and Metabolic Scaling Theories. Advances in Ecological Research. Vol. 52 (Elsevier Ltd., 2015).Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).PubMed 
PubMed Central 

Google Scholar 
Barry, K. E. et al. A graphical null model for scaling biodiversity–ecosystem functioning relationships. J. Ecol. 109, 1549–1560 (2021).
Google Scholar 
Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).PubMed 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
PubMed 

Google Scholar 
Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).PubMed 
PubMed Central 

Google Scholar 
Bond, E. M. & Chase, J. M. Biodiversity and ecosystem functioning at local and regional spatial scales. Ecol. Lett. 5, 467–470 (2002).
Google Scholar 
Delsol, R., Loreau, M. & Haegeman, B. The relationship between the spatial scaling of biodiversity and ecosystem stability. Glob. Ecol. Biogeogr. 27, 439–449 (2018).PubMed 
PubMed Central 

Google Scholar 
Price, G. R. The nature of selection. J. Theor. Biol. 175, 389-396 (1995).Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J. Ecol. 89, 118–125 (2001).
Google Scholar 
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).PubMed 
PubMed Central 

Google Scholar 
Cadotte, M., Dinnage, R. & Tilman, D. Phylogenetic diversity promotes ecosytem stability. Ecology 93, S223–S233 (2012).
Google Scholar 
Veron, S., Davies, T. J., Cadotte, M. W., Clergeau, P. & Pavoine, S. Predicting loss of evolutionary history: Where are we? Biol. Rev. 92, 271–291 (2017).PubMed 

Google Scholar 
Tucker, C. M., Davies, T. J., Cadotte, M. W. & Pearse, W. D. On the relationship between phylogenetic diversity and trait diversity. Ecology 99, 1473–1479 (2018).PubMed 

Google Scholar 
Faith, D. P. Systematics and conservation: on predicting the feature diversity of subsets of taxa. Cladistics 8, 361–373 (1992).PubMed 

Google Scholar 
Hisano, M., Searle, E. B. & Chen, H. Y. H. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems. Biol. Rev. 93, 439–456 (2018).PubMed 

Google Scholar 
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity–ecosystem-function relationships. Ecology 92, 1573–1581 (2011).PubMed 

Google Scholar 
Cadotte, M. W., Cardinale, B. J. & Oakley, T. H. Evolutionary history and the effect of biodiversity on plant productivity. Proc. Natl Acad. Sci. USA 105, 17012–17017 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Venail, P. et al. Species richness, but not phylogenetic diversity, influences community biomass production and temporal stability in a re-examination of 16 grassland biodiversity studies. Funct. Ecol. 29, 615–626 (2015).
Google Scholar 
Enquist, B., Condit, R., Peet, R., Schildhauer, M. & Thiers, B. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ Prepr. 4, e2615v2 (2016).Maitner, B. S. et al. The bien R package: a tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).
Google Scholar 
Mori, A. S. Resilience in the studies of biodiversity–ecosystem functioning. Trends Ecol. Evol. 31, 87–89 (2016).PubMed 

Google Scholar 
Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).
Google Scholar 
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 

Google Scholar 
Huete, A., Chris, J. & Leeuwen, W. Van. MODIS vegetation index (MOD 13). Algorithm theoretical basis document vol. 3 https://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (1999).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 

Google Scholar 
MacIas-Fauria, M., Forbes, B. C., Zetterberg, P. & Kumpula, T. Eurasian Arctic greening reveals teleconnections and the potential for structurally novel ecosystems. Nat. Clim. Chang. 2, 613–618 (2012).
Google Scholar 
Garcia, R. A., Cabeza, M., Rahbek, C. & Araújo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science (80-.). 344, 1247579 (2014).Zhang, Y. et al. Precipitation and carbon-water coupling jointly control the interannual variability of global land gross primary production. Sci. Rep. 6, 39748 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).CAS 
PubMed 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933 (2001).
Google Scholar 
Srivastava, D. S. et al. Phylogenetic diversity and the functioning of ecosystems. Ecol. Lett. 15, 637–648 (2012).PubMed 

Google Scholar 
Parker, I. M. et al. Phylogenetic structure and host abundance drive disease pressure in communities. Nature 520, 542–544 (2015).CAS 
PubMed 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).PubMed 

Google Scholar 
Brun, P. et al. Plant community impact on productivity: Trait diversity or key(stone) species effects? Ecol. Lett. 25, 913–925 (2022).PubMed 

Google Scholar 
Aubin, I. et al. Traits to stay, traits to move: a review of functional traits to assess sensitivity and adaptive capacity of temperate and boreal trees to climate change. Environ. Rev. 24, 164–186 (2016).
Google Scholar 
Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1216065111 (2014).Díaz, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).
Google Scholar 
Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).CAS 
PubMed 

Google Scholar 
Ye, J. S., Pei, J. Y. & Fang, C. Under which climate and soil conditions the plant productivity–precipitation relationship is linear or nonlinear? Sci. Total Environ. 616–617, 1174–1180 (2018).PubMed 

Google Scholar 
Allan, E. et al. More diverse plant communities have higher functioning over time due to turnover in complementary dominant species. Proc. Natl Acad. Sci. U. S. A. 108, 17034–17039 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. 104, 13384–13389 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Chang. 11, 543–550 (2021).
Google Scholar 
Kattge, J. et al. TRY plant trait database–enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).PubMed 

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

Google Scholar 
Li, D., Miller, J. E. D. & Harrison, S. Climate drives loss of phylogenetic diversity in a grassland community. Proc. Natl Acad. Sci. USA 116, 19989–19994 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Madani, N. et al. Future global productivity will be affected by plant trait response to climate. Sci. Rep. 8, 2870 (2018).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing Version 3.5.2. (R Core Team, 2018).Ammer, C. Diversity and forest productivity in a changing climate. N. Phytol. 221, 50–66 (2019).
Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS 
PubMed 

Google Scholar 
Larue, E. A., Hardiman, B. S., Elliott, J. M. & Fei, S. Structural diversity as a predictor of ecosystem function. Environ. Res. Lett. 14, 114011 (2019).Phillips, S. J. & Dudìk, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography (Cop.). 31, 161–175 (2008).
Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 
Diniz-Filho, J. A. F. & Bini, L. M. Modelling geographical patterns in species richness using eigenvector-based spatial filters. Glob. Ecol. Biogeogr. 14, 177–185 (2005).
Google Scholar 
Merow, C., Smith, M. J. & Silander, J. a. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (Cop.). 36, 1058–1069 (2013).
Google Scholar 
Merow, C. BIEN range methods description. http://bien.nceas.ucsb.edu/bien/wp-content/uploads/2017/06/BIEN3RangeMethodsSummary.pdf (2017).Schrodt, F. et al. BHPMF-a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Glob. Ecol. Biogeogr. 24, 1510–1521 (2015).
Google Scholar 
Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).PubMed 

Google Scholar 
Guo, W. Y. et al. Half of the world’s tree biodiversity is unprotected and is increasingly threatened by human activities. Preprint at bioRxiv https://doi.org/10.1101/2020.04.21.052464 (2020).Guo, W., Serra-diaz, J. M., Schrodt, F. & Eiserhardt, W. L. Paleoclimate and current climate collectively shape the phylogenetic and functional diversity of trees worldwide. Preprint at bioRxiv https://doi.org/10.1101/2020.06.02.128975 (2020).Diniz-Filho, J. A. F. et al. On the selection of phylogenetic eigenvectors for ecological analyses. Ecography (Cop.). 35, 239–249 (2012).
Google Scholar 
Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).
Google Scholar 
Santos, T. PVR: Phylogenetic eigenvectors regression and phylogentic signal-representation curve. R package version 0.3. Available at: http://CRAN.R-project.org/package=PVR (2018).Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gerhold, P., Cahill, J. F., Winter, M., Bartish, I. V. & Prinzing, A. Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better). Funct. Ecol. 29, 600–614 (2015).
Google Scholar 
Kendall, M. & Stuart, A. The Advanced Theory of Statistics (Macmillan, 1983).Pavoine, S. & Bonsall, M. B. Measuring biodiversity to explain community assembly: a unified approach. Biol. Rev. Camb. Philos. Soc. 86, 792–812 (2011).CAS 
PubMed 

Google Scholar 
Tucker, C. M. et al. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715 (2017).PubMed 

Google Scholar 
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 
PubMed 

Google Scholar 
Cornwell, W. K., Schwilk, L. D. W. & Ackerly, D. D. A trait-based test for habitat filtering: convex hull volume. Ecology 87, 1465–1471 (2006).PubMed 

Google Scholar 
Villéger, S., Maire, E. & Leprieur, F. On the risks of using dendrograms to measure functional diversity and multidimensional spaces to measure phylogenetic diversity: a comment on Sobral et al. (2016). Ecol. Lett. 20, 554–557 (2017).PubMed 

Google Scholar 
Laliberté, E., Legendre, P. & Shipley, B. FD: measuring functional diversity from multiple traits, an other tools for functional ecology. R package version 1.0-12 (Comprehensive R Archive Network, Vienna, Austria, 2015).Podani, J. & Schmera, D. On dendrogram-based measures of functional diversity. Oikos 115, 179–185 (2006).
Google Scholar 
Poos, M. S., Walker, S. C. & Jackson, D. A. Functional-diversity indices can be driven by methodological choices and species richness. Ecology 90, 341–347 (2009).PubMed 

Google Scholar 
Gotelli, N. J. & Graves, G. R. Null Models in Ecology (Smithsonian Institution Press, 1996).Swenson, N. G. Functional and Phylogenetic Ecology in R. (Springer, 2014).Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography (Cop.). 30, 609–628 (2007).
Google Scholar 
Kissling, W. D. & Carl, G. Spatial autocorrelation and the selection of simultaneous autoregressive models. Glob. Ecol. Biogeogr. 17, 59–71 (2008).
Google Scholar 
Bivand, R. spatialreg: Spatial Regression Analysis (R package version 1.1-5, 2019). More