Philippa Kaur

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

Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).
Google Scholar 
Gröning, J. & Hochkirch, A. Reproductive interference between animal species. Q. Rev. Biol. 83, 257–282 (2008).PubMed 

Google Scholar 
Grether, G. F., Peiman, K. S., Tobias, J. A. & Robinson, B. W. Causes and consequences of behavioral interference between species. Trends Ecol. Evol. 32, 760–772 (2017).PubMed 

Google Scholar 
Hettyey, A. & Pearman, P. B. Social environment and reproductive interference affect reproductive success in the frog Rana latastei. Behav. Ecol. 14, 294–300 (2003).
Google Scholar 
Kyogoku, D. & Sota, T. A generalized population dynamics model for reproductive interference with absolute density dependence. Sci. Rep. 7, 257–258 (2017).
Google Scholar 
Anderson, C. N. & Grether, G. F. Multiple routes to reduced interspecific territorial fighting in Hetaerina damselflies. Behav. Ecol. 22, 527–534 (2011).
Google Scholar 
Hochkirch, A., Gröning, J. & Bücker, A. Sympatry with the devil: Reproductive interference could hamper species coexistence. J. Anim. Ecol. 76, 633–642 (2007).PubMed 

Google Scholar 
Pfennig, K. S. & Pfennig, D. W. Character displacement: Ecological and reproductive responses to a common evolutionary problem. Q. Rev. Biol. 84, 253–276 (2009).PubMed 
PubMed Central 

Google Scholar 
Garrison, R. A synopsis of the genus Hetaerina with description of four new species (Odonata: Calopterygidae). Trans. Am. Entomol. Soc. 116, 175–259 (1990).
Google Scholar 
Grether, G. F., Drury, J. P., Berlin, E. & Anderson, C. N. The role of wing coloration in sex recognition and competitor recognition in rubyspot damselflies (Hetaerina spp.). Ethology 121, 674–685 (2015).
Google Scholar 
Drury, J. P. et al. A general explanation for the persistence of reproductive interference. Am. Nat. 194, 268–275 (2019).PubMed 

Google Scholar 
Cabezas Castillo, M. B. & Grether, G. F. Why are female color polymorphisms rare in territorial damselflies?. Ethology 124, 667–673 (2018).
Google Scholar 
Drury, J. P. & Grether, G. F. Interspecific aggression, not interspecific mating, drives character displacement in the wing coloration of male rubyspot damselflies (Hetaerina). Proc. R. Soc. B Biol. Sci. 281, 20141737 (2014).CAS 

Google Scholar 
Grether, G. F. Intersexual competition alone favors a sexually dimorphic ornament in the rubyspot damselfly Hetaerina americana. Evolution (N. Y.) 50, 1949 (1996).
Google Scholar 
McEachin, S., Drury, J. P., Anderson, C. N. & Grether, G. F. Mechanisms of reduced interspecific interference between territorial species. Behav. Ecol. 33, 126–136 (2022).
Google Scholar 
Vega-Sánchez, Y. M., Mendoza-Cuenca, L. F. & González-Rodríguez, A. Complex evolutionary history of the American Rubyspot damselfly, Hetaerina americana (Odonata): Evidence of cryptic speciation. Mol. Phylogenet. Evol. 139, 106536 (2019).PubMed 

Google Scholar 
Vega-Sánchez, Y. M., Mendoza-Cuenca, L. F. & González-Rodríguez, A. Hetaerina calverti (Odonata: Zygoptera: Calopterygidae) sp. Nov., a new cryptic species of the American Rubyspot complex. Zootaxa 4766, 485–497 (2020).
Google Scholar 
Paulson, D. R. Reproductive isolation in damselflies. Syst. Zool. 23, 40–49 (1974).
Google Scholar 
Sánchez-Guillén, R. A., Córdoba-Aguilar, A., Cordero-Rivera, A. & Wellenreuther, M. Rapid evolution of prezygotic barriers in non-territorial damselflies. Biol. J. Linn. Soc. 113, 485–496 (2014).
Google Scholar 
Svensson, E. I. & Waller, J. T. Ecology and sexual selection: Evolution of wing pigmentation in calopterygid damselflies in relation to latitude, sexual dimorphism, and speciation. Am. Nat. 182, E174–E195 (2013).PubMed 

Google Scholar 
Sánchez-Herrera, M., Beatty, C. D., Nunes, R., Salazar, C. & Ware, J. L. An exploration of the complex biogeographical history of the neotropical banner-wing damselflies (Odonata: Polythoridae). BMC Evol. Biol. 20, 74 (2020).PubMed 
PubMed Central 

Google Scholar 
Battin, T. J. The odonate mating system, communication, and sexual selection: A review. Boll. Zool. 60, 353–360 (1993).
Google Scholar 
Drury, J. P., Okamoto, K. W., Anderson, C. N. & Grether, G. F. Reproductive interference explains persistence of aggression between species. Proc. R. Soc. B Biol. Sci. 282, 20142256 (2015).
Google Scholar 
Svensson, E. I., Karlsson, K., Friberg, M. & Eroukhmanoff, F. Gender differences in species recognition and the evolution of asymmetric sexual isolation. Curr. Biol. 17, 1943–1947 (2007).CAS 
PubMed 

Google Scholar 
McPeek, M. A., Symes, L. B., Zong, D. M. & McPeek, C. L. Species recognition and patterns of population variation in the reproductive structures of a damselfly genus. Evolution (N. Y.) 65, 419–428 (2011).
Google Scholar 
Nagel, L. & Schluter, D. Body size, natural selection, and speciation in sticklebacks. Evolution (N. Y.) 52, 209–218 (1998).
Google Scholar 
Baube, C. L. Body size and the maintenance of reproductive isolation in stickleback, genus Gasterosteus. Ethology 114, 1122–1134 (2008).
Google Scholar 
Head, M. L., Kozak, G. M. & Boughman, J. W. Female mate preferences for male body size and shape promote sexual isolation in threespine sticklebacks. Ecol. Evol. 3, 2183–2196 (2013).PubMed 
PubMed Central 

Google Scholar 
Serrano-Meneses, M. A., López-García, K. & Carrillo-Muñoz, A. I. Assortative mating by size in the American rubyspot damselfly (Hetaerina americana). J. Insect Behav. 31, 585–598 (2018).
Google Scholar 
Kopp, M. et al. Mechanisms of assortative mating in speciation with gene flow: Connecting theory and empirical research. Am. Nat. 191, 1–20 (2018).PubMed 

Google Scholar 
Class, B. & Dingemanse, N. J. A variance partitioning perspective of assortative mating: Proximate mechanisms and evolutionary implications. J. Evol. Biol. 35, 483–490 (2022).PubMed 

Google Scholar 
Corbet, P. S. A Biology of Dragonflies 247 (Witherby, 1962).
Google Scholar 
Grether, G. F. Sexual selection and survival selection on wing coloration and body size in the Rubyspot damselfly Hetaerina americana. Evolution (N. Y.) 50, 1939 (1996).
Google Scholar 
Raihani, G., Serrano-Meneses, M. A. & Córdoba-Aguilar, A. Male mating tactics in the American rubyspot damselfly: Territoriality, nonterritoriality and switching behaviour. Anim. Behav. 75, 1851–1860 (2008).
Google Scholar 
Serrano-Meneses, M. A., Córdoba-Aguilar, A., Méndez, V., Layen, S. J. & Székely, T. Sexual size dimorphism in the American rubyspot: Male body size predicts male competition and mating success. Anim. Behav. 73, 987–997 (2007).
Google Scholar 
Contreras-Garduño, J., Buzatto, B. A., Abundis, L., Nájera-Cordero, K. & Córdoba-Aguilar, A. Wing colour properties do not reflect male condition in the American rubyspot (Hetaerina americana). Ethology 113, 944–952 (2007).
Google Scholar 
Serrano-Meneses, M. A., Córdoba-Aguilar, A., Azpilicueta-Amorín, M., González-Soriano, E. & Székely, T. Sexual selection, sexual size dimorphism and Rensch’s rule in Odonata. J. Evol. Biol. 21, 1259–1273 (2008).CAS 
PubMed 

Google Scholar 
Betts, C. R. & Wootton, R. J. Wing shape and flight behaviour in butterflies (Lepidoptera: Papilionoidea and Hesperioidea): A preliminary analysis. J. Exp. Biol. 138, 271–288 (1988).
Google Scholar 
Outomuro, D. & Johansson, F. The effects of latitude, body size, and sexual selection on wing shape in a damselfly. Biol. J. Linn. Soc. 102, 263–274 (2011).
Google Scholar 
Outomuro, D., Adams, D. C. & Johansson, F. The evolution of wing shape in ornamented-winged damselflies (Calopterygidae, Odonata). Evol. Biol. 40, 300–309 (2013).
Google Scholar 
Córdoba-Aguilar, Raihani, Serrano-Meneses, & Contreras-Garduño,. The lek mating system of Hetaerina damselflies (Insecta: Calopterygidae). Behaviour 146, 189–207 (2009).
Google Scholar 
Córdoba-Aguilar, A. Adult survival and movement in males of the damselfly Hetaerina cruentata (Odonata: Calopterygidae). Florida Entomol. 77, 256 (1994).
Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 

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

Google Scholar 
Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—A free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).PubMed 
PubMed Central 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Adams, D. C. & Otárola-Castillo, E. Geomorph: An R package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).
Google Scholar 
Viscosi, V. & Cardini, A. Correction: Leaf morphology, taxonomy and geometric morphometrics: A simplified protocol for beginners. PLoS ONE https://doi.org/10.1371/annotation/bc347abe-8d03-4553-8754-83f41a9d51ae (2012).Article 
PubMed Central 

Google Scholar 
Maia, R., Gruson, H., Endler, J. A. & White, T. E. PAVO 2: New tools for the spectral and spatial analysis of colour in R. Methods Ecol. Evol. 10, 1097–1107 (2019).
Google Scholar 
Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour thresholds. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 351–358 (1998).CAS 

Google Scholar 
Outomuro, D., Söderquist, L., Johansson, F., Ödeen, A. & Nordström, K. The price of looking sexy: Visual ecology of a three-level predator–prey system. Funct. Ecol. 31, 707–718 (2017).
Google Scholar 
Laughlin, S. B. The sensitivities of dragonfly photoreceptors and the voltage gain of transduction. J. Comp. Physiol. A 111, 221–247 (1976).
Google Scholar 
Endler, J. A. The color of light in forests and its implications. Ecol. Monogr. 63, 1–27 (1993).
Google Scholar 
Vorobyev, M., Brandt, R., Peitsch, D., Laughlin, S. B. & Menzel, R. Colour thresholds and receptor noise: Behaviour and physiology compared. Vision Res. 41, 639–653 (2001).CAS 
PubMed 

Google Scholar 
Renoult, J. P., Kelber, A. & Schaefer, H. M. Colour spaces in ecology and evolutionary biology. Biol. Rev. 92, 292–315 (2017).PubMed 

Google Scholar 
Zelditch, M. L., Swiderski, D. L., Sheets, H. D. & Fink, W. L. Geometric Morphometrics for Biologists: A Primer Vol. 95, 443 (Elsevier Academic Press, 2004).MATH 

Google Scholar 
Rohlf, F. J. TpsDig, Digitize Landmarks and Outlines v. 2.0 (Department of Ecology and Evolution, State University of New York at Stony Brook, 2004).
Google Scholar  More

Evans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, et al. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019;17:219–32.CAS 
PubMed 

Google Scholar 
Reeburgh WS. Oceanic methane biogeochemistry. Chem Rev. 2007;107:486–513.CAS 
PubMed 

Google Scholar 
Timmers PHA, Welte CU, Koehorst JJ, Plugge CM, Jetten MSM, Stams AJM. Reverse methanogenesis and respiration in methanotrophic Archaea. Archaea. 2017;2017:1–22.
Google Scholar 
Hallam SJ, Putnam N, Preston CM, Detter JC, Rokhsar D, Richardson PM, et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science. 2004;305:1457–62.CAS 
PubMed 

Google Scholar 
Knittel K, Boetius A. Anaerobic oxidation of methane: progress with an unknown process. Annu Rev Microbiol. 2009;63:311–34.CAS 
PubMed 

Google Scholar 
Vanwonterghem I, Evans PN, Parks DH, Jensen PD, Woodcroft BJ, Hugenholtz P, et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat Microbiol. 2016;1:16170.CAS 
PubMed 

Google Scholar 
McKay LJ, Dlakić M, Fields MW, Delmont TO, Eren AM, Jay ZJ, et al. Co-occurring genomic capacity for anaerobic methane and dissimilatory sulfur metabolisms discovered in the Korarchaeota. Nat Microbiol. 2019;4:614–22.CAS 
PubMed 

Google Scholar 
Wang Y, Wegener G, Hou J, Wang F, Xiao X. Expanding anaerobic alkane metabolism in the domain of Archaea. Nat Microbiol. 2019;4:595–602.CAS 
PubMed 

Google Scholar 
Wang Y, Wegener G, Ruff SE, Wang F. Methyl/alkyl‐coenzyme M reductase‐based anaerobic alkane oxidation in archaea. Environ Microbiol. 2021;23:530–41.CAS 
PubMed 

Google Scholar 
Bertram S, Blumenberg M, Michaelis W, Siegert M, Krüger M, Seifert R. Methanogenic capabilities of ANME‐archaea deduced from 13C‐labelling approaches. Environ Microbiol. 2013;15:2384–93.CAS 
PubMed 

Google Scholar 
Sousa DZ, Smidt H, Alves MM, Stams AJM. Syntrophomonas zehnderi sp. nov., an anaerobe that degrades long-chain fatty acids in co-culture with Methanobacterium formicicum. Int J Syst Evol Micr. 2007;57:609–15.CAS 

Google Scholar 
Yamada T, Sekiguchi Y, Hanada S, Imachi H, Ohashi A, Harada H, et al. Anaerolinea thermolimosa sp. nov., Levilinea saccharolytica gen. nov., sp. nov. and Leptolinea tardivitalis gen. nov., sp. nov., novel filamentous anaerobes, and description of the new classes Anaerolineae classis nov. and Caldilineae classis nov. in the bacterial phylum Chloroflexi. Int J Syst Evol Micr. 2006;56:1331–40.CAS 

Google Scholar 
Yamada T, Sekiguchi Y, Imachi H, Kamagata Y, Ohashi A, Harada H. Diversity, localization, and physiological properties of filamentous microbes belonging to Chloroflexi subphylum I in mesophilic and thermophilic methanogenic sludge granules. Appl Environ Microb. 2005;71:7493–503.CAS 

Google Scholar 
Manzoor S, Schnürer A, Bongcam-Rudloff E, Müller B. Complete genome sequence of Methanoculleus bourgensis strain MAB1, the syntrophic partner of mesophilic acetate-oxidising bacteria (SAOB). Stand Genomic Sci. 2016;11:80.PubMed 
PubMed Central 

Google Scholar 
Engelhardt T, Sahlberg M, Cypionka H, Engelen B. Biogeography of Rhizobium radiobacter and distribution of associated temperate phages in deep subseafloor sediments. ISME J. 2013;7:199–209.CAS 
PubMed 

Google Scholar 
Nölling J, Groffen A, de Vos WM. φ F1 and φF3, two novel virulent, archaeal phages infecting different thermophilic strains of the genus. Methanobacterium Microbiol. 1993;139:2511–6.
Google Scholar 
Meile L, Jenal U, Studer D, Jordan M, Leisinger T. Characterization of ψM1, a virulent phage of Methanobacterium thermoautotrophicum Marburg. Arch Microbiol. 1989;152:105–10.CAS 

Google Scholar 
Weidenbach K, Nickel L, Neve H, Alkhnbashi OS, Künzel S, Kupczok A, et al. Methanosarcina spherical virus, a novel archaeal lytic virus targeting Methanosarcina strains. J Virol. 2017;91:e00955–17.PubMed 
PubMed Central 

Google Scholar 
Molnár J, Magyar B, Schneider G, Laczi K, Valappil SK, Kovács ÁL, et al. Identification of a novel archaea virus, detected in hydrocarbon polluted Hungarian and Canadian samples. PLOS ONE. 2020;15:e0231864.PubMed 
PubMed Central 

Google Scholar 
Paul BG, Bagby SC, Czornyj E, Arambula D, Handa S, Sczyrba A, et al. Targeted diversity generation by intraterrestrial archaea and archaeal viruses. Nat Commun. 2015;6:6585.CAS 
PubMed 

Google Scholar 
Pourcel C, Touchon M, Villeriot N, Vernadet J-P, Couvin D, Toffano-Nioche C, et al. CRISPRCasdb a successor of CRISPRdb containing CRISPR arrays and cas genes from complete genome sequences, and tools to download and query lists of repeats and spacers. Nucleic Acids Res. 2019;48:D535–D544.PubMed Central 

Google Scholar 
Roux S, Hallam SJ, Woyke T, Sullivan MB. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife. 2015;4:e08490.PubMed Central 

Google Scholar 
Lever MA, Teske AP. Diversity of methane-cycling Archaea in hydrothermal sediment investigated by general and group-specific PCR primers. Appl Environ Microb. 2015;81:1426–41.
Google Scholar 
Jian H, Yi Y, Wang J, Hao Y, Zhang M, Wang S, et al. Diversity and distribution of viruses inhabiting the deepest ocean on Earth. ISME J. 2021;15:3094–110.Paez-Espino D, Pavlopoulos GA, Ivanova NN, Kyrpides NC. Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nature Protoc. 2017;12:1673–82.CAS 

Google Scholar 
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.PubMed 
PubMed Central 

Google Scholar 
Ren J, Song K, Deng C, Ahlgren NA, Fuhrman JA, Li Y, et al. Identifying viruses from metagenomic data using deep learning. Quant Biol. 2020;8:64–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Páez-Espino D, Chen I-MA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2020;49:D764–D775.PubMed Central 

Google Scholar 
Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S, Kyrpides NC. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol. 2021;39:578–85.CAS 
PubMed 

Google Scholar 
Sandaa R, Gómez‐Consarnau L, Pinhassi J, Riemann L, Malits A, Weinbauer MG, et al. Viral control of bacterial biodiversity – evidence from a nutrient‐enriched marine mesocosm experiment. Environ Microbiol. 2009;11:2585–97.CAS 
PubMed 

Google Scholar 
Howard-Varona C, Hargreaves KR, Abedon ST, Sullivan MB. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 2017;11:1511–20.PubMed 
PubMed Central 

Google Scholar 
Li Z, Pan D, Wei G, Pi W, Zhang C, Wang J-H, et al. Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity. ISME J. 2021;15:2366–78.CAS 
PubMed 
PubMed Central 

Google Scholar 
Krupovič M, Forterre P, Bamford DH. Comparative analysis of the mosaic genomes of tailed archaeal viruses and proviruses suggests common themes for virion architecture and assembly with tailed viruses of bacteria. J Mol Biol. 2010;397:144–60.PubMed 

Google Scholar 
Thiroux S, Dupont S, Nesbø CL, Bienvenu N, Krupovic M, L’Haridon S, et al. The first head‐tailed virus, MFTV1, infecting hyperthermophilic methanogenic deep‐sea archaea. Environ Microbiol. 2021;23:3614–26.CAS 
PubMed 

Google Scholar 
Jang HB, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol. 2019;37:632–9.
Google Scholar 
Hao L, Bize A, Conteau D, Chapleur O, Courtois S, Kroff P, et al. New insights into the key microbial phylotypes of anaerobic sludge digesters under different operational conditions. Water Res. 2016;102:158–69.CAS 
PubMed 

Google Scholar 
Bedoya K, Hoyos O, Zurek E, Cabarcas F, Alzate JF. Annual microbial community dynamics in a full-scale anaerobic sludge digester from a wastewater treatment plant in Colombia. Sci Total Environ. 2020;726:138479.CAS 
PubMed 

Google Scholar 
Murphy KC, Fenton AC, Poteete AR. Sequence of the bacteriophage P22 Anti-RecBCD (abc) genes and properties of P22 abc region deletion mutants. Virology. 1987;160:456–64.CAS 
PubMed 

Google Scholar 
Millman A, Bernheim A, Stokar-Avihail A, Fedorenko T, Voichek M, Leavitt A, et al. Bacterial retrons function in anti-phage defense. Cell. 2020;183:1551–61.CAS 
PubMed 

Google Scholar 
Pawluk A, Davidson AR, Maxwell KL. Anti-CRISPR: discovery, mechanism and function. Nat Rev Microbiol. 2018;16:12–7.CAS 
PubMed 

Google Scholar 
Jonge PA, de, Nobrega FL, Brouns SJJ, Dutilh BE. Molecular and evolutionary determinants of bacteriophage host range. Trends Microbiol. 2018;27:51–63.PubMed 

Google Scholar 
Daly RA, Roux S, Borton MA, Morgan DM, Johnston MD, Booker AE, et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat Microbiol. 2019;4:352–61.CAS 
PubMed 

Google Scholar 
Salmond GPC, Fineran PC. A century of the phage: past, present and future. Nat Rev Microbiol. 2015;13:777–86.CAS 
PubMed 

Google Scholar 
Rastogi S, Liberles DA. Subfunctionalization of duplicated genes as a transition state to neofunctionalization. BMC Evol Biol. 2005;5:28.PubMed 
PubMed Central 

Google Scholar 
Petitjean C, Makarova KS, Wolf YI, Koonin EV. Extreme deviations from expected evolutionary rates in archaeal protein families. Genome Biol Evol. 2017;9:2791–811.CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson CL, Sullivan MB, Fernando SC. Dietary energy drives the dynamic response of bovine rumen viral communities. Microbiome. 2017;5:155.PubMed 
PubMed Central 

Google Scholar 
Gao S-M, Schippers A, Chen N, Yuan Y, Zhang M-M, Li Q, et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome. 2020;8:89.PubMed 
PubMed Central 

Google Scholar 
Mara P, Vik D, Pachiadaki MG, Suter EA, Poulos B, Taylor GT, et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 2020;14:3079–92.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfennig N, Widdel F, Trüper HG. The prokaryotes, A handbook on habitats, isolation, and identification of bacteria. Springer-Verlag, Berlin, Germany. 1981.Moran MA, Durham BP. Sulfur metabolites in the pelagic ocean. Nat Rev Microbiol. 2019;17:665–78.CAS 
PubMed 

Google Scholar 
Kumar S, Cheng X, Klimasauskas S, Sha M, Posfai J, Roberts RJ, et al. The DNA (cytosine-5) methyltransferases. Nucleic Acids Res. 1994;22:1–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ashcroft AE, Lago H, Macedo JMB, Horn WT, Stonehouse NJ, Stockley PG. Engineering thermal stability in RNA phage capsids via disulphide bonds. J Nanosci Nanotechno. 2005;5:2034–41.CAS 

Google Scholar 
Walter M, Fiedler C, Grassl R, Biebl M, Rachel R, Hermo-Parrado XL, et al. Structure of the receptor-binding protein of bacteriophage Det7: a podoviral tail spike in a Myovirus. J Virol. 2008;82:2265–73.CAS 
PubMed 

Google Scholar 
Shai Y. Mode of action of membrane active antimicrobial peptides. Peptide Sci. 2002;66:236–48.CAS 

Google Scholar 
Thevissen K, Ferket KKA, François IEJA, Cammue BPA. Interactions of antifungal plant defensins with fungal membrane components. Peptides. 2003;24:1705–12.CAS 
PubMed 

Google Scholar 
Broderick JB, Duffus BR, Duschene KS, Shepard EM. Radical S-adenosylmethionine enzymes. Chem Rev. 2014;114:4229–317.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wildschutte H, Preheim SP, Hernandez Y, Polz MF. O‐antigen diversity and lateral transfer of the wbe region among Vibrio splendidus isolates. Environ Microbiol. 2010;12:2977–87.CAS 
PubMed 

Google Scholar 
Samuel G, Reeves P. Biosynthesis of O-antigens: genes and pathways involved in nucleotide sugar precursor synthesis and O-antigen assembly. Carbohyd Res. 2003;338:2503–19.CAS 

Google Scholar 
Polz MF, Alm EJ, Hanage WP. Horizontal gene transfer and the evolution of bacterial and archaeal population structure. Trends Genet. 2013;29:170–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Markine-Goriaynoff N, Gillet L, Etten JLV, Korres H, Verma N, Vanderplasschen A. Glycosyltransferases encoded by viruses. J Gen Virol. 2004;85:2741–54.CAS 
PubMed 

Google Scholar 
Clifford JC, Rapicavoli JN, Roper MC. A rhamnose-rich O-antigen mediates adhesion, virulence, and host colonization for the xylem-limited phytopathogen Xylella fastidiosa. Mol Plant-microbe Interac. 2013;26:676–85.CAS 

Google Scholar 
Trueba G, Zapata S, Madrid K, Cullen P, Haake D. Cell aggregation: a mechanism of pathogenic Leptospira to survive in fresh water. Int Microbiol Official J Span Soc Microbiol. 2004;7:35–40.
Google Scholar 
Trunk T, Khalil HS, Leo JC. Norway BCSG Section for Genetics and Evolutionary Biology, Department of Biosciences, University of Oslo, Oslb,. Bacterial autoaggregation. Aims Microbiol. 2018;4:140–164.CAS 
PubMed 
PubMed Central 

Google Scholar 
Guan S, Bastin DA, Verma NK. Functional analysis of the O antigen glucosylation gene cluster of Shigella flexneri bacteriophage SfX. Microbiology. 1999;145:1263–73.CAS 
PubMed 

Google Scholar 
Rakhuba DV, Kolomiets EI, Dey ES, Novik GI. Bacteriophage receptors, mechanisms of phage adsorption and penetration into host cell. Pol J Microbiol. 2010;59:145–55.CAS 
PubMed 

Google Scholar 
Silva JB, Storms Z, Sauvageau D. Host receptors for bacteriophage adsorption. FEMS Microbiol Lett. 2016;363:fnw002.
Google Scholar 
Tsuzuki K, Kimura K, Fujii N, Yokosawa N, Oguma K. The complete nucleotide sequence of the gene coding for the nontoxic-nonhemagglutinin component of Clostridium botulinum type C progenitor toxin. Biochem Bioph Res Co. 1992;183:1273–9.CAS 

Google Scholar 
Enav H, Mandel-Gutfreund Y, Béjà O. Comparative metagenomic analyses reveal viral-induced shifts of host metabolism towards nucleotide biosynthesis. Microbiome. 2014;2:9.PubMed 
PubMed Central 

Google Scholar 
Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HB, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jin M, Guo X, Zhang R, Qu W, Gao B, Zeng R. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome. 2019;7:58.PubMed 
PubMed Central 

Google Scholar 
Anderson RE, Reveillaud J, Reddington E, Delmont TO, Eren AM, McDermott JM, et al. Genomic variation in microbial populations inhabiting the marine subseafloor at deep-sea hydrothermal vents. Nat Commun. 2017;8:1114.PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
PubMed 

Google Scholar 
Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
PubMed 

Google Scholar 
Lu J, Salzberg SL. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. Microbiome. 2020;8:124.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. Peerj Comput Sci. 2017;3:e104.
Google Scholar 
Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F, Maharjan S, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife. 2021;10:e65088.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.PubMed 
PubMed Central 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
PubMed 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. Peerj. 2019;7:e7359.PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;3:1043–55.
Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;6:1925–7.
Google Scholar 
Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 2021;9:37.PubMed 
PubMed Central 

Google Scholar 
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.CAS 
PubMed 

Google Scholar 
Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.CAS 
PubMed 

Google Scholar 
Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Néron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018;46:W246–W251.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lowe TM, Eddy SR. tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence. Nucleic Acids Res. 1997;25:955–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Google Scholar 
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2019;36:2251–52.PubMed Central 

Google Scholar 
Mistry J, Bateman A, Finn RD. Predicting active site residue annotations in the Pfam database. BMC Bioinform. 2007;8:298.
Google Scholar 
Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pratama AA, Bolduc B, Zayed AA, Zhong Z-P, Guo J, Vik DR, et al. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation. Peerj. 2021;9:e11447.PubMed 
PubMed Central 

Google Scholar 
Zimmermann L, Stephens A, Nam S-Z, Rau D, Kübler J, Lozajic M, et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol. 2018;430:2237–43.CAS 
PubMed 

Google Scholar 
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan MJ, Petty NK, Beatson SA. Easyfig: a genome comparison visualizer. Bioinform Oxf Engl. 2011;27:1009–10.CAS 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.PubMed 
PubMed Central 

Google Scholar 
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, Haeseler Avon, et al. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37:1530–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:gkab301-.
Google Scholar 
Dick GJ, Andersson AF, Baker BJ, Simmons SL, Thomas BC, Yelton AP, et al. Community-wide analysis of microbial genome sequence signatures. Genome Biol. 2009;10:R85–R85.PubMed 
PubMed Central 

Google Scholar  More

Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).PubMed 
Article 

Google Scholar 
Hou, E. Q. et al. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nat. Commun. 11, 637 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cordell, D., Drangert, J.-O. & White, S. The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19, 292–305 (2009).Article 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
PubMed 
Article 

Google Scholar 
Chen, X. L., Chen, H. Y. H., Searle, E. B., Chen, C. & Reich, P. B. Negative to positive shifts in diversity effects on soil nitrogen over time. Nat. Sustain. 4, 225–234 (2021).Article 

Google Scholar 
Oelmann, Y. et al. Plant diversity effects on aboveground and belowground N pools in temperate grassland ecosystems: development in the first 5 years after establishment. Glob. Biogeochem. Cy. 25, GB2014 (2011).Article 
CAS 

Google Scholar 
Fornara, D. A. et al. Plant effects on soil N mineralization are mediated by the composition of multiple soil organic fractions. Ecol. Res. 26, 201–208 (2011).CAS 
Article 

Google Scholar 
Wright, A. J., Wardle, D. A., Callaway, R. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390 (2017).PubMed 
Article 

Google Scholar 
Oelmann, Y. et al. Above- and belowground biodiversity jointly tighten the P cycle in agricultural grasslands. Nat. Commun. 12, 4431 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, L. et al. Diversity enhances agricultural productivity via rhizosphere phosphorus facilitation on phosphorus-deficient soils. Proc. Natl Acad. Sci. USA 104, 11192–11196 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, L., Tilman, D., Lambers, H. & Zhang, F. S. Plant diversity and overyielding: insights from belowground facilitation of intercropping in agriculture. New Phytol. 203, 63–69 (2014).PubMed 
Article 
CAS 

Google Scholar 
Hacker, N. et al. Plant diversity shapes microbe–rhizosphere effects on P mobilisation from organic matter in soil. Ecol. Lett. 18, 1356–1365 (2015).PubMed 
Article 

Google Scholar 
Vance, C. P., Uhde-Stone, C. & Allan, D. L. Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytol. 157, 423–447 (2003).CAS 
PubMed 
Article 

Google Scholar 
Chen, J. et al. Long-term nitrogen loading alleviates phosphorus limitation in terrestrial ecosystems. Glob. Change Biol. 26, 5077–5086 (2020).Article 

Google Scholar 
Hinsinger, P. et al. P for two, sharing a scarce resource: soil phosphorus acquisition in the rhizosphere of intercropped species. Plant Physiol. 156, 1078–1086 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, X. J. et al. Plant diversity and species turnover co-regulate soil nitrogen and phosphorus availability in Dinghushan forests, southern China. Plant Soil 464, 257–272 (2021).CAS 
Article 

Google Scholar 
Hooper, D. U. & Vitousek, P. M. Effects of plant composition and diversity on nutrient cycling. Ecol. Monogr. 68, 121–149 (1998).Article 

Google Scholar 
Alberti, G. et al. Tree functional diversity influences belowground ecosystem functioning. Appl. Soil Ecol. 120, 160–168 (2017).Article 

Google Scholar 
Maddhesiya, P. K., Singh, K. & Singh, R. P. Effects of perennial aromatic grass species richness and microbial consortium on soil properties of marginal lands and on biomass production. Land Degrad. Dev. 32, 1008–1021 (2021).Article 

Google Scholar 
Zhang, C. B. et al. Effects of plant diversity on nutrient retention and enzyme activities in a full-scale constructed wetland. Bioresour. Technol. 101, 1686–1692 (2010).CAS 
PubMed 
Article 

Google Scholar 
Štursová, M. & Baldrian, P. Effects of soil properties and management on the activity of soil organic matter transforming enzymes and the quantification of soil-bound and free activity. Plant Soil 338, 99–110 (2011).Article 
CAS 

Google Scholar 
Wu, H. et al. Linkage between tree species richness and soil microbial diversity improves phosphorus bioavailability. Funct. Ecol. 33, 1549–1560 (2019).Article 

Google Scholar 
Steinauer, K. et al. Plant diversity effects on soil microbial functions and enzymes are stronger than warming in a grassland experiment. Ecology 96, 99–112 (2015).PubMed 
Article 

Google Scholar 
Zhang, D. S. et al. Increased soil phosphorus availability induced by faba bean root exudation stimulates root growth and phosphorus uptake in neighbouring maize. New Phytol. 209, 823–831 (2016).CAS 
PubMed 
Article 

Google Scholar 
Berendse, F., van Ruijven, J., Jongejans, E. & Keesstra, S. Loss of plant species diversity reduces soil erosion resistance. Ecosystems 18, 881–888 (2015).CAS 
Article 

Google Scholar 
Forrester, D. I. & Bauhus, J. A review of processes behind diversity–productivity relationships in forests. Curr. Rep. 2, 45–61 (2016).Article 
CAS 

Google Scholar 
Batterman, S. A. et al. Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees. Ecol. Lett. 21, 1486–1495 (2018).PubMed 
Article 

Google Scholar 
Chen, C., Chen, H. Y. H., Chen, X. & Huang, Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat. Commun. 10, 1332 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hisano, M., Chen, H. Y. H., Searle, E. B. & Reich, P. B. Species-rich boreal forests grew more and suffered less mortality than species-poor forests under the environmental change of the past half-century. Ecol. Lett. 22, 999–1008 (2019).PubMed 
Article 

Google Scholar 
Chen, X. & Chen, H. Y. H. Plant diversity loss reduces soil respiration across terrestrial ecosystems. Glob. Change Biol. 25, 1482–1492 (2019).Article 

Google Scholar 
Chen, X. & Chen, H. Y. H. Plant mixture balances terrestrial ecosystem C:N:P stoichiometry. Nat. Commun. 12, 4562 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reich, P. B. et al. Species and functional group diversity independently influence biomass accumulation and its response to CO2 and N. Proc. Natl Acad. Sci. USA 101, 10101–10106 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, X. et al. Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis. Biol. Rev. 95, 167–183 (2020).Article 

Google Scholar 
Zhang, Y., Chen, H. Y. H. & Reich, P. B. Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J. Ecol. 100, 742–749 (2012).Article 

Google Scholar 
Alewell, C. et al. Global phosphorus shortage will be aggravated by soil erosion. Nat. Commun. 11, 4546 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mueller, K. E., Tilman, D., Fornara, D. A. & Hobbie, S. E. Root depth distribution and the diversity–productivity relationship in a long-term grassland experiment. Ecology 94, 787–793 (2013).Article 

Google Scholar 
Tang, X. Y. et al. Intercropping legumes and cereals increases phosphorus use efficiency; a meta-analysis. Plant Soil 460, 89–104 (2021).CAS 
Article 

Google Scholar 
Karanika, E. D., Alifragis, D. A., Mamolos, A. P. & Veresoglou, D. S. Differentiation between responses of primary productivity and phosphorus exploitation to species richness. Plant Soil 297, 69–81 (2007).CAS 
Article 

Google Scholar 
Bünemann, E. K., Prusisz, B. & Ehlers, K. in Phosphorus in Action: Biological Processes in Soil Phosphorus Cycling (eds Bünemann, E. et al.) 37–57 (Springer, 2011).Ma, Z. L. & Chen, H. Y. H. Effects of species diversity on fine root productivity in diverse ecosystems: a global meta-analysis. Glob. Ecol. Biogeogr. 25, 1387–1396 (2016).Article 

Google Scholar 
Mellado-Vazquez, P. G. et al. Plant diversity generates enhanced soil microbial access to recently photosynthesized carbon in the rhizosphere. Soil Biol. Biochem. 94, 122–132 (2016).CAS 
Article 

Google Scholar 
Qin, Y. et al. Arbuscular mycorrhizal fungus differentially regulates P mobilizing bacterial community and abundance in rhizosphere and hyphosphere. Appl. Soil Ecol. 170, 104294 (2022).Article 

Google Scholar 
Rojo, M. J., Carcedo, S. G. & Mateos, M. P. Distribution and characterization of phosphatase and organic phosphorus in soil fractions. Soil Biol. Biochem. 22, 169–174 (1990).CAS 
Article 

Google Scholar 
Barrow, N. The effects of pH on phosphate uptake from the soil. Plant Soil 410, 401–410 (2017).CAS 
Article 

Google Scholar 
Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).CAS 
PubMed 
Article 

Google Scholar 
Yu, R. P., Li, X. X., Xiao, Z. H., Lambers, H. & Li, L. Phosphorus facilitation and covariation of root traits in steppe species. New Phytol. 226, 1285–1298 (2020).CAS 
PubMed 
Article 

Google Scholar 
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine 6, e1000097 (2009).Jenkins, D. G. & Quintana-Ascencio, P. F. A solution to minimum sample size for regressions. PLoS ONE 15, e0229345 (2020)..Rohatgi, A. WebPlotDigitizer v.4.5 (Automeris, 2021); https://automeris.io/WebPlotDigitizerJobbagy, E. G. & Jackson, R. B. The distribution of soil nutrients with depth:global patterns and the imprint of plants. Biogeochemistry 53, 51–77 (2001).CAS 
Article 

Google Scholar 
Trabucco, A. & Zomer, R. Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database (CGIAR, 2009); http://www.cgiar-csi.org/data/global-aridity-and-pet-databaseBridgham, S. D., Pastor, J., Mcclaugherty, C. A. & Richardson, C. J. Nutrient-use efficiency: a litterfall index, a model, and a test along a nutrient-availability gradient in North Carolina peatlands. Am. Nat. 145, 1–21 (1995).Article 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).CAS 
PubMed 
Article 

Google Scholar 
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).CAS 
PubMed 
Article 

Google Scholar 
Bates, D. et al. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-10 https://cran.r-project.org/web/packages/lme4/index.html (2017).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
Article 

Google Scholar 
MuMIn: Multi-model inference. R package version 1.42.1 (2018).Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Koricheva, J., Gurevitch, J. & Mengersen, K. Handbook of Meta-analysis in Ecology and Evolution (Princeton Univ. Press, 2013).Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).Article 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Long, J. A. Interactions: comprehensive, user-friendly toolkit for probing interactions. R package version 1.1.5 https://cran.r-project.org/package=interactions (2021).Adams, D. C., Gurevitch, J. & Rosenberg, M. S. Resampling tests for meta-analysis of ecological data. Ecology 78, 1277–1283 (1997).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). More

Chuine, I. Why does phenology drive species distribution? Philos. Trans. 365, 3149–3160 (2010).
Google Scholar 
Chuine, I. & Beaubien, E. G. Phenology is a major determinant of tree species range. Ecol. Lett. 4, 500–510 (2001).
Google Scholar 
Richardson, D. A. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).ADS 

Google Scholar 
Tang, J. et al. Emerging opportunities and challenges in phenology: a review. Ecosphere 7, e01436 (2016).
Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Chang. Biol. 25, 1922–1940 (2019).ADS 
MathSciNet 
PubMed 

Google Scholar 
Fu, Y. H. et al. Three times greater weight of daytime than of night‐time temperature on leaf unfolding phenology in temperate trees. N. Phytol. 212, 590–597 (2016).CAS 

Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Chang. Biol. 12, 1969–1976 (2006).ADS 

Google Scholar 
Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Penuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).CAS 
PubMed 

Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Lang, G. A. Dormancy: a new universal terminology. HortScience 22, 817–820 (1987).
Google Scholar 
Perry, T. O. Dormancy of trees in winter. Science 171, 29–36 (1971).ADS 
CAS 
PubMed 

Google Scholar 
Huang, J. et al. Intra-annual wood formation of subtropical Chinese red pine shows better growth in dry season than wet season. Tree Physiol. 38, 1225–1236 (2018).PubMed 

Google Scholar 
Knowles, J. F. et al. Montane forest productivity across a semi-arid climatic gradient. Glob. Chang. Biol. 26, 6945–6958 (2020).ADS 
PubMed 

Google Scholar 
Richard, S., Kjellsen, T. D., Schaberg, P. G. & Murakami, P. F. Dynamics of low-temperature acclimation in temperate and boreal conifer foliage in a mild winter climate. Tree Physiol. 28, 1365–1374 (2008).
Google Scholar 
Roxas, A. A., Orozco, J., Guzmán-Delgado, P. & Zwieniecki, M. A. Spring phenology is affected by fall non-structural carbohydrate concentration and winter sugar redistribution in three Mediterranean nut tree species. Tree Physiol. 41, 1425–1438 (2021).CAS 

Google Scholar 
Palacio, S., Martínez, M. M. & Montserrat-Martí, G. Seasonal dynamics of non-structural carbohydrates in two species of mediterranean sub-shrubs with different leaf phenology. Environ. Exp. Bot. 59, 34–42 (2007).CAS 

Google Scholar 
Fierravanti, A., Rossi, S., Kneeshaw, D., Grandpré, L. D. & Deslauriers, A. Low non-structural carbon accumulation in spring reduces growth and increases mortality in conifers defoliated by spruce budworm. Front. For. Glob. Change. 2, 1–13 (2019).
Google Scholar 
Oberhuber, W., Gruber, A., Lethaus, G., Winkler, A. & Wieser, G. Stem girdling indicates prioritized carbon allocation to the root system at the expense of radial stem growth in Norway spruce under drought conditions. Environ. Exp. Bot. 138, 109–118 (2017).PubMed 
PubMed Central 

Google Scholar 
Pérez-de-Lis, G., Rossi, S., Vázquez-Ruiz, R. A., Rozas, V. & García-González, I. Do changes in spring phenology affect earlywood vessels? Perspective from the xylogenesis monitoring of two sympatric ring-porous oaks. N. Phytol. 209, 521–530 (2016).
Google Scholar 
Weber, R., Gessler, A. & Hoch, G. High carbon storage in carbon-limited trees. N. Phytol. 222, 171–182 (2019).CAS 

Google Scholar 
Zani, D., Crowther, T. W., Lidong, M., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Dusenge, M. E., Duarte, A. G. & Way, D. A. Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. N. Phytol. 221, 32–49 (2019).CAS 

Google Scholar 
Lin, Y.-S., Medlyn, B. E. & Ellsworth, D. Temperature responses of leaf net photosynthesis: the role of component processes. Tree Physiol. 32, 219–231 (2012).CAS 
PubMed 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 

Google Scholar 
Terashima, I. & Hikosaka, K. Comparative ecophysiology of leaf and canopy photosynthesis. Plant Cell Environ. 18, 1111–1128 (1995).
Google Scholar 
Liang, J., Xia, J., Liu, L. & Wan, S. Global patterns of the responses of leaf-level photosynthesis and respiration in terrestrial plants to experimental warming. J. Plant. Ecol. 6, 437–447 (2013).
Google Scholar 
Duffy, K. A. et al. How close are we to the temperature tipping point of the terrestrial biosphere? Sci. Adv. 7, eaay1052 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Güsewell, S., Furrer, R., Gehrig, R. & Pietragalla, B. Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason. Glob. Chang. Biol. 23, 5189–5202 (2017).ADS 
PubMed 

Google Scholar 
Keenan, T. F., Richardson, A. D. & Hufkens, K. On quantifying the apparent temperature sensitivity of plant phenology. N. Phytol. 225, 1033–1040 (2020).
Google Scholar 
Klein, T., Vitasse, Y. & Hoch, G. Coordination between growth, phenology and carbon storage in three coexisting deciduous tree species in a temperate forest. Tree Physiol. 36, 847–855 (2016).CAS 
PubMed 

Google Scholar 
Kagawa, A., Sugimoto, A. & Maximov, T. C. Seasonal course of translocation, storage and remobilization of 13C pulse-labeled photoassimilate in naturally growing Larix gmelinii saplings. N. Phytol. 171, 793–804 (2010).
Google Scholar 
Rinne, K. T. et al. Examining the response of needle carbohydrates from Siberian larch trees to climate using compound-specific δ(13) C and concentration analyses. Plant Cell Environ. 38, 2340–2352 (2015).CAS 
PubMed 

Google Scholar 
Schädel, C., Blöchl, A., Richter, A. & Hoch, G. Short-term dynamics of nonstructural carbohydrates and hemicelluloses in young branches of temperate forest trees during bud break. Tree Physiol. 29, 901–911 (2009).PubMed 

Google Scholar 
Kaurin, A., Junttila, O. & Hanson, J. Seasonal changes in frost hardiness in cloudberry (Rubus chamaemorus) in relation to carbohydrate content with special reference to sucrose. Physiol. Plant. 52, 310–314 (1981).CAS 

Google Scholar 
Shahba, M. A., Qian, Y. L., Hughes, H. G., Koski, A. J. & Christensen, D. Relationships of soluble carbohydrates and freeze tolerance in saltgrass. Crop Sci. 43, 2148–2153 (2003).CAS 

Google Scholar 
Wang, J. et al. Contrasting temporal variations in responses of leaf unfolding to daytime and nighttime warming. Glob. Chang. Biol. 27, 5084–5093 (2021).PubMed 

Google Scholar 
Marchand, L. J. et al. Inter-individual variability in spring phenology of temperate deciduous trees depends on species, tree size and previous year autumn phenology. Agric Meteorol. 290, 108031 (2020).
Google Scholar 
Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agric Meteorol. 291, 108077 (2020).
Google Scholar 
Chen, L. et al. Long-term changes in the impacts of global warming on leaf phenology of four temperate tree species. Glob. Chang. Biol. 25, 997–1004 (2019).ADS 
PubMed 

Google Scholar 
Hanninen, H. Boreal and temperate trees in a changing climate: modelling the ecophysiology of seasonality. (Springer, 2016).Dreyer, E., Le Roux, X., Montpied, P., Daudet, F. A. & Masson, F. Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species. Tree Physiol. 21, 223–232 (2001).CAS 
PubMed 

Google Scholar 
Devi, A. F. & Garkoti, S. C. Variation in evergreen and deciduous species leaf phenology in Assam. India Trees 27, 985–997 (2013).
Google Scholar 
Bai, K., He, C., Wan, X. & Jiang, D. Leaf economics of evergreen and deciduous tree species along an elevational gradient in a subtropical mountain. AoB PLANTS 7, plv064 (2015).PubMed 
PubMed Central 

Google Scholar 
Qi, J., Fan, Z., Fu, P., Zhang, Y. & Sterck, F. Differential determinants of growth rates in subtropical evergreen and deciduous juvenile trees: carbon gain, hydraulics and nutrient-use efficiencies. Tree Physiol. 41, 12–23 (2021).CAS 
PubMed 

Google Scholar 
Fyllas, N. M. et al. Functional trait variation among and within species and plant functional types in mountainous mediterranean forests. Front. Plant Sci. 11, 1–18 (2020).
Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int J. Biometeorol. 62, 1109–1113 (2018).ADS 
PubMed 

Google Scholar 
Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
Google Scholar 
Richardson, A. D. et al. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Sci. Data. 5, 180028 (2018).PubMed 
PubMed Central 

Google Scholar 
Klosterman, S. et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11, 4305–4320 (2014).ADS 

Google Scholar 
Zhang, Y. et al. Seasonal and interannual changes in vegetation activity of tropical forests in Southeast Asia. Agric. For. Meteorol. 224, 1–10 (2016).ADS 

Google Scholar 
Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).ADS 

Google Scholar 
Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wang, X. et al. Validation of MODIS-GPP product at 10 flux sites in northern China. Int. J. Remote Sens. 34, 587–599 (2013).
Google Scholar 
Julien, Y. & Sobrino, J. Global land surface phenology trends from GIMMS database. Int J. Remote Sens. 30, 3495–3513 (2009).
Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens Environ. 84, 471–475 (2003).ADS 

Google Scholar 
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
PubMed Central 

Google Scholar 
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data. 7, 1–27 (2020).
Google Scholar 
Huang, K. et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evol. 2, 1897–1905 (2018).PubMed 

Google Scholar 
Tang, Y., Xu, X., Zhou, Z., Qu, Y. & Sun, Y. Estimating global maximum gross primary productivity of vegetation based on the combination of MODIS greenness and temperature data. Ecol. Inform. 63, 101307 (2021).
Google Scholar 
Xia, J. et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. U.S.A. 112, 2788–2793 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kalman, D. A singularly valuable decomposition: The SVD of a matrix. Coll. Math. J. 27, 2–23 (1996).MathSciNet 

Google Scholar 
Biriukova, K. et al. Performance of singular spectrum analysis in separating seasonal and fast physiological dynamics of solar-induced chlorophyll fluorescence and PRI optical signals. J. Geophys. Res. Biogeosci. 126, e2020JG006158 (2021).ADS 
CAS 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3227–3246 (2010).PubMed 
PubMed Central 

Google Scholar 
Wu, C. et al. Interannual variability of net carbon exchange is related to the lag between the end-dates of net carbon uptake and photosynthesis: Evidence from long records at two contrasting forest stands. Agric. For. Meteorol. 164, 29–38 (2012).ADS 

Google Scholar 
Cornes, R., der Schrier, G. V., den Besselaar, E. J. M. V. & Jones, P. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
Google Scholar 
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. https://CRAN.R-project.org/package=raster. R package version 3.5-15 (2022).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Erb, I. Partial correlations in compositional data analysis. Comput. Geosci. 6, 100026 (2020).
Google Scholar 
Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. U.S.A. 115, 1004–1008 (2018).CAS 
PubMed 

Google Scholar 
Kim, S. ppcor: Partial and semi-partial (part) correlation. https://CRAN.R-project.org/package=ppcor. R package version 1.1 (2015).Lefcheck, J. S. piecewiseSEM: piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 

Google Scholar 
Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Modelling species presence-only data with random forests. Ecography 44, 1731–1742 (2021).
Google Scholar 
Freeman, E. A., Moisen, G. G., Coulston, J. W. & Wilson, B. T. Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Can. J. For. Res. 46, 323–339 (2016).
Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Cutler, D. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 

Google Scholar  More

Rockström, J. et al. Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio 46, 4–17 (2017).Article 

Google Scholar 
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).Article 
CAS 

Google Scholar 
Campbell, B. M. et al. Agriculture production as a major driver of the Earth system exceeding planetary boundaries. Ecol. Soc. 22, 8 (2017).Article 

Google Scholar 
Hazell, P. & Wood, S. Drivers of change in global agriculture. Philos. Trans. R. Soc. B 363, 495–515 (2008).Article 

Google Scholar 
Lehmann, P. et al. Complex responses of global insect pests to climate warming. Front. Ecol. Environ. 18, 141–150 (2020).Article 

Google Scholar 
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).CAS 
Article 

Google Scholar 
Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).CAS 
Article 

Google Scholar 
Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W. & Mortensen, D. A. Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67, 386–391 (2017).Article 

Google Scholar 
Ecosystems and Human Well-being: Synthesis (Millenium Ecosystem Assessment, 2005); http://www.millenniumassessment.org/documents/document.356.aspx.pdfBommarco, R., Kleijn, D. & Potts, S. G. Ecological intensification: harnessing ecosystem services for food security. Trends Ecol. Evol. 28, 230–238 (2013).Article 

Google Scholar 
Kleijn, D. et al. Ecological intensification: bridging the gap between science and practice. Trends Ecol. Evol. 34, 154–166 (2018).Article 

Google Scholar 
Pingali, P. L. Green revolution: impacts, limits, and the path ahead. Proc. Natl Acad. Sci. USA 109, 12302–12308 (2012).CAS 
Article 

Google Scholar 
Wezel, A. et al. Agroecology as a science, a movement and a practice. Sustain. Agric. 2, 27–43 (2009).
Google Scholar 
Garnett, T. et al. Sustainable intensification in agriculture: premises and policies. Science 341, 33–34 (2013).CAS 
Article 

Google Scholar 
Lipper, L. et al. Climate-smart agriculture for food security. Nat. Clim. Change 4, 1068–1072 (2014).Article 

Google Scholar 
Tittonell, P. Ecological intensification of agriculture—sustainable by nature. Curr. Opin. Environ. Sustain. 8, 53–61 (2014).Article 

Google Scholar 
Jenkinson, D. S. The impact of humans on the nitrogen cycle, with focus on temperate arable agriculture. Plant Soil 228, 3–15 (2001).CAS 
Article 

Google Scholar 
Verheijen, F. G. A., Jones, R. J. A., Rickson, R. J. & Smith, C. J. Tolerable versus actual soil erosion rates in Europe. Earth Sci. Rev. 94, 23–38 (2009).Article 

Google Scholar 
Peoples, M. B. et al. in Agroecosystem Diversity: Reconciling Contemporary Agriculture and Environmental Quality (eds Lemaire, G. et al.) 123–142 (Academic Press, 2019); https://doi.org/10.1016/B978-0-12-811050-8.00008-XStorkey, J., Bruce, T., McMillan, V. & Neve, P. in Agroecosystem Diversity: Reconciling Contemporary Agriculture and Environmental Quality (eds Lemaire, G. et al.) 199–209 (Academic Press, 2019); https://doi.org/10.1016/B978-0-12-811050-8.00012-1Schröder, J. Revisiting the agronomic benefits of manure: a correct assessment and exploitation of its fertilizer value spares the environment. Bioresour. Technol. 96, 253–261 (2005).Article 
CAS 

Google Scholar 
Mhlanga, B., Ercoli, L., Pellegrino, E., Onofri, A. & Thierfelder, C. The crucial role of mulch to enhance the stability and resilience of cropping systems in southern Africa. Agron. Sustain. Dev. 41, 29–43 (2021).Article 

Google Scholar 
Barrett, C. B. & Bevis, L. E. M. The self-reinforcing feedback between low soil fertility and chronic poverty. Nat. Geosci. 8, 907–912 (2015).CAS 
Article 

Google Scholar 
Tittonell, P. & Giller, K. E. When yield gaps are poverty traps: the paradigm of ecological intensification in African smallholder agriculture. Field Crops Res. 143, 76–90 (2013).Article 

Google Scholar 
Sandén, T. et al. European long-term field experiments: knowledge gained about alternative management practices. Soil Use Manage. 34, 167–176 (2018).Article 

Google Scholar 
Storkey, J. et al. The unique contribution of Rothamsted to ecological research at large temporal scales. Adv. Ecol. Res. 55, 3–42 (2016).Article 

Google Scholar 
Johnston, A. E. & Poulton, P. R. The importance of long-term experiments in agriculture: their management to ensure continued crop production and soil fertility; the Rothamsted experience. Eur. J. Soil Sci. 69, 113–125 (2018).CAS 
Article 

Google Scholar 
Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2, 284–293 (2020).Marini, L. et al. Crop rotations sustain cereal yields under a changing climate. Environ. Res. Lett. 15, 124011 (2020).Article 

Google Scholar 
Lal, R. Carbon emission from farm operations. Environ. Int. 30, 981–990 (2004).CAS 
Article 

Google Scholar 
Cordell, D., Drangert, J. O. & White, S. The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19, 292–305 (2009).Article 

Google Scholar 
Lechenet, M., Dessaint, F., Py, G., Makowski, D. & Munier-Jolain, N. Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nat. Plants 3, 17008 (2017).Article 

Google Scholar 
Bedoussac, L. et al. Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. Agron. Sustain. Dev. 35, 911–935 (2015).Article 

Google Scholar 
Storkey, J., Mead, A., Addy, J. & MacDonald, A. J. Agricultural intensification and climate change have increased the threat from weeds. Glob. Change Biol. 27, 2416–2425 (2021).Article 

Google Scholar 
Vanlauwe, B. et al. in Integrated Plant Nutrient Management in Sub-Saharan Africa: From Concept to Practice (eds Vanlauwe, B. et al.) 173–184 (CABI, 2002).Hijbeek, R. et al. Do organic inputs matter—a meta-analysis of additional yield effects for arable crops in Europe. Plant Soil 411, 293–303 (2017).CAS 
Article 

Google Scholar 
Thierfelder, C. & Wall, P. C. Effects of conservation agriculture techniques on infiltration and soil water content in Zambia and Zimbabwe. Soil Tillage Res. 105, 217–227 (2009).Article 

Google Scholar 
Gentile, R., Vanlauwe, B., Chivenge, P. & Six, J. Interactive effects from combining fertilizer and organic residue inputs on nitrogen transformations. Soil Biol. Biochem. 40, 2375–2384 (2008).CAS 
Article 

Google Scholar 
Mupangwa, W. et al. Maize yields from rotation and intercropping systems with different legumes under conservation agriculture in contrasting agro-ecologies. Agric. Ecosyst. Environ. 306, 107170 (2021).Article 

Google Scholar 
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).CAS 
Article 

Google Scholar 
Steward, P. R. et al. The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: a meta-regression of yields. Agric. Ecosyst. Environ. 251, 194–202 (2018).Article 

Google Scholar 
Pittelkow, C. M. et al. When does no-till yield more? A global meta-analysis. Field Crops Res. 183, 156–168 (2015).Article 

Google Scholar 
Sun, W. et al. Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Glob. Change Biol. 26, 3325–3335 (2020).Article 

Google Scholar 
Kirkegaard, J. A. et al. Sense and nonsense in conservation agriculture: principles, pragmatism and productivity in Australian mixed farming systems. Agric. Ecosyst. Environ. 187, 133–145 (2014).Article 

Google Scholar 
Thierfelder, C. et al. Complementary practices supporting conservation agriculture in southern Africa. A review. Agron. Sustain. Dev. 38, 16–37 (2018).Article 

Google Scholar 
Alignier, A. et al. Configurational crop heterogeneity increases within-field plant diversity. J. Appl. Ecol. 57, 654–663 (2020).Article 

Google Scholar 
Liebman, M. et al. Ecologically sustainable weed management: how do we get from proof-of-concept to adoption? Ecol. Appl. 26, 1352–1369 (2016).Article 

Google Scholar 
Giller, K. E. The food security conundrum of sub-Saharan Africa. Glob. Food Sec. 26, 100431 (2020).Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Addy, J. W. G., Ellis, R. H., Macdonald, A. J., Semenov, M. A. & Mead, A. Changes in agricultural climate in South-Eastern England from 1892 to 2016 and differences in cereal and permanent grassland yield. Agric. For. Meteorol. 308–309, 108560 (2021).Article 

Google Scholar 
Bates, D., Kliegl, R., Vasishth, S. & Baayen, H. Parsimonious mixed models. Preprint at https://arXiv.org/abs/1506.04967v2 (2018).MacLaren, C., Glendining, M., Poulton, P., Macdonald, A. & Clark, S. Woburn Ley-Arable Experiment: Yields of Wheat as First Test Crop, 1976–2018 (e-RA Rothamsted, 2022); https://doi.org/10.23637/wrn3-wheat7618-01 .Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means: R package version 1.7.2 https://CRAN.R-project.org/package=emmeans (2020).Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
Lajeunesse, M. J. On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology 92, 2049–2055 (2011).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Liu, Y. et al. A genome and gene catalog of glacier microbiomes. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01367-2 (2022). 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