Philippa Kaur

Fischer, C. P. & Romero, L. M. Chronic captivity stress in wild animals is highly species-specific. Conserv. Physiol. 7, coz093 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McGill, I. et al. Isosporoid coccidiosis in translocated cirl buntings (Emberiza cirlus). Vet. Rec. 167, 656–660 (2010).CAS 
PubMed 
Article 

Google Scholar 
Mohajeri, M. H. et al. The role of the microbiome for human health: from basic science to clinical applications. Eur. J. Nutr. 57, 1–14 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Song, S. J. et al. Engineering the microbiome for animal health and conservation. Exp. Biol. Med. 244, 494–504 (2019).Article 
CAS 

Google Scholar 
Peters, A., Meredith, A., Skerratt, L., Carver, S. & Raidal, S. Infectious disease and emergency conservation interventions. Conserv. Biol. 34, 784–785 (2020).PubMed 
Article 

Google Scholar 
Northover, A. S. et al. Altered parasite community structure in an endangered marsupial following translocation. Int. J. Parasitol. Parasites Wildl. 10, 13–22 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Daniel, B. M. et al. A bat on the brink? A range-wide survey of the Critically Endangered Livingstone’s fruit bat Pteropus livingstonii. Oryx 51, 742–751 (2017).Article 

Google Scholar 
IUCN. Pteropus livingstonii: Sewall, B.J., Young, R., Trewhella, W.J. & Rodríguez-Clark, K.M. and Granek, E.F. IUCN Red List of Threatened Species (2016) https://doi.org/10.2305/iucn.uk.2016-2.rlts.t18732a22081502.en.IUCN Species Survival Commission. Species action plan for Livingstone’s fruit bat ‘Pteropus livingstonii’. https://portals.iucn.org/library/node/7368 (1995).Haag, A. F., Ross Fitzgerald, J. & Penadés, J. R. Staphylococcus aureus in animals. Gram-Positive Pathog. https://doi.org/10.1128/9781683670131.ch46 (2019).Article 

Google Scholar 
Pirolo, M. et al. Unidirectional animal-to-human transmission of methicillin-resistant Staphylococcus aureus ST398 in pig farming; evidence from a surveillance study in southern Italy. Antimicrob. Resist. Infect. Control 8, 1–10 (2019).Article 

Google Scholar 
Young, B. C. et al. Severe infections emerge from commensal bacteria by adaptive evolution. Elife 6, e30637 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Heaton, C. J., Gerbig, G. R., Sensius, L. D., Patel, V. & Smith, T. C. Staphylococcus aureus epidemiology in wildlife: A systematic review. Antibiotics 9, 89 (2020).PubMed Central 
Article 

Google Scholar 
Sheppard, S. K., Guttman, D. S. & Fitzgerald, J. R. Population genomics of bacterial host adaptation. Nat. Rev. Genet. 19, 549–565 (2018).CAS 
PubMed 
Article 

Google Scholar 
Richardson, E. J. et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat. Ecol. Evol. 2, 1468–1478 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Bacigalupe, R., Tormo-Mas, M. Á., Penadés, J. R. & Ross Fitzgerald, J. A multihost bacterial pathogen overcomes continuous population bottlenecks to adapt to new host species. Sci. Adv. 5, eaax0063 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Spoor, L. E. et al. Recombination-mediated remodelling of host–pathogen interactions during Staphylococcus aureus niche adaptation. Microb. Genomics 1(4), e000036. https://doi.org/10.1099/mgen.0.000036 (2015).Article 

Google Scholar 
Tong, S. Y. C., Davis, J. S., Eichenberger, E., Holland, T. L. & Fowler, V. G. Jr. Staphylococcus aureus infections: Epidemiology, pathophysiology, clinical manifestations, and management. Clin. Microbiol. Rev. 28, 603–661 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fountain, K. et al. Diversity of staphylococcal species cultured from captive Livingstone’s fruit bats (Pteropus livingstonii) and their environment. J. Zoo Wildl. Med. 50, 266–269 (2019).PubMed 
Article 

Google Scholar 
Fountain, K. et al. Fatal exudative dermatitis in island populations of red squirrels (Sciurus vulgaris): spillover of a virulent clone (ST49) from reservoir hosts. Microb. Genom. 7(5), 000565. https://doi.org/10.1099/mgen.0.000565 (2021).CAS 
Article 
PubMed Central 

Google Scholar 
Rohmer, C. & Wolz, C. The role of hlb-converting bacteriophages in Staphylococcus aureus host adaption. Microb. Physiol. 31 109–122. https://doi.org/10.1159/000516645 (2021).
PubMed 
Article 

Google Scholar 
Senghore, M. et al. Transmission of Staphylococcus aureus from humans to green monkeys in The Gambia as revealed by whole-genome sequencing. Appl. Environ. Microbiol. 82, 5910–5917 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xue, H., Lu, H. & Zhao, X. Sequence diversities of serine-aspartate repeat genes among Staphylococcus aureus isolates from different hosts presumably by horizontal gene transfer. PLoS ONE 6, e20332 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paharik, A. E. et al. The Spl serine proteases modulate protein production and virulence in a rabbit model of pneumonia. mSphere 1, e00208-16 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wein, T., Hülter, N. F., Mizrahi, I. & Dagan, T. Emergence of plasmid stability under non-selective conditions maintains antibiotic resistance. Nat. Commun. 10, 2595 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cheng, A. G., Missiakas, D. & Schneewind, O. The giant protein Ebh is a determinant of Staphylococcus aureus cell size and complement resistance. J. Bacteriol. 196, 971–981 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lin, Y.-C. et al. Staphylococcal phosphatidylinositol-specific phospholipase C potentiates lung injury via complement sensitisation. Cell. Microbiol. 21, e13085 (2019).PubMed 

Google Scholar 
Siboo, I. R., Chambers, H. F. & Sullam, P. M. Role of SraP, a serine-rich surface protein of Staphylococcus aureus, in binding to human platelets. Infect. Immun. 73, 2273–2280 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nakamura, Y. et al. Phosphatidylinositol-specific phospholipase C enhances epidermal penetration by Staphylococcus aureus. Sci. Rep. 10, 17845 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Peng, X. et al. Flight is the key to postprandial blood glucose balance in the fruit bats Eonycteris spelaea and Cynopterus sphinx. Ecol. Evol. 7, 8804–8811 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Partridge, S. R., Kwong, S. M., Firth, N. & Jensen, S. O. Mobile genetic elements associated with antimicrobial resistance. Clin. Microbiol. Rev. 31, e00088-17 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Pence, M. A. et al. Beta-lactamase repressor BlaI modulates Staphylococcus aureus cathelicidin antimicrobial peptide resistance and virulence. PLoS ONE 10, e0136605 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Raafat, D. et al. Molecular epidemiology of methicillin-susceptible and methicillin-resistant Staphylococcus aureus in wild, captive and laboratory rats: Effect of habitat on the nasal S. aureus population. Toxins 12, 80 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
National Library of Medicine (US), National Center for Biotechnology Information. Genbank. (1982).PubMLST—Public databases for molecular typing and microbial genome diversity. https://pubmlst.org/.Wick, R. R., Judd, L. M. & Holt, K. E. Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks. PLoS Comput. Biol. 14, e1006583 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).ADS 
Article 
CAS 
PubMed 
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 
PubMed Central 
Article 

Google Scholar 
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (2010).Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 
Article 

Google Scholar 
Seeman, T. MLST. Github https://github.com/tseemann/mlst.Page, A. J. et al. Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: A fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seeman, T. Snippy: Fast Bacterial Variant Calling from NGS Reads (2015).Carver, T., Harris, S. R., Berriman, M., Parkhill, J. & McQuillan, J. A. Artemis: An integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 28, 464–469 (2012).CAS 
PubMed 
Article 

Google Scholar 
Sievers, F. & Higgins, D. G. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 27, 135–145 (2018).CAS 
PubMed 
Article 

Google Scholar 
Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M. & Barton, G. J. Jalview Version 2—A multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seeman, T. Abricate; Mass screening of contigs for antimicrobial resistance or virulence genes. Github https://github.com/tseemann/abricate.Feldgarden, M. et al. Validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates. Antimicrob. Agents Chemother. 63, e00483-19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Zankari, E. et al. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, L., Zheng, D., Liu, B., Yang, J. & Jin, Q. VFDB 2016: Hierarchical and refined dataset for big data analysis–10 years on. Nucleic Acids Res. 44, D694–D697 (2016).CAS 
PubMed 
Article 

Google Scholar 
Carattoli, A. et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob. Agents Chemother. 58, 3895–3903 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gupta, S. K. et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob. Agents Chemother. 58, 212–220 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Arndt, D., Marcu, A., Liang, Y. & Wishart, D. S. PHAST, PHASTER and PHASTEST: Tools for finding prophage in bacterial genomes. Brief. Bioinform. 20, 1560–1567 (2019).CAS 
PubMed 
Article 

Google Scholar 
Antipov, D. et al. plasmidSPAdes: Assembling plasmids from whole genome sequencing data. Bioinformatics https://doi.org/10.1093/bioinformatics/btw493 (2016).Article 
PubMed 

Google Scholar 
Robertson, J. & Nash, J. H. E. MOB-suite: Software tools for clustering, reconstruction and typing of plasmids from draft assemblies. Microb. Genom. 4(8), e000206. https://doi.org/10.1099/mgen.0.000206 (2018).CAS 
Article 

Google Scholar 
Jaillard, M. et al. A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events. PLoS Genet. 14, e1007758 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

Simpson, G. G. The Major Features of Evolution (Columbia Univ. Press, 1953).Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism: American Society of Naturalists E. O. Wilson award address. Am. Nat. 175, 623–639 (2010).PubMed 
Article 

Google Scholar 
Osborn, H. F. The law of adaptive radiation. Am. Nat. 36, 353–363 (1902).Article 

Google Scholar 
Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596 (2010).CAS 
PubMed 
Article 

Google Scholar 
Robertson, G. P. et al. Soil resources, microbial activity, and primary production across an agricultural ecosystem. Ecol. Appl. 7, 158–170 (1997).Article 

Google Scholar 
Singer, D. et al. Protist taxonomic and functional diversity in soil, freshwater and marine ecosystems. Environ. Int. 146, 106262 (2021).CAS 
PubMed 
Article 

Google Scholar 
Miller, M. F. & Labandeira, C. C. Slow crawl across the salinity divide: delayed colonization of freshwater ecosystems by invertebrates. GSA Today 12, 4–10 (2002).Article 

Google Scholar 
Cnaani, A. & Hulata, G. Improving salinity tolerance in tilapias: past experience and future prospects. Isr. J. Aquac. 63, 20590 (2011).
Google Scholar 
Eiler, A. et al. Productivity and salinity structuring of the microplankton revealed by comparative freshwater metagenomics. Environ. Microbiol. 16, 2682–2698 (2014).CAS 
PubMed 
Article 

Google Scholar 
Cabello-Yeves, P. J. & Rodriguez-Valera, F. Marine-freshwater prokaryotic transitions require extensive changes in the predicted proteome. Microbiome 7, 117 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Hutchinson, G. E. A Treatise on Limnology (John Wiley & Sons, 1957).Vermeij, G. J. & Dudley, R. Why are there so few evolutionary transitions between aquatic and terrestrial ecosystems? Biol. J. Linn. Soc. 70, 541–554 (2000).Article 

Google Scholar 
Lee, C. E. & Bell, M. A. Causes and consequences of recent freshwater invasions by saltwater animals. Trends Ecol. Evol. 14, 284–288 (1999).CAS 
PubMed 
Article 

Google Scholar 
Logares, R. et al. Infrequent marine–freshwater transitions in the microbial world. Trends Microbiol. 17, 414–422 (2009).CAS 
PubMed 
Article 

Google Scholar 
Paver, S. F., Muratore, D., Newton, R. J. & Coleman, M. L. Reevaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems. mSystems 3, e00232-18 (2018).Filker, S. et al. Transition boundaries for protistan species turnover in hypersaline waters of different biogeographic regions. Environ. Microbiol. 19, 3186–3200 (2017).CAS 
PubMed 
Article 

Google Scholar 
Cavalier-Smith, T. Megaphylogeny, cell body plans, adaptive zones: causes and timing of eukaryote basal radiations. J. Eukaryot. Microbiol. 56, 26–33 (2009).PubMed 
Article 

Google Scholar 
Carr, M. et al. A six-gene phylogeny provides new insights into choanoflagellate evolution. Mol. Phylogenet. Evol. 107, 166–178 (2017).PubMed 
Article 

Google Scholar 
Simon, M., López-García, P., Moreira, D. & Jardillier, L. New haptophyte lineages and multiple independent colonizations of freshwater ecosystems. Environ. Microbiol. Rep. 5, 322–332 (2013).CAS 
PubMed 
Article 

Google Scholar 
Bråte, J., Klaveness, D., Rygh, T., Jakobsen, K. S. & Shalchian-Tabrizi, K. Telonemia-specific environmental 18S rDNA PCR reveals unknown diversity and multiple marine–freshwater colonizations. BMC Microbiol. 10, 168 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shalchian-Tabrizi, K. et al. Diversification of unicellular eukaryotes: cryptomonad colonizations of marine and fresh waters inferred from revised 18S rRNA phylogeny. Environ. Microbiol. 10, 2635–2644 (2008).CAS 
PubMed 
Article 

Google Scholar 
Von Der Heyden, S., Chao, E. E. & Cavalier-Smith, T. Genetic diversity of goniomonads: an ancient divergence between marine and freshwater species. Eur. J. Phycol. 39, 343–350 (2004).Article 
CAS 

Google Scholar 
Žerdoner Čalasan, A., Kretschmann, J. & Gottschling, M. They are young, and they are many: dating freshwater lineages in unicellular dinophytes. Environ. Microbiol. 21, 4125–4135 (2019).PubMed 
Article 

Google Scholar 
Annenkova, N. V., Giner, C. R. & Logares, R. Tracing the origin of planktonic protists in an ancient lake. Microorganisms 8, 543 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Annenkova, N. V., Hansen, G., Moestrup, Ø. & Rengefors, K. Recent radiation in a marine and freshwater dinoflagellate species flock. ISME J. 9, 1821–1834 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Annenkova, N. V., Hansen, G. & Rengefors, K. Closely related dinoflagellate species in vastly different habitats—an example of a marine–freshwater transition. Eur. J. Phycol. 55, 478–489 (2020).CAS 
Article 

Google Scholar 
Obiol, A. et al. A metagenomic assessment of microbial eukaryotic diversity in the global ocean. Mol. Ecol. Resour. 20, 718–731 (2020).CAS 
Article 

Google Scholar 
Jamy, M. et al. Long-read metabarcoding of the eukaryotic rDNA operon to phylogenetically and taxonomically resolve environmental diversity. Mol. Ecol. Resour. 20, 429–443 (2020).CAS 
PubMed 
Article 

Google Scholar 
Burki, F., Roger, A. J., Brown, M. W. & Simpson, A. G. B. The new tree of eukaryotes. Trends Ecol. Evol. 35, 43–55 (2020).PubMed 
Article 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jamy, M. et al. Data for ‘Global patterns and rates of habitat transitions across the eukaryotic tree of life’. figshare https://doi.org/10.6084/m9.figshare.15164772.v3 (2022).Dunthorn, M. et al. Placing environmental next-generation sequencing amplicons from microbial eukaryotes into a phylogenetic context. Mol. Biol. Evol. 31, 993–1009 (2014).CAS 
PubMed 
Article 

Google Scholar 
Barbera, P. et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst. Biol. 68, 365–369 (2019).PubMed 
Article 

Google Scholar 
Pagel, M. Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters. Proc. R. Soc. B 255, 37–45 (1994).Article 

Google Scholar 
Ishikawa, S. A., Zhukova, A., Iwasaki, W., Gascuel, O. & Pupko, T. A fast likelihood method to reconstruct and visualize ancestral scenarios. Mol. Biol. Evol. 36, 2069–2085 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gottschling, M., Czech, L., Mahé, F., Adl, S. & Dunthorn, M. The windblown: possible explanations for dinophyte DNA in forest soils. J. Eukaryot. Microbiol. 68, e12833 (2021).Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).PubMed 
Article 

Google Scholar 
Strassert, J. F. H., Irisarri, I., Williams, T. A. & Burki, F. A molecular timescale for eukaryote evolution with implications for the origin of red algal-derived plastids. Nat. Commun. 12, 1879 (2021).Parfrey, L. W., Lahr, D. J. G., Knoll, A. H. & Katz, L. A. Estimating the timing of early eukaryotic diversification with multigene molecular clocks. Proc. Natl Acad. Sci. USA 108, 13624–13629 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Loron, C. C. et al. Early fungi from the Proterozoic era in Arctic Canada. Nature 570, 232–235 (2019).CAS 
PubMed 
Article 

Google Scholar 
Al Jewari, C. & Baldauf, S. L. Conflict over the eukaryote root resides in strong outliers, mosaics and missing data sensitivity of site-specific (CAT) mixture models. Syst. Biol. syac029 (2022).He, D. et al. An alternative root for the eukaryote tree of life. Curr. Biol. 24, 465–470 (2014).CAS 
PubMed 
Article 

Google Scholar 
Derelle, R. & Lang, B. F. Rooting the eukaryotic tree with mitochondrial and bacterial proteins. Mol. Biol. Evol. 29, 1277–1289 (2012).CAS 
PubMed 
Article 

Google Scholar 
Del Campo, J. et al. The others: our biased perspective of eukaryotic genomes. Trends Ecol. Evol. 29, 252–259 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Boaden, P. J. S. Meiofauna and the origins of the Metazoa. Zool. J. Linn. Soc. 96, 217–227 (1989).Article 

Google Scholar 
Wiens, J. J. Faster diversification on land than sea helps explain global biodiversity patterns among habitats and animal phyla. Ecol. Lett. 18, 1234–1241 (2015).PubMed 
Article 

Google Scholar 
Oliverio, A. M. et al. The global-scale distributions of soil protists and their contributions to belowground systems. Sci. Adv. 6, eaax8787 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martijn, J. et al. Confident phylogenetic identification of uncultured prokaryotes through long read amplicon sequencing of the 16S‐ITS‐23S rRNA operon. Environ. Microbiol. 21, 2485–2498 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krehenwinkel, H. et al. Nanopore sequencing of long ribosomal DNA amplicons enables portable and simple biodiversity assessments with high phylogenetic resolution across broad taxonomic scale. Gigascience 8, giz006 (2019).Furneaux, B., Bahram, M., Rosling, A., Yorou, N. S. & Ryberg, M. Long‐ and short‐read metabarcoding technologies reveal similar spatiotemporal structures in fungal communities. Mol. Ecol. Resour. 21, 1833–1849 (2021).CAS 
PubMed 
Article 

Google Scholar 
Logares, R. et al. Phenotypically different microalgal morphospecies with identical ribosomal DNA: a case of rapid adaptive evolution? Microb. Ecol. 53, 549–561 (2007).CAS 
PubMed 
Article 

Google Scholar 
Logares, R. et al. Recent evolutionary diversification of a protist lineage. Environ. Microbiol. 10, 1231–1243 (2008).CAS 
PubMed 
Article 

Google Scholar 
Nee, S., Holmes, E. C., May, R. M. & Harvey, P. H. Extinction rates can be estimated from molecular phylogenies. Philos. Trans. R. Soc. Lond. B 344, 77–82 (1994).CAS 
Article 

Google Scholar 
Knoll, A. H. Paleobiological perspectives on early eukaryotic evolution. Cold Spring Harb. Perspect. Biol. 6, a016121 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Strother, P. K., Battison, L., Brasier, M. D. & Wellman, C. H. Earth’s earliest non-marine eukaryotes. Nature 473, 505–509 (2011).CAS 
PubMed 
Article 

Google Scholar 
Knoll, A. H., Javaux, E. J., Hewitt, D. & Cohen, P. Eukaryotic organisms in Proterozoic oceans. Philos. Trans. R. Soc. B 361, 1023–1038 (2006).CAS 
Article 

Google Scholar 
Sánchez-Baracaldo, P., Raven, J. A., Pisani, D. & Knoll, A. H. Early photosynthetic eukaryotes inhabited low-salinity habitats. Proc. Natl Acad. Sci. USA 114, E7737–E7745 (2017).PubMed 
PubMed Central 

Google Scholar 
Blank, C. E. & SÁnchez-Baracaldo, P. Timing of morphological and ecological innovations in the cyanobacteria—a key to understanding the rise in atmospheric oxygen. Geobiology 8, 1–23 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).Article 

Google Scholar 
Richards, T. A., Jones, M. D. M., Leonard, G. & Bass, D. Marine fungi: their ecology and molecular diversity. Ann. Rev. Mar. Sci. 4, 495–522 (2012).PubMed 
Article 

Google Scholar 
Amend, A. From dandruff to deep-sea vents: Malassezia-like fungi are ecologically hyper-diverse. PLoS Pathog. 10, e1004277 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Orsi, W., Biddle, J. F. & Edgcomb, V. Deep sequencing of subseafloor eukaryotic rRNA reveals active fungi across marine subsurface provinces. PLoS ONE 8, e56335 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Klein, M., Swinnen, S., Thevelein, J. M. & Nevoigt, E. Glycerol metabolism and transport in yeast and fungi: established knowledge and ambiguities. Environ. Microbiol. 19, 878–893 (2017).CAS 
PubMed 
Article 

Google Scholar 
Kaserer, A. O., Andi, B., Cook, P. F. & West, A. H. in Methods in Enzymology Vol. 471 (eds Simon M. I. et al.) 59–75 (Academic Press, 2010).Nakov, T., Beaulieu, J. M. & Alverson, A. J. Diatoms diversify and turn over faster in freshwater than marine environments. Evolution 73, 2497–2511 (2019).PubMed 
Article 

Google Scholar 
Maddison, W. P., Midford, P. E. & Otto, S. P. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).PubMed 
Article 

Google Scholar 
Nelson, D. R. et al. Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution. Cell Host Microbe 29, 250–266.e8 (2021).CAS 
PubMed 
Article 

Google Scholar 
Czech, L. & Bremer, E. With a pinch of extra salt—did predatory protists steal genes from their food? PLoS Biol. 16, e2005163 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sibbald, S. J., Eme, L., Archibald, J. M. & Roger, A. J. Lateral gene transfer mechanisms and pan-genomes in eukaryotes. Trends Parasitol. 36, 927–941 (2020).CAS 
PubMed 
Article 

Google Scholar 
Stairs, C. W. et al. Microbial eukaryotes have adapted to hypoxia by horizontal acquisitions of a gene involved in rhodoquinone biosynthesis. eLife 7, e34292 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Savory, F. R., Milner, D. S., Miles, D. C. & Richards, T. A. Ancestral function and diversification of a horizontally acquired oomycete carboxylic acid transporter. Mol. Biol. Evol. 35, 1887–1900 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McDonald, S. M., Plant, J. N. & Worden, A. Z. The mixed lineage nature of nitrogen transport and assimilation in marine eukaryotic phytoplankton: a case study of Micromonas. Mol. Biol. Evol. 27, 2268–2283 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Walsh, D. A., Lafontaine, J. & Grossart, H. P. in Lateral Gene Transfer in Evolution (ed. Gophna, U.) 55–77 (Springer, 2013).Dorrell, R. G. et al. Phylogenomic fingerprinting of tempo and functions of horizontal gene transfer within ochrophytes. Proc. Natl Acad. Sci. USA 118, e2009974118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gluck-Thaler, E. et al. Giant Starship elements mobilize accessory genes in fungal genomes. Mol. Biol. Evol. 39, msac109 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eiler, A. et al. Tuning fresh: radiation through rewiring of central metabolism in streamlined bacteria. ISME J. 10, 1902–1914 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Urbina, H., Scofield, D. G., Cafaro, M. & Rosling, A. DNA-metabarcoding uncovers the diversity of soil-inhabiting fungi in the tropical island of Puerto Rico. Mycoscience 57, 217–227 (2016).CAS 
Article 

Google Scholar 
Kalsoom Khan, F. et al. Naming the untouchable—environmental sequences and niche partitioning as taxonomical evidence in fungi. IMA Fungus 11, 23 (2020).Peura, S. et al. Ontogenic succession of thermokarst thaw ponds is linked to dissolved organic matter quality and microbial degradation potential. Limnol. Oceanogr. 65, S248–S263 (2020).CAS 
Article 

Google Scholar 
Giner, C. R. et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 14, 437–449 (2020).PubMed 
Article 

Google Scholar 
Jing, H., Zhang, Y., Li, Y., Zhu, W. & Liu, H. Spatial variability of picoeukaryotic communities in the Mariana Trench. Sci Rep. 8, 15357 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Santos, S. S. et al. Soil DNA extraction procedure influences protist 18S rRNA gene community profiling outcome. Protist 168, 283–293 (2017).CAS 
PubMed 
Article 

Google Scholar 
Derelle, E. et al. Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features. Proc. Natl Acad. Sci. USA 103, 11647–11652 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).PubMed 
Article 
CAS 

Google Scholar 
Cavalier-Smith, T., Lewis, R., Chao, E. E., Oates, B. & Bass, D. Helkesimastix marina n. sp. (Cercozoa: Sainouroidea superfam. n.) a gliding zooflagellate of novel ultrastructure and unusual ciliary behaviour. Protist 160, 452–479 (2009).PubMed 
Article 

Google Scholar 
Schwelm, A., Berney, C., Dixelius, C., Bass, D. & Neuhauser, S. The large subunit rDNA sequence of Plasmodiophora brassicae does not contain intra-species polymorphism. Protist 167, 544–554 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Heeger, F. et al. Long-read DNA metabarcoding of ribosomal RNA in the analysis of fungi from aquatic environments. Mol. Ecol. Resour. 18, 1500–1514 (2018).CAS 
PubMed 
Article 

Google Scholar 
Jamy, M. Code for ‘Global patterns and rates of habitat transitions across the eukaryotic tree of life’ v1.0.0. Zenodo https://doi.org/10.5281/zenodo.6656264 (2022).Article 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

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

Google Scholar 
Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA Genes. PLoS ONE 4, e6372 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).Article 

Google Scholar 
Adl, S. M. et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J. Eukaryot. Microbiol. 66, 4–119 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 
PubMed 
Article 

Google Scholar 
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stamatakis, A. Phylogenetic models of rate heterogeneity: a high performance computing perspective. In Proc. 20th IEEE International Parallel & Distributed Processing Symposium (IEEE Computer Society, 2006); https://ieeexplore.ieee.org/document/1639535Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Czech, L., Barbera, P. & Stamatakis, A. Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data. Bioinformatics 36, 3263–3265 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mai, U. & Mirarab, S. TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees. BMC Genomics 19, 23–40 (2018).Article 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R. P., Moret, B. M. E. & Stamatakis, A. How many bootstrap replicates are necessary? J. Comput. Biol. 17, 337–354 (2010).CAS 
PubMed 
Article 

Google Scholar 
Mahé, F. et al. Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests. Nat. Ecol. Evol. 1, 0091 (2017).Article 

Google Scholar 
Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Vaulot, D. et al. metaPR2: a database of eukaryotic 18S rRNA metabarcodes with an emphasis on protists. Preprint at bioRxiv https://doi.org/10.1101/2022.02.04.479133 (2022).Sieber, G., Beisser, D., Bock, C. & Boenigk, J. Protistan and fungal diversity in soils and freshwater lakes are substantially different. Sci Rep. 10, 20025 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaulot, D., Geisen, S., Mahé, F. & Bass, D. pr2-primers: an 18S rRNA primer database for protists. Mol. Ecol. Resour. 22, 168–179 (2022).CAS 
PubMed 
Article 

Google Scholar 
Berger, S. A., Krompass, D. & Stamatakis, A. Performance, accuracy, and Web server for evolutionary placement of short sequence reads under maximum likelihood. Syst. Biol. 60, 291–302 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 
Article 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meade, A. & Pagel, M. BayesTraits v.3.0.2. Reading Evolutionary Biology Group (2019); http://www.evolution.reading.ac.uk/BayesTraitsV3.0.2/Files/BayesTraitsV3.0.2Manual.pdfPagel, M., Meade, A. & Barker, D. Bayesian estimation of ancestral character states on phylogenies. Syst. Biol. 53, 673–684 (2004).PubMed 
Article 

Google Scholar 
Pagel, M. & Meade, A. Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo. Am. Nat. 167, 808–825 (2006).PubMed 
Article 

Google Scholar 
Varga, T. et al. Megaphylogeny resolves global patterns of mushroom evolution. Nat. Ecol. Evol. 3, 668–678 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Xie, W., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M. H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol. 60, 150–160 (2011).PubMed 
Article 

Google Scholar 
Pagel, M. & Meade, A. The deep history of the number words. Philos. Trans. R. Soc. B 373, 20160517 (2018).Article 

Google Scholar 
Baker, J. & Venditti, C. Rapid change in mammalian eye shape is explained by activity pattern. Curr. Biol. 29, 1082–1088 (2019).CAS 
PubMed 
Article 

Google Scholar 
Smith, S. A. & O’Meara, B. C. TreePL: divergence time estimation using penalized likelihood for large phylogenies. Bioinformatics 28, 2689–2690 (2012).CAS 
PubMed 
Article 

Google Scholar 
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).CAS 
PubMed 
PubMed Central 
Article 

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

Daskalova, G. N. et al. Science 368, 1341–1347 (2020).CAS 
Article 

Google Scholar 
Cardinale, B. J. et al. Nature 486, 59–67 (2012).CAS 
Article 

Google Scholar 
Hector, A. & Bagchi, R. Nature 448, 188–190 (2007).CAS 
Article 

Google Scholar 
Fanin, N. et al. Nat. Ecol. Evol. 2, 269–278 (2018).Article 

Google Scholar 
Duffy, J. E. Front. Ecol. Environ. 7, 437–444 (2009).Article 

Google Scholar 
Manning, P. et al. Adv. Ecol. Res. 61, 323–356 (2019).Article 

Google Scholar 
Lefcheck, J. S. et al. Nat. Commun. 6, 6936 (2015).CAS 
Article 

Google Scholar 
Soliveres, S. et al. Nature 536, 456–459 (2016).CAS 
Article 

Google Scholar 
Moi, D. A. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01827-7 (2022).Article 

Google Scholar 
Venter, O. et al. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Allan, E. et al. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).CAS 
Article 

Google Scholar 
Manning, P. et al. Nat. Ecol. Evol. 2, 427–436 (2018).Article 

Google Scholar 
Gamfeldt, L. et al. Nat. Commun. 4, 1340 (2013).Article 

Google Scholar 
Schuldt, A. et al. Nat. Commun. 9, 2989 (2018).Article 

Google Scholar 
Jochum, M. et al. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 

Google Scholar 
Dudgeon, D. et al. Biol. Rev. 81, 163–182 (2005).Article 

Google Scholar 
Blois, J. L. et al. Proc. Natl Acad. Sci. USA 110, 9374–9379 (2013).CAS 
Article 

Google Scholar 
França, F. et al. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

Google Scholar 
Ewers, R. M. et al. Nat. Commun. 6, 6836 (2015).CAS 
Article 

Google Scholar 
Reich, P. B. et al. Science 336, 589–592 (2012).CAS 
Article 

Google Scholar  More

Effect of different organic fertilizers on the growth of Pear-jujubeEffect of different organic fertilizers on the bearing branch length of Pear-jujubeJujube-bearing branch has the dual role of fruiting and photosynthesis32,33. It can be seen from Fig. 1 that different organic fertilizer treatments have a significant impact on the growth of jujube-bearing branches. Among them, the longest jujube-bearing branch in the SC treatment is 20.17 cm, which is significantly higher than that in CK and CF; the jujube-bearing branch length in the SC, SM and BM treatment are increased by 34%, 23% and 25% compared with that in CK, and the difference is significant (P  SM  > SC  > CK. Among them, the density of light of BM is the largest. It reaches 38.06 mol/(m2 d). CF, SC, SM and BM respectively increase by 11.54%, 8.09%, 7.96% and 15.13% compared with CK, and the difference is significant. The canopy transmittance of jujube is BM  CF  > SM  > SC. The highest Tr of BM reaches 8.66 µmol/moL. It may be related to higher LAI, and the instantaneous water use efficiency of SC is highest, which reaches 3.30%. The WUEp of CF, SC, SM and BM treatments increase by 22.4%, 64.2%, 44.3% and 30.8%, respectively, compared with that of CK. It reaches a significant difference level (P  SM  > BM  > CF  > CK. Compared with CK (9.37%), the SC, SM, BM, and CF increased by 3.69, 3.18, 1.11 and 0.40% points, respectively. Organic fertilizer is beneficial to increase the water content of the soil. Among them, soybean cake fertilizer (SC) has the largest increase, which is significantly different from CK (P  SM  > SC  > CF  > CK. The RWC of BM reaches 94.20%, which is significantly different from CK (P  SM  > BM  > CK. The total flavonoid content of SC reaches 14.35 mg/kg, which is 24.57% higher than that of CK. The total flavonoid content of SM and BM increase by 17.01% and 9.2%, respectively, compared with that of CK. Moreover, each treatment is significantly different from CK (P  More

Pigliucci, M. Phenotypic plasticity: Beyond Nature and Nurture (Johns Hopkins Univ. Press, 2001).Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Aerts, R., Boot, R. G. A. & Van Der Aart, P. J. M. The relation between above- and belowground biomass allocation patterns and competitive ability. Oecologia 87, 551–559 (1991).CAS 
Article 

Google Scholar 
Ashton, I. W., Miller, A. E., Bowman, W. D. & Suding, K. N. Niche complementarity due to plasticity in resource use: plant partitioning of chemical N forms. Ecology 91, 3252–3260 (2010).Article 

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

Google Scholar 
van Kleunen, M. & Fischer, M. Adaptive evolution of plastic foraging responses in a clonal plant. Ecology 82, 3309–3319 (2001).Article 

Google Scholar 
Relyea, R. A. Competitor-induced plasticity in tadpoles: consequences, cues, and connections to predator-induced plasticity. Ecol. Monogr. 72, 523–540 (2002).Article 

Google Scholar 
Broekman, M. J. E. et al. Signs of stabilisation and stable coexistence. Ecol. Lett. 22, 1957–1975 (2019).Article 

Google Scholar 
Callaway, R. M., Pennings, S. C. & Richards, C. L. Phenotypic plasticity and interactions among plants. Ecology 84, 1115–1128 (2003).Article 

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

Google Scholar 
Chesson, P. in Unity in Diversity: Reflections on Ecology after the Legacy of Ramon Margalef (eds F. Valladares et al.) 119–164 (Fundación Banco Bilbao Vizcaya Argentaria, 2008).Ellner, S. P., Snyder, R. E. & Adler, P. B. How to quantify the temporal storage effect using simulations instead of math. Ecol. Lett. 19, 1333–1342 (2016).Article 

Google Scholar 
Vasseur, D. A., Amarasekare, P., Rudolf, V. H. W. & Levine, J. M. Eco-evolutionary dynamics enable coexistence via neighbor-dependent selection. Am. Nat. 178, E96–E109 (2011).Article 

Google Scholar 
Hendry, A. P. Key questions on the role of phenotypic plasticity in eco-evolutionary dynamics. J. Hered. 107, 25–41 (2016).Article 

Google Scholar 
Hart, S. P., Turcotte, M. M. & Levine, J. M. Effects of rapid evolution on species coexistence. Proc. Natl Acad. Sci. USA 116, 2112–2117 (2019).CAS 
Article 

Google Scholar 
Hart, S. P., Freckleton, R. P. & Levine, J. M. How to quantify competitive ability. J. Ecol. 106, 1902–1909 (2018).Article 

Google Scholar 
Grainger, T. N., Levine, J. M. & Gilbert, B. The invasion criterion: a common currency for ecological research. Trends Ecol. Evol. 34, 925–935 (2019).Article 

Google Scholar 
Letten, A. D., Ke, P.-J. & Fukami, T. Linking modern coexistence theory and contemporary niche theory. Ecol. Monogr. 87, 161–177 (2017).Article 

Google Scholar 
Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl Acad. Sci. USA 112, 797–802 (2015).CAS 
Article 

Google Scholar 
Pfennig, D. W. & Murphy, P. J. How fluctuating competition and phenotypic plasticity mediate species divergence. Evolution 56, 1217–1228 (2002).Article 

Google Scholar 
Adler, P., HilleRisLambers, J. & Levine, J. A niche for neutrality. Ecol. Lett. 10, 95–104 (2007).Article 

Google Scholar 
Barabás, G., D’Andrea, R. & Stump Simon, M. Chesson’s coexistence theory. Ecol. Monogr. 88, 277–303 (2018).Article 

Google Scholar 
Pfennig, D. W. & Pfennig, K. S. Evolution’s Wedge: Competition and the Origins of Diversity (Univ. California Press, 2012).Ayala, F. J. Reversal of dominance in competing species of Drosophila. Am. Nat. 100, 81–83 (1966).Article 

Google Scholar 
Pease, C. M. On the evolutionary reversal of competitive dominance. Evolution 38, 1099–1115 (1984).Article 

Google Scholar 
Pimentel, D., Feinberg, E. H., Wood, P. W. & Hayes, J. T. Selection, spatial distribution, and the coexistence of competing fly species. Am. Nat. 99, 97–109 (1965).Article 

Google Scholar 
Lankau, R. A. & Strauss, S. Y. Mutual feedbacks maintain both genetic and species diversity in a plant community. Science 317, 1561–1563 (2007).CAS 
Article 

Google Scholar 
Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).CAS 
Article 

Google Scholar 
Stuart, Y. E. & Losos, J. B. Ecological character displacement: glass half full or half empty? Trends Ecol. Evol. 28, 402–408 (2013).Article 

Google Scholar 
Abrams, P. A. Alternative models of character displacement and niche shift. 2. Displacement when there is competition for a single resource. Am. Nat. 130, 271–282 (1987).Article 

Google Scholar 
Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).Article 

Google Scholar 
Harmon, E. A. & Pfennig, D. W. Evolutionary rescue via transgenerational plasticity: evidence and implications for conservation. Evol. Dev. 23, 292–307 (2021).Article 

Google Scholar 
Forsman, A. Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity 115, 276–284 (2015).CAS 
Article 

Google Scholar 
Brass, D. P. et al. Phenotypic plasticity as a cause and consequence of population dynamics. Ecol. Lett. 24, 2406–2417 (2021).Article 

Google Scholar 
Macarthur, R. H. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).Article 

Google Scholar 
Beverton, R. J. H. & Holt, S. J. On the Dynamics of Exploited Fish Populations (UK Ministry of Agriculture, Fisheries and Food, 1957).Landolt, E. Biosystematic Investigations in the Family of Duckweeds (Lemnaceae), Vol. 2: The Family of Lemnaceae—A Monographic Study, Vol.1 (Geobotanischen Institute, ETH Zürich, 1986).Wang, W. et al. The Spirodela polyrhiza genome reveals insights into its neotenous reduction fast growth and aquatic lifestyle. Nat. Commun. 5, 3311 (2014).CAS 
Article 

Google Scholar 
Hoagland, D. R. & Arnon, D. I. The Water-Culture Method for Growing Plants without Soil (College of Agriculture, Agricultural Experiment Station, Univ. California, 1950).Inouye, B. D. Response surface experimental designs for investigating interspecific competition. Ecology 82, 2696–2706 (2001).Article 

Google Scholar 
Law, R. & Watkinson, A. R. Response-surface analysis of two-species competition: an experiment on Phleum arenarium and Vulpia fasciculata. J. Ecol. 75, 871–886 (1987).Article 

Google Scholar 
MATLAB v.9.0 (MathWorks, 2016).Stan Modeling Language Users Guide and Reference Manual, v.2.27 (Stan Development Team, 2021); https://mc-stan.orgVehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

Google Scholar 
Bürkner, P.C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. https://doi.org/10.18637/jss.v080.i01 (2017).Vehtari, A. et al. loo: efficient leave-one-out cross-validation and WAIC for Bayesian models, v.2.4.1 (2020).ImageJ (US NIH, 1997–2016). More

Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Syst. 37, 637–669 (2006).
Google Scholar 
Forrest, J. & Miller-Rushing, A. J. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. Trans. R. Soc. B Biol. Sci. 365, 3101–3112 (2010).
Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change/631/158/2165/2457/631/158/2039/129/141/139 letter. Nat. Clim. Chang. 8 (2018).Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Mushegian, A. A. et al. Ecological mechanism of climate-mediated selection in a rapidly evolving invasive species. Ecol. Lett. 24, 698–707 (2021).PubMed 
PubMed Central 

Google Scholar 
Visser, M. E. & Both, C. Shifts in phenology due to global climate change: the need for a yardstick. Proc. R. Soc. B Biol. Sci. 272, 2561–2569 (2005).
Google Scholar 
Mayor, S. J. et al. Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep. 7, 1–10 (2017).ADS 

Google Scholar 
Beard, K. H., Kelsey, K. C., Leffler, A. J. & Welker, J. M. The missing angle: Ecosystem consequences of phenological mismatch. Trends Ecol. Evol. 34 (2019).Youngflesh, C. et al. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01442-y (2021).PubMed 

Google Scholar 
Forrest, J. R. Complex responses of insect phenology to climate change. Curr. Opin. Insect Sci. 17 (2016).Crimmins, T. M. et al. Short-term forecasts of insect phenology inform pest management. Ann. Entomol. Soc. Am. 113 (2020).Brakefield, P. M. Geographical variability in, and temperature effects on, the phenology of Maniola jurtina and Pyronia tithonus (Lepidoptera, Satyrinae) in England and Wales. Ecol. Entomol. 12 (1987).Dell, D., Sparks, T. H. & Dennis, R. L. H. Climate change and the effect of increasing spring temperatures on emergence dates of the butterfly Apatura iris (Lepidoptera: Nymphalidae). Eur. J. Entomol. 102, 161–167 (2005).
Google Scholar 
Van Der Kolk, H. J., Wallisdevries, M. F. & Van Vliet, A. J. H. Using a phenological network to assess weather influences on first appearance of butterflies in the Netherlands. Ecol. Indic. 69 (2016).Abarca, M. et al. Inclusion of host quality data improves predictions of herbivore phenology. Entomol. Exp. Appl. 166 (2018).Abarca, M. & Lill, J. T. Latitudinal variation in the phenological responses of eastern tent caterpillars and their egg parasitoids. Ecol. Entomol. 44 (2019).Karlsson, B. Extended season for northern butterflies. Int. J. Biometeorol. 58, 691–701 (2014).ADS 
PubMed 

Google Scholar 
Kharouba, H. M., Paquette, S. R., Kerr, J. T. & Vellend, M. Predicting the sensitivity of butterfly phenology to temperature over the past century. Glob. Chang. Biol. 20 (2014).Diamond, S. E., Frame, A. M., Martin, R. A. & Buckley, L. B. Species’ traits predict phenological responses to climate change in butterflies. Ecology 92 (2011).Diamond, S. E. et al. Unexpected phenological responses of butterflies to the interaction of urbanization and geographic temperature. Ecology 95 (2014).Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E. & Ries, L. Do growing degree days predict phenology across butterfly species?. Ecology 96, 1473–1479 (2015).
Google Scholar 
Stewart, J. E., Illán, J. G., Richards, S. A., Gutiérrez, D. & Wilson, R. J. Linking inter-annual variation in environment, phenology, and abundance for a montane butterfly community. Ecology 101 (2020).Roy, D. B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Glob. Chang. Biol. 21 (2015).Dennis, R. L. H. et al. Turnover and trends in butterfly communities on two British tidal islands: Stochastic influences and deterministic factors. J. Biogeogr. 37, 2291–2304 (2010).
Google Scholar 
Sparks, T. H. & Yates, T. J. The effect of spring temperature on the appearance dates of British butterflies 1883–1993. Ecography (Cop.). 20 (1997).Michielini, J. P., Dopman, E. B. & Crone, E. E. Changes in flight period predict trends in abundance of Massachusetts butterflies. Ecol. Lett. 24, 249–257 (2021).PubMed 

Google Scholar 
Zografou, K. et al. Species traits affect phenological responses to climate change in a butterfly community. Sci. Rep. 11 (2021).Belitz, M. W., Larsen, E. A., Ries, L. & Guralnick, R. P. The accuracy of phenology estimators for use with sparsely sampled presence-only observations. Methods Ecol. Evol. 11, 1273–1285 (2020).
Google Scholar 
Van Strien, A. J., Plantenga, W. F., Soldaat, L. L., Van Swaay, C. A. M. & WallisDeVries, M. F. Bias in phenology assessments based on first appearance data of butterflies. Oecologia 156, 227–235 (2008).ADS 
PubMed 

Google Scholar 
Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12 (1977).Taron, D. & Ries, L. Butterfly Monitoring for Conservation. in Butterfly Conservation in North America 35–57 (Springer Netherlands, 2015). https://doi.org/10.1007/978-94-017-9852-5_3.Schmucki, R. et al. A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. J. Appl. Ecol. 53, 501–510 (2016).
Google Scholar 
Prudic, K., Oliver, J., Brown, B. & Long, E. Comparisons of citizen science data-gathering approaches to evaluate urban butterfly diversity. Insects 9, 186 (2018).PubMed Central 

Google Scholar 
Prudic, K. L. et al. eButterfly: Leveraging massive online citizen science for butterfly conservation. Insects 8 (2017).Barve, V. V. et al. Methods for broad-scale plant phenology assessments using citizen scientists’ photographs. Appl. Plant Sci. 8 (2020).Seltzer, C. Making biodiversity data social, shareable, and scalable: Reflections on iNaturalist & citizen science. Biodivers. Inf. Sci. Stand. 3 (2019).Wittmann, J., Girman, D. & Crocker, D. Using inaturalist in a coverboard protocol to measure data quality: Suggestions for project design. Citiz. Sci. Theory Pract. 4 (2019).Dorazio, R. M. Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Glob. Ecol. Biogeogr. 23 (2014).Ries, L., Zipkin, E. F. & Guralnick, R. P. Tracking trends in monarch abundance over the 20th century is currently impossible using museum records. In Proceedings of the National Academy of Sciences of the United States of America vol. 116 (2019).Larsen, E. A. & Shirey, V. Method matters: Pitfalls in analysing phenology from occurrence records. Ecol. Lett. https://doi.org/10.1111/ele.13602 (2021).PubMed 
PubMed Central 

Google Scholar 
de Keyzer, C. W., Rafferty, N. E., Inouye, D. W. & Thomson, J. D. Confounding effects of spatial variation on shifts in phenology. Glob. Chang. Biol. 23 (2017).Cima, V. et al. A test of six simple indices to display the phenology of butterflies using a large multi-source database. Ecol. Indic. 110, 105885 (2020).
Google Scholar 
Zipkin, E. F. et al. Addressing data integration challenges to link ecological processes across scales. Front. Ecol. Environ. 19 (2021).Polgar, C. A., Primack, R. B., Williams, E. H., Stichter, S. & Hitchcock, C. Climate effects on the flight period of Lycaenid butterflies in Massachusetts. Biol. Conserv. 160 (2013).Brooks, S. J. et al. The influence of life history traits on the phenological response of British butterflies to climate variability since the late-19th century. Ecography (Cop.) 40, 1152–1165 (2017).
Google Scholar 
van Strien, A. J., van Swaay, C. A. M., van Strien-van Liempt, W. T. F. H., Poot, M. J. M. & WallisDeVries, M. F. Over a century of data reveal more than 80% decline in butterflies in the Netherlands. Biol. Conserv. 234 (2019).Boggs, C. L. The fingerprints of global climate change on insect populations. Curr. Opin. Insect Sci. 17 (2016).Belitz, M. et al. Climate drivers of adult insect activity are conditioned by life history traits. Authorea Prepr. (2021).Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE 9 (2014).Park, D. S., Newman, E. A. & Breckheimer, I. K. Scale gaps in landscape phenology: challenges and opportunities. Trends Ecol. Evol. 36 (2021).Kerr, J. T., Vincent, R. & Currie, D. J. Lepidopteran richness patterns in North America. Écoscience 5, 448–453 (1998).
Google Scholar 
Taylor, S. D., Meiners, J. M., Riemer, K., Orr, M. C. & White, E. P. Comparison of large-scale citizen science data and long-term study data for phenology modeling. Ecology 100 (2019).Isaac, N. J. B. et al. Data integration for large-scale models of species distributions. Trends Ecol. Evol. 35 (2020).Miller, D. A. W., Pacifici, K., Sanderlin, J. S. & Reich, B. J. The recent past and promising future for data integration methods to estimate species’ distributions. Methods Ecol. Evol. 10 (2019).Fletcher, R. J. et al. A practical guide for combining data to model species distributions. Ecology https://doi.org/10.1002/ecy.2710 (2019).PubMed 

Google Scholar 
Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J. & Haddad, N. M. Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. bioRxiv https://doi.org/10.1101/613786 (2019).
Google Scholar 
Crossley, M. S. et al. Recent climate change is creating hotspots of butterfly increase and decline across North America. Glob. Chang. Biol. 27, 2702–2714 (2021).CAS 
PubMed 

Google Scholar 
Forister, M. L. et al. Fewer butterflies seen by community scientists across the warming and drying landscapes of the American West. Science (80-) 371, 1042–1045 (2021).ADS 
CAS 

Google Scholar 
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, (2019).Kerr, N. Z. et al. Developmental trap or demographic bonanza? Opposing consequences of earlier phenology in a changing climate for a multivoltine butterfly. Glob. Chang. Biol. 26, (2020).Belth, J. E. Butterflies of Indiana: A field guide. Butterflies of Indiana: A Field Guide (2012).Betros, B. A Photographic Field Guide to the Butterflies in the Kansas City Region (Kansas City Star Books, 2008).
Google Scholar 
Bouseman, J. K., Sternburg, J. G. & Wiker, J. R. Field guide to the skipper butterflies of Illinois. (Illinois Natural History Survey Manual 11, 2006).Clark, A. H. The butterflies of the District of Columbia and vicinity. Bull. United States Natl. Museum (1932).Glassberg, J. Butterflies through Binoculars: Boston—New York—Washington Region (Oxford University Press, 1993).
Google Scholar 
Glassberg, J. Butterflies through Binoculars: The East—A Field Guide to the Butterflies of Eastern North America (Oxford University Press, 1999).
Google Scholar 
Iftner, D. C., Shuey, J. A. & Calhoun, J. V. Butterflies and skippers of Ohio (Ohio State University, 1992).
Google Scholar 
Jeffords, M. R., Post, S. L. & Wiker, J. Butterflies of Illinois: a field guide (Illinois Natural History Survey, 2019).
Google Scholar 
Schlicht, D. W., Downey, J. C. & Nekola, J. C. The butterflies of Iowa (University of Iowa Press, 2007).
Google Scholar 
Schmucki, R., Harrower, C. A. & Dennis, E. B. rbms: Computing generalised abundance indices for butterfly monitoring count data. R package version 1.1.0. https://github.com/RetoSchmucki/rbms (2021).GBIF. GBIF Occurrence download. https://doi.org/10.15468/dl.1erh15 (2019).Thornton, P. E. et al. Daymet: Daily surface weather data on a 1-km grid for North America, version 3. ORNL DAAC. (Oak Ridge, TN, 2017).Baskerville, G. L. & Emin, P. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50, (1969).R Development Core Team, R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing vol. 1 409 (2011).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version (2014).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4 (2013).Kahle, D. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J 5 (2013). More

Cardillo, M. & Meijaard, E. Are comparative studies of extinction risk useful for conservation? Trends Ecol. Evol. 27, 167–171 (2012).PubMed 
Article 

Google Scholar 
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).PubMed 
Article 

Google Scholar 
Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: The Great Acceleration. Anthr. Rev. 2, 81–98 (2015).
Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Sci. (80-.). 366, eaax3100 (2019).Article 
CAS 

Google Scholar 
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Sci. (80-.) 353, 288–291 (2016).CAS 
Article 

Google Scholar 
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Sci. (80-.). 344, 1246752–1246752 (2014).CAS 
Article 

Google Scholar 
IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo (2019) https://doi.org/10.5281/zenodo.3831674.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).CAS 
PubMed 
Article 

Google Scholar 
Rodrigues, A., Pilgrim, J., Lamoreux, J., Hoffmann, M. & Brooks, T. The value of the IUCN Red List for conservation. Trends Ecol. Evol. 21, 71–76 (2006).PubMed 
Article 

Google Scholar 
Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008).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 
Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).CAS 
PubMed 
Article 

Google Scholar 
Bachman, S. P. et al. Progress, challenges and opportunities for Red Listing. Biol. Conserv. 234, 45–55 (2019).Article 

Google Scholar 
Rondinini, C., Di Marco, M., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN red list. Conserv. Lett. 7, 126–130 (2014).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2021-2. https://www.iucnredlist.org (2021).Cazalis, V. et al. Bridging the research-implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).PubMed 
Article 

Google Scholar 
IUCN Standards and Petitions Committee. Guidelines for using the IUCN Red List Categories and Criteria. Prepared by the Standards and Petitions Committee. Downloadable from https://www.iucnredlist.org/documents/RedListGuidelines.pdf vol. 15 (2022).Bland, L. M. et al. Toward reassessing data‐deficient species. Conserv. Biol. 31, 531–539 (2017).PubMed 
Article 

Google Scholar 
Butchart, S. H. M. & Bird, J. P. Data Deficient birds on the IUCN Red List: What don’t we know and why does it matter? Biol. Conserv. 143, 239–247 (2010).Article 

Google Scholar 
Zhao, L. et al. Spatial knowledge deficiencies drive taxonomic and geographic selectivity in data deficiency. Biol. Conserv. 231, 174–180 (2019).Article 

Google Scholar 
Parsons, E. C. M. Why IUCN should replace “Data Deficient” conservation status with a precautionary “Assume Threatened” Status—A Cetacean Case Study. Front. Mar. Sci. 3, 2015–2017 (2016).
Google Scholar 
Roberts, D. L., Taylor, L. & Joppa, L. N. Threatened or Data Deficient: assessing the conservation status of poorly known species. Divers. Distrib. 22, 558–565 (2016).Article 

Google Scholar 
Jetz, W. & Freckleton, R. P. Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140016 (2015).Article 

Google Scholar 
Howard, S. D. & Bickford, D. P. Amphibians over the edge: silent extinction risk of Data Deficient species. Divers. Distrib. 20, 837–846 (2014).Article 

Google Scholar 
Jarić, I., Courchamp, F., Gessner, J. & Roberts, D. L. Potentially threatened: a Data Deficient flag for conservation management. Biodivers. Conserv. 25, 1995–2000 (2016).Article 

Google Scholar 
Mair, L. et al. A metric for spatially explicit contributions to science-based species targets. Nat. Ecol. Evol. 5, 836–844 (2021).PubMed 
Article 

Google Scholar 
Butchart, S. H. M. et al. Measuring Global Trends in the status of biodiversity: red list indices for birds. PLoS Biol. 2, e383 (2004).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
United Nations. Transforming our World: the 2030 Agenda for Sustainable Development. A/RES/70/1 (2015).Butchart, S. H. M. et al. Using Red List Indices to measure progress towards the 2010 target and beyond. Philos. Trans. R. Soc. B Biol. Sci. 360, 255–268 (2005).CAS 
Article 

Google Scholar 
Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).CAS 
PubMed 
Article 

Google Scholar 
Moran, D. & Kanemoto, K. Identifying species threat hotspots from global supply chains. Nat. Ecol. Evol. 1, 0023 (2017).Article 

Google Scholar 
Mooers, A. Ø., Faith, D. P. & Maddison, W. P. Converting endangered species categories to probabilities of extinction for Phylogenetic Conservation Prioritization. PLoS One 3, e3700 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Runting, R. K., Phinn, S., Xie, Z., Venter, O. & Watson, J. E. M. Opportunities for big data in conservation and sustainability. Nat. Commun. 11, 2003 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. 35, 502–509 (2021).PubMed 
Article 

Google Scholar 
Hino, M., Benami, E. & Brooks, N. Machine learning for environmental monitoring. Nat. Sustain 1, 583–588 (2018).Article 

Google Scholar 
Wearn, O. R., Freeman, R. & Jacoby, D. M. P. Responsible AI for conservation. Nat. Mach. Intell. 1, 72–73 (2019).Article 

Google Scholar 
Bland, L. M. et al. Cost-effective assessment of extinction risk with limited information. J. Appl. Ecol. 52, 861–870 (2015).Article 

Google Scholar 
Bland, L. M. & Böhm, M. Overcoming data deficiency in reptiles. Biol. Conserv. 204, 16–22 (2016).Article 

Google Scholar 
Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).PubMed 
Article 

Google Scholar 
Luiz, O. J., Woods, R. M., Madin, E. M. P. & Madin, J. S. Predicting IUCN extinction risk categories for the World’s Data Deficient Groupers (Teleostei: Epinephelidae). Conserv. Lett. 9, 342–350 (2016).Article 

Google Scholar 
Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Using coarse-scale species distribution data to predict extinction risk in plants. Divers. Distrib. 23, 435–447 (2017).Article 

Google Scholar 
Walls, R. H. L. & Dulvy, N. K. Tracking the rising extinction risk of sharks and rays in the Northeast Atlantic Ocean and Mediterranean Sea. Sci. Rep. 11, 15397 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Walls, R. H. L. & Dulvy, N. K. Eliminating the dark matter of data deficiency by predicting the conservation status of Northeast Atlantic and Mediterranean Sea sharks and rays. Biol. Conserv. 246, 108459 (2020).Article 

Google Scholar 
IUCN. Species Information Service. Version 2020-3. https://www.iucnredlist.org/resources/spatial-data-download (2021).IUCN. The IUCN Red List of Threatened Species. Version 2020-3. https://www.iucnredlist.org (2020).Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).Article 

Google Scholar 
Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, 1–34 (2014).Article 

Google Scholar 
Selig, E. R. et al. Global priorities for Marine biodiversity conservation. PLoS One 9, e82898 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
O’Hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K. & Halpern, B. S. Aligning marine species range data to better serve science and conservation. PLoS One 12, e0175739 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mittermeier, R. A., Goetsch Mittermeier, C., Gil, P. R. & Wilson, E. O. Megadiversity: Earth’s Biologically Wealthiest Nations. CEMEX (2005).Chamberlain, S. rredlist: ‘IUCN’ Red List Client. R package version 0.7.0. (2020).GBIF. The Global Biodiversity Information Facility: What is GBIF? https://www.gbif.org/what-is-gbif (2021).OBIS. Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. (2021).Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.6.0. https://cran.r-project.org/package=rgbif (2021).Provoost, P. & Bosch, S. robis: Ocean Biodiversity Information System (OBIS) Client. R package version 2.3.9. https://CRAN.R-project.org/package=robis. (2020).Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad, Dataset https://doi.org/10.5061/dryad.kd1d4 (2018).ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. http://maps.elie.ucl.ac.be/CCI/viewer/download.php (2017).Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch‐Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Chang. Biol. 25, 811–826 (2019).PubMed 
Article 

Google Scholar 
Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. 109, 16083–16088 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA). Cambridge, UK: UNEP-WCMC and IUCN www.protectedplanet.net (2021).Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Sci. (80-.) 342, 850–853 (2013).CAS 
Article 

Google Scholar 
Tuanmu, M. N. & Jetz, W. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 24, 1329–1339 (2015).Article 

Google Scholar 
Maggi, F., Tang, F. H. M., la Cecilia, D. & McBratney, A. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci. Data 6, 170 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Byers, L. et al. A Global Database of Power Plants. World Resour. Inst. 1–18 (2019).Mulligan, M., van Soesbergen, A. & Sáenz, L. GOODD, a global dataset of more than 38,000 georeferenced dams. Sci. Data 7, 31 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Boulay, A.-M. et al. The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE). Int. J. Life Cycle Assess. 23, 368–378 (2018).Article 

Google Scholar 
Barbarossa, V. et al. Erratum: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015. Sci. Data 5, 180078 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl Acad. Sci. 117, 3648–3655 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Domisch, S., Amatulli, G. & Jetz, W. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Sci. Data 2, 150073 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).PubMed 
Article 

Google Scholar 
Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163 (2006).PubMed 
Article 

Google Scholar 
Schlossberg, S., Chase, M. J., Gobush, K. S., Wasser, S. K. & Lindsay, K. State-space models reveal a continuing elephant poaching problem in most of Africa. Sci. Rep. 10, 10166 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Burn, R. W., Underwood, F. M. & Blanc, J. Global trends and factors associated with the illegal killing of Elephants: a hierarchical Bayesian Analysis of Carcass Encounter Data. PLoS One 6, e24165 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hauenstein, S., Kshatriya, M., Blanc, J., Dormann, C. F. & Beale, C. M. African elephant poaching rates correlate with local poverty, national corruption and global ivory price. Nat. Commun. 10, 2242 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
UNDP. Human Development Report 2020. The Next Frontier: Human Development and the Anthropocene. New York. http://hdr.undp.org/en/content/human-development-report-2020. (2020).Transparency International. Corruption Perceptions Index 2020. (2020).Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 7, 12485 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 7615 (2015).CAS 
PubMed 
Article 

Google Scholar 
Halpern, B. S. et al. A global map of human impact on marine ecosystems. Sci. (80-.) 319, 948–952 (2008).CAS 
Article 

Google Scholar 
Assis, J. et al. Bio‐ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).Article 

Google Scholar 
Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012).Article 

Google Scholar 
Zizka, A., Silvestro, D., Vitt, P. & Knight, T. M. Automated conservation assessment of the orchid family with deep learning. Conserv. Biol. 35, 897–908 (2021).PubMed 
Article 

Google Scholar 
Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning. The Elements of Statistical Learning vol. 27 (Springer New York, 2001).Kampichler, C., Wieland, R., Calmé, S., Weissenberger, H. & Arriaga-Weiss, S. Classification in conservation biology: a comparison of five machine-learning methods. Ecol. Inform. 5, 441–450 (2010).Article 

Google Scholar 
LeDell, E. et al. h2o: R Interface for the ‘H2O’ Scalable Machine Learning Platform. R package version 3.36.0.4. https://github.com/h2oai/h2o-3 (2022).H2O.ai. H2O AutoML. https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html (2022).Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 
Article 

Google Scholar 
Kuhn, M. Building Predictive Models in R using the caret Package. J. Stat. Softw. 28, 1–26 (2008).Article 

Google Scholar 
Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).Article 

Google Scholar 
Harrell Jr, F. E. Hmisc: Harrell miscellaneous. R package version 4.5-0. (2021).van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super Learner. Stat. Appl. Genet. Mol. Biol. 6 (2007).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria https://www.r-project.org/ (2021).RStudio Team. RStudio: integrated development environment for R. RStudio, PBC, Boston, MA http://www.rstudio.com/ (2021).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://cran.r-project.org/package=raster (2019).Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. https://cran.r-project.org/package=rgdal (2019).Bivand, R. & Lewin-Koh, N. maptools: Tools for Handling Spatial Objects. R package version 0.9-5. https://cran.r-project.org/package=maptools/ (2019).Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine – Open Source (‘GEOS’). R package version 0.5-1. https://cran.r-project.org/package=rgeos (2019).Bivand, R. S., Pebesma, E. & Gómez-Rubio, V. Applied Spatial Data Analysis with R. (Springer New York, 2013).Pebesma, E. Simple features for R: standardized support for Spatial Vector Data. R. J. 10, 439 (2018).Article 

Google Scholar 
Ross, N. Fasterize: Fast Polygon to Raster Conversion. R package version 1.0.3. https://CRAN.R-project.org/package=fasterize (2020).Microsoft Corporation & Weston, S. doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package. R package version 1.0.16. https://CRAN.R-project.org/package=doParallel (2020).Wickham, H. stringr: simple, consistent wrappers for common string operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr (2019).Tuszynski, J. caTools: tools: Moving Window Statistics, GIF, Base64, ROC AUC, etc. R package version 1.18.1. https://CRAN.R-project.org/package=caTools (2021).Wickham, H. et al. Welcome to the tidyverse. Journal of Open Source Software, 4, 1686. https://doi.org/10.21105/joss.01686 (2019).Dragulescu, A. & Arendt, C. xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.5. (2020).Wickham, H. & Bryan, J. readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl (2019).ESRI. ArcGIS Pro version 2.9.0. https://www.esri.com/en-us/home (2022).Kuhn, M. caret: Classification and Regression Training. R package version 6.0-86. https://CRAN.R-project.org/package=caret (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer, NY (2016).Wilke, C. O. ggridges: Ridgeline Plots in ‘ggplot2’. R package version 0.5.3. https://CRAN.R-project.org/package=ggridges (2021).South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0. https://CRAN.R-project.org/package=rnaturalearth (2017).Garnier, S. viridis: Default Color Maps from ‘matplotlib’. R package version 0.5.1. https://CRAN.R-project.org/package=viridis (2018).Borgelt, J. jannebor/dd_forecast: Code for study ‘More than half of Data Deficient species predicted to be threatened by extinction’ (v1.0.1). https://doi.org/10.5281/zenodo.6627688.Zenodo (2022). More

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

Google Scholar 
Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinctions risk. Nat. Commun. 9, 4621 (2018).PubMed 
PubMed Central 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
PubMed 

Google Scholar 
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).CAS 
PubMed 

Google Scholar 
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).CAS 
PubMed 

Google Scholar 
Schuldt, A. et al. Biodiversity across trophic levels drive multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).PubMed 
PubMed Central 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 211–220 (2020).
Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS 
PubMed 

Google Scholar 
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).PubMed 
PubMed Central 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate. Nat. Commun. 6, 8159 (2015).PubMed 

Google Scholar 
Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 

Google Scholar 
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).PubMed 

Google Scholar 
Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 

Google Scholar 
Allan, E. et al. Interannual variation in land-use intensity enhances grassland multidiversity. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).CAS 
PubMed 

Google Scholar 
Moi, D. A. et al. Regime shifts in a shallow lake over 12 years: consequences for taxonomic and functional diversities, and ecosystem multifunctionality. J. Anim. Ecol. 91, 551–565 (2022).PubMed 

Google Scholar 
Moi, D. A. et al. Multitrophic richness enhances ecosystem multifunctionality of tropical shallow lakes. Funct. Ecol. 35, 942–954 (2021).CAS 

Google Scholar 
Byrnes, J. E. K. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).
Google Scholar 
Li, F. et al. Human activitiesʼ fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China. Glob. Change Biol. 26, 6867–6879 (2020).
Google Scholar 
Enquist, B. J. et al. The megabiota are disproportionately importante for biosphere functioning. Nat. Commun. 11, 699 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Eisenhauer, N. et al. A multitrophic perspective on biodiversity–ecosystem functioning research. Adv. Ecol. Res. 61, 1–54 (2019).PubMed 
PubMed Central 

Google Scholar 
Agostinho, A. A., Thomaz, S. M. & Gomes, L. C. Threats for biodiversity in the floodplain of the Upper Paraná River: effects of hydrological regulation by dams. Ecohydrol. Hydrobiol. 4, 255–268 (2004).
Google Scholar 
Chiaravalloti, R. M., Homewood, K. & Erikson, K. Sustainability and land tenure: who owns the floodplain in the Pantanal, Brazil? Land Use Policy 64, 511–524 (2017).
Google Scholar 
Pelicice, F. M. et al. Large-scale degradation of the Tocantins–Araguaia River Basin. Environ. Manag. 68, 445–452 (2021).
Google Scholar 
Malekmohammadi, B. & Jahanishakib, F. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecol. Indic. 82, 293–303 (2017).
Google Scholar 
McIntyre, P. B. et al. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2006).
Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Google Scholar 
Heino, J. et al. Lakes in the era of global change: moving beyond single-lake thinking in maintaining biodiversity and ecosystem services. Biol. Rev. 96, 89–106 (2020).PubMed 

Google Scholar 
Bridgewater, P. & Kim, R. E. The Ramsar conservation on wetlands at 50. Nat. Ecol. Evol. 5, 268–270 (2020).
Google Scholar 
Romero, G. Q. et al. Pervasive decline of subtropical aquatic insects over 20 years driven by water transparency, non-native fish and stoichiometric imbalance. Biol. Lett. 17, 20210137 (2021).PubMed 
PubMed Central 

Google Scholar 
Lansac-Tôha, F. M. et al. Scale-depedent patterns of metacommunity structuring in aquatic organisms across floodplain systems. J. Biogeogr. 48, 872–885 (2021).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Weiss, K. C. B. & Ray, C. A. Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. Ecography 42, 2012–2020 (2019).
Google Scholar 
Laliberté, E. & Legendre, R. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 

Google Scholar 
Mackereth, F. J. H, Heron, J & Talling, J. F. Water Analysis: Some Revised Methods for Limnologists. Publication No. 36 (Freshwater Biological Association, 1978).Golterman, H. L., Clymo, R. S. & Ohnstad, M. A. M. Methods for Physical and Chemical Analysis of Freshwaters (Blackwell Scientific Publications, 1978).Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, S99–S118 (2018).
Google Scholar 
Sun, J. & Liu, D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J. Plankt. Res. 25, 1331–1346 (2003).
Google Scholar 
Froese, R. & Pauly, D. FishBase (2018); www.fishbase.orgPorter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora1. Limnol. Oceanogr. 25, 943–948 (1980).
Google Scholar 
Manning, P. et al. Redifining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).PubMed 

Google Scholar 
Hijmans, R. J. & van Etten, J. raster: Geographic analysis and modeling with raster data. R version 2.0–12 https://rspatial.org/raster (2012).World Urbanization Prospects: The 2020 Revision: Highlights (United Nations, 2020).Junk, W. J. et al. Brazilian wetlands: their definition, delineation, and classification for research, sustainable management, and protection. Aquat. Conserv. Mar. Freshwater Ecosyst. 24, 5–22 (2013).
Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects models. R version 3.1.137 https://CRAN.Rproject.org/package=nlme (2018).K. Barton, MuMIn: Model selection and model averaging based on information criteria (AICc and alike). R version 1–1 https://CRAN.R-project.org/package=MuMIn (2014).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).Schielzeth, H. Simple means to improve the interpretability ofregression coefficients. Meth. Ecol. Evol. 1, 103–113 (2010).
Google Scholar 
Aiken, L. S. & West, S. G. Multiple Regression: Testing and Interpreting Interactions (Sage Publications, 1991).Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2015).
Google Scholar 
Grace, J. B. & Bollen, K. A. Representing general theoretical concepts in structural equation models: the role of composite variables. Environ. Ecol. Stat. 15, 191–213 (2008).
Google Scholar 
R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More