Philippa Kaur

1.
Hairston NG Jr, Ellner SP, Geber MA, Yoshida T, Fox JA. Rapid evolution and the convergence of ecological and evolutionary time. Ecol Lett. 2005;8:1114–27.
Google Scholar 
2.
Ellner SP, Geber MA, Hairston NG Jr. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol Lett. 2011;14:603–14.
PubMed  Google Scholar 

3.
Gómez P, Paterson S, De Meester L, Liu X, Lenzi L, Sharma MD, et al. Local adaptation of a bacterium is as important as its presence in structuring a natural microbial community. Nat Commun. 2016;7:12453.
PubMed  PubMed Central  Google Scholar 

4.
Buckling A, Craig Maclean R, Brockhurst MA, Colegrave N. The beagle in a bottle. Nature. 2009;457:824–9.
CAS  PubMed  Google Scholar 

5.
Gómez P, Buckling A. Real-time microbial adaptive diversification in soil. Ecol Lett. 2013;16:650–5.
PubMed  Google Scholar 

6.
Lawrence D, Fiegna F, Behrends V, Bundy JG, Phillimore AB, Bell T, et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 2012;10:e1001330.
CAS  PubMed  PubMed Central  Google Scholar 

7.
Lankau RA. Rapid evolutionary change and the coexistence of species. Annu Rev Ecol Evol Syst. 2011;42:335–54.
Google Scholar 

8.
Pantel JH, Duvivier C, Meester LD. Rapid local adaptation mediates zooplankton community assembly in experimental mesocosms. Ecol Lett. 2015;18:992–1000.
PubMed  Google Scholar 

9.
Hart SP, Turcotte MM, Levine JM. Effects of rapid evolution on species coexistence. Proc Natl Acad Sci. 2019;116:2112–7.
CAS  PubMed  Google Scholar 

10.
Rainey PB, Travisano M. Adaptive radiation in a heterogeneous environment. Nature. 1998;394:69.
CAS  PubMed  Google Scholar 

11.
Hughes AR, Inouye BD, Johnson MT, Underwood N, Vellend M. Ecological consequences of genetic diversity. Ecol Lett. 2008;11:609–23.
PubMed  Google Scholar 

12.
Bolnick DI, Amarasekare P, Araújo MS, Bürger R, Levine JM, Novak M, et al. Why intraspecific trait variation matters in community ecology. Trends Ecol evolution. 2011;26:183–92.
Google Scholar 

13.
Violle C, Enquist BJ, McGill BJ, Jiang LIN, Albert CH, Hulshof C, et al. The return of the variance: intraspecific variability in community ecology. Trends Ecol Evol. 2012;27:244–52.
PubMed  Google Scholar 

14.
Bolnick DI, Ingram T, Stutz WE, Snowberg LK, Lau OL, Paull JS. Ecological release from interspecific competition leads to decoupled changes in population and individual niche width. Proc R Soc B Biol Sci. 2010;277:1789–97.
Google Scholar 

15.
Bailey SF, Dettman JR, Rainey PB, Kassen R. Competition both drives and impedes diversification in a model adaptive radiation. Proc R Soc B Biol Sci. 2013;280:20131253.
Google Scholar 

16.
Jousset A, Eisenhauer N, Merker M, Mouquet N, Scheu S. High functional diversity stimulates diversification in experimental microbial communities. Sci Adv. 2016;2:e1600124.
PubMed  PubMed Central  Google Scholar 

17.
Schluter D. Experimental evidence that competition promotes divergence in adaptive radiation. Science. 1994;266:798–801.
CAS  PubMed  Google Scholar 

18.
Ellis CN, Traverse CC, Mayo-Smith L, Buskirk SW, Cooper VS. Character displacement and the evolution of niche complementarity in a model biofilm community. Evolution. 2015;69:283–93.
PubMed  PubMed Central  Google Scholar 

19.
Zee PC, Fukami T. Priority effects are weakened by a short, but not long, history of sympatric evolution. Proc R Soc B Biol Sci. 2018;285:20171722.
Google Scholar 

20.
Schluter D. Ecological character displacement in adaptive radiation. Am Nat. 2000;156:S4–S16.
Google Scholar 

21.
Urban MC, De Meester L. Community monopolization: local adaptation enhances priority effects in an evolving metacommunity. Proc R Soc B Biol Sci. 2009;276:4129–38.
Google Scholar 

22.
De Meester L, Vanoverbeke J, Kilsdonk LJ, Urban MC. Evolving perspectives on monopolization and priority effects. Trends Ecol Evol. 2016;31:136–46.
PubMed  Google Scholar 

23.
Luján AM, Gómez P, Buckling A. Siderophore cooperation of the bacterium Pseudomonas fluorescens in soil. Biol Lett. 2015;11:20140934.
PubMed  PubMed Central  Google Scholar 

24.
O’Brien S, Hesse E, Luján A, Hodgson DJ, Gardner A, Buckling A. No effect of intraspecific relatedness on public goods cooperation in a complex community. Evolution. 2018;72:1165–73.
PubMed  PubMed Central  Google Scholar 

25.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
Google Scholar 

26.
Li H Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:13033997 2013.

27.
Garrison E, Marth G Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:12073907 2012.

28.
Garrison E Vcflib: A C++ library for parsing and manipulating VCF files. GitHub https://www.githubcom/ekg/vcflib 2012.

29.
Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Research 2016;5:1492.

30.
Maidak BL, Cole JR, Lilburn TG, Parker CT Jr, Saxman PR, Stredwick JM, et al. The RDP (ribosomal database project) continues. Nucleic Acids Res. 2000;28:173–4.
CAS  PubMed  PubMed Central  Google Scholar 

31.
Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2010;27:592–3.
PubMed  PubMed Central  Google Scholar 

32.
Hall AR, Colegrave N. How does resource supply affect evolutionary diversification? Proc R Soc B Biol Sci. 2006;274:73–78.
Google Scholar 

33.
Venail PA, MacLean RC, Bouvier T, Brockhurst MA, Hochberg ME, Mouquet N. Diversity and productivity peak at intermediate dispersal rate in evolving metacommunities. Nature. 2008;452:210.
CAS  PubMed  Google Scholar 

34.
Robertson A. Experimental design on the measurement of heritabilities and genetic correlations: biometrical genetics. Biometrics. 1959;15:219–26.
Google Scholar 

35.
Barrett RD, MacLean RC, Bell G. Experimental evolution of pseudomonas fluorescens in simple and complex environments. Am Naturalist. 2005;166:470–80.
Google Scholar 

36.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57:289–300.
Google Scholar 

37.
Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011;5:169–72.
PubMed  Google Scholar 

38.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
CAS  PubMed  PubMed Central  Google Scholar 

39.
Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community Ecol Package. 2007;10:631–7.
Google Scholar 

40.
Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.
CAS  PubMed  Google Scholar 

41.
Cailliez F. The analytical solution of the additive constant problem. Psychometrika. 1983;48:305–8.
Google Scholar 

42.
Love M, Anders S, Huber W. Differential analysis of count data–the DESeq2 package. Genome Biol. 2014;15:10–1186.
Google Scholar 

43.
McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS computational Biol. 2014;10:e1003531.
Google Scholar 

44.
Jombart T, Balloux F, Dray S. Adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics. 2010;26:1907–9.
CAS  PubMed  Google Scholar 

45.
Lenth R Emmeans: Estimated marginal means, aka least-squares means. R Package Version 2018; 1.

46.
R Core Team. R: A language and environment for statistical computing. 2013.

47.
Wickham H ggplot2: elegant graphics for data analysis. 2016. Springer.

48.
Vellend M. The consequences of genetic diversity in competitive communities. Ecology. 2006;87:304–11.
PubMed  Google Scholar 

49.
Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science. 2008;320:1081–5.
CAS  PubMed  Google Scholar 

50.
Narwani A, Alexandrou MA, Herrin J, Vouaux A, Zhou C, Oakley TH, et al. Common ancestry is a poor predictor of competitive traits in freshwater green algae. PLoS ONE. 2015;10:e0137085.
PubMed  PubMed Central  Google Scholar 

51.
Buckling A, Kassen R, Bell G, Rainey PB. Disturbance and diversity in experimental microcosms. Nature. 2000;408:961.
CAS  PubMed  Google Scholar 

52.
Castledine M, Buckling A, Padfield D. A shared coevolutionary history does not alter the outcome of coalescence in experimental populations of Pseudomonas fluorescens. J Evol Biol. 2019;32:58–65.
CAS  PubMed  Google Scholar  More

1.
Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).
CAS  PubMed  PubMed Central  Google Scholar 
2.
Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).
CAS  PubMed  Google Scholar 

3.
Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

4.
Dentro, S. C., Wedge, D. C. & Van Loo, P. Principles of reconstructing the subclonal architecture of cancers. Cold Spring Harb. Perspect. Med. 7, a026625 (2017).
PubMed  PubMed Central  Google Scholar 

5.
Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Meth. 11, 396–398 (2014).
CAS  Google Scholar 

6.
Deshwar, A. G. et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16, 35 (2015).
PubMed  PubMed Central  Google Scholar 

7.
Miller, C. A. et al. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput. Biol. 10, e1003665 (2014).
PubMed  PubMed Central  Google Scholar 

8.
Lynch, M. et al. Genetic drift, selection and the evolution of the mutation rate. Nat. Rev. Genet. 17, 704–714 (2016).
CAS  PubMed  Google Scholar 

9.
Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

10.
Kessler, D. A. & Levine, H. Large population solution of the stochastic Luria–Delbruck evolution model. Proc. Natl Acad. Sci. USA 110, 11682–11687 (2013).
CAS  PubMed  Google Scholar 

11.
Kessler, D. A. & Levine, H. Scaling solution in the large population limit of the general asymmetric stochastic Luria–Delbrück evolution process. J. Stat. Phys. 158, 783–805 (2015).
PubMed  Google Scholar 

12.
Durrett, R. Population genetics of neutral mutations in exponentially growing cancer cell populations. Ann. Appl. Probabil. 23, 230–250 (2013).
Google Scholar 

13.
Nicholson, M. D. & Antal, T. Universal asymptotic clone size distribution for general population growth. Bull. Math. Biol. 78, 2243–2276 (2016).
PubMed  PubMed Central  Google Scholar 

14.
Griffiths, R. C. & Tavaré, S. The age of a mutation in a general coalescent. Stoch. Models 14, 273–295 (1998).
Google Scholar 

15.
Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat. Genet. 49, 1015–1024 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

16.
Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

17.
Hartl, D. L. & Clark, A. G. Principles of Population Genetics (Sinauer Associates, Inc., 2006).

18.
Luria, S. E. & Delbrück, M. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491–511 (1943).
CAS  PubMed  PubMed Central  Google Scholar 

19.
Graham, T. A. & Sottoriva, A. Measuring cancer evolution from the genome. J. Pathol. 241, 183–191 (2017).
PubMed  Google Scholar 

20.
Griffith, M. et al. Optimizing cancer genome sequencing and analysis. Cell Systems 1, 210–223 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

21.
Cross, W. et al. The evolutionary landscape of colorectal tumorigenesis. Nat. Ecol. Evol. 2, 1661–1672 (2018).
PubMed  PubMed Central  Google Scholar 

22.
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1–13 (2017).
Google Scholar 

23.
Zapata, L. et al. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 19, 924 (2018).
Google Scholar 

24.
Lee, J. J.-K. et al. Tracing oncogene rearrangements in the mutational history of lung adenocarcinoma. Cell 177, 1842–1857.e21 (2019).
CAS  PubMed  Google Scholar 

25.
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
CAS  Google Scholar 

26.
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

27.
Williams, M. J. et al. Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios. eLife Sci. 9, 612 (2020).
Google Scholar 

28.
Körber, V. et al. Evolutionary trajectories of IDHWT glioblastomas reveal a common path of early tumorigenesis instigated years ahead of initial diagnosis. Cancer Cell 35, 692–704.e12 (2019).
PubMed  Google Scholar 

29.
Barthel, F. P. et al. Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576, 112–120 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

30.
Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).
CAS  PubMed  Google Scholar 

31.
Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).
CAS  PubMed  Google Scholar 

32.
Morris, L. G. T. et al. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7, 10051–10063 (2016).
PubMed  PubMed Central  Google Scholar 

33.
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
CAS  PubMed  Google Scholar 

34.
Espiritu, S. M. G. et al. The evolutionary landscape of localized prostate cancers drives clinical aggression. Cell 173, 1003–1013.e15 (2018).
CAS  PubMed  Google Scholar 

35.
Salcedo, A. et al. A community effort to create standards for evaluating tumor subclonal reconstruction. Nat. Biotechnol. 38, 97–107 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

36.
Yang, L. et al. An enhanced genetic model of colorectal cancer progression history. Genome Biol. 20, 168 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

37.
Yates, L. R. et al. Genomic evolution of breast cancer metastasis and relapse. Cancer Cell 32, 169–184.e7 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

38.
Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Noorani, A. et al. Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma. Nat. Genet. 347, 1–10 (2020).
Google Scholar 

40.
Navin, N. E. The first five years of single-cell cancer genomics and beyond. Genome Res. 25, 1499–1507 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Chkhaidze, K. et al. Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. PLoS Comput. Biol. 15, e1007243 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

42.
Fusco, D., Gralka, M., Kayser, J., Anderson, A. & Hallatschek, O. Excess of mutational jackpot events in expanding populations revealed by spatial Luria–Delbrück experiments. Nat. Commun. 7, 12760 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

43.
Teh, Y. W. Dirichlet processes. in Encyclopedia of Machine Learning (eds Sammut, C. & Webb, G.) 280–287 (Springer, 2011).

44.
Ghahramani, Z., Jordan, M. I. & Adams, R. P. Tree-structured stick breaking for hierarchical data. in Advances in Neural Information Processing Systems (eds Lafferty, J. D. et al.) 2319–2327 (Neural Information Processing Systems, 2010).

45.
Ma, Z. & Leijon, A. Bayesian estimation of beta mixture models with variational inference. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2160–2173 (2011).
PubMed  Google Scholar 

46.
Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).
Google Scholar 

47.
Schröder, C. & Rahmann, S. A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification. Algorithms Mol. Biol. 12, 21 (2017).
PubMed  PubMed Central  Google Scholar 

48.
Biernacki, C., Celeux, G. & Govaert, G. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Anal. Mach. Intell. 22, 719–725 (2000).
Google Scholar  More

1.
Boschker HTS, Nold SC, Wellsbury P, Bos D, De Graaf W, Pel R, et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392:801–4.
CAS  Article  Google Scholar 
2.
Radajewski S, Ineson P, Parekh NR, Murrell JC. Stable-isotope probing as a tool in microbial ecology. Nature. 2000;403:646–9.
CAS  Article  Google Scholar 

3.
Orphan VJ, House CH, Hinrichs K-U, McKeegan K, Delong EF. Methane-consuming archaea revealed by directly coupled isotopic and phylogenetic analysis. Science. 2001;293:484–6.
CAS  Article  Google Scholar 

4.
Manefield M, Whiteley AS, Griffiths RI, Bailey MJ. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl Environ Microbiol. 2002;68:5367–73.
CAS  Article  Google Scholar 

5.
Daebeler A, Bodelier PLE, Yan Z, Hefting MM, Jia Z, Laanbroek HJ. Interactions between Thaumarchaea, Nitrospira and methanotrophs modulate autotrophic nitrification in volcanic grassland soil. ISME J. 2014;8:1–14.
Article  Google Scholar 

6.
Gülay A, Fowler JS, Tatari K, Thamdrup B, Albrechtsen HJ, Abu Al-Soud W, et al. DNA- and RNA-SIP Reveal Nitrospira spp. as Key Drivers of Nitrification in Groundwater-Fed Biofilter. MBio. 2019;10:e01870–19.
Article  Google Scholar 

7.
Berg JS, Pjevac P, Sommer T, Buckner CRT, Philippi M, Hach PF, et al. Dark aerobic sulfide oxidation by anoxygenic phototrophs in anoxic waters. Environ Microbiol. 2019;21:1611–26.
CAS  Article  Google Scholar 

8.
Middelburg JJ, Barranguet C, Boschker HTS, Herman PMJ, Moens T, Heip CHR. The fate of intertidal microphytobenthos carbon: An in situ 13C-labeling study. Limnol Oceanogr. 2000;45:1224–34.
CAS  Article  Google Scholar 

9.
DeRito CM, Pumphrey GM, Madsen EL. Use of field-based stable isotope probing to identify adapted populations and track carbon flow through a phenol-degrading soil microbial community. Appl Environ Microbiol. 2005;71:7858–65.
CAS  Article  Google Scholar 

10.
Dumont MG, Pommerenke B, Casper P, Conrad R. DNA-, rRNA- and mRNA-based stable isotope probing of aerobic methanotrophs in lake sediment. Environ Microbiol. 2011;13:1153–67.
CAS  Article  Google Scholar 

11.
Dolinšek J, Lagkouvardos I, Wanek W, Wagner M, Daims H. Interactions of nitrifying bacteria and heterotrophs: Identification of a Micavibrio-like putative predator of Nitrospira spp. Appl Environ Microbiol. 2013;79:2027–37.
Article  Google Scholar 

12.
Neufeld JD, Schäfer H, Cox MJ, Boden R, McDonald IR, Murrell JC. Stable-isotope probing implicates Methylophaga spp and novel Gammaproteobacteria in marine methanol and methylamine metabolism. ISME J. 2007;1:480–91.
CAS  Article  Google Scholar 

13.
Ho A, Angel R, Veraart AJ, Daebeler A, Jia Z, Kim SY, et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: Methanotrophy as a model system. Front Microbiol. 2016;7:1–11.
Article  Google Scholar 

14.
Lueders T, Manefield M, Friedrich MW. Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ Microbiol. 2004;6:73–78.
CAS  Article  Google Scholar 

15.
Maxfield PJ, Hornibrook ERC, Evershed RP. Estimating high-affinity methanotrophic bacterial biomass, growth, and turnover in soil by phospholipid fatty acid 13C labeling. Appl Environ Microbiol. 2006;72:3901–7.
CAS  Article  Google Scholar 

16.
Pan C, Fischer CR, Hyatt D, Bowen BP, Hettich RL, Banfield JF. Quantitative tracking of isotope flows in proteomes of microbial communities. Mol Cell Proteom. 2011;10:1–11.
Google Scholar 

17.
Albertsen M, Hansen LBS, Saunders AM, Nielsen PH, Nielsen KL. A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. ISME J. 2012;6:1094–106.
CAS  Article  Google Scholar 

18.
Munck C, Albertsen M, Telke A, Ellabaan M, Nielsen PH, Sommer MOA. Limited dissemination of the wastewater treatment plant core resistome. Nat Commun. 2015;6:8452.
CAS  Article  Google Scholar 

19.
Krümmel A, Harms H. Effect of organic matter on growth and cell yield of ammonia-oxidizing bacteria. Arch Microbiol. 1982;133:50–54.
Article  Google Scholar 

20.
Spieck E, Lipski A. Cultivation, growth physiology, and chemotaxonomy of nitrite-oxidizing bacteria. In: Klotz MG, editor. Methods in enzymology, 1st ed. San Diego, USA: Elsevier Inc.; 2011. pp 109–30.

21.
Li W. Estimating heterotrophic bacterial productivity by inorganic radiocarbon uptake: importance of establishing time courses of uptake. Mar Ecol Prog Ser. 1982;8:167–72.
Article  Google Scholar 

22.
Roslev P, Larsen MB, Jørgensen D, Hesselsoe M. Use of heterotrophic CO2 assimilation as a measure of metabolic activity in planktonic and sessile bacteria. J Microbiol Methods. 2004;59:381–93.
CAS  Article  Google Scholar 

23.
Okabe S, Kindaichi T, Ito T. Fate of 14C-labeled microbial products derived from nitrifying bacteria in autotrophic nitrifying biofilms. Appl Environ Microbiol. 2005;71:3987–94.
CAS  Article  Google Scholar 

24.
Dirnhuber P, Schütz F. The isomeric transformation of urea into ammonium cyanate in aqueous solutions. Biochem J. 1948;42:628–32.
CAS  Article  Google Scholar 

25.
Palatinszky M, Herbold C, Jehmlich N, Pogoda M, Han P, Von BergenM, et al. Cyanate as an energy source for nitrifiers. Nature. 2015;524:105–8.
CAS  Article  Google Scholar 

26.
Kitzinger K, Padilla CC, Marchant HK, Hach PF, Herbold CW, Kidane AT, et al. Cyanate and urea are substrates for nitrification by Thaumarchaeota in the marine environment. Nat Microbiol. 2019;4:234–43.
CAS  Article  Google Scholar 

27.
Hatzenpichler R, Scheller S, Tavormina PL, Babin BM, Tirrell DA, Orphan VJ. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol. 2014;16:2568–90.
CAS  Article  Google Scholar  More

1.
Segawa, T. et al. Bipolar dispersal of red-snow algae. Nat. Commun. 9, 3094. https://doi.org/10.1038/s41467-018-05521-w (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 
2.
Tedersoo, L. et al. Fungal biogeography. Global diversity and geography of soil fungi. Science (New York, N.Y.) 346, 1256688. https://doi.org/10.1126/science.1256688 (2014).
CAS  Article  Google Scholar 

3.
Dunthorn, M., Stoeck, T., Wolf, K., Breiner, H.-W. & Foissner, W. Diversity and endemism of ciliates inhabiting Neotropical phytotelmata. Syst. Biodivers. 10, 195–205. https://doi.org/10.1080/14772000.2012.685195 (2012).
Article  Google Scholar 

4.
Siver, P. A. & Lott, A. M. Biogeographic patterns in scaled chrysophytes from the east coast of North. America 57, 451–466. https://doi.org/10.1111/j.1365-2427.2011.02711.x (2012).
Article  Google Scholar 

5.
Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227. https://doi.org/10.1038/35012228 (2000).
CAS  Article  PubMed  Google Scholar 

6.
Bass, D., Boenigk, J. & Fontaneto, D. In Biogeography of Microscopic Organisms (ed. Fontaneto, D.) 88–110 (Cambridge University Press, Cambridge, 2011).
Google Scholar 

7.
Caron, D. A. Past President’s address: protistan biogeography: why all the fuss?. J. Eukaryot. Microbiol. 56, 105–112. https://doi.org/10.1111/j.1550-7408.2008.00381.x (2009).
ADS  Article  PubMed  Google Scholar 

8.
Foissner, W. Biogeography and dispersal of micro-organisms: a review emphasizing protists (2006).

9.
Coesel, P. F. M. & Krienitz, L. Diversity and geographic distribution of desmids and other coccoid green algae. Biodivers. Conserv. 17, 381–392. https://doi.org/10.1007/s10531-007-9256-5 (2008).
Article  Google Scholar 

10.
Darling, K. F. & Wade, C. M. The genetic diversity of planktic foraminifera and the global distribution of ribosomal RNA genotypes. Mar. Micropaleontol. 67, 216–238. https://doi.org/10.1016/j.marmicro.2008.01.009 (2008).
ADS  Article  Google Scholar 

11.
Vanormelingen, P., Verleyen, E. & Vyverman, W. The diversity and distribution of diatoms: from cosmopolitanism to narrow endemism. Biodivers. Conserv. 17, 393–405. https://doi.org/10.1007/s10531-007-9257-4 (2008).
Article  Google Scholar 

12.
Stoeck, T., Bruemmer, F. & Foissner, W. Evidence for local ciliate endemism in an alpine anoxic lake. Microbiol. Ecol. 54, 478–486. https://doi.org/10.1007/s00248-007-9213-6 (2007).
Article  Google Scholar 

13.
Fernández, L. D., Hernández, C. E., Schiaffino, M. R., Izaguirre, I. & Lara, E. Geographical distance and local environmental conditions drive the genetic population structure of a freshwater microalga (Bathycoccaceae; Chlorophyta) in Patagonian lakes. FEMS Microbiol. Ecol. 93, 37. https://doi.org/10.1093/femsec/fix125 (2017).
CAS  Article  Google Scholar 

14.
Filker, S., Sommaruga, R., Vila, I. & Stoeck, T. Microbial eukaryote plankton communities of high-mountain lakes from three continents exhibit strong biogeographic patterns. Mol. Ecol. 25, 2286–2301. https://doi.org/10.1111/mec.13633 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605. https://doi.org/10.1126/science.1261605 (2015).
CAS  Article  PubMed  Google Scholar 

16.
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084-1097.e21. https://doi.org/10.1016/j.cell.2019.10.008 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Bock, C., Salcher, M., Jensen, M., Pandey, R. V. & Boenigk, J. Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled Austrian lakes. Front. Microbiol. 9, 1290. https://doi.org/10.3389/fmicb.2018.01290 (2018).
Article  PubMed  PubMed Central  Google Scholar 

18.
Boenigk, J. et al. Geographic distance and mountain ranges structure freshwater protist communities on a European scale. Metabarcoding and Metagenomics 2, e21519. https://doi.org/10.3897/mbmg.2.21519 (2018).
Article  Google Scholar 

19.
He, F. et al. Elevation, aspect, and local environment jointly determine diatom and macroinvertebrate diversity in the Cangshan Mountain, Southwest China. Ecol. Indic. 108, 105618. https://doi.org/10.1016/j.ecolind.2019.105618 (2020).
Article  Google Scholar 

20.
Shen, C. et al. Contrasting elevational diversity patterns between eukaryotic soil microbes and plants. Ecology 95, 3190–3202. https://doi.org/10.1890/14-0310.1 (2014).
Article  Google Scholar 

21.
Bryant, J. A. et al. Colloquium paper: microbes on mountainsides: contrasting elevational patterns of bacterial and plant diversity. Proc. Natl. Acad. Sci. USA 105(Suppl 1), 11505–11511. https://doi.org/10.1073/pnas.0801920105 (2008).
ADS  Article  PubMed  Google Scholar 

22.
McCain, C. M. Could temperature and water availability drive elevational species richness patterns? A global case study for bats. Global Ecol. Biogeogr. 16, 1–13. https://doi.org/10.1111/j.1466-8238.2006.00263.x (2007).
Article  Google Scholar 

23.
Desmond, A. Janet Browne, The secular ark. Studies in the history of biogeography, New Haven, Conn., and London, Yale University Press, 1983, 8vo, pp. x, 273, illus., £21.00. Med. Hist. 27, 452–453. https://doi.org/10.1017/s0025727300043611 (1983).
Article  PubMed Central  Google Scholar 

24.
Nemcová, Y., Kreidlová, J., Kosová, A. & Neustupa, J. Lakes and pools of Aquitaine region (France)—a biodiversity hotspot of Synurales in Europe. Nova Hedw 95, 1–24. https://doi.org/10.1127/0029-5035/2012/0036 (2012).
Article  Google Scholar 

25.
van de Vijver, B., Gremmen, N. J. M. & Beyens, L. The genus Stauroneis (Bacillariophyceae) in the Antarctic region. J. Biogeogr. 32, 1791–1798. https://doi.org/10.1111/j.1365-2699.2005.01325.x (2005).
Article  Google Scholar 

26.
Martiny, J. B. H. et al. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112. https://doi.org/10.1038/nrmicro1341 (2006).
CAS  Article  PubMed  Google Scholar 

27.
Lepère, C. et al. Geographic distance and ecosystem size determine the distribution of smallest protists in lacustrine ecosystems. FEMS Microbiol. Ecol. 85, 85–94. https://doi.org/10.1111/1574-6941.12100 (2013).
Article  PubMed  Google Scholar 

28.
Green, J. L. et al. Spatial scaling of microbial eukaryote diversity. Nature 432, 747–750. https://doi.org/10.1038/nature03034 (2004).
ADS  CAS  Article  PubMed  Google Scholar 

29.
Lara, E., Roussel-Delif, L., Fournier, B., Wilkinson, D. M. & Mitchell, E. A. D. Soil microorganisms behave like macroscopic organisms: patterns in the global distribution of soil euglyphid testate amoebae. J. Biogeogr. 43, 520–532. https://doi.org/10.1111/jbi.12660 (2016).
Article  Google Scholar 

30.
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl. Acad. Sci. USA 106, 9322–9327. https://doi.org/10.1073/pnas.0810306106 (2009).
ADS  Article  PubMed  Google Scholar 

31.
Orme, C. D. L. et al. Global hotspots of species richness are not congruent with endemism or threat. Nature 436, 1016–1019. https://doi.org/10.1038/nature03850 (2005).
ADS  CAS  Article  PubMed  Google Scholar 

32.
Schmitt, T. Biogeographical and evolutionary importance of the European high mountain systems. Front. Zool. 6, 9. https://doi.org/10.1186/1742-9994-6-9 (2009).
Article  PubMed  PubMed Central  Google Scholar 

33.
Vimercati, L., Darcy, J. L. & Schmidt, S. K. The disappearing periglacial ecosystem atop Mt. Kilimanjaro supports both cosmopolitan and endemic microbial communities. Sci. Rep. 9, 10676. https://doi.org/10.1038/s41598-019-46521-0 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
McCain, C. M. Elevational gradients in diversity of small mammals. Ecology 86, 366–372. https://doi.org/10.1890/03-3147 (2005).
Article  Google Scholar 

35.
Malviya, S. et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc. Natl. Acad. Sci. USA 113, E1516–E1525. https://doi.org/10.1073/pnas.1509523113 (2016).
CAS  Article  PubMed  Google Scholar 

36.
Khomich, M., Kauserud, H., Logares, R., Rasconi, S. & Andersen, T. Planktonic protistan communities in lakes along a large-scale environmental gradient. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw231 (2017).
Article  PubMed  Google Scholar 

37.
Škaloud, P. et al. Speciation in protists: Spatial and ecological divergence processes cause rapid species diversification in a freshwater chrysophyte. Mol. Ecol. 28, 1084–1095. https://doi.org/10.1111/mec.15011 (2019).
Article  PubMed  Google Scholar 

38.
Godhe, A., McQuoid, M. R., Karunasagar, I., Karunasagar, I. & Rehnstam-Holm, A.-S. Comparison of three common molecular tools for distinguishing among geographically separated clones of the diatom Skeletonema marinoi sarno et zingone (Bacillariophyceae). J. Phycol. 42, 280–291. https://doi.org/10.1111/j.1529-8817.2006.00197.x (2006).
CAS  Article  Google Scholar 

39.
Jobst, J., King, K. & Hemleben, V. Molecular evolution of the internal transcribed spacers (ITS1 and ITS2) and phylogenetic relationships among species of the family cucurbitaceae. Mol. Phylogenet. Evol. 9, 204–219. https://doi.org/10.1006/mpev.1997.0465 (1998).
CAS  Article  PubMed  Google Scholar 

40.
Lobo-Hajdu, G. Intragenomic, intra- and interspecific variation in the rDNA its of Porifera revealed by PCR-singlestrand conformation polymorphism (PCR-SSCP). Bollettino dei Musei e degli Istituti Biologici 68, 413–423 (2004).
Google Scholar 

41.
Needham, D. M., Sachdeva, R. & Fuhrman, J. A. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 11, 1614–1629. https://doi.org/10.1038/ismej.2017.29 (2017).
Article  PubMed  PubMed Central  Google Scholar 

42.
Derot, J. et al. Response of phytoplankton traits to environmental variables in French lakes: new perspectives for bioindication. Ecol. Indic. 108, 105659. https://doi.org/10.1016/j.ecolind.2019.105659 (2020).
CAS  Article  Google Scholar 

43.
Boo, S. M. et al. Complex phylogeographic patterns in the freshwater alga Synura provide new insights into ubiquity vs. endemism in microbial eukaryotes. Mol. Ecol. 19, 4328–4338. https://doi.org/10.1111/j.1365-294X.2010.04813.x (2010).
Article  PubMed  Google Scholar 

44.
Foissner, W. & Hawksworth, D. L. (eds) Protist Diversity and Geographical Distribution (Springer, Dordrecht, 2009).
Google Scholar 

45.
Schiaffino, M. R. et al. Microbial eukaryote communities exhibit robust biogeographical patterns along a gradient of Patagonian and Antarctic lakes. Environ. Microbiol. 18, 5249–5264. https://doi.org/10.1111/1462-2920.13566 (2016).
CAS  Article  PubMed  Google Scholar 

46.
Boenigk, J. et al. Evidence for geographic isolation and signs of endemism within a protistan morphospecies. Appl. Environ. Microbiol. 72, 5159–5164. https://doi.org/10.1128/AEM.00601-06 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Foissner, W., Chao, A. & Katz, L. A. Diversity and geographic distribution of ciliates (Protista: Ciliophora). Biodivers. Conserv. 17, 345–363. https://doi.org/10.1007/s10531-007-9254-7 (2008).
Article  Google Scholar 

48.
Payo, D. A. et al. Extensive cryptic species diversity and fine-scale endemism in the marine red alga Portieria in the Philippines. Proc. R. Soc. B 280, 20122660. https://doi.org/10.1098/rspb.2012.2660 (2013).
Article  PubMed  Google Scholar 

49.
Siver, P. A., Skogstad, A. & Nemcová, Y. Endemism, palaeoendemism and migration: the case for the ‘European endemic’, Mallomonas intermedia. Eur. J. Phycol. 54, 222–234. https://doi.org/10.1080/09670262.2018.1544377 (2019).
Article  Google Scholar 

50.
Cox, F., Newsham, K. K. & Robinson, C. H. Endemic and cosmopolitan fungal taxa exhibit differential abundances in total and active communities of Antarctic soils. Environ. Microbiol. 21, 1586–1596. https://doi.org/10.1111/1462-2920.14533 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Ibelings, B. W. et al. Host parasite interactions between freshwater phytoplankton and chytrid fungi (Chytridiomycota). J. Phycol. 40, 437–453. https://doi.org/10.1111/j.1529-8817.2004.03117.x (2004).
Article  Google Scholar 

52.
Logares, R. et al. Infrequent marine-freshwater transitions in the microbial world. Trends Microbiol. 17, 414–422. https://doi.org/10.1016/j.tim.2009.05.010 (2009).
CAS  Article  PubMed  Google Scholar 

53.
Hewitt, G. M. The structure of biodiversity—insights from molecular phylogeography. Front. Zool. 1, 4. https://doi.org/10.1186/1742-9994-1-4 (2004).
Article  PubMed  PubMed Central  Google Scholar 

54.
Vetaas, O. R. & Grytnes, J.-A. Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal. Global Ecol. Biogeogr. 11, 291–301. https://doi.org/10.1046/j.1466-822X.2002.00297.x (2002).
Article  Google Scholar 

55.
Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219. https://doi.org/10.1038/nature06812 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

56.
Catalán, J. et al. High mountain lakes: extreme habitats and witnesses of environmental changes. Limnetica 25, 551–584 (2006).
Google Scholar 

57.
Sommaruga, R. The role of solar UV radiation in the ecology of alpine lakes. J. Photochem. Photobiol. B Biol. 62, 35–42. https://doi.org/10.1016/S1011-1344(01)00154-3 (2001).
CAS  Article  Google Scholar 

58.
Morris, D. P. et al. The attenuation of solar UV radiation in lakes and the role of dissolved organic carbon. Limnol. Oceanogr. 40, 1381–1391. https://doi.org/10.4319/lo.1995.40.8.1381 (1995).
ADS  CAS  Article  Google Scholar 

59.
Sommaruga, R. & Augustin, G. Seasonality in UV transparency of an alpine lake is associated to changes in phytoplankton biomass. Aquat. Sci. 68, 129–141. https://doi.org/10.1007/s00027-006-0836-3 (2006).
Article  Google Scholar 

60.
Catalan, J. et al. High mountain lakes: extreme habitats and witnesses of environmental changes. Limnética 25, 551–584 (2006).
Google Scholar 

61.
Ortiz-Álvarez, R., Triadó-Margarit, X., Camarero, L., Casamayor, E. O. & Catalan, J. High planktonic diversity in mountain lakes contains similar contributions of autotrophic, heterotrophic and parasitic eukaryotic life forms. Sci. Rep. 8, 302. https://doi.org/10.1038/s41598-018-22835-3 (2018).
CAS  Article  Google Scholar 

62.
Kammerlander, B. et al. High diversity of protistan plankton communities in remote high mountain lakes in the European Alps and the Himalayan mountains. FEMS Microbiol. Ecol. 91, 429. https://doi.org/10.1093/femsec/fiv010 (2015).
CAS  Article  Google Scholar 

63.
Tartarotti, B. et al. UV-induced DNA damage in Cyclops abyssorum tatricus populations from clear and turbid alpine lakes. J. Plankton Res. 36, 557–566. https://doi.org/10.1093/plankt/fbt109 (2014).
CAS  Article  PubMed  Google Scholar 

64.
Brettum, P. & Halvorsen, G. The phytoplankton of Lake Atnsjøen, Norway—a long-term investigation. Hydrobiologia 521, 141–147. https://doi.org/10.1023/B:HYDR.0000026356.09421.e3 (2004).
Article  Google Scholar 

65.
Karlsson, J. et al. Light limitation of nutrient-poor lake ecosystems. Nature 460, 506–509. https://doi.org/10.1038/nature08179 (2009).
ADS  CAS  Article  PubMed  Google Scholar 

66.
Bergström, A.-K., Karlsson, D., Karlsson, J. & Vrede, T. N-limited consumer growth and low nutrient regeneration N: P ratios in lakes with low N deposition. Ecosphere 6, 9. https://doi.org/10.1890/ES14-00333.1 (2015).
Article  Google Scholar 

67.
Kritzberg, E. S. et al. Browning of freshwaters: consequences to ecosystem services, underlying drivers, and potential mitigation measures. Ambio 49, 375–390. https://doi.org/10.1007/s13280-019-01227-5 (2020).
Article  PubMed  Google Scholar 

68.
Gustafsson, B. G. & Westman, P. On the causes for salinity variations in the Baltic Sea during the last 8500 years. Paleoceanography 17, 12-1-12–14. https://doi.org/10.1029/2000PA000572 (2002).
Article  Google Scholar 

69.
Filker, S., Kühner, S., Heckwolf, M., Dierking, J. & Stoeck, T. A fundamental difference between macrobiota and microbial eukaryotes: protistan plankton has a species maximum in the freshwater-marine transition zone of the Baltic Sea. Environ. Microbiol. 21, 603–617. https://doi.org/10.1111/1462-2920.14502 (2019).
CAS  Article  PubMed  Google Scholar 

70.
Schiewer, U. In Ecology of Baltic Coastal Waters (ed. Schiewer, U.) 395–417 (Springer, Berlin, 2008).
Google Scholar 

71.
Falkowski, P. G. et al. The evolution of modern eukaryotic phytoplankton. Science (New York, N.Y.) 305, 354–360. https://doi.org/10.1126/science.1095964 (2004).
ADS  CAS  Article  Google Scholar 

72.
Cermeño, P., Falkowski, P. G., Romero, O. E., Schaller, M. F. & Vallina, S. M. Continental erosion and the Cenozoic rise of marine diatoms. Proc. Natl. Acad. Sci. USA 112, 4239–4244. https://doi.org/10.1073/pnas.1412883112 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

73.
Rothschild, L. J. The influence of UV radiation on protistan evolution. J. Eukaryot. Microbiol. https://doi.org/10.1111/j.1550-7408.1999.tb06074.x| (1999).
Article  PubMed  Google Scholar 

74.
Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol. Oceanogr. 52, 886–895. https://doi.org/10.4319/lo.2007.52.2.0886 (2007).
ADS  Article  Google Scholar 

75.
Ægisdóttir, H. H., Kuss, P. & Stöcklin, J. Isolated populations of a rare alpine plant show high genetic diversity and considerable population differentiation. Ann. Bot. 104, 1313–1322. https://doi.org/10.1093/aob/mcp242 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

76.
Cain, M. L., Milligan, B. G. & Strand, A. E. Long-distance seed dispersal in plant populations. Am. J. Bot. 87, 1217–1227. https://doi.org/10.2307/2656714 (2000).
CAS  Article  PubMed  Google Scholar 

77.
Nemcová, Y. & Pichrtova, M. Shape dynamics of silica scales (Chrysophyceae, Stramenopiles) associated with pH. Fottea 12, 281–291. https://doi.org/10.5507/fot.2012.020 (2012).
Article  Google Scholar 

78.
Leadbeater, B. S. C. & Green, J. C. Flagellates: Unity, Diversity and Evolution. Chapter 12: Functional Diversity of Heterotrophic Flagellates in Aquatic Ecosystems (CRC Press, Cambridge, 2000).
Google Scholar 

79.
Lange, A. et al. AmpliconDuo: a split-sample filtering protocol for high-throughput amplicon sequencing of microbial communities. PLoS ONE 10, e0141590. https://doi.org/10.1371/journal.pone.0141590 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

80.
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

81.
Jensen, M. V9_Clust.R. R-Scrift for modifying DNA-sequence-abundance-tables: clustering of related sequences (e.g. SSU-ITS1) according to 100% identical subsequences. https://github.com/manfred-uni-essen/V9-cluster (2017).

82.
Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3, e1420. https://doi.org/10.7717/peerj.1420 (2015).
Article  PubMed  PubMed Central  Google Scholar 

83.
R Core Team. R: A language and environment for statistical computing (2019).

84.
Hijmans, R. J. Spherical Trigonometry [R package geosphere version 1.5–10] (2019).

85.
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621. https://doi.org/10.1080/01621459.1952.10483441 (1952).
Article  MATH  Google Scholar 

86.
Dunn, O. J. Multiple comparisons among means. J. Am. Stat. Assoc. 56, 52–64. https://doi.org/10.1080/01621459.1961.10482090 (1961).
MathSciNet  Article  MATH  Google Scholar  More

Workflow
All data syntheses, visualisation and statistical analyses were conducted using R version 3.5.171. For conceptual diagrams of our workflow, see Supplementary Figs. 1 and 2. Effect sizes plotted on graphs were standardised by dividing the effect size by the standard deviation of the corresponding input data.
Population data
To quantify vertebrate population change (trends and fluctuations), we extracted the abundance data for 9286 population time series from 2084 species from the publicly available Living Planet Database72 (http://www.livingplanetindex.org/data_portal) that covered the period between 1970 and 2014 (Supplementary Table 1). These time series represent repeated monitoring surveys of the number of individuals in a given area, hereafter, called ‘populations’. Monitoring duration differed among populations, with a mean duration of 23.9 years and a mean sampling frequency of 23.3 time points (Supplementary Fig. 3, see Supplementary Figs. 6 and 7 for effects of monitoring duration on detected trends). In the Living Planet database, 17.9% of populations were sampled annually or in rare cases multiple times per year. The time series we analysed include vertebrate species that span a large variation in age, generation times and other demographic-rate processes. For example, from other work that we have conducted, we have found that when generation time data were available (~50.0% or 484 out of 968 bird species, and 15.6% or 48 out of 306 mammal species), the mean bird generation time is 5.0 years (min = 3.4 years, max = 14.3 years) and the mean mammal generation time is 8.3 years (min = 0.3 years, max = 25 years)45. Thus, we believe that most vertebrate time series within the LPD capture multiple generations.
In our analysis, we omitted populations which had less than five time points of monitoring data, as previous studies of similar population time series to the ones we analysed have found that shorter time series might not capture directional trends in abundance63. Populations were monitored using different metrics of abundance (e.g., population indices vs number of individuals). Before analysis, we scaled the abundance of each population to a common magnitude between zero and one to analyse within-population relationships to prevent conflating within-population relationships and between-population relationships73. Scaling the abundance data allowed us to explore trends among populations relative to the variation experienced across each time series.
Phylogenetic data
We obtained phylogenies for amphibian species from https://vertlife.org4, for bird species from https://birdtree.org8, and for reptile species from https://vertlife.org6. For each of the three classes (Amphibia, Aves and Reptilia), we downloaded 100 trees and randomly chose 10 for analysis (30 trees in total). Species-level phylogenies for the classes Actinopterygii and Mammalia have not yet been resolved with high confidence74,75.
Rarity metrics, IUCN Red List categories and threat types
We defined rarity following a simplified version of the ‘seven forms of rarity’ model76, and thus consider rarity to be the state in which species exist when they have a small geographic range, low population size, or narrow habitat specificity. We combined publicly available data from three sources: (1) population records for vertebrate species from the Living Planet Database to calculate mean population size, (2) occurrence data from the Global Biodiversity Information Facility77 (https://www.gbif.org) and range data from BirdLife78 (http://datazone.birdlife.org) to estimate geographic range size and (3) habitat specificity and Red List Category data for each species from the International Union for Conservation79 (https://www.iucnredlist.org). The populations in the Living Planet Database72 do not include species that have gone extinct on a global scale. We extracted the number and types of threats that each species is exposed to globally from their respective species’ IUCN Red List profiles79.
Quantifying population trends and fluctuations over time
In the first stage of our analysis, we used state-space models that model abundance (scaled to a common magnitude between zero and one) over time to calculate the amount of overall abundance change experienced by each population (μ,40,80). State-space models account for process noise (σ2) and observation error (τ2) and thus deliver robust estimates of population change when working with large data sets where records were collected using different approaches, such as the Living Planet Database41,81,82. Previous studies have found that not accounting for process noise and measurement error could lead to over-estimation of population declines83, but in our analyses, we found that population trends derived from state-space models were similar to those derived from linear models. Positive μ values indicate population increase and negative μ values indicate population decline. State-space models partition the variance in abundance estimates into estimated process noise (σ2) and observation or measurement error (τ2) and population trends (μ):

$$X_t = X_{t-1} + mu + varepsilon _t,$$
(1)

where Xt and Xt−1 are the scaled (observed) abundance estimates (between 0 and 1) in the present and past year, with process noise represented by εt~ gaussian(0, σ2). We included measurement error following:

$$Y_t = X_t + F_t,$$
(2)

where Yt is the estimate of the true (unobserved) population abundance with measurement error:

$$F_tsim gaussianleft( {0,,{it{T}}^2} right)$$
(3)

We substituted the estimate of population abundance (Yt) into Eq. 1:

$$Y_{t} = {it{X}}_{{it{t}} – 1} + mu + varepsilon _{it{t}} + {it{F}}_{it{t}}.$$
(4)

Given

$${it{X}}_{{it{t}} – 1} = {it{Y}}_{{it{t}} – 1} – {it{F}}_{{it{t}} – 1}$$
(5)

then:

$${it{Y}}_{it{t}} = {it{Y}}_{t – 1} + mu + varepsilon _t + F_t – F_{t – 1}$$
(6)

For comparisons of different approaches to modelling population change, see ‘Comparison of modelling approaches section’.
Quantifying rarity metrics
We tested how population change varied across rarity metrics—geographic range, mean population size and habitat specificity – on two different but complementary scales. In the main text, we presented the results of our global-scale analyses, whereas in the SI, we included the results when using only populations from the UK—a country with high monitoring intensity, Thus, we quantified rarity metrics for species monitoring globally and in the UK. The three rarity metrics used in this study were weakly correlated at both UK and global scales (Supplementary Fig. 11).
Geographic range
To estimate geographic range for bird species monitored globally, we extracted the area of occurrence in km2 for all bird species in the Living Planet Database that had records in the BirdLife Data Zone78. For mammal species’ geographic range, we used the PanTHERIA database84 (http://esapubs.org/archive/ecol/E090/184/). To estimate geographic range for bony fish, birds, amphibians, mammals and reptiles monitored in the UK (see Supplementary Table 5 for species list), we calculated a km2 occurrence area based on species occurrence data from GBIF77. Extracting and filtering GBIF data and calculating range was computationally intensive and occurrence data availability was lower for certain species. Thus, we did not estimate geographic range from GBIF data for all species part of the Living Planet Database. Instead, we focused on analysing range effects for birds and mammals globally, as they are a very well-studied taxon and for species monitored in the UK, a country with intensive and detailed biodiversity monitoring of vertebrate species. We did not use IUCN range maps, as they were not available for all of our study species, and previous studies using GBIF occurrences to estimate range have found a positive correlation between GBIF-derived and IUCN-derived geographic ranges85.
For the geographic ranges of species monitored in the UK, we calculated range extent using a minimal convex hull approach based on GBIF occurrence data77. We filtered the GBIF data to remove invalid records and outliers using the CoordinateCleaner package86. We excluded records with no decimal places in the decimal latitude or longitude values, with equal latitude or longitude, within a one-degree radius of the GBIF headquarters in Copenhagen, within 0.0001 degrees of various biodiversity institutions and within 0.1 degrees of capital cities. For each species, we excluded the lower 0.02 and upper 0.98 quantile intervals of the latitude and longitude records to account for outlier points that are records from zoos or other non-wild populations. We drew a convex hull to most parsimoniously encompass all remaining occurrence records using the chull function, and we calculated the area of the resulting polygon using areaPolygon from the geosphere package87.
Mean size of monitored populations
We calculated mean size of the monitored population, referred to as population size, across the monitoring duration using the raw abundance data, and we excluded populations, which were not monitored using population counts (i.e., we excluded indexes).
Habitat specificity
To create an index of habitat specificity, we extracted the number of distinct habitats a species occupies based on the IUCN habitat category for each species’ profile, accessed through the package rredlist88. We also quantified habitat specificity by surveying the number of breeding and non-breeding habitats for each species from its online IUCN species profile (the ‘habitat and ecology’ section). The two approaches yielded similar results (Supplementary Fig. 10, Supplementary Table 2, key for the profiling method is presented in Supplementary Table 6). We obtained global IUCN Red List Categories and threat types for all study species through their IUCN Red List profiles79.
Testing the sources of variation in population trends and fluctuations
In the second stage of our analyses, we modelled the trend and fluctuation estimates from the first stage analyses across latitude, realm, biome, taxa, rarity metrics, phylogenetic relatedness, species’ IUCN Red List Categories and threat type using a Bayesian modelling framework through the package MCMCglmm89. We included a species random intercept effect in the Bayesian models to account for the possible correlation between the trends of populations from the same species (see Supplementary Table 1 for sample sizes). The models ran for 120,000 iterations with a thinning factor of ten, a burn-in period of 20,000 iterations and the default one chain. We assessed model convergence by visually examining trace plots. We used weakly informative priors for all coefficients (an inverse Wishart prior for the variances and a normal prior for the fixed effects):

$$Prleft( mu right) sim Nleft( {0,,10^8} right)$$
(7)

$$Pr(sigma ^2) sim Inverse,Wishart,left( {V = 0,,nu = 0} right)$$
(8)

Population trends and fluctuations across latitude, biomes, realms and taxa
To investigate the geographic and taxonomic patterns of population trends and fluctuations, we modelled population trends (μ) and population fluctuations (σ2), derived from the first stage of our analyses (state-space models), as a function of (1) latitude, (2) realm (freshwater, marine, terrestrial), (3) biome (as defined by the ‘biome’ category in the Living Planet Database, e.g., ‘temperate broadleaf forest’90 and (4) taxa (Actinopterygii, bony fish; Elasmobranchii, sharks and rays; Amphibia, amphibians; Aves, birds; Mammalia, mammals; Reptilia, reptiles). We used separate models for each variable, resulting in four models testing the sources of variation in trends and four additional models focusing on fluctuations. Each categorical model from this second stage of our analyses was fitted with a zero intercept to allow us to determine whether net population trends differed from zero for each of the categories under investigation. The model structures for all models with a categorical fixed effect were identical with the exception of the identity of the fixed effect, and below we describe the taxa model:

$$mu _{i,j,k} = beta _0 + beta _{0,j} + beta _1 ast taxa_{i,j,k},$$
(9)

$$y_{i,j,k}sim gaussianleft( {mu _{i,j,k},sigma ^2} right),$$
(10)

where taxai,j,k is the taxa of the ith time series from the jth species; β0 and β1 are the global intercept (in categorical models, β0 = 1) and the slope estimate for the categorical taxa effect (fixed effect), β0j is the species-level departure from β0 (species-level random effect); yi,j,k is the estimate for change in population abundance for the ith population time series from the jth species from the kth taxa.
Population trends and fluctuations across amphibian, bird and reptile phylogenies
To determine whether there is a phylogenetic signal in the patterning of population change within amphibian, bird and reptile taxa, we modelled population trends (μ) and fluctuations (σ2) across phylogenetic and species-level taxonomic relatedness. We conducted one model per taxa per population change variable—trends or fluctuations using Bayesian linear mixed effects models using the package MCMCglmm89. We included phylogeny and taxa as random effects. The models did not include fixed effects. We assessed the magnitude of the random effects (phylogeny and species) by inspecting their posterior distributions, with a distribution pushed up against zero indicating lack of effect, as these distributions are always bounded by zero and have only positive values. We used parameter-expanded priors, with a variance-covariance structure that allows the slopes of population trend (the μ values from the first stage analysis using state-space models) to covary for each random effect. The prior and model structure were as follows:

$$Prleft( mu right) sim Nleft( {0,,10^8} right),$$
(11)

$$Prleft( {sigma ^2} right) sim Inverse,Wishart,left( {V = 1,,nu = 1} right),$$
(12)

$$mu _{i,k,m} = beta _0 + beta _{0,k} + beta _{0,m},$$
(13)

$$y_{i,k,m} sim gaussianleft( {mu _{i,k,m},,sigma ^2} right),$$
(14)

where β0 is the global intercept (β0 = 1), β0l is the phylogeny-level departure from β0 (phylogeny random effect); yi,k,m is the estimate for change in population abundance for the ith population time series for the kth species with the mth phylogenetic distance.
To account for phylogenetic uncertainty for each class, we ran ten models with identical structures, but based on different randomly selected phylogenetic trees. We report the mean estimate and its range for each class.
Population trends and fluctuations across rarity metrics
To test the influence of rarity metrics (geographic range, mean population size and habitat specificity) on variation in population trends and fluctuations, we modelled population trends (μ) and fluctuations (σ2) across all rarity metrics. We conducted one Bayesian linear model per rarity metric per scale (for both global and UK analyses) per population change variable—trends or fluctuations. The response variable was population trend (μ values from state-space models) or population fluctuation (σ2 values from state-space models), and the fixed effects were geographic range (log transformed), mean population size (log transformed) and habitat specificity (number of distinct habitats occupied). The model structures were identical across the different rarity metrics and below we outline the equations for population trends and geographic range:

$$mu _{i,k,n} = beta _0 + beta _{0,k} + beta _1 ast geographic,range_{i,k,n},$$
(15)

$$y_{i,k,n} sim gaussianleft( {mu _{i,k,n},,sigma ^2} right),$$
(16)

where geographic rangei,k,n is the logged geographic range of the kth species in the ith time series; β0 and β1 are the global intercept and slope estimate for the geographic range effect (fixed effect), β0j is the species-level departure from β0 (species-level random effect); yi,k,n is the estimate for change in population abundance for the ith population time series from the jth species with the nth geographic range.
Population trends across species’ IUCN Red List Categories
To investigate the relationship between-population change and species’ Red List Categories, we modelled population trends (μ) and fluctuations (σ2) as a function of IUCN Red List Categories (categorical variable). We conducted one Bayesian linear model per population change metric per scale (for both global and UK analyses). To test variation in population trends and fluctuations across the types and number of threats to which species are exposed, we conducted a post hoc analysis of trends and fluctuations across threat type (categorical effect) and number of threats that each species is exposed to across its range (in separate models). The model structures were identical to those presented above, except for the fixed effect which was a categorical IUCN Red List Category variable.
The analytical workflow of our analyses is summarised in conceptual diagrams (Supplementary Figs. 1 and 2) and all code is available on GitHub (https://github.com/gndaskalova/PopChangeRarity, DOI 10.5281/zenodo.3817207).
Data limitations: taxonomic and geographic gaps
Our analysis is based on 9286 monitored populations from 2084 species from the largest currently available public database of population time series, the Living Planet Database72. Nevertheless, the data are characterised by both taxonomic and geographic gaps that can influence our findings. For example, there are very few population records from the Amazon and Siberia (Fig. 1b)—two regions currently undergoing rapid environmental changes owing to land-use change and climate change, respectively. In addition, birds represent 63% of all population time series in the Living Planet Database, whilst taxa such as amphibians and sharks where we find declines are included with fewer records (Fig. 2 and Supplementary Fig. 4). On a larger scale, the Living Planet Database under-represents populations outside of Europe and North America and over-represents common and well-studied species62. We found that for the populations and species represented by current monitoring, rarity does not explain variation in population trends, but we note that the relationship between population change and rarity metrics could differ for highly endemic specialist species or species different to the ones included in the Living Planet Database17. As ongoing and future monitoring begins to fill in the taxonomic and geographic gaps in existing datasets, we will be able to reassess and test the generality of the patterns of population change across biomes, taxa, phylogenies, species traits and threats.
Data limitations: monitoring extent and survey techniques
The Living Planet Database combines population time series where survey methods were consistent within time series but varied among time series. Thus, among populations, abundance was measured using different units and over varying spatial extents. There are no estimates of error around the raw population abundance values available and detection probability likely varies among species. Thus, it is challenging to make informed decisions about baseline uncertainty in abundance estimates without prior information. We used state-space models to estimate trends and fluctuations to account for these limitations as this modelling framework is particularly appropriate for analyses of data collected using disparate methods41,81,82. Another approach to partially account for observer error that has been applied to the analysis of population trends is the use of occupancy models36. Because the precise coordinates of the polygons where the individual populations were monitored are not available, we were not able to test for the potential confounding effect of monitoring extent, but our sensitivity analysis indicated that survey units do not explain variation in the detected trends (Supplementary Fig. 12).
Data limitations: temporal gaps
The population time series we studied cover the period between 1970 and 2014, with both duration of monitoring and the frequency of surveys varying across time series. We omitted populations that had less than five time points of monitoring data, as previous studies of similar population time series data have found that shorter time series are less likely to capture directional trends in abundance63. In a separate analysis, we found significant lags in population change following disturbances (forest loss) and that population monitoring often begins decades to centuries after peak forest loss has occurred at a given site45. The findings of this related study suggest that the temporal span of the population monitoring does not always capture the period of intense environmental change and lags suggest that there might be abundance changes that have not yet manifested themselves. Thus, the detected trends and the baseline across which trends are compared might be influenced by when monitoring takes place and at what temporal frequency. Challenges of analysing time series data are present across not just the Living Planet Database that we analysed, but more broadly across population data in general, including invertebrate datasets65. Nevertheless, the Living Planet Database represents the most comprehensive compilation of vertebrate temporal population records to date, allowing for analyses of the patterns of vertebrate trends and fluctuations around the world.
Data limitations: time series with low variation
Eighty populations ( More

1.
Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).
PubMed  Google Scholar 
2.
Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).
PubMed  Google Scholar 

3.
Ollerton, J. Pollinator diversity: Distribution, ecological function, and conservation. Ann. Rev. Ecol. Evol. Syst. 48, 353–376 (2017).
Google Scholar 

4.
Botías, C. et al. Neonicotinoid residues in wildflowers, a potential route of chronic exposure for bees. Environ. Sci. Technol. 49, 12731–12740 (2015).
ADS  PubMed  Google Scholar 

5.
Botías, C., David, A., Hill, E. M. & Goulson, D. Contamination of wild plants near neonicotinoid seed-treated crops, and implications for non-target insects. Sci. Tot. Environ. 566, 269–278 (2016).
Google Scholar 

6.
Long, E. Y. & Krupke, C. H. Non-cultivated plants present a season-long route of pesticide exposure for honey bees. Nat. Commun. 7, 11629 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

7.
Mogren, C. L. & Lundgren, J. G. Neonicotinoid-contaminated pollinator strips adjacent to cropland reduce honey bee nutritional status. Sci. Rep. 6, 29608 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

8.
Tsvetkov, N. et al. Chronic exposure to neonicotinoids reduces honey-bee health near corn crops. Science 356, 1395–1397 (2017).
ADS  CAS  PubMed  Google Scholar 

9.
Wood, T. J., Kaplan, I., Zhang, Y. & Szendrei, Z. Honeybee dietary neonicotinoid exposure is associated with pollen collection from agricultural weeds. Proc. R. Soc. B 286, 20190989 (2019).
CAS  PubMed  Google Scholar 

10.
Douglas, M. R. & Tooker, J. F. Large-scale deployment of seed treatments has driven rapid increase in use of neonicotinoid insecticides and preemptive pest management in US field crops. Environ. Sci. Technol. 49, 5088–5097 (2015).
ADS  CAS  PubMed  Google Scholar 

11.
DiBartolomeis, M., Kegley, S., Mineau, P., Radford, R. & Klein, K. An assessment of acute insecticide toxicity loading (AITL) of chemical pesticides used on agricultural land in the United States. PLoS One 14, e0220029 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

12.
Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V. & Grozinger, C. M. County-level analysis reveals a rapidly shifting landscape of insecticide hazard to honey bees (Apis mellifera) on US farmland. Sci. Rep. 10, 797 (2020).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

13.
Gilburn, A. S. et al. Are neonicotinoid insecticides driving declines of widespread butterflies?. PeerJ 3, e1402 (2015).
PubMed  PubMed Central  Google Scholar 

14.
Forister, M. L. et al. Increasing neonicotinoid use and the declining butterfly fauna of lowland California. Biol. Lett. 12, 20160475 (2016).
PubMed  PubMed Central  Google Scholar 

15.
Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J. & Haddad, N. M. Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS One 14, e0216270 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

16.
Braak, N., Neve, R., Jones, A. K., Gibbs, M. & Breuker, C. J. The effects of insecticides on butterflies: A review. Environ. Pollut. 242, 507–518 (2018).
CAS  PubMed  Google Scholar 

17.
Mulé, R., Sabella, G., Robba, L. & Manachini, B. Systematic review of the effects of chemical insecticides on four common butterfly families. Front. Environ. Sci. 5, 32 (2017).
Google Scholar 

18.
Russell, C. & Schultz, C. B. Effects of grass-specific herbicides on butterflies: An experimental investigation to advance conservation efforts. J. Insect Conserv. 14, 53–63 (2009).
Google Scholar 

19.
Bohnenblust, E., Egan, J. F., Mortensen, D. & Tooker, J. Direct and indirect effects of the synthetic-auxin herbicide dicamba on two lepidopteran species. Environ. Entomol. 42, 586–594 (2013).
CAS  PubMed  Google Scholar 

20.
Hahn, M., Geisthardt, M. & Bruhl, C. A. Effects of herbicide-treated host plants on the development of Mamestra brassicae L. caterpillars. Environ. Toxicol. Chem. 33, 2633–2638 (2014).
CAS  PubMed  Google Scholar 

21.
Stark, J. D., Chen, X. D. & Johnson, C. Effects of herbicides on Behr’s Metalmark butterfly, a surrogate species for the endangered butterfly, Lange’s Metalmark. Environ. Pollut. 164, 24–27 (2012).
CAS  PubMed  Google Scholar 

22.
Eliyahu, D., Applebaum, S. W. & Rafaeli, A. Moth sex-pheromone biosynthesis is inhibited by the herbicide diclofop. Pestic. Biochem. Physiol. 77, 75–81 (2003).
CAS  Google Scholar 

23.
Srivastava, K., Sharma, S., Sharma, D. & Kumar, R. Effect of fungicides on growth and development of Spodoptera litura. Int. J. Life Sci. Sci. Res. 3, 905–908 (2017).
Google Scholar 

24.
Nicodemo, D. et al. Pyraclostrobin impairs energetic mitochondrial metabolism and productive performance of silkworm (Lepidoptera: Bombycidae) caterpillars. J. Econ. Entomol. 111, 1369 (2018).
PubMed  Google Scholar 

25.
Pilling, E. D. & Jepson, P. C. Synergism between EBI fungicides and a pyrethroid insecticide in the honeybee (Apis mellifera). Pestic. Sci. 39, 293–297 (1993).
CAS  Google Scholar 

26.
Pilling, E. D., Bromley-Challenor, K. A. C., Walker, C. H. & Jepson, P. C. Mechanism of synergism between the pyrethroid insecticide λ-cyhalothrin and the imidazole fungicide prochloraz, in the honeybee (Apis mellifera L). Pestic. Biochem. Physiol. 51, 1–11 (1995).
CAS  Google Scholar 

27.
Johnson, R. M., Ellis, M. D., Mullin, C. A. & Frazier, M. Pesticides and honey bee toxicity—USA. Apidologie 41, 312–331 (2010).
CAS  Google Scholar 

28.
Wade, A., Lin, C. H., Kurkul, C., Regan, E. & Johnson, R. M. Combined toxicity of insecticides and fungicides applied to California almond orchards to honey bee larvae and adults. Insects 10, 20 (2019).
PubMed Central  Google Scholar 

29.
Lichtenstein, E. P., Liang, T. T. & Anderegg, B. N. Synergism of insecticides by herbicides. Science 181, 847–849 (1973).
ADS  CAS  PubMed  Google Scholar 

30.
James, D. G. A neonicotinoid insecticide at a rate found in nectar reduces longevity but not oogenesis in monarch butterflies, Danaus plexippus (L.). (Lepidoptera: Nymphalidae). Insects 10, 276 (2019).
PubMed Central  Google Scholar 

31.
Sinha, S. N., Lakhani, K. H. & Davis, B. N. K. Studies on the toxicity of insecticidal drift to the first instar larvae of the large white butterfly Pieris brassicae (Lepidoptera: Pieridae). Ann. Appl. Biol. 116, 27–41 (1990).
CAS  Google Scholar 

32.
Davis, B. N. K., Lakhani, K. H. & Yates, T. J. The hazards of insecticides to butterflies of field margins. Agric. Ecosyst. Environ. 36, 151–161 (1991).
CAS  Google Scholar 

33.
Çilgi, T. & Jepson, P. C. The risks posed by deltamethrin drift to hedgerow butterflies. Environ. Pollut. 87, 1–9 (1995).
PubMed  Google Scholar 

34.
Whitehorn, P. R., Norville, G., Gilburn, A. & Goulson, D. Larval exposure to the neonicotinoid imidacloprid impacts adult size in the farmland butterfly Pieris brassicae. PeerJ 6, e4772 (2018).
PubMed  PubMed Central  Google Scholar 

35.
Agrawal, A. A. & Inamine, H. Mechanisms behind the monarch’s decline. Science 360, 1294–1296 (2018).
ADS  CAS  PubMed  Google Scholar 

36.
Belsky, J. & Joshi, N. K. Assessing role of major drivers in recent decline of monarch butterfly population in North America. Front. Environ. Sci. 6, 86 (2018).
Google Scholar 

37.
Malcolm, S. B. Anthropogenic impacts on mortality and population viability of the monarch butterfly. Ann. Rev. Entomol. 63, 277–302 (2018).
CAS  Google Scholar 

38.
Hartzler, R. G. Reduction in common milkweed (Asclepias syriaca) occurrence in Iowa cropland from 1999 to 2009. Crop Prot. 29, 1542–1544 (2010).
Google Scholar 

39.
Pleasants, J. M. & Oberhauser, K. S. Milkweed loss in agricultural fields because of herbicide use: Effect on the monarch butterfly population. Insect Conserv. Div. 6, 135–144 (2013).
Google Scholar 

40.
Thogmartin, W. E. et al. Monarch butterfly population decline in North America: Identifying the threatening processes. R. Soc. Open. Sci. 4, 170760 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

41.
Thogmartin, W. E. et al. Restoring monarch butterfly habitat in the Midwestern US: ‘all hands on deck’. Environ. Res. Lett. 12, 074005 (2017).
ADS  Google Scholar 

42.
Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E. & Zipkin, E. F. Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus. Ecography 41, 278–290 (2018).
Google Scholar 

43.
Stenoien, C. et al. Monarchs in decline: A collateral landscape-level effect of modern agriculture. Insect Sci. 25, 528–541 (2018).
PubMed  Google Scholar 

44.
Krischik, V., Rogers, M., Gupta, G. & Varshney, A. Soil-applied imidacloprid translocates to ornamental flowers and reduces survival of adult Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens lady beetles, and larval Danaus plexippus and Vanessa cardui butterflies. PLoS One 10, e0119133 (2015).
PubMed  PubMed Central  Google Scholar 

45.
Krishnan, N. et al. Assessing field-scale risks of foliar insecticide applications to monarch butterfly (Danaus plexippus) larvae. Environ. Toxicol. Chem. 39, 923–941 (2020).
CAS  PubMed  Google Scholar 

46.
Pecenka, J. R. & Lundgren, J. G. Non-target effects of clothianidin on monarch butterflies. Sci. Nat. 102, 19 (2015).
Google Scholar 

47.
Bargar, T. A., Hladik, M. L. & Daniels, J. C. Uptake and toxicity of clothianidin to monarch butterflies from milkweed consumption. PeerJ 8, e8669 (2020).
PubMed  PubMed Central  Google Scholar 

48.
Hartzler, R. G. & Buhler, D. D. Occurrence of common milkweed (Asclepias syriaca) in cropland and adjacent areas. Crop Prot. 19, 363–366 (2000).
Google Scholar 

49.
Zaya, D. N., Pearse, I. S. & Spyreas, G. Long-term trends in midwestern milkweed abundances and their relevance to monarch butterfly declines. Bioscience 67, 343–356 (2017).
Google Scholar 

50.
Agrawal, A. A. & Fishbein, M. Plant defense syndromes. Ecology 87, S132–S149 (2006).
PubMed  Google Scholar 

51.
Lefevre, T., Oliver, L., Hunter, M. D. & de Roode, J. C. Evidence for trans-generational medication in nature. Ecol. Lett. 13, 1485–1493 (2010).
PubMed  Google Scholar 

52.
Olaya-Arenas, P. & Kaplan, I. Quantifying pesticide exposure risk for monarch caterpillars on milkweeds bordering agricultural land. Front. Ecol. Evol. 7, 223 (2019).
Google Scholar 

53.
Olaya-Arenas, P., Scharf, M. E. & Kaplan, I. Do pollinators prefer pesticide-free plants? An experimental test with monarchs and milkweeds. J. Appl. Ecol. 20, 20 (2020).
Google Scholar 

54.
Bauerfeind, S. S., Fischer, K., Hartstein, S., Janowitz, S. & Martin-Creuzburg, D. Effects of adult nutrition on female reproduction in a fruit-feeding butterfly: The role of fruit decay and dietary lipids. J. Insect Physiol. 53, 964–973 (2017).
Google Scholar 

55.
Geister, T. L., Lorenz, M. W., Hoffmann, K. H. & Fischer, K. Adult nutrition and butterfly fitness: Effects of diet quality on reproductive output, egg composition, and egg hatching success. Front. Zool. 5, 10 (2008).
PubMed  PubMed Central  Google Scholar 

56.
Van Hook, T., Williams, E. H., Brower, L. P., Borkin, S. & Hein, J. A standardized protocol for ruler-based measurement of wing length in monarch butterflies, Danaus plexippus L. (Nymphalidae, Danainae). Trop. Lep. Res. 22, 42–52 (2012).
Google Scholar 

57.
Davis, A. K. & Holden, M. T. Measuring intraspecific variation in flight-related morphology of monarch butterflies (Danaus plexippus): Which sex has the best flying gear?. J. Insects 20, 591705 (2015).
Google Scholar 

58.
García-Barros, E. Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepi. 38, 59–74 (2015).
Google Scholar 

59.
Wiesweg, M. Survival Analysis: High-Level Interface for Survival Analysis and Associated Plots. R Package Version 0.1.1. (2019). https://CRAN.R-project.org/package=survivalAnalysis.

60.
Wickham, et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).
ADS  Google Scholar 

61.
Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R Package Version 0.2.5. (2020). https://CRAN.R-project.org/package=ggpubr.

62.
Kassambara, A. Rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.4.0. (2020). https://CRAN.R-project.org/package=rstatix.

63.
Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage, Thousand Oaks, 2019).
Google Scholar 

64.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, New York, 2016).
Google Scholar 

65.
Kassambara, A. ggcorrplot: Visualization of a Correlation Matrix Using ‘ggplot2’. R Package Version 0.1.3. (2019) https://CRAN.R-project.org/package=ggcorrplot.

66.
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
Google Scholar 

67.
Schaarschmidt, F. & Gerhard, D. pairwiseCI: Confidence Intervals for Two-Sample Comparisons. R Package Version 0.1-27. (2019) https://CRAN.R-project.org/package=pairwiseCI.

68.
Thompson, H. M., Fryday, S. L., Harkin, S. & Milner, S. Potential impacts of synergism in honeybees (Apis mellifera) of exposure to neonicotinoids and sprayed fungicides in crops. Apidologie 45, 545–553 (2014).
CAS  Google Scholar 

69.
Tadei, R. et al. Late effect of larval co-exposure to the insecticide clothianidin and fungicide pyraclostrobin in Africanized Apis mellifera. Sci. Rep. 9, 3277 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

70.
Després, L., David, J. P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).
PubMed  Google Scholar 

71.
Jones, P. L., Petschenka, G., Flacht, L. & Agrawal, A. A. Cardenolide intake, sequestration, and excretion by the monarch butterfly along gradients of plant toxicity and larval ontogeny. J. Chem. Ecol. 45, 264–277 (2019).
CAS  PubMed  Google Scholar 

72.
Karageorgi, M. et al. Genome editing retraces the evolution of toxin resistance in the monarch butterfly. Nature 574, 409–412 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

73.
Hardy, N. B., Peterson, D. A., Ross, L. & Rosenheim, J. A. Does a plant-eating insect’s diet govern the evolution of insecticide resistance? Comparative tests of the pre-adaptation hypothesis. Evol. Appl. 11, 739–747 (2018).
PubMed  Google Scholar 

74.
Basley, K. & Goulson, D. Effects of field-relevant concentrations of clothianidin on larval development of the butterfly Polyommatus icarus (Lepidoptera, Lycaenidae). Environ. Sci. Technol. 52, 3990–3996 (2018).
ADS  CAS  PubMed  Google Scholar 

75.
Halsch, C. et al. Pesticide contamination of milkweeds across the agricultural, urban, and open spaces of low elevation Northern California. Front. Ecol. Evol. 8, 162 (2020).
Google Scholar 

76.
Altizer, S. & Davis, A. K. Populations of monarch butterflies with different migratory behaviors show divergence in wing morphology. Evolution 64, 1018–1028 (2010).
PubMed  Google Scholar 

77.
Dockx, C. Directional and stabilizing selection on wing size and shape in migrant and resident monarch butterflies, Danaus plexippus (L.). Cuba. Biol. J. Linn. Soc. 92, 605–616 (2007).
Google Scholar 

78.
Satterfield, D. A. & Davis, A. K. Variation in wing characteristics of monarch butterflies during migration: Earlier migrants have redder and more elongated wings. Anim. Migr. 2, 1–7 (2014).
Google Scholar 

79.
Freedman, M. G. & Dingle, H. Wing morphology in migratory North American monarchs: Characterizing sources of variation and understanding changes through time. Anim. Migr. 5, 61–73 (2018).
Google Scholar 

80.
Inamine, H., Ellner, S. P., Springer, J. P. & Agrawal, A. A. Linking the continental migratory cycle of the monarch butterfly to understand its population decline. Oikos 125, 1081–1091 (2016).
Google Scholar 

81.
Nylin, S. & Gotthard, K. Plasticity in life-history traits. Ann. Rev. Entomol. 43, 63–83 (1998).
CAS  Google Scholar 

82.
Awmack, C. S. & Leather, S. R. Host plant quality and fecundity in herbivorous insects. Ann. Rev. Entomol. 47, 817–844 (2002).
CAS  Google Scholar 

83.
Jervis, M. A., Boggs, C. L. & Ferns, P. N. Egg maturation strategy and its associated trade-offs: A synthesis focusing on Lepidoptera. Ecol. Entomol. 30, 359–375 (2005).
Google Scholar 

84.
Oberhauser, K. S. Fecundity, lifespan and egg mass in butterflies: Effects of male-derived nutrients and female size. Funct. Ecol. 11, 166–175 (1997).
Google Scholar 

85.
Main, A. R., Webb, E. B., Goyne, K. W. & Mengel, D. Neonicotinoid insecticides negatively affect performance measures of non-target terrestrial arthropods: A meta-analysis. Ecol. Appl. 28, 1232–1244 (2018).
PubMed  Google Scholar  More

1.
Vaughn, C. C. Ecosystem services provided by freshwater mussels. Hydrobiologia 810, 15–27. https://doi.org/10.1007/s10750-017-3139-x (2018).
Article  Google Scholar 
2.
Christian, A. D., Smith, B. N., Berg, D. J., Smoot, J. C. & Findlay, R. H. Trophic position and potential food sources of 2 species of unionid bivalves (Mollusca:Unionidae) in 2 small Ohio streams. Freshw. Sci. 23, 101–113 (2004).
Google Scholar 

3.
Vaughn, C. C. Biodiversity losses and ecosystem function in freshwaters: emerging conclusions and research directions. Bioscience 60, 25–35. https://doi.org/10.1525/bio.2010.60.1.7 (2010).
Article  Google Scholar 

4.
Howard, J. K. & Cuffey, K. M. The functional role of native freshwater mussels in the fluvial benthic environment. Freshw. Biol. 51, 460–474. https://doi.org/10.1111/j.1365-2427.2005.01507.x (2006).
Article  Google Scholar 

5.
Izumi, T., Yagita, K., Izumiyama, S., Endo, T. & Itoh, Y. Depletion of Cryptosporidium parvum oocysts from contaminated sewage by using freshwater benthic pearl clams (Hyriopsis schlegeli). Appl. Environ. Microbiol. 78, 7420–7428. https://doi.org/10.1128/AEM.01502-12 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Ismail, N. S., Müller, C. E., Morgan, R. R. & Luthy, R. G. Uptake of contaminants of emerging concern by the bivalves Anodonta californiensis and Corbicula fluminea. Environ. Sci. Technol. 48, 9211–9219. https://doi.org/10.1021/es5011576 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

7.
Ismail, N. S. et al. Improvement of urban lake water quality by removal of Escherichia coli through the action of the bivalve Anodonta californiensis. Environ. Sci. Technol. 49, 1664–1672. https://doi.org/10.1021/es5033212 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

8.
Williams, J. D. et al. A revised list of the freshwater mussels (Mollusca: Bivalvia: Unionida) of the United States and Canada. Freshw. Mollusk Biol. Conserv. 20, 33. https://doi.org/10.31931/fmbc.v20i2.2017.33-58 (2017).
Article  Google Scholar 

9.
Lydeard, C. et al. The global decline of nonmarine mollusks. Bioscience 54, 321. https://doi.org/10.1641/0006-3568(2004)054[0321:TGDONM]2.0.CO;2 (2004).
Article  Google Scholar 

10.
Haag, W. R. North American Freshwater Mussels: Natural History, Ecology, and Conservation (Cambridge University Press, Cambridge, 2012).
Google Scholar 

11.
Strayer, D. L. Effects of alien species on freshwater mollusks in North America. J. N. Am. Benthol. Soc. 18, 74–98. https://doi.org/10.2307/1468010 (1999).
Article  Google Scholar 

12.
Haag, W. R. Reassessing enigmatic mussel declines in the United States. Freshw. Mollusk Biol. Conserv. 22, 43–60 (2019).
Article  Google Scholar 

13.
Goldberg, T. L., Dunn, C. D., Leis, E. & Waller, D. L. A novel picornalike virus in a Wabash Pigtoe (Fusconaia flava) from the Upper Mississippi River, USA. Freshw. Mollusk Biol. Conserv. 22, 81–84 (2019).
Google Scholar 

14.
Downing, J. A., Van Meter, P. & Woolnough, D. A. Suspects and evidence: a review of the causes of extirpation and decline in freshwater mussels. Anim. Biodivers. Conserv. 33, 151–185 (2010).
Google Scholar 

15.
Zipper, C. E. et al. Freshwater mussel population status and habitat quality in the Clinch Rver, Virginia and Tennessee, USA: a featured collection. J. Am. Water Resour. Assoc. 50, 807–819 (2014).
ADS  Article  Google Scholar 

16.
Jones, J. et al. Clinch River freshwater mussels upstream of Norris Reservoir, Tennessee and Virginia: a quantitative assessment from 2004 to 2009. J. Am. Water Resour. Assoc. 50, 820–836. https://doi.org/10.1111/jawr.12222 (2014).
ADS  Article  Google Scholar 

17.
Jones, J. W. et al. Collapse of the Pendleton Island mussel fauna in the Clinch River, Virginia: setting baseline conditions to guide recovery and restoration. Freshw. Mollusk Biol. Conserv. 21, 36–56 (2018).
Google Scholar 

18.
Cope, W. G. & Jones, J. W. Recent precipitous declines of endangered freshwater mussels in the Clinch River: an in situ assessment of water quality stressors related to energy development and other land-use. 244 (U.S. Fish and Wildlife Service, Southwestern Virginia Field Office, 2016).

19.
Richard, J. C. Clinch River mussel die-off. Ellipsaria 20, 1–3 (2018).
Google Scholar 

20.
Neves, R. J. Proceedings of the Workshop on Die-offs of Freshwater Mussels in the United States (U.S. Fish and Wildlife Service, Upper Mississippi River Conservation Committee, 1986).

21.
Starliper, C. E., Powell, J., Garner, J. T. & Schill, W. B. Predominant bacteria isolated from moribund Fusconaia ebena ebonyshells experiencing die-offs in Pickwick Reservoir, Tennessee River, Alabama. J. Shellfish Res. 30, 359–366. https://doi.org/10.2983/035.030.0223 (2011).
Article  Google Scholar 

22.
Grizzle, J. M. & Brunner, C. J. Infectious diseases of freshwater mussels and other freshwater bivalve mollusks. Rev. Fish. Sci. 17, 425–467 (2009).
Article  Google Scholar 

23.
Leis, E. et al. Building a response network to investigate potential pathogens associated with unionid mortality events. Ellipsaria 20, 44–45 (2018).
Google Scholar 

24.
Henley, W. F., Beaty, B. B. & Jones, J. W. Evaluations of organ tissues from Actinonaias pectorosa collected during a mussel die-off in 2016 at Kyles Ford, Clinch River, Tennessee. J. Shellfish Res. 38, 681. https://doi.org/10.2983/035.038.0320 (2019).
Article  Google Scholar 

25.
Leis, E., Erickson, S., Waller, D., Richard, J. & Goldberg, T. A comparison of bacteria cultured from unionid mussel hemolymph between stable populations in the Upper Mississippi River basin and populations affected by a mortality event in the Clinch River. Freshw. Mollusk Biol. Conserv. 22, 70–80 (2019).
Google Scholar 

26.
Garcia, C. et al. Ostreid herpesvirus 1 detection and relationship with Crassostrea gigas spat mortality in France between 1998 and 2006. Vet. Res. 42, 73. https://doi.org/10.1186/1297-9716-42-73 (2011).
ADS  Article  PubMed  PubMed Central  Google Scholar 

27.
Arzul, I., Corbeil, S., Morga, B. & Renault, T. Viruses infecting marine molluscs. J. Invertebr. Pathol. 147, 118–135. https://doi.org/10.1016/j.jip.2017.01.009 (2017).
Article  PubMed  Google Scholar 

28.
Zhang, Z., Sufang, D., Yimin, X. & Jie, W. Studies on the mussel Hyriopsis cumingii plague. I. a new viral infectious disease. Acta Hydrobiol. Sin. 26, 308–312 (1986).
Google Scholar 

29.
Zhong, L., Xiao, T.-Y., Huang, J., Dai, L.-Y. & Liu, X.-Y. Histopathological examination of bivalve mussel Hyriopsis cumingii lea artificially infected by virus. Acta Hydrobiol. Sin. 35, 666–671 (2011).
Google Scholar 

30.
Shi, M. et al. Redefining the invertebrate RNA virosphere. Nature 540, 539–543. https://doi.org/10.1038/nature20167 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

31.
Zhang, Y. Z., Shi, M. & Holmes, E. C. Using metagenomics to characterize an expanding vrosphere. Cell 172, 1168–1172. https://doi.org/10.1016/j.cell.2018.02.043 (2018).
CAS  Article  PubMed  Google Scholar 

32.
Bergoin, M. & Tijssen, P. Molecular biology of Densovirinae. Contrib. Microbiol. 4, 12–32. https://doi.org/10.1159/000060329 (2000).
CAS  Article  PubMed  Google Scholar 

33.
Mietzsch, M., Penzes, J. J. & Agbandje-McKenna, M. Twenty-five years of structural parvovirology. Viruses https://doi.org/10.3390/v11040362 (2019).
Article  PubMed  PubMed Central  Google Scholar 

34.
Cotmore, S. F. et al. ICTV virus taxonomy profile: Parvoviridae. J. Gen. Virol. 100, 367–368. https://doi.org/10.1099/jgv.0.001212 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Ganesh, B., Masachessi, G. & Mladenova, Z. Animal picobirnavirus. Virus Dis. 25, 223–238. https://doi.org/10.1007/s13337-014-0207-y (2014).
Article  Google Scholar 

36.
Gustafson, L. L. et al. Evaluation of a nonlethal technique for hemolymph collection in Elliptio complanata, a freshwater bivalve (Mollusca: Unionidae). Dis. Aquat. Organ. 65, 159–165. https://doi.org/10.3354/dao065159 (2005).
Article  PubMed  Google Scholar 

37.
Lees, D. Viruses and bivalve shellfish. Int. J. Food Microbiol. 59, 81–116. https://doi.org/10.1016/S0168-1605(00)00248-8 (2000).
CAS  Article  PubMed  Google Scholar 

38.
Faust, C., Stallknecht, D., Swayne, D. & Brown, J. Filter-feeding bivalves can remove avian influenza viruses from water and reduce infectivity. Proc. R. Soc. B 276, 3727–3735. https://doi.org/10.1098/rspb.2009.0572 (2009).
Article  PubMed  Google Scholar 

39.
Fédière, G. in Parvoviruses. From Molecular Biology to Pathology and Therapeutic Uses. Contributions to Microbiology. Vol. 4 (eds S. Faisst & J. Rommelaere) 1–11 (Karger, 2000).

40.
Kalagayan, H. et al. IHHN virus as an etiological factor in runt-deformity syndrome (RDS) of juvenile Penaeus vannamei cultured in Hawaii. J. World Aquacult. Soc. 22, 235–243. https://doi.org/10.1111/j.1749-7345.1991.tb00740.x (1991).
Article  Google Scholar 

41.
Ito, K., Kidokoro, K., Shimura, S., Katsuma, S. & Kadono-Okuda, K. Detailed investigation of the sequential pathological changes in silkworm larvae infected with Bombyx densovirus type 1. J. Invertebr. Pathol. 112, 213–218. https://doi.org/10.1016/j.jip.2012.12.005 (2013).
Article  PubMed  Google Scholar 

42.
Jiang, H. et al. Genetic engineering of Periplaneta fuliginosa densovirus as an improved biopesticide. Arch. Virol. 152, 383–394. https://doi.org/10.1007/s00705-006-0844-6 (2007).
CAS  Article  PubMed  Google Scholar 

43.
Ledermann, J. P., Suchman, E. L., Black, W. C. & Carlson, J. O. Infection and pathogenicity of the mosquito densoviruses AeDNV, HeDNV, and APeDNV in Aedes aegypti mosquitoes (Diptera: Culicidae). J. Econ. Entomol. 97, 1828–1835. https://doi.org/10.1093/jee/97.6.1828 (2004).
Article  PubMed  Google Scholar 

44.
Szelei, J. et al. Susceptibility of North-American and European crickets to Acheta domesticus densovirus (AdDNV) and associated epizootics. J. Invertebr. Pathol. 106, 394–399. https://doi.org/10.1016/j.jip.2010.12.009 (2011).
CAS  Article  PubMed  Google Scholar 

45.
Kouassi, N. et al. Pathogenicity of diatraea saccharalis densovirus to host insets and characterization of its viral genome. Virol. Sin. 22, 53–60. https://doi.org/10.1007/s12250-007-0062-8 (2007).
CAS  Article  Google Scholar 

46.
Bowater, R. et al. A parvo-like virus in cultured redclaw crayfish Cherax quadricarinatus from Queensland, Australia. Dis. Aquat. Organ. 50, 79–86. https://doi.org/10.3354/dao050079 (2002).
Article  PubMed  Google Scholar 

47.
Johnson, R. M. & Rasgon, J. L. Densonucleosis viruses (‘densoviruses’) for mosquito and pathogen control. Curr. Opin. Insect. Sci. 28, 90–97. https://doi.org/10.1016/j.cois.2018.05.009 (2018).
Article  PubMed  Google Scholar 

48.
Hewson, I. et al. Densovirus associated with sea-star wasting disease and mass mortality. Proc. Natl. Acad. Sci. USA 111, 17278–17283. https://doi.org/10.1073/pnas.1416625111 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

49.
Fritts, A. K., Peterson, J. T., Hazelton, P. D. & Bringolf, R. B. Evaluation of methods for assessing physiological biomarkers of stress in freshwater mussels. Can. J. Fish. Aquat. Sci. 72, 1450–1459. https://doi.org/10.1139/cjfas-2014-0564 (2015).
CAS  Article  Google Scholar 

50.
Cunningham, A. A., Daszak, P. & Wood, J. L. N. One health, emerging infectious diseases and wildlife: two decades of progress?. Philos. Trans. R. Soc. Lond. B https://doi.org/10.1098/rstb.2016.0167 (2017).
Article  Google Scholar 

51.
Patterson, M. A. et al. Freshwater Mussel Propagation for Restoration (Cambridge University Press, Cambridge, 2018).
Google Scholar 

52.
Toohey-Kurth, K., Sibley, S. D. & Goldberg, T. L. Metagenomic assessment of adventitious viruses in commercial bovine sera. Biologicals 47, 64–68. https://doi.org/10.1016/j.biologicals.2016.10.009 (2017).
CAS  Article  PubMed  Google Scholar 

53.
Löytynoja, A. Phylogeny-aware alignment with PRANK. Methods Mol. Biol. 1079, 155–170. https://doi.org/10.1007/978-1-62703-646-7_10 (2014).
Article  PubMed  Google Scholar 

54.
Talavera, G. & Castresana, J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 56, 564–577. https://doi.org/10.1080/10635150701472164 (2007).
CAS  Article  PubMed  Google Scholar 

55.
Abascal, F., Zardoya, R. & Telford, M. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 38, W7-13. https://doi.org/10.1093/nar/gkq291 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
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. https://doi.org/10.1093/sysbio/syq010 (2010).
CAS  Article  Google Scholar 

57.
R Core Team. R: A language and environment for statistical computing, version 3.6.3. https://www.R-project.org (R Foundation for Statistical Computing, Vienna, 2019). More

1.
Walsh, D. B. et al. Drosophila suzukii (Diptera: Drosophilidae): Invasive pest of ripening soft fruit expanding its geographic range and damage potential. J. Integr. Pest Manag 2, G1–G7 (2011).
Google Scholar 
2.
Hauser, M. A historic account of the invasion of Drosophila suzukii (Matsumura) (Diptera: Drosophilidae) in the continental United States, with remarks on their identification. Pest Manag. Sci. 67, 1352–1357 (2011).
CAS  PubMed  Google Scholar 

3.
Asplen, M. K. et al. Invasion biology of spotted wing drosophila (Drosophila suzukii): a global perspective and future priorities. J. Pest. Sci. 88, 469–494 (2015).
Google Scholar 

4.
Arnó, J., Solà, M., Riudavets, J. & Gabarra, R. Population dynamics, non-crop hosts, and fruit susceptibility of Drosophila suzukii in Northeast Spain. J. Pest. Sci. 89, 713–723 (2016).
Google Scholar 

5.
Keesey, I. W., Knaden, M. & Hansson, B. S. Olfactory specialization in Drosophila suzukii supports an ecological shift in host preference from rotten to fresh fruit. J. Chem. Ecol. 41, 121–128 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

6.
Karageorgi, M. et al. Evolution of multiple sensory systems drives novel egg-laying behavior in the fruit pest Drosophila suzukii. Curr. Biol. 27, 847–853 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Lee, J. C. et al. The susceptibility of small fruits and cherries to the spotted-wing drosophila Drosophila suzukii. Pest Manag. Sci. 67, 1358–1367 (2011).
CAS  PubMed  Google Scholar 

8.
Raffa, K. F., Bonello, P. & Orrock, J. L. Why do entomologists and plant pathologists approach trophic relationships so differently? Identifying biological distinctions to foster synthesis. New Phytol. 225, 609–620 (2020).
PubMed  Google Scholar 

9.
Scheidler, N. H., Liu, C., Hamby, K. A., Zalom, F. G. & Syed, Z. Volatile codes: Correlation of olfactory signals and reception in Drosophila-yeast chemical communication. Sci. Rep. 5, 1–13 (2015).
Google Scholar 

10.
Hamm, C. A. et al. Wolbachia do not live by reproductive manipulation alone: Infection polymorphism in Drosophila suzukii and D Subpulchrella. Mol. Ecol. 23, 4871–4885 (2014).
PubMed  PubMed Central  Google Scholar 

11.
Cha, D. H. et al. Behavioral evidence for contextual olfactory-mediated avoidance of the ubiquitous phytopathogen Botrytis cinerea by Drosophila suzukii. Insect Sci. 27, 771–779 (2019).
PubMed  Google Scholar 

12.
Bellutti, N. et al. Dietary yeast affects preference and performance in Drosophila suzukii. J. Pest. Sci. 91, 651–660 (2018).
Google Scholar 

13.
Hamby, K. A., Hernández, A., Boundy-Mills, K. & Zalom, F. G. Associations of yeasts with spotted-wing drosophila (Drosophila suzukii; Diptera: Drosophilidae) in cherries and raspberries. Appl. Environ. Microbiol. 78, 4869–4873 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

14.
Mori, B. A. et al. Enhanced yeast feeding following mating facilitates control of the invasive fruit pest Drosophila suzukii. J. Appl. Ecol. 54, 170–177 (2017).
Google Scholar 

15.
Goodhue, R. E., Bolda, M., Farnsworth, D., Williams, J. C. & Zalom, F. G. Spotted wing drosophila infestation of California strawberries and raspberries: Economic analysis of potential revenue losses and control costs. Pest Manag. Sci. 67, 1396–1402 (2011).
CAS  PubMed  Google Scholar 

16.
Barata, A., Malfeito-Ferreira, M. & Loureiro, V. The microbial ecology of wine grape berries. Int. J. Food Microbiol. 153, 243–259 (2012).
CAS  PubMed  Google Scholar 

17.
Cloonan, K. R., Abraham, J., Angeli, S., Syed, Z. & Rodriguez-Saona, C. Advances in the chemical ecology of the spotted wing drosophila (Drosophila suzukii) and its Applications. J. Chem. Ecol. 44, 922–939 (2018).
CAS  PubMed  Google Scholar 

18.
Cloonan, K. R. et al. Laboratory and field evaluation of host-related foraging odor-cue combinations to attract Drosophila suzukii (Diptera: Drosophilidae). J. Econ. Entomol. 112, 2850–2860 (2019).
PubMed  Google Scholar 

19.
Waller, T. J., Vaiciunas, J., Constantelos, C. & Oudemans, P. V. Evidence that blueberry floral extracts influence secondary conidiation and appressorial formation of Colletotrichum fioriniae. Phytopathology 108, 561–567 (2018).
PubMed  Google Scholar 

20.
Pszczółkowska, A. & Okorski, A. First report of anthracnose disease caused by Colletotrichum fioriniae on blueberry in western Poland. Plant. Dis. 100, 21–67 (2016).
Google Scholar 

21.
Wharton, P. & Diéguez-Uribeondo, J. The biology of Colletotrichum acutatum. An del Jardín Botánico Madrid 61, 3–22 (2004).
Google Scholar 

22.
Peres, N. A., Timmer, L. W., Adaskaveg, J. E. & Correll, J. C. Lifestyles of Colletotrichum acutatum. Plant Dis. 89, 784–796 (2005).
CAS  PubMed  Google Scholar 

23.
Polashock, J. J., Caruso, F. L., Averill, A. L. & Schilder, A. C. Compendium of Bluberry, Cranberry, and Lingonberry Diseases and Pests (APS Publications, St. Paul, MN, 2017).
Google Scholar 

24.
Wharton, P. S. & Schilder, A. C. Novel infection strategies of Colletotrichum acutatum on ripe blueberry fruit. Plant Pathol. 57, 122–134 (2008).
Google Scholar 

25.
Verma, N., MacDonald, L. & Punja, Z. K. Inoculum prevalence, host infection and biological control of Colletotrichum acutatum: causal agent of blueberry anthracnose in British Columbia. Plant Pathol. 55, 442–450 (2006).
Google Scholar 

26.
Verma, N., MacDonald, L. & Punja, Z. K. Environmental and host requirements for field infection of blueberry fruits by Colletotrichum acutatum in British Columbia. Plant Pathol. 56, 107–113 (2007).
Google Scholar 

27.
Miles, T. D. & Schilder, A. C. Host defenses associated with fruit infection by Colletotrichum species with an emphasis on anthracnose of blueberries. Plant Health Prog. 14, 30 (2013).
Google Scholar 

28.
Miles, T. D., Hancock, J. F., Callow, P. & Schilder, A. M. C. Evaluation of screening methods and fruit composition in relation to anthracnose fruit rot resistance in blueberries. Plant Pathol. 61, 555–566 (2012).
Google Scholar 

29.
Janzen, D. H. Why fruits rot, seeds mold, and meat spoils. Am. Nat. 111, 691–713 (1977).
CAS  Google Scholar 

30.
Cipollini, M. L. & Stiles, E. W. Fruit rot, antifungal defense, and palatability of fleshy fruits for frugivorous birds. Ecology 74, 751–762 (1993).
Google Scholar 

31.
Peris, J. E., Rodríguez, A., Penã, L. & Fedriani, J. M. Fungal infestation boosts fruit aroma and fruit removal by mammals and birds. Sci. Rep. 7, 1–9 (2017).
CAS  Google Scholar 

32.
Lee, J. C. et al. Characterization and manipulation of fruit susceptibility to Drosophila suzukii. J. Pest. Sci. 89, 771–780 (2016).
Google Scholar 

33.
Choi, M. Y. et al. Effect of non-nutritive sugars to decrease the survivorship of spotted wing drosophila Drosophila suzukii. J Insect Physiol 99, 86–94 (2017).
CAS  PubMed  Google Scholar 

34.
Tochen, S., Walton, V. M. & Lee, J. C. Impact of floral feeding on adult Drosophila suzukii survival and nutrient status. J. Pest Sci. 89, 793–802 (2016).
Google Scholar 

35.
Young, Y., Buckiewicz, N. & Long, T. A. F. Nutritional geometry and fitness consequences in Drosophila suzukii, the spotted-wing drosophila. Ecol. Evol. 8, 2842–2851 (2018).
PubMed  PubMed Central  Google Scholar 

36.
Graziosi, I. & Rieske, L. K. A plant pathogen causes extensive mortality in an invasive insect herbivore. Agric. For. Entomol. 17, 366–374 (2015).
Google Scholar 

37.
Wallingford, A. K., Hesler, S. P., Cha, D. H. & Loeb, G. M. Behavioral response of spotted-wing drosophila, Drosophila suzukii Matsumura, to aversive odors and a potential oviposition deterrent in the field. Pest Manag. Sci. 72, 701–706 (2016).
CAS  PubMed  Google Scholar 

38.
Wallingford, A. K., Cha, D. H., Linn, C. E., Wolfin, M. S. & Loeb, G. M. Robust manipulations of pest insect behavior using repellents and practical application for integrated pest management. Environ. Entomol. 46, 1041–1050 (2017).
CAS  PubMed  Google Scholar 

39.
Göhre, V. & Robatzek, S. Breaking the barriers: microbial effector molecules subvert plant immunity. Annu. Rev. Phytopathol. 46, 189–215 (2008).
PubMed  Google Scholar 

40.
Csorba, T., Kontra, L. & Burgyán, J. Viral silencing suppressors: tools forged to fine-tune host-pathogen coexistence. Virology 479–480, 85–103 (2015).
PubMed  Google Scholar 

41.
Stringlis, I. A., Zhang, H., Pieterse, C. M. J., Bolton, M. D. & De Jonge, R. Microbial small molecules-weapons of plant subversion. Nat. Prod. Rep. 35, 410–433 (2018).
CAS  PubMed  Google Scholar 

42.
McLeod, G. et al. The pathogen causing Dutch elm disease makes host trees attract insect vectors. Proc. Biol. Sci. 272, 2499–2503 (2005).
PubMed  PubMed Central  Google Scholar 

43.
Raguso, R. A. & Roy, B. A. ‘Floral’ scent production by Puccinia rust fungi that mimic flowers. Mol. Ecol. 7, 1127–1136 (1998).
CAS  PubMed  Google Scholar 

44.
Bruce, T. J. A. & Pickett, J. A. Perception of plant volatile blends by herbivorous insects—finding the right mix. Phytochemistry 72, 1605–1611 (2011).
CAS  PubMed  Google Scholar 

45.
Revadi, S. et al. Sexual behavior of Drosophila suzukii. Insects 6, 183–196 (2015).
PubMed  PubMed Central  Google Scholar 

46.
Polashock, J. J., Ehlenfeldt, M. K., Stretch, A. W. & Kramer, M. Anthracnose fruit rot resistance in blueberry cultivars. Plant Dis. 89, 33–38 (2005).
PubMed  Google Scholar 

47.
Hartung, J. S., Burton, C. & Ramsdell, D. C. Epidemiological studies of blueberry anthracnose disease caused by Colletotrichum gloeosporioides. Phytopathology 71, 449 (1981).
Google Scholar 

48.
Cai, P. et al. Potential host fruits for Drosophila suzukii: olfactory and oviposition preferences and suitability for development. Entomol. Exp. Appl. 167, 880–890 (2019).
Google Scholar 

49.
Rodriguez-Saona, C. et al. Differential susceptibility of wild and cultivated blueberries to an invasive frugivorous pest. J. Chem. Ecol. 45, 286–297 (2018).
PubMed  Google Scholar 

50.
Hodge, S. The effect of pH and water content of natural resources on the development of Drosophila melanogaster larvae. Dros. Inf. Serv. 84, 38–43 (2001).
Google Scholar 

51.
Schilder, A. M. C., Gillett, J. M. & Woodworth, J. A. The kaleidoscopic nature of blueberry fruit roots. Acta Hortic. 574, 81–83 (2002).
Google Scholar 

52.
Jaramillo, S. L., Mehlferber, E. & Moore, P. J. Life-history trade-offs under different larval diets in Drosophila suzukii (Diptera: Drosophilidae). Physiol. Entomol. 40, 2–9 (2015).
Google Scholar 

53.
Dalton, D. T. et al. Laboratory survival of Drosophila suzukii under simulated winter conditions of the Pacific Northwest and seasonal field trapping in five primary regions of small and stone fruit production in the United States. Pest Manag. Sci. 67, 1368–1374 (2011).
CAS  PubMed  Google Scholar 

54.
Miller, P. M. V-8 juice agar as a general purpose medium for fungi and bacteria. Phytopathology 45, 461–462 (1955).
Google Scholar 

55.
Feng, Y., Bruton, R., Park, A. & Zhang, A. Identification of attractive blend for spotted wing drosophila, Drosophila suzukii, from apple juice. J. Pest Sci. 91, 1251–1267 (2018).
Google Scholar 

56.
Tochen, S. et al. Temperature-related development and population parameters for Drosophila suzukii (Diptera: Drosophilidae) on cherry and blueberry. Environ. Entomol. 43, 501–510 (2014).
PubMed  Google Scholar  More