Philippa Kaur

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

1.
Van Sebille E, Wilcox C, Lebreton L, Maximenko N, Hardesty BD, Van Franeker JA, et al. A global inventory of small floating plastic debris. Environ Res Lett. 2015;10:124006.
Google Scholar 
2.
Reisser J, Shaw J, Hallegraeff G, Proietti M, Barnes DK, Thums M, et al. Millimeter-sized marine plastics: a new pelagic habitat for microorganisms and invertebrates. PLoS ONE. 2014;9:e100289.
PubMed  PubMed Central  Google Scholar 

3.
Mincer TJ, Zettler ER, Amaral-Zettler LA. Biofilms on plastic debris and their influence on marine nutrient cycling, productivity, and hazardous chemical mobility. In: Rei Yamashita KT, Bee Geok Yeo, Hideshige Takada, Jan A. van Franeker, Megan Dalton, Eric Dale, editors. Hazardous chemicals associated with plastics in the marine environment. Springer: Cham; 2016. pp. 221–33.

4.
Morét-Ferguson S, Law KL, Proskurowski G, Murphy EK, Peacock EE, Reddy CM. The size, mass, and composition of plastic debris in the western North Atlantic Ocean. Mar Pollut Bull. 2010;60:1873–8.
PubMed  Google Scholar 

5.
Eriksen M, Lebreton LC, Carson HS, Thiel M, Moore CJ, Borerro JC, et al. Plastic pollution in the world’s oceans: more than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS ONE. 2014;9:e111913.
PubMed  PubMed Central  Google Scholar 

6.
Zettler ER, Mincer TJ, Amaral-Zettler LA. Life in the “plastisphere”: microbial communities on plastic marine debris. Environ Sci Technol. 2013;47:7137–46.
CAS  PubMed  Google Scholar 

7.
Amaral-Zettler LA, Zettler ER, Slikas B, Boyd GD, Melvin DW, Morrall CE, et al. The biogeography of the plastisphere: implications for policy. Front Ecol Environ. 2015;13:541–6.
Google Scholar 

8.
De Tender CA, Devriese LI, Haegeman A, Maes S, Ruttink T, Dawyndt P. Bacterial community profiling of plastic litter in the Belgian part of the North Sea. Environ Sci Technol. 2015;49:9629–38.
PubMed  Google Scholar 

9.
De Tender CA, Schlundt C, Devriese LI, Mincer TJ, Zettler ER, Amaral-Zettler LA. A review of microscopy and comparative molecular-based methods to characterize “plastisphere” communities. Anal Methods. 2017;9:2132–43.
Google Scholar 

10.
Gong W, Marchetti A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front Mar Sci. 2019;6:219.
Google Scholar 

11.
Bonk F, Popp D, Harms H, Centler F. PCR-based quantification of taxa-specific abundances in microbial communities: quantifying and avoiding common pitfalls. J Microbiol Methods. 2018;153:139–47.
CAS  PubMed  Google Scholar 

12.
Neu TR, Lawrence JR. Innovative techniques, sensors, and approaches for imaging biofilms at different scales. Trends Microbiol. 2015;23:233–42.
CAS  PubMed  Google Scholar 

13.
Bochdansky AB, Clouse MA, Herndl GJ. Eukaryotic microbes, principally fungi and labyrinthulomycetes, dominate biomass on bathypelagic marine snow. ISME J. 2017;11:362–73.
PubMed  Google Scholar 

14.
Schlundt C, Welch JLM, Knochel AM, Zettler ER, Amaral‐Zettler LA. Spatial structure in the “plastisphere”: molecular resources for imaging microscopic communities on plastic marine debris. Mol Ecol Resour. 2020;20:620–634.
CAS  PubMed  Google Scholar 

15.
Bruinsma G, Van der Mei H, Busscher H. Bacterial adhesion to surface hydrophilic and hydrophobic contact lenses. Biomaterials. 2001;22:3217–24.
CAS  PubMed  Google Scholar 

16.
Ogonowski M, Motiei A, Ininbergs K, Hell E, Gerdes Z, Udekwu KI, et al. Evidence for selective bacterial community structuring on microplastics. Environ Microbiol. 2018;20:2796–808.
CAS  PubMed  Google Scholar 

17.
Khachikyan A, Milucka J, Littmann S, Ahmerkamp S, Meador T, Könneke M, et al. Direct cell mass measurements expand the role of small microorganisms in nature. Appl Environ Microbiol. 2019;85:e00493–19.
CAS  PubMed  PubMed Central  Google Scholar 

18.
Romanova N, Sazhin A. Relationships between the cell volume and the carbon content of bacteria. Oceanology. 2010;50:522–30.
Google Scholar 

19.
Menden-Deuer S, Lessard EJ. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol Oceanogr. 2000;45:569–79.
CAS  Google Scholar 

20.
Massana R, Logares R. Eukaryotic versus prokaryotic marine picoplankton ecology. Environ Microbiol. 2013;15:1254–61.
PubMed  Google Scholar 

21.
Loferer-Krößbacher M, Klima J, Psenner R. Determination of bacterial cell dry mass by transmission electron microscopy and densitometric image analysis. Appl Environ Microbiol. 1998;64:688–94.
PubMed  PubMed Central  Google Scholar 

22.
Lee S, Fuhrman JA. Relationships between biovolume and biomass of naturally derived marine bacterioplankton. Appl Environ Microbiol. 1987;53:1298–303.
CAS  PubMed  PubMed Central  Google Scholar 

23.
Erni-Cassola G, Zadjelovic V, Gibson MI, Christie-Oleza JA. Distribution of plastic polymer types in the marine environment; a meta-analysis. J Hazard Mater. 2019;369:691–8.
CAS  PubMed  Google Scholar 

24.
Dudek KL, Cruz BN, Polidoro B, Neuer S. Microbial colonization of microplastics in the Caribbean Sea. Limnol Oceanogr Lett. 2020;5:5–17.
Google Scholar 

25.
Carpenter EJ, Smith K. Plastics on the Sargasso Sea surface. Science. 1972;175:1240–1.
CAS  PubMed  Google Scholar 

26.
Amaral-Zettler LA, Zettler ER, Mincer TJ. Ecology of the plastisphere. Nat Rev Microbiol. 2020;18:139–51.
CAS  PubMed  Google Scholar 

27.
Patil JS, Anil AC. Biofilm diatom community structure: influence of temporal and substratum variability. Biofouling. 2005;21:189–206.
CAS  PubMed  Google Scholar 

28.
Rummel CD, Jahnke A, Gorokhova E, Kühnel D, Schmitt-Jansen M. Impacts of biofilm formation on the fate and potential effects of microplastic in the aquatic environment. Environ Sci Technol Lett. 2017;4:258–67.
CAS  Google Scholar 

29.
Michels J, Stippkugel A, Lenz M, Wirtz K, Engel A. Rapid aggregation of biofilm-covered microplastics with marine biogenic particles. Proc R Soc B. 2018;285:20181203.
PubMed  Google Scholar 

30.
Lobelle D, Cunliffe M. Early microbial biofilm formation on marine plastic debris. Mar Pollut Bull. 2011;62:197–200.
CAS  PubMed  Google Scholar 

31.
Mueller LN, de Brouwer JF, Almeida JS, Stal LJ, Xavier JB. Analysis of a marine phototrophic biofilm by confocal laser scanning microscopy using the new image quantification software PHLIP. BMC Ecol. 2006;6:1.
PubMed  PubMed Central  Google Scholar 

32.
De Tender CA, Devriese LI, Haegeman A, Maes S, Vangeyte JR, Cattrijsse A, et al. Temporal dynamics of bacterial and fungal colonization on plastic debris in the North Sea. Environ Sci Technol. 2017;51:7350–60.
PubMed  Google Scholar 

33.
Tetu SG, Sarker I, Schrameyer V, Pickford R, Elbourne LD, Moore LR, et al. Plastic leachates impair growth and oxygen production in Prochlorococcus, the ocean’s most abundant photosynthetic bacteria. Commun Biol. 2019;2:1–9.
Google Scholar 

34.
Capolupo M, Sørensen L, Jayasena KDR, Booth AM, Fabbri E. Chemical composition and ecotoxicity of plastic and car tire rubber leachates to aquatic organisms. Water Res. 2020;169:115270.
CAS  PubMed  Google Scholar 

35.
Vosshage AT, Neu TR, Gabel F. Plastic alters biofilm quality as food resource of the freshwater Gastropod Radix balthica. Environ Sci Technol. 2018;52:11387–93.
CAS  PubMed  Google Scholar 

36.
Dussud C, Meistertzheim A, Conan P, Pujo-Pay M, George M, Fabre P, et al. Evidence of niche partitioning among bacteria living on plastics, organic particles and surrounding seawaters. Environ Pollut. 2018;236:807–16.
CAS  PubMed  Google Scholar 

37.
Armitage AR, Gonzalez VL, Fong P. Decoupling of nutrient and grazer impacts on a benthic estuarine diatom assemblage. Estuar Coast Shelf Sci. 2009;84:375–82.
CAS  PubMed  PubMed Central  Google Scholar 

38.
Yokota K, Waterfield H, Hastings C, Davidson E, Kwietniewski E, Wells B. Finding the missing piece of the aquatic plastic pollution puzzle: interaction between primary producers and microplastics. Limnol Oceanogr Lett. 2017;2:91–104.
Google Scholar 

39.
Oberbeckmann S, Kreikemeyer B, Labrenz M. Environmental factors support the formation of specific bacterial assemblages on microplastics. Front Microbiol. 2018;8:2709.
PubMed  PubMed Central  Google Scholar 

40.
Kirstein IV, Wichels A, Krohne G, Gerdts G. Mature biofilm communities on synthetic polymers in seawater-specific or general? Mar Environ Res. 2018;142:147–54.
CAS  PubMed  Google Scholar 

41.
Kettner MT, Rojas‐Jimenez K, Oberbeckmann S, Labrenz M, Grossart HP. Microplastics alter composition of fungal communities in aquatic ecosystems. Environ Microbiol. 2017;19:4447–59.
CAS  PubMed  Google Scholar 

42.
Kettner MT, Oberbeckmann S, Labrenz M, Grossart HP. The eukaryotic life on microplastics in brackish ecosystems. Front Microbiol. 2019;10:538.
PubMed  PubMed Central  Google Scholar 

43.
Bayoudh S, Othmane A, Bettaieb F, Bakhrouf A, Ouada HB, Ponsonnet L. Quantification of the adhesion free energy between bacteria and hydrophobic and hydrophilic substrata. Mater Sci Eng C. 2006;26:300–5.
CAS  Google Scholar 

44.
Bendinger B, Rijnaarts HH, Altendorf K, Zehnder AJ. Physicochemical cell surface and adhesive properties of coryneform bacteria related to the presence and chain length of mycolic acids. Appl Environ Microbiol. 1993;59:3973–7.
CAS  PubMed  PubMed Central  Google Scholar 

45.
Thompson SE, Coates JC. Surface sensing and stress-signalling in Ulva and fouling diatoms–potential targets for antifouling: a review. Biofouling. 2017;33:410–32.
PubMed  Google Scholar 

46.
Araya P, Chamy R, Mota M, Alves M. Biodegradability and toxicity of styrene in the anaerobic digestion process. Biotechnol Lett. 2000;22:1477–81.
CAS  Google Scholar 

47.
Pinto M, Langer TM, Hüffer T, Hofmann T, Herndl GJ. The composition of bacterial communities associated with plastic biofilms differs between different polymers and stages of biofilm succession. PLoS ONE. 2019;14:e0217165.
CAS  PubMed  PubMed Central  Google Scholar 

48.
Datta MS, Sliwerska E, Gore J, Polz MF, Cordero OX. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat Commun. 2016;7:11965.
CAS  PubMed  PubMed Central  Google Scholar 

49.
Zobell CE. The effect of solid surfaces upon bacterial activity. J Bacteriol. 1943;46:39–56.
CAS  PubMed  PubMed Central  Google Scholar 

50.
Karl DM, Björkman KM, Dore JE, Fujieki L, Hebel DV, Houlihan T, et al. Ecological nitrogen-to-phosphorus stoichiometry at station ALOHA. Deep Sea Res Part II: Topical Stud Oceanogr. 2001;48:1529–66.
CAS  Google Scholar 

51.
Steinberg DK, Carlson CA, Bates NR, Johnson RJ, Michaels AF, Knap AH. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res II. 2001;48:1405–47.
CAS  Google Scholar 

52.
Flemming H-C, Wuertz S. Bacteria and Archaea on Earth and their abundance in biofilms. Nat Rev Microbiol. 2019;17:247.
CAS  PubMed  Google Scholar 

53.
Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci. 1998;95:6578–83.
CAS  PubMed  Google Scholar 

54.
Bjørnsen PK. Automatic determination of bacterioplankton biomass by image analysis. Appl Environ Microbiol. 1986;51:1199–204.
PubMed  PubMed Central  Google Scholar 

55.
Bloem J, Veninga M, Shepherd J. Fully automatic determination of soil bacterium numbers, cell volumes, and frequencies of dividing cells by confocal laser scanning microscopy and image analysis. Appl Environ Microbiol. 1995;61:926–36.
CAS  PubMed  PubMed Central  Google Scholar 

56.
Kallmeyer J, Pockalny R, Adhikari RR, Smith DC, D’Hondt S. Global distribution of microbial abundance and biomass in subseafloor sediment. Proc Natl Acad Sci. 2012;109:16213–6.
CAS  PubMed  Google Scholar 

57.
Pernice MC, Forn I, Gomes A, Lara E, Alonso-Sáez L, Arrieta JM, et al. Global abundance of planktonic heterotrophic protists in the deep ocean. ISME J. 2015;9:782–92.
CAS  PubMed  Google Scholar 

58.
Bölter M, Bloem J, Meiners K, Möller R. Enumeration and biovolume determination of microbial cells–a methodological review and recommendations for applications in ecological research. Biol Fertil Soils. 2002;36:249–59.
Google Scholar  More

1.
Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).
ADS  PubMed  CAS  Google Scholar 
2.
Wallace, B. P. et al. Global conservation priorities for marine turtles. PLoS ONE 6, e24510 (2011).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

3.
Davidson, A. D. et al. Drivers and hotspots of extinction risk in marine mammals. Proc. Natl. Acad. Sci. USA 109, 3395–3400 (2012).
ADS  PubMed  CAS  Google Scholar 

4.
Dulvy, N.K., et al. Extinction risk and conservation of the world’s sharks and rays. eLife 3, e00590 (2014).

5.
Slooten, E. & Davies, N. Hector’s dolphin risk assessments: old and new analyses show consistent results. J. R. Soc. NZ 42, 49–60 (2012).
Google Scholar 

6.
Cagnazzi, D., Parra, G. J., Westley, S. & Harrison, P. L. At the heart of the industrial boom: Australian snubfin dolphins in the Capricorn Coast, Queensland, need urgent conservation action. PLoS ONE 8, e56729 (2013).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

7.
Parra, G. J. & Cagnazzi, D. Conservation status of the Australian humpback dolphin (Sousa sahulensis) using the IUCN Red List criteria. Adv. Mar. Biol. 73, 157–192 (2016).
PubMed  Google Scholar 

8.
Turvey, S. T. et al. First human-caused extinction of a cetacean species?. Biol. Lett. 3, 537–540 (2007).
PubMed  PubMed Central  Google Scholar 

9.
Taylor, B. L. et al. Extinction is imminent for Mexico’s endemic porpoise unless fishery bycatch is eliminated. Conserv. Lett. 10, 588–595 (2017).
Google Scholar 

10.
Gormley, A. M. et al. First evidence that marine protected areas can work for marine mammals. J. Appl. Ecol. 49, 474–480 (2012).
Google Scholar 

11.
Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216 (2014).
ADS  PubMed  CAS  Google Scholar 

12.
Hoyt, E. Marine Protected Areas for Whales, Dolphins and Porpoises: A World Handbook for Cetacean Habitat Conservation and Planning 2nd edn. (Earthscan, London, 2011).
Google Scholar 

13.
di Sciara, G. N. et al. Place-based approaches to marine mammal conservation. Aquat. Conserv. 26, 85–100 (2016).
Google Scholar 

14.
Gregr, E. J., Baumgartner, M. F., Laidre, K. L. & Palacios, D. M. Marine mammal habitat models come of age: The emergence of ecological and management relevance. Endanger Species Res. 22, 205–212 (2013).
Google Scholar 

15.
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).
PubMed  PubMed Central  Google Scholar 

16.
Hooker, S. K. et al. Making protected area networks effective for marine top predators. Endanger Species Res. 13, 203–218 (2011).
Google Scholar 

17.
Dryden, J., Grech, A., Moloney, J. & Hamann, M. Rezoning of the Great Barrier Reef World Heritage Area: Does it afford greater protection for marine turtles?. Wildl Res 35, 477–485 (2008).
Google Scholar 

18.
Cleguer, C., Grech, A., Garrigue, C. & Marsh, H. Spatial mismatch between marine protected areas and dugongs in New Caledonia. Biol. Conserv. 184, 154–162 (2015).
Google Scholar 

19.
Oh, B. Z. L., Sequeira, A. M. M., Meekan, M. G., Ruppert, J. L. W. & Meeuwig, J. J. Predicting occurrence of juvenile shark habitat to improve conservation planning. Conserv. Biol. 31, 635–645 (2017).
PubMed  Google Scholar 

20.
Liu, M., Bejder, L., Lin, M., Zhang, P., Dong, L. & Li, S. Determining important habitats of the world’s second largest humpback dolphin population: Implications for place-based conservation and management. Aquat. Conserv. Mar. Freshw. Ecosyst. 1–11 https://doi.org/10.1002/aqc.3253 (2019).

21.
Tardin, R. H. et al. Modelling habitat use by the Guiana dolphin, Sotalia guianensis, in south-eastern Brazil: Effects of environmental and anthropogenic variables, and the adequacy of current management measures. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 775–786 (2020).
Google Scholar 

22.
Worm, B. Marine conservation: How to heal an ocean. Nature 543, 630–631 (2017).
ADS  PubMed  CAS  Google Scholar 

23.
Wood, L. J., Fish, L., Laughren, J. & Pauly, D. Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx 42, 340–351 (2008).
Google Scholar 

24.
Devillers, R. et al. Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection?. Aquat. Conserv. 25, 480–504 (2015).
Google Scholar 

25.
Bottrill, M. C. & Pressey, R. L. The effectiveness and evaluation of conservation planning. Conserv. Lett. 5, 407–420 (2012).
Google Scholar 

26.
Agardy, T. Justified ambivalence about MPA effectiveness. ICES J. Mar. Sci. 75, 1183–1185 (2018).
Google Scholar 

27.
CALM & MPRA. Management Plan for the Ningaloo Marine Park and Muiron Islands Marine Management Area, 2005–2015. Western Australian Government Department of Conservation and Land Management, and Marine Parks and Reserve Authority, Perth, Western Australia (2005). https://www.dpaw.wa.gov.au/images/documents/parks/management-plans/decarchive/ningaloo_mp_01_2005_withmaps.pdf.

28.
UNESCO. United Nations Educational, Scientific and Cultural Organisation. Decisions adopted by the World Heritage Committee at its 35th session, Paris, 7 July 2011. WHC-11/35.COM/20 (2011). https://whc.unesco.org/en/decisions/4278.

29.
Hanf, D. M., Hunt, T. N. & Parra, G. J. Humpback dolphins of Western Australia: a review of current knowledge and recommendations for future management. Adv. Mar. Biol. 73, 193–218 (2016).
PubMed  Google Scholar 

30.
Jefferson, T.A. & Rosenbaum, H.C. Taxonomic revision of the humpback dolphins (Sousa spp.), and description of a new species from Australia. Mar. Mamm. Sci. 30, 1494–1541 (2014).

31.
Parra, G. J., Corkeron, P. J. & Marsh, H. Population sizes, site fidelity and residence patterns of Australian snubfin and Indo-Pacific humpback dolphins: Implications for conservation. Biol. Conserv. 129, 167–180 (2006).
Google Scholar 

32.
Cagnazzi, D. D. B., Harrison, P. L., Ross, G. J. B. & Lynch, P. Abundance and site fidelity of Indo-Pacific humpback dolphins in the Great Sandy Strait, Queensland, Australia. Mar. Mamm. Sci. 27, 255–281 (2011).
Google Scholar 

33.
Palmer, C. et al. Estimates of abundance and apparent survival of coastal dolphins in Port Essington harbour, Northern Territory, Australia. Wildl. Res. 41, 35–45 (2014).
Google Scholar 

34.
Brown, A. M., Bejder, L., Pollock, K. H. & Allen, S. J. Site-specific assessments of the abundance of three inshore dolphin species to inform conservation and management. Front. Mar. Sci. 3, 4. https://doi.org/10.3389/fmars.2016.00004 (2016).
Article  Google Scholar 

35.
Brooks, L., Palmer, C., Griffiths, A. D. & Pollock, K. H. Monitoring variation in small coastal dolphin populations: An example from Darwin, Northern Territory, Australia. Front. Mar. Sci. 4, 94. https://doi.org/10.3389/fmars.2017.00094 (2017).
Article  Google Scholar 

36.
Hunt, T. N. et al. Demographic characteristics of Australian humpback dolphins reveal important habitat toward the southwestern limit of their range. Endanger Species Res. 32, 71–88 (2017).
Google Scholar 

37.
Brown, A. M. et al. Population differentiation and hybridisation of Australian snubfin (Orcaella heinsohni) and Indo-Pacific humpback (Sousa chinensis) dolphins in north-western Australia. PLoS ONE 9, e101427 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

38.
Parra, G. J. Resource partitioning in sympatric delphinids: space use and habitat preferences of Australian snubfin and Indo-Pacific humpback dolphins. J. Anim. Ecol. 75, 862–874 (2006).
PubMed  Google Scholar 

39.
Parra, G., Cagnazzi, D., Perrin, W. & Braulik, G.T. Sousa sahulensis. The IUCN Red List of Threatened Species 2017: e.T82031667A82031671. https://www.iucnredlist.org/details/82031667/0. (2017).

40.
Hunt, T.N., Allen, S.J., Bejder, L. & Parra, G.J. Assortative interactions revealed in a fission-fusion society of Australian humpback dolphins. Behav Ecol 1–14. https://doi.org/10.1093/beheco/arz029 (2019).

41.
Hanf, D.M. Species Distribution Modelling of Western Pilbara Inshore Dolphins. MRes thesis. Murdoch University, Perth, Western Australia (2015).

42.
Allen, S. J., Cagnazzi, D. D., Hodgson, A. J., Loneragan, N. R. & Bejder, L. Tropical inshore dolphins of north-western Australia: Unknown populations in a rapidly changing region. Pac. Conserv. Biol. 18, 56–63 (2012).
Google Scholar 

43.
Bejder, L., Hodgson, A., Loneragan, N. & Allen, S. J. Coastal dolphins in north-western Australia: The need for re-evaluation of species listings and short-comings in the Environmental Impact Assessment process. Pac. Conserv. Biol. 18, 22–25 (2012).
Google Scholar 

44.
Rob, D. & Barnes, P. Whale Shark Management Annual Report: 2016 Whale Shark Season. Progress report for the Department of Parks and Wildlife, Wildlife Management Program No. 57. (2016). Report available on request.

45.
Brown, A., Bejder, .L, Cagnazzi, D., Parra, G.J. & Allen, S.J. The North West Cape, Western Australia: A potential hotspot for Indo-Pacific humpback dolphins Sousa chinensis? Pac Conserv Biol 18, 240–246 (2012).

46.
Raudino, H. C., Hunt, T. N. & Waples, K. Records of Australian humpback dolphins (Sousa sahulensis) from an offshore island group in Western Australia. Mar. Biodivers. Rec 11, 14. https://doi.org/10.1186/s41200-018-0147-0 (2018).
Article  Google Scholar 

47.
Palmer, C., Parra, G. J., Rogers, T. & Woinarski, J. Collation and review of sightings and distribution of three coastal dolphin species in waters of the Northern Territory, Australia. Pac. Conserv. Biol. 20, 116–125 (2014).
Google Scholar 

48.
Parra, G. J., Schick, R. & Corkeron, P. J. Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphins. Ecography 29, 396–406 (2006).
Google Scholar 

49.
Cagnazzi, D. Conservation status of Australian snubfin dolphin, Orcaella heinsohni, and Indo-Pacific humpback dolphin, Sousa chinensis, in the Capricorn Coast, Central Queensland, Australia. PhD thesis. Southern Cross University, Lismore, Australia (2011).

50.
Cagnazzi, D. Review of coastal dolphins in central Queensland, particularly Port Curtis and Port Alma regions. Report produced for the Ecosystem Research and Monitoring Program Advisory Panel as part of Gladstone Ports Corporation’s Ecosystem Research and Monitoring Progra. Gladstone Ports Corporation, Queensland, Australia (2013).

51.
Beasley, I. et al. Observations on Australian humpback dolphins (Sousa sahulensis) in waters of the Pacific Islands and New Guinea. Adv. Mar. Biol. 73, 219–271 (2016).
PubMed  Google Scholar 

52.
Corkeron, P. J., Morissette, N. M., Porter, L. & Marsh, H. Distribution and status of hump-backed dolphins, Sousa chinensis, Australian waters. Asian Mar. Biol. 14, 49–59 (1997).
Google Scholar 

53.
Parra, G. J., Corkeron, P. J. & Marsh, H. The Indo-Pacific humpback dolphin, Sousa chinensis (Osbeck, 1765), in Australian waters: A summary of current knowledge. Aquat. Mamm. 30, 197–206 (2004).
Google Scholar 

54.
Jefferson, T. A. & Curry, B. E. Humpback dolphins: A brief introduction to the genus Sousa. Adv. Mar. Biol. 72, 1–16 (2015).
PubMed  Google Scholar 

55.
Koper, R. P., Karczmarski, L., du Preez, D. & Plön, S. Sixteen years later: Occurrence, group size, and habitat use of humpback dolphins (Sousa plumbea) in Algoa Bay, South Africa. Mar. Mamm. Sci. 32, 490–507 (2016).
Google Scholar 

56.
Palmer, C. Conservation biology of dolphins in coastal waters of the Northern Territory, Australia. PhD thesis. Charles Darwin University, Northern Territory, Australia (2014).

57.
Heithaus, M. R. & Dill, L. M. Food availability and tiger shark predation risk influence bottlenose dolphin habitat use. Ecology 83, 480–491 (2002).
Google Scholar 

58.
Benoit-Bird, K. J. et al. Prey patch patterns predict habitat use by top marine predators with diverse foraging strategies. PLoS ONE 8, e53348 (2013).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

59.
Pirotta, E. et al. Predicting the effects of human developments on individual dolphins to understand potential long-term population consequences. Proc. R Soc. B 282, 20151209 (2015).
Google Scholar 

60.
Parra, G. J. & Jedensjö, M. Stomach contents of Australian snubfin (Orcaella heinsohni) and Indo-Pacific humpback dolphins (Sousa chinensis). Mar. Mamm. Sci. 30, 1184–1198 (2014).
Google Scholar 

61.
Downie, R. A., Babcock, R. C., Thomson, D. P. & Vanderklift, M. A. Density of herbivorous fish and intensity of herbivory are influenced by proximity to coral reefs. Mar. Ecol. Prog. Ser. 482, 217–225 (2013).
ADS  Google Scholar 

62.
Fitzpatrick, B. M., Harvey, E. S., Langlois, T. J., Babcock, R. & Twiggs, E. Effects of fishing on fish assemblages at the reefscape scale. Mar. Ecol. Prog. Ser. 524, 241–253 (2015).
ADS  Google Scholar 

63.
Smith, F., Allen, S. J., Bejder, L. & Brown, A. M. Shark bite injuries on three inshore dolphin species in tropical northwestern Australia. Mar. Mamm. Sci. 34, 87–99. https://doi.org/10.1111/mms.12435 (2017).
Article  Google Scholar 

64.
Best, B. D. et al. Online cetacean habitat modeling system for the US east coast and Gulf of Mexico. Endanger Species Res. 18, 1–15 (2012).
Google Scholar 

65.
Bancroft, K. & Sheridan, M. The major marine habitats of the Ningaloo Marine Park and the proposed southern extension. Marine Conservation Branch, Department of Conservation and Land Management, Perth, Western Australia. MMS/PI/NMP&NSE- 26/2000 (2000).

66.
Zanardo, N., Parra, G. J., Passadore, C. & Möller, L. M. Ensemble modelling of southern Australian bottlenose dolphin Tursiops sp. distribution reveals important habitats and their potential ecological function. Mar. Ecol. Prog. Ser. 569, 253–266 (2017).

67.
Palacios, D. M., Baumgartner, M. F., Laidre, K. L. & Gregr, E. J. Beyond correlation: integrating environmentally and behaviourally mediated processes in models of marine mammal distributions. Endanger Species Res. 22, 191–203 (2013).
Google Scholar 

68.
Torres, L. G., Read, A. J. & Halpin, P. Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?. Ecol. Appl. 18, 1702–1717 (2008).
PubMed  Google Scholar 

69.
Hastie, G. D., Wilson, B., Wilson, L. J., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
Google Scholar 

70.
Hunt, T.N. Demography, habitat use and social structure of Australian humpback dolphins (Sousa sahulensis) around the North West Cape, Western Australia: Implications for conservation and management. PhD thesis. College of Science and Engineering, Flinders University, Adelaide, Australia (2018).

71.
Mitchell, J. D. et al. Quantifying shark depredation in a recreational fishery in the Ningaloo Marine Park and Exmouth Gulf, Western Australia. Mar. Ecol. Prog. Ser. 587, 141–157 (2018).
ADS  Google Scholar 

72.
Smallwood, C. B., Beckley, L. E., Moore, S. A. & Kobryn, H. T. Assessing patterns of recreational use in large marine parks: A case study from Ningaloo Marine Park, Australia. Ocean Coast Manag 54, 330–340 (2011).
Google Scholar 

73.
Great Sandy Marine Park Zoning Plan. Marine Parks (Great Sandy) Zoning Plan 2017. Marine Parks Act 2004. Queensland Government. https://www.legislation.qld.gov.au/view/pdf/inforce/current/sl-2017-0155 (2017).

74.
Smith, H., Frère, C., Kobryn, H. & Bejder, L. Dolphin sociality, distribution and calving as important behavioural patterns informing management. Anim. Conserv. 19, 462–471 (2016).
Google Scholar 

75.
Sala, E. & Giakoumi, S. No-take marine reserves are the most effective protected areas in the ocean. ICES J. Mar. Sci. 75, 1166–1168 (2018).
Google Scholar 

76.
Davies, H. N. et al. Integrating climate change resilience features into the incremental refinement of an existing marine park. PLoS ONE 11, e0161094 (2016).
PubMed  PubMed Central  Google Scholar 

77.
Cassata, L. & Collins, L. B. Coral reef communities, habitats, and substrates in and near sanctuary zones of Ningaloo Marine Park. J. Coast Res. 24, 139–151 (2008).
Google Scholar 

78.
Mann, J. Behavioral sampling methods for cetaceans: a review and critique. Mar. Mamm. Sci. 15, 102–122 (1999).
Google Scholar 

79.
Connor, R. C., Mann, J., Tyack, P. L. & Whitehead, H. Social evolution in toothed whales. Trends Ecol. Evol. 13, 228–232 (1998).
PubMed  CAS  Google Scholar 

80.
Redfern, J. et al. Techniques for cetacean–habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295 (2006).
ADS  Google Scholar 

81.
Kobryn, H. T., Wouters, K., Beckley, L. E. & Heege, T. Ningaloo reef: shallow marine habitats mapped using a hyperspectral sensor. PLoS ONE 8, e70105 (2013).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

82.
Geoscience Australia. Bathymetry Grids of Carnarvon Shelf. https://www.ga.gov.au (2008).

83.
Geoscience Australia. Australian Bathymetry and Topography Grid, June 2009. https://www.ga.gov.au (2009).

84.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org (2015).

85.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 

86.
Naimi, B., Hamm, N., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography 37, 191–203 (2014).
Google Scholar 

87.
MacLeod, C.D. An Introduction to Using GIS in Marine Mammal Research. Course Manual. Adelaide, South Australia, 15–19 July 2013 Fremantle, Western Australia, 22–26 July 2013 (2013).

88.
Gu, W. & Swihart, R. K. Absent or undetected? Effects of non-detection of species occurrence on wildlife–habitat models. Biol. Conserv. 116, 195–203 (2004).
Google Scholar 

89.
Barbet-Massin, M., Thuiller, W. & Jiguet, F. How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models?. Ecography 33, 878–886 (2010).
Google Scholar 

90.
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
PubMed  Google Scholar 

91.
Gottschalk, T. K., Aue, B., Hotes, S. & Ekschmitt, K. Influence of grain size on species-habitat models. Ecol. Model. 222, 3403–3412 (2011).
Google Scholar 

92.
Hanberry, B. B. Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models. Ecol. Inform. 15, 8–13 (2013).
Google Scholar 

93.
Passadore, C., Möller, L. M., Diaz-Aguirre, F. & Parra, G. J. Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Sci. Rep. 8, 1–14 (2018).
CAS  Google Scholar 

94.
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186 (2000).
Google Scholar 

95.
Elith, J. & Graham, C. H. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32, 66–77 (2009).
Google Scholar 

96.
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. & Thuiller, W. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59–69 (2009).
Google Scholar 

97.
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol Evol 22, 42–47 (2006).
PubMed  Google Scholar 

98.
Franklin, J. Mapping species distributions: spatial inference and prediction (Cambridge University Press, Cambridge, 2010).
Google Scholar 

99.
Grenouillet, G., Buisson, L., Casajus, N. & Lek, S. Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 34, 9–17 (2011).
Google Scholar 

100.
Sun, Y. Crested ibis in a dynamic and increasingly human-dominated landscape. PhD Thesis. Faculty of Geo-Information Science and Earth Observation, Univeristy of Twente, The Netherlands (2016).

101.
Oppel, S. et al. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biol. Conserv. 156, 94–104 (2012).
Google Scholar 

102.
Gårdmark, A. et al. Biological ensemble modeling to evaluate potential futures of living marine resources. Ecol Appl 23, 742–754 (2013).
PubMed  Google Scholar 

103.
Pikesley, S. K. et al. Modelling the niche for a marine vertebrate: A case study incorporating behavioural plasticity, proximate threats and climate change. Ecography 38, 001–010 (2015).
Google Scholar 

104.
Abrahms, B., H. et al. Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. Divers. Distrib. 25, 1182–1193 (2019).

105.
Pérez-Jorge, S. et al. Can static habitat protection encompass critical areas for highly mobile marine top predators? Insights from coastal East Africa. PLoS ONE 10, e0133265 (2015).
PubMed  PubMed Central  Google Scholar 

106.
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).
Google Scholar 

107.
Guisan, A., Edwards, T. C. Jr. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model. 157, 89–100 (2002).
Google Scholar 

108.
Friedman, J., Hastie, T. & Tibshirani, R. Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28, 337–407 (2000).
MATH  Google Scholar 

109.
De’ath, G. & Fabricius, K. E. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192 (2000).
Google Scholar 

110.
Hastie, T., Tibshirani, R. & Buja, A. Flexible discriminant analysis by optimal scoring. J. Am. Stat. Assoc. 89, 1255–1270 (1994).
MathSciNet  MATH  Google Scholar 

111.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
MATH  Google Scholar 

112.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 

113.
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 

114.
Becker, E.A., et al. Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees. Ecol Evol. https://doi.org/10.1002/ece3.6316 (2020)

115.
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
Google Scholar 

116.
Peterson, A.T., et al. Ecological Niches and Geographic Distributions (MPB-49). (Princeton University Press, 2011).

117.
Hood, G. PopTools version 3.2. 5. https://www.poptools.org. (2011).

118.
Manly, B. F. Randomization, bootstrap and Monte Carlo methods in biology 3rd edn. (Chapman & Hall, London, 2007).
Google Scholar  More

1.
Liu, X. et al. Curr. Biol. 29, 499–505.e4 (2019).
CAS  Article  Google Scholar 
2.
Sardain, A. et al. Nat. Sustain. 2, 274–282 (2019).
Article  Google Scholar 

3.
Review of Maritime Transport 2019 (United Nations, 2019).

4.
Leigh, E. G. et al. Biol. Rev. 89, 148–172 (2014).
Article  Google Scholar 

5.
Seebens, H. et al. Proc. Natl Acad. Sci. USA 113, 5646–5651 (2016).
CAS  Article  Google Scholar 

6.
Galil, B. et al. Manag. Biol. Invasion 8, 141–152 (2017).
Article  Google Scholar 

7.
Spanier, E. & Galil, B. S. Endeavour 15, 102–106 (1991).
Article  Google Scholar 

8.
Ruiz, G. M. et al. Smithson. Contrib. Mar. Sci. 38, 73–93 (2009).
Google Scholar 

9.
Muirhead, J. R. et al. Divers. Distrib. 21, 75–87 (2015).
Article  Google Scholar 

10.
Galil, B. S. et al. Biol. Invasion 17, 973–976 (2015).
Article  Google Scholar 

11.
Azzurro, E. et al. Biol. Invasion 18, 2761–2772 (2016).
Article  Google Scholar 

12.
Sharpe, D. et al. Ecology 98, 412–424 (2017).
CAS  Article  Google Scholar 

13.
Salgado, J. et al. Sci. Total Environ. 729, 138444 (2020).
CAS  Article  Google Scholar 

14.
Informe sobre la Aplicación y Eficiencia de Medidas de Mitigación para el Estudio de Impacto Ambiental del Proyecto “Ampliación del Canal de Panamá -Tercer Juego de Esclusas” (Panama Canal Authority, accessed 23 April 2020); https://go.nature.com/2FvcWMF

15.
Miller, A. W. & Ruiz, G. M. Nat. Clim. Change 4, 413–416 (2014).
Article  Google Scholar 

16.
Cramer, W. et al. Nat. Clim. Change 8, 972–980 (2018).
Article  Google Scholar 

17.
Ballew, N. G. et al. Sci. Rep. 6, 32169 (2016).
CAS  Article  Google Scholar 

18.
Savva, I. et al. J. Fish. Biol. 97, 148–162 (2020).
Article  Google Scholar 

19.
Shine, C. EPPO Bull. 37, 103–113 (2007).
Article  Google Scholar 

20.
Ballast Water Management (IMO, accessed 20 May 2020); https://go.nature.com/2DUkI2t

21.
GloFouling (IMO, accessed 20 May 2020); https://www.glofouling.imo.org/

22.
Jouffray, J.-B. et al. One Earth 2, 43–54 (2020).
Article  Google Scholar 

23.
Peleg, O. & Guy-Haim, T. Nature 575, 287 (2019).
CAS  Article  Google Scholar 

24.
Balasingham, K. D. et al. Mol. Ecol. 27, 112–127 (2018).
CAS  Article  Google Scholar 

25.
Martignac, F. et al. Fish. Fish. 16, 486–510 (2014).
Article  Google Scholar 

26.
Putland, R. L. & Mensinger, A. F. Rev. Fish. Biol. Fish. 29, 789–807 (2019).
Article  Google Scholar 

27.
Dennis, C. E. III et al. Biol. Invasion 21, 2837–2855 (2019).
Article  Google Scholar 

28.
Sepulveda, A. J. et al. Trends Ecol. Evol. 35, 668–678 (2020).
Article  Google Scholar 

29.
van Rijn, I. et al. Can. J. Fish. Aquat. Sci. 77, 752–761 (2020).
Article  Google Scholar  More