Ecology

1.Vickery, J. A. et al. The decline of Afro-Palaearctic migrants and an assessment of potential causes. Ibis (Lond. 1759) 156, 1–22 (2014).
Google Scholar 
2.Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).ADS 
CAS 
PubMed 

Google Scholar 
3.Knight, S. M. et al. Constructing and evaluating a continent-wide migratory songbird network across the annual cycle. Ecol. Monogr. 88, 445–460 (2018).
Google Scholar 
4.Alves, J. A. et al. Costs, benefits, and fitness consequences of different migratory strategies. Ecology 94, 11–17 (2013).ADS 
PubMed 

Google Scholar 
5.van Wijk, R. E., Schaub, M. & Bauer, S. Dependencies in the timing of activities weaken over the annual cycle in a long-distance migratory bird. Behav. Ecol. Sociobiol. 71, 71–73 (2017).
Google Scholar 
6.Donald, P. F., Sanderson, F. J., Burfield, I. J. & van Bommel, F. P. J. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agric. Ecosyst. Environ. 116, 189–196 (2006).
Google Scholar 
7.Bowler, D. E., Heldbjerg, H., Fox, A. D., Jong, M. & Böhning-Gaese, K. Long-term declines of European insectivorous bird populations and potential causes. Conserv. Biol. 0, 1–11 (2019).
Google Scholar 
8.Harrison, X. A., Blount, J. D., Inger, R., Norris, D. R. & Bearhop, S. Carry-over effects as drivers of fitness differences in animals. J. Anim. Ecol. 80, 4–18 (2010).PubMed 

Google Scholar 
9.Emmenegger, T., Hahn, S. & Bauer, S. Individual migration timing of common nightingales is tuned with vegetation and prey phenology at breeding sites. BMC Ecol. 14, 1–8 (2014).
Google Scholar 
10.Morrison, C. A., Alves, J. A., Gunnarsson, T. G., Þórisson, B. & Gill, J. A. Why do earlier-arriving migratory birds have better breeding success?. Ecol. Evol. 9, 8856–8864 (2019).PubMed 
PubMed Central 

Google Scholar 
11.Cooper, N. W., Murphy, M. T., Redmond, L. J. & Dolan, A. C. Reproductive correlates of spring arrival date in the Eastern Kingbird Tyrannus tyrannus. J. Ornithol. 152, 143–152 (2011).
Google Scholar 
12.Nilsson, C., Klaassen, R. H. G. & Alerstam, T. Differences in speed and duration of bird migration between spring and autumn. Am. Nat. 181, 837–845 (2013).PubMed 

Google Scholar 
13.Gow, E. A. et al. Effects of spring migration distance on tree swallow reproductive success within and among flyways. Front. Ecol. Evol. 7, 380 (2019).ADS 

Google Scholar 
14.Saino, N. et al. Sex-dependent carry-over effects on timing of reproduction and fecundity of a migratory bird. J. Anim. Ecol. 86, 239–249 (2017).PubMed 

Google Scholar 
15.Briedis, M., Hahn, S. & Adamík, P. Cold spell en route delays spring arrival and decreases apparent survival in a long-distance migratory songbird. BMC Ecol. 17, 1–8 (2017).
Google Scholar 
16.McKinnon, E. A., Macdonald, C. M., Gilchrist, H. G. & Love, O. P. Spring and fall migration phenology of an arctic-breeding passerine. J. Ornithol. 157, 681–693 (2016).
Google Scholar 
17.Woodworth, B. K. et al. Differential migration and the link between winter latitude, timing of migration, and breeding in a songbird. Oecologia 181, 413–422 (2016).ADS 
PubMed 

Google Scholar 
18.Saino, N. et al. Ecological conditions during winter predict arrival date at the breeding quarters in a trans-Saharan migratory bird. Ecol. Lett. 7, 21–25 (2004).
Google Scholar 
19.Norris, D. R., Marra, P. P., Kyser, T. K., Sherry, T. W. & Ratcliffe, L. M. Tropical winter habitat limits reproductive success on the temperate breeding grounds in a migratory bird. Proc. R. Soc. B Biol. Sci. 271, 59–64 (2004).
Google Scholar 
20.Bearhop, S., Hilton, G. M., Votier, S. C. & Waldron, S. Stable isotope ratios indicate that body condition in migrating passerines is influenced by winter habitat. Proc. R. Soc. London B Biol. Sci. 271, S215–S218 (2004).
Google Scholar 
21.Ockendon, N., Leech, D. & Pearce-Higgins, J. W. Climatic effects on breeding grounds are more important drivers of breeding phenology in migrant birds than carry- over effects from wintering grounds. Biol. Lett. 9 (2013).22.Arbeiter, S., Schulze, M., Tamm, P. & Hahn, S. Strong cascading effect of weather conditions on prey availability and annual breeding performance in European bee-eaters Merops apiaster. J. Ornithol. 157, 155–163 (2016).
Google Scholar 
23.Harrison, X. A. et al. Environmental conditions during breeding modify the strength of mass-dependent carry-over effects in a migratory bird. PLoS ONE 8, e77783 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Swift, R. J., Rodewald, A. D., Johnson, J. A., Andres, B. A. & Senner, N. R. Seasonal survival and reversible state effects in a long-distance migratory shorebird. J. Anim. Ecol. 89, 2043–2055 (2020).PubMed 

Google Scholar 
25.Brust, V., Bastian, H. V., Bastian, A. & Schmoll, T. Determinants of between-year burrow re-occupation in a colony of the European bee-eater Merops apiaster. Ecol. Evol. 5, 3223–3230 (2015).PubMed 
PubMed Central 

Google Scholar 
26.Lessells, C. M. & Krebs, J. R. Age and breeding performance of European bee-eaters. Auk 106, 375–382 (1989).
Google Scholar 
27.Pârâu, L. G. et al. Dynamics in numbers of group-roosting individuals in relation to pair-sleeping occurrence and onset of egg-laying in European Bee-eaters Merops apiaster. J. Ornithol. 158, 1119–1122 (2017).
Google Scholar 
28.Hoi, H., Darolová, A., Krištofík, J. & Hoi, C. The effect of the ectoparasite Carnus hemapterus on immune defence, condition, and health of nestling European Bee-eaters. J. Ornithol. 159, 291–302 (2018).
Google Scholar 
29.Kapun, M., Darolová, A., Krištofik, J., Mahr, K. & Hoi, H. Distinct colour morphs in nestling European Bee-eaters Merops apiaster: Is there an adaptive value?. J. Ornithol. 152, 1001–1005 (2011).
Google Scholar 
30.Lessells, C. M. & Avery, M. I. Hatching asynchrony in european bee-eaters merops apiaster. J. Anim. Ecol. 58, 815–835 (1989).
Google Scholar 
31.Arbeiter, S., Schulze, M., Todte, I. & Hahn, S. Das Zugverhalten und die Ausbreitung von in Sachsen-Anhalt brütenden Bienenfressern (Merops apiaster). Berichte der Vogelwarte Hiddensee 21, 33–40 (2012).
Google Scholar 
32.Dhanjal-Adams, K. L. et al. Spatiotemporal group dynamics in a long-distance migratory bird. Curr. Biol. 28, 2824-2830.e3 (2018).CAS 
PubMed 

Google Scholar 
33.Hahn, S. et al. Range-wide migration corridors and non-breeding areas of a northward expanding Afro-Palaearctic migrant, the European Bee-eater Merops apiaster. Ibis (Lond. 1859) 162, 345–355 (2019).
Google Scholar 
34.Fry, C. H. The bee-eaters. (T & A D Polyser Ltd, 1984).35.Ramos, R. et al. Population genetic structure and long-distance dispersal of a recently expanding migratory bird. Mol. Phylogenet. Evol. 99, 194–203 (2016).PubMed 

Google Scholar 
36.Jacobsen, L. B. et al. Annual spatiotemporal migration schedules in three larger insectivorous birds: European nightjar, common swift and common cuckoo. Anim. Biotelem1 5, 1–11 (2017).
Google Scholar 
37.Åkesson, S., Klaassen, R., Holmgren, J., Fox, J. W. & Hedenström, A. Migration routes and strategies in a highly aerial migrant, the common Swift Apus apus, revealed by light-level. Geolocators. PLoS One 7, e41195 (2012).ADS 
PubMed 

Google Scholar 
38.Carneiro, C., Gunnarsson, T. G. & Alves, J. A. Faster migration in autumn than in spring: seasonal migration patterns and non-breeding distribution of Icelandic Whimbrels Numenius phaeopus islandicus. J. Avian Biol. 50 (2019).39.Sapir, N. et al. Migration by soaring or flapping: numerical atmospheric simulations reveal that turbulence kinetic energy dictates bee-eater flight mode. Proc. R. Soc. B Biol. Sci. 278, 3380–3386 (2011).
Google Scholar 
40.Lemke, H. W. et al. Annual cycle and migration strategies of a Trans-Saharan migratory songbird: a geolocator study in the great reed warbler. PLoS ONE 8, e79209 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Briedis, M. et al. A full annual perspective on sex-biased migration timing in long-distance migratory birds. Proc. R. Soc. B Biol. Sci. 286, 20182821 (2019).
Google Scholar 
42.Fransson, T. Timing and speed of migration in North and West European populations of Sylvia warblers. J. Avian Biol. 26, 39–48 (1995).
Google Scholar 
43.Briedis, M., Hahn, S., Krist, M. & Adamík, P. Finish with a sprint: evidence for time-selected last leg of migration in a long-distance migratory songbird. Ecol. Evol. 8, 6899–6908 (2018).PubMed 
PubMed Central 

Google Scholar 
44.Alerstam, T. Strategies for the transition to breeding in time-selected bird migration. Ardea 94, 347–357 (2006).
Google Scholar 
45.Arizaga, J., Willemoes, M., Unamuno, E., Unamuno, J. M. & Thorup, K. Following year-round movements in Barn Swallows using geolocators: could breeding pairs remain together during the winter?. Bird Study 62, 141–145 (2015).
Google Scholar 
46.Tøttrup, A. P. et al. Drought in Africa caused delayed arrival of European songbirds. Science 338, 1307 (2012).ADS 
PubMed 

Google Scholar 
47.Smith, R. J. & Moore, F. R. Arrival timing and seasonal reproductive performance in a long-distance migratory landbird. Behav. Ecol. Sociobiol. 57, 231–239 (2005).
Google Scholar 
48.IPMA. Climate bulletin, June 2017, Portugal. http://www.ipma.pt/resources.www/docs/im.publicacoes/edicoes.online/20170719/bXUzZOgrqXmTjnUVRtro/cli_20170601_20170630_pcl_mm_co_pt.pdf (2017).49.Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘cold-blooded’ animals against climate warming. Proc. Natl. Acad. Sci. U.S.A. 106, 3835–3840 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Cunningham, S. J., Martin, R. O., Hojem, C. L. & Hockey, P. A. R. Temperatures in excess of critical thresholds threaten nestling growth and survival in a rapidly-warming Arid Savanna: a study of common fiscals. PLoS ONE 8 (2013).51.Cruz-Mcdonnell, K. K. & Wolf, B. O. Rapid warming and drought negatively impact population size and reproductive dynamics of an avian predator in the arid southwest. Glob. Chang. Biol. 22, 237–253 (2016).ADS 
PubMed 

Google Scholar 
52.Shukla, P. R. et al. Technical summary. IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (2019).53.Persson, C. Age structure, sex ratios and survival rates in a south Swedish Sand martin (Riparia riparia) population, 1964 to 1984. J. Zool. 1, 639–670 (1987).
Google Scholar 
54.Costa, J. S., Rocha, A. D., Correia, R. A. & Alves, J. A. Developing and validating a nestling photographic aging guide for cavity-nesting birds: an example with the European Bee-eater (Merops apiaster). Avian Res. 11, 1–8 (2020).
Google Scholar 
55.Lisovski, S., Wotherspoon, S. & Sumner, M. TwGeos: Basic data processing for light-level geolocation archival tags. R package version 0.1.2. (2016). 56.Lisovski, S. et al. Geolocation by light: accuracy and precision affected by environmental factors. Methods Ecol. Evol. 3, 603–612 (2012).
Google Scholar 
57.Wotherspoon, S., Sumner, M. & Lisovski, S. R package SGAT: solar/satellite geolocation for animal tracking (2016).58.Lisovski, S. et al. Light-level geolocator analyses: a user’s guide. J. Anim. Ecol. 89, 221–236 (2019).PubMed 

Google Scholar 
59.Lisovski, S. & Hahn, S. GeoLight—processing and analysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
Google Scholar 
60.Mazerolle, M. J. AICcmodavg: model selection and multimodel inference based on (Q)AIC(c). R package version 2.2–2. (2019).61.Team, R. C. R: a language and environment for statistical computing. (2017). More

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1.Seviour RJ, Kragelund C, Kong Y, Eales K, Nielsen JL, Nielsen PH. Ecophysiology of the Actinobacteria in activated sludge systems. Antonie Van Leeuw J Microb. 2008;94:21–33.
Google Scholar 
2.Jiang X-T, Ye L, Ju F, Wang Y-L, Zhang T. Toward an intensive longitudinal understanding of activated sludge bacterial assembly and dynamics. Environ Sci Technol. 2018;52:8224–32.CAS 
PubMed 

Google Scholar 
3.Fiałkowska E, Pajdak-Stós A. The role of Lecane rotifers in activated sludge bulking control. Water Res. 2008;42:2483–90.PubMed 

Google Scholar 
4.Madoni P. Protozoa in wastewater treatment processes: a minireview. Ital J Zool. 2011;78:3–11.
Google Scholar 
5.Ye L, Mei R, Liu W-T, Ren H, Zhang X-X. Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes. Microbiome. 2020;8:16.PubMed 
PubMed Central 

Google Scholar 
6.Peces M, Astals S, Jensen P, Clarke W. Deterministic mechanisms define the long-term anaerobic digestion microbiome and its functionality regardless of the initial microbial community. Water Res. 2018;141:366–76.CAS 
PubMed 

Google Scholar 
7.Wu L, Ning D, Zhang B, Li Y, Zhang P, Shan X, et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat Microbiol. 2019;4:1183–95.CAS 
PubMed 

Google Scholar 
8.Cox HH, Deshusses MA. Biomass control in waste air biotrickling filters by protozoan predation. Biotechnol Bioeng. 1999;62:216–24.CAS 
PubMed 

Google Scholar 
9.Madoni P. A sludge biotic index (SBI) for the evaluation of the biological performance of activated sludge plants based on the microfauna analysis. Water Res. 1994;28:67–75.CAS 

Google Scholar 
10.Ratsak C, Maarsen K, Kooijman S. Effects of protozoa on carbon mineralization in activated sludge. Water Res. 1996;30:1–12.CAS 

Google Scholar 
11.Pogue AJ, Gilbride KA. Impact of protozoan grazing on nitrification and the ammonia- and nitrite-oxidizing bacterial communities in activated sludge. Can J Microbiol. 2007;53:559–71.CAS 
PubMed 

Google Scholar 
12.Esteban G, Tellez C, Bautista LM. Dynamics of ciliated protozoa communities in activated-sludge process. Water Res. 1991;25:967–72.
Google Scholar 
13.Madoni P, Davoli D, Chierici E. Comparative analysis of the activated sludge microfauna in several sewage treatment works. Water Res. 1993;27:1485–91.CAS 

Google Scholar 
14.Otto S, Harms H, Wick LY. Effects of predation and dispersal on bacterial abundance and contaminant biodegradation. FEMS Microbiol Ecol. 2017;93:fiw241.PubMed 

Google Scholar 
15.Peralta-Maraver I, Reiss J, Robertson AL. Interplay of hydrology, community ecology and pollutant attenuation in the hyporheic zone. Sci Total Environ. 2018;610:267–75.PubMed 

Google Scholar 
16.Yang JW, Wu W, Chung C-C, Chiang K-P, Gong G-C, Hsieh C-H. Predator and prey biodiversity relationship and its consequences on marine ecosystem functioning—interplay between nanoflagellates and bacterioplankton. ISME J. 2018;12:1532–42.PubMed 
PubMed Central 

Google Scholar 
17.Seiler C, van Velzen E, Neu TR, Gaedke U, Berendonk TU, Weitere M. Grazing resistance of bacterial biofilms: a matter of predators’ feeding trait. FEMS Microbiol Ecol. 2017;93:fix112.
Google Scholar 
18.Burian A, Nielsen JM, Winder M. Food quantity-quality interactions and their impact on consumer behavior and trophic transfer. Ecol Monogr. 2020;90:e01395.
Google Scholar 
19.Schmitz OJ. Effects of predator functional diversity on grassland ecosystem function. Ecology. 2009;90:2339–45.PubMed 

Google Scholar 
20.Estes JA, Terborgh J, Brashares JS, Power ME, Berger J, Bond WJ, et al. Trophic downgrading of planet Earth. Science. 2011;333:301–6.CAS 
PubMed 

Google Scholar 
21.Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, et al. Biodiversity loss and its impact on humanity. Nature. 2012;486:59–67.CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB, et al. High plant diversity is needed to maintain ecosystem services. Nature. 2011;477:199–202.CAS 
PubMed 

Google Scholar 
23.Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.McCann KS. The diversity–stability debate. Nature. 2000;405:228.CAS 
PubMed 

Google Scholar 
25.Pennekamp F, Pontarp M, Tabi A, Altermatt F, Alther R, Choffat Y, et al. Biodiversity increases and decreases ecosystem stability. Nature. 2018;563:109–12.CAS 
PubMed 

Google Scholar 
26.Saikaly PE, Oerther DB. Diversity of dominant bacterial taxa in activated sludge promotes functional resistance following toxic shock loading. Microb Ecol. 2011;61:557–67.CAS 
PubMed 

Google Scholar 
27.Worm B, Lotze HK, Hillebrand H, Sommer U. Consumer versus resource control of species diversity and ecosystem functioning. Nature. 2002;417:848–51.CAS 
PubMed 

Google Scholar 
28.Gauzens B, Legendre S, Lazzaro X, Lacroix G. Intermediate predation pressure leads to maximal complexity in food webs. Oikos. 2016;125:595–603.
Google Scholar 
29.Chase JM, Biro EG, Ryberg WA, Smith KG. Predators temper the relative importance of stochastic processes in the assembly of prey metacommunities. Ecol Lett. 2009;12:1210–8.PubMed 

Google Scholar 
30.Paine RT. Food web complexity and species diversity. Am Nat. 1966;100:65–75.
Google Scholar 
31.Gliwicz ZM, Wursbaugh WA, Szymanska E. Absence of predation eliminates coexistence: experience from the fish–zooplankton interface. Fifty years after the “Homage to Santa Rosalia”: old and new paradigms on biodiversity in aquatic ecosystems. Springer; 2010. p. 103–17.32.Terborgh JW. Toward a trophic theory of species diversity. Proc Natl Acad Sci USA. 2015;112:11415–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Kondoh M. Unifying the relationships of species richness to productivity and disturbance. Proc R Soc B-Biol Sci. 2001;268:269–71.CAS 

Google Scholar 
34.Hutchinson GE. The paradox of the plankton. Am Nat. 1961;95:137–45.
Google Scholar 
35.Al-Shahwani S, Horan N. The use of protozoa to indicate changes in the performance of activated sludge plants. Water Res. 1991;25:633–8.CAS 

Google Scholar 
36.Torsvik V, Øvreås L, Thingstad TF. Prokaryotic diversity-magnitude, dynamics, and controlling factors. Science. 2002;296:1064–6.CAS 
PubMed 

Google Scholar 
37.Papadimitriou C, Papatheodoulou A, Takavakoglou V, Zdragas A, Samaras P, Sakellaropoulos G, et al. Investigation of protozoa as indicators of wastewater treatment efficiency in constructed wetlands. Desalination. 2010;250:378–82.CAS 

Google Scholar 
38.Rossberg AG. Food webs and biodiversity: foundations, models, data. John Wiley & Sons; 2013.39.Vage S, Bratbak G, Egge J, Heldal M, Larsen A, Norland S, et al. Simple models combining competition, defence and resource availability have broad implications in pelagic microbial food webs. Ecol Lett. 2018;21:1440–52.PubMed 

Google Scholar 
40.Landry M, Hassett R. Estimating the grazing impact of marine micro-zooplankton. Mar Biol. 1982;67:283–8.
Google Scholar 
41.Dolan J, Gallegos C, Moigis A. Dilution effects on microzooplankton in dilution grazing experiments. Mar Ecol Prog Ser. 2000;200:127–39.CAS 

Google Scholar 
42.Dottorini G, Michaelsen TY, Kucheryavskiy S, Andersen KS, Kristensen JM, Peces M, et al. Mass-immigration determines the assembly of activated sludge microbial communities. Proc Natl Acad Sci USA; 2021;118:e2021589118.CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Stevens-Garmon J, Drewes JE, Khan SJ, McDonald JA, Dickenson ERV. Sorption of emerging trace organic compounds onto wastewater sludge solids. Water Res. 2011;45:3417–26.CAS 
PubMed 

Google Scholar 
44.Gasol JM, Morán XAG. Flow cytometric determination of microbial abundances and its use to obtain indices of community structure and relative activity. Hydrocarbon and lipid microbiology protocols. Springer; 2015. p. 159–87.45.Ram AP, Chaibi-Slouma S, Keshri J, Colombet J, Sime-Ngando T. Functional responses of bacterioplankton diversity and metabolism to experimental bottom-up and top-down forcings. Microb Ecol. 2016;72:347–58.
Google Scholar 
46.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.CAS 
PubMed 

Google Scholar 
47.Hugerth LW, Muller EE, Hu YO, Lebrun LA, Roume H, Lundin D, et al. Systematic design of 18S rRNA gene primers for determining eukaryotic diversity in microbial consortia. Plos One. 2014;9:e95567.PubMed 
PubMed Central 

Google Scholar 
48.D’Amore R, Ijaz UZ, Schirmer M, Kenny JG, Gregory R, Darby AC, et al. A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling. BMC Genom. 2016;17:55.
Google Scholar 
49.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D596.PubMed 
PubMed Central 

Google Scholar 
52.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. Plos One. 2010;5:10.
Google Scholar 
53.Faith DP. Conservation evaluation and phylogenetic diversity. Biol Conserv. 1992;61:1–10.
Google Scholar 
54.Tsirogiannis C, Sandel B. PhyloMeasures: a package for computing phylogenetic biodiversity measures and their statistical moments. Ecography. 2016;39:709–14.
Google Scholar 
55.Wobbrock JO, Findlater L, Gergle D, Higgins JJ, Acm. The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. Association Computing Machinery: New York; 2011.56.Burnham KP, Anderson DR. Model selection and multimodel interference: a practical information—theoretic approach. Springer: New York, USA; 2002.57.Arndt D, Xia J, Liu Y, Zhou Y, Guo AC, Cruz JA, et al. METAGENassist: a comprehensive web server for comparative metagenomics. Nucleic Acids Res. 2012;40:W88–W95.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2015. ISBN 3-900051-07-0, http://wwwR-projectorg.59.Calbet A, Landry MR. Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol Oceanogr. 2004;49:51–57.CAS 

Google Scholar 
60.Kiorboe T. How zooplankton feed: mechanisms, traits and trade-offs. Biol Rev. 2011;86:311–39.PubMed 

Google Scholar 
61.Juergens K, Matz C. Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie Van Leeuw J Microb. 2002;81:413–34.
Google Scholar 
62.Hammill E, Kratina P, Beckerman A, Anholt BR. Precise time interactions between behavioural and morphological defences. Oikos. 2010;119:494–9.
Google Scholar 
63.Pernthaler J. Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol. 2005;3:537–46.CAS 
PubMed 

Google Scholar 
64.Visser MD, Muller‐Landau HC, Wright SJ, Rutten G, Jansen PA. Tri‐trophic interactions affect density dependence of seed fate in a tropical forest palm. Ecol Lett. 2011;14:1093–1100.PubMed 

Google Scholar 
65.Bagchi R, Gallery RE, Gripenberg S, Gurr SJ, Narayan L, Addis CE, et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature. 2014;506:85–88.CAS 
PubMed 

Google Scholar 
66.Kratina P, Vos M, Anholt BR. Species diversity modulates predation. Ecology. 2007;88:1917–23.PubMed 

Google Scholar 
67.Jaworski CC, Bompard A, Genies L, Amiens-Desneux E, Desneux N. Preference and prey switching in a generalist predator attacking local and invasive alien pests. Plos One. 2013;8:e82231.PubMed 
PubMed Central 

Google Scholar 
68.Coblentz KE, DeLong JP. Predator‐dependent functional responses alter the coexistence and indirect effects among prey that share a predator. Oikos. 2020;129:1404–14.
Google Scholar 
69.Madoni P. Estimates of ciliated protozoa biomass in activated sludge and biofilm. Bioresour Technol. 1994;48:245–9.CAS 

Google Scholar 
70.Tilman D, Knops J, Wedin D, Reich P, Ritchie M, Siemann E. The influence of functional diversity and composition on ecosystem processes. Science. 1997;277:1300–2.CAS 

Google Scholar 
71.Sato Y, Hori T, Navarro RR, Habe H, Ogata A. Functional maintenance and structural flexibility of microbial communities perturbed by simulated intense rainfall in a pilot-scale membrane bioreactor. Appl Microbiol Biot. 2016;100:6447–56.CAS 

Google Scholar 
72.Cardinale BJ, Wright JP, Cadotte MW, Carroll IT, Hector A, Srivastava DS, et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc Natl Acad Sci USA. 2007;104:18123–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Srivastava DS, Cadotte MW, MacDonald AAM, Marushia RG, Mirotchnick N. Phylogenetic diversity and the functioning of ecosystems. Ecol Lett. 2012;15:637–48.PubMed 

Google Scholar 
74.Yachi S, Loreau M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc Natl Acad Sci USA. 1999;96:1463–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Mori AS, Isbell F, Seidl R. β-diversity, community assembly, and ecosystem functioning. Trends Ecol Evol. 2018;33:549–64.PubMed 

Google Scholar 
76.Hammill E, Hawkins CP, Greig HS, Kratina P, Shurin JB, Atwood TB. Landscape heterogeneity strengthens the relationship between β‐diversity and ecosystem function. Ecology. 2018;99:2467–75.PubMed 

Google Scholar 
77.Ellingsen KE, Yoccoz NG, Tveraa T, Frank KT, Johannesen E, Anderson MJ, et al. The rise of a marine generalist predator and the fall of beta diversity. Glob Change Biol. 2020;26:2897–907.
Google Scholar 
78.Weisse T. The significance of inter-and intraspecific variation in bacterivorous and herbivorous protists. Antonie Van Leeuw J Microb. 2002;81:327–41.
Google Scholar 
79.Nierychlo M, Andersen KS, Xu Y, Green N, Jiang C, Albertsen M, et al. MiDAS 3: an ecosystem-specific reference database, taxonomy and knowledge platform for activated sludge and anaerobic digesters reveals species-level microbiome composition of activated sludge. Water Res. 2020;182:115955.CAS 
PubMed 

Google Scholar  More

1.de Souza Dias, V., Pereira da Luz, M., Medero, G. M. & Tarley Ferreira Nascimento, D. An overview of hydropower reservoirs in Brazil: Current situation, future perspectives and impacts of climate change. Water 10, 592 (2018).
Google Scholar 
2.Patias, J., Zuquette, L. V. & Rodrigues-Carvalho, J. A. Piezometric variations in the basaltic massif beneath the Itaipu hydroelectric plant (Brazil/Paraguay border): Right Buttress Dam. Bull. Eng. Geol. Environ. 74, 207–231 (2015).CAS 

Google Scholar 
3.Agostinho, A. A. Pesquisas, monitoramento e manejo da fauna aquática em empreendimentos hidrelétricos. In Seminário Sobre Fauna Aquática E O Setor Elétrico Brasileiro 38–59 (Brasil, 1994).4.Makrakis, S., Gomes, L. C., Makrakis, M. C., Fernandez, D. R. & Pavanelli, C. S. The Canal da Piracema at Itaipu Dam as a fish pass system. Neotrop. Ichthyol. 5, 185–195 (2007).
Google Scholar 
5.Dos Reis, R. B., Frota, A., Depra, G. D. C., Ota, R. R. & Da Graca, W. J. Freshwater fishes from Paraná State, Brazil: An annotated list, with comments on biogeographic patterns, threats, and future perspectives. Zootaxa 4868, 451–494 (2020).
Google Scholar 
6.Becker, R. A., Sales, N. G., Santos, G. M., Santos, G. B. & Carvalho, D. C. DNA barcoding and morphological identification of neotropical ichthyoplankton from the Upper Paraná and São Francisco. J. Fish Biol. 87, 159–168 (2015).CAS 
PubMed 

Google Scholar 
7.Milan, D. T. et al. New 12S metabarcoding primers for enhanced Neotropical freshwater fish biodiversity assessment. Sci. Rep. 10, 1–12 (2020).ADS 

Google Scholar 
8.Agostinho, A. A., Pelicice, F. M. & Gomes, L. C. Dams and the fish fauna of the Neotropical region: Impacts and management related to diversity and fisheries. Braz. J. Biol. 68, 1119–1132 (2008).CAS 
PubMed 

Google Scholar 
9.Bonar, S. A., Hubert, W. A. & Willis, D. W. Standard methods for sampling North American freshwater fishes. American Fisheries Society, Bethesda, (USA, 2009).
Google Scholar 
10.Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Conserv. 197, 131–138 (2016).
Google Scholar 
11.Reis, R. E. et al. Fish biodiversity and conservation in South America. J. Fish Biol. 89, 12–47 (2016).CAS 
PubMed 

Google Scholar 
12.Baumgartner, G. et al. Peixes do baixo rio Iguaçu. (Eduem, 2012).13.Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, 2018).
Google Scholar 
14.Taberlet, P., Coissac, E., Pompanon, F., Christian, B. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol 33, 2045–2050 (2012).
Google Scholar 
15.Ritter, C. D. et al. The pitfalls of biodiversity proxies: Differences in richness patterns of birds, trees and understudied diversity across Amazonia. Sci. Rep. 9, 1–3 (2019).CAS 

Google Scholar 
16.Sales, N. G. et al. Space-time dynamics in monitoring neotropical fish communities using eDNA metabarcoding. Sci. Total Environ. 754, 142096 (2021).ADS 
CAS 
PubMed 

Google Scholar 
17.Zinger, L. et al. Body size determines soil community assembly in a tropical forest. Mol. Ecol. 28, 528–543 (2019).CAS 
PubMed 

Google Scholar 
18.Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).PubMed 

Google Scholar 
19.Zinger, L. et al. Advances and prospects of environmental DNA in neotropical rainforests. Adv. Ecol. Res. 62, 331–373 (2020).
Google Scholar 
20.Cilleros, K. et al. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol. Ecol. Resour. 19, 27–46 (2019).CAS 
PubMed 

Google Scholar 
21.Sales, N. G., Wangensteen, O. S., Carvalho, D. C. & Mariani, S. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 119–130. https://doi.org/10.1002/edn3.14 (2020).Article 

Google Scholar 
22.Blaxter, M. et al. Defining operational taxonomic units using DNA barcode data. Philos. Trans. R. Soc. B Biol. Sci. 360(1462), 1935–1943. https://doi.org/10.1098/rstb.2005.1725 (2005).CAS 
Article 

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

Google Scholar 
24.Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv 81257 (2016).
25.Muha, T. P., Rodriguez-Barreto, D., O’Rorke, R., Garcia de Leaniz, C. & Consuegra, S. Using eDNA metabarcoding to monitor changes in fish community composition after barrier removal. Front. Ecol. Evol. 9, 28 (2021).
Google Scholar 
26.Kitano, T., Umetsu, K., Tian, W. & Osawa, M. Two universal primer sets for species identification among vertebrates. Int. J. Legal Med. 121, 423–427 (2007).PubMed 

Google Scholar 
27.Stoeckle, M. Y., Soboleva, L. & Charlop-Powers, Z. Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS One 12, e0175186 (2017).PubMed 
PubMed Central 

Google Scholar 
28.Bylemans, J. et al. An environmental DNA-based method for monitoring spawning activity: A case study, using the endangered Macquarie perch (Macquaria australasica). Methods Ecol. Evol. 8, 646–655 (2017).
Google Scholar 
29.De Souza, L. S., Godwin, J. C., Renshaw, M. A. & Larson, E. Environmental DNA (eDNA) detection probability is influenced by seasonal activity of organisms. PLoS One 11, e0165273 (2016).PubMed 
PubMed Central 

Google Scholar 
30.Ritter, C. D. et al. Locality or habitat? Exploring predictors of biodiversity in Amazonia. Ecography (Cop.) 42, 321–333 (2019).
Google Scholar 
31.CFMV-Resolução no 1000 de 11 de maio de 2012—Dispõe sobre procedimentos e métodos de eutanásia em animais e dá outras providências. (2012).32.Britski, H. A., de Silimon, K. Z. S. & Lopes, B. S. Peixes do Pantanal: manual de identificação, ampl. Brasília, DF, Embrapa Informação Tecnológica (2007).33.Ota, R. R., Deprá, G. de C., Graça, W. J. da & Pavanelli, C. S. Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated. Neotrop. Ichthyol. 16(2). https://www.scielo.br/j/ni/a/tScwvm8JLhKnbxKjtBQLPBx/abstract/?lang=en (2018).34.Neris, N., Villalba, F., Kamada, D. & Viré, S. Guía de peces del Paraguay/Guide of fishes of Paraguay. Zamphiropolos, (Paraguay, 2010).
Google Scholar 
35.Pie, M. R. et al. Development of a real-time PCR assay for the detection of the golden mussel (Limnoperna fortunei, Mytilidae) in environmental samples. An. Acad. Bras. Cienc. 89, 1041–1045 (2017).CAS 
PubMed 

Google Scholar 
36.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Boeger, W. A. et al. Testing a molecular protocol to monitor the presence of golden mussel larvae (Limnoperna fortunei) in plankton samples. J. Plankton Res. 29, 1015–1019 (2007).CAS 

Google Scholar 
38.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Google Scholar 
39.Van Rossum, G. & Drake, F. L. Python 3 References Manual. Scotts Valley CA: CreateSpace. (2009).40.R Core Team. R: the R project for statistical computing. 2019. https://www.r-project.org/ (accessed 30 Mar 2020).41.Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
42.Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).CAS 
PubMed 

Google Scholar 
43.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).
Google Scholar 
44.Team, Rs. RStudio: integrated development for R. RStudio, Inc., Boston, MA https://www.rstudio.com42, 84 (2015).45.Wickham, H. tidyverse: Easily Install and Load “Tidyverse” Packages (Version R package version 1.1. 1). (2017).46.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
47.Tang, Y., Horikoshi, M. & Li, W. ggfortify: Unified interface to visualize statistical results of popular R packages. R J. 8, 474 (2016).
Google Scholar 
48.Auguie, B. & Antonov, A. gridExtra: Miscellaneous functions for “grid” graphics (Version 2.2. 1)[Computer software]. (2016).49.Kassambara, A. & Kassambara, M. A. Package ‘ggpubr’. (2020).50.Oksanen, J. et al. Vegan: Community ecology package. R package version 1.17-4. https://cran.r-project.org. Acesso em 23, 2010 (2010).51.McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
52.Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).
Google Scholar 
53.Marcon, E., Herault, B. & Marcon, M. E. Package ‘entropart’. (2021).54.Mächler, E., Walser, J.-C. & Altermatt, F. Decision making and best practices for taxonomy-free eDNA metabarcoding in biomonitoring using Hill numbers. BioRxiv (2020).55.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
57.León, A., Reyes, J., Burriel, V. & Valverde, F. Data quality problems when integrating genomic information. In International Conference on Conceptual Modeling 173–182 (Springer, 2016).58.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 

Google Scholar 
59.Stahlhut, J. K. et al. DNA barcoding reveals diversity of hymenoptera and the dominance of parasitoids in a sub-arctic environment. BMC Ecol. 13, 2 (2013).PubMed 
PubMed Central 

Google Scholar 
60.Gillet, B. et al. Direct fishing and eDNA metabarcoding for biomonitoring during a 3-year survey significantly improves number of fish detected around a South East Asian reservoir. PLoS One 13, e0208592 (2018).PubMed 
PubMed Central 

Google Scholar 
61.Barrett, M. et al. Living planet report 2018: Aiming higher. WWF. Available at: https://www.globallandscapesforum.org/publication/living-planet-report-2018-aiming-higher/ (2018).62.Díaz, S. M. et al. The global assessment report on biodiversity and ecosystem services: Summary for policy makers. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. 56, (2019).63.Dudgeon, D. Asian river fishes in the Anthropocene: Threats and conservation challenges in an era of rapid environmental change. J. Fish Biol. 79, 1487–1524 (2011).CAS 
PubMed 

Google Scholar 
64.Dudgeon, D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr. Biol. 29, R960–R967 (2019).CAS 
PubMed 

Google Scholar 
65.He, F. et al. Disappearing giants: A review of threats to freshwater megafauna. Wiley Interdiscip. Rev. Water 4, e1208 (2017).
Google Scholar 
66.Agostinho, A. A., Thomaz, S. M. & Gomes, L. C. Threats for biodiversity in the floodplain of the Upper Paraná River: Effects of hydrological regulation by dams. (2018). Int. J. Ecohydrol. Hydrobiol Warsaw. 4(3), 267–280 (2004).
Google Scholar 
67.Santana, M. L., Carvalho, F. R. & Teresa, F. B. Broad and fine-scale threats on threatened Brazilian freshwater fish: Variability across hydrographic regions and taxonomic groups. Biota Neotrop. 21 (2). https://www.scielo.br/j/bn/a/YqFbWSy5vbfHy3QK9kNpdKp/?format=html&lang=en (2021).68.Matthews, W. J. Patterns in Freshwater Fish Ecology. (Springer Science & Business Media, 2012).69.de Oliveira Bueno, E., Alves, G. J. & Mello, C. R. Hydroelectricity water footprint in Parana hydrograph region, Brazil. Renew. Energy 162, 596–612 (2020).
Google Scholar 
70.Camacho Guerreiro, A. I., Amadio, S. A., Fabre, N. N. & da Silva Batista, V. Exploring the effect of strong hydrological droughts and floods on populational parameters of Semaprochilodus insignis (Actinopterygii: Prochilodontidae) from the Central Amazonia. Environ. Dev. Sustain. 23, 3338–3348 (2021).
Google Scholar 
71.Jespersen, H., Rasmussen, G. & Pedersen, S. Severity of summer drought as predictor for smolt recruitment in migratory brown trout (Salmo trutta). Ecol. Freshw. Fish 30, 115–124 (2021).
Google Scholar 
72.Pool, T. K., Grenouillet, G. & Villéger, S. Species contribute differently to the taxonomic, functional, and phylogenetic alpha and beta diversity of freshwater fish communities. Divers. Distrib. 20, 1235–1244 (2014).
Google Scholar 
73.de Oliveira, E. F., Goulart, E. & Minte-Vera, C. V. Fish diversity along spatial gradients in the Itaipu Reservoir, Paraná, Brazil. Braz. J. Biol. 64, 447–458 (2004).CAS 
PubMed 

Google Scholar 
74.Daga, V. S. et al. Homogenization dynamics of the fish assemblages in Neotropical reservoirs: Comparing the roles of introduced species and their vectors. Hydrobiologia 746, 327–347 (2015).
Google Scholar 
75.Vitule, J. R. S. Introdução de peixes em ecossistemas continentais brasileiros: revisão, comentários e sugestões de ações contra o inimigo quase invisível. Neotrop. Biol. Conserv. 4, 111–122 (2009).
Google Scholar 
76.Mariac, C. et al. Species‐level ichthyoplankton dynamics for 97 fishes in two major river basins of the Amazon using quantitative metabarcoding. Mol. Ecol. https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1111%2Fmec.15944 (2021).77.Jackman, J. M. et al. eDNA in a bottleneck: Obstacles to fish metabarcoding studies in megadiverse freshwater systems. Environ. DNA 3, 837–849 (2021).
Google Scholar 
78.Bessey, C. et al. Maximizing fish detection with eDNA metabarcoding. Environ. DNA 2, 493–504 (2020).
Google Scholar 
79.Evans, N. T. et al. Fish community assessment with eDNA metabarcoding: Effects of sampling design and bioinformatic filtering. Can. J. Fish. Aquat. Sci. 74, 1362–1374 (2017).CAS 

Google Scholar 
80.Prodan, A. et al. Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLoS One 15, e0227434 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 

Google Scholar 
82.Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).PubMed 
PubMed Central 

Google Scholar 
83.Pawluczyk, M. et al. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal. Bioanal. Chem. 407, 1841–1848 (2015).CAS 
PubMed 

Google Scholar 
84.Holman, L. E., Chng, Y. & Rius, M. How does eDNA decay affect metabarcoding experiments? Environ. DNA https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2Fedn3.201 (2021).85.Edgar, R. C. UNCROSS2: identification of cross-talk in 16S rRNA OTU tables. BioRxiv 400762 (2018).86.MacArthur, R. H. Geographical Ecology: Patterns in the Distribution of Species. (Princeton University Press, 1984).87.Leray, M. & Knowlton, N. Random sampling causes the low reproducibility of rare eukaryotic OTUs in Illumina COI metabarcoding. PeerJ 5, e3006 (2017).PubMed 
PubMed Central 

Google Scholar 
88.Team, Q. D. QGIS geographic information system. Open Source Geospatial Found. Proj. Versão 2, (2015). More

Microbial community samplingHippo gut microbiomeWe characterized the microbial communities in the hippo gut by collecting ten samples of fresh hippo feces adjacent to four hippo pools early in the morning (prior to desiccation by the sun) in September 2017. We collected feces from different pools and locations adjacent to the pool to include the feces of different individuals so we could estimate the similarity of the gut microbiome among individuals and across the landscape. The four hippo pools are sufficiently far apart that there was likely no intermixing of hippos among them.Each individual hippo feces sample was gently homogenized by hand and then the liquid was gently squeezed from the coarse particulate organic matter. A portion of the liquid (approximately 10 mL) was vacuum filtered through a Supor polysulfone membrane (0.2-µm pore size; Pall, Port Washington, NY, USA). After approximately 10 mL had filtered through and the filter was dry, 15 mL of RNALater was gently poured onto the filter and allowed to contact the collected biomass on the filter for 15 min before being removed by filtration. The filter was stored dry in a sterile petri dish and transferred to a refrigerator within several hours, then to a − 20 °C freezer for storage within several days.During the July 2016 survey of hippo pools, we collected an additional two samples of fresh hippo feces near a high-subsidy hippo pool and filtered approximately 10 mL of the liquid portion after homogenization as detailed above. The filter was then folded twice to preserve the biomass on the filter and stored in 14 mL of RNALater.Aquatic ecosystemWe characterized the microbial communities in the water column of hippo pools across a gradient of hippo subsidy (July 2016, N = 12 pools). We collected samples from the upstream, downstream, surface, and bottom of both pools containing hippos and pools that lacked hippos. Subsamples were also analyzed for biogeochemical variables (details provided below). We also collected water samples in four of the high-subsidy hippo pools every 2–3 days starting immediately after a flushing event until the next flushing event (August and September 2017, Supplementary Fig. S1)23. The number of hippos, discharge and volume for each pool are presented in Dutton et al (2020)23.We sampled the aquatic microbial community and biogeochemical variables along a longitudinal transect down both the Mara and Talek rivers (Supplementary Fig. S1, Supplementary Table S2). For the Mara River, we sampled an approximately 100-km transect along a gradient of hippo numbers (N = 10 locations, from 0 to ~ 4000 hippos). For the Talek River, we sampled an approximately 30-km transect to the confluence with the Mara (N = 8 locations, from 0 to 700 hippos). Mara River sites 9 and 10 are downstream of the confluence with the Talek River. Water samples were collected from each site in a well-mixed flowing section away from any hippo pools.Aquatic microbial samples were collected by filtering water samples through a Supor polysulfone filter (0.2-µm pore size; Pall, Port Washington, NY, USA) and then preserving the filter in RNALater Stabilization Solution (Ambion, Inc., Austin, TX, USA). In 2017, the filters were preserved with RNA Later and then frozen for analysis.Mesocosm experimentWe collected river water from the Mara River upstream of the distribution of hippos and placed it in 45 1-L bottles in a large water basin covered by a dark tarp to help regulate temperature and prevent algal production. Bottles were randomly assigned to the control, bacteria, and bacteria + virus treatments. We collected fresh hippo feces from multiple locations adjacent to the Mara River. After homogenization, half of the hippo feces was sterilized in a pressure cooker, which testing confirmed had similar sterilization results as an autoclave53 (see Supplementary Materials). Five grams of sterilized hippo feces was placed into each bottle to provide an organic matter substrate without viable bacteria or viruses. The unsterilized hippo feces was expressed, and the resulting liquid was filtered through 0.7-µm GF/F filters (0.7-µm pore size; Whatman, GE Healthcare Life Sciences, Pittsburgh, PA, USA) and 0.2-µm Supor filters to physically separate the bacteria (on the filter papers) from the viruses (in the filtrate). Half the filtrate was then sterilized with a UV light treatment (Supplementary Fig. S4). The UV light treatment did not significantly alter DOC quality (see Supplementary Materials).We prepared 15 bottles for each of three treatments—control, bacteria, and bacteria + virus—as follows: Control Unfiltered river water, 5 g wet weight sterilized hippo feces, and two blank Supor filters; Bacteria Unfiltered river water, 5 g wet weight sterilized hippo feces, two Supor filters containing bacteria, and 4 mL sterilized filtrate; Virus Unfiltered river water, 5 g wet weight sterilized hippo feces, two Supor filters containing bacteria, and 4 mL unsterilized filtrate containing viruses.We conducted the experiment for 27 days from September to October 2017. We terminated the experiment after 27 days because we were trying to replicate the microbial communities in hippo pools as best as we could and the hippo pools rarely go more than 1 month before they are flushed out by a flood25. Initial microbial samples of the river water, hippo feces bacteria and hippo fecal liquid filtrate were taken on day 0, and three replicate samples per treatment were destructively sampled on day 3, 9, 15, 21, and 27. During each time step, the microbial communities were sampled using the methods detailed above, and chemical analyses were done on the water samples as described below. We also measured chlorophyll a, dissolved oxygen, temperature, conductivity, total dissolved solids, turbidity, and pH with a Manta2 water quality sonde (Eureka Water Probes, Austin, TX, USA).Microbial community characterizationWe used 16S rRNA sequencing to characterize the active microbial communities. We extracted both DNA and RNA from our preserved samples, then used RNA to synthesize cDNA to represent the “active” microbial community and the total DNA in the sample to represent the “total” microbes present, including those that may not be actively replicating54. Due to the continual loading of hippo feces into pools and the long half-life of DNA, we would expect there to be significant quantities of microbial DNA derived from hippo feces within the pools. However, there would be less accumulation of RNA because of RNA’s shorter half-life. The active communities identified through this RNA-based approach are the ones that would potentially contribute to ecosystem function55 as indicated by the protein synthesis potential, although relationships between activity and rRNA concentrations in individual taxa within mixed communities can vary56. Nevertheless, this method provides an overall characterization of the microbial community’s potential activity.We used the Qiagen RNeasy Powerwater Kit (Qiagen, Hilden, Germany) to extract the DNA and RNA from the material on the filter using a slightly modified manufacturer’s protocol to allow for the extraction of both DNA and RNA. After extraction, we split the total extracted volume (100 µL per sample) into two groups. We treated one group with the DNase Max Kit (Qiagen, Hilden, Germany) to remove all DNA and serve as the RNA group of samples.We used the RNA group of samples to synthesize cDNA using the SuperScript III First Strand Synthesis Kit (Invitrogen, Carlsbad, CA, USA). DNA and cDNA were quantified using the PicoGreen dsDNA Assay Kit (Molecular Probes, Eugene, OR, USA) then normalized to 5 ng/µL. Amplicon library preparation was done using a dual-index paired-end approach57. We amplified the V4 region of the 16S rRNA gene using dual-index primers (F515/R805) and AccuPrime Pfx SuperMix (Invitrogen, Carlsbad, CA, USA) in duplicate for each sample using the manufacturer’s recommended thermocycling routine.Samples were then pooled, purified and normalized using the SequelPrep Normalization Plate Kit (Invitrogen, Carlsbad, CA, USA). Barcoded amplicon libraries were then sequenced at the Yale Center for Genome Analysis (New Haven, CT, USA) using an Illumina Miseq v2 reagent kit (Illumina, San Diego, CA, USA) to generate 2 × 250 base pair paired-end reads.Sampling took place in 2016 and 2017 and involved two separate sequencing runs. The first sequencing run included negative controls and a mock community (D6306, Zymo Research, Irvin, CA, USA). The second sequencing run included negative controls, a mock community (D6306), and a single E. coli strain. In both runs, the mock community and single E. coli strain were well reconstructed from the sequences, and there was minimal contamination in the negative controls, mock community and E. coli strain.From those two sequencing campaigns, we received over 2 million raw sequences from the first campaign and over 7 million raw sequences for the second campaign. For the microbial community analyses, only samples collected and sequenced during the same campaign are analyzed together to prevent preservation or sequencing biases. However, samples within the two separate campaigns were preserved and sequenced using identical methods with only a minor modification (mentioned above) to increase the preservation of genetic material.We de-multiplexed sequenced reads then removed barcodes, indexes, and primers using QIIME258. We used DADA2 with a standard workflow in R59 to infer exact sequence variants (ESV) for each sample60. We assigned taxonomy using a naïve Bayesian classifier and the SILVA training set v. 128 database61,62. We removed potential contamination in samples from both campaigns by using the statistical technique in the R package, decontam63. We used Phyloseq to characterize, ordinate, and compare microbial communities64 with their standard workflow59.Chemical analysesAll water samples collected in the field and in the experiment were analyzed for dissolved ferrous iron (Fe(II)), hydrogen sulfide (H2S), dissolved organic carbon (DOC), inorganic nutrients, major ions, dissolved gases, and biochemical oxygen demand following the standard methods provided in detail in Dutton et al (2020)23.Statistical analysesWe computed all statistical analyses in the R 4.1.1 statistical language in RStudio 2021.09.0 using α = 0.05 to determine significance65,66. Error bars in the figures represent standard deviation of the means. All data and R code for the statistics and data treatments are provided in the Mendeley Data Online Repository67.We used the Bray–Curtis dissimilarity matrix followed by ordination with NMDS to examine differences between individual hippo gut microbiomes; between low-, medium-, and high-subsidy hippo pools; and between a gradient of hippo pools and the environment. We used a CCA to test for the influence of biogeochemical drivers on microbial community composition using biogeochemical data that were previously published but collected concurrently with this study23. We constrained the CCA ordination by soluble reactive phosphorus, nitrate, methane, BOD, and sulfate, which were all previously shown to be important drivers in the variation between pools23. We used PERMANOVA and PERMDISP to test for significant differences between groups68.
We compared aquatic microbial communities from the bottom of high-subsidy hippo pools (N = 15), from hippo feces (N = 10, the hippo gut microbiome) and upstream of high-subsidy hippo pools (N = 15, free of hippo gut microbiome influence) using the Bray–Curtis dissimilarity matrix on the relative abundances for the active aquatic microbial communities collected from the different sample types followed by ordination with NMDS. 95% confidence ellipses were generated. We then determined the active taxa that were shared between the hippo gut microbiome (hippo feces) and the bottom of the high-subsidy hippo pools and not present in the upstream samples from high-subsidy hippo pools.We used LEfSe to calculate the differential abundance of microbial taxa between upstream (N = 14), downstream (N = 16), at the surface (N = 17) and at the bottom (N = 14) of hippo pools and calculated their effect size69. We then calculated the correlation of microbial taxa to the measured biogeochemistry using Pearson’s correlation coefficient with a false discovery rate corrected p-value in the microeco R package70.
We used SourceTracker to quantify the contribution of the hippo gut, upstream waters, or unknown sources to the active aquatic microbial communities in the bottom waters of three of the high-subsidy hippo pools between flushing flows71. We also used the Bray–Curtis dissimilarity matrix followed by ordination with NMDS to examine changes in the active aquatic microbial communities in one of the high subsidy hippo pools through time after flushing flows.For the experiment, we calculated the Bray–Curtis dissimilatory matrix followed by ordination with NMDS for the active aquatic microbial communities over time in each of the three experimental treatments. We used SourceTracker to determine the proportion of the active aquatic microbial community in each treatment that originated from the hippo gut, the river water, or unknown sources71. We analyzed the biogeochemical differences among experimental treatments by fitting a linear mixed effects model for each of the biogeochemical variables throughout the experiment with the nlme package in R72. We fit the model with the restricted maximum likelihood method and a continuous autoregressive temporal correlation structure with sample day as the repeated factor. Treatment and time were fixed effects and individual bottles were treated as random effects. We conducted a pairwise post-hoc test with an ANOVA and the emmeans package in R to estimate marginal means with a Tukey adjusted p-value for multiple comparisons73,74. More

MPs abundanceIn Table 1 MP abundance (mean value ± standard deviation) values are presented by shape, size range, color and polymer type categories for each sampling site. MP were found in all analyzed salt samples including pellets, fibers, fragments, films and lines (Fig. 3). MP total abundance values per site ranged from 74.7 to 136.7 particles kg−1 in the following order of increasing abundance: S3  black (17%)  > blue (15%)  > green and transparent (10% each)  > pink (6%)  > colorless (5%). In terms of size, most particles were in the category 500–1000 µm, except for S3 (1000–5000 µm) (Table 1). The distribution of MP particles based on size range was: 500–1000 µm (40%)  > 1000–5000 µm (34%)  > 250–500 µm (26%). For salts from the Atlantic and the Pacific Ocean, originating from Brazil, the United Kingdom, and the USA, Kim et al.12 reported a higher abundance of MP in size range 100–1000 µm while sizes in the range 100–5000 µm were reported for salt samples from the Black Sea. Seth and Shriwastay20 found that 80% of fibers found in salt samples from the Indian Sea were smaller than 2000 μm in length. MP size range differences among the various studies are suggested to depend on the degree of weathering for a given environment30, different climatic conditions such as wind, rain, temperature, salinity, and waves influencing size range composition. Also, for runoff, rivers, and atmospheric fallout transportation, smaller MP size ranges can be expected to be associated with a longer range from the initial plastic sources31,32,33. Nevertheless, more detailed information about MP polymer/color features within the size ranges are needed to achieve stronger conclusions about potential long/short-range sources.Figure 6Microplastics abundance (particles kg−1) by color in sea salt samples from stations S1 to S8.Full size imageFigure 7Microplastics abundance (particles kg−1) by size range in sea salt samples from stations S1 to S8.Full size imageMP polymer compositionFour types of polymer, namely polypropylene (PP), polystyrene (PS), polyethylene (PE), and polyethylene terephthalate (PET), were identified with FT-MIR-NIR (Supplementary Figure S1). These results are in accordance with those reported for salt samples in other studies worldwide (Table 1). These polymer types are widely used in daily life products, packaging, single-use plastics, and clothes, contributing to plastic pollution worldwide21. PET presented the highest contribution at all sampling sites, at ~ 48%, whereas PS was found to be least, at ~ 15% (Fig. 8, Table 1). Iñiguez et al.34 also reported PET predominance (83.3%) in Spanish table salt samples. PET predominance could be explained by its high density (1.30 g cm−3), making particles prone to sedimentation during the salt crystallization process19. PE (0.94 g cm−3), PP (0.90 g cm−3), and PS (1.05 cm−3) presented lower or similar densities to seawater (~ 1.02 g cm−3), making these more prone to flotation and possible loss due to wind during desiccation.Figure 8Microplastics abundances (particles kg−1) by polymer composition in sea salt samples from stations S1 to S8.Full size imageRisks assessmentDuring degradation, MP tends to emit monomers and different types of additives, these having the potential to cause harm to ecological systems and health18, 35. Results for the polymeric risks indices are presented in Fig. 9. According to polymer risk classification, all salts samples showed low risks, being similar to the entire study area. To date, none of the published studies have applied chemometric models in evaluating MP pollution in salts, posing difficulties when comparing our results. Information on the hazards of MP from ingestion to human health is still highly unclear. Other than exposure, the destiny and transit of ingested MP in the human body, including intestinal digestion and biliary discharge, have not been determined in previous research and remained largely unclear36. Some studies conducted impact assessments based on in vitro models37,38. However, whether the exposure concentrations used in such studies indicate the MP consumed and collected in humans is inconclusive. Previous studies found that toxicity, oxidative stress, and inflammation could result from MP exposure, including immune disruption and neurotoxicity effects, among others39. Therefore, an immediate effort is required to assess the health consequences of these MP when they reach the human body.Figure 9Polymeric risk indices for MP types in salts from stations S1 to S8.Full size image More

AffiliationsDepartment of Ocean Sciences, University of California, Santa Cruz, CA, USARachel A. FosterDepartment of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, GermanyRachel A. Foster, Daniela Tienken, Sten Littmann & Marcel M. M. KuypersDepartment of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, SwedenRachel A. FosterDepartment of Geosciences, Swedish Museum of Natural History, Stockholm, SwedenMartin J. WhitehouseDepartment of Oceanography, University of Hawai’i at Mānoa, Honolulu, HI, USAAngelicque E. WhiteAuthorsRachel A. FosterDaniela TienkenSten LittmannMartin J. WhitehouseMarcel M. M. KuypersAngelicque E. WhiteCorresponding authorCorrespondence to
Rachel A. Foster. More