Philippa Kaur

Boreal forests contain about half the carbon (C) of terrestrial forests worldwide, and as such, they play an immense role in the global C cycle. Therefore, accurately predicting the global C balance requires understanding of C fluxes in boreal trees and how they respond to climate change. While the relationships between climate and boreal tree growth are generally non-stationary, it remains unknown whether the same is true of the relationships between climate and C fluxes. More

Dugatkin, L. A. Principles of Animal Behavior 4th edn. (University of Chicago Press, 2018).
Google Scholar 
Lucas, M. C. & Baras, E. Migration of Freshwater Fishes (Blackwell Science, 2001).Book 

Google Scholar 
Northcote, T. G. Potamodromy in Sahnonidae—Living and moving in the fast lane. North Am. J. Fish. Manage. 17, 1029–1045 (1997).Article 

Google Scholar 
Brönmark, C. et al. There and back again: Migration in freshwater fishes. Can. J. Zool. 92, 467–479 (2013).Article 

Google Scholar 
L’Abée-Lund, J. H. & Vøllestad, L. A. Feeding migration of roach, Rutilus rutilus (L.), in Lake Arungen, Norway. J. Fish Biol. 30, 349–355 (1987).Article 

Google Scholar 
Mouchlianitis, F. A. et al. Does fragmented river connectivity alter the reproductive behavior of the potamodromous fish Alburnus vistonicus? Hydrobiologia 848, 4029 (2021).Article 

Google Scholar 
Brönmark, C., Skov, C., Brodersen, J., Nilsson, P. A. & Hansson, L.-A. Seasonal migration determined by a trade-off between predator avoidance and growth. PLoS ONE 3, 2–7 (2008).Article 

Google Scholar 
Brodersen, J., Hansen, J. H. & Skov, C. Partial nomadism in large-bodied bream (Abramis brama). Ecol. Freshw. Fish 28, 650–660 (2019).Article 

Google Scholar 
Magnuson, J. J., Crowder, L. B. & Medvick, P. A. Temperature as an ecological resource. Integr. Compar. Biol. 19, 331 (1979).
Google Scholar 
Beamish, F. W. H. Swimming capacity. Fish Physiol. 7, 101 (1978).Article 

Google Scholar 
Benitez, J. P. & Ovidio, M. The influence of environmental factors on the upstream movements of rheophilic cyprinids according to their position in a river basin. Ecol. Freshw. Fish 27, 660–671 (2018).Article 

Google Scholar 
Lucas, M. C. & Batley, E. Seasonal movements and behaviour of adult Barbel Barbus barbus, a riverine cyprinid fish: Implications for river management. J. Appl. Ecol. 33, 1345–1358 (1996).Article 

Google Scholar 
Benjamin, J. R., Vidergar, D. T. & Dunham, J. B. Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river–reservoir system. Ecol. Evol. 10, 4128 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Fernando, C. H. & Holčík, J. Fish in reservoirs. Int. Revue der gesamten Hydrobiol. Hydrogr. 76, 149–167 (1991).Article 

Google Scholar 
Kubecka, J. Succession of fish communities in reservoirs of Central and Eastern Europe. Compar. Reserv. Limnol. Water Qual. Manage. https://doi.org/10.1007/978-94-017-1096-1_11 (1993).Article 

Google Scholar 
Pfauserová, N., Slavík, O., Horký, P., Turek, J. & Randák, T. Spatial distribution of native fish species in tributaries is altered by the dispersal of non-native species from reservoirs. Sci. Total Environ. 755, 143108 (2021).ADS 
PubMed 
Article 

Google Scholar 
Hladík, M. & Kubečka, J. Fish migration between a temperate reservoir and its main tributary. Hydrobiologia 504, 251–266 (2003).Article 

Google Scholar 
Reyes-Gavilan, F. G., Garrido, R., Nicieza, A. G., Toledo, M. M. & Brana, F. Fish community variation along physical gradients in short streams of northern Spain and the disruptive effect of dams. Hydrobiologia 321, 155–163 (1996).Article 

Google Scholar 
Falke, J. A. & Gido, K. B. Spatial effects of reservoirs on fish assemblages in great plains streams in Kansas, USA. River Res. Appl. 22, 55–68 (2006).Article 

Google Scholar 
Vitule, J. R. S., Skóra, F. & Abilhoa, V. Homogenization of freshwater fish faunas after the elimination of a natural barrier by a dam in Neotropics. Divers. Distrib. 18, 111–120 (2012).Article 

Google Scholar 
Van der Zanden, M. J., Lapointe, N. W. R. & Marchetti, M. P. Non-indigenous fishes and their role in freshwater fish imperilment. In Conservation of Freshwater Fishes (eds Closs, G. P. et al.) 238–269 (Cambridge University Press, 2016).Chapter 

Google Scholar 
Moyle, P. B. & Light, T. Biological invasions of fresh water: Empirical rules and assembly theory. Biol. Conserv. 78, 149–161 (1996).Article 

Google Scholar 
Martinez, P. J., Chart, T. E., Trammell, M. A., Wullschleger, J. G. & Bergersen, E. P. Fish species composition before and after construction of a main stem reservoir on the White River, Colorado. Environ. Biol. Fish. 40, 227–239 (1994).Article 

Google Scholar 
Carey, M. P., Sanderson, B. L., Barnas, K. A. & Olden, J. D. Native invaders—Challenges for science, management, policy, and society. Front. Ecol. Environ. 10, 373–381 (2012).Article 

Google Scholar 
Cucherousset, J. & Olden, J. D. Ecological impacts of non-native freshwater fishes. Fisheries (Bethesda) 36, 215–230 (2011).Article 

Google Scholar 
Havel, J. E., Lee, C. E. & Vander Zanden, M. J. Do reservoirs facilitate invasions into landscapes? Bioscience 55, 518 (2005).Article 

Google Scholar 
Murphy, C. A., Arismendi, I., Taylor, G. A. & Johnson, S. L. Evidence for lasting alterations to aquatic food webs with short-duration reservoir draining. PLoS ONE 14, 1–12 (2019).
Google Scholar 
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).PubMed 
Article 

Google Scholar 
Pfauserová, N., Slavík, O., Horký, P., Kolářová, J. & Randák, T. Migration of non-native predator asp (Leuciscus aspius) from a reservoir poses a potential threat to native species in tributaries. Water (Basel) 11, 1306 (2019).
Google Scholar 
Hladík, M. & Kubečka, J. The effect of water level fluctuation on tributary spawning migration of reservoir fish. Ecohydrol. Hydrobiol. 4, 449–457 (2004).
Google Scholar 
Morán-López, R. & Uceda Tolosa, O. Relative leaping abilities of native versus invasive cyprinids as criteria for selective barrier design. Biol. Invas. 19, 1243–1253 (2017).Article 

Google Scholar 
Winter, J. D. Underwater biotelemetry. In Fisheries Techniques (eds Nielson, L. A. & Johnson, D. L.) 371–395 (American Fisheries Society, 1983).
Google Scholar 
Vostradovský, J. & Novák, M. Some opinions in regard to the Lipno Valley reservoir in 1958. Anim. Husb. 4, 877–888 (1959).
Google Scholar 
Balon, E. K. Reproductive guilds of fishes: A proposal and definition. J. Fish. Res. Board Can. 32, 821–864 (1975).Article 

Google Scholar 
Schiemer, F. & Waidbacher, H. Strategies for conservation of a Danubian fish fauna. River Conserv. Manage. https://doi.org/10.1016/0006-3207(92)90983-t (1992).Article 

Google Scholar 
Molls, F. New insights into the migration and habitat use by bream and white bream in the floodplain of the River Rhine. J. Fish Biol. 55, 1187–1200 (1999).Article 

Google Scholar 
Kafemann, R., Adlerstein, S. & Neukamm, R. Variation in otolith strontium and calcium ratios as an indicator of life-history strategies of freshwater fish species within a brackish water system. Fish. Res. 46, 313 (2000).Article 

Google Scholar 
le Pichon, C. et al. Summer use of the tidal freshwaters of the River Seine by three estuarine fish: Coupling telemetry and GIS spatial analysis. Estuar. Coast. Shelf Sci. 196, 83 (2017).ADS 
Article 

Google Scholar 
Jurajda, P., Roche, K., Halačka, K., Mrkvová, M. & Zukal, J. Winter activity of common bream (Abramis brama L.) in a European reservoir. Fish. Manage. Ecol. 25, 163–171 (2018).Article 

Google Scholar 
Lyons, J. & Lucas, M. C. The combined use of acoustic tracking and echosounding to investigate the movement and distribution of common bream (Abramis brama) in the River Trent, England. Hydrobiologia 483, 265–273 (2002).Article 

Google Scholar 
Gardner, C. J., Deeming, D. C. & Eady, P. E. Seasonal water level manipulation for flood risk management influences home-range size of common bream Abramis brama L. in a lowland river. River Res. Appl. 31, 165–172 (2015).Article 

Google Scholar 
Winter, E. R., Hindes, A. M., Lane, S. & Britton, J. R. Movements of common bream Abramis brama in a highly connected, lowland wetland reveal sub-populations with diverse migration strategies. Freshw. Biol. 66, 1410 (2021).CAS 
Article 

Google Scholar 
Gardner, C. J., Deeming, D. C. & Eady, P. E. Seasonal movements with shifts in lateral and longitudinal habitat use by common bream, Abramis brama, in a heavily modified lowland river. Fish. Manage. Ecol. 20, 315–325 (2013).Article 

Google Scholar 
Skov, C. et al. Sizing up your enemy: Individual predation vulnerability predicts migratory probability. Proc. R. Soc. B Biol. Sci. 278, 1414–1418 (2011).Article 

Google Scholar 
Cala, P. On the ecology of ide Leuciscus idus (L.) in the River Kävlingean, South Sweden. Rep. Inst. Freshw. Res. Drottningholm 50, 45–99 (1970).
Google Scholar 
Winter, H. V. & Fredrich, F. Migratory behaviour of ide: A comparison between the lowland rivers Elbe, Germany, and Vecht, The Netherlands. J. Fish Biol. 63, 871–880 (2003).Article 

Google Scholar 
de Leeuw, J. J. & Winter, H. V. Migration of rheophilic fish in the large lowland rivers Meuse and Rhine, the Netherlands. Fish. Manage. Ecol. 15, 409–415 (2008).Article 

Google Scholar 
Kulíšková, P., Horký, P., Slavík, O. & Jones, J. I. Factors influencing movement behaviour and home range size in ide Leuciscus idus. J. Fish Biol. 74, 1269–1279 (2009).PubMed 
Article 

Google Scholar 
Rohtla, M. et al. Review and meta-analysis of the environmental biology and potential invasiveness of a poorly-studied cyprinid, the Ide Leuciscus idus. Rev. Fish. Sci. Aquac. 29, 1–37 (2020).
Google Scholar 
Fredrich, F. Long-term investigations of migratory behaviour of asp (Aspius aspius L.) in the middle part of the Elbe River, Germany. J. Appl. Ichthyol. 19, 294–302 (2003).Article 

Google Scholar 
Šmejkal, M. et al. Climbing up the ladder: Male reproductive behaviour changes with age in a long-lived fish. Behav. Ecol. Sociobiol. https://doi.org/10.1007/s00265-020-02961-7 (2021).Article 

Google Scholar 
Šmejkal, M. et al. Seasonal and daily protandry in a cyprinid fish. Sci. Rep. 7, 1–9 (2017).Article 

Google Scholar 
Merciai, R. et al. First record of the asp Leuciscus aspius introduced into the Iberian Peninsula. Limnetica 37, 341–344 (2018).
Google Scholar 
Horký, P. & Slavík, O. Diel and seasonal rhythms of asp Leuciscus aspius (L.) in a riverine environment. Ethol. Ecol. Evol. 29, 449–459 (2017).Article 

Google Scholar 
Allouche, S., Thévenet, A. & Gaudin, P. Habitat use by chub (Leuciscus cephalus L. 1766) in a large river, the French Upper Rhone, as determined by radiotelemetry. Arch. Hydrobiol. 145, 219–236 (1999).Article 

Google Scholar 
Horký, P., Slavík, O., Bartoš, L., Kolářová, J. & Randák, T. Behavioural pattern in cyprinid fish below a weir as detected by radio telemetry. J. Appl. Ichthyol. 23, 679 (2007).Article 

Google Scholar 
Sandlund, O. T., Museth, J. & Øistad, S. Migration, growth patterns, and diet of pike (Esox lucius) in a river reservoir and its inflowing river. Fish. Res. 173, 53–60 (2016).Article 

Google Scholar 
Koed, A., Balleby, K., Mejlhede, P. & Aarestrup, K. Annual movement of adult pike (Esox lucius L.) in a lowland river. Ecol. Freshw. Fish 15, 191. https://doi.org/10.1111/j.1600-0633.2006.00136.x (2006).Article 

Google Scholar 
Kobler, A., Klefoth, T. & Arlinghaus, R. Site fidelity and seasonal changes in activity centre size of female pike Esox lucius in a small lake. J. Fish Biol. https://doi.org/10.1111/j.1095-8649.2008.01952.x (2008).Article 

Google Scholar 
Kobler, A., Klefoth, T., Mehner, T. & Arlinghaus, R. Coexistence of behavioural types in an aquatic top predator: A response to resource limitation? Oecologia. https://doi.org/10.1007/s00442-009-1415-9 (2009).Article 
PubMed 

Google Scholar 
Boeuf, G. & Falcón, J. Photoperiod and growth in fish. Vie et Milieu 51, 247–266 (2001).
Google Scholar 
Pfauserová, N., Slavík, O. & Horký, P. DATA: An increase in reservoir water levels signals non-native fish species to migrate into tributaries. Mendeley Data 1 (2021).Stroup, W. W. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications (CRC Press, 2012).MATH 

Google Scholar 
Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman & Hall/CRC, 1990).MATH 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).MATH 
Book 

Google Scholar 
Eubank, R. L. Approximate regression models and splines. Commun. Stat. Theory Methods 13, 433–484 (1984).MathSciNet 
MATH 
Article 

Google Scholar 
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3–36 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).McCulloch, C. E., Searle, S. R. & Neuhaus, J. M. Generalized, Linear, and Mixed Models (Wiley, 2008).MATH 

Google Scholar 
Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211 (1984).Article 

Google Scholar 
Maitland, P. S. & Campbell, R. N. Freshwater Fishes of the British Isles (HarperCollins Publishers, 1992).
Google Scholar 
Kottelat, M. & Freyhof, J. J. Handbook of European freshwater fishes. Copeia. https://doi.org/10.1643/OT-08-098a.1 (2007).Article 

Google Scholar 
Lucas, M. C. et al. Spatio-temporal variations in the distribution and abundance of fish in the Yorkshire Ouse system. Sci. Total Environ. 210–211, 437 (1998).ADS 
Article 

Google Scholar 
Lucas, M. C. The influence of environmental factors on movements of lowland-river fish in the Yorkshire Ouse system. Sci. Total Environ. 251–252, 223 (2000).ADS 
PubMed 
Article 

Google Scholar 
Mehner, T., Diekmann, M., Brämick, U. & Lemcke, R. Composition of fish communities in German lakes as related to lake morphology, trophic state, shore structure and human-use intensity. Freshw. Biol. 50, 70–85 (2005).CAS 
Article 

Google Scholar 
Johnson, P. T. J., Olden, J. D. & van der Zanden, M. J. Dam invaders: Impoundments facilitate biological invasions into freshwaters. Front. Ecol. Environ. 6, 357–363 (2008).Article 

Google Scholar 
Liew, J. H., Tan, H. H. & Yeo, D. C. J. Dammed rivers: Impoundments facilitate fish invasions. Freshw. Biol. 61, 1421–1429 (2016).Article 

Google Scholar 
Brito, M. F. G., Daga, V. S. & Vitule, J. R. S. Fisheries and biotic homogenization of freshwater fish in the Brazilian semiarid region. Hydrobiologia 847, 3877–3895 (2020).Article 

Google Scholar 
Kärgenberg, E. et al. Migration patterns of a potamodromous piscivore, asp (Leuciscus aspius), in a river–lake system. J. Fish Biol. 97, 996–1008 (2020).PubMed 
Article 

Google Scholar 
Poulet, N., Beaulaton, L. & Dembski, S. Time trends in fish populations in metropolitan France: Insights from national monitoring data. J. Fish Biol. 79, 1436–1452 (2011).CAS 
PubMed 
Article 

Google Scholar 
Elvira, B. Identification of Non-native freshwater Fishes Established in Europe and Assessment of Their Potential Threats to the Biological Diversity. Convention on the Conservation of European Wildlife and Natural Habitats, 35 (2001).Global Invasive Species Database (GISD). Species Profile Leuciscus idus (2021). http://www.iucngisd.org/gisd/species.php?sc=613. Accessed 15 Sept 2021.Nico, L., Fuller, P. & Neilson, M. Leuciscus idus (Linnaeus, 1758): U.S. Geological Survey, Nonindigenous Aquatic Species Database, Gainesville (2021). https://nas.er.usgs.gov/queries/factsheet.aspx?SpeciesID=557. Accessed 15 Sept 2021.Ovidio, M. & Philippart, J. C. The impact of small physical obstacles on upstream movements of six species of fish: Synthesis of a 5-year telemetry study in the River Meuse basin. Hydrobiologia 483, 55–69 (2002).Article 

Google Scholar 
Hansen, J. H. et al. Ecological consequences of animal migration: Prey partial migration affects predator ecology and prey communities. Ecosystems 23, 292–306 (2020).CAS 
Article 

Google Scholar 
Brodersen, J., Nilsson, P. A., Hansson, L.-A., Skov, C. & Brönmark, C. Condition-dependent individual decision-making determines cyprinid partial migration. Ecology 89, 1195–1200 (2008).PubMed 
Article 

Google Scholar 
Chapman, B. B. et al. To boldly go: Individual differences in boldness influence migratory tendency. Ecol. Lett. 14, 871–876 (2011).PubMed 
Article 

Google Scholar 
Harrison, P. M. et al. Personality-dependent spatial ecology occurs independently from dispersal in wild burbot (Lota lota). Behav. Ecol. 26, 483–492 (2015).Article 

Google Scholar 
Rehage, J. S., Cote, J. & Sih, A. The role of dispersal behaviour and personality in post-establishment spread. In Biological Invasions and Animal Behaviour (eds Weis, J. S. & Sol, D.) 96–116 (Cambridge University Press, 2016).Chapter 

Google Scholar 
Juette, T., Cucherousset, J. & Cote, J. Animal personality and the ecological impacts of freshwater non-native species. Curr. Zool. 60, 417–427 (2014).Article 

Google Scholar 
Sol, D. & Maspons, J. Life history, behaviour and invasion success. Biol. Invas. Anim. Behav. https://doi.org/10.1017/cbo9781139939492.006 (2016).Article 

Google Scholar 
Sol, D. & Weis, J. Highlights and insights from “biological invasions and animal behaviour”. Aquat. Invas. 14, 551–565 (2019).Article 

Google Scholar  More

J.G. was funded by The Danish Independent Research council (grant 0165-00018B). N.S. and J.W.B were funded by EU Horizon 2020 SUPERB (grant agreement 101036849). N.D.B. was funded by UK Research and Innovation’s Global Challenges Research Fund (UKRI GCRF) through the Trade, Development and the Environment Hub project (project number ES/S008160/1). More

Model overview and parametersOur mathematical model of the CF lung microbiome dynamics, originally developed in [20], is based on knowledge of the physiology and interactions among community members from experimental data and evidence in the literature. The model setting is a mucus-plugged tube, open to the air at the top and sealed at the bottom, mimicking a lung bronchiole. This setting is meant to pair with a previously established experimental microcosm called the WinCF system [21], which we use below for experiments. There is an important spatial component to the model, as oxygen penetration from the open top of the tube is constant and shapes the community structure. The consequences of these chemical gradients were first modelled in our initial study [20]. The community members are classified as either “pathogens”, representing classic CF pathogens, or “fermenters”, representing other anaerobic organisms commonly encountered in CF airways. These classifications are a significant simplification, but they can be considered as guilds, in that their individual members have similar inherent properties defined by their core metabolism, antibiotic resistance, and niche occupancy [20]. The definition of classic pathogens and anaerobic fermenters is also clinically relevant, as the former are those assayed in clinical labs for antibiotic resistance to inform treatment decisions, whereas anaerobic fermenters are not cultured or tested for susceptibility in most clinical labs. Classifications of each microbiome member into these guilds are available in Tables S2–S4. Fermenters reside in low oxygen areas and utilize sugars to produce acids [20] (Fig. 1). Pathogens, principally, but not exclusively, Pseudomonas aeruginosa, occupy high oxygen regions where they aerobically respire and utilize amino acids as a carbon source producing ammonium, which increases the surrounding pH [20] (Fig. 1). Pathogens can also respire anaerobically, with nitrate as an electron acceptor (Fig. 1). In addition to increasing the surrounding pH, they produce inhibitor molecules (such as phenazines and quinolones) that inhibit the growth of fermenters [20] (Fig. 1). This model is hereon referred to as the “mathematical model”.Fig. 1: Schematic of principles and interacations defining the mathematical model.All consitunents of the model are represented in illustrating basic assumptions and interactions. Fermenters (θf) metabolize (SG) as a carbon source, which produce acid (F) leading to an increase in hydrons (H+) (i.e. lowering the pH) under anaerobic conditions. This pH decrease inhibitis the growth of pathogens. Pathogens (θP) in the presence of oxygen (SO) (i.e., aerobic conditions) use amino acids (SA) as their primary carbon source. The byproduct of this metabolism is ammonium (P), which produces hydroxide (OH-) leading to an increase in pH, inhibiting fermenter growth. Under anaerobic conditions pathogens use nitrate (SN) as an electron acceptor. In addition to this pathogens produce a chemical inhibitor of fermenters (I).Full size imagePredicting and modelling outcomes of antibiotic therapyTo better conceputalize and compare our modeling and experimental results, we first theoretically predicted the outcomes of antimicrobial therapy against the two guilds using three theoretical drugs: one with fermenter coverage (denoted Tf), one with pathogen coverage (denoted Tp), and one with broad spectrum coverage (denoted Tw). This approach is hereon referred to as the “theoretical prediction”. To further enable comparison to experimental data we outline characteristics of the two guilds we expect to observe in the experiments. Firstly, the growth of anaerobic fermenters is positively correlated with an increase in gas production (bubble formation in the WinCF system) [21]. Second, an increase in P. aeruginosa positively correlates with an increase in its inhibitor molecule (e.g., Quinolone HHQ) and P. aeruginosa does not produce gas in the WinCF system [21]. Thirdly, based on Tables S1–S4 and the CF microbiome literature, fermenters are more diverse than pathogens [2, 43, 44]. These characteristics of our theoretical prediction enable direct comparison to microbiome measures of experimental results, such as alpha diversity, beta diversity, pathogen relative abundance, fermenter relative abundance and total bacterial load (TBL).With our theoretical prediction we expect the following outcomes when communities are exposed to antibiotics: (1) community resistance, (2) community death, (3) pathogen death, and (4) fermenter death (Fig. 2A–E). In both the complete absence of an antibiotic and community resistance, we expect TBL, pathogens, fermenters, HHQ, and gas production measures to increase until reaching carrying capacity (Fig. 2B). The opposite, community death (treatment with Tw) results in both microbial entities failing to grow (Fig. 2C). Tw treatment would not change alpha or beta diversity, as we would simply measure the initial inoculum due to total community death. Outcomes 1 and 2 have a degree of uncertainty due to the fact that it is difficult to assume the community would not change from the inoculum without an antibiotic present, but it is expected that Tw would have less impact on microbiome diversity than Tp or Tf (Fig. 1C). Treatment with Tp results in an anaerobic fermenter bloom, increasing alpha and beta diversity along with gas production and a decrease in HHQ production (Fig. 2D). Finally, in the case of Tf treatment, fermenter abundance and gas production would decrease while HHQ abundance would increase (Fig. 1E). Treatment with Tf will also result in a decrease in alpha diversity and an increase in beta diversity because of changes in community structure when the diverse anaerobic fermenters are killed (Fig. 2E).Fig. 2: Theoretical predictions and Model iteration 1.The initial microbiome is composed of both pathogens and fermenters and is illustrated in (A), but the proportions of these are unique to each patient. Under pressure of the various treatments (B) NT, (C) Tw, (D) Tp, and (E) Tf the predicted community response is illustrated. The response i.e., (expected change) in common microbiome measures as indicated in the legend (yellow = increase, red = decrease). The measures are the following: Alpha diversity (AD), Beta diversity (BD), gas production (GP), total bacterial load (TBL), pathogen abundance (P), fermenter abundance (F), and 2-heptyl-4quinolone abundance (HHQ). The model output treatment-to-NT log-ratio of (F) fermenter population and (G) pathogen population of patient 12 as an example with spatial variation at t = 50 h. Boxplots showing model outcomes of the (H) 16S rRNA gene copy ratio and (I) Pathogen to Fermenter log-ratio compared to the control. Each patients’ actual sputum Pathogen/Fermenter ratio was used as input to the model (n = 24). The dotted grey line denotes no change from treatment.Full size imageThe theoretical prediction was then tested with the mathematical model hereon referred to as “model iteration 1”. Importantly, our model parameters can use relative abundance data of the two guilds as input. Therefore, we used the sputum microbiome data of all 24 subjects as inputs for model interation 1 (Fig. 2F–H). The outputs were in line with our theoretical prediction and showed that the fermenter drug would reduce the fermenter load, with little effect on the pathogens, the pathogen drug vice versa, and the broad-spectrum antibiotic would kill both (Fig. 2F–H). However, model iteration 1 did produce some unexpected results. The TBL of the Tw decreased to similar levels as Tf and Tp, indicating similar levels of killing whether there was selection against a single guild or the whole community (Fig. 2H). In addition, the TBL and Pathogen/Fermenter log-ratio were variable, indicating the carrying capacity and community dynamics were predicated upon characteristics of this initial sputum inoculum (Fig. 2F–H). Our theoretical prediction (Fig. 2A–E), in tandem with model iteration 1 (Fig. 2F–H), provided a platform for comparison to the in vitro antibiotic experiments with the WinCF system described below.Experimental results of antibiotic therapy against the lung microbiomeWe examined the effects of antibiotics (n = 11) on the CF sputum microbiome cultured in a lung bronchiole microcosm (WinCF system, n = 24) using a combination of 16S rRNA gene amplicon sequencing, metabolomics, and qPCR analysis and compared to our theoretical prediction and model iteration 1. This is hereon referred to as the “antibiotic experiment”. The antibiotics were chosen to represent the main chemical classes commonly used in CF clinics and included: amoxicillin, azithromycin, aztreonam, ciprofloxacin, colistin, doxycycline, levofloxacin, meropenem, metronidazole, bactrim (a combination of sulfamethoxazole/trimethoprim), and tobramycin. Each of the 24 sputum samples were used as an incoculum in ASM treated with one of 11 different antibiotics cultured at 37 °C for 48 h (Table S1) and compared to a no-treatment control. WinCF tubes were also inoculated with this media/sputum/antibiotic mixture to quantify gas bubble production from fermentation (as described in [21]). The antibiotic concentration for each drug was variable and chosen to match the measured concentrations in the blood or sputum of pwCF in pharmacokinetic studies (Table S1). The most prominent genera across all samples after growth were Pseudomonas, Streptococcus, Veillonella, Haemophilus, Fusobacterium, Prevotella, Staphylococcus, Achromobacter, and Neisseria (Fig. S2). A principal component analysis (PCA) biplot, examining the top five factors by percent contribution, showed the primary genera driving community differentiation were Pseudomonas, Streptococcus, and Staphylococcus (Fig. S3). The effects of antibiotics and individual patients on the composition of the communities were compared via PERMANOVA (Table S7). Tested separately, both antibiotic and subject source had a highly significant effect on the community structure (p 40%), which occurred in 6.8% of samples. The microbiomes of outcome 6 were predominantly dominated by pathogens compared to the control samples (Fig. S7). We found this outcome to be especially interesting, with potential clinical relevance; we therefore performed follow up experiments to understand it further.Fig. 4: Characterizing outcomes in the antibiotic experiment.Weighted UniFrac distance compared to (A) rRNA gene copies, (B) Gas production, (C) Pathogen to fermenter log ratio, (D) Shannon index. Individual points are colored by antibiotic treatment (n = 11). Observed outcomes (Community resistance, community death, pathogen death, anaerobe death, niche replacement, and release of community level inhibition) are highlighted via large cogs on each of the panels colored by the outcome they represent. These highlighted regions are meant to aid in visualization of their presence in the overlying data. Cutoff values of for the outcomes are further described in Table S17.Full size imageOther interesting data relationships were found in these experiments (Fig. S8) though they were not defined as outcomes. For example, the changing UniFrac distance and change in alpha diversity were negatively correlated (Fig. S8a). A large increase in UniFrac distance (over 40% increase), was generally associated with takeover by a particular ASV, driving this phenomenon (Figs. S7 and S9). According to prevalence measures of theses samples the prominent genera in these instances were Pseudomonas and Streptococcus (Fig. S9). In the cases of meropenem and amoxicillin, UniFrac distances were increased while the Shannon indices were decreased, due to the killing of diverse anaerobic community, but there were fewer cases of an increase in alpha diversity and a significant microbiome change (observed in 3 samples only) indicating a kind of buffering of the microbiome by the diverse anaerobic community (Fig. S7a). The increase in TBL characterizing outcome 6 was rarely associated with an increase in alpha diversity (Table S17). Finally, similar to a phenomenon described in CF sputum [31], when the microbiome alpha diversity increases the metabolome diversity decreases, likely reflecting consumption of different metabolites by a more diverse microbiome (Fig. S7c).Model iteration 2 and experimental validation to explain increase in TBLBecause model iteration 1 did not predict the interesting outcome 6, we altered its parameters to determine if we could observe an increase in TBL in the presence of an antibiotic, hereon referred to as “model iteration 2”. In model iteration 1, parameter λ in the function g2(Z) was set to 0.1, which represents pH driven inhibition of fermenters on pathogen growth. Due to the inverse relationship of this parameter, reducing it to 0.05 increased the strength of inhibition, resulting in an increase in TBL for some subjects, akin to that observed in our experimental outcome 6 (Fig. 5A). This only occurred in Tf treatments in model iteration 2, corresponding to a bloom in pathogens after killing of anaerobes. Furthermore, this phenomenon was only present in modelled samples that initially contained much lower populations of the fermenter guild compared to pathogens and is dependent on the spatial structure driven by oxygen gradients that is an inherent property the modeled system (Figs. 1 and 5A). This finding suggests that outcome 6 in the antibiotic experiment may be driven by an antibiotic mediated release of community level inhibition driven by the effect of low pH from fermenters on pathogens and the inhibition of anaerobes by oxygen [20]. Thus, we set out to explore this phenomenon in more detail experimentally.Fig. 5: Model alteration and verification.(A) Model iteration 2 outcomes of 16S rRNA gene copy ratio of each patients’ actual sputum Pathogen/Fermenter ratio was used as input to the model (n = 24). Individual points are colored by antibiotic treatment (n = 11). The dotted grey line denotes no change from treatment. Subsequent experimental validation using two communities, P1 and P2 (n = 10), showing the (B) pH in relation to log rRNA gene copies, (C) Approximate pH, (D) Pathogen/Fermenter log ratio, (E) log rRNA gene copies, (F) Genera abundance, (G) Distribution based on genera-classification as classical pathogen or anaerobic fermenter. Asterisks denote p-value significance where ****p ≥ 0.0001, ***p ≥ 0.001, **p ≥ 0.01, *p ≥ 0.05.Full size imageA simple in vitro experiment was performed where three antibiotics, meropenem (Tw), tobramycin (Tp), and metronidazole (Tf), were added at 2.048 mg/L in ASM media inoculated with two representative communities obtained from pwCF: P1 and P2 (n = 10 replicates) (Fig. 5B–F). The three drugs were selected based on their common uses against CF infections based on pathogen and/or anaerobic coverage, but we acknowledge that their effects are not exclusive to these organisms. Community P1 did not contain P. aeruginosa via culturing on cetrimide agar, whereas the bacterium was isolated from the sputum of P2. This provided a unique opportunity to test the predictions from model iteration 2 on the outcomes of a community with or without P. aeruginosa. A lower concentration of antibiotics was chosen to avoid widespread killing of the communities. We examined the following: rRNA gene copies, approximate pH (based on RGB color values inferred from phenol red buffered media standards) and 16S rRNA gene amplicon sequencing (Fig. 5). This is hereon referred to as the validation experiment. The validation experiment reproduced outcome 6, where both the number of rRNA gene copies were higher when the antibiotic was present than in the no treatment control for both P1 and P2 (Fig. 5C). In contrast to model iteration 2, this only occurred in treatment Tw (paired t-test, p = 0.000831) (Fig. 5). Accordingly, this increase in TBL corresponded to an increase in pH of the cultures, validating the association of the anaerobe induced fermentation with an inhibition of the communities’ total carrying capacity (p = 1.69 × 10−9, Fig. 5B–E). In fact, there was a strong positive correlation between the TBL and media pH overall (Fig. 5B). Furthermore, P2 reached a higher bacterial load overall than P1 in the validation experiment, indicating that the pathogen’s presence drove the community to a higher carrying capacity (Fig. 5E). The lower growth in community P1 shows that a community of primarily anaerobic fermenters struggles without the aerobic pathogen present. Microbiome profiles of these follow up experiments validated the predictions of model iteration 2 and initial findings of outcome 6 (Fig. 5F, G). Meropenem killed the anaerobic community (primarily Streptococci) and the increase in TBL was driven by a bloom of Pseudomonas (P2 community) and Staphylococcus (P1 community) to a higher level than the communities’ inherent carrying capacity (Fig. 5F, G). This experiment was subsequently repeated (n = 5), with the same results observed (Fig. S10). It was interesting that a similar increase in TBL occurred from a community without a dominant pathogen (P1, Fig. 5G). We hypothesize that this result is due to the importance of both oxygen and pH in the governing dynamics. With very low levels of the pathogen guild, the community struggles to grow due to high oxygen penetration. When the anaerobes are inhibited by antibiotics, even low levels of an initial pathogen can begin to bloom, as they are not inhibited by oxygen or the antibiotic, and this leads to an increase in total carrying capacity.Antibiotic effects at the strain level in pwCFTo explore similar phenomena in outcomes 5 and 6 from pwCF treated with antibiotics we sequenced the metagenomes of sputum samples collected from subjects immediately prior to and during antibiotic treatment (n = 6) (Table S19). To minimize the effects of multiple therapies at once, a common occurrence in CF therapeutics, these samples were selected based on the treatment provided being the only known antibiotic prescribed to the subject at the time. Metagenomes were analyzed at the strain level and TBL was examined using qPCR. Overall, there was no significant decrease in TBL (Fig. 6A, Wilcoxon rank-sum test, p = 0.095), but alpha diversity significantly decreased (Fig. 6B, Wilcoxon rank-sum test, p = 0.045). Analysis of the rank abundance changes of the microbiome at the strain level showed that all six subjects had dynamic changes in their sputum microbiomes associated with antibiotic treatment despite little decrease in TBL (Fig. 6C). Thus, like outcome 5, and indicative of outcome 6, dynamic community changes occur in pwCF with minor changes in TBL.Fig. 6: In vivo changes across individuals.qPCR and shotgun metagenomics were performed on sputum samples from individuals (n = 6) before and after exacerbation. We examined the following: (A) rRNA gene copies (B) Shannon Index, and (C) Rank abundance. Each point on the rank abundance represents an individual strain. The color of lines on the rank abundance represents type of bacterium based on our model definitions where blue equates to Fermenters, red to Pathogens, and green to other.Full size image More

Tolhurst G, Heffron H, Lam YS, Parker HE, Habib AM, Diakogiannaki E, et al. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes. 2012;61:364–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vinolo MAR, Rodrigues HG, Nachbar RT, Curi R. Regulation of inflammation by short chain fatty acids. Nutrients. 2011;3:858–76.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Litvak Y, Byndloss MX, Bäumler AJ. Colonocyte metabolism shapes the gut microbiota. Science. 2018;362:t9076.Article 
CAS 

Google Scholar 
Sanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, Võsa U, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019;51:600–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parada Venegas D, De la Fuente MK, Landskron G, González MJ, Quera R, Dijkstra G, et al. Short chain fatty acids (SCFAs)-mediated gut epithelial and immune regulation and its relevance for inflammatory bowel diseases. Front Immunol. 2019;10:277.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science. 2018;359:1151–6.CAS 
PubMed 
Article 

Google Scholar 
Sitkin S, Vakhitov T, Pokrotnieks J. How to increase the butyrate-producing capacity of the gut microbiome: do IBD patients really need butyrate replacement and butyrogenic therapy? J Crohn’s Colitis. 2018;12:881–2.Article 

Google Scholar 
Lordan C, Thapa D, Ross RP, Cotter PD. Potential for enriching next-generation health-promoting gut bacteria through prebiotics and other dietary components. Gut Microbes. 2019;11:1–20.David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–63.CAS 
PubMed 
Article 

Google Scholar 
Singh V, Yeoh BS, Walker RE, Xiao X, Saha P, Golonka RM, et al. Microbiota fermentation-NLRP3 axis shapes the impact of dietary fibres on intestinal inflammation. Gut. 2019;68:1801–12.Healey G, Murphy R, Butts C, Brough L, Whelan K, Coad J. Habitual dietary fibre intake influences gut microbiota response to an inulin-type fructan prebiotic: a randomised, double-blind, placebo-controlled, cross-over, human intervention study. Brit J Nutr. 2018;119:176–89.CAS 
PubMed 
Article 

Google Scholar 
Baxter NT, Schmidt AW, Venkataraman A, Kim KS, Waldron C, Schmidt TM. Dynamics of human gut microbiota and short-chain fatty acids in response to dietary interventions with three fermentable fibers. mBio. 2019;10:e02566–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deehan EC, Yang C, Perez-Muñoz ME, Nguyen NK, Cheng CC, Triador L, et al. Precision microbiome modulation with discrete dietary fiber structures directs short-chain fatty acid production. Cell Host Microbe. 2020;27:389–404.CAS 
PubMed 
Article 

Google Scholar 
Venkataraman A, Sieber JR, Schmidt AW, Waldron C, Theis KR, Schmidt TM. Variable responses of human microbiomes to dietary supplementation with resistant starch. Microbiome. 2016;4:33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen NK, Deehan EC, Zhang Z, Jin M, Baskota N, Perez-Muñoz ME, et al. Gut microbiota modulation with long-chain corn bran arabinoxylan in adults with overweight and obesity is linked to an individualized temporal increase in fecal propionate. Microbiome. 2020;8:118.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ze X, Duncan SH, Louis P, Flint HJ. Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. ISME J. 2012;6:1535–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lahti L, Salojarvi J, Salonen A, Scheffer M, de Vos WM. Tipping elements in the human intestinal ecosystem. Nat Commun. 2014;5:4344.CAS 
PubMed 
Article 

Google Scholar 
Rodriguez J, Hiel S, Neyrinck AM, Le Roy T, Pötgens SA, Leyrolle Q, et al. Discovery of the gut microbial signature driving the efficacy of prebiotic intervention in obese patients. Gut. 2020;69:1975–87.Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F, Arora T, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015;22:971–82.CAS 
PubMed 
Article 

Google Scholar 
Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 
Article 

Google Scholar 
Davis LMG, Martínez I, Walter J, Goin C, Hutkins RW. Barcoded pyrosequencing reveals that consumption of galactooligosaccharides results in a highly specific bifidogenic response in humans. PLoS ONE. 2011;6:e25200.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Solden LM, Naas AE, Roux S, Daly RA, Collins WB, Nicora CD, et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat Microbiol. 2018;3:1274–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rakoff-Nahoum S, Coyne MJ, Comstock LE. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr Biol. 2014;24:40–9.CAS 
PubMed 
Article 

Google Scholar 
Rao C, Coyte KZ, Bainter W, Geha RS, Martin CR, Rakoff-Nahoum S. Multi-kingdom ecological drivers of microbiota assembly in preterm infants. Nature. 2021;591:633–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Koskella B, Hall LJ, Metcalf C. The microbiome beyond the horizon of ecological and evolutionary theory. Nat Ecol Evol. 2017;1:1606–15.PubMed 
Article 

Google Scholar 
Goldford JE, Lu N, Bajic D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ortiz A, Vega NM, Ratzke C, Gore J. Interspecies bacterial competition regulates community assembly in the C. elegans intestine. ISME J. 2021;15:2131–45.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu Z, de Vries B, Gerritsen J, Smidt H, Zoetendal EG. Microbiome-based stratification to guide dietary interventions to improve human health. Nutr Res. 2020;82:1–10.CAS 
PubMed 
Article 

Google Scholar 
Ahmed W, Rashid S. Functional and therapeutic potential of inulin: a comprehensive review. Crit Rev Food Sci Nutr. 2019;59:1–13.CAS 
PubMed 
Article 

Google Scholar 
Cerqueira FM, Photenhauer AL, Pollet RM, Brown HA, Koropatkin NM. Starch digestion by gut bacteria: crowdsourcing for carbs. Trends Microbiol. 2019;28:95–108.PubMed 
Article 
CAS 

Google Scholar 
Parker KD, Albeke SE, Gigley JP, Goldstein AM, Ward NL. Microbiome composition in both wild-type and disease model mice is heavily influenced by mouse facility. Front Microbiol. 2018;9:1598.PubMed 
PubMed Central 
Article 

Google Scholar 
Ericsson AC, Davis JW, Spollen W, Bivens N, Givan S, Hagan CE, et al. Effects of vendor and genetic background on the composition of the fecal microbiota of inbred mice. PLoS ONE. 2015;10:e116704.Article 
CAS 

Google Scholar 
Martino C, Morton JT, Marotz CA, Thompson LR, Tripathi A, Knight R, et al. A novel sparse compositional technique reveals microbial perturbations. mSystems. 2019;4:e00016–19.PubMed 
PubMed Central 
Article 

Google Scholar 
Lagkouvardos I, Lesker TR, Hitch TCA, Gálvez EJC, Smit N, Neuhaus K, et al. Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family. Microbiome. 2019;7:28.PubMed 
PubMed Central 
Article 

Google Scholar 
Pereira FC, Wasmund K, Cobankovic I, Jehmlich N, Herbold CW, Lee KS, et al. Rational design of a microbial consortium of mucosal sugar utilizers reduces Clostridiodes difficile colonization. Nat Commun. 2020;11:5104.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Almeida A, Mitchell AL, Boland M, Forster SC, Gloor GB, Tarkowska A, et al. A new genomic blueprint of the human gut microbiota. Nature. 2019;568:499–504.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011;334:105–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Creswell R, Tan J, Leff JW, Brooks B, Mahowald MA, Thieroff-Ekerdt R, et al. High-resolution temporal profiling of the human gut microbiome reveals consistent and cascading alterations in response to dietary glycans. Genome Med. 2020;12:59.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mackevicius EL, Bahle AH, Williams AH, Gu S, Denisenko NI, Goldman MS, et al. Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. Elife. 2019;8:e38471.PubMed 
PubMed Central 
Article 

Google Scholar 
Morjaria S, Schluter J, Taylor BP, Littmann ER, Carter RA, Fontana E, et al. Antibiotic-induced shifts in fecal microbiota density and composition during hematopoietic stem cell transplantation. Infect Immun. 2019;87:e00206.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stein RR, Bucci V, Toussaint NC, Buffie CG, Ratsch G, Pamer EG, et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput Biol. 2013;9:e1003388.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Rakoff-Nahoum S, Foster KR, Comstock LE. The evolution of cooperation within the gut microbiota. Nature. 2016;533:255–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Koropatkin NM, Cameron EA, Martens EC. How glycan metabolism shapes the human gut microbiota. Nat Rev Microbiol. 2012;10:323–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chijiiwa R, Hosokawa M, Kogawa M, Nishikawa Y, Ide K, Sakanashi C, et al. Single-cell genomics of uncultured bacteria reveals dietary fiber responders in the mouse gut microbiota. Microbiome. 2020;8:5–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou K. Strategies to promote abundance of Akkermansia muciniphila, an emerging probiotics in the gut, evidence from dietary intervention studies. J Funct Foods. 2017;33:194–201.PubMed 
PubMed Central 
Article 

Google Scholar 
Wu G, Zhao N, Zhang C, Lam YY, Zhao L. Guild-based analysis for understanding gut microbiome in human health and diseases. Genome Med. 2021;13:22.PubMed 
PubMed Central 
Article 

Google Scholar 
Patnode ML, Beller ZW, Han ND, Cheng J, Peters SL, Terrapon N, et al. Interspecies competition impacts targeted manipulation of human gut bacteria by fiber-derived glycans. Cell. 2019;179:59–73.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salonen A, Lahti L, Salojarvi J, Holtrop G, Korpela K, Duncan SH, et al. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME J. 2014;8:2218–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sze MA, Topçuoğlu BD, Lesniak NA, Ruffin MT, Schloss PD. Fecal short-chain fatty acids are not predictive of colonic tumor status and cannot be predicted based on bacterial community structure. mBio. 2019;10:e1419–54.Article 

Google Scholar 
Li L, Abou-Samra E, Ning Z, Zhang X, Mayne J, Wang J, et al. An in vitro model maintaining taxon-specific functional activities of the gut microbiome. Nat Commun. 2019;10:4146.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bucci V, Tzen B, Li N, Simmons M, Tanoue T, Bogart E, et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 2016;17:121.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Buffie CG, Bucci V, Stein RR, McKenney PT, Ling L, Gobourne A, et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature. 2015;517:205–8.CAS 
PubMed 
Article 

Google Scholar 
Lagkouvardos I, Pukall R, Abt B, Foesel BU, Meier-Kolthoff JP, Kumar N, et al. The Mouse Intestinal Bacterial Collection (miBC) provides host-specific insight into cultured diversity and functional potential of the gut microbiota. Nat Microbiol. 2016;1:16131.CAS 
PubMed 
Article 

Google Scholar 
Xiao Y, Angulo MT, Lao S, Weiss ST, Liu Y. An ecological framework to understand the efficacy of fecal microbiota transplantation. Nat Commun. 2020;11:3329.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Worthen WB, Moore JL. Higher-order interactions and indirect effects: a resolution using laboratory Drosophila communities. Am Nat. 1991;138:1092–104.Article 

Google Scholar 
Atkinson G, Batterham AM. True and false interindividual differences in the physiological response to an intervention. Exp Physiol. 2015;100:577–88.PubMed 
Article 

Google Scholar 
Schloss PD. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio. 2018;9:e00525.PubMed 
PubMed Central 
Article 

Google Scholar 
Baxter NT, Lesniak NA, Sinani H, Schloss PD, Koropatkin NM. The glucoamylase inhibitor acarbose has a diet-dependent and reversible effect on the murine gut microbiome. mSphere. 2019;4:e00528.CAS 
PubMed 
PubMed Central 

Google Scholar 
Walker AW, Ince J, Duncan SH, Webster LM, Holtrop G, Ze X, et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 2011;5:220–30.CAS 
PubMed 
Article 

Google Scholar 
Hiel S, Bindels LB, Pachikian BD, Kalala G, Broers V, Zamariola G, et al. Effects of a diet based on inulin-rich vegetables on gut health and nutritional behavior in healthy humans. Am J Clin Nutr. 2019;109:1683–95.PubMed 
PubMed Central 
Article 

Google Scholar 
Nordgaard I, Hove H, Clausen MR, Mortensen PB. Colonic production of butyrate in patients with previous colonic cancer during long-term treatment with dietary fibre (Plantago ovata seeds). Scand J Gastroenterol. 1996;31:1011–20.CAS 
PubMed 
Article 

Google Scholar 
Sakata T. Pitfalls in short-chain fatty acid research: a methodological review. Anim Sci J. 2019;90:3–13.PubMed 
Article 

Google Scholar 
McNeil NI, Cummings JH, James WP. Short chain fatty acid absorption by the human large intestine. Gut. 1978;19:819–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wu RY, Määttänen P, Napper S, Scruten E, Li B, Koike Y, et al. Non-digestible oligosaccharides directly regulate host kinome to modulate host inflammatory responses without alterations in the gut microbiota. Microbiome. 2017;5:135.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gurry T, Nguyen L, Yu X, Alm EJ. Functional heterogeneity in the fermentation capabilities of the healthy human gut microbiota. PLoS ONE. 2021;16:e254004.Article 
CAS 

Google Scholar 
Johnson AJ, Zheng JJ, Kang JW, Saboe A, Knights D, Zivkovic AM. A guide to diet-microbiome study design. Front Nutr. 2020;7:79.PubMed 
PubMed Central 
Article 

Google Scholar 
Shepherd ES, DeLoache WC, Pruss KM, Whitaker WR, Sonnenburg JL. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature. 2018;557:434–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kumar M, Ji B, Zengler K, Nielsen J. Modelling approaches for studying the microbiome. Nat Microbiol. 2019;4:1253–67.CAS 
PubMed 
Article 

Google Scholar 
Gowda K, Ping D, Mani M, Kuehn S. Genomic structure predicts metabolite dynamics in microbial communities. Cell. 2022;185:530–46.CAS 
PubMed 
Article 

Google Scholar 
Qian Y, Lan F, Venturelli OS. Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models. Curr Opin Microbiol. 2021;62:84–92.CAS 
PubMed 
Article 

Google Scholar 
Kolodziejczyk AA, Zheng D, Elinav E. Diet-microbiota interactions and personalized nutrition. Nat Rev Microbiol. 2019;17:742–53.CAS 
PubMed 
Article 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.PubMed Central 

Google Scholar 
Zhang S, Wang H, Zhu M. A sensitive GC/MS detection method for analyzing microbial metabolites short chain fatty acids in fecal and serum samples. Talanta. 2019;196:249–54.CAS 
PubMed 
Article 

Google Scholar 
Cai J, Zhang J, Tian Y, Zhang L, Hatzakis E, Krausz KW, et al. Orthogonal comparison of GC–MS and 1H NMR spectroscopy for short chain fatty acid quantitation. Anal Chem. 2017;89:7900–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jian C, Luukkonen P, Yki-Järvinen H, Salonen A, Korpela K. Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling. PLoS ONE. 2020;15:e227285.Article 
CAS 

Google Scholar 
Liu H, Zeng X, Zhang G, Hou C, Li N, Yu H, et al. Maternal milk and fecal microbes guide the spatiotemporal development of mucosa-associated microbiota and barrier function in the porcine neonatal gut. Bmc Biol. 2019;17:106.PubMed 
PubMed Central 
Article 

Google Scholar 
Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A, Becker A, et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotechnol. 2016;34:942–9.CAS 
PubMed 
Article 

Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 
Article 

Google Scholar 
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 
Article 

Google Scholar 
Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226.PubMed 
PubMed Central 
Article 

Google Scholar 
Hsieh TC, Ma KH, Chao A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol Evol. 2016;7:1451–6.Article 

Google Scholar 
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao Z, Baltar F, Herndl GJ. Linking extracellular enzymes to phylogeny indicates a predominantly particle-associated lifestyle of deep-sea prokaryotes. Sci Adv. 2020;6:z4354.Article 
CAS 

Google Scholar 
Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. Bmc Bioinform. 2010;11:119.Article 
CAS 

Google Scholar 
Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinform. 2012;28:3150–2.CAS 
Article 

Google Scholar 
Clausen PTLC, Aarestrup FM, Lund O. Rapid and precise alignment of raw reads against redundant databases with KMA. Bmc Bioinform. 2018;19:307.Article 
CAS 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nissen JN, Johansen J, Allesøe RL, Sønderby CK, Armenteros JJA, Grønbech CH, et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol. 2021;39:555–60.CAS 
PubMed 
Article 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stewart RD, Auffret MD, Roehe R, Watson M. Open prediction of polysaccharide utilisation loci (PUL) in 5414 public Bacteroidetes genomes using PULpy. 2018. https://www.biorxiv.org/content/10.1101/421024v1.full.Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM, Beckman KB, et al. Evaluating the information content of shallow shotgun metagenomics. mSystems. 2018;3:e00069–18Al-Ghalith GA, Hillmann B, Ang K, Shields-Cutler R, Knights D. SHI7 is a self-learning pipeline for multipurpose short-read DNA quality control. mSystems. 2018;3:e00202.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McDonald JH. Handbook of biological statistics, vol. Baltimore, MD: Sparky House Publishing; 2009.Bashan A, Gibson TE, Friedman J, Carey VJ, Weiss ST, Hohmann EL, et al. Universality of human microbial dynamics. Nature. 2016;534:259–62.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: a probabilistic programming language. Grantee Submission. 2017;76:1–32.
Google Scholar  More

Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120(3), 321–326 (2011).Article 

Google Scholar 
Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19(11), 915–918 (2009).CAS 
PubMed 
Article 

Google Scholar 
Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540(7632), 220–229. https://doi.org/10.1038/nature20588 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rader, R. et al. Non-bee insects are important contributors to global crop pollination. Proc. Natl. Acad. Sci. 113(1), 146–151 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Osterman, J. et al. Global trends in the number and diversity of managed pollinator species. Agr. Ecosyst. Environ. 322, 107653 (2021).Article 

Google Scholar 
Velthuis, H. H. W. & Van Doorn, A. A century of advances in bumblebee domestication and the economic and environmental aspects of its commercialization for pollination. Apidologie 37(4), 421–451 (2006).Article 

Google Scholar 
Hung, K. L. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. Royal Soc. B Biol. Sci. 285(1870), 20172140 (2018).Article 

Google Scholar 
Brown, M. J. F. & Paxton, R. J. The conservation of bees: A global perspective. Apidologie 40(3), 410–416 (2009).Article 

Google Scholar 
Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).CAS 
PubMed 
Article 

Google Scholar 
Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25(6), 345–353 (2010).PubMed 
Article 

Google Scholar 
Vanbergen, A. J. & Initiative, T. I. P. Threats to an ecosystem service: Pressures on pollinators. Front. Ecol. Environ. 11(5), 251–259 (2013).Article 

Google Scholar 
David, A. et al. Widespread contamination of wildflower and bee-collected pollen with complex mixtures of neonicotinoids and fungicides commonly applied to crops. Environ. Int. 88, 169–178. https://doi.org/10.1016/j.envint.2015.12.011 (2016).CAS 
Article 
PubMed 

Google Scholar 
Gradish, A. E. et al. Comparison of pesticide exposure in honey bees (Hymenoptera: Apidae) and bumble bees (Hymenoptera: Apidae): implications for risk assessments. Environ. Entomol. 48(1), 12–21 (2019).PubMed 
Article 

Google Scholar 
Johnson, R. M., Ellis, M. D., Mullin, C. A. & Frazier, M. Pesticides and honey bee toxicity–USA. Apidologie 41(3), 312–331 (2010).CAS 
Article 

Google Scholar 
Johnson, R. M. et al. Ecologically appropriate xenobiotics induce cytochrome P450s in Apis mellifera. PLoS ONE 7(2), e31051. https://doi.org/10.1371/journal.pone.0031051 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mullin, C. A. et al. High levels of miticides and agrochemicals in North American apiaries: Implications for honey bee health. PLoS ONE 5(3), e9754. https://doi.org/10.1371/journal.pone.0009754 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl. Acad. Sci. 108(2), 662–667 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Goulson, D., Lye, G. C. & Darvill, B. Decline and conservation of bumble bees. Annu. Rev. Entomol. 53(1), 191–208. https://doi.org/10.1146/annurev.ento.53.103106.093454 (2008).CAS 
Article 
PubMed 

Google Scholar 
Meeus, I., Brown, M. J. F., de Graaf, D. C. & Smagghe, G. Effects of invasive parasites on bumble bee declines. Conserv. Biol. 25(4), 662–671. https://doi.org/10.1111/j.1523-1739.2011.01707.x (2011).Article 
PubMed 

Google Scholar 
O’Neal, S. T., Anderson, T. D. & Wu-Smart, J. Y. Interactions between pesticides and pathogen susceptibility in honey bees. Curr. Opin. Insect Sci. 26, 57–62. https://doi.org/10.1016/j.cois.2018.01.006 (2018).Article 
PubMed 

Google Scholar 
Botías, C. et al. Multiple stressors interact to impair the performance of bumblebee Bombus terrestris colonies. J. Anim. Ecol. 90(2), 415–431 (2021).PubMed 
Article 

Google Scholar 
Dance, C., Botías, C. & Goulson, D. The combined effects of a monotonous diet and exposure to thiamethoxam on the performance of bumblebee micro-colonies. Ecotoxicol. Environ. Saf. 139, 194–201 (2017).CAS 
PubMed 
Article 

Google Scholar 
Fauser-Misslin, A., Sadd, B. M., Neumann, P. & Sandrock, C. Influence of combined pesticide and parasite exposure on bumblebee colony traits in the laboratory. J. Appl. Ecol. 51(2), 450–459 (2014).Article 

Google Scholar 
Zaragoza-Trello, C., Vilà, M., Botías, C. & Bartomeus, I. Interactions among global change pressures act in a non-additive way on bumblebee individuals and colonies. Funct. Ecol. 35(2), 420–434 (2021).Article 

Google Scholar 
Collett, M., Chittka, L. & Collett, T. S. Spatial memory in insect navigation. Curr. Biol. 23(17), R789–R800 (2013).CAS 
PubMed 
Article 

Google Scholar 
Klein, S., Cabirol, A., Devaud, J. M., Barron, A. B. & Lihoreau, M. Why bees are so vulnerable to environmental stressors. Trends Ecol. Evol. 32(4), 268–278 (2017).PubMed 
Article 

Google Scholar 
Dyer, A. G., Dorin, A., Reinhardt, V., Garcia, J. E. & Rosa, M. G. Bee reverse-learning behavior and intra-colony differences: simulations based on behavioral experiments reveal benefits of diversity. Ecol. Model. 277, 119–131 (2014).Article 

Google Scholar 
Raine, N. E. & Chittka, L. No trade-off between learning speed and associative flexibility in bumblebees: A reversal learning test with multiple colonies. PLoS ONE 7(9), e45096 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Henry, M. et al. A common pesticide decreases foraging success and survival in honey bees. Science 336(6079), 348–350 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Siviter, H., Koricheva, J., Brown, M. J. F. & Leadbeater, E. Quantifying the impact of pesticides on learning and memory in bees. J. Appl. Ecol. 55(6), 2812–2821 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Bitterman, M. E., Menzel, R., Fietz, A. & Schäfer, S. Classical conditioning of proboscis extension in honeybees (Apis mellifera). J. Comp. Psychol. 97(2), 107–119. https://doi.org/10.1037/0735-7036.97.2.107 (1983).CAS 
Article 
PubMed 

Google Scholar 
Takeda, K. Classical conditioned response in the honey bee. J. Insect Physiol. 6(3), 168–179. https://doi.org/10.1016/0022-1910(61)90060-9 (1961).CAS 
Article 

Google Scholar 
Laloi, D. et al. Olfactory conditioning of the proboscis extension in bumble bees. Entomol. Exp. Appl. 90(2), 123–129 (1999).Article 

Google Scholar 
Gómez-Moracho, T., Heeb, P. & Lihoreau, M. Effects of parasites and pathogens on bee cognition. Ecol. Entomol. 42, 51–64 (2017).Article 

Google Scholar 
Garratt, M. P. D. et al. The identity of crop pollinators helps target conservation for improved ecosystem services. Biol. Cons. 169, 128–135 (2014).CAS 
Article 

Google Scholar 
Morandin, L. A., Laverty, T. M. & Kevan, P. G. Bumble bee (Hymenoptera: Apidae) activity and pollination levels in commercial tomato greenhouses. J. Econ. Entomol. 94(2), 462–467 (2001).CAS 
PubMed 
Article 

Google Scholar 
Siviter, H. et al. No evidence for negative impacts of acute sulfoxaflor exposure on bee olfactory conditioning or working memory. PeerJ 7, e7208 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Sparks, T. C. et al. Sulfoxaflor and the sulfoximine insecticides: Chemistry, mode of action and basis for efficacy on resistant insects. Pestic. Biochem. Physiol. 107(1), 1–7. https://doi.org/10.1016/j.pestbp.2013.05.014 (2013).CAS 
Article 
PubMed 

Google Scholar 
Krupke, C. H., Hunt, G. J., Eitzer, B. D., Andino, G. & Given, K. Multiple routes of pesticide exposure for honey bees living near agricultural fields. PLoS ONE 7(1), e29268 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tomizawa, M. & Casida, J. E. Neonicotinoid insecticide toxicology: Mechanisms of selective action. Annu. Rev. Pharmacol. Toxicol. 45, 247–268 (2005).CAS 
PubMed 
Article 

Google Scholar 
Gill, R. J., Ramos-Rodriguez, O. & Raine, N. E. Combined pesticide exposure severely affects individual-and colony-level traits in bees. Nature 491(7422), 105–108 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stanley, D. A., Russell, A. L., Morrison, S. J., Rogers, C. & Raine, N. E. Investigating the impacts of field-realistic exposure to a neonicotinoid pesticide on bumblebee foraging, homing ability and colony growth. J. Appl. Ecol. 53(5), 1440–1449 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williamson, S. M. & Wright, G. A. Exposure to multiple cholinergic pesticides impairs olfactory learning and memory in honeybees. J. Exp. Biol. 216(10), 1799–1807 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, E. C., Chuang, Y. C., Chen, Y. L. & Chang, L. H. Abnormal foraging behavior induced by sublethal dosage of imidacloprid in the honey bee (Hymenoptera: Apidae). J. Econ. Entomol. 101(6), 1743–1748 (2008).CAS 
PubMed 
Article 

Google Scholar 
Yang, E., Chang, H., Wu, W. & Chen, Y. Impaired olfactory associative behavior of honeybee workers due to contamination of imidacloprid in the larval stage. PLoS ONE 7(11), e49472 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Watson, G. B., Siebert, M. W., Wang, N. X., Loso, M. R. & Sparks, T. C. Sulfoxaflor–A sulfoximine insecticide: Review and analysis of mode of action, resistance and cross-resistance. Pestic. Biochem. Physiol. 178, 104924 (2021).CAS 
PubMed 
Article 

Google Scholar 
Cordes, N. et al. Interspecific geographic distribution and variation of the pathogens Nosema bombi and Crithidia species in United States bumble bee populations. J. Invertebr. Pathol. 109(2), 209–216 (2012).PubMed 
Article 

Google Scholar 
Gillespie, S. Factors affecting parasite prevalence among wild bumblebees. Ecol. Entomol. 35(6), 737–747 (2010).Article 

Google Scholar 
Plischuk, S., Antúnez, K., Haramboure, M., Minardi, G. M. & Lange, C. E. Long-term prevalence of the protists Crithidia bombi and Apicystis bombi and detection of the microsporidium Nosema bombi in invasive bumble bees. Environ. Microbiol. Rep. 9(2), 169–173 (2017).CAS 
PubMed 
Article 

Google Scholar 
Shykoff, J. A. & Schmid-Hempel, P. Incidence and effects of four parasites in natural populations of bumble bees in Switzerland. Apidologie 22(2), 117–125 (1991).Article 

Google Scholar 
Gegear, R. J., Otterstatter, M. C. & Thomson, J. D. Bumble-bee foragers infected by a gut parasite have an impaired ability to utilize floral information. Proc. Royal Soc. B Biol. Sci. 273(1590), 1073–1078 (2006).Article 

Google Scholar 
Otterstatter, M. C., Gegear, R. J., Colla, S. R. & Thomson, J. D. Effects of parasitic mites and protozoa on the flower constancy and foraging rate of bumble bees. Behav. Ecol. Sociobiol. 58(4), 383–389 (2005).Article 

Google Scholar 
Martin, C. D., Fountain, M. T. & Brown, M. J. F. Bumblebee olfactory learning affected by task allocation but not by a trypanosome parasite. Sci. Rep. 8(1), 1–8 (2018).
Google Scholar 
Azpiazu, C. et al. Toxicity of the insecticide sulfoxaflor alone and in combination with the fungicide fluxapyroxad in three bee species. Sci. Rep. 11(1), 1–9 (2021).Article 
CAS 

Google Scholar 
European Food Safety Authority (EFSA) et al. Peer review of the pesticide risk assessment for the active substance sulfoxaflor in light of confirmatory data submitted. EFSA J. 17(3), e05633 (2019).
Google Scholar 
Linguadoca, A., Rizzi, C., Villa, S. & Brown, M. J. F. Sulfoxaflor and nutritional deficiency synergistically reduce survival and fecundity in bumblebees. Sci. Total Environ. 795, 148680 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sandor, A., Sarospataki, M. & Farkas, S. The mode of action of neonicotinoids on insects. Növényvédelem 51(1), 14–24 (2015).
Google Scholar 
Stanley, D. A., Smith, K. E. & Raine, N. E. Bumblebee learning and memory is impaired by chronic exposure to a neonicotinoid pesticide. Sci. Rep. 5, 16508 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alghamdi, A., Dalton, L., Phillis, A., Rosato, E. & Mallon, E. B. Immune response impairs learning in free-flying bumble-bees. Biol. Lett. 4(5), 479–481 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mallon, E. B., Brockmann, A. & Schmid-Hempel, P. Immune response inhibits associative learning in insects. Proc. Royal Soc. London Series B Biol. Sci. 270(1532), 2471–2473 (2003).Article 

Google Scholar 
Riddell, C. E. & Mallon, E. B. Insect psychoneuroimmunology: Immune response reduces learning in protein starved bumblebees (Bombus terrestris). Brain Behav. Immun. 20(2), 135–138 (2006).CAS 
PubMed 
Article 

Google Scholar 
Fries, I. et al. Molecular characterization of Nosema bombi (Microsporidia: Nosematidae) and a note on its sites of infection in Bombus terrestris (Hymenoptera: Apoidea). J. Apic. Res. 40(3–4), 91–96 (2001).CAS 
Article 

Google Scholar 
Siviter, H., Folly, A. J., Brown, M. J. F. & Leadbeater, E. Individual and combined impacts of sulfoxaflor and Nosema bombi on bumblebee (Bombus terrestris) larval growth. Proc. R. Soc. B 287(1932), 20200935 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Charbonneau, L. R., Hillier, N. K., Rogers, R. E., Williams, G. R. & Shutler, D. Effects of Nosema apis, N. ceranae, and coinfections on honey bee (Apis mellifera) learning and memory. Sci. Rep. 6, 22626 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gage, S. L. et al. Nosema ceranae parasitism impacts olfactory learning and memory and neurochemistry in honey bees (Apis mellifera). J. Exp. Biol. 221(4), jeb161489. https://doi.org/10.1242/jeb.161489 (2018).Article 
PubMed 

Google Scholar 
Piiroinen, S. & Goulson, D. Chronic neonicotinoid pesticide exposure and parasite stress differentially affects learning in honeybees and bumblebees. Proc. Royal Soc. B Biol. Sci. 283(1828), 20160246 (2016).Article 
CAS 

Google Scholar 
Bell, H. C., Montgomery, C. N., Benavides, J. E. & Nieh, J. C. Effects of nosema ceranae (Dissociodihaplophasida: Nosematidae) and flupyradifurone on olfactory learning in honey bees, Apis mellifera (Hymenoptera: Apidae). J. Insect Sci. https://doi.org/10.1093/jisesa/ieaa130 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Brown, M. J. F., Loosli, R. & Schmid-Hempel, P. Condition-dependent expression of virulence in a trypanosome infecting bumblebees. Oikos 91(3), 421–427. https://doi.org/10.1034/j.1600-0706.2000.910302.x (2000).Article 

Google Scholar 
Siviter, H., Brown, M. J. F. & Leadbeater, E. Sulfoxaflor exposure reduces bumblebee reproductive success. Nature 561(7721), 109–112 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Worden, B. D., Skemp, A. K. & Papaj, D. R. Learning in two contexts: The effects of interference and body size in bumblebees. J. Exp. Biol. 208(11), 2045–2053 (2005).PubMed 
Article 

Google Scholar 
Riveros, A. J. & Gronenberg, W. Olfactory learning and memory in the bumblebee Bombus occidentalis. Naturwissenschaften 96(7), 851–856. https://doi.org/10.1007/s00114-009-0532-y (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Mares, S., Ash, L. & Gronenberg, W. Brain allometry in bumblebee and honey bee workers. Brain Behav. Evol. 66(1), 50–61. https://doi.org/10.1159/000085047 (2005).Article 
PubMed 

Google Scholar 
Arce, A. N. et al. Foraging bumblebees acquire a preference for neonicotinoid-treated food with prolonged exposure. Proc. Royal Soc. B Biol. Sci. 285(1885), 20180655. https://doi.org/10.1098/rspb.2018.0655 (2018).CAS 
Article 

Google Scholar 
Muth, F., Gaxiola, R. L. & Leonard, A. S. No evidence for neonicotinoid preferences in the bumblebee Bombus impatiens. Royal Soc. Open Sci. 7(5), 191883 (2020).ADS 
CAS 
Article 

Google Scholar 
Rutrecht, S. T. & Brown, M. J. F. Differential virulence in a multiple-host parasite of bumble bees: resolving the paradox of parasite survival?. Oikos 118(6), 941–949 (2009).Article 

Google Scholar 
Schmid-Hempel, P., Puhr, K., Krüger, N., Reber, C. & Schmid-Hempel, R. Dynamic and genetic consequences of variation in horizontal transmission for a microparasitic infection. Evolution 53(2), 426–434 (1999).PubMed 
Article 

Google Scholar 
Evans, L. J., Raine, N. E. & Leadbeater, E. Reproductive environment affects learning performance in bumble bees. Behav. Ecol. Sociobiol. 70(12), 2053–2060 (2016).Article 

Google Scholar 
Cole, R. J. The application of the “triangulation” method to the purification of nosema spores from insect tissues. J. Invertebr. Pathol. 15(2), 193–195. https://doi.org/10.1016/0022-2011(70)90233-8 (1970).Article 

Google Scholar 
Folly, A. J., Barton-Navarro, M. & Brown, M. J. F. Exposure to nectar-realistic sugar concentrations negatively impacts the ability of the trypanosome parasite (Crithidia bombi) to infect its bumblebee host. Ecol. Entomol. 45(6), 1495–1498 (2020).Article 

Google Scholar 
Schlüns, H., Sadd, B. M., Schmid-Hempel, P. & Crozier, R. H. Infection with the trypanosome Crithidia bombi and expression of immune-related genes in the bumblebee Bombus terrestris. Dev. Comp. Immunol. 34(7), 705–709 (2010).PubMed 
Article 
CAS 

Google Scholar 
Yourth, C., Brown, M. J. F. & Schmid-Hempel, P. Effects of natal and novel Crithidia bombi (trypanosomatidae) infections on Bombus terrestris hosts. Insectes Soc. 55(1), 86–90. https://doi.org/10.1007/s00040-007-0974-1 (2008).Article 

Google Scholar 
Fournier, A., Rollin, O., Le Féon, V., Decourtye, A. & Henry, M. Crop-emptying rate and the design of pesticide risk assessment schemes in the honey bee and wild bees (Hymenoptera: Apidae). J. Econ. Entomol. 107(1), 38–46 (2014).PubMed 
Article 

Google Scholar 
Samuelson, E. E. W., Chen-Wishart, Z. P., Gill, R. J. & Leadbeater, E. Effect of acute pesticide exposure on bee spatial working memory using an analogue of the radial-arm maze. Sci. Rep. 6(1), 1–11 (2016).Article 
CAS 

Google Scholar 
R Core Team. (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Kassambara, A., Kosinski, M., Biecek, P., & Fabian, S. (2020). survminer: drawing survival curves using ‘ggplot2’. R package version 0.4. 8. 2019.Therneau, T. M. & Lumley, T. Package ‘survival’. R Top Doc 128(10), 28–33 (2020).
Google Scholar 
Bartoń, K. (2020). MuMIn: Multi-Model Inference. R package ver. 1.43. 17. CRAN: The Comprehensive R Archive Network, Berkeley, CA, USA.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar 
Burnham, K. P., & Anderson, D. R. (2002). A practical information-theoretic approach. Model selection and multimodel inference, 2. More

Regional distribution of airborne and surface water bacterial phyla in the Pacific and Atlantic oceansThe two open ocean sailing transects examined in this study included the western Pacific path, sampled in May 2017 from Keelung, Taiwan, towards Fiji (Fig. 1a and Supplementary Data 1), and the Atlantic crossing, sampled in June 2016 from Lorient, France, to Miami, USA (Fig. 1b and Supplementary Data 1). In the water, we found a higher homogeneity in phyla distribution within each transect (significantly lower Euclidean distances between centered log-ratio (CLR)-converted phyla counts (betadispar): Atlantic: 0.3212 compared to 0.4229 in the air, ANOVA (with Tukey’s post hoc), p  More

Permits and ethics approvalAnimal collections and all experimental procedures were conducted with ethical approval from the University of Exeter (2013/247), James Cook University (A2361) and Lizard Island Research Station, permission from the Great Barrier Reef Marine Park Authority (GBRMPA) (G17-39752.1) and under licence from the Australian Government Department of Fisheries (170251).Study speciesThe spiny chromis (Acanthochromis polyacanthus) is a damselfish that exhibits bi-parental care of eggs and juveniles at nests within shallow reef habitat in the tropical Western Pacific39 (Fig. 4). Spiny chromis are planktivores on the Great Barrier Reef, importing nutrients from the plankton to the reef40. As such, their preferred habitat is the reef edge (within ~7 m). Most pairs raise one clutch per season—in our study, second clutches occurred in only 7% of the population—so we tracked first clutches from adult pairs. Adults enhance their reproductive success by fanning eggs to oxygenate them, and chasing away potential predators from eggs and juveniles16,41. Adults also allow their offspring to eat some of their body mucus in a behaviour known as ‘glancing’ that has potential nutritional and/or immunological benefits42.Fig. 4: Spiny chromis (Acanthochromis polyacanthus) nest at the edge of coral reef habitat.Parents, juveniles and a predator (peacock grouper, Cephalopholis argus) can be seen in this photo. Photo credit: S Nedelec.Full size imageField studyWe conducted the field study at Lizard Island Research Station (LIRS) (14° 40′ S, 145° 28′ E), Great Barrier Reef, Australia over an entire spiny chromis breeding season (90 days from 23 October 2017 to 20 January 2018).Sites and nestsWe selected six coral reef edge sites (113–218 m in length) within the lagoon on the south of Lizard Island (Fig. 5). Water temperature ranged from ~26 °C at the start to ~29 °C at the end of the season. The reefs were composed mainly of a mixture of live and dead coral. Prevailing currents were wind-driven from south to north. Three of the sites were exposed to ‘busy boating’ while boating was limited at the other three. Treatments were allocated to sites partly randomly and partly allowing for ease and safety of motorboat access. Nest positions within sites did not differ between treatments in: depth of bottom next to reef (1–5 m at mid-tide with a tidal range of ~2 m, N = 67 measurements at a range of tidal heights, mean ± SE depth = 2.3 ± 0.1 m; LMM, sound treatment: Χ21 = 0.82, p = 0.365, random effect of site: variance = 0.14, standard deviation=0.38); nest height above the sand (range = 0–4 m, mean ± SE = 0.6 ± 0.1 m; sound treatment: Χ21 = 0.52, p = 0.470, site: variance=0.33, standard deviation = 0.57); or distance from the edge of the reef (range = 0–6.6 m, mean ± SE = 1.2 ± 0.2 m; t-test: t33,32 = 1.61, p = 0.112; a linear model did not fit the data). A small number of broods (mean ± SE per site = 4.7 ± 1.0) were already present at each site at the start of the season; spiny chromis occasionally breed outside the main breeding season. These were marked and ignored for the purpose of the experiment. The mean ± SD number of nesting pairs identified at each site at the start of the season was 9.5 ± 2.0 for limited-boating sites and 10.7 ± 3.2 for busy-boating sites. Nesting pairs did not always form clear, stable pairs with obvious territories and so pairs without broods were not tracked through the experiment. The mean ± SD total number of adults at each site at the start of the experiment was 185 ± 92 for limited-boating sites and 119 ± 20 for busy-boating sites.Fig. 5: Map of the experimental sites.Red sites were ‘busy-boating’ areas while blue sites were ‘limited-boating’ areas.Full size imageMotorboat traffic exposure and protectionThere is a navigable channel through the lagoon where the experiment was conducted. Fishing boats, tourist boats and research station boats pass through the channel, but the main source of traffic is research station boats. We chose six sites along the navigable route and randomly allocated these to treatments. Following random allocation, two sites were switched for safety reasons for motorboat drivers (Fig. 5). We experimentally elevated motorboat noise at three of the six sites (busy-boating treatment) to mimic typical traffic around a port, harbour or regularly visited reef. At these sites, we drove eight different 5 m aluminium motorboats with 40 hp Suzuki four-stroke outboard engines repeatedly along the length of the site within 10–30 m of the edge of the reef. Busy-boating sites received an average of 180 motorboat passes each day during 3–6 ‘exposure periods’ lasting 15–20 min each; this totalled 1.25–1.5 h per day of traffic noise at each busy-boating site. The other three sites were protected from motorboat traffic (limited-boating treatment), by marking these reefs on the research station map as areas to avoid by at least 100 m and monitoring activity in the lagoon daily. When experimenters needed to access protected sites, speed was reduced to that where no wake was created (roughly ¼ throttle) within 100 m and boats were anchored 20 m from the reef. See Supplementary Information for further details of motorboat traffic exposure and protection.Acoustic recordings and analysisWe made acoustic recordings of both pressure and particle motion at three locations within each site using an accelerometer with integrated hydrophone (M20-040 manufactured and calibrated by Geospectrum Techologies Inc. Dartmouth, Canada; sensitivity follows a curve from 0 to 5000 Hz) and a digital recorder (Zoom F4, Zoom Corporation, Tokyo, Japan; calibrated using pure sine waves measured with an oscilloscope). See Fig. 6 and Supplementary Information for further details of acoustic recordings, analysis and results.Fig. 6: Pressure power spectral density level (Lp,f (re 1 µPa2 Hz−1)) and particle acceleration power spectral density level (La,f (re 1 (µm s−2)2 Hz−1)) plots showing the mean (solid line) with 5% and 95% exceedance levels (coloured band around solid line) for busy-boating and limited-boating or no-boating treatments.A Lp,f in the wild study (average from three nests per site, ‘busy boating’ includes three passes of a motorboat at 10–250 m per recording location, recordings of limited boating were three minutes in duration per nest). B La,f in the wild study (same recording design as for pressure). C Lp,f in the parental tanks in the captive study (27 locations in a 3 × 3 × 3 grid within the tank, 1-min sample of motorboat playback and ambient reef sound playback for each case). D La,f in the parental tanks in the captive study (same recording design as for pressure), E Lp,f in the juvenile tanks in the captive study from the centre of the rearing tank (these tanks were too small to accommodate the particle motion sensor).Full size imageBreeding and reproductive successEach site was checked by snorkellers every other day to monitor breeding by spiny chromis pairs. Nests with new hatchlings were marked with flagging tape and continued to be checked every other day throughout the season to monitor reproductive success. The day in the season that broods hatched was used to test for an effect of motorboat exposure on the timing of breeding using a linear mixed-effects model (fitted in R) with site as a random effect. The proportion of nests retaining offspring at the end of the season in the two treatments was compared using a Chi-squared test.Brood sizeWe counted the number of offspring in broods within four days after hatching at a subset of 59 nests; 32 in the three busy-boating sites (N = 9, 11, 12) and 27 in the three limited-boating sites (N = 5, 10, 12). Average clutch size at hatching in the wild was 126 ± 16 (mean ± SE). Some of these nests were part of the predator presence observations (details below), some were part of the size monitoring (details below) and some were independent. We counted the number of offspring in three photos and used the highest number for analyses. We tested for an effect of motorboat treatment on brood size at hatching using a Welch’s t-test.Predator presence around nestsWe determined baseline predatory threat (counts of heterospecific piscivores, potential predators of juveniles) using video camera (GoPro 5) deployments. Thirty-three nests (not studied for size) were videoed once or twice between 1 and 11 days post-hatching; 18 in the three busy-boating sites (N = 7, 6, 5) and 15 in the three limited-boating sites (N = 6, 5, 4). A total of 55 videos were analysed. A camera stand was placed at each nest on the first or second day post-hatching and remained in place 2–3 m from the nest. For each survey, after GoPro cameras were attached to camera stands, several minutes settling time was allowed (mean ± sd: 827 ± 35 s), followed by a 30-s recording of predator presence in the absence of any motorboats. All nests that were videoed were at least 10 m away from one another (parents spend most of their time within 2 m of the nest). Videos were randomly named and analysed by KEC without sound (to remain ‘blind’ to treatment) using BORIS 7.6.143. Spiny chromis offspring are small and vulnerable to any piscivore on the reef and parents defend their offspring by chasing potential predators. The number of heterospecific piscivores at each nest was surveyed (conspecifics are known to cannibalise offspring, but this is very rare16). A negative binomial regression parameterised such that the variance is a quadratic function of the mean was fitted using glmmTMB in R with piscivore counts as the response variable, motorboat treatment and days into the season as potentially interacting fixed factors, and nest and site as random effects.Juvenile survivalIt was not possible to observe the eggs as they were laid in caves, so we studied juveniles from when they could be observed above the substrate (shortly after hatching – this species completes the larval stage inside the egg and hatches at the juvenile stage16). We also counted the number of surviving offspring at the subset of 59 nests every 4–8 days. When all juveniles from a nest could be captured in a frame, we used three photos per time point and used the highest number. As juveniles aged, they used more space and could not be captured in a single photo; then, they were counted by snorkellers with experience in fish surveys (SLN and IKD). The reliability of snorkellers’ counts was tested against one another and did not differ when there were 20 juveniles based on counts of 43 nests. Usually, however, when there were >20 juveniles at a nest, they were at an earlier developmental stage and counts could be taken from photos. Survival of juveniles was recorded as number of days from hatching until they were no longer seen at the nest. Where all offspring from a nest were apparently lost to predation (mean survival time = 21 days), the nest continued to be monitored every other day for the remainder of the season to ensure offspring had not temporarily disappeared and to check for second clutches. A Cox proportional-hazards survival model was fitted in R with motorboat treatment and initial hatching count as fixed effects, and nest and site as random effects. We discounted three nests where counts increased due to assumed experimenter error or immigration. The package Coxme was used in R to test for the interaction between treatment and start count (hatch day was not included in the model as there was no indication of an effect of treatment or site on hatch day), with nest and site as random effects. The package coxph was used to create Fig. 1B, which does not account for the random effects, but is used for illustrative purposes.Juvenile sizeWe monitored juvenile size at a subset of 22 nests; 11 in the three busy-boating sites (N = 3, 4, 4) and 11 in the three limited-boating sites (N = 3, 4, 4). Up to 10 juveniles (depending on catch success) were caught by snorkellers or SCUBA divers using hand nets from each nest each week. A total of 275 juveniles between 1 and 53 days post-hatching were sampled. Juveniles were transported to the field station in bags of fresh seawater. We measured standard length either under the microscope at 10× magnification or with a Vernier caliper, depending on fish size. All nests within a site were sampled on the same day and each site was visited for juvenile size sampling each week. Data were log-transformed and analysed using a linear mixed-effects model (fitted in R), with age and motorboat treatment as fixed effects, and nest and site as random effects.Laboratory studyWe conducted the laboratory study in the Marine and Aquaculture Research Facilities Unit (MARFU) at James Cook University, Townsville, Australia from March to July 2018. Spiny chromis adults were caught with fine monofilament barrier nets and hand nets from the section of reef on the lagoon side of Palfrey Island within the lagoon around Lizard Island in the northern Great Barrier Reef (14° 41′ S, 145° 27′ E) during November 2016. All adults would have experienced equivalent prior noise exposure. The mean ± SE standard length of adults was 10.7 ± 0.1 cm. Spiny chromis were randomly allocated to treatments and were housed in 30 male–female pairs, and maintained at a mean ± SE temperature of 27.7 ± 0.1 °C in the presence of either a busy-boating treatment (playback of four of the five recorded motorboats in a pattern matching exposures in the field) or a no-boating treatment (playback of ambient reef sound). We kept most of each brood with the parents to measure survival (cannibalism can rarely occur in this species under stress16) and isolated 50 individuals per brood as a single group in a separate tank (where parents could not compete with offspring for food) with the same playback treatment to measure size. See Supplementary Information for further details of tank setup and conditions, acoustic exposure regime, playback construction, acoustic recording analysis and results for the tanks.BreedingWe checked all adult tanks daily after lights were switched on, but before playbacks began, for the presence of a newly laid clutch. The number of days since the start of the treatment that clutches were laid was used to test for an effect of motorboat noise exposure on the timing of breeding using a two-sample Welch’s t-test.Clutch size and brood sizeWe photographed newly laid clutches and estimated clutch size by counting the number of eggs in a square on an overlaid grid and counting the number of grid squares containing eggs. All adult tanks were checked daily after lights were switched on, but before playbacks began, for the presence of a newly hatched brood. Clutch size and brood size were compared between treatments using two-sample Welch’s t-tests.Egg characteristics and embryonic developmentWe monitored clutch-level and individual-level egg and embryo characteristics at days 1 and 10 during the egg phase of the first clutch laid by each breeding pair. Measures taken were: (1) egg area, (2) yolk sac area, (3) dorsal spine length (day 10 embryos only), and (4) dry weight (at 10 days). Egg area, yolk sac area and spine length were obtained by measuring 10 randomly sampled individuals per clutch under a light microscope (Olympus SZXY). For dry weights, fish were dried in an oven for >24 h at 60 °C and weighed on a Mettler microbalance with ±0.001 mg accuracy. The number of days between laying and hatching was used as the embryonic developmental time. Linear mixed-effects models (fitted in R) were used, with clutch as a random effect and motorboat treatment as a fixed effect.Parental care of embryosWe filmed parental activity (distance moved by both parents) and time spent fanning eggs at day 10 of the egg phase during periods of playback. Two cameras were used: a Logitech HD Webcam C615 camera positioned 45 cm above the tank looking down with the entire tank in the field of view (‘top camera’), and a GoPro HERO 5 positioned inside the tank in front and looking into the nest (‘side camera’). Following a 30-minute settling time, minimising the disturbance to the fish, baseline behaviour was observed for five minutes. Then, in ‘busy-boating’ tanks, motorboat noise was played for a further five minutes, while in ‘no-boating’ tanks, a different ambient track was played for five minutes.Two key nest-caring parental behaviours were identified for analysis:

(1)

Activity – the distance travelled by parents. Average distance travelled within each breeding pair was calculated from the total distance travelled by both the male and female. Activity was observed from the top camera. Distance was calibrated by measuring a known distance on the bottom of the tank, present in all videos. The distance travelled was calculated by marking the position of the fish every second using the manual tracking feature of ImageJ version 1.52d (https://imagej.nih.gov/ij/download.html).

(2)

Fanning – the amount of time parents spent fanning the clutch of eggs. Fanning was observed from the side camera and analysed using Solomon Coder software (https://solomoncoder.com/download.php).

T-tests were used to test for effects of treatment on parental care behaviour.Juvenile survivalWe counted the number of juveniles that survived with their parents at day 21 post-hatching (maximum count from three photos for each tank). Survival was measured at day 21 because that was the mean survival time in the field; also, by day 42 post-hatching, most parents had produced a second clutch which confounded observations of survival. The fish removed at hatching were included in the final count by modelling their survival as equal to that of the rest of the brood. Survival was converted to a percentage from the number of eggs laid in the clutch. This measure of survival is conservative compared with that expected in the field because the only potential predators of the juveniles in the tanks were their parents. Percentage survival rates were non-normally distributed and so were compared between treatments using a Wilcoxon signed-ranks test.Juvenile sizeWe measured the standard lengths (from photos using ImageJ) and dry weights of ten juveniles per clutch at day 21 post-hatching and of eight juveniles per clutch at day 42 post-hatching, following humane sacrifice; fish were dried in an oven for >24 h at 60 °C and weighed on a Mettler microbalance with 0.001 mg accuracy. We measured length and weight from juveniles that were isolated from the parents to avoid the possibility that the parents could compete with the juveniles for food. Dry weights at day 21 and 42 were compared between treatments using an LMM with clutch as a random effect.General statistical approachesFor measures of parental care, or at the level of clutch, t-tests or Wilcoxon signed ranks tests were used. Where we measured several individuals from within multiple sites, clutches or broods, LMMs or GLMMs were used to control for the random effects of site, clutch or brood, provided models fit the data satisfactorily. Plots of residuals vs fitted values were examined to check model fit and where models did not fit the data, standard tests such as t-test/Wilcoxon signed ranks were used in their place. Any effects among nests (such as slight variations in water flow) were therefore controlled for by the LMM or GLMM statistical models. The variance attributed to, and standard deviation of the variance for, random effects are presented as part of the full output from models. We used the same approach for model selection as in13. To establish the best-fitting model, terms were eliminated one by one from a maximal model. Simplified models were compared with more complex ones using maximum likelihood ratio tests that employ chi-square statistics to establish whether a simpler model performed significantly worse at explaining the data than a more complex model. If the simpler model was not significantly worse when a term was removed, the simpler model was deemed better and thus the removed term was dropped. If the simpler model was significantly worse, the term was maintained in the model44. The degrees of freedom from maximum likelihood tests presented in the Results of the main paper are the difference between the degrees of freedom of the simpler and the more complex models. All potential interactions of fixed effects were examined and are only presented where their exclusion from the model made the model significantly worse at explaining the data at the significance level p  More