Philippa Kaur

Tonitto, C., Woodbury, P. B. & McLellan, E. L. Environ. Sci. Policy 87, 64–73 (2018).Article 

Google Scholar 
Carlson, K. M. et al. Nat. Clim. Change 7, 63–68 (2017).ADS 
CAS 
Article 

Google Scholar 
Carson, R., Darling, L. & Darling, L. Silent Spring (Houghton Mifflin, 1962).Audsley, E., Stacey, K. F., Parsons, D. J. & Williams, A. G. Estimation of the Greenhouse Gas Emissions from Agricultural Pesticide Manufacture and Use (Cranfield Univ., 2009).Heimpel, G. E., Yang, Y., Hill, J. D. & Ragsdale, D. W. PLoS ONE 8, e72293 (2013).ADS 
CAS 
Article 

Google Scholar 
Lal, R. Environ. Int. 30, 981–990 (2004).CAS 
Article 

Google Scholar 
Crippa, M. et al. Nat. Food 2, 198–209 (2021).CAS 
Article 

Google Scholar 
Labrie, G. et al. PLoS ONE 15, e0229136 (2020).CAS 
Article 

Google Scholar 
Tang, F. H., Lenzen, M., McBratney, A. & Maggi, F. Nat. Geosci. 14, 206–210 (2021).ADS 
CAS 
Article 

Google Scholar 
Mason, P. G. Biological Control: Global Impacts, Challenges and Future Directions of Pest Management (CSIRO, 2021).Deguine, J. P. et al. Agron. Sustain. Dev. 41, 1–35 (2021).Article 

Google Scholar 
Wyckhuys, K. A. G. et al. J. Environ. Manage. 307, 114529 (2022).Article 

Google Scholar 
Van den Berg, H. & Jiggins, J. World Dev. 35, 663–686 (2007).Article 

Google Scholar 
Godfray, H. C. J. et al. Science 327, 812–818 (2010).ADS 
CAS 
Article 

Google Scholar 
Huang, J. et al. Environ. Res. Lett. 13, 064027 (2018).ADS 
Article 

Google Scholar 
Pecenka, J. R. et al. Proc. Natl Acad. Sci. USA 118, e2108429118 (2021).CAS 
Article 

Google Scholar 
Naranjo, S. E., Ellsworth, P. C. & Frisvold, G. B. Annu. Rev. Entomol. 60, 621–645 (2015).CAS 
Article 

Google Scholar 
Tamburini, G. et al. Sci. Adv. 6, eaba1715 (2020).ADS 
Article 

Google Scholar 
Wolf, S. A. & Ghosh, R. Land Use Policy 96, 103552 (2020).Article 

Google Scholar 
Wyckhuys, K. A. G. et al. Environ. Res. Lett. 13, 094005 (2018).ADS 
Article 

Google Scholar 
Bridge, G. et al. Prog. Hum. Geogr. 44, 724–742 (2020).Article 

Google Scholar 
Gautam, M. et al. Repurposing Agricultural Policies and Support: Options to Transform Agriculture and Food Systems to Better Serve the Health of People, Economies, and the Planet (The World Bank and IFPRI, 2022).Tooker, J. F., O’Neal, M. E. & Rodriguez-Saona, C. Annu. Rev. Entomol. 65, 81–100 (2020).CAS 
Article 

Google Scholar 
van Lenteren, J. C. et al. BioControl 63, 39–59 (2018).Article 

Google Scholar 
Parnell, J. J. et al. Front. Plant Sci. 7, 1110 (2016).Article 

Google Scholar 
Herrero, M. et al. Nat. Food 1, 266–272 (2020).Article 

Google Scholar 
Rosenzweig, C. et al. Nat. Food 1, 94–97 (2020).Article 

Google Scholar 
Rana, J. & Paul, J. J. Retail. Consum. Serv. 38, 157–165 (2017).Article 

Google Scholar  More

Richardson, D.M., & Rundel, P.W. Ecology and biogeography of Pinus: An introduction. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 3–40. (Cambridge Press, 1998).Keeley, J. E. Ecology and evolution of pine life histories. Ann. For. Sci. 69, 445–453 (2012).Article 

Google Scholar 
Agee, J.K. Fire and pine ecosystems. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 193–217. (Cambridge Press, 1998).Keeley, J.E., & Zedler, P.H. Evolution of life histories in Pinus. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 219–251. (Cambridge Press, 1998).Pausas, J. G., Bradstock, R., Keith, D. A. & Keeley, J. E. Plant functional traits in relation to fire in crown-fire ecosystems. Ecology 85, 1085–1100 (2004).Article 

Google Scholar 
Hare, R. C. Contribution of bark to fire resistance of southern trees. J. For. 63, 248–251 (1965).
Google Scholar 
Jackson, J. F., Adams, D. C. & Jackson, U. B. Allometry of constitutive defense: A model and a comparative test with tree bark and fire regime. Am. Nat. 153, 614–632 (1999).PubMed 
Article 

Google Scholar 
Stephens, S. L. & Libby, W. J. Anthropogenic fire and bark thickness in coastal and island pine populations from Alta and Baja California. J. Biogeogr. 33, 648–652 (2006).Article 

Google Scholar 
Chapman, H. H. Is the longleaf type a climax?. Ecology 13, 328–334 (1932).Article 

Google Scholar 
Pile, L. S., Wang, G. G., Knapp, B. O., Liu, G. & Yu, D. Comparing morphology and physiology of southeastern US Pinus seedlings: Implications for adaptation to surface fire regimes. Ann. For. Sci. 74, 68 (2017).Article 

Google Scholar 
Rodríguez-Trejo, D. A. & Fulé, P. Z. Fire ecology of Mexican pines and a fire management proposal. Int. J. Wildl. Fire 12, 23–37 (2003).Article 

Google Scholar 
Pausas, J. G. Bark thickness and fire regime. Funct. Ecol. 29, 315–327 (2015).Article 

Google Scholar 
Little, S. & Mergen, F. External and internal changes associated with basal-crook formation in pitch and shortleaf pines. For. Sci. 12, 268–275 (1966).
Google Scholar 
Kolström, T. & Kellomäki, S. Tree survival in wildfires. Silva Fenn. 27, 277–281 (1993).Article 

Google Scholar 
Schwilk, D. W. & Ackerly, D. D. Flammability and serotiny as strategies: Correlated evolution in pines. Oikos 94, 326–236 (2001).Article 

Google Scholar 
Reyes, O. & Casal, M. Effect of high temperatures on cone opening and on the release and viability of Pinus pinaster and P. radiata seeds in NW Spain. Ann. For. Sci. 59, 327–334 (2002).Article 

Google Scholar 
Pausas, J. G. & Keeley, J. E. Epicormic resprouting in fire-prone ecosystems. Trends Plant Sci. 22, 1008–1015 (2017).CAS 
PubMed 
Article 

Google Scholar 
Fonda, R. W., Bellanger, L. A. & Burley, L. L. Burning characteristics of western conifer needles. Northwest Sci. 72, 1–9 (1998).
Google Scholar 
Fonda, R. W. Burning characteristics of needles from eight pine species. For. Sci. 47, 390–396 (2001).
Google Scholar 
Anderson, H. E. Forest fuel ignitability. Fire Tech. 6, 312–319 (1970).CAS 
Article 

Google Scholar 
Martin, R.E., et al. Assessing the flammability of domestic and wildland vegetation. in Proceedings of the 12th Conference Fire and Forest Meteorology. Jekyll Island. 130–137. (1993)Varner, J. M., Kane, J. M., Kreye, J. K. & Engber, E. The flammability of forest and wildland litter: A synthesis. Curr. For. Rep. 1, 91–99 (2015).
Google Scholar 
Fernandes, P. M. & Cruz, M. G. Plant flammability experiments offer limited insight into vegetation–fire dynamics interactions. New Phytol. 194, 606–609 (2012).PubMed 
Article 

Google Scholar 
Wenk, E. S., Wang, G. G. & Walker, J. L. Within-stand variation in understorey vegetation affects fire behaviour in longleaf pine xeric sandhills. Int. J. Wildl. Fire 20, 866–875 (2012).Article 

Google Scholar 
Whelan, A. W., Bigelow, S. W. & O’Brien, J. J. Overstory longleaf pines and hardwoods create diverse patterns of energy release and fire effects during prescribed fire. Front. For. Glob. Change. 4, 25 (2021).Article 

Google Scholar 
Mutch, R. W. Wildland fires and ecosystems—A hypothesis. Ecology 51, 1046–1051 (1970).Article 

Google Scholar 
Troumbis, A. S. & Trabaud, L. Some questions about flammability in fire ecology. Acta Oecol. 10, 167–175 (1989).
Google Scholar 
Midgley, J. J. Flammability is not selected for, it emerges. Aust. J. Bot. 61, 102–106 (2013).Article 

Google Scholar 
Snyder, J. R. The role of fire: Mutch ado about nothing?. Oikos 43, 404–405 (1984).Article 

Google Scholar 
Bond, W. J. & Midgley, J. J. Kill thy neighbour: An individualistic argument for theevolution of flammability. Oikos 73, 79–85 (1995).Article 

Google Scholar 
Gagnon, P. R. et al. Does pyrogenicity protect burning plants?. Ecology 91, 3481–3486 (2010).PubMed 
Article 

Google Scholar 
Vines, R. G. Heat transfer through bark, and the resistance of trees to fire. Aust. J. Bot. 16, 499–514 (1968).Article 

Google Scholar 
Harmon, M. E. Survival of trees after low-intensity surface fires in Great Smoky Mountains National Park. Ecology 65, 796–802 (1984).Article 

Google Scholar 
Schwilk, D. W., Gaetani, M. S. & Poulos, H. M. Oak bark allometry and fire survival strategies in the Chihuahuan Desert Sky Islands, Texas, USA. PLoS ONE 8, e79285 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Stevens, J., Kling, M., Schwilk, D., Varner, J. M. & Kane, J. M. Biogeography of fire regimes in western US conifer forests: a trait-based approach. Glob. Ecol. Biogeogr. 29, 944–955 (2020).Article 

Google Scholar 
Rosell, J. A. Bark thickness across the angiosperms: More than just fire. New Phytol. 211, 90–102 (2016).CAS 
PubMed 
Article 

Google Scholar 
Kane, J. M., Varner, J. M. & Hiers, J. K. The burning characteristics of southeastern oaks: discriminating fire facilitators from fire impeders. For. Ecol. Manag. 256, 2039–2045 (2008).Article 

Google Scholar 
Engber, E. A. & Varner, J. M. Patterns of flammability of the California oaks: The role of leaf traits. Can. J. For. Res. 42, 1965–1975 (2012).Article 

Google Scholar 
Guyette, R. P., Stambaugh, M. C., Dey, D. C. & Muzika, R. Predicting fire frequency with chemistry and climate. Ecosystems 15, 322–335 (2012).Article 

Google Scholar 
Stambaugh, M.C., Varner, J.M., & Jackson, S.T. Biogeography: An interweave of climate, fire, and humans. in Ecological Restoration and Management of Longleaf Pine Forests (Kirkman, K., Jack, S. B. Eds.). 17–38. (CRC Press, 2017).Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).Article 

Google Scholar 
Schwilk, D. W. & Caprio, A. C. Scaling from leaf traits to fire behavior: community composition predicts fire severity in a temperate forest. J. Ecol. 99, 970–980 (2011).Article 

Google Scholar 
Ormeño, E. et al. The relationship between terpenes and flammability of leaf litter. For. Ecol. Manag. 257, 471–482 (2009).Article 

Google Scholar 
Mirov, N. T. The terpenes (in relation to the biology of genus Pinus). Ann. Rev. Biochem. 17, 521–540 (1948).CAS 
PubMed 
Article 

Google Scholar 
Mitić, Z. S. et al. Needle terpenes as chemotaxonomic markers in Pinus: Subsections Pinus and Pinaster. Chem. Biodivers. 14, e1600453 (2017).Article 

Google Scholar 
Baradat, P. & Yazdani, R. Genetic expression for monoterpenes in clones of Pinus sylvestris grown on different sites. Scand. J. For. Res. 3, 25–36 (1987).Article 

Google Scholar 
Hanover, J. W. Applications of terpene analysis in forest genetics. New For. 6, 159–178 (1992).Article 

Google Scholar 
He, T., Pausas, J. G., Belcher, C. M., Schwilk, D. W. & Lamont, B. B. Fire-adapted traits of Pinus arose in the fiery Cretaceous. New Phytol. 194, 751–759 (2012).PubMed 
Article 

Google Scholar 
Saladin, B. et al. Fossils matter: Improved estimates of divergence times in Pinus reveal older diversification. Evol. Biol. 17, 95 (2017).
Google Scholar 
Kreye, J. K. et al. Effects of solar heating on the moisture dynamics of forest floor litter in humid environments: Composition, structure, and position matter. Can. J. For. Res. 48, 1331–1342 (2018).Article 

Google Scholar 
Ganteaume, A., Jappiot, M., Curt, T., Lampin, C. & Borgniet, L. Flammability of litter sampled according to two different methods: Comparison of results in laboratory experiments. Int. J. Wildl. Fire 23, 1061–1075 (2014).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019). https://www.R-project.org/.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).Article 

Google Scholar 
Orme, D., et al. Caper: Comparative Analyses of Phylogenetics and Evolution in R. Version 1.0.1. https://CRAN.R-project.org/package=caper. (2018).Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
PubMed 
Article 

Google Scholar 
Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: A test and review of evidence. Am. Nat. 160, 712–726 (2002).CAS 
PubMed 
Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R Package Version 1.43.6. https://CRAN.R-project.org/package=MuMIn. (2019).Little, E.L. Atlas of United States Trees. Vol. 1. Conifers and Important Hardwoods. 1–320. (Miscellaneous Publication 1146, USDA, Forest Service, 1971).Prasad, A.M. & Iverson, L.R. Little’s Range and FIA Importance Value Database for 135 Eastern US Tree Species. http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html. (Northeastern Research Station, USDA Forest Service). More

Odonnell, C. Population dynamics and survivorship in bats. In Ecology and Behavioral Methods for the Study of Bats (eds Kunz, T. H. & Parsons, S.) 158–176 (The Johns University Press, 2009).
Google Scholar 
Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
Gibbons, J. W. & Andrews, K. M. PIT tagging: Simple technology at its best. Bioscience 54, 447–454 (2004).Article 

Google Scholar 
Ellison, L. E. et al. A comparison of conventional capture versus PIT reader techniques for estimating survival and capture probabilities of big brown bats (Eptesicus fuscus). Acta Chiropterologica 9, 149–160 (2007).Article 

Google Scholar 
van Harten, E. et al. High detectability with low impact: Optimizing large PIT tracking systems for cave-dwelling bats. Ecol. Evol. 9, 10916–10928 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Schorr, R. A., Ellison, L. E. & Lukacs, P. M. Estimating sample size for landscape-scale mark-recapture studies of North American migratory tree bats. Acta Chiropterologica 16, 231–239 (2014).Article 

Google Scholar 
Baker, G. B. et al. The effect of forearm bands on insectivorous bats (Microchiroptera) in Australia. Wildl. Res. 28, 229–237 (2001).Article 

Google Scholar 
O’Shea, T. J., Ellison, L. E. & Stanley, T. R. Survival estimation in bats: Historical overview, critical appraisal, and suggestions for new approaches. In Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (ed. Thompson, W. L.) 297–336 (Island Press, 2004).
Google Scholar 
O’Shea, T. J. et al. Recruitment in a Colorado population of big brown bats: Breeding probabilities, litter size, and first-year survival. J. Mammal. 91, 418–428 (2010).Article 

Google Scholar 
O’Shea, T. J., Ellison, L. E. & Stanley, T. R. Adult survival and population growth rate in Colorado big brown bats (Eptesicus fuscus). J. Mammal. 92, 433–443 (2011).Article 

Google Scholar 
Schorr, R. A. & Siemers, J. L. Population dynamics of little brown bats (Myotis lucifugus) at summer roosts: Apparent survival, fidelity, abundance, and the influence of winter conditions. Ecol. Evol. 11, 7427–7438 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, C. F. J., Edmonds, H. & Hoare, J. M. Survival of PIT-tagged lesser short-tailed bats (Mystacina tuberculata) through a pest control operation using the toxin pindone in bait stations. N. Z. J. Ecol. 35, 291–295 (2011).
Google Scholar 
Edmonds, H., Pryde, M. & O’Donnell, C. Survival of PIT-tagged lesser short-tailed bats (Mystacina tuberculata) through an aerial 1080 pest control operation. N. Z. J. Ecol. 41, 186–192 (2017).
Google Scholar 
Reusch, C. et al. Differences in seasonal survival suggest species-specific reactions to climate change in two sympatric bat species. Ecol. Evol. 9, 7957–7965 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2020-2. http://www.iucnredlist.org (2020).Lentini, P. E., Bird, T. J., Griffiths, S. R., Godinho, L. N. & Wintle, B. A. A global synthesis of survival estimates for microbats. Biol. Lett. 11, 20150371 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Culina, A., Linton, D. M. & Macdonald, D. W. Age, sex, and climate factors show different effects on survival of three different bat species in a woodland bat community. Glob. Ecol. Conserv. 12, 263–271 (2017).Article 

Google Scholar 
Frick, W. F., Reynolds, D. S. & Kunz, T. H. Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. J. Anim. Ecol. 79, 128–136 (2010).PubMed 
Article 

Google Scholar 
Schorcht, W., Bontadina, F. & Schaub, M. Variation of adult survival drives population dynamics in a migrating forest bat. J. Anim. Ecol. 78, 1182–1190 (2009).PubMed 
Article 

Google Scholar 
Sendor, T. & Simon, M. Population dynamics of the pipistrelle bat: Effects of sex, age and winter weather on seasonal survival. J. Anim. Ecol. 72, 308–320 (2003).Article 

Google Scholar 
Sripathi, K., Raghuram, H., Rajasekar, R., Karuppudurai, T. & Abraham, S. G. Population size and survival in the indian false vampire bat Megaderma lyra. Acta Chiropterologica 6, 145–154 (2004).Article 

Google Scholar 
Papadatou, E., Butlin, R. K., Pradel, R. & Altringham, J. D. Sex-specific roost movements and population dynamics of the vulnerable long-fingered bat, Myotis capaccinii. Biol. Conserv. 142, 280–289 (2009).Article 

Google Scholar 
López-Roig, M. & Serra-Cobo, J. Impact of human disturbance, density, and environmental conditions on the survival probabilities of pipistrelle bat (Pipistrellus pipistrellus). Popul. Ecol. 56, 471–480 (2014).Article 

Google Scholar 
Wilkinson, G. S. & Adams, D. M. Recurrent evolution of extreme longevity in bats. Biol. Lett. 15, 20180860 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
DELWP. National Recovery Plan for the Southern Bent-wing Bat Miniopterus orianae bassanii (2020).Lumsden, L. & Gray, P. Longevity record for a southern bent-wing bat Miniopterus schreibersii bassanii. Australas. Bat Soc. Newsl. 16, 43–44 (2001).
Google Scholar 
Holz, P. H. et al. Virus survey in populations of two subspecies of bent-winged bats (Miniopterus orianae bassanii and oceanensis) in south-eastern Australia reveals a high prevalence of diverse herpesviruses. PLoS ONE 13, e0197625 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F., Marenda, M. S., Browning, G. F. & Hufschmid, J. Two subspecies of bent-winged bats (Miniopterus orianae bassanii and oceanensis) in southern Australia have diverse fungal skin flora but not Pseudogymnoascus destructans. PLoS ONE 13, e0204282 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F. & Hufschmid, J. Ectoparasites are unlikely to be a primary cause of population declines of bent-winged bats in south-eastern Australia. Int. J. Parasitol. Parasites Wildl. 7, 423–428 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F., Legione, A. R. & Hufschmid, J. Polychromophilus melanipherus and haemoplasma infections not associated with clinical signs in southern bent-winged bats (Miniopterus orianae bassanii) and eastern bent-winged bats (Miniopterus orianae oceanensis). Int. J. Parasitol. Parasites Wildl. 8, 10–18 (2019).PubMed 
Article 

Google Scholar 
Holz, P. H., Clark, P., McLelland, D. J., Lumsden, L. F. & Hufschmid, J. Haematology of southern bent-winged bats (Miniopterus orianae bassanii) from the Naracoorte Caves National Park, South Australia. Comp. Clin. Pathol. 29, 231–237 (2020).CAS 
Article 

Google Scholar 
Dwyer, P. D. The population pattern of Miniopterus schreibersii (Chiroptera) in north-eastern New South Wales. Aust. J. Zool. 14, 1073–1137 (1966).Article 

Google Scholar 
Dwyer, P. D. Mortality factors of the bent-winged bat. Vic. Nat. 83, 31–36 (1966).
Google Scholar 
Dwyer, P. D. Seasonal changes in activity and weight of Miniopterus schreibersii blepotis (Chiroptera) in north-eastern NSW. Aust. J. Zool. 12, 52–69 (1964).Article 

Google Scholar 
Bureau of Meteorology. Drought archive. http://www.bom.gov.au/climate/drought/archive.shtml (2019).Dwyer, P. D. Population ranges of Miniopterus schreibersii (Chiroptera) in south-eastern Australia. Aust. J. Zool. 17, 665–686 (1969).Article 

Google Scholar 
Fleischer, T., Gampe, J., Scheuerlein, A. & Kerth, G. Rare catastrophic events drive population dynamics in a bat species with negligible senescence. Sci. Rep. 7, 7370 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mammal. 76, 940–946 (1995).Article 

Google Scholar 
Reeder, D. M. et al. Frequent arousal from hibernation linked to severity of infection and mortality in bats with white-nose syndrome. PLoS ONE 7, e38920 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turbill, C., Bieber, C. & Ruf, T. Hibernation is associated with increased survival and the evolution of slow life histories among mammals. Proc. R. Soc. B Biol. Sci. 278, 3355–3363 (2011).Article 

Google Scholar 
van Harten, E. Population Dynamics of the Critically Endangered, Southern Bent-Winged Bat Miniopterus orianae bassanii (La Trobe University, 2020).
Google Scholar 
PIRSA. History of the south east drainage system – summary. https://www.pir.sa.gov.au/aghistory/natural_resources/water_resources_ag_dev/history_of_the_south_east_drainage_system_-_summary/history_of_the_south_east_drainage_system_-_summary#_ftnref2 (2017).Harding, C., Herpich, D. & Cranswick, R. H. Examining temporal and spatial changes in surface water hydrology of groundwater dependent ecosystems using WOfS (Water Observations from Space): Southern Border Groundwaters Agreement area, South East South Australia. (2018).Holz, P. H., Lumsden, L. F., Reardon, T., Gray, P. & Hufschmid, J. Does size matter? Morphometrics of southern bent-winged bats (Miniopterus orianae bassanii) and eastern bent-winged bats (Miniopterus orianae oceanensis). Aust. Zool. AZ https://doi.org/10.7882/AZ.2019.019 (2020).Article 

Google Scholar 
Rashid, M. M. & Beecham, S. Characterization of meteorological droughts across South Australia. Meteorol. Appl. 26, 556–568 (2019).Article 

Google Scholar 
Culina, A., Linton, D. M., Pradel, R., Bouwhuis, S. & Macdonald, D. W. Live fast, don’t die young: Survival–reproduction trade-offs in long-lived income breeders. J. Anim. Ecol. 88, 746–756 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Kunz, T. H., Whitaker, J. O. & Wadanoli, M. D. Dietary energetics of the insectivorous Mexican free-tailed bat (Tadarida brasiliensis) during pregnancy and lactation. Oecologia 101, 407–415 (1995).CAS 
PubMed 
Article 

Google Scholar 
Adams, R. A. & Hayes, M. A. Water availability and successful lactation by bats as related to climate change in arid regions of western North America. J. Anim. Ecol. 77, 1115–1121 (2008).PubMed 
Article 

Google Scholar 
Henry, M., Thomas, D. W., Vaudry, R. & Carrier, M. Foraging distances and home range of pregnant and lactating little brown bats (Myotis lucifugus). J. Mammal. 83, 767–774 (2002).Article 

Google Scholar 
Lučan, R. & Radil, J. Variability of foraging and roosting activities in adult females of Daubenton’s bat (Myotis daubentonii) in different seasons. Biologia (Bratisl.) 65 (2010).Amorim, F., Jorge, I., Beja, P. & Rebelo, H. Following the water? Landscape-scale temporal changes in bat spatial distribution in relation to Mediterranean summer drought. Ecol. Evol. 8, 5801–5814 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, C. F. J. Timing of breeding, productivity and survival of long-tailed bats Chalinolobus tuberculatus (Chiroptera: Vespertilionidae) in cold-temperate rainforest in New Zealand. J. Zool. 257, 311–323 (2002).Article 

Google Scholar 
Holz, P. H., Stent, A., Lumsden, L. F. & Hufschmid, J. Trauma found to be a significant cause of death in a pathological investigation of bent-winged bats (Miniopterus orianae). J. Zoo Wildl. Med. 50, 966–971 (2020).PubMed 
Article 

Google Scholar 
Hughes, P. M., Rayner, J. M. V. & Jonesg, G. Ontogeny of ‘true’ flight and other aspects of growth in the bat Pipistrellus pipistrellus. J. Zool. 236, 291–318 (1995).Article 

Google Scholar 
Wund, M. A. Learning and the development of habitat-specific bat echolocation. Anim. Behav. 70, 441–450 (2005).Article 

Google Scholar 
McGuire, L. P. et al. Common condition indices are no more effective than body mass for estimating fat stores in insectivorous bats. J. Mammal. 99, 1065–1071 (2018).Article 

Google Scholar 
Mispagel, C. et al. DDT and metabolites residues in the southern bent-wing bat (Miniopterus schreibersii bassanii) of south-eastern Australia. Chemosphere 55, 997–1003 (2004).CAS 
PubMed 
Article 

Google Scholar 
Allinson, G. et al. Organochlorine and trace metal residues in adult southern bent-wing bat (Miniopterus schreibersii bassanii) in southeastern Australia. Chemosphere 64, 1464–1471 (2006).CAS 
PubMed 
Article 

Google Scholar 
Kolkert, H., Andrew, R., Smith, R., Rader, R. & Reid, N. Insectivorous bats selectively source moths and eat mostly pest insects on dryland and irrigated cotton farms. Ecol. Evol. https://doi.org/10.1002/ece3.5901 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sherwin, H. A., Montgomery, W. I. & Lundy, M. G. The impact and implications of climate change for bats. Mammal Rev. 43, 171–182 (2013).Article 

Google Scholar 
O’Shea, T. J., Cryan, P. M., Hayman, D. T. S., Plowright, R. K. & Streicker, D. G. Multiple mortality events in bats: A global review. Mammal Rev. 46, 175–190 (2016).Article 

Google Scholar 
Mundinger, C., Scheuerlein, A. & Kerth, G. Long-term study shows that increasing body size in response to warmer summers is associated with a higher mortality risk in a long-lived bat species. Proc. R. Soc. B Biol. Sci. 288, 20210508 (2021).Article 

Google Scholar 
Adams, R. A. & Hayes, M. A. Assemblage-level analysis of sex-ratios in Coloradan bats in relation to climate variables: A model for future expectations. Glob. Ecol. Conserv. 14, e00379 (2018).Article 

Google Scholar 
Crichton, E. G., Seamark, R. F. & Krutzsch, P. H. The status of the corpus luteum during pregnancy in Miniopterus schreibersii (Chiroptera: Vespertilionidae) with emphasis on its role in developmental delay. Cell Tissue Res. 258, 183–201 (1989).CAS 
PubMed 
Article 

Google Scholar 
Olsen, I. C. The analysis of continuous mark-recapture data (Norwegian University of Science and Technology, 2006).
Google Scholar 
Barbour, A. B., Ponciano, J. M. & Lorenzen, K. Apparent survival estimation from continuous mark-recapture/resighting data. Methods Ecol. Evol. 4, 846–853 (2013).Article 

Google Scholar 
van Harten, E. et al. Recovery of southern bent-winged bats (Miniopterus orianae bassanii) after PIT-tagging and the use of surgical adhesive. Aust. Mammal. 42, 216–219 (2020).Article 

Google Scholar 
McDonald, T. L., Amstrup, S. C. & Manly, B. F. Tag loss can bias Jolly-Seber capture-recapture estimates. Wildl. Soc. Bull. 31, 814–822 (2003).
Google Scholar 
van Harten, E. et al. Low rates of PIT-tag loss in an insectivorous bat species. J. Wildl. Manag. 85, 1739–1743 (2021).Article 

Google Scholar 
Lebl, K. & Ruf, T. An easy way to reduce PIT-tag loss in rodents. Ecol. Res. 25, 251–253 (2010).Article 

Google Scholar 
Rigby, E. L., Aegerter, J., Brash, M. & Altringham, J. D. Impact of PIT tagging on recapture rates, body condition and reproductive success of wild Daubenton’s bats (Myotis daubentonii). Vet. Rec. 170, 101 (2012).CAS 
PubMed 
Article 

Google Scholar 
Locatelli, A. G., Ciuti, S., Presetnik, P., Toffoli, R. & Teeling, E. Long-term monitoring of the effects of weather and marking techniques on body condition in the Kuhl’s pipistrelle bat, Pipistrellus kuhlii. Acta Chiropterologica 21, 87–102 (2019).Article 

Google Scholar 
Paniw, M. et al. The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: A global analysis. J. Anim. Ecol. 90, 1398–1407 (2021).PubMed 
Article 

Google Scholar 
Frick, W. F., Kingston, T. & Flanders, J. A review of the major threats and challenges to global bat conservation. Ann. N. Y. Acad. Sci. 1469, 5–25 (2020).PubMed 
Article 

Google Scholar 
Brunet-Rossinni, A. K. & Wilkinson, G. S. Methods for age estimation and the study of senescence in bats. In Ecological and Behavioral Methods for the Study of Bats (eds Kunz, T. H. & Parsons, S.) 315–325 (Johns Hopkins University Press, 2009).
Google Scholar 
Churchill, S. Australian Bats (Allen and Unwin, 2008).
Google Scholar 
Laake, J. L. RMark: An R interface for analysis of capture-recapture data with MARK. 25 (2013).Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference (Springer, 2002). https://doi.org/10.1007/b97636.Book 
MATH 

Google Scholar 
Caswell, H. Matrix population models. In Encyclopedia of Environmetrics (eds El-Shaarawi, A. H. & Piegorsch, W. W.) (Wiley, Berlin, 2006). https://doi.org/10.1002/9780470057339.vam006m.Chapter 

Google Scholar 
Dwyer, P. D. The breeding biology of Miniopterus schreibersii blepotis (Termminck) (Chiroptera) in north-eastern NSW. Aust. J. Zool. 11, 219–240 (1963).Article 

Google Scholar 
Richardson, E. G. The biology and evolution of the reproductive cycle of Miniopterus schreibersii and M. australis (Chiroptera: Vespertilionidae). J. Zool. 183, 353–375 (1977).Article 

Google Scholar  More

Water samples were filtered twice (see Methods), first through a large filter (0.45 µm, LF or “Large Filter”) and then the filtrate was passed through a small filter (0.05 µm, UF or “Ultrafine Fraction”). Using whole genome shotgun metagenomics from four water samples (LF92 and UF92 from the 92 m depth, LF99 and UF99 from the 99 m depth) as well as one sediment sample, we provide the first comprehensive whole genome shotgun metagenomics investigation of this section of the lake and highlight both the taxonomic composition and potential metabolic strategies for survival, as well as identify areas for deeper investigation.Cell counts and dissolved nutrientsIn order to determine the habitability of the anoxic basin, the cell counts were measured in the oxycline (75 m depth) and the anoxic region (92 and 99 m depth), where oxygen content is  More

Study areaThe study was conducted in the headwaters of the Chalpi Grande River watershed, 95 km2, located inside the Cayambe-Coca National Park in the northern Andes of Ecuador at an elevation range of 3789 to 3835 m (S 0°16′ 45″, W 78° 4′49″). This watershed harbors the primary water supply system for Quito. The system includes two reservoirs and 10 water intakes placed on first and second-order streams that, altogether, provide 39% of Quito’s water supply28. We monitored the Chalpi Norte stream for ~1.5 years prior to conducting our experiment for ~0.5 years (176 days), and ~0.4 years after the manipulation. Further, in the nearby area, we monitored 21 stream sites distributed upstream and downstream water intakes from the supply system (Fig. S4).Experiment for flow manipulation and monitoring flow reduction and recoveryWe conducted our experimental flow manipulation between October 2018 and April 2019 in a mainly rain-fed stream45. The experiment manipulated natural flows encompassing stable low flows and sporadic spates characterizing the high temporal variability of headwaters45,28 (Figs. 2a, b and S1). We set up a full Before-After/Control- Impact (BACI) experiment29 to evaluate ecosystem variables under natural and manipulated flow conditions. We identified a free-flowing stream reach on the Chalpi Norte that was above any water intakes that allowed us to divert flow with an ecohydraulic structure31. The structure was located above a meander, which we used to divert flow and return it to the stream below the meander (Fig. S4). The experimental site was comprised of an upstream/free-flowing reach (L = 25 m) (reference conditions), located ~32 m above the ecohydraulic structure and a downstream/regulated reach (L = 97 m) located immediately below the flow manipulation structure (Fig. 1b–d)31. The control site was located in a free-flowing stream, a tributary of the Chalpi Norte stream, with an upstream reach separated from a downstream reach by a distance of 16 m. We manipulated the instantaneous flow of the Chalpi Norte stream through a series of fixed percentages using different v-notch weir pairs31. We started diversions to maintain in the meander 100, 80, 60, 50, 40, 30, and 20% of the incoming flow for 7-day periods (based on local observations of benthic algal colonization); then we maintained 10% of the upstream flow for 36 days. We started to return flow gradually to recover 20, 30, 40, 50, 60, 80, and 100% of the upstream flow. In response to a natural spate while we maintained the 10% of upstream flow, the manipulated flow briefly (during ~9 h) increased above the targeted reduction (i.e., 54% instead of 10%) (Fig. 2a). We registered the spate of flow on the upstream reach of the experimental site (Figs. 2b and S1).Stream monitoring in adjacent streamsWe monitored 21 stream sites between July 2017 and July 2019. We selected seven streams with water intakes placed on the main channel (Chalpi Norte, Gonzalito, Quillugsha 1, 2, 3, Venado, and Guaytaloma). We sampled one site upstream of the water intake and two sites (i.e., 10 m and 500 m) downstream to obtain a wide range of flow reduction levels (Fig. S4) (see, 30 for further details on stream sites).Global literature surveyWe performed a systematic literature review to explore benthic algae responses to flow alterations (increase or decrease), focusing on cyanobacteria in streams. We used ISI Web of Science, Google Scholar, and Google Search for the entries: “benthic cyanobacteria” + “stream”, and “river”, “benthic algal bloom” + “flow” and all available combinations (Table S1). We selected papers containing information on benthic cyanobacteria and algae biomass and flow or water level measurements; specifically, we explored detailed information regarding experiments, spatial studies with upstream and downstream sites, and temporal replicates, as well seasonal associated benthic cyanobacteria blooms. We used published and/or publicly available data to calculate the percent of flow alteration in streams and calculated a factor on cyanobacteria biomass increase or decrease (quantitative studies) according to reported baseline conditions (either temporal or spatial). Only three out of 53 study sites reported a qualitative decrease in benthic cyanobacteria biomass attributable to flow reduction (Fig. 1d). Most studies (94%, n = 50) reported biomass increases with flow reductions. Among these studies sites, 44% reported qualitative observations where low flows were proposed as one of the environmental drivers responsible for benthic cyanobacteria blooms. While 66% of study sites (n = 33) related cyanobacterium biomass increase in time or space due to flow reductions caused by droughts, extreme low flow events, water abstractions, and experimental flumes manipulations.Abiotic and biotic variables sampling and analysesWater level sensors recording every 30 min (HOBO U40L, Onset USA) were installed at both upstream and downstream sites of water intakes, and on the experimental and control stream reaches (BACI desing), where we conducted multiple wading-rod flow measurements to convert water level into discharge via stage-discharge relationships (ADC current meter, OTT Hydromet, Germany). Streamwater’s physical and chemical in situ parameters (i.e., pH, temperature, conductivity, dissolved oxygen) were measured three times during biotic sampling on both stream sites and adjacent streams using a portable sonde (YSI, Xylem, USA). We collected water samples (500 ml) during in situ samplings to analyze nutrients (i.e., nitrate and phosphate) at the water supply company’s (EPMAPS) laboratory. We also measured precipitation from a rain gauge (HOBO Onset USA) installed in the Chalpi Norte stream.Our biotic variables included three benthic algae: cyanobacteria, diatoms, and green algae), and aquatic invertebrates biomass (Table 1). To measure Chl-a from cyanobacteria and benthic algae on artificial substrates, we used a BenthoTorch® (bbe Moldaenke GmbH, Germany) on unglazed ceramic plates (200 mm × 400 mm) with a grid of 25 squares of 2500 mm2 to allow algal accrual on a standardized surface. We allowed 21 days for colonization (based on previous observations) and then we placed all substrates5 at the beginning of the experiment. We performed five readings on five squares randomly selected within each plate. To consider the effect of benthic invertebrates to flow variations, we sampled stream sites using a Surber net (mesh size = 250 µm, area = 0.0625 m2). On the experimental and control sites we measured biotic, physical, and chemical in situ parameters every two days (n = 1760), and nutrients and invertebrates every seven days (n = 500) for the duration of the flow manipulation (~0.5 years). On the monitored sites, we measured biotic, physical, and chemical in situ parameters every seven days (n = 1456) and nutrients and invertebrates every 30 days (n = 336). To evaluate differences we calculated mean abiotic and biotic variables during the different phases (BL: baseline, FR: flow reduction, FI: gradual reset to initial flow) in the four-stream reaches to apply the BACI design29: upstream and downstream reaches on the experimental and control sites. We applied a paired one-tail t-test at α = 0.05 to compare FR and FI phases to baseline conditions, based on the expected direction of the response 1,14.Statistics and reproducibilityTo quantify the relationships between environmental variables and cyanobacteria biomass under manipulated and natural flow conditions, including interaction among algae and with invertebrates, we used multivariate autoregressive state-space modeling (MARSS)14,30. We fitted models with Gaussian errors for flow, conductivity, pH, water temperature, nitrate, phosphate, cyanobacteria, benthic algae, and invertebrate biomass time series via maximum likelihood (MARSS R-package)48. The state processes Xt includes state measurements for all four benthic components (cyanobacteria, diatoms, green algae, and invertebrates’ biomasses) considering the interactions between benthic components and environmental covariates (flow, conductivity, pH, water temperature, nitrate, phosphate) evolving through time, as follows:$${X}_{t}={{BX}}_{t-1}+U+{C}_{{Ct}}+{W}_{t}; {W}_{t} sim {MVN}(0,Q)$$
(1)
$${Y}_{t}={{ZX}}_{t}+{V}_{t} ; {V}_{t} sim {MVN}(0,R)$$
(2)
with Xt a matrix of states at time t, Yt a matrix of observations at time t, Wt a matrix of process errors (multivariate normally distributed with mean 0 and variance Q), Vt is a matrix of observation errors (normally distributed with mean 0 and variance R). Z is a matrix linking the observations Yt and the correspondent state Xt. B is an interaction matrix with inter-specific interaction (diatom and green algae) and with invertebrate strengths, Ct is a matrix of environmental variables (flow, conductivity, pH, water temperature, nitrate, phosphate) at time t. C is a matrix of coefficients indicating the effect of Ct to states Xt. U describes the mean trend. We computed a total of 12 models from the most complete to the simplest, the best-fitting model was identified as having the lowest Akaike Information Criterion adjusted for small sample sizes (AICc)14,30. To detect structural breaks in cyanobacteria biomass time series we calculated the differences between the smoothed state estimates at time t and t-1 based on the multivariate models. Sudden changes in the level were detected when the standardized smoothed state residuals exceed the 95% confidence interval for a t-distribution. We estimated the strength of environmental variables on cyanobacteria biomass and fitted models independently for each stream reach.To analyze cyanobacteria biomass across a gradient of flow alterations we compared weekly paired data (n = 1456) from upstream and downstream sites (i.e., at 10 m and 500 m). We thus calculated how much downstream site(s) biomass changed in comparison to upstream site biomass and assigned a factor for the increase or decrease. We determined the relative fraction of the instantaneous upstream flow in the downstream site measured within a 30-min time-step. We applied the same analysis to data from experiments obtained on the web search. We applied the Ramer–Douglas–Peucker (RDP) algorithm to find a breakpoint (ε lower distance to breakpoint) and the best line of fit for the local and global survey data distribution, we used the kmlShape-R package 48.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

(ECHA), E. C. A. Know more about the effects of the chemicals we use in Europe (ECHA/PR/16/01). https://echa.europa.eu/de/-/know-more-about-the-effects-of-the-chemicals-we-use-in-europe (2016).Liu, W. J., Nie, H. X., Liang, D. P., Bai, Y. & Liu, H. W. Phospholipid imaging of zebrafish exposed to fipronil using atmospheric pressure matrix-assisted laser desorption ionization mass spectrometry. Talanta https://doi.org/10.1016/j.talanta.2019.120357 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sparvero, L. J. et al. Mapping of phospholipids by MALDI imaging (MALDI-MSI): Realities and expectations. Chem. Phys. Lipid. 165, 545–562. https://doi.org/10.1016/j.chemphyslip.2012.06.001 (2012).CAS 
Article 

Google Scholar 
Koizumi, S. et al. Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain. Neuroscience 168(1), 219–225. https://doi.org/10.1016/j.neuroscience.2010.03.056 (2010).CAS 
Article 
PubMed 

Google Scholar 
Hankin, J. A. et al. MALDI mass spectrometric imaging of lipids in rat brain injury models. J. Am. Soc. Mass Spectrom. 22(6), 1014–1021. https://doi.org/10.1007/s13361-011-0122-z (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, C. et al. MALDI-MS imaging reveals asymmetric spatial distribution of lipid metabolites from bisphenol s-induced nephrotoxicity. Anal. Chem. 90(5), 3196–3204. https://doi.org/10.1021/acs.analchem.7b04540 (2018).CAS 
Article 
PubMed 

Google Scholar 
Barbacci, D. C. et al. Mass spectrometric imaging of ceramide biomarkers tracks therapeutic response in traumatic brain injury. ACS Chem. Neurosci. 8(10), 2266–2274. https://doi.org/10.1021/acschemneuro.7b00189 (2017).CAS 
Article 
PubMed 

Google Scholar 
Rompp, A. et al. Histology by mass spectrometry: Label-free tissue characterization obtained from high-accuracy bioanalytical imaging. Angew. Chem. Int. Ed. 49, 3834–3838. https://doi.org/10.1002/anie.200905559 (2010).CAS 
Article 

Google Scholar 
Zemski Berry, K. A. et al. MALDI imaging of lipid biochemistry in tissues by mass spectrometry. Chem. Rev. 111, 6491–6512. https://doi.org/10.1021/cr200280p (2011).CAS 
Article 

Google Scholar 
Cornett, D. S., Reyzer, M. L., Chaurand, P. & Caprioli, R. M. MALDI imaging mass spectrometry: Molecular snapshots of biochemical systems. Nat. Methods 4, 828–833. https://doi.org/10.1038/nmeth1094 (2007).CAS 
Article 
PubMed 

Google Scholar 
Römpp, A. & Spengler, B. Mass spectrometry imaging with high resolution in mass and space. Histochem. Cell Biol. 139, 759–783. https://doi.org/10.1007/s00418-013-1097-6 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Monroe, E. B. et al. SIMS and MALDI MS imaging of the spinal cord. Proteomics 8(18), 3746-3754. https://doi.org/10.1002/pmic.200800127 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chaurand, P., Cornett, D. S., Angel, P. M. & Caprioli, R. M. From whole-body sections down to cellular level, multiscale imaging of phospholipids by MALDI mass spectrometry. Mol. Cell. Proteom. https://doi.org/10.1074/mcp.O110.004259 (2011).Article 

Google Scholar 
Lee, H.-B. & Peart, T. E. Determination of bisphenol A in sewage effluent and sludge by solid-phase and supercritical fluid extraction and gas chromatography/mass spectrometry. J. AOAC Int. 83, 290–298. https://doi.org/10.1093/jaoac/83.2.290 (2000).CAS 
Article 
PubMed 

Google Scholar 
Desbenoit, N., Walch, A., Spengler, B., Brunelle, A. & Römpp, A. Correlative mass spectrometry imaging, applying time-of-flight secondary ion mass spectrometry and atmospheric pressure matrix-assisted laser desorption/ionization to a single tissue section. Rapid Commun. Mass Spectrometry 32, 159–166. https://doi.org/10.1002/rcm.8022 (2018).ADS 
CAS 
Article 

Google Scholar 
Meding, S. et al. Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. J. Proteome Res. 11, 1996–2003. https://doi.org/10.1021/pr200784p (2012).CAS 
Article 
PubMed 

Google Scholar 
Ritschar, S. et al. Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning. Histochem. Cell Biol. https://doi.org/10.1007/s00418-021-02037-1 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Altshuler, I. et al. An integrated multi-disciplinary approach for studying multiple stressors in freshwater ecosystems: Daphnia as a model organism. Integr. Comp. Biol. 51(4), 623–633. https://doi.org/10.1093/icb/icr103 (2011).CAS 
Article 
PubMed 

Google Scholar 
Bambino, K. & Chu, J. in Zebrafish at the Interface of Development and Disease Research Vol. 124 Current Topics in Developmental Biology (ed K. C. Sadler) 331–367 (2017).Seda, J. & Petrusek, A. Daphnia as a model organism in limnology and aquatic biology: Introductory remarks. J. Limnol. 70, 337–344. https://doi.org/10.4081/jlimnol.2011.337 (2011).Article 

Google Scholar 
de Souza Anselmo, C., Sardela, V. F., de Sousa, V. P. & Pereira, H. M. G. Zebrafish (Danio rerio): A valuable tool for predicting the metabolism of xenobiotics in humans? Comp. Biochem. Physiol. Part C: Toxicol. Pharmacol. 212, 34–46. https://doi.org/10.1016/j.cbpc.2018.06.005 (2018).CAS 
Article 

Google Scholar 
Panula, P. et al. The comparative neuroanatomy and neurochemistry of zebrafish CNS systems of relevance to human neuropsychiatric diseases. Neurobiol. Dis. 40, 46–57. https://doi.org/10.1016/j.nbd.2010.05.010 (2010).CAS 
Article 
PubMed 

Google Scholar 
Korn, H. & Faber, D. S. The Mauthner cell half a century later: A neurobiological model for decision-making?. Neuron 47, 13–28. https://doi.org/10.1016/j.neuron.2005.05.019 (2005).CAS 
Article 
PubMed 

Google Scholar 
Schirmer, E., Schuster, S. & Machnik, P. Bisphenols exert detrimental effects on neuronal signaling in mature vertebrate brains. Commun. Biol. https://doi.org/10.1038/s42003-021-01966-w (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Flößner, D. Book review: Cladocera: The genus Daphnia (including Daphniopsis). Int. Rev. Hydrobiol. 90, 637. https://doi.org/10.1002/iroh.200590003 (2005).Article 

Google Scholar 
OECD. Test No. 211: Daphnia magna Reproduction Test. (2012).Muyssen, B. T. A. & Janssen, C. R. Multigeneration zinc acclimation and tolerance in Daphnia magna: Implications for water-quality guidelines and ecological risk assessment. Environ. Toxicol. Chem. 20, 2053–2060. https://doi.org/10.1002/etc.5620200926 (2001).CAS 
Article 
PubMed 

Google Scholar 
Blewett, T. A. et al. Sublethal and reproductive effects of acute and chronic exposure to flowback and produced water from hydraulic fracturing on the water flea Daphnia magna. Environ. Sci. Technol. 51, 3032–3039. https://doi.org/10.1021/acs.est.6b05179 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Yang, J. H., Kim, H. J., Lee, S. M., Kim, B. M. & Seo, Y. R. Cadmium-induced biomarkers discovery and comparative network analysis in Daphnia magna. Mol. Cell. Toxicol. 13, 327–336. https://doi.org/10.1007/s13273-017-0036-3 (2017).CAS 
Article 

Google Scholar 
Ferain, A. et al. Body lipid composition modulates acute cadmium toxicity in Daphnia magna adults and juveniles. Chemosphere 205, 328–338. https://doi.org/10.1016/j.chemosphere.2018.04.091 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ritschar, S., Narayana, V. K. B., Rabus, M. & Laforsch, C. Uncovering the chemistry behind inducible morphological defences in the crustacean Daphniamagna via micro-Raman spectroscopy. Sci. Rep. 10(1), 22408. https://doi.org/10.1038/s41598-020-79755-4 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Machnik, P., Schirmer, E., Glück, L. & Schuster, S. Recordings in an integrating central neuron provide a quick way for identifying appropriate anaesthetic use in fish. Sci. Rep. 8, 17541. https://doi.org/10.1038/s41598-018-36130-8 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Luzio, A. et al. Copper induced upregulation of apoptosis related genes in zebrafish (Danio rerio) gill. Aquat. Toxicol. 128, 183–189. https://doi.org/10.1016/j.aquatox.2012.12.018 (2013).CAS 
Article 
PubMed 

Google Scholar 
Macirella, R. & Brunelli, E. Morphofunctional alterations in zebrafish (Danio rerio) gills after exposure to mercury chloride. Int. J. Mol. Sci. https://doi.org/10.3390/ijms18040824 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Mansouri, B. & Johari, S. A. Effects of short-term exposure to sublethal concentrations of silver nanoparticles on histopathology and electron microscope ultrastructure of zebrafish (Danio rerio) gills. IJT 10, 15–20. https://doi.org/10.32598/IJT.10.1.60.4 (2016).CAS 
Article 

Google Scholar 
Perez, C. J., Tata, A., de Campos, M. L., Peng, C. & Ifa, D. R. Monitoring toxic ionic liquids in zebrafish (Danio rerio) with desorption electrospray ionization mass spectrometry imaging (DESI-MSI). J. Am. Soc. Mass Spectrom. 28, 1136–1148. https://doi.org/10.1007/s13361-016-1515-9 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Stutts, W. L. et al. Methods for cryosectioning and mass spectrometry imaging of whole-body zebrafish. J. Am. Soc. Mass Spectrom. 31, 768–772. https://doi.org/10.1021/jasms.9b00097 (2020).CAS 
Article 
PubMed 

Google Scholar 
Purves, D. & Williams, S. M. Neuroscience. 2nd edition. Vol. Chapter 11, Vision: The Eye (Sinauer Associates, 2001).
Google Scholar 
Strungaru, S. A. et al. Toxicity and chronic effects of deltamethrin exposure on zebrafish (Danio rerio) as a reference model for freshwater fish community. Ecotoxicol. Environ. Saf. 171, 854–862. https://doi.org/10.1016/j.ecoenv.2019.01.057 (2019).CAS 
Article 
PubMed 

Google Scholar 
Mishra, A. & Devi, Y. Histopathological alterations in the brain (optic tectum) of the fresh water teleost Channa punctatus in response to acute and subchronic exposure to the pesticide Chlorpyrifos. Acta Histochem. 116, 176–181. https://doi.org/10.1016/j.acthis.2013.07.001 (2014).CAS 
Article 
PubMed 

Google Scholar 
Jia, W., Mao, L., Zhang, L., Zhang, Y. & Jiang, H. Effects of two strobilurins (azoxystrobin and picoxystrobin) on embryonic development and enzyme activities in juveniles and adult fish livers of zebrafish (Danio rerio). Chemosphere 207, 573–580. https://doi.org/10.1016/j.chemosphere.2018.05.138 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Seyoum, A., Pradhan, A., Jass, J. & Olsson, P. E. Perfluorinated alkyl substances impede growth, reproduction, lipid metabolism and lifespan in Daphnia magna. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.139682 (2020).Article 
PubMed 

Google Scholar 
Scanlan, L. D. et al. Gene transcription, metabolite and lipid profiling in eco-indicator Daphnia magna indicate diverse mechanisms of toxicity by legacy and emerging flame-retardants. Environ. Sci. Technol. 49, 7400–7410. https://doi.org/10.1021/acs.est.5b00977 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Heinlaan, M. et al. Changes in the Daphnia magna midgut upon ingestion of copper oxide nanoparticles: A transmission electron microscopy study. Water Res. 45, 179–190. https://doi.org/10.1016/j.watres.2010.08.026 (2011).CAS 
Article 
PubMed 

Google Scholar 
Abe, T., Saito, H., Niikura, Y., Shigeoka, T. & Nakano, Y. Embryonic development assay with Daphnia magna: Application to toxicity of aniline derivatives. Chemosphere 45, 487–495. https://doi.org/10.1016/s0045-6535(01)00049-2 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Sengupta, N., Gerard, P. D. & Baldwin, W. S. Perturbations in polar lipids, starvation survival and reproduction following exposure to unsaturated fatty acids or environmental toxicants in Daphnia magna. Chemosphere 144, 2302–2311. https://doi.org/10.1016/j.chemosphere.2015.11.015 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Huber, K. et al. Approaching cellular resolution and reliable identification in mass spectrometry imaging of tryptic peptides. Anal. Bioanal. Chem. 410, 5825–5837. https://doi.org/10.1007/s00216-018-1199-z (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
White, R. M. et al. Transparent adult zebrafish as a tool for in vivo transplantation analysis. Cell Stem Cell 2, 183–189. https://doi.org/10.1016/j.stem.2007.11.002 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nagayoshi, S. et al. Insertional mutagenesis by the Tol2 transposon-mediated enhancer trap approach generated mutations in two developmental genes: tcf7 and synembryn-like. Development 135, 159–169. https://doi.org/10.1242/dev.009050 (2008).CAS 
Article 
PubMed 

Google Scholar 
Perciedu Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. Exp. Physiol. 105, 1459–1466. https://doi.org/10.1113/EP088870 (2020).Article 

Google Scholar 
Elendt, B. P. Selenium deficiency in Crustacea. Protoplasma 154, 25–33. https://doi.org/10.1007/BF01349532 (1990).CAS 
Article 

Google Scholar 
Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527–D532. https://doi.org/10.1093/nar/gkl838 (2007).CAS 
Article 
PubMed 

Google Scholar 
Race, A. M., Styles, I. B. & Bunch, J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J. Proteom. 75, 5111–5112. https://doi.org/10.1016/j.jprot.2012.05.035 (2012).CAS 
Article 

Google Scholar 
Robichaud, G., Garrard, K. P., Barry, J. A. & Muddiman, D. C. MSiReader: An open-source interface to view and analyze high resolving power MS imaging files on Matlab platform. J. Am. Soc. Mass Spectrom. 24, 718–721. https://doi.org/10.1007/s13361-013-0607-z (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Ryther, J. H. Photosynthesis and fish production in the sea. Sci. (80-.) 166, 72–76 (1969).ADS 
CAS 
Article 

Google Scholar 
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Sci. (80-.). 315, 1843–1846 (2007).ADS 
CAS 
Article 

Google Scholar 
Edwards, K. F., Litchman, E. & Klausmeier, C. A. Functional traits explain phytoplankton community structure and seasonal dynamics in a marine ecosystem. Ecol. Lett. 16, 56–63 (2013).PubMed 
Article 

Google Scholar 
Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Villarino, E. et al. Large-scale ocean connectivity and planktonic body size. Nat. Commun. 9, 142 (2018).Collins, S., Rost, B. & Rynearson, T. A. Evolutionary potential of marine phytoplankton under ocean acidification. Evol. Appl. 7, 140–155 (2014).CAS 
PubMed 
Article 

Google Scholar 
Rusch, D. B. et al. The Sorcerer II global ocean sampling expedition: Northwest Atlantic through Eastern Tropical Pacific. PLOS Biol. 5, e77 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Sci. (80-.). 348, 1261605–1/11 (2015).Sunagawa, S. et al. Structure and function of the global ocean microbiome. Sci. (80-.) 348, 1–10 (2015).Article 
CAS 

Google Scholar 
Fuhrman, J. A. et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl Acad. Sci. 105, 7774–7778 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, 1–11 (2019).Article 

Google Scholar 
Cermeño, P. et al. The role of nutricline depth in regulating the ocean carbon cycle. PNAS 105, 20344–20349 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J. & Follows, M. J. Patterns of diversity in marine phytoplankton. Sci. (80-.) 327, 1509–1511 (2010).ADS 
CAS 
Article 

Google Scholar 
Mantyla, A. W., Venrick, E. L. & Hayward, T. L. Primary production and chlorophyll relationships, derived from ten year of CalCOFI measurements. Calif. Cooperative Ocean. Fish. Investig. Rep. 36, 159–166 (1995).
Google Scholar 
Hayward, T. L. & Venrick, E. L. Nearsurface pattern in the California Current: Coupling between physical and biological structure. Deep. Res. Part II Top. Stud. Oceanogr. https://doi.org/10.1016/S0967-0645(98)80010-6 (1998).Article 

Google Scholar 
Venrick, E. L. Floral patterns in the California Current: The coastal-offshore boundary zone. J. Mar. Res. 67, 89–111 (2009).Article 

Google Scholar 
Powell, J. R. & Ohman, M. D. Covariability of zooplankton gradients with glider-detected density fronts in the Southern California Current System. Deep Sea Res. Part II Top. Stud. Oceanogr. 112, 79–90 (2015).ADS 
CAS 
Article 

Google Scholar 
Taylor, A. G., Landry, M. R., Selph, K. E. & Wokuluk, J. J. Temporal and spatial patterns of microbial community biomass and composition in the Southern California Current Ecosystem. Deep. Res. Part II Top. Stud. Oceanogr. 112, 117–128 (2015).Catlett, D. et al. Diagnosing seasonal to multi-decadal phytoplankton group dynamics in a highly productive coastal ecosystem. Prog. Oceanogr. 197, 102637 (2021).Article 

Google Scholar 
Lilly, L. E. & Ohman, M. D. CCE IV: El Niño-related zooplankton variability in the southern California Current System. Deep. Res. Part I Oceanogr. Res. Pap. 140, 36–51 (2018).ADS 
Article 

Google Scholar 
Richardson, A. J. et al. Using continuous plankton recorder data. Prog. Oceanogr. 68, 27–74 (2006).ADS 
Article 

Google Scholar 
Wang, Z. et al. Microbial communities across nearshore to offshore coastal transects are primarily shaped by distance and temperature. Environ. Microbiol. 1462–2920.14734. https://doi.org/10.1111/1462-2920.14734 (2019).Wang, Y. et al. Patterns and processes of free-living and particle-associated bacterioplankton and archaeaplankton communities in a subtropical river-bay system in South China. Limnol. Oceanogr. 65, S161–S179 (2020).Ibarbalz, F. M. et al. Global Trends in Marine Plankton Diversity across Kingdoms of Life. Cell 1084–1097. https://doi.org/10.1016/j.cell.2019.10.008 (2019).Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).CAS 
PubMed 
Article 

Google Scholar 
Karl, D. M. & Lukas, R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep. Res. Part II Top. Stud. Oceanogr. 43, 129–156 (1996).ADS 
CAS 
Article 

Google Scholar 
Steinberg, D. K. et al. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): A decade-scale look at ocean biology and biogeochemistry Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep. Res. Part II Top. Stud. Oceanogr. 48, 1405–1447 (2015).ADS 
Article 

Google Scholar 
Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).Zhu, Z. et al. Understanding the blob bloom: Warming increases toxicity and abundance of the harmful bloom diatom Pseudo-nitzschia in California coastal waters. Harmful Algae 67, 36–43 (2017).CAS 
PubMed 
Article 

Google Scholar 
Mcclatchie, S. et al. State of the California Current 2015–16: Comparisons with the 1997–98 El Niño. Calif. Cooperative Ocean. Fish. Investig. Rep. 57, (2016).Walker, H. J. Jr et al. Unusual occurrences of fishes in the Southern California Current System during the warm water period of 2014–2018. Estuar. Coast. Shelf Sci. 236, 106634 (2020).Article 

Google Scholar 
Kahru, M., Jacox, M. G. & Ohman, M. D. CCE1: Decrease in the frequency of oceanic fronts and surface chlorophyll concentration in the California Current System during the 2014–2016 northeast Pacific warm anomalies. Deep. Res. Part I Oceanogr. Res. Pap. 140, 4–13 (2018).ADS 
Article 

Google Scholar 
Azam, F. et al. The Ecological Role of Water-Column Microbes in the Sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 
Article 

Google Scholar 
Calbet, A. & Landry, M. R. Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol. Oceanogr. 49, 51–57 (2004).ADS 
CAS 
Article 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 
Article 

Google Scholar 
Kohonen, T. Exploration of very large databases by self-organizing maps. IEEE Int. Conf. Neural Networks – Conf. Proc. 1, (1997).Istvánovics, V. Eutrophication of Lakes and Reservoirs. Encycl. Inl. Waters 157–165 https://doi.org/10.1016/B978-012370626-3.00141-1 (2009).Partensky, F., Blanchot, J. & Vaulot, D. Differential distribution and ecology of Prochlorococcus and Synechococcus in oceanic waters: a review. Bull. Oceanogr. Monaco 19, 457–475 (1999).
Google Scholar 
Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Global Biogeochem. Cycles 14, (2000).Grover, J. P. Resource Competition in a Variable Environment: Phytoplankton Growing According to Monod’s Model. Am. Nat. 136, 771–789 (1990).Article 

Google Scholar 
Benincá, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Williams, R. G. & Follows, M. J. Ocean Dynamics and the Carbon Cycle: Principles and Mechanisms. Book (2011).Lindegren, M., Checkley, D. M., Ohman, M. D., Koslow, J. A. & Goericke, R. Resilience and stability of a pelagic marine ecosystem. Proc. R. Soc. B Biol. Sci. 283, (2016).Vallina, S. M. et al. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 1–10 https://doi.org/10.1038/ncomms5299 (2014).Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity-biodiversity relationship. Nature 416, 427–430 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jacox, M. G., Edwards, C. A., Hazen, E. L. & Bograd, S. J. Coastal Upwelling Revisited: Ekman, Bakun, and Improved Upwelling Indices for the U.S. West Coast. J. Geophys. Res. Ocean. 123, 7332–7350 (2018).ADS 
Article 

Google Scholar 
Zaba, K. D. & Rudnick, D. L. The 2014-2015 warming anomaly in the Southern California Current System observed by underwater gliders. Geophys. Res. Lett. 43, 1241–1248 (2016).ADS 
Article 

Google Scholar 
Weber, E. D. et al. State of the California Current 2019–2020: Back to the Future With Marine Heatwaves? Front. Mar. Sci. 8, (2021).Closset, I. et al. Diatom response to alterations in upwelling and nutrient dynamics associated with climate forcing in the California Current System. Limnol. Oceanogr. 1–16. https://doi.org/10.1002/lno.11705 (2021).Kenitz, K. M. et al. Environmental drivers of population variability in colony-forming marine diatoms. Limnol. Oceanogr. 65, 2515–2528 (2020).ADS 
Article 

Google Scholar 
Mullin, M. M. Biomasses of large-celled phytoplankton and their relation to the nitricline and grazing in the California current system off Southern California, 1994–1996. Calif. Cooperative Ocean. Fish. Investig. Rep. 39, 117–123 (1998).
Google Scholar 
Rykaczewski, R. R. & Checkley, D. M. Influence of ocean winds on the pelagic ecosystem in upwelling regions. PNAS 105, 1965–1970 (2007).ADS 
Article 

Google Scholar 
Grzymski, J. J. & Dussaq, A. M. The significance of nitrogen cost minimization in proteomes of marine microorganisms. ISME J. 6, 71–80 (2012).Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Ocean. Acta 1, (1978).Falkowski, P. G. & Oliver, M. J. Mix and match: How climate selects phytoplankton. Nat. Rev. Microbiol. 5, 813–819 (2007).Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).Phoma, B. S. & Makhalanyane, T. P. Depth-dependent variables shape community structure and functionality in the Prince Edward Islands. Microb. Ecol. 81, 396–409 (2021).Kahru, M. & Mitchell, B. G. Seasonal and nonseasonal variability of satellite-derived chlorophyll and colored dissolved organic matter concentration in the California Current. J. Geophys. Res. Ocean. 106, 2517–2529 (2001).ADS 
CAS 
Article 

Google Scholar 
Barth, A., Walter, R. K., Robbins, I. & Pasulka, A. Seasonal and interannual variability of phytoplankton abundance and community composition on the Central Coast of California. Mar. Ecol. Prog. Ser. 637, (2020).Powell, J. R. & Ohman, M. D. Changes in zooplankton habitat, behavior, and acoustic scattering characteristics across glider-resolved fronts in the Southern California Current System. Prog. Oceanogr. 134, 77–92 (2015).ADS 
Article 

Google Scholar 
Taylor, A. G. & Landry, M. R. Phytoplankton biomass and size structure across trophic gradients in the southern California Current and adjacent ocean ecosystems. Mar. Ecol. Prog. Ser. 592, 1–17 (2018).ADS 
CAS 
Article 

Google Scholar 
Dutkiewicz, S., Follows, M. J. & Bragg, J. G. Modeling the coupling of ocean ecology and biogeochemistry. Glob. Biogeochem. Cycles 23, 1–15 (2009).Article 
CAS 

Google Scholar 
D’Ovidio, F., De Monte, S., Alvain, S., Dandonneau, Y. & Lévy, M. Fluid dynamical niches of phytoplankton types. Proc. Natl Acad. Sci. U. S. A. 107, 18366–18370 (2010).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clayton, S., Dutkiewicz, S., Jahn, O. & Follows, M. J. Dispersal, eddies, and the diversity of marine phytoplankton. Limnol. Oceanogr. Fluids Environ. 3, 182–197 (2013).Article 

Google Scholar 
Moisan, T. A., Rufty, K. M., Moisan, J. R. & Linkswiler, M. A. Satellite observations of phytoplankton functional type spatial distributions, phenology, diversity, and ecotones. Front. Mar. Sci. 4, 1–24 (2017).Article 

Google Scholar 
Combes, V. et al. Cross-shore transport variability in the California Current: Ekman upwelling vs. eddy dynamics. Prog. Oceanogr. 109, 78–89 (2013).ADS 
Article 

Google Scholar 
Chenillat, F., Rivière, P., Capet, X., Franks, P. J. S. & Blanke, B. California coastal upwelling onset variability: cross-shore and bottom-up propagation in the planktonic ecosystem. PLoS ONE 8, (2013).Chenillat, F., Franks, P. J. S. & Combes, V. Biogeochemical properties of eddies in the California Current System. Geophys. Res. Lett. 43, 5812–5820 (2016).ADS 
CAS 
Article 

Google Scholar 
Edwards, K. F., Thomas, M. K., Klausmeier, C. A. & Litchman, E. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57, 554–566 (2012).ADS 
Article 

Google Scholar 
Wells, B. K. et al. State of the California Current 2016–17: Still anything but ‘normal’ in the north. Calif. Cooperative Ocean. Fish. Investig. Rep. 58 (2017).Thompson, A. R. et al. State of the California Current 2017–18: Still not quite normal in the north and getting interesting in the south. Calif. Cooperative Ocean. Fish. Investig. Rep. 59 (2018).Ward, C. S. et al. Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J. 11, 1412–1422 (2017).Bograd, S. J., Schroeder, I. D. & Jacox, M. G. A water mass history of the Southern California current system. Geophys. Res. Lett. 46, 6690–6698 (2019).ADS 
Article 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18 (2016).Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA Genes. PLoS ONE 4, (2009).Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.J. 17, (2011).Callahan, B. J., Mcmurdie, P. J., Rosen, M. J., Han, A. W. & A, A. J. DADA2: High resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6 (2018).Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12 (2011).Pruesse, E. et al. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35 (2007).Guillou, L. et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41 (2013).McMurdie, P. J. & Holmes, S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput. Biol. 10 (2014).Gloor, G. B., Wu, J. R., Pawlowsky-Glahn, V. & Egozcue, J. J. It’s all relative: analyzing microbiome data as compositions. Ann. Epidemiol. 26 (2016).Cameron, E. S., Schmidt, P. J., Tremblay, B. J. M., Emelko, M. B. & Müller, K. M. To rarefy or not to rarefy: Enhancing microbial community analysis through next-generation sequencing. bioRxiv. https://doi.org/10.1101/2020.09.09.290049 (2020).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. (2020).Bowman, J. S., Amaral-zettler, L. A., Rich, J. J., Luria, C. M. & Ducklow, H. W. Bacterial community segmentation facilitates the prediction of ecosystem function along the coast of the western Antarctic Peninsula. Nat. Publ. Gr. 11, 1460–1471 (2017).
Google Scholar 
Boelaert, J., Bendhaiba, L., Olteanu, M. & Villa-Vialaneix, N. SOMbrero: An R package for numeric and non-numeric self-organizing maps. Adv. Intell. Syst. Comput 295, 219–228 (2014).
Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
Article 

Google Scholar 
James, C. C. et al. Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region. https://doi.org/10.5281/zenodo.6359865 (2022).Legendre, P. & Legendre, L. Numerical ecology (Elsevier, 2012). More

Meropol, S. B., Haupt, A. A. & Debanne, S. M. Incidence and outcomes of infections caused by multidrug-resistant Enterobacteriaceae in Children, 2007–2015. J. Pediatr. Infect. Dis. Soc. 7, 36–45 (2018).Article 

Google Scholar 
Moxon, C. A. & Paulus, S. Beta-lactamases in Enterobacteriaceae infections in children. J. Infect. 72, S41–S49 (2016).PubMed 
Article 

Google Scholar 
Morrissey, I. et al. A review of ten years of the study for monitoring antimicrobial resistance trends (SMART) from 2002 to 2011. Pharmaceuticals 6, 1335–1346 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Junnila, J. et al. Changing epidemiology of methicillin-resistant Staphylococcus aureus in a low endemicity area—new challenges for MRSA control. Eur. J. Clin. Microbiol. Infect. Dis. 39, 2299–2307 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Milstone, A. M. et al. Methicillin-resistant Staphylococcus aureus colonization and risk of subsequent infection in critically ill children: Importance of preventing nosocomial methicillin-resistant Staphylococcus aureus transmission. Clin. Infect. Dis. 53, 853–859 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Lakhundi, S. & Zhang, K. Methicillin-resistant Staphylococcus aureus: Molecular characterization, evolution, and epidemiology. Clin. Microbiol. Rev. 31, e00020-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Schlesinger, Y. et al. Methicillin-resistant nasal colonization in children in Jerusalem: Community vs. chronic care institutions. Isr. Med. Assoc. J. 5, 847–851 (2003).PubMed 

Google Scholar 
Liang, B. et al. Active surveillance, drug resistance, and genotypic profiling of Staphylococcus aureus among school-age children in China. Front. Med. 8, 701494 (2021).Article 

Google Scholar 
Del Rosal, T. et al. Staphylococcus aureus nasal colonization in Spanish children. The COSACO Nationwide Surveillance Study. Infect. Drug Resist. 13, 4643–4651 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Feudtner, C., Feinstein, J. A., Zhong, W., Hall, M. & Dai, D. Pediatric complex chronic conditions classification system version 2: Updated for ICD-10 and complex medical technology dependence and transplantation. BMC Pediatr. 14, 199 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Climent Alcalá, F. J., García Fernández de Villalta, M., Escosa García, L., Rodríguez Alonso, A. & Albajara Velasco, L. A. Unidad de niños con patología crónica compleja. Un modelo necesario en nuestros hospitales. Anales de Pediatría 88, 12–18 (2018).PubMed 
Article 

Google Scholar 
Gesualdo, F. et al. Methicillin-resistant Staphylococcus aureus nasal colonization in a department of pediatrics: A cross-sectional study. Ital. J. Pediatr. 40, 3 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Yamamoto, M. et al. Effective surveillance to identify the surgical patients carrying methicillin-resistant Staphylococcus aureus on admission in a pediatric ward. Osaka City Med. J. 62, 1–9 (2016).PubMed 

Google Scholar 
Lukac, P. J., Bonomo, R. A. & Logan, L. K. Extended-spectrum-lactamase-producing Enterobacteriaceae in children: Old foe, emerging threat. Clin. Infect. Dis. https://doi.org/10.1093/cid/civ020 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Fedler, K. A., Biedenbach, D. J. & Jones, R. N. Assessment of pathogen frequency and resistance patterns among pediatric patient isolates: Report from the 2004 SENTRY Antimicrobial Surveillance Program on 3 continents. Diagn. Microbiol. Infect. Dis. 56, 427–436 (2006).CAS 
PubMed 
Article 

Google Scholar 
Caselli, D. et al. Incidence of colonization and bloodstream infection with carbapenem-resistant Enterobacteriaceae in children receiving antineoplastic chemotherapy in Italy. Infect. Dis. 48, 152–155 (2016).Article 

Google Scholar 
Logan, L. K. et al. Multidrug- and Carbapenem-Resistant Pseudomonas aeruginosa in Children, United States, 1999–2012. JPIDSJ piw064 (2016) https://doi.org/10.1093/jpids/piw064.Flokas, M. E., Alevizakos, M., Shehadeh, F., Andreatos, N. & Mylonakis, E. Extended-spectrum β-lactamase-producing Enterobacteriaceae colonisation in long-term care facilities: A systematic review and meta-analysis. Int. J. Antimicrob. Agents 50, 649–656 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bharadwaj, R. et al. Drug-resistant Enterobacteriaceae colonization is associated with healthcare utilization and antimicrobial use among inpatients in Pune, India. BMC Infect. Dis. 18, 504 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Logan, L. K. Carbapenem-resistant Enterobacteriaceae: An emerging problem in children. Clin. Infect. Dis. 55, 852–859 (2012).CAS 
PubMed 
Article 

Google Scholar 
Badal, R. E. et al. Etiology, extended-spectrum β-lactamase rates and antimicrobial susceptibility of gram-negative bacilli causing intra-abdominal infections in patients in general pediatric and pediatric intensive care units—global data from the Study for Monitoring Antimicrobial Resistance Trends 2008 to 2010. Pediatr. Infect. Dis. J. 32, 636–640 (2013).PubMed 
Article 

Google Scholar 
Wang, Q. et al. Risk factors and clinical outcomes for carbapenem-resistant Enterobacteriaceae nosocomial infections. Eur. J. Clin. Microbiol. Infect. Dis. 35, 1679–1689 (2016).CAS 
PubMed 
Article 

Google Scholar 
Sahbudak Bal, Z. et al. The prospective evaluation of risk factors and clinical influence of carbapenem resistance in children with gram-negative bacteria infection. Am. J. Infect. Control 46, 147–153 (2018).CAS 
PubMed 
Article 

Google Scholar 
Simon, T. D. et al. Pediatric medical complexity algorithm: A new method to stratify children by medical complexity. Pediatrics 133, e1647–e1654 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Román, F. et al. Characterization of methicillin-resistant Staphylococcus aureus strains colonizing the nostrils of Spanish children. MicrobiologyOpen 10, e1235 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
EUCAST. European committee on antimicrobial susceptibility testing breakpoint tables for interpretation of MICs and zone diameters. The European Committee on Antimicrobial Susceptibility Testing. (2018).Oteo, J. et al. Prospective multicenter study of carbapenemase-producing Enterobacteriaceae from 83 hospitals in Spain reveals high in vitro susceptibility to colistin and meropenem. Antimicrob. Agents Chemother. 59, 3406–3412 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maseda, E. et al. Risk factors for colonization by carbapenemase-producing enterobacteria at admission to a Surgical ICU: A retrospective study. Enferm. Infecc. Microbiol. Clin. 35, 333–337 (2017).PubMed 
Article 

Google Scholar 
Bassetti, M., Nicco, E. & Mikulska, M. Why is community-associated MRSA spreading across the world and how will it change clinical practice?. Int. J. Antimicrob. Agents 34, S15–S19 (2009).CAS 
PubMed 
Article 

Google Scholar 
El Cheikh, M. R., Barbosa, J. M., Caixêta, J. A. S. & Avelino, M. A. G. Microbiology of tracheal secretions: What to expect with children and adolescents with tracheostomies. Int. Arch. Otorhinolaryngol. 22, 50–54 (2018).PubMed 
Article 

Google Scholar 
González-Del Castillo, J. et al. BAHNG score: Predictive model for detection of subjects with the oropharynx colonized by uncommon microorganisms. Rev. Esp Quimioter. 30, 422–428 (2017).PubMed 

Google Scholar 
Hu, X. et al. Risk factors for methicillin-resistant Staphylococcus aureus colonization and infection in patients with human immunodeficiency virus infection: A systematic review and meta-analysis. J. Int. Med. Res. 50, 3000605211063019 (2022).CAS 
PubMed 

Google Scholar 
Gleeson, A., Larkin, P., Walsh, C. & O’Sullivan, N. Methicillin-resistant Staphylococcus aureus: Prevalence, incidence, risk factors, and effects on survival of patients in a specialist palliative care unit: A prospective observational study. Palliat. Med. 30, 374–381 (2016).PubMed 
Article 

Google Scholar 
Hogardt, M. et al. Current prevalence of multidrug-resistant organisms in long-term care facilities in the Rhine-Main district, Germany, 2013. Euro Surveill. 20, 21171 (2015).PubMed 
Article 

Google Scholar 
Warren, D. K. et al. Epidemiology of methicillin-resistant Staphylococcus aureus colonization in a surgical intensive care unit. Infect. Control Hosp. Epidemiol. 27, 1032–1040 (2006).PubMed 
Article 

Google Scholar 
Folgori, L. et al. Healthcare-associated infections in pediatric and neonatal intensive care units: Impact of underlying risk factors and antimicrobial resistance on 30-day case-fatality in Italy and Brazil. Infect. Control Hosp. Epidemiol. 37, 1302–1309 (2016).PubMed 
Article 

Google Scholar 
Béranger, A. et al. Early bacterial infections after pediatric liver transplantation in the era of multidrug-resistant bacteria: Nine-year single-center retrospective experience. Pediatr. Infect. Dis. J. 39, e169–e175 (2020).PubMed 
Article 

Google Scholar 
Bouras, D. et al. Staphylococcus aureus osteoarticular infections in children: An 8-year review of molecular microbiology, antibiotic resistance and clinical characteristics. J. Med. Microbiol. 67, 1753–1760 (2018).MathSciNet 
CAS 
PubMed 
Article 

Google Scholar 
Rodriguez, M., Hogan, P. G., Krauss, M., Warren, D. K. & Fritz, S. A. Measurement and impact of Staphylococcus aureus colonization pressure in households. J. Pediatr. Infect. Dis. Soc. 2, 147–154 (2013).Article 

Google Scholar 
Messina, N. L., Williamson, D. A., Robins-Browne, R., Bryant, P. A. & Curtis, N. Risk factors for carriage of antibiotic-resistant bacteria in healthy children in the community: A systematic review. Pediatr. Infect. Dis. J. 39, 397–405 (2020).PubMed 
Article 

Google Scholar 
Dualleh, N. et al. Colonization with multiresistant bacteria in acute hospital care: The association of prior antibiotic consumption as a risk factor. J. Antimicrob. Chemother. 75, 3675–3681 (2020).CAS 
PubMed 
Article 

Google Scholar 
Daskalaki, M. et al. Panton-Valentine leukocidin-positive Staphylococcus aureus skin and soft tissue infections among children in an emergency department in Madrid, Spain. Clin. Microbiol. Infect. 16, 74–77 (2010).CAS 
PubMed 
Article 

Google Scholar 
Aguilera-Alonso, D., Escosa-García, L., Saavedra-Lozano, J., Cercenado, E. & Baquero-Artigao, F. Carbapenem-resistant gram-negative bacterial infections in children. Antimicrob. Agents Chemother. 64, e02183-e2219 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Phichaphop, C. et al. High prevalence of multidrug-resistant gram-negative bacterial infection following pediatric liver transplantation. Medicine 99, e23169 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tacconelli, E. et al. ESCMID guidelines for the management of the infection control measures to reduce transmission of multidrug-resistant Gram-negative bacteria in hospitalized patients. Clin. Microbiol. Infect. 20, 1–55 (2014).PubMed 
Article 

Google Scholar 
McConville, T. H., Sullivan, S. B., Gomez-Simmonds, A., Whittier, S. & Uhlemann, A.-C. Carbapenem-resistant Enterobacteriaceae colonization (CRE) and subsequent risk of infection and 90-day mortality in critically ill patients, an observational study. PLoS ONE 12, e0186195 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tamma, P. D. et al. The likelihood of developing a carbapenem-resistant Enterobacteriaceae Infection during a hospital stay. Antimicrob. Agents Chemother. 63, e00757-e819 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Detsis, M., Karanika, S. & Mylonakis, E. ICU acquisition rate, risk factors, and clinical significance of digestive tract colonization with extended-spectrum beta-lactamase-producing Enterobacteriaceae: A systematic review and meta-analysis. Crit. Care Med. 45, 705–714 (2017).PubMed 
Article 

Google Scholar  More