Philippa Kaur

1.Hönisch, B. et al. The geological record of ocean acidification. Science 335, 1058–1063 (2012).Article 
CAS 

Google Scholar 
2.Bindoff, N. L. et al. in Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 447–588 (IPCC, 2019).3.Pörtner, H.-O. et al. in Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 35–74 (IPCC, 2019).4.Caldeira, K. & Wickett, M. E. Anthropogenic carbon and ocean pH. Nature 425, 365 (2003).CAS 
Article 

Google Scholar 
5.Cai, W. J. et al. Acidification of subsurface coastal waters enhanced by eutrophication. Nat. Geosci. 4, 766–770 (2011).CAS 
Article 

Google Scholar 
6.Wallace, R. B., Baumann, H., Grear, J. S., Aller, R. C. & Gobler, C. J. Coastal ocean acidification: the other eutrophication problem. Estuar. Coast. Shelf Sci. 148, 1–13 (2014).CAS 
Article 

Google Scholar 
7.Munday, P. L., Warner, R. R., Monro, K., Pandolfi, J. M. & Marshall, D. J. Predicting evolutionary responses to climate change in the sea. Ecol. Lett. 16, 1488–1500 (2013).Article 

Google Scholar 
8.Schlichting, C. D. & Pigliucci, M. Phenotypic Evolution: A Reaction Norm Perspective (Sinauer Associates, 1998).9.Kelly, M. W. & Hofmann, G. E. Adaptation and the physiology of ocean acidification. Funct. Ecol. 27, 980–990 (2013).Article 

Google Scholar 
10.Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013).CAS 
Article 

Google Scholar 
11.Thor, P. & Dupont, S. Transgenerational effects alleviate severe fecundity loss during ocean acidification in a ubiquitous planktonic copepod. Glob. Change Biol. 21, 2261–2271 (2015).Article 

Google Scholar 
12.Donelson, J. M., Salinas, S., Munday, P. L. & Shama, L. N. S. Transgenerational plasticity and climate change experiments: where do we go from here? Glob. Change Biol. 24, 13–34 (2018).Article 

Google Scholar 
13.Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).Article 
CAS 

Google Scholar 
14.Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, 2009).15.Byrne, M. in Oceanography and Marine Biology: An Annual Review Vol. 49 (eds Gibson, R. N. et al.) Ch. 1 (CRC Press, 2011).16.Whiteley, N. M. Physiological and ecological responses of crustaceans to ocean acidification. Mar. Ecol. Prog. Ser. 430, 257–271 (2011).CAS 
Article 

Google Scholar 
17.Cripps, G., Lindeque, P. & Flynn, K. J. Have we been underestimating the effects of ocean acidification in zooplankton? Glob. Change Biol. 20, 3377–3385 (2014).Article 

Google Scholar 
18.Baumann, H. Experimental assessments of marine species sensitivities to ocean acidification and co-stressors: how far have we come? Can. J. Zool. 97, 399–408 (2019).Article 

Google Scholar 
19.Gibbin, E. M. et al. Can multi-generational exposure to ocean warming and acidification lead to the adaptation of life history and physiology in a marine metazoan? J. Exp. Biol. 220, 551–563 (2017).
Google Scholar 
20.Gibbin, E. M., Massamba N’Siala, G., Chakravarti, L. J., Jarrold, M. D. & Calosi, P. The evolution of phenotypic plasticity under global change. Sci. Rep. 7, 17253 (2017).Article 
CAS 

Google Scholar 
21.Gonzalez, A., Ophelie, R., Ferriere, R. & Hochberg, M. E. Evolutionary rescue: an emerging focus at the intersection between ecology and evolution. Philos. Trans. R. Soc. Lond. B 368, 20120404 (2012).Article 

Google Scholar 
22.Bell, G. & Gonzalez, A. Evolutionary rescue can prevent extinction following environmental change. Ecol. Lett. 12, 942–948 (2009).Article 

Google Scholar 
23.Carlson, S. M., Cunningham, C. J. & Westley, P. A. H. Evolutionary rescue in a changing world. Trends Ecol. Evol. 29, 521–530 (2014).Article 

Google Scholar 
24.Hardy, A. The Open Sea: The World of Plankton (Fontana Collins, 1970).25.Huys, R. & Boxshall, G. A. Copepod Evolution (The Ray Society, 1991).26.Beaugrand, G. & Reid, P. C. Long-term changes in phytoplankton, zooplankton and salmon related to climate. Glob. Change Biol. 9, 801–817 (2003).Article 

Google Scholar 
27.Möllmann, C., Müller-Karulis, B., Kornilovs, G. & St John, M. A. Effects of climate and overfishing on zooplankton dynamics and ecosystem structure: regime shifts, trophic cascade, and feedback loops in a simple ecosystem. ICES J. Mar. Sci. 65, 302–310 (2008).Article 

Google Scholar 
28.Steinberg, D. K. & Landry, M. R. Zooplankton and the ocean carbon cycle. Annu. Rev. Mar. Sci. 9, 413–444 (2017).Article 

Google Scholar 
29.Mauchline, J. (ed.) The Biology of Calanoid Copepods (Academic Press, 1998).30.Turner, J. T. The Feeding Ecology of Some Zooplankters That Are Important Prey Items of Larval Fish. NOAA NMFS Technical Report (1984).31.Rice, E., Dam, H. G. & Stewart, G. Impact of climate change on estuarine zooplankton: surface water warming in Long Island Sound is associated with changes in copepod size and community structure. Estuaries Coast 38, 13–23 (2015).Article 

Google Scholar 
32.Gobler, C. J. & Baumann, H. Hypoxia and acidification in marine ecosystems: coupled dynamics and effects on ocean life. Biol. Lett. 12, 20150976 (2016).Article 
CAS 

Google Scholar 
33.Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. Lond. B 283, 20152592 (2016).
Google Scholar 
34.Burt, A. Perspective: the evolution of fitness. Evolution 49, 1–8 (1995).
Google Scholar 
35.Hendry, A. P. & Gonzalez, A. Whither adaptation? Biol. Philos. 23, 673–699 (2008).Article 

Google Scholar 
36.Arnold, S. J., Pfrender, M. E. & Jones, A. G. The adaptive landscape as a conceptual bridge between micro- and macroevolution. Genetica 112–113, 9–32 (2001).Article 

Google Scholar 
37.Caswell, H. Matrix Population Models: Construction, Analysis, and Interpretation (Sinauer Associates, 2001).38.Sasaki, M. C. & Dam, H. G. Integrating patterns of thermal tolerance and phenotypic plasticity with population genetics to improve understanding of vulnerability to warming in a widespread copepod. Glob. Change Biol. 25, 4147–4164 (2019).Article 

Google Scholar 
39.Luikart, G., England, P. R., Tallmon, D., Jordan, S. & Taberlet, P. The power and promise of population genomics: from genotyping to genome typing. Nat. Rev. Genet. 4, 981–994 (2003).CAS 
Article 

Google Scholar 
40.Black, W. C. IV, Baer, C. F., Antolin, M. F. & DuTeau, N. M. Population genomics: genome-wide sampling of insect populations. Annu. Rev. Entomol. 46, 441–469 (2001).CAS 
Article 

Google Scholar 
41.Brennan, R. et al. Loss and recovery of transcriptional plasticity after long-term adaptation to global change conditions in a marine copepod. Preprint at bioRxiv https://doi.org/10.1101/2020.01.29.925396 (2020).42.Kingsolver, J. G. & Pfennig, D. W. Patterns and power of phenotypic selection in nature. Bioscience 57, 561–572 (2007).Article 

Google Scholar 
43.Crespi, B. J. & Bookstein, F. L. A path-analytic model for the measurement of selection on morphology. Evolution 43, 18–28 (1989).Article 

Google Scholar 
44.Pigliucci, M. & Kaplan, J. Making Sense of Evolution (Univ. Chicago Press, 2006); https://doi.org/10.7208/chicago/9780226668352.001.000145.Bush, A. et al. Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecol. Lett. 19, 1468–1478 (2016).Article 

Google Scholar 
46.Riebesell, U. & Gattuso, J. Lessons learned from ocean acidification research. Nat. Clim. Change 5, 2014–2016 (2015).Article 
CAS 

Google Scholar 
47.Langer, J. A. F., Meunier, C. L., Ecker, U. & Horn, H. G. Acclimation and adaptation of the coastal calanoid copepod Acartia tonsa to ocean acidification: a long-term laboratory investigation. Mar. Ecol. Prog. Ser. 619, 35–51 (2019).CAS 
Article 

Google Scholar 
48.De Wit, P., Dupont, S. & Thor, P. Selection on oxidative phosphorylation and ribosomal structure as a multigenerational response to ocean acidification in the common copepod Pseudocalanus acuspes. Evol. Appl. 9, 1112–1123 (2016).Article 
CAS 

Google Scholar 
49.Chakravarti, L. J. et al. Can trans-generational experiments be used to enhance species resilience to ocean warming and acidification? Evol. Appl. 9, 1133–1146 (2016).CAS 
Article 

Google Scholar 
50.Carrier-Belleau, C., Drolet, D., McKindsey, C. W. & Archambault, P. Environmental stressors, complex interactions and marine benthic communities’ responses. Sci. Rep. 11, 4194 (2021).CAS 
Article 

Google Scholar 
51.Dam, H. G. & Baumann, H. in Climate Change Impacts on Fisheries and Aquaculture: A Global Analysis (eds Phillips, B. F. and Pérez-Ramírez, M.) 851–874 (Wiley, 2017).52.Bell, G. Evolutionary rescue and the limits of adaptation. Philos. Trans. R. Soc. Lond. B 368, 20120080 (2013).Article 

Google Scholar 
53.Falconer, D. S. Introduction to Quantitative Genetics (Longman Scientific and Technical, 1989).54.Angilletta, M. J. Jr Estimating and comparing thermal performance curves. J. Therm. Biol. 31, 541–545 (2006).Article 

Google Scholar 
55.Feinberg, L. R. & Dam, H. G. Effects of diet on dimensions, density and sinking rates of fecal pellets of the copepod Acartia tonsa. Mar. Ecol. Prog. Ser. 175, 87–96 (1998).Article 

Google Scholar 
56.Pierrot, D., Lewis, E. & Wallace, D. W. R. MS Excel Program Developed for CO2 System Calculations. ORNL/CDIAC-105a. (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, 2006); https://doi.org/10.3334/CDIAC/otg.CO2SYS_XLS_CDIAC105a57.Lueker, T. J., Dickson, A. G. & Keeling, C. D. Ocean (p_{{mathrm{CO}}_2}) calculated from dissolved inorganic carbon, alkalinity, and equations for K1 and K2: validation based on laboratory measurements of CO2 in gas and seawater at equilibrium. Mar. Chem. 70, 105–119 (2000).58.Dickson, A. G. Standard potential of the reaction: AgCl(s) + 12H2 (g) = Ag(s) + HCl (aq), and the standard acidity constant of the ion HSO4– in synthetic sea water from 273.15 to 318.15 K. J. Chem. Thermodyn. 22, 113–127 (1990).CAS 
Article 

Google Scholar 
59.Uppström, L. R. The boron/chlorinity ratio of deep-sea water from the Pacific Ocean. Deep Sea Res. Oceanogr. Abstr. 21, 161–162 (1974).Article 

Google Scholar 
60.Murray, C. S. & Baumann, H. You better repeat it: complex CO2× temperature effects in Atlantic silverside offspring revealed by serial experimentation. Diversity 10, 69 (2018).CAS 
Article 

Google Scholar 
61.Schank, J. C. & Koehnle, T. J. Pseudoreplication is a Pseudoproblem. J. Comp. Psychol. 123, 421–433 (2009).Article 

Google Scholar 
62.Oksanen, L. Logic of experiments in ecology: is pseudoreplication a pseudoissue? Oikos 94, 27–38 (2001).Article 

Google Scholar 
63.Therneau, T. A Package for Survival Analysis in R. R package 3.2-11 (2021); https://CRAN.R-project.org/package=survival64.Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).Article 

Google Scholar 
65.Rosseel, Y. lvaan: an R package for structural equation modeling. J. Stat. Softw. https://doi.org/10.18637/jss.v048.i02 (2012).66.Epskamp, S., Stuber, S., Nak, J., Veenman, M. & Jorgensen, T. D. semPlot: Path Diagrams and Visual Analysis of Various SEM Packages’ Output. (2019); https://CRAN.R-project.org/package=semPlot67.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
Article 

Google Scholar 
68.Jørgensen, T. S. et al. The genome and mRNA transcriptome of the cosmopolitan calanoid copepod Acartia tonsa Dana improve the understanding of copepod genome size evolution. Genome Biol. Evol. 11, 1440–1450 (2019).Article 
CAS 

Google Scholar 
69.Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).70.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article 
CAS 

Google Scholar 
71.Kofler, R. et al. Popoolation: a toolbox for population genetic analysis of next generation sequencing data from pooled individuals. PLoS One 6, e15925 (2011).CAS 
Article 

Google Scholar 
72.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020); https://www.R-project.org/73.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B 73, 3–36 (2011).Article 

Google Scholar 
74.Simpson, G. L. Modelling palaeoecological time series using generalised additive models. Front. Ecol. Evol. 6, 149 (2018).Article 

Google Scholar 
75.Dam, H. G. et al. Data and code repository for ‘Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification’. Zenodo https://doi.org/10.5281/zenodo.5115103 (2021). More

1.McClure, C. J. W. et al. State of the world’s raptors: Distributions, threats, and conservation recommendations. Biol. Conserv. 227, 390–402 (2018).Article 

Google Scholar 
2.Bernardino, J. et al. Bird collisions with power lines: State of the art and priority areas for research. Biol. Conserv. 222, 1–13 (2018).Article 

Google Scholar 
3.Hager, S. B. Human-related threats to urban raptors. J. Raptor Res. 43, 210–226 (2009).Article 

Google Scholar 
4.Molina-López, R. A., Casal, J. & Darwich, L. Causes of morbidity in wild raptor populations admitted at a wildlife rehabilitation centre in Spain from 1995–2007: A long term retrospective study. PLoS ONE 6, e24603. https://doi.org/10.1371/journal.pone.0024603 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.Rodríguez, B., Rodríguez, A., Siverio, F. & Siverio, M. Causes of raptor admissions to a wildlife rehabilitation centre in Tenerife (Canary Islands). J. Raptor Res. 44, 30–39 (2010).Article 

Google Scholar 
6.Komnenou, A. T., Georgopoulou, I., Savvas, I. & Dessiris, A. (2005) A retrospective study of presentation, treatment, and outcome of fre-ranging raptors in Greece. J. Zoo. Wildl. Med. 36, 222–228 (2005).PubMed 
Article 

Google Scholar 
7.Harris, M. C. § Sleeman, J. M. Morbidity and mortality of Bald Eagles (Haliaeetus leucocephalus) and Peregrine Falcons (Falco peregrinus) admitted to the wildlife center of Virginia, 1993–2003. J.Zoo Wildl. Med. 38, 62–66 (2007).8.IUCN Red List. 2020. Common kestrel [online]. [vid. 31st 11. 2020]. Available from: https://www.iucnredlist.org/species/22696362/935564299.Smallwood, J. A. et al. Why are American kestrel (Falco sparverius) populations declining in North America? Evidence from nest-box programs. J. Raptor Res. 43, 274–282 (2009).Article 

Google Scholar 
10.Wendell, M. D., Sleeman, J. M. & Kratz, G. Retrospective study of morbidity and mortality of raptors admitted to Colorado State University Veterinary Teaching Hospital during1995 to 1998. J. Wildl. Dis. 38, 101–106 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Costantini, D., Dell Omo, G., La Fata, I. & Casagrande, S. Reproductive performance of Eurasian Kestrel Falco tinnunculus in an agricultural landscape with a mosaic of land uses. Ibis 156, 768–776 (2014).12.Buck, A., Carrillo-Hidalgo, J., Camarero, P. R. & Mateo, R. Organochlorine pesticides and polychlorinated biphenyls in common kestrel eggs from the Canary Islands: Spatio temporal variations and effects on egg shell and reproduction. Chemosphere 261, 127722; https://doi.org/10.1016/j.chemosphere.2020.127722 (2020).13.Wienburg, C. L. & Shore, R. F. Factors influencing liver PCB concentrations in sparrowhawks (Accipiter nisus), kestrels (Falco tinnunculus) and herons (Ardea cinerea) in Britain. Environ. Pollut. 132, 41–50 (2004).CAS 
PubMed 
Article 

Google Scholar 
14.Pain, D. J. & Amiardtriquet, C. Lead poisoning of raptors in France and elsewhere. Ecotoxicol. Environ. Saf. 25, 183–192 (1993).CAS 
PubMed 
Article 

Google Scholar 
15.Newton, I., Wyllie, I. & Dale, L. Trends in the numbers and mortality patterns of sparrowhawks (Accipiter nisus) and kestrels (Falco tinnunculus) in Britain, as revealed by carcass analyses. J. Zool. 248, 139–147 (1999).Article 

Google Scholar 
16.Burfield, I. J. The conservation status and trends of raptors and owls in Europe. Ambio 37, 401–407 (2008).PubMed 
Article 

Google Scholar 
17.González, L. M. et al. Causes and spatio-temporal variations of non-natural mortality in the vulnerable Spanish imperial eagle Aquila Adalbert during a recovery period. Oryx 41, 495–502 (2007).Article 

Google Scholar 
18.Sumasgutner, P., Schulze, C. H., Krenn, H. W. & Gamauf, A. Conservation related conflicts in nest-site selection of the Eurasian kestrel (Falco tinnunculus) and the distribution of its avian prey. Landscape Urban. Plan. 127, 94–103 (2014).Article 

Google Scholar 
19.Sumasgutner, P., Adrion, M. & Gamauf, A. Carotenoid coloration and health status of urban Eurasian kestrels (Falco tinnunculus). PLoS ONE 13, e0191956. https://doi.org/10.1371/journal.pone.0191956 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Rejt, L., Rutkowvsi, R. & Gryczynska-Siemiatkowska, A. Genetic variability of urban kestrels in Warsaw – preliminary data. Zool. Pol. 49, 199–209 (2004).
Google Scholar 
21.Riddle, G. S.The kestrel in Ayrshire 1970–1978. The Journal of the Scottish Ornithologists´ Club 10, 200–216 (1979).22.Forero, M. G., Tella, J. L., Donázar, J. A. & Hiraldo, F. Canister specific competition and nest site availability explain the decrease of lesser kestrel Falcon populations?. Biol. Conserv. 78, 289–293 (1996).Article 

Google Scholar 
23.Medica, D. L., Clauser, R. & Bildstein, K. Prevalence of West Nile Virus antibodies in a breeding population of American kestrels (Falco sparverius) in Pennsylvania. J. Wildl. Dis. 43, 538–541 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Griffith, J. E., Dhand, N. K., Krockenberger, M. B. & Higgins, D. P. A retrospective study of admission trends of koalas to a rehabilitation facility over 30 years. J. Wildl. Dis. 49, 18–28 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Long, R. B., Krumlauff, K. & Young, A. M. Characterizing trends in human-wildlife conflicts in the American Midwest using wildlife rehabilitation records. PLoS ONE 15, e0238805. https://doi.org/10.1371/journal.pone.0238805 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Lukesova, G., Voslarova, E., Vecerek, V. & Vucinic, M. Trends in intake and outcomes for European hedgehog (Erinaceus europaeus) in the Czech rescue centres. PLoS One 16, e0248422; https://doi.org/10.1371/journal.pone.0248422 (2021).27.Crespo Martínez, J., Izquierdo Rosique, A. & Surroca Royo, M. Causes of admission and final dispositions of hedgehogs admitted to three Wildlife Rehabilitation Centres in eastern Spain. Hystrix 25, 107–110 (2014).28.Kelly, A. & Bland, M. Admissions, diagnoses, and outcomes for Eurasian sparrowhawks (Accipiter nisus) brought to a wildlife rehabilitation centre in England. J. Raptor. Res. 40, 231–235 (2006).Article 

Google Scholar 
29.Kübler, S., Kupko, S. & Zeller, U. The kestrel (Falco tinnunculus L.) in Berlin: Investigation of breeding biology and feeding ecology. J. Ornithol. 146, 271–278 (2005).Article 

Google Scholar 
30.Carrillo, J. & González-Dávila, E. Impact of weather on breeding success of the Eurasian kestrel Falco tinnunculus in a semi-arid island habitat. Ardea 98, 51–58 (2010).Article 

Google Scholar 
31.Carrillo, J. & Aparicio, J. M. Nest defense behavior of the Eurasian kestrel (Falco tinnunculus) against human predators. Ethology 107, 865–875 (2001).Article 

Google Scholar 
32.Strasser, E. H. & Heath, J. S. Reproductive failure of a human – tolerant species, the American kestrel, is associated with stress and human disturbance. J. Appl. Ecol. 50, 912–919 (2013).Article 

Google Scholar 
33.Baudains, T. P. & Lloyd, P. Habituation and habitat changes can moderate the impacts of human disturbance on shorebird breeding performance. Anim. Conserv. 1, 400–407 (2007).Article 

Google Scholar 
34.French, S. S., González-Suárez, M., Young, J. K., Durham, S. & Gerber, L. R. Human disturbance influences reproductive success and growth rate in California Sea Lions (Zalophus californianus). PLoS ONE 6, e17686. https://doi.org/10.1371/journal.pone.0017686 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Halfwerk, W., Holleman, L. J. M., Lessells, C., Kate, M. & Slabbekoorn, H. Negative impact of traffic noise on avian reproductive success: Traffic noise and avian reproductive success. J. Appl. Ecol. 48, 210–219 (2011).Article 

Google Scholar 
36.Ponce, C., Alonso, J. C., Argandoña, G., García Fernández, A. & Carrasco, M. Carcass removal by scavengers and search accuracy affect bird mortality estimates at power lines: Bias sources affecting power-line bird mortality estimates. Anim. Conserv. 13, 603–612 (2010).Article 

Google Scholar 
37.Lasch, U., Zerbe, S. & Lenk, M. Electrocution of raptors at power lines in Central Kazakhstan. Waldokologie Online 9, 95–100 (2010).
Google Scholar 
38.Rubolini, D., Bassi, E., Bogliani, G., Galeotti, P. & Garavaglia, R. Eagle Owl Bubo bubo and power line interactions in the Italian Alps. Bird Conserv. Int. 11, 19–324 (2001).Article 

Google Scholar 
39.López-López, P., Ferrer, M., Madero, A., Casado, E. & McGrady, M. Solving man-induced large-scale conservation problems: the Spanish Imperial Eagle and power lines. PLoS ONE 6, e17196. https://doi.org/10.1371/journal.pone.0017196 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Bevanger, K. Biological and conservation aspects of bird mortality caused by electricity power lines: A review. Biol. Conserv. 86, 67–76 (1998).Article 

Google Scholar 
41.Janss, G. F. E. Avian mortality from power lines: A morphological approach of a species-specific mortality. Biol. Conserv. 95, 353–359 (2000).Article 

Google Scholar 
42.Eccleston, D.T. & Harness, R. E. Raptor electrocutions and power line collisions in Birds of Prey (ed. Eccleston, D.T. & Harness, R. E) 273–302 (Sarasola J, Grande J &Negro J, 2018).43.Bevanger, K. (2008) Bird interactions with utility structures: Collision and electrocution, causes and mitigating measures. Ibis 136, 412–425 (2008).Article 

Google Scholar 
44.Guil, F. et al. Minimising mortality in endangered raptors due to power lines: The importance of spatial aggregation to optimize the application of mitigation measures. PLoS ONE 6, e28212. https://doi.org/10.1371/journal.pone.0028212 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Sergio, F., Marchesi, L., Pedrini, P., Ferrer, M. & Penteriani, V. Electrocution alters the distribution and density of a top predator, the eagle owl Bubo bubo: Effects of electrocution on eagle owls. J. Appl. Ecol. 41, 836–845 (2004).Article 

Google Scholar 
46.Mañosa, S. Strategies to identify dangerous electricity pylons for birds. Biodivers. Conserv. 10, 1997–2012 (2001).Article 

Google Scholar 
47.Guyonne, F. E. & Ferrer, M. Mitigation of raptor electrocution on steel power poles. Wildl. Society Bulletin 27, 263–273 (1999).
Google Scholar 
48.Zoubi, M. Y. A., Hamidan, N. A., Baker, M. A. A. & Amr, Z. Causes of raptor admissions to rehabilitation in Jordan. J. Raptor Res. 54, 273–278 (2020).Article 

Google Scholar 
49.Montesdeoca, N., Calabuig, P., Corbera, J. A., Rocha, J. & Orós, J. Final outcome of raptors admitted to the Tafira Wildlife Rehabilitation Centre, Gran Canaria Island, Spain (2003–2013). Anim. Biodivers. Conserv. 40, 211–220 (2017).Article 

Google Scholar 
50.Cooper, J. E. Raptor care and rehabilitation: precedents, progress and potential. J. Raptor Res. 21, 21–26 (1987).
Google Scholar 
51.Lam, S. W., Leenen, L. P. H., van Solinge, W. W., Hietbrink, F. & Huisman, A. Evaluation of hematological parameters on admission for the prediction of 7-day in-hospital mortality in a large trauma cohort. Clin. Chem. Lab. Med. 49, 493–499 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Sara, R. & Craig, G. A. A retrospective study of mortality and rehabilitation of raptors in the southern United States. J. Raptor Res. 38, 77–81 (2004).
Google Scholar 
53.Goldberg, H. K. Hearing impairment: a family crisis. Soc. Work Health Care 5, 33–40 (1979).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Molina-López, R. A., Mañosa, S., Torres-Riera, A., Pomarol, M. & Darwich, L. Morbidity, outcomes and cost-benefit analysis of wildlife rehabilitation in Catalonia (Spain). PLoS ONE 12, e0181331. https://doi.org/10.1371/journal.pone.0181331 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Molony, S.E., Baker, P. J., Garland, L., Cuthill, I. C. & Harris, S. 2007. Factors that can be used to predict release rates for wildlife casualties. Anim. Welfare 16, 361–367 (2007).56.Kirkwood, J. & Best, R. Treatment and rehabilitation of wildlife casualties: legal and ethical aspects. Practice 20, 214–216 (1998).Article 

Google Scholar 
57.Naldo, J. L. & Samour, J. H. Causes of morbidity and mortality in falcons in Saudi Arabia. J. Avian Med. Surg. 18, 229–241 (2004).Article 

Google Scholar 
58.Pain, D. J., Mateo, R. § Green, R. E. Effects of lead from ammunition on birds and other wildlife: A review and update. Ambio 48, 935–953 (2019).59.Thompson, L. J., Hoffman, B. & Brown, M. Causes of admissions to a raptor rehabilitation centre in KwaZulu-Natal South Africa. Afr. Zool. 48, 359–366 (2013).Article 

Google Scholar 
60.Tintó, A., Real, J. & Mañosa, S. Predicting and correcting electrocution of birds in Mediterranean areas. J. Wildl. Manage. 74, 1852–1862 (2010).Article 

Google Scholar 
61.Kemper, M. K., Court, G. S. & Beck, J. A. Estimating raptor electrocution mortality on distribution power lines in Alberta Canada. J. Wildl. Manage. 77, 1342–1352 (2013).Article 

Google Scholar 
62.Csermely, D. Duration of the rehabilitation period and familiarity with the prey affect the predatory behavior of captive wild kestrels (Falco tinnunculus). B. Zool. 60, 211–214 (1993).Article 

Google Scholar 
63.Fischer, D., Hampel, M. R. & Lierz, M. Monitoring of rehabilitated and evaluated grapevine medium Telemetry as success control. Tierarztl. Prax. K. H. 42, 29–35 (2014).CAS 
Article 

Google Scholar 
64.Republic, C. Act on the protection of animals against cruelty. Collection of Law. 50, 1284–1290 (1992).
Google Scholar  More

1.Blowes SA, Supp SR, Antão LH, Bates A, Bruelheide H, Chase JM, et al. The geography of biodiversity change in marine and terrestrial assemblages. Science. 2019;366:339–45.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Blanck H. A critical review of procedures and approaches used for assessing pollution-induced community tolerance (PICT) in biotic communities. Hum Ecol Risk Assess. 2002;8:1003–34.Article 

Google Scholar 
3.Tlili A, Berard A, Blanck H, Bouchez A, Cássio F, Eriksson KM, et al. Pollution‐induced community tolerance (PICT): towards an ecologically relevant risk assessment of chemicals in aquatic systems. Freshwat Biol. 2016;61:2141–51.CAS 
Article 

Google Scholar 
4.Duxbury T. Ecological aspects of heavy metal responses in microorganisms. In: Marshall KC, editor. Adv Microb Ecol. New York, USA: Springer; 1985. pp. 185–235.5.Carlson HK, Price MN, Callaghan M, Aaring A, Chakraborty R, Liu H, et al. The selective pressures on the microbial community in a metal-contaminated aquifer. ISME J. 2019;13:937–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Stepanauskas R, Glenn TC, Jagoe CH, Tuckfield RC, Lindell AH, McArthur J. Elevated microbial tolerance to metals and antibiotics in metal-contaminated industrial environments. Environ Sci Technol. 2005;39:3671–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Gans J, Wolinsky M, Dunbar J. Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science. 2005;309:1387–90.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Falkowski PG, Barber RT, Smetacek VV. Biogeochemical Controls and Feedbacks on Ocean Primary Production. Science. 1998;281:200–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Field CB, Michael JB, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Reusch TB, Dierking J, Andersson HC, Bonsdorff E, Carstensen J, Casini M, et al. The Baltic Sea as a time machine for the future coastal ocean. Sci Adv. 2018;4:eaar8195.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Lehtonen KK, Bignert A, Bradshaw C, Broeg K, Schiedek D. Chemical pollution and ecotoxicology. In: Snoeijs-Leijonmalm PSH, Radziejewska T, editors. Biological oceanography of the Baltic Sea. Dordrecht, The Netherlands: Springer Nature; 2017. pp. 547–89.12.Moffett JW, Brand LE, Croot PL, Barbeau KA. Cu speciation and cyanobacterial distribution in harbors subject to anthropogenic Cu inputs. Limnol Oceanogr. 1997;42:789–99.CAS 
Article 

Google Scholar 
13.Echeveste P, Agusti S, Tovar-Sanchez A. Toxic thresholds of cadmium and lead to oceanic phytoplankton: cell size and ocean basin-dependent effects. Environ Toxicol Chem. 2012;31:1887–94.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Tsiola A, Toncelli C, Fodelianakis S, Michoud G, Bucheli TD, Gavriilidou A, et al. Low-dose addition of silver nanoparticles stresses marine plankton communities. Environ Sci Nano. 2018;5:1965–80.CAS 
Article 

Google Scholar 
15.Brand LE, Sunda WG, Guillard RR. Reduction of marine phytoplankton reproduction rates by copper and cadmium. J Exp Mar Biol Ecol. 1986;96:225–50.CAS 
Article 

Google Scholar 
16.Andersson B, Godhe A, Filipsson HL, Rengefors K, Berglund O. Differences in metal tolerance among strains, populations, and species of marine diatoms-importance of exponential growth for quantification. Aquat Toxicol. 2020;226:105551.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Ning W, Nielsen A, Ivarsson LN, Jilbert T, Åkesson C, Slomp C, et al. Anthropogenic and climatic impacts on a coastal environment in the Baltic Sea over the last 1000 years. Anthropocene. 2018;21:66–79.Article 

Google Scholar 
18.Novotny A, Zamora-Terol S, Winder M. DNA metabarcoding reveals trophic niche diversity of micro and mesozooplankton species. Proc R Soc B. 2021;288:20210908.PubMed 
PubMed Central 
Article 

Google Scholar 
19.Horvatić J, Peršić V. The effect of Ni 2+, Co 2+, Zn 2+, Cd 2+ and Hg 2+ on the growth rate of marine diatom Phaeodactylum tricornutum Bohlin: microplate growth inhibition test. Bull Environ Contam Toxicol. 2007;79:494–8.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Terseleer N, Bruggeman J, Lancelot C, Gypens N. Trait‐based representation of diatom functional diversity in a plankton functional type model of the eutrophied southern North Sea. Limnol Oceanogr. 2014;59:1958–72.Article 

Google Scholar 
21.Litchman E, Klausmeier CA, Schofield OM, Falkowski PG. The role of functional traits and trade‐offs in structuring phytoplankton communities: scaling from cellular to ecosystem level. Ecol Lett. 2007;10:1170–81.PubMed 
Article 

Google Scholar 
22.Ehrlich E, Kath NJ, Gaedke U. The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton. ISME J. 2020;14:1451–62.23.Lohbeck KT, Riebesell U, Reusch TB. Adaptive evolution of a key phytoplankton species to ocean acidification. Nat Geosci. 2012;5:346.CAS 
Article 

Google Scholar 
24.Gross S, Kourtchenko O, Rajala T, Andersson B, Fernandez L, Blomberg A, et al. Optimization of a high‐throughput phenotyping method for chain‐forming phytoplankton species. Limnol Oceanogr Methods. 2017;16:57–67.Article 

Google Scholar 
25.Rynearson TA, Armbrust EV. DNA fingerprinting reveals extensive genetic diversity in a field population of the centric diatom Ditylum brightwellii. Limnol Oceanogr. 2000;45:1329–40.Article 

Google Scholar 
26.Kremp A, Oja J, LeTortorec AH, Hakanen P, Tahvanainen P, Tuimala J, et al. Diverse seed banks favour adaptation of microalgal populations to future climate conditions. Environ Microbiol. 2016;18:679–91.PubMed 
Article 

Google Scholar 
27.Sjöqvist C, Godhe A, Jonsson PR, Sundqvist L, Kremp A. Local adaptation and oceanographic connectivity patterns explain genetic differentiation of a marine diatom across the North Sea-Baltic Sea salinity gradient. Mol Ecol. 2015;24:2871–85.PubMed 
PubMed Central 
Article 

Google Scholar 
28.Rengefors K, Logares R, Laybourn‐Parry J, Gast RJ. Evidence of concurrent local adaptation and high phenotypic plasticity in a polar microeukaryote. Environ Microbiol. 2015;17:1510–9.PubMed 
Article 

Google Scholar 
29.Ajani PA, Petrou K, Larsson ME, Nielsen DA, Burke J, Murray SA. Phenotypic trait variability as an indication of adaptive capacity in a cosmopolitan marine diatom. Environ Microbiol. 2020;23:207–23.30.Collins S, Schaum CE. Diverse strategies link growth rate and competitive ability in phytoplankton responses to changes in CO2 levels. bioRxiv. 2019. https://doi.org/10.1101/651471.31.Baert JM, De Laender F, Sabbe K, Janssen CR. Biodiversity increases functional and compositional resistance, but decreases resilience in phytoplankton communities. Ecology. 2016;97:3433–40.PubMed 
Article 
PubMed Central 

Google Scholar 
32.Tatters AO, Roleda MY, Schnetzer A, Fu F, Hurd CL, Boyd PW, et al. Short-and long-term conditioning of a temperate marine diatom community to acidification and warming. Philos Trans R Soc Lond B Biol Sc. 2013;368:20120437.Article 

Google Scholar 
33.Collins S. Competition limits adaptation and productivity in a photosynthetic alga at elevated CO2. Proc R Soc B. 2011;278:247–55.PubMed 
Article 
PubMed Central 

Google Scholar 
34.Legrand C, Rengefors K, Fistarol GO, Graneli E. Allelopathy in phytoplankton-biochemical, ecological and evolutionary aspects. Phycologia. 2003;42:406–19.Article 

Google Scholar 
35.Powell N, Shilton AN, Pratt S, Chisti Y. Factors influencing luxury uptake of phosphorus by microalgae in waste stabilization ponds. Environ Sci Technol. 2008;42:5958–62.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.OECD. Test no. 201: alga, growth inhibition test. 2006. https://www.oecd-ilibrary.org/content/publication/9789264069923-en.37.Anderson SI, Rynearson TA. Variability approaching the thermal limits can drive diatom community dynamics. Limnol Oceanogr. 2020;65:1961–73.CAS 
Article 

Google Scholar 
38.Spilling K, Markager S. Ecophysiological growth characteristics and modeling of the onset of the spring bloom in the Baltic Sea. J Mar Syst. 2008;73:323–37.Article 

Google Scholar 
39.Behrenfeld MJ. Abandoning Sverdrup’s Critical Depth Hypothesis on phytoplankton blooms. Ecology. 2010;91:977–89.PubMed 
Article 

Google Scholar 
40.Follows MJ, Dutkiewicz S, Grant S, Chisholm SW. Emergent biogeography of microbial communities in a model ocean. Science. 2007;315:1843–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Abner B, Morel F, Moffett J. Trace metal control of phytochelatin production in coastal waters. Limnol Oceanogr. 1997;42:601–8.Article 

Google Scholar 
42.Behra R, Genoni GP, Joseph AL. Effect of atrazine on growth, photosynthesis, and between-strain variability in scenedesmus subspicatus (Chlorophyceae). Arch Environ Contamin Toxicol. 1999;37:36–41.CAS 
Article 

Google Scholar 
43.Tiam SK, Lavoie I, Doose C, Hamilton PB, Fortin C. Morphological, physiological and molecular responses of Nitzschia palea under cadmium stress. Ecotoxicology. 2018;27:675–88.44.Härnström K, Ellegaard M, Andersen TJ, Godhe A. Hundred years of genetic structure in a sediment revived diatom population. Proc Natl Acad Sci USA. 2011;108:4252–7.PubMed 
PubMed Central 
Article 

Google Scholar 
45.Guillard RR Culture of phytoplankton for feeding marine invertebrates. In: Smith WL, Chanley MH, editors. Culture of marine invertebrate animals. Boston, MA: Springer; 1975. pp. 29–60.46.Leal PP, Hurd CL, Sander SG, Armstrong E, Roleda MY. Copper ecotoxicology of marine algae: a methodological appraisal. Chem Ecol. 2016;32:786–800.CAS 
Article 

Google Scholar 
47.Hillebrand H, Dürselen CD, Kirschtel D, Pollingher U, Zohary T. Biovolume calculation for pelagic and benthic microalgae. J Phycol. 1999;35:403–24.Article 

Google Scholar 
48.Schreiber U. Chlorophyll fluorescence: new instruments for special applications. In: Garab G, editor. Photosynthesis: mechanisms and effects. Springer, Dordrecht: Springer; 1998. pp. 4253–8.49.MacIntyre HL, Cullen JJ. Using cultures to investigate the physiological ecology of microalgae. In Andersen RA, editor. Algal culturing techniques. Burlington, Mass: Elsevier; 2005. p. 287–326.50.Caceres C, Taboada FG, Höfer J, Anadon R. Phytoplankton growth and microzooplankton grazing in the subtropical Northeast Atlantic. Plos ONE. 2013;8:e69159.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018. https://www.R-project.org/.52.Ritz C, Baty F, Streibig JC, Gerhard D. Dose-response analysis using R. PloS ONE. 2015;10:e0146021.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Wickham H. ggplot2. WIREs Comp Stat. 2011;3:180–5.54.Pinheiro J, Bates D, DebRoy S, Sarkar D, The R Core Team. nlme: Linear and Nonlinear Mixed Effects Models [Internet]. 2021. Available from: https://CRAN.R-project.org/package=nlme.55.Wolf KK, Romanelli E, Rost B, John U, Collins S, Weigand H, et al. Company matters: the presence of other genotypes alters traits and intraspecific selection in an Arctic diatom under climate change. Glob Change Biol. 2019;25:2869–84.Article 

Google Scholar 
56.Venuleo M, Raven JA, Giordano M. Intraspecific chemical communication in microalgae. N Phytol. 2017;215:516–30.Article 

Google Scholar 
57.Esteves-Ferreira AA, Inaba M, Obata T, Fort A, Fleming GT, Araújo WL, et al. A novel mechanism, linked to cell density, largely controls cell division in Synechocystis. Plant Physiol. 2017;174:2166–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Gallo C, d’Ippolito G, Nuzzo G, Sardo A, Fontana A. Autoinhibitory sterol sulfates mediate programmed cell death in a bloom-forming marine diatom. Nat Commun. 2017;8:1–11.CAS 
Article 

Google Scholar 
59.Gresham D, Dunham MJ. The enduring utility of continuous culturing in experimental evolution. Genomics. 2014;104:399–405.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Descamps-Julien B, Gonzalez A. Stable coexistence in a fluctuating environment: an experimental demonstration. Ecology. 2005;86:2815–24.Article 

Google Scholar 
61.Wang NX, Huang B, Xu S, Wei ZB, Miao AJ, Ji R, et al. Effects of nitrogen and phosphorus on arsenite accumulation, oxidation, and toxicity in Chlamydomonas reinhardtii. Aquat Toxicol. 2014;157:167–74.CAS 
PubMed 
Article 

Google Scholar 
62.Lee J-W, Helmann JD. Functional specialization within the Fur family of metalloregulators. BioMetals. 2007;20:485.CAS 
PubMed 
Article 

Google Scholar 
63.Reusch TB, Boyd PW. Experimental evolution meets marine phytoplankton. Evolution. 2013;67:1849–59.PubMed 
Article 

Google Scholar 
64.Walworth NG, Zakem EJ, Dunne JP, Collins S, Levine NM. Microbial evolutionary strategies in a dynamic ocean. Proc Natl Acad Sci USA. 2020;117:5943–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Schaum C-E, Barton S, Bestion E, Buckling A, Garcia-Carreras B, Lopez P, et al. Adaptation of phytoplankton to a decade of experimental warming linked to increased photosynthesis. Nat Ecol Evol. 2017;1:1–7.Article 

Google Scholar 
66.Collins S, Rost B, Rynearson TA. Evolutionary potential of marine phytoplankton under ocean acidification. Evol Appl. 2014;7:140–55.CAS 
PubMed 
Article 

Google Scholar 
67.Rynearson TA, Armbrust EV. Genetic differentiation among populations of the planktonic marine diatom ditylum brightwellii (bacillariophyceae) 1. J Phycol. 2004;40:34–43.Article 

Google Scholar 
68.Soldo D, Behra R. Long-term effects of copper on the structure of freshwater periphyton communities and their tolerance to copper, zinc, nickel and silver. Aquat Toxicol. 2000;47:181–9.CAS 
Article 

Google Scholar 
69.Stokes PM. Multiple metal tolerance in copper tolerant green algae. J Plant Nutr. 1981;3:667–78.CAS 
Article 

Google Scholar 
70.Lemire JA, Harrison JJ, Turner RJ. Antimicrobial activity of metals: mechanisms, molecular targets and applications. Nat Rev Microbiol. 2013;11:371–84.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Ma J, Zhou B, Chen F, Pan K. How marine diatoms cope with metal challenge: Insights from the morphotype-dependent metal tolerance in Phaeodactylum tricornutum. Ecotoxicol Environ Saf. 2020;208:111715.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
72.Egardt J, Larsen MM, Lassen P, Dahllöf I. Release of PAHs and heavy metals in coastal environments linked to leisure boats. Mar Pollut Bull. 2018;127:664–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Falkowski PG, LaRoche J. Acclimation to spectral irradiance in algae. J Phycol. 1991;27:8–14.Article 

Google Scholar 
74.Beardall J, Young E, Roberts S. Approaches for determining phytoplankton nutrient limitation. Aquat Sci. 2001;63:44–69.CAS 
Article 

Google Scholar 
75.Boyd PW, Rynearson TA, Armstrong EA, Fu F, Hayashi K, Hu Z, et al. Marine phytoplankton temperature versus growth responses from polar to tropical waters–outcome of a scientific community-wide study. PloS ONE. 2013;8:e63091.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Johnson HL, Stauber JL, Adams MS, Jolley DF. Copper and zinc tolerance of two tropical microalgae after copper acclimation. Environ Toxicol. 2007;22:234–44.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Cid A, Herrero C, Torres E, Abalde J. Copper toxicity on the marine microalga Phaeodactylum tricornutum: effects on photosynthesis and related parameters. Aquat Toxicol. 1995;31:165–74.CAS 
Article 

Google Scholar 
78.Masmoudi S, Nguyen-Deroche N, Caruso A, Ayadi H, Morant-Manceau A, Tremblin G, et al. Cadmium, copper, sodium and zinc effects on diatoms: from heaven to hell—a review. Cryptogam Algol. 2013;34:185–225.Article 

Google Scholar  More

Study siteThe study was carried out in post-agriculture forests at different growth stages near the forest reserve of Yoko (N00°17′; E25°18′; mean elevation 435 m a.s.l.), situated between 29 and 39 km south east of Kisangani, in the Democratic Republic of the Congo. We set up 15 (40 × 40 m) plots, set out in triplicate along five successional stages (15 plots): agriculture and 5, 12, 20, 60 years old secondary forest (respectively, 5 yrs, 12 yrs, 20 yrs, 60 yrs). Additionally, soils were also characterized in three agricultural plots (Ag). We interviewed owners, farmers, and local experts to determine the time-since-disturbance of all plots. Tree height measurements were recorded at the plot level for 20% of individuals of each diameter class. The climax vegetation in the region is classified as semi-deciduous tropical. Climate falls within the Af-type following the Köppen-Geiger classification33. Annual rainfall ranges from 1418 to 1915 mm with mean monthly temperatures varying from 23.7 to 26.2 °C. Throughout the year, the region is marked by a long and a short rainy season interrupted by two small dry seasons December–January and June–August. Soils in the region are highly weathered Oxisols, being poor in nutrients, with low pH and dominated by sandy texture.Sampling and sample analysisThroughfall and bulk precipitation was collected weekly using polyethylene (PE) funnels supported by a wooden pole of 1.5 m height to which a PE tube was attached and draining into 5 L PE container. A nylon mesh was placed in the neck of the funnel to avoid contamination by large particles. The container was buried in the soil and covered by leaves to avoid the growth of algae and to keep the samples cool. We installed eight throughfall collectors in each plot as two rows of four collectors, with approximately 8 m distance between all collectors. On every sampling occasion, the water volume in each collector was measured in the field, and recipients, funnels and mesh were replaced, rinsed with distilled water. A volume-weighted composite sample of the devices per plot was made. All samples were stored in a freezer immediately and sent in batch to Belgium for chemical analysis. The volume-weighted composite samples were first filtered using a nylon membrane filter of 0.45 µm before freezing. Total phosphorus was measured by inductively coupled plasma atomic emission spectroscopy (ICP AES, IRIS interpid II XSP, Thermo Scientific, USA). Although we acknowledge the potential for microbial activity in the collectors during a 1-week, dark, in situ storage of the samples, the use of total phosphorus concentration and lack of algal growth allow for complete phosphorus recovery.Following analysis, the samples from the replicate field sites per forest stage were pooled into ‘weekly’ forest-type samples, and these were subsequently analyzed for dissolved black carbon (DBC). In short, the pooled water samples were acidified to pH 2 and analyzed for dissolved organic carbon (DOC) concentration via high-temperature catalytic oxidation on Shimadzu TOC-L total organic carbon analyzer following established methodology34. DOC was isolated from the water samples by solid phase extraction (SPE) following Dittmar et al.35. Briefly, SPE cartridges (Varian Bond Elut PPL, 1 g, 6 mL) were conditioned sequentially with methanol, ultrapure water, and ultrapure water acidified to pH 2 using concentrated HCl, then passed through the SPE cartridges by gravity. SPE cartridges were dried under a stream of high-purity N2 gas. DOC was eluted from the SPE cartridge with methanol (SPE-DOC) and stored at −20 °C until further analysis. DBC was quantified using the benzenepolycarboxylic acid (BPCA) method as detailed in Wagner et al.20. The BPCA approach to quantifying DBC involves chemothermal oxidation of condensed aromatic DOC compounds to benzenehexacarboxylic acid (B6CA) and benzenepentacarboxylic acid (B5CA) products. The B6CA and B5CA oxidation products are robustly measured and derive exclusively from pyrogenic sources36. Condensed aromatic DBC, as measured using the BPCA method, is ubiquitous in aquatic environments globally21,37,38,39. DBC has also been quantified in throughfall and stemflow in longleaf pine forests that undergo regular prescribed burning40. Therefore, we use the BPCA method as a proxy for carbon inputs from biomass burning in the current study. To analyze our samples for BPCAs, aliquots of SPE-DOC (~0.5 mg C equivalents) were combined with concentrated HNO3 in flame-sealed glass ampoules and heated to 160 °C for 6 h. The resultant BPCA-containing residue was dried and re-dissolved in mobile phase for subsequent analysis. Individual BPCAs were separated and quantified using an HPLC system (UltiMate 3000, Thermo Fisher, Germany) (CV  More

1.Robert-Gangneux, F. & Darde, M. L. Epidemiology of and diagnostic strategies for Toxoplasmosis. Clin. Microbiol. Rev. 25, 264–296 (2012).CAS 
Article 

Google Scholar 
2.VanWormer, E., Fritz, H., Shapiro, K., Mazet, J. A. K. & Conrad, P. A. Molecules to modeling: Toxoplasma gondii oocysts at the human–animal–environment interface. Comp. Immunol. Microbiol. Infect. Dis. 36, 217–231 (2013).Article 

Google Scholar 
3.Cook, A. J. C. Sources of toxoplasma infection in pregnant women: European multicentre case-control study Commentary: Congenital toxoplasmosis—further thought for food. BMJ 321, 142–147 (2000).CAS 
Article 

Google Scholar 
4.Spalding, S. M., Amendoeira, M. R. R., Klein, C. H. & Ribeiro, L. C. Serological screening and toxoplasmosis exposure factors among pregnant women in South of Brazil. Rev. Soc. Bras. Med. Trop. 38, 173–177 (2005).Article 

Google Scholar 
5.Jones, J. L. et al. Risk factors for Toxoplasma gondii infection in the United States. Clin. Infect. Dis. 49, 878–884 (2009).Article 

Google Scholar 
6.Egorov, A. I. et al. Environmental risk factors for Toxoplasma gondii infections and the impact of latent infections on allostatic load in residents of Central North Carolina. BMC Infect. Dis. 18, 421. https://doi.org/10.1186/s12879-018-3343-y (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Shapiro, K. et al. Environmental transmission of Toxoplasma gondii: Oocysts in water, soil and food. Food Waterborne Parasitol. 15, e00049; https://doi.org/10.1016/j.fawpar. (2019).8.Hill, D. et al. Identification of a sporozoite-specific antigen from Toxoplasma gondii. J. Parasitol. 97, 328–337 (2011).CAS 
Article 

Google Scholar 
9.Ballari, S. A. & Barrios-García, M. N. A review of wild boar Sus scrofa diet and factors affecting food selection in native and introduced ranges: A review of wild boar Sus scrofa diet. Mamm. Rev. 44, 124–134 (2014).Article 

Google Scholar 
10.Kodera, Y., Kanzaki, N., Ishikawa, N. & Minagawa, A. Food habits of wild boar (Sus scrofa) inhabiting Iwami District, Shimane Prefecture, western Japan (In Japanese). Mamm. Sci. 53, 279–287 (2013).
Google Scholar 
11.Chambers, L. K., Singleton, G. R. & Krebs, C. J. Movements and social organization of wild house mice (Mus domesticus) in the wheatlands of northwestern Victoria, Australia. J. Mammal. 81, 59–69 (2000).12.Oka, T. Home range and mating system of two sympatric field mouse species, Apodemus speciosus and Apodemus argenteus. Ecol. Res. 7, 163–169 (1992).Article 

Google Scholar 
13.Yatake, H., Nashimoto, M., Shimano, K., Matuki, R. & Shiraki, S. Present status and subjects of estimation methods of Japanese hare (Lepus brachyurus) density (in Japanese). Mamm. Sci. 42, 23–34 (2002).
Google Scholar 
14.Setoguchi, M. Utilization of holes and home ranges in the Japanese long-tailed mice (Apodemus argenteus) (in Japanese). Jap. J. Ecol. 31, 385–394 (1981).
Google Scholar 
15.Rostami, A. et al. The global seroprevalence of Toxoplasma gondii among wild boars: A systematic review and meta-analysis. Vet. Parasitol. 244, 12–20 (2017).Article 

Google Scholar 
16.Lopez, A. L., Pineda, E., Garakian, A. & Cherry, J. D. Effect of heat inactivation of serum on Bordetella pertussis antibody determination by enzyme-linked immunosorbent assay. Diagn. Microbiol. Infect. Dis. 30, 21–24 (1998).CAS 
Article 

Google Scholar 
17.Taniguchi, Y. et al. A Toxoplasma gondii strain isolated in Okinawa, Japan shows high virulence in Microminipigs. Parasitol. Int. 72, 101935; https://doi.org/10.1016/j.parint.2019.101935 (2019).18.Tadano, R., Nagai, A. & Moribe, J. Local-scale genetic structure in the Japanese wild boar (Sus scrofa leucomystax): insights from autosomal microsatellites. Conserv. Genet. 17, 1125–1135 (2016).Article 

Google Scholar 
19.Ikeda, T., Asano, M., Kuninaga, N. & Suzuki, M. Monitoring relative abundance index and age ratios of wild boar (Sus scrofa) in small scale population in Gifu Prefecture, Japan during classical swine fever outbreak. J. Vet. Med. Sci. 82, 861–865 (2020).Article 

Google Scholar 
20.Matsuo, K., Uetsu, H., Takashima, Y. & Abe, N. High Occurrence of Sarcocystis infection in sika deer Cervus nippon centralis and Japanese wild boar Sus scrofa leucomystax and molecular characterization of Sarcocystis and Hepatozoon isolates from their muscles (in Japanese). Jpn. J. Zoo. Wildl. Med. 21, 35–40 (2016).Article 

Google Scholar 
21.Ogedengbe, M. E. et al. Molecular phylogenetic analyses of tissue coccidia (sarcocystidae; apicomplexa) based on nuclear 18s rDNA and mitochondrial COI sequences confirms the paraphyly of the genus Hammondia. Parasitol. Open 2, e2; https://doi.org/10.1017/pao.2015.7 (2016).22.Moon, M. H. Serological cross-reactivity between Sarcocystis and Toxoplasma in pigs. Kor. J. Parasitol. 25, 188–194 (1987).Article 

Google Scholar 
23.Dubey, J. P. et al. All about Toxoplasma gondii infections in pigs: 2009–2020. Vet. Parasitol. 288, 109185 (2020).24.Puchalska, M. et al. Prevalence of Toxoplasma gondii antibodies in wild boar (Sus scrofa) from Strzałowo Forest Division, Warmia and Mazury Region, Poland. Ann. Agric. Environ. Med. 28, 237–242 (2021).25.Dubey, J. P. et al. Genotyping of viable Toxoplasma gondii from the first national survey of feral swine revealed evidence for sylvatic transmission cycle, and presence of highly virulent parasite genotypes. Parasitology 147, 295–302 (2020).CAS 
Article 

Google Scholar 
26.Kia, E. B., Mirhendi, H., Rezaeian, M., Zahabiun, F. & Sharbatkhori, M. First molecular identification of Sarcocystis miescheriana (Protozoa, Apicomplexa) from wild boar (Sus scrofa) in Iran. Exp. Parasitol. 127, 724–726 (2011).CAS 
Article 

Google Scholar 
27.Coelho, C. et al. Unraveling Sarcocystis miescheriana and Sarcocystis suihominis infections in wild boar. Vet. Parasitol. 212, 100–104 (2015).Article 

Google Scholar 
28.Gazzonis, A. L. et al. Prevalence and molecular characterization of Sarcocystis miescheriana and Sarcocystis suihominis in wild boars (Sus scrofa) in Italy. Parasitol. Res. 118, 1271–1287 (2019).Article 

Google Scholar 
29.Huang, Z. et al. Morphological and molecular characterizations of Sarcocystis miescheriana and Sarcocystis suihominis in domestic pigs (Sus scrofa) in China. Parasitol. Res. 118, 3491–3496 (2019).Article 

Google Scholar 
30.Matsuo, K. et al. Seroprevalence of Toxoplasma gondii infection in cattle, horses, pigs and chickens in Japan. Parasitol. Int. 63, 638–639 (2014).Article 

Google Scholar 
31.Singer, F., Otto, D., Tipton, A. & Hable, C. Home ranges, movements, and habitat use of European wild boar in Tennessee. J. Wildl. Manag. 45, 343–353 (1981).Article 

Google Scholar 
32.Hollings, T., Jones, M., Mooney, N. & McCallum, H. Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis. Int. J. Parasitol. Parasites Wildl. 2, 110–118 (2013).Article 

Google Scholar 
33.Maeda, T., Nakashita, R., Shionosaki, K., Yamada, F. & Watari, Y. Predation on endangered species by human-subsidized domestic cats on Tokunoshima Island. Sci. Rep. 9, 16200. https://doi.org/10.1038/s41598-019-52472-3 (2019).34.QGIS Development Team. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. http://www.qgis.org/en/site/ (2021).35.Verma, S. K., Lindsay, D. S., Grigg, M. E. & Dubey, J. P. Isolation, culture and cryopreservation of Sarcocystis species. Curr. Protoc. Microbiol. https://doi.org/10.1002/cpmc.32 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
36.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).37.Robin, X. et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).Article 

Google Scholar  More

A diverse and active DNA virosphereWe first leveraged two existing metagenomes that were constructed from the Konza native prairie soil14,15 to screen for viral sequences at the site. Each of the metagenomes was obtained from a composite of all the replicate soils collected at ambient field moisture conditions. One of the metagenomes was de novo assembled from deep sequence data (1.1 Tb)14 and the second was a hybrid assembly of short and long reads (267.0 Gb)16. The combination of the two metagenomes was used to maximize the coverage of viral sequences from the Konza prairie site. To balance between the detection limits of the viral detection tools and the wide range of viral genome size, the viral contigs > 2.5 kb in length were combined with those obtained from screening of the two largest public viral databases (i.e., IMG/VR17 and NCBI Virus16) to further increase the coverage of DNA viral sequences. We acknowledge that the length cutoff of 2.5 kb would preclude detection of some ssDNA viruses with small segmented genome sizes (e.g., Nanoviridae18). As a result, a DNA viral database for the site was curated that included 726,108 de-replicated viral contigs. The DNA viral database then served as a scaffold for mapping of metatranscriptome and metaproteome datasets to determine the activities of soil DNA viruses and their responses to differences in soil moisture. This approach was also recently applied to detect the transcriptional activity of marine prokaryotic and eukaryotic viruses19,20,21,22 and giant viruses in soil5.The metatranscriptome reads from both wet and dry treatments were mapped to a total of 416 unique DNA viral contigs using stringent criteria (% sequence identity > 95% and % sequence coverage > 80%). The 416 DNA viral contigs with an average sequence length of 19 kb were highly diverse and grouped into 139 clusters, with 111 of the clusters being singletons (Supplementary Data 1).We aimed to assign putative host taxa to the viral clusters by combining several approaches: CRISPR spacer matching, and screening for host and viral sequence similarities to respective databases (details in ‘Methods’). As a result, we assigned putative viral host taxa to 160 out of the 416 transcribed DNA viral contigs. Some of these were assigned to more than one host (Supplementary Data 1), resulting in a total of 181 virus–host pairings (Fig. 1a). Of these, 79 host–virus pairs were detected only in the dry soil treatment, 51 were only in the wet soil treatment, and an additional 51 were found in both dry and wet treatments (Fig. 1a). Consistent with previous reports4, the majority of the transcribed DNA viral contigs were annotated as bacteriophage sequences. Different sets of transcribed DNA viral contigs were unique to wet or dry soils and assigned to specific hosts at the phylum level, whereas others were shared (Fig. 1a). However, the dominant soil taxa, i.e., Proteobacteria and Actinobacteria that were previously identified by 16S rRNA gene sequencing in this soil environment, were predicted as hosts under both wet and dry conditions (Supplementary Fig. 1a). Eukaryotic DNA viruses, such as Bracovirus and Ichnovirus belonging to a family of insect viruses within the Polydnaviridae family, were also transcribed in the soils (Fig. 1a and Supplementary Data 1). Most of these insect viruses were only detected in dry soil conditions. These differences in virus–host pairings suggest that some of the respective hosts were impacted differently by the dry and wet incubation conditions.Fig. 1: Transcribed DNA viral communities and their responses to wet and dry soil conditions.a An alluvium plot that illustrates pairings of the transcribed DNA viral contigs to putative host phyla. The transcribed DNA viral community was comprised of viral contigs from the curated DNA viral databases that were mapped by quality-filtered metatranscriptomic reads. The alluvia are colored by host taxa (first x axis of each sub-panel) assigned to respective transcribed DNA viral contigs (second x axis of each sub-panel). b A Venn diagram showing the number of unique transcribed DNA viral contigs detected in both wet and dry soils and ones exclusively detected in one of the soils. c Number of unique DNA viral contigs detected. A t-Test shows significantly more DNA contigs were transcribed in dry soil (p = 0.044). d Number of transcripts that mapped to the DNA viral contigs. For panels (c) and (d), the two independent field sites of Konza Experimental Field Station are indicated as site A (circles) and site C (triangles), with the wet soil in blue and dry soil in red.Full size imageThere were 21 DNA viral contigs that were assigned to hosts across multiple bacterial phyla suggesting the presence of viral generalists1,23 (Supplementary Data 1). We recognize that host assignment based on CRISPR spacer matching, however, is limited to detection of recent or historical virus–host interactions that were captured at the time of sampling24. As bioinformatics assignment of virus–host linkages only suggests possible pairings based on sequence features, there are also chances of introducing false positives. However, we applied the most stringent criteria possible to provide confident host assignments.Increased activity of a subset of DNA viruses in wet soilSoil moisture has a strong influence on the community structures of transcribed DNA viruses. The majority of the transcriptionally active DNA viral contigs were unique to wet or dry conditions, with only 111 viral contigs (~ 26.7%) detected in both wet and dry soils, suggesting that the different soil moisture conditions may shape the activity of the DNA viral community differently (Fig. 1b). Interestingly, although a significantly higher number of transcribed DNA viral contigs were detected in dry soils (Fig. 1b, c), the levels of transcriptional activity were significantly higher (based on the normalized abundance of RNA reads that mapped to the viral contigs) for DNA viruses in wet soils irrespective of sampling site location (Fig. 1d). DNA viral contigs with mapped transcripts could represent either prophages that are passively replicated along with their host genomes, or (lytic) viruses that are actively regulating early/middle/late expression of viral gene clusters25. In soil, a lysogenic lifestyle is considered to be an adaptive strategy for viruses to cope with long periods of low host activity26,27. Therefore, the 1.5-fold increase in the number of transcribed DNA viral contigs representing transcriptionally active DNA viruses, but with lower levels of overall transcription, in dry soil suggests that the increase was due to a higher prevalence of lysogeny in dry conditions. This hypothesis is strengthened by our finding of a 20-fold increase in transcripts for lysogenic markers (i.e., integrase and excisionase) in one of our replicates (A-2) in dry compared to wet conditions (Supplementary Data 2). High number of lysogenic phages were also previously reported in dry Antarctic soils using a cultivation-independent induction assay28. By contrast, under wet soil conditions we found a 2-fold increase in transcription of fewer viral contigs representing a subset of DNA viruses, suggesting that those viruses were more transcriptionally active in response to higher soil moisture. In addition, there was a higher correlation between prokaryotic abundances, as estimated by 16S rRNA gene sequencing, with DNA viral transcript counts in wet soils (R2 = 0.593, Supplementary Fig. 1d) in comparison to dry soils (R2 = 0.069, Supplementary Fig. 1d), supporting this hypothesis.We then identified which soil DNA viruses were most transcriptionally active and how they responded to the differences in soil moisture. As the majority of the transcribed DNA viral contigs (97%) were environmental viruses with unclassified taxonomy assignment, we were not able to calculate the taxonomic abundance of each and instead compared the differential abundances of the transcribed viral contigs. There were four DNA viral contigs with significantly different levels of transcription under wet and dry conditions (VC_1, VC_19, VC_282, VC_412; Fig. 2a). Contigs VC_1 and VC_19 correspond to unclassified viral contigs deposited in IMG/VR (identifiers of ‘REF:2547132004_2547132004’ and ‘3300010038_Ga0126315_10000854’) that were previously detected in metagenomes from the Rifle site29 and from serpentine soil in the UC McLaughlin Reserve30, respectively. Contigs VC_282 and VC_412 were extracted from our Kansas metagenomes. Contigs VC_1 and VC_19 had significantly higher levels of transcriptional activity in wet soils compared to dry soils (p  More

Agrawal AF, Stinchcombe JR (2009) How much do genetic covariances alter the rate of adaptation? Proc Biol Sci 276:1183–1191PubMed 
PubMed Central 

Google Scholar 
Aitken SN, Whitlock MC (2013) Assisted gene flow to facilitate local adaptation to climate change. Annu Rev Ecol Evol S 44:367–388Article 

Google Scholar 
Andersen O (2012) Hemoglobin polymorphisms in Atlantic cod—a review of 50 years of study. Mar Genom 8:59–65Article 

Google Scholar 
Anttila K, Dhillon RS, Boulding EG, Farrell AP, Glebe BD, Elliott JA et al. (2013) Variation in temperature tolerance among families of Atlantic salmon (Salmo salar) is associated with hypoxia tolerance, ventricle size and myoglobin level. J Exp Biol 216:1183–1190CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300
Google Scholar 
Berrigan D, Charnov EL (1994) Reaction norms for age and size at maturity in response to temperature: a puzzle for life historians. Oikos 70:474–478Article 

Google Scholar 
Bontrager M, Angert AL (2019) Gene flow improves fitness at a range edge under climate change. Evol Lett 3:55–68PubMed 
Article 
PubMed Central 

Google Scholar 
Bowen SJ, Washburn KW (1984) Genetics of heat tolerance in Japanese quail. Poult Sci 63:430–435CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Bradshaw AD (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13:115–155Article 

Google Scholar 
Breau C, Cunjak RA, Bremset G (2007) Age-specific aggregation of wild juvenile Atlantic salmon Salmo salar at cool water sources during high temperature events. J Fish Biol 71:1179–1191Article 

Google Scholar 
Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) Mixed models for S language environments ASReml-R reference manual. Queensland Department of Primary Industries and Fisheries, NSW Department of Primary Industries, Brisbane, Australia
Google Scholar 
Catullo RA, Llewelyn J, Phillips BL, Moritz CC (2019) The potential for rapid evolution under anthropogenic climate change. Curr Biol 29:R996–R1007CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Charmantier A, Garant D (2005) Environmental quality and evolutionary potential: lessons from wild populations. Proc R Soc B 272:1415–1425PubMed 
PubMed Central 
Article 

Google Scholar 
Cheung WWL, Sarmiento JL, Dunne J, Frölicher TL, Lam VWY, Deng Palomares ML et al. (2012) Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat Clim Change 3:254–258Article 

Google Scholar 
Clark TD, Sandblom E, Jutfelt F (2013) Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. J Exp Biol 216:2771–2782PubMed 
Article 
PubMed Central 

Google Scholar 
Debes PV, Fraser DJ, McBride MC, Hutchings JA (2013) Multigenerational hybridisation and its consequences for maternal effects in Atlantic salmon. Heredity 111:238–247CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Debes PV, Piavchenko N, Erkinaro J, Primmer CR (2020) Genetic growth potential, rather than phenotypic size, predicts migration phenotype in Atlantic salmon. Proc R Soc B 287:20200867PubMed 
PubMed Central 
Article 

Google Scholar 
Debes PV, Piavchenko N, Ruokolainen A, Ovaskainen O, Moustakas-Verho JE, Parre N et al. (2021) Polygenic and major-locus contributions to sexual maturation timing in Atlantic salmon. Mol Ecol https://doi.org/10.1111/mec.16062Dwyer WP, Piper RG (1987) Atlantic salmon growth efficiency as affected by temperature. Prog Fish Cult 49:57–59Article 

Google Scholar 
Edmands S (2007) Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol Ecol 16:463–475PubMed 
Article 
PubMed Central 

Google Scholar 
Elliott JM, Elliott JA (2010) Temperature requirements of Atlantic salmon Salmo salar, brown trout Salmo trutta and Arctic charr Salvelinus alpinus: predicting the effects of climate change. J Fish Biol 77:1793–1817CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Etterson JR, Shaw RG (2001) Constraint to adaptive evolution in response to global warming. Science 294:151–154CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Falconer DS (1952) The problem of environment and selection. Am Nat 86:293–298Article 

Google Scholar 
Franks SJ, Hoffmann AA (2012) Genetics of climate change adaptation. Annu Rev Genet 46:185–208CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Gallaugher P, Farrell AP (1998) Hematocrit and blood oxygen-carrying capacity. In: Perry SF, Tufts BL (eds) Fish respiration. Academic Press, San Diego, California, p 185–227
Google Scholar 
Gamperl AK, Ajiboye OO, Zanuzzo FS, Sandrelli RM, Peroni EDFC, Beemelmanns A (2020) The impacts of increasing temperature and moderate hypoxia on the production characteristics, cardiac morphology and haematology of Atlantic Salmon (Salmo salar). Aquaculture 519:734874Article 

Google Scholar 
Glover KA, Otterå H, Olsen RE, Slinde E, Taranger GL, Skaala Ø (2009) A comparison of farmed, wild and hybrid Atlantic salmon (Salmo salar L.) reared under farming conditions. Aquaculture 286:203–210Article 

Google Scholar 
Glover KA, Solberg MF, Besnier F, Skaala O (2018) Cryptic introgression: evidence that selection and plasticity mask the full phenotypic potential of domesticated Atlantic salmon in the wild. Sci Rep 8:13966PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Glover KA, Solberg MF, McGinnity P, Hindar K, Verspoor E, Coulson MW et al. (2017) Half a century of genetic interaction between farmed and wild Atlantic salmon: Status of knowledge and unanswered questions. Fish Fish 18:890–927Article 

Google Scholar 
Good C, Davidson J (2016) A review of factors influencing maturation of Atlantic salmon, Salmo salar, with focus on water recirculation aquaculture system environments. J World Aquacult Soc 47:605–632Article 

Google Scholar 
Hartman KJ, Porto MA (2014) Thermal performance of three rainbow trout strains at above-optimal temperatures. Trans Am Fish Soc 143:1445–1454Article 

Google Scholar 
Henderson CR (1950) Estimation of genetic parameters. Ann Math Stat 21:309–310
Google Scholar 
Henderson CR (1973) Sire evaluation and genetic trends. J Anim Sci 1973:10–41Article 

Google Scholar 
Hill WG (2010) Understanding and using quantitative genetic variation. Philos Trans R Soc Lond B Biol Sci 365:73–85PubMed 
PubMed Central 
Article 

Google Scholar 
Hoffmann AA, Merilä J (1999) Heritable variation and evolution under favourable and unfavourable conditions. Trends Ecol Evol 14:96–101CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Huey RB, Kearney MR, Krockenberger A, Holtum JA, Jess M, Williams SE (2012) Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos Trans R Soc Lond B Biol Sci 367:1665–1679PubMed 
PubMed Central 
Article 

Google Scholar 
Huey RB, Kingsolver JG (1989) Evolution of thermal sensitivity of ectotherm performance. Trends Ecol Evol 4:131–135CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hutchings JA, Myers RA (1994) The evolution of alternative mating strategies in variable environments. Evol Ecol 8:256–268Article 

Google Scholar 
IPCC (2014) Future climate changes, risk and impacts. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, Switzerland, pp 56–74Jones OR, Wang J (2010) COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol Ecol Resour 10:551–555PubMed 
Article 
PubMed Central 

Google Scholar 
Jonsson B, Forseth T, Jensen AJ, Naesje TF (2001) Thermal performance of juvenile Atlantic Salmon, Salmo salar L. Funct Ecol 15:701–711Article 

Google Scholar 
Jonsson B, Jonsson N, Finstad AG (2013) Effects of temperature and food quality on age and size at maturity in ectotherms: an experimental test with Atlantic salmon. J Anim Ecol 82:201–210PubMed 
Article 
PubMed Central 

Google Scholar 
Jutfelt F, Norin T, Ern R, Overgaard J, Wang T, McKenzie DJ et al. (2018) Oxygen- and capacity-limited thermal tolerance: blurring ecology and physiology. J Exp Biol 221:jeb169615PubMed 
Article 
PubMed Central 

Google Scholar 
Kellermann V, van Heerwaarden B, Sgro CM (2017) How important is thermal history? Evidence for lasting effects of developmental temperature on upper thermal limits in Drosophila melanogaster. Proc R Soc B 284:20170447PubMed 
PubMed Central 
Article 

Google Scholar 
Kelly M (2019) Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes. Philos Trans R Soc Lond B Biol Sci 374:20180176PubMed 
PubMed Central 
Article 

Google Scholar 
Kenward MG, Roger JH (1997) Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–997CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Kingsolver JG, Buckley LB (2017) Quantifying thermal extremes and biological variation to predict evolutionary responses to changing climate. Philos Trans R Soc Lond B Biol Sci 372:20160147PubMed 
PubMed Central 
Article 

Google Scholar 
Kingsolver JG, Heckman N, Zhang J, Carter PA, Knies JL, Stinchcombe JR et al. (2015) Genetic variation, simplicity, and evolutionary constraints for function-valued traits. Am Nat 185:E166–181PubMed 
Article 
PubMed Central 

Google Scholar 
Kingsolver JG, Izem R, Ragland GJ (2004) Plasticity of size and growth in fluctuating thermal environments: comparing reaction norms and performance curves. Integr Comp Biol 44:450–460PubMed 
Article 
PubMed Central 

Google Scholar 
Klemetsen A, Amundsen PA, Dempson JB, Jonsson B, Jonsson N, O’Connell MF et al. (2003) Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects of their life histories. Ecol Freshwat Fish 12:1–59Article 

Google Scholar 
Komender P, Hoeschele I (1989) Use of mixed-model methodology to improve estimation of crossbreeding parameters. Livest Prod Sci 21:101–113Article 

Google Scholar 
Lande R, Arnold SJ (1983) The measurement of selection on correlated characters. Evolution 37:1210–1226PubMed 
Article 
PubMed Central 

Google Scholar 
Lenormand T (2002) Gene flow and the limits to natural selection. Trends Ecol Evol 17:183–189Article 

Google Scholar 
Lutterschmidt WI, Hutchison VH (1997) The critical thermal maximum: history and critique. Can J Zool 75:1561–1574Article 

Google Scholar 
Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer, Sunderland, Massachusetts
Google Scholar 
Mather K, Jinks JL (1982) Biometrical genetics: the study of continuous variation, 3rd edn. Chapman and Hall, LondonBook 

Google Scholar 
McKenzie DJ, Zhang Y, Eliason EJ, Schulte PM, Claireaux G, Blasco FR et al. (2021) Intraspecific variation in tolerance of warming in fishes. J Fish Biol 98:1536–1555PubMed 
Article 
PubMed Central 

Google Scholar 
Merilä J, Hendry AP (2014) Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol Appl 7:1–14PubMed 
PubMed Central 
Article 

Google Scholar 
Messmer V, Pratchett MS, Hoey AS, Tobin AJ, Coker DJ, Cooke SJ et al. (2017) Global warming may disproportionately affect larger adults in a predatory coral reef fish. Glob Change Biol 23:2230–2240Article 

Google Scholar 
Morgan R, Finnoen MH, Jensen H, Pelabon C, Jutfelt F (2020) Low potential for evolutionary rescue from climate change in a tropical fish. Proc Natl Acad Sci USA 117:33365–33372CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morita K, Tamate T, Kuroki M, Nagasawa T (2014) Temperature-dependent variation in alternative migratory tactics and its implications for fitness and population dynamics in a salmonid fish. J Anim Ecol 83:1268–1278PubMed 
Article 
PubMed Central 

Google Scholar 
Moritz C, Langham G, Kearney M, Krockenberger A, VanDerWal J, Williams S (2012) Integrating phylogeography and physiology reveals divergence of thermal traits between central and peripheral lineages of tropical rainforest lizards. Philos Trans R Soc Lond B Biol Sci 367:1680–1687PubMed 
PubMed Central 
Article 

Google Scholar 
Morrissey MB, Kruuk LE, Wilson AJ (2010) The danger of applying the breeder’s equation in observational studies of natural populations. J Evol Biol 23:2277–2288CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Morrissey MB, Liefting M (2016) Variation in reaction norms: statistical considerations and biological interpretation. Evolution 70:1944–1959PubMed 
Article 
PubMed Central 

Google Scholar 
Muff S, Niskanen AK, Saatoglu D, Keller LF, Jensen H (2019) Animal models with group-specific additive genetic variances: extending genetic group models. Genet Sel Evol 51:7PubMed 
PubMed Central 
Article 

Google Scholar 
Munday PL, Donelson JM, Domingos JA (2017) Potential for adaptation to climate change in a coral reef fish. Glob Change Biol 23:307–317Article 

Google Scholar 
Muñoz NJ, Anttila K, Chen Z, Heath JW, Farrell AP, Neff BD (2014a) Indirect genetic effects underlie oxygen-limited thermal tolerance within a coastal population of chinook salmon. Proc R Soc B 281:20141082PubMed 
PubMed Central 
Article 

Google Scholar 
Muñoz NJ, Farrell AP, Heath JW, Neff BD (2014b) Adaptive potential of a Pacific salmon challenged by climate change. Nat Clim Change 5:163–166Article 

Google Scholar 
Muñoz NJ, Farrell AP, Heath JW, Neff BD (2018) Hematocrit is associated with thermal tolerance and modulated by developmental temperature in juvenile Chinook salmon. Physiol Biochem Zool 91:757–762PubMed 
Article 
PubMed Central 

Google Scholar 
Ørsted M, Hoffmann AA, Rohde PD, Sørensen P, Kristensen TN (2019) Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait. Heredity 122:315–325PubMed 
Article 
PubMed Central 

Google Scholar 
Pörtner HO, Bock C, Mark FC (2017) Oxygen- and capacity-limited thermal tolerance: bridging ecology and physiology. J Exp Biol 220:2685–2696PubMed 
Article 
PubMed Central 

Google Scholar 
Pörtner HO, Farrell AP (2008) Physiology and climate change. Science 322:690–692PubMed 
Article 
PubMed Central 

Google Scholar 
Pörtner HO, Peck MA (2010) Climate change effects on fishes and fisheries: towards a cause-and-effect understanding. J Fish Biol 77:1745–1779PubMed 
Article 
PubMed Central 

Google Scholar 
Robertson A (1959) The sampling variance of the genetic correlation coefficient. Biometrics 15:469–485Article 

Google Scholar 
Robinson ML, Gomez-Raya L, Rauw WM, Peacock MM (2008) Fulton’s body condition factor K correlates with survival time in a thermal challenge experiment in juvenile Lahontan cutthroat trout (Oncorhynchus clarki henshawi). J Therm Biol 33:363–368Article 

Google Scholar 
Rowe DK, Thorpe JE, Shanks AM (1991) Role of fat stores in the maturation of male Atlantic salmon (Salmo salar) parr. Can J Fish Aquat Sci 48:405–413Article 

Google Scholar 
Sheridan JA, Bickford D (2011) Shrinking body size as an ecological response to climate change. Nat Clim Change 1:401–406Article 

Google Scholar 
Siepielski AM, Morrissey MB, Carlson SM, Francis CD, Kingsolver JG, Whitney KD et al. (2019) No evidence that warmer temperatures are associated with selection for smaller body sizes. Proc R Soc B 286:20191332PubMed 
PubMed Central 
Article 

Google Scholar 
Sinclair BJ, Marshall KE, Sewell MA, Levesque DL, Willett CS, Slotsbo S et al. (2016) Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures? Ecol Lett 19:1372–1385PubMed 
Article 
PubMed Central 

Google Scholar 
Solberg MF, Dyrhovden L, Matre IH, Glover KA (2016) Thermal plasticity in farmed, wild and hybrid Atlantic salmon during early development: has domestication caused divergence in low temperature tolerance? BMC Evol Biol 16:38PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Solberg MF, Fjelldal PG, Nilsen F, Glover KA (2014) Hatching time and alevin growth prior to the onset of exogenous feeding in farmed, wild and hybrid Norwegian Atlantic salmon. PLoS ONE 9:e113697PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Stillman JH (2019) Heat waves, the new normal: summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34:86–100CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Sutton SG, Bult TP, Haedrich RL (2000) Relationships among fat weight, body weight, water weight, and condition factors in wild Atlantic salmon parr. Trans Am Fish Soc 129:527–538Article 

Google Scholar 
Taggart JB (2006) FAP: an exclusion-based parental assignment program with enhanced predictive functions. Mol Ecol Notes 7:412–415Article 
CAS 

Google Scholar 
Taranger GL, Carrillo M, Schulz RW, Fontaine P, Zanuy S, Felip A et al. (2010) Control of puberty in farmed fish. Gen Comp Endocrinol 165:483–515CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Thompson RM, Beardall J, Beringer J, Grace M, Sardina P (2013) Means and extremes: building variability into community-level climate change experiments. Ecol Lett 16:799–806PubMed 
Article 
PubMed Central 

Google Scholar 
Thorpe JE (1994) Reproductive strategies in Atlantic salmon, Salmo salar L. Aquacult Res 25:77–87Article 

Google Scholar 
Tromp JJ, Jones PL, Brown MS, Donald JA, Biro PA, Afonso LOB (2018) Chronic exposure to increased water temperature reveals few impacts on stress physiology and growth responses in juvenile Atlantic salmon. Aquaculture 495:196–204Article 

Google Scholar 
Underwood ZE, Myrick CA, Rogers KB (2012) Effect of acclimation temperature on the upper thermal tolerance of Colorado River cutthroat trout Oncorhynchus clarkii pleuriticus: thermal limits of a North American salmonid. J Fish Biol 80:2420–2433CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Van Leeuwen TE, McLennan D, McKelvey S, Stewart DC, Adams CE, Metcalfe NB (2016) The association between parental life history and offspring phenotype in Atlantic salmon. J Exp Biol 219:374–382PubMed 
PubMed Central 

Google Scholar 
Walsh B, Blows MW (2009) Abundant genetic variation + strong selection = multivariate genetic constraints: a geometric view of adaptation. Annu Rev Ecol Evol S 40:41–59Article 

Google Scholar 
Whitlock MC, Phillips PC, Wade MJ (1993) Gene interaction affects the additive genetic variance in subdivided populations with migration and extinction. Evolution 47:1758–1769PubMed 
Article 
PubMed Central 

Google Scholar 
Wright S (1932) Proceedings of the Sixth International Congress on Genetics, Vol. 1. Donald FJ (ed.). The Genetics Society of America, pp 356-366Zhang T, Kong J, Liu B, Wang Q, Cao B, Luan S et al. (2014) Genetic parameter estimation for juvenile growth and upper thermal tolerance in turbot (Scophthalmus maximus Linnaeus). Acta Oceano Sin 33:106–110CAS 
Article 

Google Scholar  More

1.Karl D, Letelier R, Tupas L, Dore J, Christian J, Hebel D. The role of nitrogen fixation in biogeochemical cycling in the subtropical North Pacific Ocean. Nature. 1997;388:533–8.CAS 
Article 

Google Scholar 
2.Jickells TD, Buitenhuis E, Altieri K, Baker AR, Capone D, Duce RA, et al. A reevaluation of the magnitude and impacts of anthropogenic atmospheric nitrogen inputs on the ocean. Glob Biogeochem Cycles. 2017;31:289–305.CAS 

Google Scholar 
3.Knapp A. The sensitivity of marine N2 fixation to dissolved inorganic nitrogen. Front Microbiol. 2012;3:374.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Rees AP, Gilbert JA, Kelly-Gerreyn BA. Nitrogen fixation in the western English Channel (NE Atlantic Ocean). Mar Ecol Prog Ser. 2009;374:7–12.CAS 
Article 

Google Scholar 
5.Shiozaki T, Nagata T, Ijichi M, Furuya K. Nitrogen fixation and the diazotroph community in the temperate coastal region of the northwestern North Pacific. Biogeosciences. 2015;12:4751–64.Article 

Google Scholar 
6.Tang W, Cerdán-García E, Berthelot H, Polyviou D, Wang S, Baylay A, et al. New insights into the distributions of nitrogen fixation and diazotrophs revealed by high-resolution sensing and sampling methods. ISME J. 2020;14:2514–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Tang W, Wang S, Fonseca-Batista D, Dehairs F, Gifford S, Gonzalez AG, et al. Revisiting the distribution of oceanic N2 fixation and estimating diazotrophic contribution to marine production. Nat Commun. 2019;10:1–10.Article 
CAS 

Google Scholar 
8.Hamersley M, Turk K, Leinweber A, Gruber N, Zehr J, Gunderson T, Capone D. Nitrogen fixation within the water column associated with two hypoxic basins in the Southern California Bight. Aquat Microb Ecol. 2011;63:193–205.Article 

Google Scholar 
9.Mulholland MR, Bernhardt PW, Blanco-Garcia JL, Mannino A, Hyde K, Mondragon E, et al. Rates of dinitrogen fixation and the abundance of diazotrophs in North American coastal waters between Cape Hatteras and Georges Bank. Limnol Oceanogr. 2012;57:1067–83.CAS 
Article 

Google Scholar 
10.Mulholland MR, Bernhardt PW, Widner BN, Selden CR, Chappell PD, Clayton S, et al. High rates of N2 fixation in temperate, western North Atlantic coastal waters expands the realm of marine diazotrophy. Glob Biogeochem Cycles. 2019;33:826–40.CAS 
Article 

Google Scholar 
11.Bentzon-Tilia M, Traving SJ, Mantikci M, Knudsen-Leerbeck H, Hansen JL, Markager S, et al. Significant N2 fixation by heterotrophs, photoheterotrophs and heterocystous cyanobacteria in two temperate estuaries. ISME J. 2015;9:273–85.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Wen Z, Lin W, Shen R, Hong H, Kao SJ, Shi D. Nitrogen fixation in two coastal upwelling regions of the Taiwan Strait. Sci Rep. 2017;7:1–10.Article 
CAS 

Google Scholar 
13.Voss M, Bombar D, Loick N, Dippner JW. Riverine influence on nitrogen fixation in the upwelling region off Vietnam, South China Sea. Geophys Res Lett. 2006;33:L07604.Article 
CAS 

Google Scholar 
14.Shiozaki T, Furuya K, Kodama T, Kitajima S, Takeda S, Takemura T, et al. New estimation of N2 fixation in the western and central Pacific Ocean and its marginal seas. Glob Biogeochem Cycles. 2010;24:GB1015–n/a.15.Blais M, Tremblay JÉ, Jungblut AD, Gagnon J, Martin J, Thaler M, et al. Nitrogen fixation and identification of potential diazotrophs in the Canadian Arctic. Glob Biogeochem Cycles. 2012;26:GB3022.Article 
CAS 

Google Scholar 
16.Shiozaki T, Fujiwara A, Inomura K, Hirose Y, Hashihama F, Harada N. Biological nitrogen fixation detected under Antarctic sea ice. Nat Geosci. 2020;13:729–32.CAS 
Article 

Google Scholar 
17.Harding K, Turk-Kubo KA, Sipler RE, Mills MM, Bronk DA, Zehr JP. Symbiotic unicellular cyanobacteria fix nitrogen in the Arctic Ocean. Proc Natl Acad Sci USA. 2018;115:13371–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Thompson AW, Foster RA, Krupke A, Carter BJ, Musat N, Vaulot D, et al. Unicellular cyanobacterium symbiotic with a single-celled eukaryotic alga. Science. 2012;337:1546–50.CAS 
PubMed 
Article 

Google Scholar 
19.Zehr JP, Shilova IN, Farnelid HM, del Carmen Muñoz-MarínCarmen M, Turk-Kubo KA. Unusual marine unicellular symbiosis with the nitrogen-fixing cyanobacterium UCYN-A. Nat Microbiol. 2016;2:16214.PubMed 
Article 
CAS 

Google Scholar 
20.Zehr JP, Bench SR, Carter BJ, Hewson I, Niazi F, Shi T, et al. Globally distributed uncultivated oceanic N2-fixing cyanobacteria lack oxygenic photosystem II. Science. 2008;322:1110–2.CAS 
PubMed 
Article 

Google Scholar 
21.Tripp HJ, Bench SR, Turk KA, Foster RA, Desany BA, Niazi F, et al. Metabolic streamlining in an open-ocean nitrogen-fixing cyanobacterium. Nature. 2010;464:90–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Church MJ, Mahaffey C, Letelier RM, Lukas R, Zehr JP, Karl DM. Physical forcing of nitrogen fixation and diazotroph community structure in the North Pacific subtropical gyre. Glob Biogeochem Cycles. 2009;23:GB2020.23.Langlois RJ, Hummer D, LaRoche J. Abundances and distributions of the dominant nifH phylotypes in the Northern Atlantic Ocean. Appl Environ Microbiol. 2008;74:1922–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Moisander PH, Beinart RA, Hewson I, White AE, Johnson KS, Carlson CA, et al. Unicellular cyanobacterial distributions broaden the oceanic N2 fixation domain. Science. 2010;327:1512–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Krupke A, Lavik G, Halm H, Fuchs BM, Amann RI, Kuypers MM. Distribution of a consortium between unicellular algae and the N2 fixing cyanobacterium UCYN-A in the North Atlantic Ocean. Environ Microbiol. 2014;16:3153–67.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Shiozaki T, Bombar D, Riemann L, Hashihama F, Takeda S, Yamaguchi T, et al. Basin scale variability of active diazotrophs and nitrogen fixation in the North Pacific, from the tropics to the subarctic Bering Sea. Glob Biogeochem Cycles 2017;31:996–1009.CAS 
Article 

Google Scholar 
27.Krupke A, Musat N, Laroche J, Mohr W, Fuchs BM, Amann RI, et al. In situ identification and N2 and C fixation rates of uncultivated cyanobacteria populations. Syst Appl Microbiol. 2013;36:259–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Martínez-Pérez C, Mohr W, Löscher CR, Dekaezemacker J, Littmann S, Yilmaz P, et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat Microbiol. 2016;1:16163.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
29.Mills MM, Turk-Kubo KA, van Dijken GL, Henke BA, Harding K, Wilson ST, et al. Unusual marine cyanobacteria/haptophyte symbiosis relies on N2 fixation even in N-rich environments. ISME J. 2020;14:2395–406.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Scavotto RE, Dziallas C, Bentzon-Tilia M, Riemann L, Moisander PH. Nitrogen-fixing bacteria associated with copepods in coastal waters of the North Atlantic Ocean. Environ. Microbiol. 2015;17:3754–65.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Conroy BJ, Steinberg DK, Song B, Kalmbach A, Carpenter EJ, Foster RA. Mesozooplankton graze on cyanobacteria in the amazon river plume and western tropical North Atlantic. Front Microbiol. 2017;8:1436.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Turk-Kubo KA, Connell P, Caron D, Hogan ME, Farnelid HM, Zehr JP. In situ diazotroph population dynamics under different resource ratios in the North Pacific Subtropical Gyre. Front Microbiol. 2018;9:1616.PubMed 
PubMed Central 
Article 

Google Scholar 
33.Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat Microbiol. 2016;1:16005.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Shiozaki T, Fujiwara A, Ijichi M, Harada N, Nishino S, Nishi S, et al. Diazotroph community structure and the role of nitrogen fixation in the nitrogen cycle in the Chukchi Sea (western Arctic Ocean). Limnol Oceanogr. 2018;63:2191–205.CAS 
Article 

Google Scholar 
35.Sohm JA, Hilton JA, Noble AE, Zehr JP, Saito MA, Webb EA. Nitrogen fixation in the South Atlantic Gyre and the Benguela Upwelling system. Geophys Res Lett. 2011;38:L16608–n/a.Article 
CAS 

Google Scholar 
36.Moreira-Coello V, Mouriño-Carballido B, Marañón E, Fernández-Carrera A, Bode A, Varela MM. Biological N2 fixation in the upwelling region off NW Iberia: magnitude, relevance, and players. Front Mar Sci. 2017;4:303.Article 

Google Scholar 
37.Cabello AM, Turk-Kubo KA, Hayashi K, Jacobs L, Kudela RM, Zehr JP. Unexpected presence of the nitrogen-fixing symbiotic cyanobacterium UCYN-A in Monterey Bay, California. J Phycol. 2020;56:1521–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Deutsch C, Frenzel H, McWilliams JC, Renault L, Kessouri F, Howard E, et al. Biogeochemical variability in the California Current System. Prog Oceanogr. 2021;196:102565.Article 

Google Scholar 
39.Grasshoff K, Kremling K, Ehrhardt M, editors. Methods of seawater analysis. 3rd ed. Weinheim: Wiley-VCH; 1999.40.Welschmeyer NA. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and phaeopigments. Limnol Oceanogr. 1994;39:1985–92.CAS 
Article 

Google Scholar 
41.Moisander PH, Beinart RA, Voss M, Zehr JP. Diversity and abundance of diazotrophic microorganisms in the South China Sea during intermonsoon. ISME J. 2008;2:954–67.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Zehr JP, Jenkins BD, Short SM, Steward GF. Nitrogenase gene diversity and microbial community structure: a cross-system comparison. Environ Microbiol. 2003;5:539–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Eren AM, Maignien L, Sul WJ, Murphy LG, Grim SL, Morrison HG, et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol Evol. 2013;4:1111–9.PubMed Central 
Article 
PubMed 

Google Scholar 
47.Turk-Kubo KA, Farnelid HM, Shilova IN, Henke B, Zehr JP. Distinct ecological niches of marine symbiotic N2-fixing cyanobacterium Candidatus Atelocyanobacterium thalassa sublineages. J Phycol. 2017;53:451–61.CAS 
PubMed 
Article 

Google Scholar 
48.Henke BA, Turk-Kubo KA, Bonnet S, Zehr JP. Distributions and abundances of sublineages of the N2-fixing cyanobacterium Candidatus Atelocyanobacterium thalassa (UCYN-A) in the New Caledonian Coral Lagoon. Front Microbiol. 2018;9:554.PubMed 
PubMed Central 
Article 

Google Scholar 
49.Gradoville MR, Farnelid H, White AE, Turk‐Kubo KA, Stewart B, Ribalet F, et al. Latitudinal constraints on the abundance and activity of the cyanobacterium UCYN‐A and other marine diazotrophs in the North Pacific. Limnol Oceanogr. 2020;65:1858–75.CAS 
Article 

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

Google Scholar 
51.Church M, Jenkins B, Karl D, Zehr J. Vertical distributions of nitrogen-fixing phylotypes at Stn ALOHA in the oligotrophic North Pacific Ocean. Aquat Microb Ecol. 2005;38:3–14.Article 

Google Scholar 
52.Thompson A, Carter BJ, Turk-Kubo K, Malfatti F, Azam F, Zehr JP. Genetic diversity of the unicellular nitrogen-fixing cyanobacteria UCYN-A and its prymnesiophyte host. Environ Microbiol. 2014;16:3238–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Foster RA, Subramaniam A, Mahaffey C, Carpenter EJ, Capone DG, Zehr JP. Influence of the Amazon River plume on distributions of free-living and symbiotic cyanobacteria in the western tropical north Atlantic Ocean. Limnol Oceanogr. 2007;52:517–32.CAS 
Article 

Google Scholar 
54.Goebel NL, Turk KA, Achilles KM, Paerl R, Hewson I, Morrison AE, et al. Abundance and distribution of major groups of diazotrophic cyanobacteria and their potential contribution to N2 fixation in the tropical Atlantic Ocean. Environ Microbiol. 2010;12:3272–89.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Farnelid H, Turk-Kubo K, Munoz-Marin MD, Zehr JP. New insights into the ecology of the globally significant uncultured nitrogen-fixing symbiont UCYN-A. Aquat Microb Ecol. 2016;77:125–38.Article 

Google Scholar 
56.Mohr W, Grosskopf T, Wallace DWR, LaRoche J. Methodological underestimation of oceanic nitrogen fixation rates. PLoS ONE. 2010;5:e12583.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Montoya JP, Voss M, Kahler P, Capone DG. A simple, high-precision, high-sensitivity tracer assay for N2 fixation. Appl Environ Microbiol. 1996;62:986–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Gradoville MR, Bombar D, Crump BC, Letelier RM, Zehr JP, White AE. Diversity and activity of nitrogen-fixing communities across ocean basins. Limnol Oceanogr. 2017;62:1895–909.Article 

Google Scholar 
59.White AE, Granger J, Selden C, Gradoville MR, Potts L, Bourbonnais A, et al. A critical review of the 15N2 tracer method to measure diazotrophic production in pelagic ecosystems. Limnol Oceanogr Methods. 2020;18:129–47.Article 

Google Scholar 
60.Cornejo-Castillo FM, Cabello AM, Salazar G, Sánchez-Baracaldo P, Lima-Mendez G, Hingamp P, et al. Cyanobacterial symbionts diverged in the late Cretaceous towards lineage-specific nitrogen fixation factories in single-celled phytoplankton. Nat Commun. 2016;7:11071.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Cabello AM, Cornejo-Castillo FM, Raho N, Blasco D, Vidal M, Audic S, et al. Global distribution and vertical patterns of a prymnesiophyte-cyanobacteria obligate symbiosis. ISME J. 2016;10:693–706.PubMed 
Article 

Google Scholar 
62.Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MM. Look@ NanoSIMS–a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
PubMed 
Article 

Google Scholar 
63.Krupke A, Mohr W, LaRoche J, Fuchs BM, Amann RI, Kuypers MM. The effect of nutrients on carbon and nitrogen fixation by the UCYN-A-haptophyte symbiosis. ISME J. 2015;9:1635–47.CAS 
PubMed 
Article 

Google Scholar 
64.Meyer NR, Fortney J, Dekas AE. NanoSIMS sample preparation decreases isotope enrichment: magnitude, variability and implications for single-cell rates of microbial activity. Environ Microbiol. 2020;23:81–98.PubMed 
Article 
CAS 

Google Scholar 
65.Durazo R. Seasonality of the transitional region of the California Current System off Baja California. J Geophys Res Oceans. 2015;120:1173–96.Article 

Google Scholar 
66.Bakun A. Coastal upwelling indices, west coast of North America, 1946–71.67.Redfield AC. On the proportions of organic derivatives in sea water and their relation to the composition of plankton. Vol. 1. Liverpool: University Press of Liverpool; 1934. p. 176–92.68.Bograd SJ, Schroeder ID, Jacox MG. A water mass history of the Southern California current system. Geophys. Res. Lett. 2019;46:6690–8.Article 

Google Scholar 
69.Langlois R, Großkopf T, Mills M, Takeda S, LaRoche J. Widespread distribution and expression of gamma A (UMB), an uncultured, diazotrophic, γ-proteobacterial nifH phylotype. PLoS ONE. 2015;10:e0128912.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Dekaezemacker J, Bonnet S, Grosso O, Moutin T, Bressac M, Capone DG. Evidence of active dinitrogen fixation in surface waters of the eastern tropical South Pacific during El Niño and La Niña events and evaluation of its potential nutrient controls. Glob Biogeochem Cycles 2013;27:768–79.CAS 
Article 

Google Scholar 
71.Chen M, Lu Y, Jiao N, Tian J, Kao SJ, Zhang Y. Biogeographic drivers of diazotrophs in the western Pacific Ocean. Limnol Oceanogr. 2019;64:1403–21.CAS 
Article 

Google Scholar 
72.Turk KA, Rees AP, Zehr JP, Pereira N, Swift P, Shelley R, et al. Nitrogen fixation and nitrogenase (nifH) expression in tropical waters of the eastern North Atlantic. ISME J. 2011;5:1201–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.White AE, Foster RA, Benitez-Nelson CR, Masqué P, Verdeny E, Popp BN, et al. Nitrogen fixation in the Gulf of California and the Eastern Tropical North Pacific. Prog Oceanogr. 2013;109:1–17.Article 

Google Scholar 
74.Selden CR, Mulholland MR, Bernhardt PW, Widner B, Macías‐Tapia A, Ji Q, et al. Dinitrogen fixation across physico-chemical gradients of the eastern tropical North Pacific oxygen deficient zone. Glob Biogeochem Cycles. 2019;33:1187–202.CAS 
Article 

Google Scholar 
75.Sohm JA, Webb EA, Capone DG. Emerging patterns of marine nitrogen fixation. Nat Rev Microbiol. 2011;9:499–508.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Carlucci A, Bowes PM. Production of vitamin B12, thiamine, and biotin by phytoplankton. J Phycol. 1970;6:351–7.CAS 

Google Scholar 
77.Gledhill M, Buck KN. The organic complexation of iron in the marine environment: a review. Front Microbiol. 2012;3:69.PubMed 
PubMed Central 

Google Scholar 
78.Biddanda B, Benner R. Carbon, nitrogen, and carbohydrate fluxes during the production of particulate and dissolved organic matter by marine phytoplankton. Limnol Oceanogr. 1997;42:506–18.CAS 
Article 

Google Scholar 
79.Hernández de la Torre B, Gaxiola Castro G, Álvarez Borrego S, Gallegos García A, Aguirre Gómez R. New organic carbon in front of the Baja California Peninsula: time series and climatology. Hidrobiológica. 2015;25:74–85.
Google Scholar 
80.Xiu P, Chai F, Curchitser EN, Castruccio FS. Future changes in coastal upwelling ecosystems with global warming: the case of the California Current System. Sci. Rep. 2018;8:2866.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Kimor B, Reid F, Jordan J. An unusual occurrence of Hemiaulus membranaceus Cleve (Bacillariophyceae) with Richelia intracelluaris Schmidt (Cyanophyceae) off the coast of Southern California. Phycologia. 1978;17:162–6.Article 

Google Scholar 
82.White AE, Prahl FG, Letelier RM, Popp BN. Summer surface waters in the Gulf of California: Prime habitat for biological N2 fixation. Glob Biogeochem Cycles. 2007;21:GB2017–n/a.83.Pyle AE, Johnson AM, Villareal TA. Isolation, growth, and nitrogen fixation rates of the Hemiaulus-Richelia (diatom-cyanobacterium) symbiosis in culture. PeerJ. 2020;8:e10115.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
84.Foster RA, Kuypers MM, Vagner T, Paerl RW, Musat N, Zehr JP. Nitrogen fixation and transfer in open ocean diatom–cyanobacterial symbioses. ISME J. 2011;5:1484–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Caputo A, Nylander JAA, Foster RA. The genetic diversity and evolution of diatom-diazotroph associations highlights traits favoring symbiont integration. FEMS Microbiol Lett. 2019;366:fny297.CAS 
PubMed Central 
Article 

Google Scholar 
86.Thompson AR. State of the California Current 2017–18: still not quite normal in the north and getting interesting in the south. California cooperative oceanic fisheries investigations, Data report. 2018.87.Larkin AA, Moreno AR, Fagan AJ, Fowlds A, Ruiz A, Martiny AC. Persistent El Nino driven shifts in marine cyanobacteria populations. PLoS ONE. 2020;15:e0238405.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Hagino K, Takano Y, Horiguchi T. Pseudo-cryptic speciation in Braarudosphaera bigelowii (Gran and Braarud) Deflandre. Mar Micropaleontol. 2009;72:210–21.Article 

Google Scholar 
89.Selden CR, Chappell PD, Clayton S, Macías‐Tapia A, Bernhardt PW, Mulholland MR. A coastal N2 fixation hotspot at the Cape Hatteras front: elucidating spatial heterogeneity in diazotroph activity via supervised machine learning. Limnol Oceanogr. 2021;66:1832–49.Article 

Google Scholar 
90.Wang S, Tang W, Delage E, Gifford S, Whitby H, González AG, et al. Investigating the microbial ecology of coastal hotspots of marine nitrogen fixation in the western North Atlantic. Sci Rep. 2021;11:5508.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More