Ecology

Baker JR (1984) Mortality and morbidity in grey seal pups (Halichoerus grypus). Studies on its causes, effects of environment, the nature and sources of infectious agents and the immunological status of pups. J Zool 203:23–48Article 

Google Scholar 
Bakermans-Kranenburg MJ, van IJzendoorn MH (2008) Oxytocin receptor (OXTR) and serotonin transporter (5-HTT) genes associated with observed parenting. Soc Cogn Affect Neur 3:128–134Article 

Google Scholar 
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48Article 

Google Scholar 
Battersby S, Ogilvie AD, Smith CA, Blackwood DH, Muir WJ, Quinn JP et al. (1996) Structure of a variable number tandem repeat of the serotonin transporter gene and association with affective disorder. Psychiat Genet 6:177–181CAS 
Article 

Google Scholar 
Bengston SE, Dahan RA, Donaldson Z, Phelps SM, van Oers K, Sih A et al. (2018) Genomic tools for behavioural ecologists to understand repeatable individual differences in behaviour. Nat Ecol Evol 2:944–955PubMed 
Article 

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 Stat Methodol 57:289–300
Google Scholar 
Boake CRB, Arnold SJ, Breden F, Meffert LM, Ritchie MG, Taylor BJ et al. (2002) Genetic tools for studying adaptation and the evolution of behavior. Am Nat 160:S143–S159PubMed 
Article 

Google Scholar 
Boness DJ, Anderson SS, Cox CR (1982) Function of female aggression during the pupping and mating season of grey seals, Halichoerus grypus (Fabricius). Can J Zool 60:2270–2278Article 

Google Scholar 
Bowen WD, den Heyer CE, McMillan JI, Iverson SJ (2015) Offspring size at weaning affects survival to recruitment and reproductive performance of primiparous grey seals. Ecol Evol 5:1412–1424PubMed 
PubMed Central 
Article 

Google Scholar 
Bowen WD, Iverson SJ, McMillan JI, Boness DJ (2006) Reproductive performance in grey seals: age-related improvement and senescence in a capital breeder. J Anim Ecol 75:1340–1351CAS 
PubMed 
Article 

Google Scholar 
Bowen WD, McMillan JI, Blanchard W (2007) Reduced population growth of grey seals at Sable Island: evidence from pup production and age of primiparity. Mar Mam Sci 23:48–64Article 

Google Scholar 
Bowen WD, McMillan J, Mohn R (2003) Sustained exponential population growth of grey seals at Sable Island, Nova Scotia. ICES J Mar Sci 60:1265–1274Article 

Google Scholar 
Bowen WD, Stobo WT, Smith SJ (1992) Mass changes of grey seal Halichoerus grypus pups on Sable Island: differential maternal investment reconsidered. J Zool 227:607–622Article 

Google Scholar 
Bubac CM, Coltman DW, Bowen WD, Lidgard DC, Lang SLC, den Heyer CE (2018) Repeatability and reproductive consequences of boldness in female gray seals. Behav Ecol Sociobiol 72:100–112Article 

Google Scholar 
Bubac CM, Miller JM, Coltman DW (2020) The genetic basis of animal behavioural diversity in natural populations. Mol Ecol https://doi.org/10.1111/mec.15461Cammen KM, Andrews KR, Carroll EL, Foote AD, Humble E, Khudyakov JI et al. (2016) Genomic methods take the plunge: recent advances in high-throughput sequencing of marine mammals. J Hered 107:481–495CAS 
PubMed 
Article 

Google Scholar 
Cammen KM, Schultz TF, Bowen WD, Hammill MO, Puryear WB, Runstadler J et al. (2018b) Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds. Ecol Evol 8:6599–6614PubMed 
PubMed Central 
Article 

Google Scholar 
Cammen KM, Vincze S, Heller AS, McLeod BA, Wood SA, Bowen WD et al. (2018a) Genetic diversity from pre-bottleneck to recovery in two sympatric pinniped species in the Northwest Atlantic. Con Gen 19:555–569CAS 
Article 

Google Scholar 
Carere C, Maestripieri D (2013) Animal personalities: Behavior, physiology, and evolution. The University of Chicago Press, ChicagoBook 

Google Scholar 
Chakraborty S, Chakraborty D, Mukherjee O, Jain S, Ramakrishnan U, Sinha A (2010) Genetic polymorphism in the serotonin transporter promoter region and ecological success in macaques. Behav Genet 40:672–679PubMed 
Article 

Google Scholar 
Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale, NJ
Google Scholar 
Dochtermann NA, Schwab T, Anderson Berdal M, Dalos J, Royauté R (2019) The heritability of behavior: a meta-analysis. J Hered 110:403–410PubMed 
Article 

Google Scholar 
Dohm MR (2002) Repeatability estimates do not always set an upper limit to heritability. Funct Ecol 16:273–280Article 

Google Scholar 
Edwards HA, Hajduk GK, Durieux G, Burke T, Dugdale HL (2015) No association between personality and candidate gene polymorphisms in a wild bird population. PLoS ONE 10:e0138439. https://doi.org/10.1371/journal.pone.0138439CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Emiliano ABF, Cruz T, Pannoni V, Fudge JL (2007) The interface of oxytocin-labeled cells and serotonin transporter-containing fibers in the primate hypothalamus: a substrate for SSRIs therapeutic effects? Neuropsychopharmacol 32:977–988CAS 
Article 

Google Scholar 
Fairbanks LA, Way BM, Breidenthal, Bailey JN, Jorgensen MJ (2012) Maternal and offspring dopamine D4 receptor genotypes interact to influence juveniles impulsivity in vervet monkeys Psychol Sci 23:1099–1104. https://doi.org/10.1177/0956797612444905Article 
PubMed 

Google Scholar 
Fidler A (2011) Personality-associated genetic variation in birds and its possible significance for avian evolution, conservation, and welfare. In: Inoue-Murayama M, Kawamura S, Weiss A (eds) From Genes to Animal Behavior. Primatology Monographs. Springer, Tokyo, p 275–294
Google Scholar 
Fitzpatrick MJ, Ben-Shahar Y, Smid HM, Vet LEM, Robinson GE, Sokolowski MB (2005) Candidate genes for behavioural ecology. Trends in Ecology & Evolution 20:96–104Article 

Google Scholar 
Fulton TL, Strobeck C (2010) Multiple fossil calibrations, nuclear loci and mitochondrial genomes provide new insight into biogeography and divergence timing for true seals (Phocidae, Pinnipedia). J Biogeogr 37:814–829Article 

Google Scholar 
Garvin MR, Saitoh K, Gharrett AJ (2010) Application of single nucleotide polymorphisms to non-model species: a technical review. Mol Ecol Resour 10:915–934. https://doi.org/10.1111/j.1755-0998.02891.xCAS 
Article 
PubMed 

Google Scholar 
Göring HHH, Terwilliger JD, Blangero J (2001) Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet 69:1357–2001PubMed 
PubMed Central 
Article 

Google Scholar 
Gosling SD (2001) From mice to men: what can we learn about personality from animal research? Psychol Bull 127:45–86CAS 
PubMed 
Article 

Google Scholar 
Hadfield JD (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 33:1–22Article 

Google Scholar 
Hall AJ, McConnell BJ, Barker RJ (2001) Factors affecting first-year survival in grey seals and their implications for life-history strategy. J Anim Ecol 70:138–149
Google Scholar 
Hammill MO, den Heyer CE, Bowen WD, Lang SLC (2017) Grey seal population trends in Canadian waters, 1960–2016 and harvest advice. DFO Can Sci Advis Sec Res Doc 2017/052. v + 30pHelyar SJ, Hemmer-Hansen J, Bekkevold D, Taylor MI, Ogden R, Limborg MT et al. (2011) Applications of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol Ecol Resour 11:123–136. https://doi.org/10.1111/j.1755-0998.2010.02943.xArticle 
PubMed 

Google Scholar 
Holtmann B, Grosser S, Lagisz M, Johnson SL, Santos ESA, Lara CE et al. (2016) Population differentiation and behavioural association of the two ‘personality’ genes DRD4 and SERT in dunnocks (Prunella modularis). Mol Ecol 25:706–722CAS 
PubMed 
Article 

Google Scholar 
Howell S, Westergaard G, Hoos B, Chavanne TJ, Shoaf SE, Snoy PJ et al. (2007) Serotonergic influences on life-history outcomes in free-ranging male rhesus macaques. Am J Primatol 69:851–865CAS 
PubMed 
Article 

Google Scholar 
Iverson SJ, Bowen WD, Boness DJ, Oftedal OT (1993) The effect of maternal size and milk output on pup growth in grey seals (Halichoerus grypus). Physiol Zool 66:61–88Article 

Google Scholar 
Jacobs LN, Staiger EA, Albright JD, Brooks SA (2016) The MC1R and ASIP coat color loci may impact behavior in the horse. J Hered 107:214–219CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jaeger BC, Edwards LJ, Das K, Sen PK (2016) An R2 statistic for fixed effects in the generalized linear mixed model. J Appl Stat 44:1086–1106Article 

Google Scholar 
Jorgensen H, Riis M, Knigge U, Kjaer A, Warberg J (2003) Serotonin receptors involved in vasopressin and oxytocin secretion. J Neuroendocrinol 15:242–249CAS 
PubMed 
Article 

Google Scholar 
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S et al. (2012) Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649PubMed 
PubMed Central 
Article 

Google Scholar 
Kendrick KM (2000) Oxytocin, motherhood and bonding. Exp Physiol 85:111–124Article 

Google Scholar 
Kim SJ, Kim YS, Lee HS, Kim SY, Kim C-H (2006) An interaction between the serotonin transporter promoter region and dopamine transporter polymorphisms contributes to harm avoidance and reward dependence traits in normal healthy subjects. J Neural Transm 113:877–886CAS 
PubMed 
Article 

Google Scholar 
Kluger A, Siegfried Z, Ebstein R (2002) A meta-analysis of the association between DRD4 polymorphism and novelty seeking. Mol Psychiatry 7:712–717CAS 
PubMed 
Article 

Google Scholar 
Korsten P, Mueller JC, Hermannstädter C, van Overveld T, Patrick SC, Quinn JL et al. (2010) Association between DRD4 gene polymorphism and personality variation in great tits: a test across four populations. Mol Ecol 19:832–843CAS 
PubMed 
Article 

Google Scholar 
Laine VN, van Oers K (2017) The quantitative and molecular genetics of individual differences in animal personality. In: Vonk J, Weiss A, Kuczaj SA(eds) Personality in Nonhuman Animals. Springer, Cham, p 55–72
Google Scholar 
Laird NM, Lange C (2011) The fundamentals of modern statistical genetics. Springer, New York, NYBook 

Google Scholar 
Lang SLC, Iverson SJ, Bowen WD (2009) Repeatability in lactation performance and the consequences for maternal reproductive success in gray seals. Ecology 90:2513–2523CAS 
PubMed 
Article 

Google Scholar 
Lesch K-P, Bengel D, Heils A, Sabol SZ, Greenberg BD (1996) Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 274:1527–1531CAS 
PubMed 
Article 

Google Scholar 
Lidgard DC, Bowen WD, Boness DJ (2012) Longitudinal changes and consistency in male physical and behavioural traits have implications for mating success in the grey seal (Halichoerus grypus). Can J Zool 90:849–860Article 

Google Scholar 
Lim MM, Young LJ (2006) Neuropeptidergic regulation of affiliative behavior and social bonding in animals. Horm Behav 50:506–517CAS 
PubMed 
Article 

Google Scholar 
MacKenzie A, Quinn J (1999) A serotonin transporter gene intron 2 polymorphic region, correlated with affective disorders, has allele-dependent differential enhancer-like properties in the mouse embryo. PNAS 96:15251–15255CAS 
PubMed 
Article 

Google Scholar 
Mansfield AW, Beck B (1977) The grey seal in eastern Canada. Tech Rep. Fish Mar Serv Can 706:1–81
Google Scholar 
Maruyama T, Fuerst PA (1985) Population bottlenecks and nonequilibrium models in population genetics. II. Number alleles a small Popul that was Form a recent bottleneck Genet 111:675–689CAS 

Google Scholar 
McCann TS (1982) Aggressive and maternal activities of female southern elephant seals (Mirounga leonina). Anim Behav 30:268–276Article 

Google Scholar 
McDougall PT, Réale D, Sol D, Reader SM (2006) Wildlife conservation and animal temperament: causes and consequences of evolutionary change for captive, reintroduced, and wild populations. Anim Conserv 9:39–48Article 

Google Scholar 
Mellish JAE, Iverson SJ, Bowen WD (1999) Variation in milk production and lactation performance in grey seals and consequences for pup growth and weaning characteristics. Physiol Biochem Zool 72:677–690CAS 
PubMed 
Article 

Google Scholar 
Mitsuyasu H, Hirata N, Sakai Y, Shibata H, Takeda Y (2001) Association analysis of polymorphisms in the upstream region of the human dopamine D4 receptor gene (DRD4) with schizophrenia and personality traits. J Hum Genet 46:26–31CAS 
PubMed 
Article 

Google Scholar 
Møller AP, Jennions MD (2002) How much variance can be explained by ecologists and evolutionary biologists. Oecologia 132:492–500PubMed 
Article 

Google Scholar 
Mousseau TA, Roff DA (1987) Natural selection and the heritability of fitness components. Heredity 59:181–197PubMed 
Article 

Google Scholar 
Mueller JC, Partecke J, Hatchwell BJ, Gaston KJ, Evans KL (2013) Candidate gene polymorphisms for behavioural adaptations during urbanization in blackbirds. Mol Ecol 22:3629–3637CAS 
PubMed 
Article 

Google Scholar 
Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol 4:133–142Article 

Google Scholar 
Nei M, Li WH (1976) The transient distribution of allele frequencies under mutation pressure. Genet Res 28:205–214CAS 
PubMed 
Article 

Google Scholar 
Noren SR, Boness DJ, Iverson SJ, McMillan J, Bowen WD (2008) Body condition at weaning affects the duration of the postweaning fast in grey seal pups (Halichoerus grypus). Physiol Biochem Zool 81:269–277PubMed 
Article 

Google Scholar 
Oak JN, Oldenhof J, Van Tol HH (2000) The dopamine D4 receptor: one decade of research. Eur J Pharm 405:303–327CAS 
Article 

Google Scholar 
Pati N, Schowinsky V, Kokanovic O, Magnuson V, Ghosh S (2004) A comparison between SNaPshot, pyrosequencing, and biplex invader SNP genotyping methods: accuracy, cost, and throughput. J Biochem Biophys Methods 60:1–12CAS 
PubMed 
Article 

Google Scholar 
Prasad P, Ogawa S, Parhar IS (2015) Role of serotonin in fish reproduction. Front Neurosci 9:1–9. https://doi.org/10.3389/fnins.2015.00195Article 

Google Scholar 
R Core Team (2019) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ URLRaymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249Article 

Google Scholar 
Réale D, Gallant BY, Leblanc M, Festa-Bianchet M (2000) Consistency in bighorn ewes and correlates with behaviour and life history. Anim Behav 60:589–597PubMed 
Article 

Google Scholar 
Réale D, Reader SM, Sol D, McDougall PT, Dingemanse NJ (2007) Integrating animal temperament within ecology and evolution. Biol Rev 82:291–318PubMed 
Article 

Google Scholar 
Riyahi S, Björklund M, Mateos-Gonzalez F, Senar JC (2017) Personality and urbanization: behavioural traits and DRD4 SNP830 polymorphisms in Great Tits in Barcelona city. J Ethol 35:101–108Article 

Google Scholar 
Riyahi S, Sánchez-Delgado M, Calafell F, Monk D, Senar JC (2015) Combined epigenetic and intraspecific variation of the DRD4 and SERT genes influence novelty seeking behavior in great tit Parus major. Epigenetics 10:516–525PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson KJ, Twiss SD, Hazon N, Pomeroy PP (2015) Maternal oxytocin is linked to close mother-infant proximity in grey seals (Halichoerus grypus). PLoS One 10:e0144577. https://doi.org/10.1371/journal.pone.0144577CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Robinson KJ, Twiss SD, Hazon N, Simon M, Pomeroy PP (2017) Positive social behaviours are induced and retained after oxytocin manipulations mimicking endogenous concentrations in a wild mammal. Proc R Soc B-Biol Sci https://doi.org/10.1098/rspb.2017.0554Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8:103–106PubMed 
Article 

Google Scholar 
Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE et al. (2017) DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol 34:3299–3302CAS 
PubMed 
Article 

Google Scholar 
Sala M, Braida D, Donzelli A, Martucci R, Busnelli M, Bulgheroni E et al. (2013) Mice heterozygous for the oxytocin receptor gene (Oxtr+/−) show impaired social behaviour but not increased aggression or cognitive inflexibility: evidence of a selective haploinsufficiency gene effect. J Neuroendocrinol 25:107–118CAS 
PubMed 
Article 

Google Scholar 
Savitz JB, Ramesar RS (2004) Genetic variants implicated in personality: a review of the more promising candidates. Am J Med Genet B 131B:20–32Article 

Google Scholar 
Sih A, Bell AM, Johnson JC, Ziemba RE (2004) Behavioural syndromes: an integrative overview. Q Rev Biol 79:241–277PubMed 
Article 

Google Scholar 
Singmann H, Bolker B, Westfall J, Aust F, Ben-Shachar MS (2019) afex: Analysis of Factorial Experiments. R package version 0.25-1. https://CRAN.R-project.org/package=afexSinn DL, Gosling SD, Moltschaniwskyj NA (2008) Development of shy/bold behaviour in squid: context-specific phenotypes associated with developmental plasticity. Anim Behav 75:433–442Article 

Google Scholar 
Sloan Wilson D, Clark AB, Coleman K, Dearstyne T (1994) Shyness and boldness in humans and other animals. TREE 9:442–446CAS 
PubMed 

Google Scholar 
Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123:585–595CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Timm K, van Oers K, Tilgar V (2018) SERT gene polymorphisms are associated with risk-taking behaviour and breeding parameters in wild great tits. J Exp Biol 221:jeb171595. https://doi.org/10.1242/jeb.171595Article 
PubMed 

Google Scholar 
Twiss SD, Cairns C, Culloch RM, Richards SA, Pomeroy PP (2012) Variation in female grey seal (Halichoerus grypus) reproductive performance correlates to proactive-reactive behavioural types. PLoS ONE 7:e49598. https://doi.org/10.1371/journal.pone.0049598CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Twiss SD, Shuert CR, Brannan N, Bishop AM, Pomeroy PP (2020) Reactive stress-coping styles show more variable reproductive expenditure and fitness outcomes. Sci Rep. 10:9550PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
van Neer A, Gross S, Kesselring T, Wohlsein, Leitzen E, Siebert U (2019) Behavioural and pathological insights into a case of active cannibalism by a grey seal (Halichoerus grypus) on Helgoland, Germany. J Sea Res 148-149:12–16Article 

Google Scholar 
van Oers K (2008) Animal personality, behaviours or traits: what are we measuring? Eur J Pers 22:457–474Article 

Google Scholar 
van Oers K, Mueller JC (2010) Evolutionary genomics of animal personality. P R Soc B-Biol Sci 365:3991–4000
Google Scholar 
Wellenreuther M, Hansson B (2016) Detecting polygenic evolution: problems, pitfalls, and promises. Trends Genet 32:155–164. https://doi.org/10.1016/j.tig.2015.12.004CAS 
Article 
PubMed 

Google Scholar 
Wolf M, Weissing FJ (2010) An explanatory framework for adaptive personality differences. Proc R Soc B-Biol Sci 365:3959–3968. https://doi.org/10.1098/rstb.2010.0215Article 

Google Scholar 
Wolf M, van Doorn GS, Leimar O, Weissing FJ (2007) Life-history trade-offs favour the evolution of animal personalities. Nature 447:581–584CAS 
PubMed 
Article 

Google Scholar 
Wood SA, Frasier TR, McLeod BA, Gilbert JR, White BN et al. (2011) The genetics of recolonization: an analysis of the stock structure of grey seals (Halichoerus grypus) in the Northwest Atlantic. Can J Zool 89:490–497Article 

Google Scholar  More

1.DeVries SL, Zhang P. Antibiotics and the Terrestrial Nitrogen Cycle: a review. Curr Pollut Rep. 2016;2:51–67.CAS 
Article 

Google Scholar 
2.Kümmerer K. Antibiotics in the aquatic environment – A review – Part I. Chemosphere. 2009;75:417–34.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Franklin AM, Aga DS, Cytryn E, Durso LM, McLain JE, Pruden A, et al. Antibiotics in Agroecosystems: introduction to the Special Section. J Environ Qual. 2016;45:377–93.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Roose-Amsaleg C, Laverman AM. Do antibiotics have environmental side-effects? Impact of synthetic antibiotics on biogeochemical processes. Environ Sci Pollut Res. 2016;23:4000–12.CAS 
Article 

Google Scholar 
5.Grenni P, Ancona V, Barra, Caracciolo A. Ecological effects of antibiotics on natural ecosystems: a review. Microchemical J. 2018;136:25–39.CAS 
Article 

Google Scholar 
6.Chee-Sanford JC, Mackie RI, Koike S, Krapac IG, Lin Y-F, Yannarell AC, et al. Fate and Transport of Antibiotic Residues and Antibiotic Resistance Genes following Land Application of Manure Waste. J Environ Qual. 2009;38:1086.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Mehrtens A, Licha T, Broers HP, Burke V. Tracing veterinary antibiotics in the subsurface – A long-term field experiment with spiked manure. Environ Pollut. 2020;265:114930.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Kivits T, Broers HP, Beeltje H, van Vliet M, Griffioen J. Presence and fate of veterinary antibiotics in age-dated groundwater in areas with intensive livestock farming. Environ Pollut. 2018;241:988–98.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Gros M, Mas-Pla J, Boy-Roura M, Geli I, Domingo F, Petrović M. Veterinary pharmaceuticals and antibiotics in manure and slurry and their fate in amended agricultural soils: Findings from an experimental field site (Baix Empordà, NE Catalonia). Sci Total Environ. 2019;654:1337–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Baquero F, Negri M-C. Challenges: selective compartments for resistant microorganisms in antibiotic gradients. BioEssays. 1997;19:731–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Hermsen R, Deris JB, Hwa T. On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. Proc Natl Acad Sci. 2012;109:10775–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Andersson DI, Hughes D. Microbiological effects of sublethal levels of antibiotics. Nat Rev Microbiol. 2014;12:465–78.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355:826–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Cohen NR, Lobritz MA, Collins JJ. Microbial Persistence and the Road to Drug Resistance. Cell Host Microbe. 2013;13:632–42.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Hughes D, Andersson DI. Environmental and genetic modulation of the phenotypic expression of antibiotic resistance. FEMS Microbiol Rev. 2017;41:374–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Venter H, Arzanlou M, Chai WC, Venter H. Intrinsic, adaptive and acquired antimicrobial resistance in Gram-negative bacteria. Essays Biochem. 2017;61:49–59.PubMed 
Article 
PubMed Central 

Google Scholar 
17.Alcalde RE, Michelson K, Zhou L, Schmitz EV, Deng J, Sanford RA, et al. Motility of Shewanella oneidensis MR-1 Allows for Nitrate Reduction in the Toxic Region of a Ciprofloxacin Concentration Gradient in a Microfluidic Reactor. Environ Sci Technol. 2019;53:2778–87.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hol FJH, Hubert B, Dekker C, Keymer JE. Density-dependent adaptive resistance allows swimming bacteria to colonize an antibiotic gradient. ISME J. 2016;10:30–38.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Butler MT, Wang Q, Harshey RM. Cell density and mobility protect swarming bacteria against antibiotics. Proc Natl Acad Sci. 2010;107:3776–81.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Steel H, Papachristodoulou A. The effect of spatiotemporal antibiotic inhomogeneities on the evolution of resistance. J Theor Biol. 2020;486:110077.PubMed 
Article 
PubMed Central 

Google Scholar 
21.Lai S, Tremblay J, Déziel E. Swarming motility: a multicellular behaviour conferring antimicrobial resistance. Environ Microbiol. 2009;11:126–36.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Zhang Q, Lambert G, Liao D, Kim H, Robin K, Tung C-k, et al. Acceleration of Emergence of Bacterial Antibiotic Resistance in Connected Microenvironments. Science. 2011;333:1764–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Wu A, Loutherback K, Lambert G, Estevez-Salmeron L, Tlsty TD, Austin RH, et al. Cell motility and drug gradients in the emergence of resistance to chemotherapy. Proc Natl Acad Sci. 2013;110:16103–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Baym M, Lieberman TD, Kelsic ED, Chait R, Gross R, Yelin I, et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 2016;353:1147–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Alexandre G, Greer-Phillips S, Zhulin IB. Ecological role of energy taxis in microorganisms. FEMS Microbiol Rev. 2004;28:113–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Fenchel T. Microbial Behavior in a Heterogeneous World. Science. 2002;296:1068–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Groh JL, Luo Q, Ballard JD, Krumholz LR. Genes That Enhance the Ecological Fitness of Shewanella oneidensis MR-1 in Sediments Reveal the Value of Antibiotic Resistance. Appl Environ Microbiol. 2007;73:492–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Blair JM, Piddock LJ. Structure, function and inhibition of RND efflux pumps in Gram-negative bacteria: an update. Curr Opin Microbiol. 2009;12:512–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Fernández L, Hancock REW. Adaptive and mutational resistance: role of porins and efflux pumps in drug resistance. Clin Microbiol Rev. 2012;25:661–81.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Alvarez-Ortega C, Olivares J, Martinez JL. RND multidrug efflux pumps: what are they good for? Front Microbiol. 2013;4:7.PubMed 
PubMed Central 
Article 

Google Scholar 
31.Anes J, McCusker MP, Fanning S, Martins M. The ins and outs of RND efflux pumps in Escherichia coli. Front Microbiol. 2015;6:587.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Ma D, Alberti M, Lynch C, Nikaido H, Hearst JE. The local repressor AcrR plays a modulating role in the regulation of acrAB genes of Escherichia coli by global stress signals. Mol Microbiol. 1996;19:101–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Nies DH. Efflux-mediated heavy metal resistance in prokaryotes. FEMS Microbiol Rev. 2003;27:313–39.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Fraud S, Poole K. Oxidative Stress Induction of the MexXY Multidrug Efflux Genes and Promotion of Aminoglycoside Resistance Development in Pseudomonas aeruginosa. Antimicrobial Agents Chemother. 2011;55:1068–74.CAS 
Article 

Google Scholar 
35.El Garch F, Lismond A, Piddock LJV, Courvalin P, Tulkens PM, Van Bambeke F. Fluoroquinolones induce the expression of patA and patB, which encode ABC efflux pumps in Streptococcus pneumoniae. J Antimicrob Chemother. 2010;65:2076–82.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Zhang L, Mah T-F. Involvement of a Novel Efflux System in Biofilm-Specific Resistance to Antibiotics. J Bacteriol. 2008;190:4447–52.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.El Meouche I, Siu Y, Dunlop MJ. Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Scientific Rep. 2016;6:1–9.Article 
CAS 

Google Scholar 
38.Frade VMF, Dias M, Teixeira ACSC, Palma MSA, Frade VMF, Dias M. et al. Environmental contamination by fluoroquinolones. Braz J Pharm Sci. 2014;50:41–54.Article 

Google Scholar 
39.Riaz L, Mahmood T, Yang Q, Coyne MS, D’Angelo E. Bacteria-assisted removal of fluoroquinolones from wheat rhizospheres in an agricultural soil. Chemosphere. 2019;226:8–16.CAS 
PubMed 
Article 

Google Scholar 
40.Llanes C, Köhler T, Patry I, Dehecq B, Delden C, van, Plésiat P. Role of the MexEF-OprN Efflux System in Low-Level Resistance of Pseudomonas aeruginosa to Ciprofloxacin. Antimicrobial Agents Chemother. 2011;55:5676–84.CAS 
Article 

Google Scholar 
41.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Deatherage DE, Barrick JE. Identification of mutations in laboratory evolved microbes from next-generation sequencing data using breseq. Methods Mol Biol. 2014;1151:165–88.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Saltikov CW, Newman DK. Genetic identification of a respiratory arsenate reductase. PNAS. 2003;100:10983–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Engler C, Kandzia R, Marillonnet S. A One Pot, One Step, Precision Cloning Method with High Throughput Capability. PLOS ONE. 2008;3:e3647.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.European Committee for Antimicrobial Susceptibility Testing (EUCAST) of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID). Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin Microbiol Infect. 2003;9:ix–xv.Article 

Google Scholar 
46.Lambert RJ, Pearson J. Susceptibility testing: accurate and reproducible minimum inhibitory concentration (MIC) and non-inhibitory concentration (NIC) values. J Appl Microbiol. 2000;88:784–90.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Sonnet P, Izard D, Mullié C. Prevalence of efflux-mediated ciprofloxacin and levofloxacin resistance in recent clinical isolates of Pseudomonas aeruginosa and its reversal by the efflux pump inhibitors 1-(1-naphthylmethyl)-piperazine and phenylalanine-arginine-β-naphthylamide. Int J Antimicrobial Agents. 2012;39:77–80.CAS 
Article 

Google Scholar 
48.Lindgren PK, Karlsson Å, Hughes D. Mutation Rate and Evolution of Fluoroquinolone Resistance in Escherichia coli Isolates from Patients with Urinary Tract Infections. Antimicrobial Agents Chemother. 2003;47:3222–32.CAS 
Article 

Google Scholar 
49.Klaus W, Ross A, Gsell B, Senn H. Backbone resonance assignment of the N-terminal 24 kDa fragment of the gyrase B subunit from S. aureus complexed with novobiocin. J Biomol NMR. 2000;16:357–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Müller RT, Pos KM. The assembly and disassembly of the AcrAB-TolC three-component multidrug efflux pump. Biol Chem. 2015;396:1083–9.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
51.Oethinger M, Podglajen I, Kern WV, Levy SB. Overexpression of the marA or soxS Regulatory Gene in Clinical Topoisomerase Mutants of Escherichia coli. Antimicrob Agents Chemother. 1998;42:2089–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Praski Alzrigat L, Huseby DL, Brandis G, Hughes D. Fitness cost constrains the spectrum of marR mutations in ciprofloxacin-resistant Escherichia coli. J Antimicrob Chemother. 2017;72:3016–24.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Srikumar R, Paul CJ, Poole K. Influence of Mutations in the mexR Repressor Gene on Expression of the MexA-MexB-OprM Multidrug Efflux System of Pseudomonas aeruginosa. J Bacteriol. 2000;182:1410–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Sánchez P, Rojo F, Martı́nez JL. Transcriptional regulation of mexR, the repressor of Pseudomonas aeruginosa mexAB-oprM multidrug efflux pump. FEMS Microbiol Lett. 2002;207:63–68.PubMed 
Article 
PubMed Central 

Google Scholar 
55.Fukuda H, Hosaka M, Hirai K, Iyobe S. New norfloxacin resistance gene in Pseudomonas aeruginosa PAO. Antimicrobial Agents Chemother. 1990;34:1757–61.CAS 
Article 

Google Scholar 
56.Fukuda H, Hosaka M, Iyobe S, Gotoh N, Nishino T, Hirai K. nfxC-type quinolone resistance in a clinical isolate of Pseudomonas aeruginosa. Antimicrobial Agents Chemother. 1995;39:790–2.CAS 
Article 

Google Scholar 
57.Fetar H, Gilmour C, Klinoski R, Daigle DM, Dean CR, Poole K. mexEF-oprN Multidrug Efflux Operon of Pseudomonas aeruginosa: Regulation by the MexT Activator in Response to Nitrosative Stress and Chloramphenicol. Antimicrobial Agents Chemother. 2011;55:508–14.CAS 
Article 

Google Scholar 
58.Köhler T, Michea-Hamzehpour M, Plesiat P, Kahr AL, Pechere JC. Differential selection of multidrug efflux systems by quinolones in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 1997;41:2540–3.PubMed 
PubMed Central 
Article 

Google Scholar 
59.Galajda P, Keymer J, Dalland J, Park S, Kou S, Austin R. Funnel ratchets in biology at low Reynolds number: choanotaxis. J Mod Opt. 2008;55:3413–22.CAS 
Article 

Google Scholar 
60.Alcalde-Rico M, Hernando-Amado S, Blanco P, Martínez JL. Multidrug Efflux Pumps at the Crossroad between Antibiotic Resistance and Bacterial Virulence. Front Microbiol. 2016;7:1483.PubMed 
PubMed Central 
Article 

Google Scholar 
61.Lomovskaya O, Warren MS, Lee A, Galazzo J, Fronko R, Lee M, et al. Identification and Characterization of Inhibitors of Multidrug Resistance Efflux Pumps in Pseudomonas aeruginosa: novel Agents for Combination Therapy. Antimicrobial Agents Chemother. 2001;45:105–16.CAS 
Article 

Google Scholar 
62.Pannek S, Higgins PG, Steinke P, Jonas D, Akova M, Bohnert JA, et al. Multidrug efflux inhibition in Acinetobacter baumannii: comparison between 1-(1-naphthylmethyl)-piperazine and phenyl-arginine-beta-naphthylamide. J Antimicrob Chemother. 2006;57:970–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Deng J, Zhou L, Sanford RA, Shechtman LA, Dong Y, Alcalde RE, et al. Adaptive Evolution of Escherichia coli to Ciprofloxacin in Controlled Stress Environments: Contrasting Patterns of Resistance in Spatially Varying versus Uniformly Mixed Concentration Conditions. Environ Sci Technol. 2019;53:7996–8005.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Olivares J, Álvarez-Ortega C, Martinez JL. Metabolic Compensation of Fitness Costs Associated with Overexpression of the Multidrug Efflux Pump MexEF-OprN in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2014;58:3904–13.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Barbosa TM, Levy SB. The impact of antibiotic use on resistance development and persistence. Drug Resist Updates. 2000;3:303–11.Article 

Google Scholar 
66.Chia HE, Marsh ENG, Biteen JS. Extending fluorescence microscopy into anaerobic environments. Curr Opin Chem Biol. 2019;51:98–104.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Laverman AM, Cazier T, Yan C, Roose-Amsaleg C, Petit F, Garnier J, et al. Exposure to vancomycin causes a shift in the microbial community structure without affecting nitrate reduction rates in river sediments. Environ Sci Pollut Res. 2015;22:13702–9.CAS 
Article 

Google Scholar 
68.Li J, Romine MF, Ward MJ. Identification and analysis of a highly conserved chemotaxis gene cluster in Shewanella species. FEMS Microbiol Lett. 2007;273:180–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).Article 

Google Scholar 
3.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Both, C., van Asch, M., Bijlsma, R. G., van den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations? J. Anim. Ecol. 78, 73–83 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Visser, M. E., van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870 (1998).Article 

Google Scholar 
6.Stenseth, N. C. & Mysterud, A. Climate, changing phenology, and other life history traits: nonlinearity and match–mismatch to the environment. Proc. Natl Acad. Sci. USA 99, 13379–13381 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Blackford, C., Germain, R. M. & Gilbert, B. Species differences in phenology shape coexistence. Am. Nat. 195, E168–E180 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Møller, A. P., Rubolini, D. & Lehikoinen, E. Populations of migratory bird species that did not show a phenological response to climate change are declining. Proc. Natl Acad. Sci. USA 105, 16195–16200 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Rudolf, V. H. W. The role of seasonal timing and phenological shifts for species coexistence. Ecol. Lett. 22, 1324–1338 (2019).PubMed 
PubMed Central 

Google Scholar 
10.Mynott, J. Birds in the Ancient World: Winged Words (Oxford Univ. Press, 2018).11.Hurlbert, A. H. & Liang, Z. Spatiotemporal variation in avian migration phenology: citizen science reveals effects of climate change. PLoS ONE 7, e31662 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Mayor, S. J. et al. Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep. 7, 1902 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Horton, K. G. et al. Phenology of nocturnal avian migration has shifted at the continental scale. Nat. Clim. Change 10, 63–68 (2020).Article 

Google Scholar 
14.Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Sullivan, B. L. et al. The eBird enterprise: an integrated approach to development and application of citizen science. Biol. Conserv. 169, 31–40 (2014).Article 

Google Scholar 
16.Friedl, M., Gray, J. & Sulla-Menashe, D. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid v.6 (NASA EOSDIS Land Processes DAAC, accessed 26 March 2020); https://doi.org/10.5067/MODIS/MCD12Q2.00617.Richardson, A. D., Hufkens, K., Milliman, T. & Frolking, S. Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing. Sci. Rep. 8, 5679 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Cole, E. F., Long, P. R., Zelazowski, P., Szulkin, M. & Sheldon, B. C. Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment. Ecol. Evol. 5, 5057–5074 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Pettorelli, N. et al. The normalized difference vegetation index (NDVI): unforeseen successes in animal ecology. Clim. Res. 46, 15–27 (2011).Article 

Google Scholar 
20.Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).21.Åkesson, S. et al. Timing avian long-distance migration: from internal clock mechanisms to global flights. Philos. Trans. R. Soc. B 372, 20160252 (2017).Article 

Google Scholar 
22.Helm, B. et al. Two sides of a coin: ecological and chronobiological perspectives of timing in the wild. Philos. Trans. R. Soc. B 372, 20160246 (2017).Article 

Google Scholar 
23.Haest, B., Hüppop, O. & Bairlein, F. The influence of weather on avian spring migration phenology: what, where and when? Glob. Change Biol. 24, 5769–5788 (2018).Article 

Google Scholar 
24.Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Soc. B 278, 3437–3443 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Thorup, K. et al. Resource tracking within and across continents in long-distance bird migrants. Sci. Adv. 3, e1601360 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Post, E., Steinman, B. A. & Mann, M. E. Acceleration of phenological advance and warming with latitude over the past century. Sci. Rep. 8, 3927 (2018).27.Van der Graaf, A., Stahl, J., Klimkowska, A., Bakker, J. P. & Drent, R. H. Surfing on a green wave—how plant growth drives spring migration in the Barnacle Goose Branta leucopsis. Ardea Wagening. 94, 567 (2006).
Google Scholar 
28.Schmaljohann, H. & Both, C. The limits of modifying migration speed to adjust to climate change. Nat. Clim. Change 7, 573–576 (2017).Article 

Google Scholar 
29.Marra, P. P., Francis, C. M., Mulvihill, R. S. & Moore, F. R. The influence of climate on the timing and rate of spring bird migration. Oecologia 142, 307–315 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. Do long-distance migratory birds track their niche through seasons? J. Biogeogr. 45, 1459–1468 (2018).Article 

Google Scholar 
31.Horton, K. G. et al. Holding steady: little change in intensity or timing of bird migration over the Gulf of Mexico. Glob. Change Biol. 25, 1106–1118 (2019).Article 

Google Scholar 
32.Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Curley, S. R., Manne, L. L. & Veit, R. R. Differential winter and breeding range shifts: implications for avian migration distances. Divers. Distrib. 26, 415–425 (2020).Article 

Google Scholar 
34.Root, T. Energy constraints on avian distributions and abundances. Ecology 69, 330–339 (1988).Article 

Google Scholar 
35.La Sorte, F. A., Fink, D., Hochachka, W. M., DeLong, J. P. & Kelling, S. Population-level scaling of avian migration speed with body size and migration distance for powered fliers. Ecology 94, 1839–1847 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Somveille, M., Manica, A. & Rodrigues, A. S. L. Where the wild birds go: explaining the differences in migratory destinations across terrestrial bird species. Ecography 42, 225–236 (2019).Article 

Google Scholar 
37.Knudsen, E. et al. Challenging claims in the study of migratory birds and climate change. Biol. Rev. 86, 928–946 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Allstadt, A. J. et al. Spring plant phenology and false springs in the conterminous US during the 21st century. Environ. Res. Lett. 10, 104008 (2015).Article 

Google Scholar 
39.Franks, S. E. et al. The sensitivity of breeding songbirds to changes in seasonal timing is linked to population change but cannot be directly attributed to the effects of trophic asynchrony on productivity. Glob. Change Biol. 24, 957–971 (2018).Article 

Google Scholar 
40.Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Change 10, 406–415 (2020).Article 

Google Scholar 
42.Samplonius, J. M. et al. Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts. Nat. Ecol. Evol. 5, 155–164 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Abdala‐Roberts, L. et al. Tri‐trophic interactions: bridging species, communities and ecosystems. Ecol. Lett. 22, 2151–2167 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Burgess, M. D. et al. Tritrophic phenological match–mismatch in space and time. Nat. Ecol. Evol. 2, 970–975 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Helm, B., Van Doren, B. M., Hoffmann, D. & Hoffmann, U. Evolutionary response to climate change in migratory pied flycatchers. Curr. Biol. 29, 3714–3719 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Newson, S. E. et al. Long-term changes in the migration phenology of UK breeding birds detected by large-scale citizen science recording schemes. Ibis 158, 481–495 (2016).Article 

Google Scholar 
47.Townsend, A. K. et al. The interacting effects of food, spring temperature, and global climate cycles on population dynamics of a migratory songbird. Glob. Change Biol. 22, 544–555 (2016).Article 

Google Scholar 
48.Grüebler, M. U. & Naef-Daenzer, B. Fitness consequences of pre- and post-fledging timing decisions in a double-brooded passerine. Ecology 89, 2736–2745 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Lany, N. K. et al. Breeding timed to maximumimize reproductive success for a migratory songbird: the importance of phenological asynchrony. Oikos 125, 656–666 (2016).Article 

Google Scholar 
50.Samplonius, J. M. & Both, C. Climate change may affect fatal competition between two bird species. Curr. Biol. 29, 327–331 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Potvin, D. A., Välimäki, K. & Lehikoinen, A. Differences in shifts of wintering and breeding ranges lead to changing migration distances in European birds. J. Avian Biol. 47, 619–628 (2016).Article 

Google Scholar 
52.Bassett, F. & Cubie, D. Wintering hummingbirds in Alabama and Florida: species diversity, sex and age ratios, and site fidelity. J. Field Ornithol. 80, 154–162 (2009).Article 

Google Scholar 
53.Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl Acad. Sci. USA 114, 12976–12981 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Chmura, H. E. et al. The mechanisms of phenology: the patterns and processes of phenological shifts. Ecol. Monogr. 89, e01337 (2019).Article 

Google Scholar 
55.Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Cressie, N., Calder, C. A., Clark, J. S., Hoef, J. M. V. & Wikle, C. K. Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecol. Appl. 19, 553–570 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Miller-Rushing, A. J., Inouye, D. W. & Primack, R. B. How well do first flowering dates measure plant responses to climate change? The effects of population size and sampling frequency. J. Ecol. 96, 1289–1296 (2008).Article 

Google Scholar 
58.Miller-Rushing, A. J., Lloyd-Evans, T. L., Primack, R. B. & Satzinger, P. Bird migration times, climate change, and changing population sizes. Glob. Change Biol. 14, 1959–1972 (2008).Article 

Google Scholar 
59.Barnes, R. dggridR: Discrete global grids for R. R package version 0.1.12 https://github.com/r-barnes/dggri (2017).60.Data Zone (BirdLife International, 2019); http://datazone.birdlife.org/species/requestdis61.Sulla-Menashe, D. et al. Hierarchical mapping of Northern Eurasian land cover using MODIS data. Remote Sens. Environ. 115, 392–403 (2011).Article 

Google Scholar 
62.Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid v.6 (NASA EOSDIS Land Processes DAAC, accessed 26 February 2020); https://doi.org/10.5067/MODIS/MCD12Q2.00663.Wood, S. N. & Augustin, N. H. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Model. 157, 157–177 (2002).Article 

Google Scholar 
64.Fink, D. et al. Spatiotemporal exploratory models for broad-scale survey data. Ecol. Appl. 20, 2131–2147 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Wood, S. N. Thin plate regression splines. J. R. Stat. Soc. B 65, 95–114 (2003).Article 

Google Scholar 
66.Lindén, A., Meller, K. & Knape, J. An empirical comparison of models for the phenology of bird migration. J. Avian Biol. 48, 255–265 (2017).Article 

Google Scholar 
67.Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.1 https://mc-stan.org/rstanarm (2020).68.Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. https://www.jstatsoft.org/v076/i01 (2017).69.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434 (1998).
Google Scholar 
70.Besag, J. & Kooperberg, C. On conditional and intrinsic autoregression. Biometrika 82, 733 (1995).
Google Scholar 
71.Banerjee, S., Carlin, B. P. & Gelfand, A. E. Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC, 2004).72.Morris, M. et al. Bayesian hierarchical spatial models: implementing the Besag York Mollié model in stan. Spat. Spatiotemporal Epidemiol. 31, 100301 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3 http://mc-stan.org (2018).74.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).75.Monnahan, C. C., Thorson, J. T. & Branch, T. A. Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods Ecol. Evol. 8, 339–348 (2017).Article 

Google Scholar 
76.Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. A 182, 389–402 (2019).Article 

Google Scholar 
77.Gelman, A., Carlin, J. B, Stern, H. S. & Rubin, D. B. Bayesian Data Analysis (Chapman & Hall/CRC, 2014).78.Finley, A. O. Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence: spatially-varying coefficients models. Methods Ecol. Evol. 2, 143–154 (2011).Article 

Google Scholar 
79.Stan Modeling Language Users Guide and Reference Manual v. 2.18.0 (Stan Development Team, 2018).80.Menzel, A., von Vopelius, J., Estrella, N., Schleip, C. & Dose, V. Farmers’ annual activities are not tracking the speed of climate change. Clim. Res. 32, 201–207 (2006).
Google Scholar  More

1.Pugh, P. Gelatinous zooplankton: the forgotten fauna. Sci. Prog. 14, 67–78 (1989).
Google Scholar 
2.Robison, B. H. Deep pelagic biology. J. Exp. Mar. Biol. Ecol. 300, 253–272. https://doi.org/10.1016/j.jembe.2004.01.012 (2004).Article 

Google Scholar 
3.Condon, R. H. et al. Questioning the rise of gelatinous zooplankton in the world’s oceans. Bioscience 62, 160–169. https://doi.org/10.1525/bio.2012.62.2.9 (2012).Article 

Google Scholar 
4.Haddock, S. H. D. A golden age of gelata: past and future research on planktonic ctenophores and cnidarians. Hydrobiologia 530, 549–556. https://doi.org/10.1007/s10750-004-2653-9 (2004).Article 

Google Scholar 
5.Lebrato, M. et al. Sinking of gelatinous zooplankton biomass increases deep carbon transfer efficiency globally. Glob. Biogeochem. Cycles 33, 1764–1783. https://doi.org/10.1029/2019GB006265 (2019).ADS 
CAS 
Article 

Google Scholar 
6.Luo, J. Y. et al. Gelatinous zooplankton-mediated carbon flows in the global oceans: a data-driven modeling study. Glob. Biogeochem. Cycles https://doi.org/10.1029/2020GB006704 (2020).Article 

Google Scholar 
7.Lucas, C. H. et al. Gelatinous zooplankton biomass in the global oceans: geographic variation and environmental drivers. Glob. Ecol. Biogeogr. 23, 701–714. https://doi.org/10.1111/geb.12169 (2014).Article 

Google Scholar 
8.Robison, B. H. Conservation of deep pelagic biodiversity. Conserv. Biol. 23, 847–858 (2009).Article 

Google Scholar 
9.Décima, M., Stukel, M. R., López-López, L. & Landry, M. R. The unique ecological role of pyrosomes in the Eastern Tropical Pacific. Limnol. Oceanogr. 64, 728–743. https://doi.org/10.1002/lno.11071 (2019).ADS 
Article 

Google Scholar 
10.Henschke, N. et al. Large vertical migrations of Pyrosoma atlanticum play an important role in active carbon transport. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2018jg004918 (2019).Article 

Google Scholar 
11.Sutherland, K. R., Sorensen, H. L., Blondheim, O. N., Brodeur, R. D. & Galloway, A. W. E. Range expansion of tropical pyrosomes in the northeast Pacific Ocean. Ecology 99, 2397–2399. https://doi.org/10.1002/ecy.2429 (2018).Article 
PubMed 

Google Scholar 
12.Perissinotto, R., Mayzaud, P., Nichols, P. D. & Labat, J. P. Grazing by Pyrosoma atlanticum (Tunicata, Thaliacea) in the south Indian Ocean. Mar. Ecol. Prog. Ser. 330, 1–11 (2007).ADS 
CAS 
Article 

Google Scholar 
13.van Soest, R. W. M. A monograph of the order Pyrosomatida (Tunicata, Thaliacea). J. Plankton Res. 3, 603–631. https://doi.org/10.1093/plankt/3.4.603 (1981).Article 

Google Scholar 
14.Drits, A. V., Arashkevich, E. G. & Semenova, T. N. Pyrosoma atlanticum (Tunicata, Thaliacea): grazing impact on phytoplankton standing stock and role in organic carbon flux. J. Plankton Res. 14, 799–809. https://doi.org/10.1093/plankt/14.6.799 (1992).Article 

Google Scholar 
15.Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209. https://doi.org/10.4319/lo.2009.54.4.1197 (2009).ADS 
CAS 
Article 

Google Scholar 
16.Lebrato, M. et al. Sinking jelly-carbon unveils potential environmental variability along a continental margin. PLoS ONE 8, e82070. https://doi.org/10.1371/journal.pone.0082070 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Archer, S. K. et al. Pyrosome consumption by benthic organisms during blooms in the northeast Pacific and Gulf of Mexico. Ecology 99, 981–984. https://doi.org/10.1002/ecy.2097 (2018).Article 
PubMed 

Google Scholar 
18.Harbison, G. R. in The Biology of Pelagic Tunicates (ed Q. Bone) Ch. 12, 186–214 (Oxford University Press, 1998).19.James, G. D. & Stahl, J. C. Diet of southern Buller’s albatross (Diomedea bulleri bulleri) and the importance of fishery discards during chick rearing. NZ J. Mar. Freshwat. Res. 34, 435–454. https://doi.org/10.1080/00288330.2000.9516946 (2000).Article 

Google Scholar 
20.Hedd, A. & Gales, R. The diet of shy albatrosses (Thalassarche cauta) at Albatross Island, Tasmania. J. Zool. 253, 69–90. https://doi.org/10.1017/S0952836901000073 (2001).Article 

Google Scholar 
21.Brodeur, R. et al. An unusual gelatinous plankton event in the NE Pacific: the great pyrosome bloom of 2017. PICES Press 26, 22–27 (2018).
Google Scholar 
22.Childerhouse, S., Dix, B. & Gales, N. Diet of New Zealand sea lions at the Auckland Islands. Wildl. Res. 28, 291–298. https://doi.org/10.1071/WR00063 (2001).Article 

Google Scholar 
23.Lindsay, D., Hunt, J. & Hayashi, K.-I. Associations in the midwater zone: The penaeid shrimp Funchalia sagamiensis FUJINO 1975 and pelagic tunicates (Order: Pyrosomatida). Marine Freshwater Behav. Phys. 34, 157–170. https://doi.org/10.1080/10236240109379069 (2001).Article 

Google Scholar 
24.Andersen, V. in The Biology of Pleagic Tunicates (ed Q. Bone) Ch. 7, 125–137 (Oxford University Press, 1998).25.Madin, L. P. Production, composition and sedimentation of salp fecal pellets in oceanic waters. Mar. Biol. 67, 39–45. https://doi.org/10.1007/BF00397092 (1982).Article 

Google Scholar 
26.Thomsen, P. F. & Willerslev, E. Environmental DNA: an emerging tool in conservation for monitoring past and present biodiversity. Biol. Cons. 183, 4–18. https://doi.org/10.1016/j.biocon.2014.11.019 (2015).Article 

Google Scholar 
27.Andruszkiewicz, E. A. et al. Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLoS ONE 12, e0176343. https://doi.org/10.1371/journal.pone.0176343 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Doty, M. S. & Oguri, M. The Island mass effect. ICES J. Mar. Sci. 22, 33–37. https://doi.org/10.1093/icesjms/22.1.33 (1956).Article 

Google Scholar 
29.Gove, J. M. et al. Near-island biological hotspots in barren ocean basins. Nat. Commun. 7, 10581. https://doi.org/10.1038/ncomms10581 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Faye, S., Lazar, A., Sow, B. & Gaye, A. A model study of the seasonality of sea surface temperature and circulation in the Atlantic North-eastern tropical upwelling system. Front. Phys. https://doi.org/10.3389/fphy.2015.00076 (2015).Article 

Google Scholar 
31.Schütte, F., Brandt, P. & Karstensen, J. Occurrence and characteristics of mesoscale eddies in the tropical northeastern Atlantic Ocean. Ocean Sci. 12, 663–685. https://doi.org/10.5194/os-12-663-2016 (2016).ADS 
Article 

Google Scholar 
32.Gilly, W. F., Beman, J. M., Litvin, S. Y. & Robison, B. H. Oceanographic and biological effects of shoaling of the oxygen minimum zone. Ann. Rev. Mar. Sci. 5, 393–420. https://doi.org/10.1146/annurev-marine-120710-100849 (2013).Article 
PubMed 

Google Scholar 
33.Schütte, F. et al. Characterization of “dead-zone” eddies in the eastern tropical North Atlantic. Biogeosciences 13, 5865–5881. https://doi.org/10.5194/bg-13-5865-2016 (2016).ADS 
Article 

Google Scholar 
34.GEOMAR Helmholtz-Zentrum für Ozeanforschung. CVOO Cape Verde Ocean Observatory, http://cvoo.geomar.de/ (n.d.).35.NASA Goddard Space Flight Center, O. E. L., Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data. https://doi.org/10.5067/AQUA/MODIS/L3B/CHL/2018 (2019).36.Hoving, H. J. et al. The Pelagic in situ observation system (PELAGIOS) to reveal biodiversity, behavior, and ecology of elusive oceanic fauna. Ocean Sci. 15, 1327–1340. https://doi.org/10.5194/os-15-1327-2019 (2019).ADS 
CAS 
Article 

Google Scholar 
37.Schlining, B. & Stout, N. MBARI’s Video Annotation and reference system. Vol. 2006 (2006).38.O’Loughlin, J. H. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 102424. https://doi.org/10.1016/j.pocean.2020.102424 (2020).Article 

Google Scholar 
39.Al-Mutairi, H. & Landry, M. R. Active export of carbon and nitrogen at Station ALOHA by diel migrant zooplankton. Deep Sea Res. Part II Top. Stud. Ocean. 48, 2083–2103. https://doi.org/10.1016/S0967-0645(00)00174-0 (2001).ADS 
CAS 
Article 

Google Scholar 
40.Mayzaud, P., Boutoute, M., Gasparini, S., Mousseau, L. & Lefevre, D. Respiration in marine zooplankton—the other side of the coin: CO2 production. Limnol. Oceanogr. 50, 291–298. https://doi.org/10.4319/lo.2005.50.1.0291 (2005).ADS 
CAS 
Article 

Google Scholar 
41.GEOMAR Helmholtz-Zentrum für Ozeanforschung, Hissmann, K. & Schauer, J. Manned submersible JAGO. J. Large-Scale Res. Facil. 3, 1–12, https://doi.org/10.17815/jlsrf-3-157 (2017).42.Lavaniegos, B. E. & Ohman, M. D. Long-term changes in pelagic tunicates of the California current. Deep Sea Res. Part II Top. Stud. Ocen. 50, 2473–2498. https://doi.org/10.1016/S0967-0645(03)00132-2 (2003).ADS 
CAS 
Article 

Google Scholar 
43.GEBCO Compilation Group. GEBCO 2019 Grid. https://doi.org/10.5285/836f016a-33be-6ddc-e053-6c86abc0788e (2019).44.Schram, J. B., Sorensen, H. L., Brodeur, R. D., Galloway, A. W. E. & Sutherland, K. R. Abundance, distribution, and feeding ecology of Pyrosoma atlanticum in the Northern California current. Mar. Ecol. Prog. Ser. 651, 97–110 (2020).ADS 
CAS 
Article 

Google Scholar 
45.Goy, J. Vertical migration of zooplankton. Résultats des Campagnes à la mer, GNEXO 13, 71–73 (1977).
Google Scholar 
46.Andersen, V. & Sardou, J. Pyrosoma atlanticum (Tunicata, Thaliacea): diel migration and vertical distribution as a function of colony size. J. Plankton Res. 16, 337–349. https://doi.org/10.1093/plankt/16.4.337 (1994).Article 

Google Scholar 
47.Andersen, V., Sardou, J. & Nival, P. The diel migrations and vertical distributions of zooplankton and micronekton in the Northwestern Mediterranean Sea. 2. Siphonophores, hydromedusae and pyrosomids. J. Plankton Res. 14, 1155–1169. https://doi.org/10.1093/plankt/14.8.1155 (1992).Article 

Google Scholar 
48.Roe, H. S. J. et al. Great Meteor East: a biological characterisation (Wormley, 1987).
Google Scholar 
49.Williamson, C. E., Fischer, J. M., Bollens, S. M., Overholt, E. P. & Breckenridge, J. K. Toward a more comprehensive theory of zooplankton diel vertical migration: Integrating ultraviolet radiation and water transparency into the biotic paradigm. Limnol. Oceanogr. 56, 1603–1623. https://doi.org/10.4319/lo.2011.56.5.1603 (2011).ADS 
Article 

Google Scholar 
50.Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548. https://doi.org/10.1038/ngeo1837 (2013).ADS 
CAS 
Article 

Google Scholar 
51.Purcell, J. et al. in Coastal Hypoxia: Consequences for Living Resources and Ecosystems Vol. 58 77–100 (2001).52.Neitzel, P. The impact of the oxygen minimum zone on the vertical distribution and abundance of gelatinous macrozooplankton in the Eastern Tropical Atlantic, Christian-Albrechts-Universität Kiel, (2017).53.Hoving, H. J. T. et al. In situ observations show vertical community structure of pelagic fauna in the eastern tropical North Atlantic off Cape Verde. Sci. Rep. 10, 21798. https://doi.org/10.1038/s41598-020-78255-9 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Thuesen, E. V. et al. Intragel oxygen promotes hypoxia tolerance of scyphomedusae. J. Exp. Biol. 208, 2475. https://doi.org/10.1242/jeb.01655 (2005).Article 
PubMed 

Google Scholar 
55.Keeling, R. F., Körtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Ann. Rev. Mar. Sci. 2, 199–229. https://doi.org/10.1146/annurev.marine.010908.163855 (2009).Article 

Google Scholar 
56.Wiebe, P. H., Madin, L. P., Haury, L. R., Harbison, G. R. & Philbin, L. M. Diel vertical migration by Salpa aspera and its potential for large-scale particulate organic matter transport to the deep-sea. Mar. Biol. 53, 249–255. https://doi.org/10.1007/BF00952433 (1979).Article 

Google Scholar 
57.Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. 134, 330–342. https://doi.org/10.1016/j.pocean.2015.03.003 (2015).ADS 
Article 

Google Scholar 
58.Hernández-León, S. et al. Zooplankton and micronekton active flux across the tropical and subtropical Atlantic Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00535 (2019).Article 

Google Scholar 
59.Kiko, R. et al. Zooplankton-mediated fluxes in the eastern tropical North Atlantic. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00358 (2020).Article 

Google Scholar 
60.Cascão, I., Domokos, R. K., Lammers, M. O., Santos, R. S. & Silva, M. N. A. Seamount effects on the diel vertical migration and spatial structure of micronekton. Prog. Ocean. 175, 1–13. https://doi.org/10.1016/j.pocean.2019.03.008 (2019).Article 

Google Scholar 
61.Fock, H., Matthiessen, B., Zidowitz, H. & Westernhagen, H. Diel and habitat-dependent resource utilisation of deep-sea fishes at the Great Meteor seamount (subtropical NE Atlantic): niche overlap and support for the sound-scattering layer-interception hypothesis. Mar. Ecol. Progr. Ser. 244, 219–233. https://doi.org/10.3354/meps244219 (2002).ADS 
Article 

Google Scholar 
62.Laval, P. Hyperiid amphipods as crustacean parasitoids associated with gelatinous zooplankton. Oceanogr. Mar. Biol. Annu. Rev. 18, 11–56 (1980).
Google Scholar 
63.Madin, L. P. & Harbison, G. R. The associations of Amphipoda Hyperiidea with gelatinous zooplankton—I Associations with Salpidae. Deep-Sea Res. 24, 449–463. https://doi.org/10.1016/0146-6291(77)90483-0 (1977).ADS 
Article 

Google Scholar 
64.Gasca, R., Hoover, R. & Haddock, S. H. D. New symbiotic associations of hyperiid amphipods (Peracarida) with gelatinous zooplankton in deep waters off California. J. Mar. Biol. Assoc. UK 95, 503–511. https://doi.org/10.1017/S0025315414001416 (2015).Article 

Google Scholar 
65.Harbison, G. R., Biggs, D. C. & Madin, L. P. The associations of Amphipoda Hyperiidea with gelatinous zooplankton—II. Associations with Cnidaria, Ctenophora and Radiolaria. Deep Sea Res. 24, 465–488. https://doi.org/10.1016/0146-6291(77)90484-2 (1977).ADS 
Article 

Google Scholar 
66.Harbison, G. R., Madin, L. P. & Swanberg, N. R. On the natural history and distribution of oceanic ctenophores. Deep-Sea Res. 25, 233–256 (1978).ADS 
Article 

Google Scholar 
67.Laval, P. The barrel of the pelagic amphipod Phronima sedentaria (Forsk.) (Crustacea: hyperiidea). J. Exp. Mar. Biol. Ecol. 33, 187–211. https://doi.org/10.1016/0022-0981(78)90008-4 (1978).Article 

Google Scholar 
68.Desmarest, A.-G. in Dictionnaire des Sciences Naturelles, 28. (ed F.G. Levrault) 138–425 (Paris and Strasbourg, 1823).69.Laval, P. Observations on biology of Phronima curvipes Voss (Amphipoda Hyperidae) and description of adult male. Cah. Biol. Mar. 9, 347–362 (1968).
Google Scholar 
70.Janssen, J. & Harbison, G. R. Fish in Salps: the Association of Squaretails (Tetragonurus Spp) with Pelagic Tunicates. J. Mar. Biol. Assoc. UK. 61, 917–927. https://doi.org/10.1017/S0025315400023055 (1981).Article 

Google Scholar 
71.Choy, C. A., Haddock, S. H. D. & Robison, B. H. Deep pelagic food web structure as revealed by in situ feeding observations. Proc. R. Soc. B Biol. Sci. 284, 20172116. https://doi.org/10.1098/rspb.2017.2116 (2017).Article 

Google Scholar 
72.Robison, B. H., Sherlock, R. E., Reisenbichler, K. R. & McGill, P. R. Running the gauntlet: assessing the threats to vertical migrators. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00064 (2020).Article 

Google Scholar 
73.Hoving, H. J., Neitzel, P. & Robison, B. In situ observations lead to the discovery of the large ctenophore Kiyohimea usagi (Lobata: Eurhamphaeidae) in the eastern tropical Atlantic. Zootaxa 4526, 232–238. https://doi.org/10.11646/zootaxa.4526.2.8 (2018).Article 
PubMed 

Google Scholar 
74.Arai, M. N. Predation on pelagic coelenterates: a review. J. Mar. Biol. Assoc. UK. 85, 523–536. https://doi.org/10.1017/S0025315405011458 (2005).Article 

Google Scholar  More

1.IEA. Renewables information 2019 overview. Clim. Change 2013 Phys. Sci. Basis 53, 1–30 (2019).
Google Scholar 
2.IPCC. Summary for Policymakers. In: Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. [V. Masson-Delmotte, P. Zhai, H. O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, T. Waterfield (eds.)]. (2018).3.IPCC. Foreword, Preface, Dedication and In Memoriam. Clim. Chang. 2014 Mitig. Clim. Chang. Contrib. Work. Gr. III to Fifth Assess. Rep. Intergov. Panel Clim. Chang. 1454 (2014).4.Shakoor, A. et al. Biogeochemical transformation of greenhouse gas emissions from terrestrial to atmospheric environment and potential feedback to climate forcing. Environ. Sci. Pollut. Res. 27, 38513–38536 (2020).CAS 
Article 

Google Scholar 
5.Mani, M., Bandyopadhyay, S., Chonabayashi, S., Markandya, A. & Mosier, T. South Asia’s Hotspots: The Impact of Temperature and Precipitation Changes on Living Standards. South Asia’s Hotspots: The Impact of Temperature and Precipitation Changes on Living Standards (2018). https://doi.org/10.1596/978-1-4648-1155-5.6.Sarkar, M. S. K., Sadeka, S., Sikdar, M. M. H. & Badiuzzaman. Energy consumption and CO2 emission in Bangladesh: Trends and policy implications. Asia Pac. J. Energy Environ. 5, 41–48 (2018).Article 

Google Scholar 
7.Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
8.Dixon, R. K. et al. Carbon pools and flux of global forest ecosystems. Science 263, 185–190 (1994).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Adame, M. F. et al. Carbon stocks of tropical coastal wetlands within the Karstic landscape of the Mexican Caribbean. PLoS ONE 8, e56569 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Henry, M. et al. Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder farming systems of western Kenya. Agric. Ecosyst. Environ. 129, 238–252 (2009).CAS 
Article 

Google Scholar 
12.Kumar, B. M. Species richness and aboveground carbon stocks in the homegardens of central Kerala, India. Agric. Ecosyst. Environ. 140, 430–440 (2011).Article 

Google Scholar 
13.Mattsson, E., Ostwald, M., Nissanka, S. P. & Pushpakumara, D. K. N. G. Quantification of carbon stock and tree diversity of homegardens in a dry zone area of Moneragala district, Sri Lanka. Agrofor. Syst. 89, 435–445 (2015).Article 

Google Scholar 
14.Nair, P. K. R., Kumar, B. M. & Nair, V. D. Agroforestry as a strategy for carbon sequestration. J. Plant Nutr. Soil Sci. 172, 10–23 (2009).CAS 
Article 

Google Scholar 
15.Murthy, I. K. Carbon sequestration potential of agroforestry systems in India. J. Earth Sci. Clim. Change 4, 1–7 (2013).Article 
CAS 

Google Scholar 
16.Delgado, J. A. et al. Conservation practices to mitigate and adapt to climate change. J. Soil Water Conserv. 66, 118–129 (2011).Article 

Google Scholar 
17.Kabir, M. E. & Webb, E. L. Can homegardens conserve biodiversity in Bangladesh?. Biotropica 40, 95–103 (2008).
Google Scholar 
18.FD [Forest Department]. Bangladesh Forestry Master Plan 2017–2036, 2036 (2017).19.Mather, A. Global forest resources assessment 2000 main report. Land Use Policy 20, 195 (2003).Article 

Google Scholar 
20.FD [Forest Department]. District wise forest area of Bangladesh 2016. Preprint at http://www.bforest.gov.bd/ (2020).21.Mukul, S. A. et al. A new estimate of carbon for Bangladesh forest ecosystems with their spatial distribution and REDD+ implications. Int. J. Res. Land-use Sustain. 1, 33–41 (2014).
Google Scholar 
22.Nath, T. K., Aziz, N. & Inoue, M. Contribution of homestead forests to rural economy and climate change mitigation: A study from the ecologically critical area of Cox’s Bazar—Teknaf Peninsula, Bangladesh. Small-scale For. 14, 1–18 (2015).Article 

Google Scholar 
23.Jaman, M. S. et al. Quantification of carbon stock and tree diversity of homegardens in Rangpur District, Bangladesh. Int. J. Agric. For. 6, 169–180 (2016).
Google Scholar 
24.Wang, S. & Huang, Y. Determinants of soil organic carbon sequestration and its contribution to ecosystem carbon sinks of planted forests. Glob. Change Biol. 26, 3163–3173 (2020).ADS 
Article 

Google Scholar 
25.Khan, M. N. I. et al. Allometric relationships of stand level carbon stocks to basal area, tree height and wood density of nine tree species in Bangladesh. Glob. Ecol. Conserv. 22, e01025 (2020).Article 

Google Scholar 
26.Shen, Y. et al. Tree aboveground carbon storage correlates with environmental gradients and functional diversity in a tropical forest. Sci. Rep. 6, 25304 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Petrokofsky, G. et al. Comparison of methods for measuring and assessing carbon stocks and carbon stock changes in terrestrial carbon pools. How do the accuracy and precision of current methods compare? A systematic review protocol. Environ. Evid. 1, 1–21 (2012).Article 

Google Scholar 
28.Dondini, M., Hastings, A., Saiz, G., Jones, M. B. & Smith, P. The potential of Miscanthus to sequester carbon in soils: Comparing field measurements in Carlow, Ireland to model predictions. GCB Bioenergy 1, 413–425 (2009).CAS 
Article 

Google Scholar 
29.Ullah, M. R. & Al-Amin, M. Above- and below-ground carbon stock estimation in a natural forest of Bangladesh. J. For. Sci. 58, 372–379 (2012).Article 

Google Scholar 
30.Nouvellon, Y. et al. Age-related changes in litter inputs explain annual trends in soil CO2 effluxes over a full Eucalyptus rotation after afforestation of a tropical savannah. Biogeochemistry 111, 515–533 (2012).CAS 
Article 

Google Scholar 
31.Krishna, M. P. & Mohan, M. Litter decomposition in forest ecosystems: A review. Energy Ecol. Environ. 2, 236–249 (2017).Article 

Google Scholar 
32.Patra, P. K. et al. The carbon budget of South Asia. Biogeosciences 10, 513–527 (2013).ADS 
Article 

Google Scholar 
33.Ostertag, R., Marín-Spiotta, E., Silver, W. L. & Schulten, J. Litterfall and decomposition in relation to soil carbon pools along a secondary forest chronosequence in Puerto Rico. Ecosystems 11, 701–714 (2008).CAS 
Article 

Google Scholar 
34.Nair, P. K. R. & Garrity, D. Afroforestry—The Future of Global Land Use, Advances in Agroforestry (Springer, 2012) https://doi.org/10.1007/978-94-007-4676-3_1.Book 

Google Scholar 
35.Abrar, M. M. et al. Variations in the profile distribution and protection mechanisms of organic carbon under long-term fertilization in a Chinese Mollisol. Sci. Total Environ. 723, 138181 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Baul, T. K., Datta, D. & Alam, A. A comparative study on household level energy consumption and related emissions from renewable (biomass) and non-renewable energy sources in Bangladesh. Energy Policy https://doi.org/10.1016/j.enpol.2017.12.037 (2018).Article 

Google Scholar 
37.Mackey, B. et al. Understanding the importance of primary tropical forest protection as a mitigation strategy. Mitig. Adapt. Strateg. Glob. Change 25, 763–787 (2020).Article 

Google Scholar 
38.Zaman, M. A., Osman, K. T. & Sirajul Haque, S. M. Comparative study of some soil properties in forested and deforested areas in Cox’s Bazar and Rangamati Districts, Bangladesh. J. For. Res. 21, 319–322 (2010).CAS 
Article 

Google Scholar 
39.Akhtaruzzaman, M., Osman, K. T. & Sirajul Haque, S. M. Soil properties in two forest sites in Cox’s Bazar, Bangladesh. J. For. Environ. Sci. 31, 280–287 (2015).
Google Scholar 
40.Islam, M., Deb, G. P. & Rahman, M. Forest fragmentation reduced carbon storage in a moist tropical forest in Bangladesh: Implications for policy development. Land Use Policy 65, 15–25 (2017).Article 

Google Scholar 
41.Nair, P. K. R., Nair, V. D., Kumar, B. M. & Haile, S. G. Soil carbon sequestration in tropical agroforestry systems: A feasibility appraisal. Environ. Sci. Policy 12, 1099–1111 (2009).CAS 
Article 

Google Scholar 
42.Aryal, D. R., Gómez-González, R. R., Hernández-Nuriasmú, R. & Morales-Ruiz, D. E. Carbon stocks and tree diversity in scattered tree silvopastoral systems in Chiapas, Mexico. Agrofor. Syst. 93, 213–227 (2019).Article 

Google Scholar 
43.Islam, M., Dey, A. & Rahman, M. Effect of tree diversity on soil organic carbon content in the homegarden agroforestry system of North-Eastern Bangladesh. Small-scale For. 14, 91–101 (2015).Article 

Google Scholar 
44.Saha, S. K., Nair, P. K. R., Nair, V. D. & Kumar, B. M. Soil carbon stock in relation to plant diversity of homegardens in Kerala, India. Agrofor. Syst. 76, 53–65 (2009).Article 

Google Scholar 
45.Kothandaraman, S., Dar, J. A., Sundarapandian, S., Dayanandan, S. & Khan, M. L. Ecosystem-level carbon storage and its links to diversity, structural and environmental drivers in tropical forests of Western Ghats, India. Sci. Rep. 10, 1–15 (2020).Article 
CAS 

Google Scholar 
46.Youkhana, A. & Idol, T. Tree pruning mulch increases soil C and N in a shaded coffee agroecosystem in Hawaii. Soil Biol. Biochem. 41, 2527–2534 (2009).CAS 
Article 

Google Scholar 
47.Flessa, H. et al. Storage and stability of organic matter and fossil carbon in a Luvisol and Phaeozem with continuous maize cropping: A synthesis. J. Plant Nutr. Soil Sci. 171, 36–51 (2008).CAS 
Article 

Google Scholar 
48.Semere, M. Biomass and soil carbon stocks assessment of agroforestry systems and adjacent cultivated land, in Cheha Wereda, Gurage Zone, Ethiopia. Int. J. Environ. Sci. Nat. Resour. 20, 119–125 (2019).
Google Scholar 
49.Ramachandran Nair, P. K., Nair, V. D., Mohan Kumar, B. & Showalter, J. M. Carbon sequestration in agroforestry systems. Adv. Agron. 108, 237–307 (2010).Article 
CAS 

Google Scholar 
50.Mustafa, A. et al. Soil aggregation and soil aggregate stability regulate organic carbon and nitrogen storage in a red soil of southern China. J. Environ. Manag. 270, 110894 (2020).CAS 
Article 

Google Scholar 
51.Sayer, E. J. et al. Tropical forest soil carbon stocks do not increase despite 15 years of doubled litter inputs. Sci. Rep. 9, 1–9 (2019).ADS 
Article 
CAS 

Google Scholar 
52.Rahman, M., Biswas, J., Maniruzzaman, M., Choudhury, A. & Ahmed, F. Effect of tillage practices and rice straw management on soil environment and carbon dioxide emission. Agriculture 15, 127–142 (2017).
Google Scholar 
53.Day, M., Baldauf, C., Rutishauser, E. & Sunderland, T. C. H. Relationships between tree species diversity and above-ground biomass in Central African rainforests: Implications for REDD. Environ. Conserv. 41, 64–72 (2014).Article 

Google Scholar 
54.Poorter, L. et al. Diversity enhances carbon storage in tropical forests. Glob. Ecol. Biogeogr. 24, 1314–1328 (2015).Article 

Google Scholar 
55.Rahman, M. M., Kabir, M. E., Jahir Uddin Akon, A. S. M. & Ando, K. High carbon stocks in roadside plantations under participatory management in Bangladesh. Glob. Ecol. Conserv. 3, 412–423 (2015).Article 

Google Scholar 
56.Kamruzzaman, M., Ahmed, S., Paul, S., Rahman, M. M. & Osawa, A. Stand structure and carbon storage in the oligohaline zone of the Sundarbans mangrove forest, Bangladesh. For. Sci. Technol. 14, 23–28 (2018).
Google Scholar 
57.Asok, S. & Sobha, V. Analysis of variation of soil bulk densities with respect to different vegetation classes, in a tropical rain forest—A study in Shendurney Wildlife Sanctuary, S. Kerala, India. Glob. J. Environ. Res. 8, 17–20 (2014).
Google Scholar 
58.Périé, C. & Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci. 88, 315–325 (2008).Article 

Google Scholar 
59.Biswas, A., Alamgir, M., Haque, S. M. S. & Osman, K. T. Study on soils under shifting cultivation and other land use categories in Chittagong Hill Tracts, Bangladesh. J. For. Res. 23, 261–265 (2012).CAS 
Article 

Google Scholar 
60.Leff, J. W. et al. Experimental litterfall manipulation drives large and rapid changes in soil carbon cycling in a wet tropical forest. Glob. Change Biol. 18, 2969–2979 (2012).ADS 
Article 

Google Scholar 
61.Wang, Q., He, T., Wang, S. & Liu, L. Carbon input manipulation affects soil respiration and microbial community composition in a subtropical coniferous forest. Agric. For. Meteorol. 178–179, 152–160 (2013).ADS 
Article 

Google Scholar 
62.Ali Shah, S. A. et al. Long-term fertilization affects functional soil organic carbon protection mechanisms in a profile of Chinese loess plateau soil. Chemosphere 267, 128897 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
63.Miah, D., Uddin, M. F., Bhuiyan, M. K., Koike, M. & Shin, M. Y. Carbon sequestration by the indigenous tree species in the reforestation program in Bangladesh-aphanamixis polystachya Wall. and Parker. Forest Sci. Technol. 5, 62–65 (2009).Article 

Google Scholar 
64.Kibria, M. G. & Saha, N. Analysis of existing agroforestry practices in Madhupur Sal forest: An assessment based on ecological and economic perspectives. J. For. Res. 22, 533–542 (2011).
CAS 
Article 

Google Scholar 
65.Mikrewongel Tadesse, A. B. Estimation of carbon stored in agroforestry practices in Gununo Watershed, Wolayitta Zone, Ethiopia. J. Ecosyst. Ecogr. 05, 1–5 (2015).Article 

Google Scholar 
66.Abrar, M. M. et al. Carbon, nitrogen, and phosphorus stoichiometry mediate sensitivity of carbon stabilization mechanisms along with surface layers of a Mollisol after long-term fertilization in Northeast China. J. Soils Sediments 21, 705–723 (2021).CAS 
Article 

Google Scholar 
67.Ahmed, N. & Glaser, M. Coastal aquaculture, mangrove deforestation and blue carbon emissions: Is REDD+ a solution?. Mar. Policy 66, 58–66 (2016).Article 

Google Scholar 
68.BBS [Bangladesh Bureau of Statistics]. Statistical yearbook of Bangladesh 2018. Statistics Division, Ministry of Planning, Government of the People’s Republic of Bangladesh (2019).69.BMD [Bangladesh Metereological Department]. Cox’s Bazar region, Chittagong, Bangladesh (2020).70.Osman, K. S., Jashimuddin, M., Haque, S. M. S. & Miah, S. Effect of shifting cultivation on soil physical and chemical properties in Bandarban hill district, Bangladesh. J. For. Res. 24, 791–795 (2013).CAS 
Article 

Google Scholar 
71.SRDI. Soil resource development institute. Annu. Report. Soil Resour. Dev. Institute, Dhaka, Bangladesh (2018).72.Upazila Parishad Office. Bandarban Sadar Upazila, Bandarban District, Chittagong Hill Trcats, Bangladesh (2019).73.Blake, G. R. Bulk density. In Methods of Soil Analysis. Part 1 (eds Black, C. A. et al.) 894–895 (American Society of Agronomy Inc., 1965).
Google Scholar 
74.Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).ADS 
Article 

Google Scholar 
75.Macdicken, K. G. A guide to monitoring carbon storage in forestry and agroforestry projects (2015).76.Sattar, M. A., Bhattacharje, D. K. & Kabir, M. F. Physical and Mechanical Properties and Uses of Timbers of Bangladesh (Bangladesh Forest Research Institute, 1999).
Google Scholar 
77.Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).Article 

Google Scholar 
78.Data Set. Definitions (2020). https://doi.org/10.32388/5b0dft.79.Hairiah, K. Measuring carbon stocks: Across land use systems: a manual. Published in close cooperation with Brawijaya University and ICALRRD (Indonesian Center for Agricultural Land Resources Research and Development) (2011).80.Frangi, J. L., Lugo, A. E., Forest, F., Frangi, J. L. & Service, F. Ecosystem dynamics of a subtropical floodplain forest published by: Ecological Society of America. Ecosyst. Dyn. Subtrop. 55, 351–369 (2016).
Google Scholar 
81.Issa, S., Dahy, B., Ksiksi, T. & Saleous, N. Development of a new allometric equation correlated WTH RS variables for the assessment of date palm biomass. Proc. 39th Asian Conf. Remote Sens. Remote Sens. Enabling Prosper. ACRS 2018 2, 730–739 (2018).82.Brown, S. Estimating biomass and biomass change of tropical forests: A Primer. FAO For. Pap. 134, 13–33 (1997).
Google Scholar 
83.Margalef, R. Information theory in ecology. Gen. Syst. 3, 36–71 (1958).
Google Scholar 
84.Michael, P. Ecological Methods for Field and Laboratory Investigation (Tata Mc Graw Hill, 1990).
Google Scholar 
85.Shukla, R. S. & Chandel, P. S. Plant Ecology and Soil Science 9th edn. (S. Chand and Company, 2000).
Google Scholar 
86.Ball, D. F. Loss-on-ignition as an estimate. J. Soil Sci. 15, 84–92 (1964).CAS 
Article 

Google Scholar 
87.Pearson, T., Walker, S. & Brown, S. Sourcebook for Land Use, Land-Use Change and Forestry Projects 29 (Winrock International and the BioCarbon Fund of the World Bank, 2005).
Google Scholar 
88.Pearson, T. R. H., Brown, S. L. & Birdsey, R. A. Measurement guidelines for the sequestration of forest carbon. Gen. Tech. Rep. NRS-18. Delaware United States Dep. Agric. For. Serv. 18, 42 (2007).89.Coleman, D. C. Soil carbon balance in a successional grassland. Oikos 24, 195–199. https://doi.org/10.2307/3543875 (1973).CAS 
Article 

Google Scholar  More

These authors contributed equally: Marc Palahí, Ruben ValbuenaEuropean Forest Institute, Joensuu, FinlandMarc Palahí, Lauri Hetemäki, Pieter Johannes Verkerk & Minna KorhonenSchool of Natural Sciences, Bangor University, Bangor, UKRubén ValbuenaEcosystem Dynamics and Forest Management Group, Technical University of Munich, Munich, GermanyCornelius Senf & Rupert SeidlSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKNezha Acil, Thomas A. M. Pugh & Jonathan SadlerBirmingham Institute of Forest Research, University of Birmingham, Birmingham, UKNezha Acil, Thomas A. M. Pugh & Jonathan SadlerDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenThomas A. M. PughDepartment of Geographical Sciences, University of Maryland, College Park, MD, USAPeter PotapovInstitut Européen de la Forêt Cultivée, Cestas, FranceBarry GardinerDipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Florence, ItalyGherardo Chirici & Saverio FranciniDipartimento per l’Innovazione dei Sistemi Biologici, Agroalimentari e Forestali, Università degli Studi della Tuscia, Viterbo, ItalySaverio FranciniFaculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech RepublicTomáš Hlásny & Róbert MarušákWageningen Environmental Research, Wageningen University and Research, Wageningen, The NetherlandsBas Jan Willem Lerink & Gert-Jan NabuursDepartment of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Umeå, SwedenHåkan Olsson & Jonas FridmanJoint Research Unit CTFC – AGROTECNIO, Solsona, SpainJosé Ramón González OlabarriaDepartment of Agricultural, Forest and Food Sciences, University of Turin, Grugliasco, ItalyDavide AscoliNatural Resources Institute Finland, Joensuu, FinlandAntti AsikainenAlbert-Ludwigs-University of Freiburg, Freiburg, GermanyJürgen Bauhus & Marc HanewinkelDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenGöran BerndesLatvian State Forest Research Institute Silava, Salaspils, LatviaJanis DonisINRAE, University of Bordeaux, BIOGECO, Cestas, FranceHervé JactelEuropean Forest Institute, Bonn, GermanyMarcus LindnerUniversity of Molise, Campobasso, ItalyMarco MarchettiForest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, The NetherlandsDouglas Sheil & Gert-Jan NabuursCentro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisbon, PortugalMargarida ToméForest Science and Technology Centre of Catalonia, CTFC, Solsona, SpainAntoni TrasobaresM.P. and G.-J.N. conceived and initiated the study. R.V., C.S., N.A., T.A.M.P., G.C., S.F., T.H., B.J.W.L. and D.A. ran different parts of the analyses and demonstrations. M.P., R.V., G.-J.N., C.S., T.A.M.P., J.S., R.S., B.G. and L.H. drafted the initial version of the manuscript. P.P. provided first-hand experience of the algorithms involved in the production of GFC data. All authors offered insights from their own national statistics and local knowledge, which focused the analyses and the argumentation, and contributed critically to the interpretation of the results, revising and approving the final version of the manuscript. More

Identification of ultra-small cells associated with blooms of anoxygenic phototrophic gammaproteobacteriaThe Salada de Chiprana (NE Spain) is the only permanent athalassic hypersaline lake in Western Europe. It harbors thick, conspicuous microbial mats covering its bottom (Fig. 1a, b) and exhibits periodic stratification during which the deepest part of the water column becomes anoxic and sulfide-rich, favoring the massive development of sulfide-dependent anoxygenic photosynthetic bacteria16. We collected microbial mat fragments that were maintained in culture in the laboratory. After several weeks, we observed a bloom of anoxygenic photosynthetic bacteria containing numerous intracellular sulfur granules (Fig. 1c). Many of these bacterial cells showed one or several much smaller and darker non-flagellated cells attached to their surface (Fig. 1d, e). The infected photosynthetic cells were highly mobile, swimming at high speed with frequent changes of direction (Supplementary Movie 1), in contrast with the non-infected cells that displayed slower (approximately half speed) and more straight swimming. Sometimes, two or more photosynthetic cells were connected through relatively short filaments formed by stacked epibiont cells (Fig. 1f). Although the photosynthetic cells carrying these epibionts were often actively swimming, in some cases the epibionts were associated to empty ghost cells where only the photosynthesis-derived sulfur granules persisted. Closer scrutiny of the epibionts revealed that they actually consisted of piles of up to 10 very flattened cells of 550 ± 50 nm diameter and 220 ± 20 nm height (n = 100). These characteristics (size, morphology, and specific attachment to sulfide-dependent anoxygenic photosynthetic bacteria) perfectly matched the morphological description of the genus Vampirococcus observed over forty years ago in sulfidic freshwater lakes15.Fig. 1: Sampling site and microscopy observation of Vampirococcus cells.a General view of the microbial mat covering the shore of the Salada de Chiprana lake. b Closer view of a microbial mat section. c Natural population of blooming sulfide-dependent anoxygenic photosynthetic bacteria in waters of microbial mat containers after several weeks of growth in the laboratory; note the conspicuous refringent intracellular sulfur inclusions. d–f Closer microscopy view of anoxygenic photosynthetic bacteria infected by epibiotic Vampirococcus cells and few-cell filaments (indicated by yellow arrows). g Scanning electron microscopy image of a host cell infected by two stacking Vampirococcus cells (yellow arrow). h Transmission electron microscopy (TEM) image of a thin section of a host cell infected by Vampirococcus (yellow arrow). i Closer TEM view of a thin section of Vampirococcus cells, notice the fibrous rugose cell surface and the large space separating contiguous cells. Scale bars: 5 cm (b), 5 µm (c), 1 µm (d–h), 0.5 µm (i).Full size imageSince the first Vampirococcus description included transmission electron microscopy (TEM) images, to further ascertain this identification we examined our Chiprana Lake samples under TEM and scanning electron microscopy (SEM). SEM images confirmed the peculiar structure of the epibionts, with multiple contiguous cells separated by deep grooves (Fig. 1g). Thin sections observed under TEM confirmed that the cells were actually separated by a space of ~20–50 nm filled by a fibrous material (Fig. 1h, i). The space between epibiont and host cells was larger (~100 nm) and also filled by dense fibrous material (Fig. 1h). The sections also showed that, in contrast with the typical Gram-negative double membrane structure of the host, the epibiont cells had a single membrane surrounded by a thick layer of fibrous material that conferred a rugose aspect to the cells (Fig. 1i). In sharp contrast with the often highly vacuolated cytoplasm of the host, the epibiont cells showed a dense, homogeneous content. These observations were also in agreement with those published for Vampirococcus, reinforcing our conclusion that the epibionts we observed belonged to this genus, although most likely to a different species, as the first described Vampirococcus occurred in a non-hypersaline lake15.Using a micromanipulator coupled to an inverted microscope, we collected cells of the anoxygenic photosynthetic bacterium carrying Vampirococcus attached to their surface (Supplementary Fig. 1) and proceeded to amplify, clone, and sequence their 16 S rRNA genes. We were able to obtain sequences for both the epibiont and the host for ten infected cells and, in all cases, we retrieved the same two sequences. The host was found to be a Halochromatium-like gammaproteobacterium (Supplementary Fig. 2). Phylogenetic analysis of the epibiont sequence showed that it branched within the CPR radiation close to the Absconditabacteria (Supplementary Fig. 3), previously known as candidate phylum SR12. Since all host and epibiont cells we analyzed had identical 16 S rRNA gene sequences, suggesting that they were the result of a clonal bloom, we collected three sets of ca. 10 infected cells and carried out whole genome amplification (WGA) before sequencing (Illumina HiSeq; see Methods). This strategy allowed us to assemble the nearly complete genome sequence of the Vampirococcus epibiont (see below). In contrast with the completeness of this genome, we only obtained a very partial assembly (~15%) of the host genome, probably because of the consumption of the host DNA by the epibiont. To make more robust phylogenetic analyses of Vampirococcus, we retrieved the protein sequence set used by Hug et al. to reconstruct a multi-marker large-scale phylogeny of bacteria2. The new multi-gene maximum likelihood (ML) phylogenetic tree confirmed the affiliation of our Vampirococcus species to the Absconditabacteria with maximum support, and further placed this clade within a larger well-supported group also containing the candidate phyla Gracilibacteria and Peregrinibacteria (Fig. 2a and Supplementary Fig. 4). Therefore, our epibiotic bacterium represents the first characterized member of this large CPR clade and provides a phylogenetic identity for the predatory bacterial genus Vampirococcus described several decades ago. We propose to call this new species Candidatus Vampirococcus lugosii (see Taxonomic appendix).Fig. 2: Phylogeny and global gene content of the Vampirococcus genome.a Maximum likelihood phylogenetic tree of bacteria based on a concatenated dataset of 16 ribosomal proteins showing the position of Vampirococcus lugosii close to the Absconditabacteria (for the complete tree, see Supplementary Fig. 4). Histograms on the right show the proportion of genes retained in each species from the ancestral pool inferred for the last common ancestor of Absconditabacteria, Gracilibacteria and Peregrinibacteria. b Percentage of Vampirococcus genes belonging to the different Clusters of Orthologous Groups (COG) categories. c Genes shared by Vampirococcus and the three Absconditabacteria genomes shown in the phylogenetic tree. COG categories are: Energy production and conversion [C]; Cell cycle control, cell division, chromosome partitioning [D]; Amino acid transport and metabolism [E]; Nucleotide transport and metabolism [F]; Carbohydrate transport and metabolism [G]; Coenzyme transport and metabolism [H]; Lipid transport and metabolism [I]; Translation, ribosomal structure and biogenesis [J]; Transcription [K]; Replication, recombination and repair [L]; Cell wall/membrane/envelope biogenesis [M]; Secretion, motility and chemotaxis [N]; Posttranslational modification, protein turnover, chaperones [O]; Inorganic ion transport and metabolism [P]; General function prediction only [R]; Function unknown [S]; Intracellular trafficking, secretion, and vesicular transport [U]; Defense mechanisms [V]; Mobilome: prophages, transposons [X]; Secondary metabolites biosynthesis, transport and catabolism [Q]. Source data are provided as a Source Data file.Full size imageGenomic evidence of adaptation to predatory lifestyleWe sequenced DNA from three WGA experiments corresponding each to ~10 Halochromatium-Vampirococcus consortia. Many of the resulting (57.2 Mb) raw sequences exhibited similarity to those of available Absconditabacteria/SR1 metagenome-assembled genomes (MAGs) and, as expected, some also to Gammaproteobacteria (host-derived sequences) as well as a small proportion of potential contaminants probably present in the original sample (Bacillus- and fungi-like sequences). To bin the Vampirococcus sequences out of this mini-metagenome, we applied tetranucleotide frequency analysis on the whole sequence dataset using emergent self-organizing maps (ESOM)6. One of the ESOM sequence bins was enriched in Absconditabacteria/SR1-like sequences and corresponded to the Vampirococcus sequences, which we extracted and assembled independently. This approach yielded an assembly of 1,310,663 bp. We evaluated its completeness by searching i) a dataset of 40 universally distributed single-copy genes17 and ii) a dataset of 43 single-copy genes widespread in CPR bacteria8. We found all them as single-copy genes in the Vampirococcus genome, with the exception of two signal recognition particle subunits from the first dataset which are absent in many other CPR bacteria18. These results supported that the Vampirococcus genome assembly was complete and did not contain multiple strains or other sources of contamination. Manually curated annotation predicted 1151 protein-coding genes, a single rRNA gene operon, and 38 tRNA coding genes. As already found in other Absconditabacteria/SR1 genomes19, the genetic code of Vampirococcus is modified, with the stop codon UGA reassigned as an additional glycine codon.A very large proportion of the predicted proteins (48.9%) had no similarity to any COG class and lacked any conserved domain allowing their functional annotation (Fig. 2b). Thus, as for other CPR bacteria, a significant part of their cellular functions remains inaccessible. A comparison with three other Absconditabacteria genomes revealed a very small set of only 390 genes conserved in all them (Fig. 2c), suggesting a highly dynamic evolution of gene content in these species. Comparison with more distantly related CPR groups (Gracilibacteria and Peregrinibacteria) showed that gene loss has been a dominant trend in all these organisms, which have lost 30–50% of the 1124 genes inferred to have existed in their last common ancestor (Fig. 2a). Nevertheless, this loss of ancestral genes was accompanied by the acquisition of new ones by different mechanisms, including horizontal gene transfer (HGT). In the case of Vampirococcus, we detected, by phylogenetic analysis of all individual genes that had homologs in other organisms, the acquisition of 126 genes by HGT from various donors (Supplementary Data 1).The set of genes that could be annotated provided interesting clues about the biology and lifestyle of Vampirococcus. The most striking feature was its oversimplified energy and carbon metabolism map (Fig. 3). ATP generation in this CPR species appeared to depend entirely on substrate-level phosphorylation carried out by the phosphoenolpyruvate kinase (EC 2.7.1.40). In fact, Vampirococcus only possesses incomplete glycolysis, which starts with 3-phosphoglycerate as first substrate. This molecule is the major product of the enzyme RuBisCO and, therefore, most likely highly available to Vampirococcus from its photosynthetic host. Comparison with nearly complete MAG sequences available for other Absconditabacteria/SR1 showed that Vampirococcus has the most specialized carbon metabolism, with 3-phosphoglycerate as the only exploitable substrate, whereas the other species have a few additional enzymes that allow them to use other substrates (such as ribulose-1,5 P and acetyl-CoA) as energy and reducing power (NADH) sources (Supplementary Fig. 5). This metabolic diversification probably reflects their adaptation to other types of hosts where these substrates are abundant. Vampirococcus also lacks all the enzymes involved in some Absconditabacteria/SR1 in the 3-phosphoglycerate-synthesizing AMP salvage pathway20, including the characteristic archaeal-like type II/III RuBisCO21.Fig. 3: Metabolic and cell features inferred from the genes encoded in the Vampirococcus genome.The diagram shows the host cell surface (bottom) with two stacking Vampirococcus cells attached to its surface (as in Fig. 1h).Full size imageThe genomes of Absconditabacteria/SR1 and Vampirococcus encode several electron carrier proteins (e.g., ferredoxin, cytochrome b5, several Fe-S cluster proteins) and a membrane F1FO-type ATP synthase. However, they apparently lack any standard electron transport chain and, therefore, they seem to be non-respiring19,20,22. The electron carrier proteins may be related with the oxidative stress response and/or the reoxidation of reduced ferredoxin or NADH20. In the absence of any obvious mechanism to generate proton motive force (PMF), the presence of the membrane ATP synthase is also intriguing. It has been speculated either that CPR bacteria might tightly adhere to their hosts and scavenge protons from them or that the membrane ATP synthase might work in the opposite direction as an ATPase, consuming ATP generated by substrate level phosphorylation to extrude protons and drive antiporters9. However, in the case of Vampirococcus the direct transport of protons from the host is unlikely since, as observed in the TEM sections (Fig. 1h), it seems that there is no direct contact with the host cell membrane. In fact, parasite and host cell membranes are separated by a relatively large space of ~100 nm, which would be largely conducive to proton diffusion and inefficient transfer between cells. Alternatively, since the Chiprana lake has high Na+ concentration (1.6 g l−1), it might be possible that the ATP synthase uses Na+ instead of protons. However, the Na+-binding domain of the subunit c of typical Na+-dependent ATP synthases exhibited several differences with that of Vampirococcus (Supplementary Fig. 6). Similar differences have been considered indicative of the use of protons instead of Na+ in other organisms23. Although the proton/cation antiporters (e.g., for Na+, K+, or Ca2+) encoded by Vampirococcus and the other Absconditabacteria/SR1 may serve to produce some PMF, it is improbable that this mechanism represents a major energy transducing system as cells would accumulate cations and disrupt their ionic balance; these antiporters are most likely involved in cation homeostasis.These observations prompted us to investigate other ways that these cells might use to generate PMF usable by their ATP synthase. We found a protein (Vamp_33_45) with an atypical tripartite domain structure. The N-terminal region, containing 8 transmembrane helices, showed similarity with several flavocytochromes capable of moving electrons and/or protons across the plasma membrane (e.g., 24). The central part of the protein was a rubredoxin-like nonheme iron-binding domain likely able to transport electrons. Finally, the C-terminal region, containing an NAD-binding motif, was similar to ferredoxin reductases involved in electron transfer25. This unusual Vampirococcus 3-domain protein is well conserved in the other Absconditabacteria/SR1 genomes sequenced so far, suggesting it plays an important function in this CPR phylum. Its architecture suggests that it can transport electrons and/or protons across the membrane using ferredoxin as electron donor and makes it a strong candidate to participate in a putative new PMF-generating system. Alternatively, this protein could play a similar role to that of some oxidoreductases in the strict anaerobic archaeon Thermococcus onnurineus, including a thioredoxin reductase, which couple reactive oxygen species detoxification with NAD(P) + regeneration from NAD(P)H to maintain the intracellular redox balance and enhance O2-mediated growth despite the absence of heme-based or cytochrome-type proteins26.Although our Vampirococcus genome sequence appears to be complete, genes encoding enzymes involved in the biosynthesis of essential cell building blocks such as amino acids, nucleotides and nucleosides, cofactors, vitamins, and lipids are almost completely absent (Fig. 2b). Therefore, the classical bacterial metabolic pathways for their synthesis27 do not operate in Vampirococcus. Such simplified metabolic potential, comparable to that of intracellular parasitic bacteria such as Mycoplasma28, implies that Vampirococcus must acquire these molecules from an external source and supports the predatory nature of the interaction with its photosynthetic host. An intriguing aspect of this interaction concerns the transfer of substrates from the host to Vampirococcus, especially considering that, despite examination of several serial ultrathin sections, the cell membranes of these two partners do not appear to be in direct contact (Fig. 1h). Vampirococcus encodes several virulence factors, including divergent forms of hemolysin and hemolysin translocator (Vamp_11_169 and Vamp_9_166, respectively), a phage holin (Vamp_5_129), and a membrane-bound lytic murein transglycosylase (Vamp_144_2). These proteins are likely involved in the host cell wall and membrane disruption leading to cell content release. Hemolysin has also been found in Saccharibacteria (formerly candidate phylum TM7), the only CPR phylum for which an epibiotic parasitic lifestyle has been demonstrated so far13,14. Recent coupled lipidomic-metagenomic analyses have shown that CPR bacteria that lack complete lipid biosynthesis are able to recycle membrane lipids from other bacteria29. In Vampirococcus, also devoid of phospholipid synthesis, a phospholipase gene (Vamp_34_196) predicted to be secreted and that has homologs involved in host phospholipid degradation in several parasitic bacteria30, may not only help disrupting the host membrane but also to generate a local source of host phospholipids that it can use to build its own cell membrane. Two Vampirococcus peptidoglycan hydrolases (Vamp_68-56_103 and Vamp_145_30), also predicted to be secreted, most probably contribute to degrade the host cell wall. The Vampirococcus genome also encodes two murein DD-endopeptidases (Vamp_311_38 and Vamp_41_33). As in other predatory bacteria, such as Bdellovibrio, one probably acts to degrade the prey cell wall whereas the other is involved in self-wall remodeling31. Despite their high sequence divergence, we could align both Vampirococcus sequences with those of Bdellovibrio and other bacteria (Supplementary Fig. 7). Both sequences conserved the characteristic active site serine residue of DD endopeptidases and, in contrast with the Bdellovibrio “predatory” enzymes, also the regulatory domain III. The deletion of this regulatory domain has been associated with the capacity of the Bdellovibrio “predatory” DD endopeptidases to act promiscuously on a wide variety of peptidoglycan substrates31. This difference most likely reflects that, whereas Bdellovibrio is able to prey on very diverse bacteria, Vampirococcus is a specialized predator of Chromatiaceae that has evolved specialized enzymes to degrade the wall of its particular prey. The Vampirococcus enzymes could also be aligned with the region where the ankyrin-repeat-containing self-protective regulatory inhibitor Bd3460 binds the Bdellovibrio “predatory” DD endopeptidases32, although only partially for the C-terminal part of Vamp_41_33, like in the self-wall Bdellovibrio enzyme Bd3244 (Supplementary Fig. 7). In that sense, the Vamp_311_38 enzyme seems more similar to the “predatory” Bdellovibrio ones. Interestingly, the “predatory” endopeptidase Bd3459 and the regulatory inhibitor Bd3460 are contiguous in the genomes of Bdellovibrio and other periplasmic predators but not in epibiotic predators32. Vampirococcus confirms this pattern since, although it possesses several ankyrin-repeat-containing proteins, none of them is encoded adjacent to the DD endopeptidase genes.Vampirococcus also possesses a number of genes encoding transporters, most of them involved in the transport of inorganic molecules (Fig. 3). One notable exception is the competence-related integral membrane protein ComEC (Vamp_67_106)33 which, together with ComEA (Vamp_21_186) and type IV pili (see below), probably plays a role in the uptake of host DNA that, once transported into the epibiont, can be degraded by various restriction endonucleases (five genes encoding them are present) and recycled to provide the nucleotides necessary for growth (Supplementary Fig. 8). These proteins are widespread in other CPR bacteria where they may have a similar function34. Vampirococcus also encodes an ABC-type oligopeptide transporter (Vamp_40_40) and a DctA-like C4-dicarboxylate transporter (Vamp_41_97), known to catalyze proton-coupled symport of several Krebs cycle dicarboxylates (succinate, fumarate, malate, and oxaloacetate)35. The first, coupled with the numerous peptidases present in Vampirococcus, most likely is a source of amino acids. By contrast, the role of DctA is unclear since Vampirococcus does not have a Krebs cycle.In sharp contrast with its simplified central metabolism, Vampirococcus possesses genes related to the construction of an elaborate cell surface, which seems to be a common theme in many CPR bacteria9,12. They include genes involved in peptidoglycan synthesis, several glycosyltransferases, a Sec secretion system, and a rich repertoire of type IV pilus proteins. The retractable type IV pili are presumably involved in the tight attachment of Vampirococcus to its host and in DNA uptake in cooperation with the ComEC protein. Other proteins probably play a role in the specific recognition and fixation to the host, including several very large proteins. In fact, the Vampirococcus membrane proteome is enriched in giant proteins. The ten longest predicted proteins (between 1392 and 4163 aa, see Supplementary Table 1) are inferred to have a membrane localization and are probably responsible of the conspicuous fibrous aspect of its cell surface (Fig. 1i). Most of these proteins possess domains known to be involved in the interaction with other molecules, including protein-protein (WD40, TRP, and PKD domains) and protein–lipid (saposin domain) interactions and cell adhesion (DUF11, integrin, and fibronectin domains). Two other large membrane proteins (Vamp_6_203, 2368 aa, and Vamp_19_245, 1895 aa) may play a defensive role as they contain alpha-2-macroglobulin protease-inhibiting domains that can protect against proteases released by the host. Several other smaller proteins complete the membrane proteome of Vampirococcus, some of them also likely involved in recognition and attachment to the host thanks to a variety of protein domains, such as VWA (Vamp_41_85) and flotillin (Vamp_11_100). We did not detect genes coding for flagellar components, confirming the absence of flagella observed under the microscope (Fig. 1).New CRISPR-Cas systems and other defense mechanisms in Vampirococcus
Although most CPR phyla are devoid of CRISPR-Cas36, some have been found to contain new systems with original effector enzymes such as CasY37. In contrast with most available Absconditabacteria genomes, Vampirococcus possesses two CRISPR-Cas loci (Fig. 4a and Supplementary Fig. 9). The first is a class II type V system that contains genes coding for Cas1, Cas2, Cas4, and Cpf1 proteins associated to 34 spacer sequences of 26–32 bp. Proteins similar to those of this system are encoded not only in genomes of close relatives of the Absconditabacteria (Gracilibacteria and Peregrinibacteria) but in many other CPR phyla. These sequences form monophyletic groups in phylogenetic analyses (e.g., Cas1, see Fig. 4b), which suggests that this type V system is probably ancestral in these CPR. The second system found in Vampirococcus belongs to the class I type III and contains genes coding for Cas1, Cas2, Csm3, and Cas10/Csm1 proteins associated to a cluster of 20 longer (35–46 bp) spacers. In contrast with the previous CPR-like system, the proteins of this second system did not show strong similarity with any CPR homolog but with sequences from other bacterial phyla, suggesting that they have been acquired by HGT. Phylogenetic analysis confirmed this and supported that Vampirococcus gained this CRISPR-Cas system from different distant bacterial donors (Supplementary Fig. 10). Interestingly, these two CRISPR-Cas systems encode a number of proteins that may represent new effectors. A clear candidate is the large protein Vamp_48_93 (1158 aa), located between Cpf1 and Cas1 in the type V system (Fig. 4a), which contains a DNA polymerase III PolC motif. Very similar sequences can be found in a few other CPR (some Roizmanbacteria, Gracilibacteria, and Portnoybacteria) and in some unrelated bacteria (Supplementary Fig. 11). As in Vampirococcus, the gene coding for this protein is contiguous to genes encoding different Cas proteins in several of these bacteria, including Roizmanbacteria, Omnitrophica, and the deltaproteobacterium Smithella sp. (Supplementary Fig. 11). This gene association, as well as the very distant similarity between this protein and Cpf1 CRISPR-associated proteins of bacterial type V systems, supports that it is a new effector in type V CRISPR-Cas systems. Additional putative new CRISPR-associated proteins likely exist also in the Vampirococcus type III system (Fig. 4a). Three proteins encoded by contiguous genes (Vamp_21_116, Vamp_21_127, and Vamp_21_128) exhibit very distant similarity with type III-A CRISPR-associated Repeat Associated Mysterious Proteins (RAMP) Csm4, Csm5, and Csm6 sequences, respectively, and most probably represent new RAMP subfamilies. To date, Absconditabacteria38 and Saccharibacteria39 are the only CPR phyla for which phages have been identified. Because of its proximity to Absconditabacteria, Vampirococcus is probably infected by similar phages, so that the function of its CRISPR-Cas systems may be related to the protection against these genetic parasites. Nevertheless, we did not find any similarity between the Vampirococcus spacers and known phage sequences, suggesting that it is infected by unknown phages. Alternatively, considering that Vampirococcus -as most likely many other CPR bacteria- seems to obtain nucleotides required for growth by uptaking host DNA, an appealing possibility is that the CRISPR-Cas systems participate in the degradation of the imported host DNA.Fig. 4: CRISPR-Cas systems in Vampirococcus.a Genes in the two systems encoded in the Vampirococcus lugosii genome, elements common to the two systems are highlighted in blue. b Maximum likelihood phylogenetic tree of the Cas1 protein encoded in the class II type V system, numbers at branches indicate bootstrap support.Full size imageAlthough CPR bacteria have been hypothesized to be largely depleted of classical defense mechanisms40, we found that Vampirococcus, in addition to the two CRISP-Cas loci, is endowed with various other protection mechanisms. These include an AbiEii-AbiEi Type IV toxin-antitoxin system, also present in other CPR bacteria, which may offer additional protection against phage infection41 and several restriction-modification systems, with three type I, one type II and one type III restriction enzymes and eight DNA methylases. In addition to a defensive role, these enzymes may also participate in the degradation of the host DNA. As in its sister-groups Absconditabacteria and Gracilibacteria5,19,20,42,43, Vampirococcus has repurposed the UGA stop codon to code for glycine. The primary function of this recoding remains unknown but it has been speculated that it creates a genetic incompatibility, whereby these bacteria would be “evolutionarily isolated” from their environmental neighbors, preventing their potential competitors from acquiring their genomic innovations by HGT19. However, the opposite might be argued as well, since the UGA codon reassignment can protect Vampirococcus from foreign DNA expression upon uptake by leading to aberrant protein synthesis via read-through of the UGA stop with Gly insertion. This can be important for these CPR bacteria because they are not only impacted by phages38 but they most likely depend on host DNA import and degradation to fulfill their nucleotide requirements. In that sense, it is interesting to note that the Vampirococcus ComEA protein likely involved in DNA transport44 is encoded within the class I type III CRISPR-Cas system (Fig. 4a). More

Invertebrate composition effects on primary productionTo evaluate the effects of fiddler crabs, marsh crabs, and mussels on benthic algae and cordgrass production, the dietary sources for fiddler and marsh crabs, respectively27,28, we measured benthic diatom biomass and cordgrass stem density every 4–6 weeks and quantified cordgrass biomass and grazing damage at the conclusion of the experiment in August 2017. Diatom biomass was enhanced in enclosures with mussels and/or marsh crabs relative to enclosures with only fiddler crabs or no invertebrates, and relative to all ambient plots (F36, 200 = 1.5; P = 0.04; Tukey’s HSD, all P  More