Philippa Kaur

Aguilar R, Cristóbal-Pérez ED, Balvino-Olvera FJ, Aguilar-Aguilar MDJ, Aguirre-Acosta N, Ashworth L et al. (2019) Habitat fragmentation reduces plant progeny quality: a global synthesis. Ecol Lett 22:1163–1173Article 

Google Scholar 
Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqcBarlow J, Lennow GD, Ferreira J, Berenguer E, Lees AC, Nally RM et al. (2016) Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535:144–147Article 
CAS 

Google Scholar 
Barret SC, Eckert CG (1990) Current issues in plant reproductive ecology. Isr J Plant Sci 39:5–12
Google Scholar 
Bawa KS, Bullock SH, Perry DR, Coville RE, Grayum MH (1985) Reproductive biology of tropical lowland rain forest trees II. Pollination systems. Am J Bot 72:346–356Article 

Google Scholar 
Bello C, Galetti M, Pizo MA, Magnago LFS, Roch MF, Lima RA, et al. (2015) Defaunation affects carbon storage in tropical forests. Sci Adv 1:e1501105. https://doi.org/10.1126/sciadv.1501105Blouin MS (2003) DNA-based methods for pedigree reconstruction and kinship analysis in natural populations. Trends Ecol Evol 18:503–511Article 

Google Scholar 
Born C, Kjellberg F, Chevallier M-H, Vignes H, Dikangadissi J-T, Sanguié J et al. (2008) Colonization processes and the maintenance of genetic diversity: insight from a pioneer rainforest tree, Aucoumea Klaineana. Proc R Soc B 275:2171–2179Article 

Google Scholar 
Braun M, Dantas L, Esposito T, Pedrosa-Harand A (2020) Strong genetic differentiation on a small geographic scale in the Neotropical rainforest understory tree Paypayrola blanchetiana (Violaceae). Tree Genet Genomes. https://doi.org/10.1007/s11295-020-01477-5Campbell AJ, Carvalheiro LG, Maués MM, Jaffé R, Giannini TC, Freitas MAB et al. (2018) Anthropogenic disturbance of tropical forests threatens pollination services to açai palm in the Amazon river delta. J Appl Ecol 55:1725–1736Article 

Google Scholar 
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience https://doi.org/10.1186/s13742-015-0047-8Chiriboga-Arroyo F, Jansen M, Bardales-Lozano R, Ismail SA, Thomas E, Garcia M et al. (2021) Genetic threats to the Forest Giants of the Amazon: Habitat degradation effects on the socio-economically important Brazil nut tree (Bertholletia excelsa). Plants People Planet 3:194–210Article 

Google Scholar 
Cramer PJS, Wellman FL (1957) Review of literature of coffee research in Indonesia. SIC Editorial, Inter-American Institute of Agricultural SciencesCraparo ACW, Van Asten PJ, Läderach P, Jassogne LT, Grab SW (2015) Coffea arabica yields decline in Tanzania due to climate change: Global implications. Agric Meteorol 207:1–10Article 

Google Scholar 
Cubry P, De Bellis F, Pot D, Musoli P, Leroy P (2013) Global analysis of Coffea canephora Pierre ex Froehner (Rubiaceae) from the Guineo-Congolese region reveals impacts from climatic refuges and migration effects. Genet Resour Crop Evol 60:483–501Article 

Google Scholar 
Curtis PG, Slay CM, Harris NL, Tyukavina A, Hansen MC (2018) Classifying drivers of global forest loss. Science 361:1108–1111Article 
CAS 

Google Scholar 
Da Silva JMC, Tabarelli M (2000) Tree species impoverishment and the future flora of the Atlantic forest of northeast Brazil. Nature 404:72–74Article 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158Article 
CAS 

Google Scholar 
Davis AP, Gole TW, Baena S, Moat J (2012) The impact of climate change on indigenous arabica coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS One. https://doi.org/10.1371/journal.pone.0047981Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R, Pietrella M et al. (2014) The coffee genome provides insight into the convergent evolution of caffeine biosynthesis. Science 345:1181–1184Article 
CAS 

Google Scholar 
Depecker J, Asimonyio JA, Miteho R, Hatangi Y, Kambale J-L, Verleysen L, et al. (2022) The association between rainforest disturbance and recovery, tree community composition, and community traits in the Yangambi area in the Democratic Republic of the Congo. J Trop Ecol. https://doi.org/10.1017/S0266467422000347Dick CW, Etchelecu G, Austerlitz F (2003) Pollen dispersal of tropical trees (Dinizia excelsa: Fabaceae) by native insects and African honeybees in pristine and fragmented Amazonian rainforest. Mol Ecol 12:753–764Article 

Google Scholar 
Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bull 19:11–15
Google Scholar 
Edwards DP, Socolar JB, Mills SC, Burivalova Z, Koh LP, Wilcove DS (2019) Conservation of tropical forests in the Anthropocene. Curr Biol 29:R1008–R1020Article 
CAS 

Google Scholar 
El Mousadik A, Petit RJ (1996) High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor Appl Genet 92:832–839Article 
CAS 

Google Scholar 
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES et al. (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. https://doi.org/10.1371/journal.pone.0019379Ernst C, Mayaux P, Verhegghen A, Bodart C, Christophe M, Defourny P (2013) National forest cover change in Congo Basin: deforestation, reforestation, degradation and regeneration for the years 1990, 2000 and 2005. Glob Chang Biol 19:1173–1187Article 

Google Scholar 
Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620Article 
CAS 

Google Scholar 
FAO, UNEP (2020) The State of the World’s Forests 2020. In Forests, bio-diversity and people. FAO and UNEPFerrão RG, da Fonseca AFA, Ferrão MAG, De Mune LH (2019) Conilon Coffee: the Coffea canephora produced in Brazil. Incaper, Vitória-ES, Brasil
Google Scholar 
Gardner TA, Barlow J, Chazdon R, Ewers RM, Harvey CA, Peres CA et al. (2009) Prospects for tropical forest biodiversity in a human‐modified world. Ecol Lett 12:561–582Article 

Google Scholar 
García-Fernández C, Sánchez JA, Blanco G (2018) SNP-haplotypes: An accurate approach for parentage and relatedness inference in gilthead sea bream (Sparus aurata). Aquaculture 495:582–591Article 

Google Scholar 
Gomez C, Dussert S, Hamon P, Hamon S, De Kochko A, Poncert V (2009) Current genetic differentiation of Coffea canephora pierre ex a. Froehn in the guineo-Congolian african zone: Cumulative impact of ancient climatic changes and recent human activities. BMC Evol Biol 9:167Article 

Google Scholar 
Goudet J (2013) hierfstat: estimation and tests of hierarchical F-statistics. R Package version 0:04–10. http://CRAN.R-project.org/package=hierfstatHubbell SP, Foster RB (1986) Biology, chance and history and the structure of tropical rain forest tree communities. In: Diamond JM, Case TJ (eds) Community ecology. Harper and Row, New York, NY, p 314–329
Google Scholar 
ICO (2022) Coffee Market Report: August 2022. Donwloaded from International Coffee Organization https://www.ico.org/documents/cy2021-22/cmr-0822-e.pdfIsmail SA, Ghazoul J, Ravikanth G, Kushalappa CG, Uma Shaanker R, Kettle CJ (2017) Evaluating realized seed dispersal across fragmented tropical landscapes: A two‐fold approach using parentage analysis and the neighbourhood model. N Phytol 214:1307–1316Article 
CAS 

Google Scholar 
Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405Article 
CAS 

Google Scholar 
Jombart T, Collins C (2015) Analysing genome-wide SNP data using adegenet 2.0.0. https://adegenet.r-forge.r-project.org/files/tutorial-genomics.pdfJones AG, Small CM, Paczolt KA, Ratterman NL (2010) A practical guide to methods of parentage analysis. Mol Ecol Resour 10:6–30Article 

Google Scholar 
Jones OR, Wang J (2012) A comparison of four methods for detecting weak genetic structures from maker data. Ecol Evol 2:1048–1055Article 

Google Scholar 
Kalinowski ST, Wagner AP, Taper ML (2006) ML-Relate: a computer program for maximum likelihood estimation of relatedness and relationship. Mol Ecol Notes 6:576–579Article 
CAS 

Google Scholar 
Kearsley E, Verbeeck H, Hufkens K, Van, de Perre F, doetterl S, Baert G et al. (2017) Functional community structure of African monodominant Gilbertiodendron dewevrei forest influenced by local environmental filtering. Ecol Evol 7:295–304Article 

Google Scholar 
Kier G, Mutke J, Dinerstein E, Ricketss TH, Küper W, Kreft H et al. (2005) Global patterns of plant diversity and floristic knowledge. J Biogeogr 32:1107–1116Article 

Google Scholar 
Kiwuka C, Goudsmit E, Tournebize R, Oliveir de Aquino S, Douma JC, Bellanger L et al. (2021) Genetic diversity of native and cultivated Ugandan Robusta coffee (Coffea canephora Pierre ex A. Froehner): Climate influences, breeding potential and diversity conservation. PLoS One 16:e0245965Article 
CAS 

Google Scholar 
Kreft H, Jetz W (2007) Global patterns and determinants of vascular plant diversity. Proc Natl Acad Sci USA 104:5925–5930Article 
CAS 

Google Scholar 
Lachenaud P, Zhang D (2008) Genetic diversity and population structure in wild stands of cacao trees (Theobroma cacao L.) in French Guiana. Ann For Sci. https://doi.org/10.1051/forest:2008011Lashermes P, Combes MC, Ribas A, Cenci A, Mahé L, Etienne H (2010) Genetic and physical mapping of the SH3 region that confers resistance to leaf rust in coffee tree (Coffea arabica L.). Tree Genet Genomes 6:973–980Article 

Google Scholar 
Leroy T, Marraccini P, Dufour M, Montagnon C, Lashermes P, Sabau X et al. (2005) Construction and characterization of a Coffea canephora BAC library to study the organization of sucrose biosynthesis genes. Theor Appl Genet 111:1031–1041Article 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 14:2078–2079Article 

Google Scholar 
Li YL, Liu JX (2018) StructureSelector: A web‐based software to select and visualize the optimal number of clusters using multiple methods. Mol Ecol Resour 18:176–177Article 

Google Scholar 
Makelele IA, Verheyen K, Boeckx P, Ntaboba LC, Bazirake BM, Ewango C et al. (2021) Afrotropical secondary forests exhibit fast diversity and functional recovery, but slow compositional and carbon recovery after shifting cultivation. J Veg Sci 32:1–13Article 

Google Scholar 
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10–12Article 

Google Scholar 
Mateu-Andrés I, De Paco L (2006) Genetic diversity and the reproductive system in related species of Antirrhinum. Ann Bot 98:1053–1060Article 

Google Scholar 
Mayr E (1954) Change of genetic environment and evolution. In: Huxley A, Hardy AC, Ford EB (eds) Evolution as a process. Allen and Unwin, London, p 157–180
Google Scholar 
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A et al. (2010) The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303Article 
CAS 

Google Scholar 
Merot-L’anthoene V, Tournebize R, Darracq O, Rattina V, Lepelley M, Bellanger L et al. (2019) Development and evaluation of a genome-wide Coffee 8.5K SNP array and its application for high-density genetic mapping and for investigating the origin of Coffea arabica L. Plant Biotechnol J 17:1418–1430Article 

Google Scholar 
Musoli P, Cubry P, Aluka P, Billot C, Dufour M, De Bellis F et al. (2009) Genetic differentiation of wild and cultivated populations: diversity of Coffea canephora Pierre in Uganda. Genome 52:634–646Article 
CAS 

Google Scholar 
Neushulz EL, Mueller T, Schleuning M, Böhning-Gaese K (2016) Pollination and seed dispersal are the most threatened processes of plant regeneration. Sci Rep 6:1–6
Google Scholar 
Norden N, Chazdon RL, Chao A, Jiang YH, Vilchez-Alvarado B (2009) Resilience of tropical rain forests: tree community reassembly in secondary forests. Ecol Lett 12:385–394Article 

Google Scholar 
Nowak MD, Davis AP, Anthony F, Yoder AD (2011) Expression and trans-specific polymorphism of self-incompatibility RNases in Coffea (Rubiaceae). PLoS One. https://doi.org/10.1371/journal.pone.0021019Nyakaana S (2007) Microgeographical genetic structure of forest robusta coffee (Coffea canephora, Pierre), in Kibale National Park, Uganda. Afr J Ecol 45:71–75Article 

Google Scholar 
Oberleitner F, Egger C, Oberdorfer S, Dullinger S, Wanek W, Hietz P (2021) Recovery of aboveground biomass, species richness and composition in tropical secondary forests in SW Costa Rica. Ecol Manag 479:118580Article 

Google Scholar 
Olsson O, Nuñez-Iturri G, Smith HG, Ottosson U, Effium EO (2019) Competition, seed dispersal and hunting: what drives germination and seedling survival in an Afrotropical forest? AoB Plants https://doi.org/10.1093/aobpla/plz018Oryem-Origa H (1999) Fruit and seed ecology of wild Robusta coffee (Coffea canephora Froehner) in Kibale National Park. Uganda Afr J Ecol 37:439–448Article 

Google Scholar 
Peakall R, Smouse RPP (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539Article 
CAS 

Google Scholar 
Podani J (2000) Introduction to the exploration of multivariate biological data. Backhuys Publishers, Kerkwere
Google Scholar 
Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome. https://doi.org/10.3835/plantgenome2012.05.0005Poorter L, Craven D, Jakovac CC, van der Sande MT, Amissah L, Bongers F et al. (2021) Multidimensional tropical forest recovery. Science 374:1370–1376Article 
CAS 

Google Scholar 
Raj A, Stephens M, Pritchard JK (2014) fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197:573–589Article 

Google Scholar 
RStudio Team (2016) RStudio: Integrated Development for RSasaki N, Putz FE (2009) Critical need for new definitions of “forest” and “forest degradation” in global climate change agreements. Conserv Lett 2:226–232Article 

Google Scholar 
Sezen UU, Chazdon RL, Holsinger KE (2007) Multigenerational genetic analysis of tropical secondary regeneration in a canopy palm. Ecology 88:3065–3075Article 

Google Scholar 
Schaumont D, Veeckman E, Van der Jeugt F, Haegeman A, van Glabeke S, Bawin Y et al. (2022) Stack Mapping Anchor Points (SMAP): a versatile suite of tools for read-backed haplotyping. Preprint at bioRxiv https://doi.org/10.1101/2022.03.10.483555Shapiro AC, Grantham HS, Aguilar-Amuchastegui N, Murray NJ, Gond V, Bonfils D, et al. (2021) Forest condition in the Congo Basin for the assessment of ecosystem conservation status. Ecol Indic. https://doi.org/10.1016/j.ecolind.2020.107268Silva MDC, Várzea V, Guerra-Guimarães L, Azinheira HG, Fernandez D, Petitot AS et al. (2006) Coffee resistance to the main diseases: leaf rust and coffee berry disease. Braz J Plant Physiol 18:119–147Article 
CAS 

Google Scholar 
Theim TJ, Shirk RY, Givnish TJ (2014) Spatial genetic structure in four understorey Psychotria species (Rubiaceae) and implications for tropical forest diversity. Am J Bot 101:1189–1199Article 

Google Scholar 
Torti SD, Coley PD, Kursar TA (2001) Causes and consequences of monodominance in tropical lowland forests. Am Nat 157:141–153Article 
CAS 

Google Scholar 
Tyukavina A, Hansen MC, Potapov P, Parker D, Okpa C, Stehman SV, et al. (2018) Congo Basin forest loss dominated by increasing smallholder clearing. Sci Adv. https://doi.org/10.1126/sciadv.aat2993Vanden Abeele S, Janssens SB, Asimonyio Anio J, Bawin Y, Depecker J, Kambale B et al. (2021) Genetic diversity of wild and cultivated Coffea canephora in northeastern DR Congo and the implications for conservation. Am J Bot 108:2425–2434Article 

Google Scholar 
Vandepitte K, Gristina AS, De Hert K, Meekers T, Roldán-Ruiz I, Honnay O (2012) Recolonization after habitat restoration leads to decreased genetic variation in populations of a terrestrial orchid. Mol Ecol 21:4206–4215Article 
CAS 

Google Scholar 
Van Vliet N, Muhindo J, Kbale Nyumu J, Mushagalusa O, Nasi R (2018) Mammal depletion processes as evidenced from spatially explicit and temporal local ecological knowledge. Trop Conserv Sci 11:1–16
Google Scholar 
Vekemans X, Hardy OJ (2004) New insights from fine-scale spatial genetic structure analyses in plant populations. Mol Ecol 13:921–935Article 
CAS 

Google Scholar 
Vranckx G, Jacquemyn H, Muys B, Honnay O (2012) Meta‐analysis of susceptibility of woody plants to loss of genetic diversity through habitat fragmentation. Conserv Biol 26:228–237Article 

Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 

Google Scholar 
Wellman FL (1961) Coffee. Botany, cultivation, and utilization. Leonard Hill, London
Google Scholar 
Widmer A, Lexer C (2001) Glacial refugia: sanctuaries for allelic richness, but not for gene diversity. Trends Ecol Evol 16:267–269Article 
CAS 

Google Scholar 
Wright S (1932) The role of mutation, inbreeding, crossbreeding and selection in evolution. In: Proceedings of the sixth international congress of genetics. pp 356–366.Zhang J, Kobert K, Flouri T, Stamatakis A (2014) PEAR: a fast and accurate Illumina Paired-End read merger. Bioinformatics 30:614–620Article 
CAS 

Google Scholar  More

Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO 2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

Google Scholar 
Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).
Google Scholar 
Lovelock, C. E. & Duarte, C. M. Dimensions of Blue Carbon and emerging perspectives. Biol. Lett. 15, 20180781 (2019).Article 

Google Scholar 
Macreadie, P. I. et al. Blue carbon as a natural climate solution. Nat Rev Earth Environ 2, 826–839 (2021).Al‐Haj, A. N. & Fulweiler, R. W. A synthesis of methane emissions from shallow vegetated coastal ecosystems. Glob. Chang. Biol. 26, 2988–3005 (2020).Article 
ADS 

Google Scholar 
Rosentreter, J. A. et al. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nat. Geosci. https://doi.org/10.1038/s41561-021-00715-2 (2021).Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science (80-) 331, 50–50 (2011).Article 
ADS 
CAS 

Google Scholar 
Rosentreter, J. A., Maher, D. T., Erler, D. V., Murray, R. H. & Eyre, B. D. Methane emissions partially offset “blue carbon” burial in mangroves. Sci. Adv. 4, eaao4985 (2018).Article 
ADS 

Google Scholar 
Rosentreter, J. A., Al‐Haj, A. N., Fulweiler, R. W. & Williamson, P. Methane and nitrous oxide emissions complicate coastal blue carbon assessments. Glob. Biogeochem. Cycles 35, e2020GB006858 (2021).Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).Article 
ADS 
CAS 

Google Scholar 
Snelgrove, P. V. R. et al. Global carbon cycling on a heterogeneous seafloor. Trends Ecol. Evol. 33, 96–105 (2018).Article 

Google Scholar 
Ortega, A. et al. Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. 12, 748–754 (2019).Article 
ADS 
CAS 

Google Scholar 
Barnes, R. O. & Goldberg, E. D. Methane production and consumption in anoxic marine sediments. Geology 4, 297 (1976).Article 
ADS 
CAS 

Google Scholar 
Reeburgh, W. S. Rates of biogeochemical processes in anoxic sediments. Annu. Rev. Earth Planet. Sci. 11, 269–298 (1983).Article 
ADS 
CAS 

Google Scholar 
Wallenius, A. J., Dalcin Martins, P., Slomp, C. P. & Jetten, M. S. M. Anthropogenic and environmental constraints on the microbial methane cycle in coastal sediments. Front. Microbiol. 12, 631621 (2021).Tokoro, T. et al. Net uptake of atmospheric CO2 by coastal submerged aquatic vegetation. Glob. Chang. Biol. 20, 1873–1884 (2014).Article 
ADS 

Google Scholar 
Gallagher, J. B., Shelamoff, V. & Layton, C. Seaweed ecosystems may not mitigate CO2 emissions. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsac011 (2022).Oremland, R. S. & Taylor, B. F. Sulfate reduction and methanogenesis in marine sediments. Geochim. Cosmochim. Acta 42, 209–214 (1978).Article 
ADS 
CAS 

Google Scholar 
Egger, M., Riedinger, N., Mogollón, J. M. & Jørgensen, B. B. Global diffusive fluxes of methane in marine sediments. Nat. Geosci. 11, 421–425 (2018).Article 
ADS 
CAS 

Google Scholar 
Weber, T., Wiseman, N. A. & Kock, A. Global ocean methane emissions dominated by shallow coastal waters. Nat. Commun. 10, 1–10 (2019).Article 

Google Scholar 
Neubauer, S. C. & Megonigal, J. P. Moving beyond global warming potentials to quantify the climatic role of ecosystems. Ecosystems 18, 1000–1013 (2015).Article 

Google Scholar 
Neubauer, S. C. Global warming potential is not an ecosystem property. Ecosystems https://doi.org/10.1007/s10021-021-00631-x (2021).Howard, J., Hoyt, S., Isensee, K., Telszewski, M. & Pidgeon, E. Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses. 1–181 (2014). https://unesdoc.unesco.org/ark:/48223/pf0000372868.Berg, P., Huettel, M., Glud, R. N., Reimers, C. E. & Attard, K. M. Aquatic eddy covariance: the method and its contributions to defining oxygen and carbon fluxes in marine environments. Ann. Rev. Mar. Sci. 14, 431–455 (2022).Article 

Google Scholar 
Tokoro, T., Watanabe, K., Tada, K. & Kuwae, T. Air–water CO2 flux in shallow coastal waters: theory, methods, and empirical studies. in Blue Carbon in Shallow Coastal Ecosystems 153–184 (Springer Singapore, 2019).Saintilan, N., Rogers, K., Mazumder, D. & Woodroffe, C. Allochthonous and autochthonous contributions to carbon accumulation and carbon store in southeastern Australian coastal wetlands. Estuar. Coast. Shelf Sci. 128, 84–92 (2013).Article 
ADS 
CAS 

Google Scholar 
Ollivier, Q. R., Maher, D. T., Pitfield, C. & Macreadie, P. I. Net drawdown of greenhouse gases (CO2, CH4 and N2O) by a temperate australian seagrass meadow. Estuaries Coasts https://doi.org/10.1007/s12237-022-01068-8 (2022).Maher, D. T. et al. Novel use of cavity ring-down spectroscopy to investigate aquatic carbon cycling from microbial to ecosystem scales. Environ. Sci. Technol. 47, 12938–12945 (2013).Article 
ADS 
CAS 

Google Scholar 
Call, M. et al. Spatial and temporal variability of carbon dioxide and methane fluxes over semi-diurnal and spring–neap–spring timescales in a mangrove creek. Geochim. Cosmochim. Acta 150, 211–225 (2015).Article 
ADS 
CAS 

Google Scholar 
Maher, D. T., Cowley, K., Santos, I. R., Macklin, P. & Eyre, B. D. Methane and carbon dioxide dynamics in a subtropical estuary over a diel cycle: Insights from automated in situ radioactive and stable isotope measurements. Mar. Chem. 168, 69–79 (2015).Article 
CAS 

Google Scholar 
Attard, K. M. et al. Seasonal metabolism and carbon export potential of a key coastal habitat: The perennial canopy-forming macroalga Fucus vesiculosus. Limnol. Oceanogr. 64, 149–164 (2019).Article 
ADS 

Google Scholar 
Attard, K. M. et al. Seasonal ecosystem metabolism across shallow benthic habitats measured by aquatic eddy covariance. Limnol. Oceanogr. Lett. 4, 79–86 (2019).Article 

Google Scholar 
Trevathan-Tackett, S. M. et al. Comparison of marine macrophytes for their contributions to blue carbon sequestration. Ecology 96, 3043–3057 (2015).Article 

Google Scholar 
Pessarrodona, A. et al. Global seaweed productivity. Sci. Adv. 8, eabn2465 (2022).Machado, L., Magnusson, M., Paul, N. A., de Nys, R. & Tomkins, N. Effects of marine and freshwater macroalgae on in vitro total gas and methane production. PLoS ONE 9, e85289 (2014).Article 
ADS 

Google Scholar 
Hansson, G. Methane production from marine, green macro-algae. Resour. Conserv. 8, 185–194 (1983).Article 
CAS 

Google Scholar 
Björk, M., Rosenqvist, G., Gröndahl, F. & Bonaglia, S. Methane emissions from macrophyte beach wrack on Baltic seashores. Ambio 52, 171–181 (2023).Article 

Google Scholar 
Lundevall-Zara, M., Lundevall-Zara, E. & Brüchert, V. Sea-air exchange of methane in shallow inshore areas of the Baltic sea. Front. Mar. Sci. 8, 1–20 (2021).Article 

Google Scholar 
Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).Article 
ADS 
CAS 

Google Scholar 
Roth, F. et al. High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems. Glob. Chang. Biol. https://doi.org/10.1111/gcb.16177 (2022).Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).Article 
ADS 
CAS 

Google Scholar 
Watanabe, K. et al. Macroalgal metabolism and lateral carbon flows can create significant carbon sinks. Biogeosciences 17, 2425–2440 (2020).Article 
ADS 
CAS 

Google Scholar 
Möller, P., Pihl, L. & Rosenberg, R. Benthic faunal energy flow and biological interaction in some shallow marine soft bottom habitats. Mar. Ecol. Prog. Ser. 27, 109–121 (1985).Article 
ADS 

Google Scholar 
Frigstad, H. et al. Blue Carbon – Climate Adaptation, CO2 Uptake And Sequestration Of Carbon In Nordic Blue Forests – Results From The Nordic Blue Carbon Project. (Nordic Council of Ministers, 2021).Ikawa, H. & Oechel, W. C. Temporal variations in air-sea CO 2 exchange near large kelp beds near San Diego, California. J. Geophys. Res. Ocean. 120, 50–63 (2015).Article 
ADS 
CAS 

Google Scholar 
Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
CAS 

Google Scholar 
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).Article 
ADS 
CAS 

Google Scholar 
Oreska, M. P. J. et al. The greenhouse gas offset potential from seagrass restoration. Sci. Rep. 10, 1–15 (2020).Article 

Google Scholar 
Asplund, M. E. et al. Methane emissions from nordic seagrass meadow sediments. Front. Mar. Sci. 8, 811533 (2022).Article 

Google Scholar 
Schorn, S., Ahmerkamp, S., Bullock, E., Weber, M. & Lott, C. Diverse methylotrophic methanogenic archaea cause high methane emissions from seagrass meadows. Proc. Natl Acad. Sci. USA. https://doi.org/10.1073/pnas.2106628119/-/DCSupplemental.Published (2022).Koebsch, F., Glatzel, S. & Jurasinski, G. Vegetation controls methane emissions in a coastal brackish fen. Wetl. Ecol. Manag. 21, 323–337 (2013).Article 
CAS 

Google Scholar 
Sansone, F. J. & Martens, C. S. Methane production from acetate and associated methane fluxes from anoxic coastal sediments. Science (80-). 211, 707–709 (1981).Article 
ADS 
CAS 

Google Scholar 
Egger, M. et al. Rapid sediment accumulation results in high methane effluxes from coastal sediments. PLoS ONE 11, e0161609 (2016).Article 

Google Scholar 
Hamdan, L. J. & Wickland, K. P. Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate. Limnol. Oceanogr. 61, S3–S12 (2016).Article 
ADS 

Google Scholar 
Cai, M. et al. Metatranscriptomics reveals different features of methanogenic archaea among global vegetated coastal ecosystems. Sci. Total Environ. 802, 149848 (2022).Article 
ADS 
CAS 

Google Scholar 
Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science (80-). 350, 434–438 (2015).Article 
ADS 
CAS 

Google Scholar 
Zhang, C.-J., Pan, J., Liu, Y., Duan, C.-H. & Li, M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome 8, 94 (2020).Article 
CAS 

Google Scholar 
Hilt, S., Grossart, H., McGinnis, D. F. & Keppler, F. Potential role of submerged macrophytes for oxic methane production in aquatic ecosystems. Limnol. Oceanogr. https://doi.org/10.1002/lno.12095 (2022).Söllinger, A. & Urich, T. Methylotrophic methanogens everywhere — physiology and ecology of novel players in global methane cycling. Biochem. Soc. Trans. 47, 1895–1907 (2019).Article 

Google Scholar 
Karl, D. M. et al. Aerobic production of methane in the sea. Nat. Geosci. 1, 473–478 (2008).Article 
ADS 
CAS 

Google Scholar 
McGenity, T. J. & Sorokin, D. Y. Handbook of Hydrocarbon and Lipid Microbiology. p. 665–680 (Springer, 2010).Murray, B. C., Pendleton, L., Jenkins, W. A. & Sifleet, S. Green Payments for Blue Carbon Economic Incentives for Protecting Threatened Coastal Habitats (Nicholas Institute for Environmental Policy Solutions, 2011).Kuwae, T., Watanabe, A., Yoshihara, S., Suehiro, F. & Sugimura, Y. Implementation of blue carbon offset crediting for seagrass meadows, macroalgal beds, and macroalgae farming in Japan. Mar. Policy 138, 104996 (2022).Article 

Google Scholar 
Medvedev, I. P., Rabinovich, A. B. & Kulikov, E. A. Tides in three enclosed basins: the Baltic, Black, and Caspian Seas. Front. Mar. Sci. 3, 46 (2016).Article 

Google Scholar 
Haugen, D. A. Workshop on Micrometeorology (American Meteorological Society, 1973).Weiss, R. F. Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Mar. Chem. 2, 203–215 (1974).Article 
CAS 

Google Scholar 
Wiesenburg, D. A. & Guinasso, N. L. Equilibrium solubilities of methane, carbon monoxide, and hydrogen in water and sea water. J. Chem. Eng. Data 24, 356–360 (1979).Article 
CAS 

Google Scholar 
Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Oceanogr. Methods 12, 351–362 (2014).Article 

Google Scholar 
Gülzow, W. et al. One year of continuous measurements constraining methane emissions from the Baltic Sea to the atmosphere using a ship of opportunity. Biogeosciences 10, 81–99 (2013).Article 
ADS 

Google Scholar 
Jähne, B. et al. On the parameters influencing air-water gas exchange. J. Geophys. Res. 92, 1937 (1987).Article 
ADS 

Google Scholar 
Bonaglia, S. et al. Meiofauna improve oxygenation and accelerate sulfide removal in the seasonally hypoxic seabed. Mar. Environ. Res. 159, 104968 (2020).Article 
CAS 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).Article 
CAS 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
St John, J. SeqPrep. https://github.com/jstjohn/SeqPrep (2011).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2021).Andrews, S. FastQC: A Quality Control Tool For High Throughput Sequence Data (2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41, D590–D596 (2012).Article 

Google Scholar 
Robertson, C. E. et al. Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data. Bioinformatics 29, 3100–3101 (2013).Article 
CAS 

Google Scholar 
Coolen, M. J. L. et al. Evolution of the methane cycle in Ace Lake (Antarctica) during the Holocene: response of methanogens and methanotrophs to environmental change. Org. Geochem. 35, 1151–1167 (2004).Article 
CAS 

Google Scholar 
Wobbrock, J. O., Findlater, L., Gergle, D. & Higgins, J. J. The aligned rank transform for nonparametric factorial analyses using only anova procedures. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 143–146 (ACM, 2011).Hammer, Ø., Harper, D. & Ryan, P. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001). More

Orr, H. A. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6, 119–127 (2005).Article 
CAS 

Google Scholar 
Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).Article 

Google Scholar 
Exposito-Alonso, M., Burbano, H. A., Bossdorf, O., Nielsen, R. & Weigel, D. Natural selection on the Arabidopsis thaliana genome in present and future climates. Nature 573, 126–129 (2019).Article 
CAS 

Google Scholar 
Han, G., Wang, W. & Dong, Y. Effects of balancing selection and microhabitat temperature variations on heat tolerance of the intertidal black mussel Septifer virgatus. Integr. Zool. 15, 416–427 (2020).Article 

Google Scholar 
Meester, L. D., Stoks, R. & Brans, K. I. Genetic adaptation as a biological buffer against climate change: Potential and limitations. Integr. Zool. 13, 372–391 (2018).Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
Günter, F. et al. Genotype-environment interactions rule the response of a widespread butterfly to temperature variation. J. Evol. Biol. 33, 920–929 (2020).Article 

Google Scholar 
Lowry, D. B. et al. QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient. Proc. Natl Acad. Sci. USA 116, 12933–12941 (2019).Article 
CAS 

Google Scholar 
Bates, A. E. et al. Biologists ignore ocean weather at their peril. Nature 560, 299–301 (2018).Article 
CAS 

Google Scholar 
Plotkin, J. B. & Kudla, G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12, 32–42 (2011).Article 
CAS 

Google Scholar 
Zhao, F. et al. Genome-wide role of codon usage on transcription and identification of potential regulators. Proc. Natl Acad. Sci. USA 118, e2022590118 (2021).Hanson, G. & Coller, J. Codon optimality, bias and usage in translation and mRNA decay. Nat. Rev. Mol. Cell Bio. 19, 20–30 (2018).Article 
CAS 

Google Scholar 
Chen, S. et al. Codon-resolution analysis reveals a direct and context-dependent impact of individual synonymous mutations on mRNA level. Mol. Biol. Evol. 34, 2944–2958 (2017).Article 
CAS 

Google Scholar 
Wu, Z. et al. Expression level is a major modifier of the fitness landscape of a protein coding gene. Nat. Ecol. Evol. 6, 103–115 (2022).Article 

Google Scholar 
Lebeuf-Taylor, E., McCloskey, N., Bailey, S. F., Hinz, A. & Kassen, R. The distribution of fitness effects among synonymous mutations in a gene under directional selection. ELife. 8, e45952 (2019).Bailey, S. F., Hinz, A. & Kassen, R. Adaptive synonymous mutations in an experimentally evolved Pseudomonas fluorescens population. Nat. Commun. 5, 4076 (2014).Agashe, D. et al. Large-effect beneficial synonymous mutations mediate rapid and parallel adaptation in a bacterium. Mol. Biol. Evol. 33, 1542–1553 (2016).Article 
CAS 

Google Scholar 
Zhao, Y. et al. Synonymous mutation in growth regulating factor 15 of miR396a target sites enhances photosynthetic efficiency and heat tolerance in poplar. J. Exp. Bot. 72, 4502–4519 (2021).Article 
CAS 

Google Scholar 
Somero, G. N. The physiology of global change: linking patterns to mechanisms. Annu. Rev. Mar. Sci. 4, 39–61 (2012).Article 

Google Scholar 
Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).Article 
CAS 

Google Scholar 
Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annu Rev. Ecol. Evol. Syst. 37, 373–404 (2006).Article 

Google Scholar 
Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Side matters: microhabitat influence on intertidal heat stress over a large geographical scale. J. Exp. Mar. Biol. Ecol. 400, 200–208 (2011).Article 

Google Scholar 
Schmidt, P. S. & Rand, D. M. Intertidal microhabitat and selection at MPI: interlocus contrasts in the Northern Acorn Barnacle, Semibalanus balanoides. Evolution 53, 135 (1999).
Google Scholar 
Li, X., Tan, Y., Sun, Y., Wang, J. & Dong, Y. Microhabitat temperature variation combines with physiological variation to enhance thermal resilience of the intertidal mussel Mytilisepta virgata. Funct. Ecol. 35, 2497–2507 (2021).Article 
CAS 

Google Scholar 
Dong, Y. et al. Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress. Proc. Royal. Soc. B. 284, (2017).Li, X. & Dong, Y. Living on the upper intertidal mudflat: different behavioral and physiological responses to high temperature between two sympatric Cerithidea snails with divergent habitat-use strategies. Mar. Environ. Res. 159, 105015 (2020).Article 
CAS 

Google Scholar 
Wang, J., Peng, X. & Dong, Y. High abundance and reproductive output of an intertidal limpet (Siphonaria japonica) in environments with high thermal predictability. Mar. Life. Sci. Tech. 2, 324–333 (2020).Article 

Google Scholar 
Dong, Y., Liao, M., Han, G. & Somero, G. N. An integrated, multi-level analysis of thermal effects on intertidal molluscs for understanding species distribution patterns. Biol. Rev. 97, 554–581 (2022).Article 

Google Scholar 
Georges, A., Gros, P. & Fodil, N. USP15: a review of its implication in immune and inflammatory processes and tumor progression. Genes Immun. 22, 12–23 (2021).Article 
CAS 

Google Scholar 
Vlasschaert, C., Xia, X., Coulombe, J. & Gray, D. A. Evolution of the highly networked deubiquitinating enzymes USP4, USP15, and USP11. BMC Evol. Biol. 15, 230 (2015).Mallard, F., Nolte, V., Tobler, R., Kapun, M. & Schlötterer, C. A simple genetic basis of adaptation to a novel thermal environment results in complex metabolic rewiring in Drosophila. Genome Biol. 19, 119 (2018).Cornelissen, T. et al. The deubiquitinase USP15 antagonizes Parkin-mediated mitochondrial ubiquitination and mitophagy. Hum. Mol. Genet. 23, 5227–5242 (2014).Article 
CAS 

Google Scholar 
Morton, B. The biology and functional morphology of Septifer bilocularis and Mytilisepta virgata (Bivalvia: Mytiloidea) from corals and the exposed rocky shores, respectively, of Hong Kong. Reg. Stud. Mar. Sci. 235, 485–500 (1995).
Google Scholar 
Boroda, A. V., Kipryushina, Y. O. & Odintsova, N. A. The effects of cold stress on Mytilus species in the natural environment. Cell Stress Chaperones 25, 821–832 (2020).Article 
CAS 

Google Scholar 
Thayer, C. W. Brachiopods versus mussels: competition, predation, and palatability. Science 228, 1527–1528 (1985).Article 
CAS 

Google Scholar 
Iorio, R., Celenza, G. & Petricca, S. Mitophagy: molecular mechanisms, new concepts on Parkin activation and the emerging role of AMPK/ULK1 Axis. Cells 11, 30 (2022).Article 
CAS 

Google Scholar 
Feidantsis, K. et al. Correlation between intermediary metabolism, Hsp gene expression, and oxidative stress-related proteins in long-term thermal-stressed Mytilus galloprovincialis. Am. J. Physiol. Regul. Integr. Comp. Physiol. 319, R264–R281 (2020).Article 
CAS 

Google Scholar 
Heise, K., Puntarulo, S., Portner, H. O. & Abele, D. Production of reactive oxygen species by isolated mitochondria of the Antarctic bivalve Laternula elliptica (King and Broderip) under heat stress. Comp. Biochem. Physiol. C. Toxicol. Pharmacol. 134, 79–90 (2003).Article 
CAS 

Google Scholar 
Abele, D., Heise, K., Portner, H. O. & Puntarulo, S. Temperature-dependence of mitochondrial function and production of reactive oxygen species in the intertidal mud clam Mya arenaria. J. Exp. Biol. 205, 1831–1841 (2002).Article 
CAS 

Google Scholar 
Xiao, Q. et al. Transcriptome analysis reveals the molecular mechanisms of heterosis on thermal resistance in hybrid abalone. BMC Genom. 22, 650 (2021).Li, L. et al. Heat stress induces apoptosis through a Ca2+-mediated mitochondrial apoptotic pathway in human umbilical vein endothelial cells. PLoS ONE 9, e111083 (2014).Article 

Google Scholar 
Gu, Z. T. et al. Heat stress induced apoptosis is triggered by transcription-independent p53, Ca2+ dyshomeostasis and the subsequent Bax mitochondrial translocation. Sci. Rep. 5, 11497 (2015).Article 
CAS 

Google Scholar 
Gerdol, M., De Moro, G., Venier, P. & Pallavicini, A. Analysis of synonymous codon usage patterns in sixty-four different bivalve species. Peer J. 3, e1520 (2015).Article 

Google Scholar 
Zhou, M. et al. Non-optimal codon usage affects expression, structure and function of clock protein FRQ. Nature 495, 111–115 (2013).Article 
CAS 

Google Scholar 
Yu, C. et al. Codon usage influences the local rate of translation elongation to tegulate co-translational protein folding. Mol. Cell 59, 744–754 (2015).Article 
CAS 

Google Scholar 
Spencer, P. S., Siller, E., Anderson, J. F. & Barral, J. M. Silent substitutions predictably alter translation elongation rates and protein folding efficiencies. J. Mol. Biol. 422, 328–335 (2012).Article 
CAS 

Google Scholar 
Pechmann, S., Chartron, J. W. & Frydman, J. Local slowdown of translation by nonoptimal codons promotes nascent-chain recognition by SRP in vivo. Nat. Struct. Mol. Biol. 21, 1100–1105 (2014).Article 
CAS 

Google Scholar 
Kimchi-Sarfaty, C. et al. A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315, 525–528 (2007).Article 
CAS 

Google Scholar 
Zhou, Z. et al. Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc. Natl Acad. Sci. USA 113, E6117–E6125 (2016).Article 
CAS 

Google Scholar 
Shabalina, S. A., Spiridonov, N. A. & Kashina, A. Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Res. 41, 2073–2094 (2013).Article 
CAS 

Google Scholar 
Liao, M., Dong, Y. & Somero, G. N. Thermal adaptation of mRNA secondary structure: stability versus lability. Proc. Natl Acad. Sci. USA 118, e2113324118 (2021).Article 
CAS 

Google Scholar 
Wan, Y. et al. Genome-wide measurement of RNA folding energies. Mol. Cell. 48, 169–181 (2012).Article 

Google Scholar 
Seffens, W. & Digby, D. mRNAs have greater negative folding free energies than shuffled or codon choice randomized sequences. Nucleic Acids Res. 27, 1578–1584 (1999).Article 
CAS 

Google Scholar 
Faure, G., Ogurtsov, A. Y., Shabalina, S. A. & Koonin, E. V. Role of mRNA structure in the control of protein folding. Nucleic Acids Res. 44, 10898–10911 (2016).Article 
CAS 

Google Scholar 
Victor, M. P., Acharya, D., Begum, T. & Ghosh, T. C. The optimization of mRNA expression level by its intrinsic properties-Insights from codon usage pattern and structural stability of mRNA. Genomics 111, 1292–1297 (2019).Article 
CAS 

Google Scholar 
Backlund, M. & Kulozik, A. E. Differential analysis of the nuclear and the cytoplasmic RNA interactomes in living cells. Methods Mol. Biol. 2428, 291–304 (2022).Article 

Google Scholar 
Zaghlool, A. et al. Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular distribution of RNA transcripts. Sci. Rep. 11, 4076 (2021).Clark, M. S. et al. Life in the intertidal: cellular responses, methylation and epigenetics. Funct. Ecol. 32, 1982–1994 (2018).Article 

Google Scholar 
Jeremias, G. et al. Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems. Mol. Ecol. 27, 2790–2806 (2018).Article 

Google Scholar 
Li, L. et al. Divergence and plasticity shape adaptive potential of the Pacific oyster. Nat. Ecol. Evol. 2, 1751–1760 (2018).Article 

Google Scholar 
Chu, D. & Wei, L. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer. 19, 359 (2019).Lima, F. P. & Wethey, D. S. Robolimpets: measuring intertidal body temperatures using biomimetic loggers. Limol. Oceanogr. Methods 7, 347–353 (2009).Article 

Google Scholar 
Dong, Y. & Williams, G. A. Variations in cardiac performance and heat shock protein expression to thermal stress in two differently zoned limpets on a tropical rocky shore. Mar. Biol. 158, 1223–1231 (2011).Article 

Google Scholar 
Vito, M. Segmented: An R Package to fit regression models with broken-line relationships. R. N. 8, 20–25 (2008).
Google Scholar 
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2021).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).Article 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience. 10, giab008 (2021).Rochette, N. C., Rivera Colón, A. G. & Catchen, J. M. Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).Article 
CAS 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).Article 
CAS 

Google Scholar 
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).Article 

Google Scholar 
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).Article 
CAS 

Google Scholar 
Hao, Z. et al. RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput. Sci. 6, e251 (2020).Article 

Google Scholar 
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).Article 
CAS 

Google Scholar 
Letunic, I., Khedkar, S. & Bork, P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 49, D458–D460 (2021).Article 
CAS 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).Article 
CAS 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).Article 
CAS 

Google Scholar 
Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. USA 101, 7287–7292 (2004).Article 
CAS 

Google Scholar 
Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999).Article 
CAS 

Google Scholar 
Moyen, N. E., Somero, G. N. & Denny, M. W. Mussels’ acclimatization to high, variable temperatures is lost slowly upon transfer to benign conditions. J. Exp. Biol. 223, Pt 13 (2020).
Google Scholar 
Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).Article 

Google Scholar 
Panova, M. et al. DNA extraction protocols for whole-genome sequencing in marine organisms. Methods Mol. Biol. 1452, 13–44 (2016).Article 
CAS 

Google Scholar 
Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).Article 
CAS 

Google Scholar 
Gerdol, M. et al. The purplish bifurcate mussel Mytilisepta virgata gene expression atlas reveals a remarkable tissue functional specialization. BMC Genomics. 18, 590 (2017).Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 25, 402–408 (2001).Article 
CAS 

Google Scholar  More

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Restoration opportunity area and costsMangrove restoration programmes have a greater chance of being successful when implemented in areas where mangroves have previously grown15. These areas have either been subject to deforestation or degradation and may be under government management or private ownership. They are locations that have undergone forest conversion into other land uses, including aquaculture, crops or plantations and urban settlements. Land ownership status is an important factor to consider for determining the availability of land for mangrove restoration7. For example, a higher opportunity and priority would be given to unproductive aquaculture ponds located in the protected and production forest areas which are under government management or leasehold, rather than in areas with other land uses that may be under private ownership (Methods gives detailed forest land tenure classifications in Indonesia). Therefore, managing mangrove rehabilitation should consider factors that include land tenure status and land-cover type as well as biogeomorphology (for example, ensuring that the correct mangrove species are used in hydrologically suitable locations) across landscape scales.We calculated that ~193,367 ha of land may be feasible for implementation of mangrove rehabilitation programmes (Fig. 4). This conservative assessment suggests that the potential for restoration may be only 30% of the current mangrove rehabilitation area target (600,000 ha). Depending on the challenges and opportunities for each of the biogeomorphological categories of land use and the forest land status we considered (see Methods for detailed mapping methodology), we identified that 9% of the potential restorable area was categorized as being within the high opportunity scenario, 33% as medium and 58% as areas falling within the low opportunity scenario. Among these scenarios, ~75% of identified areas have non-protected forest status, implying a greater tenurial challenge to establishing a rehabilitation programme. We identified the five provinces that are among the top ranked of high potential for mangrove restoration in Indonesia, namely East Kalimantan (20% of national restoration potential area), North Kalimantan (20%), South Sumatra (12%), West Kalimantan (5%) and Riau provinces (5%) (Fig. 1c). All of these provinces, except South Sumatra, are among the areas already identified in the current mangrove rehabilitation programme by the BRGM as having high opportunity for rehabilitation4. At the subprovincial scale, we identified the top six regencies with restoration area opportunity >10,000 ha, namely Banyuasin, Bulungan, Tana Tidung, Paser, Berau and Nunukan (Supplementary Table 1). Mangroves across these regions were commonly deforested after 2010 and converted into aquaculture ponds despite being designated as protected forest areas (Supplementary Table 1).Fig. 4: The distribution of mangrove loss area (in hectares) between 2001 and 2020 in Indonesia.Also shown are mangrove loss proportions within different biogeomorphological typology, loss drivers (land-use types), forest land status and identified scenarios of restoration opportunity (low, medium and high).Full size imageConsidering that previous successful (85% survival rates) mangrove rehabilitation around the world has been achieved only at small landscape scales (10–400 ha) with costs varying between US$1,500 ha−1 and US$9,000 ha−1 (refs. 8,16), the large-scale mangrove rehabilitation ambition of Indonesia must be carefully planned. Rehabilitating ~200,000 ha of degraded mangroves will require between US$0.29 billion and US$1.74 billion. The 2021 annual government budget allocation for mangrove rehabilitation under BRGM alone is ~US$0.10 billion17, which is 66–94% lower than the estimated total required budget but with additional international investment18 there is potential for scalable mangrove rehabilitation success.Lessons learned from the past failuresIn Indonesia, unproductive aquaculture ponds have become targets for mangrove rehabilitation programmes (Supplementary Fig. 1). However, metrics of rehabilitation success in these settings reveal low survival rates of planted seedlings, highlighting an urgency to develop new strategies for mangrove rehabilitation and strategies to assess the effectiveness of ecosystem rehabilitation6. For example, a silviculture approach—nursery-based mangrove planting using Rhizophora species—has been adopted for mangrove restoration and management for a long time in Indonesia19. When seedlings are directly planted in unused ponds (Supplementary Fig. 1), dense monoculture plantations often form, which despite providing some ecosystem services (for example, carbon sequestration20) have limited biodiversity value21 and may be less resilient to stressors compared to a diverse assemblages of tree species22.Mangrove restoration projects have often suffered low success rates due to inadequate hydrological site assessments before revegetation23. For example, mangrove planting programmes initiated after the 2004 tsunami were focused on mono-species planting and on reporting the number of seedlings being planted in a given area24. These planting projects most often occurred on undisputed land, such as mudflats, which are inappropriate locations for long-term mangrove growth because of high inundation frequency, high water flow rates and hypersaline conditions that limit seedling establishment and survival24. Planting has also focused in mangrove areas where low canopy cover is observed. While some mangrove areas with low canopy cover may respond to plantings because they are degraded, many sites naturally support low canopy cover, reflecting suboptimal environmental conditions for growth of Rhizophora species, instead favouring growth of highly salt tolerant species such as Avicennia spp.24. Such failures in mangrove rehabilitation efforts, however, have been under-reported with more than 50% of rehabilitation studies not monitored over time (Supplementary Fig. 1).Alternative restoration approaches through repairing hydrology, including excavation and removal of pond walls and tidal gates, have also been introduced15, although this approach has been only practiced in Indonesia at limited scales, mostly in unused aquaculture ponds25. A comprehensive understanding of the opportunity for mangrove rehabilitation in Indonesia is largely unquantified. Additionally, with limited monitoring of mangrove rehabilitation projects, the effectiveness and functionality of mangrove rehabilitation in Indonesia remains largely unknown and therefore it remains challenging to assess rehabilitation effectiveness between approaches and locations in Indonesia. Yet such assessments provide important data to achieve the ambitious mangrove rehabilitation goals of Indonesia.Mangrove governance in IndonesiaMangrove conservation in Indonesia was formally adopted in 1990 (Extended Data Fig. 1 and Supplementary Table 2), when mangroves were designated as protected forests under Law 5/1990 and the Presidential Decree 32/1990. When the Asian tsunami hit Aceh province in 2004, the role of mangroves in wave attenuation and therefore minimizing disaster risks for coastal communities was recognized26. As a result, nearly 30,000 ha of damaged mangroves were rehabilitated to recover coastal resiliency through planting of nearly 24 million seedlings over 60 projects24. However, the success of these programmes was low due to a lack of planning, monitoring and critical supplemental actions24,27. Despite the failure of many mangrove rehabilitation projects post-tsunami, the implementation of the subsequent programmes have not fully adopted best-practice mangrove rehabilitation principles6,7,15,23. In 2007, similar approaches to mangrove rehabilitation and conservation were adopted at a larger, national scale under the Spatial Planning Law (Law 26/2007) and the Coastal Area and Small Islands Management Law (Law 27/2007).In 2012, the National Mangrove Management Strategy (STRANAS Mangrove) was first established and followed by the formalization of the National and Regional Mangrove Working Group whose task was to guide mangrove conservation and rehabilitation. Its main goal was to involve more stakeholders, including civil society organizations and subnational government bodies, in mangrove conservation and rehabilitation28. Until 2017, the technical regulation of strategy and performance indicators for mangrove management was implemented with targets set to rehabilitate 3.49 Mha of mangroves by 204529. In 2020, however, the Mangrove Working Group and its supporting regulations were abolished and the mangrove rehabilitation strategy was subsequently managed by BRGM4. This effectively removed the regional governments (subnational working groups) from decisions related to mangrove management and concentrated development of policy at the level of the national government. The new strategy includes a tenfold increase in the annual rehabilitation target (from 11,250 to ~120,000 ha yr−1) with an overall target of 600,000 ha to be achieved within a shorter timeline (2020–2024). Without clear planning and appropriate strategies, these ambitious targets may not be feasible. For example, the annual mangrove rehabilitation area reached between 2017 and 2020 was only 5,318 ha (50% of the target) despite 2.6 million seedlings being planted (Supplementary Table 3). Given the lessons from the previous mangrove rehabilitation and the emerging processes of mangrove governance, it is timely to set an achievable restoration framework with improved planning, evaluation and monitoring.Implication for international environmental agendasA successful mangrove rehabilitation programme can directly contribute to reducing poverty (SDG 1) and maintaining food security and livelihoods (SDG 2), thereby increasing the health and well-being of 74 million coastal people in Indonesia (see Supplementary Table 1 for total population of regions with restoration potential area >5 ha). Additionally, mangrove rehabilitation will directly contribute to other relevant SDGs, such as improving water quality (SDG 6), providing healthy coastal habitats for fish and other marine biodiversity (SDG 14), contributing to emissions reductions and improving coastal resilience from sea level rise (SDG 13) and sustainably managing and protecting terrestrial ecosystems (SDG 15). Mangrove rehabilitation contributions to SDG 1 and 2 are particularly relevant as the current rehabilitation programme is delivered as cash-for-works activities under the National Economic Recovery strategy (PEN) as part of the social welfare payments to alleviate economic impacts of the COVID-19 pandemic17. With the current annual mangrove rehabilitation budget of US$0.10 billion17, further implementation of scalable community-based mangrove restoration with technical support from subnational and non-government stakeholders could increase the benefits to local communities, if administered properly. Therefore, the large investments planned for coastal communities via a national mangrove restoration programme will not only contribute to the economy of coastal communities, potentially reducing poverty across 199 regencies but will also help in securing nearly 4% of the national greenhouse gas emissions reduction target from the land sector.Restoring 193,367 ha of mangroves in the next 5 years (2021–2025) may contribute to carbon sequestration of 22 ± 10 MtCO2e by 2030 (see Methods for detailed estimate calculation and assumptions). Moreover, stopping the current annual rates of mangrove loss of 7,436 ha yr−1 between 2021 and 2030 will reduce up to 58 ± 37 MtCO2e or 12% of the national land sector emissions reduction targets. Clearly, climate benefits from mangrove rehabilitation and conservation in Indonesia are substantial if rehabilitation and conservation can be implemented appropriately and large annual rehabilitation targets are achieved. Indonesia has submitted its updated Nationally Determined Contributions (NDCs) to the United Nations Framework Convention on Climate Change, within which integrated management and rehabilitation of mangroves is a component of the actions to enhance the resilience of coastal ecosystems30. Further ecological aquaculture practices such as silvofisheries which are commonly applied in Indonesia31,32 may provide promising potential for climate change mitigation through mangrove biomass enhancement. With the increased potential for international investment to support mangrove rehabilitation in Indonesia, there is an opportunity for Indonesia to take the lead and show the world how mangrove conservation and rehabilitation can contribute to multiple international environmental agendas.In the past three decades, the governance of mangrove conservation and rehabilitation in Indonesia has been highly variable in approach (Extended Data Fig. 1). The current approach is top-down4 which has risks and may be ineffective at achieving landscape-scale increases in mangrove extent, as was demonstrated post-tsunami24,29. This top-down approach set by national-level agencies, which are responsible for achieving rehabilitation targets, has limited involvement (or investment) by subnational governments. While we have identified key factors that determine land available for mangrove rehabilitation, the success of mangrove rehabilitation is not necessarily assured because of the limited involvement of subnational mangrove working groups. A current ‘one size fits all’ strategy of the national government may not be appropriate to achieve successful mangrove rehabilitation and thus more flexible, localized approaches may increase the likelihood of success. More

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

Google Scholar 
Lautenbach, S., Seppelt, R., Liebscher, J. & Dormann, C. F. Spatial and temporal trends of global pollination benefit. PLoS ONE 7, e35954 (2012).Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Rodger, J. G. et al. Widespread vulnerability of flowering plant seed production to pollinator declines. Sci. Adv. 7, eabd3524 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).Article 
CAS 
PubMed 

Google Scholar 
Bennett, J. M. et al. Land use and pollinator dependency drives global patterns of pollen limitation in the Anthropocene. Nat. Commun. 11, 3999 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).Article 
PubMed 

Google Scholar 
Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A.-L. & Totland, Ø. How does climate warming affect plant–pollinator interactions? Ecol. Lett. 12, 184–195 (2009).Article 
PubMed 

Google Scholar 
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).Article 
PubMed 

Google Scholar 
Lever, J. J., van Nes, E. H., Scheffer, M. & Bascompte, J. The sudden collapse of pollinator communities. Ecol. Lett. 17, 350–359 (2014).Article 
PubMed 

Google Scholar 
Valdovinos, F. S. et al. Species traits and network structure predict the success and impacts of pollinator invasions. Nat. Commun. 9, 2153 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Waser, N. M., Chittka, L., Price, M. V., Williams, N. M. & Ollerton, J. Generalization in pollination systems, and why it matters. Ecology 77, 1043–1060 (1996).Article 

Google Scholar 
Brosi, B. J. Pollinator specialization: from the individual to the community. New Phytol. 210, 1190–1194 (2016).Article 
PubMed 

Google Scholar 
Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
Waser, N. M. & Ollerton, J. Plant–Pollinator Interactions: From Specialization to Generalization (Univ. of Chicago Press, 2006).Ashman, T.-L., Arceo-Gómez, G., Bennett, J. M. & Knight, T. M. Is heterospecific pollen receipt the missing link in understanding pollen limitation of plant reproduction? Am. J. Bot. 107, 845–847 (2020).Article 
PubMed 

Google Scholar 
Garibaldi, L. A. et al. Trait matching of flower visitors and crops predicts fruit set better than trait diversity. J. Appl. Ecol. 52, 1436–1444 (2015).Article 

Google Scholar 
CaraDonna, P. J. et al. Seeing through the static: the temporal dimension of plant–animal mutualistic interactions. Ecol. Lett. 24, 149–161 (2021).Article 
PubMed 

Google Scholar 
Burkle, L. A., Marlin, J. C. & Knight, T. M. Plant–pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339, 1611–1615 (2013).Article 
CAS 
PubMed 

Google Scholar 
Jacquemin, F. et al. Loss of pollinator specialization revealed by historical opportunistic data: insights from network-based analysis. PLoS ONE 15, e0235890 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mathiasson, M. E. & Rehan, S. M. Wild bee declines linked to plant–pollinator network changes and plant species introductions. Insect Conserv. Divers. 13, 595–605 (2020).Article 

Google Scholar 
Bennett, J. M. et al. A review of European studies on pollination networks and pollen limitation, and a case study designed to fill in a gap. AoB Plants 10, ply068 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Doré, M., Fontaine, C. & Thébault, E. Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale. Glob. Change Biol. 27, 1266–1280 (2021).Article 

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

Google Scholar 
Post, E. et al. Ecological dynamics across the arctic associated with recent climate change. Science 325, 1355–1358 (2009).Article 
CAS 
PubMed 

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

Google Scholar 
Kearns, C. A. Anthophilous fly distribution across an elevation gradient. Am. Midl. Nat. 127, 172–182 (1992).Article 

Google Scholar 
Kevan, P. G. Insect pollination of high arctic flowers. J. Ecol. 60, 831–847 (1972).Article 

Google Scholar 
Tiusanen, M., Hebert, P. D. N., Schmidt, N. M. & Roslin, T. One fly to rule them all—muscid flies are the key pollinators in the arctic. Proc. Roy. Soc. B 283, 20161271 (2016).Article 

Google Scholar 
Weiner, C., Werner, M., Linsenmair, K. E. & Blüthgen, N. Land use intensity in grasslands: changes in biodiversity, species composition and specialisation in flower visitor networks. Basic Appl. Ecol. 12, 292–299 (2011).Article 

Google Scholar 
Rader, R., Edwards, W., Westcott, D. A., Cunningham, S. A. & Howlett, B. G. Pollen transport differs among bees and flies in a human-modified landscape. Divers. Distrib. 17, 519–529 (2011).Article 

Google Scholar 
Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. 3, 345–354 (2019).Article 
PubMed 

Google Scholar 
Ghisbain, G., Gérard, M., Wood, T. J., Hines, H. M. & Michez, D. Expanding insect pollinators in the Anthropocene. Biol. Rev. 96, 2755–2770 (2021).Article 
PubMed 

Google Scholar 
Silén, F. Blombiologiska iakttagelser i Kittilä Lappmark. Medd. Soc. Fauna Flora Fennica 31, 80–99 (1906).
Google Scholar 
Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: toward a global functional homogenization? Front. Ecol. Environ. 9, 222–228 (2011).Article 

Google Scholar 
Erhardt, A. Pollination of Dianthus superbus L. Flora 185, 99–106 (1991).Article 

Google Scholar 
Witt, T., Jürgens, A., Geyer, R. & Gottsberger, G. Nectar dynamics and sugar composition in flowers of Silene and Saponaria species (Caryophyllaceae). Plant Biol. 1, 334–345 (1999).Article 
CAS 

Google Scholar 
Morales, C. L. & Traveset, A. Interspecific pollen transfer: magnitude, prevalence and consequences for plant fitness. Crit. Rev. Plant Sci. 27, 221–238 (2008).Article 
CAS 

Google Scholar 
Ashman, T.-L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100, 1061–1070 (2013).Article 
PubMed 

Google Scholar 
Orford, K. A., Vaughan, I. P. & Memmott, J. The forgotten flies: the importance of non-syrphid Diptera as pollinators. Proc. R. Soc. B 282, 20142934 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Stavert, J. R. et al. Hairiness: the missing link between pollinators and pollination. PeerJ 4, e2779 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Doyle, T. et al. Pollination by hoverflies in the Anthropocene. Proc. R. Soc. B 287, 20200508 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Albrecht, M., Schmid, B., Hautier, Y. & Müller, C. B. Diverse pollinator communities enhance plant reproductive success. Proc. R. Soc. B. 279, 4845–4852 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Fründ, J., Dormann, C. F., Holzschuh, A. & Tscharntke, T. Bee diversity effects on pollination depend on functional complementarity and niche shifts. Ecology 94, 2042–2054 (2013).Article 
PubMed 

Google Scholar 
Magrach, A., Molina, F. P. & Bartomeus, I. Niche complementarity among pollinators increases community-level plant reproductive success. Peer Commun. J. 1, e1 (2021).Article 

Google Scholar 
Giménez-Benavides, L., Dötterl, S., Jürgens, A., Escudero, A. & Iriondo, J. M. Generalist diurnal pollination provides greater fitness in a plant with nocturnal pollination syndrome: assessing the effects of a Silene–Hadena interaction. Oikos 116, 1461–1472 (2007).
Google Scholar 
Vázquez, D. P., Blüthgen, N., Cagnolo, L. & Chacoff, N. P. Uniting pattern and process in plant–animal mutualistic networks: a review. Ann. Bot. 103, 1445–1457 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Vizentin-Bugoni, J., Debastiani, V. J., Bastazini, V. A. G., Maruyama, P. K. & Sperry, J. H. Including rewiring in the estimation of the robustness of mutualistic networks. Methods Ecol. Evol. 11, 106–116 (2020).Article 

Google Scholar 
Brosi, B. J. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl Acad. Sci. USA 110, 13044–13048 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pekkarinen, A. & Teräs, I. Zoogeography of Bombus and Psithyrus in northwestern Europe (Hymenoptera, Apidae). Ann. Zool. Fennici 30, 187–208 (1993).
Google Scholar 
Arbetman, M. P., Gleiser, G., Morales, C. L., Williams, P. & Aizen, M. A. Global decline of bumblebees is phylogenetically structured and inversely related to species range size and pathogen incidence. Proc. R. Soc. B 284, 20170204 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).Article 
CAS 
PubMed 

Google Scholar 
Arceo-Gómez, G., Barker, D., Stanley, A., Watson, T. & Daniels, J. Plant–pollinator network structural properties differentially affect pollen transfer dynamics and pollination success. Oecologia 192, 1037–1045 (2020).Article 
PubMed 

Google Scholar 
de Santiago-Hernández, M. H. et al. The role of pollination effectiveness on the attributes of interaction networks: from floral visitation to plant fitness. Ecology 100, e02803 (2019).Article 
PubMed 

Google Scholar 
Koch, V., Zoller, L., Bennett, J. M. & Knight, T. M. Pollinator dependence but no pollen limitation for eight plants occurring north of the Arctic Circle. Ecol. Evol. 10, 13664–13672 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Loboda, S., Savage, J., Buddle, C. M., Schmidt, N. M. & Høye, T. T. Declining diversity and abundance of High Arctic fly assemblages over two decades of rapid climate warming. Ecography 41, 265–277 (2018).Article 

Google Scholar 
Høye, T. T., Post, E., Schmidt, N. M., Trøjelsgaard, K. & Forchhammer, M. C. Shorter flowering seasons and declining abundance of flower visitors in a warmer Arctic. Nat. Clim. Change 3, 759–763 (2013).Article 

Google Scholar 
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).Article 
CAS 
PubMed 

Google Scholar 
Zattara, E. E. & Aizen, M. A. Worldwide occurrence records suggest a global decline in bee species richness. One Earth 4, 114–123 (2021).Article 

Google Scholar 
Bartomeus, I., Stavert, J. R., Ward, D. & Aguado, O. Historical collections as a tool for assessing the global pollination crisis. Philos. Trans. R. Soc. B 374, 20170389 (2019).Article 

Google Scholar 
Rakosy, D., Ashman, T.-L., Zoller, L., Stanley, A. & Knight, T. M. Integration of historic collections can shed light on patterns of change in plant–pollinator interactions and pollination service. Func. Ecol. https://doi.org/10.1111/1365-2435.14211 (2022).Hyne, C. J. C. W. Through Arctic Lapland (A. and C. Black, 1898).Knuth, P. Handbuch der Blütenbiologie, unter Zugrundelegung von Herman Müllers Werk: ‘Die Befruchtung der Blumen durch Insekten’ (W. Engelmann, 1898).Zoller, L. & Knight, T. M. Historical records of plant-insect interactions in subarctic Finland.BMC Res. Notes 15, 317 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Zoller, L. & Knight, T. M. Historical records of plant–insect interactions in subarctic Finland. figshare https://doi.org/10.6084/m9.figshare.c.5828663.v4 (2022).Zoller, L., Bennett, J. M. & Knight, T. M. Diel-scale temporal dynamics in the abundance and composition of pollinators in the arctic summer. Sci. Rep. 10, 21187 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
Klotz, S., Kühn, I. & Durka, W. Biolflor Database (UFZ—Centre for Environmental Research Leipzig-Halle, 2002); https://www.ufz.de/biolflor/index.jspOksanen, J. et al. vegan: Community ecology package. R version 2.5.7 (2020).Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62, 361–371 (2006).Article 
PubMed 

Google Scholar 
Dormann, C. F. et al. bipartite: Visualising bipartite networks and calculating some (ecological) indices. R version 2.16 (2021).Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Stefan, V. & Knight, T. M. bootstrapnet: Bootstrap network metrics. R version 1.0.0 https://valentinitnelav.github.io/bootstrapnet/ (2021).Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15, 1353–1361 (2012).Article 
PubMed 

Google Scholar 
Poisot, T. Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. Peer Community Journal 2, e35 (2022).Article 

Google Scholar 
Dormann, C. F. How to be a specialist? Quantifying specialisation in pollination networks. Netw. Biol. 1, 1 (2011).
Google Scholar  More

Plant species and pollinatorsMedicago sativa L. (Fabaceae), also called alfalfa or lucerne, is a perennial legume with flowers arranged in a cluster or raceme. It is a self-compatible plant with fairly high outcrossing rate (5.3–30%)46, and it requires insect visits for seed production47. No plant material was collected for this study. Honey bees, Apis mellifera, and alfalfa leafcutting bees, Megachile rotundata, are used as managed pollinators in alfalfa seed-production fields in the USA while bumble bees are commonly used in alfalfa breeding47.Experimental design and pollinator observationsFive 11 m × 11 m patches of M. sativa plants were set up in an east–west linear arrangement at the West Madison Agricultural Research Station in Madison, Wisconsin, USA. Within each patch, we transplanted 169 young plants grown from seeds in the greenhouse, each placed 90 cm apart. These plants grew and, at flowering, a plant had an average of 30.65 ± 16.4 stems per plant, with 4.93 ± 3.41 racemes per stem, and 7.53 ± 2.44 open flowers per raceme.A honey bee hive was placed approximately 100 m from the patches and a bumble bee hive was set up at the center of the southern edge of the patches. For leafcutting bees, a 60 × 30 × 7.6 cm bee board was set up in each of two boxes placed 1/3 and 2/3 along the southern edge of the patches and a half gallon of bees was released at periodic intervals throughout the alfalfa flowering season.Over two consecutive summers, observers followed bees foraging in the alfalfa patches, marked each raceme visited in succession within a foraging bout with a numbered clip, and recorded the number of flowers visited per raceme. After a bee had left a patch, observers went back to the marked racemes and measured the distance and direction traveled between consecutive racemes. Directions were recorded as one of the cardinal directions: North (N), South (S), East (E) or West (W), or inter-cardinal directions: Northeast (NE), Southeast (SE), Northwest (NW) and Southwest (SW). The frequency distributions of distances and directions traveled between two successive racemes are presented for each bee species each year in Figs. 1 (distances) and 2 (directions). The low pollinator abundance permitted observers to follow individual bees foraging in a patch. Little interference among bee species was observed in the patches.Figure 1Frequency distributions for distances traveled between consecutive racemes (cm) for each bee species each year.Full size imageFigure 2Frequency distributions of directions traveled between consecutive racemes for each bee species each year.Full size imageModel for the distance traveled between consecutive racemesWe first determined whether a statistical model best described the distance traveled between consecutive racemes (Modeled Distance), and examined whether the model differed among bee species. We used mixed effect linear models (proc Mixed in SAS 9.3)48 to identify the model that best described the distance traveled by pollinators between consecutive racemes. The model included loge distance as a linear function of loge flower number and bee species as fixed effects. The distance traveled between consecutive racemes and the number of flowers visited per raceme were log transformed prior to analyses in order to improve the models’ residuals. In addition, we included patch and foraging bout as random effects in the model. A foraging bout includes the racemes visited in succession from the time a bee is spotted in a patch to the time it leaves that patch. We used foraging bout instead of individual bee as the random effect because bees were not individually marked in this study. Moreover, to take into consideration the potential correlation between successive observations within a foraging bout, we added clip to the model. Clip 1 represents the first and second racemes visited in the foraging bout; clip 2, the second and third, and so on. Clip was added to the model either as a random effect or as a repeated measure with an AR(1) structure. The combination of random clip and random foraging bout creates a model that is sometimes called the “compound symmetry” model. The AR(1) structure represents correlations that decline exponentially as the gap between measurements increases such that measurements closer together in time are more strongly correlated than measurements further apart. Because we expected bees to visit flowers at close proximity when resources are abundant, we chose this correlation structure as a good potential descriptor of the way distances might be correlated within foraging bouts. We started with a full model which included loge flower number, bee species, patch, foraging bout, and clip either as a random effect or as a repeated measure with an AR(1) structure. We then removed variables and compared models by inspecting AIC values and the p values for each term in the model. We considered both low AIC and statistically significant (p  More

Agostini, S. et al. Ocean acidification drives community shifts towards simplified non-calcified habitats in a subtropical–temperate transition zone. Sci. Rep. 8, 11354 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).Article 
CAS 
PubMed 

Google Scholar 
Gilbert, B. & Levine, J. M. Ecological drift and the distribution of species diversity. Proc. Biol. Sci. 284, 20170507 (2017).PubMed 
PubMed Central 

Google Scholar 
White, E. P. et al. A comparison of the species–time relationship across ecosystems and taxonomic groups. Oikos 112, 185–195 (2006).Article 

Google Scholar 
Sax, D. F. & Gaines, S. D. Species invasions and extinction: the future of native biodiversity on islands. Proc. Natl Acad. Sci. USA 105, 11490–11497 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson, J. N. Rapid evolution as an ecological process. Trends Ecol. Evol. 13, 329–332 (1998).Article 
CAS 
PubMed 

Google Scholar 
Post, D. M. & Palkovacs, E. P. Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. Philos. Trans. R. Soc. Lond. B 364, 1629–1640 (2009).Article 

Google Scholar 
Reznick, D. N. & Travis, J. Experimental studies of evolution and eco-evo dynamics in guppies (Poecilia reticulata). Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110218-024926 (2019).Hendry, A. P. Eco-evolutionary Dynamics (Princeton Univ. Press, 2020).Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).Article 
CAS 
PubMed 

Google Scholar 
Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306 (2003).Article 
CAS 
PubMed 

Google Scholar 
Hansen, S. K., Rainey, P. B., Haagensen, J. A. J. & Molin, S. Evolution of species interactions in a biofilm community. Nature 445, 533–536 (2007).Article 
CAS 
PubMed 

Google Scholar 
Hillesland, K. L. & Stahl, D. A. Rapid evolution of stability and productivity at the origin of a microbial mutualism. Proc. Natl Acad. Sci. USA 107, 2124–2129 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Turcotte, M. M., Reznick, D. N. & Hare, J. D. The impact of rapid evolution on population dynamics in the wild: experimental test of eco-evolutionary dynamics. Ecol. Lett. 14, 1084–1092 (2011).Article 
PubMed 

Google Scholar 
Lawrence, D. et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 10, e1001330 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Celiker, H. & Gore, J. Clustering in community structure across replicate ecosystems following a long-term bacterial evolution experiment. Nat. Commun. 5, 4643 (2014).Article 
CAS 
PubMed 

Google Scholar 
Andrade-Domínguez, A. et al. Eco-evolutionary feedbacks drive species interactions. ISME J. 8, 1041–1054 (2014).Article 
PubMed 

Google Scholar 
Reznick, D. Hard and soft selection revisited: how evolution by natural selection works in the real world. J. Hered. 107, 3–14 (2016).Article 
PubMed 

Google Scholar 
Matthews, B., Aebischer, T., Sullam, K. E., Lundsgaard-Hansen, B. & Seehausen, O. Experimental evidence of an eco-evolutionary feedback during adaptive divergence. Curr. Biol. 26, 483–489 (2016).Article 
CAS 
PubMed 

Google Scholar 
Harcombe, W. R., Chacón, J. M., Adamowicz, E. M., Chubiz, L. M. & Marx, C. J. Evolution of bidirectional costly mutualism from byproduct consumption. Proc. Natl Acad. Sci. USA 115, 12000–12004 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Preussger, D., Giri, S., Muhsal, L. K., Oña, L. & Kost, C. Reciprocal fitness feedbacks promote the evolution of mutualistic cooperation. Curr. Biol. https://doi.org/10.1016/j.cub.2020.06.100 (2020).Adamowicz, E. M., Muza, M., Chacón, J. M. & Harcombe, W. R. Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica. PLoS Pathog. 16, e1008700 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez-Verdugo, A. & Ackermann, M. Rapid evolution destabilizes species interactions in a fluctuating environment. ISME J. 15, 450–460 (2021).Article 
PubMed 

Google Scholar 
Barber, J. N. et al. The evolution of coexistence from competition in experimental co-cultures of Escherichia coli and Saccharomyces cerevisiae. ISME J. 15, 746–761 (2021).Article 
CAS 
PubMed 

Google Scholar 
Hart, S. F. M., Chen, C.-C. & Shou, W. Pleiotropic mutations can rapidly evolve to directly benefit self and cooperative partner despite unfavorable conditions. eLife 10, e57838 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Kokko, H. et al. Can evolution supply what ecology demands? Trends Ecol. Evol. 32, 187–197 (2017).Article 
PubMed 

Google Scholar 
Nuismer, S. Introduction to Coevolutionary Theory (Macmillan Learning, 2017).Stoltzfus, A. & McCandlish, D. M. Mutational biases influence parallel adaptation. Mol. Biol. Evol. 34, 2163–2172 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Payne, J. L. et al. Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol. 17, e3000265 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Storz, J. F. et al. The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function. Philos. Trans. R. Soc. Lond. B 374, 20180238 (2019).Article 
CAS 

Google Scholar 
Gomez, K., Bertram, J. & Masel, J. Mutation bias can shape adaptation in large asexual populations experiencing clonal interference. Proc. Biol. Sci. 287, 20201503 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Venkataram, S., Monasky, R., Sikaroodi, S. H., Kryazhimskiy, S. & Kacar, B. Evolutionary stalling and a limit on the power of natural selection to improve a cellular module. Proc. Natl Acad. Sci. USA 117, 18582–18590 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hom, E. F. Y. & Murray, A. W. Niche engineering demonstrates a latent capacity for fungal–algal mutualism. Science 345, 94–98 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wolfe, B. E. & Dutton, R. J. Fermented foods as experimentally tractable microbial ecosystems. Cell 161, 49–55 (2015).Article 
CAS 
PubMed 

Google Scholar 
Chacón, J. M., Hammarlund, S. P., Martinson, J. N. V., Smith, L. B. & Harcombe, W. R. The ecology and evolution of model microbial mutualisms. Annu. Rev. Ecol. Evol. Syst. 52, 363–384 (2021).Article 

Google Scholar 
Blasche, S., Kim, Y., Oliveira, A. P. & Patil, K. R. Model microbial communities for ecosystems biology. Curr. Opin. Syst. Biol. 6, 51–57 (2017).Article 

Google Scholar 
Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Venkataram, S. et al. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast. Cell 166, 1585–1596.e22 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones, E. I., Bronstein, J. L. & Ferrière, R. The fundamental role of competition in the ecology and evolution of mutualisms. Ann. N. Y. Acad. Sci. 1256, 66–88 (2012).Article 
PubMed 

Google Scholar 
Boyer, S., Hérissant, L. & Sherlock, G. Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment. PLoS Genet. 17, e1009314 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blundell, J. R. et al. The dynamics of adaptive genetic diversity during the early stages of clonal evolution. Nat. Ecol. Evol. 3, 293–301 (2019).Article 
PubMed 

Google Scholar 
Good, B. H., Rouzine, I. M., Balick, D. J., Hallatschek, O. & Desai, M. M. Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc. Natl Acad. Sci. USA 109, 4950–4955 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Good, B. H., Martis, S. & Hallatschek, O. Adaptation limits ecological diversification and promotes ecological tinkering during the competition for substitutable resources. Proc. Natl Acad. Sci. USA 115, E10407–E10416 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhu, Y. O., Siegal, M. L., Hall, D. W. & Petrov, D. A. Precise estimates of mutation rate and spectrum in yeast. Proc. Natl Acad. Sci. USA 111, E2310–E2318 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dunham, M. J. et al. Characteristic genome rearrangements in experimental evolution of Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 99, 16144–16149 (2002).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yona, A. H. et al. Chromosomal duplication is a transient evolutionary solution to stress. Proc. Natl Acad. Sci. USA 109, 21010–21015 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sunshine, A. B. et al. The fitness consequences of aneuploidy are driven by condition-dependent gene effects. PLoS Biol. 13, e1002155 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gerrish, P. J. & Lenski, R. E. The fate of competing beneficial mutations in an asexual population. Genetica 102-103, 127–144 (1998).Article 
CAS 
PubMed 

Google Scholar 
Desai, M. M. & Fisher, D. S. Beneficial mutation–selection balance and the effect of linkage on positive selection. Genetics 176, 1759–1798 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Schiffels, S., Szöllosi, G. J., Mustonen, V. & Lässig, M. Emergent neutrality in adaptive asexual evolution. Genetics 189, 1361–1375 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Nguyen, Ba,A. N. et al. High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast. Nature 575, 494–499 (2019).Article 

Google Scholar 
Foster, K. R., Shaulsky, G., Strassmann, J. E., Queller, D. C. & Thompson, C. R. L. Pleiotropy as a mechanism to stabilize cooperation. Nature 431, 693–696 (2004).Article 
CAS 
PubMed 

Google Scholar 
Sachs, J. L., Mueller, U. G., Wilcox, T. P. & Bull, J. J. The evolution of cooperation. Q. Rev. Biol. 79, 135–160 (2004).Article 
PubMed 

Google Scholar 
Harcombe, W. Novel cooperation experimentally evolved between species. Evolution 64, 2166–2172 (2010).PubMed 

Google Scholar 
Vasi, F., Travisano, M. & Lenski, R. E. Long-term experimental evolution in Escherichia coli. II. Changes in life-history traits during adaptation to a seasonal environment. Am. Nat. 144, 432–456 (1994).Article 

Google Scholar 
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton Univ. Press, 2001).Reznick, D., Bryant, M. J. & Bashey, F. r- and K-selection revisited: the role of population regulation in life-history evolution. Ecology 83, 1509–1520 (2002).Article 

Google Scholar 
Mueller, L. D. & Ayala, F. J. Trade-off between r-selection and K-selection in Drosophila populations. Proc. Natl Acad. Sci. USA 78, 1303–1305 (1981).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Novak, M., Pfeiffer, T., Lenski, R. E., Sauer, U. & Bonhoeffer, S. Experimental tests for an evolutionary trade-off between growth rate and yield in E. coli. Am. Nat. 168, 242–251 (2006).Article 
PubMed 

Google Scholar 
Bachmann, H. et al. Availability of public goods shapes the evolution of competing metabolic strategies. Proc. Natl Acad. Sci. USA 110, 14302–14307 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lipson, D. A. The complex relationship between microbial growth rate and yield and its implications for ecosystem processes. Front. Microbiol. 6, 615 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Orivel, J. et al. Trade-offs in an ant–plant–fungus mutualism. Proc. Biol. Sci. 284, 20161679 (2017).PubMed 
PubMed Central 

Google Scholar 
Fritts, R. K. et al. Enhanced nutrient uptake is sufficient to drive emergent cross-feeding between bacteria in a synthetic community. ISME J. 14, 2816–2828 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wortel, M. T., Noor, E., Ferris, M., Bruggeman, F. J. & Liebermeister, W. Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield. PLoS Comput. Biol. 14, e1006010 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Cheng, C. et al. Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism. PLoS Comput. Biol. 15, e1007066 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Luckinbill, L. S. r and K selection in experimental populations of Escherichia coli. Science 202, 1201–1203 (1978).Article 
CAS 
PubMed 

Google Scholar 
Oxman, E., Alon, U. & Dekel, E. Defined order of evolutionary adaptations: experimental evidence. Evolution 62, 1547–1554 (2008).Article 
PubMed 

Google Scholar 
Jasmin, J.-N., Dillon, M. M. & Zeyl, C. The yield of experimental yeast populations declines during selection. Proc. Biol. Sci. 279, 4382–4388 (2012).PubMed 
PubMed Central 

Google Scholar 
Laan, L., Koschwanez, J. H. & Murray, A. W. Evolutionary adaptation after crippling cell polarization follows reproducible trajectories. eLife 4, e09638 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Blount, Z. D., Lenski, R. E. & Losos, J. B. Contingency and determinism in evolution: replaying life’s tape. Science 362, eaam5979 (2018).Article 
PubMed 

Google Scholar 
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).Article 

Google Scholar 
Rainey, P. B. & Travisano, M. Adaptive radiation in a heterogeneous environment. Nature 394, 69–72 (1998).Article 
CAS 
PubMed 

Google Scholar 
Meyer, J. R. et al. Repeatability and contingency in the evolution of a key innovation in phage lambda. Science 335, 428–432 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herron, M. D. & Doebeli, M. Parallel evolutionary dynamics of adaptive diversification in Escherichia coli. PLoS Biol. 11, e1001490 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hillesland, K. L. et al. Erosion of functional independence early in the evolution of a microbial mutualism. Proc. Natl Acad. Sci. USA 111, 14822–14827 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Meroz, N., Tovi, N., Sorokin, Y. & Friedman, J. Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales. Nat. Commun. 12, 2891 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
MacLean, R. C. The tragedy of the commons in microbial populations: insights from theoretical, comparative and experimental studies. Heredity 100, 471–477 (2008).Article 
CAS 
PubMed 

Google Scholar 
Dunn, B. et al. Recurrent rearrangement during adaptive evolution in an interspecific yeast hybrid suggests a model for rapid introgression. PLoS Genet. 9, e1003366 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Barillot, E., Lacroix, B. & Cohen, D. Theoretical analysis of library screening using a N-dimensional pooling strategy. Nucleic Acids Res. 19, 6241–6247 (1991).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baym, M., Shaket, L., Anzai, I. A., Adesina, O. & Barstow, B. Rapid construction of a whole-genome transposon insertion collection for Shewanella oneidensis by Knockout Sudoku. Nat. Commun. 7, 13270 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 10, e0128036 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Venkataram, S., Kuo, H., Hom, E., Kryazhimskiy, S. Early adaptation in a microbial community is dominated by mutualism-enhancing mutations. Dryad https://doi.org/10.6076/D14K5X (2022). More