Ecology

Steen AD, Carini ACP, Lloyd KG, Thrash JC, Deangelis KM, Fierer N. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 2019;13:3126–30.PubMed 
PubMed Central 

Google Scholar 
Lloyd KG, Steen AD, Ladau J, Yin J. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems. 2018;3:e00055–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Marcy Y, Ouverney C, Bik EM, Lo T, Ivanova N, Garcia H, et al. Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc Natl Acad Sci USA. 2007;104:11889–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pascoal F, Costa R, Magalhães C. The microbial rare biosphere: current concepts, methods and ecological principles. FEMS Microbiol Ecol. 2021;97:fiaa227.CAS 
PubMed 

Google Scholar 
Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA. 2006;103:12115–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gareev KG, Grouzdev DS, Kharitonskii PV, Kosterov A, Koziaeva VV, Sergienko ES, et al. Magnetotactic bacteria and magnetosomes: basic properties and applications. Magnetochemistry. 2021;7:86.CAS 

Google Scholar 
Lefevre CT, Bazylinski DA. Ecology, diversity, and evolution of magnetotactic bacteria. Microbiol Mol Biol Rev. 2013;77:497–526.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin W, Pan Y, Bazylinsky DA. Diversity and ecology of and biomineralization by magnetotactic bacteria. Environ Microbiol Rep. 2017;9:345–56.CAS 
PubMed 

Google Scholar 
Uebe R, Schüler D. Magnetosome biogenesis in magnetotactic bacteria. Nat Rev Microbiol. 2016;14:621–37.CAS 
PubMed 

Google Scholar 
Lefèvre CT, Frankel RB, Bazylinski DA. Magnetotaxis in prokaryotes. eLS. 2011. https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2.Goswami P, He K, Li J, Pan Y, Roberts AP, Lin W. Magnetotactic bacteria and magnetofossils: ecology, evolution and environmental implications. npj Biofilms Microbiomes. 2022;8:43.PubMed 
PubMed Central 

Google Scholar 
Flies CB, Jonkers HM, De Beer D, Bosselmann K, Böttcher ME, Schüler D. Diversity and vertical distribution of magnetotactic bacteria along chemical gradients in freshwater microcosms. FEMS Microbiol Ecol. 2005;52:185–95.CAS 
PubMed 

Google Scholar 
Wolfe RS, Thauer RK, Pfennig N. A’capillary racetrack’ method for isolation of magnetotactic bacteria. FEMS Microbiol Ecol. 1987;45:31–5.
Google Scholar 
Jogler C, Lin W, Meyerdierks A, Kube M, Katzmann E, Flies C, et al. Toward cloning of the magnetotactic metagenome: identification of magnetosome island gene clusters in uncultivated magnetotactic bacteria from different aquatic sediments. Appl Environ Microbiol. 2009;75:3972–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin W, Zhang W, Paterson GA, Zhu Q, Zhao X. Expanding magnetic organelle biogenesis in the domain Bacteria. Microbiome. 2020;8:152.CAS 
PubMed 
PubMed Central 

Google Scholar 
Geissinger O, Herlemann DPR, Mo E, Maier UG, Brune A. The ultramicrobacterium “Elusimicrobium minutum” gen. nov., sp. nov., the first cultivated representative of the Termite Group 1 phylum. Appl Environ Microbiol. 2009;75:2831–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wakako I-O, Brune A. Cospeciation of termite gut flagellates and their bacterial endosymbionts: Trichonympha species and ‘Candidatus Endomicrobium trichonymphae’. Mol Ecol. 2009;18:332–42.
Google Scholar 
Zheng H, Dietrich C, Radek R, Brune A. Endomicrobium proavitum, the first isolate of Endomicrobia class. nov. (phylum Elusimicrobia) – an ultramicrobacterium with an unusual cell cycle that fixes nitrogen with a Group IV nitrogenase. Environ Ecol Stat. 2016;18:191–204.CAS 

Google Scholar 
Méheust R, Castelle CJ, Carnevali PBM, Chen L, Amano Y, Hug LA, et al. Groundwater Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have a novel nitrogenase paralog. ISME J. 2020;14:2907–22.PubMed 
PubMed Central 

Google Scholar 
Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
Zhang L, Gong X, Wang L, Guo K, Cao S, Zhou Y. Science of the total environment metagenomic insights into the effect of thermal hydrolysis pre-treatment on microbial community of an anaerobic digestion system. Sci Total Environ. 2021;791:148096.CAS 
PubMed 

Google Scholar 
Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
PubMed 

Google Scholar 
Uzun M, Alekseeva L, Krutkina M, Koziaeva V, Grouzdev D. Unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases. Sci Data. 2020;7:252.PubMed 
PubMed Central 

Google Scholar 
Tully BJ, Wheat CG, Glazer BT, Huber JA. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME J. 2018;12:1–16.CAS 
PubMed 

Google Scholar 
Kirillova NP, Sileva TM, Ul’yanova TY, Rozov SY, Il’yashenko MA, Makarov MI. Digital soil map of Chashnikovo training and experimental soil ecological center, Moscow State University. Mosc Univ Soil Sci Bull. 2015;70:58–65.
Google Scholar 
Koziaeva VV, Alekseeva LM, Uzun MM, Leão P, Sukhacheva MV, Patutina EO, et al. Biodiversity of magnetotactic bacteria in the freshwater lake Beloe Bordukovskoe, Russia. Microbiology. 2020;89:348–58.CAS 

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

Google Scholar 
Edgar RC, Flyvbjerg H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics. 2015;31:3476–82.CAS 
PubMed 

Google Scholar 
Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv 2016. https://doi.org/10.1101/081257.Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Fierer N, Jackson JA, Vilgalys R, Jackson RB. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl Environ Microbiol. 2005;71:4117–20.CAS 
PubMed 
PubMed Central 

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

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

Google Scholar 
Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
PubMed 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
PubMed Central 

Google Scholar 
Lin HH, Liao YC. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep. 2016;6:12–9.
Google Scholar 
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.CAS 
PubMed 
PubMed Central 

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

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

Google Scholar 
Tatusova T, Dicuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ji R, Zhang W, Pan Y, Lin W. MagCluster: a tool for identification, annotation, and visualization of magnetosome gene clusters. Microbiol Resour Announc. 2022;11:e01031–21.CAS 
PubMed Central 

Google Scholar 
Wu S, Zhu Z, Fu L, Niu B, Li W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics. 2011;12:444.PubMed 
PubMed Central 

Google Scholar 
Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.CAS 
PubMed 

Google Scholar 
Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL. 0003 3527 8101, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 

Google Scholar 
Nguyen LT, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 
PubMed 

Google Scholar 
Kalyaanamoorthy S, Minh BQ, Wong TKF, Haeseler AVon, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoang DT, Chernomor O, Von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
Letunic I, Bork P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Coleman GA, Davín AA, Mahendrarajah TA, Szánthó LL, Spang A, Hugenholtz P, et al. A rooted phylogeny resolves early bacterial evolution. Science. 2021;372:eabe0511.CAS 
PubMed 

Google Scholar 
Parks DH. https://github.com/dparks1134/CompareM.Dombrowski N, Lee JH, Williams TA, Offre P, Spang A. Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett. 2019;366:fnz008.CAS 
PubMed Central 

Google Scholar 
Lin W, Zhang W, Zhao X, Roberts AP, Paterson GA, Bazylinski DA, et al. Genomic expansion of magnetotactic bacteria reveals an early common origin of magnetotaxis with lineage-specific evolution. ISME J. 2018;12:1508–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Urakawa H, Garcia JC, Nielsen JL, Le VQ, Kozlowski JA, Stein LY, et al. Nitrosospira lacus sp. nov., a psychrotolerant, ammonia-oxidizing bacterium from sandy lake sediment. Int J Syst Evol Microbiol. 2015;65:242–50.CAS 
PubMed 

Google Scholar 
Kalyuzhnaya MG, De Marco P, Bowerman S, Pacheco CC, Lara JC, Lidstrom ME, et al. Methyloversatilis universalis gen. nov., sp. nov., a novel taxon within the Betaproteobacteria represented by three methylotrophic isolates. Int J Syst Evol Microbiol. 2006;56:2517–22.CAS 
PubMed 

Google Scholar 
Bazylinski DA, Frankel RB, Konhauser KO. Modes of biomineralization of magnetite by microbes. Geomicrobiol J. 2007;24:465–75.CAS 

Google Scholar 
Uzun M, Koziaeva V, Dziuba M, Leão P, Krutkina M, Grouzdev D. Detection of interphylum transfers of the magnetosome gene cluster in magnetotactic bacteria. Front Microbiol. 2022;13:945734.PubMed 
PubMed Central 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996.CAS 
PubMed 

Google Scholar 
Murphy CL, Biggerstaff J, Eichhorn A, Ewing E, Shahan R, Soriano D, et al. Genomic characterization of three novel Desulfobacterota classes expand the metabolic and phylogenetic diversity of the phylum. Environ Microbiol. 2021;23:4326–43.CAS 
PubMed 

Google Scholar 
Konstantinidis KT, Rosselló-Móra R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 

Google Scholar 
Denise R, Abby SS, Rocha EPC. Diversification of the type IV filament superfamily into machines for adhesion, protein secretion, DNA uptake, and motility. PLoS Biol. 2019;17:e3000390.PubMed 
PubMed Central 

Google Scholar 
Hennell James R, Deme JC, Kjӕr A, Alcock F, Silale A, Lauber F, et al. Structure and mechanism of the proton-driven motor that powers type 9 secretion and gliding motility. Nat Microbiol. 2021;6:221–33.CAS 
PubMed 

Google Scholar 
Nolan LM, Whitchurch CB, Barquist L, Katrib M, Boinett CJ, Mayho M, et al. A global genomic approach uncovers novel components for twitching motility-mediated biofilm expansion in Pseudomonas aeruginosa. Micro Genomics. 2018;4:e000229.
Google Scholar 
Uzun M, Koziaeva V, Dziuba M, Alekseeva L, Grouzdev D. Mam protein trees. 2022. https://doi.org/10.6084/m9.figshare.c.6045158.v1.Arnoux P, Siponen MI, Lefèvre CT, Ginet N, Pignol D. Structure and evolution of the magnetochrome domains: no longer alone. Front Microbiol. 2014;5:117.PubMed 
PubMed Central 

Google Scholar 
Katzmann E, Scheffel A, Gruska M, Plitzko JM, Schüler D. Loss of the actin-like protein MamK has pleiotropic effects on magnetosome formation and chain assembly in Magnetospirillum gryphiswaldense. Mol Microbiol. 2010;77:208–24.CAS 
PubMed 

Google Scholar 
Wagner-Döbler I, Bennasar A, Vancanneyt M, Strömpl C, Brümmer I, Eichner C, et al. Microcosm enrichment of biphenyl-degrading microbial communities from soils and sediments. Appl Environ Microbiol. 1998;64:3014–22.PubMed 
PubMed Central 

Google Scholar 
Ibekwe AM, Papiernik SK, Gan J, Yates SR, Crowley DE, Yang CH. Microcosm enrichment of 1,3-dichloropropene-degrading soil microbial communities in a compost-amended soil. J Appl Microbiol. 2001;91:668–76.CAS 
PubMed 

Google Scholar 
Yakimov MM, Denaro R, Genovese M, Cappello S, D’Auria G, Chernikova TN, et al. Natural microbial diversity in superficial sediments of Milazzo Harbor (Sicily) and community successions during microcosm enrichment with various hydrocarbons. Environ Microbiol. 2005;7:1426–41.CAS 
PubMed 

Google Scholar 
Tringe SG, Von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, et al. Comparative metagenomics of microbial communities. Science. 2005;308:554–7.CAS 
PubMed 

Google Scholar 
Lefèvre CT, Trubitsyn D, Abreu F, Kolinko S, Jogler C, de Almeida LGP, et al. Comparative genomic analysis of magnetotactic bacteria from the Deltaproteobacteria provides new insights into magnetite and greigite magnetosome genes required for magnetotaxis. Environ Microbiol. 2013;15:2712–35.PubMed 

Google Scholar 
Wadhwa N, Berg HC. Bacterial motility: machinery and mechanisms. Nat Rev Microbiol. 2022;20:161–73.CAS 
PubMed 

Google Scholar 
Zhu K, Pan H, Li J, Yu-Zhang K, Zhang SD, Zhang WY, et al. Isolation and characterization of a marine magnetotactic spirillum axenic culture QH-2 from an intertidal zone of the China Sea. Res Microbiol. 2010;161:276–83.CAS 
PubMed 

Google Scholar 
Kaimer C, Zusman DR. Regulation of cell reversal frequency in Myxococcus xanthus requires the balanced activity of CheY-like domains in FrzE and FrzZ. Mol Microbiol. 2016;100:379–95.CAS 
PubMed 

Google Scholar 
Kühn MJ, Talà L, Inclan YF, Patino R, Pierrat X, Vos I, et al. Mechanotaxis directs Pseudomonas aeruginosa twitching motility. Proc Natl Acad Sci USA. 2021;118:e2101759118.PubMed 
PubMed Central 

Google Scholar  More

De Grave, S., & Fransen, C. H. J. M. Carideorum Catalogus: The Recent Species of the Dendrobranchiate, Stenopodidean, Procarididean and Caridean Shrimps (Crustacea: Decapoda). Zool. Meded. 85, (2011).Garassino, A. The macruran decapod crustaceans of the Lower Cretaceous (Lower Barremian) of Las Hoyas (Cuenca, Spain). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 137, 101–126 (1997).Bravi, S., Coppa, M. G., Garassino, A., & Patricelli, R. Palaemon vesolensis n. sp. (Crustacea, Decapoda) from the Plattenkalk of Vesole Mount (Salerno, Southern Italy). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 140, 141–169 (1999).Colleary, C. et al. Chemical, experimental, and morphological evidence for diagenetically altered melanin in exceptionally preserved fossils. Proc. Natl. Acad. Sci. U.S.A. 11241, 12592–12597 (2015).Article 
ADS 

Google Scholar 
Vinther, J., Briggs, D. E., Clarke, J., Mayr, G. & Prum, R. O. Structural coloration in a fossil feather. Biol. Lett. 6, 128–131 (2010).Article 
PubMed 

Google Scholar 
McNamara, M. E. et al. Fossilised biophotonic nanostructures reveal the original colors of 47 million-year-old moths. PLoS Biol. 9, e1001200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rietschel, S. Taphonomic biasing in the Messel Fauna and Flora. Cour. Forsch. Inst. Senckenberg 107, 169–182 (1988).
Google Scholar 
Wolf, H. W. Schätze im Schiefer (Westermann, 1991).Rabenstein, R. Messel 2000 – Das Weltnaturerbe Deutschlands (eds Forschungsinstitut Senckenberg) (2000).Gruber, G., & Micklich, N. Messel – Treasures of the Eocene (Hessisches Landesmuseum Darmstadt, 2007).Wedmann, S. Annotated taxon-list of the invertebrate animals from the Eocene fossil site Grube Messel near Darmstadt Germany. Cour. Forsch. Inst. Senckenberg 255, 103–110 (2005).
Google Scholar 
Schaal, S. F. K. & Rabenstein, R. D. Tagebau Messel in Linien und Zahlen. Senckenberg Nat. Forsch. Mus. 142, 376–377 (2012).
Google Scholar 
Moshayedi, M., Lenz, O. K., Wilde, V. & Hinderer, M. The recolonisation of volcanically disturbed Eocene habitats of Central Europe: the maar lakes of Messel and Offenthal (SW Germany) compared. Paleobiodivers. Paleoenviron. 100, 951–973 (2020).Article 

Google Scholar 
Schulz, R., Harms, F.-J. & Felder, M. Die Forschungsbohrung Messel 2001: Ein Beitrag zur Entschlüsselung der Genese einer Ölschieferlagerstätte. Z. angew. Geol. 2002, 9–17 (2002).
Google Scholar 
Felder, M. & Harms, F. J. Lithologie und genetische Interpretation der vulkano-sedimentären Ablagerungen aus der Grube Messel anhand der Forschungsbohrung Messel 2001 und weiterer Bohrungen (Eozän, Messel-Formation, Sprendlinger Horst, Südhessen). Cour. Forsch. Inst. Senckenberg 252, 151–203 (2004).
Google Scholar 
Büchel, G. N., & Schaal, S. F. K. The formation of the Messel maar in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Der, G. K. Messeler Ölschiefer – ein Algenlaminit. Cour. Forsch. Inst. Senckenberg 131, 1–143 (1990).
Google Scholar 
Lenz, O. K., Wilde, V. & Riegel, W. Recolonization of a Middle Eocene volcanic site: quantitative palynology of the initial phase of the maar lake of Messel (Germany). Rev. Palaeobot. Palynol. 145, 217–242 (2007).Article 

Google Scholar 
Bauersachs, T., Schouten, S. & Schwark, L. Characterization of the sedimentary organic matter preserved in Messel oil shale by bulk geochemistry and stable isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 410, 390–400 (2014).Article 

Google Scholar 
Mertz, D. F. & Renne, P. R. A numerical age for the Messel fossil deposit (UNESCO world natural heritage site) from 40Ar/39Ar dating. Cour. Forsch. Inst. Senckenberg 255, 67–75 (2005).
Google Scholar 
Lenz, O. K., Wilde, V., Mertz, D. F. & Riegel, W. New palynology-based astronomical and revised 40Ar/39Ar ages for the Eocene maar lake of Messel (Germany). Int. J. Earth Sci. 104, 873–889 (2015).Article 
CAS 

Google Scholar 
Lenz, O. K. & Wilde, V. Changes in Eocene plant diversity and composition of vegetation: The lacustrine archive of Messel (Germany). Paleobiology 44, 709–735 (2018).Article 

Google Scholar 
Lenz, O. K., Wilde, V, Riegel, W., & Harms, F-J. A 600 k.y. record of El Niño–Southern Oscillation (ENSO): evidence for persisting teleconnections during the Middle Eocene greenhouse climate of Central Europe. Geology 38, 627–630 (2010).Lenz, O. K., Wilde, V, & Riegel, W. Paleoclimate – Learning from the past for the future in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 16–23 (Schweizerbart, 2018).Grein, M., Utescher, T., Wilde, V. & Roth-Nebelsick, A. Reconstruction of the middle Eocene climate of Messel using palaeobotanical data. Neues Jb. Geol. Paläontol. Abh. 260, 305–318 (2011).Article 

Google Scholar 
Tütken, T. Isotope compositions (C, O, Sr, Nd) of vertebrate fossils from the Middle Eocene oil shale of Messel, Germany: Implications for their taphonomy and palaeoenvironment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 92–109 (2014).Article 

Google Scholar 
Wilde, V. The fossil flora of Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 42–61 (Schweizerbart, 2018).Smith, K. T., Schaal, S. F. K. & Habersetzer, J. (eds.) Messel: An Ancient Greenhouse Ecosystem. (Schweizerbart, 2018).Wedmann, S., Hörnschemeyer, T., Engel, M. S., Zetter, R. & Grímsson, F. The last meal of an Eocene pollen-feeding fly. Curr. Biol. 31, 2020–2026 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wedmann, S. Jewels in the oil shale – insects and other invertebrates in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Franzen J. L. Odd-toed ungulates – Early horses and tapiromorphs in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 292–301 (Schweizerbart, 2018).Franzen, J. L., Aurich, C. & Habersetzer, J. Description of a well preserved fetus of the European Eocene Equoid Eurohippus messelensis. PLoS ONE 10, e0137985 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Franzen J. L., & Gingerich, P. D. Primates – Rareties in Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 240–247 (Schweizerbart, 2018).Franzen, J. L. et al. Complete primate skeleton from the middle Eocene of Messel in Germany: Morphology and paleobiology. PLoS ONE 4(5), e5723 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Houša, V. Bechleja inopinata n. g., n. sp., nový ráček z českých třetihor (Decapoda, Palaemonidae). Ústřed. Ústavu Geol. Sborník 23, 365–377 (1957).Glaessner, M. F. Decapoda. In Part R Arthropoda 4(2) Treatise on Invertebrate Paleontology (ed Moore, R. C.) (The University of Kansas Press and The Geological Society of America, 1969).De Grave, S., Cai, Y. & Anker, A. Global diversity of shrimps (Crustacea: Decapoda: Caridea) in freshwater. Hydrobiologia 595, 287–293 (2008).Article 

Google Scholar 
Garassino, A. & Bravi, S. Palaemon antonellae new species (Crustacea, Decapoda, Caridea) from the Lower Cretaceous “Platydolomite” of profeti (Caserta, Italy). J. Paleontol. 77, 589–592 (2003).Article 

Google Scholar 
Schweitzer, C., Karasawa, H., Schweigert, G., Feldmann, R. & Garassino, A. Systematic list of fossil decapod crustacean species. Crustac. Monogr. 10, 1–222 (2010).
Google Scholar 
Plotnick, R. E. Taphonomy of a modern shrimp: implications for the arthropod fossil record. Palaios 1, 286–293 (1986).Article 
ADS 

Google Scholar 
Klompmaker, A. A., Portell, R. W. & Frick, M. G. Comparative experimental taphonomy of eight marine arthropods indicates distinct differences in preservation potential. Palaeontology 60, 773–794 (2017).Article 

Google Scholar 
Vannier, J., Schoenemann, B., Gillot, T., Charbonnier, S. & Clarkson, E. Exceptional preservation of eye structure in arthropod visual predators from the Middle Jurassic. Nat. Commun. 7, 1–9 (2016).Article 

Google Scholar 
Jauvion, C., Audo, D., Charbonnier, S. & Vannier, J. Virtual dissection and lifestyle of a 165-million-year-old female polychelidan lobster. Arthropod Struct. Dev. 45, 122–132 (2016).Article 
PubMed 

Google Scholar 
Pazinato, P. G., Jauvion, C., Schweigert, G., Haug, J. T. & Haug, C. After 100 years: a detailed view of an eumalacostracan crustacean from the Upper Jurassic Solnhofen Lagerstätte with raptorial appendages unique to Euarthropoda. Lethaia 54, 55–72 (2021).Article 

Google Scholar 
Briggs, D. E. G. & Kear, A. J. Decay and mineralization of shrimps. Palaios 9, 431–456 (1994).Article 
ADS 

Google Scholar 
Wuttke, M. Conservation-dissolution-transformation. On the behaviour of biogenic materials during fossilization In Messel: an insight into the history of life and of the earth (eds. Schaal, S. & Ziegler, W.) 263–275 (Claredon, 1992).Thompson, J. R. Comments on phylogeny of section Caridea (Decapoda Natantia) and the phylogenetic importance of the Oplophoridea. Proc. Symp. Crustacea Part 1, 314–326 (1967).
Google Scholar 
Ashelby, C. W., De Grave, S. & Johnson, M. L. Preliminary observations on the mandibles of palaemonoid shrimp (Crustacea: Decapoda: Caridea: Palaemonoidea). PeerJ 3, e846 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Felgenhauer, B. E., & Abele, L. G. Phylogenetic relationships among shrimp-like decapods. In Crustacean Phylogeny (ed Schram, F. R.) 291–311 (A. A. Balkema, 1983).Wowor, D., Cai, Y., & Ng, P. K. L. Crustacea: Decapoda, Caridea. In Freshwater Invertebrates of the Malaysian Region (eds Yule, C. M. & Y. H. Sen, Y. H.) 337–357 (Academy of Sciences Malaysia, 2004).Rodd, F. H., & Reznick, D. N. Life History Evolution in Guppies: III. The Impact of Prawn Predation on Guppy Life Histories. Oikos 62, 13–19 (1991).Felgenhauer, B. E. & Abele, L. G. Feeding structures of two atyid shrimps, with comments on Caridean phylogeny. J. Crustac. Biol. 5, 397–419 (1985).Article 

Google Scholar 
de Mazancourt, V., Marquet, G., & Keith, P. The “Pinocchio-shrimp effect”: First evidence of variation in rostrum length with the environment in Caridina H. Milne-Edwards, 1837 (Decapoda: Caridea: Atyidae). J. Crustac. Biol. 37, 249–257 (2017).Zimmermann, G. et al. Geometric morphometrics of carapace of Macrobrachium australe (Crustacea: Palaemonidae) from Reunion Island. Acta Zool. 93, 492–500 (2012).Article 

Google Scholar 
Bauer, R. T. Amphidromy in shrimps: a life cycle between rivers and the sea. Lat. Am. J. Aquat. Res. 41, 633–650 (2013).Article 

Google Scholar 
Jalihal, D. R., Sankolli, K. N. & Shenoy, S. Evolution of larval developmental patterns and the process of freshwaterization in the prawn genus Macrobrachium Bate, 1868 (Decapoda, Palaemonidae). Crustaceana 65, 365–376 (1993).Article 

Google Scholar 
Grande, L. Paleontology of the Green River Formation, with a review of the fish fauna. Bull. Geol. Surv. Wyoming 63, 1–333 (1984).
Google Scholar 
Grande, L. The Lost World of Fossil Lake: snapshots from deep time (University of Chicago Press, 2013).Micklich, N. Peculiarities of the Messel fish fauna and their palaeoecological implications: A case study. Palaeobiodivers. Palaeoenviron. 92, 585–629 (2012).Article 

Google Scholar 
Micklich, N. Actinopterygians—the fishes of the Messel lake. in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 104–111 (Schweizerbart, 2018).Christodoulou, M., Anastasiadou, C., Jugovic, J., & Tzomos, T. Freshwater Shrimps (Atyidae, Palaemonidae, Typhlocarididae) in the Broader Mediterranean Region: Distribution, Life Strategies, Threats, Conservation Challenges and Taxonomic Issues. In A Global Overview of the Conservation of Freshwater Decapod Crustaceans (eds Kawai, T. & Cumberlidge, N.) 199–236 (Springer, 2016).Anger, K. Neotropical Macrobrachium (Caridea: Palaemonidae): On the biology, origin, and radiation of freshwater-invading shrimp. J. Crustac. Biol. 33, 151–183 (2013).Article 

Google Scholar  More

LaBarbera, M. The evolution and ecology of body size. In Patterns and Processes in the History of Life (eds Raup, D. M. & Jablonski, D.) 69–98 (Springer, 1986).
Google Scholar 
Peters, R. H. & Peters, R. H. The Ecological Implications of Body Size Vol. 2 (Cambridge University Press, 1986).
Google Scholar 
Klingenberg, C. P. & Spence, J. On the role of body size for life-history evolution. Ecol. Entomol. 22(1), 55–68 (1997).
Google Scholar 
Blanckenhorn, W. U. The evolution of body size: What keeps organisms small?. Q. Rev. Biol. 75(4), 385–407 (2000).CAS 
PubMed 

Google Scholar 
Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl. Acad. Sci. USA 104(45), 17707–17712 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmidt-Nielsen, K. Scaling in biology: The consequences of size. J. Exp. Zool. 194(1), 287–307 (1975).CAS 
PubMed 

Google Scholar 
Calder, W. A. Size, Function, and Life History (Courier Corporation, 1996).
Google Scholar 
Gould, S. J. Allometry and size in ontogeny and phylogeny. Biol. Rev. 41(4), 587–638 (1966).CAS 
PubMed 

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293(5538), 2248–2251 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Gearty, W. & Payne, J. L. Physiological constraints on body size distributions in Crocodyliformes. Evolution 74(2), 245–255 (2020).PubMed 

Google Scholar 
Maurer, B. A., Brown, J. H. & Rusler, R. D. The micro and macro in body size evolution. Evolution 46(4), 939–953 (1992).PubMed 

Google Scholar 
Hone, D. W. & Benton, M. J. The evolution of large size: How does Cope’s Rule work?. Trends Ecol. Evol. 20(1), 4–6 (2005).PubMed 

Google Scholar 
Reeve, J. P. & Fairbairn, D. J. Predicting the evolution of sexual size dimorphism. J. Evol. Biol. 14(2), 244–254 (2001).
Google Scholar 
Blanckenhorn, W. U. Behavioral causes and consequences of sexual size dimorphism. Ethology 111(11), 977–1016 (2005).
Google Scholar 
Wu, H., Jiang, T., Huang, X. & Feng, J. Patterns of sexual size dimorphism in horseshoe bats: Testing Rensch’s rule and potential causes. Sci. Rep. 8(1), 1–13 (2018).ADS 

Google Scholar 
Cox, R. M., Butler, M. A. & John-Alder, H. B. The evolution of sexual size dimorphism in reptiles. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) 38–49 (Oxford University Press, 2007).
Google Scholar 
Stillwell, R. C., Blanckenhorn, W. U., Teder, T., Davidowitz, G. & Fox, C. W. Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: From physiology to evolution. Annu. Rev. Entomol. 55, 227–245 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rensch, B. Die Abhängigkeit der relativen Sexualdifferenz von der Körpergrösse. Bonn. Zool. Beitr. 1, 58–69 (1950).
Google Scholar 
Rensch, B. Evolution Above the Species Level (Columbia University Press, 1960).
Google Scholar 
Shine, R. Ecological causes for the evolution of sexual dimorphism: A review of the evidence. Q. Rev. Biol. 64(4), 419–461 (1989).CAS 
PubMed 

Google Scholar 
Portik, D. M., Blackburn, D. C. & McGuire, J. A. Macroevolutionary patterns of sexual size dimorphism among African tree frogs (Family: Hyperoliidae). J. Hered. 111(4), 379–391 (2020).PubMed 

Google Scholar 
Ceballos, C. P., Adams, D. C., Iverson, J. B. & Valenzuela, N. Phylogenetic patterns of sexual size dimorphism in turtles and their implications for Rensch’s rule. Evol. Biol. 40(2), 194–208 (2013).
Google Scholar 
Amado, T. F., Martinez, P. A., Pincheira-Donoso, D. & Olalla-Tárraga, M. Á. Body size distributions of anurans are explained by diversification rates and the environment. Glob. Ecol. Biogeogr. 30(1), 154–164 (2021).
Google Scholar 
Starostová, Z., Kubička, L. & Kratochvíl, L. Macroevolutionary pattern of sexual size dimorphism in geckos corresponds to intraspecific temperature-induced variation. J. Evol. Biol. 23(4), 670–677 (2010).PubMed 

Google Scholar 
Herczeg, G., Gonda, A. & Merilä, J. Rensch’s rule inverted–female-driven gigantism in nine-spined stickleback Pungitius pungitius. J. Anim. Ecol. 79(3), 581–588 (2010).PubMed 

Google Scholar 
Liao, W. B. & Chen, W. Inverse Rensch’s rule in a frog with female-biased sexual size dimorphism. Naturwissenschaften 99(5), 427–431 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Cooper, M. I. Sexual size dimorphism and the rejection of Rensch’s rule in Diplopoda (Arthropoda). J. Entomol. Zool. Stud. 6(1), 1582–1587 (2018).
Google Scholar 
Cheng, R. C. & Kuntner, M. Phylogeny suggests nondirectional and isometric evolution of sexual size dimorphism in argiopine spiders. Evolution 68(10), 2861–2872 (2014).PubMed 

Google Scholar 
Webb, T. J. & Freckleton, R. P. Only half right: Species with female-biased sexual size dimorphism consistently break Rensch’s rule. PLoS ONE 2(9), e897 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: Elements of a synthesis. J. Biogeogr. 35(3), 483–500 (2008).
Google Scholar 
Olalla-Tárraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: Anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16(5), 606–617 (2007).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodríguez, M. Á. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Gouveia, S. F. & Correia, I. Geographical clines of body size in terrestrial amphibians: Water conservation hypothesis revisited. J. Biogeogr. 43(10), 2075–2084 (2016).
Google Scholar 
Pincheira-Donoso, D., Meiri, S., Jara, M., Olalla-Tárraga, M. Á. & Hodgson, D. J. Global patterns of body size evolution are driven by precipitation in legless amphibians. Ecography 42(10), 1682–1690 (2019).
Google Scholar 
Nevo, E. Adaptive color polymorphism in cricket frogs. Evolution 27(3), 353–367 (1973).PubMed 

Google Scholar 
Ashton, K. G. Do amphibians follow Bergmann’s rule?. Can. J. Zool. 80(4), 708–716 (2002).MathSciNet 

Google Scholar 
Bergmann, C. Ueber die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Göttinger Studien. 1, 595–708 (1847).
Google Scholar 
Olalla-Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad-scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33(5), 781–793 (2006).
Google Scholar 
Trullas, S. C., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32(5), 235–245 (2007).
Google Scholar 
Rodríguez, M. Á., López-Sañudo, I. L. & Hawkins, B. A. The geographic distribution of mammal body size in Europe. Glob. Ecol. Biogeogr. 15(2), 173–181 (2006).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodriguez, M. A. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Womack, M. C. & Bell, R. C. Two-hundred million years of anuran body-size evolution in relation to geography, ecology and life history. J. Evol. Biol. 33(10), 1417–1432 (2020).PubMed 

Google Scholar 
Frost, D. R. Amphibian Species of the World: An online reference, version 6. http://research.amnh.org/herpetology/amphibia/index.php. Accessed 12 July 2021 (2021).
Acevedo, A. A., Armesto, O. & Palma, R. E. Two new species of Pristimantis (Anura: Craugastoridae) with notes on the distribution of the genus in northeastern Colombia. Zootaxa 4750(4), 499–523 (2020).
Google Scholar 
Heinicke, M. P., Duellman, W. E. & Hedges, S. B. Major Caribbean and Central American frog faunas originated by ancient oceanic dispersal. Proc. Natl. Acad. Sci. USA 104(24), 10092–10097 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pinto-Sánchez, N. R. et al. The great American biotic interchange in frogs: Multiple and early colonization of Central America by the South American genus Pristimantis (Anura: Craugastoridae). Mol. Phylogenet. Evol. 62(3), 954–972 (2012).PubMed 

Google Scholar 
Zumel, D., Buckley, D. & Ron, S. R. The Pristimantis trachyblepharis species group, a clade of miniaturized frogs: Description of four new species and insights into the evolution of body size in the genus. Zool. J. Linn. Soc. zlab044 (2021).Pincheira-Donoso, D. et al. The multiple origins of sexual size dimorphism in global amphibians. Glob. Ecol. Biogeogr. 30(2), 443–458 (2021).
Google Scholar 
Woolbright, L. L. Sexual selection and size dimorphism in anuran amphibia. Am. Nat. 121(1), 110–119 (1983).
Google Scholar 
Nali, R. C., Zamudio, K. R., Haddad, C. F. & Prado, C. P. Size-dependent selective mechanisms on males and females and the evolution of sexual size dimorphism in frogs. Am. Nat. 184(6), 727–740 (2014).PubMed 

Google Scholar 
Hill, R. et al. Herpetological husbandry observations on the captive reproduction of gaige’s rain frog Pristimantis gaigeae (Dunn 1931). Herpetol. Rev. 41(4), 465 (2010).
Google Scholar 
Rojas-Rivera, A., Cortés-Bedoya, S., Gutiérrez-Cárdenas, P. D. A. & Castellanos, J. M. Pristimantis achatinus (Cachabi robber frog). Parental care and clutch size. Herpetol. Rev. 42, 588–589 (2011).
Google Scholar 
Granados-Pérez, Y. & Ramirez-Pinilla, M. P. Reproductive phenology of three species of Pristimantis in an Andean cloud forest. Revista Acad. Colomb. Ci. Exact. 44(173), 1083–1098 (2020).
Google Scholar 
Levy, D. L. & Heald, R. Biological scaling problems and solutions in amphibians. Cold Spring Harb. Perspect. Biol. 8(1), a019166 (2016).PubMed Central 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Survey Data Series. 691(10), 4–9 (2012).
Google Scholar 
Valenzuela-Sánchez, A., Cunningham, A. A. & Soto-Azat, C. Geographic body size variation in ectotherms: Effects of seasonality on an anuran from the southern temperate forest. Front. Zool. 12(1), 1–10 (2015).
Google Scholar 
Parsons, J. J. The northern Andean environment. Mt. Res. Dev. 2(3), 253–264 (1982).
Google Scholar 
Navas, C. A., Carvajalino-Fernández, J. M., Saboyá-Acosta, L. P., Rueda-Solano, L. A. & Carvajalino-Fernández, M. A. The body temperature of active amphibians along a tropical elevation gradient: Patterns of mean and variance and inference from environmental data. Funct. Ecol. 27(5), 1145–1154 (2013).
Google Scholar 
Swemmer, A. M., Knapp, A. K. & Snyman, H. A. Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands. J. Ecol. 95(4), 780–788 (2007).
Google Scholar 
Losos, J. B. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (Univ. of California Press, 2011).
Google Scholar 
Pincheira-Donoso, D. & Hunt, J. Fecundity selection theory: Concepts and evidence. Biol. Rev. 92(1), 341–356 (2017).PubMed 

Google Scholar 
Morrison, C. & Hero, J. M. Geographic variation in life-history characteristics of amphibians: A review. J. Anim. Ecol. 72(2), 270–279 (2003).
Google Scholar 
Morrow, C. B., Ernest, S. M. & Kerkhoff, A. J. Macroevolution of dimensionless life-history metrics in tetrapods. Proc. Royal Soc. B. 288, 20210200 (2021).
Google Scholar 
Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57(4), 591–601 (2008).PubMed 

Google Scholar 
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368(1618), 20120341 (2013).PubMed 
PubMed Central 

Google Scholar 
Meyer, A. L. & Wiens, J. J. Estimating diversification rates for higher taxa: BAMM can give problematic estimates of rates and rate shifts. Evolution 72(1), 39–53 (2018).PubMed 

Google Scholar 
Rabosky, D. L. et al. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4(1), 1–8 (2013).
Google Scholar 
Mendoza, A. M., Ospina, O. E., Cárdenas-Henao, H. & García-R, J. C. A likelihood inference of historical biogeography in the world’s most diverse terrestrial vertebrate genus: Diversification of direct-developing frogs (Craugastoridae: Pristimantis) across the Neotropics. Mol. Phylogenet. Evol. 85, 50–58 (2015).PubMed 

Google Scholar 
Baker, J., Meade, A., Pagel, M. & Venditti, C. Adaptive evolution toward larger size in mammals. Proc. Natl. Acad. Sci. USA 112(16), 5093–5098 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hariharan, I. K., Wake, D. B. & Wake, M. H. Indeterminate growth: Could it represent the ancestral condition?. Cold Spring Harb. Perspect. Biol. 8(2), a019174 (2016).PubMed Central 

Google Scholar 
Amado, T. F., Bidau, C. J. & Olalla-Tárraga, M. Á. Geographic variation of body size in New World anurans: Energy and water in a balance. Ecography 42(3), 456–466 (2019).
Google Scholar 
Watters, J. L., Cummings, S. T., Flanagan, R. L. & Siler, C. D. Review of morphometric measurements used in anuran species descriptions and recommendations for a standardized approach. Zootaxa 4072, 477–495 (2016).PubMed 

Google Scholar 
Lovich, J. E. & Gibbons, J. W. A review of techniques for quantifying sexual size dimorphism. Growth Dev. Aging. 56, 269–269 (1992).CAS 
PubMed 

Google Scholar 
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29(6), 1695–1701 (2012).CAS 
PubMed 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7(1), 1–8 (2007).
Google Scholar 
Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4(5), e88 (2006).PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree, A Graphical Viewer of Phylogenetic Trees. (2007)Olalla-Tárraga, M. A., Bini, L. M., Diniz-Filho, J. A. & Rodríguez, M. Á. Cross-species and assemblage-based approaches to Bergmann’s rule and the biogeography of body size in Plethodon salamanders of eastern North America. Ecography 33(2), 362–368 (2010).
Google Scholar 
QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. Accessed 10 July 2021 (2022).Wei, T. et al. Package ‘corrplot’. Statistician. 56(316), e24 (2017).
Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51(3), 365–390 (1970).
Google Scholar 
Hawkins, B. A. & Felizola Diniz-Filho, J. A. Beyond Rapoport’s rule: Evaluating range size patterns of New World birds in a two-dimensional framework. Glob. Ecol. Biogeogr. 15(5), 461–469 (2006).
Google Scholar 
Eager, C. standardize: Tools for standardizing variables for regression in R. R package version 0.21 (2017).Meireles, J. E., O’Meara, B. & Cavender-Bares, J. Linking leaf spectra to the plant tree of life. In Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 155–172 (Springer, 2010).
Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401(6756), 877–884 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Pagel, M. The maximum likelihood approach to reconstructing ancestral character states of discrete characters on phylogenies. Syst. Biol. 48(3), 612–622 (1999).
Google Scholar 
Revell, L. J. Phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).
Google Scholar 
Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4(8), 754–759 (2013).
Google Scholar 
Rabosky, D. L. et al. BAMM tools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5(7), 701–707 (2014).
Google Scholar 
Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9(2), e89543 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66(4), 477–498 (2017).PubMed 
PubMed Central 

Google Scholar 
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News. 6(1), 7–11 (2006).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021). https://www.R-project.org. Accessed 1 June 2021 (2021).Fairbairn, D. J. Allometry for sexual size dimorphism: Pattern and process in the coevolution of body size in males and females. Annu. Rev. Ecol. Evol. Syst. 28(1), 659–687 (1997).
Google Scholar 
Fairbairn, D. J. Allometry for sexual size dimorphism: Testing two hypotheses for Rensch’s rule in the water strider Aquarius remigis. Am. Nat. 166(S4), S69–S84 (2005).PubMed 

Google Scholar 
Visser, A. G., Beevers, L. & Patidar, S. Complexity in hydroecological modelling: A comparison of stepwise selection and information theory. River Res. Appl. 34(8), 1045–1056 (2018).
Google Scholar 
Calcagno, V. & de Mazancourt, C. glmulti: An R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34(12), 1–29 (2010).
Google Scholar 
Callaghan, S., Guilyardi, E., Steenman-Clark, L. & Morgan, M. The METAFOR project. in Earth System Modelling-Volume 1 (Springer, 2013).Garamszegi, L. Z. & Mundry, R. Multimodel-inference in comparative analyses. In Modern Phylogenetic Comparative Methods and THEIR Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 305–331 (Springer Berlin, 2014).
Google Scholar  More

OverviewWe compiled systematically sampled empirical taxa occurrence across the landscape, and inferentially assembled respective blue and green local food webs by combining these data with a metaweb approach. We quantified key properties of the inferred food webs, then analysed with GIS-derived environmental information how focal food-web metrics change along elevation and among different land-use types in blue versus green systems. Details are given below.Assemble food webs using a metaweb approachWe applied a metaweb method to obtain the composition and structure of multiple local food webs across a landscape spatial scale10. A metaweb is an accumulation of all interactions (here, trophic relationships) among the focal taxa. In this study, we built our metaweb based on known trophic interactions derived from literature and published datasets, which themselves were all based on primary empirical natural history observations. We further complemented or refined the trophic interactions in the metaweb based on expert knowledge of primary observations that are not yet published or only accessible in grey literature. The expert knowledge covers authors and collaborators who have specific natural history knowledge on Central European plants, herbivorous insects, birds, fish, and aquatic invertebrates. Importantly, these observations were all based on empirical observations and/or unpublished data accumulated over considerable field research experience. The respective literature we referred, as well as the metaweb itself with information source of each trophic link (online repository), are provided in Supplementary Methods. By assuming that any interaction in the metaweb will realise if the interacting taxa co-occur, the metaweb approach allows an inference of local food webs if taxa occurrence is known. Such an assumption of fixed diets may lead to an over-estimation of the locally realised trophic links32, as it essentially ignores the possible intraspecific diet variation caused by resource availability61,62, predation risk63, temperature64, ontogenetic shift65, or other genetic and environmental sources66. Therefore, the food webs we inferred systematically using this method capture trophic relationships driven by community composition (species presence versus absence) but not the above-mentioned processes. Nonetheless, since the trophic interactions were based on empirical observations, the fixed diets can be seen as collapsing all intraspecific variations of diet-determining traits (or trait-matching) at species level, within which we know realisable interactions surely exist. This, together with co-occurrence as a pre-requisite, gives realistic boundaries for the potential interaction realisation, which is plausible and non-biased when applying to localised sites. With this approach, we were addressing a systematic comparison among potential local food webs between the blue and green systems and across the selected gradients. For sensitivity analyses considering the potential inaccuracy of the metaweb approach mentioned here, please see further below Food-web metrics and analyses and Supplementary Discussion.We compiled taxa occurrence of four terrestrial and two aquatic broad taxonomic groups (“focal groups”) to assemble local green and blue communities, respectively and independently, based on the well-resolved data available. Each focal group referred to a distinct taxonomic group, and the within- and among-group trophic relationships captured most of the realised interactions. These focal groups were vascular plants, butterflies, grasshoppers, and birds in the green biome, and stream invertebrates and fishes in the blue biome. Notably, with “butterflies” we refer to their larval stage and accordingly their mostly-herbivorous trophic interactions throughout this study. Larval interactions were also the predominant interaction assessed for stream invertebrates (i.e., all interactions of stream invertebrates focussed on their aquatic stage, which is predominant larval). The occurrence data of these focal groups were compiled from highly standardised multiple-year empirical surveys of various authorities, all conducted by trained biologists with fixed protocols (Supplementary Methods). The information across sites should thus be representative and can be up-scaled to the landscape. The occurrences of plants, butterflies, birds, and stream invertebrates were from the Biodiversity Monitoring Switzerland programme (BDM Coordination Office67) managed by the Swiss Federal Office for the Environment (BAFU/FOEN). The occurrences of grasshoppers and fishes were from the Swiss database on faunistic records, info fauna (CSCF), where we further complemented fish occurrence from the data of Progetto Fiumi Project (Eawag). In terms of biological resolution, taxa were resolved to species level in most cases, while the plant and butterfly groups included some multi-species complexes. Insects of the order Ephemeroptera, Plecoptera, and Trichoptera were resolved to species, while all other stream invertebrates were resolved to family level. These were each treated as a node later in our food-web assembly, and referred to as “species”, as the species within such complexes and families mostly share the same trophic role. Spatially, the occurrence datasets adopted coordinates resolved to 1 × 1 km2. The species that were recorded in the same 1 × 1 km2 grid were considered to co-occurred. We took the co-occurring four/two focal groups to form local green/blue local communities, respectively. To obtain better co-occurrence across group-specific data from different sources (e.g., BDM and info fauna), we intentionally coarsened the grasshopper and fish occurrence to 5 × 5 km2 coordinates. This is arguably a biologically acceptable approximation considering the high mobility of these two groups. Also, we only included known stream-borne fishes and dropped pure lake-borne ones to match our stream-only invertebrate occurrence data. Across all 462 green and 465 blue communities we assembled, we covered 2016 plant, 191 butterfly, 109 grasshopper, 155 bird, 248 stream invertebrate, and 78 stream fish species. Unlike the knowledge of plant occurrence in green communities, we did not have detailed occurrence information of the basal components (e.g., primary producers) in blue ones. Therefore, we assumed three mega nodes—namely plant (including all alive or dead plant materials), plankton (including zooplankton, phytoplankton, and other algae), and detritus—as the basal nodes occurring in all blue communities, without further discrimination of identities or biology within. These adding to our focal groups thus cover major taxonomic groups as well as trophic roles from producers to top consumers in both blue and green systems.Taking the above-assembled local communities then drawing trophic links among species (nodes) according to the metaweb yielded the local food webs (illustrated in Fig. 1), representatively covering the whole Swiss area. Notably, although our understanding of trophic interactions indeed encompassed some links across the blue and green taxa (e.g., between piscivorous birds and fishes), our occurrence datasets did not present sufficient spatial grids where these taxa co-occur. We, therefore, did not include such links, nor assembled blue-green interconnected food webs, but the blue and green food webs separately instead (but see Supplementary Discussion). Also, we dropped isolated nodes, i.e., basal nodes without any co-occurring consumer and consumer nodes without any co-occurring resource, from the inferred food webs. These could possibly be passing-by species that were recorded but had no trophic interaction locally, or those that interact with non-focal taxa whose occurrence information was unknown to us. We thus had to exclude them to focus on evidence-supported occurrences and trophic interactions. Nonetheless, across all cases, isolated nodes were rather rare (averaged less than 3% of species occurred in either blue or green communities).Environmental dataWe acquired environmental data across all of Switzerland (42,000 km2) on a 1 × 1 km2 grid basis (i.e., values are averaged over the grid) from GIS databases, with which we mapped environmental conditions to the grids where we assembled food webs. These included: topographical information from DHM25 (Swisstopo, FOT), land-cover information from CLC (EEA), and climate information (averaged over the decade of 2005–2015) from CHELSA. Among environmental variables, elevation and temperature are essentially highly correlated. In this study, we took elevation as the focal environmental gradient throughout, as after accounting for the main effects of elevation on temperature, the residual temperature was not a good predictor of the food-web metrics we looked at (see next section, and Supplementary Table 4). In other words, by analysing along the elevation gradient, we already captured most of the temperature influences on food webs. Based on the labels provided by the GIS databases, we categorised the originally detailed land cover into the five major land-use types that we used in this study, namely forest, scrubland, open space, farmland, and urban area. Forest includes broad-leaved, coniferous, and mixed forests. Scrub includes bushy and herbaceous vegetation, heathlands, and natural grasslands. Open space encompasses sparsely vegetated areas, such as dunes, bare rocks, glaciers and perpetual snow. Farmland include any form of arable, pastures, and agro-forestry areas. Finally, urban area is where artificial constructions and infrastructure prevail. As each grid could contain multiple land-use types, we then defined the dominant land-use type of the grid as any of the five above that occupied more than 50% of the grid’s area. Analyses separated by land-use types with subsetted food webs (land-use-specific analyses) were based on the grids’ dominant land-use type. There were a few grids where the dominant land-use type did not belong to the focal major five, e.g., wetlands or water bodies, and a few where no single land-use type covered more than 50% of the area. Food webs of these grids were still included in the overall analyses but excluded from any land-use-specific analyses (as revealed in the difference in sample sizes between all versus land-use type subsetted food webs in Fig. 2; analyses details below).Food-web metrics and analysesWe quantified five metrics as the measures of the food webs’ structural and ecological properties. For the fundamental structure of the food webs, the number of nodes (“No. Nodes”) reflects the size of the web, meanwhile represents local species richness (though the few isolated nodes were excluded as above-mentioned). Connectance is the proportion of realised links among all potential ones (thus bounded 0–1), reflecting how connected the web is. We also derived holistic topological measures, namely nestedness and modularity. Nestedness of a food web, on the one hand, describes the tendency that some nodes’ narrower diets being subsets of other’s broader diets. We adopted a recently developed UNODF index68 (bounded 0–1) that is especially suitable for quantifying such a feature in our unipartite food webs. On the other hand, modularity (bounded 0–1 with our index) reflects the tendency of a food web to form modules, where nodes are highly connected within but only loosely connected between. Nestedness and modularity are two commonly investigated structures in ecological networks and have been considered relevant to species feeding ecology24 and the stability of the system69. Finally, we measured the level of consumers’ diet niche overlap of the food webs (Horn’s index70, bounded 0–1), which essentially depends on the arrangement of trophic relationships (thus the structure of the webs), and could have strong ecological implications as niche partitioning has been recognised to be a key mechanism that drives species coexistence71,72. We selected these fundamental and holistic properties as they are potentially more relevant to the processes that may have shaped food webs across a landscape scale (e.g., community assembly), in comparison to some node- or link-centric properties. Also, addressing similar metrics as in the literature13,69 would facilitate potential cross-study comparison or validation.To first gain a glimpse of the structure of the blue and green food webs, we performed a principal component analysis (PCA; Fig. 3a) on the inferred food webs (n = 462 and 465 in green and blue, respectively) taking the four structural metrics (number of nodes, connectance, nestedness, and modularity) as the explaining variables of blue versus green system types. We then confirmed that system type, elevation, and land-use type were all important predictors of food-web metrics (whereas the residual temperature after accounting elevation effects was not) by conducting general linear model analyses, taking the former as interactive predictors while the latter response variables (Supplementary Tables 3, 4). To check how elevation influences food-web properties in blue and green systems separately, and how food-web properties depend on each other, we ran a series of piecewise structural equation modelling (SEM)73 analyses on inferred food webs (Fig. 3b, c) whose dominant land use can be defined (n = 421 and 430 in green and blue, respectively). This was also conducted on subsetted webs of each of the five major land-use types (Supplementary Figs. 1 and 2). The SEM relationships were derived from linear mixed model analyses with dominant land-use type as a random effect (assumption tests see Supplementary Figs. 12–17). The SEM structure of direct effects was set according to the literature13,69 and is illustrated in Fig. 3b. In short, this structure tests the dependencies from elevation (an environmental predictor) to food-web metrics (ecological responses). The further dependencies among food-web metrics themselves were assigned with the principle of pointing from relative lower-level properties to higher-level ones. That is, from number of nodes (purely determined by nodes) to connectance (determined by numbers of nodes and links), further to nestedness and modularity (holistic topologies, determined further by the arrangement of links), then to diet niche overlap (ecological functional outcome).Finally, to check and visualise the exact changing patterns of food webs, we applied generalised additive models (GAMs) to reveal the relationships between food-web metrics and the whole-ranged elevation (Figs. 4 and 5), as well as a particular comparison between food webs in forests and farmlands below 1500 m a.s.l. (Supplementary Fig. 5), as this elevation segment covered most of the sites belonged to these two land-use types. We also performed a series of linear models (LMs) and least-squared slope comparisons based on land-use-specific subsets of food webs (Figs. 4 and 5; Supplementary Figs. 3 and 4), to investigate whether food-web elevational patterns are different among land-use types (assumption tests see Supplementary Tables 5 and 6). In the GAMs analyses, specifically, we simulated two sets of randomised webs, i.e., “keep-group” and “fully”, as the null models to compare with the inferred ones74. Both randomisations generated ten independently simulated webs from each input inferred local food web, keeping the same number of nodes and connectance as of the latter. On the one hand, the keep-group randomisation shuffled trophic links from an input local web but only allowed them to realised fulfilling some pre-set within- and among-group relationships. That is, in green communities, birds can feed on all groups, grasshoppers on any groups but birds, while butterflies only on plants; in blue communities, fishes can feed on all groups, while invertebrates on themselves and the basal resources. These pre-set group-wide relationships captured the majority of realistic trophic interactions compiled in our metaweb. On the other hand, the fully randomised webs shuffled trophic links disregarding the biological identity of nodes. The GAMs of nestedness, modularity, and niche overlap illustrated the patterns of these randomised webs (Fig. 5). Comparing among the three types of webs, the patterns exhibited already by fully randomised webs should be those contributed by variations in web size and connectance, while the difference between keep-group and fully randomised webs by the focal-group composition of local communities, and the difference between inferred and keep-group randomised webs further by the realistic species-specific diets. In addition, we also applied the same GAMs and LMs approach to analyse node richness, as well as both realised and potential diet generality (vulnerability for plants) of each focal group (Supplementary Figs. 6–11). These analyses provided hints about the changes in community composition and species diet breadths along elevation and among land-use types, which helped explain the detected food-web responses in mechanistic ways.In addition, to check if our findings were shaped or strongly influenced by the potential inaccuracy of using the metaweb, we repeated the above PCA, SEM, and GAM analyses as a series of sensitivity analyses. We generated food webs based on our locally inferred ones (i.e., the observations) but with random 10% link removal. This procedure mimics the effect of potential intraspecific diet variation (mentioned earlier) so that some trophic interactions in the metaweb do not realise locally. Overall, these analyses with link removal showed that our conclusions are qualitatively and quantitatively highly robust, and only very minorly affected by the such potential inaccuracy of metawebs, which is also in accordance to other food-web studies (see e.g., Pearse & Altermatt 201575). All details and outcomes of these additional analyses are given in Supplementary discussion.All metric quantification and analyses were performed under R version 4.0.3 (R Core Team76). All applied packages and functions were described in Supplementary Methods, while the R scripts performing these tasks can be accessed at the online repository provided.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Intergovernmental Panel on Climate Change (IPCC). The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2022). https://doi.org/10.1017/9781009157964.Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37 (2012).ADS 

Google Scholar 
Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).PubMed 

Google Scholar 
Hall-Spencer, J. M. & Harvey, B. P. Ocean acidification impacts on coastal ecosystem services due to habitat degradation. Emerg. Top. Life Sci. 3, 197–206 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Straub, S. C. et al. Resistance, extinction, and everything in between—The diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).
Google Scholar 
Connell, S. D., Kroeker, K. J., Fabricius, K. E., Kline, D. I. & Russell, B. D. The other ocean acidification problem: CO2 as a resource among competitors for ecosystem dominance. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120442 (2013).
Google Scholar 
Kroeker, K. J., Micheli, F., Gambi, M. C. & Martz, T. R. Divergent ecosystem responses within a benthic marine community to ocean acidification. Proc. Natl. Acad. Sci. 108, 14515–14520 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Harvey, B. P., Kon, K., Agostini, S., Wada, S. & Hall-Spencer, J. M. Ocean acidification locks algal communities in a species-poor early successional stage. Glob. Change Biol. 27, 2174–2187 (2021).ADS 
CAS 

Google Scholar 
Sunday, J. M. et al. Ocean acidification can mediate biodiversity shifts by changing biogenic habitat. Nat. Clim. Change 7, 81–85 (2017).ADS 
CAS 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).
Google Scholar 
Schiel, D. R. & Foster, M. S. The population biology of large brown seaweeds: Ecological consequences of multiphase life histories in dynamic coastal environments. Annu. Rev. Ecol. Evol. Syst. 37, 343–372 (2006).
Google Scholar 
Wernberg, T. & Filbee-Dexter, K. Missing the marine forest for the trees. Mar. Ecol. Prog. Ser. 612, 209–215 (2019).ADS 

Google Scholar 
Cheminée, A. et al. Nursery value of Cystoseira forests for Mediterranean rocky reef fishes. J. Exp. Mar. Biol. Ecol. 442, 70–79 (2013).
Google Scholar 
Smale, D. A., Burrows, M. T., Moore, P., O’Connor, N. & Hawkins, S. J. Threats and knowledge gaps for ecosystem services provided by kelp forests: A northeast Atlantic perspective. Ecol. Evol. 3, 4016–4038 (2013).PubMed 
PubMed Central 

Google Scholar 
Carbajal, P., Gamarra Salazar, A., Moore, P. J. & Pérez-Matus, A. Different kelp species support unique macroinvertebrate assemblages, suggesting the potential community-wide impacts of kelp harvesting along the Humboldt Current System. Aquat. Conserv. Mar. Freshw. Ecosyst. 32, 14–27 (2022).
Google Scholar 
Filbee-Dexter, K. & Wernberg, T. Rise of turfs: A new battlefront for globally declining kelp forests. Bioscience 68, 64–76 (2018).
Google Scholar 
Pessarrodona, A. et al. Homogenization and miniaturization of habitat structure in temperate marine forests. Glob. Change Biol. 27, 5262–5275 (2021).CAS 

Google Scholar 
Orfanidis, S. et al. Effects of natural and anthropogenic stressors on Fucalean brown seaweeds across different spatial scales in the Mediterranean Sea. Front. Mar. Sci. 8, 1330 (2021).
Google Scholar 
Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl. Acad. Sci. 113, 13785–13790 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Capdevila, P. et al. Warming impacts on early life stages increase the vulnerability and delay the population recovery of a long-lived habitat-forming macroalga. J. Ecol. 107, 1129–1140 (2019).
Google Scholar 
Irving, A. D., Balata, D., Colosio, F., Ferrando, G. A. & Airoldi, L. Light, sediment, temperature, and the early life-history of the habitat-forming alga Cystoseira barbata. Mar. Biol. 156, 1223–1231 (2009).
Google Scholar 
Smith, K. E., Moore, P. J., King, N. G. & Smale, D. A. Examining the influence of regional-scale variability in temperature and light availability on the depth distribution of subtidal kelp forests. Limnol. Oceanogr. 67, 314–328 (2022).ADS 

Google Scholar 
Smale, D. A. et al. Climate-driven substitution of foundation species causes breakdown of a facilitation cascade with potential implications for higher trophic levels. J. Ecol. 00, 1–13 (2022).
Google Scholar 
Hollarsmith, J. A., Buschmann, A. H., Camus, C. & Grosholz, E. D. Varying reproductive success under ocean warming and acidification across giant kelp (Macrocystis pyrifera) populations. J. Exp. Mar. Biol. Ecol. 522, 151247 (2020).
Google Scholar 
Verdura, J. et al. Local-scale climatic refugia offer sanctuary for a habitat-forming species during a marine heatwaves. J. Ecol. 109, 1758–1773 (2021).
Google Scholar 
Mariani, S. et al. Past and present of Fucales from shallow and sheltered shores in Catalonia. Reg. Stud. Mar. Sci. 32, 100824 (2019).
Google Scholar 
Smale, D. A. Impacts of ocean warming on kelp forest ecosystems. New Phytol. 225, 1447–1454 (2020).PubMed 

Google Scholar 
Coelho, S. M., Rijstenbil, J. W. & Brown, M. T. Impacts of anthropogenic stresses on the early development stages of seaweeds. J. Aquat. Ecosyst. Stress Recov. 7, 317–333 (2000).CAS 

Google Scholar 
de Caralt, S., Verdura, J., Vergés, A., Ballesteros, E. & Cebrian, E. Differential effects of pollution on adult and recruits of a canopy-forming alga: Implications for population viability under low pollutant levels. Sci. Rep. 10, 17825 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Capdevila, P. et al. Recruitment patterns in the Mediterranean deep-water alga Cystoseira zosteroides. Mar. Biol. 162, 1165–1174 (2015).CAS 

Google Scholar 
Vadas, R. L., Johnson, S. & Norton, T. A. Recruitment and mortality of early post-settlement stages of benthic algae. Br. Phycol. J. 27, 331–351 (1992).
Google Scholar 
Koch, M., Bowes, G., Ross, C. & Zhang, X.-H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132 (2013).ADS 

Google Scholar 
Shih, P. M. et al. Biochemical characterization of predicted Precambrian RuBisCO. Nat. Commun. 7, 10382 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cornwall, C. E. et al. Inorganic carbon physiology underpins macroalgal responses to elevated CO2. Sci. Rep. 7, 46297 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hepburn, C. D. et al. Diversity of carbon use strategies in a kelp forest community: Implications for a high CO2 ocean. Glob. Change Biol. 17, 2488–2497 (2011).ADS 

Google Scholar 
Porzio, L., Buia, M. C. & Hall-Spencer, J. M. Effects of ocean acidification on macroalgal communities. J. Exp. Mar. Biol. Ecol. 400, 278–287 (2011).CAS 

Google Scholar 
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).ADS 

Google Scholar 
Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms: Biological responses to ocean acidification. Ecol. Lett. 13, 1419–1434 (2010).PubMed 

Google Scholar 
Rindi, F. et al. Coralline algae in a changing Mediterranean Sea: How can we predict their future, if we do not know their present?. Front. Mar. Sci. 6, 723 (2019).
Google Scholar 
James, R. K., Hepburn, C. D., Cornwall, C. E., McGraw, C. M. & Hurd, C. L. Growth response of an early successional assemblage of coralline algae and benthic diatoms to ocean acidification. Mar. Biol. 161, 1687–1696 (2014).CAS 

Google Scholar 
Comeau, S. & Cornwall, C. E. Contrasting effects of ocean acidification on coral reef “animal forests” versus seaweed “kelp forests.” In Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Rossi, S. et al.) 1–25 (Springer International Publishing, 2016) https://doi.org/10.1007/978-3-319-17001-5_29-1.Chapter 

Google Scholar 
Airoldi, L. Effects of disturbance, life histories, and overgrowth on coexistence of algal crusts and turfs. Ecology 81, 798–814 (2000).
Google Scholar 
Asnaghi, V. et al. Colonisation processes and the role of coralline algae in rocky shore community dynamics. J. Sea Res. 95, 132–138 (2015).ADS 

Google Scholar 
Bulleri, F., Bertocci, I. & Micheli, F. Interplay of encrusting coralline algae and sea urchins in maintaining alternative habitats. Mar. Ecol. Prog. Ser. 243, 101–109 (2002).ADS 

Google Scholar 
Villas Bôas, A. B. & Figueiredo, M. A. D. O. Are anti-fouling effects in coralline algae species specific?. Braz. J. Oceanogr. 52, 11–18 (2004).
Google Scholar 
Bulleri, F., Benedetti-Cecchi, L., Acunto, S., Cinelli, F. & Hawkins, S. J. The influence of canopy algae on vertical patterns of distribution of low-shore assemblages on rocky coasts in the northwest Mediterranean. J. Exp. Mar. Biol. Ecol. 267, 89–106 (2002).
Google Scholar 
Maggi, E., Bertocci, I., Vaselli, S. & Benedetti-Cecchi, L. Connell and Slatyer’s models of succession in the biodiversity era. Ecology 92, 1399–1406 (2011).CAS 
PubMed 

Google Scholar 
Irving, A. D., Connell, S. D., Johnston, E. L., Pile, A. J. & Gillanders, B. M. The response of encrusting coralline algae to canopy loss: An independent test of predictions on an Antarctic coast. Mar. Biol. 147, 1075–1083 (2005).
Google Scholar 
Irving, A. D., Connell, S. D. & Elsdon, T. S. Effects of kelp canopies on bleaching and photosynthetic activity of encrusting coralline algae. J. Exp. Mar. Biol. Ecol. 310, 1–12 (2004).
Google Scholar 
Melville, A. J. & Connell, S. D. Experimental effects of kelp canopies on subtidal coralline algae. Austral. Ecol. 26, 102–108 (2001).
Google Scholar 
Breitburg, D. L. Residual effects of grazing: Inhibition of competitor recruitment by encrusting coralline algae. Ecology 65, 1136–1143 (1984).
Google Scholar 
Bulleri, F., Bruno, J. F., Silliman, B. R. & Stachowicz, J. J. Facilitation and the niche: Implications for coexistence, range shifts and ecosystem functioning. Funct. Ecol. 30, 70–78 (2016).
Google Scholar 
van der Heide, T., Angelini, C., de Fouw, J. & Eklöf, J. S. Facultative mutualisms: A double-edged sword for foundation species in the face of anthropogenic global change. Ecol. Evol. 11, 29–44 (2021).PubMed 

Google Scholar 
Molinari-Novoa, E. A. & Guiry, E. Reinstatement of the genera Gongolaria Boehmer and Ericaria Stackhouse (Sargassaceae, Phaeophyceae). Notulae Algarum 1–10 (2020).Celis-Plá, P. S. M., Martinez, B., Korbee, N., Hall-Spencer, J. M. & Figueroa, F. L. Ecophysiological responses to elevated CO2 and temperature in Cystoseira tamariscifolia (Phaeophyceae). Clim. Change 142, 67–81 (2017).ADS 

Google Scholar 
Falace, A. et al. Is the South-Mediterranean canopy-forming Ericaria giacconei (= Cystoseira hyblaea) a loser from ocean warming?. Front. Mar. Sci. 8, 1758 (2021).
Google Scholar 
Hernández, C. A., Sangil, C., Fanai, A. & Hernández, J. C. Macroalgal response to a warmer ocean with higher CO2 concentration. Mar. Environ. Res. 136, 99–105 (2018).PubMed 

Google Scholar 
Falace, A., Kaleb, S., Fuente, G. D. L., Asnaghi, V. & Chiantore, M. Ex situ cultivation protocol for Cystoseira amentacea var. stricta (Fucales, Phaeophyceae) from a restoration perspective. PLoS ONE 13, e0193011 (2018).PubMed 
PubMed Central 

Google Scholar 
Bevilacqua, S. et al. Climatic anomalies may create a long-lasting ecological phase shift by altering the reproduction of a foundation species. Ecology 100, 1–4 (2019).
Google Scholar 
Savonitto, G. et al. Addressing reproductive stochasticity and grazing impacts in the restoration of a canopy-forming brown alga by implementing mitigation solutions. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 1611–1623 (2021).
Google Scholar 
Mangialajo, L. et al. Zonation patterns and interspecific relationships of fucoids in microtidal environments. J. Exp. Mar. Biol. Ecol. 412, 72–80 (2012).
Google Scholar 
Verlaque, M., Boudouresque, C.-F. & Perret-Boudouresque, M. Mediterranean seaweeds listed as threatened under the Barcelona Convention: A critical analysis. Sci. Rep. Port-Cros Natl. Park. 33, 179–214 (2019).
Google Scholar 
Benedetti-Cecchi, L. & Cinelli, F. Effects of canopy cover, herbivores and substratum type on patterns of Cystoseira spp. settlement and recruitment in littoral rockpools. Mar. Ecol. Prog. Ser. 90, 183–191 (1992).ADS 

Google Scholar 
Fuente, G. D. L., Chiantore, M., Asnaghi, V., Kaleb, S. & Falace, A. First ex situ outplanting of the habitat-forming seaweed Cystoseira amentacea var. stricta from a restoration perspective. PeerJ 7, e7290 (2019).
Google Scholar 
Orlando-Bonaca, M. et al. First restoration experiment for Gongolaria barbata in Slovenian coastal waters. What can go wrong?. Plants 10, 239 (2021).PubMed 
PubMed Central 

Google Scholar 
Christie, H. et al. Shifts between sugar kelp and turf algae in Norway: Regime shifts or fluctuations between different opportunistic seaweed species?. Front. Mar. Sci. 6, 72 (2019).
Google Scholar 
Orlando-Bonaca, M., Pitacco, V. & Lipej, L. Loss of canopy-forming algal richness and coverage in the northern Adriatic Sea. Ecol. Indic. 125, 107501 (2021).
Google Scholar 
Thibaut, T., Blanfune, A., Boudouresque, C.-F. & Verlaque, M. Decline and local extinction of Fucales in French Riviera: The harbinger of future extinctions?. Mediterr. Mar. Sci. 16, 206–224 (2015).
Google Scholar 
Thibaut, T., Pinedo, S., Torras, X. & Ballesteros, E. Long-term decline of the populations of Fucales (Cystoseira spp. and Sargassum spp.) in the Albères coast (France, North-western Mediterranean). Mar. Pollut. Bull. 50, 1472–1489 (2005).CAS 
PubMed 

Google Scholar 
Leal, P. P. et al. Copper pollution exacerbates the effects of ocean acidification and warming on kelp microscopic early life stages. Sci. Rep. 8, 14763 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Fernández, P. A., Navarro, J. M., Camus, C., Torres, R. & Buschmann, A. H. Effect of environmental history on the habitat-forming kelp Macrocystis pyrifera responses to ocean acidification and warming: A physiological and molecular approach. Sci. Rep. 11, 2510 (2021).PubMed 
PubMed Central 

Google Scholar 
Lind, A. C. & Konar, B. Effects of abiotic stressors on kelp early life-history stages. Algae 32, 223–233 (2017).CAS 

Google Scholar 
Fernández, P. A. et al. Nitrogen sufficiency enhances thermal tolerance in habitat-forming kelp: Implications for acclimation under thermal stress. Sci. Rep. 10, 3186 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Celis-Plá, P. S. M. et al. Macroalgal responses to ocean acidification depend on nutrient and light levels. Front. Mar. Sci. 2, 26 (2015).
Google Scholar 
Mancuso, F. P. et al. Influence of ambient temperature on the photosynthetic activity and phenolic content of the intertidal Cystoseira compressa along the Italian coastline. J. Appl. Phycol. 31, 3069–3076 (2019).CAS 

Google Scholar 
Vergés, A. et al. The tropicalization of temperate marine ecosystems: Climate-mediated changes in herbivory and community phase shifts. Proc. R. Soc. B Biol. Sci. 281, 20140846 (2014).
Google Scholar 
Vergés, A. et al. Tropical rabbitfish and the deforestation of a warming temperate sea. J. Ecol. 102, 1518–1527 (2014).
Google Scholar 
Gaitán-Espitia, J. D. et al. Interactive effects of elevated temperature and pCO2 on early-life-history stages of the giant kelp Macrocystis pyrifera. J. Exp. Mar. Biol. Ecol. 457, 51–58 (2014).
Google Scholar 
Leal, P. P., Hurd, C. L., Fernández, P. A. & Roleda, M. Y. Ocean acidification and kelp development: Reduced pH has no negative effects on meiospore germination and gametophyte development of Macrocystis pyrifera and Undaria pinnatifida. J. Phycol. 53, 557–566 (2017).CAS 
PubMed 

Google Scholar 
Roleda, M. Y., Morris, J. N., McGraw, C. M. & Hurd, C. L. Ocean acidification and seaweed reproduction: Increased CO2 ameliorates the negative effect of lowered pH on meiospore germination in the giant kelp Macrocystis pyrifera (Laminariales, Phaeophyceae). Glob. Change Biol. 18, 854–864 (2011).ADS 

Google Scholar 
Zhang, X. et al. Elevated CO2 concentrations promote growth and photosynthesis of the brown alga Saccharina japonica. J. Appl. Phycol. https://doi.org/10.1007/s10811-020-02108-1 (2020).Article 

Google Scholar 
Falkenberg, L. J., Russell, B. D. & Connell, S. D. Contrasting resource limitations of marine primary producers: Implications for competitive interactions under enriched CO2 and nutrient regimes. Oecologia 172, 575–583 (2013).ADS 
PubMed 

Google Scholar 
Nagelkerken, I., Russell, B. D., Gillanders, B. M. & Connell, S. D. Ocean acidification alters fish populations indirectly through habitat modification. Nat. Clim. Change 6, 89–93 (2016).ADS 
CAS 

Google Scholar 
Connell, S. D. & Russell, B. D. The direct effects of increasing CO2 and temperature on non-calcifying organisms: increasing the potential for phase shifts in kelp forests. Proc. R. Soc. B Biol. Sci. 277, 1409–1415 (2010).
Google Scholar 
Cornwall, C. E., Comeau, S. & McCulloch, M. T. Coralline algae elevate pH at the site of calcification under ocean acidification. Glob. Change Biol. 23, 4245–4256 (2017).ADS 

Google Scholar 
Martin, S. & Gattuso, J.-P. Response of Mediterranean coralline algae to ocean acidification and elevated temperature. Glob. Change Biol. 15, 2089–2100 (2009).ADS 

Google Scholar 
Cornwall, C. E. et al. Diffusion boundary layers ameliorate the negative effects of ocean acidification on the temperate coralline macroalga Arthrocardia corymbosa. PLoS ONE 9, e97235 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gefen-Treves, S. et al. The microbiome associated with the reef builder Neogoniolithon sp. in the eastern Mediterranean. Microorganisms 9, 1374 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Johnson, C. R. & Mann, K. H. The crustose coralline alga, Phymatolithon Foslie, inhibits the overgrowth of seaweeds without relying on herbivores. J. Exp. Mar. Biol. Ecol. 96, 127–146 (1986).
Google Scholar 
Keats, D. W., Knight, M. A. & Pueschel, C. M. Antifouling effects of epithallial shedding in three crustose coralline algae (Rhodophyta, Coralinales) on a coral reef. J. Exp. Mar. Biol. Ecol. 213, 281–293 (1997).
Google Scholar 
Mancuso, F., D’Hondt, S., Willems, A., Airoldi, L. & Clerck, O. Diversity and temporal dynamics of the epiphytic bacterial communities associated with the canopy-forming seaweed Cystoseira compressa (Esper) Gerloff and Nizamuddin. Front. Microbiol. 7, 476 (2016).PubMed 
PubMed Central 

Google Scholar 
Blanfuné, A., Boudouresque, C. F., Verlaque, M. & Thibaut, T. The ups and downs of a canopy-forming seaweed over a span of more than one century. Sci. Rep. 9, 1–10 (2019).
Google Scholar 
Cebrian, E. et al. A roadmap for the restoration of Mediterranean macroalgal forests. Front. Mar. Sci. 8, 1456 (2021).
Google Scholar 
Gianni, F. et al. Conservation and restoration of marine forests in the Mediterranean Sea and the potential role of Marine Protected Areas. Adv. Oceanogr. Limnol. 4, 83–101 (2013).
Google Scholar 
Gorman, D. & Connell, S. D. Recovering subtidal forests in human-dominated landscapes. J. Appl. Ecol. 46, 1258–1265 (2009).
Google Scholar 
Riquet, F. et al. Highly restricted dispersal in habitat-forming seaweed may impede natural recovery of disturbed populations. Sci. Rep. 11, 16792 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Halpern, B. S., McLeod, K. L., Rosenberg, A. A. & Crowder, L. B. Managing for cumulative impacts in ecosystem-based management through ocean zoning. Ocean Coast. Manag. 51, 203–211 (2008).
Google Scholar 
Verdura, J., Sales, M., Ballesteros, E., Cefalì, M. E. & Cebrian, E. Restoration of a canopy-forming alga based on recruitment enhancement: Methods and long-term success assessment. Front. Plant Sci. 9, 1832 (2018).PubMed 
PubMed Central 

Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
CAS 

Google Scholar 
Dickson, A. G., Sabine, C. L. & Christian, J. R. Guide to Best Practices for Ocean CO2 Measurements. https://repository.oceanbestpractices.org/handle/11329/249 (2007).Spencer Davies, P. Short-term growth measurements of corals using an accurate buoyant weighing technique. Mar. Biol. 101, 389–395. https://doi.org/10.1007/BF00428135 (1989).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. ArXiv14065823 Stat (2015).R: The R Project for Statistical Computing. https://www.r-project.org/.Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, 2018).
Google Scholar 
Lenth, R. V. et al. emmeans: Estimated Marginal Means, aka Least-Squares Means (2022). More

Mangrove cover change variables. We used the Global Mangrove Watch (GMW) v2.0 dataset from 1996 to 201656 to calculate four response variables across landscape mangrove geomorphic units24 over two time periods, 1996–2007 and 2007–2016: (1) percent net loss (units that had a net change in mangrove cover of 0), (3) percent gross loss (units that had a decrease in mangrove cover, not accounting for any increase), and (4) percent gross gain (units that had an increase in mangrove cover, not accounting for any decrease). Percent variables were calculated relative to the area at the start of the time period and were log transformed to meet the assumptions of the statistical models. We initially also considered 5 primary response variables (Supplementary Table 3), including net change in mangrove area ranging from negative (loss) to zero (no change) to positive (gain), however, the data did not meet model assumptions of equal variance (Supplementary Table 9). It was therefore necessary to separate areas of net loss and net gain and areas of gross loss and gross gain to remove zeros and log-transform to achieve normal distribution. Area of mangrove change was correlated with size of the mangrove geomorphic unit (higher area of mangrove loss or gain in bigger units), therefore we included geomorphic unit size as an explanatory variable in the models with primary response variables. We selected the transformations of these primary variables – percent net loss, percent net gain, percent gross loss, and percent gross gain to include in the analysis, because the percent changes control for differences in relative sizes of geomorphic units and because net change alone can underestimate the extent of change57.Examining mangrove change across geomorphic settings is likely to be relevant to socioeconomic and environmental conditions. Mangroves occur in the intertidal zone in diverse coastal geomorphic settings (e.g., deltas, estuaries, lagoons) shaped by rivers, tides, and waves58,59. The distribution, structure, and productivity of mangroves varies spatially with regional climate and local geomorphological processes (e.g., river discharge, tidal range, hydroperiod, and wave activity) that control soil biogeochemistry60,61,62,63. These geomorphic settings are defined by natural landscape boundaries (e.g., catchments/bays) which also often delineate boundaries of human settlements. A global mangrove biophysical typology v2.2 dataset64 was used for the delineation of landscape mangrove geomorphic units, which used a composite of the GMW dataset from the 1996, 2007, 2010, and 2016 timesteps to classify the maximal extent of mangrove cover into 4394 units (classified as delta, estuarine, lagoon or open coast). The mangrove geomorphic units do not include non-mangrove patches, unless they have been lost from the unit over time. The mean size of geomorphic units was 33.63 ha. Some splits of geomorphic units were undertaken to reduce size and divide by country boundaries. The four largest deltas (northern Brazil Delta ID 70000, Sundarbans Delta ID 70004, Niger Delta ID 70009, and Papua coast Delta ID 70013) were split into 4, 5, 4, and 2 units, respectively to aid with data processing. Mangrove geomorphic units that overlapped two countries (Peru/Ecuador, Singapore/Malaysia, and Papua New Guinea/Australia) were split by the national boundary.The country governing each geomorphic unit was assigned to match national-level variables to geomorphic units. To capture mangroves that are mapped outside of country coastline boundaries, we did a union of the GADM country shapefile v3.665 and the Exclusive Economic Zones (EEZs) v1166. The following manual country designations were made to resolve overlapping claims in the EEZs: (1) Hong Kong was merged with China as Hong Kong does not have a mapped EEZ; (2) The overlapping claim of Sudan/Egypt was maintained as a joint Sudan/Egypt designation, as this is an area of disputed land called the Halayib Triangle. However, for this study, mangrove units within this area were assigned to Egypt because Egypt currently has military control over the area; (3) Mayotte (claimed by France and Comoros) was assigned to Mayotte as it is a separate overseas territory of France recognised in GADM that has different socioeconomic variables; (4) The protected zone established under the Torres Strait Treaty was assigned to Australia as these islands are Australian territory.Areas of mangrove cover in 1996, 2007, and 2016, and gross losses and gains in each geomorphic units over the two time periods were assessed in ArcMap 10.867. Percent losses and gains were calculated in R 4.0.268. In using the GMW mapping, a minimum mapping unit of 1 ha is recommended for reliable results5, therefore we removed all geomorphic units less than 1 ha from the analysis, which reduced the available sample size from 4394 across 108 countries to 4235 units across 108 countries. In calculating percent net gains, 11 and 12 of the units returned infinity values for 1996–2007 and 2007–2016, respectively, because there was no initial mangrove cover. In these instances, 100% gain was assigned to these units.Socioeconomic variables (Supplementary Table 4)Economic growthPrevious global analyses of mangroves have been limited by data availability on economic activity to national metrics, such as a country’s Gross Domestic Product (GDP)12,18. Night-time lights satellite data provide local measures of economic activity that are comparable through time and available globally9,69. The data improve estimates of GDP in low to middle income countries69 and are strongly correlated with local indicators of human development70 and electricity consumption and GDP at the national-level71. We used the Night-time Lights Time Series v472 stable lights data, where transient lights that are deemed ephemeral, e.g., fires, have been filtered out and non-lit areas set to zero73, choosing the newer satellites where applicable70. As a proxy for local economic growth, we calculated the change in annual average stable lights within a 100 km buffer of the centroid of each geomorphic unit from 1996 to 2007 and 2007 to 2013 (no data available past 2013) using the ‘raster’ package in R74. The 100 km buffer was chosen to account for pressures from human activity within and surrounding the mangrove area, and to avoid bias with larger spatial units70.Market accessibilityTravel time to the nearest major market (national or provincial capital, landmark city, or major population centre) has been shown to be a stronger predictor of fish biomass on coral reefs than population density or linear distance to markets27. We used the global map of travel time to cities for 201575 to estimate the average travel time from each geomorphic unit to the nearest city via surface transport using the ‘raster’ package in R74, as an indicator of access to markets to trade commodities (e.g., rice, shrimp, palm oil).Economic complexityPrevious studies have examined the effect of GDP on mangrove change18, however, this is a blunt measure of country capability. Measuring a country’s economic complexity, that is the diversified capability of a nation’s economy, is preferable. For example, a country with high GDP but low economic complexity can be prone to regulatory capture by high-value natural resource industries and resource corruption26. Therefore, we used the Economic Complexity Index (ECI)76 for countries as an indicator of regulatory independence. The ECI had better coverage of countries in later years (Supplementary Table 4), therefore the ECI for the end of the time periods was used (2007 and 2016), although we recognise this may reduce the detection of trends because of potential time lags in impacts.DemocracyWe used the Varieties of Democracy (VDEM) index v10 which measures a country’s degree of freedom of association, clean elections, freedom of expression, elected executives, and suffrage77, and has been indicated to influence NDC ambition in countries to address climate change78. We adopted the VDEM index for the start of the time periods (1996 and 2007) to account for potential time lags in impacts.Community forestry supportWe determined the extent that community forestry (CF) is implemented across countries through a systematic review of articles returned in the Web of Science database (Core collection; Thomson Reuters, New York, U.S.A.). We used the search terms: TS = (“community forestry” OR “community-based forestry” OR “social forestry”) AND (TI = ”country” OR AB = ”country”) to identify how many CF case studies were reported in each country, and whether any were in mangroves. As scientific literature is biased towards particular regions, we also reviewed relevant FAO global studies79,80,81 and online databases (ICCA registry82 and REDD projects database83) to identify additional case studies (Supplementary Fig. 5). We then generated scores of 0–3 for each country based on summing values assessed using these criteria: +1 (1–50 CF case studies); +2 ( >50 CF case studies); +1 (CF case study in mangroves). There may have been some double counting as we counted the number of case studies in each article, and we will have missed CF projects not published or communicated in English. However, this is likely to have had a limited impact on the scoring method.Indigenous landThe proportion of Indigenous peoples’ land versus other land per country was calculated from national-level data84. Whilst this study involved Indigenous peoples’ land mapping at a global scale, the spatial data was not published, and thus we could only evaluate the influence of Indigenous land at the national level rather than local level.Restoration effortThe number of mangrove restoration sites per country was calculated from combining two datasets collated by C. Lovelock (2020) and Y.M. Gatt and T.A. Worthington (2020) identifying mangrove restoration project locations from web searches in English and for scientific and grey literature using Google Scholar. Duplications were removed and the number of sites was used as an indicator of effort. This will underrepresent effort in countries with few, large sites, and where restoration projects are not published or communicated in English.Climate commitmentsThe Paris Agreement is a global programme for countries to commit to climate action by submitting Nationally Determined Contributions (NDCs) to the United Nations Framework for the Convention of Climate Change (UNFCCC). First, we reviewed NDCs for mangrove-holding nations from the NDC Registry85 submitted as of 07/01/2021 to determine the extent that mangroves or coastal ecosystems were included in national climate policy (scoring method in Supplementary Table 4). We hypothesised that countries with mangrove or coastal ecosystem NDCs may be more likely to promote mangrove conservation or restoration. While the first NDCs were submitted around 2015, at the end of our time series, we suspected higher commitments would point towards a stronger baseline in environmental governance. Most countries submitted updated or second NDCs during 2021 however these were not considered relevant to the time periods assessed. Google Translate was used to interpret NDCs in languages other than English.Ramsar wetlandsThe ecological character of Ramsar wetlands have been found to be significantly better than those of wetlands generally86. The area of Ramsar coastal and marine wetlands from the Ramsar Sites Information Service87 was calculated per country. Thirty-eight mangrove-holding countries are not signatories to the Ramsar Convention, and these countries were assigned a value of 0. The area of Ramsar wetlands per country was scaled by dividing by the country’s area of mangroves in 1996.Environmental governanceWe assessed the Environmental Performance Index (EPI)88 as an indicator of a country’s effectiveness in environmental governance. The biodiversity and habitat (BDH) issue category assesses countries’ actions toward retaining natural ecosystems and protecting the full range of biodiversity within their borders. We took the BDH score for 2020 for the 2007–2016 time period and the BDH score for 2010 for the 1996–2007 time period (calculated by subtracting the ten-year change from BDH 2020). However, due to collinearity with other variables this index was excluded from the analysis (see statistical analysis).Protected area managementWe also assessed Marine Protected Area (MPA) staff capacity as an indicator of the effectiveness of management of protected areas for countries. We used published global marine protected area (MPA) management data14 which is based on the Management Effectiveness Tracking Tool (METT), the World Bank MPA Score Card, and the NOAA Coral Reef Conservation Programme’s MPA Management Assessment Checklist. Adequate staff capacity was the most important factor in explaining fish responses to MPA management globally, followed by budget capacity, but they were significantly correlated14. We, therefore, calculated the mean staff capacity across MPAs per country as our indicator. Mangroves can be included in terrestrial protected areas, which are not represented in this dataset, however, this measure provides an indicator of national governance of protected areas. However, due to collinearity with other variables this indicator was excluded from the analysis (see statistical analysis). The extent of protected areas was not included in the analysis because it has already been found to influence mangrove loss18.Biophysical variables (Supplementary Table 5)Coastal geomorphic typeMangrove extent change likely varies among different coastal geomorphic settings because human activities or environmental changes occur more commonly in some geomorphic settings than others. For example, losses of lagoonal mangroves were nearly twice as large as those in other geomorphic types24. Landscape geomorphic units from the global mangrove typology dataset v2.264 were classified as delta, estuary, lagoon or open coast.Sediment availabilityMangrove expansion and retreat are driven by sediment deposition and erosion, which are influenced by sediment availability from rivers and wave action, and alterations in hydrodynamic regimes47,89. We used the sediment trapping index from the global free-flowing rivers (FFR) dataset90 to indicate sediment availability from rivers within different geomorphic units. A mangrove catchment dataset was created based on the HydroSHEDS database91. River networks that intersected with mangrove geomorphic units were linked to that unit’s ID. Where rivers intersected multiple units, they were manually assigned by visual inspection. River basins that intersected either with the geomorphic units directly or the river networks were also linked to that unit’s ID. The FFR dataset90 was then spatially joined to the mangrove catchment dataset to identify the most downstream (i.e., the coastal outlet) segment of each FFR and its associated sediment trapping index. Not all geomorphic units (n = 3475) were linked to an FFR, however, an individual unit could be linked with several FFRs. Therefore, the unit sediment trapping index was the weighted mean of the river values, with weighting based on each FFR’s average long-term (1971–2000) naturalised discharge (m3s−1), with discharge set to the minimum value for segments with zero flow. Geomorphic units without connecting FFRs were given an index of zero (no sediment trapping). The sediment trapping index represents the percentage of the potential sediment load trapped by anthropogenic barriers along the river section. The focus on river barriers may obscure larger scale oceanic patterns that influence mangrove losses and gains (e.g., movement of mud banks from the Amazon River over 1000’s of kilometres92) or increases in sediment that could be coming from soils with catchment deforestation and erosion.Habitat fragmentationMany countries with high mangrove loss have been associated with elevated fragmentation of mangrove forests, although the relationship is not consistent at the global scale93. We calculated the clumpiness index of mangrove patches within geomorphic units within each time period, as this habitat fragmentation metric is independent of areal extent93. Whilst habitat fragmentation can be human-driven, clumpiness measures the patchy distribution of mangroves, which can also be due to natural factors inducing edge effects. We used a similar approach to Bryan-Brown, et al.86 to quantify the clumpiness index. The ‘landscape’ was defined as the combined extent of the mangrove geomorphic units across four timesteps (1996, 2007, 2010, and 2016) from the GMW dataset56. For the three focal years in this study (1996, 2007, and 2016) each geomorphic unit (n = 4394) was converted into a two-class polygon, where class one represented mangroves present during that time step and class two mangroves present in the other time steps (i.e., areas of mangrove loss). The polygons were transformed to a projected coordinate system (World Cylindrical Equal Area) and converted to rasters with a resolution of 25 m. Each raster was imported into R version 3.6.394, with clumpiness calculated using the package ‘landscapemetrics’ v1.5.095.Clumpiness describes how patches are dispersed across the landscape and ranges between minus one, where patches are maximally disaggregated, to one, where patches are maximally aggregated, a value of zero represents a case whereby patches are randomly distributed across the landscape. The clumpiness index requires that both classes are present in the landscape, therefore a no data value (NA) was returned for units where no loss of mangroves had occurred, or where there was 100% gain of mangroves in a later time period. The number of directions in which patches were connected was set to eight. The following manual fixes were conducted for NA values returned: 1) Where NA was returned for units where no loss of mangroves had occurred in another time period, i.e., class 1 (mangrove present) = 1 and class 2 (mangrove loss) = 0, assume +1 (maximally clumped); and 2) Where NA was returned for units where there was 100% gain of mangroves in a later time period, i.e., class 1 (mangrove present) = 0, class 2 (mangrove present) = 1 (100% gain), assume −1 (maximally disaggregated).Tidal amplitudeIn settings of low tidal range, mangrove vertical accretion is less likely to keep pace with rapid sea level rise3. However, in settings of high tidal range, mangroves may be more extensive and vulnerable to conversion to aquaculture or agriculture because of larger tidal flat extents. The Finite Element Solution global tide model (FES2014)96 is considered one of the most accurate tide models for shallow coastal areas97 and was selected to estimate the mean tidal amplitude within each geomorphic unit using the principal lunar semi-diurnal or M2 tidal amplitude as this is this most dominant tidal constituent98. To account for potential variation in the tidal amplitude across large geomorphic units, the raster pixel value for M2 tidal amplitude96 closest to the centroid of each mangrove patch within each unit was calculated, with the smallest value set at 0.01 m. For each geomorphic unit, the tidal amplitude was calculated as the weighted mean of the patch values, with weighting based on the patch area relative to the total unit area.Antecedent sea-level riseThe distribution of mangroves on shorelines changes over time with sediment accretion, erosion, subsidence, and sea-level rise (SLR)99, and periods of low sea level can cause mangrove dieback100. We used regional mean sea-level trends between January 1993 and December 2015 from the global sea level Essential Climate Variable (ECV) product v.2101,102 to estimate the mean antecedent SLR for each geomorphic unit. Spatial variation in regional sea-level trends generally range between −5 and +5 mm yr−1 (global mean of 3 mm yr−1)13. Extreme values ( >5 mm yr−1) observed in the dataset are subject to high levels of uncertainty (Sea Level CCI team, pers. comm.), and were therefore truncated to 5 mm yr−1. The raster pixel value for SLR102 closest to the centroid of each mangrove patch within each geomorphic unit was calculated. The geomorphic unit antecedent SLR values was calculated as the weighted mean of the patch values within the unit.DroughtWhilst long-term precipitation and temperature influence mangrove distribution globally62, periods of low rainfall have been reported to cause extensive mangrove dieback at regional scales, particularly when combined with high temperatures and low sea levels103. We used the Standardized Precipitation-Evapotranspiration Index (SPEI) from the global SPEI database v.2.6104 as an index of drought severity. SPEI is derived from precipitation and temperature and is considered an improved drought index that allows spatial and temporal comparability105,106. The mean SPEI raster pixel value was calculated for each time period and then averaged across the geomorphic units using the ‘ncdf4’107 and ‘raster’ packages74 in R.Tropical storm frequencyLarge-scale destruction of mangroves across regions have been reported from strong winds, high energy waves, and storm surges associated with tropical storms108. We used the International Best Track Archive for Climate Stewardship (IBTrACS) dataset since 1980 v4109 to calculate the number of tropical cyclone occurrences (points along their paths) within a 200 km buffer of the centroid of geomorphic units within each time period using the sf package110 in R. Maximum wind velocity and surface pressures are likely experienced within 100 km of a cyclone’s eye111, therefore the 200 km buffer zone was selected to cover the average size of geomorphic units (33.63 ha), and all tropical storms potentially influencing mangrove growth. Whilst tropical storms affect only 42% of the world’s mangroves60, they are likely to be important stressors within cyclone-impacted countries.Minimum temperatureExtreme low temperature events were a driver of mangrove loss in subtropical regions, such as Florida and Louisianan of the US, and China28,112. We used the WorldClim bioclimatic variable 6 (minimum temperature of the coldest month averaged for the years 1970–2000)113 to calculate the mean minimum temperature across the geomorphic units using the ‘sf’110 and ‘raster’ packages74 in R. Where NAs were returned due to no overlapping raster layer, the value of the closest raster pixel to the centroid of the geomorphic unit was assigned.Statistical analysisWe used multi-level linear modelling to investigate relationships between mangrove cover change variables and socioeconomic and biophysical variables to consider landscape (level 1) and country (level 2) predictors in a hierarchical approach114. For each response variable, we modelled the response for 1996–2007 and 2007–2016, using explanatory variables specific to the time-period where available. Data inspection revealed that high percent loss or gain was concentrated in small geomorphic units, therefore to avoid bias in our results, we removed geomorphic units less than 100 ha from the analysis, which further reduced the available sample size to 3134 units across 95 countries. Statistical analysis was undertaken in R 4.0.268.The response variables were log-transformed to fit normal distribution. We tested for collinearity between our explanatory variables using Pearson’s correlation coefficient (r  > 0.5) (Supplementary Tables 6 and 7). MPA staff capacity and EPI were excluded from our models because MPA staff capacity was correlated with ECI 2007 and ECI 2016 (both r = 0.54), and EPI 2020 was correlated with VDEM 2016 (r = 0.63). To improve model fit, travel time to the nearest city, mangrove restoration effort and Ramsar wetland area (relative) were log+1-transformed, and tidal amplitude was log-transformed.Two linear multi-level (mixed-effects) models were fitted for each response variable using the lme function in the ‘lme4’ package115 (Supplementary Table 8). First, a random intercept model with intercepts of landscape-level predictors varying by country was fitted. Then a random intercept and slope (coefficients) model with intercepts of landscape-level predictors varying by country, as well as slopes for socioeconomic predictors considered to have between-country variation (travel time to nearest city and night-time lights growth) was fitted, as we expect that mangrove cover change may respond to economic growth and market accessibility depending on national governance. A likelihood ratio test between the null linear model and the null random intercept model for each response variable showed that effects varied across countries and therefore we included country as a random effect (Supplementary Table 9). We also conducted likelihood ratio tests between the random intercept model and the random coefficient model to test whether the effect of travel time and night-time lights on mangrove change varies across countries. If significant, the model including random slopes for travel time and night-time lights was used (Supplementary Table 9). Mixed-effects models were fitted by maximum likelihood and model fit was validated by inspection of residual plots for the four response variables included in the analysis; percent net loss, percent net gain, percent gross loss, and percent gross gain (Supplementary Table 9).To test for spatial autocorrelation we performed spatial autoregressive (SAR) models using the errorsarlm function in the ‘spatialreg’ package116. SAR models were first fitted using a range of neighbourhood distances (50, 500, and 1000 km in 100 km intervals) for the net change variable117. Distance of 500 km showed the smallest AIC and was therefore adopted for all response variables. Neighbourhood lists of the centroid coordinates of the geomorphic units were defined with the row-standardised (‘W’) coding using the ‘spdep’ package118. We then produced Moran’s I correlograms using the correlog function in the ‘ncf’ package119 and the centroid coordinates of the geomorphic units. Correlograms for the multi-level model and SAR model were compared for each response variable (Supplementary Fig. 4). The SAR models did not improve spatial autocorrelation for any of the mangrove cover change variables and therefore the multi-level models were adopted.Hotspot estimatesWe defined hotspots as geomorphic units where raw values of percent net and gross loss and gain between 2007 and 2016 ((gamma)) differed by more than two standard deviations (sd) from the country average ((mu)).$${{{{{{rm{More}}}}}}},{{{{{{rm{loss}}}}}}}/{{{{{{rm{more}}}}}}},{{{{{{rm{gain}}}}}}}=left(gamma -mu right) , > , (2,times {{{{{{rm{sd}}}}}}})$$
(1)
$${{{{{{rm{Less}}}}}}},{{{{{{rm{loss}}}}}}},/,{{{{{{rm{less}}}}}}},{{{{{{rm{gain}}}}}}}=left(gamma -mu right) , < , -(2,times {{{{{{rm{sd}}}}}}})$$ (2) We excluded countries with only one geomorphic unit. Large deviations of the raw value from the country average were found for small units at a threshold below 50 km2, therefore we removed all units smaller than 50 km2 to overcome bias of hotspots towards smaller sites. This likely removed the identification of several hotspots. For example, Myanmar has had some large gains due to river sediments in the Gulf of Martaban (net gain of 100 % in Estuary 5834 and 39 % in Open Coast 62244), however, these areas were small (8 and 2 km2, respectively) and were therefore removed from the hotspot estimates.We analysed the factors contributing to hotspots by spatial investigation of satellite imagery in Google Earth with mangrove specialists from those countries. The hotspots were also assessed against protected area datasets for those countries120,121,122,123.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

A health-care worker in Chicago, Illinois, administers a COVID-19 vaccine aimed at the Omicron subvariant.Credit: Scott Olson/Getty

Omicron boosters protect against future variantsBooster shots against current SARS-CoV-2 variants can help to arm the human immune system against variants yet to arise. That’s the implication of two studies (W. B. Alsoussi et al. Preprint at bioRxiv https://doi.org/jhht (2022); C. I. Kaku et al. Preprint at bioRxiv https://doi.org/jhhv; 2022) that analysed how a booster shot or breakthrough infection affects antibody-producing cells. The work shows that some cells evolve to exclusively create antibodies targeting new strains, whereas others make antibodies against both new and old strains.The findings have not been peer reviewed, but provide reassurance that vaccines targeting the Omicron variant will be effective. Their utility had been questioned because of evidence that the immune system has trouble pivoting between variants.One study examined people who became infected with Omicron after receiving the original vaccine. One month after infection, nearly 97% of participants’ antibodies against the virus bound to the original strain better than to Omicron BA.1. But six months after infection, nearly half of their B cells produced antibodies that bound to Omicron BA.1 better than to the original strain — showing that the immune system continued to adapt long after the infection had passed.

White Island, also called Whakaari, is one of New Zealand’s most active volcanos.Credit: Phil Walter/Getty

Charge dropped in New Zealand volcano caseVolcanologists have applauded a judge’s decision to dismiss one of two criminal charges against New Zealand’s Earth-science research agency, GNS Science. The charges were laid in the wake of a fatal 2019 volcanic eruption on Whakaari White Island, a popular tourist destination, that killed 22 people and injured 25 others.GNS Science issues volcanic-alert bulletins for the country’s active volcanoes, which are disseminated to the media, emergency-response agencies and the public through a service called GeoNet. The dismissed charge alleged that GNS Science should have coordinated with tour operators and other agencies and reviewed its volcanic-alert bulletins to ensure that they effectively communicated the implications of volcanic activity on the island.With the charge dismissed, scientific organizations that provide information on public health and safety risks can now “breathe a bit of a sigh of relief”, says Simon Connell, a lawyer at the University of Otago in Dunedin, New Zealand.GNS Science is also charged with having failed to ensure the health and safety of helicopter pilots whom it hired to take its employees to the island. This charge will go to trial. GNS Science has pleaded not guilty.

A herd of Asian elephants wandered out of their nature reserve in southwestern China last year.Credit: Wang Zhengpeng/VCG via Getty

Asian elephants mostly roam outside protected areas — and it’s a problemAsian elephants spend most of their time outside protected areas because they prefer the food that they find there, an international team of scientists reports. But this behaviour is putting the animals and people in harm’s way, say researchers.If protected areas do not contain animals’ preferred habitats, they will wander out, says Ahimsa Campos-Arceiz, who studies Asian elephants (Elephas maximus) at the Chinese Academy of Sciences’ Xishuangbanna Tropical Botanical Garden in Menglun, China.Human–elephant conflict is the biggest threat for Asian elephants. Over the past few decades, animals in protected areas have increasingly wandered into villages. They often cause destruction, damaging crops and infrastructure and injuring and even killing people.Campos-Arceiz and his colleagues set out to get a precise picture of Asian-elephant movements. They collared 102 individuals in Peninsular Malaysia and Borneo, recording 600,000 GPS locations over a decade. They found that elephants tend to spend most of their time in habitats outside the protected areas, at the forest edge and in areas of regrowth. The findings were published in the Journal of Applied Ecology (J. A. de la Torre et al. J. Appl. Ecol. https://doi.org/gq28qp; 2022) on 18 October.The researchers suspect that the elephants venture out because they like to eat grasses, bamboo, palms and fast-growing trees, which are commonly found in disturbed forests and are relatively scarce under the canopy of old-growth forests.Philip Nyhus, a conservation biologist who specializes in human–wildlife conflict at Colby College in Waterville, Maine, says that Asian elephants live deep in dense forest and so are much more difficult to study than African elephants, which roam open savannahs. “The sample size is impressive,” he says.The research provides strong evidence for how to set up suitable protected areas that reduce the risk of elephants wandering out, he says.The results do not diminish the importance of protected areas, which provide long-term safety for the animals, says Campos-Arceiz. “But they are clearly not enough.” More

Meredith, M. et al. Polar regions. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Pörtner, H.‐O. et al. Eds.). 203–320 (2019).Nummelin, A., Ilicak, M., Li, C. & Smedsrud, L. H. Consequences of future increased Arctic runoff on Arctic Ocean stratification, circulation, and sea ice cover. J. Geophys. Res. Oceans 121, 617–637 (2016).ADS 

Google Scholar 
Smedsrud, L. H., Sorteberg, A. & Kloster, K. Recent and future changes of the Arctic sea-ice cover. Geophys. Res. Lett. 35, L20503 (2008).ADS 

Google Scholar 
Ardyna, M. & Arrigo, K. R. Phytoplankton dynamics in a changing Arctic Ocean. Nat. Clim. Change 10, 892–903. https://doi.org/10.1038/s41558-020-0905-y (2020).Article 
ADS 
CAS 

Google Scholar 
Tripathy, S. C. et al. Summer variability in bio-optical properties and phytoplankton pigment signatures in two adjacent high Arctic fjords, Svalbard. Int. J. Environ. Sci. Technol. https://doi.org/10.1007/s13762-021-03767-4 (2021).Article 

Google Scholar 
Sagan, S. & Darecki, M. Inherent optical properties and particulate matter distribution in summer season in waters of Hornsund and Kongsfjordenen, Spitsbergen. Oceanologia 60, 65–75 (2018).
Google Scholar 
Mouginot, J. et al. Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018. in Proceedings of the National Academy of Sciences of the United States of America. Vol. 116. 9239–9244. Preprint at https://doi.org/10.1073/pnas.1904242116 (2019).Rignot, E., Jacobs, S., Mouginot, J. & Scheuchl, B. Ice-shelf melting around antarctica. Science 1979(341), 266–270 (2013).ADS 

Google Scholar 
Konik, M., Darecki, M., Pavlov, A. K., Sagan, S. & Kowalczuk, P. Darkening of the Svalbard Fjords waters observed with satellite ocean color imagery in 1997–2019. Front. Mar. Sci. 8, 27 (2021).
Google Scholar 
IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (2022).Szeligowska, M. et al. The interplay between plankton and particles in the Isfjorden waters influenced by marine- and land-terminating glaciers. Sci. Total Environ. 780, 146491 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Trudnowska, E., Dąbrowska, A. M., Boehnke, R., Zajączkowski, M. & Blachowiak-Samolyk, K. Particles, protists, and zooplankton in glacier-influenced coastal svalbard waters. Estuar. Coast Shelf Sci. 242, 106842 (2020).
Google Scholar 
Maekakuchi, M., Matsuno, K., Yamamoto, J., Abe, Y. & Yamaguchi, A. Abundance, horizontal and vertical distribution of epipelagic ctenophores and scyphomedusae in the northern Bering Sea in summer 2017 and 2018: Quantification by underwater video imaging analysis. Deep Sea Res. 2 Top. Stud. Oceanogr. 181–182, 104818 (2020).
Google Scholar 
Norrbin, F., Eilertsen, H. C. & Degerlund, M. Vertical distribution of primary producers and zooplankton grazers during different phases of the Arctic spring bloom. Deep Sea Res. 2 Top. Stud. Oceanogr. 56, 1945–1958 (2009).
Google Scholar 
Stemmann, L. et al. Vertical distribution (0–1000 m) of macrozooplankton, estimated using the Underwater Video Profiler, in different hydrographic regimes along the northern portion of the Mid-Atlantic Ridge. Deep Sea Res. 2 Top. Stud. Oceanogr. 55, 94–105 (2008).
Google Scholar 
Arendt, K. E. et al. Effects of suspended sediments on copepods feeding in a glacial influenced sub-Arctic fjord. J. Plankton Res. 33, 1526–1537 (2011).CAS 

Google Scholar 
Arimitsu, M., Piatt, J. & Mueter, F. Influence of glacier runoff on ecosystem structure in Gulf of Alaska fjords. Mar. Ecol. Prog. Ser. 560, 19–40 (2016).ADS 

Google Scholar 
Renner, M., Arimitsu, M. L. & Piatt, J. F. Structure of marine predator and prey communities along environmental gradients in a glaciated fjord. Can. J. Fish. Aquat. Sci. 69, 2029–2045 (2012).
Google Scholar 
Lydersen, C. et al. The importance of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway. J. Mar. Syst. 129, 452–471. https://doi.org/10.1016/j.jmarsys.2013.09.006 (2014).Article 

Google Scholar 
Falk-Petersen, S., Pavlov, V., Timofeev, S. & Sargent, J. R. Climate variability and possible effects on arctic food chains: The role of Calanus. in Arctic Alpine Ecosystems and People in a Changing Environment. 147–166. https://doi.org/10.1007/978-3-540-48514-8_9 (Springer, 2007).Stempniewicz, L. et al. Visual prey availability and distribution of foraging little auks (Alle alle) in the shelf waters of West Spitsbergen. Polar Biol. 36, 949–955 (2013).
Google Scholar 
CAFF. Arctic Coastal Biodiversity Monitoring Plan (CAFF Monitoring Series Report No. 29). (2019).Arendt, K. E., Nielsen, T. G., Rysgaard, S. & Tönnesson, K. Differences in plankton community structure along the Godthåbsfjord, from the Greenland Ice Sheet to offshore waters. Mar. Ecol. Prog. Ser. 401, 49–62 (2010).ADS 
CAS 

Google Scholar 
Blachowiak-Samolyk, K. et al. Arctic zooplankton do not perform diel vertical migration (DVM) during periods of midnight sun. Mar. Ecol. Prog. Ser. 308, 101–116 (2006).ADS 

Google Scholar 
Cottier, F. R., Tarling, G. A., Wold, A. & Falk-Petersen, S. Unsynchronized and synchronized vertical migration of zooplankton in a high arctic fjord. Limnol. Oceanogr. 51, 2586–2599 (2006).ADS 

Google Scholar 
Hobbs, L. et al. A marine zooplankton community vertically structured by light across diel to interannual timescales. Biol Lett 17, 20200810 (2021).PubMed 
PubMed Central 

Google Scholar 
Wallace, M. I. et al. Comparison of zooplankton vertical migration in an ice-free and a seasonally ice-covered Arctic fjord: An insight into the influence of sea ice cover on zooplankton behavior. Limnol. Oceanogr. 55, 831–845 (2010).ADS 

Google Scholar 
Bandara, K. et al. Seasonal vertical strategies in a high-Arctic coastal zooplankton community. Mar. Ecol. Prog. Ser. 555, 49–64 (2016).ADS 

Google Scholar 
Rabindranath, A. et al. Seasonal and diel vertical migration of zooplankton in the High Arctic during the autumn midnight sun of 2008. Mar. Biodivers. 41, 365–382 (2011).
Google Scholar 
Piwosz, K. et al. Comparison of productivity and phytoplankton in a warm (Kongsfjorden) and a cold (Hornsund) Spitsbergen fjord in mid-summer 2002. Polar Biol. 32, 549–559 (2009).
Google Scholar 
Frank, T. M. & Widder, E. A. Effects of a decrease in downwelling irradiance on the daytime vertical distribution patterns of zooplankton and micronekton. Mar. Biol. 140, 1181–1193 (2002).
Google Scholar 
Ortega, J. C. G., Figueiredo, B. R. S., da Graça, W. J., Agostinho, A. A. & Bini, L. M. Negative effect of turbidity on prey capture for both visual and non-visual aquatic predators. J. Anim. Ecol. 89, 2427–2439. https://doi.org/10.1111/1365-2656.13329 (2020).Article 
PubMed 

Google Scholar 
Aksnes, D. et al. Coastal water darkening and implications for mesopelagic regime shifts in Norwegian fjords. Mar. Ecol. Prog. Ser. 387, 39–49 (2009).ADS 
CAS 

Google Scholar 
Urbanski, J. A. et al. Subglacial discharges create fluctuating foraging hotspots for sea birds in tidewater glacier bays. Sci. Rep. 7, 1–12 (2017).
Google Scholar 
Weslawski, J. M., Pedersen, G., Petersen, S. F. & Porazinski, K. Entrapment of macroplankton in an Arctic fjord basin, Kongsfjorden, Svalbard. Oceanologia 42, 1 (2000).
Google Scholar 
Berge, J. et al. Arctic complexity: A case study on diel vertical migration of zooplankton. J. Plankton Res. 36, 1279–1297 (2014).PubMed 
PubMed Central 

Google Scholar 
Darnis, G. et al. From polar night to midnight sun: Diel vertical migration, metabolism and biogeochemical role of zooplankton in a high Arctic fjord (Kongsfjorden, Svalbard). Limnol. Oceanogr. 62, 1586–1605 (2017).ADS 
CAS 

Google Scholar 
Descamps, S. et al. Climate change impacts on wildlife in a High Arctic archipelago – Svalbard, Norway. Glob. Chang Biol. 23, 490–502 (2017).ADS 
PubMed 

Google Scholar 
Cottier, F. R. et al. Arctic fjords: A review of the oceanographic environment and dominant physical processes. Geol. Soc. Spec. Publ. 344, 35–50 (2010).ADS 

Google Scholar 
Inall, M. E., Nilsen, F., Cottier, F. R. & Daae, R. Shelf/fjord exchange driven by coastal-trapped waves in the Arctic. J. Geophys. Res. Oceans 120, 8283–8303 (2015).ADS 

Google Scholar 
Promińska, A., Cisek, M. & Walczowski, W. Kongsfjorden and Hornsund hydrography—Comparative study based on a multiyear survey in fjords of west Spitsbergen. Oceanologia 59, 397–412 (2017).
Google Scholar 
Agrawal, Y. C. & Pottsmith, H. C. Instruments for particle size and settling velocity observations in sediment transport. Mar. Geol. 168, 89–114 (2000).ADS 

Google Scholar 
Basedow, S. L., Tande, K. S. & Zhou, M. Biovolume spectrum theories applied: Spatial patterns of trophic levels within a mesozooplankton community at the polar front. J. Plankton Res. 32, 1105–1119 (2010).PubMed 

Google Scholar 
Trudnowska, E., Basedow, S. L. & Blachowiak-Samolyk, K. Mid-summer mesozooplankton biomass, its size distribution, and estimated production within a glacial Arctic fjord (Hornsund, Svalbard). J. Mar. Syst. 137, 55–66 (2014).
Google Scholar 
Jakubas, D. et al. Foraging closer to the colony leads to faster growth in little auks. Mar. Ecol. Prog. Ser. 489, 263–278 (2013).ADS 

Google Scholar 
Basedow, S. L., Tande, K. S., Norrbin, M. F. & Kristiansen, S. A. Capturing quantitative zooplankton information in the sea: Performance test of laser optical plankton counter and video plankton recorder in a Calanus finmarchicus dominated summer situation. Prog. Oceanogr. 108, 72–80 (2013).ADS 

Google Scholar 
Woźniak, S. B., Darecki, M., Zabłocka, M., Burska, D. & Dera, J. New simple statistical formulas for estimating surface concentrations of suspended particulate matter (SPM) and particulate organic carbon (POC) from remote-sensing reflectance in the southern Baltic Sea. Oceanologia 58, 161–175 (2016).
Google Scholar 
Marker, A. The measurement of photosynthetic pigments in freshwaters and standardization of methods : Conclusions and recommendations. Arch. Hydrobiol. Beih 14, 91–106 (1980).CAS 

Google Scholar 
Stramska, M. Bio-optical relationships and ocean color algorithms for the north polar region of the Atlantic. J. Geophys. Res. 108, 3143 (2003).ADS 

Google Scholar 
Picheral, M. et al. The Underwater Vision Profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol. Oceanogr. Methods 8, 462–473 (2010).
Google Scholar 
Gabrielsen, T. M. et al. Potential misidentifications of two climate indicator species of the marine arctic ecosystem: Calanus glacialis and C. finmarchicus. Polar Biol. 35, 1621–1628 (2012).
Google Scholar 
Trudnowska, E. et al. In a comfort zone and beyond—Ecological plasticity of key marine mediators. Ecol. Evol. 10, 14067–14081 (2020).PubMed 
PubMed Central 

Google Scholar 
Jakobsson, M. et al. The International Bathymetric Chart of the Arctic Ocean version 4.0. Sci Data 7, 1–14 (2020).
Google Scholar 
van Rossum, G. & Drake, F. L. Python 3 Reference Manual. Preprint (2009).Caswell, T. A. et al. matplotlib/matplotlib: REL: v3.1.1. https://doi.org/10.5281/ZENODO.3264781 (2019).Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Google Scholar 
Mckinney, W. Data Structures for Statistical Computing in Python. (2010).Reback, J. et al. pandas-dev/pandas: Pandas 1.0.5. https://doi.org/10.5281/ZENODO.3898987 (2020).Pond, S. & Pickard, G. L. Introductory dynamical oceanography. 2nd Ed. (1983).Mojica, K. D. A. et al. Phytoplankton community structure in relation to vertical stratification along a north-south gradient in the Northeast Atlantic Ocean. Limnol. Oceanogr. 60, 1498–1521 (2015).ADS 

Google Scholar 
Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. http://www.primer-e.com (2008).Clarke, K. R. & Gorley, R. N. Getting Started with PRIMER v7 Plymouth Routines in Multivariate Ecological Research. www.primer-e.com (2015).Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).Terpilowski, M. scikit-posthocs: Pairwise multiple comparison tests in Python. J. Open Source Softw. 4, 1169 (2019).ADS 

Google Scholar 
Alcaraz, M. et al. The role of arctic zooplankton in biogeochemical cycles: Respiration and excretion of ammonia and phosphate during summer. Polar Biol. 33, 1719–1731 (2010).
Google Scholar 
Soviadan, Y. D. et al. Patterns of mesozooplankton community composition and vertical fluxes in the global ocean. Prog. Oceanogr. 200, 102717 (2022).
Google Scholar 
Falk-Petersen, S. et al. Vertical migration in high Arctic waters during autumn 2004. Deep Sea Res. 2 Top. Stud. Oceanogr. 55, 2275–2284 (2008).
Google Scholar 
Lane, P. V. Z., Llinás, L., Smith, S. L. & Pilz, D. Zooplankton distribution in the western Arctic during summer 2002: Hydrographic habitats and implications for food chain dynamics. J. Mar. Syst. 70, 97–133 (2008).
Google Scholar 
Kulk, G., Poll, W. H. & Buma, A. G. J. Photophysiology of nitrate limited phytoplankton communities in Kongsfjorden, Spitsbergen. Limnol. Oceanogr. 63, 2606–2617 (2018).ADS 
CAS 

Google Scholar 
Moskalik, M. et al. Spatiotemporal changes in the concentration and composition of suspended particulate matter in front of Hansbreen, a tidewater glacier in Svalbard. Oceanologia 60, 446–463 (2018).
Google Scholar 
Svendsen, H. et al. The physical environment of Kongsfjorden-Krossfjorden, an Arctic fjord system in Svalbard. Polar Res. 21, 133–166 (2002).
Google Scholar 
Chiswell, S. M., Calil, P. H. R. & Boyd, P. W. Spring blooms and annual cycles of phytoplankton: A unified perspective. J. Plankton Res. 37, 500–508 (2015).
Google Scholar 
Kaartvedt, S., Melle, W., Knutsen, T. & Skjoldal, H. Vertical distribution of fish and krill beneath water of varying optical properties. Mar. Ecol. Prog. Ser. 136, 51–58 (1996).ADS 

Google Scholar 
Schmid, M. S., Maps, F. & Fortier, L. Lipid load triggers migration to diapause in Arctic Calanus copepods—Insights from underwater imaging. J. Plankton Res. 40, 311–325 (2018).CAS 

Google Scholar 
Campbell, R. G. et al. Mesozooplankton prey preference and grazing impact in the western Arctic Ocean. Deep Sea Res. 2 Top. Stud. Oceanogr. 56, 1274–1289 (2009).
Google Scholar 
Hirche, H. J. Diapause in the marine copepod, calanus finmarchicus—A review. Ophelia 44, 129–143 (1996).
Google Scholar 
Pedersen, S. A. & Smidt, E. L. B. Zooplankton Investigations Off West Greenland, 1956–1984. (ICES, 1995).Reiner Vonnahme, T. et al. Early spring subglacial discharge plumes fuel under-ice primary production at a Svalbard tidewater glacier. Cryosphere 15, 2083–2107 (2021).ADS 

Google Scholar 
Majaneva, S. et al. Aggregations of predators and prey affect predation impact of the Arctic ctenophore Mertensia ovum. Mar. Ecol. Prog. Ser. 476, 87–100 (2013).ADS 

Google Scholar 
Purcell, J. E., Hopcroft, R. R., Kosobokova, K. N. & Whitledge, T. E. Distribution, abundance, and predation effects of epipelagic ctenophores and jellyfish in the western Arctic Ocean. Deep Sea Res. 2 Top Stud Oceanogr 57, 127–135 (2010).
Google Scholar 
Condon, R. H. et al. Questioning the rise of gelatinous zooplankton in the world’s oceans. Bioscience 62, 160–169 (2012).
Google Scholar 
Balazy, K., Trudnowska, E. & Błachowiak-Samołyk, K. Dynamics of Calanus copepodite structure during little Auks’ breeding seasons in two different Svalbard locations. Water (Basel) 11, 1405 (2019).CAS 

Google Scholar 
Karnovsky, N. J. & Hunt, G. L. Estimation of carbon flux to dovekies (Alle alle) in the North Water. Deep Sea Res. 2 Top. Stud. Oceanogr. 49, 5117–5130 (2002).CAS 

Google Scholar 
Renaud, P. E. et al. Is the poleward expansion by Atlantic cod and haddock threatening native polar cod, Boreogadus saida?. Polar Biol. 35, 401–412. https://doi.org/10.1007/s00300-011-1085-z (2012).Article 

Google Scholar 
Szeligowska, M. et al. Spatial patterns of particles and plankton in the warming Arctic Fjord (Isfjorden, West Spitsbergen) in seven consecutive mid-summers (2013–2019). Front. Mar. Sci. 7, 584 (2020).
Google Scholar  More