More stories

  • in

    Using click chemistry to study microbial ecology and evolution

    Saxon E, Bertozzi C. Cell surface engineering by a modified Staudinger reaction. Science. 2000;287:2007–10.CAS 

    Google Scholar 
    Staudinger H, Meyer J. Über neue organische Phosphorverbindungen III. Phosphinmethylenderivate und Phosphinimine. Helv Chim Acta. 1919;2:635–46. https://doi.org/10.1002/hlca.19190020164.Article 
    CAS 

    Google Scholar 
    Laughlin ST, Bertozzi CR. Metabolic labeling of glycans with azido sugars and subsequent glycan-profiling and visualization via Staudinger ligation. Nat Protoc. 2007;2:2930–44.CAS 

    Google Scholar 
    Oliveira BL, Guo Z, Bernardes GJL. Inverse electron demand Diels–Alder reactions in chemical biology. Chem Soc Rev. 2017;46:4895–950.CAS 

    Google Scholar 
    Lang K, Chin JW. Bioorthogonal reactions for labeling proteins. ACS Chem Biol. 2014;9:16–20. https://doi.org/10.1021/cb4009292.Article 
    CAS 

    Google Scholar 
    Kolb HC, Finn MG, Sharpless K. Click chemistry: diverse chemical function from a few good reactions. Angew Chemie-Int Ed. 2001;40:2004–21.CAS 

    Google Scholar 
    Tornøe C, Christensen C, Meldal M. Peptidotriazoles on Solid Phase: [1,2,3]-Triazoles by regiospecific Copper(I)-Catalyzed 1,3-Dipolar Cycloadditions of Terminal Alkynes to Azides. J Org Chem. 2002;67:3057–64. https://doi.org/10.1021/jo011148j.Article 
    CAS 

    Google Scholar 
    Bakkum T, Leeuwen T, van, Sarris AJC, Elsland DM, van, Poulcharidis D, Overkleeft HS, et al. Quantification of bioorthogonal stability in immune phagocytes using flow cytometry reveals rapid degradation of strained alkynes. ACS Chem Biol. 2018;13:1173–9. https://doi.org/10.1021/acschembio.8b0035.Article 
    CAS 

    Google Scholar 
    Wang Q, Chan T, Hilgraf R, Fokin R, Sharpless K, Finn M. Bioconjugation by copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:3192–3.CAS 

    Google Scholar 
    Link A, Tirrell D. Cell surface labeling of Escherichia coli via copper(I)-catalyzed [3+2] cycloaddition. J Am Chem Soc. 2003;125:11164–5.CAS 

    Google Scholar 
    Dieterich D, Link A, Tirrell D, Schuman E. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc Natl Acad Sci USA. 2006;103:9482–7.CAS 

    Google Scholar 
    McKay C, Finn M. Click chemistry in complex mixtures: bioorthogonal bioconjugation. Chem Biol. 2014;21:1075–101.CAS 

    Google Scholar 
    Agard N, Prescher J, Bertozzi C. A strain-promoted [3 + 2] Azide−Alkyne cycloaddition for covalent modification of biomolecules in living systems. J Am Chem Soc. 2004;126:15046–7. https://doi.org/10.1021/ja044996f.Article 
    CAS 

    Google Scholar 
    Weissleder R, Hilderbrand S. Tetrazine-based cycloadditions: application to pretargeted live cell imaging. Bioconjug Chem. 2008;19:2297–9.
    Google Scholar 
    Scinto SL, Bilodeau DA, Hincapie R, Lee W, Nguyen SS, Xu M, et al. Bioorthogonal chemistry. Nat Rev Methods. 2021;1:1–23.
    Google Scholar 
    Sletten E, Bertozzi C. Bioorthogonal chemistry: fishing for selectivity in a sea of functionality. Angew Chem Int Ed Engl. 2009;48:6974–98.CAS 

    Google Scholar 
    Moses JE, Moorhouse AD. The growing applications of click chemistry. Chem Soc Rev. 2007;36:1249–62.CAS 

    Google Scholar 
    Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
    CAS 

    Google Scholar 
    Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol. 2020;184:241–56.
    Google Scholar 
    Siegrist M, Whiteside S, Jewett J, Aditham A, Cava F, Bertozzi C. (D)-Amino acid chemical reporters reveal peptidoglycan dynamics of an intracellular pathogen. ACS Chem Biol. 2013;8:500–5.CAS 

    Google Scholar 
    Liechti G, Kuru E, Hall E, Kalinda A, Brun YV, VanNieuwenhze M, et al. A new metabolic cell wall labeling method reveals peptidoglycan in Chlamydia trachomatis. Nature. 2014;506:507. https://doi.org/10.1038/nature12892.Article 
    CAS 

    Google Scholar 
    Pilhofer M, Aistleitner K, Biboy J, Gray J, Kuru E, Hall E, et al. Discovery of chlamydial peptidoglycan reveals bacteria with murein sacculi but without FtsZ. Nat Commun. 2013;4:1–7.
    Google Scholar 
    Taylor JA, Bratton BP, Sichel SR, Blair KM, Jacobs HM, Demeester KE, et al. Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in helicobacter pylori. Elife. 2020;9:e52482.CAS 

    Google Scholar 
    Kuru E, Hughes HV, Brown PJ, Hall E, Tekkam S, Cava F, et al. In situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent D-amino acids. Angew Chemie Int Ed. 2012;51:12519–23. https://doi.org/10.1002/anie.201206749.Article 
    CAS 

    Google Scholar 
    van Teeseling MCF, Mesman RJ, Kuru E, Espaillat A, Cava F, Brun YV, et al. Anammox Planctomycetes have a peptidoglycan cell wall. Nat Commun. 2015;6:6878. https://doi.org/10.1038/ncomms7878.Article 
    CAS 

    Google Scholar 
    Wang W, Yang Q, Du Y, Zhou X, Du X, Wu Q. et al. Metabolic labeling of Peptidoglycan with NIR-II dye enables in vivo imaging of gut microbiota. Angew Chemie Int Ed. 2020;59:2628–33. https://doi.org/10.1002/anie.201910555.Article 
    CAS 

    Google Scholar 
    Wang W, Zhu Y, Chen X. imaging of gram-negative and gram-positive microbiotas in the mouse gut. Biochemistry. 2017;56:3889–93.CAS 

    Google Scholar 
    Geva-Zatorsky N, Alvarez D, Hudak JE, Reading NC, Erturk-Hasdemir D, Dasgupta S, et al. In vivo imaging and tracking of host-microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat Med. 2015;21:1091–100.CAS 

    Google Scholar 
    Besanceney-Webler C, Jiang H, Wang W, Baughn AD, Wu P. Metabolic labeling of fucosylated glycoproteins in Bacteroidales species. Bioorg Med Chem Lett. 2011;21:4989–92.CAS 

    Google Scholar 
    Han Z, Thuy-Boun PS, Pfeiffer W, Vartabedian VF, Torkamani A, Teijaro JR, et al. Identification of an N-acetylneuraminic acid-presenting bacteria isolated from a human microbiome. Sci Rep. 2021;11:1–12.
    Google Scholar 
    Becam J, Walter T, Burgert A, Schlegel J, Sauer M, Seibel J, et al. Antibacterial activity of ceramide and ceramide analogs against pathogenic Neisseria. Sci Rep. 2017;7:1–12.CAS 

    Google Scholar 
    Nilsson I, Lee SY, Sawyer WS, Baxter Rath CM, Lapointe G, Six DA. Metabolic phospholipid labeling of intact bacteria enables a fluorescence assay that detects compromised outer membranes. J Lipid Res. 2020;61:870–83.CAS 

    Google Scholar 
    Evershed RP, Crossman ZM, Bull ID, Mottram H, Dungait JAJ, Maxfield PJ, et al. 13C-Labelling of lipids to investigate microbial communities in the environment. Curr Opin Biotechnol. 2006;17:72–82.CAS 

    Google Scholar 
    Salic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc Natl Acad Sci USA. 2008;105:2415–20. https://doi.org/10.1073/pnas.0712168105.Article 

    Google Scholar 
    Smriga S, Samo TJ, Malfatti F, Villareal J, Azam F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat Microb Ecol. 2014;72:269–80.
    Google Scholar 
    Beauchemina ET, Hunter C, Maurice CF. Actively replicating gut bacteria identified by 5-ethynyl-2’-deoxyuridine (EdU) click chemistry and cell sorting. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.07.20.500840v2.Sinclair L, Barthelemy C, Cantrell D. Single cell glucose uptake assays: a cautionary tale. Immunometabolism. 2020;2. https://pubmed.ncbi.nlm.nih.gov/32879737/.Hu F, Chen DZ, Zhang DL, Shen Y, Wei L, Min PW. Vibrational imaging of glucose uptake activity in live cells and tissues by stimulated Raman scattering. Angew Chem Int Ed Engl. 2015;54:9821.CAS 

    Google Scholar 
    Kiick K, Saxon E, Tirrell D, Bertozzi C. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc Natl Acad Sci USA. 2002;99:19–24.CAS 

    Google Scholar 
    Kiick K, Tirrell D. Protein engineering by in vivo incorporation of non-natural amino acids: control of incorporation of methionine analogues by Methionyl-tRNA Synthetase. Tetrahedron. 2000;56:9487–93.CAS 

    Google Scholar 
    Ignacio B, Bakkum T, Bonger K, Martin N, van Kasteren S. Metabolic labeling probes for interrogation of the host-pathogen interaction. Org Biomol Chem. 2021;19:2856–70.CAS 

    Google Scholar 
    Bagert JD, Kessel JC, van, Sweredoski MJ, Feng L, Hess S, Bassler BL, et al. Time-resolved proteomic analysis of quorum sensing in Vibrio harveyi. Chem Sci. 2016;7:1797–806.CAS 

    Google Scholar 
    Babin BM, Atangcho L, Van Eldijk MB, Sweredoski MJ, Moradian A, Hess S, et al. Selective proteomic analysis of antibiotic-tolerant cellular subpopulations in pseudomonas aeruginosa biofilms. 2017. https://doi.org/10.1128/mBio.01593-17.Hatzenpichler R, Scheller S, Tavormina PL, Babin BM, Tirrell DA, Orphan VJ. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol. 2014;16:2568–90. https://doi.org/10.1111/1462-2920.12436.Article 
    CAS 

    Google Scholar 
    Samo TJ, Smriga S, Malfatti F, Sherwood BP, Azam F. Broad distribution and high proportion of protein synthesis active marine bacteria revealed by click chemistry at the single cell level. Front Mar Sci. 2014;0:48.
    Google Scholar 
    Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal-bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–78. https://doi.org/10.1073/pnas.1603757113.Article 
    CAS 

    Google Scholar 
    Couradeau E, Sasse J, Goudeau D, Nath N, Hazen TC, Bowen BP, et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun. 2019;10:1–10.CAS 

    Google Scholar 
    Leizeaga A, Estrany M, Forn I, Sebastián M. Using click-chemistry for visualizing in situ changes of translational activity in planktonic marine bacteria. Front Microbiol. 2017;0:2360.
    Google Scholar 
    Lindivat M, Larsen A, Hess-Erga OK, Bratbak G, Hoell IA. Bioorthogonal non-canonical amino acid tagging combined with flow cytometry for determination of activity in aquatic microorganisms. Front Microbiol. 2020;0:1929.
    Google Scholar 
    Chen L, Zhao B, Li X, Cheng Z, Wu R, Xia Y. Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging. Chem Eng J. 2021;418:129411.CAS 

    Google Scholar 
    McKay LJ, Smith HJ, Barnhart EP, Schweitzer HD, Malmstrom RR, Goudeau D, et al. Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane. ISME J. 2021;16:915–26.
    Google Scholar 
    Du Z, Behrens SF. Tracking de novo protein synthesis in the activated sludge microbiome using BONCAT-FACS. Water Res. 2021;205:117696.CAS 

    Google Scholar 
    Valentini TD, Lucas SK, Binder KA, Cameron LC, Motl JA, Dunitz JM, et al. Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota. Nat Commun. 2020;11:1–11.
    Google Scholar 
    Taguer M, Shapiro BJ, Maurice CF. Translational activity is uncoupled from nucleic acid content in bacterial cells of the human gut microbiota. Gut Microbes. 2021;13:1–15.
    Google Scholar 
    Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
    CAS 

    Google Scholar 
    Kavunja HW, Piligian BF, Fiolek TJ, Foley HN, Nathan TO, Swarts BM. A chemical reporter strategy for detecting and identifying O-mycoloylated proteins in Corynebacterium. Chem Commun. 2016;52:13795–8.CAS 

    Google Scholar 
    Demeester KE, Liang H, Jensen MR, Jones ZS, D’Ambrosio EA, Scinto SL, et al. Synthesis of functionalized N-Acetyl Muramic acids to probe bacterial cell wall recycling and biosynthesis. J Am Chem Soc. 2018;140:9458–65. https://doi.org/10.1021/jacs.8b03304.Article 
    CAS 

    Google Scholar 
    Moulton KD, Adewale AP, Carol HA, Mikami SA, Dube DH. Metabolic glycan labeling-based screen to identify bacterial glycosylation genes. ACS Infect Dis. 2020;6:3247–59. https://doi.org/10.1021/acsinfecdis.0c00612.Article 
    CAS 

    Google Scholar 
    Keller LJ, Babin BM, Lakemeyer M, Bogyo M. Activity-based protein profiling in bacteria: Applications for identification of therapeutic targets and characterization of microbial communities. Curr Opin Chem Biol. 2020;54:45–53.CAS 

    Google Scholar 
    Speers AE, Adam GC, Cravatt BF. Activity-based protein profiling in vivo using a copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:4686–7. https://doi.org/10.1021/ja034490.Article 
    CAS 

    Google Scholar 
    Krysiak J, Sieber SA. Activity-based protein profiling in bacteria. Methods Mol Biol. 2017;1491:57–74.CAS 

    Google Scholar 
    Jariwala PB, Pellock SJ, Cloer EW, Artola M, Simpson JB, Bhatt AP, et al. Discovering the microbial enzymes driving drug toxicity with activity-based protein profiling. ACS Chem Biol. 2020;15:217–25. https://doi.org/10.1021/acschembio.9b00788.Article 
    CAS 

    Google Scholar 
    Kovalyova Y, Hatzios SK. Activity-based protein profiling at the host-pathogen interface. Curr Top Microbiol Immunol. 2019;420:73–91.CAS 

    Google Scholar 
    Sakoula D, Smith GJ, Frank J, Mesman RJ, Kop LFM, Blom P, et al. Universal activity-based labeling method for ammonia- and alkane-oxidizing bacteria. ISME J. 2021;16:958–71.
    Google Scholar 
    Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2020;19:55–71.
    Google Scholar 
    Fitzpatrick CR, Salas-González I, Conway JM, Finkel OM, Gilbert S, Russ D, et al. The plant microbiome: from ecology to reductionism and beyond. 101146/annurev-micro-022620-014327. 2020;74:81–100. https://www.annualreviews.org/doi/abs/10.1146/annurev-micro-022620-014327.Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, Whitlock MC. Experimental evolution. Trends Ecol Evol. 2012;27:547–60.
    Google Scholar 
    Lenski RE. Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations. ISME J. 2017;11:2181–94.CAS 

    Google Scholar 
    Rodríguez-Verdugo A. Evolving Interactions and Emergent Functions in Microbial Consortia. mSystems. 2021;6. https://pubmed.ncbi.nlm.nih.gov/34427521/.Pascual-García A, Bonhoeffer S, Bell T. Metabolically cohesive microbial consortia and ecosystem functioning. Philos Trans R Soc B. 2020;375. https://royalsocietypublishing.org/doi/full/10.1098/rstb.2019.0245.Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 

    Google Scholar 
    Balaban NQ, Helaine S, Lewis K, Ackermann M, Aldridge B, Andersson DI, et al. Definitions and guidelines for research on antibiotic persistence. Nat Rev Microbiol. 2019;17:441–8.CAS 

    Google Scholar 
    Vermeersch L, Perez-Samper G, Cerulus B, Jariani A, Gallone B, Voordeckers K, et al. On the duration of the microbial lag phase. Curr Genet. 2019;65:721–7.CAS 

    Google Scholar 
    Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, et al. Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA. 2014;111:7427–32.CAS 

    Google Scholar 
    Zhang Z, Du C, de Barsy F, Liem M, Liakopoulos A, van Wezel GP, et al. Antibiotic production in Streptomyces is organized by a division of labor through terminal genomic differentiation. Sci Adv. 2020;6:eaay5781.CAS 

    Google Scholar 
    Mavridou DAI, Gonzalez D, Kim W, West SA, Foster KR. Bacteria use collective behavior to generate diverse combat strategies. Curr Biol. 2018;28:345–355.e4.CAS 

    Google Scholar 
    Levin AM, de Vries RP, Conesa A, de Bekker C, Talon M, Menke HH, et al. Spatial differentiation in the vegetative mycelium of Aspergillus niger. Eukaryot Cell. 2007;6:2311–22.CAS 

    Google Scholar 
    Zacchetti B, Wösten HAB, Claessen D. Multiscale heterogeneity in filamentous microbes. Biotechnol Adv. 2018;36:2138–49.CAS 

    Google Scholar 
    Bleichrodt R-J, Vinck A, Read ND, Wösten HAB. Selective transport between heterogeneous hyphal compartments via the plasma membrane lining septal walls of Aspergillus niger. Fungal Genet Biol. 2015;82:193–200.CAS 

    Google Scholar 
    Nürnberg DJ, Mariscal V, Bornikoel J, Nieves-Morión M, Krauß N, Herrero A, et al. Intercellular diffusion of a fluorescent sucrose analog via the septal junctions in a Filamentous Cyanobacterium. MBio. 2015;6. https://journals.asm.org/doi/full/10.1128/mBio.02109-14.Pasulka AL, Thamatrakoln K, Kopf SH, Guan Y, Poulos B, Moradian A, et al. Interrogating marine virus-host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Environ Microbiol. 2018;20:671–92. https://doi.org/10.1111/1462-2920.13996.Article 
    CAS 

    Google Scholar 
    Berjón-Otero M, Duponchel S, Hackl T, Fischer M. Visualization of giant virus particles using BONCAT labeling and STED microscopy. bioRxiv. 2020;2020.07.14.202192. https://www.biorxiv.org/content/10.1101/2020.07.14.202192v1.Steward KF, Eilers B, Tripet B, Fuchs A, Dorle M, Rawle R, et al. Metabolic implications of using BioOrthogonal Non-Canonical Amino Acid Tagging (BONCAT) for tracking protein synthesis. Front Microbiol. 2020;0:197.
    Google Scholar 
    van Elsland DM, Pujals S, Bakkum T, Bos E, Oikonomeas-Koppasis N, Berlin I, et al. Ultrastructural Imaging of Salmonella–Host interactions using super-resolution correlative light-electron microscopy of bioorthogonal pathogens. ChemBioChem. 2018;19:1766–70. https://doi.org/10.1002/cbic.201800230.Article 
    CAS 

    Google Scholar 
    Michels DE, Lomenick B, Chou T-F, Sweredoski MJ, Pasulka A. Amino acid analog induces stress response in marine Synechococcus. Appl Environ Microbiol. 2021;87:1–18. https://doi.org/10.1128/AEM.00200-21.Article 

    Google Scholar 
    Hong V, Steinmetz NF, Manchester M, Finn MG. Labeling live cells by copper-catalyzed alkyne−azide click chemistry. Bioconjug Chem. 2010;21:1912–6. https://doi.org/10.1021/bc100272z.Article 
    CAS 

    Google Scholar 
    van Geel R, Pruijn G, van Delft F, Boelens W. Preventing thiol-yne addition improves the specificity of strain-promoted azide-alkyne cycloaddition. Bioconjug Chem. 2012;23:392–8.
    Google Scholar 
    Patterson DM, Nazarova LA, Prescher JA. Finding the Right (Bioorthogonal) Chemistry. ACS Chem Biol. 2014;9:592–605. https://doi.org/10.1021/cb400828a.Article 
    CAS 

    Google Scholar 
    Ignacio BJ, Dijkstra J, Garcia NM, Slot EFJ, van Weijsten MJ, Storkebaum E, et al. THRONCAT: Efficient metabolic labeling of newly synthesized proteins using a bioorthogonal threonine analog. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.03.29.486210v1.Wright MH. Chemical proteomics of host–microbe interactions. Proteomics. 2018;18:1700333. https://doi.org/10.1002/pmic.201700333.Article 
    CAS 

    Google Scholar 
    Yu H, Schomaker J. Recent developments and strategies for mutually orthogonal bioorthogonal reactions. Chembiochem. 2021;22:3254–62.
    Google Scholar 
    Willems LI, Li N, Florea BI, Ruben M, van der Marel GA, Overkleeft HS. Triple bioorthogonal ligation strategy for simultaneous labeling of multiple enzymatic activities. Angew Chemie Int Ed. 2012;51:4431–4. https://doi.org/10.1002/anie.201200923.Article 
    CAS 

    Google Scholar 
    Simon C, Lion C, Spriet C, Baldacci-Cresp F, Hawkins S, Biot C. One, two, three: a bioorthogonal triple labelling strategy for studying the dynamics of plant cell wall formation in vivo. Angew Chemie Int Ed. 2018;57:16665–71. https://doi.org/10.1002/anie.201808493.Article 
    CAS 

    Google Scholar 
    Chio TI, Gu H, Mukherjee K, Tumey LN, Bane SL. Site-specific bioconjugation and multi-bioorthogonal labeling via rapid formation of a boron–nitrogen heterocycle. Bioconjug Chem. 2019;30:1554–64. https://doi.org/10.1021/acs.bioconjchem.9b0024.Article 
    CAS 

    Google Scholar 
    Bakkum T, Heemskerk MT, Bos E, Groenewold M, Oikonomeas-Koppasis N, Walburg KV, et al. Bioorthogonal correlative light-electron microscopy of mycobacterium tuberculosis in macrophages reveals the effect of antituberculosis drugs on subcellular bacterial distribution. ACS Cent Sci. 2020;6:1997–2007. https://doi.org/10.1021/acscentsci.0c00539.Article 
    CAS 

    Google Scholar  More

  • in

    Dynamics of aggregate-associated organic carbon after long-term cropland conversion in a karst region, southwest China

    Effects of cropland conversion on OC pool in bulk soilCropland restoration identified as an efficient ecological project to promote soil C sequestration in karst erosion areas28,30. The conversion from MS to FG resulted in the total soil OC content and stock across 0–30 cm layers increasing by 46.12% and 43.73% respectively. The result was highly coincident with previous studies observed at 0–10 cm layer, which reported that FG cultivation replaced from MS cultivation could remarkably increase soil OC pool in karst region, Southwest China28. In our study, the lower OC content and stock in MS may be partially attributed to the non-returned crop residues and increased exposure of deep soil OM to oxygen under tillage disturbance, resulting in decreased soil OC accumulation through reducing the input of OM and accelerating OM decomposition28,30,37,38. Nevertheless, the conversion from MS to FG can increase the soil OC pool by increasing inputs from crops. For detail, laregly aboverground crops are harvested and removed from the fields each every year for economic production, there is thus a lack of aboverground OC input. Therefore, the root biomass became the main source of OM inputs, and even slight changes in biomass can substantially alter soil C level39. In the present study, the root biomass in FG field was approximately 6 times that in MS field (110.06 ± 17.24 kg hm−2 averagely) (Table S2). Consequently, the higher root biomass in FG are responsible for the corresponding higher C storage of fine root in FG, which is supported by the fact that higher amount of C were stored in the fine roots of FG field compared with that of MS field (Table S2). In fact, several studies have demonstrated that cultivation of perennial grasses is efficient in stimulating soil OC accumulation owing to its great amount of fine roots and underground biomass33,40. Soil disturbance (such as tillage) is one of the main causes of soil C depletion in agricultural systems, and increased tillage practice can result in greater soil C loss41,42,43. Therefore, the frequent tillage conducted in MS field resulted in lower levels of OC than that in FG field under minimal tillage disturbance.Impacts of cropland conversion on soil aggregates structure and stabilitySoil structure plays an important role in soil environment and quality, which is strongly characterized by soil aggregates and their stability43,44. In our study, soil macro-aggregates dominated the largest portion of total soil while meso-aggregates and micro-aggregates were only accounted for a small portion, indicating that cropland conversion could facilitated the formation of macro-aggregates (Table 2). These findings are in line with other studies, wherein that macro-aggregates occupied the major portion of total soil following farmland or vegetation restoration19,30. Tillage disturbance often disrupts aggregates by bringing subsurface soil to the surface, which can readily promote soil C turnover and hinder macro-aggregate formation45. Conversely, minimal tillage experienced and greater accumulation of root residues resulted in higher C accumulation in the FG field. Furthermore, fine roots improved the soil aggregate stability via the interaction with mycorrhizal fungi, which produced exudates and binding agents and promoted the formation of soil aggregates46,47. Therefore, higher inputs of root residue in the soil could enhance the capacity of aggregate re-formation. In fact, these can be supported by the higher value of root biomass and its C stock in the FG field. In addition, forage grass cultivation can enhance the formation of large and stable soil aggregates by fine roots and fungal hyphae through the production of exudates and binding agents, such as humic compounds, polymers and roots48,49. Thus, few tillage disturbance and higher inputs of root biomass in FG field resulted in soil aggregation enhanced, especially macro-aggregates.Soil aggregate stability can also be characterized by the values of MWD and GMD. Higher MWD or GMD values indicate greater aggregate stability due to more agglomerate ability. The value of MWD in the current study varied from 1.36 to 1.96, which was classified as “stable” by LeBissonnais’ categorization of aggregate stability50.Regardless of soil depth, the FG field had the greatest MWD and GMD values, indicating that its soil aggregates were more stable than those of the other three cropland use types. We may thus draw the conclusion that FG cropland conversion can improve the stability of aggregates based on MWD and GMD.Changes in OC stocks associated –aggregates following cropland conversionCropland use change generally affects soil C sequestration through changing OM inputs and decomposition19. Our study revealed that aggregate-associated OC was significantly higher in FG field than in MS field. These increases were mainly attributed to the new C derived from root residues inputs and decreased losses of OC associated-aggregate by C mineralization in FG soil49. Generally, tillage can breakdown large aggregates into small aggregates, and thus decrease the formation of soil macro-aggregates41,42. Thus, the lower OC content and stock associated-aggregate in MS field can be attributed to the OC loss resulting from soil erosion, and OM input reduction with tillage disturbance8,30,45.In this study, the effects of cropland conversion on OC content associated-aggregate fractions occurred in the top 20 cm soil layers. In the karst region, approximate 57–89% of crop roots are concentrated in the surface soil layer, which directly affects OM inputs from underground root residues51,52. Meanwhile, tillage practices also happened on top 20 cm soil layer6,28,29. As a result, in soils below 20 cm, little or no tillage disturbance and limited OM inputs resulted in fewer or no distinctly changing levels of OC content associated with aggregate following cropland use change.Cropland use change not only affected the OC stocks in bulk soil, but also affected the OC stocks associated-aggregates (Table 1). The difference of sensitivity of OC associated-aggregate to cropland use change may affect its contribution to bulk soil OC accumulation30,38. In our study, the macro-aggregate fraction was the most important contributor to total OC stock increase, followed by meso-aggregate and micro-aggregate (Fig. 4). This is primarily due to the higher amount and OC content of macro-aggregates. Overall all cropland use types, the OC stock associated with macro-aggregate in FG field was higher than that in other three cropland types regardless of soil depth (Fig. 4). For instance, OC stocks within macro-aggregate accounted for about 85.40%, 77.72% and 97.55% of total soil OC stock at 0–10 cm, 10–20 cm and 20–30 cm, respectively, under the conversion from MS to FG. Thus, the accumulation pattern of bulk soil OC stocks could closely related with changes of OC stocks associated with macro-aggregate under cropland use change.The physical protection of OC in aggregates is regarded as one of the main mechanisms for soil OC accumulation through diminishing soil OC degradation and preventing its interaction with mineral particles53,54. In the present study, OC stock in bulk soil correlated substantially with the OC content-associated aggregate following cropland conversion (Fig. 5). Further analysised revealed that OC stocks in bulk soil was significantly correlated to OC stock associated with macro-aggregate (R2 = 0.83, p  More

  • in

    Forest edges increase pollinator network robustness to extinction with declining area

    Millard, J. et al. Global effects of land-use intensity on local pollinator biodiversity. Nat. Commun. 12, 2902 (2021).Article 
    CAS 

    Google Scholar 
    Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).Article 

    Google Scholar 
    Valiente-Banuet, A. et al. Beyond species loss: the extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

    Google Scholar 
    Rybicki, J., Abrego, N. & Ovaskainen, O. Habitat fragmentation and species diversity in competitive communities. Ecol. Lett. 23, 506–517 (2020).Article 

    Google Scholar 
    Chase, J. M., Blowes, S. A., Knight, T. M., Gerstner, K. & May, F. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020).Article 
    CAS 

    Google Scholar 
    Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).Article 

    Google Scholar 
    Didham, R. K. Ecological consequences of habitat fragmentation. In Encyclopedia of Life Sciences (ed Jansson, R.), 61, 1–39 (Wiley, UK2010).Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).Article 
    CAS 

    Google Scholar 
    Spiesman, B. J. & Inouye, B. D. Habitat loss alters the architecture of plant-pollinator interaction networks. Ecology 94, 2688–2696 (2013).Article 

    Google Scholar 
    Aizen, M. A. et al. The phylogenetic structure of plant-pollinator networks increases with habitat size and isolation. Ecol. Lett. 19, 29–36 (2016).Article 

    Google Scholar 
    Emer, C. et al. Seed-dispersal interactions in fragmented landscapes-a metanetwork approach. Ecol. Lett. 21, 484–493 (2018).Article 

    Google Scholar 
    Fortuna, M. A. & Bascompte, J. Habitat loss and the structure of plant-animal mutualistic networks. Ecol. Lett. 9, 278–283 (2006).Article 

    Google Scholar 
    Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).Article 

    Google Scholar 
    Glenn R. Matlack & John A. Litvaitis. Forest edges. In Maintaining Biodiversity in Forest Ecosystems (ed Hunter, M.) 6, 210–233 (Cambridge Univ. Press, 1999).Hadley, A. S. & Betts, M. G. The effects of landscape fragmentation on pollination dynamics: absence of evidence not evidence of absence. Biol. Rev. 87, 526–544 (2012).Article 

    Google Scholar 
    Ibanez, I., Katz, D. S. W., Peltier, D., Wolf, S. M. & Barrie, B. T. C. Assessing the integrated effects of landscape fragmentation on plants and plant communities: the challenge of multiprocess-multiresponse dynamics. J. Ecol. 102, 882–895 (2014).Article 

    Google Scholar 
    Morreale, L. L., Thompson, J. R., Tang, X., Reinmann, A. B. & Hutyra, L. R. Elevated growth and biomass along temperate forest edges. Nat. Commun. 12, 7181 (2021).Article 
    CAS 

    Google Scholar 
    Martinez-Ramos, M., Alvarez-Buylla, E. & Sarukhan, J. Tree demography and gap dynamics in a tropical rain forest. Ecology 70, 555–558 (1989).Article 

    Google Scholar 
    Yamamoto, S. I. Forest gap dynamics and tree regeneration. J. For. Res. 5, 223–229 (2000).Article 

    Google Scholar 
    Schnitzer, S. A. & Carson, W. P. Treefall gaps and the maintenance of species diversity in a tropical forest. Ecology 82, 913–919 (2001).Article 

    Google Scholar 
    Kricher, J. A Shifting Mosaic: Rain Forest Development and Dynamics. In Tropical Ecology 6, 188–226 (Princeton Univ. Press, 2011).Gayer, C. et al. Flowering fields, organic farming and edge habitats promote diversity of plants and arthropods on arable land. J. Appl. Ecol. 58, 1155–1166 (2021).Article 

    Google Scholar 
    Bailey, S. et al. Distance from forest edge affects bee pollinators in oilseed rape fields. Ecol. Evol. 4, 370–380 (2014).Article 

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

    Google Scholar 
    Hagen, M. et al. Biodiversity, species interactions and ecological networks in a fragmented world. Adv. Ecol. Res. 46, 89–210 (2012).Article 

    Google Scholar 
    Traveset, A., Castro-Urgal, R., Rotllan-Puig, X. & Lazaro, A. Effects of habitat loss on the plant-flower visitor network structure of a dune community. Oikos 127, 45–55 (2018).Article 

    Google Scholar 
    Rezende, E. L., Lavabre, J. E., Guimaraes, P. R., Jordano, P. & Bascompte, J. Non-random coextinctions in phylogenetically structured mutualistic networks. Nature 448, 925–928 (2007).Article 
    CAS 

    Google Scholar 
    Staddon, P., Lindo, Z., Crittenden, P. D., Gilbert, F. & Gonzalez, A. Connectivity, non-random extinction and ecosystem function in experimental metacommunities. Ecol. Lett. 13, 543–552 (2010).Article 

    Google Scholar 
    Wardle, D. A., Bardgett, R. D., Callaway, R. M. & Van der Putten, W. H. Terrestrial ecosystem responses to species gains and losses. Science 332, 1273–1277 (2011).Article 
    CAS 

    Google Scholar 
    Sargent, R. D. & Ackerly, D. D. Plant-pollinator interactions and the assembly of plant communities. Trends Ecol. Evol. 23, 123–130 (2008).Article 

    Google Scholar 
    Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018–1020 (2009).Article 
    CAS 

    Google Scholar 
    Rohr, R. P., Saavedra, S. & Bascompte, J. On the structural stability of mutualistic systems. Science 345, 1253497 (2014).Article 

    Google Scholar 
    Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

    Google Scholar 
    Pawar, S. Why are plant-pollinator networks nested? Science 345, 383–383 (2014).Article 
    CAS 

    Google Scholar 
    Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Muller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).Article 

    Google Scholar 
    Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).Article 

    Google Scholar 
    Ponisio, L. C., Gaiarsa, M. P. & Kremen, C. Opportunistic attachment assembles plant-pollinator networks. Ecol. Lett. 20, 1261–1272 (2017).Article 

    Google Scholar 
    Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc. Ecol. 31, 219–227 (2016).Article 

    Google Scholar 
    Zhong, L., Didham, R. K., Liu, J., Jin, Y. & Yu, M. Community re-assembly and divergence of woody plant traits in an island-mainland system after more than 50 years of regeneration. Divers. Distrib. 27, 1435–1448 (2021).Article 

    Google Scholar 
    Liu, J. et al. The asymmetric relationships of the distribution of conspecific saplings and adults in forest fragments. J. Plant Ecol. 13, 398–404 (2020).Article 
    CAS 

    Google Scholar 
    Ewers, R. M., Bartlam, S. & Didham, R. K. Altered species interactions at forest edges: contrasting edge effects on bumble bees and their phoretic mite loads in temperate forest remnants. Insect Conserv. Divers. 6, 598–606 (2013).Article 

    Google Scholar 
    Wardhaugh, C. W. The spatial and temporal distributions of arthropods in forest canopies: uniting disparate patterns with hypotheses for specialisation. Biol. Rev. Camb. Philos. Soc. 89, 1021–1041 (2015).Article 

    Google Scholar 
    Lowman, M. Life in the treetops – an overview of forest canopy science and its future directions. Plants People Planet 3, 16–21 (2021).Article 

    Google Scholar 
    Nakamura, A. et al. Forests and their canopies: achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451 (2017).Article 

    Google Scholar 
    Lennartsson, T. Extinction thresholds and disrupted plant-pollinator interactions in fragmented plant populations. Ecology 83, 3060–3072 (2002).
    Google Scholar 
    Aguilar, R., Ashworth, L., Galetto, L. & Aizen, M. A. Plant reproductive susceptibility to habitat fragmentation: review and synthesis through a meta-analysis. Ecol. Lett. 9, 968–980 (2006).Article 

    Google Scholar 
    Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).Article 

    Google Scholar 
    Goulson, D., Nicholls, E., Botias, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 

    Google Scholar 
    Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764 (2002).Article 

    Google Scholar 
    Winfree, R., Bartomeus, I. & Cariveau, D. P. Native pollinators in anthropogenic habitats. Annu. Rev. Entomol. 42, 1–22 (2011).
    Google Scholar 
    Torné-Noguera, A. et al. Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLoS ONE 9, e97255 (2014).Article 

    Google Scholar 
    Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).Article 

    Google Scholar 
    Schoereder, J. H. et al. Should we use proportional sampling for species-area studies? J. Biogeogr. 31, 1219–1226 (2004).Article 

    Google Scholar 
    Jordano, P. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677 (1987).Article 

    Google Scholar 
    Devoto, M., Medan, D. & Montaldo, N. H. Patterns of interaction between plants and pollinators along an environmental gradient. Oikos 109, 461–472 (2005).Article 

    Google Scholar 
    Petanidou, T., Kallimanis, A. S., Tzanopoulos, J., Sgardelis, S. P. & Pantis, J. D. Long-term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization. Ecol. Lett. 11, 564–575 (2008).Article 

    Google Scholar 
    Brodie, J. F. et al. Secondary extinctions of biodiversity. Trends Ecol. Evol. 29, 664–672 (2014).Article 

    Google Scholar 
    Vazquez, D. P. & Aizen, M. A. Asymmetric specialization: a pervasive feature of plant-pollinator interactions. Ecology 85, 1251–1257 (2004).Article 

    Google Scholar 
    Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).
    Google Scholar 
    Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the Anthropocene. Annu. Rev. Environ. Resour. 39, 125–159 (2014).Article 

    Google Scholar 
    Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).Article 
    CAS 

    Google Scholar 
    Fletcher, R. J. Jr et al. Is habitat fragmentation good for biodiversity? Biol. Conserv. 226, 9–15 (2018).Article 

    Google Scholar 
    Ren, P., Si, X. & Ding, P. Stable species and interactions in plant-pollinator networks deviate from core position in fragmented habitats. Ecography 2022, e06102 (2022).Article 

    Google Scholar 
    Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).
    Google Scholar 
    Bascompte, J., Jordano, P., Melian, C. J. & Olesen, J. M. The nested assembly of plant-animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).Article 
    CAS 

    Google Scholar 
    Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R. Jr, Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).Article 

    Google Scholar 
    Ulrich, W., Almeida-Neto, M. & Gotelli, N. J. A consumer’s guide to nestedness analysis. Oikos 118, 3–17 (2009).Article 

    Google Scholar 
    Dicks, L. V., Corbet, S. A. & Pywell, R. F. Compartmentalization in plant-insect flower visitor webs. J. Anim. Ecol. 71, 32–43 (2002).Article 

    Google Scholar 
    Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3, 140536 (2016).Article 

    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020). https://CRAN.R-project.org/package=veganDormann, C. F. et al. bipartite: Visualising Bipartite Networks and Calculating Some (Ecological) Indices. R package version 2.16 (2021). https://CRAN.R-project.org/package=bipartitePocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).Article 
    CAS 

    Google Scholar 
    Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).Article 
    CAS 

    Google Scholar 
    Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).Article 
    CAS 

    Google Scholar 
    Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).Article 

    Google Scholar 
    Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

    Google Scholar 
    Grace, J. B., Scheiner, S. M. & Schoolmaster, D. R. Jr. Structural equation modeling: building and evaluating causal models. In Ecological Statistics: From Principles to Applications (eds Fox, G. A. et al.), 8, 168–199 (Oxford Univ. Press, 2015).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
    Google Scholar 
    Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).Article 

    Google Scholar 
    Murphy, M. semEff: Automatic Calculation of Effects for Piecewise Structural Equation Models. R package version 0.6.0 (2021). https://CRAN.R-project.org/package=semEffDudgeon, P. A comparative investigation of confidence intervals for independent variables in linear regression. Multivar. Behav. Res. 51, 139–153 (2016).Article 

    Google Scholar 
    Gotelli, N. J. & Graves, G. R. Null Models in Ecology (Smithsonian Inst. Press, 1996).Jung, V., Violle, C., Mondy, C., Hoffmann, L. & Muller, S. Intraspecific variability and trait-based community assembly. J. Ecol. 98, 1134–1140 (2010).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

  • in

    Genetic and demographic consequences of range contraction patterns during biological annihilation

    Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. PNAS 114, E6089–E6096 (2017).ADS 
    CAS 

    Google Scholar 
    Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
    CAS 

    Google Scholar 
    Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. PNAS 117, 13596–13602 (2020).ADS 
    CAS 

    Google Scholar 
    Butchart, S. H. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
    CAS 

    Google Scholar 
    Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
    Google Scholar 
    Arenas, M., Ray, N., Currat, M. & Excoffier, L. Consequences of range contractions and range shifts on molecular diversity. Mol. Biol. Evol. 29, 207–218 (2012).CAS 

    Google Scholar 
    Banks, S. C. et al. How does ecological disturbance influence genetic diversity?. Trends Ecol. Evol. 28, 670–679 (2013).
    Google Scholar 
    Branco, C., Ray, N., Currat, M. & Arenas, M. Influence of Paleolithic range contraction, admixture and long-distance dispersal on genetic gradients of modern humans in Asia. Mol. Ecol. 29, 2150–2159 (2020).
    Google Scholar 
    Lomolino, M. V. & Channell, R. Splendid isolation: Patterns of geographic range collapse in endangered mammals. J. Mammal. 76(2), 335–347 (1995).
    Google Scholar 
    Lomolino, M. V. & Channell, R. Range collapse, re-introductions, and biogeographic guidelines for conservation. Conserv. Biol. 12, 481–484 (1998).
    Google Scholar 
    Channell, R. & Lomolino, M. V. Dynamic biogeography and conservation of endangered species. Nature 403, 84–86 (2000).ADS 
    CAS 

    Google Scholar 
    Channell, R. & Lomolino, M. V. Trajectories to extinction: Spatial dynamics of the contraction of geographical ranges. J. Biogeogr. 27, 169–179 (2000).
    Google Scholar 
    Laliberte, A. S. & Ripple, W. J. Range contractions of North American carnivores and ungulates. Bioscience 54, 123–138 (2004).
    Google Scholar 
    Donald, P. F. & Greenwood, J. J. Spatial patterns of range contraction in British breeding birds. Ibis 143, 593–601 (2001).
    Google Scholar 
    Boakes, E. H., Isaac, N. J., Fuller, R. A., Mace, G. M. & McGowan, P. J. Examining the relationship between local extinction risk and position in range. Conserv. Biol. 32, 229–239 (2018).
    Google Scholar 
    Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. PNAS 101(42), 15261–15264 (2004).ADS 
    CAS 

    Google Scholar 
    Hoelzel, A. R. et al. Elephant seal genetic variation and the use of simulation models to investigate historical population bottlenecks. J. Hered. 84, 443–449 (1993).CAS 

    Google Scholar 
    Amos, W. & Balmford, A. When does conservation genetics matter?. Heredity 87, 257–265 (2001).CAS 

    Google Scholar 
    Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).
    Google Scholar 
    Carvalho, C. D. S. et al. Habitat loss does not always entail negative genetic consequences. Front. Genet. 10, 1101 (2019).CAS 

    Google Scholar 
    Wheeler, B. A., Prosen, E., Mathis, A. & Wilkinson, R. F. Population declines of a long-lived salamander: A 20+-year study of hellbenders, Cryptobranchus alleganiensis. Biol. Cons. 109, 151–156 (2003).
    Google Scholar 
    Walkup, D. K., Leavitt, D. J. & Fitzgerald, L. A. Effects of habitat fragmentation on population structure of dune-dwelling lizards. Ecosphere 8, e01729 (2017).
    Google Scholar 
    Mikle, N., Graves, T. A., Kovach, R., Kendall, K. C. & Macleod, A. C. Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore. Proc. R. Soc. B Biol. Sci. 283, 20161467 (2016).
    Google Scholar 
    DeWoody, J. A., Harder, A. M., Mathur, S. & Willoughby, J. R. The long-standing significance of genetic diversity in conservation. Mol. Ecol. 30(17), 4147–4154 (2021).
    Google Scholar 
    Kardos, M., Armstrong, E. E., Fitzpatrick, S. W. & Funk, W. C. The crucial role of genome-wide genetic variation in conservation. PNAS 118(48), e210462118 (2021).
    Google Scholar 
    García-Dorado, A. & Caballero, A. Neutral genetic diversity as a useful tool for conservation biology. Conserv. Genet. 22, 541–545 (2021).
    Google Scholar 
    Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).CAS 

    Google Scholar 
    Eyre-Walker, A. & Keightley, P. D. The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8, 610–618 (2007).CAS 

    Google Scholar 
    Haller, B. C., Galloway, J., Kelleher, J., Messer, P. W. & Ralph, P. L. Tree-sequence recording in SLiM opens new horizons forward-time simulation of whole genomes. Mol. Ecol. Resour. 19, 552–566 (2018).
    Google Scholar 
    Kelleher, J., Thornton, K. R., Ashander, J. & Ralph, P. L. Efficient pedigree recording for fast population genetics simulation. PLoS Comput. Biol. 14, e1006581 (2018).ADS 

    Google Scholar 
    Haller, B. C. & Messer, P. W. SLiM 3: Forward genetic simulations beyond the Wright–Fisher model. Mol. Biol. Evol. 36, 632–637 (2019).CAS 

    Google Scholar 
    Rodríguez, J. P. Range contraction in declining North American bird populations. Ecol. Appl. 12, 238–248 (2002).
    Google Scholar 
    Fisher, D. O. Trajectories from extinction: where are missing mammals rediscovered?. Glob. Ecol. Biogeogr. 20, 415–425 (2011).
    Google Scholar 
    Lino, A., Fonseca, C., Rojas, D., Fischer, E. & Pereira, M. J. R. A meta-analysis of the effects of habitat loss and fragmentation on genetic diversity in mammals. Mamm. Biol. 94, 69–76 (2019).
    Google Scholar 
    Vandergast, A. G., Bohonak, A. J., Weissman, D. B. & Fisher, R. N. Understanding the genetic effects of recent habitat fragmentation in the context of evolutionary history: Phylogeography and landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus). Mol. Ecol. 16, 977–992 (2007).CAS 

    Google Scholar 
    Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 

    Google Scholar 
    Wilkins, J. F. & Wakeley, J. The coalescent in a continuous, finite, linear population. Genetics 161, 873–888 (2002).
    Google Scholar 
    Ringbauer, H., Coop, G. & Barton, N. H. Inferring recent demography from isolation by distance of long shared sequence blocks. Genetics 205, 1335–1351 (2017).
    Google Scholar 
    Bradburd, G. S. & Ralph, P. L. Spatial population genetics: It’s about time. Annu. Rev. Ecol. Evol. Syst. 50, 427–429 (2019).
    Google Scholar 
    Barton, N. H., Etheridge, A. M., Kelleher, J. & Véber, A. Inference in two dimensions: Allele frequencies versus lengths of shared sequence blocks. Theor. Popul. Biol. 87, 105–119 (2013).CAS 
    MATH 

    Google Scholar 
    Aguillon, S. M. et al. Deconstructing isolation-by-distance: The genomic consequences of limited dispersal. PLoS Genet. 13, e1006911 (2017).
    Google Scholar 
    Blanco-Pastor, J. L., Fernández-Mazuecos, M. & Vargas, P. Past and future demographic dynamics of alpine species: Limited genetic consequences despite dramatic range contraction in a plant from the Spanish Sierra Nevada. Mol. Ecol. 22, 4177–4195 (2013).CAS 

    Google Scholar 
    Chen, N. et al. Allele frequency dynamics in a pedigreed natural population. PNAS 116, 2158–2164 (2019).ADS 
    CAS 

    Google Scholar 
    Exposito-Alonso, M., Booker, T. A., Czech, L., Fukami, T., Gillespie, L., Hateley, S. et al. Quantifying the scale of genetic diversity extinction in the Anthropocene. bioRxiv (2021).Keller, I. & Largiadèr, C. R. Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proc. R. Soc. B Biol. Sci. 270, 417–423 (2003).CAS 

    Google Scholar 
    Chan, L. M. et al. Phylogeographic structure of the dunes sagebrush lizard, an endemic habitat specialist. PLoS ONE 15, 0238194 (2020).
    Google Scholar 
    Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).
    Google Scholar 
    Cayuela, H. et al. Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review. Mol. Ecol. 27, 3976–4010 (2018).
    Google Scholar 
    Battey, C. J., Ralph, P. L. & Kern, A. D. Space is the place: Effects of continuous spatial structure on analysis of population genetic data. Genetics 215, 193–214 (2020).CAS 

    Google Scholar 
    Stubbs, D. & Swingland, I. R. The ecology of a Mediterranean tortoise (Testudo hermanni): A declining population. Can. J. Zool. 63, 169–180 (1985).
    Google Scholar 
    Channell, R. The conservation value of peripheral populations: The supporting science. in Proceedings of the Species at Risk 2004 Pathways to Recovery Conference. 1–17. (Species at Risk 2004 Pathways to Recovery Conference Organizing Committee, 2004).Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124(2), 255–279 (1984).
    Google Scholar 
    Brown, J. H. Macroecology (University of Chicago Press, 1995).
    Google Scholar 
    Brown, J. H., Stevens, G. C. & Kaufman, D. M. The geographic range: Size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 27(1), 597–623 (1996).
    Google Scholar 
    Sagarin, R. D. & Gaines, S. D. The ‘abundant centre’distribution: To what extent is it a biogeographical rule?. Ecol. Lett. 5, 137–147 (2002).
    Google Scholar 
    Eckert, C. G., Samis, K. E. & Lougheed, S. C. Genetic variation across species’ geographical ranges: The central-marginal hypothesis and beyond. Mol. Ecol. 17, 1170–1188 (2008).CAS 

    Google Scholar 
    Yackulic, C. B., Sanderson, E. W. & Uriarte, M. Anthropogenic and environmental drivers of modern range loss in large mammals. PNAS 108, 4024–4029 (2011).ADS 
    CAS 

    Google Scholar 
    Fitzgerald L.A., Walkup, D. Chyn, K. Buchholtz, E. Angeli, N. & Parker M. The future for reptiles: Advances and challenges in the Anthropocene. in Encyclopedia of the Anthropocene. (eds. Dellasala, D.A., & Goldstein, M.I.). 163–174 (Elsevier, 2018).Segelbacher, G., Höglund, J. & Storch, I. From connectivity to isolation: Genetic consequences of population fragmentation in capercaillie across Europe. Mol. Ecol. 12, 1773–1780 (2003).CAS 

    Google Scholar 
    Cegelski, C. C., Waits, L. P. & Anderson, N. J. Assessing population structure and gene flow in Montana wolverines (Gulo gulo) using assignment-based approaches. Mol. Ecol. 12, 2907–2918 (2003).CAS 

    Google Scholar 
    Proctor, M. F., McLellan, B. N., Strobeck, C. & Barclay, R. M. Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations. Proc. R. Soc. B Biol. Sci. 272, 2409–2416 (2005).
    Google Scholar 
    Leavitt, D. J. & Fitzgerald, L. A. Disassembly of a dune–dwelling lizard community due to landscape fragmentation. Ecosphere 4, 97 (2013).
    Google Scholar 
    Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
    Google Scholar 
    Rogan, J.E., & Lacher Jr., T.E. Impacts of habitat loss and fragmentation on terrestrial biodiversity. in Reference Modules in Earth Systems and Environmental Sciences. 1–18 (Elsevier, 2018).Hurtado, L. A., Santamaria, C. A. & Fitzgerald, L. A. Conservation genetics of the critically endangered St. Croix ground lizard (Ameiva polops Cope 1863). Conserv. Genet. 13, 665–679 (2012).
    Google Scholar 
    Lawton, J. H. Range, population abundance and conservation. Trends Ecol. Evol. 8, 409–413 (1993).CAS 

    Google Scholar 
    Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. R. Soc. B Biol. Sci. 267, 1947–1952 (2000).CAS 

    Google Scholar 
    Cardillo, M. et al. The predictability of extinction: Biological and external correlates of decline in mammals. Proc. R. Soc. B Biol. Sci. 275, 1441–1448 (2008).
    Google Scholar 
    Templeton, A. R. Coadaptation and outbreeding depression. in Conservation Biology: The Science of Scarcity and Diversity. (ed. Soulé, M.E.). 105–116 (Sinauer, 1986). Lomolino, M. V. & Smith, G. A. Dynamic biogeography of prairie dog (Cynomys ludovicianus) towns near the edge of their range. J. Mammal. 82, 937–945 (2001).
    Google Scholar 
    Wright, S. Isolation by distance. Genetics 28, 114 (1943).CAS 

    Google Scholar 
    Maruyama, T. Rate of decrease of genetic variability in a two-dimensional continuous population of finite size. Genetics 4(1), 639–651 (1972).
    Google Scholar 
    Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 645, 330–338 (1922).
    Google Scholar 
    Kelleher, J. & EtheridgeMcVean, A. M. G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).
    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019).Greenstein, B. J. & Pandolfi, J. M. Escaping the heat: Range shifts of reef coral taxa in coastal Western Australia. Glob. Change Biol. 14, 513–528 (2008).ADS 

    Google Scholar 
    Wilcove, D. S. & Terborgh, J. W. Patterns of population decline in birds. Am. Birds 38, 10–13 (1984).
    Google Scholar 
    Gabelli, F. M. et al. Range contraction in the Pampas meadowlark Sturnella defilippii in the southern Pampas grasslands of Argentina. Oryx 38, 164–170 (2004).
    Google Scholar 
    Pomara, L. Y., LeDee, O. E., Martin, K. J. & Zuckerberg, B. Demographic consequences of climate change and land cover help explain a history of extirpations and range contraction in a declining snake species. Glob. Change Biol. 20, 2087–2099 (2014).ADS 

    Google Scholar 
    Towns, D. R. & Daugherty, C. H. Patterns of range contractions and extinctions in the New Zealand herpetofauna following human colonisation. N. Z. J. Zool. 21, 325–339 (1994).
    Google Scholar 
    Rudolph, D. C., Burgdorf, S. J., Schaefer, R. R., Conner, R. N. & Maxey, R. W. Status of Pituophis ruthveni (Louisiana pine snake). Southeast. Nat. 5(3), 463–472 (2006).
    Google Scholar 
    Russell, R. W., Lipps, G. J. Jr., Hecnar, S. J. & Haffner, G. D. Persistent organic pollutants in Blanchard’s cricket frogs (Acris crepitans blanchardi) from Ohio. Ohio J. Sci. 102, 119–122 (2002).CAS 

    Google Scholar 
    Fellers, G. M. & Drost, C. A. Disappearance of the Cascades frog Rana cascadae at the southern end of its range, California, USA. Biol. Cons. 65, 177–181 (1993).
    Google Scholar 
    Franco, A. M. et al. Impacts of climate warming and habitat loss on extinctions at species’ low-latitude range boundaries. Glob. Change Biol. 12, 1545–1553 (2006).ADS 

    Google Scholar 
    Stewart, J. A., Wright, D. H. & Heckman, K. A. Apparent climate-mediated loss and fragmentation of core habitat of the American pika in the Northern Sierra Nevada, California, USA. PLoS ONE 12, e0181834 (2017).
    Google Scholar 
    Rodríguez, A. & Delibes, M. Internal structure and patterns of contraction in the geographic range of the Iberian lynx. Ecography 25, 314–328 (2002).
    Google Scholar 
    Kattan, G. et al. Range fragmentation in the spectacled bear Tremarctos ornatus in the northern Andes. Oryx 38(2), 155–163 (2004).
    Google Scholar 
    Jones, S. J., Lima, F. P. & Wethey, D. S. Rising environmental temperatures and biogeography: poleward range contraction of the blue mussel, Mytilus edulis L., in the western Atlantic. J. Biogeogr. 37, 2243–2259 (2010).
    Google Scholar 
    Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. P. R. Soc. B Biol. Sci. 280, 20122829 (2013).
    Google Scholar  More

  • in

    Biogeochemical and historical drivers of microbial community composition and structure in sediments from Mercer Subglacial Lake, West Antarctica

    Siegert M, Ross N, Le Brocq A. Recent advances in understanding Antarctic subglacial lakes and hydrology. Philos Trans R Soc A-Math Phys Eng Sci. 2016;374:20140306.
    Google Scholar 
    Fricker H, Scambos T, Bindschadler R, Padman L. An active subglacial water system in West Antarctica mapped from space. Science. 2007;315:1544–8.CAS 

    Google Scholar 
    Livingstone S, Li Y, Rutishauser A, Sanderson R, Winter K, Mikucki J, et al. Subglacial lakes and their changing role in a warming climate. Nat Rev Earth Environ. 2022;3:106–24.
    Google Scholar 
    Tulaczyk S, Mikucki J, Siegfried M, Priscu J, Barcheck C, Beem L, et al. WISSARD at Subglacial Lake Whillans, West Antarctica: scientific operations and initial observations. Ann Glaciol. 2014;55:51–8.
    Google Scholar 
    Priscu J, Achberger A, Cahoon J, Christner B, Edwards R, Jones W, et al. A microbiologically clean strategy for access to the Whillans Ice Stream subglacial environment. Antarctitc Sci. 2013;25:637–47.
    Google Scholar 
    Christner BC, Priscu JC, Achberger AM, Barbante C, Carter SP, Christianson K, et al. A microbial ecosystem beneath the West Antarctic ice sheet. Nature. 2014;512:310–3.CAS 

    Google Scholar 
    Michaud A, Dore J, Achberger A, Christner B, Mitchell A, Skidmore M, et al. Microbial oxidation as a methane sink beneath the West Antarctic Ice Sheet. Nat Geosci. 2017;10:582–6.CAS 

    Google Scholar 
    Achberger A, Christner B, Michaud A, Priscu J, Skidmore M, Vick-Majors T, et al. Microbial community structure of Subglacial Lake Whillans, West Antarctica. Front Microbiol. 2016;7:1457.
    Google Scholar 
    Vick-Majors TJ, Mitchell AC, Achberger AM, Christner BC, Dore JE, Michaud AB, et al. Physiological ecology of microorganisms in Subglacial Lake Whillans. Front Microbiol. 2016;7:1705.
    Google Scholar 
    Vick‐Majors TJ, Michaud AB, Skidmore ML, Turetta C, Barbante C, Christner BC, et al. Biogeochemical connectivity between freshwater ecosystems beneath the West Antarctic Ice Sheet and the Sub‐Ice Marine Environment. Global Biogeochem Cycles. 2020;34:1–17.
    Google Scholar 
    Montross S, Skidmore M, Tranter M, Kivimaki A, Parkes R. A microbial driver of chemical weathering in glaciated systems. Geology. 2013;41:215–8.CAS 

    Google Scholar 
    Gill-Olivas B, Telling J, Tranter M, Skidmore M, Christner B, O’Doherty S, et al. Subglacial erosion has the potential to sustain microbial processes in Subglacial Lake Whillans, Antarctica. Commun Earth Environ. 2021;2:1–12.
    Google Scholar 
    Priscu JC, Kalin J, Winans J, Campbell T, Siegfried MR, Skidmore M, et al. Scientific access into Mercer Subglacial Lake: scientific objectives, drilling operations and initial observations. Ann Glaciol. 2021;62:340–52.
    Google Scholar 
    Fricker H, Scambos T. Connected subglacial lake activity on lower Mercer and Whillans Ice Streams, West Antarctica, 2003-2008. J Glaciol. 2009;55:303–15.
    Google Scholar 
    Carter S, Fricker H, Siegfried M. Evidence of rapid subglacial water piracy under Whillans Ice Stream, West Antarctica. J Glaciol. 2013;59:1147–62.
    Google Scholar 
    Venturelli RA, Boehman B, Davis C, Hawkings JR, Johnston SE, Gustafson CD, et al. Constraints on the timing and extent of deglacial grounding line retreat in West Antarctica from subglacial sediments. AGU Advances. 2022; (in review).Kingslake J, Scherer R, Albrecht T, Coenen J, Powell R, Reese R, et al. Extensive retreat and re-advance of the West Antarctic Ice Sheet during the Holocene. Nature. 2018;558:430–4.CAS 

    Google Scholar 
    Venturelli RA, Siegfried MR, Roush KA, Li W, Burnett J, Zook R, et al. Mid-Holocene Grounding Line Retreat and Readvance at Whillans Ice Stream, West Antarctica. Geophys Res Lett. 2020;47:e2020GL088476.
    Google Scholar 
    Scherer R, Aldahan A, Tulaczyk S, Possnert G, Engelhardt H, Kamb B. Pleistocene collapse of the West Antarctic ice sheet. Science. 1998;281:82–5.CAS 

    Google Scholar 
    Achberger A. Structure and functional potential of microbial communities in Subglacial Lake Whillans and at the Ross Ice Shelf Grounding Zone, West Antarctica: Louisiana State University; 2016.Blythe D, Duling D, Gibson D. Developing a hot-water drill system for the WISSARD project: 2. In situ water production. Ann Glaciol. 2014;55:298–310.
    Google Scholar 
    Burnett J, Rack FR, Blythe D, Swanson P, Duling D, Gibson D, et al. Developing a hot-water drill system for the WISSARD project: 3. Instrumentation and control systems. Ann Glaciol. 2014;55:303–10.
    Google Scholar 
    Rack F, Duling D, Blythe D, Burnett J, Gibson D, Roberts G, et al. Developing a hot-water drill system for the WISSARD project: 1. Basic drill system components and design. Ann Glaciol. 2014;55:285–97.
    Google Scholar 
    Michaud A, Vick-Majors T, Achberger A, Skidmore M, Christner B, Tranter M, et al. Environmentally clean access to Antarctic subglacial aquatic environments. Antarctic Sci. 2020;32:1–12.Kallmeyer J, Smith DC, Spivack AJ, D’Hondt S. New cell extraction procedure applied to deep subsurface sediments. Limnol Oceanogr Methods. 2008;6:236–45.
    Google Scholar 
    Pan D, Morono Y, Inagaki F, Takai K. An improved method for extracting viruses from sediment: detection of far more viruses in the subseafloor than previously reported. Front Microbiol. 2019;10:878.
    Google Scholar 
    Battin T, Wille A, Sattler B, Psenner R. Phylogenetic and functional heterogeneity of sediment biofilms along environmental gradients in a glacial stream. Appl Environ Microbiol. 2001;67:799–807.CAS 

    Google Scholar 
    Klock J-H, Wieland A, Seifert R, Michaelis W. Extracellular polymeric substances (EPS) from cyanobacterial mats: characterisation and isolation method optimisation. Marine Biol. 2007;152:1077–85.CAS 

    Google Scholar 
    Miyatake T, Moerdijk-Poortvliet T, Stal L, Boschker H. Tracing carbon flow from microphytobenthos to major bacterial groups in an intertidal marine sediment by using an in situ C-13 pulse-chase method. Limnol Oceanogr. 2014;59:1275–87.CAS 

    Google Scholar 
    Albalasmeh A, Berhe A, Ghezzehei T. A new method for rapid determination of carbohydrate and total carbon concentrations using UV spectrophotometry. Carbohydrate Polymers. 2013;97:253–61.CAS 

    Google Scholar 
    Lerotic M, Mak R, Wirick S, Meirer F, Jacobsen C. MANTiS: a program for the analysis of X-ray spectromicroscopy data. J Synchrotron Radiat. 2014;21:1206–12.CAS 

    Google Scholar 
    Bonneville S, Delpomdor F, Preat A, Chevalier C, Araki T, Kazemian M, et al. Molecular identification of fungi microfossils in a Neoproterozoic shale rock. Sci Adv. 2020;6:eaax7599.CAS 

    Google Scholar 
    Le Guillou C, Bernard S, De la Pena F, Le Brech Y. XANES-based quantification of carbon functional group concentrations. Anal Chem. 2018;90:8379–86.
    Google Scholar 
    Solomon D, Lehmann J, Kinyangi J, Liang B, Heymann K, Dathe L, et al. Carbon (1s) NEXAFS spectroscopy of biogeochemically relevant reference organic compounds. Soil Sci Soc Am J. 2009;73:1817–30.CAS 

    Google Scholar 
    Michaud A, Skidmore M, Mitchell A, Vick-Majors T, Barbante C, Turetta C, et al. Solute sources and geochemical processes in Subglacial Lake Whillans, West Antarctica. Geology. 2016;44:347–50.CAS 

    Google Scholar 
    Raiswell R, Hawkings J, Eisenousy A, Death R, Tranter M, Wadham J. Iron in glacial systems: speciation, reactivity, freezing behavior, and alteration during transport. Front Earth Sci. 2018;6:222.
    Google Scholar 
    Hyacinthe C, Bonneville S, Van Cappellen P. Reactive iron(III) in sediments: Chemical versus microbial extractions. Geochimica Et Cosmochimica Acta. 2006;70:4166–80.CAS 

    Google Scholar 
    Raiswell R, Benning L, Tranter M, Tulaczyk S. Bioavailable iron in the Southern Ocean: the significance of the iceberg conveyor belt. Geochem Trans. 2008;9:7.
    Google Scholar 
    Raiswell R, Vu H, Brinza L, Benning L. The determination of labile Fe in ferrihydrite by ascorbic acid extraction: Methodology, dissolution kinetics and loss of solubility with age and de-watering. Chem Geol. 2010;278:70–9.CAS 

    Google Scholar 
    Fossing H, Jorgensen B. Measurement of bacterial sulfate reduction in sediments—evaluation of a single-step chromium reduction method. Biogeochemistry. 1989;8:205–22.CAS 

    Google Scholar 
    Cline J. Spectrophotometric determination of hydrogen sulfide in natural waters. Limnol Oceanogr. 1969;14:454.CAS 

    Google Scholar 
    Kallmeyer J, Ferdelman T, Weber A, Fossing H, Jorgensen B. A cold chromium distillation procedure for radiolabeled sulfide applied to sulfate reduction measurements. Limnol Oceanogr Methods. 2004;2:171–80.
    Google Scholar 
    Roy H, Weber H, Tarpgaard I, Ferdelman T, Jorgensen B. Determination of dissimilatory sulfate reduction rates in marine sediment via radioactive S-35 tracer. Limnol Oceanogr Methods. 2014;12:196–211.
    Google Scholar 
    Caporaso J, Lauber C, Walters W, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 

    Google Scholar 
    Button DK, Robertson BR. Determination of DNA content of aquatic bacteria by flow cytometry. Appl Environ Microbiol. 2001;67:1636–45.CAS 

    Google Scholar 
    Michaud AB, Priscu JC, the Salsa Science Team. Sediment oxygen consumption in Antarctic subglacial environments. Limnology and Oceanography. 2022. (In Review).Siegfried MR, Venturelli RA, Patterson MO, Arnuk W, Campbell TD, Gustafson CD, et al. The life and death of a subglacial lake in West Antarctica. Geology. 2023; in press; https://doi.org/10.1130/G50995.1.Vyse S, Herzschuh U, Pfalz G, Pestryakova L, Diekmann B, Nowaczyk N, et al. Sediment and carbon accumulation in a glacial lake in Chukotka (Arctic Siberia) during the Late Pleistocene and Holocene: combining hydroacoustic profiling and down-core analyses. Biogeosciences. 2021;18:4791–816.CAS 

    Google Scholar 
    Oliva-Urcia B, Moreno A, Leunda M, Valero-Garces B, Gonzalez-Samperiz P, Gil-Romera G, et al. Last deglaciation and Holocene environmental change at high altitude in the Pyrenees: the geochemical and paleomagnetic record from Marbor, Lake (N Spain). J Paleolimnol. 2018;59:349–71.
    Google Scholar 
    Davis C. Ecology of subglacial lake microbial communities in West Antarctica: University of Florida; 2022.Lanoil B, Skidmore M, Priscu JC, Han S, Foo W, Vogel SW, et al. Bacteria beneath the West Antarctic ice sheet. Environ Microbiol. 2009;11:609–15.CAS 

    Google Scholar 
    Boyd E, Hamilton T, Havig J, Skidmore M, Shock E. Chemolithotrophic Primary Production in a Subglacial Ecosystem. Appl Environ Microbiol. 2014;80:6146–53.
    Google Scholar 
    Sattley WM, Madigan MT. Isolation, characterization, and ecology of cold-active, chemolithotrophic, sulfur-oxidizing bacteria from perennially ice-covered Lake Fryxell, Antarctica. Appl Environ Microbiol. 2006;72:5562–8.CAS 

    Google Scholar 
    Dieser M, Broemsen E, Cameron KA, King GM, Achberger A, Choquette K, et al. Molecular and biogeochemical evidence for methane cycling beneath the western margin of the Greenland Ice Sheet. ISME J. 2014;8:2305–16.CAS 

    Google Scholar 
    Vaclavkova S, Schultz-Jensen N, Jacobsen O, Elberling B, Aamand J. Nitrate-controlled anaerobic oxidation of pyrite by thiobacillus cultures. Geomicrobiol J. 2015;32:412–9.CAS 

    Google Scholar 
    Gustafson C, Key K, Siegfried M, Winberry J, Fricker H, Venturelli R, et al. A dynamic saline groundwater system mapped beneath an Antarctic ice stream. Science. 2022;376:640–4.CAS 

    Google Scholar 
    Priscu JC, Tulaczyk S, Studinger M, Kennicutt M, Christner BC, Foreman CM. Antarctic subglacial water: origin, evolution and ecology. Polar lakes and rivers: limnology of Arctic and Antarctic aquatic ecosystems Oxford University Press, Oxford. 2008:119–35.Whitman W, Coleman D, Wiebe W. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA 1998;95:6578–83.CAS 

    Google Scholar 
    Scherer R. Quaternary and tertiary microfossils from beneath Ice Stream-B—evidence for a dynamic West Antarctic ice-sheet history. Global Planet Change. 1991;90:395–412.
    Google Scholar 
    Haran T, Bohlander J, Scambos T, Painter T, Fahnestock M. MODIS Mosaic of Antarctica 2008–2009 (MOA2009) Image Map, Version 2. 2021; Boulder, Colorado USA NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/4ZL43A4619AF.Mouginot J, Rignot E, Scheuchl B. Continent‐Wide Interferometric SAR Phase Mapping of Antarctic Ice Velocity. Geophysical Research Letters. 2019;46:9710–8. https://doi.org/10.1029/2019GL083826.Depoorter MA, Bamber JL, Griggs JA, Lenaerts JTM, Ligtenberg SRM, van den Broeke MR, et al. Calving fluxes and basal melt rates of Antarctic ice shelves. Nature. 2013;502:89–92. https://doi.org/10.1038/nature12567. More

  • in

    Coral reef structural complexity loss exposes coastlines to waves

    Ecological sampling and structural complexity profilesThe ecological sampling consists of 10 surveys, taking place in 2005 and from 2008 to 2016, and documents changes in coral colony abundance and size distributions (i.e. width, length, and height) for the three most conspicuous taxa (i.e. Acropora, Pocillopora, and Porites) within a 10 m2 transect on the outer slope23. To quantify reef structural complexity, we built a 3D model of the coral assemblages distributed along a cross-section of the reef substrate separating the 20 m water depth from the reef crest, representing a 160 m stretch along the reef slope (Fig. 1). First, we take 200 overlapping high-resolution photos (300 dpi) of 10 individual corals from each species (i.e. n = 30 coral colonies) and built 3D models using the Agisoft Metashape software24, capturing intra- and inter-species morphological variability (Fig. 1). Then, we systematically and randomly select one of the ten 3D coral models for each taxon to add to the substrate until that the sum of the planar area for each 3D coral models match with the coral cover reported for each taxon and for each year23. We randomly place coral colonies along the 160 m reef cross-section going from 20 m depth to the reef crest (Fig. 1). The individual coral 3D models are resized in width, length, and height according to ecological surveys, and, randomly rotated between − π/2 and π/2 to ensure ecological variability. Finally, we estimated structural complexity of the 3D coral assemblage model using the function rumple_index of the LidR package25 in R 4.0.026. We repeat this approach 100 times for each year, resulting in a total of 1000 reef structural complexity profiles. Our estimates are consistent with previous reef structural complexity estimates at this location27.Figure 1(a) Representation of the three different coral species (Acropora hyacinthus in red, Pocillopora cf. verrucosa in yellow, and Porites lutea in blue). (b) A representaitive Ha’apiti reef cross-section simulation (one of 1000 total simulations) on the outer slope across a water depth range of 0–20 m.Full size imageHydrodynamic and topographic measurementsMo’orea (French Polynesia) is encircled by coral reefs, 500–700 m wide with a dominant swell direction coming from the southwest. In this study, we focus on Ha’apiti, a site with a southwest orientation that is considered as a high-energy site28. We extract 30-year offshore wave data (1980–2010) from a wave hindcast8,29 (Fig. 2a). We also collect high-frequency, in situ wave data using INW PT2X Aquistar and DHI SensorONE pressure transducers (PTs), which are logged at 4 Hz30. The sensors are installed at four locations along a cross-shelf gradient (Fig. 2b,c) covering a 250 m long stretch, including sections through the fore reef, reef crest, and reef flat. Pressure records are corrected for pressure attenuation with depth31 and are split into 15-min bursts30.Figure 2(a) Histogram of the offshore wave height (m) at Ha’apiti, Mo’orea (French Polynesia) in 2016. (b) Aerial view of Ha’apiti (WorldView-3 imagery) with an outline of the wave transect and sensor location. The ecological sampling took place near the S1 location c. Topographic cross-section of the wave transect and position of the sensors on the sea bottom.Full size imageThe beach profile and the reef morphology are measured using airborne bathymetric and topo-bathymetric lidar surveys conducted in June 2015 by the Service Hydrographique & Océanographique de la Marine (SHOM). The bathymetric data are defined by the combination of bathymetric laser (for the submerged part of the beach) and topo-bathymetric laser (for the subaerial beach). The data come at 1 m resolution and are available at https://diffusion.shom.fr.Hydraulic roughness vs structural complexitySpectral attenuation analysis of the water level measurements32,33 is used to estimate the Nikuradse (hydraulic; kn) roughness34 of the coral reef surface along the beach profile sections covered by the pressure transducers. The method is described in detail in the references provided above and uses the conservation of energy equations to obtain estimates of wave energy dissipation from friction. We obtain more than 300 kn estimates for each pair of sensors, each representing a different geomorphologic section. Since the field measurements took place in 2015, the kn outputs obtained from the fore reef section concur with the reef structural complexity estimates of that year (Fig. 3). Then, we define a coefficient factor according to the geomorphologic section as ⍺back reef = kn, back reef/kn, fore reef and ⍺reef crest = kn, reef crest/kn, fore reef. We carefully delineate the sandy section from the reef sections within the cross-shelf gradient (i.e. within the reef flat, lagoon section) and apply the following procedure. First, for the reef sections, we apply the relationship between the reef structural complexity and kn (Fig. 3) to convert our reef structural complexity estimates into continuous kn profiles through Monte Carlo simulations, using the coefficient factor of each geomorphologic section (e.g., forereef, reef crest, and back reef). Second, for the sandy section, we define kn on the grounds of the mean grain size (d50 = 63 μm). Applying this workflow (Fig. 3), we obtain 100 continuous kn profiles for each year (i.e. n = 1000 kn profiles in total).Figure 3Flow chart illustrating how the kn profiles have been obtained along the cross-section at Ha’apiti. The relationship between the Structural complexity (SC) and the Nikuradse roughness (kn) measurements can be described as kn = 0.01 × SC2.98.Full size imageHydrodynamic modelNearshore wave propagation is simulated using a nonlinear wave model based on the Boussinesq Equations35. The rationale of using a Boussinesq type model instead of other types of models (e.g. SWAN) is that the former is able to describe in detail (i.e. 1 m grid resolution) several hydrodynamic parameters (e.g. nearshore nonlinear wave propagation, shoaling, refraction, dissipation due to the bottom friction and breaking and run-up) in the swash zone. The model is defined as follows:$$frac{partial U}{partial t}+frac{1}{h}frac{partial {M}_{u}}{partial x}-frac{1}{h}Ufrac{partial left(Uhright)}{partial x}+gfrac{partialupzeta }{partial x}=frac{left({d}^{2}+2partialupzeta right)}{3}frac{{partial }^{3}U}{partial {x}^{2}partial t}+{d}_{x}hfrac{{partial }^{2}U}{partial xpartial t}+frac{{partial }^{2}}{3}left(Ufrac{{partial }^{3}U}{{partial x}^{3}}-frac{partial U}{partial x}frac{{partial }^{2}U}{partial {x}^{2}}right)+dfrac{partialupzeta }{partial mathrm{x}}frac{{partial }^{2}U}{partialupzeta partial mathrm{t}}+d{d}_{x}Ufrac{{partial }^{2}U}{partial {x}^{2}}+{d}_{x}frac{partialupzeta }{partial mathrm{x}}frac{partial mathrm{U}}{partial mathrm{t}}-dfrac{{partial }^{2}}{partial mathrm{x}partial mathrm{t}}left(delta frac{partial mathrm{U}}{partial mathrm{x}}right)+E-frac{{tau }_{b}}{rho h}+B{d}^{2}left(frac{{partial }^{3}U}{partial {x}^{2}}+gfrac{{partial }^{3}upzeta }{partial {x}^{3}}+frac{{partial }^{2}left(Ufrac{partial U}{partial x}right)}{partial {x}^{2}}right)+2Bd{d}_{x}left(frac{{partial }^{2}U}{partial xpartial t}+gfrac{{partial }^{2}upzeta }{partial {mathrm{x}}^{2}}right),$$
    (1)
    where, U is the mean over the depth horizontal velocity, ζ is the surface elevation, d is the water depth, uo is the near bottom velocity, h = d + ζ, ({M}_{u}=left(d+zeta right){u}_{0}^{2}+delta ({c}^{2}-{u}_{0}^{2})), δ is the roller thickness determined geometrically36, E is an eddy viscosity, τb is the bed friction term and B = 1/1535.In this work the wave breaking mechanism is based on the surface roller concept36. However, in the swash zone, surface roller is not present and the eddy viscosity concept is used to describe the breaking process. The term E in Eq. (1) is written:$${mathrm{E}}_{{mathrm{b}}_{mathrm{x}}}= {mathrm{B}}_{mathrm{b}}frac{1}{mathrm{h}+upeta }{left{{{mathrm{v}}_{e}left[left(mathrm{h}+upeta right)mathrm{U}right]}_{mathrm{x}}right}}_{mathrm{x}},$$
    (2)
    where ({v}_{e}) is the eddy viscosity coefficient:$${mathrm{v}}_{mathrm{e}}={{ell}}^{2}left|frac{partial {mathrm{U}}}{partial {mathrm{x}}}right|,$$
    (3)
    where ({ell}) is the mixing length ({ell}) = 3.5 h και Βb37.The width of the swash zone is assumed to extend from the run-down point (seaward boundary) up to the run-up point (landward boundary). We start from a first estimate of the run-up R using the Stockdon formula38 and the depths below R/4 are considered as the swash zone, using Eq. (2). The final wave run-up height R which comes as output is estimated by the model.The ‘dry bed’ boundary condition is used to simulate run-up35. The numerical solution is based on the fourth-order time predictor–corrector scheme39. Therefore, the bed friction term τb is calculated such as:$${tau }_{bx}=frac{1}{2}rho {f}_{w}Uleft|Uright|,$$
    (4)
    where fw is the bottom friction coefficient40, which is an explicit approximation to the implicit, semi-empirical formula given by Jonsson, 196741.$${f}_{mathrm{w}}=mathrm{exp}left[{5.213left(frac{{mathrm{k}}_{mathrm{n}}}{{mathrm{alpha }}_{0}}right)}^{0.194}-5.977right],$$
    (5)
    where αo is the amplitude of the near-bed wave orbital motion and kn is the Nikuradse roughness height.Simulations and post processingWe use our wave propagation model to assess how different coral reef states affect the impact waves have on the coast. We run an ensemble of 10,000 simulations that covers all the possible combinations of (i) 10 bottom roughness profiles expressing the different observed coral reef states (i.e. healthy vs. not unhealthy); and (ii) 1000 percentiles of wave conditions. The wave conditions are produced as follows: (i) from the weekly values, we estimate all significant wave height (Hs) percentiles from 0.1 to 100, with a step of 0.1; (ii) the resulting 1000 Hs values are linked to the corresponding peak wave period Tp using a copula expressing the dependence of the two variables42. The output of the simulations is the nearshore Hs and 2% exceedance run-up (R2%) height for each of the 1000 conditions and 10 coral reef states. To quantify how the coral reef states are altering wave propagation during extreme events, we apply extreme value analysis to estimate the R2% for different return periods43. We then compare how the return period curves changed from the two coral reef states and we define the change in frequency of extreme R2% under unhealthy coral reefs. It is important to highlight that the tidal range is  More

  • in

    Mapping the Amazon’s fish under threat

    When I first came to the Amazon from central Brazil in 1978, I was planning to stay just a year, but I was mesmerized by the size of the rainforest’s rivers and its biodiversity. I ended up staying longer and earned my master’s degree in aquatic biology in 1984 from the National Institute for Amazonian Research (INPA), in Manaus, Brazil. I then went to get my PhD in ecology and evolutionary biology at the University of Arizona in Tucson, and returned to Manaus in 1998 to work as an ichthyologist at INPA.I was part of the team that started INPA’s fish collection in 1978. At the time, most scientific information on Amazonian fish, including specimens, had been collected by researchers and stored at other institutions around the world. Brazilians couldn’t easily access any of it. Now, INPA has preserved and catalogued more than 600,000 fish, all of which are available to our graduate students and scientific community.
    Women in science
    This picture, from last June, was taken at a Manicoré River creek in northwest Brazil during a Greenpeace expedition. I’m holding a bag of small fish, collected using sieves.Since 2006, the riverside communities on the Manicoré have been advocating for a reserve to protect their land from non-sustainable practices. They asked Greenpeace to help map the area’s biodiversity to bolster their application. Greenpeace in turn invited INPA researchers for its mapping expedition. We spent 20 days collecting and registering the wide range of creatures in the Manicoré’s basins.Besides fires, the Amazon has been hit hard by deforestation and industrial activities. We registered a decline in populations of several fish species after the construction of the hydroelectric complex of Belo Monte — the second- largest in the world — in the Xingu River. These species can thrive only in the oxygenated environment of running rivers and waterfalls, which have been largely destroyed. More

  • in

    Natural hybridization reduces vulnerability to climate change

    Ackerly, D. D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 164, S165–S184 (2003).Article 

    Google Scholar 
    Kellermann, V., Van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).Article 
    CAS 

    Google Scholar 
    Hansen, M. M., Olivieri, I., Waller, D. M. & Nielsen, E. E. Monitoring adaptive genetic responses to environmental change. Mol. Ecol. 21, 1311–1329 (2012).Article 

    Google Scholar 
    Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).Article 

    Google Scholar 
    Becker, M. et al. Hybridization may facilitate in situ survival of endemic species through periods of climate change. Nat. Clim. Change 3, 1039–1043 (2013).Article 

    Google Scholar 
    Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).Article 

    Google Scholar 
    Todesco, M. et al. Hybridization and extinction. Evol. Appl. 9, 892–908 (2016).Article 
    CAS 

    Google Scholar 
    Rhymer, J. M. & Simberloff, D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).Article 

    Google Scholar 
    Taylor, S. A. & Larson, E. L. Insights from genomes into the evolutionary importance and prevalence of hybridization in nature. Nat. Ecol. Evol. 3, 170–177 (2019).Article 

    Google Scholar 
    vonHoldt, B. M., Brzeski, K. E., Wilcove, D. S. & Rutledge, L. Y. Redefining the role of admixture and genomics in species conservation. Conserv. Lett. 11, e12371 (2018).Article 

    Google Scholar 
    Hamilton, J. A. & Miller, J. M. Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conserv. Biol. 30, 33–41 (2016).Article 

    Google Scholar 
    Ralls, K., Sunnucks, P., Lacy, R. C. & Frankham, R. Genetic rescue: a critique of the evidence supports maximizing genetic diversity rather than minimizing the introduction of putatively harmful genetic variation. Biol. Conserv. 251, 108784 (2020).Article 

    Google Scholar 
    Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal) adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

    Google Scholar 
    Rellstab, C., Dauphin, B. & Exposito‐Alonso, M. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14, 1202–1212 (2021).Article 

    Google Scholar 
    Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).Article 
    CAS 

    Google Scholar 
    Rellstab, C. et al. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol. Ecol. 25, 5907–5924 (2016).Article 

    Google Scholar 
    Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).Article 

    Google Scholar 
    Exposito-Alonso, M. et al. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Nat. Ecol. Evol. 2, 352–358 (2018).Article 

    Google Scholar 
    Kindt, R. AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change. PeerJ 9, e11534 (2021).Article 

    Google Scholar 
    Gain, C. & François, O. LEA 3: factor models in population genetics and ecological genomics with R. Mol. Ecol. Resour. 21, 2738–2748 (2020).Article 

    Google Scholar 
    Aguirre-Liguori, J. A., Ramírez-Barahona, S. & Gaut, B. S. The evolutionary genomics of species’ responses to climate change. Nat. Ecol. Evol. 5, 1350–1360 (2021).Article 

    Google Scholar 
    Taylor, S. A., Larson, E. L. & Harrison, R. G. Hybrid zones: windows on climate change. Trends Ecol. Evol. 30, 398–406 (2015).Article 

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

    Google Scholar 
    McGuigan, K., Franklin, C. E., Moritz, C. & Blows, M. W. Adaptation of rainbow fish to lake and stream habitats. Evolution 57, 104–118 (2003).
    Google Scholar 
    Smith, S., Bernatchez, L. & Beheregaray, L. RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species. BMC Genomics 14, 375 (2013).Article 
    CAS 

    Google Scholar 
    Smith, S. et al. Latitudinal variation in climate‐associated genes imperils range edge populations. Mol. Ecol. 29, 4337–4349 (2020).Article 
    CAS 

    Google Scholar 
    Sandoval-Castillo, J. et al. Adaptation of plasticity to projected maximum temperatures and across climatically defined bioregions. Proc. Natl Acad. Sci. USA 117, 17112–17121 (2020).Article 
    CAS 

    Google Scholar 
    Brauer, C., Unmack, P. J., Smith, S., Bernatchez, L. & Beheregaray, L. B. On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system. Mol. Ecol. 27, 3484–3497 (2018).Article 
    CAS 

    Google Scholar 
    Attard, C. R. et al. Fish out of water: genomic insights into persistence of rainbowfish populations in the desert. Evolution 76, 171–183 (2022).Article 

    Google Scholar 
    Gates, K. et al. Environmental selection, rather than neutral processes, best explain patterns of diversity in a tropical rainforest fish. Preprint at bioRxiv https://doi.org/10.1101/2022.1105.1113.491913 (2022).Article 

    Google Scholar 
    McCairns, R. J. S., Smith, S., Sasaki, M., Bernatchez, L. & Beheregaray, L. B. The adaptive potential of subtropical rainbowfish in the face of climate change: heritability and heritable plasticity for the expression of candidate genes. Evol. Appl. 9, 531–545 (2016).Article 
    CAS 

    Google Scholar 
    McGuigan, K., Zhu, D., Allen, G. & Moritz, C. Phylogenetic relationships and historical biogeography of melanotaeniid fishes in Australia and New Guinea. Mar. Freshwat. Res. 51, 713–723 (2000).Article 

    Google Scholar 
    Unmack, P. J. et al. Malanda Gold: the tale of a unique rainbowfish from the Atherton Tablelands, now on the verge of extinction. Fish. Sahul. 30, 1039–1054 (2016).
    Google Scholar 
    Moritz, C. Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst. Biol. 51, 238–254 (2002).Article 

    Google Scholar 
    Pope, L., Estoup, A. & Moritz, C. Phylogeography and population structure of an ecotonal marsupial, Bettongia tropica, determined using mtDNA and microsatellites. Mol. Ecol. 9, 2041–2053 (2000).Article 
    CAS 

    Google Scholar 
    Hugall, A., Moritz, C., Moussalli, A. & Stanisic, J. Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875). Proc. Natl Acad. Sci. USA 99, 6112–6117 (2002).Article 
    CAS 

    Google Scholar 
    Moritz, C. et al. Identification and dynamics of a cryptic suture zone in tropical rainforest. Proc. R. Soc. B. 276, 1235–1244 (2009).Article 
    CAS 

    Google Scholar 
    Phillips, B. L., Baird, S. J. & Moritz, C. When vicars meet: a narrow contact zone between morphologically cryptic phylogeographic lineages of the rainforest skink, Carlia rubrigularis. Evolution 58, 1536–1548 (2004).
    Google Scholar 
    Krosch, M. N., Baker, A. M., Mckie, B. G., Mather, P. B. & Cranston, P. S. Deeply divergent mitochondrial lineages reveal patterns of local endemism in chironomids of the Australian Wet Tropics. Austral Ecol. 34, 317–328 (2009).Article 

    Google Scholar 
    Williams, S. E., Bolitho, E. E. & Fox, S. Climate change in Australian tropical rainforests: an impending environmental catastrophe. Proc. R. Soc. B. 270, 1887–1892 (2003).Article 

    Google Scholar 
    Whitehead, P. et al. Temporal development of the Atherton Basalt Province, north Queensland. Aust. J. Earth Sci. 54, 691–709 (2007).Article 
    CAS 

    Google Scholar 
    Moy, K. G., Unmack, P. J., Lintermans, M., Duncan, R. P. & Brown, C. Barriers to hybridisation and their conservation implications for a highly threatened Australian fish species. Ethology 125, 142–152 (2019).Article 

    Google Scholar 
    Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).Article 
    CAS 

    Google Scholar 
    Buerkle, C. A. Maximum‐likelihood estimation of a hybrid index based on molecular markers. Mol. Ecol. Notes 5, 684–687 (2005).Article 
    CAS 

    Google Scholar 
    Anderson, E. & Thompson, E. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160, 1217–1229 (2002).Article 
    CAS 

    Google Scholar 
    Dorion, S. & Landry, J. Activation of the mitogen-activated protein kinase pathways by heat shock. Cell Stress Chaperones 7, 200 (2002).Article 
    CAS 

    Google Scholar 
    Blumstein, M. et al. Protocol for projecting allele frequency change under future climate change at adaptive-associated loci. STAR Protoc. 1, 100061 (2020).Article 

    Google Scholar 
    Gougherty, A. V., Keller, S. R. & Fitzpatrick, M. C. Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat. Clim. Change 11, 166–171 (2021).Article 

    Google Scholar 
    Blumstein, M. et al. A new perspective on ecological prediction reveals limits to climate adaptation in a temperate tree species. Curr. Biol. 30, 1447–1453. e1444 (2020).Article 
    CAS 

    Google Scholar 
    Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).Article 
    CAS 

    Google Scholar 
    Goicoechea, P. G. et al. Adaptive introgression promotes fast adaptation in oaks marginal populations. Preprint available at bioRxiv https://doi.org/10.1101/731919 (2019).Lavergne, S. & Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc. Natl Acad. Sci. USA 104, 3883–3888 (2007).Article 
    CAS 

    Google Scholar 
    De Carvalho, D. et al. Admixture facilitates adaptation from standing variation in the European aspen (Populus tremula L.), a widespread forest tree. Mol. Ecol. 19, 1638–1650 (2010).Article 

    Google Scholar 
    De-Kayne, R. et al. Genomic architecture of adaptive radiation and hybridization in Alpine whitefish. Nat. Commun. 13, 4479 (2022).Article 
    CAS 

    Google Scholar 
    Baskett, M. L. & Gomulkiewicz, R. Introgressive hybridization as a mechanism for species rescue. Theor. Ecol. 4, 223–239 (2011).Article 

    Google Scholar 
    Meier, J. I. et al. The coincidence of ecological opportunity with hybridization explains rapid adaptive radiation in Lake Mweru cichlid fishes. Nat. Commun. 10, 1–11 (2019).Article 
    CAS 

    Google Scholar 
    Svardal, H. et al. Ancestral hybridization facilitated species diversification in the Lake Malawi cichlid fish adaptive radiation. Mol. Biol. Evol. 37, 1100–1113 (2020).Article 
    CAS 

    Google Scholar 
    Racimo, F., Sankararaman, S., Nielsen, R. & Huerta-Sánchez, E. Evidence for archaic adaptive introgression in humans. Nat. Rev. Genet. 16, 359–371 (2015).Article 
    CAS 

    Google Scholar 
    Jeong, C. et al. Admixture facilitates genetic adaptations to high altitude in Tibet. Nat. Commun. 5, 1–7 (2014).Article 

    Google Scholar 
    Nolte, A. W., Freyhof, J., Stemshorn, K. C. & Tautz, D. An invasive lineage of sculpins, Cottus sp. (Pisces, Teleostei) in the Rhine with new habitat adaptations has originated from hybridization between old phylogeographic groups. Proc. R. Soc. B. 272, 2379–2387 (2005).Article 

    Google Scholar 
    Fitzpatrick, M. C., Chhatre, V. E., Soolanayakanahally, R. Y. & Keller, S. R. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol. Ecol. Resour. 21, 2749–2765 (2021).Article 
    CAS 

    Google Scholar 
    Schneider, C., Cunningham, M. & Moritz, C. Comparative phylogeography and the history of endemic vertebrates in the Wet Tropics rainforests of Australia. Mol. Ecol. 7, 487–498 (1998).Article 

    Google Scholar 
    Hewitt, G. M. Quaternary phylogeography: the roots of hybrid zones. Genetica 139, 617–638 (2011).Article 

    Google Scholar 
    Pfennig, K. S., Kelly, A. L. & Pierce, A. A. Hybridization as a facilitator of species range expansion. Proc. R. Soc. B. 283, 20161329 (2016).Article 

    Google Scholar 
    Soulé, M. E. What is conservation biology? A new synthetic discipline addresses the dynamics and problems of perturbed species, communities, and ecosystems. Bioscience 35, 727–734 (1985).
    Google Scholar 
    Biermann, C. & Havlick, D. Genetics and the question of purity in cutthroat trout restoration. Restor. Ecol. 29, e13516 (2021).Article 

    Google Scholar 
    Fredrickson, R. J. & Hedrick, P. W. Dynamics of hybridization and introgression in red wolves and coyotes. Conserv. Biol. 20, 1272–1283 (2006).Article 

    Google Scholar 
    Hirashiki, C., Kareiva, P. & Marvier, M. Concern over hybridization risks should not preclude conservation interventions. Conserv. Sci. Pract. 3, e424 (2021).
    Google Scholar 
    Unmack, P. J., Allen, G. R. & Johnson, J. B. Phylogeny and biogeography of rainbowfishes (Melanotaeniidae) from Australia and New Guinea. Mol. Phylogenet. Evol. 67, 15–27 (2013).Article 

    Google Scholar 
    Allen, G. Rainbowfishes in Nature and the Aquarium (Tetra Publications, 1995).Seehausen, O. Hybridization and adaptive radiation. Trends Ecol. Evol. 19, 198–207 (2004).Article 

    Google Scholar 
    Pusey, B., Kennard, M. J. & Arthington, A. H. Freshwater Fishes of North-eastern Australia (CSIRO Publishing, 2004).Zhu, D., Degnan, S. & Moritz, C. Evolutionary distinctiveness and status of the endangered Lake Eacham rainbowfish (Melanotaenia eachamensis). Conserv. Biol. 12, 80–93 (1998).Article 

    Google Scholar 
    McGuigan, K., Chenoweth, S. F. & Blows, M. W. Phenotypic divergence along lines of genetic variance. Am. Nat. 165, 32–43 (2005).Article 

    Google Scholar 
    Sunnucks, P. & Hales, D. F. Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol. Biol. Evol. 13, 510–524 (1996).Article 
    CAS 

    Google Scholar 
    Peterson, B., Weber, J., Kay, E., Fisher, H. & Hoekstra, H. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).Article 
    CAS 

    Google Scholar 
    Catchen, J. M., Amores, A., Hohenlohe, P., Cresko, W. & Postlethwait, J. H. Stacks: building and genotyping loci de novo from short-read sequences. G3: Genes Genomes Genet. 1, 171–182 (2011).Article 
    CAS 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
    CAS 

    Google Scholar 
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).Article 
    CAS 

    Google Scholar 
    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).Article 
    CAS 

    Google Scholar 
    Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Article 

    Google Scholar 
    Goudet, J. Hierfstat, a package for R to compute and test hierarchical F‐statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Bailey, R. ribailey/gghybrid: gghybrid R package for Bayesian hybrid index and genomic cline estimation. v2.0.0 https://doi.org/10.5281/zenodo.3676498 (2020).Wringe, B. hybriddetective: automates the process of detecting hybrids from genetic data. R package version 0.1.0.9000 https://github.com/bwringe/hybriddetective (2016).Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).Article 
    CAS 

    Google Scholar 
    Malinsky, M., Matschiner, M. & Svardal, H. Dsuite‐Fast D‐statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021).Article 

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

    Google Scholar 
    Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).Article 
    CAS 

    Google Scholar 
    Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).Article 
    CAS 

    Google Scholar 
    Malinsky, M. et al. Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake. Science 350, 1493–1498 (2015).Article 
    CAS 

    Google Scholar 
    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Article 
    CAS 

    Google Scholar 
    Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

    Google Scholar 
    Karger, D. N. et al. CHELSA climatologies at high resolution for the Earth’s land surface areas (v.1.0). https://doi.org/10.1594/WDCC/CHELSA_v1 (2016).Ackerley, D. & Dommenget, D. Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures. Geosci. Model Dev. 9, 2077–2098 (2016).Article 

    Google Scholar 
    Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim: high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 1–9 (2018).Article 

    Google Scholar 
    Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21,000 years at regional and global scales. Ecography 40, 1348–1358 (2017).Article 

    Google Scholar 
    Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD–a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

    Google Scholar 
    Lemus-Canovas, M., Lopez-Bustins, J. A., Martin-Vide, J. & Royé, D. synoptReg: an R package for computing a synoptic climate classification and a spatial regionalization of environmental data. Environ. Model. Softw. 118, 114–119 (2019).Article 

    Google Scholar 
    Hao, T., Elith, J., Guillera‐Arroita, G. & Lahoz‐Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25, 839–852 (2019).Article 

    Google Scholar 
    Galpern, P., Peres‐Neto, P. R., Polfus, J. & Manseau, M. MEMGENE: spatial pattern detection in genetic distance data. Methods Ecol. Evol. 5, 1116–1120 (2014).Article 

    Google Scholar 
    Peres‐Neto, P. R. & Galpern, P. memgene: spatial pattern detection in genetic distance data using Moran’s eigenvector maps. R package version 1.0.1 https://cran.r-project.org/web/packages/memgene/ (2019).Oksanen, J. et al. vegan: community ecology package. R package version 2.3–0 https://cran.r-project.org/web/packages/vegan/ (2015).Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L. & Lasky, J. R. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecol. 25, 104–120 (2015).Article 

    Google Scholar 
    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).Article 
    CAS 

    Google Scholar 
    Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).Article 
    CAS 

    Google Scholar 
    Brauer, C. J. et al. Data for ‘Natural hybridisation reduces vulnerability to climate change’. figshare https://doi.org/10.6084/m9.figshare.21692918 (2022).Brauer, C. J. et al. Code for ‘Natural hybridisation reduces vulnerability to climate change’. GitHub https://github.com/pygmyperch/NER (2022). More