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German CR, Von Damm KL. Hydrothermal processes. In: Holland HD, Turekian KK and Elderfield H, editors. Treatise geochem, Vol. 6. The oceans and marine geochemistry. Oxford, UK:Elsevier-Pergamon, 2004;181–222.Bell JB, Woulds C, Oevelen DV. Hydrothermal activity, functional diversity and chemoautotrophy are major drivers of seafloor carbon cycling. Sci Rep. 2017;7:1–3.
Google Scholar
McCollom TM. Geochemical constraints on primary productivity in submarine hydrothermal vent plumes. Deep Res Part I Oceanogr Res Pap. 2000;47:85–101.CAS
Google Scholar
Tunnicliffe V, Baross JA, Gebruk AV, Giere O, Holland ME, Koschinsky A, et al. Group report: what are the interactions between biotic processes at vents and physical, chemical, and geological conditions. In: Halbach PE, Tunnicliffe V, and Hein JR, editors. Energy and Mass Transfer in Marine Hydrothermal Systems. Berlin-Dahlem:University Press; 2003;251–70.Nakamura K, Takai K. Theoretical constraints of physical and chemical properties of hydrothermal fluids on variations in chemolithotrophic microbial communities in seafloor hydrothermal systems. Prog Earth Planet Sci. 2014;1:1–24.
Google Scholar
Wang W, Li Z, Zeng L, Dong C, Shao Z. The oxidation of hydrocarbons by diverse heterotrophic and mixotrophic bacteria that inhabit deep-sea hydrothermal ecosystems. ISME J. 2020;14:1994–2006.CAS
Google Scholar
Sinha RK, Krishnan KP, Kurian PJ. Complete genome sequence and comparative genome analysis of Alcanivorax sp. IO_7, a marine alkane-degrading bacterium isolated from hydrothermally-influenced deep seawater of southwest Indian ridge. Genomics 2021;113:884–91.CAS
Google Scholar
Li J, Yang J, Sun M, Su L, Wang H, Gao J, et al. Distribution and succession of microbial communities along the dispersal pathway of hydrothermal plumes on the Southwest Indian Ridge. Front Mar Sci. 2020;7:581381.
Google Scholar
Meier DV, Bach W, Girguis PR, Gruber-Vodicka HR, Reeves EP, Richter M, et al. Heterotrophic Proteobacteria in the vicinity of diffuse hydrothermal venting. Environ Microbiol. 2016;18:4348–68.
Google Scholar
Li WL, Huang JM, Zhang PW, Cui GJ, Wei ZF, Wu YZ, et al. Periodic and spatial spreading of alkanes and Alcanivorax bacteria in deep waters of the Mariana Trench. Appl Environ Microbiol. 2019;85:e02089–18.CAS
Google Scholar
Brooijmans RJW, Pastink MI, Siezen RJ. Hydrocarbon-degrading bacteria: The oil-spill clean-up crew. Micro Biotechnol. 2009;2:587.CAS
Google Scholar
Scoma A, Barbato M, Borin S, Daffonchio D, Boon N. An impaired metabolic response to hydrostatic pressure explains Alcanivorax borkumensis recorded distribution in the deep marine water column. Sci Rep. 2016;6:1–3.
Google Scholar
Lai Q, Wang L, Liu Y, Fu Y, Zhong H, Wang B, et al. Alcanivorax pacificus sp. nov., isolated from a deep-sea pyrene-degrading consortium. Int J Syst Evol Microbiol. 2011;61:1370–4.CAS
Google Scholar
Wu Y, Lai Q, Zhou Z, Qiao N, Liu C, Shao Z. Alcanivorax hongdengensis sp. nov., an alkane-degrading bacterium isolated from surface seawater of the straits of Malacca and Singapore, producing a lipopeptide as its biosurfactant. Int J Syst Evol Microbiol. 2009;59:1474–9.CAS
Google Scholar
Fernández-Martínez J, Pujalte MJ, García-Martínez J, Mata M, Garay E, Rodríguez-Valera F. Description of Alcanivorax venustensis sp. nov. and reclassification of Fundibacter jadensis DSM 12178T (Bruns and Berthe-Corti 1999) as Alcanivorax jadensis comb. nov., members of the emended genus Alcanivorax. Int J Syst Evol Microbiol. 2003;53:331–8.
Google Scholar
Radwan SS, Khanafer MM, Al-Awadhi HA. Ability of the so-called obligate hydrocarbonoclastic bacteria to utilize nonhydrocarbon substrates thus enhancing their activities despite their misleading name. BMC Microbiol. 2019;19:1–2.
Google Scholar
Kalscheuer R, Stöveken T, Malkus U, Reichelt R, Golyshin PN, Sabirova JS, et al. Analysis of storage lipid accumulation in Alcanivorax borkumensis: Evidence for alternative triacylglycerol biosynthesis routes in bacteria. J Bacteriol. 2007;189:918–28.CAS
Google Scholar
Timm C, Davy B, Haase K, Hoernle KA, Graham IJ, De Ronde CEJ, et al. Subduction of the oceanic Hikurangi Plateau and its impact on the Kermadec arc. Nat Commun. 2014;5:1–9.
Google Scholar
Haase KM, Beier C, Bach W, Kleint C, Anderson MO, Rubin K, et al. SO-263 Cruise Report: Tonga Rift. 2018. https://doi.org/10.13140/RG.2.2.23035.16169.Gartman A, Hannington M, Jamieson JW, Peterkin B, Garbe-Schönberg D, Findlay AJ, et al. Boiling-induced formation of colloidal gold in black smoker hydrothermal fluids. Geology 2018;46:39–42.CAS
Google Scholar
Falkenberg JJ, Keith M, Haase KM, Bach W, Klemd R, Strauss H, et al. Effects of fluid boiling on Au and volatile element enrichment in submarine arc-related hydrothermal systems. Geochim Cosmochim Acta. 2021;307:105–32.CAS
Google Scholar
Peters C, Strauss H, Haase K, Bach W, de Ronde CEJ, Kleint C, et al. SO2 disproportionation impacting hydrothermal sulfur cycling: Insights from multiple sulfur isotopes for hydrothermal fluids from the Tonga-Kermadec intraoceanic arc and the NE Lau Basin. Chem Geol. 2021;586:120586.CAS
Google Scholar
Baker ET, Walker SL, Massoth GJ, Resing JA. The NE Lau Basin: Widespread and abundant hydrothermal venting in the back-arc region behind a superfast subduction zone. Front Mar Sci. 2019;6:382.
Google Scholar
Kim J, Lee KY, Kim JH. Metal-bearing molten sulfur collected from a submarine volcano: Implications for vapor transport of metals in seafloor hydrothermal systems. Geology 2011;39:351–4.CAS
Google Scholar
Klose L, Keith M, Hafermaas D, Kleint C, Bach W, Diehl A, et al. Trace element and isotope systematics in vent fluids and sulphides from Maka volcano, North Eastern Lau Spreading Centre: Insights into three-component fluid mixing. Front Earth Sci. 2021;9:1–26.
Google Scholar
Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS
Google Scholar
Dede B, Hansen CT, Neuholz R, Schnetger B, Kleint C, Walker S, et al. Niche differentiation of sulfur-oxidizing bacteria (SUP05) in submarine hydrothermal plumes. ISME J. 2022;16:1479–90.CAS
Google Scholar
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.
Google Scholar
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS
Google Scholar
McMurdie PJ, Holmes S. Phyloseq: An R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS
Google Scholar
Diehl A, Bach W. MARHYS (MARine HYdrothermal Solutions) Database: A global compilation of marine hydrothermal vent fluid, end member, and seawater compositions. Geochem Geophys Geosystems. 2020;21:e2020GC009385.
Google Scholar
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS
Google Scholar
Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 2012;28:1823–9.CAS
Google Scholar
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar A, et al. ARB: A software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS
Google Scholar
Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.CAS
Google Scholar
Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014;30:1312–3.CAS
Google Scholar
Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS
Google Scholar
Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.CAS
Google Scholar
Daims H, Brühl A, Amann R, Schleifer KH, Wagner M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: Development and evaluation of a more comprehensive probe set. Syst Appl Microbiol. 1999;22:434–44.CAS
Google Scholar
Wallner G, Amann R, Beisker W. Optimizing fluorescent in situ hybridization with rRNA‐targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 1993;14:136–43.CAS
Google Scholar
Stahl DA, Amann R. Development and application of nucleic acid probes in bacterial systematics. In: Nucleic acid techniques in bacterial systematics. Stackebrandt, E, Goodfellow M, editors. Chichester, UK: John Wiley & Sons Ltd; 1991. pp. 205–48.Manz W, Amann R, Ludwig W, Wagner M, Schleifer KH. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: Problems and solutions. Syst Appl Microbiol. 1992;15:593–600.
Google Scholar
Eilers H, Pernthaler J, Glöckner FO, Amann R. Culturability and in situ abundance of pelagic Bacteria from the North Sea. Appl Environ Microbiol. 2000;66:3044–51.CAS
Google Scholar
Syutsubo K, Kishira H, Harayama S. Development of specific oligonucleotide probes for the identification and in situ detection of hydrocarbon-degrading Alcanivorax strains. Environ Microbiol. 2001;3:371–9.CAS
Google Scholar
Morris RM, Rappé MS, Urbach E, Connon SA, Giovannoni SJ. Prevalence of the Chloroflexi-related SAR202 bacterioplankton cluster throughout the mesopelagic zone and deep ocean. Appl Environ Microbiol. 2004;70:2836–42.CAS
Google Scholar
Bushnell B BBMap (version 35.14). 2015. https://sourceforge.net/projects/bbmap/.Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinforma. 2010; http://www.bioinformatics.babraham.ac.uk/projects/.Rodriguez-R LM, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: Fast estimation of metagenomic coverage and sequence diversity. mSystems 2018;3:e00039–18.
Google Scholar
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.
Google Scholar
Kopylova E, Noé L, Touzet H. SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012;28:3211–7.CAS
Google Scholar
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.CAS
Google Scholar
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 2013;29:1072–5.CAS
Google Scholar
Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS
Google Scholar
Eren AM, Kiefl E, Shaiber A, Veseli I, Miller SE, Schechter MS, et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol. 2021;6:3–6.CAS
Google Scholar
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS
Google Scholar
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS
Google Scholar
Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotech. 2017;35:725–31.CAS
Google Scholar
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
Google Scholar
Seemann T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS
Google Scholar
Priest T, Heins A, Harder J, Amann R, Fuchs BM. Niche partitioning of the ubiquitous and ecologically relevant NS5 marine group. ISME J. 2022;16:1570–82.CAS
Google Scholar
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.CAS
Google Scholar
Karthikeyan S, Rodriguez‐R LM, Heritier‐Robbins P, Hatt JK, Huettel M, Kostka JE, et al. Genome repository of oil systems: An interactive and searchable database that expands the catalogued diversity of crude oil‐associated microbes. Environ Microbiol. 2020;22:2094–106.CAS
Google Scholar
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.CAS
Google Scholar
Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.CAS
Google Scholar
Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS
Google Scholar
Gomes AÉI, Stuchi LP, Siqueira NMG, Henrique JB, Vicentini R, Ribeiro ML, et al. Selection and validation of reference genes for gene expression studies in Klebsiella pneumoniae using Reverse Transcription Quantitative real-time PCR. Sci Rep. 2018;8:1–4.
Google Scholar
Guidi L, Chaffron S, Bittner L, Eveillard D, Larhlimi A, Roux S, et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 2016;532:465–70.CAS
Google Scholar
Duarte CM. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol Oceanogr Bull. 2015;24:11–4.
Google Scholar
Anantharaman K, Breier JA, Dick GJ. Metagenomic resolution of microbial functions in deep-sea hydrothermal plumes across the Eastern Lau Spreading Center. ISME J. 2016;10:225–39.CAS
Google Scholar
Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front Microbiol. 2017;8:682.
Google Scholar
Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Addendum: Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl.nov.). Front Microbiol. 2017;9:772.
Google Scholar
Green DH, Llewellyn LE, Negri AP, Blackburn SI, Bolch CJS. Phylogenetic and functional diversity of the cultivable bacterial community associated with the paralytic shellfish poisoning dinoflagellate Gymnodinium catenatum. FEMS Microbiol Ecol. 2004;47:345–57.CAS
Google Scholar
Ramasamy KP, Rajasabapathy R, Lips I, Mohandass C, James RA. Genomic features and copper biosorption potential of a new Alcanivorax sp. VBW004 isolated from the shallow hydrothermal vent (Azores, Portugal). Genomics 2020;112:3268–73.CAS
Google Scholar
Barbato M, Scoma A, Mapelli F, De Smet R, Banat IM, Daffonchio D, et al. Hydrocarbonoclastic Alcanivorax isolates exhibit different physiological and expression responses to N-dodecane. Front Microbiol. 2016;7:2056.
Google Scholar
Sevilla E, Yuste L, Rojo F. Marine hydrocarbonoclastic bacteria as whole-cell biosensors for n-alkanes. Micro Biotechnol. 2015;8:693–706.CAS
Google Scholar
Tivey MK. Black and white smokers. In: Harff J, Meschede M, Petersen S, Thiede Jö, editors. Encyclopedia of Marine Geosciences. Dordrecht: Springer Netherlands; 2016. p. 58–62.Djurhuus A, Mikalsen SO, Giebel HA, Rogers AD. Cutting through the smoke: The diversity of microorganisms in deep-sea hydrothermal plumes. R Soc Open Sci. 2017;4:160829.
Google Scholar
Leahy JG, Colwell RR. Microbial degradation of hydrocarbons in the environment. Microbiol Rev. 1990;54:305–15.CAS
Google Scholar
Atlas R, Bragg J. Bioremediation of marine oil spills: When and when not – The Exxon Valdez experience. Micro Biotechnol. 2009;2:213–21.CAS
Google Scholar
Reva ON, Hallin PF, Willenbrock H, Sicheritz-Ponten T, Tümmler B, Ussery DW. Global features of the Alcanivorax borkumensis SK2 genome. Environ Microbiol. 2008;10:614–25.CAS
Google Scholar
Gregory GJ, Morreale DP, Carpenter MR, Kalburge SS, Boyd EF. Quorum sensing regulators AphA and OpaR control expression of the operon responsible for biosynthesis of the compatible solute ectoine. Appl Environ Microbiol. 2019;85:e01543–19.CAS
Google Scholar
Richter AA, Mais CN, Czech L, Geyer K, Hoeppner A, Smits SHJ, et al. Biosynthesis of the stress-protectant and chemical chaperon ectoine: biochemistry of the transaminase EctB. Front Microbiol. 2019;10:2811.
Google Scholar
Schneiker S, Dos Santos VAPM, Bartels D, Bekel T, Brecht M, Buhrmester J, et al. Genome sequence of the ubiquitous hydrocarbon-degrading marine bacterium Alcanivorax borkumensis. Nat Biotechnol. 2006;24:997–1004.CAS
Google Scholar
Wang W, Shao Z. Enzymes and genes involved in aerobic alkane degradation. Front Microbiol. 2013;4:116.
Google Scholar
Barclay W, Rodd JA, Pflueger JC, Havard KR, Helu SP. Oil plays in the kingdom of Tonga, Southwest Pacific. PESA J. 1993;21:79–92.
Google Scholar
Chadwick WW, Rubin KH, Merle SG, Bobbitt AM, Kwasnitschka T, Embley RW. Recent eruptions between 2012-2018 discovered at West Mata submarine volcano (NE Lau Basin, SW Pacific) and characterized by new ship, AUV, and ROV data. Front Mar Sci. 2019;6:495.
Google Scholar
Baumberger T, Lilley MD, Lupton JE, Baker ET, Resing JA, Buck NJ, et al. Dissolved gas and metal composition of hydrothermal plumes from a 2008 submarine eruption on the Northeast Lau Spreading Center. Front Mar Sci. 2020;7:171.
Google Scholar
Lupton J, Rubin KH, Arculus R, Lilley M, Butterfield D, Resing J, et al. Helium isotope, C/3 He, and Ba‐Nb‐Ti signatures in the northern Lau Basin: Distinguishing arc, back‐arc, and hotspot affinities. Geochem Geophys. 2015;16:1133–55.CAS
Google Scholar
Graham DW. Noble gas isotope geochemistry of mid-ocean ridge and ocean island basalts: Characterization of mantle source reservoirs. In: Porcelli D, Wieler R, Ballentine C, editors. Noble gases in Geochemistry and cosmochemistry, Rev Mineral Geochem. Vol 47. Washington D.C.: Mineral Soc. Of Am; 2002. p. 247–318.Lupton JE, Arculus RJ, Greene RR, Evans LJ, Goddard CI. Helium isotope variations in seafloor basalts from the Northwest Lau Backarc Basin: Mapping the influence of the Samoan hotspot. Geophys Res Lett. 2009;36:L17313.
Google Scholar
Gordon GW. Naturally occurring organohalogen compounds – A comprehensive survey. Prog Chem Org Nat Prod. 1996;68:1–423.
Google Scholar
Spietz RL, Butterfield DA, Buck NJ, Larson BI, Chadwick WW, Walker SL, et al. Deep-sea volcanic eruptions create unique chemical and biological linkages between the subsurface lithosphere and the oceanic hydrosphere. Oceanography. 2018;31:128–35.
Google Scholar
Huber JA, Butterfield DA, Baross JA. Bacterial diversity in a subseafloor habitat following a deep-sea volcanic eruption. FEMS Microbiol Ecol. 2003;43:393–409.CAS
Google Scholar More
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
Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS
CAS
Google Scholar
McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the Anthropogene. Trends Ecol. Evol. 30, 104–113 (2015).
Google Scholar
Bradshaw, C. J. A., Sodhi, N. S. & Brook, B. W. Tropical turmoil: A biodiversity tragedy in progress. Front. Ecol. Environ. 7, 79–87 (2009).
Google Scholar
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS
CAS
Google Scholar
Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294, 804–808 (2001).ADS
CAS
Google Scholar
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS
CAS
Google Scholar
Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
Google Scholar
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS
CAS
Google Scholar
Pasari, J. R., Levi, T., Zavaleta, E. S. & Tilman, D. Several scales of biodiversity affect ecosystem multifunctionality. Proc. Natl. Acad. Sci. U.S.A. 110, 10219–10222 (2013).ADS
CAS
Google Scholar
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Google Scholar
Murphy, G. E. P. & Romanuk, T. N. A meta-analysis of declines in local species richness from human disturbances. Ecol. Evol. 4, 91–103 (2014).
Google Scholar
Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).ADS
CAS
Google Scholar
de Coster, G., Banks-Leite, C. & Metzger, J. P. Atlantic forest bird communities provide different but not fewer functions after habitat loss. Proc. R. Soc. B 282, 20142844 (2015).
Google Scholar
Riemann, J. C., Ndriantsoa, S. H., Rödel, M.-O. & Glos, J. Functional diversity in a fragmented landscape—habitat alterations affect functional trait composition of frog assemblages in Madagascar. Global Ecol. Conserv. 10, 173–183 (2017).
Google Scholar
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: A few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).CAS
Google Scholar
Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).
Google Scholar
van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multi-functionality. Proc. Natl. Acad. Sci. U.S.A. 113, 3557–3562 (2016).ADS
Google Scholar
Mori, A. S., Isbell, F. & Seidl, R. β-diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).
Google Scholar
Dehling, J. M. & Dehling, D. M. Conserving ecological functions of frog communities in Borneo requires diverse forest landscapes. Global Ecol. Conserv. 26, e01481 (2021).
Google Scholar
Hector, A. & Bagchi, R. Biodiversity and ecosystem multifunctionality. Nature 448, 188–190 (2007).ADS
CAS
Google Scholar
Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).ADS
CAS
Google Scholar
Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl. Acad. Sci. U.S.A. 100, 12765–12770 (2003).ADS
CAS
Google Scholar
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS
CAS
Google Scholar
Felipe-Lucia, M. R. et al. Land-use intensity alters networks between biodiversity, ecosystem functions, and services. Proc. Natl. Acad. Sci. U.S.A. 117, 28140–28149 (2020).ADS
CAS
Google Scholar
Tilman, D. Functional diversity in Encyclopedia of biodiversity, Vol. 3. (ed. Levin S. A.) 109–120 (Academic Press, 2001)Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).
Google Scholar
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity-ecosystem function relationships. Ecology 92, 1573–1581 (2011).
Google Scholar
Lean, C. & Maclaurin, J. The value of phylogenetic diversity in Biodiversity conservation and phylogenetic systematics. Topics in Biodiversity and Conservation 14. (eds. Pellens, R., Grandcolas, P.) 19–38 (Springer, 2016).Owen, N. R., Gumbs, R., Gray, C. L. & Faith, D. P. Global conservation of phylogenetic diversity captures more than just functional diversity. Nat. Commun. 10, 859 (2019).ADS
Google Scholar
Gumbs, R., Williams, R. C., Lowney, A. M. & Smith, D. Spatial and species-level metrics reveal global patterns of irreplaceable and imperiled gecko phylogenetic diversity. Israel J. Ecol. Evolut. 66, 239–252 (2020).
Google Scholar
Brooks, D. R., Mayden, R. L. & McLennan, D. A. Phylogeny and biodiversity: Conserving our evolutionary legacy. Trends Ecol. Evol. 7, 55–59 (1992).CAS
Google Scholar
Phillimore, A. B. et al. Biogeographical basis of recent phenotypic divergence among birds: a global study of subspecies richness. Evolution 61, 942–957 (2007).
Google Scholar
Miraldo, A. et al. An Anthropocene map of genetic diversity. Science 353, 1532–1535 (2016).ADS
CAS
Google Scholar
Smith, B. T., Seeholzer, G. F., Harvey, M. G., Cuervo, A. M. & Brumfield, R. T. A latitudinal phylogeographic diversity gradient in birds. PLoS Biol. 15, e2001073 (2017).
Google Scholar
Tucker, C. M. et al. Assessing the utility of conserving evolutionary history. Biol. Rev. 94, 1740–1760 (2019).
Google Scholar
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).
Google Scholar
Villéger, S., Miranda, J. R., Hernández, D. F. & Mouillot, D. Contrasting changes in taxonomic vs. functional diversity of tropical fish communities after habitat degradation. Ecological Applications 20, 1512–1522 (2010).Gibbons, J. W. et al. Remarkable amphibian biomass and abundance in an isolated wetland: Implications for wetland conservation. Conserv. Biol. 20, 1457–1465 (2006).
Google Scholar
Hocking, D. J. & Babbitt, K. J. Amphibian contributions to ecosystem services. Herpetol. Conserv. Biol. 9, 1–17 (2014).
Google Scholar
Beebee, T. J. C. Amphibian breeding and climate change. Nature 374, 219–220 (1995).ADS
CAS
Google Scholar
Kiesecker, J. M., Blaustein, A. R. & Belden, L. K. Complex causes of amphibian population declines. Nature 410, 681–684 (2001).ADS
CAS
Google Scholar
Cheng, T. L., Rovito, S. M., Wake, D. B. & Vredenburg, V. T. Coincident mass extirpation of neotropical amphibians with the emergence of the infection fungal pathogen Batrachochytrium dendrobatidis. Proc. Natl. Acad. Sci. U.S.A. 108, 9502–9507 (2011).ADS
CAS
Google Scholar
Wake, D. B. & Vredenburg, V. T. Are we in the midst of the sixth mass extinction? A view from the world of amphibians. Proc. Natl. Acad. Sci. U.S.A. 105, 11466–11473 (2008).ADS
CAS
Google Scholar
Ernst, R. & Rödel, M.-O. Patterns of community composition in two tropical tree frog assemblages: Separating spatial structure and environmental effects in disturbed and undisturbed forests. J. Trop. Ecol. 24, 111–120 (2008).
Google Scholar
Gardner, T. A. et al. The value of primary, secondary, and plantation forests for a Neotropical Herpetofauna. Conserv. Biol. 21, 775–787 (2007).
Google Scholar
Gardner, T. A., Fitzherbert, E. B., Drewes, R. C., Howell, K. M. & Caro, T. Spatial and temporal patterns of abundance and diversity of an East African leaf litter amphibian fauna. Biotropica 39, 105–113 (2007).
Google Scholar
Gillespie, G. R. et al. Conservation of amphibians in Borneo: relative value of secondary tropical forest and non-forest habitats. Biol. Cons. 152, 136–144 (2012).
Google Scholar
Angarita-M., O., Montes-Correa, A. C. & Renjifo, J. M. Amphibians and reptiles of an agroforestry system in the Colombian Caribbean. Amphibian & Reptile Conservation 8, 33–52 (2015).Jiménez-Robles, O., Guayasamin, J. M., Ron, S. R. & De la Riva, I. Reproductive traits associated with species turnover of amphibians in Amazonia and its Andean slopes. Ecol. Evol. 7, 2489–2500 (2017).
Google Scholar
Ernst, R., Linsenmair, K. E. & Rödel, M.-O. Diversity erosion beyond the species level: dramatic loss of functional diversity after selective logging in two tropical amphibian communities. Biol. Cons. 133, 143–155 (2006).
Google Scholar
Oda, F. H. et al. Anuran species richness, composition, and breeding habitat preferences: a comparison between forest remnants and agricultural landscapes in Southern Brazil. Zool. Stud. 55, 34 (2016).
Google Scholar
Sinsch, U., Lümkemann, K., Rosar, K., Schwarz, C. & Dehling, J. M. Acoustic niche partitioning in an anuran community inhabiting an Afromontane wetland (Butare, Rwanda). African Zool. 47, 60–73 (2012).
Google Scholar
Tumushimire, L., Mindje, M., Sinsch, U. & Dehling, J. M. The anuran diversity of cultivated wetlands in Rwanda: Melting pot of generalists?. Salamandra 56, 99–112 (2020).
Google Scholar
REMA. Rwanda State of Environment and Outlook Report 2017 – Achieving Sustainable Urbanization. (Rwanda Environment Management Authority, Government of Rwanda, 2017).Su, J. C., Debinski, D. M., Jakubauskas, M. E. & Kindscher, K. Beyond species richness: Community similarity as a measure of cross-taxon congruence for coarse-filter conservation. Conserv. Biol. 18, 167–173 (2004).
Google Scholar
Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).ADS
CAS
Google Scholar
Zimkus, B. M., Rödel, M.-O. & Hillers, A. Complex patterns of continental speciation: Molecular phylogenetics and biogeography of sub-Saharan puddle frogs (Phrynobatrachus). Mol. Phylogenet. Evol. 55, 883–900 (2010).
Google Scholar
Dehling, J. M. & Sinsch, U. Partitioning of morphospace in larval and adult reed frogs (Anura: Hyperoliidae: Hyperolius) of the Central African Albertine Rift. Zool. Anz. 280, 65–77 (2019).
Google Scholar
Mazel, F. et al. Prioritizing phylogenetic diversity captures functional diversity unreliably. Nat. Commun. 9, 2888 (2018).ADS
Google Scholar
Haddad, C. F. B. & Prado, C. P. A. Reproductive modes and their unexpected diversity in the Atlantic forest of Brazil. Bioscience 55, 207–217 (2005).
Google Scholar
Capinha, C., Essl, F., Seebens, H., Moser, D. & Pereira, H. M. The dispersal of alien species redefines biogeography in the Anthropocene. Science 348, 1248–1251 (2015).ADS
CAS
Google Scholar
Alroy, J. Effects of habitat disturbance on tropical forest biodiversity. Proc. Natl. Acad. Sci. U.S.A. 114, 6056–6061 (2017).ADS
CAS
Google Scholar
Dehling, J. M. & Sinsch, U. Diversity of Ptychadena in Rwanda and taxonomic status of P. chrysogaster Laurent, 1954 (Amphibia, Anura, Ptychadenidae). ZooKeys 356, 69–102 (2013).IUCN. The IUCN Red List of Threatened Species. Version 2020–1. https://www.iucnredlist.org (2020).Portillo, F., Greenbaum, E., Menegon, M., Kusamba, C. & Dehling, J. M. Phylogeography and species boundaries of Leptopelis (Anura: Arthroleptidae) from the Albertine Rift. Mol. Phylogenet. Evol. 82, 75–86 (2015).
Google Scholar
Channing, A., Dehling, J. M., Lötters, S. & Ernst, R. Species boundaries and taxonomy of the African River Frogs (Anura: Pyxicephalidae: Amietia). Zootaxa 4155, 1–76 (2016).CAS
Google Scholar
Rödel, M.-O. & Ernst, R. Measuring and monitoring amphibian diversity in tropical forests. I. An evaluation of methods with recommendations for standardization. Ecotropica 10, 1–14 (2004).Channing, A. & Howell, K. M. Amphibians of East Africa. (Chimaira, 2006).Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evolut. 2, 850–858 (2018).
Google Scholar
Villéger, S., Mason, N. W. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).
Google Scholar
Maire, E., Grenouillet, G., Brosse, S. & Villéger, S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Glob. Ecol. Biogeogr. 24, 728–740 (2015).
Google Scholar
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Cons. 61, 1–10 (1992).
Google Scholar
Dehling, D. M. et al. Functional and phylogenetic diversity and assemblage structure of frugivorous birds along an elevational gradient in the tropical Andes. Ecography 37, 1047–1055 (2014).
Google Scholar
Baselga, A. et al. betapart: partitioning beta diversity into turnover and nestedness components. R package version 1.5.6. https://CRAN.R-project.org/package=betapart (2022).Dehling, D. M. et al. Specialists and generalists fulfil important and complementary functional roles in ecological processes. Funct. Ecol. 35, 1810–1821 (2021).CAS
Google Scholar
Dehling, D. M., Barreto, E. & Graham, C. H. The contribution of mutualistic interactions to functional and phylogenetic diversity. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.05.006 (2022).Article
Google Scholar
R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2021). More
Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399 (1999).
Google Scholar
Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the ‘landscape of fear’ in Yellowstone National Park, US.A. Can. J. Zool. 79, 1401–1409 (2001).
Google Scholar
Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).CAS
Google Scholar
Laundre, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).
Google Scholar
Loggins, A. A., Shrader, A. M., Monadjem, A. & McCleery, R. A. Shrub cover homogenizes small mammals’ activity and perceived predation risk. Sci. Rep. 9, 16857 (2019).
Google Scholar
Whittingham, M. J. & Evans, K. L. The effects of habitat structure on predation risk of birds in agricultural landscapes. Ibis 146, 210–220 (2004).
Google Scholar
Marshall, K. L. A., Philpot, K. E. & Stevens, M. Microhabitat choice in island lizards enhances camouflage against avian predators. Sci. Rep. 6, 19815 (2016).CAS
Google Scholar
Stevens, M., Troscianko, J., Wilson-Aggarwal, J. K. & Spottiswoode, C. N. Improvement of individual camouflage through background choice in ground-nesting birds. Nat. Ecol. Evol. 1, 1325–1333 (2017).
Google Scholar
Wilson-Aggarwal, J. K., Troscianko, J. T., Stevens, M. & Spottiswoode, C. N. Escape distance in ground-nesting birds differs with individual level of camouflage. Am. Nat. 188, 231–239 (2016).
Google Scholar
Troscianko, J., Wilson-Aggarwal, J., Stevens, M. & Spottiswoode, C. N. Camouflage predicts survival in ground-nesting birds. Sci. Rep. 6, 19966 (2016).CAS
Google Scholar
Gaston, K. J., Duffy, J. P., Gaston, S., Bennie, J. & Davies, T. W. Human alteration of natural light cycles: causes and ecological consequences. Oecologia 176, 917–931 (2014).
Google Scholar
Gaston, K. J., Davies, T. W., Nedelec, S. L. & Holt, L. A. Impacts of artificial light at night on biological timings. Annu. Rev. Ecol. Evol. Syst. 48, 49–68 (2017).
Google Scholar
Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).
Google Scholar
Gaston, K. J. et al. Pervasiveness of biological impacts of artificial light at night. Integr. Comp. Biol. 61, 1098–1110 (2021).
Google Scholar
Sanders, D., Frago, E., Kehoe, R., Patterson, C. & Gaston, K. J. A meta-analysis of biological impacts of artificial light at night. Nat. Ecol. Evol. 5, 74–81 (2021).
Google Scholar
Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160248 (2017).
Google Scholar
Underwood, C. N., Davies, T. W. & Queir Os, A. M. Artificial light at night alters trophic interactions of intertidal invertebrates. J. Anim. Ecol. 86, 781–789 (2017).
Google Scholar
Burger, J., Howe, M. A., Hahn, D. C. & Chase, J. Effects of tide cycles on habitat selection and habitat partitioning by migrating shorebirds. Auk 94, 743–758 (1977).
Google Scholar
Granadeiro, J. P., Dias, M. P., Martins, R. C. & Palmeirim, J. M. Variation in numbers and behaviour of waders during the tidal cycle: implications for the use of estuarine sediment flats. Acta Oecologica 29, 293–300 (2006).
Google Scholar
Lourenço, P. M. et al. The energetic importance of night foraging for waders wintering in a temperate estuary. Acta Oecologica 34, 122–129 (2008).
Google Scholar
McNeil, R., Drapeau, P. & Goss-Custard, J. D. The occurrence and adaptive significance of nocturnal habits in waterfowl. Biol. Rev. 67, 381–419 (1992).
Google Scholar
Martin, G. R. Visual fields and their functions in birds. J. Ornithol. 148, 547–562 (2007).
Google Scholar
Martin, G. R. What is binocular vision for? A birds’ eye view. J. Vis. 9, 1–19 (2009).
Google Scholar
Davies, T. W., Duffy, J. P., Bennie, J. & Gaston, K. J. The nature, extent, and ecological implications of marine light pollution. Front. Ecol. Environ. 12, 347–355 (2014).
Google Scholar
Leopold, M. F., Philippart, C. J. M. & Yorio, P. Nocturnal feeding under artificial light conditions by Brown-hooded Gull (Larus maculipennis) in Puerto Madryn harbour (Chubut Province, Argentina). Hornero 25, 55–60 (2010).
Google Scholar
Pugh, A. R. & Pawson, S. M. Artificial light at night potentially alters feeding behaviour of the native southern black-backed gull (Larus dominicanus). Notornis 63, 37–39 (2016).
Google Scholar
Santos, C. D. et al. Effects of artificial illumination on the nocturnal foraging of waders. Acta Oecologica 36, 166–172 (2010).
Google Scholar
Montevecchi, W. A. Influences of Artificial Light on Marine Birds. in Ecological Consequences of Artificial Night Lighting (eds. Rich, C. & Longcore, T.) 94–113 (Island Press, 2006).Dwyer, R. G., Bearhop, S., Campbell, H. A. & Bryant, D. M. Shedding light on light: benefits of anthropogenic illumination to a nocturnally foraging shorebird. J. Anim. Ecol. 82, 478–485 (2013).
Google Scholar
Blumstein, D. T. Developing an evolutionary ecology of fear: how life history and natural history traits affect disturbance tolerance in birds. Anim. Behav. 71, 389–399 (2006).
Google Scholar
Stankowich, T. & Blumstein, D. T. Fear in animals: a meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 272, 2627–2634 (2005).
Google Scholar
Caro, T. Antipredator Defenses in Birds and Mammals. (University of Chicago Press, 2005).Tillmann, J. E. Fear of the dark: night-time roosting and anti-predation behaviour in the grey partridge (Perdix perdix L.). Behaviour 146, 999–1023 (2009).
Google Scholar
IUCN. The IUCN Red List of Threatened Species. Version 2022-1. https://www.iucnredlist.org/species/22693190/117917038 (2022).Brown, D. et al. The Eurasian Curlew—the most pressing bird conservation priority in the UK? Br. Birds 108, 660–668 (2015).
Google Scholar
Franks, S. E., Douglas, D. J. T., Gillings, S. & Pearce-Higgins, J. W. Environmental correlates of breeding abundance and population change of Eurasian Curlew Numenius arquata in Britain. Bird. Study 64, 393–409 (2017).
Google Scholar
Desholm, M. & Kahlert, J. Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005).
Google Scholar
Clarke, J. A. Moonlight’s influence on predator/prey interactions between short-eared owls (Asio flammeus) and Deermice (Peromyscus maniculatus). Behav. Ecol. Sociobiol. 13, 205–209 (1983).
Google Scholar
Mandelik, Y., Jones, M. & Dayan, T. Structurally complex habitat and sensory adaptations mediate the behavioural responses of a desert rodent to an indirect cue for increased predation risk. Evol. Ecol. Res. 5, 501–515 (2003).
Google Scholar
Alexander, R. D. The Evolution of Social Behavior | Annual Review of Ecology, Evolution, and Systematics. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).
Google Scholar
Pulliam, H. R. On the advantages of flocking. J. Theor. Biol. 38, 419–422 (1973).CAS
Google Scholar
Barnard, C. J. Flock feeding and time budgets in the house sparrow (Passer domesticus L.). Anim. Behav. 28, 295–309 (1980).
Google Scholar
Cooper, W. E. Jr. et al. Effects of risk, cost, and their interaction on optimal escape by nonrefuging Bonaire whiptail lizards, Cnemidophorus murinus. Behav. Ecol. 14, 288–293 (2003).
Google Scholar
Lagos, P. A. et al. Flight initiation distance is differentially sensitive to the costs of staying and leaving food patches in a small-mammal prey. Can. J. Zool. 87, 1016–1023 (2009).
Google Scholar
Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).
Google Scholar
Tucker, V. A., Tucker, A. E., Akers, K. & Enderson, J. H. Curved flight paths and sideways vision in peregrine falcons (Falco peregrinus). J. Exp. Biol. 203, 3755–3763 (2000).CAS
Google Scholar
Carr, J. M. & Lima, S. L. Wintering birds avoid warm sunshine: predation and the costs of foraging in sunlight. Oecologia 174, 713–721 (2014).
Google Scholar
van den Hout, P. J. & Martin, G. R. Extreme head-tilting in shorebirds: predator detection and sun avoidance. Wader Study Group Bull. 118, 18–21 (2011).
Google Scholar
Ferguson, J. W. H., Galpin, J. S. & de Wet, M. J. Factors affecting the activity patterns of black-backed jackals Canis mesomelas. J. Zool. 214, 55–69 (1988).
Google Scholar
Pyke, G. H. Optimal foraging theory: a critical review. Annu. Rev. Ecol. Syst. 15, 523–575 (1984).
Google Scholar
Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton University Press, 1986).Mouritsen, K. N. Predator avoidance in night-feeding dunlins calidris alpina: a matter of concealment. Ornis Scand. 23, 195–198 (1992).
Google Scholar
Blumstein, D. T. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manag. 67, 852–857 (2003).
Google Scholar
Troscianko, J. OSpRad; an open-source, low-cost, high-sensitivity spectroradiometer (p. 2022.12.09.519768). bioRxiv https://doi.org/10.1101/2022.12.09.519768 (2022).Article
Google Scholar
Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.4. http://florianhartig.github.io/DHARMa/ (2022).Core Team, R. R: a Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2022).
Google Scholar More
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
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
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
