More stories

  • in

    Microbiomes in the Challenger Deep slope and bottom-axis sediments

    Jamieson, A. J., Fujii, T., Mayor, D. J., Solan, M. & Priede, I. G. Hadal trenches: the ecology of the deepest places on Earth. Trends Ecol. Evol. 25, 190–197 (2010).PubMed 

    Google Scholar 
    Stewart, H. A. & Jamieson, A. J. Habitat heterogeneity of hadal trenches: considerations and implications for future studies. Prog. Oceanogr. 161, 47–65 (2018).ADS 

    Google Scholar 
    Zhu, G. et al. Along-strike variation in slab geometry at the southern Mariana subduction zone revealed by seismicity through ocean bottom seismic experiments. Geophys. J. Int. 218, 2122–2135 (2019).ADS 

    Google Scholar 
    Bao, R. et al. Tectonically-triggered sediment and carbon export to the Hadal zone. Nat. Commun. 9, 121 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kioka, A. et al. Megathrust earthquake drives drastic organic carbon supply to the hadal trench. Sci. Rep. 9, 1553 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Luo, M., Gieskes, J., Chen, L. Y., Shi, X. F. & Chen, D. F. Provenances, distribution, and accumulation of organic matter in the southern Mariana Trench rim and slope: implication for carbon cycle and burial in hadal trenches. Mar. Geol. 386, 98–106 (2017).ADS 
    CAS 

    Google Scholar 
    Glud, R. N. et al. High rates of microbial carbon turnover in sediments in the deepest oceanic trench on Earth. Nat. Geosci. 6, 284–288 (2013).ADS 
    CAS 

    Google Scholar 
    Liu, S. & Peng, X. Organic matter diagenesis in hadal setting: insights from the pore-water geochemistry of the Mariana Trench sediments. Deep Sea Res. I 147, 22–31 (2019).CAS 

    Google Scholar 
    Nunoura, T. et al. Microbial diversity in sediments from the bottom of the Challenger Deep, the Mariana Trench. Microbes Environ. 33, 186–194 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Wang, Y. et al. Genomics insights into ecotype formation of ammonia-oxidizing archaea in the deep ocean. Environ. Microbiol. 21, 716–729 (2019).CAS 
    PubMed 

    Google Scholar 
    Nunoura, T. et al. Molecular biological and isotopic biogeochemical prognoses of the nitrification-driven dynamic microbial nitrogen cycle in hadopelagic sediments. Environ. Microbiol. 15, 3087–3107 (2013).CAS 
    PubMed 

    Google Scholar 
    Mason, E. et al. Volatile metal emissions from volcanic degassing and lava–seawater interactions at Kīlauea Volcano, Hawai’i. Commun. Earth Environ. 2, 79 (2021).ADS 

    Google Scholar 
    Sun, R. et al. Methylmercury produced in upper oceans accumulates in deep Mariana Trench fauna. Nat. Commun. 11, 3389 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kalia, K. & Khambholja, D. B. in Handbook of Arsenic Toxicology (ed. Flora, S. J. S.) Ch. 28 (Elsevier, 2015).Welty, C. J., Sousa, M. L., Dunnivant, F. M. & Yancey, P. H. High-density element concentrations in fish from subtidal to hadal zones of the Pacific Ocean. Heliyon 4, e00840 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Oremland, R. S. & Stolz, J. F. The ecology of arsenic. Science 300, 939–944 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Popowich, A., Zhang, Q. & Le, X. C. Arsenobetaine: the ongoing mystery. Natl Sci. Rev. 3, 451–458 (2016).CAS 

    Google Scholar 
    Hoffmann, T. et al. Arsenobetaine: an ecophysiologically important organoarsenical confers cytoprotection against osmotic stress and growth temperature extremes. Environ. Microbiol. 20, 305–323 (2018).CAS 
    PubMed 

    Google Scholar 
    Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).
    Google Scholar 
    Hoffmann, A. A. & Hercus, M. J. Environmental stress as an evolutionary force. Bioscience 50, 217–226 (2000).
    Google Scholar 
    Cui, G., Li, J., Gao, Z. & Wang, Y. Spatial variations of microbial communities in abyssal and hadal sediments across the Challenger Deep. PeerJ 7, e6961 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Hiraoka, S. et al. Microbial community and geochemical analyses of trans-trench sediments for understanding the roles of hadal environments. ISME J. 14, 740–756 (2020).CAS 
    PubMed 

    Google Scholar 
    Morono, Y. et al. Aerobic microbial life persists in oxic marine sediment as old as 101.5 million years. Nat. Commun. 11, 3626 (2020).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, X. et al. Metagenomics reveals microbial diversity and metabolic potentials of seawater and surface sediment from a hadal biosphere at the Yap Trench. Front. Microbiol. 9, 2402 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Logares, R. et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ. Microbiol. 16, 2659–2671 (2014).CAS 
    PubMed 

    Google Scholar 
    Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of Hydrothermarchaeota in hydrothermal sediment. mSystems 5, e00795-00719 (2020).
    Google Scholar 
    Dombrowski, N., Teske, A. P. & Baker, B. J. Expansive microbial metabolic versatility and biodiversity in dynamic Guaymas Basin hydrothermal sediments. Nat. Commun. 9, 4999 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dong, X. et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nat. Commun. 10, 1816 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Laso-Pérez, R. et al. Anaerobic degradation of non-methane alkanes by “Candidatus Methanoliparia” in hydrocarbon seeps of the Gulf of Mexico. mBio 10, e01814–e01819 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Gao, Z. M. et al. In situ meta-omic insights into the community compositions and ecological roles of hadal microbes in the Mariana Trench. Environ. Microbiol. 21, 4092–4108 (2019).CAS 
    PubMed 

    Google Scholar 
    Varliero, G., Bienhold, C., Schmid, F., Boetius, A. & Molari, M. Microbial diversity and connectivity in deep-sea sediments of the South Atlantic polar front. Front. Microbiol. 10, 665 (2019).Su, X. et al. Identifying and predicting novelty in microbiome studies. mBio 9, e02099-02018 (2018).
    Google Scholar 
    Jing, G. et al. Microbiome Search Engine 2: a platform for taxonomic and functional search of global microbiomes on the whole-microbiome level. mSystems 6, e00943-00920 (2021).
    Google Scholar 
    Baltar, F., Zhao, Z. H. & Herndl, G. J. Potential and expression of carbohydrate untilization by marine fungi in the global ocean. Microbiome 9, 106 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quemener, M. et al. Meta-omics highlights the diversity, activity and adaptations of fungi in deep oceanic crust. Environ. Microbiol. 22, 3950–3967 (2020).CAS 

    Google Scholar 
    Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).CAS 
    PubMed 

    Google Scholar 
    Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Bobay, L. M. & Ochman, H. The evolution of bacterial genome architecture. Front. Genet. 8, 72 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Huang, L. et al. dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. Nucleic Acids Res. 46, D516–D521 (2018).CAS 
    PubMed 

    Google Scholar 
    Xu, Y., Ge, H. & Fang, J. Biogeochemistry of hadal trenches: Recent developments and future perspectives. Deep Sea Res. II Top. Stud. Oceanogr. 155, 19–26 (2018).ADS 
    CAS 

    Google Scholar 
    Jørgensen, B. B. & Boetius, A. Feast and famine — microbial life in the deep-sea bed. Nat. Rev. Microbiol. 5, 770–781 (2007).PubMed 

    Google Scholar 
    Pérez Castro, S. et al. Degradation of biological macromolecules supports uncultured microbial populations in Guaymas Basin hydrothermal sediments. ISME J. 15, 3480–3497 (2021).Rastelli, E. et al. Drivers of bacterial α- and β-diversity patterns and functioning in subsurface hadal sediments. Front. Microbiol. 10, 2609 (2019).Vetter, Y. A. & Deming, J. W. Extracellular enzyme-activity in the Arctic northeast water polynya. Mar. Ecol. Prog. Ser. 114, 23–34 (1994).ADS 
    CAS 

    Google Scholar 
    Li, J. et al. Recycling and metabolic flexibility dictate life in the lower oceanic crust. Nature 579, 250–255 (2020).ADS 
    CAS 

    Google Scholar 
    Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: reaction mechanism, physiological significance, and hyperglycinemia. Proc. Jpn. Acad. 84, 246–263 (2008).CAS 

    Google Scholar 
    Chakraborty, A. et al. Hydrocarbon seepage in the deep seabed links subsurface and seafloor biospheres. Proc. Natl Acad. Sci. USA 117, 11029–11037 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu, J. et al. Proliferation of hydrocarbon-degrading microbes at the bottom of the Mariana Trench. Microbiome 7, 47 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xue, C.-X. et al. Insights into the vertical stratification of microbial ecological roles across the deepest seawater column on Earth. Microorganisms 8, 1309 (2020).CAS 
    PubMed Central 

    Google Scholar 
    Thamdrup, B. et al. Anammox bacteria drive fixed nitrogen loss in hadal trench sediments. Proc. Natl Acad. Sci. USA 118, e2104529118 (2021).CAS 
    PubMed 

    Google Scholar 
    Wu, J. et al. Unexpectedly high diversity of anammox bacteria detected in deep-sea surface sediments of the South China Sea. FEMS Microbiol. Ecol. 95, fiz013 (2019).Kartal, B. et al. Molecular mechanism of anaerobic ammonium oxidation. Nature 479, 127–130 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Maalcke, W. J. et al. Characterization of anammox hydrazine dehydrogenase, a key N2-producing enzyme in the global nitrogen cycle. J. Biol. Chem. 291, 17077–17092 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kartal, B. et al. How to make a living from anaerobic ammonium oxidation. FEMS Microbiol. Rev. 37, 428–461 (2013).CAS 
    PubMed 

    Google Scholar 
    Oshiki, M., Ali, M., Shinyako-Hata, K., Satoh, H. & Okabe, S. Hydroxylamine-dependent anaerobic ammonium oxidation (anammox) by “Candidatus Brocadia sinica”. Environ. Microbiol. 18, 3133–3143 (2016).CAS 
    PubMed 

    Google Scholar 
    Mateos, L. M. et al. in Advances in Applied Microbiology (eds Sariaslani, S. & Gadd, G. M.) Ch. 4 (Academic Press, 2017).Ben Fekih, I. et al. Distribution of arsenic resistance genes in prokaryotes. Front. Microbiol. 9, 2473 (2018).Wang, P. P., Sun, G. X. & Zhu, Y. G. Identification and characterization of arsenite methyltransferase from an archaeon, methanosarcina acetivorans C2A. Environ. Sci. Technol. 48, 12706–12713 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Masuda, H., Yoshinishi, H., Fuchida, S., Toki, T. & Even, E. Vertical profiles of arsenic and arsenic species transformations in deep-sea sediment, Nankai Trough, offshore Japan. Prog. Earth Planet Sci. 6, 28 (2019).ADS 

    Google Scholar 
    Dunivin, T. K., Yeh, S. Y. & Shade, A. A global survey of arsenic-related genes in soil microbiomes. BMC Biol. 17, 45 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Teske, A. et al. The Guaymas Basin hiking guide to hydrothermal mounds, chimneys, and microbial mats: complex seafloor expressions of subsurface hydrothermal circulation. Front. Microbiol. 7, 75 (2016).O’Day, P. A., Vlassopoulos, D., Root, R. & Rivera, N. The influence of sulfur and iron on dissolved arsenic concentrations in the shallow subsurface under changing redox conditions. Proc. Natl Acad. Sci. USA 101, 13703–13708 (2004).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Galinski, E. A. Osmoadaptation in bacteria. Adv. Microb. Physiol. 37, 273–328 (1995).CAS 

    Google Scholar 
    Papini, C. M., Pandharipande, P. P., Royer, C. A. & Makhatadze, G. I. Putting the piezolyte hypothesis under pressure. Biophys. J. 113, 974–977 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Caumette, G., Koch, I. & Reimer, K. J. Arsenobetaine formation in plankton: a review of studies at the base of the aquatic food chain. J. Environ. Monit. 14, 2841–2853 (2012).CAS 
    PubMed 

    Google Scholar 
    Whaley-Martin, K. J., Koch, I., Moriarty, M. & Reimer, K. J. Arsenic speciation in blue mussels (Mytilus edulis) along a highly contaminated arsenic gradient. Environ. Sci. Technol. 46, 3110–3118 (2012).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Oremland, R. S. et al. Anaerobic oxidation of arsenite in Mono Lake water and by a facultative, arsenite-oxidizing chemoautotroph, strain MLHE-1. Appl. Environ. Microbiol. 68, 4795–4802 (2002).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rhine, E. D., Phelps, C. D. & Young, L. Y. Anaerobic arsenite oxidation by novel denitrifying isolates. Environ. Microbiol. 8, 899–908 (2006).CAS 
    PubMed 

    Google Scholar 
    Rhine et al. LY. The arsenite oxidase genes (aroAB) in novel chemoautotrophic arsenite oxidizers. Biochem. Biophys. Res. Commun. 354, 662–667 (2007).CAS 
    PubMed 

    Google Scholar 
    Saunders, J. K., Fuchsman, C. A., Mckay, C. & Rocap, G. Complete arsenic-based respiratory cycle in the marine microbial communities of pelagic oxygen-deficient zones. Proc. Natl Acad. Sci. USA 116, 9925–9930 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Couture, R. M., Sekowska, A., Fang, G. & Danchin, A. Linking selenium biogeochemistry to the sulfur‐dependent biological detoxification of arsenic. Environ. Microbiol. 14, 1612–1623 (2012).CAS 
    PubMed 

    Google Scholar 
    Zhang, Y. & Gladyshev, V. N. Trends in selenium utilization in marine microbial world revealed through the analysis of the Global Ocean Sampling (GOS) project. PLoS Genet. 4, e1000095 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    Peng, T., Lin, J., Xu, Y.-Z. & Zhang, Y. Comparative genomics reveals new evolutionary and ecological patterns of selenium utilization in bacteria. ISME J. 10, 2048–2059 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Labunskyy, V. M., Hatfield, D. L. & Gladyshev, V. N. Selenoproteins: molecular pathways and physiological roles. Physiol. Rev. 94, 739–777 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yin, K., Wang, Q., Lv, M. & Chen, L. Microorganism remediation strategies towards heavy metals. Chem. Eng. J. 360, 1553–1563 (2019).CAS 

    Google Scholar 
    O’Day, P. A., Vlassopoulos, D., Root, R. & Rivera, N. The influence of sulfur and iron on dissolved arsenic concentrations in the shallow subsurface under changing redox conditions. Proc. Natl Acad. Sci. USA 101, 13703–13708 (2004).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chen, S. F., Zhou, Y. Q., Chen, Y. R. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, 884–890 (2018).
    Google Scholar 
    Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
    PubMed 

    Google Scholar 
    Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huang, Y., Gilna, P. & Li, W. Z. Identification of ribosomal RNA genes in metagenomic fragments. Bioinformatics 25, 1338–1340 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhou, Y. Microbiomes in the Challenger Deep slope and bottom-axis sediments. Zenodo https://doi.org/10.5281/zenodo.6061243 (2022).Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jing, G. C. et al. Parallel-META 3: comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities. Sci. Rep. 7, 40371 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
    PubMed 

    Google Scholar 
    Kang, D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).CAS 
    PubMed 

    Google Scholar 
    Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–887 (2013).CAS 
    PubMed 

    Google Scholar 
    Yamada, K. D., Tomii, K. & Katoh, K. Application of the MAFFT sequence alignment program to large data-reexamination of the usefulness of chained guide trees. Bioinformatics 32, 3246–3251 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 

    Google Scholar 
    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pachiadaki, M. G. et al. Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358, 1046–1051 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Perry, M. heatmaps: flexible heatmaps for functional genomics and sequence features. R package version 1.14.0 (Bioconductor, 2020).Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Aramaki, T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2019).PubMed Central 

    Google Scholar 
    Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).CAS 
    PubMed 

    Google Scholar 
    Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).CAS 
    PubMed 

    Google Scholar 
    Zhou, Y. Microbiomes in the Challenger Deep slope and bottom-axis sediments. Figshare https://doi.org/10.6084/m6089.figshare.12979709 (2022). More

  • in

    Fingerprint analysis reveals sources of petroleum hydrocarbons in soils of different geographical oilfields of China and its ecological assessment

    Concentration of TPHs in surface soilsStatistical results of TPHs concentrations at different geographic oilfields were showed in Fig. 2, and grid regional distribution of TPHs in YC Oilfield surface soils (Y6–Y25) were shown in Fig. 3. Results are given as mean value of triplicate analysis of each sample. The results of TPHs concentration in soil samples showed that the three oilfields all suffered from varying degrees of petroleum pollution, and 60.92% of the 47 sampling points was significantly higher than the soil critical value (500 mg/kg). The average concentration of the TPHs in each study areas conformed to be in the following law: SL Oilfield (average: 5.36 × 103 mg/kg) ( >) NY Oilfield (average: 1.73 × 103 mg/kg) ( >) YC Oilfield (average: 1.37 × 103 mg/kg). The highest concentration of the TPHs were found in SL Oilfield surface soils, ranging from 1.21 × 102 to 6.66 × 104 mg/kg, and NY Oilfield had the second highest TPHs concentrations in the range from 15.82 to 7.42 × 103 mg/kg. The concentrations of TPHs in YC Oilfield ranged from 12.34 to 5.38 × 103 mg/kg. The petroleum contamination mainly derived from abandoned and working oil wells. S4 and S8 soils were collected near the abandoned oil well and working oil well, respectively, and had the highest concentration of TPHs up to 5.28 × 104 and 6.66 × 104 mg/kg. Y1, N8 near the abandoned oil well also had high concentration of TPHs with 5.39 × 103 and 7.42 × 103 mg/kg, respectively. Pollution caused by grounded crude oil in exploitation process has been a serious problem in oilfield area. Our previous research reported that the TPHs content in Dagang Oilfield soils collected adjacent to working oil wells were about 20-folds higher than that in corn soils and living area soils25. Concentration contour map of TPHs in YC Oilfield by grid sampling method showed that regional pollution in the northwest and southeast area are more serious than other sites. Y6 near the gas station and Y15, Y21, Y23 adjacent to the working oil wells have higher concentration (2.12 × 103–5.34 × 103 mg/kg) of TPHs than other farmland and grass soils. Previous study reported that the concentrations of TPHs ranged 7.0 × 102–4.0 × 103 mg/kg in oil exploitation areas of the loess plateau region (34°20′N,107°10′E), showing a similar pollution level with this study26.Figure 2The concentration of TPHs in three oilfield soils.Full size imageFigure 3Grid regional distribution of TPHs in YC Oilfield.Full size imageThe percentage composition of total PAHs, SHs and polar components of petroleum hydrocarbons were shown in Table 1. In general, the dominant petroleum component was saturated hydrocarbons in all soils, accounting more than 50%. Yet, the percentage proportion of PAHs and SHs in contamination soils adjacent to working and abandon oil wells were significantly different (p  BbF (14.16–21.87%) ≫ BaA, Chr, InP, and BkF (less than 10%). This result aligned to the previous study that the contribution of individual PAHs to the TEQs of ∑PAH16 was BaP (45%)  > DBA (33%) in urban surface dust of Xi’an city, China46. Therefore, contamination control should priority focus on the individual PAHs of BaP, DBA, BbF in these areas. In addition, the ecological risk with abandoned time ranging 0–15 years has been assessed, and the descriptive statistic TEQBap of PAHs was shown in Supporting Information, Table S6. The highest TEQs of ∑PAH16 and ∑PAH7 with mean of 1422.27 μg/kg and 1400.48 μg/kg, respectively, were present in soils adjacent to abandoned oil well with abandoned time of 0—5 years. And the TEQs of ∑PAH16 and ∑PAH7 decreased with the abandoned time though the percentage proportion of PAHs increased. The TEQs of ∑PAH16 and ∑PAH7 were close between abandoned time of 5–10 years and 10—15 years while both had high content. It demonstrated that high ecological risk was persistent in abandoned oil well areas over abandoned time of 15 years, and basically stable after 5 years. Therefore, abandoned oil well areas need to be blocked to prevent PAHs entering the external environment, and combine physical–chemical technology for petroleum remediation instead of simple weathering biological processes.Table 3 Descriptive statistic TEQBap of PAHs in different sampling area.Full size tableAs referred the PAHs standard of Dutch soil, TEQs of ∑PAH7 was 32.02 μg/kg, calculated by ten individual PAHs times TEFs. In this study, the mean TEQs of ∑PAH7 were about 35- and 10-folds of Dutch soil in petro-related area soils and grassland soils, indicating a high and medium ecological risk in these soils respectively. However, the mean TEQs of ∑PAH7 in farmland soils (18.80 μg/kg) was below Dutch soil, presenting a low potential ecological risk. It should be noted that the minimum of TEQs of ∑PAH7 in grassland soil was 26.24 μg/kg less than TEQs of ∑PAH7 in Dutch soil, but it was vulnerable affected by the surrounding soils with high TEQs of ∑PAH7. In this study, except the farmland soils, TEQs of ∑PAH7 exhibited higher TEQ values than those reported soils in Santiago, Chile47 and Nepal24, and road dust in Tianjin, China48. Overall, the most threat of ecological risk in petro-related soils caused by the anthropogenic PAHs input, such like oil leakage, oil refining, and fossil energy combustion. Preventing oil spills accident and developing the remediation methods are the main significant ways to reduce the ecological risks in these areas. The medium ecological risk in grassland might result from the migration of PAHs via rainfall pathway. Therefore, establishment the oil-blocking isolation zones is the critical way for medium ecological risk areas to control petroleum inflow. Even though the low ecological risk was identified in farmland soils, PAHs source analysis indicated that the biomass combustion should be controlled in these areas. More

  • in

    Sociality predicts orangutan vocal phenotype

    Lipkind, D. et al. Stepwise acquisition of vocal combinatorial capacity in songbirds and human infants. Nature 498, 104–108 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goldstein, M., King, A. P. & West, M. J. Social interaction shapes babbling: testing parallels between birdsong and speech. Proc. Natl Acad. Sci. USA 100, 8030–8035 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fehér, O., Ljubičić, I., Suzuki, K., Okanoya, K. & Tchernichovski, O. Statistical learning in songbirds: from self-tutoring to song culture. Phil. Trans. R. Soc. B 372, 20160053 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Tchernichovski, O., Lints, T., Mitra, P. P. & Nottebohm, F. Vocal imitation in zebra finches is inversely related to model abundance. Proc. Natl Acad. Sci. USA 96, 12901–12904 (1999).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tchernichovski, O. Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291, 2564–2569 (2001).CAS 

    Google Scholar 
    Fehér, O., Wang, H., Saar, S., Mitra, P. P. & Tchernichovski, O. De novo establishment of wild-type song culture in the zebra finch. Nature 459, 564–568 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Takahashi, D. et al. The developmental dynamics of marmoset monkey vocal production. Science 349, 734–738 (2015).CAS 

    Google Scholar 
    Takahashi, D. Y., Liao, D. A. & Ghazanfar, A. A. Vocal learning via social reinforcement by infant marmoset monkeys. Curr. Biol. 27, 1844–1852.E6 (2017).Takahashi, D. Y., Fenley, A. R. & Ghazanfar, A. A. Early development of turn-taking with parents shapes vocal acoustics in infant marmoset monkeys. Phil. Trans. R. Soc. B 371, 20150370 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Gultekin, Y. B. & Hage, S. R. Limiting parental interaction during vocal development affects acoustic call structure in marmoset monkeys. Sci. Adv. 4, eaar4012 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Gultekin, Y. B. & Hage, S. R. Limiting parental feedback disrupts vocal development in marmoset monkeys. Nat. Commun. 8, 14046 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jarvis, E. D. Evolution of vocal learning and spoken language. Science 366, 50–54 (2019).CAS 

    Google Scholar 
    Snowdon, C. T. Learning from monkey “talk”. Science 355, 1120–1122 (2017).CAS 

    Google Scholar 
    Malik, K. Rights and wrongs. Nature 406, 675–676 (2000).
    Google Scholar 
    Wise, S. M. & Goodall, J. Rattling the Cage: Toward Legal Rights for Animals (Da Capo Press, 2017).Grayson, L. Animals in Research: For and Against (British Library, 2000).Nater, A. et al. Morphometric, behavioral, and genomic evidence for a new orangutan species. Curr. Biol. 27, 3487–3498.E10 (2017).CAS 

    Google Scholar 
    Estrada, A. et al. Impending extinction crisis of the world’s primates: why primates matter. Sci. Adv. 3, e1600946 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Ross, S. et al. Inappropriate use and portrayal of chimpanzees. Science 319, 1487 (2008).CAS 

    Google Scholar 
    Wich, S. A. et al. Land-cover changes predict steep declines for the Sumatran orangutan (Pongo abelii). Sci. Adv. 2, e1500789 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Wich, S. A. et al. Understanding the impacts of land-use policies on a threatened species: is there a future for the Bornean orangutan? PLoS ONE 7, e49142 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wich, S. A. et al. Will oil palm’s homecoming spell doom for Africa’s great apes? Curr. Biol. https://doi.org/10.1016/j.cub.2014.05.077 (2014).Fitch, T. W. Empirical approaches to the study of language evolution. Psychon. Bull. Rev. 24, 3–33 (2017).Hauser, M. D. et al. The mystery of language evolution. Front. Psychol. https://doi.org/10.3389/fpsyg.2014.00401 (2014)Corballis, M. C. Crossing the Rubicon: behaviorism, language, and evolutionary continuity. Front. Psychol. 11, 653 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Berwick, R. C. & Chomsky, N. All or nothing: no half-merge and the evolution of syntax. PLoS Biol. 17, e3000539 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolhuis, J. J. & Wynne, C. D. Can evolution explain how minds work? Nature 458, 832–833 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hayes, K. J. & Hayes, C. The intellectual development of a home-raised chimpanzee. Proc. Am. Phil. Soc. 95, 105–109 (1951).
    Google Scholar 
    Premack, D. Language in chimpanzee? Science 172, 808–822 (1971).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Terrace, H., Petitto, L., Sanders, R. & Bever, T. Can an ape create a sentence? Science 206, 891–902 (1979).CAS 

    Google Scholar 
    Patterson, F. & Linden, E. The Education of Koko (Holt, Rinehart and Winston, 1981).Leavens, D. A., Bard, K. A. & Hopkins, W. D. BIZARRE chimpanzees do not represent “the chimpanzee”. Behav. Brain Sci. 33, 100–101 (2010).
    Google Scholar 
    Lameira, A. R. Bidding evidence for primate vocal learning and the cultural substrates for speech evolution. Neurosci. Biobehav. Rev. 83, 429–439 (2017).
    Google Scholar 
    Lameira, A. R. et al. Speech-like rhythm in a voiced and voiceless orangutan call. PLoS ONE 10, e116136 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Lameira, A. R. & Shumaker, R. W. Orangutans show active voicing through a membranophone. Sci. Rep. 9, 12289 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Lameira, A. R., Hardus, M. E., Mielke, A., Wich, S. A. & Shumaker, R. W. Vocal fold control beyond the species-specific repertoire in an orangutan. Sci. Rep. 6, 30315 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lameira, A. R. et al. Orangutan (Pongo spp.) whistling and implications for the emergence of an open-ended call repertoire: a replication and extension. J. Acoust. Soc. Am. 134, 2326–2335 (2013).
    Google Scholar 
    Perlman, M. & Clark, N. Learned vocal and breathing behavior in an enculturated gorilla. Anim. Cogn. 18, 1165–1179 (2015).
    Google Scholar 
    Wich, S. et al. A case of spontaneous acquisition of a human sound by an orangutan. Primates 50, 56–64 (2009).
    Google Scholar 
    Lameira, A. R., Maddieson, I. & Zuberbuhler, K. Primate feedstock for the evolution of consonants. Trends Cogn. Sci. 18, 60–62 (2014).
    Google Scholar 
    Lameira, A. R. The forgotten role of consonant-like calls in theories of speech evolution. Behav. Brain Sci. 37, 559–560 (2014).
    Google Scholar 
    Boë, L.-J. et al. Which way to the dawn of speech? Reanalyzing half a century of debates and data in light of speech science. Sci. Adv. 5, eaaw3916 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Boë, L. J. et al. Evidence of a vocalic proto-system in the baboon (Papio papio) suggests pre-hominin speech precursors. PLoS ONE 12, e0169321 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Fitch, T. W., Boer, B., Mathur, N. & Ghazanfar, A. A. Monkey vocal tracts are speech-ready. Sci. Adv. 2, e1600723 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Pereira, A. S., Kavanagh, E., Hobaiter, C., Slocombe, K. E. & Lameira, A. R. Chimpanzee lip-smacks confirm primate continuity for speech-rhythm evolution. Biol. Lett. 16, 20200232 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Lameira, A. R. et al. Proto-consonants were information-dense via identical bioacoustic tags to proto-vowels. Nat. Hum. Behav. 1, 0044 (2017).
    Google Scholar 
    Lameira, A. R. et al. Orangutan information broadcast via consonant-like and vowel-like calls breaches mathematical models of linguistic evolution. Biol. Lett. 17, 20210302 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Watson, S. K. et al. Nonadjacent dependency processing in monkeys, apes, and humans. Sci. Adv. 6, eabb0725 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Lameira, A. R. & Call, J. Time-space–displaced responses in the orangutan vocal system. Sci. Adv. 4, eaau3401 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Belyk, M. & Brown, S. The origins of the vocal brain in humans. Neurosci. Biobehav. Rev. 77, 177–193 (2017).
    Google Scholar 
    Crockford, C., Wittig, R. M. & Zuberbuhler, K. Vocalizing in chimpanzees is influenced by social-cognitive processes. Sci. Adv. 3, e1701742 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Taglialatela, J. P., Reamer, L., Schapiro, S. J. & Hopkins, W. D. Social learning of a communicative signal in captive chimpanzees. Biol. Lett. 8, 498–501 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Russell, J. L., Joseph, M., Hopkins, W. D. & Taglialatela, J. P. Vocal learning of a communicative signal in captive chimpanzees, Pan troglodytes. Brain Lang. 127, 520–525 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Hopkins, W. D. et al. Genetic factors and orofacial motor learning selectively influence variability in central sulcus morphology in chimpanzees (Pan troglodytes). J. Neurosci. 37, 5475–5483 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Staes, N. et al. FOXP2 variation in great ape populations offers insight into the evolution of communication skills. Sci. Rep. 7, 16866 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Martins, P. T. & Boeckx, C. Vocal learning: beyond the continuum. PLoS Biol. 18, e3000672 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Watson, S. K. et al. Vocal learning in the functionally referential food grunts of chimpanzees. Curr. Biol. 25, 495–499 (2015).CAS 

    Google Scholar 
    Hopkins, W. D., Taglialatela, J. P. & Leavens, D. A. Chimpanzees differentially produce novel vocalizations to capture the attention of a human. Anim. Behav. 73, 281–286 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    Bianchi, S., Reyes, L. D., Hopkins, W. D., Taglialatela, J. P. & Sherwood, C. C. Neocortical grey matter distribution underlying voluntary, flexible vocalizations in chimpanzees. Sci. Rep. 6, 34733 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wich, S. A. et al. Call cultures in orangutans? PLoS ONE 7, e36180 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Crockford, C., Herbinger, I., Vigilant, L. & Boesch, C. Wild chimpanzees produce group-specific calls: a case for vocal learning? Ethology 110, 221–243 (2004).
    Google Scholar 
    Whiten, A. et al. Cultures in chimpanzees. Nature 399, 682–685 (1999).CAS 

    Google Scholar 
    van Schaik, C. P. et al. Orangutan cultures and the evolution of material culture. Science 299, 102–105 (2003).
    Google Scholar 
    Whiten, A. Culture extends the scope of evolutionary biology in the great apes. Proc. Natl Acad. Sci. USA 114, 7790–7797 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Koops, K., Visalberghi, E. & van Schaik, C. The ecology of primate material culture. Biol. Lett. 10, 20140508 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Kalan, A. K. et al. Chimpanzees use tree species with a resonant timbre for accumulative stone throwing. Biol. Lett. 15, 20190747 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Hardus, M., Lameira, A. R., Van Schaik, C. P. & Wich, S. A. Tool use in wild orangutans modifies sound production: a functionally deceptive innovation? Proc. R. Soc. B https://doi.org/10.1098/rspb.2009.1027 (2009).Lameira, A. R. et al. Population-specific use of the same tool-assisted alarm call between two wild orangutan populations (Pongo pygmaeus wurmbii) indicates functional arbitrariness. PLoS ONE 8, e69749 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hohmann, G. & Fruth, B. Culture in bonobos? Between‐species and within‐species variation in behavior. Curr. Anthropol. 44, 563–571 (2003).
    Google Scholar 
    Robbins, M. M. et al. Behavioral variation in gorillas: evidence of potential cultural traits. PLoS ONE 11, e0160483 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Kühl, H. S. et al. Human impact erodes chimpanzee behavioral diversity. Science 363, 1453–1455 (2019).
    Google Scholar 
    van Schaik, C. P. Fragility of Traditions: the disturbance hypothesis for the loss of local traditions in orangutans. Int. J. Primatol. 23, 527–538 (2002).
    Google Scholar 
    Delgado, R. A. & van Schaik, C. P. The behavioral ecology and conservation of the orangutan (Pongo pygmaeus): a tale of two islands. Evol. Anthropol. 9, 201–218 (2000).
    Google Scholar 
    van Schaik, C. The socioecology of fission–fusion sociality in orangutans. Primates 40, 69–86 (1999).
    Google Scholar 
    Nater, A. et al. Sex-biased dispersal and volcanic activities shaped phylogeographic patterns of extant orangutans (genus: Pongo). Mol. Biol. 28, 2275–2288 (2011).CAS 

    Google Scholar 
    Arora, N. et al. Parentage-based pedigree reconstruction reveals female matrilineal clusters and male-biased dispersal in nongregarious Asian great apes, the Bornean orangutans (Pongo pygmaeus). Mol. Ecol. 21, 3352–3362 (2012).CAS 

    Google Scholar 
    Kavanagh, E. et al. Dominance style is a key predictor of vocal use and evolution across nonhuman primates. R. Soc. Open Sci. 8, 210873 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Husson, S. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 6 (Oxford Univ. Press, 2009).van Noordwijk, M. A. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 12 (Oxford Univ Press, 2009).Singleton, I., Knott, C., Morrogh-Bernard, H., Wich, S. & van Schaik, C. P. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 13 (Oxford Univ. Press, 2009).Wich, S. et al. Life history of wild Sumatran orangutans (Pongo abelii). J. Hum. Evol. 47, 385–398 (2004).CAS 

    Google Scholar 
    Wich, S. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 5 (Oxford Univ. Press, 2009).Shumaker, R. W., Wich, S. A. & Perkins, L. Reproductive life history traits of female orangutans (Pongo spp.). Primate Reprod. Aging 36, 147–161 (2008).CAS 

    Google Scholar 
    Freund, C., Rahman, E. & Knott, C. Ten years of orangutan-related wildlife crime investigation in West Kalimantan, Indonesia. Am. J. Primatol. 79, 22620 (2016).
    Google Scholar 
    van Noordwijk, M. A. & van Schaik, C. P. Development of ecological competence in Sumatran orangutans. Am. J. Phys. Anthropol. 127, 79–94 (2005).
    Google Scholar 
    Knot, C. D. et al. The Gunung Palung Orangutan Project: Twenty-five years at the intersection of research and conservation in a critical landscape in Indonesia. Biol. Conserv. 255, 108856 (2021).
    Google Scholar 
    Guillermo, S.-B., Gershenson, C. & Fernández, N. A package for measuring emergence, self-organization, and complexity based on shannon entropy. Front. Robot. AI 4, 174102 (2017).
    Google Scholar 
    Santamaría-Bonfil, G., Fernández, N. & Gershenson, C. Measuring the complexity of continuous distributions. Entropy 18, 72 (2016).
    Google Scholar 
    Kalan, A. K., Mundry, R. & Boesch, C. Wild chimpanzees modify food call structure with respect to tree size for a particular fruit species. Anim. Behav. 101, 1–9 (2015).
    Google Scholar 
    Fedurek, P. & Slocombe, K. E. The social function of food-associated calls in male chimpanzees. Am. J. Primatol. 75, 726–739 (2013).
    Google Scholar 
    Luef, E., Breuer, T. & Pika, S. Food-associated calling in gorillas (Gorilla g. gorilla) in the wild. PLoS ONE 11, e0144197 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Clay, Z. & Zuberbuhler, K. Food-associated calling sequences in bonobos. Anim. Behav. 77, 1387–1396 (2009).
    Google Scholar 
    Hardus, M. E. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 4 (Oxford Univ. Press, 2009).Wich, S. A. et al. Forest fruit production is higher on Sumatra than on Borneo. PLoS ONE 6, e21278 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lameira, A. R. & Wich, S. Orangutan long call degradation and individuality over distance: a playback approach. Int. J. Primatol. 29, 615–625 (2008).
    Google Scholar 
    Lameira, A. R., Delgado, R. & Wich, S. Review of geographic variation in terrestrial mammalian acoustic signals: human speech variation in a comparative perspective. J. Evolut. Psychol. 8, 309–332 (2010).
    Google Scholar 
    Lameira, A. R. et al. Predator guild does not influence orangutan alarm call rates and combinations. Behav. Ecol. Sociobiol. 67, 519–528 (2013).
    Google Scholar 
    Derex, M. & Mesoudi, A. Cumulative cultural evolution within evolving population structures. Trends Cogn. Sci. 24, 654–667 (2020).
    Google Scholar 
    Scerri, E. M. et al. Did our species evolve in subdivided populations across Africa, and why does it matter? Trends Ecol. Evol. 33, 582–594 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Kaya, F. et al. The rise and fall of the Old World savannah fauna and the origins of the African savannah biome. Nat. Ecol. Evol. 2, 241–246 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Bobe, R. The expansion of grassland ecosystems in Africa in relation to mammalian evolution and the origin of the genus Homo. Palaeogeogr. Palaeoclimatol. Palaeoecol. 207, 399–420 (2004).
    Google Scholar 
    Zhu, D., Galbraith, E. D., Reyes-García, V. & Ciais, P. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition. Nat. Ecol. Evol. 5, 1536–1545 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    DeCasien, A. R., Williams, S. A. & Higham, J. P. Primate brain size is predicted by diet but not sociality. Nat. Ecol. Evol. 1, 0112 (2017).
    Google Scholar 
    Mauricio, G.-F. & Gardner, A. Inference of ecological and social drivers of human brain-size evolution. Nature 557, 554–557 (2018).
    Google Scholar 
    Lindenfors, P., Wartel, A. & Lind, J. ‘Dunbar’s number’ deconstructed. Biol. Lett. 17, 20210158 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Schuppli, C., van Noordwijk, M., Atmoko, S. U. & van Schaik, C. Early sociability fosters later exploratory tendency in wild immature orangutans. Sci. Adv. 6, eaaw2685 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Schuppli, C. et al. Observational social learning and socially induced practice of routine skills in immature wild orangutans. Anim. Behav. 119, 87–98 (2016).
    Google Scholar 
    Jaeggi, A. V. et al. Social learning of diet and foraging skills by wild immature Bornean orangutans: implications for culture. Am. J. Primatol. 72, 62–71 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Schuppli, C. et al. The effects of sociability on exploratory tendency and innovation repertoires in wild Sumatran and Bornean orangutans. Sci. Rep. 7, 15464 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Ehmann, B. et al. Immature wild orangutans acquire relevant ecological knowledge through sex-specific attentional biases during social learning. PLoS Biol. 19, e3001173 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Meijaard, E. et al. Declining orangutan encounter rates from Wallace to the present suggest the species was once more abundant. PLoS ONE 5, e12042 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Marshall, A. J. et al. The blowgun is mightier than the chainsaw in determining population density of Bornean orangutans (Pongo pygmaeus morio) in the forests of East Kalimantan. Biol. Conserv. 129, 566–578 (2006).
    Google Scholar 
    Gail, C.-S., Miran, C.-S., Singleton, I. & Linkie, M. Raiders of the lost bark: orangutan foraging strategies in a degraded landscape. PLoS ONE 6, e20962 (2011).
    Google Scholar 
    Schuppli, C. & van Schaik, C. P. Animal cultures: how we’ve only seen the tip of the iceberg. Evol. Hum. Sci. 1, e2 (2019).
    Google Scholar 
    Langergraber, K. E. et al. Vigilant, generation times in wild chimpanzees and gorillas suggest earlier divergence times in great ape and human evolution. Proc. Natl Acad. Sci. USA 109, 15716–15721 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fernández, N., Maldonado, C. & Gershenson, C. in Guided Self-Organization: Inception (ed Prokopenko, M.) 19–51 (Springer Berlin Heidelberg, 2014).JAST Team, JASP (Univ. of Amsterdam, 2020).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009).Auguie, B. gridExtra: Functions in grid graphics. R version 0.9.1 (2012).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
    Google Scholar 
    Korthauer, K. et al. A practical guide to methods controlling false discoveries in computational biology. Genome Biol. 20, 118 (2019).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Identifying conservation technology needs, barriers, and opportunities

    Pimm, S. L. et al. Emerging technologies to conserve biodiversity. Trends Ecol. Evol. 30, 685–696 (2015).Article 

    Google Scholar 
    Marvin, D. C. et al. Integrating technologies for scalable ecology and conservation. Glob. Ecol. Conserv. 7, 262–275 (2016).Article 

    Google Scholar 
    Wall, J., Wittemyer, G., Klinkenberg, B. & Douglas-Hamilton, I. Novel opportunities for wildlife conservation and research with real-time monitoring. Ecol. Appl. 24, 593–601 (2014).Article 

    Google Scholar 
    Snaddon, J., Petrokofsky, G., Jepson, P. & Willis, K. J. Biodiversity technologies: tools as change agents. Biol. Lett. 9, 20121029 (2013).Article 

    Google Scholar 
    Pettorelli, N., Safi, K., Turner, W. Satellite remote sensing, biodiversity research and conservation of the future. Philos. Trans. R. Soc. B Biol. Sci. 369, 20130190 (2014).Ripperger, S. P. et al. Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging. PLOS Biol. 18, e3000655 (2020).CAS 
    Article 

    Google Scholar 
    Xu, H., Wang, K., Vayanos, P. & Tambe, M. Strategic coordination of human patrollers and mobile sensors with signaling for security games. 8 (2018).Liu, Y. et al. AI for Earth: Rainforest conservation by acoustic surveillance. 2 (2019).Joppa, L. N. Technology for nature conservation: an industry perspective. Ambio 44, 522–526 (2015).Article 

    Google Scholar 
    Koh, L. P. & Wich, S. A. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 5, 121–132 (2012).Article 

    Google Scholar 
    Hahn, N. et al. Unmanned aerial vehicles mitigate human–elephant conflict on the borders of Tanzanian Parks: a case study. Oryx 51, 513–516 (2017).Article 

    Google Scholar 
    Pomerantz, A. et al. Real-time DNA barcoding in a rainforest using nanopore sequencing: opportunities for rapid biodiversity assessments and local capacity building. GigaScience 7, (2018).Van Doren, B. M. & Horton, K. G. A continental system for forecasting bird migration. Science 361, 1115–1118 (2018).ADS 
    Article 

    Google Scholar 
    Howson, P. Building trust and equity in marine conservation and fisheries supply chain management with blockchain. Mar. Policy 115, 103873 (2020).Article 

    Google Scholar 
    Speaker, T. et al. A global community-sourced assessment of the state of conservation technology. Conserv. Biol. cobi. https://doi.org/10.1111/cobi.13871 (2022).Article 

    Google Scholar 
    Pearce, J. M. Building research equipment with free Open-Source Hardware. Science 337, 1303–1304 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    Gibb, R., Browning, E., Glover-Kapfer, P. & Jones, K. E. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol. Evol. 10, 169–185 (2019).Article 

    Google Scholar 
    current constraints and future priorities for development. Glover-Kapfer, P., Soto-Navarro, C. A. & Wearn, O. R. Camera-trapping version 3.0. Remote Sens. Ecol. Conserv. 5, 209–223 (2019).Article 

    Google Scholar 
    Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725 (2018).CAS 
    Article 

    Google Scholar 
    Berger-Tal, O. & Lahoz-Monfort, J. J. Conservation technology: the next generation. Conserv. Lett. 11, 1–6 (2018).Article 

    Google Scholar 
    Hill, A. P. et al. AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods Ecol. Evol. 9, 1199–1211 (2018).Article 

    Google Scholar 
    Zárybnická, M., Kubizňák, P., Šindelář, J. & Hlaváč, V. Smart nest box: a tool and methodology for monitoring of cavity-dwelling animals. Methods Ecol. Evol. 7, 483–492 (2016).Article 

    Google Scholar 
    Kalmár, G. et al. Animal-Borne Anti-Poaching System. in Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services 91–102 (ACM, 2019). https://doi.org/10.1145/3307334.3326080.Weise, F. J. et al. Lions at the gates: Trans-disciplinary design of an early warning system to improve human-lion coexistence. Front. Ecol. Evol. 6, 242 (2019).Article 

    Google Scholar 
    Beery, S., Van Horn, G. & Perona, P. Recognition in Terra Incognita. in Proceedings of the European Conference on Computer Vision (ECCV) (eds. Ferrari, V., Hebert, M., Sminchisescu, C. & Weiss, Y.) 472–489 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-030-01270-0_28.Crego, R. D., Masolele, M. M., Connette, G. & Stabach, J. A. Enhancing animal movement analyses: spatiotemporal matching of animal positions with remotely sensed data using google earth engine and R. Remote Sens. 13, 4154 (2021).ADS 
    Article 

    Google Scholar 
    Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 
    Article 

    Google Scholar 
    Vulcan. EarthRanger. https://earthranger.com.Ahumada, J. A. et al. Wildlife insights: A platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environ. Conserv. 47, 1–6 (2020).MathSciNet 
    Article 

    Google Scholar 
    Lahoz-Monfort, J. J. et al. A call for international leadership and coordination to realize the potential of conservation technology. Bioscience 69, 823–832 (2019).Article 

    Google Scholar 
    Group Gets – AudioMoth. https://groupgets.com/manufacturers/open-acoustic-devices/products/audiomoth.Kulits, P., Wall, J., Bedetti, A., Henley, M. & Beery, S. ElephantBook: A semi-automated human-in-the-loop system for elephant re-identification. in ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) 88–98 (ACM, 2021). https://doi.org/10.1145/3460112.3471947.Pardo, L. E. et al. Snapshot Safari: A large-scale collaborative to monitor Africa’s remarkable biodiversity. South Afr. J. Sci. 117, (2021).Iacona, G. et al. Identifying technology solutions to bring conservation into the innovation era. Front. Ecol. Environ. 17, 591–598 (2019).Article 

    Google Scholar 
    Cooper, R. G. What’s next?: After stage-gate. Res.-Technol. Manag. 57, 20–31 (2014).ADS 

    Google Scholar 
    Cooper, R. G. The drivers of success in new-product development. Ind. Mark. Manag. 76, 36–47 (2019).Article 

    Google Scholar 
    Pearce, J. M. The case for open source appropriate technology. Environ. Dev. Sustain. 14, 425–431 (2012).Article 

    Google Scholar 
    Mair, J., Battilana, J. & Cardenas, J. Organizing for society: A typology of social entrepreneuring models. J. Bus. Ethics 111, 353–373 (2012).Article 

    Google Scholar 
    Meissner, D. Public-private partnership models for science, technology, and innovation cooperation. J. Knowl. Econ. 10, 1341–1361 (2019).Article 

    Google Scholar 
    Likert, R. A technique for the measurement of attitudes. Arch. Psychol. 22, 1–55.Mayer, A. L. & Wellstead, A. M. Questionable survey methods generate a questionable list of recommended articles. Nat. Ecol. Evol. 2, 1336–1337 (2018).Article 

    Google Scholar 
    Archie, K. M., Dilling, L., Milford, J. B. & Pampel, F. C. Climate Change and Western Public Lands: a Survey of U.S. Federal Land Managers on the Status of Adaptation Efforts. Ecol. Soc. 17 (2012).Jimenez, M. F. et al. Underrepresented faculty play a disproportionate role in advancing diversity and inclusion. Nat. Ecol. Evol. 3, 1030–1033 (2019).Article 

    Google Scholar 
    Christensen, R. ordinal – Regression Models for Ordinal Data. R package version 2019.12-10. (2019).R Core Team. R: A language and environment for statistical computing. (2020).Arnold, T. W. Uninformative parameters and model selection using Akaike’s information criterion. J. Wildl. Manag. 74, 1175–1178 (2010).Article 

    Google Scholar 
    QSR International Pty Ltd. Nvivo 12 Pro. (2020).Glesne, C. Making words fly: Developing understanding through interviewing. Becom. Qual. Res. Introd. 3, (2006).Creswell, J. W. & Creswell, J. D. Research design: Qualitative, quantitative, and mixed methods approaches. (Sage publications 2017). More

  • in

    Metabarcoding analysis of the soil fungal community to aid the conservation of underexplored church forests in Ethiopia

    Balami, S., Vašutová, M., Godbold, D., Kotas, P. & Cudlín, P. Soil fungal communities across land use types. Forest Biogeosci. For. 13, 548–558 (2020).
    Google Scholar 
    Deacon, J. Fungal Biology (Wiley, 2009).
    Google Scholar 
    Ruiz-Almenara, C., Gándara, E. & Gómez-Hernández, M. Comparison of diversity and composition of macrofungal species between intensive mushroom harvesting and non-harvesting areas in Oaxaca, Mexico. PeerJ 7, e8325 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 

    Google Scholar 
    Egli, S. Mycorrhizal mushroom diversity and productivity—an indicator of forest health?. Ann. For. Sci. 68, 81–88 (2011).
    Google Scholar 
    Westover, K. M. & Bever, J. D. Mechanisms of plant species coexistence: Roles of rhizosphere bacteria and root fungal pathogens. Ecology 82, 3285–3294 (2001).
    Google Scholar 
    Deacon, J. Fungal Biology (Wiley, 2006).
    Google Scholar 
    Fernandez, C. W., Nguyen, N. H. U. H., Stefanski, A. & Han, Y. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Chang. Biol. 23, 1598–1609 (2017).ADS 
    PubMed 

    Google Scholar 
    Heilmann-Clausen, J. et al. A fungal perspective on conservation biology. Conserv. Biol. 29, 61–68 (2015).PubMed 

    Google Scholar 
    Shay, P.-E., Winder, R. S. & Trofymow, J. A. Nutrient-cycling microbes in coastal Douglas-fir forests: Regional-scale correlation between communities, in situ climate, and other factors. Front. Microbiol. 6, 5897 (2015).
    Google Scholar 
    van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).PubMed 

    Google Scholar 
    Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. Regional environmental conditions shape microbial community structure stronger than local forest management intensity. For. Ecol. Manag. 409, 250–259 (2018).
    Google Scholar 
    Monkai, J., Hyde, K. D., Xu, J. & Mortimer, P. E. Diversity and ecology of soil fungal communities in rubber plantations. Fungal Biol. Rev. 31, 1–11 (2017).
    Google Scholar 
    White, F. Vegetation of Africa—a descriptive memoir to accompany the UNESCO/AETFAT/UNSO vegetation map of Africa, Natural Resources Research Report XX. U.N. Educational, Scientific and Cultural Organization, Paris (1983).Aynekulu, E. et al. Plant diversity and regeneration in a disturbed isolated dry Afromontane forest in northern Ethiopia. Folia Geobot. 51, 115–127 (2016).
    Google Scholar 
    Wassie, A., Sterck, F. J. & Bongers, F. Species and structural diversity of church forests in a fragmented Ethiopian Highland landscape. J. Veg. Sci. 21, 938–948 (2010).
    Google Scholar 
    Alem, D., Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Survey of macrofungal diversity and analysis of edaphic factors influencing the fungal community of church forests in Dry Afromontane areas of Northern Ethiopia. For. Ecol. Manag. 496, 119391 (2021).
    Google Scholar 
    Aerts, R. et al. Conservation of the Ethiopian church forests: Threats, opportunities and implications for their management. Sci. Total Environ. 551–552, 404–414 (2016).ADS 
    PubMed 

    Google Scholar 
    Wassie, A., Teketay, D. & Powell, N. Church forests in North Gonder administrative zone, Northern Ethiopia. For. Trees Livelihoods 15, 349–373 (2005).
    Google Scholar 
    Wsaaie, A., Teketay, D. & Powell, N. Church forests in North Gondar Administative Zone, Northern Ethioopia. For. Trees Livelihoods 15, 349–373 (2005).
    Google Scholar 
    Lemenih, M. & Bongers, F. Dry forests of Ethiopia and their silviculture. In Silviculture in the Tropics, Tropical Forestry 8 (ed. S. G€unter et al.) 261–272 (Springer, Heidelberg, 2011). https://doi.org/10.1007/978-3-642-19986-8_17.Fernández, A., Sánchez, S., García, P. & Sánchez, J. Macrofungal diversity in an isolated and fragmented Mediterranean Forest ecosystem. Plant Biosyst. Int. J. Deal. Asp. Plant Biol. 154, 139–148 (2020).
    Google Scholar 
    Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant-fungal interactions. New Phytol. 204, 180–191 (2014).PubMed 

    Google Scholar 
    Burgess, N. D., Hales, J. D. A., Ricketts, T. H. & Dinerstein, E. Factoring species, non-species values and threats into biodiversity prioritisation across the ecoregions of Africa and its islands. Biol. Conserv. 127, 383–401 (2006).
    Google Scholar 
    Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398, 37–47 (2017).
    Google Scholar 
    Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 1–14 (2020).
    Google Scholar 
    Tedersoo, L. et al. Global diversity and geography of soil fungi. Science (80-. ). 346 (2014).Hawksworth, D. L. Global species numbers of fungi: are tropical studies and molecular approaches contributing to a more robust estimate?. Biodivers. Conserv. 21, 2425–2433 (2012).
    Google Scholar 
    Crous, P. W. et al. How many species of fungi are there at the tip of Africa?. Stud. Mycol. 55, 13–33 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    Martínez, M. L. et al. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. For. Ecol. Manag. 258, 1856–1863 (2009).
    Google Scholar 
    Phillips, H. et al. The effects of global change on soil faunal communities: a meta-analytic approach. Res. Ideas Outcomes 5 (2019).Riutta, T. et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol. Biochem. 49, 124–131 (2012).CAS 

    Google Scholar 
    Rantalainen, M., Haimi, J., Fritze, H., Pennanen, T. & Setala, T. Soil decomposer community as a model system in studying the effects of habitat fragmentation and habitat corridors. Soil Biol. Biochem. 40, 853–863 (2008).CAS 

    Google Scholar 
    Newsham, K. K. et al. Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nat. Clim. Chang. 6, 182–186 (2016).ADS 

    Google Scholar 
    Bahram, M., Põlme, S., Kõljalg, U., Zarre, S. & Tedersoo, L. Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran. New Phytol. 193, 465–473 (2012).PubMed 

    Google Scholar 
    Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).PubMed 

    Google Scholar 
    Krüger, C. et al. Plant communities rather than soil properties structure arbuscular mycorrhizal fungal communities along primary succession on a mine spoil. Front. Microbiol. 8, 1–16 (2017).
    Google Scholar 
    Bahram, M., Peay, K. G. & Tedersoo, L. Local-scale biogeography and spatiotemporal variability in communities of mycorrhizal fungi. New Phytol. 205, 1454–1463 (2015).CAS 
    PubMed 

    Google Scholar 
    Li, P. et al. Spatial variation in soil fungal communities across paddy fields in Subtropical China. mSystems 5 (2020).Grilli, G., Urcelay, C. & Galetto, L. Forest fragment size and nutrient availability: Complex responses of mycorrhizal fungi in native–exotic hosts. Plant Ecol. 213, 155–165 (2012).
    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. Shrub Resprouting Response After Fuel Reduction Treatments: Comparison of Prescribed Burning, Clearing and Mastication (Elsevier, 2013).
    Google Scholar 
    Tedersoo, L., Sadam, A., Zambrano, M., Valencia, R. & Bahram, M. Low diversity and high host preference of ectomycorrhizal fungi in Western Amazonia, a neotropical biodiversity hotspot. ISME J. 4, 465–471 (2010).PubMed 

    Google Scholar 
    Glassman, S. I., Wang, I. J. & Bruns, T. D. Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales. Mol. Ecol. 26, 6960–6973 (2017).CAS 
    PubMed 

    Google Scholar 
    Colwell, R. K. EstimateS: statistical estimation of species richness and shared species from samples. Version 9. User’s Guide and application published at: http://purl.oclc.org/estimates (2013).Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).CAS 
    PubMed 

    Google Scholar 
    Pan, W. et al. DNA polymerase preference determines PCR priming efficiency. BMC Biotechnol. 14, 10 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Kirk, P. M., Cannon, P. F., Minter, D. W. & J.A, S. Dictionary of the Fungi (The Centre for Agriculture and Bioscience International (CABI), 2008).Rossman, A., Samuel, G., Rogerson, C. & Lowen, R. Genera of bionectriaceae, hypocreaceae and nectriaceae (hypocreales, ascomycetes). Stud. Mycol. 42, 1–260 (1999).
    Google Scholar 
    Samuels, G. Trichoderma: A review of biology and systematics of the genus. Mycol. Res. 923–935 (1996).Alem, D. et al. Soil fungal communities and succession following wildfire in Ethiopian dry Afromontane forests, a highly diverse underexplored ecosystem. For. Ecol. Manag. 474, 118328 (2020).
    Google Scholar 
    Muleta, D., Woyessa, D. & Teferi, Y. Mushroom consumption habits of Wacha Kebele residents, southwestern Ethiopia. Glob. Res. J. Agric. Biol. Sci. 4, 6–16 (2013).
    Google Scholar 
    Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Edible wild mushrooms of Ethiopia: Neglected non-timber forest products. Rev. Fitotec. Mex. 40, 391–397 (2017).
    Google Scholar 
    Tedersoo, L. et al. Disentangling global soil fungal diversity. Science (80-) 346, 1052–1053 (2014).
    Google Scholar 
    Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398 (2017).Dang, P. et al. Changes in soil fungal communities and vegetation following afforestation with Pinus tabulaeformis on the Loess Plateau. Ecosphere 9 (2018).Gilbert, G. S., Ferrer, A. & Carranza, J. Polypore fungal diversity and host density in a moist tropical forest. Biodivers. Conserv. 11, 947–957 (2002).
    Google Scholar 
    Kottke, I., Beck, A., Oberwinkler, F., Homeier, J. & Neill, D. Arbuscular endomycorrhizas are dominant in the organic soil of a neotropical montane cloud forest. J. Trop. Ecol. 20, 125–129 (2004).
    Google Scholar 
    Barnes, C. J., Van der Gast, C. J., Burns, C. A., McNamara, N. P. & Bending, G. D. Temporally variable geographical distance effects contribute to the assembly of root-associated fungal communities. Front. Microbiol. 7, 1–13 (2016).
    Google Scholar 
    Tian, J. et al. Environmental factors driving fungal distribution in freshwater lake sediments across the Headwater Region of the Yellow River, China. Sci. Rep. 8, 4–11 (2018).
    Google Scholar 
    Rosales-Castillo, J. et al. Fungal community and ligninolytic enzyme activities in Quercus deserticola Trel. litter from forest fragments with increasing levels of disturbance. Forests 9, 11 (2017).
    Google Scholar 
    Kuhar, F., Barroetaveña, C. & Rajchenberg, M. New species of Tomentella (Thelephorales) from the Patagonian Andes forests. Mycologia 108, 780–790 (2016).CAS 
    PubMed 

    Google Scholar 
    Alem, D., Dejene, T., Oria-de-Rueda, J. A., Geml, J. & Martín-Pinto, P. Soil fungal communities under Pinus patula Schiede ex Schltdl. & Cham. Plantation forests of different ages in Ethiopia. Forests 11, 1109 (2020).
    Google Scholar 
    Tedersoo, L. et al. Terrestrial and lignicolous macrofungi. ISME J. 10, 1228–1239 (2016).
    Google Scholar 
    Ruiz, R., Decock, C., Saikawa, M., Gene, J. & Guarro, J. Polyschema obclaviformis sp. Nov., and some new records of hyphomycetes from Cuba. Cryptogam. Mycol. 21, 215–220 (2000).
    Google Scholar 
    Kaygusuz, O. New locality records of Trichoglossum hirsutum (Geoglossales: Geoglossaceae) based on molecular analyses, and prediction of its potential distribution in Turkey. Curr. Res. Environ. Appl. Mycol. 10, 443–456 (2020).
    Google Scholar 
    Mayer, P. M. Ecosystem and decomposer effects on litter dynamics along an old field to old-growth forest successional gradient. Acta Oecol. 33, 222–230 (2008).ADS 

    Google Scholar 
    Krishna, M. P. & Mohan, M. Litter decomposition in forest ecosystems: A review. Energy Ecol. Environ. 2, 236–249 (2017).
    Google Scholar 
    Kirschbaum, M. U. F. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biol. Biochem. 27, 753–760 (1995).CAS 

    Google Scholar 
    Mayer, P. M., Tunnell, S. J., Engle, D. M., Jorgensen, E. E. & Nunn, P. Invasive grass alters litter decomposition by influencing macrodetritivores. Ecosystems 8, 200–209 (2005).
    Google Scholar 
    Epstein, H. E., Burke, I. C. & Lauenroth, W. K. Regional patterns of decomposition and primary production rates in the U.S. great plains. Ecology 83, 320 (2002).
    Google Scholar 
    Sharon, R., Degani, G. & Warburg, M. Comparing the soil macro-fauna in two oak-wood forests: Does community structure differ under similar ambient conditions?. Pedobiologia (Jena). 45, 355–366 (2001).
    Google Scholar 
    Clocchiatti, A., Hannula, S. E., van den Berg, M., Korthals, G. & de Boer, W. The hidden potential of saprotrophic fungi in arable soil: Patterns of short-term stimulation by organic amendments. Appl. Soil Ecol. 147, 103434 (2020).
    Google Scholar 
    Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lauber, C., Hamady, M., Knigh, R. & Fierer, N. Pyrosequencing based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ullah, S. et al. The response of soil fungal diversity and community composition to long-term fertilization. Appl. Soil Ecol. 140, 35–41 (2019).
    Google Scholar 
    Bååth, E. & Anderson, T.-H. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35, 955–963 (2003).
    Google Scholar 
    Zhang, T., Wang, N.-F., Liu, H.-Y., Zhang, Y.-Q. & Yu, L.-Y. Soil pH is a key determinant of soil fungal community composition in the Ny-Ålesund Region, Svalbard (high arctic). Front. Microbiol. 7 (2016).Tian, D. et al. Effects of nitrogen deposition on soil microbial communities in temperate and subtropical forests in China. Sci. Total Environ. 607–608, 1367–1375 (2017).ADS 
    PubMed 

    Google Scholar 
    Zhao, A. et al. Influences of canopy nitrogen and water addition on am fungal biodiversity and community composition in a mixed deciduous forest of China. Front. Plant Sci. 9 (2018).He, J. et al. Greater diversity of soil fungal communities and distinguishable seasonal variation in temperate deciduous forests compared with subtropical evergreen forests of eastern China. FEMS Microbiol. Ecol. 93, 1–12 (2017).
    Google Scholar 
    Shi, L. et al. Variation in forest soil fungal diversity along a latitudinal gradient. Fungal Divers. 64, 305–315 (2014).
    Google Scholar 
    Gebeyehu, G., Soromessa, T., Bekele, T. & Teketay, D. Plant diversity and communities along environmental, harvesting and grazing gradients in dry afromontane forests of Awi Zone, northwestern Ethiopia. Taiwania 64, 307–320 (2019).
    Google Scholar 
    Zegeye, H., Teketay, D. & Kelbessa, E. Diversity and regeneration status of woody species in Tara Gedam and Abebaye forests, northwestern Ethiopia. J. For. Res. 22, 315–328 (2011).
    Google Scholar 
    Abere, F., Belete, Y., Kefalew, A. & Soromessa, T. Carbon stock of Banja forest in Banja district, Amhara region, Ethiopia: An implication for climate change mitigation. J. Sustain. For. 36, 604–622 (2017).
    Google Scholar 
    Masresha, G., Soromessa, T. & Kelbessa, E. Status and species diversity of Alemsaga Forest, Northwestern Ethiopia 14 (2015).Rudolph, S., Maciá-Vicente, J. G., Lotz-Winter, H., Schleuning, M. & Piepenbring, M. Temporal variation of fungal diversity in a mosaic landscape in Germany. Stud. Mycol. 89, 95–104 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    De la Varga, H., Águeda, B., Martínez-Peña, F., Parladé, J. & Pera, J. Quantification of extraradical soil mycelium and ectomycorrhizas of Boletus edulis in a Scots pine forest with variable sporocarp productivity. Mycorrhiza 22, 59–68 (2012).PubMed 

    Google Scholar 
    Voříšková, J. & Baldrian, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7, 477–486 (2013).PubMed 

    Google Scholar 
    Reeuwijk, L. Procedures for Soil Analysis (International Soil Reference and Information Centre, 2002).
    Google Scholar 
    Walkley, A. & Black, I. A. An examination of the digestion method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 34, 29–38 (1934).ADS 

    Google Scholar 
    Kim, J., Kreller, C. R. & Greenberg, M. M. Preparation and analysis of oligonucleotides containing the C4’-oxidized abasic site and related mechanistic probes. J. Org. Chem. 70, 8122–8129 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kim, H. T. Soil sampling, preparation and analysis. 139–145 (1996).Bouyoucos, G. H. A reclamation of the hydrometer for making mechanical analysis. Soil. Agro. J. 43, 434–438 (1951).CAS 

    Google Scholar 
    Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).CAS 
    PubMed 

    Google Scholar 
    White, T. ., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. in PCR Protocols: A Guide to Methods and Applications (eds. Innis, M. A., Gelfand, D. H., Sninsky, J. J. & White, T. J.) 315–322 (Academic Press, 1990).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).
    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).PubMed 

    Google Scholar 
    Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105 (2020).Hedberg, I. & Edwards, S. Flora of Ethiopia and Eritria (1989).Collins, C. G., Stajich, J. E., Weber, S. E., Pombubpa, N. & Diez, J. M. Shrub range expansion alters diversity and distribution of soil fungal communities across an alpine elevation gradient. Mol. Ecol. 27, 2461–2476 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Schön, M. E., Nieselt, K. & Garnica, S. Belowground fungal community diversity and composition associated with Norway spruce along an altitudinal gradient. PLoS ONE 13, e0208493 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Castaño, C. et al. Changes in fungal diversity and composition along a chronosequence of Eucalyptus grandis plantations in Ethiopia. Fungal Ecol. 39, 328–335 (2019).
    Google Scholar 
    Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1949).MATH 

    Google Scholar 
    Kent, M. & Coker, P. Vegetation Description and Analysis: A Practical Approach (Belhaven Press, 1993).
    Google Scholar 
    Magurran, A. E. Ecological Diversity and Its Measurement (Princeton University Press, 1988).
    Google Scholar 
    Jost, L., Chao, A. & Chazdon, R. Compositional similarity and β (beta) diversity. in Biological Diversity. Frontiers in Measurement and Assessment (eds. A.E., Magurran & B.J., M.) 66–84 (Oxford University Press, 2011).Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. (World Agroforestry Centre (ICRAF), 2005).R Core Team. A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2020).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1-128. http://CRAN.R-project.org/package=nlme (2016).Tóthmérész, B. Comparison of different methods for diversity ordering. J. Veg. Sci. 6, 283–290 (1995).
    Google Scholar 
    Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in marine communities: an approach to statistical analysis and interpretation. (PRIMER-E, Plymouth, 2014).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar  More

  • in

    Assessment of deep convolutional neural network models for species identification of forensically-important fly maggots based on images of posterior spiracles

    Of which at the third instar, the external morphology of larvae is quite similar; thus, the morphological identification used to differentiate between its genera or species, generally includes cephalophalyngeal skeleton, anterior spiracle, and posterior spiracles. The morphology of the posterior spiracle is one of the important characteristics for identification. A typical morphology of the posterior spiracle of third stage larvae was shown in Fig. 2. Based on studying under light microscopy, the posterior spiracle of M. domestica was clearly distinguished from the others. On the other hand, the morphology of the posterior spiracle of C. megacephala and A. rufifacies was quite similar. For C. megacephala and C. rufifacies, the peritreme, a structure encircling the three spiracular openings (slits), was incomplete and slits were straight as shown Fig. 2A,B, respectively. The complete peritreme encircling three slits was found in L. cuprina and M. domestica as shown in Fig. 2C,D, respectively. However, only the slits of M. domestica were sinuous like the M-letter (Fig. 2D). Their morphological characteristics found in this study were like the descriptions in the previous reports23,24,25.Figure 2Morphology of posterior spiracles of four different fly species after inverting the image colors; (A) Chrysomya (Achoetandrus) ruffifacies, (B) Chrysomya megacephala, (C) Lucilia cuprina, (D) Musca domestica.Full size imageFor model training, four of the CNN models used for species-level identification of fly maggots provided 100% accuracy rates and 0% loss. Number of parameter (#Params), model speed, model size, macro precision, macro recall, f1-score, and support value were also presented in Table 1. The result demonstrated that the AlexNet model provided the best performance in all indicators when compared among four models. The AlexNet model used the least number of parameters while the Resnet101 model used the most. For model speed, the AlexNet model provided the fastest speed, while the Densenet161 model provided the slowest speed. For the model size, the AlexNet model was the smallest, while the Resnet101 model was the largest which corresponded to the number of parameters used. Macro precision, macro recall, f1-score and support value of all models were the same.Table 1 Comparison of model size, speed, and performances of each studied model (The text in bold indicates the best value in each category).Full size tableAs the training results presented in the supplementary data (Fig. S1), all models provided 100% accuracy and 0% loss in the early stage of training ( More

  • in

    European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.)

    Hill, L. et al. The£ 15 billion cost of ash dieback in Britain. Curr. Biol. 29(9), R315–R316 (2019).CAS 
    PubMed 

    Google Scholar 
    Pliûra, A. & Heuertz, M. EUFORGEN Technical Guidelines for Genetic Conservation and Use for Common Ash (Fraxinus excelsior) (Bioversity International, 2003).
    Google Scholar 
    Dufour, S. & Piégay, H. Geomorphological controls of Fraxinus excelsior growth and regeneration in floodplain forests. Ecology 89(1), 205–215 (2008).CAS 
    PubMed 

    Google Scholar 
    Mitchell, R. J. et al. Ash dieback in the UK: a review of the ecological and conservation implications and potential management options. Biol. Conserv. 175, 95–109 (2014).
    Google Scholar 
    Przybył, K. Fungi associated with necrotic apical parts of Fraxinus excelsior shoots. For. Pathol. 32(6), 387–394 (2002).
    Google Scholar 
    Vasaitis, R., & Enderle, R. Dieback of European ash (Fraxinus spp.)-consequences and guidelines for sustainable management. Dieback of European ash (Fraxinus spp.). Report on COST Action FP1103 FRAXBACK. ISBN978-91-576-8696-1. (SLU Swedish University of Agricultural Sciences, 2017).Børja, I. et al. Ash dieback in Norway-current situation. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 166–175 (Swedish University of Agricultural Sciences, 2017).
    Google Scholar 
    Ghelardini, L. et al. From the Alps to the Apennines: Possible spread of ash dieback in Mediterranean areas. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 140–149 (Swedish University of Agricultural Sciences, 2017).
    Google Scholar 
    Marçais, B., Husson, C., Godart, L. & Cael, O. Influence of site and stand factors on Hymenoscyphus fraxineus-induced basal lesions. Plant. Pathol. 65(9), 1452–1461 (2016).
    Google Scholar 
    Queloz, V., Hopf, S., Schoebel, C. N., Rigling, D. & Gross, A. Ash dieback in Switzerland: History and scientific achievements. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 68–78 (Swedish University of Agricultural Sciences, 2017).
    Google Scholar 
    Orton, E. S. et al. Population structure of the ash dieback pathogen, Hymenoscyphus fraxineus, in relation to its mode of arrival in the UK. Plant. Pathol. 67(2), 255–264 (2018).CAS 
    PubMed 

    Google Scholar 
    Enderle, R., Stenlid, J. & Vasaitis, R. An overview of ash (Fraxinus spp.) and the ash dieback disease in Europe. CAB Rev. 14, 1–12 (2019).
    Google Scholar 
    Heinze, B., Tiefenbacher, H., Litschauer, R. & Kirisits, T. Ash dieback in Austria: History, current situation and outlook. in Dieback of European Ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management, 33–52 (2017).Coker, T. L. et al. Estimating mortality rates of European ash (Fraxinus excelsior) under the ash dieback (Hymenoscyphus fraxineus) epidemic. Plants People Planet 1(1), 48–58 (2019).
    Google Scholar 
    Cleary, M., Nguyen, D., Stener, L. G., Stenlid, J., & Skovsgaard, J. P. Ash and ash dieback in Sweden: A review of disease history, current status, pathogen and host dynamics, host tolerance and management options in forests and landscapes. Dieback of European Ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management, 195–208 (2017).Stocks, J. J., Buggs, R. J. & Lee, S. J. A first assessment of Fraxinus excelsior (common ash) susceptibility to Hymenoscyphus fraxineus (ash dieback) throughout the British Isles. Sci. Rep. 7(1), 1–7 (2017).
    Google Scholar 
    Díaz-Yáñez, O. et al. The invasive forest pathogen Hymenoscyphus fraxineus boosts mortality and triggers niche replacement of European ash (Fraxinus excelsior). Sci. Rep. 10(1), 1–10 (2020).
    Google Scholar 
    Enderle, R., Metzler, B., Riemer, U. & Kändler, G. Ash dieback on sample points of the national forest inventory in south-western Germany. Forests 9(1), 25 (2018).
    Google Scholar 
    Klesse, S. et al. Spread and severity of ash dieback in Switzerland: Tree characteristics and landscape features explain varying mortality probability. Front. For. Glob. Change 4, 18 (2021).
    Google Scholar 
    Timmermann, V., Potočić, N., Ognjenović, M. & Kirchner, T. Tree crown condition in 2020. In Forest Condition in Europe: The 2021 Assessment ICP Forests Technical Report under the UNECE Convention on Long-range Transboundary Air Pollution (Air Convention) (eds Michel, A. et al.) (Thünen Institute, 2021).
    Google Scholar 
    Chumanová, E. et al. Predicting ash dieback severity and environmental suitability for the disease in forest stands. Scand. J. For. Res. 34(4), 254–266 (2019).
    Google Scholar 
    Solheim, H. & Hietala, A. M. Spread of ash dieback in Norway. Balt. For. 23(1), 1–6 (2017).
    Google Scholar 
    Kjær, E. D. et al. Genetics of ash dieback resistance in a restoration context: Experiences from Denmark. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 106–114 (Swedish University of Agricultural Sciences, 2017).
    Google Scholar 
    Madsen, C. L. et al. Combined progress in symptoms caused by Hymenoscyphus fraxineus and Armillaria species, and corresponding mortality in young and old ash trees. For. Ecol. Manage. 491, 119177 (2021).
    Google Scholar 
    Trapiello, E., Schoebel, C. N. & Rigling, D. Fungal community in symptomatic ash leaves in Spain. Balt. For. 23(1), 68–73 (2017).
    Google Scholar 
    Grosdidier, M., Ioos, R. & Marçais, B. Do higher summer temperatures restrict the dissemination of Hymenoscyphus fraxineus in France?. For. Pathol. 48(4), e12426. https://doi.org/10.1111/efp.12426 (2018).Article 

    Google Scholar 
    Stroheker, S., Queloz, V. & Nemesio-Gorriz, M. First report of Hymenoscyphus fraxineus causing ash dieback in Spain. New Dis. Rep. 44(2), e12054 (2021).
    Google Scholar 
    Chandelier, A., Gerarts, F., San Martin, G., Herman, M. & Delahaye, L. Temporal evolution of collar lesions associated with ash dieback and the occurrence of Armillaria in Belgian forests. For. Pathol. 46(4), 289–297. https://doi.org/10.1111/efp.12258 (2016).Article 

    Google Scholar 
    Gross, A., Holdenrieder, O., Pautasso, M., Queloz, V. & Sieber, T. N. H ymenoscyphus pseudoalbidus, the causal agent of E uropean ash dieback. Mol. Plant Pathol. 15(1), 5–21 (2014).CAS 
    PubMed 

    Google Scholar 
    Clark, J. & Webber, J. The ash resource and the response to ash dieback in Great Britain. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 228–237 (Swedish University of Agricultural Sciences, 2017).
    Google Scholar 
    Dandy, N., Marzano, M., Porth, E. F., Urquhart, J. & Potter, C. Who has a stake in ash dieback? A conceptual framework for the identification and categorisation of tree health stakeholders. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 15–26 (Swedish University of Agricultural Sciences, 2017).
    Google Scholar 
    Kjær, E. D., McKinney, L. V., Nielsen, L. R., Hansen, L. N. & Hansen, J. K. Adaptive potential of ash (Fraxinus excelsior) populations against the novel emerging pathogen Hymenoscyphus pseudoalbidus. Evol. Appl. 5(3), 219–228 (2012).PubMed 

    Google Scholar 
    Plumb, W. J. et al. The viability of a breeding programme for ash in the British Isles in the face of ash dieback. Plants People Planet 2(1), 29–40 (2020).
    Google Scholar 
    Evans, M. R. Will natural resistance result in populations of ash trees remaining in British woodlands after a century of ash dieback disease?. R. Soc. Open Sci. 6(8), 190908 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Buggs, R. J. A. Changing perceptions of tree resistance research. Plants People Planet 2, 2–4. https://doi.org/10.1002/ppp3.10089 (2020).Article 

    Google Scholar 
    Tomlinson, I. & Potter, C. ‘Too little, too late’? Science, policy and Dutch Elm Disease in the UK. J. Hist. Geogr. 36(2), 121–131 (2010).
    Google Scholar 
    Kelly, L. J. et al. Convergent molecular evolution among ash species resistant to the emerald ash borer. Nat. Ecol. Evol. 4, 1116–1128. https://doi.org/10.1038/s41559-020-1209-3 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sollars, E. S. et al. Genome sequence and genetic diversity of European ash trees. Nature 541(7636), 212–216 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Stocks, J. J. et al. Genomic basis of European ash tree resistance to ash dieback fungus. Nat. Ecol. Evol. 3(12), 1686–1696 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Volkovitsh, M. G., Bieńkowski, A. O. & Orlova-Bienkowskaja, M. J. Emerald ash borer approaches the borders of the european union and kazakhstan and is confirmed to infest European ash. Forests 12(6), 691 (2021).
    Google Scholar 
    Eichhorn, J. et al. Part IV: Visual Assessment of Crown Condition and Damaging Agents. in Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests. (Thünen Institute of Forest Ecosystems, 2016). Annex http://www.icp-forests.org/manual.htm.Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality. Nat. Commun. 12(1), 1–13 (2021).
    Google Scholar 
    Taccoen, A. et al. Climate change impact on tree mortality differs with tree social status. For. Ecol. Manage. 489, 119048 (2021).
    Google Scholar 
    Therneau, T. A Package for Survival Analysis in R. https://cran.r-project.org/web/packages/survival/vignettes/survival.pdf. Accessed 26 May 2021Godaert, L. et al. Prognostic factors of inhospital death in elderly patients: A time-to-event analysis of a cohort study in Martinique (French West Indies). BMJ Open 8(1), e018838 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Sargeran, K., Murtomaa, H., Safavi, S. M. R., Vehkalahti, M. M. & Teronen, O. Survival after diagnosis of cancer of the oral cavity. Br. J. Oral Maxillofac. Surg. 46(3), 187–191 (2008).PubMed 

    Google Scholar 
    Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. B 34(2), 187–202 (1972).MathSciNet 
    MATH 

    Google Scholar 
    Aalen, O. O. A linear regression model for the analysis of life times. Stat. Med. 8(8), 907–925 (1989).CAS 
    PubMed 

    Google Scholar 
    Therneau, T. M., & Grambsch, P. M. The cox model. In Modeling survival data: extending the Cox model, pp. 39–77. (Springer, 2000).Neumann, M., Mues, V., Moreno, A., Hasenauer, H. & Seidl, R. Climate variability drives recent tree mortality in Europe. Glob. Change Biol. 23(11), 4788–4797 (2017).ADS 

    Google Scholar 
    Senf, C., Buras, A., Zang, C. S., Rammig, A. & Seidl, R. Excess forest mortality is consistently linked to drought across Europe. Nat. Commun. 11(1), 1–8 (2020).
    Google Scholar 
    Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, D20 (2008).
    Google Scholar 
    R Development Core Team. RStudio, R: A Language and Environment for Statistical Computing (R Development Core Team, 2017).Holt, C. C. Forecasting Trends and Season-Als by Exponentially Weighted Averages. (Carnegie Institute of Technology, Pittsburgh ONR memorandum no. 52, 1957)Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 27(3), 1–22 (2008).
    Google Scholar  More

  • in

    Frequency-dependent Batesian mimicry maintains colour polymorphism in a sea snake population

    Van Gossum, H., Sherratt, T. N., Cordero-Rivera, A. & Córdoba-Aguilar, A. The evolution of sex-limited colour polymorphism. In Dragonflies and Damselflies: Model Organisms for Ecological and Evolutionary Research (ed. Córdoba-Aguilar, A.) 219–231 (Oxford University Press, 2008).
    Google Scholar 
    Hughes, J. M. & Jones, M. P. Shell colour polymorphism in a mangrove snail Littorina sp. (Prosobranchia: Littorinidae). Biol. J. Linn. Soc. 25, 365–378 (1985).
    Google Scholar 
    Sinervo, B., Bleay, C. & Adamopoulou, C. Social causes of correlational selection and the resolution of a heritable throat color polymorphism in a lizard. Evolution 55, 2040–2052 (2001).CAS 
    PubMed 

    Google Scholar 
    Westerman, E. L. et al. Does male preference play a role in maintaining female limited polymorphism in a Batesian mimetic butterfly? Behav. Process. 150, 47–58 (2018).CAS 

    Google Scholar 
    Vane-Wright, R. I. An integrated classification for polymorphism and sexual dimorphism in butterflies. J. Zool. 177, 329–337 (1975).
    Google Scholar 
    Timmermans, M. J., Srivathsan, A., Collins, S., Meier, R. & Vogler, A. P. Mimicry diversification in Papilio dardanus via a genomic inversion in the regulatory region of engrailed–invected. Proc. R. Soc. B 287, 20200443 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brodie, E. D. III. & Janzen, F. J. Experimental studies of coral snake mimicry: Generalized avoidance of ringed snake patterns by free-ranging avian predators. Funct. Ecol. 9, 186–190 (1995).
    Google Scholar 
    Banci, K. R., Eterovic, A., Marinho, P. S. & Marques, O. A. Being a bright snake: Testing aposematism and mimicry in a neotropical forest. Biotropica 52, 1229–1241 (2020).
    Google Scholar 
    Wüster, W. et al. Do aposematism and Batesian mimicry require bright colours? A test, using European viper markings. Proc. R. Soc. B 271, 2495–2499 (2004).PubMed 
    PubMed Central 

    Google Scholar 
    Valkonen, J. K. & Mappes, J. Resembling a viper: Implications of mimicry for conservation of the endangered smooth snake. Conserv. Biol. 28, 1568–1574 (2014).PubMed 

    Google Scholar 
    Sinervo, B. & Lively, C. M. The rock–paper–scissors game and the evolution of alternative male strategies. Nature 380, 240–243 (1996).ADS 
    CAS 

    Google Scholar 
    Moon, R. M. & Kamath, A. Re-examining escape behaviour and habitat use as correlates of dorsal pattern variation in female brown anole lizards, Anolis sagrei (Squamata: Dactyloidae). Biol. J. Linn. Soc. 126, 783–795 (2019).
    Google Scholar 
    Le Rouzic, A., Hansen, T. F., Gosden, T. P. & Svensson, E. I. Evolutionary time-series analysis reveals the signature of frequency-dependent selection on a female mating polymorphism. Am. Nat. 185, E182–E196 (2015).PubMed 

    Google Scholar 
    Udyawer, V. et al. Future directions in the research and management of marine snakes. Front. Mar. Sci. 5, 399 (2018).
    Google Scholar 
    Goiran, C., Bustamante, P. & Shine, R. Industrial melanism in the seasnake Emydocephalus annulatus. Curr. Biol. 27, 2510–2513 (2017).CAS 
    PubMed 

    Google Scholar 
    Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).
    Google Scholar 
    Shine, R., Shine, T. & Shine, B. Intraspecific habitat partitioning by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae): The effects of sex, body size, and colour pattern. Biol. J. Linn. Soc. 80, 1–10 (2003).
    Google Scholar 
    Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).
    Google Scholar 
    Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing, 1999).
    Google Scholar 
    Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).
    Google Scholar 
    Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 

    Google Scholar 
    Olsson, M., Stuart-Fox, D. & Ballen, C. Genetics and evolution of colour patterns in reptiles. Semin. Cell Dev. Biol. 24, 529–541 (2013).PubMed 

    Google Scholar 
    Shine, R., Brischoux, F. & Pile, A. J. A seasnake’s colour affects its susceptibility to algal fouling. Proc. R. Soc. B 277, 2459–2464 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
    Google Scholar 
    Packard, G. C. & Boardman, T. J. The misuse of ratios, indices, and percentages in ecophysiological research. Physiol. Zool. 61, 1–9 (1988).
    Google Scholar 
    Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Shine, R. All at sea: Aquatic life modifies mate-recognition modalities in sea snakes (Emydocephalus annulatus, Hydrophiidae). Behav. Ecol. Sociobiol. 57, 591–598 (2005).
    Google Scholar 
    Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Population dynamics of the sea snake Emydocephalus annulatus (Elapidae, Hydrophiinae). Sci. Rep. 11, 20701 (2021).ADS 

    Google Scholar 
    Rancurel, P. & Intes, A. Le requin tigre, Galeocerdo cuvieri Lacepede, des eaux neocaledoniennes examen des contenus stomacaux. Tethys 10, 195–199 (1982).
    Google Scholar 
    Heatwole, H. Predation on sea snakes. In The Biology of Sea Snakes (ed. Dunson, W. A.) 233–250 (University Park Press, 1975).
    Google Scholar 
    Ineich, I. & Laboute, P. Les serpents marins de Nouvelle-Calédonie (IRD éditions, 2002).
    Google Scholar 
    Kerford, M. R., Wirsing, A. J., Heithaus, M. R. & Dill, L. M. Danger on the rise: diurnal tidal state mediates an exchange of food for safety by the bar-bellied sea snake Hydrophis elegans. Mar. Ecol. Progr. Ser. 358, 289–294 (2008).ADS 

    Google Scholar 
    Masunaga, G., Kosuge, T., Asai, N. & Ota, H. Shark predation of sea snakes (Reptilia: Elapidae) in the shallow waters around the Yaeyama Islands of the southern Ryukyus, Japan. Mar. Biodivers. Rec. 1, e96 (2008).
    Google Scholar 
    Wirsing, A. J. & Heithaus, M. R. Olive-headed sea snakes Disteria major shift seagrass microhabitats to avoid shark predation. Mar. Ecol. Progr. Ser. 387, 287–293 (2009).ADS 

    Google Scholar 
    Goiran, C. & Shine, R. The ability of damselfish to distinguish between dangerous and harmless sea snakes. Sci. Rep. 10, 1377 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Norman, M. D., Finn, J. & Tregenza, T. Dynamic mimicry in an Indo-Malayan octopus. Proc. R. Soc. B 268, 1755–1758 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pernetta, J. C. Observations on the habits and morphology of the sea snake Laticauda colubrina (Schneider) in Fiji. Can. J. Zool. 55, 1612–1619 (1977).
    Google Scholar 
    Randall, J. E. A review of mimicry in marine fishes. Zool. Stud. 44, 299–328 (2005).
    Google Scholar 
    Dudgeon, C. L. & White, W. T. First record of potential Batesian mimicry in an elasmobranch: Juvenile zebra sharks mimic banded sea snakes? Mar. Freshw. Res. 63, 545–551 (2012).
    Google Scholar 
    Sullivan Caldwell, G. & Wolff Rubinoff, R. Avoidance of venomous sea snakes by naive herons and egrets. Auk 100, 195–198 (1983).
    Google Scholar 
    Sanders, K. L., Malhotra, A. & Thorpe, R. S. Evidence for a Müllerian mimetic radiation in Asian pitvipers. Proc. R. Soc. B 273, 1135–1141 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Raveendran, D. K., Deepak, V., Smith, E. N. & Smart, U. A new colour morph of Calliophis bibroni (Squamata: Elapidae) and evidence for Müllerian mimicry in Tropical Indian coral snakes. Herpetol. Notes 10, 209–217 (2017).
    Google Scholar  More