Swift, M. J., Heal, O. W. & Anderson, J. M. Decomposition in Terrestrial Ecosystems (Blackwell Scientific, 1979).
Carter, D. O., Yellowlees, D. & Tibbett, M. Cadaver decomposition in terrestrial ecosystems. Naturwissenschaften 94, 12–24 (2007).
Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E. & van der Heijden, M. G. A. Fungal–bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 10, 4841 (2019).
Schroeter, S. A. et al. Microbial community functioning during plant litter decomposition. Sci. Rep. 12, 7451 (2022).
Strickland, M. S., Lauber, C., Fierer, N. & Bradford, M. A. Testing the functional significance of microbial community composition. Ecology 90, 441–451 (2009).
Metcalf, J. L. et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351, 158–162 (2016).
Pechal, J. L. et al. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int. J. Leg. Med. 128, 193–205 (2014).
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
Parmenter, R. R. & MacMahon, J. A. Carrion decomposition and nutrient cycling in a semiarid shrub–steppe ecosystem. Ecol. Monogr. 79, 637–661 (2009).
Barton, P. S., Cunningham, S. A., Lindenmayer, D. B. & Manning, A. D. The role of carrion in maintaining biodiversity and ecological processes in terrestrial ecosystems. Oecologia 171, 761–772 (2013).
Barton, P. S. et al. Towards quantifying carrion biomass in ecosystems. Trends Ecol. Evol. 34, 950–961 (2019).
Putman, R. J. Flow of energy and organic matter from a carcase during decomposition: decomposition of small mammal carrion in temperate systems 2. Oikos 31, 58–68 (1978).
DeVault, T. L., Brisbin, I. L. Jr & Rhodes, O. E. Jr Factors influencing the acquisition of rodent carrion by vertebrate scavengers and decomposers. Can. J. Zool. 82, 502–509 (2004).
Aneja, M. K. et al. Microbial colonization of beech and spruce litter—influence of decomposition site and plant litter species on the diversity of microbial community. Microb. Ecol. 52, 127–135 (2006).
Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).
Dangerfield, C. R., Frehner, E. H., Buechley, E. R., Şekercioğlu, Ç. H. & Brazelton, W. J. Succession of bacterial communities on carrion is independent of vertebrate scavengers. PeerJ 8, e9307 (2020).
Johnson, H. R. et al. A machine learning approach for using the postmortem skin microbiome to estimate the postmortem interval. PLoS ONE 11, e0167370 (2016).
Metcalf, J. L. et al. A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system. eLife 2, e01104 (2013).
Singh, B. et al. Temporal and spatial impact of human cadaver decomposition on soil bacterial and arthropod community structure and function. Front. Microbiol. 8, 2616 (2017).
Hong, E. S., Bang, S. H., Kim, Y.-H. & Min, J. Treatment of livestock carcasses in soil using Corynebacterium glutamicum and lysosomal application to livestock burial. Environ. Health Toxicol. 33, e2018009 (2018).
Fey, S. B. et al. Recent shifts in the occurrence, cause, and magnitude of animal mass mortality events. Proc. Natl Acad. Sci. USA 112, 1083–1088 (2015).
Metcalf, J. L. Estimating the postmortem interval using microbes: knowledge gaps and a path to technology adoption. Forensic Sci. Int. Genet. 38, 211–218 (2019).
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).
Weiss, S., Carter, D. O., Metcalf, J. L. & Knight, R. Carcass mass has little influence on the structure of gravesoil microbial communities. Int. J. Leg. Med. 130, 253–263 (2015).
Carter, D. O., Metcalf, J. L., Bibat, A. & Knight, R. Seasonal variation of postmortem microbial communities. Forensic Sci. Med. Pathol. 11, 202–207 (2015).
Shukla, S. P. et al. Microbiome-assisted carrion preservation aids larval development in a burying beetle. Proc. Natl Acad. Sci. USA 115, 11274–11279 (2018).
Megyesi, M. S., Nawrocki, S. P. & Haskell, N. H. Using accumulated degree-days to estimate the postmortem interval from decomposed human remains. J. Forensic Sci. 50, 618–626 (2005).
Connor, M., Baigent, C. & Hansen, E. S. Measuring desiccation using qualitative changes: a step toward determining regional decomposition sequences. J. Forensic Sci. 64, 1004–1011 (2019).
Towne, E. G. Prairie vegetation and soil nutrient responses to ungulate carcasses. Oecologia 122, 232–239 (2000).
Vass, A. A., Bass, W. M., Wolt, J. D., Foss, J. E. & Ammons, J. T. Time since death determinations of human cadavers using soil solution. J. Forensic Sci. 37, 1236–1253 (1992).
Coe, M. The decomposition of elephant carcases in the Tsavo (East) National Park, Kenya. J. Arid Environ. 1, 71–86 (1978).
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).
Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).
Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).
DeBruyn, J. M. et al. Comparative decomposition of humans and pigs: soil biogeochemistry, microbial activity and metabolomic profiles. Front. Microbiol. 11, 608856 (2020).
Keenan, S. W., Schaeffer, S. M., Jin, V. L. & DeBruyn, J. M. Mortality hotspots: nitrogen cycling in forest soils during vertebrate decomposition. Soil Biol. Biochem. 121, 165–176 (2018).
Carbonero, F., Benefiel, A. C., Alizadeh-Ghamsari, A. H. & Gaskins, H. R. Microbial pathways in colonic sulfur metabolism and links with health and disease. Front. Physiol. 3, 448 (2012).
Parr, W. R. G. J. Water Potential Relations in Soil Microbiology (Soil Science Society of America, 1981).
Stark, J. M. & Firestone, M. K. Mechanisms for soil moisture effects on activity of nitrifying bacteria. Appl. Environ. Microbiol. 61, 218–221 (1995).
Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Ågren, G. I. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79–91 (2012).
Martino, C. et al. A novel sparse compositional technique reveals microbial perturbations. mSystems 4, e00016–e00019 (2019).
Drobish, A. M. et al. Oblitimonas alkaliphila gen. nov., sp. nov., in the family Pseudomonadaceae, recovered from a historical collection of previously unidentified clinical strains. Int. J. Syst. Evol. Microbiol. 66, 3063–3070 (2016).
Ashe, E. C., Comeau, A. M., Zejdlik, K. & O’Connell, S. P. Characterization of bacterial community dynamics of the human mouth throughout decomposition via metagenomic, metatranscriptomic, and culturing techniques. Front. Microbiol. 12, 689493 (2021).
Dong, N. et al. Prevalence, transmission, and molecular epidemiology of tet(X)-positive bacteria among humans, animals, and environmental niches in China: an epidemiological, and genomic-based study. Sci. Total Environ. 818, 151767 (2022).
Cobaugh, K. L., Schaeffer, S. M. & DeBruyn, J. M. Functional and structural succession of soil microbial communities below decomposing human cadavers. PLoS ONE 10, e0130201 (2015).
Keenan, S. W. et al. Spatial impacts of a multi-individual grave on microbial and microfaunal communities and soil biogeochemistry. PLoS ONE 13, e0208845 (2018).
Tomberlin, J. K. et al. Interkingdom responses of flies to bacteria mediated by fly physiology and bacterial quorum sensing. Anim. Behav. 84, 1449–1456 (2012).
Shi, Z. et al. Putrescine is an intraspecies and interkingdom cell–cell communication signal modulating the virulence of Dickeya zeae. Front. Microbiol. 10, 1950 (2019).
Valdés-Santiago, L. & Ruiz-Herrera, J. Stress and polyamine metabolism in fungi. Front. Chem. 1, 42 (2013).
Tofalo, R., Cocchi, S. & Suzzi, G. Polyamines and gut microbiota. Front. Nutr. 6, 16 (2019).
Challacombe, J. F. et al. Genomes and secretomes of Ascomycota fungi reveal diverse functions in plant biomass decomposition and pathogenesis. BMC Genomics 20, 976 (2019).
Fu, X. et al. Fungal succession during mammalian cadaver decomposition and potential forensic implications. Sci. Rep. 9, 12907 (2019).
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl Acad. Sci. USA 109, 21390–21395 (2012).
Dini-Andreote, F., Stegen, J. C., van Elsas, J. D. & Salles, J. F. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl Acad. Sci. USA 112, E1326–E1332 (2015).
Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/mmbr.00002-17 (2017).
Zhou, J. et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc. Natl Acad. Sci. USA 111, E836–E845 (2014).
Waring, B., Gee, A., Liang, G. & Adkins, S. A quantitative analysis of microbial community structure–function relationships in plant litter decay. iScience 25, 104523 (2022).
Aerts, R. Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos 79, 439–449 (1997).
Purahong, W. et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol. Ecol. 25, 4059–4074 (2016).
Pechal, J. L., Crippen, T. L., Cammack, J. A., Tomberlin, J. K. & Benbow, M. E. Microbial communities of salmon resource subsidies and associated necrophagous consumers during decomposition: potential of cross-ecosystem microbial dispersal. Food Webs 19, e00114 (2019).
Hyde, E. R., Haarmann, D. P., Petrosino, J. F., Lynne, A. M. & Bucheli, S. R. Initial insights into bacterial succession during human decomposition. Int. J. Leg. Med. 129, 661–671 (2015).
Vogel, H. et al. The digestive and defensive basis of carcass utilization by the burying beetle and its microbiota. Nat. Commun. 8, 15186 (2017).
Deel, H. L. et al. The microbiome of fly organs and fly–human microbial transfer during decomposition. Forensic Sci. Int. 340, 111425 (2022).
Mason, A. R. et al. Body mass index (BMI) impacts soil chemical and microbial response to human decomposition. mSphere 7, e0032522 (2022).
Burkepile, D. E. et al. Chemically mediated competition between microbes and animals: microbes as consumers in food webs. Ecology 87, 2821–2831 (2006).
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems https://doi.org/10.1128/msystems.00009-15 (2016).
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE 4, e6372 (2009).
Sanders, J. G. et al. Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads. Genome Biol. 20, 226 (2019).
Glenn, T. C. et al. Adapterama I: universal stubs and primers for 384 unique dual-indexed or 147,456 combinatorially-indexed Illumina libraries (iTru & iNext). PeerJ 7, e7755 (2019).
Didion, J. P., Martin, M. & Collins, F. S. Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ 5, e3720 (2017).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191-16 (2017).
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
Robeson, M. S. 2nd et al. RESCRIPt: reproducible sequence taxonomy reference database management. PLoS Comput. Biol. 17, e1009581 (2021).
Janssen, S. et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems 3, e00021-18 (2018).
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).
Chen, J. et al. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 28, 2106–2113 (2012).
Anderson, M. J. A new method for non‐parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Wickham, H. Ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
McDonald, D. et al. American Gut: an open platform for citizen science microbiome research. mSystems 3, e00031-18 (2018).
Kodama, W. A. et al. Trace evidence potential in postmortem skin microbiomes: from death scene to morgue. J. Forensic Sci. 64, 791–798 (2019).
Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796–798 (2018).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Kluyver, T. et al. in Positioning and Power in Academic Publishing: Players, Agents and Agendas (eds Loizides, F. & Scmidt, B.) 87–90 (IOS Press, 2016).
Stegen, J. C. et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 7, 2069–2079 (2013).
Stegen, J. C., Lin, X., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Microbiol. 6, 370 (2015).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
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).
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).
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).
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).
Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).
Gower, J. C. Generalized procrustes analysis. Psychometrika 40, 33–51 (1975).
Jackson, D. A. PROTEST: a PROcrustean Randomization TEST of community environment concordance. Écoscience 2, 297–303 (1995).
Peres-Neto, P. R. & Jackson, D. A. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129, 169–178 (2001).
Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).
Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. 14, 639–702 (2019).
Bittinger, K. et al. Bacterial colonization reprograms the neonatal gut metabolome. Nat. Microbiol. 5, 838–847 (2020).
Chan, S. H. J., Simons, M. N. & Maranas, C. D. SteadyCom: predicting microbial abundances while ensuring community stability. PLoS Comput. Biol. 13, e1005539 (2017).
Nothias, L.-F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020).
Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010).
Dührkop, K. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. Methods 16, 299–302 (2019).
Ludwig, M. et al. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. Nat. Mach. Intell. 2, 629–641 (2020).
Kim, S., Kramer, R. W. & Hatcher, P. G. Graphical method for analysis of ultrahigh-resolution broadband mass spectra of natural organic matter, the van Krevelen diagram. Anal. Chem. 75, 5336–5344 (2003).
Boye, K. et al. Thermodynamically controlled preservation of organic carbon in floodplains. Nat. Geosci. 10, 415–419 (2017).
van den Berg, R. A., Hoefsloot, H. C. J., Westerhuis, J. A., Smilde, A. K. & van der Werf, M. J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142 (2006).
Lin, H. & Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514 (2020).
Keshavan, R. H., Montanari, A. & Oh, S. Matrix completion from a few entries. IEEE Trans. Inf. Theory 56, 2980–2998 (2010).
Fedarko, M. W. et al. Visualizing ’omic feature rankings and log-ratios using Qurro. NAR Genom. Bioinform. 2, lqaa023 (2020).
Martino, C. et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat. Biotechnol. 39, 165–168 (2021).
McDonald, D. et al. redbiom: a rapid sample discovery and feature characterization system. mSystems 4, e00215–e00219 (2019).
McDonald, D. et al. Greengenes2 unifies microbial data in a single reference tree. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01845-1 (2023).
Cantrell, K. et al. EMPress enables tree-guided, interactive, and exploratory analyses of multi-omic data sets. mSystems 6, e01216–e01220 (2021).
Pedregosa, F., Varoquaux, G. & Gramfort, A. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Dr. Thomas Hughes is a UK-based scientist and science communicator who makes complex topics accessible to readers. His articles explore breakthroughs in various scientific disciplines, from space exploration to cutting-edge research.
