Philippa Kaur

1.Hillebrand, H. et al. Thresholds for ecological responses to global change do not emerge from empirical data. Nat. Ecol. Evol. 4, 1502–1509 (2020).Article 

Google Scholar 
2.Biggs, R. O., Peterson, G. D. & Rocha, J. C. C. The Regime Shifts Database: a framework for analyzing regime shifts in social-ecological systems. Ecol. Soc. 23, 9 (2018).Article 

Google Scholar 
3.Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).CAS 
Article 

Google Scholar 
4.Walker, B. & Meyers, J. A. Thresholds in ecological and social–ecological systems: a developing database. Ecol. Soc. 9, 3 (2004).Article 

Google Scholar 
5.Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).CAS 
Article 

Google Scholar 
6.Scheffer, M. et al. Creating a safe operating space for iconic ecosystems. Science 347, 1317–1319 (2015).CAS 
Article 

Google Scholar 
7.Eppinga, M. B. et al. Long-term transients help explain regime shifts in consumer-renewable resource systems. Commun. Earth Environ. 2, 42 (2021).8.Hughes, T. P., Linares, C., Dakos, V., van de Leemput, I. A. & van Nes, E. H. Living dangerously on borrowed time during slow, unrecognized regime shifts. Trends Ecol. Evol. 28, 149–155 (2013).Article 

Google Scholar 
9.Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20130263 (2015).Article 

Google Scholar 
10.Santos, F. C. & Pacheco, J. M. Risk of collective failure provides an escape from the tragedy of the commons. Proc. Natl Acad. Sci. USA 108, 10421–10425 (2011).CAS 
Article 

Google Scholar 
11.Rocha, J. C., Schill, C., Saavedra-Díaz, L. M., Del Pilar Moreno, R. & Maldonado, J. H. Cooperation in the face of thresholds, risk, and uncertainty: experimental evidence in fisher communities from Colombia. PLoS ONE 15, e0242363 (2020).CAS 
Article 

Google Scholar 
12.Barrett, S. & Dannenberg, A. Climate negotiations under scientific uncertainty. Proc. Natl Acad. Sci. USA 109, 17372–17376 (2012).CAS 
Article 

Google Scholar 
13.Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. Ambio 43, 579–591 (2014).Article 

Google Scholar 
14.Peterson, G. D., Carpenter, S. R. & Brock, W. A. Uncertainty and the management of multistate ecosystems: an apparently rational route to collapse. Ecology 84, 1403–1411 (2003).Article 

Google Scholar 
15.Vea, E. B., Ryberg, M., Richardson, K. & Hauschild, M. Z. Framework to define environmental sustainability boundaries and a review of current approaches. Environ. Res. Lett. 15, 103003 (2020).Article 

Google Scholar 
16.McCool, S. F. Planning for sustainable nature dependent tourism development. Tour. Recreat. Res. 19, 51–55 (1994).
Google Scholar 
17.Bruckner, T., Petschel-Held, G., Leimbach, M. & Toth, F. L. Methodological aspects of the tolerable windows approach. Clim. Change 56, 73–89 (2003).Article 

Google Scholar 
18.UN Environment Programme. Convention on Biological Diversity. Aichi Biodiversity Targets https://www.cbd.int/sp/targets/ (2010).19.Dearing, J. A. et al. Safe and just operating spaces for regional social-ecological systems. Glob. Environ. Change 28, 227–238 (2014).Article 

Google Scholar 
20.Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
Article 

Google Scholar  More

1.Harvell, D., Altizer, S., Cattadori, I. M., Harrington, L. & Weil, E. Climate change and wildlife diseases: when does the host matter the most? Ecology 90, 912–920 (2009).2.Wood, C. L. & Johnson, P. T. J. A world without parasites: exploring the hidden ecology of infection. Front. Ecol. Environ. 13, 425–434 (2015).3.Randall, C. J. & Van Woesik, R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat. Clim. Chang. 5, 375–379 (2015).Article 

Google Scholar 
4.Bruno, J. F. et al. Thermal stress and coral cover as drivers of coral disease outbreaks. PLoS Biol. 5, 1220–1227 (2007).Article 
CAS 

Google Scholar 
5.Pollock, F. J. et al. Sediment and turbidity associated with offshore dredging increase coral disease prevalence on nearby reefs. PLoS ONE 9, e102498 (2014).6.Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).7.Lafferty, K. D. & Kuris, A. M. Mass mortality of abalone Haliotis cracherodii on the California Channel Islands: tests of epidemiological hypotheses. Mar. Ecol. Prog. Ser. 96, 239–239 (1993).8.Miner, C. M. et al. Large-scale impacts of sea star wasting disease (SSWD) on intertidal sea stars and implications for recovery. PLoS ONE 13, e0192870 (2018).9.Patterson, K. L. et al. The etiology of white pox, a lethal disease of the Caribbean elkhorn coral, Acropora palmata. Proc. Natl Acad. Sci. USA 99, 8725–8730 (2002).10.Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. Hydrobiologia. 460, 25–38 (2001).Article 

Google Scholar 
11.Weil, E., Croquer, A. & Urreiztieta, I. Temporal variability and impact of coral diseases and bleaching in La Parguera, Puerto Rico from 2003-2007. Caribb. J. Sci. 45, 221–246 (2009).12.Muller, E. et al. Coral disease following massive bleaching in 2005 causes 60% decline in coral cover on reefs in the US Virgin Islands. Coral Reefs. 28, 925–937 (2009).Article 

Google Scholar 
13.Jones, G. P., McCormick, M. I., Srinivasan, M. & Eagle, J. V. Coral decline threatens fish biodiversity in marine reserves. Proc. Natl Acad. Sci. USA 101, 8251–8253 (2004).14.Jones, D. O. B. et al. Global reductions in seafloor biomass in response to climate change. Glob Chang Biol. 20, 1861–1872 (2014).15.Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Glob. Chang Biol. 21, 2479–2487 (2015).16.Weil, E., Smith, G. & Gil-Agudelo, D. L. Status and progress in coral reef disease research. Dis. Aquatic Organ. 69, 1–7 (2006).17.Sutherland, K. P., Porter, J. W. & Torres, C. Disease and immunity in Caribbean and Indo-Pacific zooxanthellate corals. Marine Ecol. Progress Ser. 266, 273–302 (2004).18.Pollock, F. J., Morris, P. J., Willis, B. L. & Bourne, D. G. The urgent need for robust coral disease diagnostics. PLoS Pathog. 7, e1002183 (2011).19.Williams, L., Smith, T. B., Burge, C. A. & Brandt, M. E. Species-specific susceptibility to white plague disease in three common Caribbean corals. Coral Reefs 39, 27–31 (2020).20.Velthuis, A. G. J., Bouma, A., Katsma, W. E. A., Nodelijk, G. & De Jong, M. C. M. Design and analysis of small-scale transmission experiments with animals. Epidemiol. Infect. 135, 202–217 (2007).21.Richardson, L. L., Goldberg, W. M., Carlton, R. G. & Halas, J. C. Coral disease outbreak in the Florida keys: Plague type II. Rev. Biol. Trop. 46, 187–198 (1998).
Google Scholar 
22.Frias-Lopez, J., Klaus, J. S., Bonheyo, G. T. & Fouke, B. W. Bacterial community associated with black band disease in corals. Appl. Environ. Microbiol. 70, 5955–5962 (2004).23.Soffer, N., Brandt, M. E., Correa, A. M. S., Smith, T. B. & Thurber, R. V. Potential role of viruses in white plague coral disease. ISME J. 8, 271–283 (2014).24.Sweet, M. et al. Compositional homogeneity in the pathobiome of a new, slow-spreading coral disease. Microbiome 7, 1–14 (2019).25.Sweet, M. J. & Bulling, M. T. On the importance of the microbiome and pathobiome in coral health and disease. Front. Marine Sci. 4, 9 (2017).26.Egan, S. & Gardiner, M. Microbial dysbiosis: rethinking disease in marine ecosystems. Front. Microbiol. 7, 991 (2016).27.Ezzat, L. et al. Parrotfish predation drives distinct microbial communities in reef-building corals. Anim. Microbiome 2, 5 (2020).28.Ezzat, L. et al. Surgeonfish feces increase microbial opportunism in reef-building corals. Mar. Ecol. Prog. Ser. 631, 81–97 (2019).29.Meyer, J. L. et al. Microbial community shifts associated with the ongoing stony coral tissue loss disease outbreak on the Florida reef tract. Front. Microbiol. 10, 2244 (2019).30.Lima, L. F. O. et al. Modeling of the coral microbiome: the influence of temperature and microbial network. MBio. 11, e02691–19 (2020).31.Thurber, R. V. et al. Deciphering coral disease dynamics: integrating host, microbiome, and the changing environment. Front. Ecol. Evol. 8, 402 (2020).
Google Scholar 
32.Cárdenas, A., Rodriguez-R, L. M., Pizarro, V., Cadavid, L. F. & Arévalo-Ferro, C. Shifts in bacterial communities of two Caribbean reef-building coral species affected by white plague disease. ISME J. 6, 502–512 (2012).Article 
CAS 

Google Scholar 
33.Meyer, J. L., Gunasekera, S. P., Scott, R. M., Paul, V. J. & Teplitski M. Microbiome shifts and the inhibition of quorum sensing by Black Band Disease cyanobacteria. ISME J. 10, 1204–1216 (2016).34.Sweet M. J., Burian A., Bulling M. Corals as canaries in the coalmine: towards the incorporation of marine ecosystems into the ‘One Health’ concept. OSF Prepr. https://doi.org/10.31219/osf.io/gv6s7 (2020).35.Glasl, B. et al. Microbial indicators of environmental perturbations in coral reef ecosystems. Microbiome 7, 1–13 (2019).36.Zaneveld, J. R., McMinds, R. & Thurber, R. V. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 1–8 (2017).37.Fan, L., Liu, M., Simister, R., Webster, N. S. & Thomas T. Marine microbial symbiosis heats up: the phylogenetic and functional response of a sponge holobiont to thermal stress. ISME J. 7, 991–1002 (2013).38.Darling, E. S., Alvarez-Filip, L., Oliver, T. A., Mcclanahan, T. R. & Côté, I. M. Evaluating life-history strategies of reef corals from species traits. Ecol. Lett. 15, 1378–1386 (2012).39.Calnan, J. M., Smith, T. B., Nemeth, R. S., Kadison, E. & Blondeau, J. Coral disease prevalence and host susceptibility on mid-depth and deep reefs in the United States Virgin Islands. Rev. Biol. Trop. 56, 223–224 (2008).40.Perry, C. T. et al. Regional-scale dominance of non-framework building corals on Caribbean reefs affects carbonate production and future reef growth. Glob Chang Biol. 21, 1153–1164 (2015).41.Okazaki, R. R. et al. Species-specific responses to climate change and community composition determine future calcification rates of Florida Keys reefs. Glob. Chang Biol. 23, 1023–1035 (2017).42.Green, D. H., Edmunds, P. J. & Carpenter, R. C. Increasing relative abundance of Porites astreoides on Caribbean reefs mediated by an overall decline in coral cover. Mar. Ecol. Prog. Ser. 359, 1–10 (2008).43.Pinzón, C. J. H., Beach-Letendre, J., Weil, E. & Mydlarz, L. D. Relationship between phylogeny and immunity suggests older caribbean coral lineages are more resistant to disease. PLoS ONE 9, e104787 (2014).44.Smith, T. B. et al. Convergent mortality responses of Caribbean coral species to seawater warming. Ecosphere 4, 1–40 (2013).45.Jolles, A. E., Sullivan, P., Alker, A. P., Harvell, C. D. Disease transmission of aspergillosis in sea fans: Inferring process from spatial pattern. Ecology 83, 2373–2378 (2002).46.Shore, A. & Caldwell, J. M. Modes of coral disease transmission: how do diseases spread between individuals and among populations? Mar. Biol. 166, 45 (2019).Article 

Google Scholar 
47.Glasl, B., Herndl, G. J., Frade, P. R. The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance. ISME J. 10, 2280–2292 (2016).48.Lesser, M. P., Bythell, J. C., Gates, R. D. & Johnstone, R. W., Hoegh-Guldberg, O. Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data. J. Exp. Mar. Bio. Ecol. 346, 36–44 (2007).49.Muller, E. M. & Van Woesik, R. Caribbean coral diseases: primary transmission or secondary infection? Glob. Chang Biol. 18, 3529–3535 (2012).50.Fernandes, N. et al. Genomes and virulence factors of novel bacterial pathogens causing bleaching disease in the marine red alga Delisea pulchra. PLoS ONE 6, e27387 (2011).51.Vandecandelaere, I. et al. Nautella italica gen. nov., sp. nov., isolated from a marine electroactive biofilm. Int. J. Syst. Evol. Microbiol. 59, 811–817 (2009).52.Dang, H. & Lovell, C. R. Bacterial primary colonization and early succession on surfaces in marine waters as determined by amplified rRNA gene restriction analysis and sequence analysis of 16S rRNA genes. Appl. Environ. Microbiol. 66, 467–475 (2000).53.Kviatkovski, I. & Minz, D. A member of the Rhodobacteraceae promotes initial biofilm formation via the secretion of extracellular factor(s). Aquat. Microb. Ecol. 75, 155–167 (2015).54.Rosales, S. M., Clark, A. S., Huebner, L. K., Ruzicka, R. R. & Muller E. M. Rhodobacterales and Rhizobiales are associated with stony coral tissue loss disease and its suspected sources of transmission. Front. Microbiol. 11, 681 (2020).55.Campbell, A. H., Harder, T., Nielsen, S., Kjelleberg, S. & Steinberg, P. D. Climate change and disease: bleaching of a chemically defended seaweed. Glob. Chang. Biol. 17, 2958–2970 (2011).56.Kumar, V., Zozaya-Valdes, E., Kjelleberg, S., Thomas, T. & Egan, S. Multiple opportunistic pathogens can cause a bleaching disease in the red seaweed Delisea pulchra. Environ. Microbiol. 18, 3962–3975 (2016).57.Brandt, M. E. & Mcmanus, J. W. Disease incidence is related to bleaching extent in reef-building corals. Ecology 90, 2859–2867 (2009).58.Brandt, M. E., Smith, T. B., Correa, A. M. S. & Vega-Thurber, R. Disturbance driven colony fragmentation as a driver of a coral disease outbreak. PLoS ONE 8, e57164 (2013).59.Godwin, S., Bent, E., Borneman, J. & Pereg, L. The role of coral-associated bacterial communities in Australian subtropical white Syndrome of Turbinaria mesenterina. PLoS ONE 7, e44243 (2012).60.Ranson, H. J. et al. Draft Genome Sequence of the Putative Marine Pathogen Thalassobius sp. I31.1. Microbiol. Resour. Announc. 8, e01431–18 (2019).61.Miller A. W., Richardson L. L. Fine structure analysis of black band disease (BBD) infected coral and coral exposed to the BBD toxins microcystin and sulfide. J. Invertebr. Pathol. 109, 27–33 (2012).62.Geffen, Y., Ron, E. Z. & Rosenberg, E. Regulation of release of antibacterials from stressed scleractinian corals. FEMS Microbiol. Lett. 295, 103–109 (2009).63.Beurmann, S. et al. Pseudoalteromonas piratica strain OCN003 is a coral pathogen that causes a switch from chronic to acute Montipora white syndrome in Montipora capitata. PLoS ONE (2017).64.Apprill, A., Marlow, H. Q., Martindale, M. Q., Rappé, M. S. Specificity of associations between bacteria and the coral Pocillopora meandrina during early development. Appl. Environ. Microbiol. 78, 7467–7475 (2012).65.Shnit-Orland, M., Sivan, A. & Kushmaro, A. Antibacterial activity of Pseudoalteromonas in the Coral Holobiont. Microb. Ecol. 64, 851–859 (2012).66.Sunagawa, S. et al. Bacterial diversity and white Plague disease-associated community changes in the caribbean coral montastraea faveolata. ISME J. 3, 512–521 (2009).Article 
CAS 

Google Scholar 
67.Bettarel, Y. et al. Corallivory and the microbial debacle in two branching scleractinians. ISME J. 12, 1109–1126 (2018).68.Ritchie, K. B. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar. Ecol. Prog. Ser. 322, 1–14 (2006).69.Bayer, T. et al. The microbiome of the red sea coral stylophora pistillata is dominated by tissue-associated endozoicomonas bacteria. Appl. Environ. Microbiol. 79, 4759–4762 (2013).70.Lesser, M. P. & Jarett, J. K. Culture-dependent and culture-independent analyses reveal no prokaryotic community shifts or recovery of Serratia marcescens in Acropora palmata with white pox disease. FEMS Microbiol. Ecol. 88, 457–467 (2014).Article 
CAS 

Google Scholar 
71.Morrow, K. M., Muller, E. & Lesser, M. P. How does the coral microbiome cause, respond to, or modulate the bleaching process? Coral Bleaching 233, 153–188 (2018).72.Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 1–13 (2018).73.Price, E. P. et al. Accurate and rapid identification of the Burkholderia pseudomallei near-neighbour, Burkholderia ubonensis, using real-time PCR. PLoS ONE 8, e71647 (2013).74.Price, E. P. et al. Phylogeographic, genomic, and meropenem susceptibility analysis of Burkholderia ubonensis. PLoS Negl. Trop. Dis. 11, e0005928 (2017).75.Therneau, T. Package Survival: A Package for Survival Analysis in R. R Package version 238. (2015).76.Oksanen, J. et al. Vegan: community ecology. R package version 2.2-1. (2015).77.Andres, B., David, O., Sebastien, V., Julien, De B. & Fabien, L. betapart: partitioning Beta Diversity into Turnover and Nestedness Components. R Packag. (1.5.1). https://cran.r-project.org/package=betapart (2018).78.nmacknight. nmacknight/16sCommunityAnalysis: First Release. 2021 Mar 24 [cited 2021 Mar 24]: https://doi.org/10.5281/zenodo.4635319#.YFvOI-zLDNs.mendeley. (2021). More

Exp 1: chemo-tactile conditioning of the proboscis extension response (PER)Bee foragers may assess the quality of floral nectars through chemo-sensilla located on their antennae47. In this first experiment, we asked whether nectar-relevant concentrations of GABA, β-alanine, taurine, citrulline and ornithine can be detected by bees through their antennae. To this aim, we used a chemo-tactile differential conditioning of PER protocol48 in which different groups of bees were trained to discriminate one of the five NPAAs from water. Briefly, tethered bees experienced five pairings of a neutral stimulus (either NPAA-laced water or water) (CS+) with a 30% sucrose solution reinforcement (US) and five pairings (either water or NPAA-laced water) (CS−) with a saturated NaCl solution (US) used as punishment. The results showed that bees increased their response to both the rewarded (CS+) and the punished (CS−) stimuli over the ten conditioning trials (GLMM, trial: GABA: n = 76, χ2 = 65.75, df = 1, p  More

1.Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).CAS 
Article 

Google Scholar 
2.Song, J. et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. Evol. 3, 1309–1320 (2019).Article 

Google Scholar 
3.Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).CAS 
Article 

Google Scholar 
4.Houghton, R. A. The contemporary carbon cycle. Treatise Geochem. 8, 473–513 (2003).Article 

Google Scholar 
5.Paterson, E., Midwood, A. J. & Millard, P. Through the eye of the needle: a review of isotope approaches to quantify microbial processes mediating soil carbon balance. New Phytol. 184, 19–33 (2009).CAS 
Article 

Google Scholar 
6.Bader, M. K. F. & Körner, C. No overall stimulation of soil respiration under mature deciduous forest trees after 7 years of CO2 enrichment. Glob. Change Biol. 16, 2830–2843 (2010).Article 

Google Scholar 
7.Reynolds, L. L., Lajtha, K., Bowden, R. D., Johnson, B. R. & Bridgham, S. D. The carbon quality–temperature hypothesis does not consistently predict temperature sensitivity of soil organic matter mineralization in soils from two manipulative ecosystem experiments. Biogeochemistry 136, 249–260 (2017).CAS 
Article 

Google Scholar 
8.Knorr, W., Prentice, I. C., House, J. & Holland, E. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298–301 (2005).CAS 
Article 

Google Scholar 
9.Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil–carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
10.Kirschbaum, M. U. F. The temperature dependence of organic-matter decomposition—still a topic of debate. Soil Biol. Biochem. 38, 2510–2518 (2006).CAS 
Article 

Google Scholar 
11.Feng, X., Simpson, A. J., Wilson, K. P., Williams, D. D. & Simpson, M. J. Increased cuticular carbon sequestration and lignin oxidation in response to soil warming. Nat. Geosci. 1, 836–839 (2008).CAS 
Article 

Google Scholar 
12.Pries, C. E. H., Castanha, C., Porras, R. & Torn, M. The whole-soil carbon flux in response to warming. Science 355, 1420–1423 (2017).Article 
CAS 

Google Scholar 
13.Li, J. et al. Reduced carbon use efficiency and increased microbial turnover with soil warming. Glob. Change Biol. 25, 900–910 (2019).Article 

Google Scholar 
14.Schaphoff, S. et al. Contribution of permafrost soils to the global carbon budget. Environ. Res. Lett. 8, 014026 (2013).CAS 
Article 

Google Scholar 
15.Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584, 234–237 (2020).CAS 
Article 

Google Scholar 
16.Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).CAS 
Article 

Google Scholar 
17.Koven, C. D. et al. The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4. Biogeosciences 10, 7109–7131 (2013).CAS 
Article 

Google Scholar 
18.Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E. & Pacala, S. W. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Change 4, 1099–1102 (2014).CAS 
Article 

Google Scholar 
19.Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).CAS 
Article 

Google Scholar 
20.Wieder, W. R. et al. Explicitly representing soil microbial processes in Earth system models. Glob. Biogeochem. Cycles 29, 1782–1800 (2015).CAS 
Article 

Google Scholar 
21.Gonzalez-Dominguez, B. et al. Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics. Sci. Rep. 9, 6422 (2019).CAS 
Article 

Google Scholar 
22.Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140 (2018).23.Koven, C. D. et al. Permafrost carbon–climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).CAS 
Article 

Google Scholar 
24.Angst, G. et al. Soil organic carbon stocks in topsoil and subsoil controlled by parent material, carbon input in the rhizosphere, and microbial-derived compounds. Soil Biol. Biochem. 122, 19–30 (2018).CAS 
Article 

Google Scholar 
25.Abramoff, R. et al. The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century. Biogeochemistry 137, 51–71 (2017).Article 

Google Scholar 
26.Doetterl, S. et al. Links among warming, carbon and microbial dynamics mediated by soil mineral weathering. Nat. Geosci. 11, 589–593 (2018).CAS 
Article 

Google Scholar 
27.Hamdi, S., Moyano, F., Sall, S., Bernoux, M. & Chevallier, T. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol. Biochem. 58, 115–126 (2013).CAS 
Article 

Google Scholar 
28.Hashimoto, S. et al. Global spatiotemporal distribution of soil respiration modeled using a global database. Biogeosciences 12, 4121–4132 (2015).Article 

Google Scholar 
29.Varney, R. M. et al. A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming. Nat. Commun. 11, 5544 (2020).CAS 
Article 

Google Scholar 
30.Wu, D., Piao, S., Liu, Y., Ciais, P. & Yao, Y. Evaluation of CMIP5 Earth System Models for the spatial patterns of biomass and soil carbon turnover times and their linkage with climate. J. Clim. 31, 5947–5960 (2018).Article 

Google Scholar 
31.Wieder, W. R. et al. Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Glob. Change Biol. 24, 1563–1579 (2018).Article 

Google Scholar 
32.Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Change 7, 817–822 (2017).CAS 
Article 

Google Scholar 
33.Mahecha, M. D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem level. Science 329, 838–840 (2010).CAS 
Article 

Google Scholar 
34.Foereid, B., Ward, D., Mahowald, N., Paterson, E. & Lehmann, J. The sensitivity of carbon turnover in the Community Land Model to modified assumptions about soil processes. Earth Syst. Dynam. 5, 211–221 (2014).Article 

Google Scholar 
35.Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).Article 

Google Scholar 
36.Post, H., Vrugt, J. A., Fox, A., Vereecken, H. & Hendricks Franssen, H. J. Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites. J. Geophys. Res. Biogeosci. 122, 661–689 (2017).CAS 
Article 

Google Scholar 
37.Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Glob. Biogeochem. Cycles 30, 40–56 (2016).CAS 
Article 

Google Scholar 
38.Bailey, V. L. et al. Soil carbon cycling proxies: understanding their critical role in predicting climate change feedbacks. Glob. Change Biol. 24, 895–905 (2018).Article 

Google Scholar 
39.Conant, R. T. et al. Temperature and soil organic matter decomposition rates—synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404 (2011).Article 

Google Scholar 
40.Meyer, N., Welp, G. & Amelung, W. The temperature sensitivity (Q10) of soil respiration: controlling factors and spatial prediction at regional scale based on environmental soil classes. Glob. Biogeochem. Cycles 32, 306–323 (2018).CAS 
Article 

Google Scholar 
41.Doetterl, S. et al. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780–783 (2015).CAS 
Article 

Google Scholar 
42.Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–105 (2017).CAS 
Article 

Google Scholar 
43.Kramer, M. G. & Chadwick, O. A. Climate-driven thresholds in reactive mineral retention of soil carbon at the global scale. Nat. Clim. Change 8, 1104–1108 (2018).CAS 
Article 

Google Scholar 
44.Cusack, D. F. et al. Decadal-scale litter manipulation alters the biochemical and physical character of tropical forest soil carbon. Soil Biol. Biochem. 124, 199–209 (2018).CAS 
Article 

Google Scholar 
45.Wang, X. et al. Are ecological gradients in seasonal Q10 of soil respiration explained by climate or by vegetation seasonality? Soil Biol. Biochem. 42, 1728–1734 (2010).CAS 
Article 

Google Scholar 
46.Warner, D. L., Bond‐Lamberty, B., Jian, J., Stell, E. & Vargas, R. Spatial predictions and associated uncertainty of annual soil respiration at the global scale. Glob. Biogeochem. Cycles 33, 1733–1745 (2019).CAS 
Article 

Google Scholar 
47.Todd-Brown, K., Zheng, B. & Crowther, T. W. Field-warmed soil carbon changes imply high 21st-century modeling uncertainty. Biogeosciences 15, 3659–3671 (2018).CAS 
Article 

Google Scholar 
48.He, Y. et al. Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century. Science 353, 1419–1424 (2016).CAS 
Article 

Google Scholar 
49.Haddix, M. L. et al. The role of soil characteristics on temperature sensitivity of soil organic matter. Soil Sci. Soc. Am. J. 75, 56–68 (2011).CAS 
Article 

Google Scholar 
50.Lara, M. J., Lin, D. H., Andresen, C., Lougheed, V. L. & Tweedie, C. E. Nutrient release from permafrost thaw enhances CH4 emissions from Arctic tundra wetlands. J. Geophys. Res. Biogeosci. 124, 1560–1573 (2019).CAS 
Article 

Google Scholar 
51.Prater, I. et al. From fibrous plant residues to mineral-associated organic carbon–the fate of organic matter in Arctic permafrost soils. Biogeosciences 17, 3367–3383 (2020).CAS 
Article 

Google Scholar 
52.Åkerman, H. J. & Johansson, M. Thawing permafrost and thicker active layers in sub‐arctic Sweden. Permafr. Periglac. Process. 19, 279–292 (2008).Article 

Google Scholar 
53.Jilling, A. et al. Minerals in the rhizosphere: overlooked mediators of soil nitrogen availability to plants and microbes. Biogeochemistry 139, 103–122 (2018).CAS 
Article 

Google Scholar 
54.Jones, M. C. et al. Rapid carbon loss and slow recovery following permafrost thaw in boreal peatlands. Glob. Change Biol. 23, 1109–1127 (2017).Article 

Google Scholar 
55.Korell, L., Auge, H., Chase, J. M., Harpole, W. S. & Knight, T. M. We need more realistic climate change experiments for understanding ecosystems of the future. Glob. Change Biol. 26, 325–327 (2019).Article 

Google Scholar 
56.Raich, J. W. & Schlesinger, W. H. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44, 81–99 (1992).Article 

Google Scholar 
57.Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).58.Crowther, T. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).59.R Core Team. C. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).60.Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).CAS 
Article 

Google Scholar 
61.Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).Article 

Google Scholar 
62.Conover, W. J., Johnson, M. E. & Johnson, M. M. A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics 23, 351–361 (1981).Article 

Google Scholar 
63.Chen, X., Zhao, P. L. & Zhang, J. A note on ANOVA assumptions and robust analysis for a cross‐over study. Stat. Med. 21, 1377–1386 (2002).Article 

Google Scholar 
64.McGuinness, K. A. Of rowing boats, ocean liners and tests of the ANOVA homogeneity of variance assumption. Austral. Ecol. 27, 681–688 (2002).Article 

Google Scholar 
65.Zimmerman, D. W. & Zumbo, B. D. Relative power of the Wilcoxon test, the Friedman test, and repeated-measures ANOVA on ranks. J. Exp. Educ. 62, 75–86 (1993).Article 

Google Scholar 
66.Tomczak, M. & Tomczak, E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends Sport Sci. 1, 19–25 (2014).
Google Scholar 
67.Thornley, J. & Cannell, M. Soil carbon storage response to temperature: an hypothesis. Ann. Bot. 87, 591–598 (2001).CAS 
Article 

Google Scholar 
68.Lloyd, J. & Taylor, J. On the temperature dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994).69.Libohova, Z. et al. The anatomy of uncertainty for soil pH measurements and predictions: implications for modellers and practitioners. Eur. J. Soil Sci. 70, 185–199 (2019).Article 

Google Scholar 
70.Kirkby, C. A. et al. Carbon–nutrient stoichiometry to increase soil carbon sequestration. Soil Biol. Biochem. 60, 77–86 (2013).CAS 
Article 

Google Scholar 
71.Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).CAS 
Article 

Google Scholar 
72.Beer, C. et al. Temporal and among‐site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23, GB2018 (2009).Article 
CAS 

Google Scholar 
73.Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543 (2014).CAS 
Article 

Google Scholar 
74.Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. 4, 333 (2013).Article 

Google Scholar 
75.Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer, 2001).76.Efron, B., Hastie, T., Johnstone, I. & Tibshirani, R. Least angle regression. Ann. Stat. 32, 407–499 (2004).Article 

Google Scholar 
77.Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).Article 

Google Scholar 
78.Kuhn, M. & Johnson, K. Applied Predictive Modeling Vol. 26 (Springer, 2013).79.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
80.Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).81.Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
Google Scholar 
82.Quinlan, J. R. Learning with Continuous Classes in Proceedings of the 5th Australian Joint Conference on Artificial Intelligence (eds Adams, A. & Sterling, L.) 343–348 (World Scientific, 1992).83.Boulesteix, A. L., Janitza, S., Kruppa, J. & König, I. R. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIRES Data Mining Knowl. Discov. 2, 493–507 (2012).Article 

Google Scholar 
84.Xu, Q.-S. & Liang, Y.-Z. Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 56, 1–11 (2001).CAS 
Article 

Google Scholar 
85.Shcherbakov, M. V. et al. A survey of forecast error measures. World Appl. Sci. J. 24, 171–176 (2013).
Google Scholar 
86.James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).87.Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28 (2008).88.Grömping, U. Variable importance assessment in regression: linear regression versus random forest. Am. Statistician 63, 308–319 (2009).Article 

Google Scholar 
89.Wei, P., Lu, Z. & Song, J. Variable importance analysis: a comprehensive review. Reliab. Eng. Syst. Saf. 142, 399–432 (2015).Article 

Google Scholar 
90.Yang, R.-M. et al. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Indic. 60, 870–878 (2016).CAS 
Article 

Google Scholar 
91.Greenwell, B. M. pdp: an R package for constructing partial dependence plots. R J. 9, 421–436 (2017).Article 

Google Scholar 
92.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
Article 

Google Scholar 
93.Land Cover CCI Product User Guide Version 2 (ESA, 2017); maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf94.Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 
CAS 

Google Scholar 
95.Moran, P. A. A test for the serial independence of residuals. Biometrika 37, 178–181 (1950).CAS 
Article 

Google Scholar 
96.Legendre, P. Spatial autocorrelation: trouble or new paradigm? Ecology 74, 1659–1673 (1993).Article 

Google Scholar  More

1.Norse, E. A. et al. Sustainability of deep-sea fisheries. Mar. Policy 36, 307–320 (2012).Article 

Google Scholar 
2.Simpfendorfer, C. A. & Kyne, P. M. Limited potential to recover from overfishing raises concerns for deep-sea sharks, rays and chimaeras. Environ. Conserv. 36, 97–103 (2009).Article 

Google Scholar 
3.Kyne, P. & Simpfendorfer, C. In Sharks and Their Relatives II: Biodiversity, Physiology, and Conservation (eds Carrier, J. C. et al.) 37–113 (CRC Press, 2010).
Google Scholar 
4.Dunn, M. R., Szabo, A., McVeagh, M. S. & Smith, P. J. The diet of deepwater sharks and the benefits of using DNA identification of prey. Deep Sea Res. Part I 57, 923–930 (2010).CAS 
Article 

Google Scholar 
5.Mauchline, J. & Gordon, J. Diets of the sharks and chimaeroids of the Rockall Trough, northeastern Atlantic Ocean. Mar. Biol. 75, 269–278 (1983).Article 

Google Scholar 
6.Cortes, E. Standardized diet compositions and trophic levels in sharks. ICES J. Mar. Sci. 56, 707–717 (1999).Article 

Google Scholar 
7.Peterson, B. J. & Fry, B. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320 (1987).Article 

Google Scholar 
8.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
9.Estrada, J. A., Rice, A. N., Lutcavage, M. E. & Skomall, G. B. Predicting trophic position in sharks of the north-west Atlantic Ocean using stable isotope analysis. J. Mar. Biol. Assoc. UK 83, 1347–1350 (2003).CAS 
Article 

Google Scholar 
10.Hussey, N. E. et al. Stable isotopes and elasmobranchs: Tissue types, methods, applications and assumptions. J. Fish. Biol. 20, 1449–1484 (2012).Article 
CAS 

Google Scholar 
11.Meyer, L., Pethybridge, H., Nichols, P. D., Beckmann, C. & Huveneers, C. Abiotic and biotic drivers of fatty acid tracers in ecology: A global analysis of chondrichthyan profiles. Funct. Ecol. 20, 20 (2019).
Google Scholar 
12.Munroe, S., Meyer, L. & Heithaus, M. Dietary biomarkers in shark foraging and movement ecology. Shark Res. Emerg. Technol. Appl. Field Lab. 20, 20 (2018).

Google Scholar 
13.Hobson, K. A., Barnett-Johnson, R. & Cerling, T. E. In Isoscapes: Understanding Movement, Pattern, and Process on Earth Through Isotope Mapping (eds West, J. B. et al.) 273–298 (Springer, 2010).
Google Scholar 
14.Michener, R. H. & Kaufman, L. In Stable Isotopes in Ecology and Environmental Science (eds Michener, R. & Lajtha, K.) 238–282 (Blackwell, 2007).
Google Scholar 
15.West, J. B., Bowen, G. J., Cerling, T. E. & Ehleringer, J. R. Stable isotopes as one of nature’s ecological recorders. Trends Ecol. Evol. 21, 408–414 (2006).PubMed 
Article 

Google Scholar 
16.DeNiro, M. J. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–345 (1981).ADS 
CAS 
Article 

Google Scholar 
17.DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).ADS 
CAS 
Article 

Google Scholar 
18.Tieszen, L. L., Boutton, T. W., Tesdahl, K. G. & Slade, N. A. Fractionation and turnover of stable carbon isotopes in animal tissues: Implications for δ13C analysis of diet. Oecologia 57, 32–37 (1983).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.MacNeil, M. A., Skomal, G. B. & Fisk, A. T. Stable isotopes from multiple tissues reveal diet switching in sharks. Mar. Ecol. Prog. Ser. 302, 199–206 (2005).ADS 
Article 

Google Scholar 
20.Kim, S. L., Martinez del Rio, C., Casper, D. & Koch, P. L. Isotopic incorporation rates for shark tissues from a long-term captive feeding study. J Exp Biol 215, 2495–2500 (2012).21.Madigan, D. J. et al. Tissue turnover rates and isotopic trophic discrimination factors in the endothermic teleost, Pacific bluefin tuna (Thunnus orientalis). PLoS One 7, e49220 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Carlisle, A. B. et al. Using stable isotope analysis to understand the migration and trophic ecology of northeastern Pacific white sharks (Carcharodon carcharias). PLoS One 7, 30492 (2012).ADS 
Article 
CAS 

Google Scholar 
23.Madigan, D. J. et al. Reconstructing transoceanic migration patterns of Pacific bluefin tuna using a chemical tracer toolbox. Ecology 95, 1674–1683 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Ackman, R.G. & Macpherson, E.J. Coincidence of cis-and trans-monoethylenic fatty acids simplifies the open-tubular gas-liquid chromatography of butyl esters of butter fatty acids. Food chem. 50(1), 45–52 (1994).25.Sargent, J., Bell, G., McEvoy, L., Tocher, D. & Estevez, A. Recent developments in the essential fatty acid nutrition of fish. Aquaculture., 177(1–4), 191–199 (1999).26.Tocher, D.R. Metabolism and functions of lipids and fatty acids in teleost fish. Rev. Fish. Sci. 11(2), 107–184 (2003).27.McMeans, B. C. et al. The role of Greenland sharks (Somniosus microcephalus) in an Arctic ecosystem: Assessed via stable isotopes and fatty acids. Mar. Biol. 160, 1223–1238. https://doi.org/10.1007/s00227-013-2174-z (2013).Article 

Google Scholar 
28.Pethybridge, H. R., Nichols, P. D., Virtue, P. & Jackson, G. D. The foraging ecology of an oceanic squid, Todarodes filippovae: The use of signature lipid profiling to monitor ecosystem change. Deep Sea Res. Part II 95, 119–128 (2013).CAS 
Article 

Google Scholar 
29.Pethybridge, H. et al. Lipid and mercury profiles of 61 mid-trophic species collected off south-eastern Australia. Mar. Freshw. Res. 61, 1092–1108 (2010).CAS 
Article 

Google Scholar 
30.Beckmann, C. L., Mitchell, J. G., Stone, D. A. & Huveneers, C. A controlled feeding experiment investigating the effects of a dietary switch on muscle and liver fatty acid profiles in Port Jackson sharks Heterodontus portusjacksoni. J. Exp. Mar. Biol. Ecol. 448, 10–18 (2013).CAS 
Article 

Google Scholar 
31.Pethybridge, H. R., Choy, C. A., Polovina, J. J. & Fulton, E. A. Improving marine ecosystem models with biochemical tracers. Ann. Rev. Mar. Sci. 10, 199–228 (2018).PubMed 
Article 

Google Scholar 
32.Belicka, L. L., Matich, P., Jaffé, R. & Heithaus, M. R. Fatty acids and stable isotopes as indicators of early-life feeding and potential maternal resource dependency in the bull shark Carcharhinus leucas. Mar. Ecol. Prog. Ser. 455, 245–256 (2012).ADS 
CAS 
Article 

Google Scholar 
33.Every, S. L., Fulton, C. J., Pethybridge, H. R., Kyne, P. M. & Crook, D. A. A seasonally dynamic estuarine ecosystem provides a diverse prey base for Elasmobranchs. Estuar. Coasts 42, 580–595 (2019).CAS 
Article 

Google Scholar 
34.Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 20, e00547 (2019).Article 

Google Scholar 
35.Soininen, E. M. et al. Shedding new light on the diet of Norwegian lemmings: DNA metabarcoding of stomach content. Polar Biol 36, 1069–1076 (2013).Article 

Google Scholar 
36.De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).Article 
CAS 

Google Scholar 
37.Deagle, B. E., Kirkwood, R. & Jarman, S. N. Analysis of Australian fur seal diet by pyrosequencing prey DNA in faeces. Mol. Ecol. 18, 2022–2038 (2009).CAS 
PubMed 
Article 

Google Scholar 
38.Bade, L. M., Balakrishnan, C. N., Pilgrim, E. M., McRae, S. B. & Luczkovich, J. J. A genetic technique to identify the diet of cownose rays, Rhinoptera bonasus: Analysis of shellfish prey items from North Carolina and Virginia. Environ. Biol. Fishes 97, 999–1012 (2014).Article 

Google Scholar 
39.Jensen, M. R., Knudsen, S. W., Munk, P., Thomsen, P. F. & Møller, P. R. Tracing European eel in the diet of mesopelagic fishes from the Sargasso Sea using DNA from fish stomachs. Mar. Biol. 165, 130 (2018).Article 
CAS 

Google Scholar 
40.Compagno, L. FAO species catalogue. Vol. 4. Sharks of the world. An annotated and illustrated catalogue of sharks species known to date. Part 1. Hexanchiformes to Lammiformes. FAO Fish. Synop. 20, 1–249 (1984).
Google Scholar 
41.Jahn, A. & Haedrich, R. Notes on the pelagic squaloid shark Isistius brasiliensis. Biol. Oceanogr. 5, 297–309 (1988).
Google Scholar 
42.Nakano, H. & Tabuchi, M. Occurrence of the cookiecutter shark Isistius brasiliensis in surface waters of the North Pacific Ocean. Jpn. J. Ichthyol. 37, 60–63 (1990).
Google Scholar 
43.Hubbs, C. L., Iwai, T. & Matsubara, K. External and internal characters, horizontal and vertical distributions, luminescence, and food of the dwarf pelagic shark, Euprotomicrus bispinatus. (1967).44.Papastamatiou, Y. P., Wetherbee, B. M., O’Sullivan, J., Goodmanlowe, G. D. & Lowe, C. G. Foraging ecology of cookiecutter sharks (Isistius brasiliensis) on pelagic fishes in Hawaii, inferred from prey bite wounds. Environ. Biol. Fishes 88, 361–368 (2010).Article 

Google Scholar 
45.Feunteun, A. et al. First evaluation of the cookie-cutter sharks (Isistius sp.) predation pattern on different cetacean species in Martinique. Environ. Biol. Fishes 20, 1–11 (2018).
Google Scholar 
46.Jones, E. Isistius brasiliensis, a squaloid shark, probable cause of crater wounds on fishes and cetaceans. Fish Bull. 69, 791–798 (1971).
Google Scholar 
47.Strasburg, D. W. The diet and dentition of Isistius brasiliensis, with remarks on tooth replacement in other sharks. Copeia 20, 33–40 (1963).Article 

Google Scholar 
48.Widder, E. A. A predatory use of counter illumination by the squaloid shark, Isistius brasiliensis. Environ. Biol. Fishes 53, 267–273 (1998).Article 

Google Scholar 
49.Moore, M., Steiner, L. & Jann, B. Cetacean surveys in the Cape Verde Islands and the use of cookiecutter shark bite lesions as a population marker for fin whales. Aquat. Mamm. 29, 383–389 (2003).Article 

Google Scholar 
50.Muñoz-Chápuli, R., Salgado, J. R. & de La Serna, J. Biogeography of Isistius brasiliensis in the north-eastern Atlantic, inferred from crater wounds on swordfish (Xiphias gladius). J. Mar. Biol. Assoc. U K 68, 315–321 (1988).Article 

Google Scholar 
51.Murakami, C., Yoshida, H. & Yonezaki, S. Cookie-cutter shark Isistius brasiliensis eats Bryde’s whale Balaenoptera brydei. Ichthyol. Res. 65, 398–404 (2018).Article 

Google Scholar 
52.Castro, J., Anllo, T., Mejuto, J. & García, B. Ichnology applied to the study of Cookiecutter shark (Isistius brasiliensis) biogeography in the Gulf of Guinea. Environ. Biol. Fishes 101, 579–588 (2018).Article 

Google Scholar 
53.Kim, S. L. et al. Carbon and nitrogen discrimination factors for elasmobranch soft tissues based on a long-term controlled feeding study. Environ. Biol. Fishes 95, 37–52 (2012).Article 

Google Scholar 
54.Le Boeuf, B., McCosker, J. & Hewitt, J. Crater wounds on northern elephant seals: The Cookiecutter Shark strikes again. Fish Bull. 85, 20 (1987).
Google Scholar 
55.Niella, Y. et al. Cookie-cutter shark Isistius spp. predation upon different tuna species from the south-western Atlantic Ocean. J. Fish. Biol. 92, 1082–1089 (2018).CAS 
PubMed 
Article 

Google Scholar 
56.Manlick, P. J., Petersen, S. M., Moriarty, K. M. & Pauli, J. N. Stable isotopes reveal limited Eltonian niche conservatism across carnivore populations. Funct. Ecol. 33, 335–345 (2019).Article 

Google Scholar 
57.McMeans, B.C., Arts, M.T. & Fisk, A.T. Similarity between predator and prey fatty acid profiles is tissue dependent in Greenland sharks (Somniosus microcephalus): Implications for diet reconstruction. J. Exp. Mar. Biol. Ecol. 429, 55–63 (2012).58.Waugh, C.A., Nichols, P.D., Schlabach, M., Noad, M. & Nash, S.B. Vertical distribution of lipids, fatty acids and organochlorine contaminants in the blubber of southern hemisphere humpback whales (Megaptera novaeangliae). Mar. Environ. Res. 94, 24–31 (2014).59.Sigler, M. F. et al. Diet of Pacific sleeper shark, a potential Steller sea lion predator, in the north-east Pacific Ocean. J. Fish. Biol. 69, 392–405 (2006).Article 

Google Scholar 
60.Leclerc, L.-M. et al. Greenland sharks (Somniosus microcephalus) scavenge offal from minke (Balaenoptera acutorostrata) whaling operations in Svalbard (Norway). Polar. Res. 30, 7342 (2011).Article 

Google Scholar 
61.Yano, K., Stevens, J. & Compagno, L. Distribution, reproduction and feeding of the Greenland shark Somniosus (Somniosus) microcephalus, with notes on two other sleeper sharks, Somniosus (Somniosus) pacificus and Somniosus (Somniosus) antarcticus. J. Fish. Biol. 70, 374–390 (2007).Article 

Google Scholar 
62.Preti, A. et al. Comparative feeding ecology of shortfin mako, blue and thresher sharks in the California current. Environ. Biol. Fishes https://doi.org/10.1007/s10641-10012-19980-x (2012).Article 

Google Scholar 
63.Phillips, D. L. et al. Best practices for use of stable isotope mixing models in food-web studies. Can. J. Zool. 92, 823–835 (2014).Article 

Google Scholar 
64.Smith, J. A., Mazumder, D., Suthers, I. M. & Taylor, M. D. To fit or not to fit: Evaluating stable isotope mixing models using simulated mixing polygons. Methods Ecol. Evol. 4, 612–618 (2013).Article 

Google Scholar 
65.Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. https://doi.org/10.1111/ele.12226 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Childress, J. J. & Nygaard, M. H. Deep Sea Research and Oceanographic Abstracts 1093–1109 (Elsevier, 1973).
Google Scholar 
67.Childress, J., Price, M., Favuzzi, J. & Cowles, D. Chemical composition of midwater fishes as a function of depth of occurrence off the Hawaiian Islands: Food availability as a selective factor?. Mar. Biol. 105, 235–246 (1990).Article 

Google Scholar 
68.Choy, C. A., Popp, B. N., Hannides, C. C. & Drazen, J. C. Trophic structure and food resources of epipelagic and mesopelagic fishes in the North Pacific Subtropical Gyre ecosystem inferred from nitrogen isotopic compositions. Limnol. Oceanogr. 60, 1156–1171 (2015).ADS 
Article 

Google Scholar 
69.Gloeckler, K. et al. Stable isotope analysis of micronekton around Hawaii reveals suspended particles are an important nutritional source in the lower mesopelagic and upper bathypelagic zones. Limnol. Oceanogr. 63, 1168–1180 (2018).ADS 
CAS 
Article 

Google Scholar 
70.Hannides, C. C., Popp, B. N., Choy, C. A. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: A stable isotope perspective. Limnol. Oceanogr. 58, 1931–1946 (2013).ADS 
CAS 
Article 

Google Scholar 
71.Dunstan, G.A., Sinclair, A.J., O’Dea, K. & Naughton, J.M. The lipid content and fatty acid composition of various marine species from southern Australian coastal waters. Comp. Biochem. Physiol. B: Comp. Biochem. 91(1), 165–169 (1988).72.Semeniuk, C.A., Speers-Roesch, B. & Rothley, K.D. Using fatty-acid profile analysis as an ecologic indicator in the management of tourist impacts on marine wildlife: a case of stingray-feeding in the Caribbean. Environ. Manag. 40(4), 665–677 (2007).73.Wai, T.C., Leung, K.M., Sin, S.Y., Cornish, A., Dudgeon, D. & Williams, G.A. Spatial, seasonal, and ontogenetic variations in the significance of detrital pathways and terrestrial carbon for a benthic shark, Chiloscyllium plagiosum (Hemiscylliidae), in a tropical estuary. Limnol. Oceanogr. 56(3), 1035–1053 (2011).74.Ebert, D. A., Fowler, S. L., Compagno, L. J. & Dando, M. Sharks of the World: A Fully Illustrated Guide (Wild Nature Press, 2013).
Google Scholar 
75.Vaudo, J. J., Matich, P. & Heithaus, M. R. Mother-offspring isotope fractionation in two species of placentatrophic sharks. J. Fish. Biol. 77, 1724–1727 (2010).CAS 
PubMed 
Article 

Google Scholar 
76.Olin, J. A. et al. Maternal meddling in neonatal sharks: Implications for interpreting stable isotopes in young animals. Rapid Commun. Mass Spectrom. 25, 1008–1016 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
77.Grubbs, R. D. In Sharks and Their Relatives II: Biodiversity, Physiology, and Conservation (eds Carrier, J. C. et al.) 319–350 (CRC Press, 2010).
Google Scholar 
78.Yano, K. & Tanaka, S. Size at maturity, reproductive cycle, fecundity, and depth segregation of the deep sea squaloid sharks Centroscymnus owstoni and C. coelolepis in Suruga Bay Japan. Nippon Suisan Gakkaishi 54, 20 (1988).
Google Scholar 
79.Yano, K. & Tanaka, S. Review of the deep sea squaloid shark genus Scymnodon of Japan, with a description of a new species. Jpn. J. Ichthyol. 30, 341–360 (1984).
Google Scholar 
80.Munoz-Chapuli, R. Ethologie de la reproduction chez quelques requins de l’Atlantique Nord-Est. Cybium 8, 1–14 (1984).
Google Scholar 
81.Jakobsdóttir, K. B. Biological aspects of two deep-water squalid sharks: Centroscyllium fabricii (Reinhardt, 1825) and Etmopterus princeps (Collett, 1904) in Icelandic waters. Fish Res. 51, 247–265 (2001).Article 

Google Scholar 
82.Wetherbee, B. M. Distribution and reproduction of the southern lantern shark from New Zealand. J. Fish. Biol. 49, 1186–1196. https://doi.org/10.1111/j.1095-8649.1996.tb01788.x (1996).Article 

Google Scholar 
83.MacNeil, M. A., Drouillard, K. G. & Fisk, A. T. Variable uptake and elimination of stable nitrogen isotopes between tissues in fish. Can. J. Fish. Aquat. Sci. 63, 345–353 (2006).CAS 
Article 

Google Scholar 
84.Logan, J. M. & Lutcavage, M. Stable isotope dynamics in elasmobranch fishes. Hydrobiologia 644, 231–244 (2010).CAS 
Article 

Google Scholar 
85.Weidel, B. C., Carpenter, S. R., Kitchell, J. F. & Vander Zanden, M. J. Rates and components of carbon turnover in fish muscle: Insights from bioenergetics models and a whole-lake 13C addition. Can. J. Fish. Aquat. Sci. 68, 387–399 (2011).CAS 
Article 

Google Scholar 
86.Carlisle, A. B. et al. Interactive effects of urea and lipid content confound stable isotope analysis in elasmobranch fishes. Can. J. Fish. Aquat. Sci. 74, 419–428 (2016).Article 
CAS 

Google Scholar 
87.Kim, S. L. & Koch, P. L. Methods to collect, preserve, and prepare elasmobranch tissues for stable isotope analysis. Environ. Biol. Fishes 95, 53–63 (2012).Article 

Google Scholar 
88.Witteveen, B. H., Worthy, G. A. J. & Roth, J. D. Tracing migratory movements of breeding North Pacific humpback whales using stable isotope analysis. Mar. Ecol. Prog. Ser. 393, 173–183. https://doi.org/10.3354/meps08231 (2009).ADS 
Article 

Google Scholar 
89.Parry, M. P. The trophic ecology of two ommastrephid squid species, Ommastrephes bartamii and Sthenoteuthis oualaniensis, in the North Pacific sub-tropical gyre Ph.D. thesis, University of Hawaii, (2003).90.Parry, M. P. Trophic variation with length in two ommastrephid squids, Ommastrephes bartramiii and Sthenoteuthis oualaniensis. Mar. Biol. 153, 249–256 (2008).Article 

Google Scholar 
91.Graham, B. S. Trophic dynamics and movements of tuna in tropical Pacific Ocean inferred from stable isotope analyses Ph. D. thesis thesis, University of Hawaii, (2007).92.Graham, B. S., Grubbs, D., Holland, K. & Popp, B. N. A rapid ontogenetic shift in the diet of juvenile yellowfin tuna from Hawaii. Mar. Biol. 150, 647–658 (2007).Article 

Google Scholar 
93.Carlisle, A. B. et al. Stable isotope analysis of vertebrae reveals ontogenetic changes in habitat in an endothermic pelagic shark. Proc. R. Soc. B-Biol. Sci. 282, 20141446. https://doi.org/10.1098/rspb.2014.1446 (2015).CAS 
Article 

Google Scholar 
94.Stock, B. C. & Semmens, B. X. MixSIAR GUI user manual, version 1.0. http://conserver.iugo-cafe.org/user/brice.semmens/MixSIAR (2013).95.Folch, J., Lees, M. & Stanley, G.S. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226(1), 497–509 (1957).96.Kartikasari, L.R., Hughes, R.J., Geier, M.S., Makrides, M. & Gibson, R.A. Dietary alpha-linolenic acid enhances omega-3 long chain polyunsaturated fatty acid levels in
chicken tissues. Prostaglandins Leukot. Essent. Fatty Acids. 87(4–5), 103–109 (2012).97.Froese, R. & D. Pauly. Editors. 2021. FishBase. World Wide Web electronic publication. https://www.fishbase.org, version (02/2021).98.Clarke, K. & Gorley, R. (PRIMER-E: Plymouth, 2006).
99.Riaz, T. et al. ecoPrimers: Inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Res. 39, e145–e145 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
100.Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS One 9, e86175 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
101.Stoeckle, M. Y., Soboleva, L. & Charlop-Powers, Z. Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS ONE 12, e0175186 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
102.Port, J. A. et al. Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Mol. Ecol. 25, 527–541 (2016).CAS 
PubMed 
Article 

Google Scholar 
103.Shehzad, W. et al. Carnivore diet analysis based on next-generation sequencing: Application to the leopard cat (Prionailurus bengalensis) in Pakistan. Mol. Ecol. 21, 1951–1965 (2012).CAS 
PubMed 
Article 

Google Scholar 
104.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
Article 

Google Scholar 
105.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
106.Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate illumina paired-end reAd mergeR. Bioinformatics 30, 614–620 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
107.Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated-reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15, 1289–1303 (2015).CAS 
PubMed 
Article 

Google Scholar 
108.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).Article 
CAS 

Google Scholar 
109.Oksanen, J. et al. Vegan: Community ecology package. R package version 1.17–4. http://cran.r-project.org. Acesso em 23, 2010 (2010). More

Fieldwork and rock sample analysisThe primary objective of our fieldwork was to collect sedimentological data that would allow us to interpret the processes responsible for the deposition of the beds of the Greater Phyllopod Bed. These parameters could then be incorporated into our experimental design and recreation of Burgess Shale-type flows. To understand the complex sedimentary deposits of the Burgess Shale Formation, we targeted individual beds (Fig. 3, Supplementary Figs. 2–5) that were logged at outcrop for informative mm-scale and cm-scale sedimentary structures. Grain size analysis was conducted in the field using a grain-size comparator and hand-lens and during petrographic analysis. The Greater Phyllopod Bed has been logged in considerable detail in the field20,33, and so logs produced from our work can be used to compare to previous studies. Detailed descriptions of the intervals sampled included color, bounding surfaces, micro-sedimentary structures, grain size, and textures. Larger-scale field mapping and analysis of sedimentary architecture were not undertaken and so we were not attempting to answers questions on the relationship of the Cathedral Escarpment to the fossil-bearing deposits or the precise provenance of the organisms.We collected whole-rock samples from the Greater Phyllopod Bed of the Walcott Quarry at stratigraphic heights of 111.6, 136, 149.95, 184.83, and 226.68 cm (labeled Bed A to E, respectively) above the top of the Wash Limestone Member. All sedimentological samples for this study were collected in situ from this location under the Parks Canada collection and research permit (YNP-2015-19297). The permit for our fieldwork allowed us to collect and sample sedimentological material exclusively. These were subsequently sampled for laboratory analysis and thin-section preparation.Petrographic analysis was performed on all samples using a Leica DM750P microscope. Each thin section was scanned with an Epson scanner to observe details of the millimeter-scale structures and textures (Fig. 3, Supplementary Figs. 2–5). Plain and cross-polarized light micrographs were taken of areas of particular sedimentological interest from each thin section and documented along with the petrological analysis. These samples were processed for further geochemical and elemental analysis.Sample analysisX-Ray Diffraction (XRD) was used to characterize the mineralogical content of the matrix of Bed A (111.6 cm above the top of the Wash Limestone Member) from the Walcott Quarry. For whole-rock bulk powder analyses, the sample was ground into a powder, and XRD was conducted using a PANalytical X’Pert3 diffractometer. For clay analysis, we applied the fractions to orientated glass slides. Organics were removed from each sample by H2O2 treatment before disaggregating the material using ultrasonic vibration. The suspended material was decanted from the ultrasonic bath in centrifuge bottles, which were topped up with deionized water so that each bottle weighed within the same gram. The bottles were placed in the centrifuge for two treatments, first at 1000 rpm for 4 min, and then again at 4000 rpm for 20 min. After the first treatment, the supernatant was transferred to new centrifuge bottles. The three lightest bottles were topped up with deionized water in order to reach the weight of the heaviest. The resultant concentrated sample yield ( More

Selection of materials for joint repair of chromium-contaminated soilTable 1 shows the results of the orthogonal test. Range analysis was performed according to the results of Table 1. The range-analysis results are shown in Table 2.Table 1 Orthogonal design scheme and results.Full size tableTable 2 Orthogonal test results range analysis calculation table.Full size tableTable 2 shows that, from the perspective of unconfined compressive strength, the primary and secondary order of the 28 day strength, factors affecting the combined repair of chromium-contaminated soil were cement content → fly-ash synthetic zeolite content → CaS5 content. The best test ratio was: CaS5 content 3 times, synthetic zeolite content 15%, and cement content 20%. The unconfined compressive strength of the contaminated soil after remediation increased with the increase in cement content, but the relationship between the content of CaS5 and synthetic zeolite, and the unconfined compressive strength of the specimen was not very obvious. From the perspective of toxicity leaching, the primary and secondary order of factors affecting the total chromium leaching concentration of the combined remediation of chromium-contaminated soil were cement content → fly-ash synthetic zeolite content → CaS5 content. The primary and secondary order of factors affecting the leaching concentration of Cr(VI) in the combined remediation of contaminated soil were CaS5 content → cement content → fly-ash synthetic zeolite content. The best test ratios of the total chromium and Cr(VI) toxicity leaching test were: CaS5 content is 4 times, synthetic zeolite content 15%, and cement content 20%. Total chromium and Cr(VI) leaching concentration of the chromium-contaminated soil after joint remediation was negatively correlated with the content of CaS5, synthetic zeolite, and cement content. The change of total chromium leaching concentration was most significantly affected by cement content and synthetic zeolite. Second, the change of Cr(VI) leaching concentration was most significantly affected by CaS5 content. From the perspective of leaching concentration, when reducing agent CaS5, adsorbent synthetic zeolite, and curing agent cement were all at maximum, the leaching effect of total chromium and Cr(VI) was best. However, considering the actual engineering cost and dosage of the preparation should be reduced as much as possible for meeting the requirements. Therefore, comprehensive balance analysis determined the optimal ratio for joint repair of chromium-contaminated soil to be 3 times the dosage of CaS5, 15% synthetic zeolite, and cement amount 20%.Strength change of combined repair of chromium-contaminated soil under action of dry–wet cycleThe test compared the variation of unconfined compressive strength with the number of dry and wet cycles under different conditions of chromium content, combined to repair standard specimens of chromium-contaminated soil, and test results are shown in Fig. 1.Figure 1The relationship between unconfined compressive strength and the number of dry wet cycles.Full size imageFigure 1 shows that, in the beginning, the unconfined compressive strength of the combined repair of chromium-contaminated soil increased with the increase in the number of wet and dry cycles. After reaching the maximal value, it gradually decreased as the number of dry–wet cycles continued to increase. In the initial stage of the dry–wet cycle, the unconfined compressive strength of the combined repair of chromium-contaminated soil increased to varying degrees. For 1000 and 3000 mg/kg of chromium-contaminated soil, the peak of the unconfined compressive strength appeared at 2 times during the dry–wet cycle, and the peak of the unconfined compressive strength of 5000 mg/kg chromium-contaminated soil appeared at 4 dry–wet cycles. After that, unconfined compressive strength gradually decreased with the progress of dry–wet cycles, and the decrease rate became slower. From strength-loss analysis, the higher the chromium content was, the greater the change in strength loss. After 16 wet and dry cycles, the strength-loss rates of 1000, 3000, and 5000 mg/kg chromium-contaminated soil were 17.95%, 22.27%, and 28.73%, respectively, and strength loss was within 30%, showing better water stability21,22.From analysis of the strength-change process, after 28 days of curing for the joint repair of chromium-contaminated soil, the physical and chemical interaction between cement hydrate and soil in the repair preparation was still occurring, as was the strength increase and dry–wet cycle caused by its hydration products. The weakening effect on strength is a dynamic equilibrium process of mutual decline and growth, and the equilibrium state of the two reaction degrees directly affected the strength of solidified chromium-contaminated soil23. In the initial stage of the dry–wet cycle, the strength increase caused by the interaction between remediation agent and chromium-contaminated soil continued. At that time, the destructive effect of the dry–wet cycle on the joint repair of chromium-contaminated soil was not significant in comparison. As the number of dry–wet cycles increased, hydration products formed and became stable. Dry shrinkage and wet expansion cause internal stress in the joint repair of chromium-contaminated soil, and the soil has cracks due to internal stress changes. A dry–wet cycle has a relatively destructive effect that is gradually noticeable and resulting in a decrease in strength. After many instances of drying and wetting, the strength of repairing chromium-contaminated soil was decreased and stabilized.Figure 1 also shows that, compared with low-content chromium-contaminated soil, the high-content chromium-contaminated-soil solidified body strength peak appeared later, and the peak value was low. This is because the higher the chromium ion content was, the more serious the delay of the hydration reaction of the repair agent was, and the more obvious the weakening effect on the strength of the cured body was, which is not conducive to strength growth. The weakening effect of the dry–wet cycle on strength continued to exist, which led to the repaired contaminated soil with a high content of chromium having lower strength.Toxic-leaching changes of combined remediation of chromium-contaminated soil under dry–wet cycleThe experiment compared the variation of hexavalent chromium and total chromium leaching concentration with the number of dry–wet cycles in standard specimens of the combined repair of chromium-contaminated soil under different chromium-content conditions of the contaminated soil. Test results are shown in Fig. 2.Figure 2Effect of drying–wetting cycle timeson leaching concentration of Cr.Full size imageFigure 2 shows that the leaching concentration of Cr(VI) and total chromium decreased in the initial stage of the dry–wet cycle of the remediation of chromium-contaminated soil. After that, as the number of dry–wet cycles increased, leaching concentration also increased, but the content was low (1000 mg/kg). The medium content (3000 mg/kg) of chromium-contaminated soil Cr(VI) and total chromium leaching concentration fluctuated slightly, and the change was relatively stable, while the high content of chromium-contaminated soil (5000 mg/kg) Cr(VI) leaching the concentration fluctuated greatly, and total chromium increased significantly. Compared with the low-content chromium-contaminated soil, the leaching concentration of the solidified body of high-content chromium-contaminated soil was higher.In the beginning of the dry–wet cycle, the physical and chemical interaction between the cement hydrate and the soil in the repair preparation was still happening. The fly-ash synthetic zeolite had the adsorption effect of metal chromium ions and hydroxide precipitation in the alkaline environment. The formation of chromium ions could meet the requirements of curing/stabilizing chromium ions, and heavy-metal chromium ions are not easy to leach. With the increase in the number of dry–wet cycles, a series of evolutionary processes occurred, such as the expansion of local microcracks, the increase in macropores, the appearance of internal cracks in the contaminated soil, and the appearance of cracks and peeling phenomena on the outside of the contaminated-soil damage. At this time, the contact area between the heavy-metal ions in the contaminated soil and the external environment, especially water, increased, which reduced the ability of the repair agent to adsorb and wrap chromium ions, so that chromium ions were easily leached. In the leaching test, the use of the acidic leaching solution also destroyed the pH balance of the repaired chromium-contaminated soil, the hydrated gel was dissolved and desorbed, and the heavy metals changed, thereby accelerating the leaching of heavy-metal ions24.From analysis of the leaching law shown by the contaminated soil with different chromium content levels, when chromium content in the contaminated soil was low, the remediation agent could effectively solidify/stabilize most of the chromium ions in the soil Cr(VI) and low total chromium leaching. When the chromium content in the contaminated soil was high, the limited content of the repair agent showed an insufficient solidification/stabilization effect of the heavy-metal chromium ions. Because a higher concentration of chromium ions hindered the formation of hydration products of the repair agent, it weakened the adsorption and binding capacity of the hydrated gel. The heavy-metal chromium ions existed in the pores of the contaminated soil in a free state, making the repair agent solidify the chromium ions, the stabilization effect decreased, and the leaching of Cr(VI) and total chromium increased.Overall, the effect of the dry–wet cycle on the joint repair of chromium-contaminated soil was limited, and the joint repair of chromium-contaminated soil had strong resistance to dry–wet cycles, especially the low- and medium-content chromium-polluted soil.Combined repair of quality loss of chromium-contaminated soil under action of dry–wet cyclesThe cumulative mass-loss rate of the sample was calculated from Formula (1), and the result is shown in Fig. 3. With the increase in the number of wet and dry cycles, the cumulative mass-loss rate of the composite preparation to repair chromium-contaminated soil gradually increased; and the higher the chromium content of the contaminated soil was, the greater the cumulative mass-loss rate was. The cumulative mass-loss rate of 16 wet and dry cycles was less than 1%, which shows that the joint repair of chromium-contaminated soil had strong resistance to dry and wet cycles.Figure 3Change of cumulative mass loss rate during dry wet cycle.Full size imageFigure 4 is a photograph of the appearance change of a solidified 5000 mg/kg chromium-contaminated-soil sample after a dry–wet cycle. The soundness-evaluation results of the sample after each dry–wet cycle are shown in Fig. 5.Figure 4Appearance changes of cured chromium contaminated soil samples with dry and wet cycles at (a) 0 times; (b) 2 times; (c) 4 times; (d) 8 times; and (e) 16 times.Full size imageFigure 5Soundness evaluation results of cured chromium contaminated soil samples.Full size imageFigures 4 and 5 show that, after two dry–wet cycles of the joint repair of chromium-contaminated soil, the appearance of the sample did not significantly change, compared with 0 cycles, the surface changed from smooth to rough. Slight cracks appeared from the fourth cycle. Obvious cracks appeared in the sample at the end of the eighth cycle, and a small part of the sample fell off. The sample began to show obvious cracks from the end of the 15th dry–wet cycle, and large pieces of slack simultaneously appeared. The sample was subjected to 16 wet and dry cycles, and soundness was still not at e–h level, indicating that the joint repair of chromium-contaminated soil had strong resistance to dry and wet cycles.Combined repair of chromium-contaminated-soil microstructure changes under action of dry–wet cyclesAfter the joint repair of chromium-contaminated-soil specimens underwent a certain number of wet and dry cycles, the strength, leaching characteristics, and appearance of the specimens significantly changed. From the microstructure, there had to be corresponding changes. Therefore, scanning electron microscope (SEM) and X-ray diffraction (XRD) were used to further analyze the microstructure changes of specimens with different chromium content levels under the action of different wet and dry cycles, as shown in Figs. 6 and 7.Figure 6SEM images of 5000 mg/kg chromium contaminated soil specimens after different dry wet cycles at (a) 0 times; (b) 2 times; (c) 8 times; and (d) 16 times.Full size imageFigure 7XRD pattern of 5000 mg/kg chromium contaminated soil specimen after different dry wet cycles.Full size imageFigure 6 shows that the combined repair of chromium-contaminated soil after 28 days of curing had many pores in the specimen at 0 dry–wet cycles (standard sample), the physical and chemical interaction between the cement hydrate and the soil in the repair preparation still continued, and there were platelike calcium hydroxide crystals on the surface. After two dry–wet cycles, the contaminated soil was denser, and the overall structure was more complete than that in the samples without dry–wet cycles. The plate-shaped calcium hydroxide crystals were reduced, and a large number of fibrous and flocculent hydrated gels could be seen on the surface of the structure. This shows that the reaction between remediation agent and chromium-contaminated soil continued, which is consistent with the law that strength did not drop but rose during the two dry and wet cycles in the unconfined-compressive-strength test. After the test piece had undergone 8 dry–wet cycles, the surface of the test piece not only had a large increase in pores, but also had local cracks, indicating that the structure of the test piece was damaged under the action of the dry–wet cycle, which is consistent with the unconfined compressive strength found in the experiment, coinciding with a sharp drop. After 16 wet and dry cycles, the surface of the specimen not only showed a large number of pores and cracks, but also had obvious roughness. It showed that the dry–wet cycle effect caused the hydration products and cement materials in the soil to be destroyed and dissolved out, and the coupling and supporting forces between soil particles are weakened, and the strength of the soil is reduced accordingly, which was consistent with the macroscopic test results.Figure 7 shows that the main crystal phases of the chromium-contaminated soil were SiO2 and Al2O3 for the samples that did not undergo a dry–wet cycle. A small number of CSH, CAH, Ca(OH)2, and CaCO3 crystals could also be detected from the diffraction peaks. Cr3+ and Cr6+ formed hydroxide precipitates in a highly alkaline environment and wrapped them on the surface of cement, hindering their contact reaction with water. Compared with 0 cycles, SiO2 and Al2O3 in the second cycle were decreased, while the contents of CSH, CAH, Ca(OH)2, and CaCO3 significantly increased. This is because in the process of dry and wet cycles, the sample is fully exposed to moisture and air, so the hydration, depolymerization-cementation, pozzolanic, and carbonation reactions between composite preparation and chromium-contaminated soil continued. After two dry–wet cycles, more hydration products were generated than in the specimens without dry–wet cycles, which filled the pores between the particles of the solidified body, effectively blocking the permeability of the pores, and making the contaminated soil denser, and more structured and complete. At the same time, the full progress of the hydration reaction also delayed the damage rate of the water body to the soil in the dry–wet cycle, so that the soil could maintain a certain strength in the harsh environment, which is consistent with the above-mentioned growth trend of the soil strength. At the same time, the extension of a large amount of fibrous calcium silicate hydrate greatly increased the internal specific surface area of the soil. Free-state Cr3+ and Cr6+ were adsorbed or produced hydroxide precipitation and filled in the pores of the soil, and free ion concentration was also greatly reduced, which is consistent with the above ion-leaching test results. For the specimens with 8 dry and wet cycles, the content of hydration products such as CAH and CSH was reduced. This is due to a series of evolutionary processes such as the expansion of local microcracks, the increase in macropores, the appearance of internal cracks in the contaminated soil, and the appearance of cracks and peeling on the outside of the contaminated soil. Structural integrity was destroyed, and strength was accordingly reduced. By 16 wet and dry cycles, a large amount of fibrous CSH disappeared, which weakened the cementation between soil particles. At this time, the heavy-metal ions originally wrapped in the contaminated soil solidified the body and the external environment, the contact area with the water was increased, the pH value of the environment was decreased, hydrate CSH was decalcified, and Ca/Si ratio was decreased. This reduced the adsorption capacity of the compound formulation to chromium ions, so that chromium ions were dissolved out of the soil. More

Wildlife Conservation Research Unit, Recanati-Kaplan Centre, Zoology, University of Oxford, Oxford, UKHans Bauer & Claudio Sillero-ZubiriEvolutionary Ecology Group, Biology, University of Antwerp, Antwerp, BelgiumHans BauerLaboratory for Applied Ecology, Natural Resource Conservation, University of Abomey-Calavi, Cotonou, BeninAristide Comlan TehouDepartment of HydroSciences and Environment, University Iba Der Thiam, Thiès, SénégalMallé GueyeDirection de la Faune, de la Chasse et des Parcs et Réserves, Ministère de l’Environnement de la Salubrité Urbaine et du Développement Durable, Niamey, NigerHamissou GarbaDirection de la Faune et des Chasses, Ministère de l’Environnement et du Développement Durable, Ouagadougou, Burkina FasoBenoit DoambaNational Parks Directorate, Ministry of Environment and Sustainable Development, Dakar, SenegalDjibril DiouckThe Born Free Foundation, Horsham, UKClaudio Sillero-Zubiri More