Philippa Kaur

Pogoreutz C, Voolstra CR, Rädecker N, Weis V, Cardenas A, Raina J-B. The coral holobiont highlights the dependence of cnidarian animal hosts on their associated microbes. In Bosch TCG, Hadfield MG, editors. Cellular Dialogues in the Holobiont. CRC Press; 2020. pp. 91–118. https://doi.org/10.1201/9780429277375-7Rohwer F, Seguritan V, Azam F, Knowlton N. Diversity and distribution of coral-associated bacteria. Mar Ecol Prog Ser. 2002;243:1–10.
Google Scholar 
LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol. 2018;28:2570–80.e6CAS 
PubMed 

Google Scholar 
Muscatine L, Porter JW. Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience 1977;27:454–60.
Google Scholar 
Christian R, Voolstra DJ, Suggett RS, Peixoto JE, Parkinson KM, Quigley CB, et al. Extending the natural adaptive capacity of coral holobionts. Nature Reviews Earth & Environment. 2021;2:747–762. https://doi.org/10.1038/s43017-021-00214-3Article 

Google Scholar 
Bourne DG, Morrow KM, Webster NS. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu Rev Microbiol. 2016;70:317–40.CAS 
PubMed 

Google Scholar 
Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.PubMed 

Google Scholar 
Matthews JL, Raina JB, Kahlke T, Seymour JR, van Oppen MJ, Suggett DJ. Symbiodiniaceae‐bacteria interactions: rethinking metabolite exchange in reef‐building corals as multi‐partner metabolic networks. Environ Microbiol 2020;22:1675–87.PubMed 

Google Scholar 
Kimes NE, Van Nostrand JD, Weil E, Zhou J, Morris PJ. Microbial functional structure of Montastraea faveolata, an important Caribbean reef‐building coral, differs between healthy and yellow‐band diseased colonies. Environ Microbiol. 2010;12:541–56.CAS 
PubMed 

Google Scholar 
Neave MJ, Apprill A, Ferrier-Pagès C, Voolstra CR. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl Environ Micro. 2016;100:8315–24.CAS 

Google Scholar 
Neave MJ, Michell CT, Apprill A, Voolstra CR. Endozoicomonas genomes reveal functional adaptation and plasticity in bacterial strains symbiotically associated with diverse marine hosts. Sci Rep. 2017;7:1–12.
Google Scholar 
Krediet CJ, Ritchie KB, Alagely A, Teplitski M. Members of native coral microbiota inhibit glycosidases and thwart colonization of coral mucus by an opportunistic pathogen. ISME J. 2013;7:980–90.CAS 
PubMed 

Google Scholar 
Raina J-B, Tapiolas D, Motti CA, Foret S, Seemann T, Tebben J, et al. Isolation of an antimicrobial compound produced by bacteria associated with reef-building corals. PeerJ 2016;4:e2275.PubMed 
PubMed Central 

Google Scholar 
Diaz JM, Hansel CM, Apprill A, Brighi C, Zhang T, Weber L, et al. Species-specific control of external superoxide levels by the coral holobiont during a natural bleaching event. Nat Commun. 2016;7:1–10.
Google Scholar 
Dunlap WC, Shick JM. Ultraviolet radiation‐absorbing mycosporine‐like amino acids in coral reef organisms: a biochemical and environmental perspective. J Phycol. 1998;34:418–30.
Google Scholar 
Webster NS, Smith LD, Heyward AJ, Watts JE, Webb RI, Blackall LL, et al. Metamorphosis of a scleractinian coral in response to microbial biofilms. Appl Environ Microbiol. 2004;70:1213–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gómez-Lemos LA, Doropoulos C, Bayraktarov E, Diaz-Pulido G. Coralline algal metabolites induce settlement and mediate the inductive effect of epiphytic microbes on coral larvae. Sci Rep. 2018;8:1–11.
Google Scholar 
Pernice M, Raina J-B, Rädecker N, Cárdenas A, Pogoreutz C, Voolstra CR. Down to the bone: the role of overlooked endolithic microbiomes in reef coral health. ISME J. 2020;14:325–34.PubMed 

Google Scholar 
Ricci F, Marcelino VR, Blackall LL, Kühl M, Medina M, Verbruggen H. Beneath the surface: community assembly and functions of the coral skeleton microbiome. Microbiome 2019;7:1–10.
Google Scholar 
Marcelino VR, Verbruggen H. Multi-marker metabarcoding of coral skeletons reveals a rich microbiome and diverse evolutionary origins of endolithic algae. Sci Rep. 2016;6:1–9.
Google Scholar 
Verbruggen H, Marcelino VR, Guiry MD, Cremen MCM, Jackson CJ. Phylogenetic position of the coral symbiont Ostreobium (Ulvophyceae) inferred from chloroplast genome data. J Phycol. 2017;53:790–803.CAS 
PubMed 

Google Scholar 
Del Campo J, Pombert J-F, Šlapeta J, Larkum A, Keeling PJ. The ‘other’coral symbiont: Ostreobium diversity and distribution. ISME J 2017;11:296–9.PubMed 

Google Scholar 
Massé A, Domart-Coulon I, Golubic S, Duché D, Tribollet A. Early skeletal colonization of the coral holobiont by the microboring Ulvophyceae Ostreobium sp. Sci Rep. 2018;8:1–11.
Google Scholar 
Halldal P. Photosynthetic capacities and photosynthetic action spectra of endozoic algae of the massive coral Favia. Biol Bull. 1968;134:411–24.CAS 

Google Scholar 
Fork D, Larkum A. Light harvesting in the green alga Ostreobium sp., a coral symbiont adapted to extreme shade. Mar Biol. 1989;103:381–5.
Google Scholar 
Fine M, Steindler L, Loya Y. Endolithic algae photoacclimate to increased irradiance during coral bleaching. Mar Freshw Res. 2004;55:115–21.CAS 

Google Scholar 
Fine M, Roff G, Ainsworth T, Hoegh-Guldberg O. Phototrophic microendoliths bloom during coral “white syndrome”. Coral Reefs. 2006;25:577–81.
Google Scholar 
Galindo-Martínez CT, Weber M, Avila-Magaña V, Enríquez S, Kitano H, Medina M, et al. The role of the endolithic alga Ostreobium spp. during coral bleaching recovery. Sci Rep. 2022;12:1–12.
Google Scholar 
Fine M, Loya Y. Endolithic algae: an alternative source of photoassimilates during coral bleaching. Proc R Soc B Biol Sci. 2002;269:1205–10.
Google Scholar 
Schlichter D, Zscharnack B, Krisch H. Transfer of photoassimilates from endolithic algae to coral tissue. Naturwissenschaften 1995;82:561–4.CAS 

Google Scholar 
Sangsawang L, Casareto BE, Ohba H, Vu HM, Meekaew A, Suzuki T, et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R Soc Open Sci. 2017;4:171201.PubMed 
PubMed Central 

Google Scholar 
Marcelino VR, Morrow KM, van Oppen MJ, Bourne DG, Verbruggen H. Diversity and stability of coral endolithic microbial communities at a naturally high pCO2 reef. Mol Ecol. 2017;26:5344–57.CAS 
PubMed 

Google Scholar 
Marcelino VR, Van Oppen MJ, Verbruggen H. Highly structured prokaryote communities exist within the skeleton of coral colonies. ISME J. 2018;12:300–3.PubMed 

Google Scholar 
Yang S-H, Tandon K, Lu C-Y, Wada N, Shih C-J, Hsiao SS-Y, et al. Metagenomic, phylogenetic, and functional characterization of predominant endolithic green sulfur bacteria in the coral Isopora palifera. Microbiome 2019;7:1–13.
Google Scholar 
Ferrer L, Szmant A, editors. Nutrient regeneration by the endolithic community in coral skeletons. Proceedings of the 6th International Coral Reef Symposium; 1988: AIMS Townsville, Australia.Eakin CM, Devotta D, Heron S, Connolly S, Liu G, Geiger E, et al. The 2014-17 global coral bleaching event: The most severe and widespread coral reef destruction. Research Square. 2022. https://doi.org/10.21203/rs.3.rs-1555992/v1Article 

Google Scholar 
Hughes TP, Kerry JT, Álvarez-Noriega M, Álvarez-Romero JG, Anderson KD, Baird AH, et al. Global warming and recurrent mass bleaching of corals. Nature 2017;543:373–7.CAS 
PubMed 

Google Scholar 
Hughes TP, Anderson KD, Connolly SR, Heron SF, Kerry JT, Lough JM, et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 2018;359:80–3.CAS 
PubMed 

Google Scholar 
Hughes TP, Kerry JT, Baird AH, Connolly SR, Dietzel A, Eakin CM, et al. Global warming transforms coral reef assemblages. Nature 2018;556:492–6.CAS 
PubMed 

Google Scholar 
Veron J, Stafford-Smith M, Corals of the World, Volumes 1-3. Australian Institute of Marine Science. Odyssey Publishing; 2000.Brown B, Dunne R, Phongsuwan N, Patchim L, Hawkridge J. The reef coral Goniastrea aspera: a ‘winner’becomes a ‘loser’during a severe bleaching event in Thailand. Coral Reefs. 2014;33:395–401.
Google Scholar 
Klepac C, Barshis D. Reduced thermal tolerance of massive coral species in a highly variable environment. Proc R Soc B Biol Sci. 2020;287:20201379.CAS 

Google Scholar 
Nicolas R, Evensen CR, Voolstra M, Fine G, Perna C, Buitrago-López A, et al. Empirically derived thermal thresholds of four coral species along the Red Sea using a portable and standardized experimental approach. Coral Reefs. 2022;41:239–52. https://doi.org/10.1007/s00338-022-02233-yArticle 

Google Scholar 
Madin JS, Anderson KD, Andreasen MH, Bridge TC, Cairns SD, Connolly SR, et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci Data. 2016;3:1–22.
Google Scholar 
Roth F, Karcher DB, Rädecker N, Hohn S, Carvalho S, Thomson T, et al. High rates of carbon and dinitrogen fixation suggest a critical role of benthic pioneer communities in the energy and nutrient dynamics of coral reefs. Funct Ecol. 2020;34:1991–2004.
Google Scholar 
Harrison PJ, Waters RE, Taylor F. A broad spectrum artificial sea water medium for coastal and open ocean phytoplankton. J Phycol. 1980;16:28–35.
Google Scholar 
Andersson AF, Lindberg M, Jakobsson H, Bäckhed F, Nyrén P, Engstrand L. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS One. 2008;3:e2836.PubMed 
PubMed Central 

Google Scholar 
Bayer T, Neave MJ, Alsheikh-Hussain A, Aranda M, Yum LK, Mincer T, et al. The microbiome of the Red Sea coral Stylophora pistillata is dominated by tissue-associated Endozoicomonas bacteria. Appl Environ Microbiol. 2013;79:4759–62.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D6.PubMed 
PubMed Central 

Google Scholar 
Wickham H. ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3:180–5.
Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dixon P. The vegan package. J Veg Sci. 2003;14:927–30.
Google Scholar 
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:1–11.CAS 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, et al. MEGAHIT v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
Peng Y, Leung HC, Yiu S-M, Chin FY. Meta-IDBA: a de Novo assembler for metagenomic data. Bioinformatics 2011;27:i94–i101.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 2013;29:1072–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:1–11.
Google Scholar 
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.
Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 2020;36:2251–2.CAS 
PubMed 

Google Scholar 
Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology 1973;54:427–32.
Google Scholar 
Bates D, Sarkar D, Bates MD, Matrix L. The lme4 package. R Package Version. 2007;2:74.
Google Scholar 
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Micro Ecol Health Dis. 2015;26:27663.
Google Scholar 
Rivera-Pinto J, Egozcue JJ, Pawlowsky-Glahn V, Paredes R, Noguera-Julian M, Calle ML. Balances: a new perspective for microbiome analysis. mSystems. 2018;3:e00053–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019;7:e7359.PubMed 
PubMed Central 

Google Scholar 
Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016;32:605–7.CAS 
PubMed 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018;6:1–13.
Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020;36:1925–7.CAS 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 
PubMed 

Google Scholar 
Zhou Z, Tran P, Briester AM, Liu Y, Kieft K, Cowley ES, et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome. 2022;10:33 https://doi.org/10.1186/s40168-021-01213-8CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Na S-I, Kim YO, Yoon S-H, Ha S-M, Baek I, Chun J. UBCG: up-to-date bacterial core gene set and pipeline for phylogenomic tree reconstruction. J Microbiol. 2018;56:280–5.CAS 
PubMed 

Google Scholar 
Morel J, Jay S, Féret J-B, Bakache A, Bendoula R, Carreel F, et al. Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology. Sci Rep. 2018;8:1–13.CAS 

Google Scholar 
Calamita F, Imran HA, Vescovo L, Mekhalfi ML, La, Porta N. Early identification of root rot disease by using hyperspectral reflectance: the case of pathosystem Grapevine/Armillaria. Remote Sens. 2021;13:2436.
Google Scholar 
Brumfield KD, Huq A, Colwell RR, Olds JL, Leddy MB. Microbial resolution of whole-genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data. PLoS One. 2020;15:e0228899.PubMed 
PubMed Central 

Google Scholar 
Khachatryan L, de Leeuw RH, Kraakman ME, Pappas N, Te Raa M, Mei H, et al. Taxonomic classification and abundance estimation using 16S and WGS—A comparison using controlled reference samples. Forensic Sci Int Genet. 2020;46:102257.CAS 
PubMed 

Google Scholar 
Cardénas A, Voolstra C. 75 Coral Endolith Bacterial Genomes (MAGs) from Red Sea corals Goniastrea edwardsi and Porites lutea (Version 1) [Data set]. Zenodo. 2021. https://doi.org/10.5281/zenodo.5606932Article 

Google Scholar 
Branson O, Bonnin EA, Perea DE, Spero HJ, Zhu Z, Winters M, et al. Nanometer-scale chemistry of a calcite biomineralization template: Implications for skeletal composition and nucleation. Proc Natl Acad Sci. 2016;113:12934–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sauvage T, Schmidt WE, Suda S, Fredericq S. A metabarcoding framework for facilitated survey of endolithic phototrophs with tufA. BMC Ecol. 2016;16:1–21.
Google Scholar 
Wegley L, Edwards R, Rodriguez‐Brito B, Liu H, Rohwer F. Metagenomic analysis of the microbial community associated with the coral Porites astreoides. Environ Microbiol. 2007;9:2707–19.CAS 
PubMed 

Google Scholar 
Robbins S, Song W, Engelberts J, Glasl B, Slaby BM, Boyd J, et al. A genomic view of the microbiome of coral reef demosponges. ISME J 2021;15:1641–54.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang S-Y, Lu C-Y, Tang S-L, Das RR, Sakai K, Yamashiro H, et al. Effects of ocean acidification on coral endolithic bacterial communities in Isopora palifera and Porites lobata. Front Mar Sci. 2020;7:603293.
Google Scholar 
Yang SH, Lee ST, Huang CR, Tseng CH, Chiang PW, Chen CP, et al. Prevalence of potential nitrogen‐fixing, green sulfur bacteria in the skeleton of reef‐building coral Isopora palifera. Limnol Oceanogr. 2016;61:1078–86.
Google Scholar 
Cai L, Zhou G, Tian R-M, Tong H, Zhang W, Sun J, et al. Metagenomic analysis reveals a green sulfur bacterium as a potential coral symbiont. Sci Rep. 2017;7:1–11.
Google Scholar 
Kühl M, Holst G, Larkum AW, Ralph PJ. Imaging of oxygen dynamics within the endolithic algal community of the massive coral Porites Lobata. J Phycol. 2008;44:541–50.PubMed 

Google Scholar 
Roberty S, Bailleul B, Berne N, Franck F, Cardol P. PSI Mehler reaction is the main alternative photosynthetic electron pathway in Symbiodinium sp., symbiotic dinoflagellates of cnidarians. N. Phytol. 2014;204:81–91.CAS 

Google Scholar 
Shigeoka S, Ishikawa T, Tamoi M, Miyagawa Y, Takeda T, Yabuta Y, et al. Regulation and function of ascorbate peroxidase isoenzymes. J Exp Bot. 2002;53:1305–19.CAS 
PubMed 

Google Scholar 
Roberty S, Fransolet D, Cardol P, Plumier J-C, Franck F. Imbalance between oxygen photoreduction and antioxidant capacities in Symbiodinium cells exposed to combined heat and high light stress. Coral Reefs. 2015;34:1063–73.
Google Scholar 
Petersen JM, Zielinski FU, Pape T, Seifert R, Moraru C, Amann R, et al. Hydrogen is an energy source for hydrothermal vent symbioses. Nature 2011;476:176–80.CAS 
PubMed 

Google Scholar 
McCollom T, Amend J. A thermodynamic assessment of energy requirements for biomass synthesis by chemolithoautotrophic micro‐organisms in oxic and anoxic environments. Geobiology 2005;3:135–44.CAS 

Google Scholar 
Heijnen J, Van Dijken J. In search of a thermodynamic description of biomass yields for the chemotrophic growth of microorganisms. Biotechnol Bioeng. 1992;39:833–58.CAS 
PubMed 

Google Scholar 
Bar-Even A, Noor E, Milo R. A survey of carbon fixation pathways through a quantitative lens. J Exp Bot. 2012;63:2325–42.CAS 
PubMed 

Google Scholar 
Schulze E-D, Mooney HA, Biodiversity and ecosystem function: Springer Science & Business Media; 2012.Lawton JH, Brown VK, Redundancy in ecosystems. Biodiversity and Ecosystem Function: Springer; 1994. p. 255–70.Mori AS, Furukawa T, Sasaki T. Response diversity determines the resilience of ecosystems to environmental change. Biol Rev. 2013;88:349–64.PubMed 

Google Scholar 
Nyström M. Redundancy and response diversity of functional groups: implications for the resilience of coral reefs. Ambio 2006;35:30–5.PubMed 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
Ziegler M, Grupstra CG, Barreto MM, Eaton M, BaOmar J, Zubier K, et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat Commun. 2019;10:1–11.CAS 

Google Scholar 
Dikou A, Van, Woesik R. Survival under chronic stress from sediment load: spatial patterns of hard coral communities in the southern islands of Singapore. Mar Pollut Bull. 2006;52:1340–54.CAS 
PubMed 

Google Scholar 
Hennige SJ, Smith DJ, Walsh S-J, McGinley MP, Warner ME, Suggett DJ. Acclimation and adaptation of scleractinian coral communities along environmental gradients within an Indonesian reef system. J Exp Mar Biol Ecol. 2010;391:143–52.
Google Scholar 
Cárdenas A, Neave MJ, Haroon MF, Pogoreutz C, Rädecker N, Wild C, et al. Excess labile carbon promotes the expression of virulence factors in coral reef bacterioplankton. ISME J. 2018;12:59–76.PubMed 

Google Scholar 
Cárdenas A, Ye J, Ziegler M, Payet JP, McMinds R, Thurber RV, et al. Coral-associated viral assemblages from the Central Red Sea align with host species and contribute to holobiont genetic diversity. Front Microbiol. 2020;11:572534.PubMed 
PubMed Central 

Google Scholar 
McCook GD-PLJ. The fate of bleached corals: patterns and dynamics of algal recruitment. Mar Ecol Prog Ser. 2002;232:115–28.
Google Scholar 
Reshef L, Koren O, Loya Y, Zilber‐Rosenberg I, Rosenberg E. The coral probiotic hypothesis. Environ Microbiol. 2006;8:2068–73.CAS 
PubMed 

Google Scholar 
Voolstra CR, Ziegler M. Adapting with microbial help: microbiome flexibility facilitates rapid responses to environmental change. BioEssays 2020;42:2000004.
Google Scholar 
Rosenberg E, Zilber-Rosenberg I. The hologenome concept of evolution after 10 years. Microbiome 2018;6:1–14.
Google Scholar 
Wiedenmann J, D’Angelo C, Smith EG, Hunt AN, Legiret F-E, Postle AD, et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat Clim Change. 2013;3:160–4.CAS 

Google Scholar 
DeCarlo TM, Gajdzik L, Ellis J, Coker DJ, Roberts MB, Hammerman NM, et al. Nutrient-supplying ocean currents modulate coral bleaching susceptibility. Sci Adv. 2020;6:eabc5493.PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Rädecker N, Cardenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Change Biol 2017;23:3838–48.
Google Scholar  More

Calculating Potential Evapotranspiration using Penman-MonteithAmong several equations used to estimate PET, an implementation of the Penman-Monteith equation originally presented by the Food and Agriculture Organization FAO-561, is considered a standard method3,12,13,49. FAO-561 defined PET as the ET of a reference crop (ET0) under optimal conditions, in this case with the specific characteristics of well-watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter and an albedo of 0.231. Less specifically, “reference evapotranspiration”, generally referred to as “ET0”, measures the rate at which readily available soil water is evaporated from specified vegetated surfaces2,13, i.e., from a uniform surface of dense, actively growing vegetation having specified height and surface resistance, not short of soil water, and representing an expanse of at least 100 m of the same or similar vegetations1,13. ET0 is one of the essential hydrological variables used in many research efforts, such as study of the hydrologic water balance, crop yield simulation, irrigation system management and in water resources management, allowing researchers and practitioners to study the evaporative demand of the atmosphere independent of crop type, crop development and management practices2,4,13,49. ET0 values measured or calculated at different locations or in different seasons are comparable as they refer to the ET from the same reference surface. The factors affecting ET0 are climatic parameters, and crop specific resistances coefficients solved for reference vegetation. Other crop specific coefficients (Kc) may then be used to determine the ET of specific crops (ETc), and which can in turn be determined from ET01.As the Penman-Monteith methodology is predominately a climatic approach, it can be applied globally as it does not require estimations of additional site-specific parameters. However, a major drawback of the Penman-Monteith method is its relatively high need for specific data for a variety of parameters (i.e., windspeed, relative humidity, solar radiation). Zomer et al.18 compared five methods of calculating PET with parameters from data available at the time and settled upon using a Modified Hargreaves-Thornton equation50 which required less parametrization to produce the Global-AI_PET_v116,17,18. Several other attempts to produce global PET datasets with concurrently available global datasets came to similar conclusions51,52,53. The Modified Hargreaves-Thornton method required less parameterization with relatively good results, relying on datasets which were available at the time for a globally applicable modeling effort. The Global-AI_PET_v1 used the WorldClim_v1.420 downscaled climate dataset (30 arcseconds; averaged over the period 1960–1990) for input into the global geospatial implementation of the Modified Hargreaves-Thornton equation, applied on a per grid cell basis at approximately 1 km resolution (30 arcseconds). More recently, the UK Climate Research Unit released the “CRU_TS Version 4.04”, which now includes a Penman-Monteith calculated PET (ET0) global coverage, however at a relatively coarse resolution of 0.5 × 0.5 degrees. A number of satellite-based remote sensing datasets22,54,55,56,57 are now available and in use to provide the parameters for ET0 estimates, in some cases providing high spatial and/or temporal resolution and are likely to become increasingly utilized as the historical data record lengthens and sensors improve.The latest 2.0 versions of WorldClim58 (currently version 2.1; released January 2020), in addition to being updated with improved data and analysis, and a revised baseline (1970–2000), includes several additional primary climatic variables, beyond temperature and precipitation, namely: solar radiation, wind speed and water vapor pressure. The addition of these variables allowed that the global data now available was sufficient to effectively parameterize the FAO-56 equation to estimate ET0 globally at the 30 arc seconds scale (~1 km at equator).The FAO-56 Penman-Monteith equation, described in detail below, has been implemented on a per grid cell basis at 30 arc seconds resolution, using the Python programming language (version 3.2). The data to parametrize the various components equations required to arrive at the ET0 estimate were obtained from the Worlclim 2.158 climatological dataset, which provides values averaged over the time period 1970–2000 for minimum, maximum and average temperature; solar radiation; wind speed, and water vapor pressure. Subroutines in the program include calculation of the psychrometric constant (aerodynamic resistance), saturation vapor pressure, vapor pressure deficit, slope of vapour pressure curve, air density at constant pressure, net shortwave radiation at crop surface, clear-sky solar radiation, net longwave radiation at crop surface, net radiation at the crop surface, and the calculation of daily and monthly ET0. This process is described below. Geospatial processing and analysis were done using ArcGIS Pro v 2.9 (ESRI, 2020), Python (ArcPy) programming language (version 3.2), and Microsoft Excel for further data analysis, graphics and presentation.Global Reference Evapotranspiration (Global-ET0)Penman59, in 1948, first combined the radiative energy balance with the aerodynamic mass transfer method and derived an equation to compute evaporation from an open water surface from standard climatological records of sunshine, temperature, humidity and wind speed. This combined approach eliminated the need for the parameter “most difficult” to measure, surface temperature, and allowed for the first time an opportunity to make theoretical estimates of ET from standard meteorological data. Consequently, these estimates could also now be made retrospectively. This so-called combination method was further developed by many researchers and extended to cropped surfaces by introducing resistance factors. Among the various derivations of the Penman equation is the inclusion of a bulk surface resistance term60, with the resulting equation now called the Penman-Monteith equation3, as standardized in FAO-561 and subsequently by the American Society of Civil Engineers – Technical Committee on Standardization of Reference Evapotranspiration12,13,49,61. The FAO-56 Penman-Monteith form of the combination equation to estimate ET0 is calculated as:$$ETo=frac{Delta left({R}_{n}-Gright)+{rho }_{a}{c}_{p}frac{({e}_{s}-{e}_{a})}{{r}_{a}}}{Delta +gamma left(1+frac{{r}_{s}}{{r}_{a}}right)}$$
(1)
WhereET0 is the evapotranspiration for reference crop, as mm day−1Rn is the net radiation at the crop surface, as MJ m−2 day−1G is the soil heat flux density, as MJ m−2 day−1cp is the specific heat of dry airpa is the air density at constant pressurees is the saturation vapour pressure, as kPaea is the actual vapour pressure, as kPaes – ea is the saturation vapour pressure deficit, as kPa(Delta ) is the slope vapour pressure curve, as kPa °C−1(gamma ) is the psychrometric constant, as kPa °C−1rs is the bulk surface resistance, as m s−1ra is the aerodynamic resistance, as m s−1Psychrometric Constant (γ)The Atmospheric Pressure (Pr, [KPa]) is the pressure exerted by the weight of the atmosphere and is thus dependent on elevation (elev, [m]). To a certain (and limited) extent evaporation is promoted at higher elevations:$$Pr=101.3ast {left(frac{293-0.0065ast elev}{293}right)}^{5.26}$$
(2)
Instead, the psychrometric constant, [γ, kPa C−1] is expressed as:$$gamma =frac{{c}_{p}ast Pr}{varepsilon ast lambda }=frac{0.001013ast Pr}{0.622ast 2.45}$$
(3)
Where cp is the specific heat at constant pressure [MJ kg−1 °C−1] and is equal to 1.013 10−3, λ is the latent heat of vaporization [MJ kg−1] and is equal to 2.45, while ε is the molecular weight ratio between water vapour and dry air and is equal to 0.622.Elevation data has been obtained from the Shuttle Radar Topography Mission (SRTM) aggregated to 30 arc-second spatial resolution62 and combined with the USGS GTOPO3063 database for the areas north of 60°N and south of 60°S where no SRTM data was available (available at https://worldclim.org).Air Density at Constant Pressure [ρa]The mean Air Density at Constant Pressure [ρa, Kg m−3] can be represented as:$${rho }_{a}=frac{Pr}{{T}_{Kv}ast R}$$
(4)
While R is the specific heat constant (0.287, KJ Kg−1 K−1), the virtual temperature TKv can be represented as well as:$${T}_{Kv}=1.01ast ({T}_{avg}+273)$$
(5)
With Tavg as the mean daily air temperature at 2 m height [C°].Saturation Vapor Pressure [KPa]Saturation Vapor Pressure [KPa] is strictly related to temperature values (T)$${e}_{s_T}=0.6108ast ex{p}^{left[frac{17.27ast T}{T+237.3}right]}$$
(6)
Values of saturation vapor pressures, as function of temperature, are calculated for both Minimum Temperature [Tmin, C°] and Maximum temperature [Tmax, C°]. Due to nonlinearity of the equation, the mean saturation vapour pressure [es, KPa] is calculated as the average of saturation vapour pressure at minimum [es_min] and maximum temperature [es_max]$${e}_{s}=frac{{e}_{s_Tmax}+{e}_{s_Tmin}}{2}$$
(7)
The actual vapour pressure [ea, KPa] is the vapour pressure exerted by the water in the air and is usually calculated as function of Relative Humidity [RH]. Water vapour pressure is already available as one of the Worldclim 2.1 variables.$${e}_{a}=RH/100,ast ,{e}_{s}$$
(8)
The vapour pressure deficit (es-ea), [KPa] is the difference between the saturation (es) and actual vapour pressure (({e}_{a})).Slope of Saturation Vapor Pressure (Δ)The Slope of Saturation Vapor Pressure [Δ, kPa C−1] at a given temperature is given as function of average temperature:$$Delta =frac{4098ast 0.6108,ex{p}^{left(frac{17.27ast {T}_{avg}}{{T}_{avg}+237.3}right)}}{{left({T}_{avg}+237.3right)}^{2}}$$
(9)
Where Tavg [C°] is the average temperature.Net Radiation At The Crop Surface (R
n)Net radiation [Rn, MJ m−2 day−1] is the difference between the net shortwave radiation [Rns, MJ m−2 day−1] and the net longwave radiation [Rnl, MJ m−2 day−1], and is calculated using solar radiation (Rs). In Worldclim 2.1 solar radiation (Rs) is given as KJ m−2 day−1. Thus, for computation of ET0, its unit should be converted to MJ m−2 day−1 and thus its value should be divided by 1000. The net accounting of either longwave and shortwave radiation sums up the incoming and outgoing components.$${R}_{n}={R}_{ns}-{R}_{nl}$$
(10)
The net shortwave radiation [Rns, MJ m−2 day−1] is the fraction of the solar radiation Rs that is not reflected from the surface. The fraction of the solar radiation reflected by the surface is known as the albedo [α]. For the green grass reference crop, α is assumed to have a value of 0.23. The value of Rns is:$${R}_{ns}={R}_{s},ast ,(1-alpha )$$
(11)
The difference between outgoing and incoming longwave radiation is called the net longwave radiation [Rnl]. As the outgoing longwave radiation is almost always greater than the incoming longwave radiation, Rnl represents an energy loss. Longwave energy emission is related to surface temperature following Stefan-Boltzmann law. Thus, longwave radiation emission is calculated as positive in the outward direction, while shortwave radiation is positive in the downward direction. The net energy flux leaving the earth’s surface is influenced as well by humidity and cloudiness$${R}_{nl}=sigma ast left(frac{{T}_{max,,K}^{4}+{T}_{min,,K}^{4}}{2}right)ast left(0.34-0.14ast sqrt{{e}_{a}}right)ast left(1.35ast frac{{R}_{s}}{{R}_{so}}-0.35right)$$
(12)
Where σ represent the Stefan-Boltzmann constant (4.903 10-9 MJ K−4 m−2 day−1), Tmax,K and Tmin,K the maximum and minimum absolute temperature (in Kelvin; K = C° + 273.16), ea is the actual vapour pressure; Rs the measured solar radiation [MJ m−2 day−1] and Rso is the calculated clear-sky radiation [MJ m−2 day−1]. Rso is calculated as function of extraterrestrial solar radiation [Ra, MJ m−2 day−1] and elevation (elev, m):$${R}_{so}={R}_{a}ast (0.75+0.00002ast elev)$$
(13)
The extraterrestrial radiation, [Ra, MJ m−2 day−1], is estimated from the solar constant, solar declination and day of the year. It requires specific information about latitude and Julian day to accomplish a trigonometric computation of the amount of solar radiation reaching the top of the atmosphere following trigonometric computations as shown in Allen et al.1.Although the soil heat flux is small compared to Rn, particularly when the surface is covered by vegetation, changes of soil heat flux may still be relevant at monthly scale. However, accurate assessments of soil heat flux may require computation of soil heat capacity, related to its mineral composition and water content, which in turn may be rather inaccurate at global scale at resolution of 30 arc sec. Thus, for simplicity, changes in soil heat fluxes are ignored (G = 0).Bulk Surface Resistance (r
s)The resistance nomenclature distinguishes between aerodynamic resistance and surface resistance factors. The surface resistance parameters are often combined into one parameter, the ‘bulk’ surface resistance parameter which operates in series with the aerodynamic resistance. The surface resistance, rs, describes the resistance of vapour flow through stomata openings, total leaf area and soil surface. The aerodynamic resistance, ra, describes the resistance from the vegetation upward and involves friction from air flowing over vegetative surfaces. Although the exchange process in a vegetation layer is too complex to be fully described by the two resistance factors, good correlations can be obtained between measured and calculated evapotranspiration rates, especially for a uniform grass reference surface.A general equation for the bulk surface resistance (rs, [s m−1]) describes a ratio between the bulk stomatal resistance of a well illuminated leaf (rl) and the active sunlit leaf area of the vegetation:$${r}_{s}=frac{{r}_{l}}{LA{I}_{active}}$$
(14)
The stomatal resistance of a single leaf under well-watered conditions has a value of about 100 s m−1. It can be assumed that about half (0.5) of the total LAI is actively contributing to vapour transfer, while it can also be roughly generalized that for short crops there is a linear relation between LAI and crop height (h):$$LAI=24ast h$$
(15)
When the evapotranspiration simulated with the Penman-Monteith method is referred to a specific reference crop, denoted as ET0, a simplified computation of the method can occur that defines a priori specific variables into constant values. In this case, the reference surface is a hypothetical grass reference crop, well-watered grass of uniform height, actively growing and completely shading the ground, with an assumed crop height of 0.12 m, and an albedo of 0.23. The surface resistance for this hypothetical grass can be simplified to the following:$${r}_{s}=frac{100}{0.5ast 24ast h}$$
(16)
For such reference crop the surface resistance is fixed to 70 s m−1 and implies a moderately dry soil surface resulting from about a weekly irrigation frequency.Aerodynamic Resistance (r
a)The aerodynamic resistance [s m−1] verifies the transfer of water vapour and heat from the vegetation surface into the air, and is controlled by both vegetation status but also atmospheric turbulence under theoretical aspect as:$${r}_{a}=frac{lnleft[frac{{z}_{m}-d}{{z}_{om}}right]ast lnleft[frac{{z}_{h}-d}{{z}_{oh}}right]}{{k}^{2}{u}_{z}}$$
(17)
Zm [m] is the height [h] of wind measurements and Zh [m] is the height of humidity measurements. These are normally set at 2 meters height, although several climate models may provide them for higher heights (e.g. 10 m). The zero plane displacement (d [m]) term can be estimated as two thirds of crop height, while Zom is the roughness length governing momentum transfer, and can be calculated as Zom = 0.123 * h.The roughness length governing transfer of heat and vapour, Zoh [m], can be approximated as one tenth of Zom. k is the von Karman’s constant, equal to 0.41, and uz [m s-1] is the wind speed at height z.The reference surface, as stated, is a hypothetical grass reference crop, well-watered grass of uniform height, actively growing and completely shading the ground, with an assumed crop height of 0.12 m, and an albedo of 0.23. For such reference crop the surface resistance is fixed to 70 s m-1 and implies a moderately dry soil surface resulting from about a weekly irrigation frequency.When crop height is equal to 0.12 and wind/humidity measurements are taken at 2 meters height, then the aerodynamic resistance can be simplified as:$${r}_{a}=frac{208}{{u}_{2}}$$
(18)
Reference Evapotranspiration (ET
0)Given the above, and the specific properties of the standard reference crop, the FAO-56 Penman-Monteith method to estimate ET0 then can be calculated as:$$ETo=frac{0.408ast Delta ast left({R}_{n}-Gright)+gamma frac{900}{{T}_{avg}+273}ast {u}_{2}ast left({e}_{s}-{e}_{a}right)}{Delta +gamma left(1+frac{{r}_{s}}{{r}_{a}}right)}$$
(19)
Aridity Index (AI)Aridity is often expressed as a generalized function of precipitation and PET. The ratio of precipitation over PET (or ET0). That is, the precipitation available in relation to atmospheric water demand64 quantifies water availability for plant growth after ET demand has been met, comparing incoming moisture totals with potential outgoing moisture65.Geospatial analysis and global mapping of the AI for the averaged 1970–2000 time period has been calculated on a per grid cell basis, as:$$Al=MA_Prec/MA_E{T}_{0}$$
(20)
where:AI = Aridity IndexMA_Prec = Mean Annual PrecipitationMA_ET0 = Mean Annual Reference EvapotranspirationMean annual precipitation (MA_Prec) values were obtained from the WorldClim v 2.158, as averaged over the period 1970–2000, while ET0 datasets estimated on a monthly average basis by the Global-ET0 (i.e., modeled using the method described above) were aggregated to mean annual values (MA_ET0). Using this formulation, AI values are unitless, increasing with more humid condition and decreasing with more arid conditions.As a general reference, a climate classification scheme for Aridity Index values provided by UNEP64 provides an insight into the climatic significance of the range of moisture availability conditions described by the AI.
Aridity Index Value

Climate Class

0.65

Humid More

Overall characteristics of air pollutantsThe results of previous studies indicated that local pollution is highly important in determining the emissions of air pollutant. Therefore, in this study, we estimated the changes in pollution and the AQI between the pre-COVID and COVID lockdown periods and among the different regions in Ji’nan. A comparison of the different pollutant concentrations analysed in this study shows that the concentrations of almost all pollutants decreased during the COVID lockdown period; only the concentration of O3 increased continuously as the COVID lockdown period progressed (Fig. 1).Figure 1Spatial distributions of the different observation sites and industrial enterprises above a designated size threshold in Ji’nan city. JCE, machine tool factory No. 2; LSX, technical college; JNS, Ji’nan fourth building group; KFQ, economic development zone; KGS, Kegansuo; LWZ, Laiwu memorial hall; NKS, Agricultural Scientific Institute; SZZ, Seed warehouse of Shandong Province; SJC, Ji’nan monitoring station; TXG, Taixing company; CQD, Changqing school. Red circles, red triangles and red squares represent stations in urban, urban-industrial and suburban regions, respectively. The map of Observation site was completed by the geostatistical analysis module of ArcGIS (version 10.3, https://developers.arcgis.com/).Full size imageDuring the observation period, the daily average mass concentrations of PM10, PM2.5, SO2, NO2, CO, and O3 in Ji’nan were 137.09 µg/m3, 101.35 µg/m3, 22.70 µg/m3, 39.77 µg/m3, 1.28 mg/m3, and 71.84 µg/m3, respectively (Fig. 2). The mass concentrations of PM10 and PM2.5 exceeded the daily average Grade I values (50 µg/m3 and 35 µg/m3) of the Ambient Air Quality Standard of China (CAAQS, GB 3095-2012) during the whole observation period. In contrast, the mass concentrations of NO2, SO2, CO and O3 were substantially lower than the daily average Grade I values (80 µg/m3, 50 µg/m3, 4 mg/m3 and 100 µg/m3, respectively) of the CAAQS each day. During the pre-COVID period, the daily average mass concentrations of PM10, PM2.5, SO2, NO2, CO, and O3 in Ji’nan were 177.03 µg/m3, 125.94 µg/m3, 26.39 µg/m3, 54.52 µg/m3, 1.59 mg/m3, and 60.72 µg/m3, respectively. The mass concentrations of all these pollutants, except NO2, CO and O3, exceeded the daily average Grade I values of the CAAQS. The mass concentration trends during the COVID lockdown period were consistent with those during the pre-COVID period, but there were significant differences in the concentrations between the periods. In summary, the air quality in Ji’nan was generally good from January 24 to February 7, 2020, mainly due to the strict prevention and control measures for COVID-19.Figure 2Temporal variations in the mass concentrations of air pollutants (PM10, PM2.5, NO2, SO2, CO and O3) at the urban site in Ji’nan during the observation period.Full size imageEffects of regional differences and lockdown on air pollutantsOur results reveal that the PM10, PM2.5, NO2, SO2, CO and O3 concentrations in the urban, suburban and urban-industrial regions differed significantly between the COVID lockdown and pre-COVID periods (Figs. 3, 4).Figure 3Mean concentrations (± SD, mg/m3) of PM10, PM2.5, NO2, SO2, CO and O3 during the pre-COVID and COVID lockdown periods in 2020; the values were determined by combining the urban, suburban and urban-industrial areas at the regional scale. *, ** and *** represent significant differences between the pre-COVID and COVID lockdown periods in the same region (Duncan test, *p = 0.05; **p = 0.01; ***p = 0.001), with nonsignificant results being excluded.Full size imageFigure 4General reductions in the concentrations of major air pollutants.Full size imageNOx, one of the most important pollutants and a major health hazard, was studied in different countries across the world during COVID-19-related lockdowns. In all three regions studied herein, the highest rate of reduction in NO2 concentrations was observed during the COVID lockdown period (Fig. 4), with the NO2 levels in the COVID lockdown period being 54.02% on average lower than those during the pre-COVID period (53.07% in urban area, 48.31% in the suburban areas and 55.74% in the urban-industrial area) (Fig. 4); this reduction is greater than that reported at other sites by 26–42%11 and 14–38%18 but lower than that (50–62%) in Barcelona and Madrid in Spain33. As shown in Fig. 3E, the NO2 concentrations in the urban, suburban and urban-industrial areas were significantly higher in the pre-COVID period than in the COVID lockdown period, with the pre-COVID the NO2 levels in the urban area being 13.46% and 27.63% higher than those in the suburban and urban-industrial areas, respectively. During the COVID-19 lockdown period, the NO2 levels in urban areas were 4.69% and 31.75% higher than those in the suburban and urban-industrial areas, respectively. Blocking and controlling the air pollution associated with COVID-19 has helped reduce ground NO2 levels34 and this effect might be correlated with the tropospheric NO2 column density27. Among all sources of NO2, automobile emissions and power generation are the most important5. A systematic review confirmed that a short-term increase in the NO2 concentration in urban areas correlates to an increase in the number of pneumonia hospitalizations5,35.The trends in the CO concentration were similar to those in the NO2 level. During the COVID-lockdown period, the average CO mass concentrations in the urban, suburban and urban industrial areas were 1.08 mg/m3, 1.16 mg/m3 and 1.14 mg/m3, respectively, which decreased by 27.78%, 29.46% and 36.61%, respectively, compared with those during the pre-COVID period. The highest levels of PM10 were also observed during the pre-COVID period in the urban, suburban, and urban-industrial areas in Ji’nan (Fig. 4). The reductions in PM2.5 and CO emissions in urban and urban-industrial areas are generally higher than those in suburban areas25, supporting our findings. Notably, PM2.5 and CO are generated mainly by construction activities and from road dust, natural soil dust and dust from urban-industrial activities36. In contrast, the differences in the PM10 concentrations among the three regions were not significant during either the pre-COVID period or the COVID-lockdown period (Fig. 3A), which suggests that particles in Ji’nan are strongly diffused. However, the COVID lockdown period had a significant effect on the PM10 concentrations, with 42.86%, 44.26% and 50.60% differences in the PM10 concentration between the pre-COVID and COVID lockdown periods in the urban, suburban and urban-industrial areas, respectively (average of 44.92%, Fig. 4). The main reasons for the decreases in the concentration of PM were the severe restrictions on vehicle traffic, the cessation of industrial activities, and the stopping of construction projects, which are important sources of floating dust in the urban air37. Despite the overall consistency among the observed changes in all regions for the different air pollutants (except O3), at the regional level, some differences were statistically significant, while others were not due to the variability among stations, with the differences being more pronounced at the urban, suburban and urban-industrial stations.O3 is a secondary pollutant involved in different atmospheric reaction mechanisms and acts as both a source and sink. Generally, the impact of lockdowns on O3 was mixed, with its levels generally falling within ± 20%38, but total O3 levels remained relatively stable18. In this study, by comparing the regional mean concentrations throughout the COVID-19 period, we found that O3 concentrations were higher during the COVID lockdown period than during the pre-COVID period, especially in the urban regions (Fig. 3). Furthermore, the mean O3 concentration at all stations during the COVID lockdown period was 37.42% higher than that during the pre-COVID period (46.84% in the urban areas, 18.27% in the suburban area, and 19.84% in the urban-industrial areas) (Fig. 4); this finding is consistent with the outcomes of other studies, which reported that O3 concentrations increased by (on average) 20% during lockdowns39, potentially due, in part, to atmospheric reactivity37. The higher lockdown O3 concentrations can be attributed to the following three reasons: (1) low PM concentrations can result in more sunlight passing through the atmosphere, encouraging increased photochemical activities and thus higher O3 production40; (2) a reduction in NOx emissions increases O3 formation41; and (3) lower PM2.5 concentrations means their role as a sink for hydroperoxy radicals (HO2) is less effective, which would increase peroxy radical-mediated O3 production42. During the pre-COVID period, the O3 levels were not significantly different among the region, and the same results were observed during the COVID lockdown period. However, in the urban and urban-industrial areas, the O3 levels during the COVID lockdown period were significantly higher than those in the pre-COVID period (p  More

Gamma source and dose modelingThe general literature contains conflicting results on whether the energies of photons interacting with fungi affects the radiotrophic response. As such, we sought to control critical variables while irradiating the fungi with ionizing radiation from a sealed Cs-137 source and a UV source. The Cs-137 source emitted a photon at 662 keV along with other lower energy photons near 30 keV (Table S1).A review of previous studies was conducted to identify the gamma dose rate and total dose that should be targeted for exposure (Table S2). Those dose rates ranged from 600,000 rad delivered in 1.5 h to 0.08 rad delivered in 16 h. Even among studies examining the same fungi attributes, the total dose varied dramatically. For the present study, we used a Health Physics code to target a 50-rad dose over a one-week exposure. This dose was selected as it changes blood count observed in most humans24. We hypothesized that this dose would induce physiological changes in the fungi without causing a high rate of lethality. A MicroShield (Grove Software, Inc.) model was created to identify the quantity of radioactive material and distance between source and sample necessary to achieve the dose of 50 rad in seven days. From a sensitivity analysis of the MicroShield model, it was determined that ~ 350 µCi of Cs-137 would create a dose rate of ~ 50 rad in seven days (Fig. 1; Table S3), if placed 1.8 cm from the surface of the fungi. It should be noted that Microshield values are often conservative and likely underestimate the actual dose on target. In addition, 50 rad falls in the middle of the large range for energies previously reported in the general literature (Table S2).Figure 1Time required on target to achieve an exposure of 50 rad determined in MicroShield and based on an activity of ~ 350 µCi for Cs-137 source and the vertical distance between the source and fungus.Full size imageThe dose from the Cs-137 source on the fungal mycelium is also dependent on the radial growth of the fungus from the center plug used to initiate growth. As the fungus grows away from the source, the leading edge will experience a lower total dose of radiation. Although a uniform dose would have been ideal, a source with activity sufficient to create a uniform radiation field would have initiated a variety of safety controls deemed impractical for this experiment. The background radiation dose at the testing site in Albuquerque is approximately 10 µrem h−1; the dose at the outermost area of the Petri dish was measured at 65,553 µrad h−1. As this dose was primarily from gamma emissions, rad and rem can be considered equivalent. To validate the simulation, a dose rate study was performed using thermoluminescent dosimeters (TLD) placed at varying distances from the center of the source. The TLD placed directly under the source measured ~ 100 rad over the seven-day exposure, which is double the prediction from the simulation (50 rad; Fig. 2A). However, at a radial distance of 3.5 cm, the measured and estimated total dose over seven days were much closer, 12.3 and 11.4 rad, respectively. A comparison of the measured and estimated dose on target demonstrated a non-linear correlation (Fig. 2B), in which the simulation better approximated the dose at larger radial distances from the source.Figure 2(A) The total gamma dose on the fungal mycelial at 7 days as a function of the radial distance from the central mycelium plug based on empirical measurements (-●-) and estimated from simulations (-○-). (B) Observed correlation between the measured and estimated doses at varying radial distances.Full size imageIn order to normalize the energy deposited in the fungi from Cs-137 and the UV lamp sources, the units of MeV g−1 s−1 were selected for additional simulations. Monte Carlo N-Particle transport code (MCNP) simulations were used to determine this quantity for the Cs-137. The materials and geometry of the Petri dish and fungus used for these simulations are shown in Fig. 3. The Cs-137 was simulated as a point source located 1.5 cm from the top of the fungi. The Petri dish was set on a bakelite table. The setup was located in the center of a notional 5 m × 5 m × 5 m room with 30 cm thick concrete walls and filled with air. Leads bricks set on the table surrounded the petri dish and source. The International Commission on Radiological Protection (ICRP) material definitions did not contain data for fungal mycelia. Thus, we selected for skin as the closest approximation of the properties of the fungal mycelium25. This simulation gave a result for the energy deposited per particle as 6.53 × 10–4 MeV g−1, which for a 350 μCi activity, the rate of energy deposition was determined to be 7907 MeV g−1 s−1.Figure 3Top (upper left) and side (upper right) view of the Petri dish and fungi materials and distances used to determine energy deposition rates in MCNP. The overall geometry used for the radiation transport simulations, including the lead bricks, is shown from the top down (lower left) and from the side (lower right).Full size imageUV source and irradiationOur intent was to match the energy absorbed by the fungi to control for all variables except the photon energy difference between the Cs-137 source and UV lamp. The spectrum of energies emitted from the Cs-137 source varied significantly from those of the UV lamp, which in this case was a 30 W deuterium lamp that emitted from 185 to 400 nm (Fig. S1). This wide bandwidth represented photon energies ranging from 3.1 to 6.7 eV. The bandwidth of the UV exposure was limited to 300–350 nm using a 50-nm bandpass filter centered at 325 nm to ensure that incident photons would be in the UV energy range and not form ozone. Because we chose to match the overall energy deposited from the UV source to the gamma source it was necessary to attenuate the beam to the right power level. We assumed that all the UV energy would be absorbed near the surface rather than in the bulk since the fungi were melanized. This simplified the calculations and reduced risk, given the challenge of accurately estimating the absorbance of the fungi. The power deposited by the gamma source was calculated as the rate of energy deposition was determined to be 7907 MeV g−1 s−1 (1.3 nW g−1 s−1). Given the initial size of the plug was 1 cm in diameter, the desired lamp fluence needed to be ~ 2.8 nW cm−2. Across the spectrum of interest, the lamp power was determined to be 3.202 × 10–4 mW, thus requiring an attenuation of 8.7 × 10–9 (OD 8.06), reducing the lamp power to ~ 3 pW cm−2 and achieving a reasonably close power density to the target. Due to the sensitivity of UV detectors, the required power densities could not be measured directly. Alternatively, we measured the neutral density filters to verify the prescription was indeed correct.Response of P. variotii to irradiationUniform plugs (~ 5-mm in diameter) of actively growing mycelia of P. variotii were cut using the end of a Pasteur pipette and transferred a Petri plate containing potato dextrose agar (PDA) one day prior to initiating exposure experiments. The diameter of the mycelium was measured from four images, separated by precisely six hours, over the course of seven days and used to measure the growth rate. Differences in the pigmentation of the fungi under the different conditions was quantified in Fiji26 through analysis of grayscale images collected at day seven, following the method described by Brilhante et al.27 A ratiometric value was derived from the grayscale values and the white background, which corrected for variations in lighting across or between images.Significant differences in the pigmentation but not growth rates of P. variotii were associated with exposure to UV and gamma to irradiation, based on One-Way ANOVA analyses (Fig. 4A; Table S4). P. variotii is a ubiquitous filamentous fungus commonly inhabiting soil, decaying plants, and food products and was reported to be present on the surface of the walls of Unit-4 at ChNPP22,28. P. variotii is also a common food contaminant and is resistant to high temperature and metals29,30, despite being more sensitive to gamma irradiation than other fungi such as Aspergillus fumigatus31. In the present study, we hypothesized that positive radiation-induced effects in P. variotii would result in enhanced growth rates due to gamma irradiation. Across all conditions, the average growth rate of P. variotii was ~ 5.6 ± 0.9 mm d−1 (mean ± standard deviation). While the growth rate of P. variotii exposed to gamma irradiation was greater compared with the control and UV-irradiated samples (Fig. 4A), the difference in the mean growth rates was not significant (P = 0.255) by ANOVA.Figure 4(A) Growth rate and pigmentation of control (orange square), gamma- (blue square), and UV- (red square) irradiated cultures of P. variotti (mean ± standard deviation). (B) Estimated total irradiation dose experienced by the mycelial as a function of the distance from the central source. Exponential decay fit: − 3.6 + 105.7*exp(− 0.75*x); Adjusted R2 = 0.998. (C) Graphical representation of the irradiation dose based on the growth rate and duration of exposure for zones of mycelia as a function of radial distance from the central plug.Full size imageWe also hypothesized that the pigmentation of P. variotii would increase with exposure to gamma and UV irradiation. While P. variotti does not produce melanin, it does produce a pigment, Ywa1, from a polyketide synthesis (PKS) gene cluster and has been shown to protect the fungus against UV-C irradiation28. In some melanized fungi, Ywa1 serves as precursor and can be hydrolyzed to 1,3,6,8-tetrahydroxynaphthalen (T4HN). T4HN may then be converted to 1,8-dihydroxynaphthalene (1,8-DHN) melanin through the DHN pathway32. However, Lim et al.28 concluded that P. variotii does not produce true melanin as the pigmentation was maintained when the DHN-melanin pathway was inhibited. Significant differences in the pigmentation of P. variotii were observed among the three different sample types (P  More

Losey, J. E. & Vaughan, M. The economic value of ecological services provided by insects. Bioscience 56, 311–323 (2006).Article 

Google Scholar 
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1–6 (2019).ADS 
CAS 
Article 

Google Scholar 
Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Seto, K. C., Güneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. 109, 16083–16088 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).PubMed 
Article 
CAS 

Google Scholar 
Petersen, H. & Luxton, M. A comparative analysis of soil fauna populations and their role in decomposition processes. Oikos 39, 288–388 (1982).Article 

Google Scholar 
Frizzo, T. L., Souza, L. M., Sujii, E. R. & Togni, P. H. Ants provide biological control on tropical organic farms influenced by local and landscape factors. Biol. Control 151, 104378 (2020).CAS 
Article 

Google Scholar 
Elizalde, L. et al. The ecosystem services provided by social insects: Traits, management tools and knowledge gaps. Biol. Rev. 95, 1418–1441 (2020).PubMed 
Article 

Google Scholar 
Zhong, Z. et al. Soil engineering by ants facilitates plant compensation for large herbivore removal of aboveground biomass. Ecology 102, e03312 (2021).PubMed 
Article 

Google Scholar 
Ortiz, D. P., Elizalde, L. & Pirk, G. I. Role of ants as dispersers of native and exotic seeds in an understudied dryland. Ecol. Entomol. 46, 626–636 (2021).Article 

Google Scholar 
Li, X. et al. A facilitation between large herbivores and ants accelerates litter decomposition by modifying soil microenvironmental conditions. Funct. Ecol. 35, 1822–1832 (2021).Article 

Google Scholar 
Wendt, C. F. et al. Local environmental variables are key drivers of ant taxonomic and functional beta-diversity in a Mediterranean dryland. Sci. Rep. 11, 1–10 (2021).ADS 
Article 
CAS 

Google Scholar 
Lach, L. Invasive ant establishment, spread, and management with changing climate. Curr. Opin. Insect Sci. 47, 119–124 (2021).PubMed 
Article 

Google Scholar 
Buczkowski, G. & Richmond, D. S. The effect of urbanization on ant abundance and diversity: A temporal examination of factors affecting biodiversity. PLoS ONE 7, e41729 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Holway, D. A. & Suarez, A. V. Homogenization of ant communities in mediterranean California: The effects of urbanization and invasion. Biol. Conserv. 127, 319–326 (2006).Article 

Google Scholar 
Miguelena, J. G. & Baker, P. B. Effects of urbanization on the diversity, abundance, and composition of ant assemblages in an arid city. Environ. Entomol. 48, 836–846 (2019).PubMed 
Article 

Google Scholar 
Nielsen, A. B., van den Bosch, M., Maruthaveeran, S. & van den Bosch, C. K. Species richness in urban parks and its drivers: A review of empirical evidence. Urban Ecosyst. 17, 305–327 (2014).Article 

Google Scholar 
Clarke, K. M., Fisher, B. L. & LeBuhn, G. The influence of urban park characteristics on ant (Hymenoptera, Formicidae) communities. Urban Ecosyst. 11, 317–334 (2008).Article 

Google Scholar 
Peng, M.-H., Hung, Y.-C., Liu, K.-L. & Neoh, K.-B. Landscape configuration and habitat complexity shape arthropod assemblage in urban parks. Sci. Rep. 10, 16043 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Santos, M. N., Delabie, J. H. & Queiroz, J. M. Biodiversity conservation in urban parks: A study of ground-dwelling ants (Hymenoptera: Formicidae) in Rio de Janeiro City. Urban Ecosyst. 22, 927–942 (2019).Article 

Google Scholar 
McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).Article 

Google Scholar 
Lahr, E. C., Dunn, R. R. & Frank, S. D. Getting ahead of the curve: Cities as surrogates for global change. Proc. R. Soc. B Biol. Sci. 285, 20180643 (2018).Article 
CAS 

Google Scholar 
Abdel-Dayem, M. S. et al. Ant diversity and composition patterns along the urbanization gradients in an arid city. J. Nat. Hist. 55, 2521–2547 (2021).Article 

Google Scholar 
Nooten, S. S., Lee, R. H. & Guénard, B. Evaluating the conservation value of sacred forests for ant taxonomic, functional and phylogenetic diversity in highly degraded landscapes. Biol. Conserv. 261, 109286 (2021).Article 

Google Scholar 
Bhagwat, S. A. & Rutte, C. Sacred groves: Potential for biodiversity management. Front. Ecol. Environ. 4, 519–524 (2006).Article 

Google Scholar 
Ballullaya, U. P. et al. Stakeholder motivation for the conservation of sacred groves in south India: An analysis of environmental perceptions of rural and urban neighbourhood communities. Land Use Policy 89, 104213 (2019).Article 

Google Scholar 
Lowman, M. D. & Sinu, P. A. Can the spiritual values of forests inspire effective conservation?. Bioscience 67, 688–690 (2017).Article 

Google Scholar 
Rajesh, T. P., Ballullaya, U. P., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of South India. Urban Ecosyst. 23, 1335–1348 (2020).Article 

Google Scholar 
Rajesh, T. P., Unni, A. P., Ballullaya, U. P., Manoj, K. & Sinu, P. A. An insight into the quality of sacred groves–an island habitat–using leaf-litter ants as an indicator in a context of urbanization. J. Trop. Ecol. 37, 82–90 (2021).Article 

Google Scholar 
Holway, D. A., Lach, L., Suarez, A. V., Tsutsui, N. D. & Case, T. J. The causes and consequences of ant invasions. Annu. Rev. Ecol. Syst. 33, 181–233 (2002).Article 

Google Scholar 
Plowes, R. M., Dunn, J. G. & Gilbert, L. E. The urban fire ant paradox: Native fire ants persist in an urban refuge while invasive fire ants dominate natural habitats. Biol. Invasions 9, 825–836 (2007).Article 

Google Scholar 
Rajesh, T. P., Ballullaya, U. P., Surendran, P. & Sinu, P. A. Ants indicate urbanization pressure in sacred groves of southwest India: A pilot study. Curr. Sci. 113, 317–322 (2017).Article 

Google Scholar 
Wetterer, J. K. Worldwide distribution and potential spread of the long-legged ant, Anoplolepis gracilipes (Hymenoptera: Formicidae). Sociobiology 45, 77–97 (2005).
Google Scholar 
Bhagwat, S. A., Kushalappa, C. G., Williams, P. H. & Brown, N. D. A landscape approach to biodiversity conservation of sacred groves in the Western Ghats of India. Conserv. Biol. 19, 1853–1862 (2005).Article 

Google Scholar 
Chandrashekara, U. M. & Sankar, S. Ecology and management of sacred groves in Kerala, India. For. Ecol. Manag. 112, 165–177 (1998).Article 

Google Scholar 
Asha, G., Navya, K. K., Rajesh, T. P. & Sinu, P. A. Roller dung beetles of dung piles suggest habitats are alike, but that of guarding pitfall traps suggest habitats are different. J. Trop. Ecol. 37, 209–213 (2021).Article 

Google Scholar 
Manoj, K. et al. Diversity of Platygastridae in leaf litter and understory layers of tropical rainforests of the Western Ghats Biodiversity Hotspot, India. Environ. Entomol. 46, 685–692 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hariraveendra, M., Rajesh, T. P., Unni, A. P. & Sinu, P. A. Prey–predator interaction suggests sacred groves are not functionally different from neighbouring used lands. J. Trop. Ecol. 36, 220–224 (2020).Article 

Google Scholar 
Bingham, C. T. The fauna of British India, including Ceylon and Burma. Hymenoptera, Vol. II. Ants and Cuckoo-wasps. (1903).Bolton, B. Identification Guide to the Ant Genera of the World (Harvard University Press, 1994).
Google Scholar 
Bellow, J. G. & Nair, P. K. R. Comparing common methods for assessing understory light availability in shaded-perennial agroforestry systems. Agric. For. Meteorol. 114, 197–211 (2003).ADS 
Article 

Google Scholar 
Dobson, A. J. & Barnett, A. G. An Introduction to Generalized Linear Models (Chapman and Hall/CRC, 2018).MATH 

Google Scholar 
Fox, J. et al. Package ‘car’, Vol. 16, (R Foundation for Statistical Computing, 2012).Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (H ill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
Kim, T. N., Savannah, B., Bill, D. W., Douglas, A. L. & Claudio, G. Disturbance differentially affects alpha and beta diversity of ants in tallgrass prairies. Ecosphere 9, e02399 (2018).Article 

Google Scholar 
Hartig, F. & Hartig, M. F. Package ‘DHARMa’. R package (2017).Nash, J. C. On best practice optimization methods in R. J. Stat. Softw. 60, 1–14 (2014).Article 

Google Scholar 
Berman, M., Andersen, A. N. & Ibanez, T. Invasive ants as back-seat drivers of native ant diversity decline in New Caledonia. Biol. Invasions 15, 2311–2331 (2013).Article 

Google Scholar 
Melliger, R. L., Braschler, B., Rusterholz, H.-P. & Baur, B. Diverse effects of degree of urbanisation and forest size on species richness and functional diversity of plants, and ground surface-active ants and spiders. PLoS ONE 13, e0199245 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Guénard, B., Cardinal-De Casas, A. & Dunn, R. R. High diversity in an urban habitat: Are some animal assemblages resilient to long-term anthropogenic change?. Urban Ecosyst. 18, 449–463 (2015).Article 

Google Scholar 
Slipinski, P., Zmihorski, M. & Czechowski, W. Species diversity and nestedness of ant assemblages in an urban environment. Eur. J. Entomol. 109, 197 (2012).Article 

Google Scholar 
Heterick, B. E., Lythe, M. & Smithyman, C. Urbanisation factors impacting on ant (Hymenoptera: Formicidae) biodiversity in the Perth metropolitan area, Western Australia: Two case studies. Urban Ecosyst. 16, 145–173 (2013).Article 

Google Scholar 
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 1–13 (2020).Article 
CAS 

Google Scholar 
Goodman, M. & Warren, R. J. II. Non-native ant invader displaces native ants but facilitates non-predatory invertebrates. Biol. Invasions 21, 2713–2722 (2019).Article 

Google Scholar 
Philpott, S. M. & Armbrecht, I. Biodiversity in tropical agroforests and the ecological role of ants and ant diversity in predatory function. Ecol. Entomol. 31, 369–377 (2006).Article 

Google Scholar 
Philpott, S. M., Perfecto, I. & Vandermeer, J. Effects of management intensity and season on arboreal ant diversity and abundance in coffee agroecosystems. Biodivers. Conserv. 15, 139–155 (2006).Article 

Google Scholar 
García-Cárdenas, R., Montoya-Lerma, J. & Armbrecht, I. Ant diversity under three coverages in a Neotropical coffee landscape. Rev. Biol. Trop. 66, 1373–1389 (2018).Article 

Google Scholar 
Sinu, P. A. et al. Invasive ant (Anoplolepis gracilipes) disrupts pollination in pumpkin. Biol. Invasions 19, 2599–2607 (2017).Article 

Google Scholar 
Tsang, T. P., Dyer, E. E. & Bonebrake, T. C. Alien species richness is currently unbounded in all but the most urbanized bird communities. Ecography 42, 1426–1435 (2019).Article 

Google Scholar 
D’ettorre, P. Invasive eusocieties: commonalities between ants and humans. In Human Dispersal and Species Movement (eds Boivin, N. et al.) (Cambridge University Press, 2017).
Google Scholar 
Wetterer, J. K. Worldwide spread of the longhorn crazy ant, Paratrechina longicornis (Hymenoptera: Formicidae). Myrmecol. News 11, 137–149 (2008).
Google Scholar 
Lizon à l’Allemand, S. & Witte, V. sophisticated, modular communication contributes to ecological dominance in the invasive ant Anoplolepis gracilipes. Biol. invasions 12, 3551–3561 (2010).Article 

Google Scholar 
Silverman, J. & Buczkowski, G. Behaviours mediating ant invasions. In Biological Invasions and Animal Behaviour (eds Weis, J. S. & Sol, D.) (Cambridge University Press, 2016).
Google Scholar  More

Gause, G. F. Experimental analysis of Vito Volterra’s mathematical theory of the struggle for existence. Science 79, 16–17 (1934).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Amarasekare, P. Competitive coexistence in spatially structured environments: A synthesis. Ecol. Lett. 6, 1109–1122 (2003).Article 

Google Scholar 
HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).Article 

Google Scholar 
Wisz, M. S. et al. The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling. Biol. Rev. 88, 15–30 (2013).PubMed 
Article 

Google Scholar 
Frey, S., Fisher, J. T., Burton, A. C. & Volpe, J. P. Investigating animal activity patterns and temporal niche partitioning using camera-trap data: Challenges and opportunities. Remote Sens. Ecol. Conserv. 3, 123–132 (2017).Article 

Google Scholar 
Davis, C. L. et al. Ecological correlates of the spatial co-occurrence of sympatric mammalian carnivores worldwide. Ecol. Lett. 21, 1401–1412 (2018).PubMed 
Article 

Google Scholar 
Durant, S. M. Competition refuges and coexistence: An example from Serengeti carnivores. J. Anim. Ecol. 67, 370–386 (1998).Article 

Google Scholar 
Fedriani, J. M., Fuller, T. K., Sauvajot, R. M. & York, E. C. Competition and intraguild predation among three sympatric carnivores. Oecologia 125, 258–270 (2000).ADS 
PubMed 
Article 

Google Scholar 
Kamler, J. F., Ballard, W. B., Gilliland, R. L. & Mote, K. Spatial relationships between swift foxes and coyotes in northwestern Texas. Can. J. Zool. 81, 168–172 (2003).Article 

Google Scholar 
Vanak, A. T. et al. Moving to stay in place: Behavioral mechanisms for coexistence of African large carnivores. Ecology 94, 2619–2631 (2013).PubMed 
Article 

Google Scholar 
Donadio, E. & Buskirk, S. W. Diet, morphology, and interspecific killing in carnivora. Am. Nat. 167, 524–536 (2006).PubMed 
Article 

Google Scholar 
Tsunoda, H. et al. Food niche segregation between sympatric golden jackals and red foxes in central Bulgaria. J. Zool. 303, 64–71 (2017).Article 

Google Scholar 
Palomares, F. & Caro, T. M. Interspecific killing among mammalian carnivores. Am. Nat. 153, 492–508 (1999).CAS 
PubMed 
Article 

Google Scholar 
Linnell, J. D. C. & Strand, O. Interference interactions, co-existence and conservation of mammalian carnivores. Divers. Distrib. 6, 169–176 (2000).Article 

Google Scholar 
Kamler, J. F., Stenkewitz, U., Klare, U., Jacobsen, N. F. & MacDonald, D. W. Resource partitioning among cape foxes, bat-eared foxes, and black-backed jackals in South Africa. J. Wildl. Manag. 76, 1241–1253 (2012).Article 

Google Scholar 
Di Bitetti, M. S., Di Blanco, Y. E., Pereira, J. A., Paviolo, A. & Pírez, I. J. Time Partitioning favors the coexistence of sympatric crab-eating foxes (Cerdocyon thous) and Pampas Foxes (Lycalopex gymnocercus). J. Mammal. 90, 479–490 (2009).Article 

Google Scholar 
Lesmeister, D. B., Nielsen, C. K., Schauber, E. M. & Hellgren, E. C. Spatial and temporal structure of a mesocarnivore guild in Midwestern North America. Wildl. Monogr. 191, 1–61 (2015).Article 

Google Scholar 
Di Bitetti, M. S., De Angelo, C. D., Di Blanco, Y. E. & Paviolo, A. Niche partitioning and species coexistence in a Neotropical felid assemblage. Acta Oecologica 36, 403–412 (2010).ADS 
Article 

Google Scholar 
Monterroso, P., Alves, P. C. & Ferreras, P. Plasticity in circadian activity patterns of mesocarnivores in southwestern Europe: Implications for species coexistence. Behav. Ecol. Sociobiol. 68, 1403–1417 (2014).Article 

Google Scholar 
Tsunoda, H., Ito, K., Peeva, S., Raichev, E. & Kaneko, Y. Spatial and temporal separation between the golden jackal and three sympatric carnivores in a human-modified landscape in central Bulgaria. Zool. Ecol. 28, 172–179 (2018).Article 

Google Scholar 
Tsunoda, H. et al. Spatio-temporal partitioning facilitates mesocarnivore sympatry in the Stara Planina Mountains, Bulgaria. Zoology 141, 125801 (2020).PubMed 
Article 

Google Scholar 
Ramesh, T., Kalle, R., Sankar, K. & Qureshi, Q. Spatio-temporal partitioning among large carnivores in relation to major prey species in Western Ghats. J. Zool. 287, 269–275 (2012).Article 

Google Scholar 
Gómez-Ortiz, Y., Monroy-Vilchis, O. & Castro-Arellano, I. Temporal coexistence in a carnivore assemblage from central Mexico: Temporal-domain dependence. Mammal Res. 64, 333–342 (2019).Article 

Google Scholar 
Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).MathSciNet 
MATH 
Article 

Google Scholar 
Meredith, M. & Ridout, M. Overlap: Estimates of coefficient of overlapping for animal activity patterns. https://cran.r-project.org/web/packages/overlaphttps://cran.r-project.org/web/packages/overlap/index.html (2018).Marinho, P. H., Fonseca, C. R., Sarmento, P., Fonseca, C. & Venticinque, E. M. Temporal niche overlap among mesocarnivores in a Caatinga dry forest. Eur. J. Wildl. Res. 66, 1–13 (2020).Article 

Google Scholar 
Vilella, M., Ferrandiz-Rovira, M. & Sayol, F. Coexistence of predators in time: Effects of season and prey availability on species activity within a Mediterranean carnivore guild. Ecol. Evol. 10, 11408–11422 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao, G. et al. Spatio-temporal coexistence of sympatric mesocarnivores with a single apex carnivore in a fine-scale landscape. Glob. Ecol. Conserv. 21, e00897 (2020).Article 

Google Scholar 
Farmer, M. J., Allen, M. L., Olson, E. R., Van Stappen, J. & Van Deelen, T. R. Agonistic interactions and island biogeography as drivers of carnivore spatial and temporal activity at multiple scales. Can. J. Zool. 99, 309–317 (2021).Article 

Google Scholar 
Watabe, R. & Saito, M. U. Diel activity patterns of three sympatric medium-sized carnivores during winter and spring in a heavy snowfall area in northeastern Japan. Mammal Study 46, 69–75 (2021).Article 

Google Scholar 
Lashley, M. A. et al. Estimating wildlife activity curves: comparison of methods and sample size. Sci. Rep. 8, 4173 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Niedballa, J., Wilting, A., Sollmann, R., Hofer, H. & Courtiol, A. Assessing analytical methods for detecting spatiotemporal interactions between species from camera trapping data. Remote Sens. Ecol. Conserv. 5, 272–285 (2019).Article 

Google Scholar 
Karanth, K. U. et al. Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient. Proc. R. Soc. B Biol. Sci. 284, 20161860 (2017).Article 

Google Scholar 
Cusack, J. J. et al. Revealing kleptoparasitic and predatory tendencies in an African mammal community using camera traps: A comparison of spatiotemporal approaches. Oikos 126, 812–822 (2017).Article 

Google Scholar 
Balme, G. et al. Big cats at large: density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267 (2019).Article 

Google Scholar 
Li, Z. et al. Coexistence of two sympatric flagship carnivores in the human-dominated forest landscapes of Northeast Asia. Landsc. Ecol. 34, 291–305 (2019).Article 

Google Scholar 
Lahkar, D., Ahmed, M. F., Begum, R. H., Das, S. K. & Harihar, A. Inferring patterns of sympatry among large carnivores in Manas National Park: A prey-rich habitat influenced by anthropogenic disturbances. Anim. Conserv. 24, 589–601 (2021).Article 

Google Scholar 
Paúl, M. J., Layna, J. F., Monterroso, P. & Álvares, F. Resource partitioning of sympatric African Wolves (Canis lupaster) and side-striped jackals (Canis adustus) in an arid environment from West Africa. Diversity 12, 477 (2020).Article 

Google Scholar 
Prat-Guitart, M., Onorato, D. P., Hines, J. E. & Oli, M. K. Spatiotemporal pattern of interactions between an apex predator and sympatric species. J. Mammal. 101, 1279–1288 (2020).Article 

Google Scholar 
Stone, L. & Roberts, A. The checkerboard score and species distributions. Oecologia 85, 74–79 (1990).ADS 
PubMed 
Article 

Google Scholar 
Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in r. J. Stat. Softw. 69, 1–17 (2016).Article 

Google Scholar 
Noor, A., Mir, Z. R., Veeraswami, G. G. & Habib, B. Activity patterns and spatial co-occurrence of sympatric mammals in the moist temperate forest of the Kashmir Himalaya, India. Folia Zool. 66, 231–241 (2017).Article 

Google Scholar 
de Satgé, J., Teichman, K. & Cristescu, B. Competition and coexistence in a small carnivore guild. Oecologia 184, 873–884 (2017).ADS 
PubMed 
Article 

Google Scholar 
Kass, J. M., Tingley, M. W., Tetsuya, T. & Koike, F. Co-occurrence of invasive and native carnivorans affects occupancy patterns across environmental gradients. Biol. Invasions 22, 2251–2266 (2020).Article 

Google Scholar 
Louppe, V., Herrel, A., Pisanu, B., Grouard, S. & Veron, G. Assessing occupancy and activity of two invasive carnivores in two Caribbean islands: implications for insular ecosystems. J. Zool. 313, 182–194 (2020).Article 

Google Scholar 
Proulx, G. et al. World distribution and status of the genus Martes in 20. In Martens and Fishers (Martes) in Human-Altered Environments (eds Harrison, D. J. et al.) 21–76 (Springer, Berlin, 2005). https://doi.org/10.1007/b99487.Chapter 

Google Scholar 
Ohdachi, S. D., Ishibashi, Y., Iwasa, M., Fukuki, D. & Saitoh, T. The Wild Mammals of Japan 2nd edn. (Shokadoh Book Seller, Kyoto, 2015).
Google Scholar 
Kauhala, K. & Saeki, M. Nyctereutes procyonoides. The IUCN Red List of Threatened Species. https://www.iucnredlist.org/species/14925/85658776 (2016).Yamamoto, Y. Comparative analyses on food habits of Japanese marten, red fox, badger and raccoon dog in the Mt. Nyugasa, Nagano Prefecture, Japan. Nat. Environ. Sci. Res. 7, 45–52 (1994) (in Japanese with English summary).
Google Scholar 
Hisano, M. et al. A comparison of visual and genetic techniques for identifying Japanese marten scats enabling diet examination in relation to seasonal food availability in a sub-alpine area of Japan. Zool. Sci. 34, 137–146 (2017).Article 

Google Scholar 
Lindstrom, E. R., Brainerd, S. M., Helldin, J. O. & Overskaug, K. Pine marten-red fox interactions: A case of intraguild predation?. Ann. Zool. Fenn. 32, 123–130 (1995).
Google Scholar 
Waggershauser, C. N., Ruffino, L., Kortland, K. & Lambin, X. Lethal interactions among forest-grouse predators are numerous, motivated by hunger and carcasses, and their impacts determined by the demographic value of the victims. Ecol. Evol. 11, 7164–7186 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Watabe, R., Saito, M. U., Enari, H. S. & Enari, H. Mammalian fauna of the Kaminagawa Experimental Forest of Yamagata University detected by camera traps. Tohoku J. For. Sci. 25, 37–40 (2020) (in Japanese).
Google Scholar 
Hofmeester, T. R., Rowcliffe, J. M. & Jansen, P. A. A simple method for estimating the effective detection distance of camera traps. Remote Sens. Ecol. Conserv. 3, 81–89 (2017).Article 

Google Scholar 
Di Bitetti, M. S., Paviolo, A. & De Angelo, C. Camera trap photographic rates on roads vs. off roads: Location does matter. Mastozoología Neotrop. 21, 37–46 (2014).
Google Scholar 
Borcard, D. & Legendre, P. Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93, 1473–1481 (2012).PubMed 
Article 

Google Scholar 
Oksanen, J. et al. Vegan: community ecology package. https://cran.r-project.org/web/packages/veganhttps://cran.r-project.org/web/packages/vegan/index.html (2019).R Core Team. R: a language environment for statistical computing. r foundation for statistical computing, Vienna, Austria. https://www.r-project.org/https://www.r-project.org/ (2021).Linkie, M. & Ridout, M. S. Assessing tiger-prey interactions in Sumatran rainforests. J. Zool. 284, 224–229 (2011).Article 

Google Scholar 
Watabe, R. & Saito, M. U. Effects of vehicle-passing frequency on forest roads on the activity patterns of carnivores. Landsc. Ecol. Eng. 17, 225–231 (2021).Article 

Google Scholar 
Furukawa, G. genkiFurukawa/rSetDayNightAttr documentation. https://rdrr.io/github/genkiFurukawa/rSetDayNightAhttps://rdrr.io/github/genkiFurukawa/rSetDayNightAttr/ (2019).Mielke, P. W., Berry, K. J. & Johnson, E. S. Multi-response permutation procedures for a priori classifications. Commun. Stat. Theory Methods 5, 1409–1424 (1976).MATH 
Article 

Google Scholar 
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

Google Scholar 
Monterroso, P., Alves, P. C. & Ferreras, P. Catch me if you can: Diel activity patterns of mammalian prey and predators. Ethology 119, 1044–1056 (2013).Article 

Google Scholar 
Hendrichsen, D. K. & Tyler, N. J. C. How the timing of weather events influences early development in a large mammal. Ecology 95, 1737–1745 (2014).CAS 
PubMed 
Article 

Google Scholar 
Herfindal, I. et al. Weather affects temporal niche partitioning between moose and livestock. Wildlife Biol. https://doi.org/10.2981/wlb.00275 (2017).Article 

Google Scholar 
Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: How mesopredators avoid costly interactions with apex predators. Oecologia 187, 573–583 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barrull, J. et al. Factors and mechanisms that explain coexistence in a Mediterranean carnivore assemblage: An integrated study based on camera trapping and diet. Mamm. Biol. 79, 123–131 (2014).Article 

Google Scholar 
Tattersall, E. R., Burgar, J. M., Fisher, J. T. & Burton, A. C. Boreal predator co-occurrences reveal shared use of seismic lines in a working landscape. Ecol. Evol. 10, 1678–1691 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Moll, R. J. et al. Humans and urban development mediate the sympatry of competing carnivores. Urban Ecosyst. 21, 765–778 (2018).Article 

Google Scholar 
McCreadie, J. W. & Bedwell, C. R. Patterns of co-occurrence of stream insects and an examination of a causal mechanism: Ecological checkerboard or habitat checkerboard?. Insect Conserv. Divers. 6, 105–113 (2013).Article 

Google Scholar  More

Wu, A. Social buffering of stress – Physiological and ethological perspectives. Appl. Anim. Behav. Sci. 239, 105325 (2021).
Google Scholar 
Hennessy, M. B., Kaiser, S. & Sachser, N. Social buffering of the stress response: diversity, mechanisms, and functions. Front. Neuroendocrinol. 30, 470–482 (2009).CAS 
PubMed 

Google Scholar 
Young, C., Majolo, B., Heistermann, M., Schülke, O. & Ostner, J. Responses to social and environmental stress are attenuated by strong male bonds in wild macaques. Proc. Natl Acad. Sci. USA 111, 18195–18200 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stanton, M. E., Patterson, J. M. & Levine, S. Social influences on conditioned cortisol secretion in the squirrel monkey. Psychoneuroendocrinology 10, 125–134 (1985).CAS 
PubMed 

Google Scholar 
Caldji, C., Diorio, J. & Meaney, M. J. Variations in maternal care in infancy regulate the development of stress reactivity. Biol. Psychiatry 48, 1164–1174 (2000).CAS 
PubMed 

Google Scholar 
Novak, M. A., Hamel, A. F., Kelly, B. J., Dettmer, A. M. & Meyer, J. S. Stress, the HPA axis, and nonhuman primate well-being: a review. Appl. Anim. Behav. Sci. 143, 135–149 (2013).PubMed 
PubMed Central 

Google Scholar 
Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21, 55–89 (2000).CAS 
PubMed 

Google Scholar 
Liu, D. et al. Maternal Care, hippocampal glucocorticoid receptors, and hypothalamic-pituitary-adrenal responses to stress. Sci. Ment. Heal. Stress Brain 9, 75–78 (1997).
Google Scholar 
Gjerstad, J. K., Lightman, S. L. & Spiga, F. Role of glucocorticoid negative feedback in the regulation of HPA axis pulsatility. Stress 21, 403–416 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Spiga, F., Walker, J. J., Terry, J. R. & Lightman, S. L. HPA axis-rhythms. Compr. Physiol. 4, 1273–1298 (2014).PubMed 

Google Scholar 
Sapolsky, R. M. Why Zebras Don’t Get Ulcers (Henry Holt and Company, LLC, 2004).Campos, F. A. et al. Glucocorticoid exposure predicts survival in female baboons. Sci. Adv. 7, 1–10 (2021).
Google Scholar 
Banerjee, S. B., Arterbery, A. S., Fergus, D. J. & Adkins-Regan, E. Deprivation of maternal care has long-lasting consequences for the hypothalamic-pituitary-adrenal axis of zebra finches. Proc. R. Soc. B Biol. Sci. 279, 759–766 (2012).
Google Scholar 
Hennessy, M. B., Nigh, C. K., Sims, M. L. & Long, S. J. Plasma cortisol and vocalization responses of postweaning age guinea pigs to maternal and sibling separation: evidence for filial attachment after weaning. Dev. Psychobiol. 28, 103–115 (1995).CAS 
PubMed 

Google Scholar 
Hennessy, M. B., O’Leary, S. K., Hawke, J. L. & Wilson, S. E. Social influences on cortisol and behavioral responses of preweaning, periadolescent, and adult guinea pigs. Physiol. Behav. 76, 305–314 (2002).CAS 
PubMed 

Google Scholar 
Wiener, S. G., Johnson, D. F. & Levine, S. Influence of postnatal rearing conditions on the response of squirrel monkey infants to brief perturbations in mother-infant relationships. Physiol. Behav. 39, 21–26 (1987).CAS 
PubMed 

Google Scholar 
Girard-Buttoz, C. et al. Early maternal loss leads to short-but not long-term effects on diurnal cortisol slopes in wild chimpanzees. Elife 10, e64134 (2021).Rosenbaum, S. et al. Social bonds do not mediate the relationship between early adversity and adult glucocorticoids in wild baboons. Proc. Natl Acad. Sci. USA 117, 20052–20062 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moss, C. Elephant Memories: Thirteen Years in the Life of an Elephant Family (Univ. Chicago Press, 1988).Douglas-Hamilton, I., Bhalla, S., Wittemyer, G. & Vollrath, F. Behavioural reactions of elephants towards a dying and deceased matriarch. Appl. Anim. Behav. Sci. 100, 87–102 (2006).
Google Scholar 
Shoshani, J., Kupsky, W. J. & Marchant, G. H. Elephant brain. Part I: gross morphology, functions, comparative anatomy, and evolution. Brain Res. Bull. 70, 124–157 (2006).PubMed 

Google Scholar 
Goldenberg, S. Z. & Wittemyer, G. Orphaned female elephant social bonds reflect lack of access to mature adults. Sci. Rep. 7, 14408 (2017).PubMed 
PubMed Central 

Google Scholar 
Goldenberg, S. Z. & Wittemyer, G. Orphaning and natal group dispersal are associated with social costs in female elephants. Anim. Behav. 143, 1–8 (2018).
Google Scholar 
Lee, P. C. Allomothering among African elephants. Anim. Behav. 35, 278–291 (1987).
Google Scholar 
Parker, J. M. et al. Poaching of African elephants indirectly decreases population growth through lowered orphan survival. Curr. Biol. 31, 4156–4162.e5 (2021).Wittemyer, G. et al. Where sociality and relatedness diverge: the genetic basis for hierarchical social organization in African elephants. Proc. R. Soc. B Biol. Sci. 276, 3513–3521 (2009).
Google Scholar 
Goldenberg, S. Z., Douglas-Hamilton, I. & Wittemyer, G. Vertical transmission of social roles drives resilience to poaching in elephant metworks. Curr. Biol. 26, 75–79 (2016).CAS 
PubMed 

Google Scholar 
Gobush, K. S., Mutayoba, B. M. & Wasser, S. K. Long-term impacts of poaching on relatedness, stress physiology, and reproductive output of adult female African elephants. Conserv. Biol. 22, 1590–1599 (2008).CAS 
PubMed 

Google Scholar 
Gobush, K. S. et al. Loxodonta africana (African Savanna Elephant). Loxodonta africana: the IUCN red list of threatened species 2021 e.T181008073A181022663 https://doi.org/10.2305/IUCN.UK.2021-1.RLTS.T181008073A181022663.en (2021).Wittemyer, G. et al. Illegal killing for ivory drives global decline in African elephants. Proc. Natl Acad. Sci. USA 111, 13117–13121 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wittemyer, G., Daballen, D. & Douglas-Hamilton, I. Comparative Demography of an At-Risk African Elephant Population. PLoS ONE 8, e53726 (2013).McCormick, S. D. & Romero, L. M. Conservation endocrinology. Bioscience 67, 429–442 (2017).
Google Scholar 
Wittemyer, G. The elephant population of Samburu and Buffalo Springs National Reserves, Kenya. Afr. J. Ecol. 39, 357–369 (2001).
Google Scholar 
Cockrem, J. F. Individual variation in glucocorticoid stress responses in animals. Gen. Comp. Endocrinol. 181, 45–58 (2013).CAS 
PubMed 

Google Scholar 
Taff, C. C., Schoenle, L. A. & Vitousek, M. N. The repeatability of glucocorticoids: a review and meta-analysis. Gen. Comp. Endocrinol. 260, 136–145 (2018).CAS 
PubMed 

Google Scholar 
Hooten, M. B. & Hobbs, N. T. A guide to Bayesian model selection for ecologists. Ecol. Monogr. 85, 3–28 (2015).
Google Scholar 
Wittemyer, G. & Getz, W. M. Hierarchical dominance structure and social organization in African elephants, Loxodonta africana. Anim. Behav. 73, 671–681 (2007).
Google Scholar 
Heim, C., Ehlert, U. & Hellhammer, D. H. The potential role of hypocortisolism in the pathophysiology of stress-related bodily disorders. Psychoneuroendocrinology 25, 1–35 (2000).CAS 
PubMed 

Google Scholar 
Dickens, M. J. & Romero, L. M. A consensus endocrine profile for chronically stressed wild animals does not exist. Gen. Comp. Endocrinol. 191, 177–189 (2013).CAS 
PubMed 

Google Scholar 
Ma, D., Serbin, L. A. & Stack, D. M. How children’s anxiety symptoms impact the functioning of the hypothalamus–pituitary–adrenal axis over time: a cross-lagged panel approach using hierarchical linear modeling. Dev. Psychopathol. 31, 1–15 (2018).
Google Scholar 
Blas, J., Bortolotti, G. R., Tella, J. L., Baos, R. & Marchant, T. A. Stress response during development predicts fitness in a wild, long lived vertebrate. Proc. Natl Acad. Sci. USA 104, 8880–8884 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boonstra, R. Reality as the leading cause of stress: rethinking the impact of chronic stress in nature. Funct. Ecol. 27, 11–23 (2013).
Google Scholar 
Gunnar, M. R. & Vazquez, D. M. Low cortisol and a flattening of expected daytime rhythm: Potential indices of risk in human development. Dev. Psychopathol. 13, 515–538 (2001).CAS 
PubMed 

Google Scholar 
Perry, R. E. et al. Corticosterone administration targeting a hypo-reactive HPA axis rescues a socially-avoidant phenotype in scarcity-adversity reared rats. Dev. Cogn. Neurosci. 40, 100716 (2019).Fries, E., Hesse, J., Hellhammer, J. & Hellhammer, D. H. A new view on hypocortisolism. Psychoneuroendocrinology 30, 1010–1016 (2005).CAS 
PubMed 

Google Scholar 
Dorsey, C., Dennis, P., Guagnano, G., Wood, T. & Brown, J. L. Decreased baseline fecal glucocorticoid concentrations associated with skin and oral lesions in black rhinoceros (Diceros bicornis). J. Zoo. Wildl. Med. 41, 616–625 (2010).PubMed 

Google Scholar 
Pawluski, J. et al. Low plasma cortisol and fecal cortisol metabolite measures as indicators of compromised welfare in domestic horses (Equus caballus). PLoS ONE 12, 1–18 (2017).
Google Scholar 
Feng, X. et al. Maternal separation produces lasting changes in cortisol and behavior in rhesus monkeys. Proc. Natl Acad. Sci. USA 108, 14312–14317 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
González Ramírez, C. et al. The NR3C1 gene expression is a potential surrogate biomarker for risk and diagnosis of posttraumatic stress disorder. Psychiatry Res. 284, 112797 (2020).PubMed 

Google Scholar 
Cluver, L., Fincham, D. S. & Seedat, S. Posttraumatic stress in AIDS-orphaned children exposed to high levels of trauma: the protective role of perceived social support. J. Trauma. Stress 22, 106–112 (2009).PubMed 

Google Scholar 
Bastille-Rousseau, G. et al. Landscape-scale habitat response of African elephants shows strong selection for foraging opportunities in a human dominated ecosystem. Ecography 43, 149–160 (2020).
Google Scholar 
Foley, C. A. H., Papageorge, S. & Wasser, S. K. Noninvasive stress and reproductive measures of social and ecological pressures in free-ranging African elephants. Conserv. Biol. 15, 1134–1142 (2001).
Google Scholar 
Wittemyer, G., Getz, W. M., Vollrath, F. & Douglas-Hamilton, I. Social dominance, seasonal movements, and spatial segregation in African elephants: a contribution to conservation behavior. Behav. Ecol. Sociobiol. 61, 1919–1931 (2007).
Google Scholar 
Wittemyer, G., Daballen, D. & Douglas‐Hamilton, I. Differential influence of human impacts on age‐specific demography underpins trends in an African elephant population. Ecosphere 12, e03720 (2021).Brown, J. L. et al. Individual and environmental risk factors associated with fecal glucocorticoid metabolite concentrations in zoo-housed Asian and African elephants. PLoS ONE 14, 1–18 (2019).
Google Scholar 
Goldenberg, S. Z. et al. Increasing conservation translocation success by building social functionality in released populations. Glob. Ecol. Conserv. 18, e00604 (2019).Dantzer, B., Fletcher, Q. E., Boonstra, R. & Sheriff, M. J. Measures of physiological stress: a transparent or opaque window into the status, management and conservation of species? Conserv. Physiol. 2, 1–18 (2014).
Google Scholar 
Kaisin, O., Fuzessy, L., Poncin, P., Brotcorne, F. & Culot, L. A meta-analysis of anthropogenic impacts on physiological stress in wild primates. Conserv. Biol. 0, 1–14 (2020).CAS 

Google Scholar 
Ganswindt, A., Rasmussen, H. B., Heistermann, M. & Hodges, J. K. The sexually active states of free-ranging male African elephants (Loxodonta africana): defining musth and non-musth using endocrinology, physical signals, and behavior. Horm. Behav. 47, 83–91 (2005).CAS 
PubMed 

Google Scholar 
Santymire, R. M. et al. Using ACTH challenges to validate techniques for adrenocortical activity analysis in various African wildlife species. Int. J. Anim. Vet. Adv. 4, 99–108 (2012).CAS 

Google Scholar 
Watson, R. et al. Development of a versatile enzyme immunoassay for non-invasive assessment of glucocorticoid metabolites in a diversity of taxonomic species. Gen. Comp. Endocrinol. 186, 16–24 (2013).CAS 
PubMed 

Google Scholar 
Oduor, S. et al. Differing physiological and behavioral responses to anthropogenic factors between resident and non-resident African elephants at Mpala Ranch, Laikipia County, Kenya. PeerJ 8, e10010 (2020).Brown, J. L., Kersey, D. C., Freeman, E. W. & Wagener, T. Assessment of diurnal urinary cortisol excretion in Asian and African elephants using different endocrine methods. Zoo. Biol. 29, 274–283 (2010).PubMed 

Google Scholar 
Justice, C. O. et al. The moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 36, 1228–1249 (1998).
Google Scholar 
Lafferty, D. J. R., Zimova, M., Clontz, L., Hackländer, K. & Mills, L. S. Noninvasive measures of physiological stress are confounded by exposure. Sci. Rep. 9, 1–6 (2019).
Google Scholar 
O’Dwyer, K., Dargent, F., Forbes, M. R. & Koprivnikar, J. Parasite infection leads to widespread glucocorticoid hormone increases in vertebrate hosts: a meta-analysis. J. Anim. Ecol. 89, 519–529 (2020).PubMed 

Google Scholar 
Parker, J. M., Goldenberg, S. Z., Letitiya, D. & Wittemyer, G. Strongylid infection varies with age, sex, movement and social factors in wild African elephants. Parasitology 147, 348–359 (2020).PubMed 

Google Scholar 
Gibbons, L., Jacobs, D. E., Fox, M. T. & Hansen, J. The RVC/FAO guide to veterinary diagnostic parasitology. McMaster egg-counting technique. http://www.rvc.ac.uk/review/Parasitology/EggCount/Purpose.htm (2004)R Core Team. A language and environment for statistical computing. https://www.r-project.org/. (2020).Rstudio Team. RStudio: integrated development for R. http://www.rstudio.com/ (2020).Plummer, M. rjags: Bayesian graphical models using MCMC. https://cran.r-project.org/package=rjags (2019).Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).
Google Scholar 
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–511 (1992).
Google Scholar 
Wickham, H. ggplot2: elegant graphics for data analysis. https://ggplot2.tidyverse.org (2016).Youngflesh, C. MCMCvis: tools to visualize, manipulate, and summarize MCMC output. J. Open Source Softw. 3, 640 (2018).
Google Scholar 
Parker, J. M. The Physiological Condition of Orphaned African Elephants (Loxodonta africana). Doctoral dissertation, Colorado State University. (2021). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More