More stories

  • in

    Immune-mediated competition benefits protective microbes over pathogens in a novel host species

    Alizon S, de Roode JC, Michalakis Y (2013) Multiple infections and the evolution of virulence. Ecol Lett 16(4):556–67PubMed 

    Google Scholar 
    Bian G, Zhou G, Lu P, Xi Z (2013) Replacing a native Wolbachia with a novel strain results in an increase in endosymbiont load and resistance to dengue virus in a mosquito vector. PLoS Negl Trop Dis 7(6):e2250PubMed 
    PubMed Central 

    Google Scholar 
    Bjørnstad ON, Harvill ET (2005) Evolution and emergence of Bordetella in humans. Trends Microbiol 13(8):355–9PubMed 

    Google Scholar 
    Bosch TC (2013) Cnidarian-microbe interactions and the origin of innate immunity in metazoans. Annu Rev Microbiol 67:499–518CAS 
    PubMed 

    Google Scholar 
    Bull JJ, Turelli M (2013) Wolbachia versus dengue: Evolutionary forecasts. Evol Med Public Health 2013(1):197–207PubMed 
    PubMed Central 

    Google Scholar 
    Cabreiro F, Gems D (2013) Worms need microbes too: microbiota, health and aging in Caenorhabditis elegans. EMBO Mol Med 5(9):1300–10CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chen F, Krasity BC, Peyer SM, Koehler S, Ruby EG, Zhang X et al. (2017) Bactericidal permeability-increasing proteins shape host-microbe interactions. mBio 8:e00040–17CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chrostek E, Pelz-Stelinski K, Hurst GDD, Hughes GL (2017) Horizontal Transmission of Intracellular Insect Symbionts via Plants. Front Microbiol 8:2237PubMed 
    PubMed Central 

    Google Scholar 
    Chrostek E, Teixeira L (2015) Mutualism breakdown by amplification of Wolbachia genes. PLoS Biol 13(2):e1002065PubMed 
    PubMed Central 

    Google Scholar 
    Cisani G, Varaldo PE, Grazi G, Soro O (1982) High-level potentiation of lysostaphin anti-staphylococcal activity by lysozyme. Antimicrob Agents Chemother 21(4):531–5CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clark LC, Hodgkin J (2014) Commensals, probiotics and pathogens in the Caenorhabditis elegans model. Cell Microbiol 16(1):27–38CAS 
    PubMed 

    Google Scholar 
    Coolon JD, Jones KL, Todd TC, Carr BC, Herman MA (2009) Caenorhabditis elegans genomic response to soil bacteria predicts environment-specific genetic effects on life history traits. PLOS Genet 5:e1000503PubMed 
    PubMed Central 

    Google Scholar 
    Dierking K, Yang W, Schulenburg H (2016) Antimicrobial effectors in the nematode Caenorhabditis elegans: an outgroup to the Arthropoda. Philos Trans R Soc Lond B Biol Sci 371:1695
    Google Scholar 
    Dong Y, Manfredini F, Dimopoulos G (2009) Implication of the mosquito midgut microbiota in the defense against malaria parasites. PLoS Pathog 5(5):e1000423PubMed 
    PubMed Central 

    Google Scholar 
    Drew GC, King KC (2022) More or less? The effect of symbiont density in protective mutualisms. Am Nat 199(4):443–54PubMed 

    Google Scholar 
    Ford SA, Kao D, Williams D, King KC (2016) Microbe-mediated host defence drives the evolution of reduced pathogen virulence. Nat Commun 7:13430CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ford SA, King KC (2016) Harnessing the Power of Defensive Microbes: Evolutionary Implications in Nature and Disease Control. PLoS Pathog 12(4):e1005465PubMed 
    PubMed Central 

    Google Scholar 
    Ford SA, King KC (2021) In Vivo Microbial Coevolution Favors Host Protection and Plastic Downregulation of Immunity. Mol Biol Evol 38(4):1330–1338CAS 
    PubMed 

    Google Scholar 
    Frank SA (1996) Models of parasite virulence. Q Rev Biol 71(1):37–78CAS 
    PubMed 

    Google Scholar 
    Félix MA, Braendle C (2010) The natural history of Caenorhabditis elegans. Curr Biol 20(22):R965–9PubMed 

    Google Scholar 
    Garsin DA, Sifri CD, Mylonakis E, Qin X, Singh KV, Murray BE et al. (2001) A simple model host for identifying Gram-positive virulence factors. Proc Natl Acad Sci USA 98(19):10892–7CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gerardo NM, Parker BJ (2014) Mechanisms of symbiont-conferred protection against natural enemies: an ecological and evolutionary framework. Curr Opin Insect Sci 4:8–14PubMed 

    Google Scholar 
    Gravato-Nobre MJ, Hodgkin J (2005) Caenorhabditis elegans as a model for innate immunity to pathogens. Cell Microbiol 7(6):741–51CAS 
    PubMed 

    Google Scholar 
    Habets MG, Rozen DE, Brockhurst MA (2012) Variation in Streptococcus pneumoniae susceptibility to human antimicrobial peptides may mediate intraspecific competition. Proc Biol Sci 279(1743):3803–11CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Heath BD, Butcher RD, Whitfield WG, Hubbard SF (1999) Horizontal transfer of Wolbachia between phylogenetically distant insect species by a naturally occurring mechanism. Curr Biol 9(6):313–6CAS 
    PubMed 

    Google Scholar 
    Heikkilä MP, Saris PE (2003) Inhibition of Staphylococcus aureus by the commensal bacteria of human milk. J Appl Microbiol 95(3):471–8PubMed 

    Google Scholar 
    Hoffmann AA, Ross PA, Rašić G (2015) Wolbachia strains for disease control: ecological and evolutionary considerations. Evol Appl 8(8):751–68PubMed 
    PubMed Central 

    Google Scholar 
    Hope IA (1999) C. elegans: a practical approach. Oxford University Press, Oxford
    Google Scholar 
    Huigens ME, de Almeida RP, Boons PA, Luck RF, Stouthamer R (2004) Natural interspecific and intraspecific horizontal transfer of parthenogenesis-inducing Wolbachia in Trichogramma wasps. Proc Biol Sci 271(1538):509–15CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jaenike J, Polak M, Fiskin A, Helou M, Minhas M (2007) Interspecific transmission of endosymbiotic Spiroplasma by mites. Biol Lett 3(1):23–5CAS 
    PubMed 

    Google Scholar 
    Kaltenpoth M, Engl T (2014) Defensive microbial symbionts in Hymenoptera. Funct Ecol 28(2):315–27
    Google Scholar 
    King KC (2019) Quick guide: defensive symbionts. Curr Biol 29:R78–R80CAS 
    PubMed 

    Google Scholar 
    King KC, Brockhurst MA, Vasieva O, Paterson S, Betts A, Ford SA et al. (2016) Rapid evolution of microbe-mediated protection against pathogens in a worm host. ISME J 10(8):1915–24CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kong C, Tan MW, Nathan S (2014) Orthosiphon stamineus protects Caenorhabditis elegans against Staphylococcus aureus infection through immunomodulation. Biol Open 3(7):644–55PubMed 
    PubMed Central 

    Google Scholar 
    Kopylova E, Noé L, Touzet H (2012) SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 14(24):3211–17
    Google Scholar 
    Koziel J, Potempa J (2013) Protease-armed bacteria in the skin. Cell Tissue Res 351:325–37CAS 
    PubMed 

    Google Scholar 
    Lysenko ES, Ratner AJ, Nelson AL, Weiser JN (2005) The role of innate immune responses in the outcome of interspecies competition for colonization of mucosal surfaces. PLoS Pathog 1(1):e1PubMed 
    PubMed Central 

    Google Scholar 
    Magalhaes T, Bergren NA, Bennett SL, Borland EM, Hartman DA, Lymperopoulos K et al. (2019) Induction of RNA interference to block Zika virus replication and transmission in the mosquito Aedes aegypti. Insect Biochem Mol Biol 111:103169CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Margolis E, Yates A, Levin BR (2010) The ecology of nasal colonization of Streptococcus pneumoniae, Haemophilus influenzae and Staphylococcus aureus: the role of competition and interactions with host’s immune response. BMC Microbiol 10:59PubMed 
    PubMed Central 

    Google Scholar 
    Marra A, Hanson MA, Kondo S, Erkosar B, Lemaitre B (2021) Drosophila Antimicrobial Peptides and Lysozymes Regulate Gut Microbiota Composition and Abundance. mBio 12(4):e0082421CAS 
    PubMed 

    Google Scholar 
    Martinez J, Cogni R, Cao C, Smith S, Illingworth CJ, Jiggins FM (2016) Addicted? Reduced host resistance in populations with defensive symbionts. Proc Biol Sci 283:1833
    Google Scholar 
    Martín-Platero AM, Valdivia E, Ruíz-Rodríguez M, Soler JJ, Martín-Vivaldi M, Maqueda M et al. (2006) Characterization of antimicrobial substances produced by Enterococcus faecalis MRR 10-3, isolated from the uropygial gland of the hoopoe (Upupa epops). Appl Environ Microbiol 72(6):4245–9PubMed 
    PubMed Central 

    Google Scholar 
    Mason KL, Stepien TA, Blum JE, Holt JF, Labbe NH, Rush JS et al. (2011) From commensal to pathogen: translocation of Enterococcus faecalis from the midgut to the hemocoel of Manduca sexta. MBio 2(3):e00065–11PubMed 
    PubMed Central 

    Google Scholar 
    Matthews AC, Mikonranta L, Raymond B (2019) Shifts along the parasite-mutualist continuum are opposed by fundamental trade-offs. Proc Biol Sci 286(1900):20190236CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    May G, Nelson P (2014) Defensive mutualisms: do microbial interactions within hosts drive the evolution of defensive traits? Funct Ecol 28(2):356–63
    Google Scholar 
    Mejía LC, Herre EA, Sparks JP, Winter K, García MN, Van Bael SA et al. (2014) Pervasive effects of a dominant foliar endophytic fungus on host genetic and phenotypic expression in a tropical tree. Front Microbiol 5:479PubMed 
    PubMed Central 

    Google Scholar 
    Mergaert P (2018) Role of antimicrobial peptides in controlling symbiotic bacterial populations. Nat prod Rep. 35(4):336–56CAS 
    PubMed 

    Google Scholar 
    Metcalf CJE, Koskella B (2019) Protective microbiomes can limit the evolution of host pathogen defense. Evol Lett 3:534–43PubMed 
    PubMed Central 

    Google Scholar 
    Montalvo-Katz S, Huang H, Appel MD, Berg M, Shapira M (2013) Association with soil bacteria enhances p38-dependent infection resistance in Caenorhabditis elegans. Infect Immun 81(2):514–20CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moreira LA, Iturbe-Ormaetxe I, Jeffery JA, Lu G, Pyke AT, Hedges LM et al. (2009) A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell 139(7):1268–78PubMed 

    Google Scholar 
    O’Neill SL, Ryan PA, Turley AP, Wilson G, Retzki K, Iturbe-Ormaetxe I et al. (2018) Scaled deployment of Wolbachia to protect the community from Aedes transmitted arboviruses. Gates Open Res 2:36PubMed 

    Google Scholar 
    Oliver KM, Campos J, Moran NA, Hunter MS (2008) Population dynamics of defensive symbionts in aphids. Proc Biol Sci 275(1632):293–9PubMed 

    Google Scholar 
    Oliver KM, Smith AH, Russell JA (2014) Defensive symbiosis in the real world ‘96 advancing ecological studies of heritable, protective bacteria in aphids and beyond. Funct Ecol 28(2):341–55
    Google Scholar 
    Pan X, Pike A, Joshi D, Bian G, McFadden MJ, Lu P et al. (2018) The bacterium Wolbachia exploits host innate immunity to establish a symbiotic relationship with the dengue vector mosquito Aedes aegypti. ISME J 12(1):277–88CAS 
    PubMed 

    Google Scholar 
    Parker BJ, Barribeau SM, Laughton AM, de Roode JC, Gerardo NM (2011) Non-immunological defense in an evolutionary framework. Trends Ecol Evol 26(5):242–8PubMed 

    Google Scholar 
    Pastar I, O’Neill K, Padula L, Head CR, Burgess JL, Chen V et al. (2020) Staphylococcus epidermidis Boosts Innate Immune Response by Activation of Gamma Delta T Cells and Induction of Perforin-2 in Human Skin. Front Immunol 11:550946CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pees B, Kloock A, Nakad R, Barbosa C, Dierking K (2017) Enhanced behavioral immune defenses in a C. elegans C-type lectin-like domain gene mutant. Dev Comp Immunol 74:237–42CAS 
    PubMed 

    Google Scholar 
    Peleg AY, Tampakakis E, Fuchs BB, Eliopoulos GM, Moellering RC, Mylonakis E (2008) Prokaryote-eukaryote interactions identified by using Caenorhabditis elegans. Proc Natl Acad Sci USA 105(38):14585–90CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Petersen C, Dirksen P, Schulenburg H (2015) Why we need more ecology for genetic models such as C. elegans. Trends Genet 31(3):120–7CAS 
    PubMed 

    Google Scholar 
    Pimentel H, Bray NL, Puente S, Melsted P, Pachter L (2017) Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods 14(7):687–90CAS 
    PubMed 

    Google Scholar 
    Portal-Celhay C, Blaser MJ (2012) Competition and resilience between founder and introduced bacteria in the Caenorhabditis elegans gut. Infect Immun 80(3):1288–99CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Raberg L, de Roode JC, Bell AS, Stamou P, Gray D, Read AF (2006) The role of immune-mediated apparent competition in genetically diverse malaria infections. Am Nat 168(1):41–53PubMed 

    Google Scholar 
    Rafaluk-Mohr C, Ashby B, Dahan DA, King KC (2018) Mutual fitness benefits arise during coevolution in a nematode-defensive microbe model. Evol Lett 2(3):246–56PubMed 
    PubMed Central 

    Google Scholar 
    Ragland SA, Criss AK (2017) From bacterial killing to immune modulation: Recent insights into the functions of lysozyme. PLoS Pathog 13(9):e1006512PubMed 
    PubMed Central 

    Google Scholar 
    Rancès E, Ye YH, Woolfit M, McGraw EA, O’Neill SL (2012) The relative importance of innate immune priming in Wolbachia-mediated dengue interference. PLoS Pathog 8(2):e1002548PubMed 
    PubMed Central 

    Google Scholar 
    Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H et al. (2019) g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res 47(W1):W191–W198CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Raymann K, Shaffer Z, Moran NA (2017) Antibiotic exposure perturbs the gut microbiota and elevates mortality in honeybees. PLoS Biol 15(3):e2001861PubMed 
    PubMed Central 

    Google Scholar 
    Rossouw W, Korsten L (2017) Cultivable microbiome of fresh white button mushrooms. Lett Appl Microbiol 64(2):164–70CAS 
    PubMed 

    Google Scholar 
    Russell JA, Moran NA (2005) Horizontal transfer of bacterial symbionts: heritability and fitness effects in a novel aphid host. Appl Environ Microbiol 71(12):7987–94CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ryu H, Kim SH, Lee HY, Bai JY, Nam YD, Bae JW et al. (2008) Innate immune homeostasis by the homeobox gene Caudal and commensal-gut mutualism in Drosophila. Science 319:777–82CAS 
    PubMed 

    Google Scholar 
    Sellegounder D, Liu Y, Wibisono P, Chen CH, Leap D, Sun J (2019) Neuronal GPCR NPR-8 regulates C. elegans defense against pathogen infection. Sci Adv 5(11):eaaw4717CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sifri CD, Begun J, Ausubel FM, Calderwood SB (2003) Caenorhabditis elegans as a model host for Staphylococcus aureus pathogenesis. Infect Immun 71(4):2208–17CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Singh UB, Malviya D, Wasiullah, Singh S, Pradhan JK, Singh BP et al. (2016) Bio-protective microbial agents from rhizosphere eco-systems trigger plant defense responses provide protection against sheath blight disease in rice (Oryza sativa L.). Microbiol Res 192:300–12CAS 
    PubMed 

    Google Scholar 
    Trevelline BK, Fontaine SS, Hartup BK, Kohl KD (2019) Conservation biology needs a microbial renaissance: a call for the consideration of host-associated microbiota in wildlife management practices. Proc Biol Sci 286(1895):20182448PubMed 
    PubMed Central 

    Google Scholar 
    Ulrich Y, Schmid-Hempel P (2012) Host modulation of parasite competition in multiple infections. Proc Biol Sci 279(1740):2982–9PubMed 
    PubMed Central 

    Google Scholar 
    Vaishnava S, Yamamoto M, Severson KM, Ruhn KA, Yu X, Koren O et al. (2011) The antibacterial lectin RegIIIgamma promotes the spatial segregation of microbiota and host in the intestine. Science 334(653):255–8CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Varahan S, Iyer VS, Moore WT, Hancock LE (2013) Eep confers lysozyme resistance to enterococcus faecalis via the activation of the extracytoplasmic function sigma factor SigV. J Bacteriol 195(14):3125–34CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Visvikis O, Ihuegbu N, Labed SA, Luhachack LG, Alves AF, Wollenberg AC et al. (2014) Innate host defense requires TFEB-mediated transcription of cytoprotective and antimicrobial genes. Immunity 40(6):896–909CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vorburger C, Ganesanandamoorthy P, Kwiatkowski M (2013) Comparing constitutive and induced costs of symbiont-conferred resistance to parasitoids in aphids. Ecol Evol 3(3):706–13PubMed 
    PubMed Central 

    Google Scholar 
    Wang S, Dos-Santos ALA, Huang W, Liu KC, Oshaghi MA, Wei G et al. (2017) Driving mosquito refractoriness to Plasmodium falciparum with engineered symbiotic bacteria. Science 357(6358):1399–1402CAS 
    PubMed 

    Google Scholar 
    Wilke AB, Marrelli MT (2015) Paratransgenesis: a promising new strategy for mosquito vector control. Parasit Vectors 8:342PubMed 
    PubMed Central 

    Google Scholar 
    Wong D, Bazopoulou D, Pujol N, Tavernarakis J, Ewbank J (2007) Genome-wide investigation reveals pathogen-specific and shared signatures in the response of Caenorhabditis elegans to infection. Genome Biol 8:R194PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning

    To build Sentinel2GlobalLULC, we followed two main steps. First, we established a spatio-temporal consensus between 15 global LULC products for 29 LULC classes. Then, we extracted the maximum number of Sentinel-2 RGB images representing each class. Each image is a tile that has 224 × 224 pixels at 10 × 10 m spatial resolution and was built as a cloud-free composite from all the Sentinel-2 images acquired between June 2015 and October 2020. Both tasks were implemented using GEE, an efficient programming, processing and visualisation platform that allowed us to have free manipulation and access to all used LULC products and Sentinel-2 imagery, simultaneously.Finding spatio-temporal agreement across 15 global LULC productsTo establish the spatio-temporal consensus between different LULC products for each one of the 29 LULC classes, we followed four steps: (1)Identification of the LULC products to be used in the consensus, (2)Standardization and harmonization of the LULC legend that was subsequently used to annotate the image tiles, (3)Spatio-temporal aggregation across LULC products, and (4)Spatial reprojection and tile selection based on optimized spatial purity thresholds.Global LULC products selectionThe adopted purity measure for spatio-temporal agreement across the 15 global LULC products we selected from GEE (Table 2) aims to find areas of high consensus to maximize the annotation quality. Spatial and temporal consensus across such rich diversity of LULC products, in terms of spatial resolution, time coverage, satellite source, LULC classes and accuracy, was used as a source of robustness for our subsequent LULC annotation. Products outside GEE were not used due to computing limitations.Table 2 Main characteristics of the 15 global Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) that were combined to find consensus in the global distribution of 29 main LULC classes.Full size tableStandardization and Harmonization of LULC legendsLand cover (LC) data describes the main type of natural ecosystem that occupies an area; either by vegetation types such as shrublands, grasslands and forests, or by other biophysical classes such as permanent snow, bare land and water bodies. Land use (LU) includes the way in which humans modify or exploit an area, such as urban areas or agricultural fields.To build our 29 LULC classes nomenclature, we established a standardization and harmonization approach based on expert knowledge. During this process, we took into account both the needs of different practitioners in the global and regional LULC mapping field and the thematic resolution of the global LULC legends available in GEE. Our nomenclature consists of 23 LC and 6 LU distinct classes identified through specific consensus rules across 15 LULC products (see Table 4). A six-level (L0 to L5) hierarchical structure was adopted in the creation of these 29 LULC classes (Fig. 2). To facilitate the inter-operability of our 29 legends at the finest level L5 across all LULC products and with the widely used FAO’s hierarchical Land Cover Classification System (LCCS)1, we have established an LULC classification system where the 29 classes can be mapped directly to FAO’s LCCS as explained in the table of Supplementary File 1. The LC part in our dataset contains 20 terrestrial ecosystems and 3 aquatic ecosystems. The terrestrial systems are: Barren lands, Grasslands, Permanent snow, Moss and Lichen lands, Close shrublands, Open shrublands, in addition to 12 Forests classes that differed in their tree cover, phenology, and leaf type. The aquatic classes are: Marine water bodies, Continental water bodies, and Wetlands; furthermore, wetlands were divided into 3 classes: Marshlands, Mangroves and Swamps. The LU part is composed of urban areas and 5 coarse cropland types that differed in their irrigation regime and leaf type. In Table 3, you can find the semantic definition of each one of the 29 classes in Sentinel2GlobalLULC. We provided a table in Supplementary File 2, for a more detailed definition of each LULC class.Fig. 2Tree representation of the six-level (L0 to L5) hierarchical structure of the Land-Use and Land-Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset. Outter circular leafs represent the final or most detailed 29 LULC classes (C1 to C29) of level L5. The followed path to define each class is represented through inner ellipses that contain the names of intermediate classes at different levels between the division of the Earth’s surface (square) into LU and LC (level L0) and the final class circle (level L5). All LULC classes belong to three levels at least, except the 12 forest classes that belong to L5 only.Full size imageTable 3 Semantic signification of each one of the 29 Land Use and Land Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset according to the six-level (L0 to L5) hierarchical structure.Full size tableCombining products across time and spaceFor each one of the 29 LULC classes, we combined in space and time the global LULC information among the 15 GEE LULC products. This way, each image was annotated with a LULC class only if all combined products agreed in its corresponding tile (i.e., 100% of agreement in space and time). For each product and LULC type, we first set one or more criteria to create a global mask at the native resolution of the product in which each pixel was classified as 1 or 0 depending on whether it met the criteria for belonging to that LULC type or not, respectively (see first stage in Table 4). For certain LULC classes, some products did not provide any relevant information, so they were not used. For example (Table 4), in Grasslands (C3), Open Shrublands (C4) and Close Shrublands (C5), we combined 14 products, while in UrbanBlUpArea (C29) and Permanent Snow (C23) we only combined 10 and 7 products, respectively.Table 4 First stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableThen (see second stage in Table 5), for each LULC type, we calculated the average of all the masks obtained from each product to create a final global probability map from all products with values ranging between 0 and 1. Value 1 meant that all products agreed to assign that pixel to a particular LULC class, while 0 meant that none of the products assigned it to that particular class (Fig. 3). These 0-to-1 values are interpreted as the spatio-temporal purity level of each pixel to belong to a particular LULC class and are provided as metadata with each image.Table 5 Second stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableFig. 3Example of the process of building the final global probability map for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”) by means of spatio-temporal agreement of the 15 LULC products available in Google Earth Engine (GEE). The final map is normalized to values between 0 (white, i.e., areas with no presence of C1 in any product) and 1 (black spots, i.e., areas containing or compatible with the presence of C1 in all 15 products), whereas the shades of grey corresponds to the values in between (i.e., areas that did not contain or were not compatible with the presence of C1 in some of the products). This process is divided into two stages: the first stage (the blue part, see details in Table 4) and the second stage (the yellow part, see details in Table 5). LULC products available for several years are represented with superposed rectangles, while single year products are represented with single rectangles. GMP: global probability map, NA: Not Available.Full size imageAs an example of the first stage (see details in Table 4), to specify if a given pixel belongs to Dense Evergreen Needleleaf Forest, we evaluated its tree cover level using “ ≤ “ and “ ≥ “, while for bands containing the leaf type information, we used the equal operator “ = “. For the spatio-temporal combination of multiple criteria we have used the following operators: “AND”,“OR” and “ADD”. For example, we combined the tree cover percentage criteria with the leaf type criteria using “AND” to select forest pixels that met both conditions. To combine many years instances of the same product, we used “ADD”, except for product P13, where we used “AND” to identify permanent water areas only. Whenever we used the “ADD” operator, we normalized pixel values afterwards to bring it back to a probability interval between 0 and 1 using the division by the total number of combined years or criteria.In the second stage (see details in Table 5), we combined for each LULC class the 15 global probability maps previously derived from each product to create a final global probability map (Fig. 3). This combination was carried out using various operators such as “ADD”, “MULTIPLY” and “OR”, depending on the LULC type. When “ADD” was used, the final pixel values were normalized by dividing the final addition value of each pixel by the total number of added products. The “MULTIPLY” operator was mostly used at the end, to remove urban areas from non-urban LULC classes, or to remove water from non-water LULCs. The multiplication operator was also adopted to make sure that a certain criteria was respected in the final probability map. For instance, for the swamp class, we multiplied all pixels in the final stage by a water mask where saline water areas have a value of 0 in order to eliminate mangrove from swamp pixels and vice versa. Finally, we used “OR” operator between different water related products to take advantage of the fact that they complement each other in terms of spatial-temporal coverage and accuracy.In GEE, when two products are aggregated using “ADD”, “MULTIPLY” or any other operator, the output is aggregated at the spatial resolution of the product at the left of the operator. Hence, to maintain the finest spatial resolution in the final probability map, we multiplied everything by product P15 and placed it at the left of the final “MULTIPLY” operation (See Table 5). Hence, all the 29 final probability maps were generated at the P15 spatial resolution of 30 m/pixel (except the urban class C29 which maintained the 30 m/pixel resolution of product P14).Re-projection and Selection of purity thresholdSince our objective was finding pure Sentinel-2 image tiles of 224 × 224 10-m pixels representing each LULC class, we reprojected the 30 m/pixel probability maps to 2240 m/pixel using the spatial mean reducer in GEE. That is, each pixel value at 2240 m resolution was computed using the mean over all the 30m-pixel values contained within it. Hence, the resulting pixel values at 2240 m resolution represent the purity level that each Sentinel-2 image tile of 224 × 224 10-m pixels has. We illustrated the reprojection and selection processes in Fig. 4.Fig. 4Example of the workflow to obtain a Sentinel-2 image tile of 2240 × 2240 m for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”). The process starts with the reprojected final global probability map obtained from stage two (Table 5) and ends with its exportation to the repository of a Sentinel-2 image tile of 224 × 224 pixels. The white rectangle is the only one having a probability value of 1 (Recall that the purity threshold used for Barren was 1, i.e., 100%). The black pixels has a null probability value, while the probability values between 0 and 1 are represented in gray scale levels.Full size imageFor each one of the reprojected maps, we defined a pixel value threshold to decide whether a given 2240 × 2240 m tile was representative of each LULC class or not. Since training DL image classification models needs a large number of high quality (both in terms of image quality and annotation quality) image tiles to reach a good accuracy, when the spatial purity of 100% (full agreement across products in all the pixels of the 224 × 224 tile) resulted in a small number of agreement tiles for a particular class, the purity threshold was decreased for that class until the number of tiles was larger than 1000 or further decreased in less abundant classes to a minimum of 75% of purity. The found purity value is always provided as metadata for each image in the dataset, so the user can always restrict its analysis to those image tiles and classes at any desired purity level. Decreasing the purity threshold down to 75% for the less abundant classes (e.g swamp, mangrove, etc.) was a trade-off between maintaining a good data annotation quality and providing a sufficient number of tiles in each class. In Table 6, we present the number of agreement tiles found at different purity thresholds ranging from 75% to 100% for each LULC class. This spatial purity was not further decreased since machine learning image classification models are known to be robust when the target class is spatially dominant in each training image (it occupies more than 60% of the pixels in the scene)42. On the other hand, when the number of pure tiles for a LULC class was too large to be downloaded (i.e., greater than 14000), we applied a selection algorithm as described in the Supplementary File 3, to download a maximum of 14000 spatially representative images. For this, the world was divided into a one-degree squared cell grid. If a cell contained less than 50 image tiles, we selected them all. If it contained more than 50, we applied that automatic maximum geographic distance algorithm that selected images as far from each other as possible in a number proportional to the number of existing images in that cell. The map in Fig. 6 shows the global distribution of the selected 194877 image tiles contained in Sentinel2GlobalLULC and distributed in 29 LULC classes.Table 6 Summary of the varying number of found and eventually selected Sentinel-2 image tiles of 224 × 224 pixels depending on the different consensus level reached across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableData extractionSentinel2GlobalLULC provides the user with two types of data: Sentinel-2 RGB images (jpeg and geotif versions) and CSV files with associated metadata. In the following subsections, we describe the process for associating metadata, including the Global Human Modification (GHM) index.Global human modification index extractionAs an additional metadata related to the level of human influence in each image, we calculated for each tile in GEE, the spatial mean of the global human modification index for terrestrial lands43, where 0 means no human modification and 1 means complete transformation. Since the original GHM product was mapped at 1 × 1 km resolution, we reprojected it to 2240 × 2240 m using the same reprojection procedure explained in (Re-projection and Selection of purity threshold).CSV files generationOnce the tiles were selected, for each LULC class we listed the image tiles in descendent order of purity. Metadata included: geographical coordinates of each tile centroid, tile purity value, name and ID of the LULC class, and average GHM index for that tile. Then, we used the geographical coordinates of each tile to identify its exact administrative address geolocation. To implement this reverse geo-referencing operation, we used a free request-unlimited python module called reverse_geocoder. This way, we assigned a country code, two levels of administrative departments, and the locality to each tile.For LULC classes that had more than 14000 pure tiles, we have released the coordinates before and after the distance-based selection in case the user wants to download more tiles or use our consensus coordinates for other purposes.Sentinel-2 RGB images exportationAfter extracting all these pieces of information and grouping them into CSV files, we went back to the geographic center coordinates of each tile and used them to extract the corresponding 224 × 224 Sentinel-2 RGB tiles using GEE. Each exported image was identical to the 2240 × 2240 m area covered by its Sentinel-2 tile.We chose “Sentinel-2 MSI (Multi-Spectral Instrument) product” since it is free and publicly available in GEE at the fine resolution of 10 × 10 m. We chose “Level-1C” (i.e., top-of-atmosphere reflectance) since it provides the longest data availability of Sentinel-2 images without any modification of the data. To build RGB images, we extracted the three bands B4, B3 and B2 that correspond to Red, Green and Blue channels, respectively. More bands available in Sentinel-2 or even in Sentinel-1 images can be incorporated in the future to our dataset. However, computational limitations (i.e., the size of the dataset would be impractical) did not allowed us to handle it as a first goal. In addition, the spatial resolution of the images would be heterogeneous across bands.To minimize the inherent noise due to atmospheric conditions (e.g. clouds, aerosols, smoke, etc.) that could affect the satellite RGB images, every image was built as a temporal aggregation of all images gathered by Sentinel-2 satellites between June 2015 and October 2020. During this aggregation, only the highest quality images in the corresponding image collection were considered, as we firstly discarded all image instances where the cloud probability exceeded 20% according to the metadata provided in their corresponding Sentinel-2 collection. Then, we calculated the 25th-percentile value between all remaining images for each reflectance band (R, G, and B), and built the final image with the obtained 25-percentile values in each pixel for its RGB bands. The 25th-percentile choice was adopted giving its suitability in atmospheric noise reduction44,45,46,47,48.Usually, Sentinel-2 MSI product includes true colour images in JPEG2000 format, except for the “Level-1C” collection used here. The three original bands (B4, B3, and B2) required a saturation mapping of their reflectance values into 0–255 RGB digital values. Thus, we mapped the saturation reflectance of 3558 into 255 to obtain true RGB channels with digital values between 0 and 255. The choice of these mapping numbers was taken from the Sentinel-2 true colour image recommendations section of Sentinel user guidelines. Finally, after exporting the selected tiles for each LULC class as “.tif” images, we converted them into “.jpeg” format using a lossless conversion algorithm.Technical implementationTo implement all our methodology steps, we first created a javascript in GEE for each LULC class. Each script is a multi-task javascript where we implemented a switch command to control which task we want to execute (between the spatio-temporal aggregation task, the spatial reprojection and tiles selection task, or the data exportation task). In each one of these scripts, we selected from GEE LULC datasets repository the 15 LULC products used to build the consensus of that LULC class. Each script was responsible of elaborating the spatio-temporal combination of the selected products and generating the final consensus map for that LULC class as described in the subsection “Combining products across time and space”. Then, it exports the final global probability map as an asset into GEE server storage to make its reprojection faster. In the same script, once the consensus map exportation was done, we imported it from the GEE assets storage and reprojected it to 2240 × 2240 m resolution; then, we exported the new reprojected map into GEE assets storage again to make its analysis and processing faster. Afterwards, we imported the reprojected map into the same script and applied different processing tasks. During this processing phase, many purity threshold values were evaluated. Then, we elaborated in this same script the pure tiles identification and their center coordinates exportation into a CSV file. A distinct GEE script was developed to import, reproject and export the global GHM map. The resulted GHM map was saved as an asset too, then imported and used in each one of the 29 LULC multi-task scripts.A python script was developed separately to read the exported CSV files for each LULC class and apply the reverse geo-referencing on their pure tiles coordinates then add the found geolocalization data (country code, locality…etc) to the original CSV files as new columns. Then, another python script was implemented to read the new resulted CSV files with all their added columns (reverse geo-referencing data, GHM data) and use the center coordinates of each pure tile in that class to export first its corresponding Sentinel-2 satellite geotiff image within GEE through the python API. Finally, after downloading all the selected geotiff images from our Google drive, we created another python script to convert these geotiff images into JPEG format. More

  • in

    Javanese Homo erectus on the move in SE Asia circa 1.8 Ma

    Dubois, E. On Pithecanthropus Erectus: a transitional form between man and the apes. J. Anthropol. Inst. G. B. Irel. 25, 240–255 (1896).
    Google Scholar 
    von Koenigswald, G. H. R. Neue Pithecanthropus-funde, 1936-1938 : ein beitrag zur Kenntnis der Praehominiden Wetenschappelijke Mededeelingen ; no. 28 (Landsdrukkerij, Batavia, 1940).Janssen, R. et al. Tooth enamel stable isotopes of Holocene and Pleistocene fossil fauna reveal glacial and interglacial paleoenvironments of hominins in Indonesia. Quatern. Sci. Rev. 144, 145–154 (2016).ADS 

    Google Scholar 
    Bettis, E. A. et al. Way out of Africa: Early Pleistocene paleoenvironments inhabited by Homo erectus in Sangiran, Java. J. Hum. Evol. 56(1), 11–24 (2009).PubMed 

    Google Scholar 
    Huffman, O. Geologic context and age of the Perning/Mojokerto Homo erectus, East Java. J. Hum. Evol. 40(4), 353–362 (2001).PubMed 

    Google Scholar 
    Sarr, A.-C. et al. Subsiding Sundaland. Geology (Boulder) 47(2), 119–122 (2019).ADS 

    Google Scholar 
    Salles, T. et al. Quaternary landscape dynamics boosted species dispersal across Southeast Asia. Commun. Earth Environ. 2(1), 1–12 (2021).MathSciNet 

    Google Scholar 
    Husson, L., Boucher, F. C., Sarr, A., Sepulchre, P. & Cahyarini, S. Y. Evidence of Sundaland’s subsidence requires revisiting its biogeography. J. Biogeogr. 47(4), 843–853 (2020).Winder, I. C. et al. Evolution and dispersal of the genus Homo: A landscape approach. J. Hum. Evol. 87, 48–65 (2015).PubMed 

    Google Scholar 
    Carotenuto, F. et al. Venturing out safely: The biogeography of Homo erectus dispersal out of Africa. J. Hum. Evol. 95, 1–12 (2016).PubMed 

    Google Scholar 
    Larick, R. et al. Early Pleistocene 40Ar/39Ar ages for Bapang Formation hominins, Central Jawa, Indonesia. Proc. Natl. Acad. Sci. PNAS 98(9), 4866–4871 (2001).ADS 
    PubMed 

    Google Scholar 
    Swisher, C. C., Curtis, G. H., Jacob, T., Getty, A. G. & Suprijo, A. Age of the earliest known hominids in Java, Indonesia. Science 263(5150), 1118–1121 (1994).ADS 
    PubMed 

    Google Scholar 
    Sémah, F., Saleki, H., Falguŕes, C., Féraud, G. & Djubiantono, T. Did early man reach Java during the Late Pliocene?. J. Archaeol. Sci. 27(9), 763–769 (2000).
    Google Scholar 
    Bettis, E. et al. Landscape development preceding Homo erectus immigration into Central Java, Indonesia: The Sangiran Formation Lower Lahar. Palaeogeogr. Palaeoclimatol. Palaeoecol. 206(1), 115–131 (2004).
    Google Scholar 
    Matsu’ura, S. et al. Age control of the first appearance datum for Javanese Homo erectus in the Sangiran area. Science 367(6474), 210–214 (2020).ADS 
    PubMed 

    Google Scholar 
    Granger, D. E. & Muzikar, P. F. Dating sediment burial with in situ-produced cosmogenic nuclides: Theory, techniques, and limitations. Earth Planet. Sci. Lett. 188(1), 269–281 (2001).ADS 

    Google Scholar 
    Shen, G., Gao, X., Gao, B. & Granger, D. E. Age of Zhoukoudian Homo erectus determined with 26Al/10Be burial dating. Nature 458(7235), 198–200 (2009).ADS 
    PubMed 

    Google Scholar 
    Pappu, S. et al. Early Pleistocene presence of Acheulian Hominins in South India. Science 331(6024), 1596–1599 (2011).ADS 
    PubMed 

    Google Scholar 
    Lebatard, A.-E. et al. Dating the Homo erectus bearing travertine from Kocabaş (Denizli, Turkey) at at least 1.1 Ma. Earth Planet. Sci. Lett.390, 8–18 (2014).Lebatard, A.-E., Bourlès, D. L. & Braucher, R. Absolute dating of an Early Paleolithic site in Western Africa based on the radioactive decay of in situ-produced 10Be and 26Al. Nucl. Instrum. Methods Phys. Res. Sect. B 456, 169–179 (2019).ADS 

    Google Scholar 
    Braucher, R., Oslisly, R., Mesfin, I., Ntoutoume Mba, P. P. & Team, A. In situ-produced 10 Be and 26 Al indirect dating of Elarmékora Earlier Stone Age artifacts: First attempt in a savannah forest mosaic in the middle Ogooué valley, Gabon. Philos. Trans. Biol. Sci. (2021) .Grimaud-Hervé, D. et al. Position of the posterior skullcap fragment from Sendang Klampok (Sangiran Dome, Java, Indonesia) among the Javanese Homo erectus record. Quatern. Int. 416, 193–209 (2016).
    Google Scholar 
    Sartono, S. Observations on a new skull of Pithecanthropus erectus (Pithecanthropus VIII), from Sangiran, Central Java. Koninklijke Akademie Wetenschappen te Amsterdam 74, 185–194 (1971).
    Google Scholar 
    Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J. & Wobbe, F. Generic mapping tools: Improved version released. EOS Trans. Am. Geophys. Union 94(45), 409–410. https://doi.org/10.1002/2013EO450001 (2013).Article 
    ADS 

    Google Scholar 
    Antón, S., Potts, R. & Aiello, L. Evolution of Early Homo: An integrated biological perspective. Science (New York, N.Y.)345 (2014). https://doi.org/10.1126/science.1236828.Luo, L. et al. The first radiometric age by isochron 26Al/10Be burial dating for the Early Pleistocene Yuanmou hominin site, southern China. Quat. Geochronol. 55, 101022. https://doi.org/10.1016/j.quageo.2019.101022 (2019).Article 

    Google Scholar 
    Zaim, Y. et al. New 1.5 million-year-old Homo erectus maxilla from Sangiran (Central Java, Indonesia). J. Hum. Evol.61(4), 363–376 (2011).Rizal, Y. et al. Last appearance of Homo erectus at Ngandong, Java, 117,000–108,000 years ago. Nature 577(7790), 381–385 (2020).PubMed 

    Google Scholar 
    McRae, B. & Beier, P. Circuit theory predicts gene flow in plant and animal populations. Proc. Natl. Acad. Sci. USA 104, 19885–90. https://doi.org/10.1073/pnas.0706568104 (2008).Article 
    ADS 

    Google Scholar 
    Quaglietta, L. & Porto, M. SiMRiv: An R package for mechanistic simulation of individual, spatially-explicit multistate movements in rivers, heterogeneous and homogeneous spaces incorporating landscape bias. Mov. Ecol. https://doi.org/10.1186/s40462-019-0154-8 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Landau, V. A., Shah, V. B., Anantharaman, R. & Hall, K. R. Omniscape.jl: Software to compute omnidirectional landscape connectivity. J. Open Source Softw.6(57), 2829 (2021). https://doi.org/10.21105/joss.02829.Salles, T., Mallard, C. & Zahirovic, S. gospl: Global Scalable Paleo Landscape Evolution. J. Open Source Softw.5(56), 2804 (2020). https://doi.org/10.21105/joss.02804.Husson, L. et al. Slow geodynamics and fast morphotectonics in the far East Tethys. Geochem. Geophys. Geosyst. 23(1), n/a (2022).Valdes, P., Scotese, C. & Lunt, D. Deep ocean temperatures through time. Climate Past 17, 1483–1506. https://doi.org/10.5194/cp-17-1483-2021 (2021).Article 
    ADS 

    Google Scholar 
    Hyodo, M. et al. High-resolution record of the Matuyama–Brunhes transition constrains the age of Javanese Homo erectus in the Sangiran dome, Indonesia. Proc. Natl. Acad. Sci. PNAS 108(49), 19563–19568 (2011).ADS 
    PubMed 

    Google Scholar 
    Brasseur, B., Sémah, F., Sémah, A.-M. & Djubiantono, T. Pedo-sedimentary dynamics of the Sangiran dome hominid bearing layers (Early to Middle Pleistocene, central Java, Indonesia): A palaeopedological approach for reconstructing ‘Pithecanthropus’ (Javanese Homo erectus) palaeoenvironment. Quatern. Int. 376, 84–100 (2015).
    Google Scholar 
    Falguéres, C. et al. Geochronology of early human settlements in Java: What is at stake?. Quatern. Int. 416, 5–11 (2016).
    Google Scholar 
    Roach, N. et al. Pleistocene footprints show intensive use of lake margin habitats by Homo erectus groups. Sci. Rep. 121 (2016). https://doi.org/10.1038/srep26374.Simandjuntak, T. O. & Barber, A. J. Contrasting tectonic styles in the Neogene orogenic belts of Indonesia. Geol. Soc. Spec. Pub. 106(1), 185–201 (1996).
    Google Scholar 
    Clements, B., Hall, R., Smyth, H. R. & Cottam, M. A. Thrusting of a volcanic arc; a new structural model for Java. Pet. Geosci. 15(2), 159–174 (2009).
    Google Scholar 
    Joordens, J., Wesselingh, F., de Vos, J., Vonhof, H. & Kroon, D. Relevance of aquatic environments for hominins: A case study from Trinil (Java, Indonesia). J. Hum. Evol. 57(6), 656–671 (2009).PubMed 

    Google Scholar 
    Berghuis, H. et al. Hominin homelands of East Java: Revised stratigraphy and landscape reconstructions for Plio-Pleistocene Trinil. Quatern. Sci. Rev. 260, 106912 (2021).
    Google Scholar 
    Fort, J., Pujol, T. & Cavalli-Sforza, L. Palaeolithic populations and waves of advance. Camb. Archaeol. J. 14, 53–61. https://doi.org/10.1017/S0959774304000046 (2004).Article 

    Google Scholar 
    Hamilton, M. & Buchanan, B. Spatial gradients in Clovis-age radiocarbon dates across North America suggest rapid colonization from the north. Proc. Natl. Acad. Sci. USA 104, 15625–30. https://doi.org/10.1073/pnas.0704215104 (2007).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hazelwood, L. & Steele, J. Spatial dynamics of human dispersals: Constraints on modelling and archaeological validation. J. Archaeol. Sci. 31, 669–679. https://doi.org/10.1016/j.jas.2003.11.009 (2004).Article 

    Google Scholar 
    Bae, C., Li, F., Liuling, C., Wang, W. & Hanlie, H. Hominin distribution and density patterns in pleistocene China: Climatic influences. Palaeogeogr. Palaeoclimatol. Palaeoecol. 512 (2018). https://doi.org/10.1016/j.palaeo.2018.03.015.Timmermann, A. et al. Climate effects on archaic human habitats and species successions. Nature 604, 1–7. https://doi.org/10.1038/s41586-022-04600-9 (2022).Article 

    Google Scholar 
    Bailey, G. N., Reynolds, S. C. & King, G. C. Landscapes of human evolution: Models and methods of tectonic geomorphology and the reconstruction of hominin landscapes. J. Hum. Evol. 60(3), 257–280 (2011).PubMed 

    Google Scholar 
    Sarr, A., Sepulchre, P. & Husson, L. Impact of the Sunda Shelf on the Climate of the Maritime Continent. J. Geophys. Res. Atmos. 124(5), 2574–2588 (2019).ADS 

    Google Scholar 
    Louys, J. & Roberts, P. Environmental drivers of megafauna and hominin extinction in Southeast Asia. Nature 586(7829), 402–406 (2020).ADS 
    PubMed 

    Google Scholar 
    Raia, P. et al. Past extinctions of homo species coincided with increased vulnerability to climatic change. One Earth 3(4), 480–490 (2020).ADS 

    Google Scholar 
    Zhu, Z. et al. Hominin occupation of the Chinese Loess Plateau since about 2.1 million years ago. Nature 559(7715), 608–612 (2018).Gabunia, L. et al. Earliest Pleistocene hominid cranial remains from Dmanisi, Republic of Georgia: Taxonomy, geological setting, and age. Science 288, 1019–1025. https://doi.org/10.1126/science.288.5468.1019 (2000).Article 
    ADS 
    PubMed 

    Google Scholar 
    Lordkipanidze, D. et al. A complete skull from Dmanisi, Georgia, and the evolutionary biology of early homo. Science 342(6156), 326–331 (2013).ADS 
    PubMed 

    Google Scholar 
    Baba, H. et al. Homo erectus calvarium from the pleistocene of java. Sci. (Am. Assoc. Adv. Sci.) 299 (5611), 1384–1388 (2003) .Ciochon, R. L. & Bettis, E. A. III. Asian Homo erectus converges in time. Nature 458(7235), 153–154 (2009).ADS 
    PubMed 

    Google Scholar 
    Dennell, R. & Roebroeks, W. An Asian perspective on early human dispersal from Africa. Nature 438(7071), 1099–1104 (2005).ADS 
    PubMed 

    Google Scholar 
    Martinon-Torres, M. et al. Dental evidence on the hominin dispersals during the Pleistocene. Proc. Natl. Acad. Sci. PNAS 104(33), 13279–13282 (2007).ADS 
    PubMed 

    Google Scholar 
    Wood, B. Did early Homo migrate “out of’’ or “in to’’ Africa?. Proc. Natl. Acad. Sci. PNAS 108(26), 10375–10376 (2011).ADS 
    PubMed 

    Google Scholar 
    Shen, G. et al. Isochron 26Al/10Be burial dating of Xihoudu: Evidence for the earliest human settlement in northern China. Anthropologie 124, 102790. https://doi.org/10.1016/j.anthro.2020.102790 (2020).Article 

    Google Scholar 
    Chmeleff, J., von Blanckenburg, F., Kossert, K. & Jakob, D. Determination of the 10Be half-life by multicollector ICP-MS and liquid scintillation counting. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms268(2), 192–199 (2010).Korschinek, G. et al. A new value for the half-life of 10Be by Heavy-Ion Elastic Recoil Detection and liquid scintillation counting. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 268(2), 187–191 (2010) .Nishiizumi, K. Preparation of 26Al AMS standards. Nucl. Inst. and Meth. in Phys. Res. 223-224, 388–392 (2004).Norris, T. L., Gancarz, A. J., Rokop, D. J. & Thomas, K. W. Half-life of 26Al. J. Geophys. Res. Solid Earth 88(S01), B331–B333 (1983).ADS 

    Google Scholar 
    Braucher, R., Merchel, S., Borgomano, J. & Bourlès, D. Production of cosmogenic radionuclides at great depth: A multi element approach. Earth Planet. Sci. Lett. 309(1), 1–9 (2011).ADS 

    Google Scholar 
    Braucher, R. et al. Preparation of ASTER in-house 10Be/9Be standard solutions. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms361, 335–340 (2015) .Merchel, S. & Bremser, W. First international 26Al interlaboratory comparison—Part I. Nucl. Instrum. Methods Phys. Res. 223–224, 393–400 (2004).ADS 

    Google Scholar 
    Arnold, M. et al. The French accelerator mass spectrometry facility ASTER: Improved performance and developments. Nucl. Instrum. Methods Phys. Res. 268(11), 1954–1959 (2010).ADS 

    Google Scholar 
    Borchers, B. et al. Geological calibration of spallation production rates in the CRONUS-Earth project. Quat. Geochronol. 31, 188–198 (2016).
    Google Scholar 
    Stone, J. O. Air pressure and cosmogenic isotope production. J. Geophys. Res. Solid Earth 105(B10), 23753–23759 (2000).
    Google Scholar 
    Bintanja, R. & van de Wal, R. S. W. North American ice-sheet dynamics and the onset of 100,000-year glacial cycles. Nature 454, 869–872. https://doi.org/10.1038/nature07158 (2008).Article 
    ADS 
    PubMed 

    Google Scholar 
    Field, J. & Mirazon Lahr, M. Assessment of the Southern Dispersal: GIS-Based Analyses of Potential Routes at Oxygen Isotopic Stage 4. J. World Prehist. 19, 1–45 (2005). https://doi.org/10.1007/s10963-005-9000-6.Howey, M. Multiple pathways across past landscapes: Circuit theory as a complementary geospatial method to least cost path for modeling past movement. J. Archaeol. Sci. 38, 2523–2535. https://doi.org/10.1016/j.jas.2011.03.024 (2011).Article 

    Google Scholar 
    Tassi, F. et al. Early modern human dispersal from Africa: Genomic evidence for multiple waves of migration. Investig. Genet. 6, 13. https://doi.org/10.1186/s13323-015-0030-2 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kealy, S., Louys, J. & O’Connor, S. Least-cost pathway models indicate northern human dispersal from Sunda to Sahul. J. Hum. Evol. 125, 59–70. https://doi.org/10.1016/j.jhevol.2018.10.003 (2018).Article 
    PubMed 

    Google Scholar 
    Dennell, R. W., Rendell, H. M. & Hailwood, E. Late pliocene artefacts from northern Pakistan. Curr. Anthropol. 29(3), 495–498 (1988).
    Google Scholar 
    Zhu, R. et al. Early evidence of the genus homo in east asia. J. Hum. Evol. 55(6), 1075–1085 (2008).PubMed 

    Google Scholar 
    Gowen, K. M. & de Smet, T. S. Testing least cost path (LCP) models for travel time and kilocalorie expenditure: Implications for landscape genomics. PLoS ONE 15(9), 1–20. https://doi.org/10.1371/journal.pone.0239387 (2020).Article 

    Google Scholar 
    Walt, S. et al. scikit-image: Image processing in Python. PeerJ 2, e453. https://doi.org/10.7287/peerj.preprints.336v2 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mueller, T. & Fagan, W. Search and navigation in dynamic environments—From individual behaviors to population distributions. Oikos 117, 654–664. https://doi.org/10.1111/j.0030-1299.2008.16291.x (2008).Article 

    Google Scholar 
    Bastille-Rousseau, G., Douglas-Hamilton, I., Blake, S., Northrup, J. & Wittemyer, G. Applying network theory to animal movements to identify properties of landscape space use. Ecol. Appl. 28 (2018). https://doi.org/10.1002/eap.1697.Michelot, T., Langrock, R. & Patterson, T. moveHMM: An R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol. Evol. 7 (2016). https://doi.org/10.1111/2041-210X.12578 .Benhamou, S. How many animals really do the Lévy Walk. Ecology 88, 1962–9. https://doi.org/10.1890/06-1769.1 (2007).Article 
    PubMed 

    Google Scholar 
    Turchin, P. Quantitative Analysis of Movement: Measuring and Modeling Population Redistribution of Plants and Animals (Sinauer Associates, Sunderland, 1998).
    Google Scholar 
    Lieberman, D. E. The Story of the Human Body: Evolution, Health, and Disease (Pantheon Books, New York, 2013).
    Google Scholar 
    Braun, D. et al. Early hominin diet included diverse terrestrial and aquatic animals 1.95 Ma in East Turkana, Kenya. Proc. Natl. Acad. Sci. USA 107, 10002–7 (2010). https://doi.org/10.1073/pnas.1002181107.O’Connor, S., Louys, J., Kealy, S. & Samper Carro, S. C. Hominin dispersal and settlement east of huxley’s line: The role of sea level changes, island size, and subsistence behavior. Curr. Anthropol. 58(S17), S567–S582 (2017).Macaulay, V. et al. Single, rapid coastal settlement of asia revealed by analysis of complete mitochondrial genomes. Science (New York, N.Y.)308, 1034–6 (2005). https://doi.org/10.1126/science.1109792. More

  • in

    The expanding value of long-term studies of individuals in the wild

    Lack, D. J. Anim. Ecol. 33, 159–173 (1964).Article 

    Google Scholar 
    Pemberton, J. et al. The unusual value of long-term studies of individuals: the example of the Isle of Rum red deer project. Annu. Rev. Ecol. Evol. Syst. (in the press).Clutton-Brock, T. & Sheldon, B. C. Trends Ecol. Evol. 25, 562–573 (2010).Article 
    PubMed 

    Google Scholar 
    Weimerskirch, H. J. Anim. Ecol. 87, 945–955 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Höner, O. P. et al. Nature 448, 798–801 (2007).Article 
    PubMed 

    Google Scholar 
    Rodríguez-Muñoz, R. et al. Evolution 73, 317–328 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Czorlich, Y., Aykanat, T., Erkinaro, J., Orell, P. & Primmer, C. R. Science 376, 420–423 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sparkman, A. M., Arnold, S. J. & Bronikowski, A. M. Proc. R. Soc. Lond. B 274, 943–950 (2007).
    Google Scholar 
    Doak, D. F. & Morris, W. F. Nature 467, 959–962 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Campos, F. A. et al. Proc. Natl Acad. Sci. USA 119, e2117669119 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Forchhammer, M. C., Clutton-Brock, T. H., Lindström, J. & Albon, S. D. J. Anim. Ecol. 70, 721–729 (2001).Article 

    Google Scholar 
    Bonnet, T. et al. PLoS Biol. 17, e3000493 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McCleery, R. H. & Perrins, C. M. Nature 391, 30–31 (1998).Article 
    CAS 

    Google Scholar 
    Charmantier, A. et al. Science 320, 800–803 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vedder, O., Bouwhuis, S. & Sheldon, B. C. PLoS Biol. 11, e1001605 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Simmonds, E. G., Cole, E. F., Sheldon, B. C. & Coulson, T. Ecol. Lett. 23, 1766–1775 (2020).Article 
    PubMed 

    Google Scholar 
    Cole, E. F., Regan, C. E. & Sheldon, B. C. Nat. Clim. Chang. 11, 872–878 (2021).Article 

    Google Scholar 
    Huisman, J., Kruuk, L. E., Ellis, P. A., Clutton-Brock, T. & Pemberton, J. M. Proc. Natl Acad. Sci. USA 113, 3585–3590 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Johnston, S. E., Bérénos, C., Slate, J. & Pemberton, J. M. Genetics 203, 583–598 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stoffel, M. A., Johnston, S. E., Pilkington, J. G. & Pemberton, J. M. Nat. Commun. 12, 2972 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grieneisen, L. et al. Science 373, 181–186 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Björk, J. R. et al. Nat. Ecol. Evol. 6, 955–964 (2022).Article 
    PubMed 

    Google Scholar 
    Lamichhaney, S. et al. Science 352, 470–474 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bosse, M. et al. Science 358, 365–368 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    De Villemereuil, P. et al. Proc. Natl Acad. Sci. USA 117, 31969–31978 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bailey, L. D. et al. Nat. Commun. 13, 2112 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonnet, T. et al. Science 376, 1012–1016 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Culina, A. et al. J. Anim. Ecol. 90, 2147–2160 (2021).Article 
    PubMed 

    Google Scholar  More

  • in

    Rebooting GDP: new ways to measure economic growth gain momentum

    The numbers are heading in the wrong direction. If the world continues on its current track, it will fall well short of achieving almost all of the 17 Sustainable Development Goals (SDGs) that the United Nations set to protect the environment and end poverty and inequality by 2030.The projected grade for:Eliminating hunger: F.Ensuring healthy lives for all: F.Protecting and sustainably using ocean resources: F.The trends were there before 2020, but then problems increased with the COVID-19 pandemic, war in Ukraine and the worsening effects of climate change. The world is in “a new uncertainty complex”, says economist Pedro Conceição, lead author of the United Nations Human Development Report.One measure of this is the drastic change in the Human Development Index (HDI), which combines educational outcomes, income and life expectancy into a single composite indicator. After 2019, the index has fallen for two successive years for the first time since its creation in 1990. “I don’t think this is a one-off, or a blip. I think this could be a new reality,” Conceição says.UN secretary-general António Guterres is worried. “We need an urgent rescue effort for the SDGs,” he wrote in the foreword to the latest progress report, published in July. Over the past year, Guterres and the heads of big UN agencies, such as the Statistics Division and the UN Development Programme, have been assessing what’s gone wrong and what needs to be done. They’re converging on the idea that it’s time to stop using gross domestic product (GDP) as the world’s main measure of prosperity, and to complement it with a dashboard of indicators, possibly ones linked to the SDGs. If this happens, it would be the biggest shift in how economies are measured since nations first started using GDP in 1953, almost 70 years ago1.
    Get the Sustainable Development Goals back on track
    Guterres’s is the latest in a crescendo of voices calling for GDP to be dropped as the world’s primary go-to indicator, and for a dashboard of metrics instead. In 2008, then French president Nicolas Sarkozy endorsed such a call from a team of economists, including Nobel laureates Amartya Sen and Joseph Stiglitz.And in August, the White House announced a 15-year plan to develop a new summary statistic that would show how changes to natural assets — the natural wealth on which economies depend — affect GDP. The idea, according to the project’s main architect, economist Eli Fenichel at the White House Office of Science and Technology Policy, is to help society to determine whether today’s consumption is being accomplished without compromising the future opportunities that nature provides. “GDP only gives a partial and — for many common uses — an incomplete, picture of economic progress,” Fenichel says.The fact that Guterres has made this a priority, amid so many major crises, is a sign that “going beyond GDP has been picked up at the highest level”, says Stefan Schweinfest, the director of the UN Statistics Division, based in New York City.Grappling with growth GDP is a measure of economic activity that has ended up becoming the world’s main index for economic progress. By a commonly used definition, it is the numerical sum of countries’ consumer and government spending and their business investments, adding the value of exports minus imports. When governments and businesses talk, as they regularly do, about boosting ‘economic growth’, what they mean is boosting GDP.But GDP is more than a growth target. It is also the benchmark for how countries measure themselves against each other (see ‘Growth gaps’). The United States is the world’s largest economy, as measured by GDP. China, currently second, is on a path to overtake it.

    Source: World Bank

    GDP also matters greatly to politicians. When India leapfrogged the United Kingdom to become the world’s fifth largest economy earlier this year, it made headline news. Last month, China reportedly delayed publication of its latest (and less-than-flattering) quarterly GDP figures so they would not appear during the Communist party’s national congress, at which Xi Jinping took a third term as president.“GDP is without question the superstar of indicators,” says Rutger Hoekstra, a researcher who studies sustainability metrics at Leiden University in the Netherlands and author of Replacing GDP by 2030.The problem with using GDP as a proxy for prosperity, says Hoekstra, is that it doesn’t reflect equally important indicators that have been heading in the opposite direction. Global GDP has increased exponentially since the Industrial Revolution, but this has coincided with high levels of income and wealth inequality, according to data compiled by the economist Thomas Piketty at the World Inequality Lab in Paris2. This is not a coincidence. Back in the 1950s, when countries pivoted economies to maximizing GDP, they knew it would mean “making the labourer produce more than he is allowed to consume”, as Pakistan’s then chief economist Mahbub ul Haq graphically put it3. “It is well to recognize that economic growth is a brutal, sordid process.”What is more, to boost GDP, nations need to indulge in environmentally damaging behaviour. In his 2021 report, entitled Our Common Agenda, Guterres writes: “Absurdly, GDP rises when there is overfishing, cutting of forests or burning of fossil fuels. We are destroying nature, but we count it as an increase in wealth.”This tension is apparent when it comes to the SDGs. GDP growth is associated with several SDG targets; in fact SDG 8 is about boosting growth. But GDP growth “can also come at the expense of progress towards other goals”, such as climate and biodiversity action, says environmental economist Pushpam Kumar, who directs a UN Environment Programme (UNEP) project, called the Inclusive Wealth Report, to measure sustainability and inequality. The latest report will be published next month.The one-number problemThe present effort by Guterres and his colleagues is not the first time policymakers have tried to improve on GDP. In 1990, a group of economists led by ul Haq and Sen designed the HDI. They were motivated in part by frustration that their countries’ often impressive growth rates masked more-dismal quality-of-life data, such as life expectancy or education.More recently, environment ministers have found that GDP-boosting priorities have got in the way of their SDG efforts. Carlos Manuel Rodríguez, the former environment minister of Costa Rica, says he urged his finance and economics colleagues to take account of the impact of economic development on water, soils, forests and fish. But they were concerned about possible reductions in GDP calculations, says Rodríguez, now chief executive of the Global Environment Facility, based in Washington DC. Costa Rica didn’t want to be the first country to implement such a change only to possibly see itself slide down the growth rankings as a result.

    Industrial production, such as the work at this automobile plant in Japan, goes into GDP calculations.Credit: Akio Kon/Bloomberg via Getty

    China’s environmental policymakers were confronted with a similar response when, in 2006, they tried to implement a plan called Green GDP4. Local authorities were asked to measure the economic cost of pollution and environmental damage, and offset that against their economic growth targets. “They panicked and the project was shelved,” says Vic Li, a political economist at the Education University of Hong Kong, who has studied the episode. “Reducing GDP would have affected their performance reviews, which needed GDP to always increase,” he says.It’s been a similar story in Italy. In 2019, then research minister Lorenzo Fioramonti helped to establish an agency, Well-being Italy, attached to the prime minister’s office. It was intended to test economic policy decisions against sustainability targets. “It was an uphill battle because the various economic ministries did not see this as a priority,” says Fioramonti, now an economist at the University of Surrey in Guildford, UK.Revising the rulesSo, can the latest attempt to complement GDP succeed? Economists and national statisticians who help to determine GDP’s rules say it will be a struggle.Guterres and his colleagues are proposing to include 10–20 indicators alongside GDP. But that’s a tough sell because countries see a lot of value (not to mention ease of use) in relying on one number. And GDP’s great success is that countries produce their own figures, according to internationally agreed rules, which allow for cross-comparison over time. “It’s not a metric compiled by Washington DC, Beijing or London,” says Schweinfest.At the same time, GDP is not something that can just be turned on or off. In each country, tracking the data that goes into calculating GDP is an industrial-scale operation involving government data as well as surveys of households and businesses.
    Are there limits to economic growth? It’s time to call time on a 50-year argument
    China, Costa Rica and Italy’s experiences suggest that an environment-adjusted GDP might be accepted only if every country signs up to the concept at the same time. In theory, this could happen at the point when GDP’s rules — known as the System of National Accounts — are being reviewed, an event that takes place roughly once every 15 years.The next revision to the rules is under way and is due to be completed in 2025. The final decision will be made by the UN Statistical Commission, a group of chief statisticians from different nations, together with the European Commission, the International Monetary Fund, the World Bank and the Organisation for Economic Co-operation and Development (OECD), the network of the world’s wealthy countries.Because the UN oversees this process, Guterres has some influence over the questions that the review is asking. As part of their research, national statisticians are exploring how to measure well-being and sustainability, along with improving the way the digital economy is valued. Economists Diane Coyle and Annabel Manley, both at the University of Cambridge, say that technology and data companies, which make up seven out of the global top ten firms by stock-market capitalization, are probably undervalued in national accounts5.However, according to Peter van de Ven, a former OECD statistician who is the lead editor of the GDP revision effort, some aspects of digital-economy valuation, along with putting a value on the environment, are unlikely to make it into a revised GDP formula, and will instead be part of the report’s supplementary data tables. One of the reasons, he says, is that national statisticians have not agreed on a valuation methodology for the environment. Nor is there agreement on how to value digital services such as when people use search engines or social-media accounts that (like the environment) are not bought and sold for money.Yet other economists, including Fenichel, say that there are well-established methods that economists use to value both digital and environmental goods and services. One way involves asking people what they would be willing to pay to keep or use something that might otherwise be free, such as a forest or an Internet search engine. Another method involves asking what people would be willing to accept in exchange for losing something otherwise free. Management scientists Erik Brynjolfsson and Avinash Collis, both at the Massachusetts Institute of Technology in Cambridge, did an experiment6 in which they computed the value of social media by paying people to give up using it.The value of natureEconomist Gretchen Daily at Stanford University in California says it’s not true that valuing the environment would make economies look smaller. It all depends on what you value. Daily is among the principal investigators of a project called Gross Ecosystem Product (GEP) that has been trialled across China and is now set to be replicated in other countries. GEP adds together the value of different kinds of ecosystem goods and services, such as agricultural products, water, carbon sequestration and recreational sites. The researchers found7 that in the Chinese province of Qinghai, the region’s total GEP exceeded its GDP.Although past efforts to avoid using GDP have stalled, this time could be different. It’s likely, as van de Ven says, that national statisticians will not add nature (or indeed the value of social media and Internet search) to the GDP formula. But the pressure for change is greater than at any time in the past.GDP is like a technical standard, such as the voltage of household electricity or driving on the left, says Coyle. “So if you want to switch to the right, you need to align people on the same approach. Everyone needs to agree.” More

  • in

    Phosphate limitation intensifies negative effects of ocean acidification on globally important nitrogen fixing cyanobacterium

    Laboratory experimentsCulturingThe marine cyanobacterium Trichodesmium erythraeum IMS101 was obtained from the National Center for Marine Algae and Microbiota (Maine, USA) and was grown in Aquil-tricho medium prepared with 0.22 µm-filtered and microwave-sterilized oligotrophic South China Sea surface water6. The medium was enriched with various concentrations of chelexed and filter-sterilized NaH2PO4 as where indicated, and filter-sterilized vitamins and trace metals buffered with 20 µM EDTA6. The cultures were unialgal, and although they were not axenic, sterile trace metal clean techniques were applied for culturing and experimental manipulations. T. erythraeum was pre-adapted to low P condition by semi-continuously culturing at 0.5 μM PO43− and at two pCO2 levels (400 and 750 µatm) for more than one year. To start the chemostat culture, three replicates per treatment were grown in 1-L Nalgene® magnetic culture vessels (Nalgene Nunc International, Rochester, NY, USA), in which the cultures were continuously mixed by bubbling with humidified and 0.22 µm-filtered CO2–air mixtures and stirring using a suspended magnetic stir bar. The reservoirs contained Aquil-tricho medium with 1.2 μM NaH2PO4, which was delivered to the culture vessels using a peristaltic pump (Masterflex® L/S®, USA) at the dilution rate of 0.2 d−1. In all experiments, cultures were grown at ;27 °C and ~80 μmol photons m−2 s−1 (14 h:10 h light–dark cycle) in an AL-41L4 algae chamber (Percival). The concentration of Chlorophyll a (Chla) was monitored daily in the middle of the photoperiod as an indicator of biomass. When the Chla concentration remained constant for more than one generation, the system was considered to have reached steady-state, and was maintained for at least another four generations prior to sampling for further analysis.Carbonate chemistry manipulationpCO2/pH of seawater media in the culture vessels and in the reservoir was controlled by continuously bubbling with humidified and 0.22 µm-filtered CO2-air mixtures generated by CO2 mixers (Ruihua Instrument & Equipment Ltd.). During the experimental period, the pHT (pH on the total scale) of media was monitored daily using a spectrophotometric method46. The dissolved inorganic carbon (DIC) of media was analyzed by acidification and subsequent quantification of released CO2 with a CO2 analyzer (LI 7000, Apollo SciTech). Calculations of alkalinity and pCO2 were made using the CO2Sys program47, based on measurements of pHT and DIC, and the carbonate chemistry of the experiments are shown in Supplementary Table 1.Chla concentration and cell density and sizeChla concentration was measured daily following Hong et al.6. Briefly, T. erythraeum was filtered onto 3 μm polycarbonate membrane filters (Millipore), followed by heating at 65 °C for 6 min in 90% (vol/vol) methanol. After extraction the filter was removed and cell debris were spun down via centrifugation (5 min at 20,000×g) before spectrophotometric analysis. Cell density and the average cell length and width were determined at regular intervals when the chemostat cultures reached steady-state using ImageJ software. Photographs of Trichodesmium were taken using a camera (Canon DS126281, Japan) connected with an inverted microscope (Olympus CKX41, Japan). Total number and length of filaments in 1 mL of culture were measured, and the cell number of ~20 filaments was counted. The average length of cells was obtained by dividing the total length of the 20 filaments by their total cell number. The cell density of the culture was then calculated by dividing the total length of filaments in 1 mL culture by the average cell length. The average cell width was determined by measuring the width of around 1000 cells in each treatment.Elemental compositionTo determine particulate organic C (POC) and N (PON), at the end of the chemostat culturing T. erythraeum cells were collected on pre-combusted 25 mm GF/F filters (Whatman) and stored at −80 °C. Prior to analysis, the filters were dried overnight at 60 °C, treated with fuming HCl for 6 h to remove all inorganic carbon, and dried overnight again at 60 °C. After being packed in tin cups, the samples were subsequently analyzed on a PerkinElmer Series II CHNS/O Analyzer 2400.Particulate organic P (POP) was measured following Solorzano et al.48. Cells were filtered on pre-combusted 25 mm GF/F filters and rinsed twice with 2 mL of 0.17 M Na2SO4. The filters were then placed in combusted glass bottles with the addition of 2 mL of 0.017 M MgSO4, and subsequently evaporated to dryness at 95 °C and baked at 450 °C for 2 h. After cooling, 5 mL of 0.2 M HCl was added to each bottle. The bottle was then tightly capped and heated at 80 °C for 30 min, after which 5 mL Milli-Q H2O was added. Dissolved phosphate from the digested POP sample was measured colorimetrically following the standard phosphomolybdenum blue method.C uptake and N2 fixation ratesRates of short-term C uptake were determined at the end of the chemostat culturing. 100 µM NaH14CO3 (PerkinElmer) was added to 50 mL of cultures in the middle of the photoperiod, which was then incubated for 20 min under the growth conditions. After incubation, the samples were collected onto 3 μm polycarbonate membrane filters (Millipore), which were then washed with 0.22 µm-filtered oligotrophic seawater and placed on the bottom of scintillation vials. The filters were acidified to remove inorganic C by adding 500 µL of 2% HCl. The radioactivity was determined using a Tri-Carb 2800TR Liquid Scintillation Analyzer (PerkinElmer). Rates of N2 fixation (nitrogenase activity) were measured in the middle of the photoperiod for 2 h by the acetylene reduction assay49, using a ratio of 4:1 to convert ethylene production to N2 fixation.Soluble reactive phosphate (SRP) analysisWhen the chemostat cultures reached a steady-state, SRP concentrations in the culture vessels were measured at regular intervals, using the classic phosphomolybdenum blue (PMB) method with an additional step to enrich PMB on an Oasis HLB cartridge50. Briefly, 100 mL of GF/F filtered medium sample was fortified with 2 mL of ascorbic acid (100 g L−1) and 2 mL of mixed reagent (MR, the mixture of 100 mL of 130 g L−1 ammonium molybdate tetrahydrate, 100 mL of 3.5 g L−1 potassium antimony tartrate, and 300 mL of 1:1 diluted H2SO4), and then mixed completely. After standing at room temperature for 5 min, the solution was loaded onto a preconditioned Oasis HLB cartridge (3 cm3/60 mg, P/N: WAT094226, Waters Corp.) via a peristaltic pump, and then 1 mL eluent solution (0.2 M NaOH) was added to elute the sample into a cuvette, to which 0.06 mL of MR and 0.03 mL of ascorbic acid solution was added to fully develop PMB. Finally, the absorbance of PMB was measured at 700 nm using a spectrophotometer.Alkaline phosphatase (AP) activityAP activities were measured in the middle of the photoperiod using p-nitrophenylphosphate (pNPP) as a substrate51. Briefly, 5 mL of culture was incubated with 250 μL of 10 mM pNPP, 675 μL of Tris-glycine buffer (50 mM, pH 8.5) and 67.5 μL of 1 mM MgCl2 for 2 h under growth conditions. The absorbance of formed p-nitrophenol (pNP) was measured at 410 nm using a spectrophotometer.PolyP analysisAt the end of the chemostat culturing, T. erythraeum cells were filtered in the middle of the photoperiod onto 3 μm polycarbonate membrane filters (Millipore), flash frozen in liquid nitrogen, and stored at −80 °C until analysis. PolyP was quantified fluorometrically following Martin and Van Mooy22 and Martin et al.23. Briefly, samples were re-suspended in 1 mL Tris buffer (pH 7.0), sonicated for 30 s, immersed in boiling water for 5 min, sonicated for another 30 s, and then digested by 10 U DNase (Takara), RNase (2.5 U RNase A + 100 U RNase T1) (Invitrogen) and 20 μl of 20 mg mL−1 proteinase K at 37 °C for 30 min. After centrifugation for 5 min at 14,000×g, the supernatant was diluted with Tris buffer according to the range of standards curve, stained with 60 μL of 100 μM 4, 6-diamidino-2-phenylindole (DAPI) per 500 μL of samples, incubated for 7 min and then vortexed. The samples were then loaded onto a black 96-well plate and the absorption of fluorescence at an excitation wavelength of 415 nm and emission wavelength of 550 nm was measured using a PerkinElmer EnSpire® Multimode Plate Reader. PolyP standard (sodium phosphate glass Type 45) was purchased from Sigma-Aldrich. This method gives a relative measure of polyP concentration23 that is expressed as femto-equivalents of the standard per cell (feq cell−1).Cellular ATP measurementCellular ATP contents were determined when the chemostat cultures reached a steady state. T. erythraeum cells were collected in the middle of the photoperiod using an ATP Assay Kit (Beyotime Biotechnology, Shanghai, China) according to the manufacturer’s instructions. Briefly, the sample was lysed and centrifuged, and the supernatant (100 μL) was mixed with ATP detection working reagent (100 μL) and loaded onto a black 96-well plate. The luminescence was measured using a PerkinElmer EnSpire® Multimode Plate Reader.Intracellular metabolites measurementsNAD(H), NADP(H), and Glu were measured at the end of the chemostat culturing, using the liquid chromatography-tandem quadrupole mass spectrometry (LC–MS/MS) method modified from Luo et al.52. Briefly, T. erythraeum cells were gently filtered at the middle of photoperiod onto 3 μm polycarbonate membrane filters (Millipore), rapidly suspended in −80 °C precooled methanol-water (60%, v/v) mixture. After being kept in −80 °C freezer for 30 min, the sample was sonicated for 30 s, centrifuged at 12,000×g and 4 °C for 5 min, and the supernatant was filtered through a 0.2 μm filter (Jinteng®, China) and stored at −80 °C for further LC–MS/MS analysis.A 2.0 × 50 mm Phenomenex® Gemini 5u C18 110 Å column (particle size 5.2 µm, Phenomenex, USA) was used for the analysis. The mobile phases consisted of two solvents: mobile phase A (10 mM tributylamine aqueous solution, pH 4.95 with 15 mM acetic acid) and mobile phase B (100% methanol), which were delivered using an Agilent 1290 UPLC binary pump (Agilent Technologies, Palo Alto, CA, USA) at a flow rate of 200 µL min−1, with a linear gradient program implemented as follows: hold isocratic at 0% B (0–2 min); linear gradient from 0% to 85% B (2–28 min); hold isocratic at 0% B (28–34 min). The effluent from the LC column was delivered to an Agilent 6490 triple-quadrupole mass spectrometer, equipped with an electrospray ionization source operating in negative-ion mode. NAD, NADH, NADP, NADPH, and Glu were monitored in the multiple reaction monitoring modes with the transition events at m/z 662.3  > 540, 664.3  > 79, 742  > 620, 744  > 79, and 147  > 84, respectively.RNA extraction, library preparation, and sequencingAt the end of the chemostat culturing, T. erythraeum was collected in the middle of the photoperiod by filtering onto 3 μm polycarbonate membrane filters (Millipore), flash frozen in liquid nitrogen and stored at −80 °C until extraction. Total RNA was extracted using TRIzol® Reagent (Invitrogen) combined with a physical cell disruption approach by glass beads according to the manufacturer’s instructions. Genomic DNA was removed thoroughly by treating it with RNAase-free DNase I (Takara, Japan). Ribosomal RNA was removed from a total amount of 3 µg RNA using Ribo-Zero rRNA Removal kit (Illumina, USA). Subsequently, cDNA libraries were generated according to the manufacturer’s protocol of NEBNext® UltraTM Directional RNA Library Prep Kit for Illumina® (NEB, USA). The quality of the library was assessed on the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Libraries were sequenced on an Illumina Hiseq 2500 platform, yielding 136-bp paired-end reads.RNA-Seq bioinformaticsClean reads were obtained from raw data by removing reads containing adapter, ploy-N and low-quality read. Qualified sequences were mapped to the Trichodesmium erythraeum IMS101 genome (https://www.ncbi.nlm.nih.gov/nuccore/NC_008312.1) by using Bowtie2-2.2.353. Differential expression analysis for high/low pCO2 with P limitation was performed using the DESeq2 R package54. The resulting p-values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted p-value  More

  • in

    Characterization of bacterial diversity between two coastal regions with heterogeneous soil texture

    Soil sampling and determination of soil physical properties and synoptic dataSoil samples were taken from two coastal deserts in the north and south of Iran. Details of their geographic distribution and eco-physiological characterization were shown in Table 1. A total of 2 kg of soil samples were collected from 2 distinct sampling locations ranging in depth from 0 to 30 cm, and the samples were dried for 3 days at room temperature and in the dark before sifting. The soil samples were sieved using a 2 mm sieve to remove stones and other inert material before being stored in zip-top bags. Table 1 lists the soil samples’ physical characteristics, including soil texture (sand 2–0.02 mm; silt 0.02–0.002 mm; clay 0.002 mm), pH, and the proportions of clay, silt, and sand. Synoptic data from the past 10 years (2009–2019), including the average annual temperature, maximum temperature, minimum temperature, average rainfall, average annual wind speed, and maximum wind speed, were obtained from the I.R.OF Iran Meteor (http://www.irimo.ir/far/index.php).Bacterial isolation and effect of manure-based medium on their growthAccording to Chen et al. 2005, the soil-borne bacteria were isolated using direct-spreading method. For this essence soil samples were treated through a series of dilutions. The mixture of 1 g of soil sample was vortexed for 1 min after being suspended in 2 ml of sterile physiological saline (0.9% w/v NaCl). The mixture was then diluted serially (typically 10–1 to 10–7), and level 100 μl of the diluted soil samples were scattered on the surface of solidified plates using glass spreaders. The samples were then incubated for 1 to 3 days at 30 °C in an inverted posture without light. For bacterial isolation, we used eleven culture media including Nutrient Agar (NA), Nutrient Agar plus MnSO4 (NA + MnSO4), LB, Moller Hinton Agar (MHA), Acidithiobacillus (APH) medium, Violet Red Bile Lactose (VRB) agar medium, GYM Streptomyces medium, DPM medium, Azospirillum medium, Azotobacter medium and Manure based medium (MB).To prepare MB medium, dry animal manure and distilled water (1:6 w/v) were combined to create MB medium, which was then let to sit at room temperature for 16 h. The resulting mixture was then centrifuged at 5000 rcf for 30 min after being filtered twice. The next stage involved adding Hoagland salts (10% w/v) to the final extract, adjusting the medium’s pH to 5.8 ± 0.02, and autoclaving it for 20 min at 121 °C and 1.5 kPa. Before sterilization, bacteriological agar (1.5 w/v) was employed as a gelling agent to solidify the medium.After bacterial isolation on NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospibrillum media, the growth of all isolates was evaluated on an MB medium. To investigate isolates biomass in the same condition, we elected MB medium. First, the bacteria were grown in the liquid form of NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospirillum and Azotobacter media at 30 °C for 48 h, then 103 cells of each isolate were transferred to 48 wells plates containing MB medium, and plates were incubated at 30 °C for 10 h. Then, the growth of bacteria was read at an optical density (OD) of 630 nm 10 h after inoculation, the experiment was performed with three replicates. In the following step, CFU/ml equivalent to each OD was obtained by inoculating the uniform amount of liquid culture of the isolates on the solid form of MB medium at 30 °C for 16 h.Phenotypic characterization and biochemical identification of bacterial isolatesThe morphological analysis of the cell shape, colony (i.e., shape, color, and size), and biochemical tests were used to identify the bacterial isolates. Biochemical characterization was carried out By using gram staining, KOH27, oxidase, and catalase tests. For this essence, following Bartholomew’s method28, gram staining of bacteria was studied 48 h after inoculation on MHA, and the non-staining KOH method was used to confirm the results. Using 0.5 ml of a 10% hydrogen peroxide solution, a catalase test was conducted, and the generation of gas bubbles was monitored. Using biochemical oxidase discs, the oxidative activity of 27 isolates was investigated.Effect of abiotic stresses on bacterial isolatesTo determine the effect of abiotic stresses on isolates alkaline (MH medium with pH  10), salinity (MH medium supplemented with the final concentration of 100 mM NaCl), osmotic [MH medium supplemented with 25% polyethylene glycol (PEG) Mn6000], and thermal stresses (MH medium incubated at 15 °C for cold stress and 60 °C for heat stress) were screened. For all experiments, the incubation period was 15 h, and plates were kept in a dark condition.MALDI-TOF MS identification of isolatesSoil bacterial isolates were subcultured twice on MHA and incubated at 30 °C for 24 h before MALDI-TOF MS measurement. Then ∼0.1 µg of cell material was directly transferred from a bacterial colony or smear of colonies to a MALDI target spot. After drying at laboratory temperature, sample spots were overlaid with 1 μl of matrix solution (10 mg/mL a-cyano-4-hydroxycinnamic acid in 50% acetonitrile and 2.5% trifluoroacetic acid) and each measurement was carried out in triplicate (technical replicates). MS analysis was performed on an Autoflex MALDI-TOF mass spectrometer (Bruker Daltonics, Germany) using Flex Control 3.4 software (Bruker Daltonics, Germany). Calibration was carried out with the use of the Bacterial Test Standard (Bruker Daltonics, Germany). Soil isolates with a valid MALDI-TOF MS score of 2 were undoubtedly assigned to the genus/species level. For bacterial classification and identification, BioTyper 3.1 software (Bruker Daltonics, Germany) equipped with MBT 6903 MPS Library (released in April 2016), the MALDI Biotyper Preprocessing Standard Method, and the MALDI Biotyper MSP Identification Standard Method adjusted by the manufacturer (Bruker Daltonics, Germany) were used. Only the highest score value of all mass spectra belonging to individual cultures (biological and technical replicates) was recorded25. The score between 2.3 and 3.00 shows highly probable species-level identification and between 2.0 and 2.29 represents genus-level identification and probable species level of identification. A score between 1.7 and 1.99 indicates probable genus-level identification29.Effects of bacterial isolates on plants growthThe Seed and Plant Improvement Institute of Karaj (Karaj, Iran; http://www.spii.ir/homepage.aspx?site=DouranPortal&tabid=1&lang=faIR) provided the maize, canola, and wheat seeds (Zea mays. Var Kosha; Brassica napus Var Nima; Triticum aestivum Var Kalate). In greenhouse trials, 2 × 103 cells/seed of soil-borne isolates cultured in a manure-based medium were inoculated to maize, canola, and wheat plants. During the studies, sand that had been acid washed and autoclaved was used for planting. For three weeks, seedlings were kept under a 16/8 h day/night photoperiod with a 25 °C temperature. Three replications of a complete randomized block design were used for the colonization experiment’s treatments. Under the bacterial treatments, measurements were made of the plant growth parameters including shoot dry biomass (mg), root dry biomass (mg), shoot length (cm), root length (cm), shoot density (mg/cm), root density (mg/cm), and shoot/root weight (mg). Samples were dried at 60 °C for three days to measure dry biomass.Statistical analysisStatistical analysis was done by R software (version 4.1.3). One-way analysis of variance (ANOVA) was used to determine the significance of the experiment, and Fisher’s protected Least Significant Difference (LSD) test with a P-value of 0.01 was performed to separate the means. Furthermore, PCA analysis has been carried out based on the Clustvis package and the SVD imputation approach.Ethics approval and consent to participateAll authors agree to the ethics and consent to participate in this article and declare that this submission follows the policies of Scientific Reports. Accordingly, the material is the author’s original work, which has not been previously published elsewhere. The paper is not being considered for publication elsewhere. All authors have been personally and actively involved in substantial work leading to the paper and will take public responsibility for its content.Ethics for research involving plantsAll authors confirmed that experimental research and field studies on plants, including receiving the seeds from the Seed and Plant Improvement Institute of Karaj, complied with relevant institutional, national, and international guidelines and legislation. Furthermore, methods were conducted according to the relevant guidelines and regulations. More

  • in

    Carcass appearance does not influence scavenger avoidance of carnivore carrion

    DeVault, T. L., Rhodes, O. E. Jr. & Shivik, J. A. Scavenging by vertebrates: Behavioral, ecological, and evolutionary perspectives on an important energy transfer pathway in terrestrial ecosystems. Oikos 102, 225–234 (2003).
    Google Scholar 
    Wilson, E. E. & Wolkovich, E. M. Scavenging: How carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).PubMed 

    Google Scholar 
    Barton, P. S., Cunningham, S. A., Lindenmayer, D. B. & Manning, A. D. The role of carrion in maintaining biodiversity and ecological processes in terrestrial ecosystems. Oecologia 171, 761–772 (2013).ADS 
    PubMed 

    Google Scholar 
    Benbow, M. E. et al. Necrobiome framework for bridging decomposition ecology of autotrophically and heterotrophically derived organic matter. Ecol. Monogr. 89, e01331 (2019).
    Google Scholar 
    Carter, D. O., Yellowlees, D. & Tibbett, M. Cadaver decomposition in terrestrial ecosystems. Naturwissenschaften 94, 12–24 (2007).ADS 
    PubMed 
    CAS 

    Google Scholar 
    Bump, J. K., Peterson, R. O. & Vucetich, J. A. Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology 90, 3159–3167 (2009).PubMed 

    Google Scholar 
    Beasley, J. C., Olson, Z. H. & DeVault, T. L. Ecological role of vertebrate scavengers. In Carrion Ecology, Evolution, and Their Applications (eds Benbow, E. M. et al.) 107–127 (CRC Press, 2015).
    Google Scholar 
    DeVault, T. L., Brisbin, I. L. Jr. & Rhodes, O. E. Jr. Factors influencing the acquisition of rodent carrion by vertebrate scavengers and decomposers. Can. J. Zool. 82, 502–509 (2004).
    Google Scholar 
    Moleón, M., Sánchez-Zapata, J. A., Sebastián-González, E. & Owen-Smith, N. Carcass size shapes the structure and functioning of an African scavenging assemblage. Oikos 124, 1391–1403 (2015).
    Google Scholar 
    Turner, K. L., Abernethy, E. F., Conner, L. M., Rhodes, O. E. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).PubMed 

    Google Scholar 
    Selva, N. The Role of Scavenging in the Predator Community of Białowieża Primeval Forest (E Poland) (Univeristy of Sevilla, 2004).
    Google Scholar 
    Moleón, M. et al. Carnivore carcasses are avoided by carnivores. J. Anim. Ecol. 86, 1179–1191 (2017).PubMed 

    Google Scholar 
    Selva, N. & Fortuna, M. A. The nested structure of a scavenger community. Proc. R. Soc. B Biol. Sci. 274, 1101–1108 (2007).
    Google Scholar 
    Abernethy, E. F. et al. Carcasses of invasive species are predominantly utilized by invasive scavengers in an island ecosystem. Ecosphere 7, e01496 (2016).
    Google Scholar 
    DeVault, T. L., Seamans, T. W., Linnell, K. E., Sparks, D. W. & Beasley, J. C. Scavenger removal of bird carcasses at simulated wind turbines: Does carcass type matter?. Ecosphere 8, e01994 (2017).
    Google Scholar 
    Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Carcass type affects local scavenger guilds more than habitat connectivity. PLoS ONE 11, e0147798 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Muñoz-Lozano, C. et al. Avoidance of carnivore carcasses by vertebrate scavengers enables colonization by a diverse community of carrion insects. PLoS ONE 14, e0221890 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Peers, M. J. L. et al. Vertebrate scavenging dynamics differ between carnivore and herbivore carcasses in the northern boreal forest. Ecosphere 12, e03691 (2021).
    Google Scholar 
    Pfennig, D. W. Effect of predator-prey phylogenetic similarity on the fitness consequences of predation: A trade-off between nutrition and disease?. Am. Nat. 155, 335–345 (2000).PubMed 

    Google Scholar 
    Polis, G. A. The evolution and dynamics of intraspecific predation. Annu. Rev. Ecol. Syst. 12, 225–251 (1981).
    Google Scholar 
    Elgar, M. A. & Crespi, B. J. Cannibalism: Ecology and Evolution Among Diverse Taxa (Oxford University Press, 1992).
    Google Scholar 
    Fouilloux, C., Ringler, E. & Rojas, B. Cannibalism. Curr. Biol. 29, R1295–R1297 (2019).PubMed 
    CAS 

    Google Scholar 
    Oliva-Vidal, P., Tobajas, J. & Margalida, A. Cannibalistic necrophagy in red foxes: Do the nutritional benefits offset the potential costs of disease transmission?. Mamm. Biol. https://doi.org/10.1007/s42991-021-00184-5 (2021).Article 

    Google Scholar 
    Mateo, J. M. Recognition systems and biological organization: The perception component of social recognition. Ann. Zool. Fenn. 41, 729745 (2004).
    Google Scholar 
    Dangles, O., Irschick, D., Chittka, L. & Casas, J. Variability in sensory ecology: Expanding the bridge between physiology and evolutionary biology. Q. Rev. Biol. 84, 51–74 (2009).PubMed 

    Google Scholar 
    Janzen, D. H. Why fruits rot, seeds mold, and meat spoils. Am. Nat. 111, 691–713 (1977).CAS 

    Google Scholar 
    Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).PubMed 
    CAS 

    Google Scholar 
    Gonzálvez, M., Martínez-Carrasco, C., Sánchez-Zapata, J. A. & Moleón, M. Smart carnivores think twice: red fox delays scavenging on conspecific carcasses to reduce parasite risk. Appl. Anim. Behav. Sci. 243, 105462 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Selva, N., Jedrzejewska, B., Jedrzejewski, W. & Wajrak, A. Scavenging on European bison carcasses in Bialowieza Primeval Forest (eastern Poland). Écoscience 10, 303–311 (2003).
    Google Scholar 
    Carr, W. J., Hirsch, J. T., Campellone, B. E. & Marasco, E. Some determinants of a natural food aversion in Norway rats. J. Comp. Physiol. Psychol. 93, 899–906 (1979).
    Google Scholar 
    Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: Spatial patterns of risk perception and response. Trends Ecol. Evol. 34, 355–368 (2019).PubMed 

    Google Scholar 
    Moleón, M. & Sánchez-Zapata, J. A. The role of carrion in the landscapes of fear and disgust: a review and prospects. Diversity 13, 28 (2021).
    Google Scholar 
    Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models (Springer, 2022).
    Google Scholar 
    Hothorn, T., Winell, H., Hornik, K., van de Wiel, M. A. & Zeileis, A. Coin: Conditional Inference Procedures in a Permutation Test Framework (Springer, 2021).
    Google Scholar 
    Owings, C. G., Gilhooly, W. P. & Picard, C. J. Blow fly stable isotopes reveal larval diet: A case study in community level anthropogenic effects. PLoS ONE 16, e0249422 (2021).PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Matuszewski, S., Konwerski, S., Frątczak, K. & Szafałowicz, M. Effect of body mass and clothing on decomposition of pig carcasses. Int. J. Legal Med. 128, 1039–1048 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Cunningham, C. X. et al. Top carnivore decline has cascading effects on scavengers and carrion persistence. Proc. R. Soc. B. 285, 1–10 (2018).
    Google Scholar 
    Huang, S., Bininda-Emonds, O. R. P., Stephens, P. R., Gittleman, J. L. & Altizer, S. Phylogenetically related and ecologically similar carnivores harbour similar parasite assemblages. J. Anim. Ecol. 83, 671–680 (2014).PubMed 

    Google Scholar 
    Hill, D. E., Chirukandoth, S. & Dubey, J. P. Biology and epidemiology of Toxoplasma gondii in man and animals. Anim. Health Res. Rev. 6, 41–61 (2005).PubMed 

    Google Scholar 
    Hill, D. E. et al. Trichinella murrelli in scavenging mammals from south-central Wisconsin, USA. J. Wildl. Dis. 44, 629–635 (2008).PubMed 
    CAS 

    Google Scholar 
    Sandfoss, M., DePerno, C., Patton, S., Flowers, J. & Kennedy-Stoskopf, S. Prevalence of antibody to Toxoplasma gondii and Trichinella spp. in feral pigs (Sus scrofa) of eastern North Carolina. J. Wildl. Dis. 47, 338–343 (2011).PubMed 

    Google Scholar 
    Butler, J. R. A., du Toit, J. T. & Bingham, J. Free-ranging domestic dogs (Canis familiaris) as predators and prey in rural Zimbabwe: Threats of competition and disease to large wild carnivores. Biol. Conserv. 115, 369–378 (2004).
    Google Scholar 
    Mendenhall, I. H. et al. Evidence of canine parvovirus transmission to a civet cat (Paradoxurus musangus) in Singapore. One Health 2, 122–125 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Han, B. A., Castellanos, A. A., Schmidt, J. P., Fischhoff, I. R. & Drake, J. M. The ecology of zoonotic parasites in the Carnivora. Trends Parasitol. 37, 1096–1110 (2021).PubMed 

    Google Scholar 
    Malmberg, J. L., White, L. A. & VandeWoude, S. Bioaccumulation of pathogen exposure in top predators. Trends Ecol. Evol. 36, 411–420 (2021).PubMed 

    Google Scholar 
    Mammal Diversity Database (Version 1.9). https://doi.org/10.5281/zenodo.6407053 (2022).Han, B. A., Kramer, A. M. & Drake, J. M. Global patterns of zoonotic disease in mammals. Trends Parasitol. 32, 565–577 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Digby, Z. et al. Evolutionary loss of inflammasomes in the Carnivora and implications for the carriage of zoonotic infections. Cell Rep. 36, 109614 (2021).PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Buck, J. C., Weinstein, S. B. & Young, H. S. Ecological and evolutionary consequences of parasite avoidance. Trends Ecol. Evol. 33, 619–632 (2018).PubMed 
    CAS 

    Google Scholar 
    Hart, B. L. & Hart, L. A. How mammals stay healthy in nature: The evolution of behaviours to avoid parasites and pathogens. Philos. Trans. R. Soc. B 373, 20170205 (2018).
    Google Scholar 
    Brown, C. J. & Plug, I. Food choice and diet of the bearded vulture Gypaetus barbatus in southern Africa. S. Afr. J. Zool. 25, 169–177 (1990).
    Google Scholar 
    Rossi, L., Interisano, M., Deksne, G. & Pozio, E. The subnivium, a haven for Trichinella larvae in host carcasses. Int. J. Parasitol. Parasit. Wildl. 8, 229–233 (2019).
    Google Scholar 
    Micozzi, M. S. Experimental study of postmortem change under field conditions: Effects of freezing, thawing, and mechanical injury. J. Forensic Sci. 31, 953–961 (1986).PubMed 
    CAS 

    Google Scholar 
    Mayntz, D. & Toft, S. Nutritional value of cannibalism and the role of starvation and nutrient imbalance for cannibalistic tendencies in a generalist predator. J. Anim. Ecol. 75, 288–297 (2006).PubMed 

    Google Scholar 
    Margalida, A. Bearded vultures (Gypaetus barbatus) prefer fatty bones. Behav. Ecol. Sociobiol. 63, 187–193 (2008).
    Google Scholar 
    Parmenter, R. R. & MacMahon, J. A. Carrion decomposition and nutrient cycling in a semiarid shrub–steppe ecosystem. Ecol. Monogr. 79, 637–661 (2009).
    Google Scholar 
    Evans, B. E., Mosby, C. E. & Mortelliti, A. Assessing arrays of multiple trail cameras to detect North American mammals. PLoS ONE 14, 1–18 (2019).
    Google Scholar 
    Ivan, J. S. & Newkirk, E. S. CPW Photo Warehouse: A custom database to facilitate archiving, identifying, summarizing and managing photo data collected from camera traps. Methods Ecol. Evol. 7, 499–504 (2016).
    Google Scholar 
    Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, 2000).MATH 

    Google Scholar 
    Kassambara, A., Kosinski, M. & Biecek, P. survminer: Drawing Survival Curves Using ‘ggplot2’ (Springer, 2020).
    Google Scholar 
    Nenadic, O. & Greenacre, M. Correspondence analysis in R, with two- and three-dimensional graphics: the ca package. J. Stat. Softw. 20, 1–13 (2007).
    Google Scholar 
    Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses (Springer, 2020).
    Google Scholar 
    Greenacre, M. The contributions of rare objects in correspondence analysis. Ecology 94, 241–249 (2013).PubMed 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).
    Google Scholar  More