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    Extinction magnitude of animals in the near future

    Selection of environmental-biotic events to be studiedIn global warming events associated with mass extinctions, the current environmental changes are similar to those recorded during the end-Ordovician, end-Guadalupian, and end-Permian mass extinctions. Therefore, I analyzed global surface temperature anomalies, mercury pollution concentrations, and deforestation percentages in these three mass extinctions and in the current crisis. The asteroid impact at the K–Pg boundary and nuclear war cause the formation of stratospheric soot aerosols distributed globally, thus inducing sunlight reductions and global cooling (impact winter and nuclear winter). I also analyzed stratospheric soot aerosols as a possible cause of future extinctions.Most likely case and worst caseThe most likely case corresponds to the reduction of CO2 emissions resulting from human conduct, the protection of forests, and the introduction of anti-pollution measures in the future under the Paris Agreement on Climate change and Sustainable Development Goals (SDGs). The worst case corresponds to the scenario in which humans fail to stop increasing global surface temperatures, pollution, and deforestation until 2100–2200 CE.I use the average of the RCP4.5 and RCP6.0 cases in the Intergovernmental Panel on Climate Change (IPCC)8 as the most likely case of GHG emissions, representing the middle of the four potential GHG emissions cases (RCP2.6, 4.5, 6.0, and 8.5) in Fifth Assessment Report of the IPCC8, approximately corresponding to the middle of SSP2-4.5 and SSP3-7.0 in Sixth Assessment Report of the IPCC9. The timing of decreased global GHG emissions is 2060–2080 CE. Therefore, I use the average GHG emissions and global surface temperature anomalies of the RCP4.5 and RCP6.0 cases as the most likely values and those of the RCP8.5 case as the worst-case scenario, marked by stopping GHG emissions from 2090 to 2100 CE8,9, as this case corresponds to the highest GHG emissions8,9.Surface temperature anomaly, environment, and extinction magnitude dataData on surface temperature anomalies and extinction percentages are from Kaiho4. Changes in industrial GHG emissions and global surface temperature anomalies are sourced from the Fifth and Sixth Assessment Report of the IPCC8,9.Pollution can be represented by mercury concentrations measured in sedimentary rocks recording mass extinctions8 and in recent sediments deposited in seas and lakes25,26 because mercury is toxic to plants and animals and because its sources include volcanic eruptions, meteorite impacts, and the combustion of fossil fuels10,33, which are common sources of pollutants, and because it can be commonly measured from sedimentary rocks recording mass extinctions33. The mercury concentration is related to the CO2 emission amount during global warming because of the common sources of mercury and CO2 (volcanism and fossil fuel combustion influencing global warming). Thus, the future mercury concentrations are estimated based on the CO2 emission amounts estimated by the IPCC8,9. Since mercury and the other pollutants mainly come from oil, coal, and vegetation33, the amount of mercury released should change in parallel with industrial CO2 emissions because there is a good correlation between mercury and CO2 emissions11.Deforestation occurs by the expansion of agricultural areas and urban areas, which are strongly related to human populations13,28. Thus, future deforestation percentages are estimated based on estimated future population data27 (Supplementary Table S2). The severity of deforestation in each event is expressed by the occupancy % of the deforested area in the pre-event forest area in (i) the Permian–Triassic transition marked by the largest mass extinction based on plant fossil records24 and (ii) 2005–2015 CE as a representative of the Anthropocene epoch12,13,28 based on the actual forest area relative to the pre-agriculture phase before 4000 BP. Deforestation is related to the human population because agriculture and urbanization have caused deforestation13,28. I estimate the past and future deforestation percentage using human population data in the past and future21 based on the parallel growth of the human population and deforestation13,28.Amount of stratospheric soot was calculated using a method of Kaiho and Oshima34 (Supplementary Table S1). I obtained global surface temperature anomaly caused by stratospheric soot using Fig. 5 of Kaiho and Oshima34.I then use those data to estimate the future extinction magnitude based on the assumption that the Earth and contemporary life at the time of each crisis are more or less mutually comparable throughout time and to the present day.I estimate the magnitude of the species animal extinction crisis between 2000 and 2500 CE using Figs. 1, 2 and Supplementary Tables S1 and S2 in each cause under the most likely case and worst case under three nuclear war scenarios (zero, minor, and major; Fig. 2d)15 in the PETM and mass extinction cases, respectively (Supplementary Tables S3, S4; Fig. 3). Finally, I estimate the magnitude of current animal extinction crisis by the four causes as an average of the species extinction magnitude by the four causes in Fig. 3. I use two different contribution rates of temperature anomalies, pollution, deforestation, and stratospheric soot by nuclear wars, 1:0.2:0.1:1 for marine animals and 1:0.5:1:1 for terrestrial tetrapods (different contribution case considering lower influence of pollution and deforestation to marine animals rather than terrestrial animals) and 1:1:1:1 for marine animals and 1:1:1:1 for terrestrial tetrapods (equal contribution case considering high influence of pollution and deforestation to marine animals via rain and soil erosion) (Supplementary Tables S5–S9). These contribution rates are estimated as end-members to show ranges of animal species extinction magnitude (%). More

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    Composition and metabolic potential of microbiomes associated with mesopelagic animals from Monterey Canyon

    McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, et al. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA. 2013;110:3229–36.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hammer TJ, Sanders JG, Fierer N. Not all animals need a microbiome. FEMS Microbiol Lett. 2019;366:fnz117.Article 
    CAS 
    PubMed 

    Google Scholar 
    Bagge LE, Osborn KJ, Johnsen S. Nanostructures and monolayers of spheres reduce surface reflections in hyperiid amphipods. Curr Biol. 2016;26:3071–6.Article 
    CAS 
    PubMed 

    Google Scholar 
    Apprill A. Marine animal microbiomes: toward understanding host–microbiome interactions in a changing ocean. Front Mar Sci. 2017;4:222.Article 

    Google Scholar 
    Wilkins LGE, Leray M, O’Dea A, Yuen B, Peixoto RS, Pereira TJ, et al. Host-associated microbiomes drive structure and function of marine ecosystems. PLoS Biol. 2019;17:e3000533.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moitinho-Silva L, Nielsen S, Amir A, Gonzalez A, Ackermann GL, Cerrano C, et al. The sponge microbiome project. GigaScience. 2017;6:gix077.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    van Oppen MJH, Blackall LL. Coral microbiome dynamics, functions and design in a changing world. Nat Rev Microbiol. 2019;17:557–67.Article 
    PubMed 

    Google Scholar 
    Henehan MJ, Hull PM, Penman DE, Rae JWB, Schmidt DN. Biogeochemical significance of pelagic ecosystem function: an end-Cretaceous case study. Phil Trans R Soc B. 2016;371:20150510.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    De Corte D, Srivastava A, Koski M, Garcia JAL, Takaki Y, Yokokawa T, et al. Metagenomic insights into zooplankton-associated bacterial communities. Environ Microbiol. 2018;20:492–505.Article 
    PubMed 

    Google Scholar 
    Egerton S, Culloty S, Whooley J, Stanton C, Ross RP. The gut microbiota of marine fish. Front Microbiol. 2018;9:873.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Scott JJ, Adam TC, Duran A, Burkepile DE, Rasher DB. Intestinal microbes: an axis of functional diversity among large marine consumers. Proc R Soc B. 2020;287:20192367.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sanders JG, Beichman AC, Roman J, Scott JJ, Emerson D, McCarthy JJ, et al. Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nat Commun. 2015;6:8285.Article 
    CAS 
    PubMed 

    Google Scholar 
    Preheim SP, Boucher Y, Wildschutte H, David LA, Veneziano D, Alm EJ, et al. Metapopulation structure of Vibrionaceae among coastal marine invertebrates. Environ Microbiol. 2011;13:265–75.Article 
    CAS 
    PubMed 

    Google Scholar 
    Sullam KE, Essinger SD, Lozupone CA, O’Connor MP, Rosen GL, Knight R, et al. Environmental and ecological factors that shape the gut bacterial communities of fish: a meta-analysis. Mol Ecol. 2012;21:3363–78.Article 
    PubMed 

    Google Scholar 
    Huang Q, Sham RC, Deng Y, Mao Y, Wang C, Zhang T, et al. Diversity of gut microbiomes in marine fishes is shaped by host‐related factors. Mol Ecol. 2020;29:5019–34.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Webb TJ, Vanden Berghe E, O’Dor R. Biodiversity’s big wet secret: the global distribution of marine biological records reveals chronic under-exploration of the deep pelagic ocean. PLoS ONE. 2010;5:e10223.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Irigoien X, Klevjer TA, Røstad A, Martinez U, Boyra G, Acuña JL, et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat Commun. 2014;5:3271.Article 
    PubMed 

    Google Scholar 
    Drazen JC, Sutton TT. Dining in the deep: the feeding ecology of deep-sea fishes. Annu Rev Mar Sci. 2017;9:337–66.Article 

    Google Scholar 
    Boyd PW, Claustre H, Levy M, Siegel DA, Weber T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature. 2019;568:327–35.Article 
    CAS 
    PubMed 

    Google Scholar 
    Klevjer TA, Irigoien X, Røstad A, Fraile-Nuez E, Benítez-Barrios VM, Kaartvedt S. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci Rep. 2016;6:19873.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davison PC, Checkley DM, Koslow JA, Barlow J. Carbon export mediated by mesopelagic fishes in the northeast Pacific Ocean. Prog Oceanogr. 2013;116:14–30.Article 

    Google Scholar 
    Steinberg DK, Landry MR. Zooplankton and the ocean carbon cycle. Annu Rev Mar Sci. 2017;9:413–44.Article 

    Google Scholar 
    Stenvers VI, Hauss H, Osborn KJ, Neitzel P, Merten V, Scheer S, et al. Distribution, associations and role in the biological carbon pump of Pyrosoma atlanticum (Tunicata, Thaliacea) off Cabo Verde, NE Atlantic. Sci Rep. 2021;11:9231.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Robinson C, Steinberg DK, Anderson TR, Aristegui J, Carlson CA, Frost JR, et al. Mesopelagic zone ecology and biogeochemistry–a synthesis. Deep Sea Res II. 2010;57:1504–18.Article 
    CAS 

    Google Scholar 
    Iacuaniello CM. An examination of intestinal microbiota of mesopelagic fish reveals microbial community diversity across fish families. Master’s Thesis, University of California San Diego. 2019.Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.Article 
    PubMed 

    Google Scholar 
    Bernal A, Olivar MP, Maynou F, Fernández de Puelles ML. Diet and feeding strategies of mesopelagic fishes in the western Mediterranean. Prog Oceanogr. 2015;135:1–17.Article 

    Google Scholar 
    Bollens SM, Frost BW, Lin TS. Recruitment, growth, and diel vertical migration of Euphausia pacifica in a temperate fjord. Mar Biol. 1992;114:219–28.Article 

    Google Scholar 
    Hoving HJT, Neitzel P, Hauss H, Christiansen S, Kiko R, Robison BH, et al. In situ observations show vertical community structure of pelagic fauna in the eastern tropical North Atlantic off Cape Verde. Sci Rep. 2020;10:21798.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Judkins H, Vecchione M. Vertical distribution patterns of cephalopods in the northern Gulf of Mexico. Front Mar Sci. 2020;7:47.Article 

    Google Scholar 
    Miya M, Nemoto T. Reproduction, growth and vertical distribution of the meso- and bathypelagic fish Cyclothone atraria (Pisces: Gonostomatidae) in Sagami Bay, Central Japan. Deep Sea Res I. 1987;34:1565–77.Article 

    Google Scholar 
    Osborn KJ. Phylogenetics and ecology of pelagic munnopsid isopods (Crustacea, Asellota). Dissertation, University of California Berkeley. 2007.Pearcy WG, Forss CA. Depth distribution of oceanic shrimps (Decapoda; Natantia) off Oregon. J Fish Res Bd Can. 1966;23:1135–43.Article 

    Google Scholar 
    Russell FS. The vertical distribution of marine macroplankton. An observation on diurnal changes. J Mar Biol Ass. 1925;13:769–809.Article 

    Google Scholar 
    Watanabe H, Moku M, Kawaguchi K, Ishimaru K, Ohno A. Diel vertical migration of myctophid fishes (family Myctophidae) in the transitional waters of the western North Pacific. Fish Oceanogr. 1999;8:115–27.Article 

    Google Scholar 
    Madin LP. Gelatinous grazers: an underestimated force in ocean carbon cycles. 4th International Zooplankton Production Symposium, May 28–June 1. Hiroshima, Japan. 2007.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.Article 
    CAS 
    PubMed 

    Google Scholar 
    Ivanova NV, Zemlak TS, Hanner RH, Hebert PDN. Universal primer cocktails for fish DNA barcoding. Mol Ecol Notes. 2007;7:544–8.Article 
    CAS 

    Google Scholar 
    Geller J, Meyer C, Parker M, Hawk H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all‐taxa biotic surveys. Mol Ecol Resour. 2013;13:851–61.Article 
    CAS 
    PubMed 

    Google Scholar 
    Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 2013;10:34.Article 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.Article 
    CAS 
    PubMed 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl Acids Res. 2013;41:D590–6.Article 
    CAS 
    PubMed 

    Google Scholar 
    Robeson MS II, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, et al. RESCRIPt: reproducible sequence taxonomy reference database management. PLoS Comput Biol. 2021;17:e1009581.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Janssen S, McDonald D, Gonzalez A, Navas-Molina JA, Jiang L, Xu ZZ, et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems. 2018;3:e00021–18.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McMurdie PJ, Holmes S. phyloseq: an R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria; 2021.Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Knight R, Knights D. SHOGUN: a modular, accurate and scalable framework for microbiome quantification. Bioinformatics. 2020;36:4088–90.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Al-Ghalith G, Knights D. Faster and lower-memory metagenomic profiling with UTree. https://doi.org/10.5281/zenodo.998252.Jovel J, Patterson J, Wang W, Hotte N, O’Keefe S, Mitchel T, et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol. 2016;7:459.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    DeWitt FA, Cailliet GM. Feeding habits of two bristlemouth fishes, Cyclothone acclinidens and C. signata (Gonostomatidae). Copeia. 1972;1972:868.Article 

    Google Scholar 
    Fauchald K, Jumars PA. The diet of worms: a study of polychaete feeding guilds. Oceanogr Mar Biol Annu Rev. 1979;17:193–284.
    Google Scholar 
    Flock ME, Hopkins TL. Species composition, vertical distribution, and food habits of the sergestid shrimp assemblage in the eastern Gulf of Mexico. J Crustacean Biol. 1992;12:210–23.Article 

    Google Scholar 
    Uttal L, Buck KR. Dietary study of the midwater polychaete Poeobius meseres in Monterey Bay, California. Mar Biol. 1996;125:333–43.Article 

    Google Scholar 
    Tanimata N, Yamamura O, Sakurai Y, Azumaya T. Dietary shift and feeding intensity of Stenobrachius leucopsarus in the Bering Sea. J Oceanogr. 2008;64:185–94.Article 

    Google Scholar 
    Hoving HJT, Robison BH. Vampire squid: detritivores in the oxygen minimum zone. Proc R Soc B. 2012;279:4559–67.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Berning M. The feeding ecology of two species of holopelagic munnopsid isopods from the North Pacific (Acanthamunnopsis milleri and Munneurycope murrayi) using SEM analysis. Honors Thesis, Florida State University. 2014.Du X, Peterson W. Feeding rates and selectivity of adult Euphausia pacifica on natural particle assemblages in the coastal upwelling zone off Oregon, USA, 2010. J Plankton Res. 2014;36:1031–46.Article 

    Google Scholar 
    Henschke N, Everett JD, Richardson AJ, Suthers IM. Rethinking the role of salps in the ocean. Trends Ecol Evol. 2016;31:720–33.Article 
    PubMed 

    Google Scholar 
    Gruber-Vodicka HR, Seah BKB, Pruesse E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems. 2020;5:e00920–20.Article 
    CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Karlicki M, Antonowicz S, Karnkowska A. Tiara: deep learning-based classification system for eukaryotic sequences. Bioinformatics. 2022;38:344–50.Article 
    CAS 

    Google Scholar 
    Levy Karin E, Mirdita M, Söding J. MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome. 2020;8:48Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH.UniProt Consortium UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics. 2015;31:926–32.Article 
    CAS 
    PubMed 

    Google Scholar 
    Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chamberlain SA, Szöcs E. taxize: taxonomic search and retrieval in R. F1000Res. 2013;2:191.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.Article 
    CAS 
    PubMed 

    Google Scholar 
    Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9.Article 
    CAS 
    PubMed 

    Google Scholar 
    Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5:27.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McKnight DT, Huerlimann R, Bower DS, Schwarzkopf L, Alford RA, Zenger KR. Methods for normalizing microbiome data: an ecological perspective. Methods Ecol Evol. 2019;10:389–400.Article 

    Google Scholar 
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2020.Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10:1200–2.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Molnár K, Ostoros G, Dunams-Morel D, Rosenthal B. Eimeria that infect fish are diverse and are related to, but distinct from, those that infect terrestrial vertebrates. Infect Genet Evol. 2012;12:1810–5.Article 
    PubMed 

    Google Scholar 
    Domozych D. Algal cell walls. In: John Wiley & Sons, Ltd, editor. eLS. 1st ed. Wiley, Hoboken, NJ; 2019. p. 1–11.Gallet A, Koubbi P, Léger N, Scheifler M, Ruiz-Rodriguez M, Suzuki MT, et al. Low-diversity bacterial microbiota in Southern Ocean representatives of lanternfish genera Electrona, Protomyctophum and Gymnoscopelus (family Myctophidae). PLoS ONE. 2019;14:e0226159.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Reji L, Tolar BB, Chavez FP, Francis CA. Depth-differentiation and seasonality of planktonic microbial assemblages in the Monterey Bay upwelling system. Front Microbiol. 2020;11:1075.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    De Corte D, Lekunberri I, Sintes E, Garcia J, Gonzales S, Herndl G. Linkage between copepods and bacteria in the North Atlantic Ocean. Aquat Microb Ecol. 2014;72:215–25.Article 

    Google Scholar 
    Russell SL. Transmission mode is associated with environment type and taxa across bacteria-eukaryote symbioses: a systematic review and meta-analysis. FEMS Microbiol Lett. 2019;366:fnz013.Article 
    CAS 
    PubMed 

    Google Scholar 
    Akbar S, Li X, Ding Z, Liu Q, Huang J, Zhou Q, et al. Disentangling diet- and medium-associated microbes in shaping Daphnia gut microbiome. Microb Ecol. 2022;84:911–21. https://doi.org/10.1007/s00248-021-01900-x.Eckert EM, Anicic N, Fontaneto D. Freshwater zooplankton microbiome composition is highly flexible and strongly influenced by the environment. Mol Ecol. 2021;30:1545–58.Article 
    PubMed 

    Google Scholar 
    Rakusa-Suszczewski S. Predation of chaetognatha by Tomopteris helgolandica Greff. ICES J Mar Sci. 1968;32:226–31.Article 

    Google Scholar 
    Aldredge AL, Silver MW. Characteristics, dynamics and significance of marine snow. Prog Oceanogr. 1988;20:41–82.Article 

    Google Scholar 
    Jumars PA, Dorgan KM, Lindsay SM. Diet of worms emended: an update of polychaete feeding guilds. Ann Rev Mar Sci. 2015;7:497–520.Article 
    PubMed 

    Google Scholar 
    Pfenning-Butterworth A, Cooper RO, Cressler CE. Daily feeding rhythm linked to microbiome composition in two zooplankton species. PLoS ONE. 2022;17:e0263538.Pappalardo P, Collins AG, Pagenkopp Lohan KM, Hanson KM, Truskey SB, et al. The role of taxonomic expertise in interpretation of metabarcoding studies. ICES J Mar Sci. 2021;78:3397–410.Article 

    Google Scholar 
    Hunt DE, Gevers D, Vahora NM, Polz MF. Conservation of the chitin utilization pathway in the Vibrionaceae. Appl Environ Microbiol. 2008;74:44–51.Article 
    CAS 
    PubMed 

    Google Scholar 
    Turner JT. Zooplankton fecal pellets, marine snow, phytodetritus and the ocean’s biological pump. Prog Oceanogr. 2015;130:205–48.Article 

    Google Scholar 
    Chavez FP. Forcing and biological impact of onset of the 1992 El Niño in central California. Geophys Res Lett. 1996;23:265–8.Article 

    Google Scholar 
    Pennington TJ, Chavez FP. Seasonal fluctuations of temperature, salinity, nitrate, chlorophyll and primary production at station H3/M1 over 1989-96 in Monterey Bay, California. Deep Sea Res II. 2000;47:947–73.Article 
    CAS 

    Google Scholar  More

  • in

    Fertilization treatments affect soil CO2 emission through regulating soil bacterial community composition in the semiarid Loess Plateau

    Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Crippa, M. et al. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat. Food 2, 198–209 (2021).Article 
    CAS 

    Google Scholar 
    Shakoor, A. et al. Effect of animal manure, crop type, climate zone, and soil attributes on greenhouse gas emissions from agricultural soils—A global meta-analysis. J. Clean Prod. 278, 124019. https://doi.org/10.1016/j.jclepro.2020.124019 (2021).Article 
    CAS 

    Google Scholar 
    Wu, L. et al. Soil organic matter priming and carbon balance after straw addition is regulated by long-term fertilization. Soil Biol. Biochem. 135, 383–391 (2019).Article 
    CAS 

    Google Scholar 
    Chen, F. et al. Effects of N addition and precipitation reduction on soil respiration and its components in a temperate forest. Agr. Forest. Meteorol. 271, 336–345 (2019).Article 

    Google Scholar 
    Lei, J. et al. Temporal changes in global soil respiration since 1987. Nat. Commun. 12, 1–9 (2021).
    Google Scholar 
    Wang, R. et al. Nitrogen application increases soil respiration but decreases temperature sensitivity: Combined effects of crop and soil properties in a semiarid agroecosystem. Geoderma 353, 320–330 (2019).Article 
    CAS 

    Google Scholar 
    Du, K. et al. Influence of no-tillage and precipitation pulse on continuous soil respiration of summer maize affected by soil water in the North China Plain. Sci. Total Environ. 766, 144384. https://doi.org/10.1016/j.scitotenv.2020.144384 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Chen, X. & Chen, H. Y. Plant diversity loss reduces soil respiration across terrestrial ecosystems. Global Change Biol. 25, 1482–1492 (2019).Article 

    Google Scholar 
    Lang, A. K., Jevon, F. V., Ayres, M. P. & Matthes, J. H. Higher soil respiration rate beneath arbuscular mycorrhizal trees in a northern hardwood forest is driven by associated soil properties. Ecosystems 23, 1243–1253 (2020).Article 
    CAS 

    Google Scholar 
    Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, eabb8508 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xiao, H. et al. The regulatory effects of biotic and abiotic factors on soil respiration under different land-use types. Ecol. Indic. 127, 107787. https://doi.org/10.1016/j.ecolind.2021.107787 (2021).Article 
    CAS 

    Google Scholar 
    Liu, Y.-R. et al. New insights into the role of microbial community composition in driving soil respiration rates. Soil Biol. Biochem. 118, 35–41 (2018).Article 
    CAS 

    Google Scholar 
    Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E. & van der Heijden, M. G. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 10, 1–10 (2019).Article 
    CAS 

    Google Scholar 
    Chen, L.-F. et al. Linkages between soil respiration and microbial communities following afforestation of alpine grasslands in the northeastern Tibetan Plateau. Appl. Soil Ecol. 161, 103882. https://doi.org/10.1016/j.apsoil.2021.103882 (2021).Article 

    Google Scholar 
    Choudhary, M. et al. Long-term effects of organic manure and inorganic fertilization on sustainability and chemical soil quality indicators of soybean-wheat cropping system in the Indian mid-Himalayas. Agr. Ecosyst. Environ. 257, 38–46 (2018).Article 

    Google Scholar 
    Zhang, M. et al. Increasing yield and N use efficiency with organic fertilizer in Chinese intensive rice cropping systems. Field Crop. Res. 227, 102–109 (2018).Article 

    Google Scholar 
    Bonanomi, G. et al. Repeated applications of organic amendments promote beneficial microbiota, improve soil fertility and increase crop yield. Appl. Soil Ecol. 156, 103714. https://doi.org/10.1016/j.apsoil.2020.103714 (2020).Article 

    Google Scholar 
    Gai, X. et al. Long-term benefits of combining chemical fertilizer and manure applications on crop yields and soil carbon and nitrogen stocks in North China Plain. Agr. Water Manage. 208, 384–392 (2018).Article 

    Google Scholar 
    Lai, R. et al. Manure fertilization increases soil respiration and creates a negative carbon budget in a Mediterranean maize (Zea mays L.)-based cropping system. Catena 151, 202–212 (2017).Article 
    CAS 

    Google Scholar 
    Yan, T. et al. Negative effect of nitrogen addition on soil respiration dependent on stand age: Evidence from a 7-year field study of larch plantations in northern China. Agr. Forest Meteorol. 262, 24–33 (2018).Article 

    Google Scholar 
    Peng, Q. et al. Effects of nitrogen fertilization on soil respiration in temperate grassland in Inner Mongolia. China. Environ. Earth Sci. 62, 1163–1171 (2011).Article 
    CAS 

    Google Scholar 
    Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).Article 
    CAS 

    Google Scholar 
    Levine, U. Y., Teal, T. K., Robertson, G. P. & Schmidt, T. M. Agriculture’s impact on microbial diversity and associated fluxes of carbon dioxide and methane. ISME J. 5, 1683–1691 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chen, Q., Liu, Z., Zhou, J., Xu, X. & Zhu, Y. Long-term straw mulching with nitrogen fertilization increases nutrient and microbial determinants of soil quality in a maize–wheat rotation on China’s Loess Plateau. Sci. Total. Environ. 775, 145930. https://doi.org/10.1016/j.scitotenv.2021.145930 (2021).Article 
    CAS 

    Google Scholar 
    Wang, J. et al. The impact of fertilizer amendments on soil autotrophic bacteria and carbon emissions in maize field on the semiarid Loess Plateau. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.664120 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Subke, J. A., Inglima, I. & Francesca Cotrufo, M. Trends and methodological impacts in soil CO2 efflux partitioning: a metaanalytical review. Global Change Biol. 12, 921–943 (2006).Article 

    Google Scholar 
    Yan, W., Zhong, Y., Liu, J. & Shangguan, Z. Response of soil respiration to nitrogen fertilization: Evidence from a 6-year field study of croplands. Geoderma 384, 114829. https://doi.org/10.1016/j.geoderma.2020.114829 (2021).Article 
    CAS 

    Google Scholar 
    Lamptey, S., Xie, J., Li, L., Coulter, J. A. & Jagadabhi, P. S. Influence of organic amendment on soil respiration and maize productivity in a semi-arid environment. Agronomy 9, 611. https://doi.org/10.3390/agronomy9100611 (2019).Article 
    CAS 

    Google Scholar 
    Chen, Z. et al. Nitrogen fertilization stimulated soil heterotrophic but not autotrophic respiration in cropland soils: A greater role of organic over inorganic fertilizer. Soil Biol. Biochem. 116, 253–264 (2018).Article 
    CAS 

    Google Scholar 
    Zheng, J., Zhang, X., Li, L., Zhang, P. & Pan, G. Effect of long-term fertilization on C mineralization and production of CH4 and CO2 under anaerobic incubation from bulk samples and particle size fractions of a typical paddy soil. Agr. Ecosyst. Environ. 120, 129–138 (2007).Article 
    CAS 

    Google Scholar 
    Shen, J., Zhang, L., Guo, J., Ray, J. & He, J. Impact of long-term fertilization practices on the abundance and composition of soil bacterial communities in Northeast China. Appl. Soil Ecol. 46, 119–124 (2010).Article 

    Google Scholar 
    Chen, Q., An, X., Zheng, B., Ma, Y. & Su, J. Long-term organic fertilization increased antibiotic resistome in phyllosphere of maize. Sci. Total. Environ. 645, 1230–1237 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zhang, W., Yu, C., Wang, X. & Hai, L. Increased abundance of nitrogen transforming bacteria by higher C/N ratio reduces the total losses of N and C in chicken manure and corn stover mix composting. Bioresource Technol. 297, 122410. https://doi.org/10.1016/j.biortech.2019.122410 (2020).Article 
    CAS 

    Google Scholar 
    Chen, X. et al. Microbial carbon use efficiency, biomass turnover, and necromass accumulation in paddy soil depending on fertilization. Agr. Ecosyst. Environ. 292, 106816. https://doi.org/10.1016/j.agee.2020.106816 (2020).Article 
    CAS 

    Google Scholar 
    Wang, J. et al. Nitrogen application increases soil microbial carbon fixation and maize productivity on the semiarid Loess Plateau. Plant Soil https://doi.org/10.1007/s11104-022-05457-7 (2022).Article 

    Google Scholar 
    Li, J. et al. The more straw we deep-bury, the more soil TOC will be accumulated: When soil bacteria abundance keeps growing. J. Soil Sediment 22, 162–171 (2022).Article 

    Google Scholar 
    Siczek, A., Frąc, M., Wielbo, J. & Kidaj, D. Benefits of flavonoids and straw mulch application on soil microbial activity in pea rhizosphere. Int. J. Environ. Sci. Te. 15, 755–764 (2018).Article 
    CAS 

    Google Scholar 
    Zhao, S. et al. Change in straw decomposition rate and soil microbial community composition after straw addition in different long-term fertilization soils. Appl. Soil Ecol. 138, 123–133 (2019).Article 

    Google Scholar 
    Zhang, S. et al. Cow manure application effectively regulates the soil bacterial community in tea plantation. BMC Microbiol. 20, 1–11 (2020).Article 

    Google Scholar 
    Jiang, Y. et al. Crop rotations alter bacterial and fungal diversity in paddy soils across East Asia. Soil Biol. Biochem. 95, 250–261 (2016).Article 
    CAS 

    Google Scholar 
    Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rath, K. M., Fierer, N., Murphy, D. V. & Rousk, J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 13, 836–846 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zhao, F. et al. Changes of the organic carbon content and stability of soil aggregates affected by soil bacterial community after afforestation. CATENA 171, 622–631 (2018).Article 
    CAS 

    Google Scholar 
    Goldfarb, K. C. et al. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front. Microbiol. 2, 94. https://doi.org/10.3389/fmicb.2011.00094 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhao, J. et al. Response of soil microbial community to vegetation reconstruction modes in mining areas of the Loess Plateau, China. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.714967 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, Y. et al. Fertilization shapes bacterial community structure by alteration of soil pH. Front. Microbiol. 8, 1325. https://doi.org/10.3389/fmicb.2017.01325 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, X. et al. Organic amendments drive shifts in microbial community structure and keystone taxa which increase C mineralization across aggregate size classes. Soil Biol. Biochem. 153, 108062. https://doi.org/10.1016/j.soilbio.2020.108062 (2021).Article 
    CAS 

    Google Scholar 
    Lin, Y. et al. Long-term manure application increases soil organic matter and aggregation, and alters microbial community structure and keystone taxa. Soil Biol. Biochem. 134, 187–196 (2019).Article 
    CAS 

    Google Scholar 
    Woyke, T. et al. Symbiosis insights through metagenomic analysis of a microbial consortium. Nature 443, 950–955 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zhang, B., Zhang, J., Liu, Y., Shi, P. & Wei, G. Co-occurrence patterns of soybean rhizosphere microbiome at a continental scale. Soil Biol. Biochem. 118, 178–186 (2018).Article 
    CAS 

    Google Scholar 
    Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).Article 
    PubMed 

    Google Scholar 
    Deng, Y. et al. Molecular ecological network analyses. BMC Bioinf. 13, 1–20 (2012).Article 

    Google Scholar 
    Liao, H. et al. Complexity of bacterial and fungal network increases with soil aggregate size in an agricultural Inceptisol. Appl. Soil Ecol. 154, 103640. https://doi.org/10.1016/j.apsoil.2020.103640 (2020).Article 

    Google Scholar 
    Herren, C. M. & McMahon, K. D. Keystone taxa predict compositional change in microbial communities. Environ. Microbiol. 20, 2207–2217 (2018).Article 
    PubMed 

    Google Scholar 
    Zhang, C., Jiao, S., Shu, D. & Wei, G. Inter-phylum negative interactions affect soil bacterial community dynamics and functions during soybean development under long-term nitrogen fertilization. Stress Biol. 1, 1–13 (2021).Article 
    CAS 

    Google Scholar 
    Su, Y. G., Huang, G., Lin, Y. J. & Zhang, Y. M. No synergistic effects of water and nitrogen addition on soil microbial communities and soil respiration in a temperate desert. CATENA 142, 126–133 (2016).Article 
    CAS 

    Google Scholar 
    Yang, C. et al. Assessing the effect of soil salinization on soil microbial respiration and diversities under incubation conditions. Appl. Soil Ecol. 155, 103671. https://doi.org/10.1016/j.apsoil.2020.103671 (2020).Article 

    Google Scholar 
    Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).Article 
    CAS 

    Google Scholar 
    Chen, L.-F., He, Z.-B., Zhao, W.-Z., Kong, J.-Q. & Gao, Y. Empirical evidence for microbial regulation of soil respiration in alpine forests. Ecol. Indic. 126, 107710. https://doi.org/10.1016/j.ecolind.2021.107710 (2021).Article 
    CAS 

    Google Scholar 
    Liu, S. et al. Decoupled diversity patterns in bacteria and fungi across continental forest ecosystems. Soil Biol. Biochem. 144, 107763. https://doi.org/10.1016/j.soilbio.2020.107763 (2020).Article 
    CAS 

    Google Scholar 
    Lynch, M. D. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Chen, L. et al. Competitive interaction with keystone taxa induced negative priming under biochar amendments. Microbiome 7, 1–18 (2019).
    Google Scholar 
    Chiba, A. et al. Soil bacterial diversity is positively correlated with decomposition rates during early phases of maize litter decomposition. Microorganisms 9, 357 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, S., Wang, S., Fan, M., Wu, Y. & Shangguan, Z. Interactions between biochar and nitrogen impact soil carbon mineralization and the microbial community. Soil Till. Res. 196, 104437. https://doi.org/10.1016/j.still.2019.104437 (2020).Article 

    Google Scholar 
    Bao, S. Soil agrochemical analysis 30 (China Agricultural Press, Beijing, Chinese, 2000).
    Google Scholar 
    Zhai, L., Liu, H., Zhang, J., Huang, J. & Wang, B. Long-term application of organic manure and mineral fertilizer on N2O and CO2 emissions in a red soil from cultivated maize-wheat rotation in China. Agr. Sci. China 10, 1748–1757 (2011).Article 

    Google Scholar 
    Xia, W. et al. Autotrophic growth of nitrifying community in an agricultural soil. ISME J. 5, 1226–1236 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pruesse, E. et al. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids. Res. 35, 7188–7196 (2007).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Liaw, A. & Wiener, M. Classification and regression by randomForest. R news 2, 18–22 (2002).
    Google Scholar 
    Archer, E. rfPermute: Estimate permutation p-values for random Forest importance metrics. R package version 2(1), 81 (2020).MathSciNet 

    Google Scholar 
    Hooper, D., Coughlan, J. & Mullen, M. Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods 6(1), 53–60 (2008).
    Google Scholar  More

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    Drivers and potential distribution of anthrax occurrence and incidence at national and sub-county levels across Kenya from 2006 to 2020 using INLA

    Data sourcesWe analyzed records of confirmed and suspected livestock deaths attributed to anthrax occurring from 1 January 2006 to 31 December 2020 across Kenya (available online along with full code for the analysis in this paper https://github.com/spatialmodels/Kenyan_anthrax_model). The case records covering the entire country were reported from the Kenya Directorate of Veterinary Services (KDVS) located in Nairobi and the five Regional Veterinary Investigation Laboratories located in Nakuru, Eldoret, Karatina, Kericho, and Mariakani. The anthrax outbreaks were considered as any livestock (cattle, goats, sheep, pigs, camels) or wildlife deaths confirmed through clinical and laboratory diagnosis. Clinical diagnosis was defined as an acute disease accompanied by sudden death, bleeding from body orifices, swelling, lack of rigor mortis, and oedema of the neck and face in pigs. Laboratory confirmation was done through methylene blue staining to identify the characteristic bacterial capsule and the rod-shaped bacilli in clinical specimens collected from the infected carcasses.We extracted data from old paper records of livestock anthrax cases into Microsoft Excel. These records comprised the location of the livestock outbreaks, name of the farmer, number of animals dead and herd size, species affected, date, method of diagnosis, and the details of the reporting veterinary doctor. Since the locations of livestock anthrax outbreaks were reported at sub-county/district levels (districts refer to the old naming given to current sub-counties before the rollout of the current constitution), we recorded the geographic coordinates of livestock cases at the district level. During data cleaning, we removed duplicate coordinates, outliers, and entries with missing variables. In the end, we had 540 livestock cases that we used for analysis. The spatial granularity and prolonged surveillance period of these data allow for a more detailed perspective on the major drivers of anthrax across Kenya. We also collected wildlife data from the Kenya Wildlife Service (KWS). Most of the data from KWS was lacking information on the geographic coordinates of the outbreaks, so we visited the actual locations and collected the coordinates. We recorded 20 wildlife cases that we used to validate the performance of the spatial model.Processing socio-economic and ecological covariatesWe gathered geospatial data on ecological and socio-economic correlates of B. anthracis ecology and distribution. For the spatial model, we obtained the following variables: rainfall, vegetation, elevation, distance to permanent water bodies, and soil patterns. For the spatiotemporal models, we used human population estimates (total population, population density, and male and female population per sub-county), host population (livestock producing households, total number of indigenous, exotic dairy, and exotic beef cattle per sub-county), and agricultural practices that lead to soil disturbance (agricultural area under cultivation, number of farming households, and crop-producing households).We chose seven environmental covariates for the spatial model based on known correlates of B. anthracis suitability identified from previous peer-reviewed studies9,10,13,15,21,22,23. These comprised three soil variables, including soil pH (× 10) in H2O at a depth of 0 cm, exchangeable calcium at a depth of 0–20 cm, and soil water availability (volume of water per unit volume of soil) retrieved at a resolution of 250 m from the International Soil Reference and Information Centre (ISRIC) data hub (https://data.isric.org/geonetwork/srv/eng/catalog.search#/home). We used the shallowest depth available because although the bacterial spores can persist in the surface soil for up to five years and indefinitely in much deeper soils24, the spores in the surface soils are more likely to trigger host infection25. We retrieved monthly Enhanced Vegetation Index (EVI) data from 1 January 2006 to 31 December 2020 (180 tiles in total) from The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3 v.6) at a resolution of 1 km2 (https://lpdaac.usgs.gov/products/myd13a3v006/). The mean EVI was then calculated using QGIS by averaging all 180 tiles. EVI reduces variations in the canopy background and retains precision over dense vegetation conditions. Monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data from rain gauge and satellite observations was retrieved from the United States Geological Service (USGS) at a resolution of 0.05 degrees (https://climateserv.servirglobal.net/map). Since the rainfall data also comprised 180 tiles, the mean rainfall was calculated by averaging all 180 tiles using QGIS. We also collected data on the distance to permanent water bodies from a global hydrology map obtained from ArcGIS version 10.6.1.26 and elevation data at 1 km2 resolution from the Global Multi-resolution Terrain Elevation Data (GMTED2010) dataset available from USGS (Table 1).Table 1 Summary of the environmental variables used in the spatial model including variable name, unit, and spatial resolution.Full size tableFor the spatiotemporal sub-county-based models, we accessed the population data per sub-county (total population, male population, female population, and population density) from the 2019 Kenyan census report provided via the Humanitarian Data Exchange platform (https://data.humdata.org/dataset/kenya-population-per-county-from-census-report-2019). We also obtained data on livestock population (numbers of exotic dairy and beef cattle, and indigenous cattle), area of agricultural land in hectares, number of farming households, and the number of households actively practicing agriculture (crop production and livestock production) aggregated to the sub-county level from the 2019 Kenya Population and Housing Census volume IV provided by the OpenAfrica platform (https://open.africa/dataset/2019-kenya-population-and-housing-census).We conducted data exploration to check for outliers, collinearity, and the relationships between the covariates and the response variables. We used Cleveland dot plots to check for outliers. We measured collinearity using variance inflation factors (VIF), Pearson correlation coefficients, and pairs plots. For VIF scores, the covariates with scores higher than 3 were eliminated one-by-one until all the scores were equal to or less than 3. All the covariates included in the study had correlation coefficient values of less than 0.6 (Figs. 1, 2).Figure 1Results of correlation between covariates using Pearson’s correlation coefficient test for the spatial model. Correlation between covariates is shown by red numbers (negative correlation) and blue numbers (positive correlation). Correlations with a p-value  > 0.01 are regarded as insignificant and the correlation coefficient values are left blank. The figure was generated using R software v. 4.1.028.Full size imageFigure 2Results of correlation between covariates using Pearson’s correlation coefficient test for the spatiotemporal model. Correlation between covariates is shown by red numbers (negative correlation) and blue numbers (positive correlation). Correlations with a p-value  > 0.01 are regarded as insignificant and the correlation coefficient values are left blank. The figure was generated using R software v. 4.1.028.Full size imageSpatial model analysisWe used R version 4.1.0 together with the packages raster version 4.1.127, and R-INLA version 4.1.128 to conduct the data processing and statistical modelling. The R-INLA package applies the INLA framework in designing models. We used Quantum Global Information System (QGIS) version 3.16 (https://qgis.org) to create a 50 km buffer polygon around all the observed livestock outbreak points. We then created a 20 km2 grid within this buffer and counted the number of points within each grid cell to create a regular lattice with a given number of counts per cell. We then extracted the coordinates of the centroids of each cell to create marked locations with a given number of livestock cases per location. We essentially converted the data into a count process (number of livestock outbreaks per location). We had 95 cells with one or more counts which formed our new presence locations. We then randomly selected 95 pseudoabsences within the 50 km buffer polygon but at a distance of 10 km from the presence locations as shown in Fig. 3.Figure 3Spatial distribution of thinned livestock anthrax case locations across Kenya from 2006 to 2020. The map shows livestock anthrax case locations (n = 540) thinned to pixels of 20 km2 to form 95 new marked locations. The orange dots show the new presence locations which are marked points with colour intensity representing the number of livestock cases per location. The white triangles show the random pseudo-absence locations. The yellow squares are the wildlife cases obtained from the Kenya Wildlife Service. The green polygon is the background calibration buffer used to derive the random pseudo-absence locations. This map was generated using Quantum Geographical Information Systems (QGIS) v. 3.16.11 (https://www.qgis.org/en/site/forusers/download.html).Full size imageWe defined a Zero-inflated Poisson (ZIP) regression model with spatially correlated random effects, implemented as a generalized additive model (GAM) with anthrax incidence as the response variable. The model is defined as shown in Eqs. (1), (2), and (3)$${C}_{i} sim zero-inflated, Poisson left({mu }_{i},{p}_{i}right),$$
    (1)
    $$expectedleft({C}_{i}right)=left(1- {p}_{i}right)times {mu }_{i},$$
    (2)
    $$mathrm{log}left({mu }_{i}right)= alpha + sum_{j}{beta }_{j}{X}_{j,i}+ sum_{k}{delta }_{k,i}+{u}_{i},$$
    (3)
    where (Ci) denotes the observed number of anthrax livestock cases at location i, ({mu }_{i}) and ({p}_{i}) are parameters of the ZIP distribution. (expectedleft({C}_{i}right)) refers to the expected number of outbreaks at location i, (alpha) is the intercept, (beta) are the beta coefficients for the covariates, X is the matrix with all the covariates, (delta k) are the non-linear effects (cubic regression splines), and ({u}_{i}) is the spatial random effect at location i.To test whether the addition of the GAM smoothers and the spatially correlated random effects improved the fit of the model, we also considered candidate models without smoothers and spatial random effects. We tested three versions of the spatial model: the first used distance to water, elevation, and EVI as linear covariates without spatial random effects, the second applied non-linear terms to elevation and EVI also without spatial random effects, and the final model was similar to the second model but with the addition of spatial random effects. We then measured the DIC values of the candidate models to select the final spatial model.We conducted model validation by assessing the posterior distributions of the parameters and the residuals for adherence to the distributional assumptions. We checked whether the residuals were independent and normally distributed. We also plotted a sample variogram to check for any residual spatial auto-correlation using a well-defined method29. We then ran 1000 simulations to check whether the model was capable of handling zeros.The estimated model was used to map posterior predicted distributions for the incidence of anthrax disease (plotted as mean and 95% credible intervals). We validated the model using independent evaluation data withheld from the model calibration. This evaluation dataset comprises the wildlife cases collected from KWS. We then calculated the sensitivity by estimating the proportion of wildlife case locations correctly identified by the model, using the minimum presence training threshold (minimum value of the fitted presence training points).Spatiotemporal model analysisOur second objective was to investigate the socio-economic, population-based drivers of livestock anthrax risk at the sub-county level. These socioeconomic variables are usually collected at the sub-county level. Therefore, we developed a second areal model with the number of observations per sub-county as the new response variable. The occurrence data, gathered by the Kenya Directorate for Veterinary Services (KDVS), consisted of monthly case reports of livestock anthrax cases collected by all 290 sub-counties across Kenya between January 2006 to December 2020. We analyzed the whole monthly case time series from the year 2006 to 2020 and mapped out the annual counts of confirmed and suspected livestock anthrax cases across Kenya at the sub-county level to analyse the spatial and temporal trends throughout the surveillance period. The sub-county shapefiles that were used for mapping and modelling were derived from Humanitarian Data Exchange version 1.57.16 under a Creative Commons Attribution for Intergovernmental Organisations license (https://data.humdata.org/dataset/ken-administrative-boundaries).Due to the sparsity of data, we aggregated the monthly case counts and modelled the quarterly occurrence and incidence of anthrax at the sub-county-level scale, including spatial and temporal effects, to determine the spatial socio-economic drivers of livestock anthrax disease risk across Kenya. We used R-INLA version 4.1.1 (26) to conduct the data processing and statistical modelling. We used quarterly case counts that were confirmed per sub-county across the 15 years of surveillance (2006–2020) as a measure of anthrax incidence. Due to the zero-inflated and over-dispersed nature of the distribution, which is difficult to fit incidence counts, we employed a two-stage modelling approach using the hurdle model distribution to separately model anthrax occurrence (presence or absence) across all sub-counties via logistic regression, and incidence counts using a zero-inflated Poisson distribution. We were then able separately to estimate the contributions of the various socio-ecological factors that drive disease occurrence (the presence or absence of anthrax) and total incidence counts.We model the quarterly anthrax occurrence (n = 290 sub-counties over 60 quarters; 17,400 observations) where ({Y}_{i,t}) refers to the binary presence (denoted as 1) or absence (denoted as 0) of anthrax in sub-county i during year t, and ({P}_{i,t}) is the probability of anthrax occurrence, thus:$${Y}_{i,t} sim Bernoullileft({P}_{i,t}right).$$
    (4)
    We model quarterly anthrax incidence counts ({C}_{i,t}) using a zero-inflated Poisson process with parameters ({mu }_{i,t}) and ({p}_{i,t}) (see Eq. (5)). Equation (6) denotes the expected values for the ZIP distribution at sub-county i during year t.$${C}_{i,t} sim Zero-inflated, Poisson left({mu }_{i,t},{p}_{i,t}right),$$
    (5)
    $$expectedleft({C}_{i,t}right)=left(1- {p}_{i,t}right)times {mu }_{i,t}.$$
    (6)
    Both the Bernoulli and the ZIP distributions are modelled separately as functions of the covariates and the spatial and temporal random effects using a general linear predictor as shown in Eqs. (7) and (8):$$logit left({P}_{i,t}right)= alpha + sum_{j}{beta }_{j}{X}_{j,i}+{u}_{i,t}+{v}_{i,t}+{y}_{i,t},$$
    (7)
    $$mathrm{log}left({mu }_{i,t}right)= alpha + sum_{j}{beta }_{j}{X}_{j,i}+{u}_{i,t}+{v}_{i,t}+{y}_{i,t},$$
    (8)
    $${y}_{i,t}= {y}_{i,t-1}+ {w}_{i,t},$$
    (9)
    where α denotes the intercept; (X) signifies a matrix made up of the socio-economic covariates accompanied by their linear coefficients denoted as (beta); spatiotemporal reporting trends at the sub-county level were accounted for in the models using spatially structured (({u}_{i,t}); conditional autoregressive) and unstructured noise (({v}_{i,t}); i.i.d—independent and identically distributed) random-effects specified jointly as a Besag–York–Mollie model30,31, as well as temporally structured (({y}_{i,t})) random effects of the first order where ({w}_{i,t}) is a pure noise term that is normally distribute with a mean of zero and a variance of σ2. We used uninformative priors with a Gaussian distribution for the fixed effects and penalized complexity priors for the hyperparameters of all the random effects.For the two spatiotemporal models, we applied linear effects for all the variables: population density, total population, number of exotic dairy cattle, agricultural land area, and number of livestock producing households. We scaled the continuous covariates by standardizing them (to a mean of 0 and standard deviation of 1) before fitting the linear fixed effects.We used R-INLA to conduct model inference and selection and used DIC to evaluate the model fit for both the occurrence and incidence models. For both models (occurrence and incidence), we created 4 candidate models, compared them, and selected the model with the lowest DIC as the final model. The candidate models included: a baseline intercept only model; a second model with the intercept and covariates; a third model with the intercept, covariates, and the spatial random effects; and a fourth model with the intercept, covariates, spatial random effects, and a temporal trend.We evaluated the posterior distributions of the parameters and the residuals for adherence to the distributional assumptions. We assessed the residuals to check whether they were independent and normally distributed. We also plotted the residuals against the covariates to check for any non-linear patterns using a well-defined method29. We then ran 1000 simulations to check whether the model was capable of handling zeros.Ethics statementLicence to conduct the research was granted by the National Council for Science, Technology, and Innovation (NACOSTI) under reference number 651983, and the Kenya Wildlife Service under reference number KWS-0003-01-21. More

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    Wildflower phenological escape differs by continent and spring temperature

    We used a hierarchical Bayesian modeling approach to evaluate the relationship between the spring phenology of tree and wildflower species and various climate drivers (see Methods). Following model selection, our final model structure included fixed effects of average spring (March–April) temperature and elevation, as well as species-level random effects. We show continental distributions of spring temperature values in Fig. 1b (means and standard deviations are listed in Table S2). We report estimates for spring temperature sensitivities from the final model structure in the main text. Parameter estimates for elevation sensitivities as well as the model performance of other potential drivers and combinations of drivers are reported in Tables S3 and S4. An extended discussion of model assumptions and limitations is included in the Supplementary Information.Sensitivity differences by strataTree leaf out phenology (LOD) was substantially more sensitive to average spring temperature in North America (mean = −3.62 days °C−1; 95% credible interval (CI) = [−3.76, −3.49]) than in Europe (mean = −2.79; CI = [−3.27, −2.30]) and Asia (mean = −2.62; CI = [−2.97, −2.26]; Fig. 2). These values are consistent with previously reported phenological sensitivities in North America7 (−5.5 to −3.3 days °C−1) and Europe8 (−4.1 to −3.0 days °C−1), as the credible intervals from our results overlap with the reported credible intervals of prior studies. However, the Asian LOD sensitivity was less sensitive than previously reported27 (−3.50 to −3.03 days °C−1), potentially owing to differences in species selection28 or model structure. Previously reported sensitivities were determined in separate studies using either observational data7,8 or long-term observation-based weather station data27. The general consistency between our findings suggests that phenology data from herbarium collections are good indicators of patterns in natural systems29,30,31, a point supported by a recent study of phenological sensitivity derived from herbaria and from observed citizen science data32. These herbarium-based results provide evidence that phenological sensitivity differs across the temperate forest biome (but see ref. 33 for evidence of differences in response to warming and chilling accumulation). To our knowledge, our study is the first to contrast overstory and understory phenology across multiple continents and, therefore, to find differences in phenological sensitivity between trees and forest wildflowers across continents. We recommend future studies explore these differences using alternative approaches and methodologies that focus on the physiological basis for and mechanisms that underlie these patterns.Fig. 2: Posterior estimated means and 95% credible intervals for spring temperature sensitivity.Shapes represent parameter estimates for wildflower First Flower Date (FFD, blue circles; n = 1418, 618, and 1060 for Asia, Europe, and North America, respectively) and canopy tree Leaf Out Date (LOD, yellow triangles; n = 899, 532, and 995, for Asia, Europe, and North America, respectively). Estimates are considered different from 0 if credible intervals do not overlap the dashed 0 line and are considered different from each other if credible intervals do not overlap.Full size imageIn contrast to trees, wildflower sensitivity to spring temperature was similar across all three continents and exhibited no strong differences (i.e., overlap in 95% Bayesian credible intervals) among continents (means and 95% credible intervals in brackets: North America = −3.14, [−3.28, −3.00]; Europe = −3.02, [−3.48, −2.56]; Asia = −3.12, [−3.36, −2.86]; Fig. 2). These values are also generally consistent with those reported elsewhere in the literature (i.e., 95% credible intervals overlap with those reported in other studies; −2.2, [−3.7, −0.76] days °C−1 in North America7 and −3.6, [−4.04, −3.18] days °C−1 in Europe9), although we are unaware of any studies that have estimated phenological sensitivity for Asian forest wildflowers in days °C−1. Ge et al.3 report herbaceous plant sensitivity of −5.71 days per decade in Asia (±7.90 standard deviation; based primarily on long-term observational data), which appears to be roughly consistent with our model results, but the difference in units makes this more speculative than the other comparisons. Discrepancies in mean responses between this study and others may be due in part to different types of data (herbarium specimens versus field observations) and to choice in focal taxa, as temperature sensitivity has been shown to vary widely across taxa28.Particularly noticeable in our results was that r2 coefficients of predicted versus observed phenology were much higher in North America (0.70 and 0.76 for wildflower and tree models, respectively) compared to Asian (0.40 and 0.44, respectively) and European models (0.41 and 0.25, respectively). This difference in model performance could be due to the higher interannual variability of spring temperatures in North America33, leading to greater selective pressure for strong sensitivity to spring temperatures in North American plants. This difference could explain why North American species exhibit higher correlation of phenology with average spring temperatures (Table S4). Alternatively, European and Asian species may have stronger phenological responses to alternative spring forcing windows, winter chilling temperatures, or photoperiod, relative to the March–April temperature period used in this study (see Methods). We think the latter explanation is unlikely, given the strong correlations of phenology with spring temperature across all continents (see Supplementary Information – Justification for March–April Temperature Window).Herbarium-based phenological models may be improved by accounting for spatial autocorrelation within the dataset. For example, Willems et al.9 found that including spatial autocorrelation significantly improved predictability of European herbaceous flowering phenology, even when accounting for multiple drivers of spring phenology. We followed a similar approach as their study and found similar improvements in model performance with the addition of spatial autocorrelation (Tables S3–S4) that had substantial positive effects on r2 values of Asian and European models. However, spatial distributions of specimens differed substantially among continents (see Figs. S2–S4), and these differences could lead to artifacts that make results unreliable to interpret (see Supplementary Information). Therefore, we focus here on results for models without spatial autocorrelation while acknowledging that spatial aggregation of herbarium specimens in Europe and Asia may be partially responsible for the relatively lower r2 values. We encourage other researchers to explore this question further both with our data set and other datasets.Climate change and spring light windowsThe relative difference between wildflower and tree sensitivity varied substantially among continents, with wildflowers being approximately equally as sensitive to spring temperature as trees in Asia and Europe but substantially less sensitive (i.e., 95% BCI do not overlap) than trees in North America (Fig. 2). Importantly, these differences were driven by changes in tree phenological sensitivities among continents and resulted in different expectations for spring light window duration (i.e., the difference in time between estimated wildflower flowering date and canopy tree leaf out date) on different continents under current climate conditions (Fig. 3), based on modeled leaf out and flowering under a climate scenario derived from average climate conditions from 2009–2018 (Fig. S5).Fig. 3: Current estimated phenological escape duration in northern temperate deciduous forests.Estimated mean difference between wildflower First Flower Date (FFD) and canopy tree Leaf Out Date (LOD) (in days) under current climate conditions (averaged from 2009–2018, see methods) in a Asia, b Europe, and c North America. Negative values indicate tree LOD is estimated to occur before wildflower FFD. Estimations were cropped by the estimated area of broadleaf and mixed-broadleaf forest (see methods). Dark gray regions indicate areas where the consensus land classification is More

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    Natural selection under conventional and organic cropping systems affect root architecture in spring barley

    Root morphological traitsThe wild-type parent ISR42-8 produced longer root length (RL) than the modern cultivar parent Golf and tested lines (Table S2, Fig. 1A.h,A.f [h = hydroponic; f = field]). The tested lines of the two evolving barley populations displayed significant variations under hydroponic conditions. Barley lines evolved under OCS had on average 3484 mm longer roots compared to CCS under hydroponic treatment (Fig. 1A.h, Table 2). Complementary results under field conditions show as well higher RL for the OCS lines, even though the variance was significantly less pronounced (Fig. 1A.f). In addition, a less evident variance was observed in the field within both groups compared to the hydroponic (Fig. 1A.d). Across both experimental setups, the observed range of RL was higher in the OCS lines [Standard deviation (SD)OCS = 883, SDCCS = 597] (Table 2).Figure 1Significantly variant root morphological phenotypes. Boxplots illustrate the overall distribution of observed data points for the parents Golf and ISR 42-8 as well as for the conventional (CCS) and organic (OCS) lines. Density plots highlight the overall distribution of organic and conventional adapted lines. (A)—Root length (RL)—the sum of all roots harvested in millimeters (mm), illustrated for all four groups. (A.h)—root length measured in the hydroponic experiment; (A.f)—field experiment; A.d—distribution histogram for root length in both field and hydroponic experiment for CCS and OCS adapted lines. (B)—the ratio of root length to volume (L/V). Data available for hydroponics (B.h), field (B.f), and distribution of the ratio of root length to volume illustrated in B.d. (C.f)—Root mass density (RMD) from the field; (D.f)—Root angle (RA) from the field, distribution of the root angle illustrated in (D.d); (E.f)—root tip per plant count from the field, corresponding histogram visualized in (E.d). (F.f)—root fork per plant count from the field.Full size imageTable 2 Comparison of organic and conventional population root phenotypes under field and hydroponic evaluation.Full size tableThe root length to volume (L/V) is an important indicator of the soil volume that can be explored by the roots. Under hydroponics conditions, variations were found for L/V between the parental genotypes as well as between the OCS and CCS populations (Tables 2 & S3). The organic lines were characterized by a significantly higher L/V, indicating a much more distinct exploration of the soil by these lines (Fig. 1B.h,B.f). In comparison to field, highest diversity in L/V was found under hydroponic experiments within both OCS and CCS populations (Fig. 1B.d).The root mass density (RMD) is the ratio of root volume for a given root mass and is a key indicator of root thickness. Although significant variations existed between ISR42-8 and Golf under hydroponics conditions, such significant variations were not found between the OCS and CCS groups (P = 0.09) (Fig. 1C.f and Tables 2 and S3).The root angle (RA) measurements were only performed under field conditions since plants grown under hydroponics conditions were placed in uniform growing vessels and the direction of root growth is restricted by tubes. Significant variation was observed for the RA between the two parental lines, which was also reflected in the CCS and OCS lines (Fig. 1D.f). ISR42-8 was characterized by an 11.5° average narrower RA than Golf (Table S3). The RA was 4.1° bigger in the OCS compared to the CCS population (P = 0.005) (Table 2). However, a higher diversity in RA was observed in the OCS compared to the CCS lines (Fig. 1D.d, Table 2).In addition to the RA, the number of root tips and forks was measured under field conditions only. Both tips and forks indicate a similar pattern, where the OCS lines produced on average more for both PForks = 0.014, PTips = 0.0041 (Fig. 1E.f,F.f). After applying a P-adjustment, the number of forks count was no longer significantly different between OCS and CCS (PForks = 0.07, Table 2). Complementary, ISR 42-8 was observed to produce more tips and forks than Golf, which remained highly significant even after probability adjustment (Fig. 1E.f,F.f, Table S3). The distribution and the standard deviation of observed phenotypes highlight once more the fact that the OCS lines tend to have a higher variation (Fig. 1E.d, Table 2). Similarly, a significant increasing trend was recorded in root surface area (RSA) and root average diameter (RAD) by ISR42-8 as compared to Golf under hydroponics (Table S3). Contrasting to the parental genotypes, no variation was observed between OCS and CCS lines for RSA (Table 2).Root anatomical traitsWithin the observed anatomical traits, four were considered due to their relevance and variation between the systems. In both hydroponic and field experiments, significant variations were observed for the late metaxylem number (LMXN) between the parental lines as well as OCS and CCS lines (Tables 2 & S3, Fig. 2A.h,A.f). An increased LMXN for ISR 42-8 compared to Golf was observed (Fig. 2A.h). Regarding the CCS and OCS lines, a heterogenic scenario was presented over both experimental setups. While the median LMXN under CCS was identical with ISR 42-8 in the seedling stages of plant development (Fig. 2A.h), it was much lower in flowering stages under field conditions (Fig. 1A.f). Additionally, the LMXN was significantly higher in the CCS lines in the seedling stage compared to OCS lines, vice-versa LMXN was observed at the flowering time point (Fig. 2A.d).Figure 2Significantly variant root anatomical traits. Boxplots illustrate the overall distribution of observed data points for the parents Golf and ISR 42-8 as well as for the conventional (CCS) and organic (OCS) lines. (A) –Late metaxylem number (LMN)—the sum of all roots harvested and expressed by plant−1, illustrated for all four groups. A.h—Late metaxylem number measured in the hydroponic experiment; A.f—field experiment; A.d—distribution histogram for late metaxylem number in both field and hydroponic experiment for CCS and OCS adapted lines. (B)—Aerenchyma area (AA). Data available for hydroponics (B.h), field (B.f), and distribution of the aerenchyma area illustrated in (B.d). (C.f)—Total cortical area (TCA) from the field; (D.f)—Root cross-section area (RA) from the field, distribution of the total cortical area and root cross-section area illustrated in C.d and D.d, respectively.Full size imageThe intercellular space, represented by the aerenchyma area (AA), was observed to be significantly more pronounced in the tested CCS compared to OCS lines in both environments (Fig. 2B.h,B.f). Furthermore, the OCS population did not show significant differences to both parents under hydroponics conditions, however, when grown under field conditions, it was noted that Golf had a significantly higher AA mean value as compared to the OCS population (Table S3). As illustrated by the values, the AA expended from early to late stages by a magnitude of 10-folds (Fig. 2B.d). In general, although the two parents did not indicate phenotypic variations, OCS and CCS lines showed significant variations (Table S4).A 0.12 mm2 decreased average total cortical area (TCA) was recorded in the OCS compared to the CCS population under field conditions (P = 0.003, Fig. 2C.f), although substantial variations for TCA was observed within OCS and CCS populations (Fig. 2C.d). The root cross-section area (RXA) is a two-dimensional axis of the root which is an important indicator of root thickness. In the hydroponic examination of the seedling stage, significant variations existed between the CCS and ISR42-8 as well as OCS population (Tables 2 and S3). The complementary study under field conditions observed a noticeable variation for OCS from both parental genotypes and the CCS (Table S3). About 0.13 mm2 increased average value for RXA was identified for CCS (Fig. 2D.f), while consistent significant variations were also observed between the populations in the under field experiment, where 0.13 mm2 increased average value for RXA was identified for CCS (Fig. 2D.f). Analog (Fig. 2D.d). Analogue to the AA, the RXA indicates a lower root extension in the OCS compared to the CCS population. For the stele area (SA), significant variations were only observed at the flowering stage, where ISR42-8 generally had the highest SA and varies significantly between Golf and its progeny lines (Tables 2 and S3).Shoot-related traitsBeyond the root-related phenotypic observations, above-ground characteristics were also recorded to assess the root-borne shoot dynamics (Figs. 3 and S2). Among the OCS and CCS populations and the parents, ISR42-8 had the longest duration of emergence. While CCS-adapted lines took on average 5.8 days of emergence (DE), OCS-adapted lines emerged 1.8 days later (7.6 days) (Fig. S2). No variation was observed for the tiller number (TN) throughout all tested groups, while ISR 42-8 tends to produce much more leaf number (LN), accompanied by a lower plant height (PH) and higher shoot dry weight (SDW) (Table S4, Fig. S2). The OCS and CCS plants significantly differed in PH as well as SDW (Fig. 3B,C). The LN was marginally above the probability threshold of 0.05 (p = 0.058, Fig. 3A), with a clear tendency of increased variability in phenotypic variation (Fig. 3D). Similar trend was recorded for the SDW (Fig. 3F).Figure 3Above-ground plant characteristics. Boxplots illustrate the overall distribution of observed data points for the parents Golf and ISR 42-8 as well as for the conventional (CCS) and organic (OCS) lines under the hydroponic experiment. (A)—Leaf number (LN) expressed by; (B)—Plant height (PH) and C-Shoot dry weight (SDW). The data distribution of the leaf number, plant height and shoot dry weight is illustrated in (D,E,F), respectively.Full size imageInterconnection of root-shoot traitsWe performed inter-trait correlation analysis to unravel association among root traits and in between root and shoot phenotypes (Fig. 4). Pearson correlation coefficient revealed significant correlations among root-shoot traits. LN, PH and SDW had strong positive associations with all root architectural traits under hydroponic conditions (P  0.30) in both CCS and OCS, while DE has negative association with all shoot traits (r = −0.17 to −0.48) (Fig. 4A.h,B.h). A consistent negative relationship was observed for L/V with shoot traits such as LN, PH and SDW and root morphological traits such as RL, RSA and RAD in both CCS and OSC populations (Fig. 4A.h,B.h). A strong negative association existed between RL and all shoot morphological, root architectural and anatomical traits in both populations, except for L/V where a weak negative (r = −0.09) association was displayed only in the OCS. Likewise, all above-ground traits and all root architectural traits exhibited significant positive associations with all root anatomical features in both groups with an exception for the AA (Fig. 4A.h,B.h). Moreover, correlation analysis revealed strong positive relationships in both groups of SDW and root dry weight (RDW) to all above-ground traits, below-ground traits including, RL, SA, and RAD, as well as in all root anatomical traits (Fig. 4A.h,B.h). This means that the growth of tissue and organ is proportional to the increase in total dry biomass. More importantly, we observed a significant positive correlation among most of the root morphological, architectural, and anatomical traits in both OCS and CCS adapted populations, with few exceptions such as L/V (Fig. 4A.h,B.h).Figure 4Correlation matrix for shoot morphological (only in hydroponic conditions; A.h and B.h), root architectural and anatomical traits in two groups of barley populations and their parental lines grown across two growing conditions. (A)—conventional and (B)—organic cropping systems. (A.h)—conventional under hydroponic, (B.h)—organic under hydroponic, (A.f)—conventional under field, and B.f—organic under field conditions. The color scale represents Spearman’s ranked correlation coefficient. A larger circle size indicates a smaller p-value; blank cells represent that correlation was non-significant at P  −0.90) and RDW (r =   > −0.80) (Fig. 4A.f,B.f) for CCS and OCS populations respectively, which means that narrower the angle of the nodal roots, the longer was the root system. The two root branching traits, the number of tips and number of roots forks which were known to be associated and dependent on the RL have a strong positive correlation reflected by r = 0.81 and 0.90 in CCS and r = 0.74 and 0.84 in OCS developed lines, respectively, while they have a significant negative correlation with RA (r = −0.72 to −0.77) in both barley groups. In addition, RA had also strong negative relationship to RDW contributing architectural traits including, RMD (r = −0.81 to −0.84) and L/V (r = −0.34 to −0.44). However, no positive associations were observed for RA and all root anatomical traits in both OCS and CCS populations (Fig. 4A.f,B.f).Allometry analysisThe correlation analysis identified interconnection among root and shoot-related traits. Therefore, we checked if these correlations can be explained by allometric relations (Tables 3 and 4).Table 3 Summary of allometric analysis of root-shoot system traits under hydroponic condition.Full size tableTable 4 Summary of allometric analysis of root-shoot system traits under field condition.Full size tableIn the hydroponic environment, we observed a total of ten allometric relations, from which six were annotated to the PH. The PH was allometrically related to the SDW, the RSA, the RV, the RDW, the SRL, and the RMD (Table 3). Besides, the SDW was allometrically associated with the RSD. Furthermore, the TCA was related to the RXA. Finally, an allometry relationship was detected between the LMXN and AA (Table 3).In the field experimental setup, we detected in total ten allometric relations (P  More

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    Cutmarked bone of drought-tolerant extinct megafauna deposited with traces of fire, human foraging, and introduced animals in SW Madagascar

    Each sedimentary sequence from the three excavated ponds (Tampolove [TAMP], Ankatoke [ANKA], and Andranobe [ANDR]) includes a layer of clay (defined as zone 2), which separates the surface soil formation (zone 1) from the underlying fossiliferous muddy sand and bedrock (zone 3, Figs. S4–S7 & S9). Details regarding the composition of this sediment and its microfossils are given in Appendix-Results-Excavation (Figs. S9–S12).Subfossils and chronologyCoastal survey recovered mostly zebu bones on exposed sandy surfaces, some pygmy hippo and giant tortoise bones on the margins of shallow ponds, and giant tortoise carapace under overhanging limestone outcrops (Appendix-Results-Survey, Fig. S3). A high proportion of surface bone failed 14C analysis (~ 55%, Table S1), yet the successfully analyzed specimens (n = 8) span up to 3390–3220 calibrated years before present (cal BP, PSUAMS 8681, 3150 ± 15 14C BP, a hippo molar). Pond deposits that are relatively deep include bones that cover a relatively long period of time (Figs. S14–S16, Dataset S6). This span ranges from ~ 6000 years at TAMP (~ 120 cm deep) to ~ 2500 years at ANDR (~ 100 cm deep), with the oldest bones present in the fossiliferous sedimentary zone 3 and scarce bones in the overlying clay (zone 2).Zone 3Most bones in this layer are relatively intact and include readily identifiable pygmy hippo long bones and cranial fragments (e.g., Fig. S13a,f), giant tortoise carapace and plastron fragments (Fig. S13d), ratite eggshell and long bones (Fig. S13c,m), and crocodile scutes, cranial fragments, and teeth (Fig. S13b). Scarce bones of a duck (genus Anas) were recovered at ANDR. Remains of subfossil lemurs were scarce or absent, but they may be represented by an unknown type of bone fragment identified through protein fingerprinting (ANDR-1-5-55, Dataset S3). The widespread success of collagen extraction from these bones attests to the excellent preservation of organics in this zone. ANKA also includes keratin (mostly in the form of crocodile claws, e.g., Fig. S13i), as well as two rounded agates found associated with ratite eggshell (Fig. S13m).Remains of a juvenile pygmy hippo were recovered from both TAMP and ANDR (a femur and tibia, respectively, Dataset S3). The epiphyses of some of the pygmy hippo long bones have gnaw marks (Fig. S13f), and none of the bones include chop marks. In association with these bones towards the top of this zone are some large ( > 1 cm diameter) charcoal fragments and scarce bones of bushpig (Fig. S13k) and zebu (Fig. S13e). Protein fingerprinting identified a screened fragment of a non-zebu bovid in ANKA zone 3 and confirmed that a tentatively identified bushpig canine fragment (ANKA 1-4-151) belonged to a hippo. This zone at TAMP and ANDR also includes occasional mangrove whelk (Terebralia palustris) shells (Fig. S13g). These whelks currently live at least ~ 500 m distant from these ponds, and whelk shells at ANDR each have an irregular hole above the operculum.The span of time represented by bones in zone 3 ranges up to ~ 4000 years (~ 6000–2000 cal BP at TAMP, Fig. S14). Confirmed introduced animal bones from zone 3 failed direct 14C analysis. There are multiple examples of directly 14C-dated bone in close stratigraphic association that nonetheless differ in age by  > 1000 years, and there are a couple of examples of bones from the same individual that are separated stratigraphically. For example, two giant tortoise carapace and plastron fragments from TAMP that have indistinguishable 14C ages are separated by 22 cm of sediment (PSUAMS 8670 comes from 112 cm depth, and PSUAMS 8668 comes from 90 cm depth).Although ANKA produced what is thus far the oldest directly 14C dated pygmy hippo bone from a coastal subfossil site (PSUAMS 9383, 4380 ± 25 BP, 5030–4840 cal BP), the mean calibrated age of hippos from the Tampolove excavations (n = 11, x̄ = 2858 cal BP, SD = 972 yr) is significantly less than that of the giant tortoises (n = 9, x̄ = 4582 cal BP, SD = 705 yr, t(18) = − 4.4, p  2000 years older than a closely associated charcoal sample (38 cm depth, PSUAMS 8849, 575 ± 30 14C BP, 630–510 cal BP), which makes this molar comparable in age to bone from zone 3. Consequently, the youngest directly 14C-dated ancient bone from the Tampolove excavations comes from the lowermost zone 3: a pygmy hippo’s vertebra recovered at 90 cm depth at TAMP (PSUAMS 8730, 1865 ± 15 14C BP, 1819–1705 cal BP). Though poorly constrained in time, the deposition of zone 2 sediment came sometime within the past two millennia, which witnessed marine regression and dry intervals recorded in both the δ18O record of a nearby speleothem27 and the salinization of a nearby pan36. Previously directly 14C-dated bone collected around Tampolove attests to the local persistence of at least pygmy hippos and giant tortoises until the start of the last millennium (n = 15), and an atlas from Lamboara/Lamboharana is in fact the most recent confidently dated pygmy hippo bone from the island (PSUAMS 5629, 1100 ± 15 14C BP, 980–930 cal BP).Figure 4Cutmarked pygmy hippo femur recovered from Tampolove during recent excavation at ~ 40 cm depth (TAMP-1-2-61, above), and previously-recovered and directly 14C-dated (~ 3500 and 1600 cal BP37) cutmarked pygmy hippo femora from the nearby site of Lamboara/Lamboharana that are currently housed in the National Museum of Natural History in Paris (MAD 1709 & MAD 1710, below). Four views highlight three locations of cutmarks on the broken shaft of TAMP-1-2-61, and the inset frames show 20 × magnification of these areas, with corresponding orientations given by red lines. Note that the false color insets of TAMP-1-2-61 are meant to highlight linear edges and crevices, and the overview photos of all three femur fragments are on the same scale.Full size imageZone 1A fragment of iron (from TAMP, 16 cm depth) and sparse ceramic fragments (from ANKA, 3 & 9 cm depth) are present only in zone 1, and three 14C dates from TAMP and ANKA suggest that these specimens span the past ~ 200 years (Figs. S14–S15).CharcoalThe directly 14C dated charcoal spans all three stratigraphic zones yet consistently dates to the past millennium (Figs. S14–16). Multiple charcoal samples from different excavated ponds have practically indistinguishable 14C ages (Table S2), and much of the charcoal from Tampolove formed during peaks in the deposition of macrocharcoal at nearby Namonte (17 km distant; Fig. 5A). The onset of directly 14C-dated charcoal deposition approximately coincides with a decrease in Asafora speleothem δ18O values and with multiple directly 14C-dated first and final local occurrences of large animals. While directly 14C dated charcoal is limited to the past millennium, microcharcoal particles were abundant in all TAMP sediment samples (x̄ ± SD = 2.0 × 106 ± 2.8 × 106 particles). Additionally, microcharcoal is relatively abundant near the bottom of TAMP and ANKA, which contains bones that span ~ 6000–2000 cal BP (Fig. 5B).Figure 5Records of fire, drought, and faunal turnover from the vicinity of Tampolove within the past 1200 years, with dashed horizontal lines for reference (5A), and macrocharcoal concentrations from the excavated ponds, with depth intervals containing directly 14C-dated charcoal that spans the past millennium marked in red (5B). The past 1200 years includes the entire summed calibrated distribution of the 10 directly dated prebomb charcoal fragments from the Tampolove excavations. The calibrated probability distributions associated with the latest dates from endemic megafauna bone (giant tortoises and pygmy hippos) and earliest dates from introduced animal bone (zebu cattle and bushpigs) are shown as black distributions, and 95% of each distribution is bracketed. Considering directly dated remains within the past 4 ka from hippos (n = 26), giant tortoises (n = 18), and zebu (n = 9) and the assumption that bones were deposited uniformly over time, the grey distributions and bracketed 95% credible intervals give estimates of extirpation and arrival times. As in Fig. 3, the red line on the Asafora record follows from BCPA.Full size image More

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    Dark wing pigmentation as a mechanism for improved flight efficiency in the Larinae

    Caro, T., Izzo, A., Reiner, R. C., Walker, H. & Stankowich, T. The function of zebra stripes. Nat. Commun. 5, 3535 (2014).Article 
    PubMed 

    Google Scholar 
    Merilaita, S., Scott-Samuel, N. E. & Cuthill, I. C. How camouflage works. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160341 (2017).Article 

    Google Scholar 
    Rowland, H. M. From Abbott Thayer to the present day: what have we learned about the function of countershading? Philos. Trans. R. Soc. B Biol. Sci. 364, 519–527 (2009).Article 

    Google Scholar 
    Rogalla, S. et al. The evolution of darker wings in seabirds in relation to temperature-dependent flight efficiency. J. R. Soc. Interface 18, 20210236.Malling Olsen, K. Gulls of the World. (Princeton University Press, 2018).Jawor, J. M. & Breitwisch, R. Melanin Ornaments, Honesty, and Sexual Selection. Auk 120, 249–265 (2003).Article 

    Google Scholar 
    Field, D. J. et al. Melanin Concentration Gradients in Modern and Fossil Feathers. PLOS ONE 8, e59451 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McNamara, M. E. et al. Decoding the Evolution of Melanin in Vertebrates. Trends Ecol. Evol. 36, 430–443 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dufour, P. et al. Plumage colouration in gulls responds to their non-breeding climatic niche. Glob. Ecol. Biogeogr. 29, 1704–1715 (2020).Article 

    Google Scholar 
    Hassanalian, M., Abdelmoula, H., Ben Ayed, S. & Abdelkefi, A. Thermal impact of migrating birds’ wing color on their flight performance: Possibility of new generation of biologically inspired drones. J. Therm. Biol. 66, 27–32 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hassanalian, M., Throneberry, G., Ali, M., Ben Ayed, S. & Abdelkefi, A. Role of wing color and seasonal changes in ambient temperature and solar irradiation on predicted flight efficiency of the Albatross. J. Therm. Biol. 71, 112–122 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Spear, L. B. & Ainley, D. G. Flight behaviour of seabirds in relation to wind direction and wing morphology. Ibis 139, 221–233 (1997).Article 

    Google Scholar 
    Sullivan, T. N., Meyers, M. A. & Arzt, E. Scaling of bird wings and feathers for efficient flight. Sci. Adv. 5, eaat4269.Pennycuick, C. J. Modelling the Flying Bird. (Elsevier, 2008).Buffo, J., Fritschen, L. J. & Murphy, J. L. Direct Solar Radiation on Various Slopes from 0 to 60 Degrees North Latitude. (Pacific Northwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, 1972).Hansen, T. F. Stabilizing Selection and the Comparative Analysis of Adaptation. Evolution 51, 1341–1351 (1997).Article 
    PubMed 

    Google Scholar 
    Hansen, T. F., Pienaar, J. & Orzack, S. H. A Comparative Method for Studying Adaptation to a Randomly Evolving Environment. Evolution 62, 1965–1977 (2008).PubMed 

    Google Scholar 
    Roulin, A. Condition-dependence, pleiotropy and the handicap principle of sexual selection in melanin-based colouration. Biol. Rev. 91, 328–348 (2016).Article 
    PubMed 

    Google Scholar 
    Rayner, J. M. V. FORM AND FUNCTION IN AVIAN FLIGHT. in Current Ornithology vol. 5 1–66 (Plenum Press, 1988).Schreiber, E. A. & Burger, J. Biology of Marine Birds. (CRC Press, 2001).Clusella Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).Article 

    Google Scholar 
    Shamoun-Baranes, J. & van Loon, E. Energetic influence on gull flight strategy selection. J. Exp. Biol. 209, 3489–3498 (2006).Article 
    PubMed 

    Google Scholar 
    Pennycuick, C. J. & Lighthill, M. J. The flight of petrels and albatrosses (procellariiformes), observed in South Georgia and its vicinity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 300, 75–106 (1982).Article 

    Google Scholar 
    Rogalla, S., Shawkey, M. D. & D’Alba, L. Thermal effects of plumage coloration. Ibis 164, 933–948 (2022).Article 

    Google Scholar 
    Flinks, H. & Salewski, V. Quantifying the effect of feather abrasion on wing and tail lengths measurements. J. Ornithol. 153, 1053–1065 (2012).Article 

    Google Scholar 
    Hill, G. E. Sexiness, Individual Condition, and Species Identity: The Information Signaled by Ornaments and Assessed by Choosing Females. Evol. Biol. 42, 251–259 (2015).Article 

    Google Scholar 
    Sonsthagen, S. A. et al. Recurrent hybridization and recent origin obscure phylogenetic relationships within the ‘white-headed’ gull (Larus sp.) complex. Mol. Phylogenet. Evol. 103, 41–54 (2016).Article 
    PubMed 

    Google Scholar 
    Howell, S. & Dunn, J. A reference guide to gulls of the Americas. (Houghton Mifflin Company, 2007).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581–597 (2022).Article 
    PubMed 

    Google Scholar 
    Yalden, D. Wing area, wing growth and wing loading of Common Sandpipers Actitis hypoleucos. Wader Study Group Bull. 119, 84–88 (2012).
    Google Scholar 
    Ho, L. S. T. & Ane, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).Article 
    PubMed 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. (2021).Cooper, N., Thomas, G. H., Venditti, C., Meade, A. & Freckleton, R. P. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. Linn. Soc. 118, 64–77 (2016).Article 

    Google Scholar 
    Harmon, L. J. Phylogenetic Comparative Methods: Learning from Trees. (CreateSpace Independent Publishing Platform, 2018).Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

    Google Scholar 
    Douma, J. C. & Weedon, J. T. Analysing continuous proportions in ecology and evolution: a practical introduction to beta and Dirichlet regression. Methods Ecol. Evol. 10, 1412–1430 (2019).Article 

    Google Scholar 
    Li, M. & Bolker, B. wzmli/phyloglmm: First release of phylogenetic comparative analysis in lme4-verse. https://doi.org/10.5281/zenodo.2639887 (2019).Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).Article 
    PubMed 

    Google Scholar 
    Goumas, M. Dark wing pigmentation as a mechanism for improved flight efficiency in the Larinae. Zenodo, https://doi.org/10.5281/zenodo.7156454 (2022). More