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    Subsurface Archaea associated with rapid geobiological change in a model Yellowstone hot spring

    Acidification of CPHistorical geochemical data suggest that the water chemistry of Cinder Pool (CP) has been relatively stable from the time of first reported geochemical data in 1947 until autumn 2018, followed by pronounced acidification between winter and spring 2019 (Supplementary Data 1, Fig. 1a, b). Images and documentation dating to even earlier (1927) reveal the presence of cinders covering ~50% of the spring surface at that time, a temperature near boiling (91.5 °C), and a description of having high sulfate and chloride levels (although data was not provided), suggesting that its chemistry has been generally stable since its discovery1. Spring pH ranged between ~3.6 and 4.5 in 22 yearly measurements spanning 71 years (1947–2018; multiple measurements in the same year were averaged to represent each year) (Fig. 1b), while the pH has been subsequently measured after 2018 as low as 2.5 (Fig. 1b). A single pH measurement of 2.5 was also recorded in a 2003 publication27, although other measurements in 2003, 2000, and 2001 were more consistent with the long-term average (i.e., pH 4.2–4.3; Supplementary Data 1). Scrutiny of chemical data accompanying the pH 2.5 measurement in 2003 indicates a SO42− concentration (~48 mg L−1) that is considerably lower than would be expected for CP, even when the pH is much higher (SO42− = 80 mg L−1; pH = 4.2–4.3). Considering that sulfuric acid is the predominant buffer of pH in these systems7,28, the pH 2.5 reading in 2003 is considered questionable. Nevertheless, the 2018 shift in pH towards more acidic conditions was accompanied by a notable change in the appearance of CP. Prior to autumn 2018, the spring waters were cloudy gray with the considerable suspension of kaolinite clay particles20 and black cinders10. However, between autumn 2018 and spring 2019, the spring waters visibly turned blue-green and contained colloidal S° particles that were also deposited along the pool shelves, while the pool also lacked its characteristic black cinders (Fig. 1a). The spring has maintained this appearance since spring 2019 until at least July 2022.Fig. 1: Historical geochemistry of Cinder Pool (CP).a Top panel shows the visual change in the appearance of CP in 2016 (left) and 2020 (right). Scale bars in the bottom right are ∼1 m. b Measurements of pH (n = 21; black line) and sulfate (SO42−) concentrations (n = 12; red line) in CP waters between 1947 and 2021. Years with multiple measurements were averaged to represent the entire year. c Paired measurements of SO42− and chloride (Cl−) concentrations (n = 12) between 1947 and 2021 in the context of the same measurements for 488 YNP springs derived from previous studies. Paired points for CP are colored based on the year they were recorded (averaged for multiple measurements/year as described above). End member fluid compositions as described in the manuscript text are indicated based on the abbreviations: MO meteoric only, HO hydrothermal only, MG meteoric plus gas, HB hydrothermal plus boiling, HBG hydrothermal plus boiling plus gas. Points for 2016, 2018, 2019, 2020, and 2021 are indicated by “16”, “18”, “19”, “20”, and “21”, respectively.Full size imageThe source of fluids in YNP hot springs can be broadly defined by concentrations of sulfate (SO42−) and chloride (Cl−)2,7. These indicators have been previously used to define the source of YNP springs as either (1) hydrothermal only (HO) waters that have moderate concentrations of SO42− (~30 mg L−1 depending on the depth of boiling; described below) but high concentrations of Cl− (~300 mg L−1), (2) meteoric-only (MO) waters containing lower concentrations of both solutes, or (3) MO waters infused with gas (MG) that have lower Cl− concentrations and higher SO42− concentrations (Fig. 1c). Subsequent boiling and/or evaporation of HO waters can concentrate Cl− and SO42− to higher concentrations (termed hydrothermal plus boiling; HB), while additional gas input into HO or HB waters can lead to particularly high concentrations of both Cl− and SO42− (hydrothermal + boiling + gas; HBG)7 (Fig. 1c). Geochemical data from surveys spanning 1947 to 2018 suggest that CP was largely sourced by hydrothermal (HO) waters that have undergone boiling and/or evaporation (HB) during this time frame (Fig. 1c).HO and HB waters are typically circumneutral7, while CP (which is also sourced by HB waters) has maintained a moderately acidic pH of ~4 until autumn 2018 (Fig. 1b). Several other low pH HB waters have been previously observed within the NGB7. The moderately acidic pH in CP (prior to 2018) has been attributed to the hydrolysis of molten S° that occurs at depths of >18 m that leads to the formation of S2O32– 11. Oxygen (O2)-dependent oxidation of S2O32−, catalyzed by trace iron sulfide in the cinders, forms SxO62− that can then react with sulfide to yield S2O32− and S° 11. Alternatively, SxO62− can be disproportionated to form S2O32− and SO42− 11. The relative rates of these reactions in CP prior to 2018 are not known although similar concentrations of S2O32− measured between 1995 and 1997 suggest that rates of S° hydrolysis and rates of S2O32− formation have been relatively constant over yearly time scales11. The consumption of O2 by reaction with S2O32− and the consumption of sulfide involving reactions with SxO62− would limit the amount of sulfuric acid that could be formed, thereby maintaining a less acidic pH than other sulfuric acid buffered acidic springs in YNP7.Between November 2018 and March 2019, the pH of CP markedly decreased to 2.8 in 2019, 2.7 in 2020, and 2.6 in 2021. This coincided with a marked increase in SO42− concentrations of ~3–5 fold above historical ranges (Fig. 1b), while Cl− concentrations fluctuated without clear trends during this time (Supplementary Fig. 1c). Thus, CP transitioned from an HB water type to an HBG water type between autumn 2018 and spring 2019 and has remained this way since (Fig. 1c). This is interpreted to reflect a substantial increase in H2S/S° oxidation that results in the formation of SO42− and H+ (sulfuric acid). Several observations suggest a fundamental restructuring of CP’s unique sulfur cycling due to dramatic physical and chemical changes at this time. As described in more detail below, the molten S° layer was detected at a depth of 18 m in 2016. However, in 2020 and 2021 there was no evidence of molten S° at ~18 to 20 m depth as previously documented, and sampling equipment could be freely dropped to a depth of 22 m (length of the cable) without interruption. In the absence of the molten S° at depth, the S° hydrolysis product S2O32−, and the cinders that catalyze SxO62− formation from S2O32− and H2S, it is possible that such reactions that previously competed for H2S or O2 (i.e., those involving S2O32− and SxO62−) are no longer taking place in CP. This in turn would allow for sulfur compounds (H2S and S°) to now be oxidized, thereby contributing to spring acidification.Alternative scenarios underlying the dramatic changes in CP waters also warrant consideration, and the three most logical are presented below. First, it is possible that the waters sourcing CP may have shifted either via replacement of the primary source or by altered mixing of multiple water sources. Water isotope values (δ2H and δ18O) can be used to further deconvolute the sources of hydrothermal waters because distinctive isotope values are associated with distinct water sources and the various influences upon them including meteoric water recharge, boiling (and/or evaporation), and water–rock interactions7,29. The water isotope values measured among the measured depths in CP in 2020 were near the range of water isotope values observed in CP across multiple months in 201613 (depth-resolved water isotope measurements were not made in 2016). The 2020 CP water isotope values were slightly right-shifted relative to those of 2016, suggesting a minor increase in the evaporation and concentration of CP water isotopes between 2016 and 20207 (Supplementary Fig. 2). These data thus do not support the hypothesis that the source of waters in CP dramatically shifted between 2016 and 2020, consistent with the SO42− and Cl− measurements indicating that the primary change to CP waters was increased input or availability of H2S for oxidation.A second alternative explanation is that a change in the water level of CP could potentially alter residence times which could allow for more oxidation of sulfur compounds in the spring and increased acidification. Such a scenario would also likely result in increased evaporation and concentration of solutes. However, the minimal increase in water isotope values (Supplementary Fig. 2) and similar Cl− concentrations (Supplementary Fig. 1c) accompanying a ~3–5 fold increase in SO42− concentration pre- and post-acidification (Fig. 1b) argue that increased residence time was of minimal importance in acidification.A third possible explanation is that a change in the plumbing system of CP is now delivering more vapor phase gas that contributes H2S and acidity when oxidized. Such a scenario could be consistent with increased surface deformation, subsurface gas accumulation, and seismic activity that has been taking place near NGB just prior to these changes21, and the transition from HB-type to HBG-type waters in CP. Sulfur species isotope analyses would help deconvolute the sources of SO42− in CP, but samples for sulfur isotopic analyses were not collected prior to acidification. Thus, it is unclear if this process may also be contributing to the acidification of CP. Regardless, the disappearance of the molten S° cap either by consumption or displacement would in effect make H2S more available for oxidation, similar to increased vapor phase input. The acidification of hot springs involves the oxidation of H2S by O230. More specifically, partial oxidation of H2S at acidic pH (90% amino acid identity to other homologs from UYS MAGs), but that was only present on unbinned contig sequences. Proteins are grouped based on their functionalities and associations in complexes. TetH (tetrathionate hydrolase), SQO sulfide:quinone oxidoreductase, SOR sulfur oxygenase reductase, SoxABCD Sulfolobus oxidase, SoxM Sulfolobus oxidase, CbsAB cytochrome b 558/566, SoxLN cytochrome ba complex, DoxBCE Desulfurolobus oxidase, DoxAD/TQOab Desulfurolobus oxidase/thiosulfate-quinone oxidoreductase, HdrAB1C1B2C2 (heterodisulfide reductase), DsrE3 DsrE3 sulfurtransferase, Dld dihydrolipoamide dehydrogenase, LplA lipoate-protein ligase A, LbpA lipoate binding protein A/glycine cleavage system H protein, TusA tRNA 2-thiouridine synthesizing protein A, SreABC sulfur reductase, SAOR sulfite:acceptor oxidoreductase, HcaLS [NiFe]-hydrogenase group 1 g. SoxEFGHI and FoxABCDEFGH (ferrous iron oxidation) gene sets were also investigated, but not identified in any of the MAGs and not shown here for brevity. A complete description of the enzymes/proteins found in individual UYS MAGs is provided in Supplementary Data 4.Full size imageTo assess the potential role of the UYS in sulfur biogeochemical cycling, the metabolic functional potentials of these populations were evaluated in greater detail based on their reconstructed genomes (Fig. 5, Supplementary Data 3). The UYS encoded the capacity for autotrophy via full complements of enzymes involved in the 3-hydroxypropionate/4-hydroxybutyrate cycle (3HP-4HB) (Supplementary Data 4), consistent with the general potential for autotrophy in most other Sulfolobales36. Consistently, the SoxM subunit that has been suggested as a marker for (facultatively) heterotrophic growth of Sulfolobales37 was absent in all UYS MAGs (Fig. 5, Supplementary Data 4). Given that all known Acidilobus and Vulcanisaeta spp. are characterized heterotrophs without known autotrophic capacity38,39, the UYS are likely the sole primary producers in the CP surface and subsurface waters, consistent with their considerable dominance in CP water communities over time.Also consistent with almost all other Sulfolobales36, the UYS universally encode the ability to reduce O2 via terminal cytochrome oxidases, although not via Sulfolobus oxidase (SoxABCD) complexes that are common among many Sulfolobales36 but rather via Desulfurolobus oxidase complexes (DoxBCE) (Fig. 5, Supplementary Data 4). An additional terminal oxidase complex (CbsAB-SoxLN) was encoded in the 2020 CP MAGs along with several other UYS MAGs from other YNP springs, although homologs of CbsAB-SoxLN were not present in the 2016 CP MAGs or several others recovered from sediments of other hot springs (Fig. 5). Thus, a potentially important metabolic difference between the pre- and post-acidification (2016 and 2020, respectively) CP Sulfolobales was the ability to use different terminal cytochrome oxidase compliments for aerobic respiration. The capacity to use multiple terminal oxidases has been suggested as an adaptation to varying oxygen tensions/availabilities37,40 that likely substantively differed between the low ORP 2016 CP waters and the high ORP 2020 CP waters (Fig. 2c). Consequently, these data point to the ecological succession of UYS strains within CP that are, at least in part, related to strain-level differences in aerobic respiration capacities.A defining feature of most cultured Sulfolobales is the ability to grow chemolithoautotrophically by coupling the oxidation of sulfur compounds (e.g., S0) to aerobic respiration37. The slow kinetics associated with abiotic oxidation of S0 with O2 at temperatures More

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    Seasonal bacterial niche structures and chemolithoautotrophic ecotypes in a North Atlantic fjord

    Cloern, J. E., Foster, S. Q. & Kleckner, A. E. Phytoplankton primary production in the world’s estuarine-coastal ecosystems. Biogeosciences 11, 2477–2501 (2014).ADS 
    Article 

    Google Scholar 
    Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science (80-) 281, 237–240 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    Gervais, C. R., Champion, C. & Pecl, G. T. Species on the move around the Australian coastline: A continental scale review of climate-driven species redistribution in marine systems. Glob. Chang. Biol. 685, 171–181 (2021).
    Google Scholar 
    Scanes, E., Scanes, P. R. & Ross, P. M. Climate change rapidly warms and acidifies Australian estuaries. Nat. Commun. 11, 1–11 (2020).Article 
    CAS 

    Google Scholar 
    Rodrigues, J. G. et al. Marine and coastal cultural ecosystem services: knowledge gaps and research priorities. One Ecosyst. 2 (2017).O’Brien, T. D., Lorenzoni, L., Isensee, K. & Valdés, L. What are marine ecological time series telling us about the ocean. A status report. IOC Tech. Ser. 129, 1–297 (2017).
    Google Scholar 
    Ajani, P. A., Davies, C. H., Eriksen, R. S. & Richardson, A. J. Global warming impacts micro-phytoplankton at a long-term Pacific Ocean Coastal Station. Front. Mar. Sci. 7, 878 (2020).Article 

    Google Scholar 
    Wiltshire, K. H. et al. Helgoland roads, North Sea: 45 years of change. Estuaries Coasts 33, 295–310 (2010).CAS 
    Article 

    Google Scholar 
    Benway, H. M. et al. Ocean time series observations of changing marine ecosystems: An era of integration, synthesis, and societal applications. Front. Mar. Sci. 6, 393 (2019).Article 

    Google Scholar 
    Wilson, J. M., Chamberlain, E. J., Erazo, N., Carter, M. L. & Bowman, J. S. Recurrent microbial community types driven by nearshore and seasonal processes in coastal Southern California. Environ. Microbiol. 23, 3225 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Keeling, C. D. et al. Atmospheric carbon dioxide variations at Mauna Loa observatory, Hawaii. Tellus 28, 538–551 (1976).ADS 
    CAS 

    Google Scholar 
    Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: The other CO2 problem. Ann. Rev. Mar. Sci. 1, 169–192 (2009).PubMed 
    Article 

    Google Scholar 
    Falkowski, P. G. Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean. Nature 387, 272–275 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    Brown, M. V. et al. Systematic, continental scale temporal monitoring of marine pelagic microbiota by the Australian Marine Microbial Biodiversity Initiative. Sci. Data 5, 180130 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Buttigieg, P. L. et al. Marine microbes in 4D—Using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health. Curr. Opin. Microbiol. 43, 169–185 (2018).PubMed 
    Article 

    Google Scholar 
    Chow, C.-E.T. et al. Temporal variability and coherence of euphotic zone bacterial communities over a decade in the southern California Bight. ISME J. 7, 2259–2273 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Krabberød, A. K. et al. Long-term patterns of an interconnected core marine microbiota. bioRxiv 2021.03.18.435965. https://doi.org/10.1101/2021.03.18.435965 (2021).Lambert, S. et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 13, 388–401 (2019).PubMed 
    Article 

    Google Scholar 
    Auladell, A. et al. Seasonal niche differentiation among closely related marine bacteria. ISME J. https://doi.org/10.1038/s41396-021-01053-2 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Robicheau, B. M., Tolman, J., Bertrand, E. M. & LaRoche, J. Highly-resolved interannual phytoplankton community dynamics of the coastal Northwest Atlantic. ISME Commun. 2(1), 1–12 (2022).Article 

    Google Scholar 
    Legendre, L., Rivkin, R. B., Weinbauer, M. G., Guidi, L. & Uitz, J. The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean. Prog. Oceanogr. 134, 432–450 (2015).ADS 
    Article 

    Google Scholar 
    Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 1–11 (2017).Article 
    CAS 

    Google Scholar 
    Gross, T., Rudolf, L., Levin, S. A. & Dieckmann, U. Generalized models reveal stabilizing factors in food webs. Science (80-). 325, 747–750 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Karl, D. M. & Church, M. J. Microbial oceanography and the Hawaii Ocean time-series programme. Nat. Rev. Microbiol. 12, 699–713 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Walsh, D. A. et al. Metagenome of a versatile chemolithoautotroph from expanding oceanic dead zones. Science (80-). 326, 578–582 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Shan, S., Sheng, J., Thompson, K. R. & Greenberg, D. A. Simulating the three-dimensional circulation and hydrography of Halifax Harbour using a multi-nested coastal ocean circulation model. Ocean Dyn. 61, 951–976 (2011).ADS 
    Article 

    Google Scholar 
    Petrie, B. & Yeats, P. Simple models of the circulation, dissolved metals, suspended solids and nutrients in Halifax Harbour. Water Qual. Res. J. 25, 325–350 (1990).CAS 
    Article 

    Google Scholar 
    WK, W. L. The State of Phytoplankton and Bacterioplankton at the Compass Buoy Station: Bedford Basin Monitoring Program 1992–2013. (Fisheries and Oceans Canada = Pêches et Océans Canada, 2014).Haas, S. et al. Physical mixing in coastal waters controls and decouples nitrification via biomass dilution. Proc. Natl. Acad. Sci. 118, e2004877118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084-1097.e21 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mittelbach, G. G. et al. What is the observed relationship between species richness and productivity?. Ecology 82, 2381–2396 (2001).Article 

    Google Scholar 
    Pernthaler, J. Competition and niche separation of pelagic bacteria in freshwater habitats. Environ. Microbiol. 19, 2133–2150 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science (80-) 336, 608–611 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    Vallina, S. M. et al. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 5, 4299 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Wietz, M. et al. The polar night shift: Annual dynamics and drivers of microbial community structure in the Arctic Ocean. bioRxiv 2021.04.08.436999. https://doi.org/10.1101/2021.04.08.436999 (2021).Ladau, J. et al. Global marine bacterial diversity peaks at high latitudes in winter. ISME J. 7, 1669–1677 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science (80-). 348, 1261359 (2015).Article 
    CAS 

    Google Scholar 
    Brown, J. H. Why are there so many species in the tropics?. J. Biogeogr. 41, 8–22 (2014).PubMed 
    Article 

    Google Scholar 
    Raes, E. J. et al. Oceanographic boundaries constrain microbial diversity gradients in the South Pacific Ocean. Proc. Natl. Acad. Sci. 115, E8266–E8275 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fuhrman, J. A. et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl. Acad. Sci. 105, 7774–7778 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Raes, E. J., Bodrossy, L., van de Kamp, J., Bissett, A. & Waite, A. M. Marine bacterial richness increases towards higher latitudes in the eastern Indian Ocean. Limnol. Oceanogr. Lett. 3, 10–19 (2018).Article 

    Google Scholar 
    Oksanen, J. et al. The vegan package. Commun. Ecol. Packag. 10, 719 (2007).
    Google Scholar 
    Mestre, M. et al. Sinking particles promote vertical connectivity in the ocean microbiome. Proc. Natl. Acad. Sci. 115, E6799–E6807 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    El-Swais, H., Dunn, K. A., Bielawski, J. P., Li, W. K. W. & Walsh, D. A. Seasonal assemblages and short-lived blooms in coastal north-west A tlantic O cean bacterioplankton. Environ. Microbiol. 17, 3642–3661 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Raes, E. J. et al. Metabolic pathways inferred from a bacterial marker gene illuminate ecological changes across South Pacific frontal boundaries. Nat. Commun. 12, 2213 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Musat, N. et al. A single-cell view on the ecophysiology of anaerobic phototrophic bacteria. Proc. Natl. Acad. Sci. 105, 17861–17866 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wong, H. L., MacLeod, F. I., White, R. A., Visscher, P. T. & Burns, B. P. Microbial dark matter filling the niche in hypersaline microbial mats. Microbiome 8, 1–14 (2020).Article 
    CAS 

    Google Scholar 
    De Cáceres, M. How to use the indicspecies package (ver. 1.7.1). R Proj. 2, 29 (2013).
    Google Scholar 
    Hood, R. R. et al. Pelagic functional group modeling: Progress, challenges and prospects. Deep Sea Res. Part II Top. Stud. Oceanogr. 53, 459–512 (2006).ADS 
    Article 

    Google Scholar 
    Sun, S., Jones, R. B. & Fodor, A. A. Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories. Microbiome 8, 1–9 (2020).CAS 
    Article 

    Google Scholar 
    Lynam, C. P. et al. Interaction between top-down and bottom-up control in marine food webs. Proc. Natl. Acad. Sci. 114, 1952–1957 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhou, Z. et al. Gammaproteobacteria mediating utilization of methyl-, sulfur- and petroleum organic compounds in deep ocean hydrothermal plumes. ISME J. 14, 3136–3148 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dede, B. et al. Niche differentiation of sulfur-oxidizing bacteria (SUP05) in submarine hydrothermal plumes. ISME J. 16(6), 1479–1490 (2022).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lavik, G. et al. Detoxification of sulphidic African shelf waters by blooming chemolithotrophs. Nature 457, 581–584 (2009).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science (80-) 333, 1296–1300 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    Taguchi, S. & Platt, T. Assimilation of 14CO2 in the dark compared to phytoplankton production in a small coastal inlet. Estuar. Coast. Mar. Sci. 5, 679–684 (1977).ADS 
    CAS 
    Article 

    Google Scholar 
    Platt, T. & Irwin, B. Phytoplankton Production and Nutrients in Bedford Basin, 1969–1970. (1971).Vega, S. et al. Morphological plasticity in a sulfur-oxidizing marine bacterium from the SUP05 clade enhances dark carbon fixation. MBio 10, e00216-e219 (2021).
    Google Scholar 
    Mattes, T. E., Ingalls, A. E., Burke, S. & Morris, R. M. Metabolic flexibility of SUP05 under low DO growth conditions. Environ. Microbiol. 23, 2823 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Brown, M. V. et al. Global biogeography of SAR11 marine bacteria. Mol. Syst. Biol. 8, 595 (2012).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Martiny, A. C., Coleman, M. L. & Chisholm, S. W. Phosphate acquisition genes in Prochlorococcus ecotypes: Evidence for genome-wide adaptation. Proc. Natl. Acad. Sci. 103, 12552–12557 (2006).Zorz, J. et al. Drivers of regional bacterial community structure and diversity in the Northwest Atlantic Ocean. Front. Microbiol. 10, 281 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Comeau, A. M., Douglas, G. M. & Langille, M. G. I. Microbiome helper: A custom and streamlined workflow for microbiome research. MSystems 2, e00127 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1, e00009-15 (2016).PubMed 
    Article 

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

    Google Scholar 
    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2, 191–16 (2017).
    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lahti L. & Shetty, S.A. Tools for Microbiome Analysis in R. Microbiome Package Version 1.7.21. R/Bioconductor http://microbiome.github.com/microbiome. (2017).Team, R. C. R: A Language and Environment for Statistical Computing. (2013).Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).Article 

    Google Scholar 
    Schlitzer, R. Ocean Data View. 2018. Available odv. awi. (2015).Hijmans, R. J., Williams, E., Vennes, C. & Hijmans, M. R. J. Package ‘geosphere’. in Spherical Trigonometry. Vol. 1 (2017).Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).
    Google Scholar 
    Groemping, U. & Matthias, L. Package ‘relaimpo’. (2021).Clarke, K. R. & Gorley, R. N. Primer. Prim. Plymouth (2006).Chytrý, M., Tichý, L., Holt, J. & Botta-Dukát, Z. Determination of diagnostic species with statistical fidelity measures. J. Veg. Sci. 13, 79–90 (2002).Article 

    Google Scholar 
    Tichy, L. & Chytry, M. Statistical determination of diagnostic species for site groups of unequal size. J. Veg. Sci. 17, 809–818 (2006).Article 

    Google Scholar  More

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    Seasonal variation in bull semen quality demonstrates there are heat-sensitive and heat-tolerant bulls

    Intra-bull semen quality variationTo understand variation in bull semen quality, we assessed 1271 ejaculates from 79 different bulls (11 different breeds) housed at Rockhampton stud farm, in the state of Queensland, Australia, over a period of 5 years (2014–2018). The raw data, together with the semen analysis and when the samples for each individual bull were collected is available in Supplementary 1. The climate in this area (23.3786° S, 150.5089° E) is considered sub-tropical, ranging from 16 °C in winter to over 30 °C in summer. A comprehensive semen analysis was undertaken, including sperm morphology and motility. To determine the variation in semen quality, we plotted the percentage of sperm normal forms for each bull that had 5 or more ejaculates taken annually. Morphology was used as a measure of sperm quality, as Söderquist et al.17 demonstrated that sperm motility is heavily influenced by the collection/collectors and, therefore potentially unreliable and irreproducible. This resulted in the analysis of 1178 ejaculates from 50 bulls, with an average of 23 ejaculates per bull. The percentage of sperm normal forms as a box and whiskers plot for each bull is given (Fig. 1). As shown, many bulls demonstrated extremely high variation between ejaculates, with several males ranging from  70% (considered an outright “pass” in terms of cryopreservation potential) of normal sperm morphology. On the contrary, some bulls appeared to produce consistent semen samples across the year.Figure 1Changes in sperm normal forms. Semen samples were taken from bulls via electroejaculation and the percentage sperm normal forms were counted. The data show a box and whiskers plot consisting of 50 bulls, each of which had at least 5 different ejaculates across a minimum one month. Each box and whiskers plot represents an individual Bull showing the median, upper and lower quartile range. Outliers are represented by individual dots.Full size imageTo determine the amplitude and the proportion of bulls demonstrating variation in the number of normal sperm forms, we measured the difference between the maximum and the minimum values recorded for each animal. From this analysis we found that: 9 (18%) bulls showed less than 20% variation in normal forms; 15 (30%) bulls had between 20–40% variation; 13 (26%) bulls were between 40–60% and for 13 (26%) bulls this number was over 60%. These data have major implications when interpreting semen analysis, since a bull could be classified as either fertile or infertile depending on which ejaculate was considered. This data also sheds light into why correlations between the vBBSE parameters such as morphology and the bull fertility are so variable.Seasonal effect on semen qualitySeveral sources of environmental influence have been suggested to affect bull sperm quality. These include feed availability (i.e., higher conception rates in rainy seasons)27, excessive protein intake28, day length29, thermal heat stress and age30,31. To better understand the dynamics of semen quality variation within our samples, we plotted sample “pass” and “fail” cryopreservation criteria against the month of collection. A raw bull semen sample is classified as “pass” when motility is above 60% and normal forms greater than 70%. When samples were between 30 and 60% motility and 50–70% normal forms, they were classified as a “compensatory” (or qualified) pass (q-pass). The compensatory pass relied on there being the ability to have at least 10 million motile normal forms of spermatozoa in each straw to allow for conception. An outright failure was given to any sample with less than 30% progressive motility or 50% normal forms. This allowed each ejaculate to be placed into a binary “pass” or “fail”.The data for the percentage of total males that “failed” within each month (1271 ejaculates) is shown (Fig. 2A). Clearly, there is a seasonal pattern, with over 90% pass rate in winter (June–August) that fell to 50% or lower in summer (Dec-Feb). Considering that all bulls were greater than 4 years old, housed on the same stud farm and received the same dietary supplement we found no relationship in terms of “pass” or “fail” rates to these parameters. Thus, the data clearly suggested that Temperature/Temperature-Humidity or day length were responsible for the increased failure rates seen during Summer. Therefore, to understand if there was any causal relationship, we correlated either the average monthly temperature (Fig. 2B) or daylight (Fig. 2C) with monthly failure rates. The data showed a correlation with monthly temperature (r2 = 0.55; and temperature-humidity index – see further modelling below) but not with daylight hours (r2 = 0.05). Combined, these data suggest that temperature was the most likely reason for increased failure rates during the warm/hot months.Figure 2Seasonal variation in the semen quality of 1271 bull semen ejaculates. Semen samples were taken from bulls via electroejaculation and a full semen analysis was undertaken. Each sample was then classified as a pass or fail as described in Materials and Methods. (A) The percentage failure rate for each month is shown for all bulls. The number above each column indicate how many semen ejaculates were processed that month. (B). Scatter plot showing the average monthly temperature of Rockhampton and the percentage of samples that fail/month. Line of best fit indicates and r2 = 0.55. (C) Scatter plot showing the average daily sunlight in Rockhampton and the percentage of samples that fail/month. Line of best fit indicates and r2 = 0.04.Full size imageChanges in normal sperm forms categorised by breedThe present study investigated 11 different breeds of cattle, and we reasoned that maybe one, or more breed(s) contributed to failure rates more than others. Therefore, we plotted the percentage of normal forms for every ejaculate against the breed (Fig. 3). All breeds showed similar variation except for the Belmont Red, Boran and Wagyu. However, a relatively small number of bulls from the Belmont Red and Boran breeds were assessed in this study, therefore, it is unclear if they are indeed more resistant to heat. In the case of the Wagyu, it is worth mentioning that only one animal exhibited poor sperm morphology in several ejaculates (Fig. 3 circled) during winter. A close inspection of the records showed that during this time the animal had a fever episode, with body temperature reaching 39.4 °C, and that the sperm morphology returned to normal in approximately 70 days.Figure 3Variation in Semen quality as judged by Bull breed. Semen sample was collected and analysed for sperm morphology. The animals were then separated according to breed and the percentage normal forms for each ejaculate are shown.Full size imageSome bulls are heat-sensitive, whilst others are heat-tolerantAnalysis of the present data clearly illustrated that some bulls showed marked variation in terms of their semen quality throughout the year (Fig. 1). Meanwhile, others demonstrated much less variation, and were reasonably consistent. To further clarify these differences, we closely analysed the percentage of sperm morphology from two bulls, both of whom had several ejaculates were taken throughout the year, including during and after summer (Fig. 4). There was a clear pattern, and evidence of two types of bulls. Prior to the summer season, bull 1 (Fig. 4, red), designated here as “heat-sensitive”, exhibited  > 70% normal forms of spermatozoa. This value decreases dramatically, reaching its lowest point (10%) mid-January, before undergoing a recovery by April ( > 70%). In contrast, bull 2 (Fig. 4, green) showed a consistent semen profile throughout the year. The data suggest this bull was more “heat-tolerant”.Figure 4Identification of Heat-Sensitive and Heat-Tolerant bulls. The percentage normal sperm morphology from two bulls, both Droughtmasters, which had several ejaculates taken over the course of the year were plotted against the month in which the semen sample was taken. The first bull (red) is an example of a heat-sensitive bull. The second bull (Green) an example of heat-tolerant response.Full size imageTo further explore the concept of “heat-tolerant” and “heat-sensitive” bulls, we subjected 20 Wagyu bulls to a single event of controlled heat stress (40 °C, 12 h). This experiment was performed during Winter, at Singleton (New South Wales, Australia, 32.5695° S, 151.1788° E), where the average temperature was 17 °C and never exceeded 18 °C. Prior to the heat stress event, baseline semen samples were taken from each animal. After heat stress, semen samples were taken every week for 11 weeks. During the experiment, two bulls were removed from the program due to infection and sickness whilst a 3rd bull was removed as it refused to co-operate with electroejaculation procedure. From the remaining bulls, we were able to reproduce the heat-sensitive and heat-tolerant bull phenomenon. The raw data from this work is given in Supplementary 1, and an example of the data is shown (Fig. 5). For 14 bulls, we found no difference in terms of their baseline samples, which were between 70–90% normal forms. This is consistent with the Wagyu bull characteristics and their heat-tolerance (Fig. 5, yellow, green, blue lines). Within these “heat-tolerant” bulls, there was a variation of 16–22% sperm normal forms. For the other three bulls, two of them showed a decline in sperm quality, which began 2–3 weeks after the heat event, dropping from a baseline of 85% and 90% normal forms to 55% and 59%, respectively (30–31% variation in normal forms; Fig. 5, grey and orange line). The third bull showed a greater degree of heat-sensitivity. Starting at 77% morphologically normal sperm, the spermiogram of this bull illustrated a rapid decrease in normal forms in a short time (2 weeks), reaching around 40% after 4–5 weeks. Sperm morphology remained at this level (37% variation in normal form) for four weeks, before recovery. These data show that under experimental condition, the phenomenon of heat-sensitive and heat-tolerant animals can be reproduced. Further, it appears that there are degrees of heat-sensitivity.Figure 5Heating of Wagyu bulls to identify heat-sensitive and heat-tolerant effect. Twenty Wagyu bulls all 3 years of age and over were heated to 40 °C for 12 h in an insulated barn. Before heating, bassline samples were taken (week 1). After heating, electroejaculation was used to collect semen every week for 11 weeks. For every sample, sperm morphology was counted by a qualified theriogenologist. The data show the percentage normal morphology for 5 bulls. The light blue line indicates a heat-sensitive bulls, whose morphology was affected by heat, then returned back to baseline. The orange and grey line represent two related bulls (same father) who also produced less than 70% normal forms. The yellow, green and dark blue lines represent three heat-tolerant bulls, whose semen profile did not drop below the 70% normal spermatozoa threshold.Full size imageEnvironmental heat stress leads to poor sperm quality 17 days laterSimilar to previous reports, we noted that sperm quality does not begin to deteriorate until 2–3 weeks after the heat stress event of the bulls32. Based on the timing of spermatogenesis, this is consistent with reports that meiotic cells are more susceptible to heat stress following a heating event, with poor quality spermatozoa appearing in the ejaculate around 2–3 weeks later. To better understand the relationship between a “heat-event” and the production of poor-quality spermatozoa, we modelled both maximum temperature and maximum temperature humidity index (THI) and their relationship to the proportion of morphologically normal spermatozoa. The THI is an index representing the effect of humidity on the heat stress of an animal. THI was obtained using the following formula:$$mathrm{THI}=0.8* frac{{T}_{max}}{100}+frac{left(humidity*left({t}_{max}-14.4right)right)}{1}+46.4$$where Tmax = maximum temperature, (oF), and H = relative humidity.We plotted the correlation between semen quality and Tmax on the day, and every day prior (up to 40 days) to semen collection (Fig. 6). This modelling demonstrated that poor semen quality was due to maximum daytime temperature 17 days prior (Fig. 6a, arrow). Notably, 1 day of heat-stress appears to be sufficient to cause poor sperm quality, since if we take the average of 2 (Fig. 6b) or 3-day maximal temperatures prior to collection (Fig. 6c) the correlation patterns were similar. Supplementary 3 shows further modelling for Tmax and THI using between 1 and 5-day average temperatures prior to semen collection.Figure 6Bull semen quality (as percentage sperm normal forms) is related to the temperature that occurred 17–19 days ago. Correlation between sperm quality and maximum Temperature (Tmax). The Y axis is the Pearson correlation coefficient and X axis represents the number of days before the day the sperm sample was taken. (a) Uses one day of Tmax data whilst (b) averages two and (c) averages three consecutive days of Tmax data. The arrow shows the best correlation between Tmax and poor sperm quality, which occurs around 17–19 days before the semen sample is collected.Full size imageUnderstanding the temperatures at which heat-sensitive bulls failTo determine the Tmax at which bulls in the paddock begin to produce poor quality spermatozoa, we modelled data using both parameters measured at 17 days prior to the heat event, and plotted samples from 12 heat-sensitive bulls (6 Brahmans, 4 Drought Masters and 2 Santa Gertrudis). The relationship between sperm morphology and Tmax 17 days prior to heat even was plotted, with a spline smoothing cure to show the mean quality as a function of Tmax (Fig. 7a). As the temperature increase, so the quality of sperm morphology decreases as expected. To gain further clarity, we next fitted a nominal logistic regression analysis to model the proportion of spermatozoa that would either pass, Q-pass or fail sperm cryopreservation criteria as a function of Tmax 17 days prior. Tmax effect was highly significant for both outcome categories, with both p  More

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    Differences in acute phase response to bacterial, fungal and viral antigens in greater mouse-eared bats (Myotis myotis)

    Wibbelt, G., Moore, M. S., Schountz, T. & Voigt, C. C. Emerging diseases in Chiroptera: Why bats?. Biol. Let. 6, 438–440 (2010).Article 

    Google Scholar 
    Gonzalez, V. & Banerjee, A. Molecular, ecological, and behavioural drivers of the bat-virus relationship. iScience 25, 104779 (2022).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Brook, C. E. & Dobson, A. P. Bats as ‘special’reservoirs for emerging zoonotic pathogens. Trends Microbiol. 23, 172–180 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kosoy, M. et al. Bartonella spp. in bats, Kenya. Emerg. Infect. Dis. 16, 1875–1881 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Becker, D. J. et al. Livestock abundance predicts vampire bat demography, immune profiles and bacterial infection risk. Philos. Trans. R. Soc. Biol. Sci. 373, 20170089 (2018).Article 
    CAS 

    Google Scholar 
    Muehldorfer, K. Bats and bacterial pathogens: A review. Zoonoses Public Health 60, 93–103 (2013).Article 

    Google Scholar 
    Taylor, M. L. et al. Geographical distribution of genetic polymorphism of the pathogen Histoplasma capsulatum isolated from infected bats, captured in a central zone of Mexico. FEMS Immunol. Med. Microbiol. 45, 451–458 (2005).PubMed 
    Article 
    CAS 

    Google Scholar 
    Schaer, J. et al. High diversity of West African bat malaria parasites and a tight link with rodent Plasmodium taxa. Proc. Natl. Acad. Sci. 110, 17415–17419 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Evans, N., Bown, K., Timofte, D., Simpson, V. & Birtles, R. Fatal borreliosis in bat caused by relapsing fever spirochete, United Kingdom. Emerg. Infect. Dis. 15, 1331–1333 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Muehldorfer, K., Speck, S. & Wibbelt, G. Diseases in free-ranging bats from Germany. BMC Vet. Res. 7, 61 (2011).Article 

    Google Scholar 
    Muehldorfer, K., Wibbelt, G., Haensel, J., Riehm, J. & Speck, S. Yersinia species isolated from bats, Germany. Emerg. Infect. Dis. 16, 578–581 (2010).Article 

    Google Scholar 
    Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227–227 (2009).PubMed 
    Article 
    CAS 

    Google Scholar 
    Barlow, A., Jolliffe, T., Tomlin, M., Worledge, L. & Miller, H. Mycotic dermatitis in a vagrant parti-coloured bat (Vespertilio murinus) in Great Britain. Vet. Rec. 169, 614–614 (2011).PubMed 
    Article 

    Google Scholar 
    Simpson, V. R., Borman, A. M., Fox, R. I. & Mathews, F. Cutaneous mycosis in a Barbastelle bat (Barbastella barbastellus) caused by Hyphopichia burtonii. J. Vet. Diagn. Invest. 25, 551–554 (2013).PubMed 
    Article 

    Google Scholar 
    Frick, W. F. et al. An emerging disease causes regional population collapse of a common North American bat species. Science 329, 679–682 (2010).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Hecht-Höger, A. et al. Plasma proteomic profiles differ between European and North American myotid bats colonized by Pseudogymnoascus destructans. Mol. Ecol. 29, 1745–1755 (2020).PubMed 
    Article 

    Google Scholar 
    Baker, M., Schountz, T. & Wang, L. F. Antiviral immune responses of bats: A review. Zoonoses Public Health 60, 104–116 (2013).PubMed 
    Article 
    CAS 

    Google Scholar 
    Baker, M. L. & Zhou, P. in Bats and Viruses Vol. 1 (eds Lin-Fa Wang & Christopher Cowled) Ch. 14, 327–348 (John Wiley & Sons, Inc., 2015).Wang, L.-F., Walker, P. J. & Poon, L. L. M. Mass extinctions, biodiversity and mitochondrial function: Are bats ‘special’ as reservoirs for emerging viruses?. Curr. Opin. Virol. 1, 649–657 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Lee, K. A. Linking immune defenses and life history at the levels of the individual and the species. Integr. Comp. Biol. 46, 1000–1015 (2006).PubMed 
    Article 
    CAS 

    Google Scholar 
    Murphy, K. Janeway’s Immunobiology 8th edn. (Garland Science, 2012).
    Google Scholar 
    Gruys, E., Toussaint, M., Niewold, T. & Koopmans, S. Acute phase reaction and acute phase proteins. J. Zhejiang Univ. Sci. B Biomed. Biotechnol. 6, 1045–1056 (2005).CAS 

    Google Scholar 
    Cray, C., Zaias, J. & Altman, N. H. Acute phase response in animals: A review. Comp. Med. 59, 517–526 (2009).PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Hart, B. L. Biological basis of the behavior of sick animals. Neurosci. Biobehav. Rev. 12, 123–137 (1988).PubMed 
    Article 
    CAS 

    Google Scholar 
    Owen-Ashley, N. T. & Wingfield, J. C. Acute phase responses of passerine birds: Characterization and seasonal variation. J. Ornithol. 148, S583–S591 (2007).Article 

    Google Scholar 
    Kozak, W., Conn, C. A. & Kluger, M. J. Lipopolysaccharide induces fever and depresses locomotor-activity in unrestrained mice. Am. J. Physiol. 266, R125–R135 (1994).PubMed 
    CAS 

    Google Scholar 
    Copeland, S. et al. Acute inflammatory response to endotoxin in mice and humans. Clin. Diagn. Lab. Immunol. 12, 60–67 (2005).PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Evans, S. S., Repasky, E. A. & Fisher, D. T. Fever and the thermal regulation of immunity: The immune system feels the heat. Nat. Rev. Immunol. 15, 335–349 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Stockmaier, S., Dechmann, D. K. N., Page, R. A. & Teague O’Mara, M. No fever and leucocytosis in response to a lipopolysaccharide challenge in an insectivorous bat. Biol. Let. 11, 20150576 (2015).Article 
    CAS 

    Google Scholar 
    Martin, L. B., Scheuerlein, A. & Wikelski, M. Immune activity elevates energy expenditure of house sparrows: A link between direct and indirect costs?. Proc. R. Soc. Lond. B Biol. Sci. 270, 153–158 (2003).Article 

    Google Scholar 
    Sheldon, B. C. & Verhulst, S. Ecological immunology: Costly parasite defences and trade-offs in evolutionary ecology. Trends Ecol. Evol. 11, 317–321 (1996).PubMed 
    Article 
    CAS 

    Google Scholar 
    Bonneaud, C. et al. Assessing the cost of mounting an immune response. Am. Nat. 161, 367–379 (2003).PubMed 
    Article 

    Google Scholar 
    Audebert, H. J., Pellkofer, T. S., Wimmer, M. L. & Haberl, R. L. Progression in lacunar stroke is related to elevated acute phase parameters. Eur. Neurol. 51, 125–131 (2004).PubMed 
    Article 
    CAS 

    Google Scholar 
    Lee, K. A., Martin, L. B. & Wikelski, M. C. Responding to inflammatory challenges is less costly for a successful avian invader, the house sparrow (Passer domesticus), than its less-invasive congener. Oecologia 145, 244–251 (2005).ADS 
    PubMed 
    Article 

    Google Scholar 
    Owen-Ashley, N. T., Turner, M., Hahn, T. P. & Wingfield, J. C. Hormonal, behavioral, and thermoregulatory responses to bacterial lipopolysaccharide in captive and free-living white-crowned sparrows (Zonotrichia leucophrys gambelii). Horm. Behav. 49, 15–29 (2006).PubMed 
    Article 
    CAS 

    Google Scholar 
    Coon, C. A. C., Warne, R. W. & Martin, L. B. Acute-phase responses vary with pathogen identity in house sparrows (Passer domesticus). Am. J. Physiol. Regul. Integr. Comp. Physiol. 300, R1418–R1425 (2011).PubMed 
    Article 
    CAS 

    Google Scholar 
    Kimura, M. et al. Comparison of acute phase responses induced in rabbits by lipopolysaccharide and double-stranded RNA. Am. J. Physiol. Regul. Integr. Comp. Physiol. 267, R1596–R1605 (1994).Article 
    CAS 

    Google Scholar 
    Gomez, C. R., Goral, J., Ramirez, L., Kopf, M. & Kovacs, E. J. Aberrant acute-phase response in aged interleukin-6 knockout mice. Shock 25, 581–585 (2006).PubMed 
    Article 
    CAS 

    Google Scholar 
    Barrientos, R. M., Watkins, L. R., Rudy, J. W. & Maier, S. F. Characterization of the sickness response in young and aging rats following E. coli infection. Brain Behav Immun. 23, 450–454 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sköld-Chiriac, S., Nord, A., Tobler, M., Nilsson, J. -Å. & Hasselquist, D. Body temperature changes during simulated bacterial infection in a songbird: Fever at night and hypothermia during the day. J. Exp. Biol. 218, 2961–2969 (2015).PubMed 

    Google Scholar 
    Sköld-Chiriac, S., Nord, A., Nilsson, J. -Å. & Hasselquist, D. Physiological and behavioral responses to an acute-phase response in zebra finches: Immediate and short-term effects. Physiol. Biochem. Zool. 87, 288–298 (2014).PubMed 
    Article 

    Google Scholar 
    Fritze, M. et al. Immune response of hibernating European bats to a fungal challenge. Biol. Open 8, bio046078 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Triana-Llanos, C., Guerrero-Chacón, A. L., Rivera-Ruíz, D., Rojas-Díaz, V. & Niño-Castro, A. The acute phase response elicited by a viral-like molecular pattern increases energy expenditure in Artibeus lituratus. Biologia 74, 667–673 (2019).Article 

    Google Scholar 
    Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Inflammatory challenge increases measures of oxidative stress in a free-ranging, long-lived mammal. J. Exp. Biol. 216, 4514–4519 (2013).PubMed 
    CAS 

    Google Scholar 
    Allen, L. C. et al. Roosting ecology and variation in adaptive and innate immune system function in the Brazilian free-tailed bat (Tadarida brasiliensis). J. Comp. Physiol. B. 179, 315–323 (2009).PubMed 
    Article 

    Google Scholar 
    Otálora-Ardila, A., Herrera, M. L. G., Flores-Martínez, J. J. & Welch, K. C. Jr. Metabolic cost of the activation of immune response in the fish-eating myotis (Myotis vivesi): The effects of inflammation and the acute phase response. PLoS ONE 11, e0164938 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ohmer, M. E. B. et al. Applied ecoimmunology: Using immunological tools to improve conservation efforts in a changing world. Conserv. Physiol. 9, coab074 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Becker, D. J., Seifert, S. N. & Carlson, C. J. Beyond infection: Intergrating competence into reservoir host prediction. Trends Ecol. Evol. 35, P1062–P1065 (2020).Article 

    Google Scholar 
    Kacprzyk, J. et al. A potent anti-inflammatory response in bat macrophages may be linked to extended longevity and viral tolerance. Acta Chiropterologica 19, 219–228 (2017).Article 

    Google Scholar 
    Langlois, M. R. & Delanghe, J. R. Biological and clinical significance of haptoglobin polymorphism in humans. Clin. Chem. 42, 1589–1600 (1996).PubMed 
    Article 
    CAS 

    Google Scholar 
    Field, K. A. et al. The white-nose syndrome transcriptome: activation of anti-fungal host responses in wing tissue of hibernating little brown myotis. PLoS Pathog. 11, e1005168 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Fritze, M. et al. Determinants of defence strategies of a hibernating European bat species towards the fungal pathogen Pseudogymnoascus destructans. Dev. Comp. Immunol. 119, 104017 (2021).PubMed 
    Article 
    CAS 

    Google Scholar 
    Moreno, K. et al. Sick bats stay home alone: Fruit bats practice social distancing when faced with an immunological challenge. Ann. N. Y. Acad. Sci. 1505, 178–190 (2021).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Otálora-Ardila, A., Herrera, M. L. G., Flores-Martínez, J. J. & Welch, K. C. Jr. The effect of short-term food restriction on the metabolic cost of the acute phase response in the fish-eating Myotis (Myotis vivesi). Mamm. Biol. 82, 41–47 (2017).Article 

    Google Scholar 
    Voigt, C. C. et al. The immune response of bats differs between pre-migration and migration seasons. Sci. Rep. 10, 17384 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Guerrero-Chacón, A. L., Rivera-Ruíz, D., Rojas-Díaz, V., Triana-Llanos, C. & Niño-Castro, A. Metabolic cost of acute phase response in the frugivorous bat, Artibeus lituratus. Mamm. Res. 63, 397–404 (2018).Article 

    Google Scholar 
    Weise, P., Czirják, G. Á., Lindecke, O., Bumrungsri, S. & Voigt, C. C. Simulated bacterial infection disrupts the circadian fluctuation of immune cells in wrinkle-lipped bats (Chaerephon plicatus). PeerJ 5, e3570 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Cabrera-Martínez, L. V., Herrera, M. L. G. & Cruz-Neto, A. P. The energetic cost of mounting an immune response for Pallas’s long-tongued bat (Glossophaga soricina). PeerJ 6, e4627 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Cabrera-Martinez, L. V., Herrera, M. L. G. & Cruz-Neto, A. P. Food restriction, but not seasonality, modulates the acute phase response of a neotropical bat. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 229, 93–100 (2019).PubMed 
    Article 
    CAS 

    Google Scholar 
    Stockmaier, S., Bolnick, D. I., Page, R. A. & Carter, G. G. An immune challenge reduces social grooming in vampire bats. Anim. Behav. 140, 141–149 (2018).Article 

    Google Scholar 
    Scheiermann, C., Kunisaki, Y. & Frenette, P. S. Circadian control of the immune system. Nat. Rev. Immunol. 13, 190–198 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Measures of the constitutive immune system are linked to diet and roosting habits of Neotropical bats. PLoS ONE 8, e54023 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hasselquist, D. Comparative immunoecology in birds: Hypotheses and tests. J. Ornithol. 148, 571–582 (2007).Article 

    Google Scholar 
    Becker, D. J. et al. Leukocyte profiles reflect geographic range limits and local food abundance in a widespread Neotropical bat. Integr. Comp. Biol. 59, 1176–1189 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Vermeulen, A., Eens, M., Zaid, E. & Müller, W. Baseline innate immunity does not affect the response to an immune challenge in female great tits (Parus major). Behav. Ecol. Sociobiol. 70, 585–592 (2016).Article 

    Google Scholar 
    Melhado, G., Herrera, M. L. G. & Cruz-Neto, A. P. Bats respond to simulated bacterial infection during the active phase by reducing food intake. J. Exp. Zool. A 333, 536–542 (2020).Article 
    CAS 

    Google Scholar 
    Costantini, D. et al. Induced bacterial sickness causes inflammation but not blood oxidative stress in Egyptian fruit bats (Rousettus aegyptiacus). Conserv. Physiol. 10, coac028 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Viljoen, H., Bennett, N. C. & Lutermann, H. Life-history traits, but not season, affect the febrile response to a lipopolysaccharide challenge in highveld mole-rats. J. Zool. 285, 222–229 (2011).Article 

    Google Scholar 
    Ahn, M., Cui, J., Irving, A. T. & Wang, L. F. Unique loss of the PYHIN gene family in bats amongst mammals: Implications for inflammasome sensing. Sci. Rep. 6, 21722 (2016).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Lilley, T. et al. Immune responses in hibernating little brown myotis (Myotis lucifugus) with white-nose syndrome. Proc. R. Soc. Lond. B Biol. Sci. 284, 20162232 (2017).
    Google Scholar 
    Mayberry, H. W., McGuire, L. P. & Willis, C. K. Body temperatures of hibernating little brown bats reveal pronounced behavioural activity during deep torpor and suggest a fever response during white-nose syndrome. J. Comp. Physiol. B. 188, 333–343 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    Watkins, L. R., Maier, S. F. & Goehler, L. E. Immune activation: The role of pro-inflammatory cytokines in inflammation, illness responses and pathological pain states. Pain 63, 289–302 (1995).PubMed 
    Article 

    Google Scholar 
    Grimble, R. F. Interaction between nutrients, pro-inflammatory cytokines and inflammation. Clin. Sci. 91, 121–130 (1996).Article 
    CAS 

    Google Scholar 
    Schultz, E. M., Hahn, T. P. & Klasing, K. C. Photoperiod but not food restriction modulates innate immunity in an opportunistic breeder, Loxia curvirostra. J. Exp. Biol. 220, 722–730 (2016).PubMed 

    Google Scholar 
    Brinkmann, V. & Zychlinsky, A. Neutrophil extracellular traps: Is immunity the second function of chromatin?. J. Cell Biol. 198, 773–783 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Davis, A. K., Maney, D. L. & Maerz, J. C. The use of leukocyte profiles to measure stress in vertebrates: A review for ecologists. Funct. Ecol. 22, 760–772 (2008).Article 

    Google Scholar 
    Bouma, H. R., Carey, H. V. & Kroese, F. G. Hibernation: The immune system at rest?. J. Leukoc. Biol. 88, 619–624 (2010).PubMed 
    Article 
    CAS 

    Google Scholar 
    Crameri, G. et al. Establishment, immortalisation and characterisation of pteropid bat cell lines. PLoS ONE 4, e8266 (2009).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Neely, B. A. et al. Surveying the vampire bat (Desmodus rotundus) serum proteome: A resource for identifying immunological proteins and detecting pathogens. J. Proteome Res. 20, 2547–2559 (2021).PubMed 
    Article 
    CAS 

    Google Scholar 
    Hecht, A. M. et al. Plasma proteomic analysis of active and torpid greater mouse-eared bats (Myotis myotis). Sci. Rep. 5, 16604 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Barclay, R. M. R. et al. Can external radiotransmitters be used to assess body temperature and torpor in bats?. J. Mammal. 77, 1102–1106 (1996).Article 

    Google Scholar 
    Pap, P. L., Czirják, G. Á., Vágási, C. I., Barta, Z. & Hasselquist, D. Sexual dimorphism in immune function changes during the annual cycle in house sparrows. Naturwissenschaften 97, 891–901 (2010).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Heinrich, S. K. et al. Feliform carnivores have a distinguished constitutive innate immune response. Biol. Open 5, 550–555 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Heinrich, S. K. et al. Cheetahs have a stronger constitutive innate immunity than leopards. Sci. Rep. 7, 44837 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Morell, V., Lundgren, E. & Gillott, A. Predicting severity of trauma by admission white blood cell count, serum potassium level, and arterial pH. South. Med. J. 86, 658–659 (1993).PubMed 
    Article 
    CAS 

    Google Scholar 
    R Core Team. A Language and Environment for Statistical Computing. (R foundation for statistical computing, 2018).Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. Linear and nonlinear mixed effects models. R Package Version 3, 57 (2007).
    Google Scholar 
    Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE, 2011).
    Google Scholar 
    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
    PubMed 
    MATH 
    Article 

    Google Scholar  More

  • in

    Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series

    Carson, R. The Sea Around Us. Oxford University Press, Oxford, UK 1951.Beverton, R. J. H. & Holt, S. J. A review of the lifespans and mortality rates of fish in nature, and their relation to growth and other physiological characteristics. In: Ciba Foundation Symposium – The Lifespan of Animals (Colloquia on Ageing, Vol. 5) 142–180 (John Wiley & Sons, Ltd, 2008).Kiørboe, T., Visser, A. & Andersen, K. H. A trait-based approach to ocean ecology. ICES Journal of Marine Science 75, 1849–1863 (2018).Article 

    Google Scholar 
    Froese, R. Cube law, condition factor and weight-length relationships: History, meta-analysis and recommendations. Journal of Applied Ichthyology 22, 241–253 (2006).Article 

    Google Scholar 
    Juan-Jordá, M. J., Mosqueira, I., Freire, J. & Dulvy, N. K. Life in 3-D: Life history strategies in tunas, mackerels and bonitos. Reviews in Fish Biology and Fisheries 23, 135–155 (2012).Article 

    Google Scholar 
    Beukhof, E. et al. Marine fish traits follow fast-slow continuum across oceans. Scientific Reports 9 (2019).Pauly, D. Tropical fishes: patterns and propensities. Journal of Fish Biology 53, 1–17 (1998).ADS 

    Google Scholar 
    Munch, S. B. & Salinas, S. Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proceedings of the National Academy of Sciences 106, 13860–13864 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Gislason, H., Daan, N., Rice, J. C. & Pope, J. G. Size, growth, temperature and the natural mortality of marine fish. Fish and Fisheries 11, 149–158 (2010).Article 

    Google Scholar 
    Froese, R. & Pauly, D. FishBase https://fishbase.org/ (2021).Winemiller, K. O. & Rose, K. A. Patterns of life-history diversification in North American Fishes: implications for population regulation. Canadian Journal of Fisheries and Aquatic Sciences 49, 2196–2218 (1992).Article 

    Google Scholar 
    Cortés, E. Life History patterns and correlations in sharks. Reviews in Fisheries Science 8, 299–344 (2000).Article 

    Google Scholar 
    Juan-Jordá, M. J., Mosqueira, I., Freire, J., Ferrer-Jordá, E. & Dulvy, N. K. Global scombrid life history data set. Ecology 97, 809–809 (2016).Article 

    Google Scholar 
    Kindsvater, H. K., Mangel, M., Reynolds, J. D. & Dulvy, N. K. Ten principles from evolutionary ecology essential for effective marine conservation. Ecology and Evolution 6, 2125–2138 (2016).Article 

    Google Scholar 
    Kindsvater, H. K. et al. Overcoming the data crisis in biodiversity conservation. Trends in Ecology & Evolution 33, 676–688 (2018).Article 

    Google Scholar 
    Ricard, D., Minto, C., Jensen, O. P. & Baum, J. K. Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish and Fisheries 13, 380–398 (2011).Article 

    Google Scholar 
    Maureaud, A. et al. Are we ready to track climate‐driven shifts in marine species across international boundaries? ‐ A global survey of scientific bottom trawl data. Global Change Biology 27, 220–236 (2020).ADS 
    Article 

    Google Scholar 
    Sherley, R. B. et al. Estimating IUCN Red List population reduction: JARA-A decision‐support tool applied to pelagic sharks. Conservation Letters 13 (2019).McAllister, M. K., Pikitch, E. K. & Babcock, E. A. Using demographic methods to construct Bayesian priors for the intrinsic rate of increase in the Schaefer model and implications for stock rebuilding. Canadian Journal of Fisheries and Aquatic Sciences 58, 1871–1890 (2001).Article 

    Google Scholar 
    Froese, R., Demirel, N., Coro, G. & Kleisner, K. M. & Winker, H. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18, 506–526 (2016).Article 

    Google Scholar 
    Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).Article 

    Google Scholar 
    Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Scientific Data 4 (2017).Inchausti, P. & Halley, J. Investigating Long-Term Ecological Variability Using the Global Population Dynamics Database. Science 293, 655–657 (2001).CAS 
    Article 

    Google Scholar 
    Collen, B. et al. Monitoring change in vertebrate abundance: the Living Planet Index. Conservation Biology 23, 317–327 (2009).Article 

    Google Scholar 
    Thorson, J. T., Munch, S. B., Cope, J. M. & Gao, J. Predicting life history parameters for all fishes worldwide. Ecological Applications 27, 2262–2276 (2017).Article 

    Google Scholar 
    Heinicke, S. et al. Advancing conservation planning for western chimpanzees using IUCN SSC A.P.E.S.-the case of a taxon-specific database. Environmental Research Letters 14, 064001 (2019).ADS 
    Article 

    Google Scholar 
    Horswill, C. et al. Global reconstruction of life‐history strategies: A case study using tunas. Journal of Applied Ecology 56, 855–865 (2019).Article 

    Google Scholar 
    Thorson, J. T. Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data‐integrated life‐history model. Fish and Fisheries 21, 237–251 (2019).Article 

    Google Scholar 
    Brown, C. J. & Roff, G. Life-history traits inform population trends when assessing the conservation status of a declining tiger shark population. Biological Conservation 239, 108230 (2019).Article 

    Google Scholar 
    Walls, R. H. L. & Dulvy, N. K. Eliminating the dark matter of data deficiency by predicting the conservation status of Northeast Atlantic and Mediterranean Sea sharks and rays. Biological Conservation 246, 108459 (2020).Article 

    Google Scholar 
    Guy, C. S. et al. A paradoxical knowledge gap in science for critically endangered fishes and game fishes during the sixth mass extinction. Scientific Reports 11 (2021).Compagno, L. J. V. Alternative life-history styles of cartilaginous fishes in time and space. In Alternative life-history styles of fishes 33–75 (Springer Netherlands, 1990).Stein, R. W. et al. Global priorities for conserving the evolutionary history of sharks, rays and chimaeras. Nature Ecology & Evolution 2, 288–298 (2018).ADS 
    Article 

    Google Scholar 
    Yopak, K. E. et al. A conserved pattern of brain scaling from sharks to primates. Proceedings of the National Academy of Sciences 107, 12946–12951 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    Mull, C. G., Yopak, K. E. & Dulvy, N. K. Maternal Investment, Ecological Lifestyle, and Brain Evolution in Sharks and Rays. The American Naturalist 195, 1056–1069 (2020).Article 

    Google Scholar 
    Mull, C. G., Pennel, M. W., Yopak, K. E. & Dulvy, N. K. Maternal investment evolves with larger body size and higher diversification rate in sharks and rays. BioRxiv TBC (2022).Dulvy, N. D. & Reynolds, J. D. Evolutionary transitions among egg-laying, live-bearing, and maternal inputs in sharks and rays. Proceedings of the Royal Society B: Biological Sciences 264, 1309–1315 (1997).ADS 
    Article 

    Google Scholar 
    Heithaus, M. R. et al. Advances in our understanding of the ecological importance of sharks and their relatives. In: Biology of sharks and their relatives, 3rd Ed. Carrier, J. C., Simpfendorfer, C. A., Heithaus, M. R., & Yopak, K. E. (Ed).Simpfendorfer, C. A., Heupel, M. R., White, W. T. & Dulvy, N. K. The importance of research and public opinion to conservation management of sharks and rays: a synthesis. Marine and Freshwater Research 62, 518 (2011).CAS 
    Article 

    Google Scholar 
    Dulvy, N. K. et al. Overfishing drives over one-third of all sharks and rays toward a global extinction crisis. Current Biology 31, 4773–4787.e8 (2021).CAS 
    Article 

    Google Scholar 
    Cortés, E., Brooks, E. N. & Shertzer, K. W. Risk assessment of cartilaginous fish populations. ICES Journal of Marine Science 72, 1057–1068 (2014).Article 

    Google Scholar 
    D’Alberto, B. M., Carlson, J. K., Pardo, S. A. & Simpfendorfer, C. A. Population productivity of shovelnose rays: Inferring the potential for recovery. PLOS ONE 14, e0225183 (2019).Article 

    Google Scholar 
    Sharkipedia: elasmobranch traits & trends http://www.sharkipedia.org.Bibliography Database. Shark-References http://www.shark-references.com.Weigmann, S. Annotated checklist of the living sharks, batoids and chimaeras (Chondrichthyes) of the world, with a focus on biogeographical diversity. Journal of Fish Biology 88, 837–1037 (2016).CAS 
    Article 

    Google Scholar 
    Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–571 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Spalding, M. D. et al. Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. BioScience 57, 573–583 (2007).Article 

    Google Scholar 
    Spalding, M. D. et al. Pelagic provinces of the world: A biogeographic classification of the world’s surface pelagic waters. Ocean & Coastal Management 60, 19–30 (2012).Article 

    Google Scholar 
    Rohatgi, A. WebPlotDigitizer. Extract data from plots, images, and maps https://automeris.io/WebPlotDigitizer/.Mull, C. G. et al. Sharkipedia: A database of shark and ray life history traits and abundance time-series. Zenodo https://doi.org/10.5281/zenodo.6656525 (2012). More

  • in

    Empirical analysis of the role of the environmental accountability system in energy conservation and emission reduction in China

    Arora, V. K. et al. Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett. https://doi.org/10.1029/2010GL046270 (2011).Article 

    Google Scholar 
    Tutak, M. & Brodny, J. Renewable energy consumption in economic sectors in the EU-27. The impact on economics, environment and conventional energy sources. A 20-year perspective. J. Clean. Prod. 345, 131076 (2022).Article 

    Google Scholar 
    Acheampong, A. O. & Boateng, E. B. Modelling carbon emission intensity: Application of artificial neural network. J. Clean. Prod. 225, 833–856 (2019).Article 

    Google Scholar 
    Zhou, L. A. Governing China’s local officials: An analysis of promotion tournament model. Econ. Res. J. 07, 36–50 (2007) (in Chinese).
    Google Scholar 
    Luo, Z. & Qi, B. The effects of environmental regulation on industrial transfer and upgrading and banking synergetic development—Evidence from water pollution control in the Yangtze River Basin. Econ. Res. J. 56(02), 174–189 (2021).
    Google Scholar 
    Blumstein, C., Krieg, B., Schipper, L. & York, C. Overcoming social and institutional barriers to energy conservation. Energy 5(4), 355–371 (1980).Article 

    Google Scholar 
    Zhang, L. Energy conservation and emission reduction: An inevitable choice of China’s energy strategy. Sustain. Energy 6, 21–30 (2016).ADS 
    Article 

    Google Scholar 
    Bhuiyan, M. A. H., Siwar, C., Ismail, S. M. & Islam, R. The role of government for ecotourism development: Focusing on east coast economic region. J. Soc. Sci. 7(4), 557 (2011).
    Google Scholar 
    Fan, G., Su, M. & Cao, J. An economic analysis of consumption and carbon emission responsibility. Econ. Res. J. 45(01), 4–14 (2010) (in Chinese).
    Google Scholar 
    Xie, J. G. & Jiang, P. S. Embodied energy in international trade of China: Calculation and decomposition. China Econ. Q. 13(04), 1365–1392 (2014) (in Chinese).
    Google Scholar 
    Wu, J., Cui, C., Mei, X., Xu, Q. & Zhang, P. Migration of manufacturing industries and transfer of carbon emissions embodied in trade: Empirical evidence from China and Thailand. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-021-14674-z (2021).Article 

    Google Scholar 
    Porter, M. E. & Van der Linde, C. Toward a new conception of the environment-competitiveness relationship. J. Econ. Perspect. 9(4), 97–118 (1995).Article 

    Google Scholar 
    Zhang, X. P. & Cheng, X. M. Energy consumption, carbon emissions, and economic growth in China. Ecol. Econ. 68(10), 2706–2712 (2009).Article 

    Google Scholar 
    Wang, B. & Liu, G. T. Energy conservation, emission reduction and green economic growth in China: From the perspective of total factor productivity. China Ind. Econ. 05, 57–69 (2015) (in Chinese).
    Google Scholar 
    Cheng, Y. Q., Wang, Z. Y., Zhang, S. Z., Ye, X. Y. & Jiang, H. M. Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China. Acta Geogr. Sin. 68(10), 1418–1431 (2013) (in Chinese).
    Google Scholar 
    Peng, X. & Cui, H. R. Research on the effects of energy structure adjustment in China on Carbon Intensity. J. Dalian Univ. Technol. (Soc. Sci. Ed.) 37(01), 11–16 (2016) (in Chinese).
    Google Scholar 
    Xiao, T. & Liu, H. Empirical research on industrial structure adjustment and energy conservation and emission reduction. Economist 09, 58–68 (2014) (in Chinese).
    Google Scholar 
    Sheng, P., He, Y. & Guo, X. The impact of urbanization on energy consumption and efficiency. Energy Environ. 28(7), 673–686 (2017).Article 

    Google Scholar 
    Sun, H., Samuel, C. A., Amissah, J. C. K., Taghizadeh-Hesary, F. & Mensah, I. A. Non-linear nexus between CO2 emissions and economic growth: A comparison of OECD and B&R countries. Energy 212, 118637 (2020).Article 

    Google Scholar 
    He, J. K. Economic analysis and effectiveness evaluation on China’s CO2 emission mitigation target. Stud. Sci. Sci. 01, 9–17 (2011) (in Chinese).
    Google Scholar 
    Meng, W. et al. Study on the developmental strategy of the energy saving and environmental protection industry in China. Strat. Study CAE 18(04), 1–8 (2016) (in Chinese).
    Google Scholar 
    He, J., Wang, M. M., Zhang, Z. L., Li, M. & Shi, H. X. Equal attention should be paid to boyh construction and operation of buildings for energy efficiency and emission reduction: Findings from current data on resource and environment loads in China’s building industry. Sci. Technol. Rev. 36(05), 8–13 (2018) (in Chinese).
    Google Scholar 
    Xie, C. X. & Gao, Y. B. Research on innovative development path of energy conservation and emission reduction from the perspective of low carbon economy. China Resour. Compr. Util. 37(12), 92–94 (2019) (in Chinese).
    Google Scholar 
    Dong, J. F., Deng, C., Wang, X. M. & Zhang, X. L. Multilevel index decomposition of energy-related carbon emissions and their decoupling from economic growth in Northwest China. Energies 9(9), 680 (2016).Article 

    Google Scholar 
    Duan, Y. Q. & Xu, S. L. Command-based environmental regulation and heavy polluters’ investment: incentive or disincentive? A quasi-Natural experiment based on the new environmental protection law. J. Financ. Dev. Res. 07, 54–61 (2021) (in Chinese).
    Google Scholar 
    Cai, W. & Xu, F. The impact of the new environmental protection law on eco-innovation: Evidence from green patent data of Chinese listed companies. Environ. Sci. Pollut. Res. 29(7), 10047–10062 (2022).Article 

    Google Scholar 
    Ning, Y. et al. Energy conservation and emission reduction path selection in China: A simulation based on bi-level multi-objective optimization model. Energy Policy 137, 111116 (2020).Article 

    Google Scholar 
    Hughes, S., Giest, S. & Tozer, L. Accountability and data-driven urban climate governance. Nat. Clim. Change 10(12), 1085–1090 (2020).ADS 
    Article 

    Google Scholar 
    Feng, L., Chen, Z. & Chen, H. Does the central environmental protection inspectorate accountability system improve environmental quality?. Sustainability 14(11), 6575 (2022).Article 

    Google Scholar 
    Ulucak, R. How do environmental technologies affect green growth? Evidence from BRICS economies. Sci. Total Environ. 712, 136504 (2020).ADS 
    PubMed 
    Article 

    Google Scholar 
    Shahbaz, M., Raghutla, C., Chittedi, K. R., Jiao, Z. & Vo, X. V. The effect of renewable energy consumption on economic growth: Evidence from the renewable energy country attractive index. Energy 207, 118162 (2020).Article 

    Google Scholar 
    Yang, Y. & Niu, X. Impact of the new “Environmental Protection Law” on the efficiency of listed companies in heavily polluting industries in China: Based on the research perspective of “Porter Hypothesis”. Manag. Rev. 33(10), 55–69 (2021).
    Google Scholar 
    Wong, C. W., Wong, C. Y., Boon-Itt, S. & Tang, A. K. Strategies for building environmental transparency and accountability. Sustainability 13(16), 9116 (2021).Article 

    Google Scholar 
    Liu, Z. et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524(7565), 335–338 (2015).ADS 
    PubMed 
    Article 

    Google Scholar 
    Litman, T. Comprehensive evaluation of energy conservation and emission reduction policies. Transp. Res. Part A Policy Pract. 47, 153–166 (2013).Article 

    Google Scholar 
    Zhou, P., Ang, B. W. & Han, J. Y. Total factor carbon emission performance: A Malmquist index analysis. Energy Econ. 32(1), 194–201 (2010).Article 

    Google Scholar 
    Steg, L. Promoting household energy conservation. Energy Policy 36(12), 4449–4453 (2008).Article 

    Google Scholar 
    Yang, Q. & Liu, H. J. Regional difference decomposition and influence factors of China’s carbon dioxide emissions. J. Quant. Tech. Econ. 29(05), 36–49 (2012) (in Chinese).
    Google Scholar 
    Yao, L. J. & Sun, C. Y. Italy’s low carbon economic development policy. Sci. Technol. Ind. China 11, 58–60 (2007) (in Chinese).
    Google Scholar 
    Li, L. et al. Energy conservation and emission reduction policies for the electric power industry in China. Energy Policy 39(6), 3669–3679 (2011).Article 

    Google Scholar 
    Dong, F. et al. Drivers of carbon emission intensity change in China. Resour. Conserv. Recycl. 129, 187–201 (2018).Article 

    Google Scholar 
    Li, X., Hu, Z., Cao, J. & Xu, X. The impact of environmental accountability on air pollution: A public attention perspective. Energy Policy 161, 112733 (2022).Article 

    Google Scholar 
    Ehrlich, P. R. & Holdren, J. P. Impact of Population Growth: Complacency concerning this component of man’s predicament is unjustified and counterproductive. Science 171(3977), 1212–1217 (1971).ADS 
    PubMed 
    Article 

    Google Scholar 
    York, R., Rosa, E. A. & Dietz, T. STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecol. Econ. 46(3), 351–365 (2003).Article 

    Google Scholar 
    Shao, S., Yang, L. L. & Cao, J. H. Study on influencing of CO2 emissions from industrial energy consumption: An empirical analysis based on STIRPAT model and industrial sectors’ dynamic panel data in Shanghai. J. Finance Econ. 36(11), 16–27 (2010) (in Chinese).
    Google Scholar 
    Tseng, S. W. Analysis of energy-related carbon emissions in Inner Mongolia, China. Sustainability 11(24), 7008 (2019).Article 

    Google Scholar 
    Lin, B. & Ouyang, X. Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry. Energy 68, 688–697 (2014).Article 

    Google Scholar 
    Wang, D., He, W. & Shi, R. How to achieve the dual-control targets of China’s CO2 emission reduction in 2030? Future trends and prospective decomposition. J. Clean. Prod. 213, 1251–1263 (2019).Article 

    Google Scholar 
    Card, D., & Krueger, A. B. Minimum wages and employment: A case study of the fast food industry in New Jersey and Pennsylvania (1993).Abadie, A. & Gardeazabal, J. The economic costs of conflict: A case study of the Basque Country. Am. Econ. Rev. 93(1), 113–132 (2003).Article 

    Google Scholar 
    Kaul, A., Klößner, S., Pfeifer, G. & Schieler, M. Standard synthetic control methods: The case of using all preintervention outcomes together with covariates. J. Bus. Econ. Stat. 40(3), 1362–1376 (2022).MathSciNet 
    Article 

    Google Scholar 
    Lin, B. Q. & Li, J. L. Transformation of China’s energy structure under environmental governance constraints: A peak value analysis of coal and carbon dioxide. Soc. Sci. China 09, 84–107 (2015) (in Chinese).
    Google Scholar 
    Long, X., Naminse, E. Y., Du, J. & Zhuang, J. Nonrenewable energy, renewable energy, carbon dioxide emissions and economic growth in China from 1952 to 2012. Renew. Sustain. Energy Rev. 52, 680–688 (2015).Article 

    Google Scholar 
    Zhang, W., Zhu, Q. G. & Gao, H. Upgrading of industrial structure, optimizing of energy structure, and low carbon development of industrial system. Econ. Res. J. 51(12), 62–75 (2016) (in Chinese).
    Google Scholar 
    Wen, Z., Zhang, L., Hou, J. & Liu, H. Mediating effect test procedure and it application. Acta Psychol. Sin. 36(5), 614–620 (2004).
    Google Scholar 
    He, Y., Yu, W. L. & Yang, M. Z. CEOs with rich career experience, corporate risk-taking and the value of enterprises. China Ind. Econ. 09, 155–173 (2019) (in Chinese).
    Google Scholar 
    Zhou, D. Q., Wang, Q., Su, B., Zhou, P. & Yao, L. X. Industrial energy conservation and emission reduction performance in China: A city-level nonparametric analysis. Appl. Energy 166, 201–209 (2016).Article 

    Google Scholar 
    Waheed, R., Sarwar, S. & Wei, C. The survey of economic growth, energy consumption and carbon emission. Energy Rep. 5, 1103–1115 (2019).Article 

    Google Scholar 
    Yang, Y., Zhou, Y., Poon, J. & He, Z. China’s carbon dioxide emission and driving factors: A spatial analysis. J. Clean. Prod. 211, 640–651 (2019).Article 

    Google Scholar 
    Apergis, N. & Payne, J. E. Coal consumption and economic growth: Evidence from a panel of OECD countries. Energy Policy 38(3), 1353–1359 (2010).Article 

    Google Scholar 
    Mujtaba, A., Jena, P. K., Bekun, F. V. & Sahu, P. K. Symmetric and asymmetric impact of economic growth, capital formation, renewable and non-renewable energy consumption on environment in OECD countries. Renew. Sustain. Energy Rev. 160, 112300 (2022).Article 

    Google Scholar 
    Wolde-Rufael, Y. Coal consumption and economic growth revisited. Appl. Energy 87(1), 160–167 (2010).Article 

    Google Scholar  More

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    Ursids evolved early and continuously to be low-protein macronutrient omnivores

    The giant panda’s preference for culm over leaves occurred even though leaves had far more protein than did culm, which is inconsistent with the suggestion that giant pandas are high protein carnivores1. The giant panda’s preference for culm over leaves in the spring was likely driven by the increased availability of mono- and polysaccharides in culm relative to leaves31. This preference by giant pandas for a high-carbohydrate, low protein diet is similar to the brown bear’s preference for carbohydrate-rich but protein-poor berries or apples over protein- and energy-rich salmon, although both needed to be consumed to produce the most efficient diet2,10. The preference for culm over leaves created a protein ME in the diet of giant pandas from January to March (~ 20%) when digestible carbohydrates were most plentiful and for the entire year (27 ± 10%) that was comparable to the macronutrient proportions in giant panda milk and the milk and diets selected by other ursids (Table 1, Fig. 3) that minimize energy expenditure and maximize the efficiency of gain3.Table 1 The protein and fat metabolizable energy concentrations (%) in ursid milks and in the diets selected by brown bears, polar bears, and sloth bears when given ad libitum access to foods rich in protein, fat, and digestible carbohydrates (PFC) or protein and fat only (PF)1,3,4,29,32,40,54,55.Full size tableRelative to the suggestion that giant pandas are not well adapted to consuming the more omnivorous macronutrient proportions characteristic of the diets of other ursids1, captive giant pandas are often fed various combinations of bamboo and high-carbohydrate supplements that include rice, baby cereal, bread, beans, wheat, millet, apples, carrots, ground corn, sorghum, sugar cane, and sugar in addition to milk, eggs, vegetables, and various meats5,32,33. The dry matter of giant panda diets in five Chinese zoos in which successful reproduction occurred (i.e., Beijing Zoo, Chengdu Zoo, China Conservation and Research Center, Fuzhou Zoo, and Xian Zoo) averaged 11.6 ± 2.4% protein, 39.0 ± 13.6% neutral detergent fiber (NDF) or cell wall, 5.0 ± 2.0% fat, and 5.4 ± 0.6% ash32. If we estimate soluble carbohydrates as 100 – (NDF + protein + fat + ash)3, the soluble carbohydrate content was 39.0 ± 11.2%. This approach likely underestimates digestible carbohydrates in that it assumes a zero digestibility for the hemicellulose fraction of the NDF. However, even with these assumptions, the average macronutrient ME distribution was 19 ± 4% protein, 18 ± 7% fat, and 63 ± 18% carbohydrate, or again a low-protein macronutrient ratio typical of the other ursid diets (Table 1).Several errors may have been made in the previous giant panda study1 that likely influenced their conclusion. These included initially air-drying their bamboo samples in a dark room prior to laboratory drying and analyses34. When plants are cut and allowed to dry slowly, soluble carbohydrates are lost as they are metabolized to carbon dioxide, water, and energy until death of the plant cells35,36. The loss of soluble carbohydrates increases when drying occurs slowly, as would occur with air-drying in a dark room. Protein also may be metabolized, but the nitrogen remains and is only converted to different nitrogen-containing compounds, such as amides, free amino acids and peptides that would be part of a crude protein estimate36.Thus, if there are significant amounts of soluble carbohydrates in fresh bamboo, air-drying of bamboo samples will lead to an underestimate of the importance of carbohydrates and thereby an overestimate of the importance of protein. Indeed, starch accounted for 16 ± 11% of the digestible macronutrients and 23 ± 13% of the digestible carbohydrates in bamboo during the current study. Also, the previous study1 assumed a hemicellulose digestibility of 22%37, which significantly underestimated that found in our digestion studies (46 ± 9%).Another potential error in the previous study1 was in using a concept they termed “relative efficiencies” of macronutrient absorption in which the macronutrient profiles of bamboo were directly compared to that of giant panda feces. Such a comparison is often meaningless without knowing the amounts of food consumed and feces produced because the proportions of macronutrients in the feces reflect the extraordinarily complex interaction between the variable absorption of digestible products, passage of indigestible components, and excretion of metabolic products. Thus, only by providing data showing a close linkage between relative efficiencies and digestibility or measuring digestibility as we did can one be certain of estimating the relative importance of macronutrients.The macronutrient intake of wild sloth bears has not been measured, although the dietary proportions and energy content of termites, ants, and fruits have been estimated17. Soldiers and worker termites and ants are generally low in fat and high in protein (excluding the nitrogen in their chitin exoskeleton), whereas alate and alate nymphs (winged reproductive termites) can be very low in protein and high in fat (i.e.,  > 50% fat)38. Joshi et al.17 surmised that sloth bears consumed primarily termite eggs and defending soldiers based on the residues in bear feces and the absence of eggs and soldiers at termite mounds after sloth bear feeding bouts. Although not measured, the dry matter of termite eggs is likely high in both protein and fat, which would create a high fat ME because of the much greater energy content of fat than protein39. The high fruit diet of the summer will be low in protein and fat and high in carbohydrates if not supplemented with other fat-rich foods (e.g., grubs or insect larvae)17. Thus, depending on season and which stage of the ant and termite life cycle the bears consume, wild sloth bears could be consuming either high or low-protein or fat diets.The preference for fat that we observed differs markedly from current zoo diets. Zoo diets can be classified into two macronutrient types: 1) high carbohydrate, low protein, low fat diets that use grains, often in cooked porridges or soups, with fruits and vegetables or 2) diets having more modest or intermediate levels of protein, fat, and carbohydrates that include dog food, bear chows, or omnivore dry or canned products supplemented with fruits and vegetables (Fig. 3). Examples of the first type of diet are more common in Germany [e.g., Leipzig Zoo (ME protein 11%, fat 5%, and carbohydrate 84%)] and the various bear rescue centers in India [e.g., Bannerghatta Bear Rescue Centre (ME protein 10%, fat 9%, and carbohydrate 81%)]. Examples of the second type of diet are more common in US and other European zoos and have more protein and fat than the high grain diets but are much lower in fat than what bears selected in the current study22 (Fig. 3). Nevertheless, bears consuming all past and current zoo diets are prone to developing hepatobiliary cancer and inflammatory bowel disease.If these problems are dietary in origin and not due to something unique to feeding on termites and ants (e.g., development of a unique gastrointestinal microbiome or consumption of formic acid in ants or chitin in both ants and termites), there are two broad types of diets not fed in captivity (i.e., high protein diets and high fat diets) (Fig. 3). In evaluating if either one of those might be more suitable for sloth bears, the protein ME ratios of ursid milks and the diets voluntarily selected by brown bears, polar bears, giant pandas, and sloth bears are low and do not differ from each other (t(3) = 2.449, p = 0.092), which minimizes maintenance energy requirements and maximizes the efficiency of gain1,3,4,29,40 (Table 1). Additionally, brown bears and sloth bears prefer high fat, low carbohydrate diets when given a choice between foods rich in either carbohydrates or fats3 (Table 1, Fig. 3). This fat preference in the adult ursid diet is virtually identical to that occurring in ursid milks (t(2) = -0.726, p = 0.543) even though omnivorous ursids likely have a strong preference for sweet flavors41.While an understanding of the link between dietary macronutrient content and biliary cancer is lacking, we hypothesize that bears, such as polar bears and apparently sloth bears that prefer or evolved to consume high-fat diets, have high resting rates of bile production. Consequently, when sloth bears consume a high-carbohydrate, low-fat diet long term, bile is not secreted into the digestive tract as fast as it is being produced and may back up in the bile ducts, cause bile duct dilation and inflammation, and ultimately biliary cancer. An example of this process is a rare congenital disease in humans and other animals known as choledochal cyst disease. Sacs or outpocketings may develop along the bile ducts in this disease. Bile sitting in those sacs or in the bile ducts causes inflammation of the duct walls and, if not treated by surgical excision, biliary cancer42.If we assume the macronutrient characteristics of ursid milks and the preferences for low protein, low carbohydrate, high fat diets exhibited by brown bears, polar bears, and sloth bears are healthy, current and past sloth bear zoo diets have provided too little fat, too much digestible carbohydrate, and often too much protein (Fig. 3). While this mismatch between the diets fed in captivity and what sloth bears prefer might explain the high incidence of hepatobiliary cancer, inflammatory bowel disease, and poor reproduction world-wide, we cannot dismiss the possibility that the bears’ preference for avocados and fat and the avoidance of apples, baked yams, and digestible carbohydrates in the current study has nothing to do with their macronutrient content and would be unhealthy long-term. Thus, additional feeding studies are needed to determine if a high fat, low protein, low carbohydrate diet might be the key to improving the health, reproduction, and longevity of captive sloth bears.Finally, the selection of lower protein diets by giant pandas, polar bears, sloth bears, and brown bears and the often low-protein omnivorous diets of the other four ursids indicate that all ursids can modulate liver catabolic enzyme activity when needed to conserve protein. This would suggest that this ability to conserve protein occurred early in the evolution of ursids from a high protein carnivore ancestor and may have been critical to the spread of ursids world-wide by opening niches that could not be filled by another high protein carnivore. While all ursids at times may consume foods with a much higher protein content than that of a low protein omnivore, that selection process can only be evaluated relative to the other available dietary choices interacting with foraging and metabolic constraints and does not indicate their preferred diet is that of a high protein carnivore2,43,44. More

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    Seed germination ecology of hood canarygrass (Phalaris paradoxa L.) and herbicide options for its control

    Effects of light intensity and temperatureThe germination of P. paradoxa (91 to 95%) and wheat (93 to 97%) was not affected by light intensity (data not shown). Our results conform to previous studies which revealed that light intensity had little role in influencing P. paradoxa germination24.The germination of wheat and P. paradoxa was influenced by temperature regimes (Fig. 1). At temperature regimes of 15/5 °C and 20/10 °C, germination of wheat and P. paradoxa did not vary. Seed germination in wheat remained similar at temperatures ranging between 15/5 °C to 30/20 °C. However, in P. paradoxa, germination was reduced at higher temperature regimes (35/25 C) compared with lower temperature regimes (15/5 °C to 25/15 °C). At the highest temperature regime (35/25 °C), the germination of wheat was 79%, while, at this temperature regime, the germination of P. paradoxa was only 1%. This suggests that wheat can germinate at high-temperature ranges, while, germination of P. paradoxa may be reduced at high temperatures (35/25 °C). These results implied that at the time of planting wheat in Australia if the air temperature is low, the chances of emergence of P. paradoxa are very high. This suggests that efforts should be made towards early control of P. paradoxa in wheat if the air temperature in the winter season falls early. These results also suggest that early planting of wheat could reduce the emergence of P. paradoxa as the prevailing temperature conditions are relatively high in early planting (e.g., end of April). In the Indo-Gangetic Plains, better control of P. minor was observed in the early planting of wheat (high-temperature conditions) due to less emergence of P. minor25.Figure 1Effect of alternating day/night temperatures (15/5 to 35/25 °C) on germination of Phalaris paradoxa and wheat seeds (incubated for 21 d) under light/dark (12-h photoperiod). LSD: Least significant difference at the 5% level of significance.Full size imagePrevious studies have also revealed that germination of P. paradoxa was highest at 10 °C and then failed to germinate at 30 °C 24,26, however, these studies were conducted at constant temperatures and the germination response of P. paradoxa was not studied in comparison with wheat in those studies.Effect of radiant heatThe germination of P. paradoxa seeds that were stored at room temperature (25 °C) was 97%, which reduced to 88% after exposure to the 100 °C pretreatment for 5 min and became nil at 150 °C (Fig. 2). About 88% of P. paradoxa at 100 °C suggests that it can tolerate heat stress for short periods.Figure 2Effect of high-temperature pretreatment for 5 min (℃) on germination of Phalaris paradoxa seeds. LSD: Least significant difference at the 5% level of significance.Full size imageGermination was nil at 150 °C and above, suggesting that burning could help in managing P. paradoxa, particularly in a no-till field where seeds are on the soil surface or at shallow depths. Exposure of seeds to fire could inhibit germination by desiccating the seed coat or by damaging the embryo27,28,29.Burning of residue in the fields could kill weed seeds and other pests in the topsoil layer30. Windrow burning proved to be an effective tool for killing weed seeds in paddocks31. However, the crop residue burning may cause environmental destruction by killing microbes and polluting the air. Also, it reduces the amount of soil organic matter due to the high heat, causing soil degradation. Therefore, these aspects should also be considered while formulating weed management strategies through crop residue burning. Burning may also release the dormancy of other weed seeds present in the subsoil and thus may increase infestation; therefore, this technique should be used cautiously32,33.Effect of osmotic stressGermination of P. paradoxa was highest (95%) in the control treatment and germination reduced to 75% at an osmotic potential of −0.8 MPa, and became nil at −1.6 MPa (Fig. 3). However, in wheat, germination did not reduce with an increase in water potential and it was 94% in the control treatment.Figure 3Effect of osmotic potential on germination of Phalaris paradoxa and wheat seeds at alternating day/night temperatures of 20/10 °C under 12 h photoperiod. Seeds were incubated for 21 d. LSD: Least significant difference at the 5% level of significance.Full size imageAt a very high concentration of PEG, the metabolic activity of P. paradoxa might be reduced due to water stress. Seed germination is affected when seeds are not able to get critical moisture threshold levels for imbibitions34,35. These results indicate that high water stress may inhibit the seed germination of P. paradoxa. However, under no water stress or mild water stress conditions, P. paradoxa may infest the wheat crop.Contrary to these results, previous studies reported that germination of P. paradoxa was reduced by 90% at an osmotic potential of −0.25 MPa25. Good germination of wheat at high osmotic potential indicates that the wheat variety used in this study may have water stress tolerance traits for germination. It was observed that wheat could germinate well (75%) at a high-water stress level (−1.6 MPa)36. This suggests that it is possible to menace P. paradoxa by growing stress-tolerant varieties of wheat and manipulating irrigation. In a previous study, less infestation of P. paradoxa was observed in drip-irrigated wheat crops due to optimal soil moisture conditions for the crop37.Effect of salt stressGermination of P. paradoxa was highest (93%) in the control treatment, and at a NaCl of 150 mM, germination was reduced to 76% (Fig. 4). Similarly, in wheat, germination was highest (94%) in the control treatment and at a salt concentration of 150 and 200 mM, germination was reduced to 84 and 79%, respectively. These results suggest that at a high salt concentration, P. paradoxa may infest the wheat crop owing to its ability to germinate under high salt concentrations.Figure 4Effect of sodium chloride concentration on germination of Phalaris paradoxa and wheat seeds at alternating day/night temperatures of 20/10 °C under 12 h photoperiod. Seeds were incubated for 21 d. LSD: Least significant difference at the 5% level of significance.Full size imageContrary to this, in Iran, it was observed that germination of P. paradoxa was reduced by 70% at a NaCl of 160 mM24. Most of the Australian soils are saline; therefore, it is quite possible that P. paradoxa in Australia might have developed traits for salt tolerance38. The variable response of populations of P. paradoxa to salt concentrations in Iran and Australia might be due to genetic differences between the P. paradoxa populations38. These observations suggest that P. paradoxa could invade the agroecosystem under the saline conditions of Australia.Effect of seed burial depth on emergenceGermination of P. paradoxa was very low (10%) on the soil surface, and seedling emergence was highest (74%) at a soil burial depth of 0.5 cm (Fig. 5). Seedling emergence was similar when seeds were buried in the soil at a depth ranging from 0.5 to 4 cm. Seedling emergence was 32% at a burial depth of 8 cm.Figure 5Effect of seed burial depth on seedling emergence of Phalaris paradoxa. LSD: Least significant difference at the 5% level of significance.Full size imageThe results from this experiment suggest that a no-till production system may inhibit the germination of P. paradoxa. This study also suggests that deep tillage ( > 4 cm) could reduce the emergence of P. paradoxa to some extent; therefore, inversion tillage could be a weed management strategy if the seedbank is in the shallow layer of the soil. It has been reported that the emergence of small-seeded weeds is reduced from deeper burial depths, as the soil-gas exchange is limited 21. However, it is important to know the seed longevity of this weed in different soil and environmental conditions when considering tillage operations39.Likewise, previous studies also reported that seed germination of P. paradoxa was lowest on the soil surface and no seedlings emerged from a soil depth of 10-cm2,40. Contrary to this in Iran, germination of P. paradoxa was found to be  > 65% on the soil surface 24.Evaluation of PRE-herbicidesResults revealed that cinmethylin, pyroxasulfone, and trifluralin provided 100% control of P. paradoxa. Atrazine, bixlozone, imazethapyr, isoxaflutole, prosulfocarb + s-metolachlor, and s-metolachlor were not found to be effective against P. paradoxa (Table 1). Pendimethalin and triallate controlled P. paradoxa by 80 and 42%, respectively, compared with the nontreated control.Table 1 Effect of PRE herbicides on the survival of Phalaris paradoxa and wheat seedlings (28 d after spray).Full size tableIn wheat, all tested herbicides performed similarly for plant survival except dimethenamid-P and prosulfocarb + s-metolachlor, which caused wheat mortality by 41 and 16%, respectively, compared with the nontreated control. These results suggest that pyroxasulfone, pendimethalin, and trifluralin can be successfully used for the management of P. paradoxa in wheat. Alternative use of these herbicides in wheat crops could provide sustainable weed control of P. paradoxa. In previous studies conducted in Australia, herbicides namely cinmethylin, pyroxasulfone, and trifluralin were found safe for wheat and provided excellent grass weed control41.Efficacy of PRE-herbicides in relation to crop residue coverCinmethylin, pendimethalin, and pyroxasulfone were proven to be very effective against P. paradoxa under no residue cover conditions (Table 2). However, at the residue cover of 6 t ha-1 (high output systems), the efficacy of these herbicides decreased and these three herbicides failed to provide effective control of P. paradoxa. At the residue cover of 2 t ha-1 (low output systems), the efficacy of pyroxasulfone in controlling P. paradoxa was not affected; however, cinmethylin and pendimethalin at the residue load of 2 t ha-1 did not control P. paradoxa. These results suggest that in a residue-retained, no-till system, pyroxasulfone could provide better control of P. paradoxa compared with cinmethylin and pendimethalin.Table 2 The interaction of PRE herbicides and wheat residue amount on the survival of Phalaris paradoxa seedlings at 28 d after spray.Full size tableThe crop residue binds some herbicides, which results in a reduced dose to target weeds and provides poor weed control42. A crop residue cover of 1 t ha-1 may prevent 50% of the herbicide from reaching the target weed seeds in the soil and thus provide poor weed control43.Efficacy of POST herbicides in relation to plant sizeWhen plants were sprayed at the 4-leaf stage, the herbicides clodinafop and propaquizafop were not effective against P. paradoxa compared with the other tested herbicides (Table 3). The efficacy of clethodim, glyphosate, haloxyfop, and paraquat in controlling P. paradoxa was not decreased even when plants were sprayed at the 10-leaf stage. In previous studies, poor control of P. paradoxa was observed with ACCase-inhibiting herbicides44,45. These results also suggest that under noncropped or fallow situations, early and late cohorts of P. paradoxa can be controlled successfully by delaying applications of clethodim, paraquat, haloxyfop, and glyphosate.Table 3 The interaction effect of plant size (large plants-10 leaves and small plants-4 leaves) and herbicide treatments on the survival of Phalaris paradoxa seedlings at 28 d after spray.Full size tableGermination of P. paradoxa at 25/15 °C (day/night) was lower compared with 20/10 °C. This suggests that early sowing of wheat (relatively high-temperature conditions) could reduce the emergence of P. paradoxa in fields. Phalaris paradoxa did not germinate after exposure to radiant heat of 150 °C (for 5 min), which suggests that burning may be a useful tool for managing P. paradoxa, particularly when seeds are on the soil surface or at the shallow surface. A high level of tolerance of P. paradoxa to water and salt stress was observed. These observations suggest that this weed can dominate under saline and water stress conditions in Australia. Low germination of P. paradoxa was observed on the soil surface, suggesting that a no-till system could provide better control of P. paradoxa. PRE herbicides cinmethylin, pyroxasulfone, pendimethalin, and trifluralin were effective for control of P. paradoxa in wheat; however, under a conservation tillage system, pyroxasulfone provided better control of P. paradoxa compared with other herbicides. Haloxyfop and clethodim were the most effective herbicides among the ACCase-inhibiting herbicides. Under noncropped or fallow land situations, larger plants of P. paradoxa can be successfully controlled with the application of clethodim, glyphosate, and paraquat. More