More stories

  • in

    Empirical leucine-to-carbon conversion factors in north-eastern Atlantic waters (50–2000 m) shaped by bacterial community composition and optical signature of DOM

    1.Yamada, N., Fukuda, H., Ogawa, H., Saito, H. & Suzumura, M. Heterotrophic bacterial production and extracellular enzymatic activity in sinking particulate matter in the western North Pacific Ocean. Front. Microbiol. 3, 379. https://doi.org/10.3389/fmicb.2012.00379 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.del Giorgio, P., Cole, J. & Cimbleris, A. Respiration rates in bacteria exceed phytoplankton production in unproductive aquatic systems. Nature 385, 148–151 (1997).ADS 

    Google Scholar 
    3.Teira, E. et al. Sample dilution and bacterial community composition influence empirical leucine-to-carbon conversion factors in surface waters of the world’s oceans. Appl. Environ. Microbiol. 81, 8224–8232 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Dobal-Amador, V. et al. Vertical stratification of bacterial communities driven by multiple environmental factors in the waters (0–5000 m) off the Galician coast (NW Iberian margin). Deep-Sea Res. I(114), 1–11 (2016).
    Google Scholar 
    5.Kirchman, D., Ducklow, H. W. & Mitchell, R. Estimates of bacterial growth from changes in uptake rates and biomass. Appl. Environ. Microbiol. 44, 1296–1307 (1982).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Simon, M. & Azam, F. Protein content and protein synthesis rates of planktonic marine bacteria. Mar. Ecol. Prog. Ser. 51, 201–213 (1989).ADS 
    CAS 

    Google Scholar 
    7.Ducklow, H. Bacterial production and biomass in the ocean. In Microbial Ecology of the Oceans (ed. Kirchman, D.) 85–120 (Wiley, 2000).
    Google Scholar 
    8.Varela, M. M., Bode, A., Morán, X. A. G. & Valencia, J. Dissolved organic nitrogen (DON) release and bacterial activity in the upper layers of the Atlantic Ocean. Microb. Ecol. 51, 487–500 (2006).CAS 
    PubMed 

    Google Scholar 
    9.Calvo-Díaz, A. & Morán, X. A. G. Empirical leucine-to-carbon conversion factors for estimating heterotrophic bacterial production: Seasonality and predictability in a temperate coastal ecosystem. Appl. Environ. Microbiol. 75, 3216–3221 (2009).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Alonso-Sáez, L., Pinhassi, J., Pernthaler, J. & Gasol, J. M. Leucine-to-carbon empirical conversion factor experiments: Does bacterial community structure have an influence?. Environ. Microbiol. 12, 2988–2997 (2010).PubMed 

    Google Scholar 
    11.Gasol, J. M. et al. Mesopelagic prokaryotic bulk and single-cell heterotrophic activity and community composition in the NW Africa-Canary Islands coastal-transition zone. Prog. Oceanogr. 83, 189–196 (2009).ADS 

    Google Scholar 
    12.Baltar, F., Aristegui, J., Gasol, J. M. & Herndl, G. Prokaryotic carbon utilization in the dark ocean: Growth efficiency, leucine-to-carbon conversion factors, and their relation. Aquat. Microb. Ecol. 60, 227–232 (2010).
    Google Scholar 
    13.Varela, M. M., Rodríguez-Ramos, T., Guerrero-Feijóo, E. & Nieto-Cid, M. Changes in activity and community composition shape bacterial responses to size-fractionated marine DOM. Front. Microbiol. 11, 586148. https://doi.org/10.3389/fmicb.2020.586148 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Sarmento, H., Morana, C. & Gasol, J. M. Bacterioplankton niche partitioning in the use of phytoplankton-derived dissolved organic carbon: Quantity is more important than quality. ISME J. 10, 2582–2592 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Guerrero-Feijóo, E. et al. Optical properties of dissolved organic matter relate to different depth-specific patterns of archaeal and bacterial community structure in the North Atlantic Ocean. FEMS Microbiol. Ecol. 93, 1–14 (2017).
    Google Scholar 
    16.Helms, J. R. et al. Absorption spectral slopes and slope ratios as indicators of molecular weight, source and photobleaching of chromophoric dissolved organic matter. Limnol. Oceanogr. 51, 2170–2180 (2008).
    Google Scholar 
    17.Stedmon, C. A. & Nelson, N. B. The optical properties of DOM in the ocean. In Biogeochemistry of Marine Dissolved Organic Matter (eds Hansell, D. A. & Carlson, C. A.) 481–508 (Academic Press, 2015).
    Google Scholar 
    18.Nieto-Cid, M., Álvarez-Salgado, X. A. & Pérez, F. F. Microbial and photochemical reactivity of fluorescent dissolved organic matter in a coastal upwelling system. Limnol. Oceanogr. 51, 1391–1400 (2006).ADS 
    CAS 

    Google Scholar 
    19.Rodríguez-Ramos, T., Nieto-Cid, M., Auladell, A., Guerrero-Feijóo, E. & Varela, M. M. Vertical niche partitioning of archaea and bacteria linked to shifts in dissolved organic matter quality and hydrography in North Atlantic waters. Front. Mar. Sci. 8, 673171. https://doi.org/10.3389/fmars.2021.673171 (2021).Article 

    Google Scholar 
    20.Bode, A., Álvarez-Osorio, M. T., Cabanas, J. M., Miranda, A. & Varela, M. Recent trends in plankton and upwelling intensity off Galicia (NW Spain). Prog. Oceanogr. 83, 342–350 (2009).ADS 

    Google Scholar 
    21.Teira, E. et al. Plankton carbon budget in a coastal wind-driven upwelling station off A Coruña (NW Iberian Peninsula). Mar. Ecol. Prog. Ser. 265, 31–43 (2003).ADS 
    CAS 

    Google Scholar 
    22.Ruiz-Villarreal, M. et al. Oceanographic conditions in North and Northwest Iberia and their influence on the Prestige oil spill. Mar. Pollut. Bull. 53, 220–238 (2006).CAS 
    PubMed 

    Google Scholar 
    23.Lavin, A. et al. The Bay of Biscay: The encountering of the ocean and the shelf. In The Sea, Volume 14B: The Global Coastal Ocean (eds Robinson, A. & Brink, K.) 993–1001 (Harvard University Press, 2006).
    Google Scholar 
    24.Pedrós-Alió, C., Calderón-Paz, J. I., Guixa-Boixereu, N., Estrada, M. & Gasol, J. M. Bacterioplankton and phytoplankton biomass and production during summer stratification in the northwestern Mediterranean Sea. Deep-Sea Res. I(46), 985–1019 (1999).
    Google Scholar 
    25.Barbosa, A. B. et al. Short-term variability of heterotrophic bacterioplankton during upwelling off the NW Iberian margin. Prog. Oceanogr. 51, 339–359 (2001).ADS 

    Google Scholar 
    26.Morán, X. A., Gasol, J. M., Pedrós-Alió, C. & Estrada, M. Partitioning of phytoplankton organic carbon production and bacterial production along a coastal-offshore gradient in the NE Atlantic during different hydrographic regimes. Aquat. Microb. Ecol. 29, 239–252 (2002).
    Google Scholar 
    27.Martínez-García, S. et al. Differential responses of phytoplankton and heterotrophic bacteria to organic and inorganic nutrient additions in coastal waters off the NW Iberian Peninsula. Mar. Ecol. Prog. Ser. 416, 17–33 (2010).ADS 

    Google Scholar 
    28.Li, X., Xu, J., Shi, Z., Li, Q. & Li, R. Variability in the empirical leucine-to-carbon conversion factors along an environmental gradient. Acta Oceanol. Sin. 37, 77–82 (2018).
    Google Scholar 
    29.Doval, M. D., Nogueira, E. & Pérez, F. F. Spatio-temporal variability of the thermohaline and biogeochemical properties and dissolved organic carbon in a coastal embayment affected by upwelling: The Ría de Vigo (NW Spain). J. Mar. Sys. 14, 135–150 (1998).
    Google Scholar 
    30.Valencia, J. et al. Variations in planktonic bacterial biomass and production, and phytoplankton blooms off A Coruña (NW Spain). Sci. Mar. 67, 143–157 (2003).
    Google Scholar 
    31.Bode, A., Álvarez-Osorio, M. T. & Varela, M. Phytoplankton and macrophyte contributions to littoral food webs in the Galician upwelling estimated from stable isotopes. Mar. Ecol. Prog. Ser. 318, 89–102 (2006).ADS 
    CAS 

    Google Scholar 
    32.Bode, A., Varela, M., Canle, M. & González, N. Dissolved and particulate organic nitrogen in shelf waters of northern Spain during spring. Mar. Ecol. Prog. Ser. 214, 43–54 (2001).ADS 
    CAS 

    Google Scholar 
    33.Lønborg, C. & Álvarez-Salgado, X. A. Tracing dissolved organic matter cycling in the eastern boundary of the temperate North Atlantic using absorption and fluorescence spectroscopy. Deep Res. Part I 85, 35–46 (2014).
    Google Scholar 
    34.Lønborg, C., Yokokawa, T., Herndl, G. & Álvarez-Salgado, X. A. Production and degradation of fluorescent dissolved organic matter in surface waters of the eastern North Atlantic Ocean. Deep Res. Part I(96), 28–37 (2015).
    Google Scholar 
    35.Lønborg, C., Davidson, K., Álvarez-Salgado, X. A. & Miller, A. E. J. Bioavailability 904 and bacterial degradation rates of dissolved organic matter in a temperate coastal area 905 during an annual cycle. Mar. Chem. 113, 219–226 (2009).
    Google Scholar 
    36.Teira, E. et al. Plankton carbon budget in a coastal wind-driven upwelling station off A Coruna (NW Iberian Peninsula). Mar. Ecol. Prog. Ser. 265, 31–43 (2003).ADS 
    CAS 

    Google Scholar 
    37.Álvarez-Salgado, X. A., Arístegui, J., Barton, E. D. & Hansell, D. A. Contribution of upwelling filaments to offshore carbon export in the subtropical Northeast Atlantic Ocean. Limnol. Oceanogr. 52, 1287–1292 (2007).ADS 

    Google Scholar 
    38.Álvarez-Salgado, X. A. et al. Off-shelf fluxes of labile materials by an upwelling filament in the NW Iberian Upwelling System. Prog. Oceanogr. 51, 321–337 (2001).ADS 

    Google Scholar 
    39.del Giorgio, P. A. et al. Coherent patterns in bacterial growth, growth efficiency, and leucine metabolism along a northeastern Pacific inshore-offshore transect. Limnol. Oceanogr. 56, 1–16 (2011).ADS 

    Google Scholar 
    40.Aristegui, J., Gasol, J. M., Duarte, C. M. & Herndl, G. J. Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr. 54, 1501–1529 (2009).ADS 
    CAS 

    Google Scholar 
    41.Herndl, G. J. & Reinthaler, T. Microbial control of the dark end of the biological pump. Nat. Geosci. 6, 718–724 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Landry, Z., Swan, B. K., Herndl, G. J., Stepanauskas, R. & Giovannoni, S. J. SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter. MBio 8, e00413-17. https://doi.org/10.1128/mBio.00413-17 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Catalá, T. et al. Dissolved Organic Matter (DOM) in the open Mediterranean Sea. I. Basin–wide distribution and drivers of chromophoric DOM. Prog. Oceanogr. 165, 35–51 (2018).ADS 

    Google Scholar 
    44.Bjørnsen, P. K. & Kuparinen, J. Determination of bacterioplankton biomass, net production and growth efficiency in the Southern Ocean. Mar. Ecol. Prog. Ser. 71, 185–194 (1991).ADS 

    Google Scholar 
    45.Weinbauer, M. G. et al. Synergistic and antagonistic effects of viral lysis and protistan grazing on bacterial biomass, production and diversity. Environ. Microbiol. 9(3), 777–788 (2007).CAS 
    PubMed 

    Google Scholar 
    46.Evans, C. et al. Shift from carbon flow through the microbial loop to the viral shunt in coastal Antarctic waters during austral summer. Microorganisms 9, 460 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Gasol, J. M., Zweifel, U., Peters, F., Fuhrman, J. D. & Hagström, H. Significance of size and nucleic acid content heterogeneity as measured by flow cytometry in Natural Planktonic Bacteria. Appl. Environ. Microbiol. 65, 104475–104483 (1999).ADS 

    Google Scholar 
    48.Calvo-Díaz, A. & Morán, X. A. G. Seasonal dynamics of picoplankton in shelf waters of the southern Bay of Biscay. Aquat. Microb. Ecol. 42, 159–174 (2006).
    Google Scholar 
    49.Norland, S. The relationship between biomass and volume of bacteria. In Handbook of Methods in Aquatic Microbial Ecology (eds Kemp, P. F. et al.) 303–307 (CRC Press, 1993).
    Google Scholar 
    50.Kirchman, D., Knees, E. & Hodson, R. Leucine incorporation and its potential as a measure of protein synthesis by bacteria in natural aquatic systems. Appl. Environ. Microbiol. 49, 599–607 (1985).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Smith, D. C. & Azam, F. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar. Microb. Food Webs 6, 107–114 (1992).
    Google Scholar 
    52.Massana, R. et al. Vertical distribution and phylogenetic characterization of marine planktonic Archaea in the Santa Barbara Channel. Appl. Environ. Microbiol. 63, 50–56 (1997).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Herlemann, D. P. et al. Transitions in bacterial communities along the 2000km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    55.R Core Team. R: A language and environment for statistical computing http://www.r-project.org (2018).56.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
    PubMed 

    Google Scholar 
    57.Huse, S. M., Welch, D. M., Morrison, H. G. & Sogin, M. L. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12, 1889–1898 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.4–6 http://www.CRAN.R-project.org/package=vegan (2018).59.Álvarez-Salgado, X. A. & Miller, A. E. J. Simultaneous determination of dissolved organic carbon and total dissolved nitrogen in seawater by high temperature catalytic oxidation: Conditions for precise shipboard measurements. Mar. Chem. 62, 325–333 (1998).
    Google Scholar 
    60.Green, S. A. & Blough, N. V. Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters. Limnol. Oceanogr. 29, 1903–1916 (1994).ADS 

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

    Google Scholar 
    62.Pearson, K. Notes on the history of correlation. Biometrika 13, 25–45 (1920).
    Google Scholar 
    63.Addinsoft XLSTAT statistical and data analysis solution http://www.xlstat.com (2020).64.Burnham, K. & Anderson, D. Information and Likelihood Theory: A Basis for Model Selection and Inference in Model Selection and Inference: A Practical Information-Theoretic Approach 60–64 (Springer, 2002).65.Clarke, K. Nonparametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).
    Google Scholar 
    66.Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience 8, giz107. https://doi.org/10.1093/gigascience/giz107 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Responses of functional traits in cavity-nesting birds to logging in subtropical and temperate forests of the Americas

    1.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    2.Chaudhary, A., Burivalova, Z., Koh, L. P. & Hellweg, S. Impact of forest management on species richness: global meta-analysis and economic trade-offs. Sci. Rep. 6, 23954 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Edwards, F. A., Edwards, D. P., Hamer, K. C. & Davies, R. G. Impacts of logging and conversion of rainforest to oil palm on the functional diversity of birds in Sundaland. Ibis 155, 313–326 (2013).
    Google Scholar 
    4.Bicknell, J. E., Struebig, M. J. & Davies, Z. G. Reconciling timber extraction with biodiversity conservation in tropical forests using reduced-impact logging. J. Appl. Ecol. 52, 379–388 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    5.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures: animal species diversity driven by habitat heterogeneity. J. Biogeogr. 31, 79–92 (2004).
    Google Scholar 
    6.Robles, H. et al. Sylvopastoral management and conservation of the middle spotted woodpecker at the south-western edge of its distribution range. For. Ecol. Manag. 242, 343–352 (2007).
    Google Scholar 
    7.Aleixo, A. Effects of selective logging on a bird community in the brazilian atlantic forest. Condor 101, 537–548 (1999).
    Google Scholar 
    8.Robles, H., Ciudad, C. & Matthysen, E. Tree-cavity occurrence, cavity occupation and reproductive performance of secondary cavity-nesting birds in oak forests: the role of traditional management practices. For. Ecol. Manag. 261, 1428–1435 (2011).
    Google Scholar 
    9.Burivalova, Z. et al. Avian responses to selective logging shaped by species traits and logging practices. Proc. R. Soc. B. 282, 20150164 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    10.Wiebe, K. L. Nest sites as limiting resources for cavity-nesting birds in mature forest ecosystems: a review of the evidence. J. Field Ornithol. 82, 239–248 (2011).
    Google Scholar 
    11.Politi, N., Hunter, M. & Rivera, L. Assessing the effects of selective logging on birds in Neotropical piedmont and cloud montane forests. Biodivers. Conserv. 21, 3131–3155 (2012).
    Google Scholar 
    12.Bergner, A. et al. Influences of forest type and habitat structure on bird assemblages of oak (Quercus spp.) and pine (Pinus spp.) stands in southwestern Turkey. For. Ecol. Manag. 336, 137–147 (2015).
    Google Scholar 
    13.van der Hoek, Y., Gaona, G. V. & Martin, K. The diversity, distribution and conservation status of the tree-cavity-nesting birds of the world. Divers. Distrib. 23, 1120–1131 (2017).
    Google Scholar 
    14.Aitken, K. E. H. & Martin, K. The importance of excavators in hole-nesting communities: availability and use of natural tree holes in old mixed forests of western Canada. J. Ornithol. 148, 425–434 (2007).
    Google Scholar 
    15.Cockle, K. L., Martin, K. & Wesołowski, T. Woodpeckers, decay, and the future of cavity-nesting vertebrate communities worldwide. Front. Ecol. Environ. 9, 377–382 (2011).
    Google Scholar 
    16.Schaaf, A. A. et al. Tree use, niche breadth and overlap for excavation by woodpeckers in subtropical piedmont forests of Northwestern Argentina. Acta Ornithol. 55 (2020).17.Sekercioglu, C. H. Effects of forestry practices on vegetation structure and bird community of Kibale National Park, Uganda. Biol. Conserv. 12 (2002).18.Stratford, J. A. & Robinson, W. D. Gulliver travels to the fragmented tropics: geographic variation in mechanisms of avian extinction. Front. Ecol. Environ. 3, 85–92 (2005).
    Google Scholar 
    19.Moore, R. P., Robinson, W. D., Lovette, I. J. & Robinson, T. R. Experimental evidence for extreme dispersal limitation in tropical forest birds. Ecol. Lett. 11, 960–968 (2008).CAS 
    PubMed 

    Google Scholar 
    20.Woltmann, S. Bird community responses to disturbance in a forestry concession in lowland Bolivia. 16.21.Strubbe, D. & Matthysen, E. Experimental evidence for nest-site competition between invasive ring-necked parakeets (Psittacula krameri) and native nuthatches (Sitta europaea). Biol. Conserv. 142, 1588–1594 (2009).
    Google Scholar 
    22.Rivera, L., Politi, N. & Bucher, E. H. Nesting habitat of the Tucuman Parrot Amazona tucumana in an old-growth cloud-forest of Argentina. Bird Conserv. Int. 22, 398–410 (2012).
    Google Scholar 
    23.Schaaf, A. A., Tallei, E., Politi, N. & Rivera, L. Cavity-tree use and frequency of response to playback by the Tropical Screech-Owl in northwestern Argentina. NBC 14, 99–107 (2019).
    Google Scholar 
    24.Schepps, J., Lohr, L. & Martin, T. E. Does tree hardness influence nest-tree selection by primary cavity nesters?. Auk 116, 658–665 (1999).
    Google Scholar 
    25.Rudolph, D. C., Conner, R. N. & Turner, J. Competition for red-cockaded woodpecker roost and nest cavities: effects of resin age and entrance diameter. Wilson Bull. 102(1), 23–36 (1990).
    Google Scholar 
    26.Drever, M. C. & Martin, K. Response of woodpeckers to changes in forest health and harvest: implications for conservation of avian biodiversity. For. Ecol. Manag. 259, 958–966 (2010).
    Google Scholar 
    27.Styring, A. R. & Hussin, M. Z. Effects of logging on woodpeckers in a Malaysian rain forest: the relationship between resource availability and woodpecker abundance. J. Trop. Ecol. 20, 495–504 (2004).
    Google Scholar 
    28.Ruggera, R. A., Schaaf, A. A., Vivanco, C. G., Politi, N. & Rivera, L. O. Exploring nest webs in more detail to improve forest management. For. Ecol. Manag. 372, 93–100 (2016).
    Google Scholar 
    29.Ibarra, J. T., Martin, M., Cockle, K. L. & Martin, K. Maintaining ecosystem resilience: functional responses of tree cavity nesters to logging in temperate forests of the Americas. Sci. Rep. 7, 4467 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Dı́az, S., Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evolut. 16, 646–655 (2001).31.Córdova-Tapia, F. & Zambrano, L. Functional diversity in community ecology. ECOS 24, 78–87 (2015).
    Google Scholar 
    32.Leaver, J., Mulvaney, J., Ehlers Smith, D. A., Ehlers Smith, Y. C. & Cherry, M. I. Response of bird functional diversity to forest product harvesting in the Eastern Cape, South Africa. For. Ecol. Manag. 445, 82–95 (2019).33.Georgiev, K. B. et al. Salvage logging changes the taxonomic, phylogenetic and functional successional trajectories of forest bird communities. J. Appl. Ecol. 57, 1103–1112 (2020).
    Google Scholar 
    34.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
    Google Scholar 
    35.Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity: experimental evolution in variable environments. J. Evolut. Biol. 15, 173–190 (2002).
    Google Scholar 
    36.Scherer-Lorenzen, M. Biodiversity and ecosystem functioning: basic principles. Struct. Funct. 10 (2005).37.Devictor, V., Julliard, R. & Jiguet, F. Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation. Oikos 117, 507–514 (2008).
    Google Scholar 
    38.Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 

    Google Scholar 
    39.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 

    Google Scholar 
    40.Schaaf, A. A. et al. Functional diversity of tree cavities for secondary cavity-nesting birds in logged subtropical Piedmont forests of the Andes. For. Ecol. Manag. 464, 118069 (2020).
    Google Scholar 
    41.Lindenmayer, D. B., Margules, C. R. & Botkin, D. B. Indicators of biodiversity for ecologically sustainable forest management. Conserv. Biol. 14, 941–950 (2000).
    Google Scholar 
    42.Gregory, R. D. et al. The generation and use of bird population indicators in Europe. Bird Conserv. Int. 18, S223–S244 (2008).
    Google Scholar 
    43.Prado, D. E. Seasonally dry forests of tropical South America: from forgottenecosystems to a new phytogeographic unit. Edinb. J. Bot. 57, 437–461 (2000).
    Google Scholar 
    44.Arias, M. Estadísticas climatológicas de la Provincia de Salta. Dirección de Medio Ambiente y Recursos Naturales, Provincia de Salta, Estación Experimental Agropecuaria Salta, Inta. (1996).45.Brown, A. D. & Malizia, L. R. Las Selvas Pedemontanas de las Yungas. Ciencia hoy 14, 52–63 (2004).
    Google Scholar 
    46.Politi, N., Hunter, M. Jr. & Rivera, L. Nest selection by cavity-nesting birds in subtropical montane forests of the andes: implications for sustainable forest management. Biotropica 41, 354–360 (2009).
    Google Scholar 
    47.Politi, N., Hunter, M. & Rivera, L. Availability of cavities for avian cavity nesters in selectively logged subtropical montane forests of the Andes. For. Ecol. Manag. 260, 893–906 (2010).
    Google Scholar 
    48.Eliano, P. M., Badinier, C. & Malizia, L. R. Manejo forestal sustentable en Yungas: protocolo para el desarrollo de un plan de manejo forestal e implementación en una finca piloto. Ediciones del Subtrópico, San Miguel de Tucumán (2009).49.Ralph, C. J., Droege, S. & Sauer, J. R. Managing and monitoring birds using point counts: standards and applications 1: 3-8 (1995).50.Hill, D. Handbook of biodiversity methods: survey, evaluation and monitoring (Cambridge University Press, 2005).
    Google Scholar 
    51.Ibarra, J. T. & Martin, K. Biotic homogenization: loss of avian functional richness and habitat specialists in disturbed Andean temperate forests. Biol. Conserv. 192, 418–427 (2015).
    Google Scholar 
    52.Schaaf, A. A. et al. Identification of tree groups used by secondary cavity-nesting birds to simplify forest management in subtropical forests. J. For. Res. 31, 1417–1424 (2020).
    Google Scholar 
    53.Blendinger, P. G. & Álvarez, M. E. Aves de la Selva Pedemontana de las Yungas australes. In: Selva Pedemontana de las Yungas. Historia Natural, Ecología y Manejo de un Ecosistema en Peligro. (Eds AD Brown, A. D et al.) 233–272 (2009).54.Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals: Ecol. Arch. Ecol. 95, 2027–2027 (2014).55.del Hoyo, J. A., Sargatal, J., Christie, D. A. & de Juana, E. Handbook of the Birds of the World Alive. (Lynx Edicions, 2017).56.Schaaf, A. A. et al. Influence of logging on nest density and nesting microsites of cavity-nesting birds in the subtropical forests of the Andes. For. Int. J. For. Res. https://doi.org/10.1093/forestry/cpab032 (2021).Article 

    Google Scholar 
    57.Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111, 112–118 (2005).
    Google Scholar 
    58.R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2016).59.Laliberté, E., Legendre, P. & Shipley, B. FD: measuring functional diversity (FD) from multiple traits, and other tools for functional ecology. http://cran.r-project.org/web/packages/FD (2011).60.Ghadiri Khanaposhtani, M., Kaboli, M., Karami, M., Etemad, V. & Baniasadi, S. Effects of logged and unlogged forest patches on avifaunal diversity. Environ. Manag. 51, 750–758 (2013).61.Tilman, D. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).CAS 

    Google Scholar 
    62.Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules: functional diversity measures. Funct. Ecol. 24, 867–876 (2010).
    Google Scholar 
    63.Mackey, B. et al. Policy options for the world’s primary forests in multilateral environmental agreements. Conserv. Lett. 8, 139–147 (2015).
    Google Scholar 
    64.Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 

    Google Scholar 
    65.Azeria, E. T. et al. Differential response of bird functional traits to post-fire salvage logging in a boreal forest ecosystem. Acta Oecol. 37, 220–229 (2011).ADS 

    Google Scholar  More

  • in

    Synergistic effects of crop residue and microbial inoculant on soil properties and soil disease resistance in a Chinese Mollisol

    1.Yang, W. Y. et al. Soil properties and geography shape arbuscular mycorrhizal fungal communities in black land of China. Appl. Soil Ecol. 167, 104109. https://doi.org/10.1016/j.apsoil.2021.104109 (2021).Article 

    Google Scholar 
    2.Li, H. Y. et al. Effects of different slopes and fertilizer types on the grey water footprint of maize production in the black soil region of China. J. Clean. Prod. 246, 119077. https://doi.org/10.1016/j.jclepro.2019.119077 (2020).CAS 
    Article 

    Google Scholar 
    3.Li, X. Y., Wang, D. Y., Ren, Y. X., Wang, Z. M. & Zhou, Y. H. Soil quality assessment of croplands in the black soil zone of Jilin Province, China: Establishing a minimum data set model. Ecol. Indic. 107, 105251. https://doi.org/10.1016/j.ecolind.2019.03.028 (2019).CAS 
    Article 

    Google Scholar 
    4.Mao, L. G. et al. Flame soil disinfestation: A novel, promising, non-chemical method to control soilborne nematodes, fungal and bacterial pathogens in China. Crop. Prot. 83, 90–94. https://doi.org/10.1016/j.cropro.2016.02.002 (2016).ADS 
    Article 

    Google Scholar 
    5.Rasool, M. et al. Role of biochar, compost and plant growth promoting rhizobacteria in the management of tomato early blight disease. Sci. Rep. 11, 6092. https://doi.org/10.1038/s41598-021-85633-4 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Solorzano, C. D. & Malvick, D. K. Effects of fungicide seed treatments on germination, population, and yield of maize grown from seed infected with fungal pathogens. Field. Crop. Res. 122(3), 173–178. https://doi.org/10.1016/j.fcr.2011.02.011 (2011).Article 

    Google Scholar 
    7.An-le, H. E. et al. Soil application of Trichoderma asperellum GDFS1009 granules promotes growth and resistance to Fusarium graminearum in maize. J. Integr. Agric. 18(3), 599–606. https://doi.org/10.1016/S2095-3119(18)62089-1 (2019).Article 

    Google Scholar 
    8.Xu, X. G. et al. Isolation and characterization of Bacillus amyloliquefaciens MQ01, a bifunctional biocontrol bacterium with antagonistic activity against Fusarium graminearum and biodegradation capacity of zearalenone. Food Control 130, 108259. https://doi.org/10.1016/j.foodcont.2021.108259 (2021).CAS 
    Article 

    Google Scholar 
    9.Bonanomi, G., Antignani, V. & Scala, C. P. Suppression of soilborne fungal diseases with organic amendments. J. Plant. Pathol. 89(3), 311–324 (2007).
    Google Scholar 
    10.Shafique, H. A., Sultana, V., Ehteshamul-Haque, S. & Athar, M. Management of soil-borne diseases of organic vegetables. J. Plan. Protect. Res. https://doi.org/10.1515/jppr-2016-0043 (2016).Article 

    Google Scholar 
    11.Li, H. et al. Evaluation on the production of food crop straw in China from 2006 to 2014. Bioenerg. Res. 10, 949–957. https://doi.org/10.1007/s12155-017-9845-4 (2017).Article 

    Google Scholar 
    12.Zhang, P., Wei, T., Jia, Z. K., Han, Q. F. & Ren, X. L. Soil aggregate and crop yield changes with different rates of straw incorporation in semiarid areas of northwest China. Geoderma 230–231, 41–49. https://doi.org/10.1016/j.geoderma.2014.04.007 (2014).ADS 
    Article 

    Google Scholar 
    13.Yang, H. S. et al. The impacts of ditch-buried straw layers on the interface soil physicochemical and microbial properties in a rice-wheat rotation system. Soil. Till. Res. 202, 146656. https://doi.org/10.1016/j.still.2020.104656 (2020).Article 

    Google Scholar 
    14.Song, X. Y. et al. Stable isotopes reveal the formation diversity of humic substances derived from different cotton straw-based materials. Sci. Total. Environ. 740, 140202. https://doi.org/10.1016/j.scitotenv.2020.140202 (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Mi, Y. Z. et al. Changes in soil quality, bacterial community and anti-pepper Phytophthora disease ability after combined application of straw and multifunctional composite bacterial strains. Eur. J. Soil. Biol. 105, 103329. https://doi.org/10.1016/j.ejsobi.2021.103329 (2021).CAS 
    Article 

    Google Scholar 
    16.Guo, X. X., Liu, H. T. & Wu, S. B. Humic substances developed during organic waste composting: Formation mechanisms, structural properties, and agronomic functions. Sci. Total. Environ. 662, 501–510. https://doi.org/10.1016/j.scitotenv.2019.01.137 (2019).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    17.Baldock, J. A. & Skjemstad, J. O. Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Org. Geochem. 31(7–8), 697–710. https://doi.org/10.1016/S0146-6380(00)00049-8 (2000).CAS 
    Article 

    Google Scholar 
    18.Chaparro, J. M. et al. Manipulating the soil microbiome to increase soil health and plant fertility. Biol. Fert. Soils. 48(5), 489–499. https://doi.org/10.1007/s00374-012-0691-4 (2012).Article 

    Google Scholar 
    19.Hu, Y. et al. Integrated biocontrol of tobacco bacterial wilt by antagonistic bacteria and marigold. Sci. Rep. 11, 16360. https://doi.org/10.1038/s41598-021-95741-w (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Hyder, S. et al. Characterization of native plant growth promoting rhizobacteria and their anti-oomycete potential against Phytophthora capsici affecting chilli pepper (Capsicum annum L.). Sci. Rep. 10, 13859. https://doi.org/10.1038/s41598-020-69410-3 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Paterson, E., Sim, A., Osborne, S. & Murray, P. J. Long-term exclusion of plant-inputs to soil reduces the functional capacity of microbial communities to mineralise recalcitrant root-derived carbon sources. Soil. Biol. Biochem. 43(9), 1873–1880. https://doi.org/10.1016/j.soilbio.2011.05.006 (2011).CAS 
    Article 

    Google Scholar 
    22.Wang, H., Guo, Q., Li, X., Li, X. & Zhang, C. Effects of long-term no-tillage with different straw mulching frequencies on soil microbial community and the abundances of two soil-borne pathogens. Appl. Soil. Ecol. 148, 103488. https://doi.org/10.1016/j.apsoil.2019.103488 (2020).Article 

    Google Scholar 
    23.Ndzelu, B. S., Dou, S. & Zhang, X. W. Changes in soil humus composition and humic acid structural characteristics under different corn straw returning modes. Soil. Res. 58, 452–460. https://doi.org/10.1071/SR20025 (2020).CAS 
    Article 

    Google Scholar 
    24.De Corato, U. Agricultural waste recycling in horticultural intensive farming systems by on-farm composting and compost-based tea application improves soil quality and plant health: A review under the perspective of a circular economy. Sci. Total. Environ. 738, 139840. https://doi.org/10.1016/j.scitotenv.2020.139840 (2021).CAS 
    Article 

    Google Scholar 
    25.Wong, M. & Swift, R. S. Role of organic matter in alleviating soil acidity. in Handbook of Soil Acidity. http://espace.library.uq.edu.au/view/UQ:191317 (2003).26.Xie, W. J. et al. Coastal saline soil aggregate formation and salt distribution are affected by straw and nitrogen application: A 4-year field study. Soil. Till. Res. 198, 104535. https://doi.org/10.1016/j.still.2019.104535 (2020).Article 

    Google Scholar 
    27.Cathal, N. et al. Soil aggregates formed in vitro by saprotrophic Trichocomaceae have transient water-stability. Soil. Biol. Biochem. 48, 151–161. https://doi.org/10.1016/j.soilbio.2012.01.010 (2012).CAS 
    Article 

    Google Scholar 
    28.Lou, Y. L., Xu, M. G., Wang, W., Sun, X. L. & Zhao, K. Return rate of straw residue affects soil organic C sequestration by chemical fertilization. Soil. Till. Res. 113(1), 70–73. https://doi.org/10.1016/j.still.2011.01.007 (2011).Article 

    Google Scholar 
    29.Loffredo, E., Berloco, M. & Senesi, N. The role of humic fractions from soil and compost in controlling the growth in vitro of phytopathogenic and antagonistic soil-borne fungi. Ecotoxicol. Environ. Saf. 69(3), 350–357. https://doi.org/10.1016/j.ecoenv.2007.11.005 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    30.Bhatia, A. et al. Diversity of bacterial isolates during full scale rotary drum composting. Waste Manag. 33(7), 1595–1601. https://doi.org/10.1016/j.wasman.2013.03.019 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Dou, S., Zhang, J. J. & Li, K. Effect of organic matter applications on 13C-NMR spectra of humic acids of soil. Eur. J. Soil. Sci. 59(3), 532–539. https://doi.org/10.1111/j.1365-2389.2007.01012.x (2008).CAS 
    Article 

    Google Scholar 
    32.De, V. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9(1), 3033. https://doi.org/10.1038/s41467-018-05516-7 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Sanaullah, M. et al. How do microbial communities in top and subsoil respond to root litter addition under field conditions?. Soil Biol. Biochem. 103, 28–38. https://doi.org/10.1016/j.soilbio.2016.07.017 (2016).CAS 
    Article 

    Google Scholar 
    34.Song, Y. et al. Identification of the produced volatile organic compounds and the involved soil bacteria during decomposition of watermelon plant residues in a Fusarium-infested soil. Geoderma 315, 178–187. https://doi.org/10.1016/j.geoderma.2017.11.021 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Vida, C., Cazorla, F. M. & Vicente, A. D. Characterization of biocontrol bacterial strains isolated from a suppressiveness-induced soil after amendment with composted almond shells. Res. Microbiol. 168(6), 583–593. https://doi.org/10.1016/j.resmic.2017.03.007 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Liu, J. G., Li, X. G., Jia, Z. J., Zhang, T. L. & Wang, X. X. Effect of benzoic acid on soil microbial communities associated with soilborne peanut diseases. Appl. Soil. Ecol. 110, 34–42. https://doi.org/10.1016/j.apsoil.2016.11.001 (2017).ADS 
    Article 

    Google Scholar 
    37.Zhao, S. C. et al. Ciampitti dynamic of fungal community composition during maize residue decomposition process in north-central China. Appl. Soil Ecol. 167, 104057. https://doi.org/10.1016/j.apsoil.2021.104057 (2021).Article 

    Google Scholar 
    38.Zhang, J., Xu, Y., Liang, S., Ma, X. & Sun, F. Synergistic effect of klebsiella sp. fh-1 and arthrobacter sp. nj-1 on the growth of the microbiota in the black soil of northeast china. Ecotox. Environ. Safe 190, 110079. https://doi.org/10.1016/j.ecoenv.2019.110079 (2019).CAS 
    Article 

    Google Scholar 
    39.Wang, X. W. et al. Diversity and taxonomy of Chaetomium and chaetomium-like fungi from indoor environments. Stud. Mycol. 84, 145–224. https://doi.org/10.1016/j.simyco.2016.11.005 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Chen, W. H. et al. High-throughput sequencing analysis of endophytic fungal diversity in cynanchum sp.. S. Afr. J. Bot. 134, 349–358. https://doi.org/10.1016/j.sajb.2020.04.010 (2020).CAS 
    Article 

    Google Scholar 
    41.Voriskova, J. & Baldrain, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7(3), 477–486. https://doi.org/10.1038/ismej.2012.116 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    42.Kerdraon, L., Laval, V. & Suffert, F. Microbiomes and pathogen survival in crop residues, an ecotone between plant and soil. Phytobiomes J. 3, 246–255. https://doi.org/10.1094/pbiomes-02-19-0010-rvw (2019).Article 

    Google Scholar 
    43.Rahman, S. F. S. A. et al. Emerging microbial biocontrol strategies for plant pathogens. Plant Sci. 267, 102–111. https://doi.org/10.1016/j.plantsci.2017.11.012 (2018).CAS 
    Article 

    Google Scholar 
    44.Wachowska, U., Irzykowski, W., Jedryczka, M., Stasiulewicz-Paluch, A. D. & Glowacka, K. Biological control of winter wheat pathogens with the use of antagonistic Sphingomonas bacteria under greenhouse conditions. Biocontrol. Sci. Technol. 23, 1110–1122. https://doi.org/10.1080/09583157.2013.812185 (2013).Article 

    Google Scholar 
    45.Liu, J. J. et al. Soil carbon content drives the biogeographical distribution of fungal communities in the black soil zone of northeast China. Soil Biol. Biochem. 83(0038–0017), 29–39. https://doi.org/10.1016/j.soilbio.2015.01.009 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    46.Xiong, W. et al. Distinct roles for soil fungal and bacterial communities associated with the suppression of vanilla Fusarium wilt disease. Soil Biol. Biochem. 107, 198–207. https://doi.org/10.1016/j.soilbio.2017.01.010 (2017).CAS 
    Article 

    Google Scholar 
    47.Raaijmakers, J. M. & Mazzola, M. Diversity and natural functions of antibiotics produced by beneficial and plant pathogenic bacteria. Annu. Rev. Phytopathol. 50, 403–424. https://doi.org/10.1146/annurev-phyto-081211-172908 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    48.Deng, X. H. et al. Rhizosphere bacteria assembly derived from fumigation and organic amendment triggers the direct and indirect suppression of tomato bacterial wilt disease. Appl. Soil Ecol. 147, 103364. https://doi.org/10.1016/j.apsoil.2019.103364 (2020).Article 

    Google Scholar 
    49.Li, C. N. et al. Microbial inoculation influences bacterial community succession and physicochemical characteristics during pig manure composting with corn straw. Bioresour. Technol. 289, 121653. https://doi.org/10.1016/j.biortech.2019.121653 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    50.Lydia, S., Tymon, P. M., Gundersen, B. & Inglis, D. A. Potential of endophytic fungi collected from Cucurbita pepo roots grown under three different agricultural mulches as antagonistic endophytes to Verticillium dahliae in western Washington. Microbiol. Res. 240, 126535. https://doi.org/10.1016/j.micres.2020.126535 (2020).CAS 
    Article 

    Google Scholar 
    51.Mehmood, M. A. et al. Sclerotia of a phytopathogenic fungus restrict microbial diversity and improve soil health by suppressing other pathogens and enriching beneficial microorganisms. J. Environ. Manag. 259, 109857. https://doi.org/10.1016/j.jenvman.2019.109857 (2020).Article 

    Google Scholar 
    52.Ding, J. L. et al. Influence of inorganic fertilizer and organic manure application on fungal communities in a long-term field experiment of Chinese Mollisols. Appl. Soil. Ecol. 111, 114–122. https://doi.org/10.1016/j.apsoil.2016.12.003 (2017).ADS 
    Article 

    Google Scholar 
    53.Zhao, Y. Y. et al. Characterization of Lysobacter spp. strains and their potential use as biocontrol agents against pear anthracnose. Microbiol. Res. 242, 126624. https://doi.org/10.1016/j.micres.2020.126624 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    54.Liu, X. S. et al. Organic amendment improves rhizosphere environment and shapes soil bacterial community in black and red soil under lead stress. J. Hazard. Mater. 416, 125805. https://doi.org/10.1016/j.jhazmat.2021.125805 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    55.Qiao, J. Q., Tian, D. W., Huo, R., Wu, H. J. & Gao, X. W. Functional analysis and application of the cryptic plasmid pBSG3 harboring the RapQ–PhrQ system in Bacillus amyloliquefaciens B3. Plasmid 65(2), 141–149. https://doi.org/10.1016/j.plasmid.2010.11.008 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    56.Coutte, F. et al. Effect of pps disruption and constitutive expression of srfa on surfactin productivity, spreading and antagonistic properties of Bacillus subtilis 168 derivatives. J. Appl. Microbiol. 109(2), 480–491. https://doi.org/10.1111/j.1365-2672.2010.04683.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    57.Leclere, V. et al. Mycosubtilin overproduction by Bacillus subtilis bbg100 enhances the organism’s antagonistic and biocontrol activities. Appl. Environ. Microb. 71(8), 4577. https://doi.org/10.1128/AEM.71.8.4577-4584.2005 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    58.Choi, S. K., Jeong, H., Kloepper, J. W. & Ryu, C. M. Genome sequence of Bacillus amyloliquefaciens GB03, an active ingredient of the first commercial biological control product. Genome Announc. 2(5), 1092–1106. https://doi.org/10.1128/genomeA.01092-14 (2014).Article 

    Google Scholar 
    59.Kim, S. Y., Lee, S. Y., Weon, H. Y., Sang, M. K. & Song, J. Complete genome sequence of Bacillus velezensis M75, a biocontrol agent against fungal plant pathogens, isolated from cotton waste. J. Biotechnol. 241, 112–115. https://doi.org/10.1016/j.jbiotec.2016.11.023 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Abbasi, S. et al. Streptomyces strains modulate dynamics of soil bacterial communities and their efficacy in disease suppression caused by Phytophthora capsici. Sci. Rep. 11, 9317. https://doi.org/10.1038/s41598-021-88495-y (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Saravanakumar, K. et al. Effect of Trichoderma harzianum on maize rhizosphere microbiome and biocontrol of Fusarium stalk rot. Sci. Rep. 7, 1771. https://doi.org/10.1038/s41598-017-01680-w (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Yan, F., Li, C., Ye, X., Lian, Y. & Wang, X. Antifungal activity of lipopeptides from Bacillus amyloliquefaciens mg3 against colletotrichum gloeosporioides in loquat fruits. Biol. Control 146, 104281. https://doi.org/10.1016/j.biocontrol.2020.104281 (2020).CAS 
    Article 

    Google Scholar 
    63.Qi, Y., Liu, H., Wang, J. & Wang, Y. Effects of different straw biochar combined with microbial inoculants on soil environment of ginseng. Sci. Rep. 11, 14685. https://doi.org/10.21203/rs.3.rs-189319/v1 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Wang, Y. et al. Evaluation and spatial variability of paddy soil fertility in typical county of northeast China. J. Plant Nutr. Fertil. 26(2), 256–266. https://doi.org/10.11674/zwyf.19128 (2020).CAS 
    Article 

    Google Scholar 
    65.Cambardella, C. A. & Elliott, E. T. Carbon and nitrogen distribution in aggregates from cultivated and native grassland soils. Soil Sci. Soc. Am. J. 57(4), 1071–1076. https://doi.org/10.2136/sssaj1993.03615995005700040032x (1993).ADS 
    CAS 
    Article 

    Google Scholar 
    66.Zhang, X., Dou, S., Ndzelu, B. S., Guan, X. W. & Bai, Y. Effects of different corn straw amendments on humus composition and structural characteristics of humic acid in black soil. Commun. Soil. Sci. Plan. 51(1), 1–11. https://doi.org/10.1080/00103624.2019.1695827 (2019).CAS 
    Article 

    Google Scholar 
    67.Edgar, R. C. Uparse: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods. 10(10), 996. https://doi.org/10.1038/NMETH.2604 (2021).Article 

    Google Scholar 
    68.Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353. https://doi.org/10.1038/ismej (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Lourenço, K. S. et al. Resilience of the resident soil microbiome to organic and inorganic amendment disturbances and to temporary bacterial invasion. Microbiome 6, 142. https://doi.org/10.1186/s40168-018-0525-1 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Exploring the potential of moringa leaf extract as bio stimulant for improving yield and quality of black cumin oil

    Plant height (cm)Plant height of black cumin as affected by moringa leaf extract applied at various growth stages is reported in Table 1. Both concentrations of moringa leaf extract significantly affected plant height of black cumin. All growth stages also showed statistically significant results. Mean comparison of control vs treatments and water spray vs rest were also found significant for plant height (cm) of black cumin. Whereas, interaction of moringa leaf extract concentrations and growth stages remained non-significant. With increase in interval of spraying moringa leaf extract, plant height enhanced and thus taller plants (68.15 cm) were recorded when moringa leaf extract was sprayed at stage-7 (40 + 80 + 120 days after sowing), followed by (65.15 cm) stage-4 (40 + 80 days after sowing), while lower plants height (47.45 cm) was recorded in stage-3 (120 days after sowing). The use of moringa leaf extract during critical vegetative development phases increased the black cumin crop’s plant height. Similar results were recorded by Abbas et al.14 that moringa leaf extract enhanced plant height and improved fresh and dried weight of wheat root when compared to control. Taller (62.2 cm) plants were recorded in 20% moringa leaf extract sprayed plots followed by (55.8 cm) 10% moringa leaf extract. Spraying moringa leaf extract on a variety of field crops can boost plants and increase vegetative development15.Table 1 Plant height (cm), number of branches plant−1 fixed oil content (% vw−1) and essential oil content (% vw−1) of black cumin as affected by moringa leaf extract applied at various growth stages.Full size tableBranches plant−1
    Branches plant−1 of black cumin were significantly influenced by moringa leaf extract concentrations, stage of application as well as their interaction (Table 1). The planned mean comparison of control vs rest and water spray vs rest were also found significant for branches plant−1. The unsprayed against sprayed treatments of moringa leaf extract showed that in unsprayed plots number of branches plant−1 (39) were less than plants sprayed with moringa leaf extract (61.19). Highest number of branches plant−1 (62.19) were observed 20% moringa leaf extract treated plots. These results are in agreement with Mahmood16 who found that foliar application of MLE contains an adequate amount of stimulating substances that promote cell division and enlargement at a faster rate. Zeatin, a growth hormone found in moringa leaf extract, encourages the growth of lateral buds, which leads to an increase in the number of branches. After pounding 100 g of Moringa leaves in 8 L of water, foliar spray of moringa leaf extract enhanced branches plant−1 in okra17. More number of branches plant−1 (70.66) were attained in plots sprayed with moringa leaf extract at growth stage 7 (40 + 80 + 120 days after sowing), followed by growth stage 4 (40 + 80 days after sowing). The effect of the application of MLE at the rate of 20% at 40 days’ interval increased the number of branches and this may be because of the abundant supply of macro and micronutrients and growth hormones. The result of yield parameters revealed that the yield increased as the frequency of moringa leaf extract increased. This is because hormone enhances formation and development of flowers and ripening of fruits. Hormones also enhance growth and yield by altering photosynthetic distributive pattern within the plants. The findings were also in line with that of Manzoor et al.18 who found that an aqueous extract of moringa significantly influence yield and yield components such as number of branches, number of fruits per plant and fruit weight of tomato. The significant interaction of MLE and growth stages is presented in Fig. 1. Applying moringa leaf extract @ 20% at all growth stages enhanced branches plant−1. Maximum branches plant−1 was observed when moringa leaf extract was sprayed @ 20% at growth stage 7 (40 + 80 + 120 days after sowing) whereas, minimum branches plant−1 was recorded in plants sprayed with 10% moringa leaf extract at growth stage-3 (120 days after sowing). Moringa leaf extract (MLE) increased number of branches. Similar results were recorded by Jain et al.19), who reported MLE positively enhanced plant growth attributes of wheat. He also stated that with increasing MLE concentration and application intervals, the growth parameters such as branches plant−1 were increased in arithmetic order. Plant growth regulators are essential for controlling growth and development of plants20. These plant growth regulators increased yield by changing the dry matter distribution pattern or controlling the growth characteristics in crop plants, depending on the dosage and time of application21. In comparison to control, foliar application of moringa leaf extract resulted in a markedly higher branches plant−1. The increased number of branches plant−1 might be due to Zeatin present in moringa leaf extract, which is very effective in delaying the abscission response10.Figure 1Number of branches plant−1 of black cumin as affected by moringa leaf extract applied at various growth stages.Full size imageFixed oil content (% vw−1)Data concerning fixed oil content (% vw−1) in response to moringa leaf extract applied at various growth stages is given in Table 1 and Fig. 2. Statistical analysis of data indicated that foliar application of various concentrations of moringa leaf extract, their stage of application and interaction of concentrations and growth stages had significantly affected fixed oil content (% vw−1) of black cumin crop. The planned mean comparison of control vs rest and water spray vs rest had significant effect on fixed oil content (% vw−1). Highest fixed oil percentage (35.39%) was recorded when moringa leaf extract was sprayed @ 20%, followed by (34.06%) 10% moringa leaf extract, whereas, control (31.48%) showed lowest fixed oil %. Sakr et al.22 indicated that foliar applications of MLE significantly improved the oil percentage and yield plant−1 and feddan of geranium plants. Application of MLE at growth stage-7 (40 + 80 + 120 days after sowing) showed maximum fixed oil content percentage (37.08%) as compared to all other growth stages. Minimum fixed oil percentage was recorded in growth stage-1 (40 days after sowing). Concerning the interaction of moringa leaf extract vs application stage, highest fix oil (37.45%) was observed when moringa leaf extract @ 20% was applied as foliar spray at growth stage-7 (40 + 80 + 120 days after sowing), followed by (36.71%) moringa leaf extract @ 10% applied at growth stage-7. Lowest fixed oil percentage (31.83%) was observed in plants sprayed with 10% moringa leaf extract at stage 1 (40 days after sowing). According to Rady et al.23, biosynthesis of cytokinins promotes the movement of stem reserves to new shoots, resulting in stable plant development, the prevention of premature leaf senescence, and the preservation of more leaf area for photosynthetic action.Figure 2Fixed oil content (%) of black cumin as affected by moringa leaf extract applied at various growth stages.Full size imageEssential oil content (% vw−1)Essential oil content (% vw−1) is a vital oil component of black cumin. Moringa leaf extract concentrations and stage of their application had significant effect on essential oil content of black cumin while the interaction remained non-significant (Table 1). Application of MLE at 20% resulted in higher essential oil yield (0.38%) followed by 10% moringa leaf extract (0.37) sprayed plots. Control plots resulted in lower essential oil (0.33%) content of black cumin. Many research ventures around the world are currently focusing on increasing the biomass yield and volatile oil output of aromatic plants. Moringa leaf extract has been discovered to be an excellent bio-stimulant for enhancing not only crop growth but also yield24,25. According to Aslam et al.26, Plant treated with MLE had major impacts, including an average rise in oil concentrations. Interestingly, MLE treatment not only increased the coriander fruit yield but also improved the fruits volatile oil suggesting that MLE could be a promise plant growth promoter that improved the content of volatile oil in coriander. MLE application also positively affected the volatile oil constituents (Table 2). Increasing the volatile oil in coriander by MLE could be due to the MLE components including amino acids, nutrient elements and phytohoromes that motivate the accumulation of secondary metabolites27. The phytohormones affect the pathway of terpenoids through motivating the responsible physiological and biochemical processes28. Concerning the application stages of moringa leaf extract, higher essential oil content % of black cumin (0.42%) was observed in growth stage-7 (40 + 80 + 120 days after sowing), followed by (0.39%) growth stage-4 (40 + 80 days after sowing), whereas, lower essential oil content % (0.36%) of black cumin was observed in growth stage-1 (40 days after sowing). Plant growth regulators are essential for controlling the amount, type, and direction of plant growth, development, and yield20. These plant growth regulators increased yield by changing the dry matter distribution pattern or controlling the growth characteristics in crop plants, depending on the dosage and time of application21. Exogenous application of MLE resulted in higher yield and quality29.Table 2 Peroxidase value (meq kg−1) and Iodine value (g of I2/100 g) of black cumin as affected by moringa leaf extract applied at various growth stages.Full size tablePeroxidase value (meq kg−1)The response of MLE and stage of MLE application recorded for peroxidase value is stated in Table 2. The data depicted that moringa leaf extract concentrations, stage of application and their interaction had significant (P ≤ 0.05) variation in peroxidase value of black cumin. Similarly, when means were compared, that of control vs treatments and water spray check vs treatments were found significant for peroxidase value (%). Mean value of data indicated that highest peroxidase value (6.32%) was recorded in 20% moringa leaf extract treated plots, followed by (6.03%) 10% moringa leaf extract. While in case of application stages, highest peroxidase value (6.42%) was recorded when moringa leaf extract was applied at stage-7 (40 + 80 + 120 days after sowing), followed by (6.39%) stage-6 (80 + 120 days after sowing). Whereas lowest peroxidase value (5.73%) was recorded in plots treated with moringa leaf extract at stage-3 (120 days after sowing). Interaction of moringa leaf extract concentrations and stage of application in Fig. 3 showed that increasing moringa leaf extract concentration from 10 to 20% applied at growth stage-7 increased peroxidase value of black cumin crop. However, application of moringa leaf extract @ 10% applied at growth stage-3 (120 days after sowing) showed lowest peroxidase value. The phytohormones affect the pathway of terpenoids through motivating the responsible physiological and biochemical processes28. Our results are in agreement with the reports of Ali et al.27 in geranium and Abdel-Rahman and Abdel-Kader30 in fennel who observed that MLE application improves both the volatile oil yield and its components. The fact that MLE application improved black cumin growth and quality characters suorts the study’s hypothesis that MLE is an important plant growth enhancer. In agreement with our results, Rady and Mohamed28 concluded that MLE is considered one of the important plant bio stimulants because it contains antioxidants, phenols, basic nutrients, ascorbates, and phytohormones. Furthermore, foliar application of moringa leaf extract may have a positive effect on endogenous phytohormone concentrations, resulting in improved plant growth and quality10,37.Figure 3Peroxidase value (meq kg−1) of black cumin as affected by moringa leaf extract applied at various growth stages.Full size imageIodine value (g of I2/100 g)Data concerning iodine value of black cumin oil in response to various concentrations of MLE applied at various growth stages is given in Table 2 and Fig. 4. Statistical analysis of data indicated that both the concentrations of moringa leaf extract, stage of application as well as their interaction had significant effect on iodine value of black cumin oil. The planned mean comparison of control vs rest and water spray vs rest treatments had significant effect on iodine value. Highest iodine value (85.3) was recorded with application of moringa leaf extract @ 20% whereas, lowest (78.28) was observed in control. Regarding the stage of application, highest iodine value (87.35) was observed in plots sprayed with moringa leaf extract at stage-7 (40 + 80 + 120 days after sowing), followed by (85.61) plots sprayed with moringa leaf extract at growth stage-6 (80 + 120 days after sowing). Concerning the interaction of MLE concentrations and stage of application of MLE, highest iodine value (6.49) was observed with 20% moringa leaf extract sprayed at stage-7 (40 + 80 + 120 days after sowing) whereas, lowest iodine value was observed in plants sprayed with moringa leaf extract @ 20% applied at stage-3 (120 days after sowing). The use of plant growth regulators is very specific and depends to achieve specific results like for example; enhanced plant growth, betterment in yield and yield related attributes, and to modify the fruit and plant bio-constituents. Several previous studies reveled that MLE are enriched with many phtyo-hormones especially zeatin31. In addition to that MLEs are embedded with many essential amino acids, vitamins (A, B1, B2, B3, C and E), minerals as well as several antioxidants like phenolic32,33. This unique biochemical composition of MLE showed that they can be utilized as bio stimulant which have the potential to promote crop growth, productivity as well as quality which in return depends on its application time34.Figure 4Iodine value (meq kg−1) of black cumin as affected by moringa leaf extract applied at various growth stages.Full size imageTotal free amino acidsThe data presented in Table 2 revealed that moringa leaf extract concentrations and application stages had significantly affected total free amino acid content of black cumin crop during rabi 2019-20 under agro-climatic conditions of Haripur whereas, their interaction remained non-significant. The planned mean comparison of control vs rest and water spray vs rest had significant effect on total free amino acids of black cumin. Highest amino acids (336.3) were observed with the application of moringa leaf extract @ 20%, followed by application of moringa leaf extract @ 10%. Regarding application stages, highest total free amino acids (364.2) were observed with the application of moringa leaf extract at 40 + 80 + 120 days after sowing, followed by (355.9) application of MLE at 40 + 80 days after sowing. Lowest total free amino acids (290.3) were recorded with moringa leaf extract sprayed at 40 days after sowing. Several investigations have demonstrated that MLE can alter both primary and secondary metabolism, resulting in an increase in antioxidant molecule concentrations35,36. The content of phenolic antioxidants, total soluble proteins, and total free amino acids increased in spinach plants treated with synthetic growth regulators and MLE26. MLE can also increase fruit quality metrics in ‘Kinnow’ mandarins, such as soluble solid contents, vitamin C, sugars, total antioxidant, phenolic contents, and superoxide dismutase and catalase enzyme activities, when treated at various growth stages37.Total phenolicPhenolic have acquired much importance because of their properties of disease preventing and health promoting. The effect of moringa leaf extract concentrations, stage of application and their interaction is presented in Table 2. Analysis of variance revealed that moringa leaf extract concentrations and stage of application of moringa leaf extract had significant effect on total phenolic content of black cumin while their interaction remained non-significant. Our results depict that all MLE levels enhanced the total phenolic content of black cumin leaves relative to the control. Highest phenolic content (71.59 mg g−1) was observed with application of moringa leaf extract at the rate of 20%, followed by (68.72 mg g−1) moringa leaf extract application at the rate of 10%. Regarding application stages, highest phenolic content (81.23 mg g−1) was observed with the application of moringa leaf extract at growth stage-7 (40 + 80 + 120 days after sowing), followed by (76.66 mg g−1) stage-6 (80 + 120 days after sowing), whereas, lowest phenolic content (55.25 mg g−1) was observed in crop sprayed with moringa leaf extract at stage-3 (120 days after sowing). In the medicinal, biological, and agricultural areas, phenolic and their derivatives gained scientists attention. Recent studies had focused on their potential as antioxidant-rich natural chemicals38. The increased content of phenolics, flavonoids, and phytohormones in moringa leaves, which may have contributed to the enhanced total phenolic content in black cumin leaves, can be linked to the higher content of phenolics, flavonoids, and phytohormones in MLE treated plants26. Furthermore, the proper concentrations of minerals, vitamins, and -carotene found in moringa leaves may have influenced metabolic processes in a way that increased the internal phenolic content in black cumin leaves, either directly or indirectly39. Therefore, these aspects assist MLE to serve as growth enhancer and natural antioxidant40. Our results supported by the previous report of Nasir et al.37 who revealed that the total phenolic content was enhanced as a result of MLE application at critical stages of plant growth. More

  • in

    Soil minerals affect taxon-specific bacterial growth

    1.Roselló-Mora R, Amann R. The species concept for prokaryotes. FEMS Microbiol Rev. 2001;25:39–67.
    Google Scholar 
    2.Certini G, Campbell CD, Edwards AC. Rock fragments in soil support a different microbial community from the fine earth. Soil Biol Biochem. 2004;36:1119–28.CAS 

    Google Scholar 
    3.Carson JK, Rooney D, Gleeson DB, Clipson N. Altering the mineral composition of soil causes a shift in microbial community structure. FEMS Microbiol Ecol. 2007;61:414–23.CAS 
    PubMed 

    Google Scholar 
    4.Uroz S, Kelly LC, Turpault M, Lepleux C, Frey-Klett P. The mineralosphere concept: mineralogical control of the distribution and function of mineral-associated bacterial communities. Trends Microbiol. 2015;23:751–62.CAS 
    PubMed 

    Google Scholar 
    5.Ahmed E, Hugerth LW, Logue JB, Brüchert V, Andersson AF, Holmström SJ. Mineral type structures soil microbial communities. Geomicrobiol J 2017;34:538–45.CAS 

    Google Scholar 
    6.Whitman T, Neurath R, Perera A, Chu-Jacoby I, Ning D, Zhou J, et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ Microbiol. 2018;20:4444–60.CAS 
    PubMed 

    Google Scholar 
    7.Kandeler E, Gebala A, Boeddinghaus RS, Müller K, Rennert T, Soares M, et al. The mineralosphere—succession and physiology of bacteria and fungi colonising pristine minerals in grassland soils under different land-use intensities. Soil Biol Biochem. 2019;136:107534.CAS 

    Google Scholar 
    8.Hassink J, Bouwman LA, Zwart KB, Bloem J, Brussaard L. Relationships between soil texture, physical protection of organic-matter, soil biota, and C-mineralization and N-mineralization in grassland soils. Geoderma 1993;57:105–28.CAS 

    Google Scholar 
    9.Mayer LM, Schick LL, Hardy KR, Wagai R, McCarthy J. Organic matter in small mesopores in sediments and soils. Geochim Cosmochim Acta. 2004;68:3868–72.
    Google Scholar 
    10.Chenu C, Stotzky G. Interaction between microorganisms and soil particles: an overview. In: Huang PM, Bollag JM, Senesi N, editors. Interactions between soil particles and microorganism: impact on the terrestrial ecosystem. New York: Wiley; 2002. p. 3–40.11.Hemkemeyer M, Pronk GJ, Heister K, Kögel-Knabner I, Martens R, Tebbe CC. Artificial soil studies reveal domain-specific preferences of microorganisms for the colonisation of different soil minerals and particle size fractions. FEMS Microbiol Ecol. 2014;90:770–82.CAS 
    PubMed 

    Google Scholar 
    12.Six J, Elliott ET, Paustian K. Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture. Soil Biol Biochem. 2000;32:2099–103.CAS 

    Google Scholar 
    13.Totsche KU, Amelung W, Gerzabek MH, Guggenberger G, Klumpp E, Knief C, et al. Microaggregates in soils. J Plant Nutr Soil Sci. 2018;181:104–36.CAS 

    Google Scholar 
    14.Rasmussen C, Southard RJ, Horwath WR. Litter type and soil minerals control temperate forest soil carbon response to climate change. Glob Change Biol 2008;14:2064–80.
    Google Scholar 
    15.Hemingway JD, Rothman DH, Grant KE, Rosengard SZ, Eglinton TI, Derry LA, et al. Mineral protection regulates long-term global preservation of natural organic carbon. Nature 2019;570:228–31.CAS 
    PubMed 

    Google Scholar 
    16.Ranjard L, Richaume A. Quantitative and qualitative microscale distribution of bacteria in soil. Res Microbiol. 2001;152:707–16.CAS 
    PubMed 

    Google Scholar 
    17.Poll C, Thiede A, Wermbter N, Sessitsch A, Kandeler E. Micro-scale distribution of microorganisms and microbial enzyme activities in a soil with long-term organic amendment. Eur J Soil Sci. 2003;54:715–24.
    Google Scholar 
    18.Neumann D, Heuer A, Hemkemeyer M, Martens R, Tebbe CC. Response of microbial communities to long-term fertilization depends on their microhabitat. FEMS Microbiol Ecol. 2013;86:71–84.CAS 
    PubMed 

    Google Scholar 
    19.Nie M, Pendall E, Bell C, Wallenstein MD. Soil aggregate size distribution mediates microbial climate change feedbacks. Soil Biol Biochem. 2014;68:357–365.CAS 

    Google Scholar 
    20.Chenu C, Hassink J, Bloem J. Short-term changes in the spatial distribution of microorganisms in soil aggregates as affected by glucose addition. Biol Fertil Soils. 2001;34:349–56.CAS 

    Google Scholar 
    21.Saidy AR, Smernik RJ, Baldock JA, Kaiser K, Sanderman J. The sorption of organic carbon onto differing clay minerals in the presence and absence of hydrous iron oxide. Geoderma. 2013;209:15–21.
    Google Scholar 
    22.Mikutta R, Kleber M, Torn MS, Jahn R. Stabilization of soil organic matter: association with minerals or chemical recalcitrance? Biogeochemistry 2006;77:25–56.CAS 

    Google Scholar 
    23.Gadd GM. Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology. 2010;156:609–43.CAS 
    PubMed 

    Google Scholar 
    24.Lehmann J, Kleber M. The contentious nature of soil organic matter. Nature 2015;528:60–68.CAS 
    PubMed 

    Google Scholar 
    25.Sokol NW, Sanderman J, Bradford MA. Pathways of mineral‐associated soil organic matter formation: integrating the role of plant carbon source, chemistry, and point of entry. Glob Change Biol. 2019;25:12–24.
    Google Scholar 
    26.Skjemstad JO, Janik LJ, Head MJ, McClure SG. High energy ultraviolet photo‐oxidation: a novel technique for studying physically protected organic matter in clay‐and silt‐sized aggregates. J Soil Sci. 1993;44:485–99.CAS 

    Google Scholar 
    27.Goldfarb KC, Karaoz U, Hanson CA, Santee CA, Bradford MA, Treseder KK, et al. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front Microbiol. 2011;2:1–10.
    Google Scholar 
    28.Kleber M, Sollins P, Sutton R. A conceptual model of organo-mineral interactions in soils: self-assembly of organic molecular fragments into zonal structures on mineral surfaces. Biogeochemistry 2007;85:9–24.
    Google Scholar 
    29.Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM. Mineral control of soil organic carbon storage and turnover content. Nature 1997;389:3601–3.
    Google Scholar 
    30.Dahlgren RA, Saigusa M, Ugolini FC. The nature, properties and management of volcanic soils. Adv Agron. 2004;82:113–82.CAS 

    Google Scholar 
    31.Mikutta R, Kleber M, Jahn R. Poorly crystalline minerals protect organic carbon in clay subfractions from acid subsoil horizons. Geoderma 2005;128:106–15.CAS 

    Google Scholar 
    32.Keiluweit M, Bougoure JJ, Nico PS, Pett-Ridge J, Weber PK, Kleber M. Mineral protection of soil carbon counteracted by root exudates. Nat Clim Change. 2015;5:588–95.CAS 

    Google Scholar 
    33.Rasmussen C, Throckmorton H, Liles G, Heckman K, Meding S, Horwath WR. Controls on soil organic carbon partitioning and stabilization in the California Sierra Nevada. Soil Syst. 2018;2:1–18.
    Google Scholar 
    34.Zhou Z, Wang C, Luo Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat Comm. 2020;11:1–10.CAS 

    Google Scholar 
    35.Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microb. 2015;81:7570–81.CAS 

    Google Scholar 
    36.Hayer M, Schwartz E, Marks JC, Koch BJ, Morrissey EM, Schuettenberg AA, et al. Identification of growing bacteria during litter decomposition in freshwater through H218O quantitative stable isotope probing. Environ Microbiol Rep. 2016;8:975–82.CAS 
    PubMed 

    Google Scholar 
    37.Papp K, Hungate BA, Schwartz E. Microbial rRNA synthesis and growth compared through quantitative stable isotope probing with H218O. Appl Environ Microbiol. 2018;84:1–17.
    Google Scholar 
    38.Finley BK, Dijkstra P, Rasmussen C, Schwartz E, Liu XA, van Gestel N, et al. Soil mineral assemblage and substrate quality effects on microbial priming. Geoderma2018;322:38–47.CAS 

    Google Scholar 
    39.Rasmussen C, Southard RJ, Horwath WR. Mineral control of organic carbon mineralization in a range of temperate conifer forest soils. Glob Change Biol. 2006;12:834–47.
    Google Scholar 
    40.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012;22:939–46.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    44.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
    Google Scholar 
    45.Morrissey EM, Mau RL, Schwartz E, McHugh TA, Dijkstra P, Koch BJ, et al. Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 2017;11:1890–9.PubMed 
    PubMed Central 

    Google Scholar 
    46.R Core Team. R: a language and environment for statistical computiong. Vienna: R Foundation for Statistical Computing; 2021. https://www.R-project.org/.47.Dowle M, Srinivasan A. data.table: Extensions of ‘data.frame’. R package version 1.13.6. 2020.48.Oksanen J, Blanchet FG, Kindt R, Legendre P, O’hara RB, Simpson GL, et al. Vegan: community ecology package. R package version 1.17-4. 2010. http://cran.r-project.org.49.Morrissey EM, Mau RL, Hayer M, Liu XJ, Schwartz E, Dijkstra P, et al. Evolutionary history constrains microbial traits across environmental variation. Nat Ecol Evol. 2019;3:1064–9.PubMed 

    Google Scholar 
    50.Pinheiro J, Bates D, DebRoy S, Sarkar D. R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3. 1–137, 2018. https://CRAN.R-project.org/package=nlme .51.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2018;35:526–8.
    Google Scholar 
    52.Barter RL, Yu B. Superheat: an R package for creating beautiful and extendable heatmaps for visualizing complex data. J Comput Graph Stat. 2018;27:910–22.PubMed 
    PubMed Central 

    Google Scholar 
    53.Demoling F, Figueroa D, Bååth E. Comparison of factors limiting bacterial growth in different soils. Soil Biol Biochem. 2007;39:2485–95.CAS 

    Google Scholar 
    54.Kaiser K, Zech W. Sorption of dissolved organic nitrogen by acid subsoil horizons and individual mineral phases. Eur J Soil Sci. 2000;51:403–11.CAS 

    Google Scholar 
    55.Barnhisel RI, Bertsch PM. Chlorites and hydroxy-interlayered vermiculite and smectite. In: Dixon JB, Weed SB editors. Minerals in soils environments, 2nd edn. Madison: Soil Science Society of America, Inc.; 1989. p. 729–88.56.Zunino H, Borie F, Aguilera S, Martin JP, Haider K. Decomposition of C-14- labeled glucose, plant and microbial products and phenols in volcanic ash-derived soils of Chile. Soil Biol Biochem. 1982;14:37–43.CAS 

    Google Scholar 
    57.Baldock JA, Nelson PN. In: Sumner ME editor. Handbook of soil science. Boca Raton: CRC Press; 2000. B25–B84.58.Matus F, Rumpel C, Neculman R, Panichini M, Mora ML. Soil carbon storage and stabilisation in andic soils: a review. Catena. 2014;120:102–10.CAS 

    Google Scholar 
    59.Nottingham AT, Griffiths H, Chamberlain PM, Stott AW, Tanner EVJ. Soil priming by sugar and leaf-litter substrates: a link to microbial groups. Appl Soil Ecol. 2009;42:183–90.
    Google Scholar 
    60.McMahon SK, Williams MA, Bottomley PJ, Myrold DD. Dynamics of microbial communities during decomposition of carbon-13 labeled ryegrass fractions in soil. Soil Sci Soc Am J 2005;69:1238–47.CAS 

    Google Scholar 
    61.Vieira S, Sikorski J, Gebala A, Boeddinghaus RS, Marhan S, Rennert T, et al. Bacterial colonization of minerals in grassland soils is selective and highly dynamic. Environ Microbiol. 2020;22:917–33.CAS 
    PubMed 

    Google Scholar 
    62.Mille-Lindblom C, Fischer H, Tranvik LJ. Antagonism between bacteria and fungi: substrate competition and a possible tradeoff between fungal growth and tolerance towards bacteria. Oikos 2006;113:233–42.
    Google Scholar  More

  • in

    Effectiveness assessment of using riverine water eDNA to simultaneously monitor the riverine and riparian biodiversity information

    Driven by the land-to-river and upstream-to-downstream WBIF, biodiversity information across terrestrial and aquatic biomes could be detected in riverine water eDNA6,16, and the monitoring effectiveness of riverine water eDNA relies on the transportation effectiveness of corresponding WBIF6,17,18,19,20. The transportation effectiveness of WBIF mainly relies on the transport capacity, degradation rate, and environmental filtration of WBIF15,21,22,23, which can vary with different seasons and weather conditions26. We hypothesized that the monitoring effectiveness would vary with the seasons and weather conditions. In the present case, the bacterial community richness in riparian soil did not vary with season, whereas the bacterial community composition in riverine water was richest in the autumn, followed by the summer (Figs. 2, 3). The transportation effectiveness of riparian-to-river and upstream-to-downstream WBIF in spring frozen days was significantly lower than in summer rainy days and autumn cloudy days (Tables 1, 2, Supplementary Tables S3, S4). Considering the insufficient read depth on the riverine water samples of summer and autumn groups (Supplementary Fig. S1), the riverine water bacterial community richness and the riparian-to-river transportation effectiveness on summer and autumn were already underestimated. It indicates that the monitoring effectiveness varied with different seasons and weather conditions, and summer and autumn were the optimal seasons, along with rainy days being the optimal weather condition, for using riverine water eDNA to simultaneously monitor the holistic biodiversity information in riverine sites and riparian sites.The biodiversity information detected by water eDNA could originate from living and dead organisms23,26. The detection of biodiversity information that originates from a living organism mainly depends on the dispersal of this living organism11,20. The detection of biodiversity information that originates from a dead organism mainly depends on its transport capacity and degradation rate12,22,29. In summer and autumn, as driven by active organisms, more eDNA was input into the river system. In particular, the surface runoff caused by rain can input more eDNA from terrestrial soil into the river system and can preserve them in soil aggregates30. In the present study, the highest proportion of bacteria in riparian soil was detected in riverine water in summer and autumn, and the rain promoted this phenomenon (Fig. 3 and Table 1, Supplementary Table S3). The proportion of effective upstream-to-downstream WBIF was significantly higher in summer and autumn than in spring, as well as being higher on rainy days than on cloudy days (Table 2). eDNA (originated from dead organisms) degrades over time in a logistic manner (a half-life time)12,22,27,31, which was described in this study as degrading by half-life distance in a lotic system, which integrates the transport capacity and the degradation rate. In the present work, as driven by runoff discharge and flow velocity (Supplementary Table S1), the half-life distance of noneffective WBIF was significantly farther in the summer than in autumn and in spring (Table 2).The biodiversity information monitoring effectiveness of riverine water eDNA, as approximated by the transportation effectiveness of WBIF, was impacted by the eDNA degradation rate in WBIF, and there were taxonomy-specific eDNA degradation rates27, species-specific eDNA degradation rates17, and form-specific eDNA degradation rates28. We hypothesized that the monitoring effectiveness of riverine water eDNA would vary with taxonomic communities. In the present case, the results revealed the detection of a significantly higher monitoring effectiveness of riverine water eDNA (both riparian-to-river and downstream-to-upstream) for bacterial communities than for eukaryotic communities (Tables 3, 4). Considering the insufficient read depth on the bacterial community (16S rRNA gene, Supplementary Fig. S2), the detection capacity on bacterial group was already underestimated. A significantly higher monitoring effectiveness of riverine water eDNA was found for micro-eukaryotic communities (fungi) than for overall eukaryotic communities (including micro- and macro-organisms) (Tables 3, 4). This indicates that the monitoring effectiveness varied with different taxonomic communities, and the effectiveness of monitoring eukaryotic communities was significantly lower than for monitoring bacterial communities; in addition, the effectiveness of monitoring macrobe communities was significantly lower than for monitoring microbe communities.eDNA surveys that are based on metabarcoding can actually acquire information across the taxonomic tree of life5,6,11,32,33. However, eDNA that originates from different taxonomic groups has a different probability of being left in the environment and input into water6,8,9,34. van Bochove et al. inferred that the eDNA contained inside of cells and mitochondria is especially resilient against degradation (i.e., intracellular vs. extracellular effects)28. In the present case, more bacteria than eukaryotes and more microorganisms than macroorganisms (both OTU and species levels) in riparian soil could be detected in riverine water (Table 3). The half-life distance of noneffective WBIF for bacteria (detected by the 16 s RNA gene) was much farther than that for unicellular eukaryotes (detected by the ITS gene, which is mainly unicellular), than that for multicellular eukaryotes (as detected by the CO1 gene, which is mainly multicellular) (Table 4). We inferred that the eDNA contained inside of bacterial cells was more resilient against degradation than that contained inside of unicellular eukaryotic cells (i.e., prokaryotic cells vs. eukaryotic cells), as well as compared to the eDNA contained inside of multicellular eukaryotic cells or extracellular mitochondria (i.e., unicellular eukaryotic cells vs. multicellular eukaryotic cells or extracellular mitochondria).In previous studies, the effectiveness of using water eDNA to monitor terrestrial organisms was indicated by the detection probability8,9,34, and the effectiveness of using downstream water eDNA to monitor upstream organisms was indicated by the detectable distance7,12,17,19,20,35. In this study, we approximated the biodiversity information monitoring effectiveness by the WBIF transportation effectiveness and proposed its assessment framework, in which we described the riparian-to-river monitoring effectiveness with the proportion of biodiversity information in riparian soil that was detected by using riverine water eDNA samples. Additionally, we described the downstream-to-upstream monitoring effectiveness with the proportion of biodiversity information in upstream site water eDNA samples that was detected by 1-km downstream site water eDNA samples, and the runoff distance of that 50% of dead bioinformation (i.e., the bioinformation labeling the biological material that lacked life activity and fertility) could be monitored. These indicators provided new usable assessment tools for designing monitoring projects and for evaluating monitoring results.In the optimal monitoring season and weather condition (a summer rainy day) in the Shaliu river basin on the Qinghai–Tibet Plateau, by using riverine water eDNA, we were able to monitor as much as 87.95% of bacterial species, 76.18% of fungal species, and 53.52% of eukaryotic species from riparian soil, along with as much as 98.69% of bacterial species, 95.71% of fungal species, and 92.41% of eukaryotic species from 1 km upstream (Table 4). The half-life distance of the noneffective WBIF was respectively 17.82 km, 5.96 km, and 5.02 km for bacteria, fungi, and metazoans at the species level (Table 4). When considering the fact that the monitoring effectiveness of eDNA can not only vary with season, weather, and taxonomic communities, but can also vary with rivers and watersheds with different environmental conditions12,17,19,23, more studies on the monitoring effectiveness for each taxonomic community in other watersheds with different environmental conditions are needed.eDNA metabarcoding surveys are relatively cheaper, more efficient, and more accurate than traditional surveys in aquatic systems10,13, although this is certainly not true in all circumstances36. Sales et al. show that the detection probability of using riverine water eDNA to monitor the semi-aquatic and terrestrial mammals in natural lotic ecosystems in the UK was 40–67%, which provided comparable results to conventional survey methods per unit of survey effort for three species (water vole, field vole and red deer); in other words, the results from 3 to 6 water replicates would be equivalent to the results from 3 to 5 latrine surveys and 5–30 weeks of single camera deployment9. In the current case, the riverine water eDNA samples detected 53.52% of eukaryotic species from riparian soil samples. As the bioinformation in WBIF includes the biodiversity information of all taxonomic communities, the information of all taxonomic communities could be monitored by using riverine water eDNA, although variability in monitoring effectiveness exists among different taxonomic communities. We anticipate that, in future biodiversity research, conservation, and management, we will be able to efficiently monitor and assess the aquatic and terrestrial biodiversity by simply using riverine water eDNA samples.In summary, to test the idea of using riverine water eDNA to simultaneously monitor aquatic and terrestrial biodiversity, we proposed a monitoring effectiveness assessment framework, in which the land-to-river monitoring effectiveness was indicated by detection probability, and the upstream-to-downstream monitoring effectiveness was described by the detection probability per kilometer runoff distance and by the half-life distance of dead bioinformation. In our case study, in the Shaliu River watershed on the Qinghai-Tibet Plateau, and on summer rainy days, 43–76% of species information in riparian sites could be detected in adjacent riverine water eDNA samples, 92–99% of species information from upstream sites could be detected in a 1-km downstream eDNA sample, and the half-life distances of dead bioinformation for bacteria was approximately 13–19 km and was approximately 4–6 km for eukaryotes. The indicators in the assessment framework that describe the monitoring effectiveness provide usable assessment tools for designing monitoring projects and for evaluating monitoring results. In future ecological research, biodiversity conservation, and ecosystem management, riverine water eDNA may be a general diagnostic procedure for routine watershed biodiversity monitoring and assessment. More

  • in

    Serotonin transporter (SERT) polymorphisms, personality and problem-solving in urban great tits

    1.Dingemanse, N. J. & Wolf, M. Recent models for adaptive personality differences: A review. Phil. Trans. R. Soc. B 365, 3947–3958. https://doi.org/10.1098/rstb.2010.0221 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Wolf, M., van Doorn, G., Leimar, O. & Weissing, F. J. Life-history trade-offs favour the evolution of animal personalities. Nature 447, 581–584. https://doi.org/10.1038/nature05835 (2007).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. B. 271, 847–852. https://doi.org/10.1098/rspb.2004.2680 (2004).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Sih, A. & Bell, A. M. Insights for behavioral ecology from behavioral syndromes. Adv. Study Behav. 38, 227–281. https://doi.org/10.1016/S0065-3454(08)00005-3 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Sih, A., Bell, A. M. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378. https://doi.org/10.1016/j.tree.2004.04.009 (2004).Article 
    PubMed 

    Google Scholar 
    6.Drent, P. J., van Oers, K. & van Noordwijk, A. J. Realized heritability of personalities in the great tit (Parus major). Proc. R. Soc. B. 270, 45–51. https://doi.org/10.1098/rspb.2002.2168 (2003).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Sol, D., Griffin, A. S., Bartomeus, I. & Boyce, H. Exploring or avoiding novel food resources? The novelty conflict in an invasive bird. PLoS ONE 6, e19535. https://doi.org/10.1371/journal.pone.0019535 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Dammhahn, M., Mazza, V., Schirmer, A., Göttsche, C. & Eccard, J. C. Of city and village mice: Behavioural adjustments of striped field mice to urban environments. Sci. Rep. 10, 13056. https://doi.org/10.1038/s41598-020-69998-6 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Sih, A. & Del Giudice, M. Linking behavioural syndromes and cognition: A behavioural ecological perspective. Phil. Trans. R. Soc. B 367, 2762–2772. https://doi.org/10.1098/rstb.2012.0216 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Stoewe, M. & Kotrschal, K. Behavioural phenotypes may determine whether social context facilitates or delays novel object exploration in ravens (Corvus corax). J. Ornithol. 148, S179–S184. https://doi.org/10.1007/s10336-007-0145-1 (2007).Article 

    Google Scholar 
    11.Guillette, L. M., Reddon, A. R., Hoeschele, M. & Sturdy, C. B. Sometimes slower is better: Slow-exploring birds are more sensitive to changes in a vocal discrimination task. Proc. R. Soc. B 278, 767–773. https://doi.org/10.1098/rspb.2010.1669 (2011).Article 
    PubMed 

    Google Scholar 
    12.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: Heritability of personality. Proc. R. Soc. B 282, 20142201. https://doi.org/10.1098/rspb.2014.2201 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Van Oers, K., De Jong, G., Van Noordwijk, A. J., Kempenaers, B. & Drent, P. J. Contribution of genetics to the study of animal personalities: A review of case studies. Behaviour 142, 1185–1206. https://doi.org/10.1163/156853905774539364 (2005).Article 

    Google Scholar 
    14.Van Oers, K. & Mueller, J. C. Evolutionary genomics of animal personality. Phil. Trans. R. Soc. B 365, 3991–4000. https://doi.org/10.1098/rstb.2010.0178 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Croston, R., Branch, C. L., Kozlovsky, D. Y., Dukas, R. & Pravosudov, V. V. Heritability and the evolution of cognitive traits. Behav. Ecol. 26, 1447–1459. https://doi.org/10.1093/beheco/arv088 (2015).Article 

    Google Scholar 
    16.Quinn, J. L., Cole, E. F., Reed, T. E. & Morand-Ferron, J. Environmental and genetic determinants of innovativeness in a natural population of birds. Phil. Trans. R. Soc. B 371, 20150184. https://doi.org/10.1098/rstb.2015.0184 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Evans, J., Boudreau, K. & Hyman, J. Behavioural syndromes in urban and rural populations of song sparrows. Ethology 116, 588–595. https://doi.org/10.1111/j.1439-0310.2010.01771.x (2010).Article 

    Google Scholar 
    18.Bókony, V., Kulcsár, A., Tóth, Z. & Liker, A. Personality traits and behavioral syndromes in differently urbanized populations of house sparrows (Passer domesticus). PLoS ONE 7, 36639. https://doi.org/10.1371/journal.pone.0036639 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Charmantier, A., Deyeyrier, V., Lambrechts, M., Perret, S. & Grégoire, A. Urbanization is associated with divergence in pace-of-life in great tits. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00053 (2017).Article 

    Google Scholar 
    20.Isaksson, C., Rodewald, A. D. & Gil, D. Editorial: Behavioural and ecological consequences of urban life in birds. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00050 (2018).Article 

    Google Scholar 
    21.Audet, J.-N., Ducatez, S. & Lefebvre, L. The town bird and the country bird: Problem solving and immunocompetence vary with urbanization. Behav. Ecol. 27, 637–644. https://doi.org/10.1093/beheco/arv201 (2016).Article 

    Google Scholar 
    22.Miranda, A. C., Schielzeth, H., Sonntag, T. & Partecke, J. Urbanization and its effects on personality traits: A result of microevolution or phenotypic plasticity. Glob. Change Biol. 19, 2634–2644. https://doi.org/10.1111/gcb.12258 (2013).ADS 
    Article 

    Google Scholar 
    23.Riyahi, S., Björklund, M., Mateos-Gonzalez, F. & Senar, J. C. Personality and urbanization: Behavioural traits and DRD4 SNP830 polymorphisms in great tits in Barcelona city. J. Ethol. 35, 101–108. https://doi.org/10.1007/s10164-016-0496-2 (2017).Article 

    Google Scholar 
    24.Schinka, J. A., Letsch, E. A. & Crawford, F. C. DRD4 and novelty seeking: Results of meta-analyses. Am. J. Med. Genet. 114, 643–648. https://doi.org/10.1002/ajmg.10649 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Chen, C. S., Burton, M., Greenberger, E. & Dmitrieva, J. Population migration and the variation of Dopamine D4 Receptor (DRD4) allele frequencies around the globe. Evol. Hum. Behav. 20, 309–324. https://doi.org/10.1016/S1090-5138(99)00015-X (1999).Article 

    Google Scholar 
    26.Shimada, M. K. et al. Polymorphism in the second intron of dopamine receptor D4 gene in humans and apes. Biochem. Biophys. Res. Commun. 316, 1186–1190. https://doi.org/10.1016/j.bbrc.2004.03.006 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    27.Fidler, A. E. et al. Drd4 gene polymorphisms are associated with personality variation in a passerine bird. Proc. R. Soc. B. 274, 1685–1691. https://doi.org/10.1098/rspb.2007.0337 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Mueller, J. C. et al. Haplotype structure, adaptive history and associations with exploratory behaviour of the DRD4 gene region in four great tit (Parus major) populations. Mol. Ecol. 22, 2797–2809. https://doi.org/10.1111/mec.12282 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Korsten, P. et al. Association between DRD4 gene polymorphism and personality variation in great tits: A test across four wild populations. Mol. Ecol. 19, 832–843. https://doi.org/10.1111/j.1365-294X.2009.04518.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    30.Jiang, W., Shang, S. & Su, Y. Genetic influences on insight problem solving: The role of catechol-O-methyltransferase polymorphisms. Front. Psychol. 6, 1569. https://doi.org/10.3389/fpsyg.2015.01569 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Hopkins, W. et al. Genetic influences on receptive joint attention in chimpanzees (Pan troglodytes). Sci. Rep. 4, 3774. https://doi.org/10.1038/srep03774 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Fitzpatrick, M. J. et al. Candidate genes for behavioural ecology. Trends Ecol. Evol. 20, 96–104. https://doi.org/10.1016/j.tree.2004.11.017 (2005).Article 
    PubMed 

    Google Scholar 
    33.Munafo, M. R., Brown, S. M. & Harkless, K. C. Serotonin transporter (5-HTTLPR) genotype and amygdala activation: A meta-analysis. Biol. Psychiatry 63, 852–857. https://doi.org/10.1016/j.biopsych.2007.08.016 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    34.Staes, N. et al. Serotonin receptor 1A variation is associated with anxiety and agonistic behavior in chimpanzees. Mol. Biol. Evol. 36, 1418–1429. https://doi.org/10.1093/molbev/msz061 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Mueller, J. C. et al. Behaviour-related DRD4 polymorphisms in invasive bird populations. Mol. Ecol. 23, 2876–2885. https://doi.org/10.1111/mec.12763 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Timm, K., Tilgar, V. & Saag, P. DRD4 gene polymorphism in great tits: Gender-specific association with behavioural variation in the wild. Behav. Ecol. Sociobiol. 69, 729–735. https://doi.org/10.1007/s00265-015-1887-z (2015).Article 

    Google Scholar 
    37.Riyahi, S., Sánchez-Delgado, M., Calafell, F., Monk, D. & Senar, J. C. Combined epigenetic and intraspecific variation of the DRD4 and SERT genes influence novelty seeking behaviour in great tit Parus major. Epigenetics 10, 516–525. https://doi.org/10.1080/15592294.2015.1046027 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Holtmann, B. et al. Population differentiation and behavioural association of the two ‘personality’ genes DRD4 and SERT in dunnocks (Prunella modularis). Mol. Ecol. 25, 706–722. https://doi.org/10.1111/mec.13514 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Krause, E. T., Kjaer, J. B., Lüders, C. & van Phi, L. A polymorphism in the 5′-flanking region of the serotonin transporter (5-HTT) gene affects fear-related behaviors of adult domestic chickens. Behav. Brain Res. 14, 92–96. https://doi.org/10.1016/j.bbr.2017.04.051 (2017).CAS 
    Article 

    Google Scholar 
    40.Timm, K., van Oers, K. & Tilgar, V. SERT gene polymorphisms are associated with risk-taking behaviour and breeding parameters in wild great tits. J. Exp. Biol. 221, jeb171595. https://doi.org/10.1242/jeb.171595 (2018).Article 
    PubMed 

    Google Scholar 
    41.Timm, K., Koosa, K. & Tilgar, V. The serotonin transporter gene could play a role in anti-predator behaviour in a forest passerine. J. Ethol. 37, 221–227. https://doi.org/10.1007/s10164-019-00593-7 (2019).Article 

    Google Scholar 
    42.Berger, M., Gray, J. A. & Roth, B. L. The expanded biology of serotonin. Annu. Rev. Med. 60, 355–366. https://doi.org/10.1146/annurev.med.60.042307.110802 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Lesch, K. P. & Merschdorf, U. Impulsivity, aggression, and serotonin: A molecular psychobiological perspective. Behav. Sci. Law 18, 581–604 (2000).CAS 
    Article 

    Google Scholar 
    44.Duke, A. A., Bègue, L., Bell, R. & Eisenlohr-Moul, T. Revisiting the serotonin-aggression relation in humans: A meta-analysis. Psychol. Bull. 139, 1148–1172. https://doi.org/10.1037/a0031544 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Ferrari, P. F., Palanza, P., Parmigiani, S., de Almeida, R. M. & Miczek, K. A. Serotonin and aggressive behavior in rodents and nonhuman primates: Predispositions and plasticity. Eur. J. Pharmacol. 526, 259–273. https://doi.org/10.1016/j.ejphar.2005.10.002 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    46.Bacqué-Cazenave, J. et al. Serotonin in animal cognition and behavior. Int. J. Mol. Sci. 21, 1649. https://doi.org/10.3390/ijms21051649 (2020).CAS 
    Article 
    PubMed Central 

    Google Scholar 
    47.Walker, S. C. et al. Selective prefrontal serotonin depletion impairs acquisition of a detour-reaching task. Eur. J. Neurosci. 23, 3119–3123. https://doi.org/10.1111/j.1460-9568.2006.04826.x (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Cools, R., Roberts, A. C. & Robbins, T. W. Serotoninergic regulation of emotional and behavioural control processes. Trends Cogn. Sci. 12, 31–40. https://doi.org/10.1016/j.tics.2007.10.011 (2008).Article 
    PubMed 

    Google Scholar 
    49.Rudnick, G. & Sandtner, W. Serotonin transport in the 21st century. J. Gen. Physiol. 151, 1248–1264. https://doi.org/10.1085/jgp.201812066 (2018).CAS 
    Article 

    Google Scholar 
    50.Lesch, K. P. et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 274, 1527–1531. https://doi.org/10.1126/science.274.5292.1527 (1996).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    51.Sen, S., Burmeister, M. & Ghosh, D. Meta-analysis of the association between a serotonin transporter promoter polymorphism (5- HTTLPR) and anxiety-related personality traits. Am. J. Med. Genet. 127, 85–89. https://doi.org/10.1002/ajmg.b.20158 (2004).Article 

    Google Scholar 
    52.Karg, K., Burmeister, M., Shedden, K. & Sen, S. The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: Evidence of genetic moderation. Arch. Gen. Psychiatry 68, 444–454. https://doi.org/10.1001/archgenpsychiatry.2010.189 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Beversdorf, D. Q. et al. Influence of serotonin transporter SLC6A4 genotype on the effect of psychosocial stress on cognitive performance: An exploratory pilot study. Cogn. Behav. Neurol. 31, 79–85. https://doi.org/10.1097/WNN.0000000000000153 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Canli, T. & Lesch, P.-K. Long story short: The serotonin transporter in emotion regulation and social cognition. Nat. Neurosci. 10, 1103–1109. https://doi.org/10.1038/nn1964 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    55.Jarrell, H. et al. Polymorphisms in the serotonin reuptake transporter gene modify the consequences of social status on metabolic health in female rhesus monkeys. Physiol. Behav. 93, 807–819. https://doi.org/10.1016/j.physbeh.2007.11.042 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    56.Bennett, A. et al. Early experience and serotonin transporter gene variation interact to influence primate CNS function. Mol. Psychiatry 7, 118–122. https://doi.org/10.1038/sj.mp.4000949 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    57.Golebiowska, J. et al. Serotonin transporter deficiency alters socioemotional ultrasonic communication in rats. Sci. Rep. 9, 20283. https://doi.org/10.1038/s41598-019-56629-y (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Thys, B. et al. The serotonin transporter gene and female personality variation in a free-living passerine. Sci. Rep. 11, 8577. https://doi.org/10.1038/s41598-021-88225-4 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Audet, J.-N. et al. Divergence in problem-solving skills is associated with differential expression of glutamate receptors in wild finches. Sci. Adv. 4, eaao6369. https://doi.org/10.1126/sciadv.aao6369 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Grunst, A. S., Grunst, M. L., Pinxten, R. & Eens, M. Personality and plasticity in neophobia levels vary with anthropogenic disturbance but not toxic metal exposure in urban great tits. Sci. Total Environ. 656, 997–1009. https://doi.org/10.1016/j.scitotenv.2018.11.383 (2019).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    61.Grunst, A. S., Grunst, M. L., Pinxten, R. & Eens, M. Sources of individual variation in problem-solving performance in urban great tits (Parus major): Exploring effects of metal pollution, urban disturbance and personality. Sci. Tot. Environ. 749, 141436. https://doi.org/10.1016/j.scitotenv.2020.141436 (2020).CAS 
    Article 

    Google Scholar 
    62.Thys, B. et al. The female perspective of personality in a wild songbird: Repeatable aggressiveness relates to exploration behavior. Sci. Rep. 7, 7656. https://doi.org/10.1038/s41598-017-08001-1 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Grunst, A. S. et al. An important personality trait varies with blood and plumage metal concentrations in a free-living songbird. Environ. Sci. Technol. 53, 10487–10496. https://doi.org/10.1021/acs.est.9b03548 (2019).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    64.Grunst, A. S. et al. Variation in personality traits across a metal pollution gradient in a free-living songbird. Sci. Total Environ. 630, 668–678. https://doi.org/10.1016/j.scitotenv.2018.02.19 (2018).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    65.Laucht, M. et al. Interaction between the 5-HTTLPR serotonin transporter polymorphism and environmental adversity for mood and anxiety psychopathology: Evidence from a high-risk community sample of young adults. Int. J. Neuropharmacol. 12, 737–747. https://doi.org/10.1017/S1461145708009875 (2009).CAS 
    Article 

    Google Scholar 
    66.Wang, Z. et al. Genome-wide gene by lead exposure interaction analysis identifies UNC5D as a candidate gene for neurodevelopment. Environ. Health 16, 81. https://doi.org/10.1186/s12940-017-0288-3 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Grunst, A. S., Grunst, M. L., Pinxten, R. & Eens, M. Proximity to roads, but not exposure to metal pollution, is associated with accelerated developmental telomere shortening in nestling great tits. Environ. Pollut. 256, 113373. https://doi.org/10.1016/j.envpol.2019.113373 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Dingemanse, N. J. et al. Repeatability and heritability of exploratory behaviour in great tits from the wild. Anim. Behav. 64, 929–937. https://doi.org/10.1006/anbe.2002.2006 (2002).Article 

    Google Scholar 
    69.Solé, X. et al. SNPStats: A web tool for the analysis of association studies. Bioinformatics 22, 1928–1929. https://doi.org/10.1093/bioinformatics/bti283 (2005).Article 

    Google Scholar 
    70.Hecht, M., Bromberg, Y. & Rost, B. Better prediction of functional effects for sequence variants from VarI-SIG 2014: Identification and annotation of genetic variants in the context of structure, function and disease. BMC Genom. 16, S1. https://doi.org/10.1186/1471-2164-16-S8-S1 (2015).CAS 
    Article 

    Google Scholar 
    71.Choi, Y. & Chan, A. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31, 2745–2747. https://doi.org/10.1093/bioinformatics/btv195 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Omasits, U., Ahrens, C. H., Müller, S. & Wollscheid, B. Protter: Interactive protein feature visualization and integration with experimental proteomic data. Bioinformatics 30(6), 884–886. https://doi.org/10.1093/bioinformatics/btt607 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    73.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2019). URL https://www.R-project.org/.74.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2014).Article 

    Google Scholar 
    75.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

    Google Scholar 
    76.Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644. https://doi.org/10.1111/2041-210X.12797 (2017).Article 

    Google Scholar 
    77.Harrison, X. A. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2, e616. https://doi.org/10.7717/peerj.616 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.4.3.01 (2019). https://CRAN.R-project.org/package=emmeans.79.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300. https://doi.org/10.2307/2346101 (1995).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    80.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x (2013).Article 

    Google Scholar 
    81.Lüdecke, D., Makowski, D., Waggoner, P. & Patil, I. performance: Assessment of Regression Models Performance. R package version 0.4.6 (2020). https://CRAN.R-project.org/package=performance.82.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.2.6 (2019). https://CRAN.R-project.org/package=DHARMa.83.Mikros, E. & Diallinas, G. Tales of tails in transporters. Open Biol. 9, 190083. https://doi.org/10.1098/rsob.190083 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    84.Kern, C. et al. The N teminus specifies the switch between transporter modes of the human serotonin transporter. J. Biol. Chem. 292, 3603–3613. https://doi.org/10.1074/jbc.M116.771360 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    85.Visser, M. E., Van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870. https://doi.org/10.1098/rspb.1998.0514 (1998).Article 
    PubMed Central 

    Google Scholar 
    86.Hunt, R., Sauna, Z. E., Ambudkar, S. V., Gottesman, M. M. & Kimchi-Sarfaty, C. Silent (Synonymous) SNPs: Should we care about them? In Single Nucleotide Polymorphisms Methods in Molecular Biology (Methods and Protocols) Vol. 578 (ed. Komar, A.) (Humana Press, 2009). https://doi.org/10.1007/978-1-60327-411-1_2.Chapter 

    Google Scholar 
    87.Grunst, A.S., Grunst, M.L. & Staes, N., Bert, T., Pinxten, R., Eens, M. Data for: Serotonin Transporter (SERT) Polymorphisms, Personality and Problem-Solving in Urban Great Tits. (Dryad Digital Repository, 2021). More

  • in

    The belowground growing season

    1.Piao, S. et al. Glob. Change Biol. 25, 1922–1940 (2019).Article 

    Google Scholar 
    2.Richardson, A. D. et al. Nature 560, 368–371 (2018).CAS 
    Article 

    Google Scholar 
    3.Ma, H. et al. Nat. Ecol. Evol. 5, 1110–1122 (2021).Article 

    Google Scholar 
    4.Mokany, K., Raison, R. J. & Prokushkin, A. S. Glob. Change Biol. 12, 84–96 (2006).Article 

    Google Scholar 
    5.Radville, L., McCormack, M. L., Post, E. & Eissenstat, D. M. J. Exp. Bot. 67, 3617–3628 (2016).CAS 
    Article 

    Google Scholar 
    6.Liu, H. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01244-x (2021).7.Freschet, G. T. et al. New Phytol. 232, 1123–1158 (2021).Article 

    Google Scholar 
    8.Clemmensen, K. E. et al. Science 339, 1615–1618 (2013).CAS 
    Article 

    Google Scholar 
    9.Sokol, N. W. & Bradford, M. A. Nat. Geosci. 12, 46–53 (2019).CAS 
    Article 

    Google Scholar 
    10.Jones, D. L., Nguyen, C. & Finlay, R. D. Plant Soil 321, 5–33 (2009).CAS 
    Article 

    Google Scholar 
    11.Abramoff, R. Z. & Finzi, A. C. New Phytol. 205, 1054–1061 (2015).Article 

    Google Scholar 
    12.Warren, J. M. et al. New Phytol. 205, 59–78 (2015).Article 

    Google Scholar 
    13.Blume-Werry, G., Wilson, S. D., Kreyling, J. & Milbau, A. New Phytol. 209, 978–986 (2016).CAS 
    Article 

    Google Scholar 
    14.Sloan, V. L., Fletcher, B. J. & Phoenix, G. K. J. Ecol. 104, 239–248 (2016).CAS 
    Article 

    Google Scholar 
    15.Fu, Y. H. et al. Nature 526, 104–107 (2015).CAS 
    Article 

    Google Scholar  More