Philippa Kaur

To investigate whether vagrancy is associated with geomagnetic disturbance and solar activity, we developed a method for quantifying the relative vagrancy of spatiotemporal records for 152 North American landbird species (nfall = 150, nspring = 124). While vagrancy is often treated as a binary classification (i.e., an individual is either a vagrant or not) and then used as a discrete variable (i.e., a count of total vagrants in an area)16,18, here we calculated it as a continuous variable by combining two large-scale ornithological datasets—captures and encounters of individually marked birds from the USGS Bird Banding Lab (BBL)49 and weekly, species-specific abundance maps for the continental United States from the eBird Status and Trends (hereafter, eBird S&T; via the R package ‘ebirdst’, version 2.1.0)69. Banding records have the advantage over other potential databases of vagrancy records (such as eBird or rare bird lists) in that efforts are long-term, continent-wide, have limited false positives, and have only one record per individual. Additionally, eBird S&T has the advantage over static range maps in that they provide weekly predictions and incorporate relative abundance. With these two data sources, we constructed a species-specific vagrancy value (Fig. S1), that measures the spatiotemporal rarity for every banding record. Inclusion of all banding records rather than just rare records allowed for the analysis of the dispersion of whole species populations, mitigating the potential bias of effort in banding operations (i.e., more vagrant records with greater effort). We then used hierarchical Bayesian random-effects models to estimate the strength of the association between geomagnetic disturbance, solar activity, and avian vagrancy.Species data and inclusionWe considered all full—or partial-migrant landbird species with a breeding, non-breeding, or migratory range in the United States or Canada. To do this, we used species distribution maps accessed through Birds of the World70. Landbird species likely to be caught through banding efforts (excluding species like raptors, nightjars, and swifts) that regularly occur in  > 3 but  10 km. Each banding record included the date, latitude and longitude (and precision), species, and age (if known;71). Banding records were filtered to those captures that occurred during the species-specific migration period as defined by eBird S&T69. eBird S&T approximates stationary and migratory periods by determining when the distribution of whole species population is moving69. Our use of banding records within species-specific eBird S&T migratory periods was designed to maximize the proportion of migrant birds in the analysis, but likely excludes some early and late records of migrating individuals.Banding records of species that underwent taxonomic divisions or aggregations during the study period were eliminated if the date occurred during a period in which the species identity according to modern taxonomy is indeterminate (see Supplement 2). Taxonomic reclassifications were not considered when species divisions/aggregations would only affect records from outside North America, such as the split of a Southern American taxon, Chestnut-collared Swallow (Petrochelidon rufocollaris) from its North American counterpart, Cave Swallow (Petrochelidon fulva). In these cases, we assumed all banding records during the study period were of the North American species. For a full list of periods where species records were excluded, see Supplement 2. Species with  More

Wingard, G. L., Bernhardt, C. E. & Wachnicka, A. H. The role of paleoecology in restoration and resource management—the past as a guide to future decision-making: review and example from the Greater Everglades ecosystem, U.S.A. Front. Ecol. Evol 5, 11 (2017).Article 

Google Scholar 
Gillson, L., Dirk, C. & Gell, P. Using long-term data to inform a decision pathway for restoration of ecosystem resilience. Anthropocene 36, 100315 (2021).Article 

Google Scholar 
Nieto-Lugilde, D. et al. Time to better integrate paleoecological research infrastructures with neoecology to improve understanding of biodiversity long-term dynamics and to inform future conservation. Environ. Res. Lett. 16, 095005 (2021).Article 

Google Scholar 
Leo, G. A. D. & Levin, S. A. The multifaceted aspects of ecosystem integrity. Conserv. Ecol. 1, 3 (1997).
Google Scholar 
Mason, N. & Mouillot, D. in Encyclopedia of Biodiversity (ed. Levin, S. A.) 597–608 (Elsevier, 2013).Carvalho, F. et al. A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages. PLoS ONE 14, e0216698 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brussel, T. & Brewer, S. C. Functional paleoecology and the pollen-plant functional trait linkage. Front. Ecol. Evol 8, 564609 (2021).Article 

Google Scholar 
Brussel, T., Minckley, T. A., Brewer, S. C. & Long, C. J. Community-level functional interactions with fire track long-term structural development and fire adaptation. J. Veg. Sci. 29, 450–458 (2018).Article 

Google Scholar 
Barboni, D. et al. Relationships between plant traits and climate in the Mediterranean region: a pollen data analysis. J. Veg. Sci. 15, 635–646 (2004).Article 

Google Scholar 
Reitalu, T. et al. Novel insights into post-glacial vegetation change: functional and phylogenetic diversity in pollen records. J. Veg. Sci. 26, 911–922 (2015).Article 

Google Scholar 
Blaus, A. et al. Modern pollen-plant diversity relationships inform palaeoecological reconstructions of functional and phylogenetic diversity in calcareous fens. Front. Ecol. Evol 8, 207 (2020).Article 

Google Scholar 
Morris, J. L. et al. Stable or seral? Fire-driven alternative states in aspen forests of western North America. Biol. Lett. 15, 20190011 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Ordonez, A. & Svenning, J.-C. Greater tree species richness in eastern North America compared to Europe is coupled to denser, more clustered functional trait space filling, not to trait space expansion. Glob. Ecol. Biogeogr. 27, 1288–1299 (2018).Article 

Google Scholar 
van der Sande, M. T. et al. A 7000-year history of changing plant trait composition in an Amazonian landscape; the role of humans and climate. Ecol. Lett. 22, 925–935 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Lacourse, T. & Adeleye, M. A. Climate and species traits drive changes in Holocene forest composition along an elevation gradient in Pacific Canada. Front. Ecol. Evol 10, 838545 (2022).Article 

Google Scholar 
Lacourse, T. Environmental change controls postglacial forest dynamics through interspecific differences in life-history traits. Ecology 90, 2149–2160 (2009).Article 
PubMed 

Google Scholar 
Veeken, A., Santos, M. J., McGowan, S., Davies, A. L. & Schrodt, F. Pollen-based reconstruction reveals the impact of the onset of agriculture on plant functional trait composition. Ecol. Lett. 25, 1937–1951 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
GILL, A. M. Fire and the Australian flora: a review. Aust. For. 38, 4–25 (1975).Article 

Google Scholar 
Crisp, M. D., Burrows, G. E., Cook, L. G., Thornhill, A. H. & Bowman, D. M. J. S. Flammable biomes dominated by eucalypts originated at the Cretaceous–Palaeogene boundary. Nat. Commun. 2, 193 (2011).Article 
PubMed 

Google Scholar 
Keith, D. A. Australian Vegetation (Cambridge Univ. Press, 2017).Woinarski, J. C. Z., Burbidge, A. A. & Harrison, P. L. Ongoing unraveling of a continental fauna: decline and extinction of Australian mammals since European settlement. Proc. Natl Acad. Sci. USA 112, 4531–4540 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Broadhurst, L. & Coates, D. Plant conservation in Australia: current directions and future challenges. Plant Divers. 39, 348–356 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Adeleye, M. A., Connor, S. E., Haberle, S. G., Herbert, A. & Brown, J. European colonization and the emergence of novel fire regimes in southeast Australia. Anthr. Rev. https://doi.org/10.1177/205301962110446 (2021).Gallagher, R. V. et al. High fire frequency and the impact of the 2019–2020 megafires on Australian plant diversity. Divers. Distrib. 27, 1166–1179 (2021).Article 

Google Scholar 
Gallagher, R. V. et al. An integrated approach to assessing abiotic and biotic threats to post-fire plant species recovery: lessons from the 2019–2020 Australian fire season. Glob. Ecol. Biogeogr. 31, 2056–2069.Mariani, M. et al. Disruption of cultural burning promotes shrub encroachment and unprecedented wildfires. Front. Ecol. Environ. 20, 292–300 (2022).Article 

Google Scholar 
Williams, A. N., Mooney, S. D., Sisson, S. A. & Marlon, J. Exploring the relationship between Aboriginal population indices and fire in Australia over the last 20,000 years. Palaeogeogr. Palaeoclimatol. Palaeoecol. 432, 49–57 (2015).Article 

Google Scholar 
Bird, M. I., O’Grady, D. & Ulm, S. Humans, water, and the colonization of Australia. Proc. Natl Acad. Sci. USA 113, 11477–11482 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Adeleye, M. A., Haberle, S. G., Connor, S. E., Stevenson, J. & Bowman, D. M. J. S. Indigenous fire-managed landscapes in Southeast Australia during the Holocene—new insights from the Furneaux Group Islands, Bass Strait. Fire 4, 17 (2021).Article 

Google Scholar 
Fletcher, M.-S., Romano, A., Connor, S., Mariani, M. & Maezumi, S. Y. Catastrophic bushfires, Indigenous fire knowledge and reframing science in Southeast Australia. Fire 4, 61 (2021).Article 

Google Scholar 
Fletcher, M.-S., Hall, T. & Alexandra, A. N. The loss of an indigenous constructed landscape following British invasion of Australia: an insight into the deep human imprint on the Australian landscape. Ambio 50, 138–149 (2021).Article 
CAS 
PubMed 

Google Scholar 
Adeleye, M. A. et al. Long-term drivers of vegetation turnover in Southern Hemisphere temperate ecosystems. Glob. Ecol. Biogeogr. 30, 557–571 (2021).Article 

Google Scholar 
Kershaw, A. P., D’Costa, D. M., McEwen Mason, J. R. C. & Wagstaff, B. E. Palynological evidence for Quaternary vegetation and environments of mainland southeastern Australia. Quat. Sci. Rev. 10, 391–404 (1991).Article 

Google Scholar 
Colhoun, E. A. & Shimeld, P. W. in Peopled Landscapes: Archaeological and Biogeographic Approaches to Landscapes (eds. Haberle, S. G. & David, B.) 297–328 (ANU Press, 2012).Madani, N. et al. Future global productivity will be affected by plant trait response to climate. Sci. Rep. 8, 2870 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Squire, D. T. et al. Likelihood of unprecedented drought and fire weather during Australia’s 2019 megafires. npj Clim. Atmos. Sci. 4, 64 (2021).Article 

Google Scholar 
Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G. & Pitman, A. J. Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophys. Res. Lett. 47, e2020GL087820 (2020).Article 

Google Scholar 
Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).Article 
PubMed 

Google Scholar 
Arias, P. A. et al. In Climate Change 2021: The Physical Science Basis (eds. Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Laliberté, E., Legendre, P. & Shipley, B. FD: measuring (FD) from multiple traits, and other tools for functional ecology. R package version 1.0-12 (2014).Laliberté, E. & Legendre, P. A distance-based framework for measuring from multiple traits. Ecology 91, 299–305 (2010).Article 
PubMed 

Google Scholar 
Tilman, D. in Encyclopedia of Biodiversity (ed. Levin, S. A.) 109–120 (Elsevier, 2001).Fletcher, M.-S. & Moreno, P. I. Have the Southern Westerlies changed in a zonally symmetric manner over the last 14,000 years? A hemisphere-wide take on a controversial problem. Quat. Int. 253, 32–46 (2012).Article 

Google Scholar 
Markgraf, V., Bradbury, J. P. & Busby, J. R. Paleoclimates in Southwestern Tasmania during the last 13,000 years. PALAIOS 1, 368 (1986).Article 

Google Scholar 
Moros, M. et al. Hydrographic shifts south of Australia over the last deglaciation and possible interhemispheric linkages. Quat. Res. 102, 130–141 (2021).Article 

Google Scholar 
Perner, K. et al. Heat export from the tropics drives mid to late Holocene palaeoceanographic changes offshore southern Australia. Quat. Sci. Rev. 180, 96–110 (2018).Article 

Google Scholar 
Mariani, M. & Fletcher, M.-S. Long-term climate dynamics in the extra-tropics of the South Pacific revealed from sedimentary charcoal analysis. Quat. Sci. Rev. 173, 181–192 (2017).Article 

Google Scholar 
McWethy, D. B. et al. A conceptual framework for predicting temperate ecosystem sensitivity to human impacts on fire regimes. Glob. Ecol. Biogeogr. 22, 900–912 (2013).Article 

Google Scholar 
Baker, A. G., Catterall, C. & Benkendorff, K. Invading rain forest pioneers initiate positive fire suppression feedbacks that reinforce shifts from open to closed forest in eastern Australia. J. Veg. Sci. 32, e13102 (2021).Article 

Google Scholar 
Lambeck, K. & Chappell, J. Sea level change through the last glacial cycle. Science 292, 679–686 (2001).Article 
CAS 
PubMed 

Google Scholar 
Sloss, C. R., Murray-Wallace, C. V. & Jones, B. G. Holocene sea-level change on the southeast coast of Australia: a review. Holocene 17, 999–1014 (2007).Article 

Google Scholar 
Adeleye, M. A. et al. Holocene heathland development in temperate oceanic Southern Hemisphere: key drivers in a global context. J. Biogeogr. 48, 1048–1062 (2021).Article 

Google Scholar 
McWethy, D. B., Haberle, S. G., Hopf, F. & Bowman, D. M. J. S. Aboriginal impacts on fire and vegetation on a Tasmanian island. J. Biogeogr. 44, 1319–1330 (2017).Article 

Google Scholar 
Hope, G. Vegetation and fire response to late Holocene human occupation in island and mainland north west Tasmania. Quat. Int. 59, 47–60 (1999).Article 

Google Scholar 
Sim, R. The Archaeology of Isolation? Prehistoric Occupation in the Furneaux Group of Islands, Bass Strait, Tasmania. PhD thesis, Australian National Univ. (1998).Lourandos, H. Intensification: a late Pleistocene-Holocene archaeological sequence from Southwestern Victoria. Archaeol. Ocean. 18, 81–94 (1983).Article 

Google Scholar 
Bowman, D. M. J. S. The impact of Aboriginal landscape burning on the Australian biota. New Phytol. 140, 385–410 (1998).Article 
CAS 
PubMed 

Google Scholar 
Iversen, J. in Systematics of Today (ed. Hedberg, O.) 210–215 (Acta Universitatis Upsaliensis/Uppsala Universitets Årsskrift, 1958).Colhoun, E. A. Application of Iversen’s glacial–interglacial cycle to interpretation of the late last glacial and Holocene vegetation history of western Tasmania. Quat. Sci. Rev. 15, 557–580 (1996).Article 

Google Scholar 
Adeleye, M. A., Haberle, S. G., Ondei, S. & Bowman, D. M. J. S. Ecosystem transformation following the mid-nineteenth century cessation of Aboriginal fire management in Cape Pillar, Tasmania. Reg. Environ. Change 22, 99 (2022).Article 

Google Scholar 
Mccann, K. The diversity–stability debate. Nature 405, 228–233 (2000).Article 
CAS 
PubMed 

Google Scholar 
Hallett, L. M., Stein, C. & Suding, K. N. Functional diversity increases ecological stability in a grazed grassland. Oecologia 183, 831–840 (2017).Article 
PubMed 

Google Scholar 
Bello, Fde et al. Functional trait effects on ecosystem stability: assembling the jigsaw puzzle. Trends Ecol. Evol. 36, 822–836 (2021).Article 
PubMed 

Google Scholar 
Lucini, F. A., Morone, F., Tomassone, M. S. & Makse, H. A. Diversity increases the stability of ecosystems. PLoS ONE 15, e0228692 (2020).Article 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).Article 
CAS 
PubMed 

Google Scholar 
Gallagher, R. V., Hughes, L. & Leishman, M. R. Species loss and gain in communities under future climate change: consequences for functional diversity. Ecography 36, 531–540 (2013).Article 

Google Scholar 
Song, Y., Wang, P., Li, G. & Zhou, D. Relationships between and ecosystem functioning: a review. Acta Ecol. Sin. 34, 85–91 (2014).Article 
CAS 

Google Scholar 
Li, W. et al. Plant can be independent of species diversity: observations based on the impact of 4-yrs of nitrogen and phosphorus additions in an alpine meadow. PLoS ONE 10, e0136040 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Lewis, C. J., Huang, Y., Siems, S. T. & Manton, M. J. Wintertime orographic precipitation over western Tasmania. J. South. Hemisphere Earth Syst. Sci. 68, 22–40 (2018).Article 

Google Scholar 
Andrew, S. C. et al. Functional diversity of the Australian flora: strong links to species richness and climate. J. Veg. Sci. 32, e13018 (2021).Article 

Google Scholar 
Biswas, S. R. & Mallik, A. U. Species diversity and relationship varies with disturbance intensity. Ecosphere 2, art52 (2011).Article 

Google Scholar 
Gallagher, R. V. et al. A guide to using species trait data in conservation. One Earth 4, 927–936 (2021).Article 

Google Scholar 
Siefert, A. et al. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecol. Lett. 18, 1406–1419 (2015).Article 
PubMed 

Google Scholar 
Harris, S. & Kitchener, A. From Forest to Fjaeldmark. Descriptions of Tasmania’s Vegetation (Department of Primary Industries, Water and Environment, Tasmania, 2005).Adeleye, M. A., Haberle, S. G., McWethy, D., Connor, S. E. & Stevenson, J. Environmental change during the last glacial on an ancient land bridge of southeast Australia. J. Biogeogr. 48, 2946–2960 (2021).Article 

Google Scholar 
Hopf, F. V. L., Colhoun, E. A. & Barton, C. E. Late-glacial and Holocene record of vegetation and climate from Cynthia Bay, Lake St Clair, Tasmania. J. Quat. Sci. 15, 725–732 (2000).Article 

Google Scholar 
Stahle, L. N., Whitlock, C. & Haberle, S. G. A 17,000-year-long record of vegetation and fire from Cradle Mountain National Park, Tasmania. Front. Ecol. Evol 4, 82 (2016).Article 

Google Scholar 
Michael-Shawn, F. et al. The influence of climatic change, fire and species invasion on a Tasmanian temperate rainforest system over the past 18,000 years. Quat. Sci. Rev. 260, 106824 (2021).Article 

Google Scholar 
Climate and Water Availability in South-Eastern Australia: A Synthesis of Findings From Phase 2 of the South-Eastern Australian Climate initiative (SEACI) (CSIRO, 2012); https://doi.org/10.4225/08/584af3986fe96Australian Climate Influences (Commonwealth of Australia, Bureau of Meteorology, 2010); http://www.bom.gov.au/watl/about-weather-and-climate/australian-climate-influences.shtmlRisbey, J. S., Pook, M. J., McIntosh, P. C., Wheeler, M. C. & Hendon, H. H. On the remote drivers of rainfall variability in Australia. Mon. Weather Rev. 137, 3233–3253 (2009).Article 

Google Scholar 
Mariani, M., Fletcher, M.-S., Holz, A. & Nyman, P. ENSO controls interannual fire activity in southeast Australia. Geophys. Res. Lett. 43, 10891–10900 (2016).Article 

Google Scholar 
Mariani, M. & Fletcher, M.-S. The Southern Annular Mode determines interannual and centennial-scale fire activity in temperate southwest Tasmania, Australia. Geophys. Res. Lett. 43, 1702–1709 (2016).Article 

Google Scholar 
Herbert, A. V. & Harrison, S. P. Evaluation of a modern-analogue methodology for reconstructing Australian palaeoclimate from pollen. Rev. Palaeobot. Palynol. 226, 65–77 (2016).Article 

Google Scholar 
Blaauw, M. et al. rbacon: Age-depth modelling using Bayesian statistics. R package version 4.2.0 (2022).Hogg, A. G. et al. SHCal20 Southern Hemisphere calibration, 0–55,000 years cal BP. Radiocarbon 62, 759–778 (2020).Article 
CAS 

Google Scholar 
Falster, D. et al. AusTraits, a curated plant trait database for the Australian flora. Sci. Data 8, 254 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pérez-Harguindeguy, N. et al. Corrigendum to: New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 64, 715–716 (2016).Article 

Google Scholar 
Wright, I. J. et al. Global climatic drivers of leaf size. Science 357, 917–921 (2017).Article 
CAS 
PubMed 

Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).Article 
CAS 
PubMed 

Google Scholar 
Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).Article 

Google Scholar 
Moles, A. T. & Westoby, M. Seed size and plant strategy across the whole life cycle. Oikos 113, 91–105 (2006).Article 

Google Scholar 
Leishman, M. R. & Westoby, M. The role of seed size in seedling establishment in dry soil conditions—experimental evidence from semi-arid species. J. Ecol. 82, 249–258 (1994).Article 

Google Scholar 
Falster, D. S. & Westoby, M. Plant height and evolutionary games. Trends Ecol. Evol. 18, 337–343 (2003).Article 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).Article 
PubMed 

Google Scholar 
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).Article 

Google Scholar 
Pakeman, R. J. Functional trait metrics are sensitive to the completeness of the species’ trait data? Methods Ecol. Evol. 5, 9–15 (2014).Article 

Google Scholar 
Scheiner, S. M., Kosman, E., Presley, S. J. & Willig, M. R. Decomposing. Methods Ecol. Evol. 8, 809–820 (2017).Article 

Google Scholar 
Ripley, B. et al. MASS: Support functions and datasets for venables and Ripley’s MASS. R package version ??? (2022).Moy, C. M., Seltzer, G. O., Rodbell, D. T. & Anderson, D. M. Variability of El Niño/Southern Oscillation activity at millennial timescales during the Holocene epoch. Nature 420, 162–165 (2002).Article 
CAS 
PubMed 

Google Scholar  More

Doran JW. Soil health and global sustainability: translating science into practice. Agric Ecosyst Environ. 2002;88:119–27.Article 

Google Scholar 
Wander MM, Cihacek LJ, Coyne M, Drijber RA, Grossman JM, Gutknecht JLM, et al. Developments in Agricultural Soil Quality and Health: Reflections by the Research Committee on Soil Organic Matter Management. Front Environ Sci. 2019;7:1–9.Article 

Google Scholar 
Stewart RD, Jian J, Gyawali AJ, Thomason WE, Badgley BD, Reiter MS, et al. What we talk about when we talk about soil health. Agric Environ Lett. 2018;3:5–9.Article 

Google Scholar 
Rinot O, Levy GJ, Steinberger Y, Svoray T, Eshel G. Soil health assessment: A critical review of current methodologies and a proposed new approach. Sci Total Environ. 2019;648:1484–91.Article 
CAS 

Google Scholar 
Hurisso TT, Culman SW, Zhao K. Repeatability and spatiotemporal variability of emerging soil health indicators relative to routine soil nutrient tests. Soil Sci Soc Am J. 2018;82:939–48.Article 
CAS 

Google Scholar 
Lilburne L, Sparling G, Schipper L. Soil quality monitoring in New Zealand: Development of an interpretative framework. Agric Ecosyst Environ. 2004;104:535–44.Article 

Google Scholar 
Moebius-Clune BN, Moebius-Clune DJ, Gugino BK, Idowu OJ, Schindelbeck RR, Ristow AJ, et al. Comprehensive assessment of soil health – the Cornell framework manual, 3rd ed. Ithaca, NY:Cornell University; 2017.Fierer N, Wood SA, Bueno de Mesquita CP. How microbes can, and cannot, be used to assess soil health. Soil Biol Biochem. 2021;153:108111.Article 
CAS 

Google Scholar 
Amsili JP, van Es HM, Schindelbeck RR. Cropping system and soil texture shape soil health outcomes and scoring functions. Soil Secur. 2021;4:100012.Article 

Google Scholar 
Wade J, Culman SW, Gasch CK, Lazcano C, Maltais-Landry G, Margenot AJ, et al. Rigorous, empirical, and quantitative: a proposed pipeline for soil health assessments. Soil Biol Biochem. 2022;170:108710.Article 
CAS 

Google Scholar 
Simonin M, Voss KA, Hassett BA, Rocca JD, Wang SY, Bier RL, et al. In search of microbial indicator taxa: shifts in stream bacterial communities along an urbanization gradient. Environ Microbiol. 2019;21:3653–68.Article 

Google Scholar 
Bissett A, Brown MV, Siciliano SD, Thrall PH. Microbial community responses to anthropogenically induced environmental change: Towards a systems approach. Ecol Lett. 2013;16:128–39.Article 

Google Scholar 
Wilhelm RC, Cardenas E, Maas KR, Leung H, McNeil L, Berch S, et al. Biogeography and organic matter removal shape long-term effects of timber harvesting on forest soil microbial communities. ISME J. 2017;11:2552–68.Article 

Google Scholar 
Gibbons SM, Scholz M, Hutchison AL, Dinner AR, Gilbert JA, Colemana ML, et al. Disturbance regimes predictably alter diversity in an ecologically complex bacterial system. MBio. 2016;7:1–10.Article 

Google Scholar 
Trivedi P, Delgado-Baquerizo M, Anderson IC, Singh BK. Response of soil properties and microbial communities to agriculture: Implications for primary productivity and soil health indicators. Front Plant Sci. 2016;7:1–13.Article 

Google Scholar 
Jiao S, Xu Y, Zhang J, Hao X. Core microbiota in agricultural soils and their potential associations with nutrient cycling. mSystems. 2019;4:1–16.Article 

Google Scholar 
Chang HX, Haudenshield JS, Bowen CR, Allen R, Iii W, Parnell JJ, et al. Metagenome-wide association study and machine learning prediction of bulk soil microbiome and crop productivity. Front Microbiol. 2017;8:519.Article 

Google Scholar 
Trivedi P, Delgado-Baquerizo M, Jeffries TC, Trivedi C, Anderson IC, Lai K, et al. Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content. Environ Microbiol. 2017;19:3070–86.Article 
CAS 

Google Scholar 
Armbruster M, Goodall T, Hirsch PR, Ostle N, Puissant J, Fagan KC, et al. Bacterial and archaeal taxa are reliable indicators of soil restoration across distributed calcareous grasslands. Eur J Soil Sci. 2021;72:2430–44.Rieke EL, Cappellazzi SB, Cope M, Liptzin D, Mac Bean G, Greub KLH, et al. Linking soil microbial community structure to potential carbon mineralization: A continental scale assessment of reduced tillage. Soil Biol Biochem. 2022;168:108618.Article 
CAS 

Google Scholar 
Wilhelm RC, Van Es HM, Buckley DH. Predicting measures of soil health using the microbiome and supervised machine learning. Soil Biol Biochem. 2022;164:108472.Article 
CAS 

Google Scholar 
Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2: An improved and customizable approach for metagenome inference 2. bioRxiv. 2020. https://doi.org/10.1101/672295.Gravuer K, Eskelinen A. Nutrient and rainfall additions shift phylogenetically estimated traits of soil microbial communities. Front Microbiol. 2017;8:1–16.Article 

Google Scholar 
Chen Y, Maier RM, Barberán A, Neilson JW, Kushwaha P, Maier RM, et al. Life-history strategies of soil microbial communities in an arid ecosystem. ISME J. 2021;15:649–57.Article 
CAS 

Google Scholar 
Fierer N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.Article 
CAS 

Google Scholar 
Malik AA, Martiny JBHH, Brodie EL, Martiny AC, Treseder KK, Allison SD, et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 2020;14:1–9.Article 
CAS 

Google Scholar 
Roller BRK, Stoddard SF, Schmidt TM. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat Microbiol. 2016;1:1–7.Nunan N, Schmidt H, Raynaud X, Schmidt H. The ecology of heterogeneity: Soil bacterial communities and C dynamics. Philos Trans R Soc B Biol Sci. 2020;375:20190249.Article 
CAS 

Google Scholar 
Grime JP. Evidence for the existence of three primary strategies in plants and its relevance for ecological and evolutionary theory. Am Nat. 1977;111:1169–94.Article 

Google Scholar 
Barnett S, Youngblut ND, Koechli CN, Buckley DH. Multisubstrate DNA stable isotope probing reveals guild structure of bacteria that mediate soil carbon cycling. PNAS. 2021;118:e2115292118.Wilhelm RC, Pepe-Ranney C, Weisenhorn P, Lipton M, Buckley DH. Competitive exclusion and metabolic dependency among microorganisms structure the cellulose economy of an agricultural soil. MBio. 2021;12:1–19.Article 

Google Scholar 
Schmidt R, Gravuer K, Bossange AV, Mitchell J, Scow K. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS ONE. 2018;13:1–19.Article 

Google Scholar 
Neal AL, Hughes D, Clark IM, Jansson JK, Hirsch PR. Microbiome Aggregated Traits and Assembly Are More Sensitive to Soil Management than Diversity. mSystems 2021;6:e0105620.Lupatini M, Korthals GW, de Hollander M, Janssens TKS, Kuramae EE. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front Microbiol. 2017;7:1–13.Article 

Google Scholar 
Koechli C, Campbell AN, Pepe-ranney C, Buckley DH. Assessing fungal contributions to cellulose degradation in soil by using high- throughput stable isotope probing. Soil Biol Biochem. 2019;130:150–8.Article 
CAS 

Google Scholar 
Furtak K, Grządziel J, Gałązka A, Niedźwiecki J. Prevalence of unclassified bacteria in the soil bacterial community from floodplain meadows (fluvisols) under simulated flood conditions revealed by a metataxonomic approachss. Catena. 2020;188:104448.Article 
CAS 

Google Scholar 
Schmidt R, Mitchell J, Scow K. Cover cropping and no-till increase diversity and symbiotroph: saprotroph ratios of soil fungal communities. Soil Biol Biochem. 2019;129:99–109.Article 
CAS 

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

Google Scholar 
Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.Article 

Google Scholar 
Levy R, Borenstein E. Reverse Ecology: From systems to environments and back. Adv Exp Med Biol. 2012;751:329–45.Article 
CAS 

Google Scholar 
Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.Article 

Google Scholar 
Hamilton JP, Neeno-Eckwall EC, Adhikari BN, Perna NT, Tisserat N, Leach JE, et al. The Comprehensive Phytopathogen Genomics Resource: a web-based resource for data-mining plant pathogen genomes. Database. 2011;2011:bar053.Detheridge AP, Brand G, Fychan R, Crotty FV, Sanderson R, Griffith GW, et al. The legacy effect of cover crops on soil fungal populations in a cereal rotation. Agric Ecosyst Environ. 2016;228:49–61.Article 

Google Scholar 
McKenna TP, Crews TE, Kemp L, Sikes BA. Community structure of soil fungi in a novel perennial crop monoculture, annual agriculture, and native prairie reconstruction. PLoS ONE. 2020;15:1–15.Article 

Google Scholar 
Rocca JD, Simonin M, Blaszczak JR, Ernakovich JG, Gibbons SM, Midani FS, et al. The Microbiome Stress Project: Toward a global meta-analysis of environmental stressors and their effects on microbial communities. Front Microbiol. 2019;9:3272.Article 

Google Scholar 
Ramirez KS, Knight CG, De Hollander M, Brearley FQ, Constantinides B, Cotton A, et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat Microbiol. 2018;3:189–96.Article 
CAS 

Google Scholar 
Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.Article 
CAS 

Google Scholar 
Lagkouvardos I, Joseph D, Kapfhammer M, Giritli S, Horn M, Haller D, et al. IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci Rep. 2016;6:1–9.Article 

Google Scholar 
Jurburg SD, Konzack M, Eisenhauer N, Heintz-Buschart A. The archives are half-empty: a field-wide assessment of the availability of microbial community sequencing data. Commun Biol. 2020;3:474.Emerson JB, Everhart SE, Eversole K, Frost KE, Herr JR, Huerta AI, et al. Community-driven metadata standards for agricultural microbiome research. Phytobiomes J. 2020; 4:115-121.Anderson TH, Martens R. DNA determinations during growth of soil microbial biomasses. Soil Biol Biochem. 2013;57:487–95.Article 
CAS 

Google Scholar 
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the Miseq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20.Article 
CAS 

Google Scholar 
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.Article 
CAS 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.Article 

Google Scholar 
Harrell F, Dupont C. Hmisc: Harrell miscellaneous. R Package 2015.Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc. 1995;57:289–300.
Google Scholar 
Weiss S, Van Treuren W, Lozupone C, Faust K, Friedman J, Deng Y, et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 2016;10:1669–81.Article 
CAS 

Google Scholar 
Mills RH, Dulai PS, Vázquez-Baeza Y, Sauceda C, Daniel N, Gerner RR, et al. Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity. Nat Microbiol. 2022;7:262–76.Article 
CAS 

Google Scholar 
De Cáceres M, Legendre P, De Caceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.Article 

Google Scholar 
Markowitz VM, Ivanova NN, Szeto E, Palaniappan K, Chu K, Dalevi D, et al. IMG/M: A data management and analysis system for metagenomes. Nucleic Acids Res. 2008;36:534–8.Article 

Google Scholar 
Stoddard SF, Smith BJ, Hein R, Roller BRK, Schmidt M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 2015;43:593–8.R Core Team. R: a language and environment for statistical computing. R Foundation. 2020.Wickham H. Reshaping data with the reshape package. J Stat Soft. 2007;21:1–20.Article 

Google Scholar 
Wickham H. The split-apply-combine strategy for data analysis. J Stat Soft. 2009;40:1–29.
Google Scholar 
Wickham H. Elegant graphics for data analysis. Media. 2009;35:211.
Google Scholar 
McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 2013;8:e61217.Grömping U. Relative importance for linear regression in R: the package relaimpo. J Stat Softw. 2006;17:1–27.Article 

Google Scholar 
Bastian M, Heymann S. Gephi: an open source software for exploring and manipulating networks. Proc Int AAAI Conf Web Soc Media. 2009:361–2.Hu Y. Efficient, high-quality force-directed graph drawing. Math J. 2006;10:37–71.
Google Scholar 
Ranea JAG, Grant A, Thornton JM, Orengo CA. Microeconomic principles explain an optimal genome size in bacteria. Trends Genet. 2005;21:21–5.Article 
CAS 

Google Scholar 
Nielsen DA, Fierer N, Geoghegan JL, Gillings MR, Gumerov V, Madin JS, et al. Aerobic bacteria and archaea tend to have larger and more versatile genomes. Oikos. 2021;130:501–11.Article 
CAS 

Google Scholar 
Chen Y, Leung PM, Wood JL, Bay SK, Kessler AJ, Shelley G, et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021;15:2986–3004.Article 
CAS 

Google Scholar 
Brewer TE, Handley KM, Carini P, Gilbert JA, Fierer N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat Microbiol. 2016;2:16198.Willms IM, Rudolph AY, Göschel I, Bolz SH, Schneider D, Penone C, et al. Globally Abundant “Candidatus Udaeobacter” Benefits from Release of Antibiotics in Soil and Potentially Performs Trace Gas Scavenging. mSphere. 2020;5:1–17.Article 

Google Scholar 
Kaboré OD, Godreuil S, Drancourt M. Planctomycetes as host-associated bacteria: a perspective that holds promise for their future isolations, by mimicking their native environmental niches in clinical microbiology laboratories. Front Cell Infect Microbiol. 2020;10:1–19.Article 

Google Scholar 
Martens-Habbena W, Berube PM, Urakawa H, De La Torre JR, Stahl DA. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature. 2009;461:976–9.Article 
CAS 

Google Scholar 
Zhalnina K, De Quadros PD, Gano KA, Davis-Richardson A, Fagen JR, Brown CT, et al. Ca. Nitrososphaera and Bradyrhizobium are inversely correlated and related to agricultural practices in long-term field experiments. Front Microbiol. 2013;4:1–13.Article 

Google Scholar 
Land M, Hauser L, Jun S, Nookaew I, Leuze MR, Ahn T, et al. Insights from 20 years of bacterial genome sequencing. Funct Integr Genom. 2015;15:141–61.Article 
CAS 

Google Scholar 
Gil R, Latorre A, Postal A. Factors behind junk DNA in bacteria. Genes (Basel). 2012;3:634–50.Article 

Google Scholar 
Williamson KE, Radosevich M, Wommack KE. Abundance and diversity of viruses in six Delaware soils. Appl Environ Microbiol. 2005;71:3119–25.Article 
CAS 

Google Scholar 
Williamson KE, Corzo KA, Drissi CL, Buckingham JM, Thompson CP, Helton RR. Estimates of viral abundance in soils are strongly influenced by extraction and enumeration methods. Biol Fertil Soils. 2013;49:857–69.Article 

Google Scholar 
Van Goethem MW, Swenson TL, Trubl G, Roux S, Northen TR. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. MBio. 2019;10:e02287-19.Westra ER, Van Houte S, Gandon S, Whitaker R, Van Houte S, Gandon S, et al. The ecology and evolution of microbial CRISPR-Cas adaptive immune systems. Philos Trans R Soc B Biol Sci. 2019;374:20190101.Martinez-Gutierrez CA, Aylward FO. Genome size distributions in bacteria and archaea are strongly linked to evolutionary history at broad phylogenetic scales. PLoS Genet. 2022;18:1–17.Article 

Google Scholar 
Saifuddin M, Bhatnagar JM, Finzi AC, Segrè D, Finzi AC. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat Commun. 2019;10:1–10.Article 
CAS 

Google Scholar  More

Swift, M. J., Heal, O. W. & Anderson, J. M. Decomposition in Terrestrial Ecosystems. Vol. 5.5 (Blackwell, 1979).Aerts, R. Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos 79, 439 (1997).Article 

Google Scholar 
Makkonen, M. et al. Highly consistent effects of plant litter identity and functional traits on decomposition across a latitudinal gradient. Ecol. Lett. 15, 1033–1041 (2012).Article 

Google Scholar 
Coûteaux, M. M., Bottner, P. & Berg, B. Litter decomposition, climate and liter quality. Trends Ecol. Evol. 10, 63–66 (1995).Article 

Google Scholar 
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).Article 

Google Scholar 
Bradford, M. A. et al. Climate fails to predict wood decomposition at regional scales. Nat. Clim. Change 4, 625–630 (2014).Article 
CAS 

Google Scholar 
Bradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R. & Wood, S. A. Understanding the dominant controls on litter decomposition. J. Ecol. 104, 229–238 (2016).Article 
CAS 

Google Scholar 
Joly, F.-X. et al. Tree species diversity affects decomposition through modified micro-environmental conditions across European forests. New Phytol. 214, 1281–1293 (2017).Article 
CAS 

Google Scholar 
Bradford, M. A. et al. A test of the hierarchical model of litter decomposition. Nat. Ecol. Evol. 1, 1836–1845 (2017).Article 

Google Scholar 
Berg, B. et al. Litter mass loss rates in pine forests of Europe and Eastern United States: some relationships with climate and litter quality. Biogeochemistry 20, 127–159 (1993).Article 

Google Scholar 
Powers, J. S. et al. Decomposition in tropical forests: a pan-tropical study of the effects of litter type, litter placement and mesofaunal exclusion across a precipitation gradient. J. Ecol. 97, 801–811 (2009).Article 
CAS 

Google Scholar 
Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394 (2018).Article 

Google Scholar 
Cornelissen, J. H. C. & Thompson, K. Functional leaf attributes predict litter decomposition rate in herbaceous plants. New Phytol. 135, 109–114 (1997).Article 
CAS 

Google Scholar 
Coq, S., Souquet, J.-M., Meudec, E., Cheynier, V. & Hättenschwiler, S. Interspecific variation in leaf litter tannins drives decomposition in a tropical rain forest of French Guiana. Ecology 91, 2080–2091 (2010).Article 

Google Scholar 
Sun, T. et al. Contrasting dynamics and trait controls in first-order root compared with leaf litter decomposition. Proc. Natl Acad. Sci. USA 115, 10392–10397 (2018).Article 
CAS 

Google Scholar 
Baeten, L. et al. A novel comparative research platform designed to determine the functional significance of tree species diversity in European forests. Perspect. Plant Ecol. Evol. Syst. 15, 281–291 (2013).Article 

Google Scholar 
Hobbie, S. E. et al. Tree species effects on decomposition and forest floor dynamics in a common garden. Ecology 87, 2288–2297 (2006).Article 

Google Scholar 
von Arx, G., Graf Pannatier, E., Thimonier, A. & Rebetez, M. Microclimate in forests with varying leaf area index and soil moisture: potential implications for seedling establishment in a changing climate. J. Ecol. 101, 1201–1213 (2013).Article 

Google Scholar 
Ayres, E. et al. Home-field advantage accelerates leaf litter decomposition in forests. Soil Biol. Biochem. 41, 606–610 (2009).Article 
CAS 

Google Scholar 
Freschet, G. T., Aerts, R. & Cornelissen, J. H. C. Multiple mechanisms for trait effects on litter decomposition: moving beyond home-field advantage with a new hypothesis. J. Ecol. 100, 619–630 (2012).Article 

Google Scholar 
Meentemeyer, V. Macroclimate and lignin control of litter decomposition rates. Ecology 59, 465–472 (1978).Article 
CAS 

Google Scholar 
Currie, W. S. et al. Cross-biome transplants of plant litter show decomposition models extend to a broader climatic range but lose predictability at the decadal time scale. Glob. Change Biol. 16, 1744–1761 (2010).Article 

Google Scholar 
Canessa, R. et al. Relative effects of climate and litter traits on decomposition change with time, climate and trait variability. J. Ecol. 109, 447–458 (2021).Article 

Google Scholar 
García-Palacios, P., Shaw, E. A., Wall, D. H. & Hättenschwiler, S. Temporal dynamics of biotic and abiotic drivers of litter decomposition. Ecol. Lett. 19, 554–563 (2016).Article 

Google Scholar 
Prescott, C. E. Litter decomposition: what controls it and how can we alter it to sequester more carbon in forest soils? Biogeochemistry 101, 133–149 (2010).Article 
CAS 

Google Scholar 
Prescott, C. E. & Vesterdal, L. Decomposition and transformations along the continuum from litter to soil organic matter in forest soils. For. Ecol. Manage. 498, 119522 (2021).Article 

Google Scholar 
Stadler, S. J. in Encyclopedia of World Climatology 89–94 (Springer, 2005).Moore, T. R., Bubier, J. L. & Bledzki, L. Litter decomposition in temperate peatland ecosystems: the effect of substrate and site. Ecosystems 10, 949–963 (2007).Article 

Google Scholar 
Austin, A. T. Has water limited our imagination for aridland biogeochemistry. Trends Ecol. Evol. 26, 229–235 (2011).Article 

Google Scholar 
Joly, F.-X., Kurupas, K. & Throop, H. Pulse frequency and soil-litter mixing alter the control of cumulative precipitation over litter decomposition. Ecology 98, 2255–2260 (2017).Article 

Google Scholar 
Scherer-Lorenzen, M., Bonilla, J. L. & Potvin, C. Tree species richness affects litter production and decomposition rates in a tropical biodiversity experiment. Oikos 116, 2108–2124 (2007).Article 

Google Scholar 
Vivanco, L. & Austin, A. T. Tree species identity alters forest litter decomposition through long-term plant and soil interactions in Patagonia, Argentina. J. Ecol. 96, 727–736 (2008).Article 
CAS 

Google Scholar 
Fanin, N. et al. Home‐field advantage of litter decomposition: from the phyllosphere to the soil. New Phytol. 231, 1353–1358 (2021).Article 

Google Scholar 
Hättenschwiler, S., Tiunov, A. V. & Scheu, S. Biodiversity and litter decomposition in terrestrial ecosystems. Annu. Rev. Ecol. Evol. Syst. 36, 191–218 (2005).Article 

Google Scholar 
Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).Article 

Google Scholar 
Thakur, M. P. et al. Reduced feeding activity of soil detritivores under warmer and drier conditions. Nat. Clim. Change 8, 75–78 (2018).Article 

Google Scholar 
Harrison, A. F., Latter, P. M. & Walton, D. W. H. (eds) Cotton Strip Assay: An Index of Decomposition in Soils (Institute of Terrestrial Ecology, 1988).García-Palacios, P., Maestre, F. T., Kattge, J. & Wall, D. H. Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes. Ecol. Lett. 16, 1045–1053 (2013).Article 

Google Scholar 
Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).Article 

Google Scholar 
Dawud, S. M. et al. Tree species functional group is a more important driver of soil properties than tree species diversity across major European forest types. Funct. Ecol. 31, 1153–1162 (2017).Article 

Google Scholar 
Pollastrini, M. et al. Taxonomic and ecological relevance of the chlorophyll a fluorescence signature of tree species in mixed European forests. New Phytol. 212, 51–65 (2016).Article 
CAS 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2013).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar  More

Griggs, D. et al. Sustainable development goals for people and planet. Nature 495, 305–307 (2013).Article 
CAS 

Google Scholar 
Charlop-Powers, Z. et al. Urban park soil microbiomes are a rich reservoir of natural product biosynthetic diversity. Proc. Natl Acad. Sci. USA 113, 14811 (2016).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci. Adv. 7, eabg5809 (2021).Article 
CAS 

Google Scholar 
Martínez, J. L. Antibiotics and antibiotic resistance genes in natural environments. Science 321, 365–367 (2008).Article 

Google Scholar 
Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).Article 
CAS 

Google Scholar 
Byrnes, J. E. K. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evolution 5, 111–124 (2014).Article 

Google Scholar 
Gamfeldt, L. & Roger, F. Revisiting the biodiversity–ecosystem multifunctionality relationship. Nat. Ecol. Evolution 1, 0168 (2017).Article 

Google Scholar 
Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).Article 
CAS 

Google Scholar 
Zavaleta, E. S. et al. Sustaining multiple ecosystem functions in grassland communities requires higher biodiversity. Proc. Natl Acad. Sci. USA 107, 1443 (2010).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).Article 
CAS 

Google Scholar 
Wagg, C. et al. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266 (2014).Article 
CAS 

Google Scholar 
van der Heijden, M. G. A. et al. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396, 69–72 (1998).Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).Article 

Google Scholar 
Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. R. Soc. B 281, 20141988 (2014).Article 

Google Scholar 
Schittko, C. et al. Biodiversity maintains soil multifunctionality and soil organic carbon in novel urban ecosystems. J. Ecol. https://doi.org/10.1111/1365-2745.13852 (2022).Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).Article 
CAS 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above-and belowground biodiversity are mediated by climate. Nat. Commun. 6, 1–8 (2015).Article 

Google Scholar 
Kadowaki, K. et al. Mycorrhizal fungi mediate the direction and strength of plant–soil feedbacks differently between arbuscular mycorrhizal and ectomycorrhizal communities. Commun. Biol. 1, 196 (2018).Article 

Google Scholar 
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).Article 
CAS 

Google Scholar 
Berman, J. J. in Taxonomic Guide to Infectious Diseases (ed. Berman, J. J.) 37–47 (Academic Press, 2012).Berman, J. J. in Taxonomic Guide to Infectious Diseases (ed. Berman, J. J.) 25–31 (Academic Press, 2012).Busse, H.-J. in Methods in Microbiology (eds Rainey, F. & Oren. A.) Vol. 38, 239–259 (Academic Press, 2011).van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).Article 

Google Scholar 
van Bergeijk, D. A. et al. Ecology and genomics of Actinobacteria: new concepts for natural product discovery. Nat. Rev. Microbiol. 18, 546–558 (2020).Article 

Google Scholar 
Orellana, L. H. et al. Verrucomicrobiota are specialist consumers of sulfated methyl pentoses during diatom blooms. ISME J. 16, 630–641 (2022).Article 
CAS 

Google Scholar 
Fincker, M. et al. Metabolic strategies of marine subseafloor Chloroflexi inferred from genome reconstructions. Environ. Microbiol. 22, 3188–3204 (2020).Article 
CAS 

Google Scholar 
Stralis-Pavese, N. et al. Analysis of methanotroph community composition using a pmoA-based microbial diagnostic microarray. Nat. Protoc. 6, 609–624 (2011).Article 
CAS 

Google Scholar 
Berube, P. M. et al. Physiology and evolution of nitrate acquisition in Prochlorococcus. ISME J. 9, 1195–1207 (2015).Article 
CAS 

Google Scholar 
Liang, J.-L. et al. Novel phosphate-solubilizing bacteria enhance soil phosphorus cycling following ecological restoration of land degraded by mining. ISME J. 14, 1600–1613 (2020).Article 
CAS 

Google Scholar 
Hättenschwiler, S. & Gasser, P. Soil animals alter plant litter diversity effects on decomposition. Proc. Natl Acad. Sci. USA 102, 1519 (2005).Article 

Google Scholar 
Erktan, A. et al. The physical structure of soil: determinant and consequence of trophic interactions. Soil Biol. Biochem. 148, 107876 (2020).Article 
CAS 

Google Scholar 
Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).Article 

Google Scholar 
Barberán, A. et al. Why are some microbes more ubiquitous than others? Predicting the habitat breadth of soil bacteria. Ecol. Lett. 17, 794–802 (2014).Article 

Google Scholar 
Chen, Q. L. et al. Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils. Soil Biol. Biochem. 141, 107686 (2020).Article 
CAS 

Google Scholar 
Zhang, Z. et al. Rare species-driven diversity-ecosystem multifunctionality relationships are promoted by stochastic community assembly. mBio. mBio. 13, e00449–22 (2022).Article 

Google Scholar 
Domínguez-García, V. et al. Unveiling dimensions of stability in complex ecological networks. Proc. Natl Acad. Sci. USA 116, 25714 (2019).Article 

Google Scholar 
Zhang, L. et al. Signal beyond nutrient, fructose, exuded by an arbuscular mycorrhizal fungus triggers phytate mineralization by a phosphate solubilizing bacterium. ISME J. 12, 2339–2351 (2018).Article 
CAS 

Google Scholar 
Couturier, M. et al. Lytic xylan oxidases from wood-decay fungi unlock biomass degradation. Nat. Chem. Biol. 14, 306–310 (2018).Article 
CAS 

Google Scholar 
Steinberg, G. et al. A lipophilic cation protects crops against fungal pathogens by multiple modes of action. Nat. Commun. 11, 1608 (2020).Article 
CAS 

Google Scholar 
Johnston, A. S. A. & Sibly, R. M. The influence of soil communities on the temperature sensitivity of soil respiration. Nat. Ecol. Evol. 2, 1597–1602 (2018).Article 

Google Scholar 
Watson, C. J. et al. Ecological and economic benefits of low-intensity urban lawn management. J. Appl. Ecol. 57, 436–446 (2020).Article 

Google Scholar 
Williams, N. S. G. et al. A conceptual framework for predicting the effects of urban environments on floras. J. Ecol. 97, 4–9 (2009).Article 

Google Scholar 
Trabucco, A. & Zomer, R. Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2 (figshare, 2019); https://doi.org/10.6084/m9.figshare.7504448.v3Kettler, T. A. et al. Simplifed method for soil particle-size determination to accompany soil-quality analyses. Soil Sci. Soc. Am. J. 65, 849–852 (2001).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. Changes in belowground biodiversity during ecosystem development. Proc. Natl Acad. Sci. USA 116, 6891 (2019).Article 
CAS 

Google Scholar 
Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. Biol. Sci. 281, 22 (2014).
Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in Northern Europe. Front. Microbiol. 11, 1953 (2020).Article 

Google Scholar 
Bastida, F. et al. Microbiological degradation index of soils in a semiarid climate. Soil Biol. Biochem. 38, 3463–3473 (2006).Article 
CAS 

Google Scholar 
Lugato, E. et al. Different climate sensitivity of particulate and mineral-associated soil organic matter. Nat. Geosci. 14, 295–300 (2021).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. The influence of soil age on ecosystem structure and function across biomes. Nat. Commun. 11, 4721 (2020).Article 
CAS 

Google Scholar 
Frostegård, Å. et al. Use and misuse of PLFA measurements in soils. Soil Biol. Biochem. 43, 1621–1625 (2011).Article 

Google Scholar 
Olsson, P. A. et al. The use of phospholipid and neutral lipid fatty acids to estimate biomass of arbuscular mycorrhizal fungi in soil. Mycol. Res. 99, 623–629 (1995).Article 
CAS 

Google Scholar 
Campbell, C. D. et al. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).Article 
CAS 

Google Scholar 
Bell, C. W. et al. High-throughput fluorometric measurement of potential soil extracellular enzyme activities. J. Vis. Exp. 15, e50961 (2013).
Google Scholar 
Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl Acad. Sci. USA 109, 21390–21395 (2012).Article 
CAS 

Google Scholar 
Fierer, N. et al. Reconstructing the microbial diversity and function of pre-agricultural tallgrass prairie soils in the United States. Science 342, 621–624 (2013).Article 
CAS 

Google Scholar 
Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).Article 
CAS 

Google Scholar 
Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evolution 2, 427–436 (2018).Article 

Google Scholar 
Legendre, P. & Legendre, L. Interpretation of Ecological Structures Numerical Ecology 3rd English edn (Elsevier Science BV, 2012).Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, 2006).Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417 (2014).Article 
CAS 

Google Scholar 
Fan, K. et al. Soil biodiversity supports the delivery of multiple ecosystem functions in urban greenspaces. figshare https://doi.org/10.6084/m9.figshare.21175492.v3 (2022). More

Bacterial strains, media and batch culturesWe used the wildtype strain Vibrio natriegens ATCC 14048 and Alteromonas macleodii sp. 4B03 (non-clumping variant) isolated from marine particles [8]. Strains were cultured in Marine Broth (MB, Difco 2216) and grown overnight at 25 °C. In total, 1 ml of cell culture was centrifuged (13,000 rpm for 2 min) in a 1.5 ml microfuge tube. After discarding the supernatant, the cells were washed with 1 ml of MBL minimal medium medium without carbon source. Cells were centrifuged again and the cell pellet was resuspended in 1 ml of MBL (marine minimal medium) [30, 34] adjusted to an 0.002 OD600. Cells from these cultures were used for experiments in MBL minimal medium containing 0.1% (weight/volume) Pentaacetyl-Chitopentaose (Megazyme, Ireland). The carbon source was added to the MBL minimum medium and filter sterilized using 0.22 μm Surfactant-Free Cellulose Acetate filters (Corning, USA). A total of 500 µl of the prepared cultures (250 µl + 250 µl for co-cultures) were added to 9.5 ml of MBL + 0.1 % chitopentaose (v/w) in serum flasks. This resulted in a starting OD of 0.0001. The flasks included a stirrer and were sealed with a rubber seal. Serum flasks were stored on a bench top magnetic stirrer (500 rpm) and connected to the microfluidics setup via Hamilton NDL NO HUB needles (ga21/135 mm/pst 2).MicrofluidicsMicrofluidics experiments were performed as described previously [35,36,37,38]. Cell growth was imaged within mother machine channels of 25 × 1.4 × 1.26 μm (length × width × height). Within these channels, cells could experience the batch culture medium that diffused through the main flow channels. The microfluidic device consisted of a PDMS flow cell (50 µm/23 µm). The PDMS flow cell was fabricated by mixing the SYLGARD 184 Silicone Elastomer Kit chemicals 10:1 (w/v), pouring the mix on a master waver and hardening it at 80 °C for 1 h. The solid PDMS flow cell was cut out of the master waver and holes were pierced at both ends of each flow channel prior to binding it to a cover glass (Ø 50 mm) by applying the “high” setting for 30 s on the PDC-32G Plasma Cleaner by Harrick Plasma. The flow cell was connected via 40 mm Adtech PTFE tubing (0.3 mm ID × 0.76 mm OD) to a Ismatech 10 K Pump with 40 mm of Ismatech tubing (ID 0.25 mm, OD 0.90 mm) which again was connected via 80 mm Adtech PTFE tubing (0.3 mm ID × 0.76 mm OD) via a 5 mm short Cole-Parmer Tygon microbore tubing (EW-06418-03) (ID 0.762 mm OD 2.286 mm) connector tubing to a Hamilton NDL NO HUB needle (ga21/135 mm/pst 2) that was inserted into the feeding culture. During the whole experiment the pump flow was set to 1.67 µl/min (0.1 ml/h).Time-lapse microscopyMicroscopy imaging was done using fully automated Olympus IX81 or IX83 inverted microscope systems (Olympus, Japan), equipped with a ×100 NA1.3 oil immersion, phase contrast objective, an ORCA-flash 4.0 v2 sCMOS camera (Hamamatsu, Japan), an automated stage controller (Marzhauser Wetzlar, Germany), shutter, and laser-based autofocus system (Olympus ZDC 1 and 2). Detailed information about the microscopy setup has been described by D’Souza et al. [39]. Channels on the same PDMS Chip were imaged in parallel, and phase-contrast images of each position were taken every 5 min. The microscopy units and PDMS chip were maintained at room temperature. All experiments were run at a flow rate of 0.1 ml h−1, which ensures nutrients enter the chamber through diffusion. Four biological replicates were performed. These replicates consist of four independent microfluidics channels (two for each of the strains). These channels were connected to one of two independent batch cultures.The microscopy dataset consists of 200 mother machine channels; 49 channels for the degrader on co-culture, 51 for the degrader on mono-culture, 40 for the cross-feeder on mono-culture and 60 for the cross-feeder on co-culture.Image analysisImage processing was performed using a modified version of the Vanellus image analysis software (Daan Kiviet, https://github.com/daankiviet/vanellus), together with Ilastik [40] and custom written Matlab scripts.Movies were registered to compensate for stage movement and cropped to the region of growth channels. Subsequently, segmentation was done on the phase contrast images using Ilastik’s supervised pixel classification workflow and cell tracking was done using the Vanellus build-in tracking algorithm.After visual curation of segmentation and tracking for each mother machine and at every frame growth parameters were calculated using custom written matlab scripts [36]. Lengths of individual cells were estimated by finding the cell center line by fitting a third-degree polynomial to the cell mask; then the cell length was calculated as the length of the center line between the automatically detected cell pole positions (see Kiviet et al. [33] for details).We quantified cell growth by calculating single-cell elongation rates r from measured cell length trajectories: L(t) = L(0)∙e^(r ∙ t). Cell lengths and growth rates varied drastically over the time course of the experiment; we thus developed a robust procedure that can reliably estimate elongation rates both for large fast-growing cells as well as for small non-growing cells. We first log-transformed cell lengths, which were subsequently smoothed over a moving time window with a length of 5 h (60 time points). We used a second order local regression using weighted linear least squares (rloess method of Matlab smooth function) in order to minimize noise while maintaining sensitivity to changes in elongation rates. Subsequently the instantaneous elongation rate was estimated as the slope of a linear regression over a moving time window of 30 min (7 time points). Time points for which the fit quality was bad (χ2  > 10−4) were removed from the analysis [32]. All parameters were optimized manually by visually inspecting the fitting procedure of many cell length trajectories randomly selected from across all replicates.As cells are continuously lost from the mother machine channels it is non-trivial to calculate the total amount of biomass produced in the chip. We thus need to estimate this quantity from the observed single-cell elongation rates. Specifically, we estimated the total amount of biomass produced per individual mother machine until a given time point as:$$B_T = e^{Delta tmathop {sum}limits_{i = 1}^T { < r_i > } }$$Where is the average growth rate of all cells in a given replicate at time point i, and where Δt is the time interval between two timepoints. By using the average growth rate, we ignore the variation in growth rates between cells. However, it is difficult to calculate population growth when growth rates vary both with time and between cells and the current method still allows us to capture the overall effect of interactions on cell growth.Datasets and statistical analysisAll microfluidics experiments were replicated four times. No cells were excluded from the analysis after visual curation. For V. natriegens 2227 cells were analyzed on mono-culture, and 1707 cells were analyzed on co-culture. For A. macleodii 2657 cells were analyzed on mono-culture, and 3901 cells were analyzed on co-culture. Each mother machine channel was treated as an independent sample. All statistical analysis was performed in Rstudio v1.2.5033. Percent increases were calculated using the relative differences of estimated between the corresponding values. For mixed effect models analysis the LmerTest package (Version 3.1-3) [41] with the following equation were used: y ~ Batch + (1 | Replicate) The Tukey Post hoc test was performed using the Multcomp package (Version 1.4-15) [42].Chitinase assayDegrader and Cross-feeder cells were cultured in Marine Broth (MB, Difco 2216) and grown overnight at 25 °C. In total, 1 ml of cell culture was centrifuged (13,000 rpm for 2 min) in a 1.5 ml microfuge tube. After discarding the supernatant the cells were washed with 1 ml of MBL minimal medium medium without carbon source. Cells were centrifuged again and the cell pellet was resuspended in 1 ml of MBL adjusted to an 0.002 OD600. A total of 10 µl of cell culture was added to 190 µl of MBL containing 0.1% Chitopentaose (w/v). Cultures were grown to exponential phase in a plate reader (Eon, BioTek) at 25 °C. Cell free supernatants were generated by sterile filtering cultures using a multi-well filter plate (AcroPrep) into a fresh 96 well plate. Chitinase activity of cell free supernatants was measured using a commercially available fluorometric chitinase assay kit (CS1030, Sigma-Aldrich) following the protocol. In short, 10 µl of sterile supernatant was added to 90 µl of the assay mix. The solution was incubated in the dark at 25 °C for 40 min before measuring fluorescence (Excitation 360 nm, Emission 450 nm) in a plate reader (Synergy MX, Biotek). Logarithmic chitinase activity per OD600 was analyzed for eight replicates.Chitinase activity in units per ml was calculated using a standard concentration. Using the following Formula: ({{{{{rm{Units}}}}}}/{{{{{rm{ml}}}}}} = frac{{left( {{{{{{rm{FLU}}}}}} – {{{{{rm{FLUblank}}}}}}} right) times 1.9 times 0.3 times {{{{{rm{DF}}}}}}}}{{{{{{{rm{FLUstandard}}}}}} times {{{{{rm{time}}}}}} times {{{{{rm{Venz}}}}}}}})Here, FLU indicates measured fluorescence, DF indicates the dilution factor, and V indicates the volume of the sample in ml [43].Acetate assayCell cultures were prepared and grown in serum flasks as described above. At different time intervals 1 ml of culture was removed and OD600 was measured. Cultures were filter sterilized using 0.22 μm Surfactant-Free Cellulose Acetate filters (Corning, USA) into a 1.5 ml microfuge tube. Cell free supernatants were stored at −4 °C until they were used for acetate measurements. Acetate concentrations were measured using a colorimetric assay kit (MAK086, Sigma-Aldrich) following the protocol. In short, 50 µl of cell free supernatant was added to 50 µl of assay mix. The solution was incubated in the dark at 25 °C for 30 min. Acetate concentrations were measured in a plate reader (Eon, Biotek) at 450 nm [44].Growth on spend mediaDegrader and Cross-feeder cells were cultured in Marine Broth (MB, Difco 2216) and grown overnight at 25 °C. In total, 1 ml of cell culture was centrifuged (13,000 rpm for 2 min) in a 1.5 ml microfuge tube. After discarding the supernatant, the cells were washed with 1 ml of MBL minimal medium medium without carbon source. Cells were centrifuged again and the cell pellet was resuspended in 1 ml of MBL adjusted to an 0.002 OD600. A total of 10 µl of cell culture was added to the 190 µl cell free supernatant described above. Cultures were grown in a plate reader (Eon, BioTek) at 25 °C. Cell free supernatants after this growth assay were generated by sterile filtering cultures using a multi-well filter plate (AcroPrep) into a fresh 96 well plate. These supernatants were used as described above to measure acetate levels after growth on spend media. More

Environmental parametersSampling locations, air temperature, water temperature, water depth, salinity, nutrient concentrations (NO3-N, NO2-N, NH4-N, SiO4, PO4-P), and incubation temperatures are shown in Table 1. The air and water temperatures of the studied locations were 11 and 16.6 °C, 3.1 and 3.8 °C, 3.1 and 3.7 °C, 24.5 and 25.9 °C, 24.5 and 18.8 °C, respectively in the Kuroshio Current, SPG surface layer, SPG chlorophyll maximum zone, STG surface layer, and STG chlorophyll maximum zone. At SPG, the values of different parameters were quite similar (p = 0.62, two-tail t-Test; at 5% level of significance) between surface (5 m) and chlorophyll maximum (37 m), indicating the vertical mixing in the upper water column. At STG, the values were relatively different (p = 0.39, two-tail t-Test; at 5% level of significance) between surface (5 m) and chlorophyll maximum (125 m), suggesting the vertical stratification of the water column. The in-situ (water) temperatures (6.4 °C, 0.2 °C, 0.3 °C and 4.2 °C) were lower than the incubation temperatures compared to those of Kuroshio Current, SPG surface layer, SPG chlorophyll maximum zone, and STG chlorophyll maximum zone, while 2.9 °C higher than the incubation temperature of the STG surface layer. Nutrient assays revealed a big difference in nutrient concentrations between SPG and STG; the waters from the station STG were nutrient-poor. The incubation temperatures of the onboard microcosms were 23 ± 1 °C, ~ 4 °C, and 23 ± 1 °C in the case of Kuroshio Current, SPG, and STG, respectively (Table 1).Table 1 Environmental properties of three water samples used in microcosm experiments. Microcosm experiments were conducted on board during the KH-14-2 cruise in May–June 2014.Full size tableBacterial cell densities and cell volumesAt initial incubation periods (12 h to 24 h), the cell densities between the glucose-amended and non-treated microcosms were similar (p = 0.74, two-tail t-Test; at 5% level of significance). Highly significant differences (p  More

Study species and spider collectionAustralomisidia ergandros is a subsocial spider inhabiting South-Eastern Australia. They live in communal kin-groups in nests usually built with leaves from Eucalyptus trees bound by silk threads. Group size usually ranges from 5 to 45 spiderlings. Groups are comprised of the offspring of a single female who provides maternal care until her death28. Offspring continue to live in groups for 5 to 7 months after the mother’s death29,30. One of the females inherit the natal nest while the remaining females disperse to found new nests. It is not entirely clear if A. ergandros inbreed with natal kin or if spiders show a mandatory pre-mating dispersal.We collected 29 A. ergandros nests from a population along Yass River Road in New South Wales, Australia (34° 55′ 20.50′′ S, 149° 6′ 15.53′′ E) in February 2016. At this time of year, the spiderlings are very young and the presence of immigrants, who might influence the extent of social foraging, is improbable9,31. For our experiments, we transferred the original nests to the laboratory at Macquarie University in Sydney.Group composition effectsOur experiments spanned a duration of 56 days. To investigate group composition (cooperators vs. defectors) effects, we first assessed the hunting types of individuals within ‘initial’ groups (phase 1) and subsequently composed and tested ‘sorted’ groups of cooperators or defectors only (phase 2). The formation of initial groups was dictated by special requirements. Basically, we randomly selected up to 30 individuals per original nest and split these individuals into two to three initial groups of ten (Nnests = 10, N groups = 25). Each selected individual received a unique color mark (©Plaka-Farbe) and was weighed to the nearest 0.01 mg on an electronic balance (Mettler Toledo New Classic MS). Each group was then transferred to a petri dish (100 mm in diameter) which served as the test arena for the hunting type assessment. An acclimatization period of four days ensured that the spiders weaved silk threads which amplify vibrations by prey32.Phase 1We assessed hunting types with a modified version of the ‘communal feeding experiment’ originally used by Dumke et al.16 to establish hunting specialization in A. ergandros. For each initial group, we completed 7 feeding trials over 24 days (1 trial every 4 days), during which we offered living Musca domestica flies and observed the foraging behaviour of all group members (Fig. 1). Each fly was weighed before being placed into the petri dish and either removed after two hours if not captured, or after two hours post capture. For each trial, we documented the attack latency, the attacker IDs and the IDs of the feeding individuals in 10-min intervals over two hours. From these data, we determined the feeding frequency of each individual (i.e. the number of trials it was feeding) and calculated the proportions to which it cooperated vs. defected. We thus obtained comparable quantifications of hunting types16. All individuals except those that died during the assessment (56 of 250 spiders) were weighed two days after the last trial to assess weight gain1 (= log (end weight1/start weight133).Phase 2Following phase 1, we regrouped individuals into ‘sorted’ groups of cooperators or defectors only, and this time gave three days acclimatization time since the re-grouping took one day. We formed experimental cooperator groups by selecting nine to ten individuals with the highest cooperating tendencies from the original colony. Next, we formed experimental defector groups analogously from that same pool (Fig. 1). Thus, we achieved paired relatedness between cooperator groups and defector groups, to control for nest origin and nest experience (matched pairs design). We further ensured comparability of cooperator groups and defector groups in the individuals’ physical state (details in Supplementary Methods). Owing to mortality in three nests and restricted possibilities to realize balanced conditions between groups in two nests, we could establish five cooperator-defector group pairs with nine individuals per group.To explore group composition effects on social foraging behaviour and individual fitness payoffs, we tested each sorted group over another seven feeding trials over 24 days. The trials were conducted in exactly the same manner as for the feeding type assessment (phase 1). From the recorded data (attack latency, IDs of attackers, IDs of feeding individuals), we calculated a set of variables that quantified social foraging behaviour (data points per trial and group). To examine individual fitness payoffs, we checked the petri dishes for dead individuals and noted their IDs prior to every trial. As an additional fitness payoff measure for those individuals still alive at the end of phase 2, we determined individual weight gain2 (= log (end weight2/start weight2)).The role of sex in cooperator vs. defector typesTo examine the role of sex in cooperation-defection scenarios, we collected another eight nests from Yass River Road in June 2016. Around this time, A. ergandros individuals reached the subadult stage, at which sex can be visually determined17. Three nests contained subadult males and females in sufficient numbers, so that we formed three groups, each with ten males and ten females from the same nest (in total: N males = 30, N females = 30). All group members were weighed and color marked before they were tested in another, extended feeding type assessment over ten trials.Based on the IDs of attackers and individuals that hunted in these trials, we generated social network graphs and visualized the foraging interactions within groups31,34,35. Individuals were represented by ‘nodes’; a directed line (‘edge’) was drawn from one node to another if the specific individual had cooperated by sharing prey with the other. The lines received weights reflecting the frequency of the respective interaction. We quantified individual prey sharing tendencies using the node-level metric out-strength: the weight sum of all outgoing edges from a particular node35. This metric comprehensively reflects an individual’s prey sharing tendency, as it incorporates the frequency and the spread of prey sharing behaviour. To visualize social networks and calculate the individuals’ out-strengths, we used the software UCINET 636.Statistical analysesAll model analyses were performed in R version 3.2.2, whereas all social network analyses were conducted in UCINET 636.Group composition effectsWe modelled the effect of group composition on social foraging behaviour separately for each response variable with binomial or gamma GEEs (generalized estimation equations). GEEs are adequate to analyse data from repeated measurements over time within same groups because they allow adjustment for the dependence of these measurements37. Defining the dependence structure of our data, we set sorted-group ID as a grouping variable and specified the temporal correlation AR-1. Group composition constituted the explanatory variable of interest, fly weight and group size were included as additional variables to control for prey mass and mortality. An exception was the model for the scrounging degree, in which group size was controlled by the variable itself. We assessed the significance of group composition effects by dropping each explanatory variable in turn and then comparing the full model to its nested models based on Wald test statistics. The least significant variable was removed, and model comparisons were repeated until all remaining variables were significant.Mortality was compared between cooperator groups and defector groups using a Chi-squared test. The difference between group compositions in individual weight gain2 was analysed in a GLS (generalized least squares) model that incorporated an exchangeable correlation structure with sorted-group ID as the grouping variable.Sex differencesWe conducted a node-based Monte Carlo randomization test to determine whether the observed difference in mean out-strength between sexes deviated significantly from the difference expected if producing associations occurred randomly and hence independent of sex. The observed data were shuffled in 10,000 node-label randomizations that preserved group membership. The sum of the differences between mean male out-strength (σm) and mean female out-strength (σf) within groups was used as the test statistic A (A = sumnolimits_{(i = 1)}^{3} {left( {sigma_{{(m_{i} )}} – sigma_{{left( {f_{i} } right)}} } right)}^{ – }), where i denotes group identity. To produce a probability value, we compared the observed test statistic to the distribution of random test statistics drawn from the 10,000 Monte Carlo simulations34. More