Ecology

Dudgeon, D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr. Biol. 29, R942–R995 (2019).Article 

Google Scholar 
Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).Article 
CAS 

Google Scholar 
Arrigo, K. R. et al. Synergistic interactions among growing stressors increase risk to an Arctic ecosystem. Nat. Commun. 11, 6255 (2020).Article 
CAS 

Google Scholar 
Kopf, R. K., Finlayson, C. M., Humphries, P., Sims, N. C. & Hladyz, S. Anthropocene baselines: assessing change and managing biodiversity in human-dominated aquatic ecosystems. Bioscience 65, 798–811 (2015).Article 

Google Scholar 
Chapin, F. S. et al. Ecosystem stewardship: sustainability strategies for a rapidly changing planet. Trends Ecol. Evol. 25, 241–249 (2010).Article 

Google Scholar 
Seastedt, T. R., Hobbs, R. J. & Suding, K. N. Management of novel ecosystems: are novel approaches required? Front. Ecol. Environ. 6, 547–553 (2008).Article 

Google Scholar 
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).Article 
CAS 

Google Scholar 
Bellwood, D. R. et al. Coral reef conservation in the Anthropocene: confronting spatial mismatches and prioritizing functions. Biol. Conserv. 236, 604–615 (2019).Article 

Google Scholar 
Graham, N. A. J., Cinner, J. E., Norström, A. V. & Nyström, M. Coral reefs as novel ecosystems: embracing new futures. Curr. Opin. Environ. Sustain. 7, 9–14 (2014).Article 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).Article 
CAS 

Google Scholar 
Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1264 (2019).Article 
CAS 

Google Scholar 
Fisher, R. et al. Species richness on coral reefs and the pursuit of convergent global estimates. Curr. Biol. 25, 500–505 (2015).Article 
CAS 

Google Scholar 
Brandl, S. J. et al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445–454 (2019).Article 

Google Scholar 
Teh, L. S. L., Teh, L. C. L. & Sumaila, U. R. A global estimate of the number of coral reef fishers. PLoS ONE 8, e65397 (2013).Article 
CAS 

Google Scholar 
Ferrario, F. et al. The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nat. Commun. 5, 3794 (2014).Article 
CAS 

Google Scholar 
Skirving, W. J. et al. The relentless march of mass coral bleaching: a global perspective of changing heat stress. Coral Reefs 38, 547–557 (2019).Article 

Google Scholar 
Donovan, M. K. et al. Local conditions magnify coral loss after marine heatwaves. Science 372, 977–980 (2021).Article 
CAS 

Google Scholar 
Gilmour, J. P., Smith, L. D., Heyward, A. J., Baird, A. H. & Pratchett, M. S. Recovery of an isolated coral reef system following severe disturbance. Science 340, 69–71 (2013).Article 

Google Scholar 
Diaz-Pulido, G. & McCook, L. J. The fate of bleached corals: patterns and dynamics of algal recruitment. Mar. Ecol. Prog. Ser. 232, 115–128 (2002).Article 

Google Scholar 
Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).Article 
CAS 

Google Scholar 
Jouffray, J. B. et al. Parsing human and biophysical drivers of coral reef regimes. Proc. R. Soc. B Biol. Sci. 286, 20182544 (2019).Article 

Google Scholar 
Reverter, M., Helber, S. B., Rohde, S., Goeij, J. M. & Schupp, P. J. Coral reef benthic community changes in the Anthropocene: biogeographic heterogeneity, overlooked configurations, and methodology. Glob. Chang. Biol. 28, 1956–1971 (2022).Article 

Google Scholar 
Cheal, A. J., MacNeil, M. A., Emslie, M. J. & Sweatman, H. The threat to coral reefs from more intense cyclones under climate change. Glob. Chang. Biol. 23, 1511–1524 (2017).Article 

Google Scholar 
Done, T. in Perspectives on Coral Reefs (ed. Barnes, D. J.) 107–147 (Brian Clouston, 1983).Bruno, J. F., Côté, I. M. & Toth, L. T. Climate change, coral loss, and the curious case of the parrotfish paradigm: why don’t marine protected areas improve reef resilience? Ann. Rev. Mar. Sci. 11, 307–334 (2019).Article 

Google Scholar 
Gardner, T. A., Cote, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).Article 
CAS 

Google Scholar 
Schutte, V. G. W., Selig, E. R. & Bruno, J. F. Regional spatio-temporal trends in Caribbean coral reef benthic communities. Mar. Ecol. Prog. Ser. 402, 115–122 (2010).Article 

Google Scholar 
Hughes, T. P. Catastrophes, phase shifts and large-scale degradation of a Caribbean coral reef. Science 265, 1547–1551 (1994).Article 
CAS 

Google Scholar 
Souter, D. et al. Status of Coral Reefs of the World: 2020 (Global Coral Reef Monitoring Network, 2021).Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: timing, extent, and subregional comparisons. PLoS One 2, e711 (2007).Article 

Google Scholar 
Ateweberhan, M., McClanahan, T. R., Graham, N. A. J. & Sheppard, C. R. C. Episodic heterogeneous decline and recovery of coral cover in the Indian Ocean. Coral Reefs 30, 739–752 (2011).Article 

Google Scholar 
Bellwood, D. R., Hemingson, C. R. & Tebbett, S. B. Subconscious biases in coral reef fish studies. Bioscience 70, 621–627 (2020).Article 

Google Scholar 
Kench, P. S. et al. Sustained coral reef growth in the critical wave dissipation zone of a Maldivian atoll. Commun. Earth Environ. 3, 9 (2022).Article 

Google Scholar 
Eddy, T. D. et al. Global decline in capacity of coral reefs to provide ecosystem services. One Earth 4, 1278–1285 (2021).Article 

Google Scholar 
Mumby, P. J., Hastings, A. & Edwards, H. J. Thresholds and the resilience of Caribbean coral reefs. Nature 450, 98–101 (2007).Article 
CAS 

Google Scholar 
Roff, G. & Mumby, P. J. Global disparity in the resilience of coral reefs. Trends Ecol. Evol. 27, 404–413 (2012).Article 

Google Scholar 
Bruno, J. F., Sweatman, H., Precht, W. F., Selig, E. R. & Schutte, V. G. W. Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs. Ecology 90, 1478–1484 (2009).Article 

Google Scholar 
Renema, W. et al. Hopping hotspots: global shifts in marine biodiversity. Science 321, 654–657 (2008).Article 
CAS 

Google Scholar 
Bellwood, D. R., Goatley, C. H. R. & Bellwood, O. The evolution of fishes and corals on reefs: form, function and interdependence. Biol. Rev. 92, 878–901 (2017).Article 

Google Scholar 
Roff, G. Evolutionary history drives biogeographic patterns of coral reef resilience. Bioscience 71, 26–39 (2021).
Google Scholar 
Siqueira, A. C., Bellwood, D. R. & Cowman, P. F. The evolution of traits and functions in herbivorous coral reef fishes through space and time. Proc. R. Soc. B Biol. Sci. 286, 20182672 (2019).Article 

Google Scholar 
Birrell, C. L., McCook, L. J., Willis, B. L. & Diaz-Pulido, G. A. Effects of benthic algae on the replenishment of corals and the implications for the resilience of coral reefs. Oceanogr. Mar. Biol. Annu. Rev. 46, 25–63 (2008).
Google Scholar 
Speare, K. E., Duran, A., Miller, M. W. & Burkepile, D. E. Sediment associated with algal turfs inhibits the settlement of two endangered coral species. Mar. Pollut. Bull. 144, 189–195 (2019).Article 
CAS 

Google Scholar 
Diaz-Pulido, G., Harii, S., McCook, L. J. & Hoegh-Guldberg, O. The impact of benthic algae on the settlement of a reef-building coral. Coral Reefs 29, 203–208 (2010).Article 

Google Scholar 
Johns, K. A. et al. Macroalgal feedbacks and substrate properties maintain a coral reef regime shift. Ecosphere 9, e02349 (2018).Article 

Google Scholar 
Houk, P. et al. Commercial coral-reef fisheries across Micronesia: a need for improving management. Coral Reefs 31, 13–26 (2012).Article 

Google Scholar 
Edwards, C. B. et al. Global assessment of the status of coral reef herbivorous fishes: evidence for fishing effects. Proc. R. Soc. B Biol. Sci. 281, 20131835 (2014).Article 
CAS 

Google Scholar 
Choat, J. H. & Clements, K. D. Vertebrate herbivores in marine and terrestrial environments: a nutritional ecology perspective. Annu. Rev. Ecol. Syst. 29, 375–403 (1998).Article 

Google Scholar 
Tebbett, S. B., Morais, R. A., Goatley, C. H. R. & Bellwood, D. R. Collapsing ecosystem functions on an inshore coral reef. J. Environ. Manag. 289, 112471 (2021).Article 

Google Scholar 
Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proc. Natl Acad. Sci. USA. 118, e2015265118 (2021).Article 
CAS 

Google Scholar 
Diaz-Pulido, G. et al. Greenhouse conditions induce mineralogical changes and dolomite accumulation in coralline algae on tropical reefs. Nat. Commun. 5, 3310 (2014).Article 

Google Scholar 
Nash, M. C. et al. Dolomite-rich coralline algae in reefs resist dissolution in acidified conditions. Nat. Clim. Chang. 3, 268–272 (2013).Article 
CAS 

Google Scholar 
Lyons, M., Larsen K. & Skone, M. Allen Coral Atlas. Imagery, maps and monitoring of the world’s tropical coral reefs. Zenodo https://doi.org/10.5281/zenodo.3833242 (2020).Tebbett, S. B. & Bellwood, D. R. Algal turf sediments on coral reefs: what’s known and what’s next. Mar. Pollut. Bull. 149, 110542 (2019).Article 
CAS 

Google Scholar 
Nugues, M. M. & Bak, R. P. M. Long-term dynamics of the brown macroalga Lobophora variegata on deep reefs in Curaçao. Coral Reefs 27, 389–393 (2008).Article 

Google Scholar 
Tsounis, G. & Edmunds, P. J. Three decades of coral reef community dynamics in St. John, USVI: a contrast of scleractinians and octocorals. Ecosphere 8, e01646 (2017).Article 

Google Scholar 
Toth, L. T. et al. Do no-take reserves benefit Florida’s corals? 14 years of change and stasis in the Florida Keys National Marine Sanctuary. Coral Reefs 33, 565–577 (2014).Article 

Google Scholar 
Smith, J. E. et al. Re-evaluating the health of coral reef communities: baselines and evidence for human impacts across the central Pacific. Proc. R. Soc. B Biol. Sci. 283, 20151985 (2016).Article 

Google Scholar 
Wolfe, K., Kenyon, T. M. & Mumby, P. J. The biology and ecology of coral rubble and implications for the future of coral reefs. Coral Reefs 40, 1769–1806 (2021).Article 

Google Scholar 
Harris, J. L., Lewis, L. S. & Smith, J. E. Quantifying scales of spatial variability in algal turf assemblages on coral reefs. Mar. Ecol. Prog. Ser. 532, 41–57 (2015).Article 

Google Scholar 
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & Group, T. P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097 (2009).Article 

Google Scholar 
Crisp, S. K., Tebbett, S. B. & Bellwood, D. R. A critical evaluation of benthic phase shift studies on coral reefs. Mar. Environ. Res. 178, 105667 (2022).Article 
CAS 

Google Scholar 
WebPlotDigitizer v. 4.3 (A. Rohatgi, 2020); https://automeris.io/WebPlotDigitizerKulbicki, M. et al. Global biogeography of reef fishes: a hierarchical quantitative delineation of regions. PLoS ONE 8, e81847 (2013).Article 

Google Scholar 
Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).Article 

Google Scholar 
R Core Team: R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).Zychaluk, K., Bruno, J. F., Clancy, D., McClanahan, T. R. & Spencer, M. Data-driven models for regional coral-reef dynamics. Ecol. Lett. 15, 151–158 (2012).Article 

Google Scholar 
Dudgeon, S. R., Aronson, R. B., Bruno, J. F. & Precht, W. F. Phase shifts and stable states on coral reefs. Mar. Ecol. Prog. Ser. 413, 201–216 (2010).Article 

Google Scholar 
Jost, L., Chao, A. & Chazdon, R. L. in Biological Diversity: Frontiers in Measurement and Assessment (eds Magurran, A. E. & McGill, B. J.) 66–84 (Oxford Univ. Press, 2011).Oksanen, J. F. et al. Vegan: Community ecology package. R package version 2.5-6 (2019).Calenge, C. The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
Worton, B. J. Kernel methods for estimating the utilization distribution in home‐range studies. Ecology 70, 164–168 (1989).Article 

Google Scholar 
Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455 (2018).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: an Introduction with R 2nd edn (Chapman & Hall/CRC, 2017).Gräler, B., Pebesma, E. & Heuvelink, G. Spatio-temporal interpolation using gstat. R. J. 8, 204–218 (2016).Article 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.3.0 (2020).Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.5.1 (2020).Wickham, H. et al. tidyverse: easily install and load the ‘tidyverse’. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Pebesma, E. Simple features for R: standardized support for spatial vector data. R. J. 10, 439–446 (2018).Article 

Google Scholar 
South, A. rnaturalearth: World map data from natural earth. R package version 0.1.0 (2017).Hamilton, N. E. & Ferry, M. ggtern: ternary diagrams using ggplot2. J. Stat. Softw., Code Snippets 87, 1–17 (2018).
Google Scholar 
Pedersen, T. L. patchwork: The composer of plots. R package version 1.1.1 (2020). 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

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

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

Funicular bodies: structure, function, and developmentThe ultrastructural and functional complexity of funicular bodies in bryozoans25,46,47 (our data), as well as the reduction of the genome in their symbiotic bacteria (e.g., Bugula neritina48), point at a long-term co-evolution between these organisms, also suggesting that infected bryozoan colonies spend a significant part of their energy budget supporting numerous bacteria inside FBs. All known prokaryote symbionts are apparently non-pathogenic for bryozoans49,62. Instead, the overall evidence indicates a mutualistic relationship. Symbiotic bacteria are known to produce toxic substances (bryostatins in Bugula63; bryoanthrathiophene in Watersipora64) that protect bryozoan larvae from predators42,44,45. Another potential role of bacterial secondary metabolites is the chemical defense of early developmental stages during larval settlement and metamorphosis40,65. Such chemical defence may also prevent epibiotic overgrowth of the bryozoan colonies by other bacteria and algae (reviewed in66,67). Similar functions can be assumed for symbiotic prokaryotes of Dendrobeania fruticosa, although experimental evidence is still required.FBs with bacterial symbionts in colonies of D. fruticosa show signs of high specialization. The walls of the funicular body completely isolate its internal cavity from the surrounding zooidal cavity: the cells of the outer layer overlap each other, whereas cells of the inner layer have tight Z-shaped contacts. Such isolation probably creates a specific environment inside the symbiont-containing space that helps maintain a growing population of bacterial symbionts. A massive protein-synthesizing apparatus was observed in the cells of the inner layer. In addition, the ‘pocket’-like structure of the internal cavity of FB and the abundance of cytoplasmic processes, some of which protruded deep into this cavity, contribute to increasing both the general inner surface area of FB and the contact area between its inner cells and bacteria. From their side, bacteria provide this contact by numerous pili. Numerous pits and microvesicles associated with the apical membrane of the inner cells imply an active exchange of substances between the host and its symbionts (e.g., nutrition provided to growing and multiplying bacteria and absorption of the potential wastes they produce).The ultrastructure and functional morphology of FBs in Aquiloniella scabra, the only species in which the ultrastructure of these organs was described, are very similar to those in D. fruticosa. Its FBs also consist of two cell types, but the inner cell layer is formed by one or a few cells, whereas external cells form a multilayered envelope25. In both species, the main function of FBs was considered to be the chambers/organs for symbiont incubation and nourishment.FBs with symbiotic bacteria have been found in several bryozoan species from four different families20,25,47,49,51 (our data). Remarkably, all these families (Bugulidae, Beaniidae, Candidae, Epistomiidae) belong to the clade Buguloidea. Encapsulated aggregations of bacteria within the zooidal cavity were also described in two species of Watersipora from the phylogenetically distant family Watersiporidae (Smittinoidea)62,68. In that case, prokaryotes (“mollicutes”) were also enveloped by flattened bryozoan cells, although the current scarcity of data makes it difficult to compare these cell aggregations with funicular bodies.Among Buguloidea, FBs show the same basic structure, although species described by Lutaud49, Dyrynda and King51, and Mathew with co-authors46 based on light microscopy require re-examination by TEM. Moreover, FBs are in all instances associated with the funicular system of the colony. This similarity could indicate the single origin of the bacterial symbiosis and FBs within Buguloidea. Still, it remains unknown (although rather probable) whether that system transports nutrients from feeding polypides to FBs because no communication between the lacunae of the funicular cords and the FB internal cavity was detected. Another, more likely option, however, is the independent acquisition of bacteria (see also below) and a similar ‘reaction’ of host tissues to invaders, resulting in the formation of bacterial organs (FB) with a similar bauplan. The presence of incapsulated bacteria in non-related Watersipora supports the second interpretation. Answering this question will require both ultrastructural studies and molecular identification of bacteria.As for the initial source of somatic cells and development of FBs, two variants have been suggested. Describing the development of FBs in Bugulina turbinata, Lutaud49 stated that the epithelial cells of the cystid wall were transformed into the inner cell layer of the FB, whereas peritoneal cells formed its external lining. By contrast, while studying Aquiloniella scabra, Karagodina with co-authors25 suggested that bacteria are engulfed by one of the funicular cells, which becomes a ‘bacteriocyte’ that is later enveloped by neighboring funicular cells. In the latter case, FBs are considered to be modified expanded parts of the funicular system. This is consistent with experiments by Sharp and co-authors40, who detected groups of labeled bacteria in the funicular cords of Bugula neritina, and also multiple bacteria developing inside enlarged funicular cords (in fact, very large FBs) in the related Paralicornia sinuosa20.Our TEM study of FBs in D. fruticosa showed that they are not swollen parts of the funicular cords, as was stated by Vishnyakov with co-authors for B. neritina20. It is more likely that the funicular cords and processes of their cells contact the external cell layer of FBs. According to the third scenario that we present here, the inner cell layer of FBs in D. fruticosa, as well as in other studied bugulids, most likely originates from the coelomocytes which accumulate bacteria via phagocytosis. Such solitary cells were described inside the zooidal cavity in B. neritina20. Instead of being digested, engulfed bacteria could trigger the coelomocyte divisions resulting in the formation of the inner cell layer. In contrast, in A. scabra, the coelomocyte can remain single or undergo only a few divisions. We propose that the external cell layer of FB in D. fruticosa originates from the funicular cells because these cell types are ultrastructurally similar. Finally, it is also possible that the exact process and sources of FB formation differ in different species.Multiple and lobed FBs found in two zooids of D. fruticosa could indicate a potential mode of their multiplication. The case of P. sinuosa requires additional study, but currently we believe that its bacteria-bearing ‘funicular cords’ are very large, elongated FBs, as well (see20).Symbiont circulation in the bryozoan life cycleThe taxonomic diversity of bryozoan hosts and their symbiotic bacteria—supported by a variety of sites in the bryozoan zooids and larvae where symbionts have been found—unambiguously point to multiple independent origins of symbiotic associations between bacteria and cheilostome Bryozoa20,25.Rod-shaped bacteria are the most common symbionts in the superfamily Buguloidea. Although superficially similar, these bacteria strongly differ in their maximum size, suggesting the presence of different procaryote species. Thus, bacteria detected in the larvae of Bugulina simplex and in FBs of Aquiloniella scabra can reach 10 μm in length25,69, while healthy symbionts (see below) inside coelomocytes and presumably peritoneal cells of Bugula neritina were only 2.5 μm long20. The maximum length of bacteria in FBs of D. fruticosa never exceeded 5 μm. Coccoid bacteria in the tentacles of B. neritina were 0.5–0.7 μm in diameter20. Else, oval or irregularly-shaped mycoplasma-like α-Proteobacteria were detected in the genus Watersipora62,68,70. By contrast, the symbionts identified in B. neritina and B. simplex belong to γ-Proteobacteria69,71,72.Apart from FBs, prokaryote symbionts were described extracellularly in colonies of different cheilostome species: in vestibular glands of autozooids, inside polymorphic zooids (avicularia)38,39,50, in tentacles and funicular cords20. They have also been found intracellularly: inside coelomocytes, epithelial and peritoneal cells of the body wall, and pharyngeal cells20,50. In addition, Woollacott and Zimmer73 described bacteria in the ‘channels’ of the funicular cords associated with brood chambers. However, the TEM image they published shows bacteria inside large vacuoles of the funicular cells—seemingly not in the lacunae between these cells, recalling the aforementioned idea of a ‘bacteriocyte’. Finally, bacteria have also been found in the pallial sinus of bryozoan larvae (62,68,74; reviewed in72). All these diverse data have led to two opposite views on the acquisition and circulation of symbionts in the bryozoan life cycle.Discovery of bacteria in both colonies and larvae of B. neritina was regarded as possible evidence of their transmission from larvae to adults74. This assumption was experimentally proven using both labeled bacteria and their metabolites (bryostatins) in the larvae and preancestrulae developing from them40. Moreover, the presence of bacteria within brood chambers (ovicells) in this species was considered as proof for the next step—the transition of symbionts from the colony to the incubated larvae, i.e., the vertical transfer of symbionts. Symbiotic bacteria populating larvae (and making them unpalatable for predators) are incorporated into the preancestrular tissues during larval metamorphosis, and then found inside zooidal buds in early colonies (a symbiont association with the host cells was not specified) and funicular cords of rhizoids in adult colonies40.The next step of the bacterial development could be the formation of FBs as a locus of symbiont reproduction. Mature FBs, full of bacteria, were considered to be the starting point for the transfer of prokaryotes (by an unknown mechanism) from the zooid to the brood cavity (via funicular cords associated with both FB and ovicells), and then to the incubated larvae47. Light microscopic data demonstrated: (1) the association of FBs with tube-like funicular cords47, and (2) the presence of groups of ‘bacterial bodies’ (small aggregations of bacteria) inside the ooecial vesicle (membranous-epithelial ‘plug’ that closes the entrance to the ovicell; mentioned in B. neritina47, and shown in images of the related Bugulina flabellata75,76,77). In addition, ultrastructural data proved the presence of bacteria inside funicular cords, more precisely—inside their funicular cells (see above), “extending to the ooecial vesicle” (73, p. 362). Elsewhere, Sharp and co-authors (40, p. 697) used fluorescence microscopy to demonstrate the presence of bacteria “within the ovicells”, and suggested that they are transported there across the colony via funicular cords that also house bacteria. Combined, all these data imply that bacteria move from FBs to the ooecial vesicle, accumulate there, and then somehow enter the brood cavity, which contains a larva, either through or in-between the epithelial cells and the cuticle of the ooecial vesicle. Findings of bacteria inside larvae and adult colonies of two species from the non-related genus Watersipora62,68 further strengthened the hypothesis of vertical transfer, which has subsequently been widely accepted by many authors20,30,47,78. Despite extensive TEM studies, no bacteria have been found inside the funicular cords in B. neritina (Vishnyakov & Ostrovsky, unpublished data), which contradicts the data of Sharp and coauthors40 obtained by fluorescent microscopy. Accordingly, it was suggested that coelomocytes carry symbionts to the ooecial vesicle instead20.Nonetheless, the hypothesis of vertical transfer faces serious objections based on life history, molecular and morphological data. In Dendrobeania fruticosa in the White Sea, for example, larval production occurs predominantly in autumn (mainly in the distal parts of branches, Fig. 1A), and no bacteria are present in colonies during this period. In addition, molecular population studies revealed that B. neritina is a complex of sibling species, both symbiotic and aposymbiotic, some of which live in sympatry, with the horizontal transfer between colonies being the most parsimonious explanation for the distribution of bacteria between siblings79. A study of the genome of symbiotic bacteria showed that they may be able to live outside the host48, which is consistent with the hypothesis of horizontal transfer (which is not the same as environmental transmission, see below).TEM data showed no communication between the FB cavity and lacunae of the funicular cords in the studied species, in particular in B. neritina (Vishnyakov & Ostrovsky, unpublished data), Aquiloniella scabra25 and D. fruticosa (this study). Coelomocytes (and presumed peritoneal cells) with bacteria embedded in their cytoplasm were indeed recorded inside the ooecial vesicle in B. neritina20. Nevertheless, extensive TEM studies of B. neritina ovicells at various stages of placental development (Vishnyakov and Ostrovsky, unpublished data) have not revealed bacteria between placental cells adjoining a developing embryo. The fact that these cells are provided with both tight and adherens junctions (e.g., B. neritina and Bicellariella ciliata73,80), and additionally are covered by a cuticle (albeit thin) raises the question of whether coelomocytes with bacteria and/or bacteria alone can move through the very thick hypertrophied placental epithelium. Interestingly, Miller with co-authors48 detected a gene encoding chitinase in the genome of the symbiont of B. neritina that could potentially be used for cuticle piercing.Another opportunity for the vertical transfer of symbionts is their transport via the supraneural coelomopore during oviposition (see20,81). In this case, free bacteria in the cavity of the maternal zooid could stick to the ovulated oocyte before its transfer into the brood cavity via the coelomopore. However, free bacteria were never recorded in the zooidal cavity. For the Watersipora species, an assumed variant of symbiont transmission is through a strand of mucus extending from the maternal zooid to the released larva and tethering it for a few minutes68.Environmental transmission, when bacteria are acquired from the surrounding seawater, is an alternative option for symbiont acquisition. It may potentially occur either via infection of brooded larvae inside the ovicell by bacteria entering the brood cavity from the external environment or via infection of larvae during the free-swimming period by bacteria from the water column. Published images by Sharp and co-authors40 showed the presence of both symbiotic bacteria and bryostatins both inside the ooecial vesicle and in the peripheral part of the brood cavity, close to the entrance of the ovicell. Although these authors stated that such close “locations of both the bacterial symbionts and the bryostatins demonstrate that the B. neritina–‘E. sertula’ association has a delivery system for both the symbionts and the bryostatins to embryos within the ovicell” (40, p. 699), we argue, based on the above-mentioned data, that this statement remains a probable yet unproven speculation. Our numerous unpublished TEM images indicate the presence of a large number of bacteria filling the brood space between the embryos and the ovicell wall in the brood chambers with and without developing larvae. These bacteria, attracted by some chemical signal(s), can enter the ovicells from outside and infect larvae. Until the transfer of bacteria through the wall of the ooecial vesicle or during oviposition is documented, environmental transmission remains the more probable method. Notably, recent studies on sponge microbiotas showed that the environmental transmission is widespread in this group of suspension feeders82.The hypothesis of external acquisition of bacteria by the bryozoan hosts leaves different infection pathways open. Some of these could potentially develop into vertical transfer. Beyond the infection of larvae, prokaryotes could enter feeding autozooids via the mouth (and further through the intestinal epithelium into the zooidal cavity), through the coelomopore—a presumed entrance for alien sperm76,81,83, or by direct infection of the tentacles (probably by penetration through the outer epithelium).We should stress that FBs were absent in zooidal buds and the youngest zooids with functional polypides in the growing branch tip of one colony of D. fruticosa collected in June. This suggests that bacteria are not transmitted from the older colony parts (and, thus, are not inherited from the founding larvae), but obtained from the external medium since older (and more proximal) zooids had FBs. This idea is supported by the lack of signs of transfer of bacteria between zooids via communication pores and their pore-cell complexes in Dendrobeania fruticosa and Aquiloniella scabra in our TEMs. In contrast, fluorescence microscopy showed symbionts in the non-feeding zooidal bud of the newly-formed small colony of B. neritina40. Bacteria were also present in the preancestrula formed during larval metamorphosis. It remains unknown whether they can move from the preancestrula to the bud along with coelomocytes, with growing funicular cords or both before the formation of transverse walls that isolate newly budded zooids. So, interzooidal transfer to budding sites is possible, and the youngest zooids with functional polypides in the growing tips of D. fruticosa could already receive bacteria too, but FBs were not yet developed. Thus the question of interzooidal/intracolonial transport of bacteria remains open.Two infection pathways—via larvae and by direct penetration through the tissues of the functional polypide, potentially exist in the same species. This is the case in B. neritina, which has morphologically different symbionts in FBs and in the tentacles20. Bacteria in the epithelial wall of the tentacle sheath and ooecial vesicle (see above) could potentially get there via both pathways, or enter the zooid via the coelomopore (third way), subsequently becoming entrapped by coelomocytes or cells of the cystid wall.Finally, the presence of bryozoan sibling species, some of which have symbionts while others do not, and the presence of symbiotic and aposymbiotic colonies within the same species79, suggests that bacteria can be lost and acquired anew at both short- and long-term time scales, as occurs in hermatypic corals and their symbiotic zooxanthellae84,85,86. In this light, it would be important to know whether FBs can develop anew in D. fruticosa after overwintering in the same zooids, or whether they appear only in newly budding zooids. What is the source of bacteria in overwintered colonies? Is it an external infection, or some ‘survivor’-cells (descendants of the bacterial pool from the larva that overwintered inside epithelial cells and/or coelomocytes), or both? This will require further study.Symbiont population dynamics in Dendrobeania fruticosa and its potential driversWhatever the route used by bacteria to enter zooids, they are apparently immediately ‘trapped’ by somatic cells. Free bacteria have never been observed inside the zooidal cavity, another argument against their passage through the wall of the ooecial vesicle to the ovicell.We have shown that regardless of the as-yet-unknown mode of FB development, these temporary organs and the symbionts inside them undergo seasonal changes. Early and mature FBs with non-modified morphology and ‘healthy’ bacteria were found in young zooids only in the colonies collected in June. In one of these colonies, older zooids contained FBs at the initial stage of degradation. In the same month, one colony possessed FBs either at the early-advanced degradation stage (in young zooids) still containing numerous bacteria or at the late-advanced and even terminal stages in old, presumably overwintered zooids. At the initial stage of modification, the slightly developed ‘interlayer’ space between the inner and outer FB cell layers contained fibrils (presumed virus-like particles). Interestingly, the cells of the inner lining apparently engulfed some of the bacterial cells by phagocytosis, supporting our interpretation of the origin of these cells from coelomocytes in this species.One to two months later (August–September), all examined FBs were either at the middle-, or the late-advanced stages of degradation. The number of bacteria in the FB internal cavity distinctly decreased, the inner layer of cells became thinner, and in some regions remained only as a double membrane. The protein-synthesis apparatus was seen only occasionally, and engulfed bacteria were no longer visible inside the inner cells. A wide ILS, formed between the cells of the outer and inner layers, contained abundant putative virus-like particles (Figs. 2, 10). All these FBs were recorded in feeding and non-feeding zooids in the non-growing distal parts of colony branches.Polypide recycling and a seasonal drop of planktonic food alone cannot explain these changes. Firstly, modified FBs were found in zooids with both degenerated and functioning polypides. Secondly, the initial stages of FBs degradation were detected in June when phytoplankton is abundant in the White Sea (e.g.87). We therefore propose the following scenario for the sequence of changes in FBs and their possible causes. In June, newly-formed zooids build funicular bodies containing bacteria that were acquired either from outside or via internal transfer from older colony parts. During that month, FBs begin to degrade. This process continues throughout the rest of the summer. The mid-advanced stage of FB degradation, with few modified bacteria surviving and distributed on the periphery of the FB cavity, was recorded in August. This stage is reminiscent of the final stage in Lutaud’s49 descriptions of the gradual destruction of FBs accompanied by the disappearance of bacterial symbionts in Bugulina turbinata. In late September, FBs change and bacteria disappear, probably through viral lysis (the induction of prophages) (Fig. 2). Young and non-modified FBs were never encountered in August and September, indicating that development of FBs occurs only in young zooids at the growing tips of colony branches in June.Vishnyakov et al.20 recently described the degradation of symbiotic prokaryotes in Bugula neritina and Paralicornia sinuosa accompanied by a change in bacterial morphology similar to bacteriophage-mediated lysis. Degradation process was accompanied by the appearance of polyhedral VLPs in B. neritina and by the formation of structures similar to the so-called metamorphosis-associated contractile complexes (MACs) in P. sinuosa. These complexes are phage-related structures whose activity eventually results in the cell lysis, see88. Although the fate of FBs was not analyzed in their paper, these two VLP variants were observed both inside the bacteria and in the FB internal space by Vishnyakov and co-authors20.In D. fruticosa, presumed VLPs in the form of spherical complexes (as clusters of straight filaments) are present inside bacteria and, together with their fragments, inside the ILS. The filaments were also observed in the free state, frequently curved (apparently flexible) in the internal cavity of FBs. ILS was mostly filled with ‘fibrils’ (potentially representing modified/corrupted filaments) and ‘globules’, although filaments were incidentally recorded inside ILS too. We suggest that, in D. fruticosa, filaments may represent bacteriophage virions. This interpretation is supported by their appearance being associated with the degradation of bacterial cells, as in the case of VLP in B. neritina and P. sinuosa. Nonetheless, the morphology of the spherical complexes built from the filaments in D. fruticosa is unique: they do not resemble any known group of bacterial viruses. We found these putative VLPs inside ILS between the outer and inner FB cell layers. It remains unclear whether they travel there from the FB internal cavity or self-assemble inside ILS from individual filaments that were incidentally met there too. We add that spherical complexes, complete or partial, were recorded inside ILS of the funicular bodies in non-overwintered (collected on 31 September 2019) and presumably overwintered (14 June 2021) zooids.A filamentous morphology is known from only one bacteriophage from the order Tubulavirales, which includes two families Inoviridae and Plectoviridae89. Although inoviruses or plectoviruses have never been reported to assemble in any regular macrocomplexes, the ability of the filamentous Pf phages (inoviruses) of Pseudomonas aeruginosa to form nearly regular liquid crystalline assemblages was recently demonstrated90 (see also review91). Interestingly the formation of such crystals required interactions with bacterial or eukaryotic polymeric molecules such as polysaccharides, DNA and probably mucin90,91.The development of filamentous phages in bacterial cells usually does not kill the cells because these viruses assemble, along with extrusion from the infected bacterium, without disrupting its cell wall (reviewed in92). However, cell death mediated by filamentous prophage induction has been reported in P. aeruginosa due to the emergence of so-called superinfective phage variants93,94,95. Accordingly, the degradation of bacteria in D. fruticosa associated with bacterial viruses is possible.Since the assembly of known filamentous phages is associated with their extrusion from the cell, virions should not accumulate inside bacteria. Although filamentous assembly intermediates may be present (see92), they are not expected to accumulate in such large quantities and/or form superstructures in the bacterial cytoplasm like the ones we found in D. fruticosa (Fig. 9). Our observations revealed nothing resembling the extrusion of a filamentous phage from the surface of a bacterial cell. Therefore, if the described filaments are indeed VLPs, they may represent a new type of bacterial virus.Even though many details remain unknown, we assume that the filaments, ‘globules’, ’fibrils’, and spherical structures in D. fruticosa are of viral origin. Their development following the total disappearance of bacteria in FBs indicates their bacteriophage nature. If so, our observations support the idea that viruses control the number of symbionts in their bryozoan host20, as has been reported in some insects85,86,96. More

Pausas, J. G. & Bond, W. J. On the three major recycling pathways in terrestrial ecosystems. Trends Ecol. Evol. 35, 767–775 (2020).
Google Scholar 
Manzano, P. & White, S. R. Intensifying pastoralism may not reduce greenhouse gas emissions: wildlife-dominated landscape scenarios as a baseline in life cycle analysis. Clim. Res. 77, 91–97 (2019).
Google Scholar 
Röös, E. et al. Greedy or needy? Land use and climate impacts of food in 2050 under different livestock futures. Glob. Environ. Change 47, 1–12 (2017).
Google Scholar 
Harwatt, H., Ripple, W. J., Chaudhary, A., Betts, M. G. & Hayek, M. N. Scientists call for renewed Paris pledges to transform agriculture. Lancet Planet. Health 4, E9–E10 (2020).
Google Scholar 
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 

Google Scholar 
Barnosky, A. D. Megafauna biomass trade-off as a driver of Quaternary and future extinctions. Proc. Natl Acad. Sci. USA 105, 11543–11548 (2008).CAS 

Google Scholar 
Smith, F. A. et al. Exploring the influence of ancient and historic megaherbivore extirpations on the global methane budget. Proc. Natl Acad. Sci. USA 113, 874–879 (2016).CAS 

Google Scholar 
Zimov, S. A., Zimov, N. S., Tikhonov, A. N. & Chapin, F. S. III Mammoth steppe: a high-productivity phenomenon. Quat. Sci. Rev. 57, 26–45 (2012).
Google Scholar 
Adams, J. M., Faure, H., Faure-Denard, L., McGlade, J. M. & Woodward, F. I. Increases in terrestrial carbon storage from the Last Glacial Maximum to the present. Nature 348, 711–714 (1990).CAS 

Google Scholar 
Nogués-Bravo, D., Rodríguez, J., Hortal, J., Batra, P. & Araújo, M. B. Climate change, humans, and the extinction of the woolly mammoth. PLoS Biol. 6, e79 (2008).
Google Scholar 
Bond, W. J. Open Ecosystems: Ecology and Evolution Beyond the Forest Edge (Oxford Univ. Press, 2019).Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).CAS 

Google Scholar 
Carpio Camargo, A. J. et al. Assessing red deer hunting management in the Iberian Peninsula: the importance of longitudinal studies. PeerJ 9, e10872 (2021).
Google Scholar 
Gordon, I. J., Manning, A. D., Navarro, L. M. & Rouet-Leduc, J. Domestic livestock and rewilding: are they mutually exclusive? Front. Sustain. Food Syst. 5, 550410 (2021).
Google Scholar 
Bond, W. J. Large parts of the world are brown or black: a different view on the ‘Green World’ hypothesis. J. Veg. Sci. 16, 261–266 (2005).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
ESRI. ArcGIS Desktop: Release 10.3. (Environmental Systems Research Institute, Redlands, CA, 2014).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).
Google Scholar 
Fløjgaard, C., Pedersen, P. B. M., Sandom, C. J., Svenning, J.-C. & Ejrnæs, R. Exploring a natural baseline for large-herbivore biomass in ecological restoration. J. Appl. Ecol. 59, 18–24 (2022).
Google Scholar 
Haller, T. et al. Conflicts, security and marginalisation: institutional change of the pastoral commons in a ‘glocal’ world. Rev. Sci. Tech. Off. Int. Epiz. 35, 405–416 (2016).CAS 

Google Scholar 
Torrents-Ticó, M., Fernández-Llamazares, A., Burgas, D. & Cabeza, M. Convergences and divergences between scientific and Indigenous and Local Knowledge contribute to inform carnivore conservation. Ambio 50, 990–1002 (2021).
Google Scholar 
Griffith, E. F., Pius, L., Manzano, P. & Jost, C. C. COVID-19 in pastoral contexts in the greater Horn of Africa: implications and recommendations. Pastoralism 10, 1–12 (2020).
Google Scholar 
Schieltz, J. M. & Rubenstein, D. I. Evidence-based review: positive versus negative effects of livestock grazing on wildlife. What do we really know? Environ. Res. Lett. 11, 113003 (2016).
Google Scholar 
García Sanz, A. La ganadería española entre 1750–1865: los efectos de la reforma agraria liberal. Agricultura y Sociedad 72, 81–120 (1991).
Google Scholar 
San Miguel, A., Roig, S. & Perea, R. The pastures of Spain. Pastos 46, 6–39 (2016).
Google Scholar 
Epp, H. & Dyck, I. Early human-bison population interdependence in the Plains ecosystem. Gt. Plains Res. 12, 323–337 (2002).
Google Scholar 
Hristov, A. N. Historic, pre-European settlement, and present-day contribution of wild ruminants to enteric methane emissions in the United States. J. Anim. Sci 90, 1371–1375 (2012).CAS 

Google Scholar 
Bond, W. J. Ancient grasslands at risk. Science 351, 120–122 (2016).CAS 

Google Scholar 
Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).
Google Scholar 
Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci. USA 118, e2023483118 (2021).CAS 

Google Scholar 
Swette, B. & Lambin, E. F. Institutional changes drive land use transitions on rangelands: the case of grazing on public lands in the American West. Glob. Environ. Change 66, 102220 (2021).
Google Scholar 
Hayek, M. N., Harwatt, H., Ripple, W. J. & Mueller, N. D. The carbon opportunity cost of animal-sourced food production on land. Nat. Sustain. 4, 21–24 (2021).
Google Scholar 
Kristensen, J. A., Svenning, J.-C., Georgiou, K. & Mahli, Y. Can large herbivores enhance ecosystem carbon persistence? Trends Ecol. Evol. 37, 117–128 (2022).CAS 

Google Scholar 
Carmona, C. P., Azcárate, F. M., Oteros-Rozas, E., González, J. A. & Peco, B. Assessing the effects of seasonal grazing on holm oak regeneration: Implications for the conservation of Mediterranean dehesas. Biol. Cons. 159, 240–247 (2013).
Google Scholar 
García-Fernández, A. et al. Herbivore corridors sustain genetic footprint in plant populations: a case for Spanish drove roads. PeerJ 7, e7311 (2019).
Google Scholar 
Scoones, I. Living with Uncertainty: New Directions in Pastoralism Development in Africa, Ch. 1 (ITDG, 1995).Pardo, G., Casas, R., del Prado, A. & Manzano, P. Carbon footprint of transhumant sheep farms: accounting for natural baseline emissions in Mediterranean systems. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-1838904/v1 (2022).Odhiambo, M. & Manzano, P. Making Way. Developing National Legal and Policy Frameworks for Pastoral Mobility (FAO, 2022).del Prado, A., Manzano, P. & Pardo, G. The role of the European small ruminant dairy sector on stabilizing global temperatures: lessons from GWP* warming-equivalent emission metrics. J. Dairy Res. 8, 8–15 (2021).
Google Scholar 
Molina-Flores, B., Manzano-Baena, P. & Coulibaly, M. A. The Role of Livestock in Food Security, Poverty Reduction and Wealth Creation in West Africa (FAO, 2020).Lasanta, T., Cortijos-López, M., Errea, M. P., Khorchani, M. & Nadal-Romero, E. An environmental management experience to control wildfires in the mid-mountain Mediterranean area: shrub clearing to generate mosaic landscapes. Land Use Policy 118, 106147 (2022).
Google Scholar 
Torres-Miralles, M. et al. Contribution of High Nature Value farming systems to sustainable livestock production: a case from Finland. Sci. Total Environ. 839, 156267 (2022).CAS 

Google Scholar 
Manzano, P. et al. Towards a holistic understanding of pastoralism. One Earth 4, 651–665 (2021).
Google Scholar 
Karlsson, J. O., Parodi, A., Van Zanten, H. H., Hansson, P. A. & Röös, E. Halting European Union soybean feed imports favours ruminants over pigs and poultry. Nat. Food 2, 38–46 (2021).
Google Scholar 
Leroy, F. et al. Transformation of animal agriculture should be evidence-driven and respectful of livestock’s benefits and contextual aspects. Animal 16, 100644 (2022).
Google Scholar 
Jackson, R. D. Grazed perennial grasslands can match current beef production while contributing to climate mitigation and adaptation. Agric. Environ. Lett. 7, e20059 (2022).
Google Scholar 
Mahli, Y. et al. The role of large wild animals in climate change mitigation and adaptation. Curr. Biol. 32, R181–R196 (2022).
Google Scholar 
O’Bryan, C. J. et al. Unrecognized threat to global soil carbon by a widespread invasive species. Glob. Change Biol. 28, 877–882 (2022).
Google Scholar 
Karp, A. T., Faith, J. T., Marlon, J. R. & Straver, A. C. Global response of fire activity to late Quaternary grazer extinctions. Science 374, 1145–1148 (2021).CAS 

Google Scholar 
Ripple, W. J. et al. World scientists’ warning of a climate emergency 2021. Bioscience 71, 894–898 (2021).
Google Scholar 
Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).
Google Scholar 
Mann, D. H., Groves, P., Kunz, M. L., Reanier, R. E. & Gaglioti, B. V. Ice-age megafauna in Arctic Alaska: extinction, invasion, survival. Quat. Sci. Rev. 70, 91–108 (2013).
Google Scholar 
Sandom, C., Faurby, S., Sandel, B. & Svenning, J. C. Global late Quaternary megafauna extinctions linked to humans, not climate change. Proc. Royal Soc. B 281, 20133254 (2014).
Google Scholar 
Fariña, R. A., Czerwonogora, A. & di Giacomo, M. Splendid oddness: revisiting the curious trophic relationships of South American Pleistocene mammals and their abundance. An. Acad. Bras. Ciênc. 86, 311–331 (2014).
Google Scholar  More

EU. Regulation (EU) No 1380/2013 of the European Parliament and of the Council. Off. J. Eur. Union 354 22–61 (2013).Sguotti, C. et al. Catastrophic dynamics limit Atlantic cod recovery. Proc. R. Soc. B Biol. Sci. 286, 20182877 (2019).Article 

Google Scholar 
ICES. in Report of the ICES Advisory Committee 2012. (2012).ICES. Advice basis. in Report of the ICES Advisory Committee 17 (2019).Emeis, K.-C. et al. The North Sea—A shelf sea in the Anthropocene. J. Mar. Syst. 141, 18–33 (2015).Article 

Google Scholar 
Beare, D. et al. An increase in the abundance of anchovies and sardines in the northwestern North Sea since 1995. Glob. Chang. Biol. 10, 1209–1213 (2004).Article 

Google Scholar 
Petitgas, P. et al. Anchovy population expansion in the North Sea. Mar. Ecol. Prog. Ser. 444, 1–13 (2012).Article 

Google Scholar 
Baudron, A. R. et al. Changing fish distributions challenge the effective management of European fisheries. Ecography (Cop.) 43, 494–505 (2020).Article 

Google Scholar 
Sguotti, C. et al. Non-linearity in stock–recruitment relationships of Atlantic cod: Insights from a multi-model approach. ICES J. Mar. Sci. 77, 1492–1502 (2020).Article 

Google Scholar 
Möllmann, C. et al. Tipping point realized in cod fishery. Sci. Rep. 11, 1–12 (2021).Article 

Google Scholar 
Sguotti, C. & Cormon, X. Regime shifts—A global challenge for the sustainable use of our marine resources. in YOUMARES 8—Oceans Across Boundaries: Learning from each other 155–166 (Springer International Publishing, 2018).Beisner, B. E., Haydon, D. T. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).Article 

Google Scholar 
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).Article 
CAS 

Google Scholar 
Sguotti, C. et al. Irreversibility of regime shifts in the North Sea. Front. Mar. Sci. 1830, (2022).Yang, Y. & Yamakawa, T. Re-examination of stock-recruitment relationships: A meta-analysis. ICES J. Mar. Sci. 79, 1380–1393 (2022).Article 

Google Scholar 
Perälä, T. A., Swain, D. P. & Kuparinen, A. Examining nonstationarity in the recruitment dynamics of fishes using Bayesian change point analysis. Can. J. Fish. Aquat. Sci. 74, 751–765 (2017).Article 

Google Scholar 
Beaugrand, G. The North Sea regime shift: Evidence, causes, mechanisms and consequences. Prog. Oceanogr. 60, 245–262 (2004).Article 

Google Scholar 
ICES. ICES Stock Assessment Database. https://standardgraphs.ices.dk/stockList.aspx (2019).Szuwalski, C. S., Vert-Pre, K. A., Punt, A. E., Branch, T. A. & Hilborn, R. Examining common assumptions about recruitment: A meta-analysis of recruitment dynamics for worldwide marine fisheries. Fish Fish. 16, 633–648 (2015).Article 

Google Scholar 
Szuwalski, C. S. et al. Global forage fish recruitment dynamics: A comparison of methods, time-variation, and reverse causality. Fish. Res. 214, 56–64 (2019).Article 

Google Scholar 
Otto, S. A., Plonus, R., Funk, S. & Keth, A. INDperform: Evaluation of Indicator Performances for Assessing Ecosystem States. R Package Version 0.1.1. (2018).ICES. Plaice (Pleuronectes platessa) in Subarea 4 (North Sea) and Subdivision 20 (Skagerrak). (2019).Støttrup, J. G., Munk, P., Kodama, M. & Stedmon, C. Changes in distributional patterns of plaice Pleuronectes platessa in the central and eastern North Sea; Do declining nutrient loadings play a role?. J. Sea Res. 127, 164–172 (2017).Article 

Google Scholar 
ICES. Hake (Merluccius merluccius) in subareas 4, 6, and 7, and divisions 3.a, 8.a–b, and 8.d, Northern stock (Greater North Sea, Celtic Seas, and the northern Bay of Biscay). (2019).ICES. Herring (Clupea harengus) in Subarea 4 and divisions 3.a and 7.d, autumn spawners (North Sea, Skagerrak and Kattegat, eastern English Channel). (2019).EU. Council Regulation (EC) No. 811/2004 of 21.4.2004 establishing measures for the recovery of the Northern hake stock. (2004).ICES. Haddock (Melanogrammus aeglefinus) in Subarea 4, Division 6.a, and Subdivision 20 (North Sea, West of Scotland, Skagerrak). (2019).ICES. Cod (Gadus morhua) in Subarea 4, Division 7.d, and Subdivision 20 (North Sea, eastern English Channel, Skagerrak). (2019).Rijnsdorp, A. D. & Vingerhoed, B. Feeding of plaice Pleuronectes platessa L. and sole Solea solea (L.) in relation to the effects of bottom trawling. J. Sea Res. 45, 219–229 (2001).Article 

Google Scholar 
Rijnsdorp, A., Van Damme, C. & Witthames, P. R. Implications of fisheries-induced changes in stock structure and reproductive potential for stock recovery of a sex-dimorphic species, North Sea plaice. ICES J. Mar. Sci. 67, 1931–1938 (2010).Article 

Google Scholar 
Engelhard, G. H., Pinnegar, J. K., Kell, L. T. & Rijnsdorp, A. D. Nine decades of North Sea sole and plaice distribution. ICES J. Mar. Sci. 68, 1090–1104 (2011).Article 

Google Scholar 
Baudron, A. R. & Fernandes, P. G. Adverse consequences of stock recovery: European hake, a new “choke” species under a discard ban?. Fish Fish. 16, 563–575 (2015).Article 

Google Scholar 
Huang, B. et al. Extended reconstructed sea surface temperature version 4 (ERSST.v4). J. Clim. 28, 911–930 (2015).Article 

Google Scholar 
Quante, M. & Colijn, F. North Sea Region Climate Change Assessment (Springer Nature, 2016).Book 

Google Scholar 
IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (2014).Erdman, C. & Emerson, J. W. bcp: An R package for performing a Bayesian analysis of change point problems. J. Stat. Softw. 23, 1–13 (2007).Article 

Google Scholar 
Killick, R. & Eckley, I. A. changepoint: An R package for changepoint analysis. J. Stat. Softw. 58, 1–19 (2014).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. (2018).Zeileis, A., Leisch, F., Hornik, K. & Kleiber, C. Strucchange. An R package for testing for structural change in linear regression models. J. Stat. Softw. 7, 1–38 (2002).Article 

Google Scholar 
Ogle, D. H., Wheeler, P. & Dinno, A. FSA: Fisheries Stock Analysis. R package version 0.8.30. (2020).Beverton, R. J. H. & Holt, S. J. On the Dynamics of Exploited Fish Populations (Fisheries Investigations, 1957).
Google Scholar 
Ricker, W. E. Stock and recruitment. J. Fish. Board Can. 11, 559–623 (1954).Article 

Google Scholar 
Muggeo, V. M. R. Segmented: An R package to fit regression models with broken-line relationships. R News 8, 20–25 (2008).
Google Scholar 
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning (Springer, New York, 2013).Book 
MATH 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer, 2009).Book 
MATH 

Google Scholar 
Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 
MATH 

Google Scholar 
Pedersen, T. L. Patchwork: The Composer of Plots. R package version 1.1.1. (2020). More