More stories

  • in

    Meta-analysis shows that plant mixtures increase soil phosphorus availability and plant productivity in diverse ecosystems

    Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).PubMed 
    Article 

    Google Scholar 
    Hou, E. Q. et al. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nat. Commun. 11, 637 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cordell, D., Drangert, J.-O. & White, S. The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19, 292–305 (2009).Article 

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

    Google Scholar 
    Chen, X. L., Chen, H. Y. H., Searle, E. B., Chen, C. & Reich, P. B. Negative to positive shifts in diversity effects on soil nitrogen over time. Nat. Sustain. 4, 225–234 (2021).Article 

    Google Scholar 
    Oelmann, Y. et al. Plant diversity effects on aboveground and belowground N pools in temperate grassland ecosystems: development in the first 5 years after establishment. Glob. Biogeochem. Cy. 25, GB2014 (2011).Article 
    CAS 

    Google Scholar 
    Fornara, D. A. et al. Plant effects on soil N mineralization are mediated by the composition of multiple soil organic fractions. Ecol. Res. 26, 201–208 (2011).CAS 
    Article 

    Google Scholar 
    Wright, A. J., Wardle, D. A., Callaway, R. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390 (2017).PubMed 
    Article 

    Google Scholar 
    Oelmann, Y. et al. Above- and belowground biodiversity jointly tighten the P cycle in agricultural grasslands. Nat. Commun. 12, 4431 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, L. et al. Diversity enhances agricultural productivity via rhizosphere phosphorus facilitation on phosphorus-deficient soils. Proc. Natl Acad. Sci. USA 104, 11192–11196 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, L., Tilman, D., Lambers, H. & Zhang, F. S. Plant diversity and overyielding: insights from belowground facilitation of intercropping in agriculture. New Phytol. 203, 63–69 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    Hacker, N. et al. Plant diversity shapes microbe–rhizosphere effects on P mobilisation from organic matter in soil. Ecol. Lett. 18, 1356–1365 (2015).PubMed 
    Article 

    Google Scholar 
    Vance, C. P., Uhde-Stone, C. & Allan, D. L. Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytol. 157, 423–447 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen, J. et al. Long-term nitrogen loading alleviates phosphorus limitation in terrestrial ecosystems. Glob. Change Biol. 26, 5077–5086 (2020).Article 

    Google Scholar 
    Hinsinger, P. et al. P for two, sharing a scarce resource: soil phosphorus acquisition in the rhizosphere of intercropped species. Plant Physiol. 156, 1078–1086 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liu, X. J. et al. Plant diversity and species turnover co-regulate soil nitrogen and phosphorus availability in Dinghushan forests, southern China. Plant Soil 464, 257–272 (2021).CAS 
    Article 

    Google Scholar 
    Hooper, D. U. & Vitousek, P. M. Effects of plant composition and diversity on nutrient cycling. Ecol. Monogr. 68, 121–149 (1998).Article 

    Google Scholar 
    Alberti, G. et al. Tree functional diversity influences belowground ecosystem functioning. Appl. Soil Ecol. 120, 160–168 (2017).Article 

    Google Scholar 
    Maddhesiya, P. K., Singh, K. & Singh, R. P. Effects of perennial aromatic grass species richness and microbial consortium on soil properties of marginal lands and on biomass production. Land Degrad. Dev. 32, 1008–1021 (2021).Article 

    Google Scholar 
    Zhang, C. B. et al. Effects of plant diversity on nutrient retention and enzyme activities in a full-scale constructed wetland. Bioresour. Technol. 101, 1686–1692 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Štursová, M. & Baldrian, P. Effects of soil properties and management on the activity of soil organic matter transforming enzymes and the quantification of soil-bound and free activity. Plant Soil 338, 99–110 (2011).Article 
    CAS 

    Google Scholar 
    Wu, H. et al. Linkage between tree species richness and soil microbial diversity improves phosphorus bioavailability. Funct. Ecol. 33, 1549–1560 (2019).Article 

    Google Scholar 
    Steinauer, K. et al. Plant diversity effects on soil microbial functions and enzymes are stronger than warming in a grassland experiment. Ecology 96, 99–112 (2015).PubMed 
    Article 

    Google Scholar 
    Zhang, D. S. et al. Increased soil phosphorus availability induced by faba bean root exudation stimulates root growth and phosphorus uptake in neighbouring maize. New Phytol. 209, 823–831 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Berendse, F., van Ruijven, J., Jongejans, E. & Keesstra, S. Loss of plant species diversity reduces soil erosion resistance. Ecosystems 18, 881–888 (2015).CAS 
    Article 

    Google Scholar 
    Forrester, D. I. & Bauhus, J. A review of processes behind diversity–productivity relationships in forests. Curr. Rep. 2, 45–61 (2016).Article 
    CAS 

    Google Scholar 
    Batterman, S. A. et al. Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees. Ecol. Lett. 21, 1486–1495 (2018).PubMed 
    Article 

    Google Scholar 
    Chen, C., Chen, H. Y. H., Chen, X. & Huang, Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat. Commun. 10, 1332 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hisano, M., Chen, H. Y. H., Searle, E. B. & Reich, P. B. Species-rich boreal forests grew more and suffered less mortality than species-poor forests under the environmental change of the past half-century. Ecol. Lett. 22, 999–1008 (2019).PubMed 
    Article 

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

    Google Scholar 
    Chen, X. & Chen, H. Y. H. Plant mixture balances terrestrial ecosystem C:N:P stoichiometry. Nat. Commun. 12, 4562 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reich, P. B. et al. Species and functional group diversity independently influence biomass accumulation and its response to CO2 and N. Proc. Natl Acad. Sci. USA 101, 10101–10106 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chen, X. et al. Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis. Biol. Rev. 95, 167–183 (2020).Article 

    Google Scholar 
    Zhang, Y., Chen, H. Y. H. & Reich, P. B. Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J. Ecol. 100, 742–749 (2012).Article 

    Google Scholar 
    Alewell, C. et al. Global phosphorus shortage will be aggravated by soil erosion. Nat. Commun. 11, 4546 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mueller, K. E., Tilman, D., Fornara, D. A. & Hobbie, S. E. Root depth distribution and the diversity–productivity relationship in a long-term grassland experiment. Ecology 94, 787–793 (2013).Article 

    Google Scholar 
    Tang, X. Y. et al. Intercropping legumes and cereals increases phosphorus use efficiency; a meta-analysis. Plant Soil 460, 89–104 (2021).CAS 
    Article 

    Google Scholar 
    Karanika, E. D., Alifragis, D. A., Mamolos, A. P. & Veresoglou, D. S. Differentiation between responses of primary productivity and phosphorus exploitation to species richness. Plant Soil 297, 69–81 (2007).CAS 
    Article 

    Google Scholar 
    Bünemann, E. K., Prusisz, B. & Ehlers, K. in Phosphorus in Action: Biological Processes in Soil Phosphorus Cycling (eds Bünemann, E. et al.) 37–57 (Springer, 2011).Ma, Z. L. & Chen, H. Y. H. Effects of species diversity on fine root productivity in diverse ecosystems: a global meta-analysis. Glob. Ecol. Biogeogr. 25, 1387–1396 (2016).Article 

    Google Scholar 
    Mellado-Vazquez, P. G. et al. Plant diversity generates enhanced soil microbial access to recently photosynthesized carbon in the rhizosphere. Soil Biol. Biochem. 94, 122–132 (2016).CAS 
    Article 

    Google Scholar 
    Qin, Y. et al. Arbuscular mycorrhizal fungus differentially regulates P mobilizing bacterial community and abundance in rhizosphere and hyphosphere. Appl. Soil Ecol. 170, 104294 (2022).Article 

    Google Scholar 
    Rojo, M. J., Carcedo, S. G. & Mateos, M. P. Distribution and characterization of phosphatase and organic phosphorus in soil fractions. Soil Biol. Biochem. 22, 169–174 (1990).CAS 
    Article 

    Google Scholar 
    Barrow, N. The effects of pH on phosphate uptake from the soil. Plant Soil 410, 401–410 (2017).CAS 
    Article 

    Google Scholar 
    Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yu, R. P., Li, X. X., Xiao, Z. H., Lambers, H. & Li, L. Phosphorus facilitation and covariation of root traits in steppe species. New Phytol. 226, 1285–1298 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine 6, e1000097 (2009).Jenkins, D. G. & Quintana-Ascencio, P. F. A solution to minimum sample size for regressions. PLoS ONE 15, e0229345 (2020)..Rohatgi, A. WebPlotDigitizer v.4.5 (Automeris, 2021); https://automeris.io/WebPlotDigitizerJobbagy, E. G. & Jackson, R. B. The distribution of soil nutrients with depth:global patterns and the imprint of plants. Biogeochemistry 53, 51–77 (2001).CAS 
    Article 

    Google Scholar 
    Trabucco, A. & Zomer, R. Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database (CGIAR, 2009); http://www.cgiar-csi.org/data/global-aridity-and-pet-databaseBridgham, S. D., Pastor, J., Mcclaugherty, C. A. & Richardson, C. J. Nutrient-use efficiency: a litterfall index, a model, and a test along a nutrient-availability gradient in North Carolina peatlands. Am. Nat. 145, 1–21 (1995).Article 

    Google Scholar 
    Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

    Google Scholar 
    Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bates, D. et al. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-10 https://cran.r-project.org/web/packages/lme4/index.html (2017).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
    Article 

    Google Scholar 
    MuMIn: Multi-model inference. R package version 1.42.1 (2018).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).Koricheva, J., Gurevitch, J. & Mengersen, K. Handbook of Meta-analysis in Ecology and Evolution (Princeton Univ. Press, 2013).Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).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 
    Long, J. A. Interactions: comprehensive, user-friendly toolkit for probing interactions. R package version 1.1.5 https://cran.r-project.org/package=interactions (2021).Adams, D. C., Gurevitch, J. & Rosenberg, M. S. Resampling tests for meta-analysis of ecological data. Ecology 78, 1277–1283 (1997).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). More

  • in

    Natural forest growth and human induced ecosystem disturbance influence water yield in forests

    Forest complexity increases hydrological resistance to disturbancesIn general, natural forests, old forests, forests with high coverage, and forests located in low aridity regions (P/PET ≥ 1) are characterized by higher ecosystem complexity than planted forests, young forests, forests with low coverage, and forests located in arid regions (P/PET  More

  • in

    Chaos is not rare in natural ecosystems

    May, R. M. Biological populations with nonoverlapping generations: stable points, stable cycles, and chaos. Science 186, 645–647 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    Beddington, J. R., Free, C. A. & Lawton, J. H. Dynamic complexity in predator–prey models framed in difference equations. Nature 255, 58–60 (1975).Article 

    Google Scholar 
    Hastings, A., Hom, C. L., Ellner, S., Turchin, P. & Godfray, H. C. J. Chaos in ecology: is Mother Nature a strange attractor? Annu. Rev. Ecol. Syst. 24, 1–33 (1993).Article 

    Google Scholar 
    Cressie, N. & Wikle, C. K. Statistics for Spatio-Temporal Data (John Wiley & Sons, 2011).The State of World Fisheries and Aquaculture 2020 (FAO, 2020).Hastings, A. & Powell, T. Chaos in a three-species food chain. Ecology 72, 896–903 (1991).Article 

    Google Scholar 
    Huisman, J. & Weissing, F. J. Biodiversity of plankton by species oscillations and chaos. Nature 402, 407–410 (1999).Article 

    Google Scholar 
    Doebeli, M. & Ispolatov, I. Chaos and unpredictability in evolution. Evolution 68, 1365–1373 (2014).PubMed 
    Article 

    Google Scholar 
    Pearce, M. T., Agarwala, A. & Fisher, D. S. Stabilization of extensive fine-scale diversity by ecologically driven spatiotemporal chaos. Proc. Natl Acad. Sci. USA 117, 14572–14583 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Costantino, R. F., Desharnais, R. A., Cushing, J. M. & Dennis, B. Chaotic dynamics in an insect population. Science 275, 389–391 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Becks, L., Hilker, F. M., Malchow, H., Jürgens, K. & Arndt, H. Experimental demonstration of chaos in a microbial food web. Nature 435, 1226–1229 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Benincá, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).PubMed 
    Article 
    CAS 

    Google Scholar 
    Tilman, D. & Wedin, D. Oscillations and chaos in the dynamics of a perennial grass. Nature 353, 653–655 (1991).Article 

    Google Scholar 
    Turchin, P. & Ellner, S. P. Living on the edge of chaos: population dynamics of fennoscandian voles. Ecology 81, 3099–3116 (2000).Article 

    Google Scholar 
    Ferrari, M. J. et al. The dynamics of measles in sub-Saharan Africa. Nature 451, 679–684 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Benincà, E., Ballantine, B., Ellner, S. P. & Huisman, J. Species fluctuations sustained by a cyclic succession at the edge of chaos. Proc. Natl Acad. Sci. USA 112, 6389–6394 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hassell, M. P., Lawton, J. H. & May, R. M. Patterns of dynamical behaviour in single-species populations. J. Anim. Ecol. 45, 471–486 (1976).Article 

    Google Scholar 
    Sibly, R. M., Barker, D., Hone, J. & Pagel, M. On the stability of populations of mammals, birds, fish and insects. Ecol. Lett. 10, 970–976 (2007).PubMed 
    Article 

    Google Scholar 
    Shelton, A. O. & Mangel, M. Fluctuations of fish populations and the magnifying effects of fishing. Proc. Natl Acad. Sci USA. 108, 7075–7080 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Salvidio, S. Stability and annual return rates in amphibian populations. Amphib. Reptil. 32, 119–124 (2011).Article 

    Google Scholar 
    Snell, T. W. & Serra, M. Dynamics of natural rotifer populations. Hydrobiologia 368, 29–35 (1998).Article 

    Google Scholar 
    Gross, T., Ebenhöh, W. & Feudel, U. Long food chains are in general chaotic. Oikos 109, 135–144 (2005).Article 

    Google Scholar 
    Ispolatov, I., Madhok, V., Allende, S. & Doebeli, M. Chaos in high-dimensional dissipative dynamical systems. Sci. Rep. 5, 12506 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Clark, T. J. & Luis, A. D. Nonlinear population dynamics are ubiquitous in animals. Nat. Ecol. Evol. 4, 75–81 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sivakumar, B., Berndtsson, R., Olsson, J. & Jinno, K. Evidence of chaos in the rainfall-runoff process. Hydrol. Sci. J. 46, 131–145 (2001).CAS 
    Article 

    Google Scholar 
    Hanski, I., Turchin, P., Korpimäki, E. & Henttonen, H. Population oscillations of boreal rodents: regulation by mustelid predators leads to chaos. Nature 364, 232–235 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    Turchin, P. & Taylor, A. D. Complex dynamics in ecological time series. Ecology 73, 289–305 (1992).Article 

    Google Scholar 
    Munch, S. B., Brias, A., Sugihara, G. & Rogers, T. L. Frequently asked questions about nonlinear dynamics and empirical dynamic modelling. ICES J. Mar. Sci. 77, 1463–1479 (2020).Article 

    Google Scholar 
    Sugihara, G. & May, R. M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ellner, S. P. & Turchin, P. Chaos in a noisy world: new methods and evidence from time-series analysis. Am. Nat. 145, 343–375 (1995).Article 

    Google Scholar 
    Nychka, D., Ellner, S., Gallant, A. R. & McCaffrey, D. Finding chaos in noisy systems. J. R. Stat. Soc. B 54, 399–426 (1992).
    Google Scholar 
    Webber, C. L. & Zbilut, J. P. Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol. 76, 965–973 (1994).PubMed 
    Article 

    Google Scholar 
    Bandt, C. & Pompe, B. Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88, 174102 (2002).PubMed 
    Article 
    CAS 

    Google Scholar 
    Luque, B., Lacasa, L., Ballesteros, F. & Luque, J. Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80, 46103 (2009).CAS 
    Article 

    Google Scholar 
    Toker, D., Sommer, F. T. & D’Esposito, M. A simple method for detecting chaos in nature. Commun. Biol. 3, 11 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pikovsky, A. & Politi, A. Lyapunov Exponents: A Tool to Explore Complex Dynamics (Cambridge Univ. Press, 2016).Rosenstein, M. T., Collins, J. J. & De Luca, C. J. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65, 117–134 (1993).Article 

    Google Scholar 
    Dämmig, M. & Mitschke, F. Estimation of Lyapunov exponents from time series: the stochastic case. Phys. Lett. A 178, 385–394 (1993).Article 

    Google Scholar 
    Prendergast, J., Bazeley-White, E., Smith, O., Lawton, J. & Inchausti, P. The Global Population Dynamics Database (KNB, 2010); https://doi.org/10.5063/F1BZ63Z8Thibaut, L. M. & Connolly, S. R. Hierarchical modeling strengthens evidence for density dependence in observational time series of population dynamics. Ecology 101, e02893 (2020).PubMed 
    Article 

    Google Scholar 
    Knape, J. & de Valpine, P. Are patterns of density dependence in the Global Population Dynamics Database driven by uncertainty about population abundance? Ecol. Lett. 15, 17–23 (2012).PubMed 
    Article 

    Google Scholar 
    Takens, F. in Dynamical Systems and Turbulence (eds Rand, D. A. & Young, L. S.) 366–381 (Springer, 1981).Sugihara, G. Nonlinear forecasting for the classification of natural time series. Philos. Trans. R. Soc. A 348, 477–495 (1994).
    Google Scholar 
    Loh, J. et al. The Living Planet Index: using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. B 360, 289–295 (2005).Article 

    Google Scholar 
    Kendall, B. E. Cycles chaos, and noise in predator–prey dynamics. Chaos Solitons Fractals 12, 321–332 (2001).Article 

    Google Scholar 
    Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Anderson, D. M. & Gillooly, J. F. Allometric scaling of Lyapunov exponents in chaotic populations. Popul. Ecol. 62, 364–369 (2020).Article 

    Google Scholar 
    Graham, D. W. et al. Experimental demonstration of chaotic instability in biological nitrification. ISME J. 1, 385–393 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Turchin, P. Nonlinear time-series modeling of vole population fluctuations. Res. Popul. Ecol. 38, 121–132 (1996).Article 

    Google Scholar 
    Becks, L. & Arndt, H. Different types of synchrony in chaotic and cyclic communities. Nat. Commun. 4, 1359 (2013).PubMed 
    Article 
    CAS 

    Google Scholar 
    Becks, L. & Arndt, H. Transitions from stable equilibria to chaos, and back, in an experimental food web. Ecology 89, 3222–3226 (2008).PubMed 
    Article 

    Google Scholar 
    Rezende, E. L., Albert, E. M., Fortuna, M. A. & Bascompte, J. Compartments in a marine food web associated with phylogeny, body mass, and habitat structure. Ecol. Lett. 12, 779–788 (2009).PubMed 
    Article 

    Google Scholar 
    Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    The IUCN Red List of Threatened Species Version 2020-2 (IUCN, 2020); https://www.iucnredlist.orgFreckleton, R. P. & Watkinson, A. R. Are weed population dynamics chaotic? J. Appl. Ecol. 39, 699–707 (2002).Article 

    Google Scholar 
    May, R. M. Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the ocean? PLoS Biol. 9, e1001127 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: a global meta-analysis. Fish Fish. 19, 964–973 (2018).Article 

    Google Scholar 
    Boettiger, C., Harte, T., Chamberlain, S. & Ram, K. rgpdd: R Interface to the Global Population Dynamics Database. https://docs.ropensci.org/rgpdd, https://github.com/ropensci/rgpdd (2019).Brook, B. W., Traill, L. W. & Bradshaw, C. J. A. Minimum viable population sizes and global extinction risk are unrelated. Ecol. Lett. 9, 375–382 (2006).PubMed 
    Article 

    Google Scholar 
    Baars, J. W. M. Autecological investigations of marine diatoms, 2. Generation times of 50 species. Hydrobiol. Bull. 15, 137–151 (1981).Article 

    Google Scholar 
    Lavigne, A. S., Sunesen, I. & Sar, E. A. Morphological, taxonomic and nomenclatural analysis of species of Odontella, Trieres and Zygoceros (Triceratiaceae, Bacillariophyta) from Anegada Bay (Province of Buenos Aires, Argentina). Diatom Res. 30, 307–331 (2015).Article 

    Google Scholar 
    Anderson, D. M. & Gillooly, J. F. Physiological constraints on long-term population cycles: a broad-scale view. Evol. Ecol. Res. 18, 693–707 (2017).
    Google Scholar 
    Janes, M. J. Oviposition studies on the chinch bug, Blissus leucopterus (Say). Ann. Entomol. Soc. Am. 28, 109–120 (1935).Article 

    Google Scholar 
    Cook, L. M. Food-plant specialization in the moth Panaxia dominula L. Evolution 15, 478–485 (1961).Article 

    Google Scholar 
    Casey, T. M. Flight energetics of sphinx moths: power input during hovering flight. J. Exp. Biol. 64, 529–543 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kobayashi, A., Tanaka, Y. & Shimada, M. Genetic variation of sex allocation in the parasitoid wasp Heterospilus prosopidis. Evolution 57, 2659–2664 (2003).PubMed 
    Article 

    Google Scholar 
    Hozumi, N. & Miyatake, T. Body-size dependent difference in death-feigning behavior of adult Callosobruchus chinensis. J. Insect Behav. 18, 557–566 (2005).Article 

    Google Scholar 
    Huntley, M. E. & Lopez, M. D. G. Temperature-dependent production of marine copepods: a global synthesis. Am. Nat. 140, 201–242 (1992).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cohen, R. E. & Lough, R. G. Length–weight relationships for several copepods dominant in the Georges Bank–Gulf of Maine area. J. Northwest Atl. Fish. Sci. 2, 47–52 (1981).Article 

    Google Scholar 
    World Register of Marine Species (WoRMS, accessed 1 November 2020); https://doi.org/10.14284/170Nakamura, Y. Growth and grazing of a large heterotrophic dinoflagellate, Noctiluca scintillans, in laboratory cultures. J. Plankton Res. 20, 1711–1720 (1998).Article 

    Google Scholar 
    Boulding, E. G. & Platt, T. Variation in photosynthetic rates among individual cells of a marine dinoflagellate. Mar. Ecol. Prog. Ser. 29, 199–203 (1986).CAS 
    Article 

    Google Scholar 
    Rimet, F. et al. The Observatory on LAkes (OLA) database: sixty years of environmental data accessible to the public. J. Limnol. https://doi.org/10.4081/jlimnol.2020.1944 (2020).Rudstam, L. Zooplankton Survey of Oneida Lake, New York, 1964 to Present (KNB, 2020); https://knb.ecoinformatics.org/view/kgordon.17.99https://knb.ecoinformatics.org/knb/metacat/kgordon.17.67/defaultDumont, H. J., Van de Velde, I. & Dumont, S. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecologia 19, 75–97 (1975).PubMed 
    Article 

    Google Scholar 
    Geller, W. & Müller, H. Seasonal variability in the relationship between body length and individual dry weight as related to food abundance and clutch size in two coexisting Daphnia species. J. Plankton Res. 7, 1–18 (1985).Article 

    Google Scholar 
    Branstrator, D. K. Contrasting life histories of the predatory cladocerans Leptodora kindtii and Bythotrephes longimanus. J. Plankton Res. 27, 569–585 (2005).Article 

    Google Scholar 
    Rosen, R. A. Length–dry weight relationships of some freshwater zooplankton. J. Freshw. Ecol. 1, 225–229 (1981).Article 

    Google Scholar 
    Peters, R. H. & Downing, J. A. Empirical analysis of zooplankton filtering and feeding rates. Limnol. Oceanogr. 29, 763–784 (1984).Article 

    Google Scholar 
    Eckmann, J. P., Kamphorst, S. O. & Ruelle, D. Recurrence plots of dynamical systems. Europhys. Lett. 4, 973–977 (1987).Article 

    Google Scholar 
    Luque, B., Lacasa, L., Ballesteros, F. J. & Robledo, A. Analytical properties of horizontal visibility graphs in the Feigenbaum scenario. Chaos 22, 013109 (2012).PubMed 
    Article 

    Google Scholar 
    McCaffrey, D. F., Ellner, S., Gallant, A. R. & Nychka, D. W. Estimating the Lyapunov exponent of a chaotic system with nonparametric regression. J. Am. Stat. Assoc. 87, 682–695 (1992).Article 

    Google Scholar 
    Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

    Google Scholar 
    Ricker, W. E. Stock and recruitment. J. Fish. Board Can. 11, 559–623 (1954).Article 

    Google Scholar  More

  • in

    Warming-induced increase in carbon uptake is linked to earlier spring phenology in temperate and boreal forests

    Chuine, I. Why does phenology drive species distribution? Philos. Trans. 365, 3149–3160 (2010).
    Google Scholar 
    Chuine, I. & Beaubien, E. G. Phenology is a major determinant of tree species range. Ecol. Lett. 4, 500–510 (2001).
    Google Scholar 
    Richardson, D. A. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).ADS 

    Google Scholar 
    Tang, J. et al. Emerging opportunities and challenges in phenology: a review. Ecosphere 7, e01436 (2016).
    Google Scholar 
    Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Chang. Biol. 25, 1922–1940 (2019).ADS 
    MathSciNet 
    PubMed 

    Google Scholar 
    Fu, Y. H. et al. Three times greater weight of daytime than of night‐time temperature on leaf unfolding phenology in temperate trees. N. Phytol. 212, 590–597 (2016).CAS 

    Google Scholar 
    Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Chang. Biol. 12, 1969–1976 (2006).ADS 

    Google Scholar 
    Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Penuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).CAS 
    PubMed 

    Google Scholar 
    Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lang, G. A. Dormancy: a new universal terminology. HortScience 22, 817–820 (1987).
    Google Scholar 
    Perry, T. O. Dormancy of trees in winter. Science 171, 29–36 (1971).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Huang, J. et al. Intra-annual wood formation of subtropical Chinese red pine shows better growth in dry season than wet season. Tree Physiol. 38, 1225–1236 (2018).PubMed 

    Google Scholar 
    Knowles, J. F. et al. Montane forest productivity across a semi-arid climatic gradient. Glob. Chang. Biol. 26, 6945–6958 (2020).ADS 
    PubMed 

    Google Scholar 
    Richard, S., Kjellsen, T. D., Schaberg, P. G. & Murakami, P. F. Dynamics of low-temperature acclimation in temperate and boreal conifer foliage in a mild winter climate. Tree Physiol. 28, 1365–1374 (2008).
    Google Scholar 
    Roxas, A. A., Orozco, J., Guzmán-Delgado, P. & Zwieniecki, M. A. Spring phenology is affected by fall non-structural carbohydrate concentration and winter sugar redistribution in three Mediterranean nut tree species. Tree Physiol. 41, 1425–1438 (2021).CAS 

    Google Scholar 
    Palacio, S., Martínez, M. M. & Montserrat-Martí, G. Seasonal dynamics of non-structural carbohydrates in two species of mediterranean sub-shrubs with different leaf phenology. Environ. Exp. Bot. 59, 34–42 (2007).CAS 

    Google Scholar 
    Fierravanti, A., Rossi, S., Kneeshaw, D., Grandpré, L. D. & Deslauriers, A. Low non-structural carbon accumulation in spring reduces growth and increases mortality in conifers defoliated by spruce budworm. Front. For. Glob. Change. 2, 1–13 (2019).
    Google Scholar 
    Oberhuber, W., Gruber, A., Lethaus, G., Winkler, A. & Wieser, G. Stem girdling indicates prioritized carbon allocation to the root system at the expense of radial stem growth in Norway spruce under drought conditions. Environ. Exp. Bot. 138, 109–118 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Pérez-de-Lis, G., Rossi, S., Vázquez-Ruiz, R. A., Rozas, V. & García-González, I. Do changes in spring phenology affect earlywood vessels? Perspective from the xylogenesis monitoring of two sympatric ring-porous oaks. N. Phytol. 209, 521–530 (2016).
    Google Scholar 
    Weber, R., Gessler, A. & Hoch, G. High carbon storage in carbon-limited trees. N. Phytol. 222, 171–182 (2019).CAS 

    Google Scholar 
    Zani, D., Crowther, T. W., Lidong, M., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Dusenge, M. E., Duarte, A. G. & Way, D. A. Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. N. Phytol. 221, 32–49 (2019).CAS 

    Google Scholar 
    Lin, Y.-S., Medlyn, B. E. & Ellsworth, D. Temperature responses of leaf net photosynthesis: the role of component processes. Tree Physiol. 32, 219–231 (2012).CAS 
    PubMed 

    Google Scholar 
    Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Terashima, I. & Hikosaka, K. Comparative ecophysiology of leaf and canopy photosynthesis. Plant Cell Environ. 18, 1111–1128 (1995).
    Google Scholar 
    Liang, J., Xia, J., Liu, L. & Wan, S. Global patterns of the responses of leaf-level photosynthesis and respiration in terrestrial plants to experimental warming. J. Plant. Ecol. 6, 437–447 (2013).
    Google Scholar 
    Duffy, K. A. et al. How close are we to the temperature tipping point of the terrestrial biosphere? Sci. Adv. 7, eaay1052 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Güsewell, S., Furrer, R., Gehrig, R. & Pietragalla, B. Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason. Glob. Chang. Biol. 23, 5189–5202 (2017).ADS 
    PubMed 

    Google Scholar 
    Keenan, T. F., Richardson, A. D. & Hufkens, K. On quantifying the apparent temperature sensitivity of plant phenology. N. Phytol. 225, 1033–1040 (2020).
    Google Scholar 
    Klein, T., Vitasse, Y. & Hoch, G. Coordination between growth, phenology and carbon storage in three coexisting deciduous tree species in a temperate forest. Tree Physiol. 36, 847–855 (2016).CAS 
    PubMed 

    Google Scholar 
    Kagawa, A., Sugimoto, A. & Maximov, T. C. Seasonal course of translocation, storage and remobilization of 13C pulse-labeled photoassimilate in naturally growing Larix gmelinii saplings. N. Phytol. 171, 793–804 (2010).
    Google Scholar 
    Rinne, K. T. et al. Examining the response of needle carbohydrates from Siberian larch trees to climate using compound-specific δ(13) C and concentration analyses. Plant Cell Environ. 38, 2340–2352 (2015).CAS 
    PubMed 

    Google Scholar 
    Schädel, C., Blöchl, A., Richter, A. & Hoch, G. Short-term dynamics of nonstructural carbohydrates and hemicelluloses in young branches of temperate forest trees during bud break. Tree Physiol. 29, 901–911 (2009).PubMed 

    Google Scholar 
    Kaurin, A., Junttila, O. & Hanson, J. Seasonal changes in frost hardiness in cloudberry (Rubus chamaemorus) in relation to carbohydrate content with special reference to sucrose. Physiol. Plant. 52, 310–314 (1981).CAS 

    Google Scholar 
    Shahba, M. A., Qian, Y. L., Hughes, H. G., Koski, A. J. & Christensen, D. Relationships of soluble carbohydrates and freeze tolerance in saltgrass. Crop Sci. 43, 2148–2153 (2003).CAS 

    Google Scholar 
    Wang, J. et al. Contrasting temporal variations in responses of leaf unfolding to daytime and nighttime warming. Glob. Chang. Biol. 27, 5084–5093 (2021).PubMed 

    Google Scholar 
    Marchand, L. J. et al. Inter-individual variability in spring phenology of temperate deciduous trees depends on species, tree size and previous year autumn phenology. Agric Meteorol. 290, 108031 (2020).
    Google Scholar 
    Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agric Meteorol. 291, 108077 (2020).
    Google Scholar 
    Chen, L. et al. Long-term changes in the impacts of global warming on leaf phenology of four temperate tree species. Glob. Chang. Biol. 25, 997–1004 (2019).ADS 
    PubMed 

    Google Scholar 
    Hanninen, H. Boreal and temperate trees in a changing climate: modelling the ecophysiology of seasonality. (Springer, 2016).Dreyer, E., Le Roux, X., Montpied, P., Daudet, F. A. & Masson, F. Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species. Tree Physiol. 21, 223–232 (2001).CAS 
    PubMed 

    Google Scholar 
    Devi, A. F. & Garkoti, S. C. Variation in evergreen and deciduous species leaf phenology in Assam. India Trees 27, 985–997 (2013).
    Google Scholar 
    Bai, K., He, C., Wan, X. & Jiang, D. Leaf economics of evergreen and deciduous tree species along an elevational gradient in a subtropical mountain. AoB PLANTS 7, plv064 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Qi, J., Fan, Z., Fu, P., Zhang, Y. & Sterck, F. Differential determinants of growth rates in subtropical evergreen and deciduous juvenile trees: carbon gain, hydraulics and nutrient-use efficiencies. Tree Physiol. 41, 12–23 (2021).CAS 
    PubMed 

    Google Scholar 
    Fyllas, N. M. et al. Functional trait variation among and within species and plant functional types in mountainous mediterranean forests. Front. Plant Sci. 11, 1–18 (2020).
    Google Scholar 
    Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int J. Biometeorol. 62, 1109–1113 (2018).ADS 
    PubMed 

    Google Scholar 
    Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
    Google Scholar 
    Richardson, A. D. et al. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Sci. Data. 5, 180028 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Klosterman, S. et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11, 4305–4320 (2014).ADS 

    Google Scholar 
    Zhang, Y. et al. Seasonal and interannual changes in vegetation activity of tropical forests in Southeast Asia. Agric. For. Meteorol. 224, 1–10 (2016).ADS 

    Google Scholar 
    Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).ADS 

    Google Scholar 
    Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, X. et al. Validation of MODIS-GPP product at 10 flux sites in northern China. Int. J. Remote Sens. 34, 587–599 (2013).
    Google Scholar 
    Julien, Y. & Sobrino, J. Global land surface phenology trends from GIMMS database. Int J. Remote Sens. 30, 3495–3513 (2009).
    Google Scholar 
    Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens Environ. 84, 471–475 (2003).ADS 

    Google Scholar 
    Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data. 7, 1–27 (2020).
    Google Scholar 
    Huang, K. et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evol. 2, 1897–1905 (2018).PubMed 

    Google Scholar 
    Tang, Y., Xu, X., Zhou, Z., Qu, Y. & Sun, Y. Estimating global maximum gross primary productivity of vegetation based on the combination of MODIS greenness and temperature data. Ecol. Inform. 63, 101307 (2021).
    Google Scholar 
    Xia, J. et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. U.S.A. 112, 2788–2793 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kalman, D. A singularly valuable decomposition: The SVD of a matrix. Coll. Math. J. 27, 2–23 (1996).MathSciNet 

    Google Scholar 
    Biriukova, K. et al. Performance of singular spectrum analysis in separating seasonal and fast physiological dynamics of solar-induced chlorophyll fluorescence and PRI optical signals. J. Geophys. Res. Biogeosci. 126, e2020JG006158 (2021).ADS 
    CAS 

    Google Scholar 
    Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3227–3246 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Wu, C. et al. Interannual variability of net carbon exchange is related to the lag between the end-dates of net carbon uptake and photosynthesis: Evidence from long records at two contrasting forest stands. Agric. For. Meteorol. 164, 29–38 (2012).ADS 

    Google Scholar 
    Cornes, R., der Schrier, G. V., den Besselaar, E. J. M. V. & Jones, P. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
    Google Scholar 
    Hijmans, R. J. et al. raster: Geographic data analysis and modeling. https://CRAN.R-project.org/package=raster. R package version 3.5-15 (2022).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Erb, I. Partial correlations in compositional data analysis. Comput. Geosci. 6, 100026 (2020).
    Google Scholar 
    Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. U.S.A. 115, 1004–1008 (2018).CAS 
    PubMed 

    Google Scholar 
    Kim, S. ppcor: Partial and semi-partial (part) correlation. https://CRAN.R-project.org/package=ppcor. R package version 1.1 (2015).Lefcheck, J. S. piecewiseSEM: piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 

    Google Scholar 
    Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Modelling species presence-only data with random forests. Ecography 44, 1731–1742 (2021).
    Google Scholar 
    Freeman, E. A., Moisen, G. G., Coulston, J. W. & Wilson, B. T. Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Can. J. For. Res. 46, 323–339 (2016).
    Google Scholar 
    Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
    Google Scholar 
    Cutler, D. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 

    Google Scholar  More

  • in

    Regenerative living cities and the urban climate–biodiversity–wellbeing nexus

    CIAT Global Rural-Urban Mapping Project, v1 (GRUMPv1): Urban Extents Grid (NASA SEDAC, 2011).Global Status Report for Buildings and Construction: Towards a Zero-Emission, Efficient and Resilient Buildings and Construction Sector (UNEP, 2020).Harris, N. L. et al. Nat. Clim. Change 11, 234–240 (2021).Article 

    Google Scholar 
    Reid, W. V. et al. Ecosystems and Human Well-being: Biodiversity Synthesis (Millenium Ecosystem Assessment, World Resources Institute, 2005).Xu, C. et al. Resour. Conserv. Recycl. 151, 104478 (2019).Article 

    Google Scholar 
    Su, J., Friess, D. A. & Gasparatos, A. Nat. Commun. 12, 5050 (2021).CAS 
    Article 

    Google Scholar 
    van den Berg, M. et al. Urban For. Urban Green. 14, 806–816 (2015).Article 

    Google Scholar 
    Aerts, R., Honnay, O. & Van Nieuwenhuyse, A. Br. Med. Bull. 127, 5–22 (2018).Article 

    Google Scholar 
    Lindenmayer, D. et al. Ecol. Lett. 11, 78–91 (2008).
    Google Scholar 
    Knapp, S., Jaganmohan, M. & Schwarz, N. in Atlas of Ecosystem Services: Drivers, Risks, and Societal Responses (eds Schröter, M. et al.) 167–172 (Springer, 2019).Kim, H. Y. Geomat. Nat. Hazards Risk 12, 1181–1194 (2021).Article 

    Google Scholar 
    Vargas-Hernández, J. G., Pallagst, K. & Zdunek-Wielgołaska, J. in Handbook of Engaged Sustainability (ed. Marques, J.) 885–916 (Springer, 2018).Manso, M. et al. Renew. Sustain. Energy Rev. 135, 110111 (2021).Article 

    Google Scholar 
    Assimakopoulos, M.-N. et al. Sustainability 12, 3772 (2020).CAS 
    Article 

    Google Scholar 
    Mora-Melià, D. et al. Sustainability 10, 1130 (2018).Article 

    Google Scholar 
    IPBES. Curr. Opin. Environ. Sustain. 26, 7–16 (2017).
    Google Scholar 
    Schröpfer, T. & Menz, S. in Dense and Green Building Typologies: Research, Policy and Practice Perspectives (eds Schröpfer, T. & Menz, S.) 1–4 (Springer, 2019).Pedersen Zari, M. & Hecht, K. Biomimetics 5, 18 (2020).Article 

    Google Scholar  More

  • in

    Value wild animals’ carbon services to fill the biodiversity financing gap

    Pettorelli, N. et al. J. Appl. Ecol. 58, 2384–2393 (2021).Article 

    Google Scholar 
    CBD High-Level Panel Resourcing the Aichi Biodiversity Targets: An Assessment of Benefits, Investments and Resource Needs for Implementing the Strategic Plan for Biodiversity 2011–2020 (Secretariat of the Convention on Biological Diversity, 2014).Schmitz, O. J. et al. Science 362, eaar3213 (2018).Article 

    Google Scholar 
    Krause, T. & Nielsen, M. R. Forests 10, 344 (2019).Article 

    Google Scholar 
    Jørgensen, D. BioScience 63, 719–720 (2013).Article 

    Google Scholar 
    Berzaghi, F., Chami, R., Cosimano, T. & Fullenkamp, C. Proc. Natl Acad. Sci. USA 119, e2120426119 (2022).Article 

    Google Scholar 
    van Duuren, E., Plantinga, A. & Scholtens, B. J. Bus. Ethics 138, 525–533 (2016).Article 

    Google Scholar 
    Broadstock, D. C., Chan, K., Cheng, L. T. W. & Wang, X. Finance Res. Lett. 38, 101716 (2021).Article 

    Google Scholar 
    Joos, F., Meyer, R., Bruno, M. & Leuenberger, M. Geophys. Res. Lett. 26, 1437–1440 (1999).CAS 
    Article 

    Google Scholar 
    Wang, F. et al. Biol. Conserv. 253, 108913 (2021).Article 

    Google Scholar 
    Sullivan, S. Antipode 45, 198–217 (2013).Article 

    Google Scholar 
    Kamilaris, A., Cole, I. R. & Prenafeta-Boldú, F. X., in Food Technology Disruptions (ed. Galanakis, C. M.) 247–284 (Academic Press, 2021).O’Donnell, E. & Talbot-Jones, J. Ecol. Soc. 23, 7 (2018).Article 

    Google Scholar 
    Anderson, K. & Peters, G. Science 354, 182–183 (2016).CAS 
    Article 

    Google Scholar 
    Berzaghi, F. et al. Nat. Geosci. 12, 725–729 (2019).CAS 
    Article 

    Google Scholar 
    Mariani, G. et al. Sci. Adv. 6, eabb4848 (2020).CAS 
    Article 

    Google Scholar 
    Martin, A. H., Pearson, H. C., Saba, G. K. & Olsen, E. M. One Earth 4, 680–693 (2021).Article 

    Google Scholar 
    Durfort, A., Mariani, G., Troussellier, M., Tulloch, V. & Mouillot, D. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-92037/v1 (2021).Norris, K., Terry, A., Hansford, J. P. & Turvey, S. T. Trends Ecol. Evol. 35, 919–926 (2020).Article 

    Google Scholar 
    Berzaghi, F. et al. Ecography 41, 1934–1954 (2018).Article 

    Google Scholar  More

  • in

    Network analysis suggests changes in food web stability produced by bottom trawl fishery in Patagonia

    Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
    PubMed 

    Google Scholar 
    FAO. The State of World Fisheries and Aquaculture 2018—Meeting the Sustainable Development Goals. (2018).Teh, L. C. L. & Sumaila, U. R. Contribution of marine fisheries to worldwide employment. Fish Fish. 14, 77–88 (2013).
    Google Scholar 
    Halpern, B. S., Selkoe, K. A., Micheli, F. & Kappel, C. V. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conserv. Biol. 21, 1301–1315 (2007).PubMed 

    Google Scholar 
    Kaiser, M. J., Collie, J. S., Hall, S. J., Jennings, S. & Poiner, I. R. Modification of marine habitats by trawling activities: Prognosis and solutions. Fish Fish. 3, 114–136 (2002).
    Google Scholar 
    Hiddink, J. G. et al. Selection of indicators for assessing and managing the impacts of bottom trawling on seabed habitats. J. Appl. Ecol. 57, 1199–1209 (2020).
    Google Scholar 
    Funes, M., Marinao, C. & Galván, D. E. Does trawl fisheries affect the diet of fishes? A stable isotope analysis approach. Isotop. Environ. Health Stud. 10, 1–17 (2019).
    Google Scholar 
    Preciado, I. et al. Small-scale spatial variations of trawling impact on food web structure. Ecol. Ind. 98, 442–452 (2019).
    Google Scholar 
    Su, L. et al. Decadal-scale variation in mean trophic level in Beibu Gulf based on bottom-trawl survey data. Mar. Coast. Fish. 13, 174–182 (2021).
    Google Scholar 
    Jennings, S., van Hal, R., Hiddink, J. G. & Maxwell, T. A. D. Fishing effects on energy use by North Sea fishes. J. Sea Res. 60, 74–88 (2008).ADS 

    Google Scholar 
    de Ruiter, P. C., Neutel, A.-M. & Moore, J. C. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science 269, 1257–1260 (1995).ADS 
    PubMed 

    Google Scholar 
    Bascompte, J. Disentangling the web of life. Science 325, 416–419 (2009).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 
    Wootton, K. L. Omnivory and stability in freshwater habitats: Does theory match reality?. Freshw. Biol. 62, 821–832 (2017).
    Google Scholar 
    Borrelli, J. J. & Ginzburg, L. R. Why there are so few trophic levels: Selection against instability explains the pattern. Food Webs 1, 10–17 (2014).
    Google Scholar 
    Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl. Acad. Sci. USA 108, 3648–52 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Márquez-Velásquez, V., Raimundo, R. L. G., de Souza Rosa, R. & Navia, A. F. The use of ecological networks as tools for understanding and conserving marine biodiversity. In Marine Coastal Ecosystems Modelling and Conservation: Latin American Experiences, pp 179–202 (eds Ortiz, M. & Jordán, F.) (Springer, 2021). https://doi.org/10.1007/978-3-030-58211-1_9.Chapter 

    Google Scholar 
    Neutel, A.-M. & Thorne, M. A. S. Interaction strengths in balanced carbon cycles and the absence of a relation between ecosystem complexity and stability. Ecol. Lett. 17, 651–661 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Neutel, A.-M. & Thorne, M. A. S. Beyond connectedness: Why pairwise metrics cannot capture community stability. Ecol. Evol. 6, 7199–7206 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Saravia, L. A., Marina, T. I., Kristensen, N. P., De Troch, M. & Momo, F. R. Ecological network assembly: How the regional metaweb influences local food webs. J. Anim. Ecol. 3, 25 (2021).
    Google Scholar 
    Góngora, M. E., GonzalezZevallos, D., Pettovello, A. & Mendia, L. Caracterizacion de las principales pesquerias del golfo San Jorge Patagonia, Argentina. Latin Am. J. Aquat. Res. 40, 1–11 (2012).
    Google Scholar 
    Yorio, P. Marine protected areas, spatial scales, and governance: Implications for the conservation of breeding seabirds. Conserv. Lett. 2, 171–178 (2009).
    Google Scholar 
    Rincón-Díaz, M. P., Bovcon, N. D., Cochia, P. D., Góngora, M. E. & Galván, D. E. Fish functional diversity as an indicator of resilience to industrial fishing in Patagonia Argentina. J. Fish Biol. 99, 1650–1667 (2021).PubMed 

    Google Scholar 
    González-Zevallos, D. & Yorio, P. Consumption of discards and interactions between Black-browed Albatrosses (Thalassarche melanophrys) and Kelp Gulls (Larus dominicanus) at trawl fisheries in Golfo San Jorge, Argentina. J. Ornithol. 152, 827–838 (2011).
    Google Scholar 
    Vinuesa, J. H. & Varisco, M. Trophic ecology of the lobster krill Munida gregaria in San Jorge Gulf, Argentina. Investig. Mar. 35, 25–34 (2007).
    Google Scholar 
    Belleggia, M. et al. Trophic ecology of yellownose skate Zearaja chilensis, a top predator in the south-western Atlantic Ocean. J. Fish Biol. 88, 1070–1087 (2016).CAS 
    PubMed 

    Google Scholar 
    Pasti, A. T. et al. The diet of Mustelus schmitti in areas with and without commercial bottom trawling (Central Patagonia, Southwestern Atlantic): Is it evidence of trophic interaction with the Patagonian shrimp fishery?. Food Webs 29, e00214 (2021).
    Google Scholar 
    Yorio, P., Bertellotti, M., Gandini, P. & Frere, E. Kelp gulls Larus dominicanus breeding on the argentine coast: Population status and relationship with coastal management and conservation. Mar. Ornithol. 26, 11–18 (1998).
    Google Scholar 
    Dans, S. et al. El golfo san jorge como área prioritaria de investigación, manejo y conservación en el marco de la iniciativa pampa azul. Rev. Cie. Investig. 71, 21–43 (2021).
    Google Scholar 
    de la Garza, J. M., Ferníndez, M. & Ravalli, C. Langostino patagónico (Pleoticus muelleri). Inf. Campa 20, 20 (2013).
    Google Scholar 
    Varisco, M. & La Vinuesa, J. H. Alimentación de Munida gregaria (Fabricius, 1793) (Crustacea:Anomura:Galatheidae) en fondos de pesca del Golfo San Jorge, Argentina. Rev. Biol. Mar. Oceanogr. 42, 221–229 (2007).
    Google Scholar 
    Tschopp, A., Cristiani, F., García, N. A., Crespo, E. A. & Coscarella, M. A. Trophic niche partitioning of five skate species of genus Bathyraja in northern and central Patagonia, Argentina. J. Fish. Biol. 97, 656–667 (2020).PubMed 

    Google Scholar 
    Kasinsky, T., Yorio, P., Dell’Arciprete, P., Marinao, C. & Suárez, N. Geographical differences in sex-specific foraging behaviour and diet during the breeding season in the opportunistic Kelp Gull (Larus dominicanus). Mar. Biol. 168, 14 (2021).CAS 

    Google Scholar 
    González-Zevallos, D. & Yorio, P. Seabird use of discards and incidental captures at the Argentine hake trawl fishery in the Golfo San Jorge, Argentina. Mar. Ecol. Progress Ser. 316, 175–183 (2006).ADS 

    Google Scholar 
    Crespo, E. A. et al. Direct and indirect effects of the Highseas fisheries on the marine mammal populations in the northern and central Patagonian coast. J. Northw. Atl. Fish. Sci. 22, 189–207 (1997).
    Google Scholar 
    Gandini, P. A., Frere, E., Pettovello, A. D. & Cedrola, P. V. Interaction between Magellanic Penguins and Shrimp Fisheries in Patagonia, Argentina. Condor 101, 783–789 (1999).
    Google Scholar 
    Fu, C. et al. Making ecological indicators management ready: Assessing the specificity, sensitivity, and threshold response of ecological indicators. Ecol. Ind. 105, 16–28 (2019).
    Google Scholar 
    Olivier, P. et al. Exploring the temporal variability of a food web using long-term biomonitoring data. Ecography 42, 2107–2121 (2019).
    Google Scholar 
    Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407 (2002).MATH 

    Google Scholar 
    Gellner, G. & McCann, K. Reconciling the omnivory-stability debate. Am. Nat. 179, 22–37 (2012).PubMed 

    Google Scholar 
    Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 26113 (2004).ADS 
    CAS 

    Google Scholar 
    Reichardt, J. & Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 74, 16110 (2006).ADS 
    MathSciNet 

    Google Scholar 
    Allesina, S. & Pascual, M. Network structure, predator-prey modules, and stability in large food webs. Theor. Ecol. 1, 55–64 (2008).
    Google Scholar 
    Strona, G., Nappo, D., Boccacci, F., Fattorini, S. & San-Miguel-Ayanz, J. A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals. Nat. Commun. 5, 4114 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Scholz, F. W. & Stephens, M. A. K-sample Anderson–Darling tests. J. Am. Stat. Assoc. 82, 918–924 (1987).MathSciNet 

    Google Scholar 
    Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Front. Psychol. 4, 863 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Saravia, L. A. Multiweb: An R Package for Multiple Interaction Ecological Networks (Zenodo, 2019). https://doi.org/10.5281/zenodo.3370397.Book 

    Google Scholar 
    Kortsch, S. et al. Disentangling temporal food web dynamics facilitates understanding of ecosystem functioning. J. Anim. Ecol. 20, 20 (2021).
    Google Scholar 
    Marina, T. I. et al. Architecture of marine food webs: To be or not be a “small-world’’. PLoS One 13, 1–13 (2018).
    Google Scholar 
    Panel, E. P. A. Ecosystem-based Fishery Management: A Report to Congress by the Ecosystem Principles Advisory Panel. https://repository.library.noaa.gov/view/noaa/23730 (1998)Armoškaitė, A. et al. Establishing the links between marine ecosystem components, functions and services: An ecosystem service assessment tool. Ocean Coast. Manage. 193, 105229 (2020).
    Google Scholar 
    Navia, A. F., Cruz-Escalona, V. H., Giraldo, A. & Barausse, A. The structure of a marine tropical food web, and its implications for ecosystem-based fisheries management. Ecol. Model. 328, 23–33 (2016).
    Google Scholar 
    Agnetta, D. et al. Benthic-pelagic coupling mediates interactions in Mediterranean mixed fisheries: An ecosystem modeling approach. PLoS One 14, e0210659 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Baum, J. K. et al. Collapse and conservation of shark populations in the Northwest Atlantic. Sciencehttps://doi.org/10.1126/science.1079777 (2003).Article 
    PubMed 

    Google Scholar 
    Bearzi, G. et al. Overfishing and the disappearance of short-beaked common dolphins from western Greece. Endang. Species Res. 5, 1–12 (2008).
    Google Scholar 
    Lotze, H. K., Coll, M., Magera, A. M., Ward-Paige, C. & Airoldi, L. Recovery of marine animal populations and ecosystems. Trends Ecol. Evol. 26, 595–605 (2011).PubMed 

    Google Scholar 
    Reyes, L. M. Cetaceans of Central Patagonia, Argentina. Aquat. Mammals 32, 20–30 (2006).
    Google Scholar 
    Lisnizer, N., Garcia-Borboroglu, P. & Yorio, P. Spatial and temporal variation in population trends of Kelp Gulls in northern Patagonia, Argentina. Emu Austral Ornithol. 111, 259–267 (2011).
    Google Scholar 
    Yorio, P. et al. Population trends of Imperial Cormorants (Leucocarbo atriceps) in northern coastal Argentine Patagonia over 26 years. Emu Austral Ornithol. 120, 114–122 (2020).
    Google Scholar 
    Irigoyen, A. & Trobbiani, G. Depletion of trophy large-sized sharks populations of the Argentinean coast, south-western Atlantic: Insights from fishers’ knowledge. Neotrop. Ichthyol. 14, 20 (2016).
    Google Scholar 
    Vasas, V., Lancelot, C., Rousseau, V. & Jordán, F. Eutrophication and overfishing in temperate nearshore pelagic food webs: A network perspective. Mar. Ecol. Prog. Ser. 336, 1–14 (2007).ADS 
    CAS 

    Google Scholar 
    Gilarranz, L. J., Mora, C. & Bascompte, J. Anthropogenic effects are associated with a lower persistence of marine food webs. Nat. Commun. 7, 10737 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. 3, 345–354 (2019).PubMed 

    Google Scholar 
    May, R. M. Stability and Complexity in Model Ecosystems Vol. 6 (Princeton University Press, 1974).
    Google Scholar 
    McCann, K. S. The diversity-stability debate. Nature 405, 228–233 (2000).CAS 
    PubMed 

    Google Scholar 
    van Altena, C., Hemerik, L. & de Ruiter, P. C. Food web stability and weighted connectance: The complexity-stability debate revisited. Theor. Ecol. 9, 49–58 (2016).
    Google Scholar 
    Dougoud, M., Vinckenbosch, L., Rohr, R. P., Bersier, L.-F. & Mazza, C. The feasibility of equilibria in large ecosystems: A primary but neglected concept in the complexity-stability debate. PLoS Comput. Biol. 14, e1005988 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    McCann, K. & Hastings, A. Re-evaluating the omnivory-stability relationship in food webs. Proc. R. Soc. Lond. B 264, 1249–1254 (1997).ADS 

    Google Scholar 
    Pimm, S. L. & Lawton, J. H. On feeding on more than one trophic level. Nature 275, 542–544 (1978).ADS 

    Google Scholar 
    Link, J. Does food web theory work for marine ecosystems?. Mar. Ecol. Prog. Ser. 230, 1–9 (2002).ADS 

    Google Scholar 
    Bieg, C. et al. Linking humans to food webs: A framework for the classification of global fisheries. Front. Ecol. Environ. 16, 412–420 (2018).
    Google Scholar 
    Shephard, S. et al. Scavenging on trawled seabeds can modify trophic size structure of bottom-dwelling fish. ICES J. Mar. Sci. 71, 398–405 (2014).
    Google Scholar 
    Gilarranz, L. J., Rayfield, B., Liñán-Cembrano, G., Bascompte, J. & Gonzalez, A. Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science 357, 199–201 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E. & Fontaine, C. Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities. Ecol. Lett. 24, 2364–2377 (2021).PubMed 

    Google Scholar 
    Shanafelt, D. W. & Loreau, M. Stability trophic cascades in food chains. R. Soc. Open Sci. 5, 180995 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barbier, M. & Loreau, M. Pyramids and cascades: A synthesis of food chain functioning and stability. Ecol. Lett. 22, 405–419 (2019).PubMed 

    Google Scholar 
    Sánchez, M. F. et al. Caracterización ecológica del Golfo San Jorge (Argentina) mediante modelación ecotrófica multiespecífica. 30 https://www.inidep.edu.ar/wordpress/?page_id=1959 (2009)Gaitán, E. N. Tramas Tróficas en Sistemas Frontales del Mar Argentino: Estructura, Dinámica y Complejidad Analizada Mediante Isótopos Estables (Universidad Nacional de Mar del Plata, Facultad de Ciencias Exactas y Naturales, 2012).
    Google Scholar 
    Pinnegar, J. K. & Polunin, N. V. C. Differential fractionation of 13C and 15N among fish tissues: Implications for the study of trophic interactions. Funct. Ecol. 13, 225–231 (1999).
    Google Scholar 
    Philippsen, J. S. & Benedito, E. Discrimination factor in the trophic ecology of fishes: A review about sources of variation and methods to obtain it. Oecol. Aust. 17, 205–2016 (2013).
    Google Scholar 
    Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).PubMed 

    Google Scholar 
    Lefebvre, S. & Dubois, S. The stony road to understand isotopic enrichment and turnover rates: Insight into the metabolic part. Vie Milieu-life Environ. 66, 305–314 (2016).
    Google Scholar 
    Funes, M., Irigoyen, A. J., Trobbiani, G. A. & Galván, D. E. Stable isotopes reveal different dependencies on benthic and pelagic pathways between Munida gregaria ecotypes. Food Webs 17, e00101 (2018).
    Google Scholar 
    Santos, B. & Villarino, M. F. Evaluación del Estado de Explotación del Efectivo sur de 41 S de la Merluza (Merluccius hubbsi) y Estimación de la Captura Biológicamente Aceptable Para 2014. Informe Técnico Oficial INIDEP. 1–30 (2013).Belleggia, M., Giberto, D. & Bremec, C. Adaptation of diet in a changed environment: Increased consumption of lobster krill Munida gregaria (Fabricius, 1793) by Argentine hake. Mar. Ecol. 38, e12445 (2017).ADS 

    Google Scholar 
    Diez, M. J., Cabreira, A. G., Madirolas, A. & Lovrich, G. A. Hydroacoustical evidence of the expansion of pelagic swarms of Munida gregaria (Decapoda, Munididae) in the Beagle Channel and the Argentine Patagonian Shelf, and its relationship with habitat features. J. Sea Res. 114, 1–12 (2016).ADS 

    Google Scholar 
    Ravalli, C., De La Garza, J. & Greco, L. L. Distribución de los morfotipos gregaria y subrugosa de la langostilla Munida gregaria (Decapoda, Galatheidae) en el Golfo San Jorge en la campaña de verano AE-01/2011. Integración de resultados con las campañas 2009 y 2010. Rev. Invest. Desarr. Pesq. 22, 29–41 (2013).
    Google Scholar 
    Belleggia, M. et al. Are hakes truly opportunistic feeders? A case of prey selection by the Argentine hake Merluccius hubbsi off southwestern Atlantic. Fish. Res. 214, 166–174 (2019).
    Google Scholar 
    Roux, A., Piñero, R., Moriondo, P. & Fernández, M. Diet of the red shrimp Pleoticus muelleri (Bate, 1888) in Patagonian fishing grounds, Argentine. Rev. Biol. Mar. Oceanogr. 44, 25 (2009).
    Google Scholar 
    de la Garza, J. et al. An Overview of the Argentine Red Shrimp (Pleoticus muelleri, Decapoda, Solenoceridae) Fishery in Argentina: Biology, Fishing, Management and Ecological Interactions (Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), 2017).
    Google Scholar 
    Sánchez, M. F. & Prenski, L. B. Ecología trófica de peces demersales en el Golfo San Jorge. Trophic Ecol. Demersal Fish San Jorge Gulf 10, 57–71 (1996).
    Google Scholar 
    Copello, S., Quintana, F. & Pérez, F. Diet of the southern giant petrel in Patagonia: Fishery-related items and natural prey. Endang. Species Res. 6, 15–23 (2008).
    Google Scholar 
    Alonso, R. B. et al. The opportunistic sense: The diet of Argentine hake Merluccius hubbsi reflects changes in prey availability. Region. Stud. Mar. Sci. 27, 100540 (2019).
    Google Scholar 
    Marón, C. F. et al. Increased wounding of southern right whale (Eubalaena australis) calves by kelp gulls (Larus dominicanus) at Península Valdés, Argentina. PLoS One 10, e0139291 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Fazio, A., Argüelles, M. B. & Bertellotti, M. Change in southern right whale breathing behavior in response to gull attacks. Mar. Biol. 162, 267–273 (2015).
    Google Scholar 
    Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Kéfi, S. et al. Network structure beyond food webs: Mapping non-trophic and trophic interactions on Chilean rocky shores. Ecology 96, 291–303 (2015).
    Google Scholar 
    Mougi, A. The roles of amensalistic and commensalistic interactions in large ecological network stability. Sci. Rep. 6, 29929 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mougi, A. & Kondoh, M. Diversity of interaction types and ecological community stability. Science 337, 349–351 (2012).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 
    Kéfi, S., Miele, V., Wieters, E. A., Navarrete, S. A. & Berlow, E. L. How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience. PLoS Biol. 14, e1002527 (2016).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Manure amendment can reduce rice yield loss under extreme temperatures

    Zhu, C. et al. Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Sci. Adv. 4, eaaq1012 (2018).
    Google Scholar 
    Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision (FAO Agricultural Development Economics Division, 2012).Arunrat, N., Pumijumnong, N., Sereenonchai, S., Chareonwong, U. & Wang, C. Assessment of climate change impact on rice yield and water footprint of large-scale and individual farming in Thailand. Sci. Total Environ. 726, 137864 (2020).CAS 

    Google Scholar 
    Lafferty, D. C. et al. Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields. Commun. Earth Environ. 2, 196 (2021).
    Google Scholar 
    Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food. 2, 54–65 (2021).
    Google Scholar 
    Wang, X. et al. Emergent constraint on crop yield response to warmer temperature from field experiments. Nat. Sustain. 3, 908–916 (2020).
    Google Scholar 
    Sun, T. et al. Current rice models underestimate yield losses from short-term heat stresses. Glob. Chang. Biol. 27, 402–416 (2020).
    Google Scholar 
    Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang. 4, 287–291 (2014).
    Google Scholar 
    Iizumi, T. & Ramankutty, N. Changes in yield variability of major crops for 1981–2010 explained by climate change. Environ. Res. Lett. 11, 034003 (2016).
    Google Scholar 
    Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).
    Google Scholar 
    Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 1–10 (2020).
    Google Scholar 
    Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 494, 390 (2013).CAS 

    Google Scholar 
    Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).CAS 

    Google Scholar 
    Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).CAS 

    Google Scholar 
    Guo, J. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).CAS 

    Google Scholar 
    Galloway, J. et al. Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. Science 320, 889–892 (2008).CAS 

    Google Scholar 
    Xia, L., Lam, S. K., Yan, X. & Chen, D. How does recycling of livestock manure in agroecosystems affect crop productivity, reactive nitrogen losses, and soil carbon balance? Environ. Sci. Technol. 51, 7450–7457 (2017).CAS 

    Google Scholar 
    Zhang, T. et al. Replacing synthetic fertilizer by manure requires adjusted technology and incentives: A farm survey across China. Resour. Conserv. Recycl. 168, 105301 (2021).
    Google Scholar 
    Bi, L. et al. Long-term effects of organic amendments on the rice yields for double rice cropping systems in subtropical China. Agric. Ecosyst. Environ. 129, 534–541 (2009).
    Google Scholar 
    Du, Y. et al. Effects of manure fertilizer on crop yield and soil properties in China: A meta-analysis. Catena 193, 104617 (2020).CAS 

    Google Scholar 
    Wang, K., Zhang, X. & Ervin, E. Antioxidative responses in roots and shoots of creeping bentgrass under high temperature: Effects of nitrogen and cytokinin. J. Plant Physiol. 169, 492–500 (2012).CAS 

    Google Scholar 
    Jespersen, D. & Huang, B. Proteins associated with heat‐induced leaf senescence in creeping bentgrass as affected by foliar application of nitrogen, cytokinins, and an ethylene inhibitor. Proteomics. 15, 798–812 (2015).CAS 

    Google Scholar 
    Xi, Y. et al. Exogenous phosphite application alleviates the adverse effects of heat stress and improves thermotolerance of potato (Solanum tuberosum L.) seedlings. Ecotoxicol. Environ. Saf. 190, 110048 (2020).CAS 

    Google Scholar 
    Waraich, E. A., Ahmad, R., Halim, A. & Aziz, T. Alleviation of temperature stress by nutrient management in crop plants: a review. J. Soil Sci. Plant Nut. 12, 221–244 (2012).
    Google Scholar 
    Yamori, W., Noguchi, K., Hikosaka, K. & Terashima, I. Phenotypic plasticity in photosynthetic temperature acclimation among crop species with different cold tolerances. Plant Physiol. 152, 388–399 (2010).CAS 

    Google Scholar 
    Mittler, R. Oxidative stress, antioxidants and stress tolerance. Trends. Plant Sci. 7, 405–410 (2002).CAS 

    Google Scholar 
    Wang, Q., Chen, J., He, N. & Guo, F. Metabolic reprogramming in chloroplasts under heat stress in plants. Int. J. Mol. Sci. 19, 849 (2018).
    Google Scholar 
    Cheng, Q. et al. An alternatively spliced heat shock transcription factor, OsHSFA2dI, functions in the heat stress-induced unfolded protein response in rice. Plant Biol. 17, 419–429 (2015).CAS 

    Google Scholar 
    Miura, K. et al. SIZ1-mediated sumoylation of ICE1 controls CBF3/DREB1A expression and freezing tolerance in Arabidopsis. Plant Cell 19, 1403–1414 (2007).CAS 

    Google Scholar 
    Xie, G., Kato, H., Sasaki, K. & Imai, R. A cold-induced thioredoxin h of rice, OsTrx23, negatively regulates kinase activities of OsMPK3 and OsMPK6 in vitro. FEBS Lett. 583, 2734–2738 (2009).CAS 

    Google Scholar 
    Hasanuzzaman, M., Hossain, M. A. & Fujita, M. Nitric oxide modulates antioxidant defense and the methylglyoxal detoxification system and reduces salinity-induced damage of wheat seedlings. Plant Biotechnol. Rep. 5, 353 (2011).
    Google Scholar 
    Uchida, A., Jagendorf, A. T., Hibino, T., Takabe, T. & Takabe, T. Effects of hydrogen peroxide and nitric oxide on both salt and heat stress tolerance in rice. Plant Sci. 163, 515–523 (2002).CAS 

    Google Scholar 
    Khan, S. et al. Plants mechanisms and adaptation strategies to improve heat tolerance in rice. A review. Plants 8, 508 (2019).CAS 

    Google Scholar 
    Li, Y., Gao, Y., Xu, X., Shen, Q. & Guo, S. Light-saturated photosynthetic rate in high-nitrogen rice (Oryza sativa L.) leaves is related to chloroplastic CO2 concentration. J. Exp. Bot. 60, 2351–2360 (2009).CAS 

    Google Scholar 
    Xiong, D. et al. Rapid responses of mesophyll conductance to changes of CO2 concentration, temperature, and irradiance are affected by N supplements in rice. Plant. Cell Environ. 38, 2541–2550 (2015).CAS 

    Google Scholar 
    Waraich, E. A., Ahmad, R., Ashraf, M. Y., Saifullah & Ahmad, M. Improving agricultural water use effciency by nutrient management in crop plants. Acta Agric. Scand. Sect.-B Soil. Plant Sci. 61, 291–304 (2011).CAS 

    Google Scholar 
    Dias, A. S. & Lidon, F. C. Bread and durum wheat tolerance under heat stress: A synoptical overview. Emir. J. Food Agric. 22, 412–436 (2010).
    Google Scholar 
    Meshah, E. A. E. Effect of irrigation regimes and foliar spraying of potassium on yield, yield components and water use efficiency of wheat in sandy soils. World J. Agric. Sci. 5, 662–669 (2009).
    Google Scholar 
    Huang, G., Zhang, Q., Wei, X., Peng, S. & Li, Y. Nitrogen can alleviate the inhibition of photosynthesis caused by high temperature stress under both steady-state and flecked irradiance. Front. Plant Sci. 8, 945 (2017).
    Google Scholar 
    Zhou, Y. et al. High nitrogen input reduces yield loss from low temperature during the seedling stage in early-season rice. Field Crop. Res. 228, 68–75 (2018).
    Google Scholar 
    Hou, L. et al. Effects of different phosphate fertilizer application on permeability of membrane and antioxidative enzymes in rice under low temperature stress. Acta Agriculturae. Boreali-Sinica 27, 118–123 (2012).
    Google Scholar 
    Dong, W. et al. Effect of different fertilizer application on the soil fertility of paddy soils in red soil region of southern China. PLoS One 7, e44504 (2012).CAS 

    Google Scholar 
    Bertollo, A. M. et al. Precrops alleviate soil physical limitations for soybean root growth in an Oxisol from southern Brazil. Soil Till. Res. 206, 104820 (2021).
    Google Scholar 
    Ren, Y. et al. Functional compensation dominates plant rhizosphere microbiota assembly of plant rhizospheric bacterial community. Soil Biol. Biochem. 150, 107968 (2020).CAS 

    Google Scholar 
    Oka, Y. Mechanisms of nematode suppression by organic soil amendments—a review. Appl. Soil Ecol. 44, 101–115 (2010).
    Google Scholar 
    Rose, M. T. et al. A meta-analysis and review of plant-growth response to humic substances: Practical implications for agriculture. Adv. Agron 124, 37–89 (2014).CAS 

    Google Scholar 
    García, A. C. et al. Vermicompost humic acids modulate the accumulation and metabolism of ROS in rice plants. J. Plant Physiol. 192, 56–63 (2016).
    Google Scholar 
    Dieleman, W. I. et al. Simple additive effects are rare: A quantitative review of plant biomass and soil process responses to combined manipulations of CO2 and temperature. Glob. Chang. Biol. 18, 2681–2693 (2012).
    Google Scholar 
    Muhammad, Q. et al. Yield sustainability, soil organic carbon sequestration, and nutrients balance under long-term combined application of manure and inorganic fertilizers in acidic paddy soil. Soil Till. Res. 198, 104509 (2020).
    Google Scholar 
    Zhang, X. et al. Benefits and trade-offs of replacing synthetic fertilizers by animal manures in crop production in China: A meta‐analysis. Glob. Chang. Biol. 26, 888–900 (2020).
    Google Scholar 
    Zhang, X. et al. Significant residual effects of wheat fertilization on greenhouse gas emissions in succeeding soybean growing season. Soil Till. Res. 169, 7–15 (2017).
    Google Scholar 
    Latare, A. M., Kumar, O., Singh, S. K. & Gupta, A. Direct and residual effect of sewage sludge on yield, heavy metals content and soil fertility under rice–wheat system. Ecol. Eng. 69, 17–24 (2014).
    Google Scholar 
    Zhang, J. et al. Long-term straw incorporation increases rice yield stability under high fertilization level conditions in the rice–wheat system. Crop J. 9, 1191–1197 (2021).
    Google Scholar 
    Pachauri, R. K. et al. Climate change 2014: Synthesis Report. Contribution of Working Groups I, II, and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC, 2014).Choi, W. J., Lee, M. S., Choi, J. E., Yoon, S. & Kim, H. Y. How do weather extremes affect rice productivity in a changing climate? An answer to episodic lack of sunshine. Glob. Chang. Biol. 19, 1300–1310 (2013).
    Google Scholar 
    FAO. FAOSTAT Online Statistical Service. https://www.fao.org/faostat/en/#data/RFN, (FAO, 2016).Carlson, K. M. et al. Greenhouse gas emissions intensity of global croplands. Nat. Clim. Chang. 7, 63–68 (2017).CAS 

    Google Scholar 
    Sheldrick, W., Syers, J. K. & Lingard, J. Contribution of livestock excreta to nutrient balances. Nutr. Cycling Agroecosyst. 66, 119–131 (2003).
    Google Scholar 
    Thangarajan, R., Bolan, N. S., Tian, G., Naidu, R. & Kunhikrishnan, A. Role of organic amendment application on greenhouse gas emission from soil. Sci. Total Environ. 465, 72–96 (2013).CAS 

    Google Scholar 
    Aryal, J. P. et al. Factors affecting farmers’ use of organic and inorganic fertilizers in South Asia. Environ. Sci. Pollut. Res. 28, 51480–51496 (2021).CAS 

    Google Scholar 
    Zhang, Q. et al. Targeting hotspots to achieve sustainable nitrogen management in China’s smallholder-dominated cereal production. Agronomy 11, 557 (2021).
    Google Scholar 
    Tyagi, V. K. et al. Anaerobic co-digestion of organic fraction of municipal solid waste (OFMSW): Progress and challenges. Renewable Sustain. Energy Rev. 93, 380–399 (2018).
    Google Scholar 
    Schlesinger, W. H. Carbon sequestration in soils: Some cautions amidst optimism. Agric. Ecosyst. Environ. 82, 121–127 (2000).CAS 

    Google Scholar 
    Potter, P., Ramankutty, N., Bennett, E. M. & Donner, S. D. Characterizing the spatial patterns of global fertilizer application and manure production. Earth Interact. 14, 1–22 (2010).
    Google Scholar 
    Zhao, F., Yang, L., Chen, L., Li, S. & Sun, L. Bioaccumulation of antibiotics in crops under long-term manure application: Occurrence, biomass response, and human exposure. Chemosphere 219, 882–895 (2019).CAS 

    Google Scholar 
    Chadwick, D. R. et al. Strategies to reduce nutrient pollution from manure management in China. Front. Agr. Sci. Eng. 7, 45–55 (2020).
    Google Scholar 
    Jin, S. et al. Decoupling livestock and crop production at the household level in China. Nat. Sustain 4, 48–55 (2021).
    Google Scholar 
    Chen, D., Yuan, L., Liu, Y., Ji, J. & Hou, H. Long-term application of manures plus chemical fertilizers sustained high rice yield and improved soil chemical and bacterial properties. Eur. J. Agron. 90, 34–42 (2017).
    Google Scholar 
    Siddik, M. A. et al. Responses of indica rice yield and quality to extreme high and low temperatures during the reproductive period. Eur. J. Agron. 106, 30–38 (2019).
    Google Scholar 
    Bates, L. S., Waldren, R. P. & Teare, I. D. Rapid determination of free proline for water stress studies. Plant Soil 39, 205–207 (1973).CAS 

    Google Scholar 
    Page, A. L., Miller, R. H. & Dennis, R. K. Methods of Soil Analysis. Part 2 Chemical Methods (ed Page, A. L.) (Soil Science Society of America, 1982).Black, C. A. Methods of Soil Analysis Part II. Chemical and Microbiological Properties (ed Norman, A. G.) (American Society of Agriculture, 1965).Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).CAS 

    Google Scholar 
    Knudsen, D., Peterson, G. A. & Pratt, P. F. Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (ed Page, A. L.) (American Society of Agriculture, 1982).Olsen, S. R. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate (United States Department of Agriculture Circular, 1954).Lewis, S. L., Brando, P. M., Phillips, O. L., Van Der Heijden, G. M. F. & Nepstad, D. The 2010 amazon drought. Science 331, 554–554 (2011).CAS 

    Google Scholar 
    Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta‐analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
    Google Scholar 
    van Groenigen, K. J., Van Kessel, C. & Hungate, B. A. Increased greenhouse-gas intensity of rice production under future atmospheric conditions. Nat. Clim. Chang. 3, 288–291 (2013).
    Google Scholar 
    Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles 22, GB1022 (2008).
    Google Scholar 
    Laborte, A. G. et al. RiceAtlas, a spatial database of global rice calendars and production. Sci. Data 4, 170074 (2017).
    Google Scholar  More