Ecology

Rockström, J. et al. A safe operation space for humanity. Nature 461, 472–475 (2009).Article 

Google Scholar 
Chancel, L., Piketty, T., Saez, E. & Zucman, G. World Inequality Report 2022 (Belknap Press, 2022).Ivanova, D. et al. Environmental impact assessment of household consumption. J. Ind. Ecol. 20, 526–536 (2016).Article 
CAS 

Google Scholar 
Steen-Olsen, K., Weinzettel, J., Cranston, G., Ercin, A. E. & Hertwich, E. G. Carbon, land, and water footprint accounts for the European Union: consumption, production, and displacements through international trade. Environ. Sci. Technol. 46, 10883–10891 (2012).Article 
CAS 

Google Scholar 
Tukker, A. et al. Environmental and resource footprints in a global context: Europe’s structural deficit in resource endowments. Glob. Environ. Change 40, 171–181 (2016).Article 

Google Scholar 
Bruckner, B., Hubacek, K., Shan, Y., Zhong, H. & Feng, K. Impacts of poverty alleviation on national and global carbon emissions. Nat. Sustain. 5, 311–320 (2022).Article 

Google Scholar 
Hubacek, K. et al. Global carbon inequality. Energy, Ecol. Environ. 2, 361–369 (2017).Article 

Google Scholar 
Yu, Y., Feng, K. & Hubacek, K. Tele-connecting local consumption to global land use. Glob. Environ. Change 23, 1178–1186 (2013).Article 

Google Scholar 
Wilting, H. C., Schipper, A. M., Bakkenes, M., Meijer, J. R. & Huijbregts, M. A. J. Quantifying biodiversity losses due to human consumption: a global-scale footprint analysis. Environ. Sci. Technol. 51, 3298–3306 (2017).Article 
CAS 

Google Scholar 
Lucas, P. L., Wilting, H. C., Hof, A. F. & Van Vuuren, D. P. Allocating planetary boundaries to large economies: distributional consequences of alternative perspectives on distributive fairness. Glob. Environ. Change 60, 102017 (2020).Article 

Google Scholar 
Beylot, A. et al. Assessing the environmental impacts of EU consumption at macro-scale. J. Clean. Prod. 216, 382–393 (2019).Article 

Google Scholar 
Koslowski, M., Moran, D. D., Tisserant, A., Verones, F. & Wood, R. Quantifying Europe’s biodiversity footprints and the role of urbanization and income. Glob. Sustain. 3, e1 (2020).Lutter, S., Pfister, S., Giljum, S., Wieland, H. & Mutel, C. Spatially explicit assessment of water embodied in European trade: a product-level multi-regional input-output analysis. Glob. Environ. Change 38, 171–182 (2016).Article 

Google Scholar 
Stadler, K. et al. EXIOBASE 3 (3.8.1) [Data set]. Zenodo https://doi.org/10.5281/ZENODO.4588235 (2021).Roadmap to a Resource Efficient Europe (European Commission, 2011).Steinmann, Z. J. N. et al. Headline environmental indicators revisited with the global multi-regional input–output database EXIOBASE. J. Ind. Ecol. 22, 565–573 (2018).Article 

Google Scholar 
Ivanova, D. et al. Mapping the carbon footprint of EU regions. Environ. Res. Lett. 12, 054013 (2017).Wiedmann, T. O. et al. The material footprint of nations. Proc. Natl Acad. Sci. USA 112, 6271–6276 (2015).Article 
CAS 

Google Scholar 
Lenzen, M. et al. Implementing the material footprint to measure progress towards Sustainable Development Goals 8 and 12. Nat. Sustain. 5, 157–166 (2022).Dorninger, C. et al. The effect of industrialization and globalization on domestic land-use: a global resource footprint perspective. Glob. Environ. Change 69, 102311 (2021).Article 

Google Scholar 
Mekonnen, M. M. & Gerbens-Leenes, W. The water footprint of food. Water 12, 12 (2020).Article 

Google Scholar 
Prell, C. & Feng, K. Unequal carbon exchanges: the environmental and economic impacts of iconic U.S. consumption items. J. Ind. Ecol. 20, 537–546 (2016).Article 

Google Scholar 
Prell, C., Feng, K., Sun, L., Geores, M. & Hubacek, K. The economic gains and environmental losses of US consumption: a world-systems and input-output approach. Soc. Forces 93, 405–428 (2014).Article 

Google Scholar 
Prell, C. Wealth and pollution inequalities of global trade: a network and input-output approach. Soc. Sci. J. 53, 111–121 (2016).Article 

Google Scholar 
World Economic Outlook (October 2022) (International Monetary Fund, 2022); https://www.imf.org/external/datamapper/datasets/WEOWilting, H. C., Schipper, A. M., Ivanova, O., Ivanova, D. & Huijbregts, M. A. J. Subnational greenhouse gas and land-based biodiversity footprints in the European Union. J. Ind. Ecol. 25, 79–94 (2021). https://doi.org/10.1111/jiec.13042Cabernard, L. & Pfister, S. A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress. Sci. Total Environ. 755, 142587 (2021).Jakob, M., Ward, H. & Steckel, J. C. Sharing responsibility for trade-related emissions based on economic benefits. Glob. Environ. Chang. 66, 102207 (2021).Article 

Google Scholar 
Wood, R. et al. The structure, drivers and policy implications of the European carbon footprint. Clim. Policy 20, S39–S57 (2020).Article 

Google Scholar 
Wood, R. et al. Growth in environmental footprints and environmental impacts embodied in trade: resource efficiency indicators from EXIOBASE3. J. Ind. Ecol. 22, 553–564 (2018).Article 

Google Scholar 
Hubacek, K., Chen, X., Feng, K., Wiedmann, T. & Shan, Y. Evidence of decoupling consumption-based CO2 emissions from economic growth. Adv. Appl. Energy 4, 100074 (2021).Article 

Google Scholar 
Wiedmann, T. & Lenzen, M. Environmental and social footprints of international trade. Nat. Geosci. 11, 314–321 (2018).Article 
CAS 

Google Scholar 
Dorninger, C. et al. Global patterns of ecologically unequal exchange: Implications for sustainability in the 21st century. Ecol. Econ. 179, 106824 (2021).Article 

Google Scholar 
Hickel, J., Dorninger, C., Wieland, H. & Suwandi, I. Imperialist appropriation in the world economy: drain from the global South through unequal exchange, 1990–2015. Glob. Environ. Change 73, 102467 (2022).Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).Article 
CAS 

Google Scholar 
Ivanova, D. et al. Quantifying the potential for climate change mitigation of consumption options. Environ. Res. Lett. 15, 093001 (2020).Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).Article 
CAS 

Google Scholar 
Ivanova, D. & Wood, R. The unequal distribution of household carbon footprints in Europe and its link to sustainability. Glob. Sustain. 3, e18 (2020).Hickel, J., O’Neill, D. W., Fanning, A. L. & Zoomkawala, H. National responsibility for ecological breakdown: a fair-shares assessment of resource use, 1970–2017. Lancet Planet. Heal. 6, e342–e349 (2022).Article 

Google Scholar 
Otto, I. M., Kim, K. M., Dubrovsky, N. & Lucht, W. Shift the focus from the super-poor to the super-rich. Nat. Clim. Change 9, 82–84 (2019).Article 

Google Scholar 
Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Nielsen, K. S., Nicholas, K. A., Creutzig, F., Dietz, T. & Stern, P. C. The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions. Nat. Energy 6, 1011–1016 (2021).Article 

Google Scholar 
Jakob, M. Why carbon leakage matters and what can be done against it. One Earth 4, 609–614 (2021).Article 

Google Scholar 
Lave, L. B. Using input–output analysis to estimate economy-wide discharges. Environ. Sci. Technol. 29, 420A–426A (1995).Article 
CAS 

Google Scholar 
Wiedmann, T. A review of recent multi-region input–output models used for consumption-based emission and resource accounting. Ecol. Econ. 69, 211–222 (2009).Article 

Google Scholar 
Ewing, B. R. et al. Integrating ecological and water footprint accounting in a multi-regional input–output framework. Ecol. Indic. 23, 1–8 (2012).Article 

Google Scholar 
Brizga, J., Feng, K. & Hubacek, K. Household carbon footprints in the Baltic States: a global multi-regional input–output analysis from 1995 to 2011. Appl. Energy 189, 780–788 (2017).Hertwich, E. G. & Peters, G. P. Carbon footprint of nations: a global, trade-linked analysis. Environ. Sci. Technol. 43, 6414–6420 (2009).Article 
CAS 

Google Scholar 
Zhong, H., Feng, K., Sun, L., Cheng, L. & Hubacek, K. Household carbon and energy inequality in Latin American and Caribbean countries. J. Environ. Manag. 273, 110979 (2020).Article 

Google Scholar 
Stadler, K. et al. EXIOBASE 3: developing a time series of detailed environmentally extended multi-regional input–output tables. J. Ind. Ecol. 22, 502–515 (2018).Article 

Google Scholar 
Hardadi, G., Buchholz, A. & Pauliuk, S. Implications of the distribution of German household environmental footprints across income groups for integrating environmental and social policy design. J. Ind. Ecol. 25, 95–113 (2021).Zhang, Q. et al. Transboundary health impacts of transported global air pollution and international trade. Nature 543, 705–709 (2017).Article 
CAS 

Google Scholar 
Hoekstra, A. Y., Mekonnen, M. M., Chapagain, A. K., Mathews, R. E. & Richter, B. D. Global monthly water scarcity: blue water footprints versus blue water availability. PLoS ONE 7, e32688 (2012).Article 
CAS 

Google Scholar 
IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).Schmidt, S. et al. Understanding GHG emissions from Swedish consumption—current challenges in reaching the generational goal. J. Clean. Prod. 212, 428–437 (2019).Article 

Google Scholar 
Huijbregts, M. A. J. Priority Assessment of Toxic Substances in the Frame of LCA. Development and Application of the Multi-Media Fate, Exposure and Effect Model USES-LCA (Interfaculty Department of Envrionmental Science, 1999).Huijbregts, M. A. J. Priority Assessment of Toxic Substances in the Frame of LCA. Time Horizon Dependency in Toxicity Potentials Calculated with the Multi-Media Fate, Exposure and Effects Model USES-LCA (Institute for Biodiversity and Ecosystem Dynamics, 2000).International Reference Life Cycle Data System (ILCD) Handbook (Publications Office EU, 2011).Verones, F., Moran, D., Stadler, K., Kanemoto, K. & Wood, R. Resource footprints an d their ecosystem consequences. Sci. Rep. 7, 40743 (2017).Chaudhary, A., Pfister, S. & Hellweg, S. Spatially explicit analysis of biodiversity loss due to global agriculture, pasture and forest land use from a producer and consumer perspective. Environ. Sci. Technol. 50, 3928–3936 (2016).Article 
CAS 

Google Scholar 
Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species-area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
CAS 

Google Scholar 
Marquardt, S. G. et al. Consumption-based biodiversity footprints—do different indicators yield different results? Ecol. Indic. 103, 461–470 (2019).Article 

Google Scholar 
World Development Indicators DataBank (World Bank, 2022); https://databank.worldbank.org/source/world-development-indicatorsWorld Population Prospects 2022 (United Nations, 2022); https://population.un.org/wpp/Natural Earth Vector (Natural Earth, 2022); https://www.naturalearthdata.com/Lahti, L., Huovari, J., Kainu, M. & Biecek, P. Retrieval and analysis of eurostat open data with the Eurostat package. R J. 9, 385–392 (2017).Castellani, V., Beylot, A. & Sala, S. Environmental impacts of household consumption in Europe: comparing process-based LCA and environmentally extended input-output analysis. J. Clean. Prod. 240, 117966 (2019).Article 

Google Scholar  More

Essl, F. et al. A conceptual framework for range-expanding species that track human-induced environmental change. BioScience 69, 908–919 (2019).Article 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Article 

Google Scholar 
Freeman, B. G., Lee-Yaw, J. A., Sunday, J. M. & Hargreaves, A. L. Expanding, shifting and shrinking: the impact of global warming on species’ elevational distributions. Glob. Ecol. Biogeogr. 27, 1268–1276 (2018).Article 

Google Scholar 
van Kleunen, M. et al. Global exchange and accumulation of non-native plants. Nature 525, 100–103 (2015).Article 

Google Scholar 
Alexander, J. M. et al. Lags in the response of mountain plant communities to climate change. Glob. Change Biol. 24, 563–579 (2018).Article 

Google Scholar 
Graae, B. J. et al. Stay or go—how topographic complexity influences alpine plant population and community responses to climate change. Perspect. Plant Ecol. Evol. Syst. 30, 41–50 (2018).Article 

Google Scholar 
Rumpf, S. B., Hülber, K., Zimmermann, N. E. & Dullinger, S. Elevational rear edges shifted at least as much as leading edges over the last century. Glob. Ecol. Biogeogr. 28, 533–543 (2019).Article 

Google Scholar 
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).Article 
CAS 

Google Scholar 
Mamantov, M. A., Gibson-Reinemer, D. K., Linck, E. B. & Sheldon, K. S. Climate-driven range shifts of montane species vary with elevation. Glob. Ecol. Biogeogr. 30, 784–794 (2021).Article 

Google Scholar 
Alexander, J. M. et al. Plant invasions into mountains and alpine ecosystems: current status and future challenges. Alp. Bot. 126, 89–103 (2016).Article 

Google Scholar 
Pauchard, A. et al. Ain’t no mountain high enough: plant invasions reaching new elevations. Front. Ecol. Environ. 7, 479–486 (2009).Article 

Google Scholar 
Alexander, J. M., MIREN Consortium et al. Assembly of nonnative floras along elevational gradients explained by directional ecological filtering. Proc. Natl Acad. Sci. USA 108, 656–661 (2011).Article 
CAS 

Google Scholar 
Seipel, T. et al. Processes at multiple spatial scales determine non-native plant species richness and similarity in mountain regions around the world. Glob. Ecol. Biogeogr. 21, 236–246 (2012).Article 

Google Scholar 
Dainese, M. et al. Human disturbance and upward expansion of plants in a warming climate. Nat. Clim. Change 7, 577–580 (2017).Article 

Google Scholar 
McDougall, K. L. et al. Running off the road: roadside non-native plants invading mountain vegetation. Biol. Invasions 20, 3461–3473 (2018).Article 

Google Scholar 
Petitpierre, B. et al. Will climate change increase the risk of plant invasions into mountains? Ecol. Appl. 26, 530–544 (2016).Article 

Google Scholar 
Lembrechts, J. J. et al. Microclimate variability in alpine ecosystems as stepping stones for non‐native plant establishment above their current elevational limit. Ecography 41, 900–909 (2017).Article 

Google Scholar 
Haider, S. et al. Mountain roads and non-native species modify elevational patterns of plant diversity. Glob. Ecol. Biogeogr. 27, 667–678 (2018).Article 

Google Scholar 
Wolf, A., Zimmerman, N. B., Anderegg, W. R. L., Busby, P. E. & Christensen, J. Altitudinal shifts of the native and introduced flora of California in the context of 20th-century warming. Glob. Ecol. Biogeogr. 25, 418–429 (2016).Article 

Google Scholar 
Seipel, T., Alexander, J. M., Edwards, P. J. & Kueffer, C. Range limits and population dynamics of non-native plants spreading along elevation gradients. Perspect. Plant Ecol. Evol. Syst. 20, 46–55 (2016).Article 

Google Scholar 
Koide, D., Yoshida, K., Daehler, C. C. & Mueller-Dombois, D. An upward elevation shift of native and non-native vascular plants over 40 years on the island of Hawai’i. J. Vegetation Sci. 28, 939–950 (2017).Article 

Google Scholar 
Becker, T., Dietz, H., Billeter, R., Buschmann, H. & Edwards, P. J. Altitudinal distribution of alien plant species in the Swiss Alps. Perspect. Plant Ecol. Evol. Syst. 7, 173–183 (2005).Article 

Google Scholar 
Haider, S. et al. The role of bioclimatic origin, residence time and habitat context in shaping non-native plant distributions along an altitudinal gradient. Biol. Invasions 12, 4003–4018 (2010).Article 

Google Scholar 
Pyšek, P., Jarošík, V., Pergl, J. & Wild, J. Colonization of high altitudes by alien plants over the last two centuries. Proc. Natl Acad. Sci. USA 108, 439–440 (2011).Article 

Google Scholar 
Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Change 2, 111–115 (2012).Article 

Google Scholar 
Lenoir, J. et al. Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography 33, 295–303 (2010).
Google Scholar 
Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).Article 
CAS 

Google Scholar 
Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).Article 
CAS 

Google Scholar 
Scherrer, D. & Körner, C. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J. Biogeogr. 38, 406–416 (2011).Article 

Google Scholar 
Kelly, C. & Price, T. D. Correcting for regression to the mean in behavior and ecology. Am. Nat. 166, 700–707 (2005).Article 

Google Scholar 
Mazalla, L. & Diekmann, M. Regression to the mean in vegetation science. J. Vegetation Sci. 33, e13117 (2022).Article 

Google Scholar 
Colwell, R. K. & Lees, D. C. The mid-domain effect: geometric constraints on the geography of species richness. Trends Ecol. Evol. 15, 70–76 (2000).Article 
CAS 

Google Scholar 
Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).Article 
CAS 

Google Scholar 
Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110 (2021).Article 

Google Scholar 
Haider, S. et al. Think globally, measure locally: the MIREN standardized protocol for monitoring plant species distributions along elevation gradients. Ecol. Evol. 12, e8590 (2022).Article 

Google Scholar 
Jacobsen, D. The dilemma of altitudinal shifts: caught between high temperature and low oxygen. Front. Ecol. Environ. 18, 211–218 (2020).Article 

Google Scholar 
Kueffer, C. et al. in Plant Invasions in Protected Areas Vol. 7 (eds Foxcroft, L. C. et al.) 89–113 (Springer, 2013).Halbritter, A. H., Alexander, J. M., Edwards, P. J. & Billeter, R. How comparable are species distributions along elevational and latitudinal climate gradients? Glob. Ecol. Biogeogr. 22, 1228–1237 (2013).Article 

Google Scholar 
Vitasse, Y. et al. Phenological and elevational shifts of plants, animals and fungi under climate change in the European Alps. Biol. Rev. 96, 1816–1835 (2021).Article 

Google Scholar 
Angert, A. L. et al. Do species’ traits predict recent shifts at expanding range edges? Ecol. Lett. 14, 677–689 (2011).Article 

Google Scholar 
Matteodo, M., Wipf, S., Stöckli, V., Rixen, C. & Vittoz, P. Elevation gradient of successful plant traits for colonizing alpine summits under climate change. Environ. Res. Lett. 8, 024043 (2013).Article 

Google Scholar 
Lembrechts, J. et al. Disturbance is the key to plant invasions in cold environments. Proc. Natl Acad. Sci. USA 113, 14061–14066 (2016).Article 
CAS 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

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

Google Scholar 
Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R J. 5, 144–161 (2013). http://journal.r-project.org/archive/2013-1/kahle-wickham.pdfSeipel, T., Haider, S. & MIREN consortium. MIREN survey of plant species in mountains (v2.0). Zenodo https://doi.org/10.5281/zenodo.5529072 (2022). More

Ceballos, G. et al. Accelerated modern human-induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).Article 

Google Scholar 
Dereniowska, M. & Meinard, Y. The unknownness of biodiversity: its value and ethical significance for conservation action. Biol. Conserv. 260, 109199 (2021).Article 

Google Scholar 
Maron, M. et al. Towards a threat assessment framework for ecosystem services. Trends Ecol. Evol. 32, 240–248 (2017).Article 

Google Scholar 
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).Article 
CAS 

Google Scholar 
Taborsky, B. et al. Towards an evolutionary theory of stress responses. Trends Ecol. Evol. 36, 39–48 (2021).Article 

Google Scholar 
van de Leemput, I. A., Dakos, V., Scheffer, M. & van Nes, E. H. Slow recovery from local disturbances as an indicator for loss of ecosystem resilience. Ecosystems 21, 141–152 (2018).Article 

Google Scholar 
Fagan, W. F. & Holmes, E. E. Quantifying the extinction vortex. Ecol. Lett. 9, 51–60 (2005).
Google Scholar 
Williams, N. F., McRae, L., Freeman, R., Capdevila, P. & Clements, C. F. Scaling the extinction vortex: body size as a predictor of population dynamics close to extinction events. Ecol. Evol. 11, 7069–7079 (2021).Article 

Google Scholar 
Clements, C. F. & Ozgul, A. Indicators of transitions in biological systems. Ecol. Lett. 21, 905–919 (2018).Article 

Google Scholar 
Shaffer, M. L. in Challenges in the Conservation of Biological Resources (eds. Decker, D. J., Krasny, M. E., Goff, G. R., Smith, C. R. & Gross, D. W.) 107–118 (Routledge, 2019).Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).Article 
CAS 

Google Scholar 
Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).Article 

Google Scholar 
Coulson, T., Mace, G. M., Hudson, E. & Possingham, H. The use and abuse of population viability analysis. Trends Ecol. Evol. 16, 219–221 (2001).Article 
CAS 

Google Scholar 
Clements, C. F., Drake, J. M., Griffiths, J. I. & Ozgul, A. Factors influencing the detectability of early warning signals of population collapse. Am. Nat. 186, 50–58 (2015).Article 

Google Scholar 
Patterson, A. C., Strang, A. G. & Abbott, K. C. When and where we can expect to see early warning signals in multispecies systems approaching tipping points: insights from theory. Am. Nat. 198, E12–E26 (2021).Article 

Google Scholar 
Vinton, A. C., Gascoigne, S. J. L., Sepil, I. & Salguero-Gómez, R. Plasticity’s role in adaptive evolution depends on environmental change components. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.08.008 (2022).Levin, S. A. The problem of pattern and scale in ecology: the Robert H. MacArthur Award lecture. Ecology 73, 1943–1967 (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 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).Article 
CAS 

Google Scholar 
Haberle, I., Marn, N., Geček, S. & Klanjšček, T. Dynamic energy budget of endemic and critically endangered bivalve Pinna nobilis: a mechanistic model for informed conservation. Ecol. Model. 434, 109207 (2020).Article 

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

Google Scholar 
Jennings, S. & Blanchard, J. L. Fish abundance with no fishing: predictions based on macroecological theory. J. Anim. Ecol. 73, 632–642 (2004).Article 

Google Scholar 
Valderrama, D. & Fields, K. H. Flawed evidence supporting the metabolic theory of ecology may undermine goals of ecosystem-based fishery management: the case of invasive Indo-Pacific lionfish in the western Atlantic. ICES J. Mar. Sci. 74, 1256–1267 (2017).Article 

Google Scholar 
Marshall, D. J. & McQuaid, C. D. Warming reduces metabolic rate in marine snails: adaptation to fluctuating high temperatures challenges the metabolic theory of ecology. Proc. R. Soc. B 278, 281–288 (2011).Article 

Google Scholar 
Rombouts, I., Beaugrand, G., Ibaňez, F., Chiba, S. & Legendre, L. Marine copepod diversity patterns and the metabolic theory of ecology. Oecologia 166, 349–355 (2011).Article 

Google Scholar 
Allen, A. P. & Gillooly, J. F. The mechanistic basis of the metabolic theory of ecology. Oikos 116, 1073–1077 (2022).Article 

Google Scholar 
Lawton, J. H. From physiology to population dynamics and communities. Funct. Ecol. 5, 155–161 (1991).Article 

Google Scholar 
Ames, E. M. et al. Striving for population-level conservation: integrating physiology across the biological hierarchy. Conserv. Physiol. 8, coaa019 (2020).Article 

Google Scholar 
Berger-Tal, O. et al. Integrating animal behavior and conservation biology: a conceptual framework. Behav. Ecol. 22, 236–239 (2011).Article 

Google Scholar 
Baruah, G., Clements, C. F., Guillaume, F. & Ozgul, A. When do shifts in trait dynamics precede population declines? Am. Nat. 193, 633–644 (2019).Article 

Google Scholar 
Dakos, V. et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7, e41010 (2012).Article 
CAS 

Google Scholar 
Ward, R. J., Griffiths, R. A., Wilkinson, J. W. & Cornish, N. Optimising monitoring efforts for secretive snakes: a comparison of occupancy and N-mixture models for assessment of population status. Sci. Rep. 7, 18074 (2017).Article 

Google Scholar 
Thompson, W. Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (Island Press, 2013).Clements, C. F., Blanchard, J. L., Nash, K. L., Hindell, M. A. & Ozgul, A. Body size shifts and early warning signals precede the historic collapse of whale stocks. Nat. Ecol. Evol. 1, 0188 (2017).Article 

Google Scholar 
Burant, J. B., Park, C., Betini, G. S. & Norris, D. R. Early warning indicators of population collapse in a seasonal environment. J. Anim. Ecol. 90, 1538–1549 (2021).Article 

Google Scholar 
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).Article 

Google Scholar 
Mazza, V., Dammhahn, M., Lösche, E. & Eccard, J. A. Small mammals in the big city: behavioural adjustments of non-commensal rodents to urban environments. Glob. Change Biol. 26, 6326–6337 (2020).Article 

Google Scholar 
Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29 (2008).Article 

Google Scholar 
Speakman, J. R., Król, E. & Johnson, M. S. The functional significance of individual variation in basal metabolic rate. Physiol. Biochem. Zool. 77, 900–915 (2004).Article 

Google Scholar 
Péron, G. et al. Evidence of reduced individual heterogeneity in adult survival of long-lived species. Evolution 70, 2909–2914 (2016).Article 

Google Scholar 
Fleming, A. H., Clark, C. T., Calambokidis, J. & Barlow, J. Humpback whale diets respond to variance in ocean climate and ecosystem conditions in the California Current. Glob. Change Biol. 22, 1214–1224 (2016).Article 

Google Scholar 
Kirkwood, T. B. L., Rose, M. R., Harvey, P. H., Partridge, L. & Southwood, S. R. Evolution of senescence: late survival sacrificed for reproduction. Phil. Trans. R. Soc. Lond. B 332, 15–24 (1991).Article 
CAS 

Google Scholar 
Mallela, A. & Hastings, A. The role of stochasticity in noise-induced tipping point cascades: a master equation approach. Bull. Math. Biol. 83, 53 (2021).Article 

Google Scholar 
Burthe, S. J. et al. Do early warning indicators consistently predict nonlinear change in long-term ecological data? J. Appl. Ecol. 53, 666–676 (2016).Article 

Google Scholar 
Vucetich, J. A. & Waite, T. A. Erosion of heterozygosity in fluctuating populations. Conserv. Biol. 13, 860–868 (1999).Article 

Google Scholar 
Kramer, A. M. & Drake, J. M. Experimental demonstration of population extinction due to a predator-driven Allee effect. J. Anim. Ecol. 79, 633–639 (2010).Article 

Google Scholar 
Oram, E. & Spitze, K. Depth selection by Daphnia pulex in response to Chaoborus kairomone. Freshw. Biol. 58, 409–415 (2013).Article 

Google Scholar 
Trites, A. W. & Donnelly, C. P. The decline of Steller sea lions Eumetopias jubatus in Alaska: a review of the nutritional stress hypothesis. Mammal. Rev. 33, 3–28 (2003).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).Article 

Google Scholar 
Dakos, V. et al. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 3, 355–362 (2019).Article 

Google Scholar 
Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).Article 

Google Scholar 
Tanner, R. L. & Dowd, W. W. Inter-individual physiological variation in responses to environmental variation and environmental change: integrating across traits and time. Comp. Biochem. Physiol. A 238, 110577 (2019).Article 
CAS 

Google Scholar 
Patrick, S. C., Martin, J. G. A., Ummenhofer, C. C., Corbeau, A. & Weimerskirch, H. Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Glob. Change Biol. 27, 4564–4574 (2021).Article 
CAS 

Google Scholar 
Fayet, A. L., Clucas, G. V., Anker‐Nilssen, T., Syposz, M. & Hansen, E. S. Local prey shortages drive foraging costs and breeding success in a declining seabird, the Atlantic puffin. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13442 (2021).Pierce, C. L. Predator avoidance, microhabitat shift, and risk-sensitive foraging in larval dragonflies. Oecologia 77, 81–90 (1988).Article 
CAS 

Google Scholar 
Leibold, M. & Tessier, A. J. Contrasting patterns of body size for Daphnia species that segregate by habitat. Oecologia 86, 342–348 (1991).Article 

Google Scholar 
Charmantier, A. & Gienapp, P. Climate change and timing of avian breeding and migration: evolutionary versus plastic changes. Evol. Appl. 7, 15–28 (2014).Article 

Google Scholar 
Kopp, M. & Matuszewski, S. Rapid evolution of quantitative traits: theoretical perspectives. Evol. Appl. 7, 169–191 (2014).Article 

Google Scholar 
Williams, J. W., Ordonez, A. & Svenning, J.-C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).Article 

Google Scholar 
Jaureguiberry, P. et al. The direct drivers of recent global anthropogenic biodiversity loss. Sci. Adv. 8, eabm9982 (2022).Article 

Google Scholar 
Chevin, L.-M., Collins, S. & Lefèvre, F. Phenotypic plasticity and evolutionary demographic responses to climate change: taking theory out to the field. Funct. Ecol. 27, 967–979 (2013).Article 

Google Scholar 
Ferriere, R. & Legendre, S. Eco-evolutionary feedbacks, adaptive dynamics and evolutionary rescue theory. Phil. Trans. R. Soc. B 368, 20120081 (2013).Article 

Google Scholar 
Rebecchi, L., Boschetti, C. & Nelson, D. R. Extreme-tolerance mechanisms in meiofaunal organisms: a case study with tardigrades, rotifers and nematodes. Hydrobiologia 847, 2779–2799 (2020).Article 

Google Scholar 
Hansson, B. & Westerberg, L. On the correlation between heterozygosity and fitness in natural populations. Mol. Ecol. 11, 2467–2474 (2002).Article 

Google Scholar 
Mammola, S., Carmona, C. P., Guillerme, T. & Cardoso, P. Concepts and applications in functional diversity. Funct. Ecol. 35, 1869–1885 (2021).Article 
CAS 

Google Scholar 
McClanahan, T. R. et al. Highly variable taxa-specific coral bleaching responses to thermal stresses. Mar. Ecol. Prog. Ser. 648, 135–151 (2020).Article 

Google Scholar 
Reside, A. E. et al. Beyond the model: expert knowledge improves predictions of species’ fates under climate change. Ecol. Appl. 29, e01824 (2019).Article 

Google Scholar 
Desjonquères, C., Gifford, T. & Linke, S. Passive acoustic monitoring as a potential tool to survey animal and ecosystem processes in freshwater environments. Freshw. Biol. 65, 7–19 (2020).Article 

Google Scholar 
Sequeira, A. M. M. et al. A standardisation framework for bio-logging data to advance ecological research and conservation. Methods Ecol. Evol. 12, 996–1007 (2021).Article 

Google Scholar 
Shimada, T. et al. Optimising sample sizes for animal distribution analysis using tracking data. Methods Ecol. Evol. 12, 288–297 (2021).Article 

Google Scholar 
Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).Article 

Google Scholar 
Krause, D. J., Hinke, J. T., Perryman, W. L., Goebel, M. E. & LeRoi, D. J. An accurate and adaptable photogrammetric approach for estimating the mass and body condition of pinnipeds using an unmanned aerial system. PLoS ONE 12, e0187465 (2017).Article 

Google Scholar 
Besson, M. et al. Towards the fully automated monitoring of ecological communities. Ecol. Lett. https://doi.org/10.1111/ele.14123 (2022).Article 

Google Scholar 
Cavender-Bares, J. et al. Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat. Ecol. Evol. 6, 506–519 (2022).Article 

Google Scholar 
Ingram, D. J., Ferreira, G. B., Jones, K. E. & Mace, G. M. Targeting conservation actions at species threat response thresholds. Trends Ecol. Evol. 36, 216–226 (2021).Article 

Google Scholar 
Keith, S. A. et al. Synchronous behavioural shifts in reef fishes linked to mass coral bleaching. Nat. Clim. Change 8, 986–991 (2018).Article 

Google Scholar 
Drake, J. M. & Griffen, B. D. Early warning signals of extinction in deteriorating environments. Nature 467, 456–459 (2010).Article 
CAS 

Google Scholar 
Enquist, B. J. et al. in Advances in Ecological Research Vol. 52 (eds Pawar, S. et al.) 249–318 (Academic Press, 2015).Wei, W. W. S. Multivariate Time Series Analysis and Applications (John Wiley & Sons, 2018).Holmes, E. E., Ward, E. J. & Wills, K. MARSS: multivariate autoregressive state-space models for analyzing time-series data. R J. 4, 11–19 (2012).Article 

Google Scholar 
Zhu, M., Yamakawa, T. & Sakai, T. Combined use of trawl fishery and research vessel survey data in a multivariate autoregressive state-space (MARSS) model to improve the accuracy of abundance index estimates. Fish. Sci. 84, 437–451 (2018).Article 
CAS 

Google Scholar 
Lai, G., Chang, W.-C., Yang, Y. & Liu, H. Modeling long- and short-term temporal patterns with deep neural networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval 95–104, https://doi.org/10.1145/3209978.3210006 (ACM, 2018).Bury, T. M. et al. Deep learning for early warning signals of tipping points. Proc. Natl Acad. Sci. USA 118, e2106140118 (2021).Article 
CAS 

Google Scholar 
Lara-Benítez, P., Carranza-García, M. & Riquelme, J. C. An experimental review on deep learning architectures for time series forecasting. Int. J. Neural Syst. 31, 2130001 (2021).Article 

Google Scholar 
Guo, Q. et al. Application of deep learning in ecological resource research: theories, methods, and challenges. Sci. China Earth Sci. 63, 1457–1474 (2020).Article 

Google Scholar 
Rogers, T. L., Johnson, B. J. & Munch, S. B. Chaos is not rare in natural ecosystems. Nat. Ecol. Evol. 6, 1105–1111 (2022).Article 

Google Scholar 
Samplonius, J. M. et al. Phenological sensitivity to climate change is higher in resident than in migrant bird populations among European cavity breeders. Glob. Change Biol. 24, 3780–3790 (2018).Article 

Google Scholar 
Menzel, A. et al. Climate change fingerprints in recent European plant phenology. Glob. Change Biol. 26, 2599–2612 (2020).Article 

Google Scholar 
Koleček, J., Adamík, P. & Reif, J. Shifts in migration phenology under climate change: temperature vs. abundance effects in birds. Clim. Change 159, 177–194 (2020).Article 

Google Scholar 
Altermatt, F. et al. Big answers from small worlds: a user’s guide for protist microcosms as a model system in ecology and evolution. Methods Ecol. Evol. 6, 218–231 (2015).Article 

Google Scholar 
Beermann, A. J. et al. Multiple-stressor effects on stream macroinvertebrate communities: a mesocosm experiment manipulating salinity, fine sediment and flow velocity. Sci. Total Environ. 610–611, 961–971 (2018).Article 

Google Scholar 
Clements, C. F. & Ozgul, A. Including trait-based early warning signals helps predict population collapse. Nat. Commun. 7, 10984 (2016).Article 
CAS 

Google Scholar 
Jacquet, C. & Altermatt, F. The ghost of disturbance past: long-term effects of pulse disturbances on community biomass and composition. Proc. R. Soc. B 287, 20200678 (2020).Article 

Google Scholar 
Greggor, A. L. et al. Research priorities from animal behaviour for maximising conservation progress. Trends Ecol. Evol. 31, 953–964 (2016).Article 

Google Scholar 
Couvillon, M. J., Schürch, R. & Ratnieks, F. L. W. Waggle dance distances as integrative indicators of seasonal foraging challenges. PLoS ONE 9, e93495 (2014).Article 

Google Scholar 
Hamilton, C. D., Lydersen, C., Ims, R. A. & Kovacs, K. M. Predictions replaced by facts: a keystone species’ behavioural responses to declining Arctic sea-ice. Biol. Lett. 11, 20150803 (2015).Article 

Google Scholar 
Holt, R. E. & Jørgensen, C. Climate change in fish: effects of respiratory constraints on optimal life history and behaviour. Biol. Lett. 11, 20141032 (2015).Article 

Google Scholar 
Gauzens, B. et al. Adaptive foraging behaviour increases vulnerability to climate change. Preprint at https://doi.org/10.1101/2021.05.05.442768 (2021).Lenda, M., Witek, M., Skórka, P., Moroń, D. & Woyciechowski, M. Invasive alien plants affect grassland ant communities, colony size and foraging behaviour. Biol. Invasions 15, 2403–2414 (2013).Article 

Google Scholar 
Hertel, A. G. et al. Don’t poke the bear: using tracking data to quantify behavioural syndromes in elusive wildlife. Anim. Behav. 147, 91–104 (2019).Article 

Google Scholar 
Tini, M. et al. Use of space and dispersal ability of a flagship saproxylic insect: a telemetric study of the stag beetle (Lucanus cervus) in a relict lowland forest. Insect Conserv. Divers. 11, 116–129 (2018).Article 

Google Scholar 
Kunc, H. P. & Schmidt, R. Species sensitivities to a global pollutant: a meta-analysis on acoustic signals in response to anthropogenic noise. Glob. Change Biol. 27, 675–688 (2021).Article 

Google Scholar 
Anestis, A., Lazou, A., Pörtner, H. O. & Michaelidis, B. Behavioral, metabolic, and molecular stress responses of marine bivalve Mytilus galloprovincialis during long-term acclimation at increasing ambient temperature. Am. J. Physiol. 293, R911–R921 (2007).CAS 

Google Scholar 
Pacherres, C. O., Schmidt, G. M. & Richter, C. Autotrophic and heterotrophic responses of the coral Porites lutea to large amplitude internal waves. J. Exp. Biol. 216, 4365–4374 (2013).
Google Scholar 
Ban, S. S., Graham, N. A. J. & Connolly, S. R. Evidence for multiple stressor interactions and effects on coral reefs. Glob. Change Biol. 20, 681–697 (2014).Article 

Google Scholar 
Singh, R., Prathibha, P. & Jain, M. Effect of temperature on life-history traits and mating calls of a field cricket, Acanthogryllus asiaticus. J. Therm. Biol. 93, 102740 (2020).Article 

Google Scholar 
Pellegrini, A. Y., Romeu, B., Ingram, S. N. & Daura-Jorge, F. G. Boat disturbance affects the acoustic behaviour of dolphins engaged in a rare foraging cooperation with fishers. Anim. Conserv. 24, 613–625 (2021).Article 

Google Scholar 
McMahan, M. D. & Grabowski, J. H. Nonconsumptive effects of a range-expanding predator on juvenile lobster (Homarus americanus) population dynamics. Ecosphere 10, e02867 (2019).Article 

Google Scholar 
Vilhunen, S., Hirvonen, H. & Laakkonen, M. V.-M. Less is more: social learning of predator recognition requires a low demonstrator to observer ratio in Arctic charr (Salvelinus alpinus). Behav. Ecol. Sociobiol. 57, 275–282 (2005).Article 

Google Scholar 
Ortega, Z., Mencía, A. & Pérez-Mellado, V. Rapid acquisition of antipredatory responses to new predators by an insular lizard. Behav. Ecol. Sociobiol. 71, 1 (2017).Article 

Google Scholar 
Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Phil. Trans. R. Soc. B 374, 20180174 (2019).Article 

Google Scholar 
Pigeon, G., Ezard, T. H. G., Festa-Bianchet, M., Coltman, D. W. & Pelletier, F. Fluctuating effects of genetic and plastic changes in body mass on population dynamics in a large herbivore. Ecology 98, 2456–2467 (2017).Article 

Google Scholar 
Lomolino, M. V. & Perault, D. R. Body size variation of mammals in a fragmented, temperate rainforest. Conserv. Biol. 21, 1059–1069 (2007).Article 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 

Google Scholar 
Thoral, E. et al. Changes in foraging mode caused by a decline in prey size have major bioenergetic consequences for a small pelagic fish. J. Anim. Ecol. 90, 2289–2301 (2021).Article 

Google Scholar 
Stirling, I. & Derocher, A. E. Effects of climate warming on polar bears: a review of the evidence. Glob. Change Biol. 18, 2694–2706 (2012).Article 

Google Scholar 
Spanbauer, T. L. et al. Body size distributions signal a regime shift in a lake ecosystem. Proc. R. Soc. B 283, 20160249 (2016).Article 

Google Scholar 
Bjorndal, K. A. et al. Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic. Glob. Change Biol. 23, 4556–4568 (2017).Article 

Google Scholar 
Eshun-Wilson, F., Wolf, R., Andersen, T., Hessen, D. O. & Sperfeld, E. UV radiation affects antipredatory defense traits in Daphnia pulex. Ecol. Evol. 10, 14082–14097 (2020).Article 

Google Scholar 
Zhang, H., Hollander, J. & Hansson, L.-A. Bi-directional plasticity: rotifer prey adjust spine length to different predator regimes. Sci. Rep. 7, 10254 (2017).Article 

Google Scholar 
Simbula, G., Vignoli, L., Carretero, M. A. & Kaliontzopoulou, A. Fluctuating asymmetry as biomarker of pesticides exposure in the Italian wall lizards (Podarcis siculus). Zoology 147, 125928 (2021).Article 

Google Scholar 
Leary, R. F. & Allendorf, F. W. Fluctuating asymmetry as an indicator of stress: implications for conservation biology. Trends Ecol. Evol. 4, 214–217 (1989).Article 
CAS 

Google Scholar 
Gavrilchuk, K. et al. Trophic niche partitioning among sympatric baleen whale species following the collapse of groundfish stocks in the Northwest Atlantic. Mar. Ecol. Prog. Ser. 497, 285–301 (2014).Article 

Google Scholar 
Kershaw, J. L. et al. Declining reproductive success in the Gulf of St. Lawrence’s humpback whales (Megaptera novaeangliae) reflects ecosystem shifts on their feeding grounds. Glob. Change Biol. 27, 1027–1041 (2021).Article 
CAS 

Google Scholar 
Rode, K. D., Amstrup, S. C. & Regehr, E. V. Reduced body size and cub recruitment in polar bears associated with sea ice decline. Ecol. Appl. 20, 768–782 (2010).Article 

Google Scholar 
Obbard, M. E. et al. Re-assessing abundance of Southern Hudson Bay polar bears by aerial survey: effects of climate change at the southern edge of the range. Arct. Sci. 4, 634–655 (2018).Article 

Google Scholar 
Hutchings, J. A. The cod that got away. Nature 428, 899–900 (2004).Article 
CAS 

Google Scholar 
Zhang, F. Early warning signals of population productivity regime shifts in global fisheries. Ecol. Indic. 115, 106371 (2020).Article 

Google Scholar 
Fulton, G. R. The Bramble Cay melomys: the first mammalian extinction due to human-induced climate change. Pac. Conserv. Biol. 23, 1–3 (2017).Article 

Google Scholar  More

Bradford, M. A. et al. Soil carbon science for policy and practice. Nat. Sustain. 2, 1070–1072 (2019).Article 

Google Scholar 
Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68 (2015).Article 

Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 17105 (2017).Article 

Google Scholar 
Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019).Article 

Google Scholar 
Wang, B. R., An, S. S., Liang, C., Liu, Y. & Kuzyakov, Y. Microbial necromass as the source of soil organic carbon in global ecosystems. Soil Biol. Biochem. 162, 108422 (2021).Article 

Google Scholar 
Kästner, M. & Miltner, A. in The Future of Soil Carbon (eds Garcia, C. et al.) Ch. 5 (Academic Press, 2018).Buckeridge, K. M. et al. Sticky dead microbes: rapid abiotic retention of microbial necromass in soil. Soil Biol. Biochem. 149, 107929 (2020).Article 

Google Scholar 
Kallenbach, C. M., Grandy, A. S., Frey, S. D. & Diefendorf, A. F. Microbial physiology and necromass regulate agricultural soil carbon accumulation. Soil Biol. Biochem. 91, 279–290 (2015).Article 

Google Scholar 
Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016).Article 

Google Scholar 
Emerson, J. B. et al. Schrödinger’s microbes: tools for distinguishing the living from the dead in microbial ecosystems. Microbiome 5, 86 (2017).Article 

Google Scholar 
Zhang, Y. et al. Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model. Biogeosciences 18, 3147–3171 (2021).Article 

Google Scholar 
Ackermann, M., Stearns Stephen, C. & Jenal, U. Senescence in a bacterium with asymmetric division. Science 300, 1920–1920 (2003).Article 

Google Scholar 
Aguilaniu, H., Gustafsson, L., Rigoulet, M. & Nyström, T. Asymmetric inheritance of oxidatively damaged proteins during cytokinesis. Science 299, 1751–1753 (2003).Article 

Google Scholar 
Maheshwari, R. & Navaraj, A. Senescence in fungi: the view from Neurospora. FEMS Microbiol. Lett. 280, 135–143 (2008).Article 

Google Scholar 
See, C. R. et al. Hyphae move matter and microbes to mineral microsites: integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob. Change Biol. 28, 2527–2540 (2022).Article 

Google Scholar 
Pusztahelyi, T. et al. Comparative studies of differential expression of chitinolytic enzymes encoded by chiA, chiB, chiC and nagA genes in Aspergillus nidulans. Folia Microbiologica 51, 547–554 (2006).Article 

Google Scholar 
Bartoszewska, M. & Kiel, J. A. The role of macroautophagy in development of filamentous fungi. Antioxid. Redox Signal. 14, 2271–2287 (2011).Article 

Google Scholar 
Josefsen, L. et al. Autophagy provides nutrients for nonassimilating fungal structures and is necessary for plant colonization but not for infection in the necrotrophic plant pathogen Fusarium graminearum. Autophagy 8, 326–337 (2012).Article 

Google Scholar 
Heaton, L. L., Jones, N. S. & Fricker, M. D. Energetic constraints on fungal growth. Am. Nat. 187, E27–E40 (2016).Article 

Google Scholar 
Taiz, L. & Zeiger, E. Plant Physiology 4th edn (Spektrum Akademischer Verlag, 2008).Bowman, E. J. & Bowman, B. J. in Cellular and Molecular Biology of Filamentous Fungi (eds Borkovich, K. & Ebbole, D.) 179–190 (ASM Press, 2010).Voigt, O. & Pöggeler, S. Self-eating to grow and kill: autophagy in filamentous ascomycetes. Appl. Microbiol. Biotechnol. 97, 9277–9290 (2013).Article 

Google Scholar 
Grimmett, I. J., Shipp, K. N., Macneil, A. & Barlocher, F. Does the growth rate hypothesis apply to aquatic hyphomycetes? Fungal Ecol. 6, 493–500 (2013).Article 

Google Scholar 
Camenzind, T., Philipp Grenz, K., Lehmann, J. & Rillig, M. C. Soil fungal mycelia have unexpectedly flexible stoichiometric C:N and C:P ratios. Ecol. Lett. 24, 208–218 (2021).Article 

Google Scholar 
Mason-Jones, K., Robinson, S. L., Veen, G. F., Manzoni, S. & van der Putten, W. H. Microbial storage and its implications for soil ecology. ISME J. 16, 617–629 (2022).Article 

Google Scholar 
Gow, N. A. R., Latge, J. P. & Munro, C. A. The fungal cell wall: structure, biosynthesis, and function. Microbiol. Spectr. 5, FUNK-0035–2016 (2017).Article 

Google Scholar 
Steiner, U. K. Senescence in bacteria and its underlying mechanisms. Front. Cell Dev. Biol. 9, 668915 (2021).Article 

Google Scholar 
Allocati, N., Masulli, M., Di Ilio, C. & De Laurenzi, V. Die for the community: an overview of programmed cell death in bacteria. Cell Death Dis. 6, e1609 (2015).Article 

Google Scholar 
Peeters, S. H. & de Jonge, M. I. For the greater good: programmed cell death in bacterial communities. Microbiol. Res. 207, 161–169 (2018).Article 

Google Scholar 
Wang, J. & Bayles, K. W. Programmed cell death in plants: lessons from bacteria? Trends Plant Sci. 18, 133–139 (2013).Article 

Google Scholar 
Nagamalleswari, E., Rao, S., Vasu, K. & Nagaraja, V. Restriction endonuclease triggered bacterial apoptosis as a mechanism for long time survival. Nucleic Acids Res. 45, 8423–8434 (2017).Article 

Google Scholar 
Kysela, D. T., Brown, P. J. B., Huang, K. C. & Brun, Y. V. Biological consequences and advantages of asymmetric bacterial growth. Annu. Rev. Microbiol. 67, 417–435 (2013).Article 

Google Scholar 
Bayles, K. W. Bacterial programmed cell death: making sense of a paradox. Nat. Rev. Microbiol. 12, 63–69 (2014).Article 

Google Scholar 
Flemming, H.-C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).Article 

Google Scholar 
Coleman, D. C. & Wall, D. H. in Soil Microbiology, Ecology and Biochemistry 4th edn (ed. Paul, E. A.) Ch. 5 (Academic Press, 2015).Hungate, B. A. et al. The functional significance of bacterial predators. mBio 12, e00466-21 (2021).Article 

Google Scholar 
Kuzyakov, Y. & Mason-Jones, K. Viruses in soil: nano-scale undead drivers of microbial life, biogeochemical turnover and ecosystem functions. Soil Biol. Biochem. 127, 305–317 (2018).Article 

Google Scholar 
Williamson, K. E., Fuhrmann, J. J., Wommack, K. E. & Radosevich, M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu. Rev. Virol. 4, 201–219 (2017).Article 

Google Scholar 
Sokol, N. W. et al. Life and death in the soil microbiome: how ecological processes influence biogeochemistry. Nat. Rev. Microbiol. 20, 415–430 (2022).Article 

Google Scholar 
Bonkowski, M. & Clarholm, M. J. A. P. Stimulation of plant growth through interactions of bacteria and protozoa: testing the auxiliary microbial loop hypothesis. Acta Protozool. 51, 237–247 (2012).
Google Scholar 
Potapov, A. M., Pollierer, M. M., Salmon, S., Šustr, V. & Chen, T.-W. Multidimensional trophic niche revealed by complementary approaches: gut content, digestive enzymes, fatty acids and stable isotopes in Collembola. J. Anim. Ecol. 90, 1919–1933 (2021).Article 

Google Scholar 
Esteban, G. F. & Fenchel, T. M. in Ecology of Protozoa: The Biology of Free-living Phagotrophic Protists (eds Esteban, G. F. & Fenchel, T. M.) 33–54 (Springer, 2020).Koksharova, O. A. Bacteria and phenoptosis. Biochemistry 78, 963–970 (2013).
Google Scholar 
Tilman, D. Resource Competition and Community Structure (Princeton Univ. Press, 1982).Boddy, L. Interspecific combative interactions between wood-decaying basidiomycetes. FEMS Microbiol. Ecol. 31, 185–194 (2000).Article 

Google Scholar 
Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15–25 (2010).Article 

Google Scholar 
Müller, S. et al. Predation by Myxococcus xanthus induces Bacillus subtilis to form spore-filled megastructures. Appl. Environ. Microbiol. 81, 203–210 (2015).Article 

Google Scholar 
Laskowska, E. & Kuczynska-Wisnik, D. New insight into the mechanisms protecting bacteria during desiccation. Curr. Genet. 66, 313–318 (2020).Article 

Google Scholar 
Rillig, M. C., Ryo, M. & Lehmann, A. Classifying human influences on terrestrial ecosystems. Glob. Change Biol. 27, 2273–2278 (2021).Article 

Google Scholar 
Dörr, T., Moynihan, P. J. & Mayer, C. Bacterial cell wall structure and dynamics. Front. Microbiol. 10, 02051 (2019).Article 

Google Scholar 
Corredor, B., Lang, B. & Russell, D. Effects of nitrogen fertilization on soil fauna in a global meta-analysis. Preprint at Res. Sq. https://doi.org/10.21203/rs.3.rs-1438491/v1 (2022).Blankinship, J. C., Niklaus, P. A. & Hungate, B. A. A meta-analysis of responses of soil biota to global change. Oecologia 165, 553–565 (2011).Article 

Google Scholar 
Manzoni, S., Chakrawal, A., Spohn, M. & Lindahl, B. D. Modeling microbial adaptations to nutrient limitation during litter decomposition. Front. For. Glob. Change 4, 686945 (2021).Article 

Google Scholar 
Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).Article 

Google Scholar 
Gunina, A. & Kuzyakov, Y. From energy to (soil organic) matter. Glob. Change Biol. 28, 2169–2182 (2022).Article 

Google Scholar 
Fernandez, C. W. & Koide, R. T. Initial melanin and nitrogen concentrations control the decomposition of ectomycorrhizal fungal litter. Soil Biol. Biochem. 77, 150–157 (2014).Article 

Google Scholar 
Kästner, M., Miltner, A., Thiele-Bruhn, S. & Liang, C. Microbial necromass in soils—linking microbes to soil processes and carbon turnover. Front. Environ. Sci. 9, 756378 (2021).Article 

Google Scholar 
Buckeridge, K. M., Creamer, C. & Whitaker, J. Deconstructing the microbial necromass continuum to inform soil carbon sequestration. Funct. Ecol. 36, 1396–1410 (2022).Article 

Google Scholar 
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).Article 

Google Scholar 
Blazewicz, S. J. et al. Taxon-specific microbial growth and mortality patterns reveal distinct temporal population responses to rewetting in a California grassland soil. ISME J. 14, 1520–1532 (2020).Article 

Google Scholar 
Kallenbach, C. M., Wallenstein, M. D., Schipanksi, M. E. & Grandy, A. S. Managing agroecosystems for soil microbial carbon use efficiency: ecological unknowns, potential outcomes, and a path forward. Front. Microbiol. 10, 1146 (2019).Article 

Google Scholar 
Liang, C. Soil microbial carbon pump: mechanism and appraisal. Soil Ecol. Lett. 2, 241–254 (2020).Article 

Google Scholar 
Sinsabaugh, R. L., Manzoni, S., Moorhead, D. L. & Richter, A. Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecol. Lett. 16, 930–939 (2013).Article 

Google Scholar 
van Groenigen, J. W. et al. Sequestering soil organic carbon: a nitrogen dilemma. Environ. Sci. Technol. 51, 4738–4739 (2017).Article 

Google Scholar 
Greenlon, A. et al. Quantitative stable-isotope probing (qSIP) with metagenomics links microbial physiology and activity to soil moisture in Mediterranean-climate grassland ecosystems (in the press).Mafla-Endara, P. M. et al. Microfluidic chips provide visual access to in situ soil ecology. Commun. Biol. 4, 889 (2021).Article 

Google Scholar 
Schaible, G. A., Kohtz, A. J., Cliff, J. & Hatzenpichler, R. Correlative SIP-FISH-Raman-SEM-NanoSIMS links identity, morphology, biochemistry, and physiology of environmental microbes. ISME Commun. 2, 52 (2022).Article 

Google Scholar 
See, C. R. et al. Distinct carbon fractions drive a generalisable two-pool model of fungal necromass decomposition. Funct. Ecol. 35, 796–806 (2021).Article 

Google Scholar 
Wang, C. et al. Stabilization of microbial residues in soil organic matter after two years of decomposition. Soil Biol. Biochem. 141, 107687 (2020).Article 

Google Scholar 
Veresoglou, S. D., Halley, J. M. & Rillig, M. C. Extinction risk of soil biota. Nat. Commun. 6, 8862 (2015).Article 

Google Scholar 
Potapov, A. M. et al. Feeding habits and multifunctional classification of soil-associated consumers from protists to vertebrates. Biol. Rev. 97, 1057–1117 (2022).Article 

Google Scholar 
Trap, J., Bonkowski, M., Plassard, C., Villenave, C. & Blanchart, E. Ecological importance of soil bacterivores for ecosystem functions. Plant Soil 398, 1–24 (2016).Article 

Google Scholar 
Dooley, S. R. & Treseder, K. K. The effect of fire on microbial biomass: a meta-analysis of field studies. Biogeochemistry 109, 49–61 (2012).Article 

Google Scholar 
Muñoz-Leoz, B., Ruiz-Romera, E., Antigüedad, I. & Garbisu, C. Tebuconazole application decreases soil microbial biomass and activity. Soil Biol. Biochem. 43, 2176–2183 (2011).Article 

Google Scholar 
Meyer, M., Diehl, D., Schaumann, G. E. & Muñoz, K. Agricultural mulching and fungicides—impacts on fungal biomass, mycotoxin occurrence, and soil organic matter decomposition. Environ. Sci. Pollut. Res. 28, 36535–36550 (2021).Article 

Google Scholar 
Thiery, S. & Kaimer, C. The predation strategy of Myxococcus xanthus. Front. Microbiol. 11, 2 (2020).Article 

Google Scholar 
Laloux, G. Shedding light on the cell biology of the predatory bacterium Bdellovibrio bacteriovorus. Front. Microbiol. 10, 3136 (2020).Article 

Google Scholar  More

Figure 1 presents the average monthly energy expenditure at the household level based on USD across the 37 surveyed nations. The households in Singapore expend the most amount of energy, that is, 748 USD each month on average. The energy consumption appears positively associated with the economic development level; for example, households from high-income countries, including France, Italy, Japan and the US, tend to consume more energy than those from low-income countries (e.g., Kazakhstan, Myanmar, and Mongolia). In India, Indonesia, and Vietnam, households with higher income expend more on energy than rural/slum households. For the energy expenditure to household income ratio, strong trends were not found between developing and developed countries. Notably, middle-income countries (e.g., Greece, Chile, Brazil, Egypt) spend a relatively higher share of total income on energy.Figure 1Average monthly energy expenditure at the household level across the 37 surveyed nations. Data source: Original survey.Full size imageThe relationship between subjective well-being and energy consumption expenditure based on the ordered logit, ordered probit, and OLS models is shown in Table 2, panel A. The LR Chi-Square test and Pseudo R-squared for the ordered logistic regression model and the ordered probit model were applied to measure the goodness of the fit, whereas F-statistics and adjusted R-squared were used for the OLS model. For the validation of the measurement of subjective well-being, life satisfaction and happiness measures were used. Importantly, the results from variated regression models are consistent, indicating a positive relationship between household energy consumption expenditure and the improvement of individuals’ subjective well-being. Regarding the model’s goodness of fit, the LR Chi-Square test with ordered logit and probit models, and the F-statistic in the OLS model are all statistically significant at 0.1%, which validates the regression model. As the consistency of the robustness results is derived from different models, the ordered logit model is applied in Table 2 (Panel B).Table 2 Association between energy consumption expenditure and subjective well-being in high- and non-high-income countries.Full size tableWith the control variables being constant, energy consumption expenditure improves subjective well-being, including life satisfaction and happiness. The coefficients for the relationship of energy consumption with life satisfaction and with happiness are 0.018 and 0.008, respectively, and they are statistically significant at the 1% level; in other words, there is increased energy consumption for people who are satisfied with their lives and are happier. This is because electricity, water, gas, or gasoline are indispensable consumption goods in daily life. The results suggest that when policies lead to a reduction in the consumption of these goods at the household level, the life satisfaction of citizens is likely to decrease. When reducing energy consumption at the household level to reduce the emission of greenhouse gases, the conflicts of interest of individuals in these households (given that they derive life satisfaction from energy consumption) pose a challenge to policymakers; therefore, policymakers should devise strategies to improve both citizens’ living standards and environmental preservation.Referring to the criteria developed by the World Bank, the standard classification of high-income nations and non-high-income nations is as follows. Based on the 2017 gross national income (GNI) per capita, the World Bank List of Economies (June 2018) presented the following criteria for nations to be classified as high-income and non-high-income nations, respectively: a GNI per capita of $12,056 or higher, and less than $12,056. According to this standard of classification, in this study, high-income nations comprise Japan, Singapore, Chile, Australia, the United States, Germany, the United Kingdom, France, Spain, Italy, Sweden, Canada, Netherlands, Greece, Hungary, Poland, and the Czech Republic, whereas non-high-income nations comprise Thailand, Malaysia, Indonesia, Vietnam, Philippines, Mexico, Venezuela, Brazil, Colombia, South Africa, India, Myanmar, Kazakhstan, Mongolia, Egypt, Russia, China, Turkey, Romania, and Sri Lanka.Regarding the comparison of high- and non-high-income countries, energy consumption at the household level is more likely to lead to life satisfaction in non-high-income than in high-income countries. In high-income countries, the coefficients for the relationship of energy consumption with life satisfaction and with happiness are 0.010 and 0.003, respectively; these coefficients are 0.035 and 0.015, respectively, among non-high-income countries. Hence, in both high-income and non-high-income countries, an increase in energy consumption leads to an increase in life satisfaction; nonetheless, energy consumption is more crucial for households in non-high-income countries. Compared to the effect of energy consumption on satisfaction in high-income countries and non-high-income countries, individuals living in less urbanized countries appear more satisfied with energy consumption.Table 3 presents the association between life satisfaction and energy consumption expenditure at the household level in each country by estimating Eq. (2) based on the ordered logit model for each country. There is a positive relationship between energy consumption expenditure and life satisfaction in 27 out of the 37 nations. For example, the coefficient of this relationship is 0.062 in Brazil, and is statistically significant at the 1% level. An increase in energy consumption expenditure positively impacts the life satisfaction of households in Brazil, meaning that individuals with greater energy expenditure tend to be satisfied with their lives. Similar results are found in other countries: Canada, Chile, China, Egypt, France, Germany, Greece, India, Indonesia, Italy, and Japan. As life satisfaction is a proxy of well-being, energy consumption is expected to increase when households can afford more energy to obtain higher life satisfaction. These results indicate that most of the developed and developing countries analyzed face a conflict of interest in addressing individuals’ life satisfaction and environment conservation goals; these countries include China and India that are home to large populations that have a positive desire for energy consumption.Table 3 Relationship between energy expenditure and life satisfaction for each country.Full size tableHowever, the association between life satisfaction and energy consumption expenditure at the household level was non-significant across some countries. In Australia, the coefficient of this association is positive but not statistically significant; hence, an increase in energy expenditure is not completely associated with life satisfaction at the household level here. Similar results are found in the Netherlands, Hungary, Sweden, Singapore, Poland, the Czech Republic, and Colombia. In these countries, energy consumption is at an adequate level, and additional energy consumption does not lead to higher life satisfaction. It may be that households consume an adequate amount of energy with their income and energy price.Tables 4, 5, 6, and 7 display the determinant factors of household energy consumption in 37 nations by estimating the energy demand equation for each country using Eq. (3). The key energy consumption metric is the quantity of energy consumed (e.g., kWh) across the targeted households. Since price information is limited, transforming consumption expenditure into a quantity (e.g., kWh) is problematic. As explained earlier, this study adopted the energy demand equation.Table 4 Household socioeconomic and demographic determinants of household energy consumption expenditure I.Full size tableTable 5 Household socioeconomic and demographic determinants of household energy consumption expenditure II.Full size tableTable 6 Household socioeconomic and demographic determinants of household energy consumption expenditure III.Full size tableTable 7 Household socioeconomic and demographic determinants of household energy consumption expenditure IV.Full size tableThere are positive relationships between energy consumption expenditure at the household level and household income across countries. If the coefficients for household income are positive and statistically significant, this means that energy consumption expenditure at the household level would increase with an increase in household income ensuing from economic development in the country, ceteris paribus. The positive coefficients for the association between energy consumption expenditure and household income range from 0.756 (Japan) to 3.613 (the Philippines) in our sample, indicating that an additional 10,000 USD would lead to an additional energy consumption expenditure at the household level of approximately 17.3% (Japan) – 445% (Mongolia). The number is calculated using the magnitude of the coefficient/energy consumption expenditure. The results also show that homeowners tend to consume more energy than renters in Australia, Brazil, Canada, Chile, China, Colombia, Germany, India, Italy, Japan, Malaysia, Mexico, Russia, the United States, and Vietnam. This indicates that if individuals live in their own houses, the household energy consumption expenditure tends to be higher owing to the wealth effect, as energy is a normal consumption good. Overall, the wealth effect on energy consumption expenditure at the household level is increasing in our sample, and with economic development, energy consumption may increase.The following factors are confirmed to reduce energy consumption at the household level: (1) energy-curtailment behavior regarding electricity, (2) higher education, and (3) age. The energy-saving effect is confirmed in households. In Canada, the coefficient of energy-saving behaviors is -0.642, indicating that households consume 12.5% less energy when they adopt both energy curtailment behavior and non-saving groups (64.2/513). The Canadian household average energy consumption is 513 USD. Similar results are seen in Colombia, Germany, India, Indonesia, Italy, Japan, the Netherlands, Poland, Russia, Turkey, the United Kingdom, and the United States. The magnitude of the effect of energy curtailment behavior ranged from 6.4% (Russia) to 32% (India) less energy consumption expenditure. Hence, energy-saving behaviors have a favorable effect on environmentally preferable outcomes. By contrast, households in Indonesia save electricity as they tend to spend more on purchasing energy.Individuals with higher education tend to save energy in 23 out of the 37 nations. For instance, the coefficient for individuals with university-level education is -2.292 and statistically significant at the 1% level. This suggests that households with individuals who have university-level education have less energy consumption expenditure than households with individuals with junior high school or lower levels of education. Similar results are seen in Brazil, Canada, Chile, Colombia, the Czech Republic, France, Germany, Hungary, India, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Poland, Russia, Singapore, South Africa, Spain, Sweden, Turkey, the United Kingdom, and the United States. Encouraging households to engage in energy curtailment behaviors and higher educational attainment may lead to environment-friendly outcomes.Surprisingly, purchasing energy-saving household products has a limited effect on reducing energy consumption expenditure at the household level. The coefficients for purchasing energy-saving household products are negative, ranging between -0.044 and -0.763, and are statistically significant in Australia, Canada, the Czech Republic, Italy, and Kazakhstan. Hence, the purchase of these products in these five countries decreases energy expenditure from 2.9% (China) to 14% (Australia). However, the relationship between energy consumption expenditure at the household level and purchasing energy-saving household products is non-significant in the other countries. Moreover, in Poland and Turkey, households that purchase these products consume more energy than those that do not. Therefore, purchasing energy-saving household products has a limited contribution to energy saving at the household level.The findings also show that older individuals tend to have lower energy consumption. The coefficients for the age variable are negative and statistically significant in 30 countries (out of 37). The effect of age on energy consumption expenditure ranges between -0.003 and -0.148, indicating that as the average age of individuals increases by one year, their monthly energy consumption expenditure reduces from 0.3–14.8 USD. This may be because older individuals are more likely to live frugally. More

DatasetsImage acquisitionFor this study, we assembled three datasets: one for training of the RootDetector Convolutional Neural Network (Training-Set), one for a performance comparison between humans and RootDetector in segmenting roots in minirhizotron images (Comparison-Set), and one for the validation of the algorithm (Validation-Set). The Training-Set contained 129 images comprised of 17 randomly selected minirhizotron images sampled in a mesocosm experiment (see “Mesocosm sampling” Section), 47 randomly selected minirhizotron images sampled in a field study (see “Field sampling” Section) as well as the 65 minirhizotron images of soy roots published by Wang et al.15. The Comparison-Set contained 25 randomly selected minirhizotron images from the field-study which all were not part of the images included in the Training- and Validation-Sets. The Validation-Set contained 10 randomly selected minirhizotron images from the same field study, which had not been used in the Training-Set. All images were recorded with 2550 ✕ 2273 pixels at 300 dpi with a CI-600 In-Situ Root Imager (CID Bio-Science Inc., Camas, WA, USA) and stored as .tiff files to reduce compression loss. For all training and evaluation purposes we used raw, unprocessed output images from the CI-600.Mesocosm samplingThe mesocosm experiment was established in 2018 on the premises of the Institute for Botany and Landscape Ecology of the University of Greifswald (Fig. S1). It features 108 heavy duty plastic buckets of 100 l each, filled to two thirds of their height with moderately decomposed sedge fen peat. Each mesocosm contained one minirhizotron (inner diameter: 64 mm, outer diameter: 70 mm, length: 650 mm) installed at a 45°angle and capped in order to avoid penetration by light. The mesocosms were planted with varying compositions of plant species that typically occur in north-east German sedge fens (Carex rostrata, Carex acutiformis, Glyceria maxima, Equisetum fluviatile, Juncus inflexus, Mentha aquatica, Acorus calamus and Lycopus europaeus). The mesocosms were subjected to three different water table regimes: stable at soil surface level, stable at 20 cm below soil surface and fluctuating between the two levels every two weeks. The minirhizotrons were scanned weekly at two levels of soil depth (0–20 cm and 15–35 cm) between April 2019 and December 2021, resulting in roughly 9500 minirhizotron images of 216 × 196 mm. Manual quantification of root length would, based on own experience, take approximately three hours per image, resulting in approximately 28,500 h of manual processing for the complete dataset. Specimens planted were identified by author Dr. Blume-Werry, however no voucher specimen were deposited. All methods were carried out in accordance with relevant institutional, national, and international guidelines and legislation.Field samplingThe field study was established as part of the Wetscapes project in 201716. The study sites were located in Mecklenburg-Vorpommern, Germany, in three of the most common wetland types of the region: alder forest, percolation fen and coastal fen (Fig. S2). For each wetland type, a pair of drained versus rewetted study sites was established. A detailed description of the study sites and the experimental setup can be found in Jurasinski et al.16. At each site, 15 minirhizotrons (same diameter as above, length: 1500 mm) were installed at 45° angle along a central boardwalk. The minirhizotrons have been scanned biweekly since April 2018, then monthly since January 2019 at two to four levels of soil depth (0–20 cm, 20–40 cm, 40–60 cm and 60–80 cm), resulting in roughly 12,000 minirhizotron images of 216 × 196 cm, i.e. an estimated 36,000 h of manual processing for the complete dataset. Permission for the study was obtained from the all field owners. Figure 1Overview of the RootDetector system. The main component is a semantic segmentation network based on the U-Net architecture. The root length is estimated by skeletonizing the segmentation output and applying the formula introduced by Kimura et al.17. During training only, a weight map puts more emphasis on fine roots.Full size imageThe CNN RootDetectorImage annotationFor the generation of training data for the CNN, human analysts manually masked all root pixels in the 74 images of the Training-Set using GIMP 2.10.12. The resulting ground truth data are binary, black-and-white images in Portable Network Graphics (.png) format, where white pixels represent root structures and black pixels represent non-root objects and soil (Fig. 2b). All training data were checked and, if required, corrected by an expert (see “Selection of participants” for definition). The Validation-Set was created in the same way but exclusively by experts.Figure 2Example of segmentation and result of skeletonization. A 1000 by 1000 pixel input image (a), the manually annotated ground truth image (b), the RootDetector estimation image (c), the combined representation image (error map, d with green indicating true positives, red indicating false positive, blue indicating false negatives), the skeletonized RootDetector estimation image (e), and the skeletonized ground truth image (f).Full size imageArchitectureRootDetector’s core consists of a Deep Neural Network (DNN) based on the U-Net image segmentation architecture[27]nd is implemented in TensorFlow and Keras frameworks18. Although U-Net was originally developed for biomedical applications, it has since been successfully applied to other domains due to its generic design.RootDetector is built up of four down-sampling blocks, four up-sampling blocks and a final output block (Fig. 1). Every block contains two 3 × 3 convolutional layers, each followed by rectified linear units (ReLU). The last output layer instead utilizes Sigmoid activation. Starting from initial 64 feature channels, this number is doubled in every down-block and the resolution is halved via 2 × 2 max-pooling. Every up-block again doubles the resolution via bilinear interpolation and a 1 × 1 convolution which halves the number of channels. Importantly, after each up-sampling step, the feature map is concatenated with the corresponding feature map from the down-sampling path. This is crucial to preserve fine spatial details.Our modifications from the original architecture include BatchNormalization19 after each convolutional layer which significantly helps to speed up the training process and zero-padding instead of cropping as suggested by Ronneberger, Fischer, & Brox20 to preserve the original image size.In addition to the root segmentation network, we trained a second network to detect foreign objects, specifically the adhesive tape that is used as a light barrier on the aboveground part of the minirhizotrons. We used the same network architecture as above and trained in a supervised fashion with the binary cross-entropy loss. During inference, the result is thresholded (predefined threshold value: 0.5) and used without post-processing.TrainingWe pre-trained RootDetector on the COCO dataset21 to generate a starting point. Although the COCO dataset contains a wide variety of image types and classes not specifically related to minirhizotron images, Majurski et al.22 showed, that for small annotation counts, transfer-learning even from unrelated datasets may improve a CNNs performance by up to 20%. We fine-tuned for our dataset with the Adam optimizer23 for 15 epochs and trained on a total of 129 images from the Training-Set (17 mesocosm images, 47 field-experiment images, 65 soy root images). To enhance the dataset size and reduce over-fitting effects, we performed a series of augmentation operations as described by Shorten & Khoshgoftaar24. In many images, relatively coarse roots ( > 3 mm) occupied a major part of the positive (white) pixel space, which might have caused RootDetector to underestimate fine root details overall. Similarly, negative space (black pixels) between tightly packed, parallel roots was often very small and might have impacted the training process to a lesser extent when compared to large areas with few or no roots (Fig. 2). To mitigate both effects, we multiplied the result of the cross-entropy loss map with a weight map which emphasizes positive–negative transitions. This weight map is generated by applying the following formula to the annotated ground truth images:$$omega left( x right) = 1 – left( {tanh left( {2tilde{x} – 1} right)} right)^{2}$$
(1)
where ω(x) is the average pixel value of the annotated weight map in a 5 × 5 neighborhood around pixel x. Ronneberger, Fischer, & Brox20 implemented a similar weight map, however with stronger emphasis on space between objects. As this requires computation of distances between two comparatively large sets of points, we adapted and simplified their formula to be computable in a single 5 × 5 convolution.For the loss function we applied a combination of cross-entropy and Dice loss 25:$${mathcal{L}} = {mathcal{L}}_{CE} + lambda {mathcal{L}}_{Dice} = – frac{1}{N}sumnolimits_{i} {wleft( {x_{i} } right)y_{i} log left( {x_{i} } right) + lambda frac{{2sumnolimits_{i} {x_{i} y_{i} } }}{{sumnolimits_{i} {x_{i}^{2} sumnolimits_{i} {y_{i}^{2} } } }}}$$
(2)

where x are the predicted pixels, y the corresponding ground truth labels, N the number of pixels in an image and λ a balancing factor which we set to 0.01. This value was derived empirically. The Dice loss is applied per-image to counteract the usually high positive-to-negative pixel imbalance. Since this may produce overly confident outputs and restrict the application of weight maps, we used a relatively low value for λ.Output and post-processingRootDetector generates two types of output. The first type of output are greyscale .png files in which white pixels represent pixels associated with root structures and black pixels represent non-root structures and soil (Fig. 2c). The advantage of .png images is their lossless ad artifact-free compression at relatively small file sizes. RootDetector further skeletonizes the output images and reduces root-structures to single-pixel representations using the skeletonize function of scikit-image v. 0.17.1 (26; Fig. 2e,f). This helps to reduce the impact of large diameter roots or root-like structures such as rhizomes in subsequent analyses and is directly comparable to estimations of root length. The second type of output is a Comma-separated values (.csv) file, with numerical values indicating the number of identified root pixels, the number of root pixels after skeletonization, the number of orthogonal and diagonal connections between pixels after skeletonization and an estimation of the physical combined length of all roots for each processed image. The latter is a metric commonly used in root research as in many species, fine roots provide most vital functions such as nutrient and water transport3. Therefore, the combined length of all roots in a given space puts an emphasis on fine roots as they typically occupy a relatively smaller fraction of the area in a 2D image compared to often much thicker coarse roots. To derive physical length estimates from skeletonized images, RootDetector counts orthogonal- and diagonal connections between pixels of skeletonized images and employs the formula proposed by Kimura et al.17 (Eq. 3).$$L = left[ {N_{d}^{2} + left( {N_{d} + N_{o} /2} right)^{2} } right]^{{1/2}} + N_{o} /2$$
(3)
where Nd is the number of diagonally connected and No the number of orthogonally connected skeleton pixels. To compute Nd we convolve the skeletonized image with two 2 × 2 binary kernels, one for top-left-to-bottom-right connections and another for bottom-left-to-top-right connections and count the number of pixels with maximum response in the convolution result. Similarly, No is computed with a 1 × 2 and a 2 × 1 convolutional kernels.Performance comparisonSelection of participantsFor the performance comparison, we selected 10 human analysts and divided them into three groups of different expertise levels in plant physiology and with the usage of digital root measuring tools. The novice group consisted of 3 ecology students (2 bachelor’s, 1 master’s) who had taken or were taking courses in plant physiology but had no prior experience with minirhizotron images or digital root measuring tools. This group represents undergraduate students producing data for a Bachelor thesis or student assistants employed to process data. The advanced group consisted of 3 ecology students (1 bachelor’s, 2 master’s) who had already taken courses in plant physiology and had at least 100 h of experience with minirhizotron images and digital root measuring tools. The expert group consisted of 4 scientists (2 PhD, 2 PhD candidates) who had extensive experience in root science and at least 250 h of experience with digital root measuring tools. All methods were carried out in accordance with relevant institutional, national, and international guidelines and legislation and informed consent was obtained from all participants.Instruction and root tracingAll three groups were instructed by showing them a 60 min live demo of an expert tracing roots in minirhizotron images, during which commonly encountered challenges and pitfalls were thoroughly discussed. Additionally, all participants were provided with a previously generated, in-depth manual containing guidelines on the identification of root structures, the correct operation of the root tracing program and examples of often encountered challenges and suggested solutions. Before working on the Comparison-Set, all participants traced roots in one smaller-size sample image and received feedback from one expert.Image preparation and root tracingBecause the minirhizotron images acquired in the field covered a variety of different substrates, roots of different plant species, variance in image quality, and because tracing roots is very time consuming, we decided to maximize the number of images by tracing roots only in small sections, in order to cover the largest number of cases possible. To do this, we placed a box of 1000 × 1000 pixels (8.47 × 8.47 cm) at a random location in each of the images in the Comparison-Set and instructed participants to trace only roots within that box. Similarly, we provided RootDetector images where the parts of the image outside the rectangle were occluded. All groups used RootSnap! 1.3.2.25 (CID Bio-Science Inc., Camas, WA, USA;27), a vector based tool to manually trace roots in each of the 25 images in the comparison set. We decided on RootSnap! due to our previous good experience with the software and its’ relative ease of use. The combined length of all roots was then exported as a csv file for each person and image and compared to RootDetector’s output of the Kimura root length.ValidationWe tested the accuracy of RootDetector on a set of 10 image segments of 1000 by 1000 pixels cropped from random locations of the 10 images of the Validation-Set. These images were annotated by a human expert without knowledge of the estimations by the algorithm and were exempted from the training process. As commonly applied in binary classification, we use the F1 score as a metric to evaluate the performance RootDetector. F1 is calculated from precision (Eq. 4) and recall (Eq. 5) and represents their harmonic mean (Eq. 6). Ranging from 0 to 1, higher values indicate high classification (segmentation) performance. As one of the 10 image sections contained no roots and thus no F1 Score was calculable, it was excluded from the validation. We calculated the F1 score for each of the nine remaining image sections and averaged the values as a metric for overall segmentation performance.$$Precision;(P) = frac{{tp}}{{tp + fp}}$$
(4)
$$Recall;(R) = frac{{tp}}{{tp + fn}}$$
(5)
$$F1 = 2*frac{{P*R}}{{P + R}}$$
(6)
where P = precision, R = recall, tp = true positives; fp = false positives, fn = false negatives.Statistical analysisWe used R Version 4.1.2 (R Core Team, 2021) for all statistical analyses and R package ggplot2 Version 3.2.128 for visualizations. Pixel identification-performance comparisons were based on least-squares fit and the Pearson method. Root length estimation-performance comparisons between groups of human analysts (novice, advanced, expert) and RootDetector were based on the respective estimates of total root length plotted over the minirhizotron images in increasing order of total root length. Linear models were calculated using the lm function for each group of analysts. To determine significant differences between the groups and the algorithm, 95% CIs as well as 83% CIs were displayed and RootDetector root length outside the 95% CI were considered significantly different from the group estimate at α = 0.0529. The groups of human analysts were considered significantly different if their 83% CIs did not overlap, as the comparison of two 83% CIs approximates an alpha level of 5%30,31.This study is approved by Ethikkommission der Universitätsmedizin Greifswald, University of Greifswald, Germany. More

Lauchlan, S. S. & Nagelkerken, I. Species range shifts along multistressor mosaics in estuarine environments under future climate. Fish Fish. 21, 32–46 (2020).Article 

Google Scholar 
Gao, G., Zhao, X., Jiang, M. & Gao, L. Impacts of marine heatwaves on algal structure and carbon sequestration in conjunction with ocean warming and acidification. Front. Mar. Sci. 8, 758651 (2021).Article 

Google Scholar 
Asch, R. G. Climate change and decadal shifts in the phenology of larval fishes in the California Current ecosystem. Proc. Natl. Acad. Sci. 112, E4065–E4074 (2015).Article 
ADS 
CAS 

Google Scholar 
Lonhart, S. I., Jeppesen, R., Beas-Luna, R., Crooks, J. A. & Lorda, J. Shifts in the distribution and abundance of coastal marine species along the eastern Pacific Ocean during marine heatwaves from 2013 to 2018. Mar. Biodivers. Rec. 12, 13 (2019).Article 

Google Scholar 
Morley, J. W. et al. Projecting shifts in thermal habitat for 686 species on the North American continental shelf. PLoS ONE 13, e0196127 (2018).Article 

Google Scholar 
Vergés, A. et al. The tropicalization of temperate marine ecosystems: Climate-mediated changes in herbivory and community phase shifts. Proc. R. Soc. B 281, 20140846 (2014).Article 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).Article 
ADS 
CAS 

Google Scholar 
Cheung, W. W. L. et al. Marine high temperature extremes amplify the impacts of climate change on fish and fisheries. Sci. Adv. https://doi.org/10.1126/sciadv.abh0895 (2021).Article 

Google Scholar 
Ling, S. D., Johnson, C. R., Frusher, S. D. & Ridgway, K. R. Overfishing reduces resilience of kelp beds to climate-driven catastrophic phase shift. Proc. Natl. Acad. Sci. 106, 22341–22345 (2009).Article 
ADS 
CAS 

Google Scholar 
Pessarrodona, A. et al. Tropicalization unlocks novel trophic pathways and enhances secondary productivity in temperate reefs. Funct. Ecol. 36, 659–673 (2022).Article 

Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).Article 
ADS 

Google Scholar 
Holbrook, N. J. et al. A global assessment of marine heatwaves and their drivers. Nat. Commun. 10, 2624 (2019).Article 
ADS 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Chang. 9, 306–312 (2019).Article 
ADS 

Google Scholar 
Cheung, W. W. L. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 6678 (2020).Article 
ADS 
CAS 

Google Scholar 
Garrabou, J. et al. Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea. Glob. Change Biol. 28, 5708–5725 (2022).Article 
CAS 

Google Scholar 
Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change 3, 78–82 (2013).Article 
ADS 

Google Scholar 
Cure, K. et al. Distributional responses to marine heat waves: insights from length frequencies across the geographic range of the endemic reef fish Choerodon rubescens. Mar. Biol. 165, 1 (2018).Article 

Google Scholar 
Jacox, M. G., Tommasi, D., Alexander, M. A., Hervieux, G. & Stock, C. A. Predicting the evolution of the 2014–2016 California current system marine heatwave from an ensemble of coupled global climate forecasts. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00497 (2019).Article 

Google Scholar 
Gentemann, C. L., Fewings, M. R. & García-Reyes, M. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophys. Res. Lett. 44, 312–319 (2017).Article 
ADS 

Google Scholar 
Cavanaugh, K. C., Reed, D. C., Bell, T. W., Castorani, M. C. N. & Beas-Luna, R. Spatial variability in the resistance and resilience of giant kelp in southern and baja California to a multiyear heatwave. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00413 (2019).Article 

Google Scholar 
Cavole, L. M. et al. Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: Winners, losers, and the future. Oceanography 29, 273–285 (2016).Article 

Google Scholar 
Sen Gupta, A. et al. Drivers and impacts of the most extreme marine heatwave events. Sci. Rep. 10, 19359 (2020).Article 
ADS 
CAS 

Google Scholar 
Rykaczewski, R. R. & Checkley, D. M. Influence of ocean winds on the pelagic ecosystem in upwelling regions. Proc. Natl. Acad. Sci. 105, 1965–1970 (2008).Article 
ADS 
CAS 

Google Scholar 
Thompson, A. R. et al. Putting the Pacific marine heatwave into perspective: The response of larval fish off southern California to unprecedented warming in 2014–2016 relative to the previous 65 years. Glob. Change Biol. 28, 1766–1785 (2022).Article 
CAS 

Google Scholar 
Suryan, R. M. et al. Ecosystem response persists after a prolonged marine heatwave. Sci. Rep. 11, 6235 (2021).Article 
ADS 
CAS 

Google Scholar 
Bates, A. E. et al. Resilience and signatures of tropicalization in protected reef fish communities. Nat. Clim. Change 4, 62–67 (2014).Article 
ADS 

Google Scholar 
Behrens, M. & Lafferty, K. Effects of marine reserves and urchin disease on southern Californian rocky reef communities. Mar. Ecol. Prog. Ser. 279, 129–139 (2004).Article 
ADS 

Google Scholar 
Bernhardt, J. R. & Leslie, H. M. Resilience to climate change in coastal marine ecosystems. Ann. Rev. Mar. Sci. 5, 371–392 (2013).Article 

Google Scholar 
Caselle, J. E., Davis, K. & Marks, L. M. Marine management affects the invasion success of a non-native species in a temperate reef system in California, USA. Ecol. Lett. 21, 43–53 (2018).Article 

Google Scholar 
Micheli, F. et al. Evidence that marine reserves enhance resilience to climatic impacts. PLoS ONE 7, e40832 (2012).Article 
ADS 
CAS 

Google Scholar 
Olds, A. D. et al. Marine reserves help coastal ecosystems cope with extreme weather. Glob. Change Biol. 20, 3050–3058 (2014).Article 
ADS 

Google Scholar 
Freedman, R. M., Brown, J. A., Caldow, C. & Caselle, J. E. Marine protected areas do not prevent marine heatwave-induced fish community structure changes in a temperate transition zone. Sci. Rep. 10, 21081 (2020).Article 
ADS 
CAS 

Google Scholar 
Bates, A. E. et al. Climate resilience in marine protected areas and the ‘Protection Paradox’. Biol. Cons. 236, 305–314 (2019).Article 

Google Scholar 
Kirlin, J. et al. California’s Marine Life Protection Act Initiative: Supporting implementation of legislation establishing a statewide network of marine protected areas. Ocean Coast. Manag. 74, 3–13 (2013).Article 

Google Scholar 
Saarman, E. T. et al. An ecological framework for informing permitting decisions on scientific activities in protected areas. PLoS ONE 13, e0199126 (2018).Article 

Google Scholar 
Caselle, J. E., Rassweiler, A., Hamilton, S. L. & Warner, R. R. Recovery trajectories of kelp forest animals are rapid yet spatially variable across a network of temperate marine protected areas. Sci. Rep. 5, 14102 (2015).Article 
ADS 
CAS 

Google Scholar 
Hamilton, S. L., Caselle, J. E., Malone, D. P. & Carr, M. H. Incorporating biogeography into evaluations of the Channel Islands marine reserve network. PNAS 107, 18272–18277 (2010).Article 
ADS 
CAS 

Google Scholar 
Wendt, D. E. & Starr, R. M. Collaborative research: An effective way to collect data for stock assessments and evaluate marine protected areas in California. Mar. Coast. Fish. 1, 315–324 (2009).Article 

Google Scholar 
Côté, I. M. & Darling, E. S. Rethinking ecosystem resilience in the face of climate change. PLoS Biol. 8, e1000438 (2010).Article 

Google Scholar 
Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).Article 

Google Scholar 
Li, L. et al. Subregional differences in groundfish distributional responses to anomalous ocean bottom temperatures in the northeast Pacific. Glob. Change Biol. 25, 2560–2575 (2019).Article 
ADS 

Google Scholar 
Dawson, M. N. Phylogeography in coastal marine animals: A solution from California?. J. Biogeogr. 28, 723–736 (2001).Article 

Google Scholar 
Horn, M. H., Allen, L. G. & Lea, R. N. Biogeography. In The Ecology of Marine Fishes: California and Adjacent Waters (ed. Allen, L.) 3–25 (University of California Press, 2006). https://doi.org/10.1525/california/9780520246539.003.0001.Chapter 

Google Scholar 
Horn, M. H. & Allen, L. G. A distributional analysis of California coastal marine fishes. J. Biogeogr. 5, 23–42 (1978).Article 

Google Scholar 
Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).Article 
ADS 

Google Scholar 
Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. R. Soc. B 280, 20122829 (2013).Article 

Google Scholar 
O’Leary, B. C. et al. Addressing criticisms of large-scale marine protected areas. Bioscience 68, 359–370 (2018).Article 

Google Scholar 
California Department of Fish and Wildlife. California Sheephead, Bodianus (formerly Semicossyphus) pulcher, Enhanced Status Report. (2021).Pinsky, M. L., Selden, R. L. & Kitchel, Z. J. Climate-driven shifts in marine species ranges: Scaling from organisms to communities. Ann. Rev. Mar. Sci. 12, 153–179 (2020).Article 

Google Scholar 
Francour, P., Mangialajo, L. & Pastor, J. Mediterranean marine protected areas and non-indigenous fish spreading. In Fish Invasions of the Mediterranean Sea: Change and Renewal (eds Golani, D. & Appelbaum-Golani, B.) 127–144 (Pensoft Publisher, 2010).
Google Scholar 
Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Glob. Change Biol. 19, 3592–3606 (2013).Article 
ADS 

Google Scholar 
Bennett, S., Wernberg, T., Harvey, E. S., Santana-Garcon, J. & Saunders, B. J. Tropical herbivores provide resilience to a climate-mediated phase shift on temperate reefs. Ecol. Lett. 18, 714–723 (2015).Article 

Google Scholar 
Trainer, V. L. et al. Pelagic harmful algal blooms and climate change: Lessons from nature’s experiments with extremes. Harmful Algae 91, 101591 (2020).Article 

Google Scholar 
Gliwicz, Z. M., Babkiewicz, E., Kumar, R., Kunjiappan, S. & Leniowski, K. Warming increases the number of apparent prey in reaction field volume of zooplanktivorous fish. Limnol. Oceanogr. 63, S30–S43 (2018).Article 
ADS 

Google Scholar 
Nielsen, J. M. et al. Responses of ichthyoplankton assemblages to the recent marine heatwave and previous climate fluctuations in several Northeast Pacific marine ecosystems. Glob. Change Biol. 27, 506–520 (2021).Article 
ADS 

Google Scholar 
du Pontavice, H., Gascuel, D., Reygondeau, G., Stock, C. & Cheung, W. W. L. Climate-induced decrease in biomass flow in marine food webs may severely affect predators and ecosystem production. Glob. Change Biol. 27, 2608–2622 (2021).Article 
ADS 

Google Scholar 
Arimitsu, M. L. et al. Heatwave-induced synchrony within forage fish portfolio disrupts energy flow to top pelagic predators. Glob. Change Biol. 27, 1859–1878 (2021).Article 
ADS 
CAS 

Google Scholar 
Oken, K. L., Essington, T. E. & Fu, C. Variability and stability in predation landscapes: A cross-ecosystem comparison on the potential for predator control in temperate marine ecosystems. Fish Fish. 19, 489–501 (2018).Article 

Google Scholar 
Baum, J. K. & Worm, B. Cascading top-down effects of changing oceanic predator abundances. J. Anim. Ecol. 78, 699–714 (2009).Article 

Google Scholar 
Jacox, M. G. et al. Impacts of the 2015–2016 El Niño on the California current system: Early assessment and comparison to past events. Geophys. Res. Lett. 43, 7072–7080 (2016).Article 
ADS 

Google Scholar 
Brodeur, R. D., Auth, T. D. & Phillips, A. J. Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00212 (2019).Article 

Google Scholar 
Field, J. C. et al. Spatiotemporal patterns of variability in the abundance and distribution of winter-spawned pelagic juvenile rockfish in the California Current. PLoS ONE 16, e0251638 (2021).Article 
CAS 

Google Scholar 
Schroeder, I. D. et al. Source water variability as a driver of rockfish recruitment in the California current ecosystem: Implications for climate change and fisheries management. Can. J. Fish. Aquat. Sci. 76, 950–960 (2019).Article 
CAS 

Google Scholar 
Echeverria, T. W. Thirty-four species of California rockfishes: Maturity and seasonality of reproduction. Fish. Bull. 85, 229–250 (1987).
Google Scholar 
Miller, A. & Sydeman, W. Rockfish response to low-frequency ocean climate change as revealed by the diet of a marine bird over multiple time scales. Mar. Ecol. Prog. Ser. 281, 207–216 (2004).Article 
ADS 

Google Scholar 
Johnson, K. F. et al. Status of lingcod (Ophiodon elongatus) along the southern U.S. west coast in 2021. 195 p. (2021).Winemiller, K. O. & Rose, K. A. Patterns of life-history diversification in North American fishes: Implications for population regulation. Can. J. Fish. Aquat. Sci. 49, 2196–2218 (1992).Article 

Google Scholar 
Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring along the great barrier reef following mass coral bleaching. Nature 560, 92–96 (2018).Article 
ADS 
CAS 

Google Scholar 
Starr, R. M. et al. Variation in responses of fishes across multiple reserves within a network of marine protected areas in temperate waters. PLoS ONE 10, e0118502 (2015).Article 

Google Scholar 
Ziegler, S. L. et al. External fishing effort regulates positive effects of no-take marine protected areas. Biol. Cons. 269, 109546 (2022).Article 

Google Scholar 
Jarvis, E. T. & Lowe, C. G. The effects of barotrauma on the catch-and-release survival of southern California nearshore and shelf rockfish (Scorpaenidae, Sebastes spp.). Can. J. Fish. Aquat. Sci. 65, 1286–1296 (2008).Article 

Google Scholar 
Brooks, R. et al. Nearshore Fishes Abundance and Distribution Data, California Collaborative Fisheries Research Program (CCFRP). (2022).García-Reyes, M. & Sydeman, W. J. California multivariate ocean climate indicator (MOCI) and marine ecosystem dynamics. Ecol. Ind. 72, 521–529 (2017).Article 

Google Scholar 
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2021).Oksanen, J. et al. vegan: Community Ecology Package. (2020).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. (2021). More

Our findings highlight the critical role of polyP in Sodalinema stali-formed cyanobacterial mats, as it was dynamically accumulated and recycled during acclimation to P fluctuations.Cellular response to progressive P starvationAnalogous to planktonic cyanobacteria, growth under low P availability could be sustained by recycling polyP, which acted as a primary P source (Fig. 2a) [16, 23, 24]. We further attribute the rapid reduction of easily dispensable cellular P-containing compounds to the substitution of cellular phospholipids with S- or N-containing membrane lipids to maintain growth at the onset of P stress (Fig. 2a) [15, 23]. However, the exhaustion of this easily dispensable P pool limited proliferation in Phase 2, and the metabolic strategy switched from a focus on growth towards maintenance (Fig. 5). The interpretation of prevailing cellular processes based on our results is graphically summarized and explained in detail below (Fig. 5).Fig. 5: Schematic interpretation of cellular phosphorus (P) cycling in a cyanobacterial mat, based on significant changes of the monitored parameters (arbitrary units).a At low P availability, initially contained polyphosphate (polyP) was recycled simultaneously with phosphate uptake to sustain growth at constant C:N:P ratios. Further proliferation at the onset of P stress in Phase 1 was sustained by mobilization of cellular P, e.g. phospholipids, which led to rapidly increasing C:N:P ratios. Severe P stress in Phase 2, indicated by increasing APase activity, prevented proliferation and photosynthesis, indicated by a loss of green chlorophyll pigments. PolyP accumulation by deficiency response occurs with severely increasing P stress, whereby globular DNA accumulation indicates the allocation of P contained in DNA into polyP. P re-addition to the P-stressed cells in Phase 3 triggered overplus uptake and narrow C:N:P ratios, transitioning to luxury uptake at higher C:N:P ratios following polyP recycling. b At high P availability, polyP in Phase 1 was accumulated by overplus uptake at narrow C:N:P ratios, transitioning to luxury uptake at higher C:N:P ratios during polyP recycling in Phase 2. P-deprivation in Phase 3 did not affect the cells, which we attributed to a sufficient amount of phosphate in the residual medium or within the biofilm matrix. Arrows indicate phosphorus transformation processes, whereby arrows pointing towards DNA represent cell growth. Yellow granules = polyP, blue granules = globular DNA spheres, P = phospholipids, S = substitute lipids.Full size imageSevere P stress in Phase 2 was indicated by the colour change from green towards yellow-green (Fig. S1) and increasing APase activity (Fig. 2a). The colour change suggested the loss of photosynthetic pigments [40], but we could not clarify whether this occurred through active cellular pigment reduction or degradation of available chlorophyll e.g., by oxidation. The increasing APase activity (Fig. 2a) suggested that Sodalinema stali is capable of hydrolysing organic P [14]. Even though APase expression did not trigger proliferation, it likely hydrolysed a potentially available organic P pool, as increasing DIC, NH4 and decreasing pH indicated progressive decay and remineralisation of organic matter (Fig. 1a). This suggests that in analogous oligotrophic environments with often fluctuating conditions, the strategy has to be maximizing the utilization of external P sources contained in organic and inorganic sediment particles that get trapped in the EPS [41]. The sediment can contain large amounts of organic P [42] and the fluctuating physico-chemical gradients in the EPS matrix due to high daytime pH and low oxygen conditions at night, facilitate P desorption from metal oxides, leading to higher dissolved phosphate concentrations within the mat, compared to the overlying water body [3]. However, alternating redox conditions at the SWI could also trigger polyP release from benthic microorganisms to the sediments, where it could act as a P source for the benthic food-chain, or ultimately trigger the formation of mineral P phases [32], to sustainably remove P from the aquatic cycle. Either way, we suggest that polyP-containing cyanobacterial mats critically impact P fluxes at the SWI.With persisting severe P stress and increasing APase activity in Phase 2, polyP accumulation as a deficiency response was observed (Fig. 2a), which has been reported from planktonic cyanobacteria of different habitats [24, 29, 23], as well as stream periphyton [28]. However, the reasons causing this deficiency response remain unresolved. In marine phytoplankton of the oligotrophic Sargasso Sea, Martin et al. [23] excluded that polyP-rich cells were in a perpetual overplus state with ‘undetectable’ pulses driving this state and suggested that polyP accumulation occurred as a cellular stress response. In other studies, reduced biosynthesis of P-rich rRNA coincided with deficiency responses [26, 28] and led to the suggestion that polyP accumulation at P concentrations below a certain threshold required for growth occurs because of P allocation changes away from growth and towards storage. Further, APase can hydrolytically cleave phosphate groups from nucleic acids and convert DNA-lipid-P to DNA-lipids, which were shown to self-assemble into globular lipid-based DNA micelles [43]. These preferentially anchor on cell membranes [44], and indeed, such DNA spheres were found to accumulate at the cell’s polar membranes in our experiments adjacent to polyP during deficiency response (Fig. 4a: Phase 2,c). Therefore, we suggest that intracellular P recovery by cleavage from P-rich DNA and reallocation to polyP, and potentially reduced rRNA synthesis [31], is also a strategy in benthic mats of Sodalinema stali as a response to severe P stress when P availability is too low to sustain growth. This supports the theory of a reallocation of resources away from growth towards flexibly available P and energy storage. Such direct intracellular P cycling could be beneficial to help retain P within the cyanobacterial population; while external P moieties such as dissolved organic P within the matrix can act as an additional P source, they are also likely to be subject to nutrient competition between cyanobacteria and other organisms inhabiting the matrix.Such effects of potential interactions in terms of nutrient competition or provision between cyanobacteria and mutualistic microorganisms contained within the same EPS matrix are difficult to assess and we cannot exclude some potential effects on our results. However, mutualistic microorganisms that are naturally contained in many cyanobacterial or algal cultures are often critical for metacommunity functioning and hence, working with axenic mat-forming strains may even further falsify any obtained results. Furthermore, microscopic analyses revealed that Sodalinema always dominated the biomass and hence, it is here considered reasonable to work with a non-axenic culture.Cellular response to a simulated P pulseIn P-deficient cells, the affinity of the P uptake system is typically increased to maximize P uptake for future pulses [13, 45]. The simulated P pulse to the P-stressed cells in Phase 3 led to a rapid increase of the cellular P content by 1260% relative to C within 3 days (Fig. 2a), whereby P was accumulated to a significant part as polyP, which is characteristic for overplus uptake [25]. Many different types of oligotrophic aquatic habitats experience only temporal P pulses, e.g., from redox changes at the benthic interface leading to P release from the sediment [32], storm run-off [28], upwelling [46], or excretions of aquatic animals [47]. The capability of microorganisms to immediately take up, store, and efficiently re-use this P by overplus uptake is hence of critical importance for a population to sustain a potential subsequent period of low P availability. Overplus uptake is typically accompanied by the overall slow growth of the population and cellular recovery from P starvation, including ultrastructural organization and recovery of the photosynthetic apparatus [48]. This took one week after re-feeding of P-starved Nostoc sp. PCC 7118 cells [48]—a timeframe very similar to the delayed onset of photosynthesis observed in our study, indicated by the elevated pH at day 9 (Fig. 1a). Regarding overplus-triggering mechanisms following P pulses, Solovochenko et al. [48] suggested that overplus uptake occurs due to a delayed down-regulation of high-affinity Pi transporters, which are active during P starvation, and emphasized the simultaneous advantage of osmotically inert polyP accumulation as a response to dramatically high phosphate concentrations in the cells. Even though APase levels declined following our experimental P re-addition, they were significantly elevated for at least 9 days (Fig. 2a). As our experimental design involved replacing the medium with APase-free, BG11 + medium after Phase 2, we assume that the APase detected in Phase 3 was actively produced, and we conclude that previously relevant, low-P response mechanisms are slowly disengaged with some sort of lag, even when ambient P is repleted. Following cellular recovery, Sodalinema now recycled stored polyP instead of further accumulating it during the transition from overplus-to luxury uptake, which was reflected in the increasing C:N:P molar ratios and decreasing polyP levels without significant additional phosphate uptake (Figs. 1a, 2a, 5).Qualitative observations on polyP distributionMost methods applied to analyse polyP in microorganisms are quantitative and do not contain information on its spatial distribution within a population. The here observed variable distribution of polyP between the cells during luxury uptake and deficiency response, as well as the retention of polyP in few individual filaments during polyP recycling in Phase 1 of the low P experiment (Fig. 4) suggests strategies of either slow growth with a retention of polyP, or of high growth with polyP recycling. This was also suggested for cells of a unicellular Synechocystis sp. PCC 6803 population during overplus uptake [47]. In contrast, polyP in our experiment was distributed homogeneously between all cyanobacterial cells during overplus uptake (Fig. 4a: Phase 3, Fig. 4b: Phase 1). Yet, we are unaware of any polyP distribution study in multicellular or mat-forming cyanobacteria and hence, further mechanisms of interactions, e.g., cell-to-cell communication [49, 50], might also contribute to purposeful differentiation of cells or filaments within a common matrix.In summary, our study shows that the mat-forming Sodalinema stali (1) is capable of luxury uptake, overplus uptake and deficiency response with a heterogenous polyP distribution during polyP recycling, luxury uptake and deficiency response, while (2) dynamically adjusting cellular P content to changing phosphate concentrations. (3) Proliferation is sustained under the expense of polyP, followed by P acquisition from other easily dispensable cellular P-containing compounds under the onset of P stress. (4) Further, biosynthetic allocation changes away from growth towards maintenance with relative polyP accumulation at the expense of P-rich DNA are conducted under severe P stress. Our findings demonstrate the extraordinary capabilities of mat-forming cyanobacteria to adapt their P acquisition strategies to strong P fluctuations. While lasting proliferation under P limitation requires the mobilization of additional P sources through regeneration of P from particulate matter, the transition to net P accumulation under excess ambient P is rapid and effective. Since current projections of climate and land use change include intensified pulses of P load to aquatic ecosystems [50], e.g., through external input from surplus of agriculture fertilizer, inefficient wastewater treatment plants, and internal loads via the mobilization of legacy P, these P ‘bioaccumulators’ could form an important component in P remediation by temporarily accumulating P within the mat, and synthesizing polyP that could ultimately stimulate the formation of mineral P phases to sustainably remove P from the aquatic cycle. More