Ecology

1.
Huwyler, F., Kappeli, J., Serafimova, K., Swanson, E. & Tobin, J. Conservation Finance: Moving Beyond Donor Funding Toward an Investor-driven Approach (WWF, Credit Suisse and McKinsey & Company, 2014); http://go.nature.com/2Ka5Y2u
2.
Deutz, A. et al. Financing Nature: Closing the Global Biodiversity Financing Gap: Full Report (Paulson Institute, Nature Conservancy and Cornell Atkinson Center for Sustainability, 2020).

3.
Halpern, B. et al. Gaps and mismatches between global conservation priorities and spending. Conserv. Biol. 20, 56–64 (2006).
Article  Google Scholar 

4.
James, A., Gaston, K. J. & BalmfordA. Can we afford to conserve biodiversity? BioScience 51, 43–52 (2001).
Article  Google Scholar 

5.
McCarthy, D. et al. Financial costs of meeting global biodiversity conservation targets: current spending and unmet needs. Science 338, 946–949 (2012).
CAS  Article  Google Scholar 

6.
Nature’s Dangerous Decline ‘Unprecedented’; Species Extinction Rates ‘Accelerating’ (IPBES, 2019); http://go.nature.com/2V4ZBN9

7.
The Global Risks Report 2020 (WEF, 2020); https://go.nature.com/3ahNfg8

8.
IUCN Views on the Preparation, Scope and Content of the Post-2020 Global Biodiversity Framework (IUCN, 2018); https://go.nature.com/2WlW3ti

9.
Biodiversity: Finance and the Economic and Business Case for Action (OECD, 2019); https://go.nature.com/3h0F9Kc

10.
Parker, C. & Cranford, M. The Little Biodiversity Finance Book. A Guide to Proactive Investment in Natural Capital (Global Canopy Program, 2010); https://go.nature.com/3mwyxUJ

11.
Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front. Ecol. Environ. 17, 259–264 (2019).
Article  Google Scholar 

12.
Kearney, S. G. et al. Estimating the benefit of well-managed protected areas for threatened species conservation. ORYX 54, 276–284 (2020).
Article  Google Scholar 

13.
Waldron, A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (IIASA, 2020); https://go.nature.com/387GkDq

14.
Stepping, K. M. K. & Meijer, K. S. The challenges of assessing the effectiveness of biodiversity-related development aid. Trop. Conserv. Sci. https://doi.org/10.1177/1940082918770995 (2018).

15.
Waldron, A. et al. Targeting global conservation funding to limit immediate biodiversity declines. Proc. Natl Acad. Sci. USA 110, 12144–12148 (2018).
Article  Google Scholar 

16.
Gallo-Cajiao, E. et al. Crowdfunding biodiversity conservation. Conserv. Biol. 32, 1426–1435 (2018).
Article  Google Scholar 

17.
Parker, C., Cranford, M., Oakes, N. & Leggett, M. The Little Biodiversity Finance Book 3rd edn (Global Canopy Programme, 2012).

18.
Arlaud, M. et al. in Towards a Sustainable Bioeconomy: Principles, Challenges and Perspectives (eds Filho, W. L. et al.) Ch. 5 (Springer, 2018); https://doi.org/10.1007/978-3-319-73028-8_5

19.
Rawat, U. S. & Agarwal, N. K. Biodiversity: concept, threats and conservation. Environ. Conserv. J. 16, 19–28 (2015).
Article  Google Scholar 

20.
Gorobets, A. Wild fauna conservation: IUCN-CITES match is required. Ecol. Indic. 112, 106091 (2020).
Article  Google Scholar 

21.
Rodrigues, A. S. L. et al. The value of the IUCN Red List for conservation. Trends Ecol. Evol. 21, 71–76 (2006).
Article  Google Scholar 

22.
Rao, M., Naro-Maciel, E. & Sterling, E. Protected Areas and Biodiversity Conservation II: Management and Effectiveness (Network of Conservation Educators and Practitioners, 2009).

23.
Adams, V. M., Iacona, G. D. & Possingham, H. P. Weighing the benefits of expanding protected areas versus managing existing ones. Nat. Sustain. 2, 404–411 (2019).
Article  Google Scholar 

24.
BIOFIN The Biodiversity Finance Initiative Workbook 2018 (United Nations Development Programme, 2018).

25.
Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).
CAS  Article  Google Scholar 

26.
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).
Article  Google Scholar 

27.
Naidoo, R. et al. Global mapping of ecosystem services and conservation priorities. Proc. Natl Acad. Sci. USA 105, 9495–9500 (2008).
CAS  Article  Google Scholar 

28.
Turner, W. et al. Global conservation of biodiversity and ecosystem services. BioScience 57, 868–873 (2007).
Article  Google Scholar 

29.
Balmford, A. et al. Economic reasons for conserving wild nature. Science 297, 950–953 (2002).
CAS  Article  Google Scholar 

30.
Hily, E. et al. Assessing the cost-effectiveness of a biodiversity conservation policy: a bio-econometric analysis of Natura 2000 contracts in forests. Ecol. Econ. 119, 197-208 (2015).

31.
Ferraro, P. J., McIntosh, C. & Ospina, M. The effectiveness of the US endangered special act: an econometric analysis using matching methods. J. Environ. Econ. Manag. 54, 245–261 (2007).
Article  Google Scholar 

32.
Waldron, A. et al. Targeting global conservation funding to limit immediate biodiversity declines. Proc. Natl Acad. Sci. USA 110, 12144–12148 (2013).
CAS  Article  Google Scholar 

33.
Waldron, A. et al. Reductions in global biodiversity loss predicted from conservation spending. Nature 551, 364–367 (2017).
CAS  Article  Google Scholar 

34.
Richerzhagen, C. et al. Why We Need More and Better Biodiversity Aid Briefing Paper 13 (German Development Institute, 2016); https://go.nature.com/2K0S9Dz

35.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).

36.
Karousakis, K. Evaluating the Effectiveness of Policy Instruments for Biodiversity: Impact Evaluation, Cost-effectiveness Analysis and Other Approaches Environment Working Paper No.141 (OECD, 2018).

37.
Isaza, C., Bofill, W. & Cabrera, H. Cost-effective species conservation: an application to Huemul (Hippocamelus bisulcus) in Chile. Environ. Dev. Econ. 12, 535–551 (2007).
Article  Google Scholar 

38.
Alix-Garcia, J. M., Shapiro, E. N. & Sims, K. R. Forest conservation and slippage: evidence from Mexico’s national payments for ecosystem services program. Land Econ. 88, 613–638 (2012).
Article  Google Scholar 

39.
Bare, M. Assessing the impact of international conservation aid on deforestation in sub-Saharan Africa. Environ. Res. Lett. 10, 125010 (2015).
Article  Google Scholar 

40.
Ferraro, P. J. et al. More strictly protected areas are not necessarily more protective: evidence from Bolivia, Costa Rica, Indonesia, and Thailand. Environ. Res. Lett. 8, 025011 (2013).
Article  Google Scholar 

41.
Lindsey, P. A. et al. More than $1 billion needed annually to secure Africa’s protected areas with lions. Proc. Natl Acad. Sci. USA 115, E10788–E10796 (2018).
CAS  Article  Google Scholar 

42.
Bonham, C. et al. Conservation trust funds, protected area management effectiveness and conservation outcomes: lessons from the global conservation fund. Parks 20, 89–100 (2014).
Article  Google Scholar 

43.
Hein, Lars et al. Progress in natural capital accounting for ecosystems. Science 367, 514–515 (2020).
CAS  Article  Google Scholar 

44.
Natural Capital Accounting and Valuing Ecosystem Services Project (UN, 2019); http://go.nature.com/2K2jsxn

45.
Ecosystem Valuation and Natural Capital Accounting (Gaborone Declaration for Sustainability in Africa, 2012); http://www.gaboronedeclaration.com/nca

46.
Climate Public Expenditure and Institutional Review (CPEIR) (UNDP, 2015); https://go.nature.com/2K0C7tp

47.
BIOFIN Workbook: Mobilising Resources for Biodiversity and Sustainable Development (UND, 2016); https://go.nature.com/3p1PDMb

48.
Shieh, G. Effect size, statistical power, and sample size for assessing interactions between categorical and continuous variables. Br. J. Math. Stat. Psychol. 72, 136–154 (2019).
Article  Google Scholar 

49.
Leon, A. C. & Heo, M. Sample sizes required to detect interactions between two binary fixed-effects in a mixed-effects linear regression model. Comput. Stat. Data Anal. 53, 603–608 (2009).
Article  Google Scholar 

50.
Marques, A. et al. Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol. 3, 628–637 (2019).
Article  Google Scholar 

51.
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).
CAS  Article  Google Scholar 

52.
Luther, D. A. et al. Determinants of bird conservation—action implementation and associated population trends of threatened species. Conserv. Biol. 30, 1338–1346 (2016).
Article  Google Scholar 

53.
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).
CAS  Article  Google Scholar 

54.
Brooks, T. M. et al. Analysing biodiversity and conservation knowledge products to support regional environmental assessments. Sci. Data 3, I60007 (2016).
Article  Google Scholar 

55.
Keith, D. A. et al. Scientific foundations for an IUCN Red List of ecosystems. PLoS ONE 8, e62111 (2013).
CAS  Article  Google Scholar 

56.
Kaufmann, D., Kraay, A. & Mastruzzi, M. The worldwide governance indicators: methodology and analytical issues. Hague J. Rule Law 3, 220–246 (2011).
Article  Google Scholar 

57.
Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle (Academiai Kiado, 1973).

58.
Bozdogan, H. Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987).
Article  Google Scholar 

59.
Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton Univ. Press, 2009); http://go.nature.com/3r5t6zA

60.
Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data 2nd edn (MIT Press, 2010). More

1.
Galloway, J. N. et al. Transformation of the nitrogen cycle: recent trends, questions, and potential solutions. Science 320, 889–892 (2008).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Zhou, X., Zhang, Y. & Downing, A. Non-linear response of microbial activity across a gradient of nitrogen addition to a soil from the gurbantunggut desert, northwestern China. Soil Biol. Biochem. 47, 67–77 (2012).
CAS  Article  Google Scholar 

3.
Liu, X. et al. Enhanced nitrogen deposition over China. Nature 494, 459–462 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Fang, Y. T., Gundersen, P., Mo, J. M. & Zhu, W. X. Input and output of dissolved organic and inorganic nitrogen in subtropical forests of south China under high air pollution. Biogeosciences 5, 339–352 (2008).
ADS  CAS  Article  Google Scholar 

5.
Hoorens, B., Aerts, R. & Stroetenga, M. Does initial litter chemistry explain litter mixture effects on decomposition?. Oecologia 137, 578–586 (2003).
ADS  PubMed  Article  Google Scholar 

6.
Passarinho, J. A. P., Lamosa, P., Baeta, J. P., Santos, H. & Ricardo, C. P. P. Annual changes in the concentration of minerals and organic compounds of Quercus suber leaves. Physiol. Plantarum 127, 100–110 (2006).
CAS  Article  Google Scholar 

7.
Shen, F. F. et al. Litterfall ecological stoichiometry and soil available nutrients under long-term nitrogen deposition in a Chinese fir plantation. Acta Ecol. Sin. 38, 7477–7487 (2018).
Google Scholar 

8.
Huangfu, C. & Wei, Z. Nitrogen addition drives convergence of leaf litter decomposition rates between Flaveria bidentis and native plant. Plant Ecol. 219, 1355–1368 (2018).
Article  Google Scholar 

9.
Vivanco, L. & Austin, A. Nitrogen addition stimulates forest litter decomposition and disrupts species interactions in Patagonia, Argentina. Global Change Biol. 17, 1963–1974 (2011).
ADS  Article  Google Scholar 

10.
Li, H., Wei, Z., Huangfu, C., Chen, X. & Yang, D. Litter mixture dominated by leaf litter of the invasive species, Flaveria bidentis, accelerates decomposition and favors nitrogen release. J. Plant Res. 130, 167–180 (2017).
CAS  PubMed  Article  Google Scholar 

11.
Aerts, R. D. C. H. Nutritional and plant-mediated controls on leaf litter decomposition of Carex species. Ecology 78, 244–260 (1997).
Article  Google Scholar 

12.
Osono, T. & Takeda, H. Accumulation and release of nitrogen and phosphorus in relation to lignin decomposition in leaf litter of 14 tree species. Ecol. Res. 19, 593–602 (2004).
Article  Google Scholar 

13.
Bradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R. & Wood, S. A. Understanding the dominant controls on litter decomposition. J. Ecol. 104, 229–238 (2016).
CAS  Article  Google Scholar 

14.
García-Palacios, P., Shaw, E. A., Wall, D. H. & Hättenschwiler, S. Temporal dynamics of biotic and abiotic drivers of litter decomposition. Ecol. Lett. 19, 554–563 (2016).
PubMed  Article  Google Scholar 

15.
Song, C., Liu, D., Yang, G., Song, Y. & Mao, R. Effect of nitrogen addition on decomposition of Calamagrostis angustifolia litters from freshwater marshes of northeast China. Ecol. Eng. 37, 1578–1582 (2011).
Article  Google Scholar 

16.
Zhang, D., Hui, D., Luo, Y. & Zhou, G. Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors. J. Plant Ecol. 1, 85–93 (2008).
Article  Google Scholar 

17.
Chen, F. et al. Nitrogen deposition effect on forest litter decomposition is interactively regulated by endogenous litter quality and exogenous resource supply. Plant Soil. 437, 413 (2019).
CAS  Article  Google Scholar 

18.
Wang, Q., Kwak, J., Choi, W. & Chang, S. X. Long-term N and S addition and changed litter chemistry do not affect trembling aspen leaf litter decomposition, elemental composition and enzyme activity in a boreal forest. Environ. Pollut. 250, 143–154 (2019).
CAS  PubMed  Article  Google Scholar 

19.
Hou, S. et al. Increasing rates of long-term nitrogen deposition consistently increased litter decomposition in a semi-arid grassland. New Phytol. 229, 296–307 (2020).
PubMed  Article  Google Scholar 

20.
Yu, Z. et al. Nitrogen addition enhances home-field advantage during litter decomposition in subtropical forest plantations. Soil Biol. Biochem. 90, 188–196 (2015).
CAS  Article  Google Scholar 

21.
Pichon, N. et al. Decomposition disentangled: A test of the multiple mechanisms by which nitrogen enrichment alters litter decomposition. Funct. Ecol. 34, 1485–1496 (2020).
Article  Google Scholar 

22.
Hobbie, S. et al. Response of decomposing litter and its microbial community to multiple forms of nitrogen enrichment. Ecol. Monogr. 82, 389–405 (2012).
Article  Google Scholar 

23.
Knops, J., Naeem, S. & Reich, P. The impact of elevated CO2, increased nitrogen availability and biodiversity on plant tissue quality and decomposition. Global Change Biol. 13, 1960–1971 (2007).
ADS  Article  Google Scholar 

24.
Prescott, C. E. Does nitrogen availability control rates of litter decomposition in forests?. Plant Soil. 168, 83–88 (1995).
Article  Google Scholar 

25.
Zhou, Y., Wang, L., Chen, Y., Zhang, J. & Liu, Y. Litter stoichiometric traits have stronger impact on humification than environment conditions in an alpine treeline ecotone. Plant Soil 453, 545–560 (2020).
CAS  Article  Google Scholar 

26.
Mooshammer, M. et al. Stoichiometric controls of nitrogen and phosphorus cycling in decomposing beech litter. Ecology 93, 770–782 (2012).
PubMed  Article  Google Scholar 

27.
Remy, E. et al. Driving factors behind litter decomposition and nutrient release at temperate forest edges. Ecosystems 24, 755–771 (2017).
Google Scholar 

28.
Zhou, S. et al. Simulated nitrogen deposition significantly suppresses the decomposition of forest litter in a natural evergreen broad-leaved forest in the rainy area of western China. Plant Soil 420, 135–145 (2017).
CAS  Article  Google Scholar 

29.
Cornwell, W. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).
PubMed  Article  Google Scholar 

30.
Norris, M., Avis, P., Reich, P. & Hobbie, S. E. Positive feedbacks between decomposition and soil nitrogen availability along fertility gradients. Plant Soil 367, 347–361 (2013).
CAS  Article  Google Scholar 

31.
Berg, B. & McClaugherty, C. Plant Litter: Decomposition, Humus Formation, Carbon Sequestration 2nd edn. (Springer, Berlin, 2008).
Google Scholar 

32.
Cuchietti, A., Marcotti, E., Gurvich, D. E., Cingolani, A. M. & Harguindeguy, N. P. Leaf litter mixtures and neighbour effects: Low-nitrogen and high-lignin species increase decomposition rate of high-nitrogen and low-lignin neighbours. Appl. Soil Ecol. 82, 44–51 (2014).
Article  Google Scholar 

33.
Jing, H. & Wang, G. Temporal dynamics of Pinus tabulaeformis litter decomposition under nitrogen addition on the loess plateau of China. For. Ecol. Manag. 476, 118465 (2020).
Article  Google Scholar 

34.
Sun, T., Dong, L., Wang, Z., Lü, X. & Mao, Z. Effects of long-term nitrogen deposition on fine root decomposition and its extracellular enzyme activities in temperate forests. Soil Biol. Biochem. 93, 50–59 (2016).
CAS  Article  Google Scholar 

35.
Carrera, A. L. & Bertiller, M. B. Combined effects of leaf litter and soil microsite on decomposition process in arid rangelands. J. Environ. Manag. 114, 505–511 (2013).
CAS  Article  Google Scholar 

36.
Sun, Z. et al. The effect of nitrogen addition on soil respiration from a nitrogen-limited forest soil. Agr. For. Meteorol. 197, 103–110 (2014).
Article  Google Scholar 

37.
He, X., Lin, Y., Han, G. & Ma, T. Litterfall interception by understorey vegetation delayed litter decomposition in Cinnamomum camphora plantation forest. Plant Soil 372, 207–219 (2013).
CAS  Article  Google Scholar 

38.
Wang, Q. et al. Impact of 36 years of nitrogen fertilization on microbial community composition and soil carbon cycling-related enzyme activities in rhizospheres and bulk soils in northeast China. Appl. Soil Ecol. 136, 148–157 (2019).
Article  Google Scholar 

39.
Chen, J. et al. Co-stimulation of soil glycosidase activity and soil respiration by nitrogen addition. Global Change Biol. 23, 1328–1337 (2016).
ADS  Article  Google Scholar 

40.
Wang, C. et al. Responses of soil microbial community to continuous experimental nitrogen additions for 13 years in a nitrogen-rich tropical forest. Soil Biol. Biochem. 121, 103–112 (2018).
CAS  Article  Google Scholar 

41.
Jing, X. et al. Neutral effect of nitrogen addition and negative effect of phosphorus addition on topsoil extracellular enzymatic activities in an alpine grassland ecosystem. Appl. Soil Ecol. 107, 205–213 (2016).
Article  Google Scholar 

42.
Jing, X. et al. Nitrogen deposition has minor effect on soil extracellular enzyme activities in six Chinese forests. Sci. Total Environ. 607–608, 806–815 (2017).
ADS  PubMed  Article  CAS  Google Scholar 

43.
Wang, Q., Kwak, J., Choi, W. & Chang, S. X. Decomposition of trembling aspen leaf litter under long-term nitrogen and sulfur deposition: effects of litter chemistry and forest floor microbial properties. For. Ecol. Manag. 412, 53–61 (2018).
Article  Google Scholar 

44.
Huang, X. et al. Autotoxicity hinders the natural regeneration of Cinnamomum migao H W. Li in southwest China. Forests 10, 919 (2019).
Article  Google Scholar 

45.
Feng, H., Xue, L. & Chen, H. Responses of decomposition of green leaves and leaf litter to stand density, N and P additions in Acacia auriculaeformis stands. Eur. J. For. Res. 137, 819–830 (2018).
Article  Google Scholar 

46.
Diepen, L. V. et al. Changes in litter quality caused by simulated nitrogen deposition reinforce the N-induced suppression of litter decay. Ecosphere 6, t205 (2015).
Article  Google Scholar 

47.
Zechmeister-Boltenstern, S. et al. The application of ecological stoichiometry to plant–microbial–soil organic matter transformations. Ecol. Monogr. 85, 133–155 (2015).
Article  Google Scholar 

48.
Hobbie, S. E. Nitrogen effects on decomposition: A five-year experiment in eight temperate sites. Ecology 89, 2633–2644 (2008).
PubMed  Article  Google Scholar 

49.
Hobbie, S. Interactions between litter lignin and nitrogenitter lignin and soil nitrogen availability during leaf litter decomposition in a hawaiian montane forest. Ecosystems 3, 484–494 (2000).
CAS  Article  Google Scholar 

50.
Zhang, J. et al. Effect of nitrogen and phosphorus addition on litter decomposition and nutrients release in a tropical forest. Plant Soil 454, 139–153 (2020).
CAS  Article  Google Scholar 

51.
Apolinário, V. et al. Litter decomposition of signalgrass grazed with different stocking rates and nitrogen fertilizer levels. Agron. J. 106, 1–6 (2014).
Article  Google Scholar 

52.
Takeda, H. Decomposition Processes of Litter Along a Latitudinal Gradient (Springer, Dordrecht, 1998).
Google Scholar 

53.
Torreta, N. K. & Takeda, H. Carbon and nitrogen dynamics of decomposing leaf litter in a tropical hill evergreen forest. Eur. J. Soil Biol. 35, 57–63 (1999).
CAS  Article  Google Scholar 

54.
Song, Y., Song, C., Ren, J., Zhang, X. & Jiang, L. Nitrogen input increases Deyeuxia angustifolia litter decomposition and enzyme activities in a marshland ecosystem in Sanjiang plain, northeast China. Wetlands. 39, 549–557 (2019).
Article  Google Scholar 

55.
Sinsabaugh, R. L., Hill, B. H. & Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462, 795–798 (2009).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Xia, M. A. T. A. Long-term simulated atmospheric nitrogen deposition alters leaf and fine root decomposition. Ecosystems 21, 1–14 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Chen, F., Feng, X. & Liang, C. Endogenous versus exogenous nutrient affects C, N, and P dynamics in decomposing litters in mid-subtropical forests of China. Ecol. Res. 27, 923–932 (2012).
CAS  Article  Google Scholar 

58.
Zhou, Z., Wang, C., Zheng, M., Jiang, L. & Luo, Y. Patterns and mechanisms of responses by soil microbial communities to nitrogen addition. Soil Biol. Biochem. 115, 433–441 (2017).
CAS  Article  Google Scholar 

59.
He, X. et al. Diversity and decomposition potential of endophytes in leaves of a Cinnamomum camphora plantation in China. Ecol. Res. 27, 273 (2011).
ADS  Article  Google Scholar 

60.
Berg, B. R. & Laskowski, R. Litter Decomposition: A Guide to Carbon and Nutrient Turnover, Advances in Ecological Research Vol. 38 (Academic Press, Waltham, 2006).
Google Scholar 

61.
Hall, S., Huang, W., Timokhin, V. & Hammel, K. Lignin lags, leads, or limits the decomposition of litter and soil organic carbon. Ecology 101, e03113 (2020).
PubMed  Article  Google Scholar 

62.
Tu, L. et al. Nitrogen addition significantly affects forest litter decomposition under high levels of ambient nitrogen deposition. PLoS ONE 9, e88752 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

63.
Zhou, X. & Zhang, Y. Temporal dynamics of soil oxidative enzyme activity across a simulated gradient of nitrogen deposition in the gurbantunggut desert, northwestern China. Geoderma 213, 261–267 (2014).
ADS  CAS  Article  Google Scholar 

64.
Hao, C. et al. Effects of experimental nitrogen and phosphorus addition on litter decomposition in an old-growth tropical forest. PLoS ONE 8, e84101 (2013).
ADS  Article  Google Scholar 

65.
Cameron, K. C., Di, H. J. & Moir, J. Nitrogen losses from the soil/plant system: a review. Ann. Appl. Biol. 162, 145–173 (2013).
CAS  Article  Google Scholar 

66.
Waldrop, M. P., Zak, D. R., Sinsabaugh, R. L., Gallo, M. & Lauber, C. Nitrogen deposition modifies soil carbon storage through changes in microbial enzymatic activity. Ecol. Appl. 14, 1172–1177 (2004).
Article  Google Scholar 

67.
Freedman, Z. B., Upchurch, R. A., Zak, D. R. & Cline, L. C. Anthropogenic N deposition slows decay by favoring bacterial metabolism: Insights from metagenomic analyses. Front. Microbiol. 7, 259 (2016).
PubMed  PubMed Central  Article  Google Scholar 

68.
Marklein, A. R. & Houlton, B. Z. Nitrogen inputs accelerate phosphorus cycling rates across a wide variety of terrestrial ecosystems. New Phytol. 193, 696–704 (2012).
CAS  PubMed  Article  Google Scholar 

69.
Weand, M. P., Arthur, M. A., Lovett, G. M., McCulley, R. L. & Weathers, K. C. Effects of tree species and N additions on forest floor microbial communities and extracellular enzyme activities. Soil Biol. Biochem. 42, 2161–2173 (2010).
CAS  Article  Google Scholar 

70.
Wang, C. et al. Response of litter decomposition and related soil enzyme activities to different forms of nitrogen fertilization in a subtropical forest. Ecol. Res. 26, 505–513 (2011).
CAS  Article  Google Scholar 

71.
Feng, H., Xue, L. & Chen, H. Responses of decomposition of green leaves and leaf litter to stand density, N and P additions in Acacia auriculaeformis stands. Eur. J. Forest Res. 137, 819 (2018).
Article  Google Scholar 

72.
Frey, S. D., Knorr, M., Parrent, J. L. & Simpson, R. T. Chronic nitrogen enrichment affects the structure and function of the soil microbial community in temperate hardwood and pine forests. For. Ecol. Manag. 196, 159–171 (2004).
Article  Google Scholar 

73.
Zheng, Z. et al. Effects of nutrient additions on litter decomposition regulated by phosphorus-induced changes in litter chemistry in a subtropical forest, China. For. Ecol. Manag. 400, 123–128 (2017).
Article  Google Scholar 

74.
Mo, J. et al. Nitrogen addition reduces soil respiration in a mature tropical forest in southern China. Glob. Change Biol. 14, 403–412 (2008).
ADS  Article  Google Scholar 

75.
Liu, G., Jiang, N. & Zhang, L. D. Soil Physical and Chemical Analysis and Description of Soil Profiles (Standards Press of China, Beijing, 1996).
Google Scholar 

76.
Bao, S. D. Soil and Agricultural Chemistry Analysis 3rd edn. (China Agricultural Press, Beijing, 2013).
Google Scholar 

77.
Allen, S. E. Chemical analysis of Ecological Materials, 2nd edn, Vol. 13 (Blackwell Scientific Publications, Oxford, 1989).
Google Scholar 

78.
Rowland, A. P. & Roberts, J. D. Lignin and cellulose fractionation in decomposition studies using acid-detergent fibre methods. Commun. Soil Sci. Plan. 25, 269–277 (1994).
CAS  Article  Google Scholar 

79.
Olson, J. Energy storage and the balance of producers and decomposers in ecological systems. Ecology 44, 322–331 (1963).
Article  Google Scholar 

80.
Bockheim, J., Jepsen, E. A. & Heisey, D. M. Nutrient dynamics in decomposing leaf litter of four tree species on a sandy soil in northwestern Wisconsin. Can. J. For. Res. 21, 803–812 (1991).
CAS  Article  Google Scholar  More

Our results are consistent with a non-industrial gut harboring a more resilient ecology with respect to SCFA production, while the industrial gut ecology would be vulnerable to disruption of such pathways, yet the pattern is complex and nuanced. The increased gene abundance in non-industrial populations and overall ratio of acetate:butyrate:propionate generally agrees with previous studies of SCFAs5,12. Similarly, the higher genus-level diversity of bacteria encoding acetate, compared to the other SCFAs, is expected and matches studies that have documented the taxa that encode different SCFAs13,17,19. The overall high richness, high diversity at Hill numbers 1 and 2, and high Gini-Simpson indices found in non-industrial populations at the genus level indicates a highly diverse and evenly distributed production of SCFAs. From an ecological perspective, uneven production of SCFA dominated by a few bacteria in industrial gut microbiomes means lower functional diversity and less redundancy, which ultimately leads to an expectation of decreased resilience. In other words, this study finds that industrial gut microbiomes are at a higher risk of reduced SCFA production because SCFA synthesis is dominated by only a few genera. Given the lower resilience, factors that disrupt the gut ecology are expected to have a more extreme consequence to those living an industrial lifestyle.
While there is an overall trend of increased genus-level functional diversity and redundancy for SCFA production in non-industrial populations, variation exists when examining the SCFAs and populations individually. At the genus-level, the pastoral and rural agricultural populations have increased richness of genera encoding genes involved in acetate and butyrate synthesis, while there is similarity across the different lifestyles for genus richness for propionate encoding taxa. Although hunter-gatherers have similar, or lower, genus richness as industrial populations, they have significantly higher diversity at Hill number orders 1 and 2 and Gini-Simpson indices for butyrate and propionate. Additionally, the pastoralists have a generally similar profile to the industrial populations for acetate and propionate Hill number diversity, as well as similarity to the industrial populations in species PD, which may be linked to this pastoralist group having a diet similar to some industrial populations; namely, a diet high in dairy and red meat consumption, coupled with few dietary sources of plant-derived fibers23. This paints a complex picture. Non-industrial populations have a high diversity of genera encoding butyrate synthesis, and butyrate production is spread more evenly across genera in non-industrial populations than in industrial populations. Hunter-gatherers and rural agriculturalists have significantly greater evenness of propionate production, even though they have fewer number of total genera encoding this SCFA. Finally, the richness and evenness of genera encoding acetate is similar between industrial and non-industrial populations. Ecologically, we would expect the industrial populations to be less resilient for production of butyrate and propionate when faced with a shift in taxonomic composition, while non-industrial populations may be only marginally more resilient for acetate production compared to industrial populations. Intriguingly, SCFA relative abundance does not appear to correlate to resilience profile. Acetate and butyrate are significantly more abundant in non-industrial populations but only butyrate shows much stronger resilience profile for non-industrial populations. Additionally, propionate is slightly more abundant in industrial populations, although not significantly, yet our results indicate greater resilience in non-industrial groups for propionate production. This indicates that measuring only total gene, and/or molar, abundance is not enough to make statements about metabolic processes in the human microbiome; rather, ecological approaches are necessary to understand diversity in functional potential of the human microbiome.
The increased species-level alpha diversity in industrial populations initially runs counter to the genus-level results but the genus and species level results ultimately yield similar interpretations after accounting for ecology and ascertainment bias, as discussed below. The substantially higher species richness in industrial populations is striking; however, the differences in PD between industrial and non-industrial populations are not nearly as extreme. This means that the high species richness in the industrial populations is driven by species that are closely phylogenetically related. Indeed, we observed SCFA producing genera found at high abundance in industrial populations (Bacteroides and Clostridium) to have up to nine species encoding SCFAs, while highly abundant non-industrial genera only have one or two species. Therefore, what first appears to indicate high species-level ecological resilience in SCFA production in the industrial populations is actually the result of closely related species performing the same function. It follows that closely related species may be prone to changes in abundance or even elimination after certain types of ecosystem shift events. For example, narrow-spectrum antibiotics33 and exposure to various xenobiotic compounds that lead to variable bacterial metabolic responses34 are events that can affect a limited range of bacteria and lead to shifts in microbial abundance and metabolic activity. While this result has ecological implications, it is also likely the result of historical trends of microbiology research. Bacterial taxa at high abundance in non-industrial gut microbiomes have not been a focus of microbiological isolation and species identification until recently; therefore, we expect more species to be identified from non-industrial gut microbiomes in the future35. Additionally, classification of bacteria into distinct genera and species is undergoing a revolution in the genomic era36 meaning that the high number of species classified to Bacteroides and Clostridium may ultimately be reclassified to different genera. Nevertheless, the fact that we observe a large jump in species richness, but only a minor increase in species PD, in the industrial gut microbiomes suggests that the high industrial species richness is driven by closely related species and therefore, results in the same interpretation as the genus richness results: diversity is high in non-industrial populations.
Ascertainment bias extends to the databases used to identify taxa and genes: fewer genes were identified in non-industrial populations and a smaller proportion of these genes can be linked back to bacteria at every taxonomic level, in non-industrial gut microbiomes. In some cases, such as butyrate synthesis genes, less than 10% of genes are identified to species for non-industrial populations, while over 50% of such identifications were possible for industrial populations. A decreased ability to identify the genus and species encoding SCFA synthesis genes in non-industrial populations means that the ecological metrics underestimate the true ecological diversity of these genes. Moreover, the drop-off in classification from the genus to the species level was significantly greater in non-industrial populations compared to industrial populations. This drop-off means a much lesser ability to identify species compared to genera in non-industrial populations, which helps explain why species diversity was substantially lower in non-industrial populations. Nevertheless, the statistically significant differences observed at the genus-level send a strong signal of the high functional diversity, and potential resilience, of SCFA synthesis genes in non-industrial gut microbiomes.
The metagenome-wide poor performance in terms of gene identification and classifying SCFA genes to genera and species indicates a bias in reference databases that underrepresents diversity in non-industrial gut microbiomes, which is unsurprising. Bias is expected because the vast majority of human gut microbiome studies have used samples from industrial populations. There is an immense challenge in including non-industrial communities in biomedical research, including recruiting research participants, sustaining longitudinal sampling, building culturally appropriate community relationships, and even securing transport of samples35. This has resulted in comparatively few metagenomic studies of human gut microbiomes from non-industrial settings35. Nevertheless, our data demonstrate the extent of this bias and how it can hinder more in-depth study of human gut microbiome health. Given this sizable ascertainment bias favored industrial populations, the non-industrial populations are likely even more diverse, more resilient, than our databases can sufficiently characterize, making our genus-level results even stronger. Without a serious investment to include such populations, the characterization of microbiomes will remain naive to the ecological breadth of the core, healthy, human gut. Imagine studying forest ecology, with only city parks at your disposal. This has been, overwhelmingly, the analogous practice of human microbiome research.
The relative lack of microbiome studies with non-industrial populations means an underrepresentation of not only metagenomic data and genome annotation but also fewer opportunities for cultivation and validation of novel species of bacteria. This ultimately leads to an inequality in the depth to which researchers can describe microbiome samples from non-industrial communities, compared to industrial microbiomes, as diverse groups of novel taxa may be grouped into a single group of “unknown” or “unclassified” bacteria35. Similarly, an incomplete picture of microbial functional potential means that genes may be misidentified or even unannotated completely. Unknown taxa and misidentified genes may be playing key roles in ecological and metabolic processes but researchers are unable to confidently identify them, let alone make statements about their importance in a microbial ecology35. Recent human gut microbiome metagenome studies from diverse populations will undoubtedly improve database representation but the number of studies and metagenomic samples from non-industrial populations still pales in comparison to industrial gut microbiomes26,35,37,38.
Limitations in annotating the full extent of microbial diversity impacts health research. Recently proposed ‘Microbiota Insufficiency Syndrome (MIS)’2 postulates that, while the microbiome has adapted to industrialization, these adaptations are maladaptive to human health. The decreased phylogenetic diversity and loss of specific taxa (e.g. Prevotellaceae, Succinivibrionaceae, and Spirochaetaceae) observed in industrial gut microbiomes may contribute to the increase in non-communicable chronic diseases found at higher prevalence in industrial populations. The root cause of MIS in industrial populations is undoubtedly multifactorial; however, diet is suggested to play a major role2. This syndrome is compelling and we postulate that this insufficiency precisely rests on the stability of functional capacity. Our findings of decreased resilience in industrial populations, as well as species-level diversity driven by a few closely related species, fits in well with MIS. Low resilience in SCFA production may ultimately manifest itself as altered colonocyte function and/or autoimmune disruptions (both symptoms of MIS) due to a decrease in SCFA bioavailability after a group SCFA-producing bacteria were wiped-out during an ecological shift, such as antibiotic or xenobiotic exposure. Similar to MIS, diet is likely to play an important role in SCFA resilience. The non-industrial populations studied in this paper consume much more fiber than industrial populations, on average3,5,14,25,26, and microbial fermentation of dietary fibers is a major source of SCFAs in the human digestive tract39. A diet poor in dietary fiber means less substrate for microbial fermentation and therefore less SCFA production and also higher competition for that fiber, potentially resulting in competitive exclusion and less microbial diversity. Nevertheless, if we are unable to fully characterize and annotate non-industrial gut microbiomes then we will be unable to paint a complete picture of MIS. Currently, we have confidence that there is a wealth of undiscovered resilience in non-industrial gut microbiomes. Once we describe the extent of this diversity/resilience, through increased sampling and focus on partnerships with research institutes in industrializing countries, we will have a more complete picture of MIS and possibly develop therapeutic approaches to combat non-communicable chronic diseases related to the human gut microbiome.
Improved sampling, metabolic profiling, and annotation will not only improve our understanding of SCFA resilience, but it will also permit more detailed picture microbiome wide resilience. Our work shows the value of focusing on specific SCFA genes, due to their importance in human biology and previously reported variation in SCFA molar abundance between industrial and non-industrial populations31,32; however, future work will undoubtedly add to our findings. One avenue for future work is through analyzing SCFA molar concentrations in fecal samples in a longitudinal setting and comparing these results to predicted SCFA resilience from metagenome panels. Unlike genomic data, where we can infer about SCFA production potential via taxonomic diversity, one-time measures of fecal SCFA molar concentrations will not inform about future resilience because SCFA molar concentrations carry no information about which taxa produce each SCFA. Longitudinal SCFA concentration and metagenomic data from non-industrial populations, or animal models, is necessary to inform about SCFA resilience and production in diverse lifestyles. Another avenue for future work is to focus resilience analysis on other microbiome functions of interest, such as resilience of antibiotic resistance genes and amino acid biosynthetic pathways. These valuable studies would be valuable for comparing microbiome resilience dynamics for different functions, with the caveat that there is sufficient genomic annotation data to yield interpretable results.
Lack of sample diversity is not unique to human microbiome research, as human genetics research has been grappling with this very issue for decades. In 2009, 96% of individuals included in human genome-wide association studies (GWAS) claimed European ancestry, as compared to 78% in 201940. Thus, while there have been improvements, GWAS clearly fail to reflect the breadth of human diversity. Incorporating diverse populations in human genome and microbiome research has the potential to greatly benefit the scientific community’s understanding of human biology and develop treatments that are based on human diversity rather than European-ancestry genetics and microbiomes. A key component of increasing representation in genetics and microbiome studies is that these studies are designed as partnerships with minority and/or indigenous communities in a manner that builds both trust between the community and researchers, as well as facilitates the ability for the sample donors to exercise their rights on how data are treated and shared41. More

1.
Gruber, N. et al. The oceanic sink for anthropogenic CO2 from 1994 to 2007. Science 363, 1193–1199 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).
ADS  Article  Google Scholar 

3.
Caldeira, K. & Wickett, M. E. Anthropogenic carbon and ocean pH. Nature 425, 365 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Feely, R. A. et al. Impact of anthropogenic CO2 on the CaCO3 system in the oceans. Science 305, 362–366 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

5.
Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: The other CO2 problem. Ann. Rev. Mar. Sci. 1, 169–192 (2009).
PubMed  Article  PubMed Central  Google Scholar 

6.
Riebesell, U. et al. Reduced calcification of marine plankton in response to increased atmospheric CO2. Nature 407, 364–367 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681–686 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Gazeau, F. et al. Impacts of ocean acidification on marine shelled molluscs. Mar. Biol. 160, 2207–2245 (2013).
CAS  Article  Google Scholar 

9.
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).
ADS  Article  Google Scholar 

10.
Waldbusser, G. G. et al. Saturation-state sensitivity of marine bivalve larvae to ocean acidification. Nat. Clim. Change 5, 273–280 (2015).
ADS  CAS  Article  Google Scholar 

11.
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Moy, A. D., Howard, W. R., Bray, S. G. & Trull, T. W. Reduced calcification in modern Southern Ocean planktonic foraminifera. Nat. Geosci. 2, 276–280 (2009).
ADS  CAS  Article  Google Scholar 

13.
Bednaršek, N. et al. Extensive dissolution of live pteropods in the Southern Ocean. Nat. Geosci. 5, 881–885 (2012).
ADS  Article  CAS  Google Scholar 

14.
Bednaršek, N. et al. Limacina helicina shell dissolution as an indicator of declining habitat suitability owing to ocean acidification in the California Current Ecosystem. Proc. R. Soc. B Biol. Sci. 281, 20140123 (2014).
Article  CAS  Google Scholar 

15.
Manno, C. et al. Shelled pteropods in peril: Assessing vulnerability in a high CO2 ocean. Earth-Sci. Rev. 169, 132–145 (2017).
ADS  CAS  Article  Google Scholar 

16.
Lischka, S., Büdenbender, J., Boxhammer, T. & Riebesell, U. Impact of ocean acidification and elevated temperatures on early juveniles of the polar shelled pteropod Limacina helicina: Mortality, shell degradation, and shell growth. Biogeosciences 8, 919–932 (2011).
ADS  CAS  Article  Google Scholar 

17.
Bednaršek, N. et al. Exposure history determines pteropod vulnerability to ocean acidification along the US West Coast. Sci. Rep. 7, 1–12 (2017).
Article  CAS  Google Scholar 

18.
Comeau, S. et al. Impact of aragonite saturation state changes on migratory pteropods. Proc. R. Soc. B Biol. Sci. 279, 732–738 (2011).
Article  Google Scholar 

19.
Moya, A. et al. Near-future pH conditions severely impact calcification, metabolism and the nervous system in the pteropod Heliconoides inflatus. Glob. Change Biol. 22, 3888–3900 (2016).
ADS  Article  Google Scholar 

20.
Maas, A., Lawson, G. L., Bergan, A. J. & Tarrant, A. M. Exposure to CO2 influences metabolism, calcification and gene expression of the thecosome pteropod Limacina retroversa. J. Exp. Biol. 221, 164400 (2018).
Article  Google Scholar 

21.
Johnson, K. M. & Hofman, G. E. A transcriptome resource for the Antarctic pteropod Limacina helicina antarctica. Mar. Genom. 28, 25–28 (2016).
Article  Google Scholar 

22.
Feely, R. A. et al. Chemical and biological impacts of ocean acidification along the west coast of North America. Estuar. Coast. Shelf Sci. 183, 260–270 (2016).
ADS  CAS  Article  Google Scholar 

23.
Bednaršek, N. et al. El Niño-related thermal stress coupled with ocean acidification negatively impacts cellular to population-level responses in pteropods along the California Current System with implications for increased bioenergetic costs. Front. Mar. Sci. 5, 486 (2018).
Article  Google Scholar 

24.
Peck, V. L., Tarling, G. A., Manno, C., Harper, E. M. & Tynan, E. Outer organic layer and internal repair mechanism protects pteropod Limacina helicina from ocean acidification. Deep-Sea Res. II 127, 53–56 (2016).
Article  Google Scholar 

25.
Peck, V. L., Oakes, R. L., Harper, E. M., Manno, C. & Tarling, G. A. Pteropods counter mechanical damage and dissolution through extensive shell repair. Nat. Commun. 9, 264 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Howes, E. L., Eagle, R. A., Gattuso, J.-P. & Bijma, J. Comparison of Mediterranean pteropod shell biometrics and ultrastructure from historical (1910 and 1921) and present day (2012) samples provides baseline for monitoring effects of global change. PLoS ONE 1, 1–23 (2017).
Google Scholar 

27.
Oakes, R. L. & Sessa, J. A. Determining how biotic and abiotic variables affect the shell condition and parameters of Heliconoides inflatus pteropods from a sediment trap in the Cariaco Basin. Biogeosciences 7, 1975–1990 (2020).
ADS  Article  Google Scholar 

28.
Feely, R. A., Sabine, C. L., Hernandez-Ayon, J. M., Ianson, D. & Hales, B. Evidence for upwelling of corrosive “acidified” water onto the continental shelf. Science 320, 1490–1492 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Alin, S. R., et al. Dissolved inorganic carbon, total alkalinity, pH on total scale, and other variables collected from profile and discrete sample observations using CTD, Niskin bottle, and other instruments from NOAA Ship Ronald H. Brown in the U.S. West Coast California Current System from 2016-05-08 to 2016-06-06 (NCEI Accession 0169412). Version 1.1. NOAA National Centers for Environmental Information dataset (2017). https://doi.org/10.7289/V5V40SHG.

30.
Northcott, D. et al. Impacts of urban carbon dioxide emissions on sea-air flux and ocean acidification in nearshore waters. PLoS ONE 14, e0214403 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Wang, K., Hunt, B. P. V., Liang, C., Pauly, D. & Pakhomov, E. A. Reassessment of the life cycle of the pteropod Limacina helicina from a high resolution interannual time series in the temperate North Pacific. ICES J. Mar. Sci. 74, 1906–1920 (2017).
Article  Google Scholar 

32.
Shimizu, K. et al. Phylogeography of the pelagic snail Limacina helicina (Gastropoda: Thecosomata) in the subarctic western North Pacific. J. Mollus. Stud. 84, 30–37 (2017).
Article  Google Scholar 

33.
Sromek, L., Lasota, R. & Wolowicz, M. Impact of glaciations on genetic diversity of pelagic mollusks: Antarctic Limacina Antarctica and Arctic Limacina helicina. Mar. Ecol. Prog. Ser. 525, 143–152 (2015).
ADS  Article  Google Scholar 

34.
Hunt, B. et al. Poles apart: the ‘bipolar’ pteropod species Limacina helicina is genetically distinct between the Arctic and Antarctic Oceans. PLoS ONE 5, e9835 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Bednaršek, N. et al. Systematic review and meta-analysis towards synthesis of thresholds of ocean acidification impacts on calcifying pteropods and interactions with warming. Front. Mar. Sci. 6, 227 (2019).
Article  Google Scholar 

36.
Vaquer-Sunyer, R. & Duarte, C. M. Thresholds of hypoxia for marine biodiversity. Proc. Natl. Acad. Sci. 105, 15452–15457 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Legaard, K. R. & Thomas, A. C. Spatial patterns in seasonal and interannual variability of chlorophyll and sea surface temperature in the California Current. J. Geophys. Res. 111, C06032 (2006).
ADS  Article  Google Scholar 

38.
Thomsen, J., Casties, I., Pansch, C., Körtzinger, A. & Melzner, F. Food availability outweighs ocean acidification effects in juvenile Mytilus edulis: Laboratory and field experiments. Glob. Change Biol. 19, 1017–1027 (2013).
ADS  Article  Google Scholar 

39.
Maas, A. E., Elder, L. E., Dierssen, H. M. & Seibel, B. A. Metabolic response of Antarctic pteropods (Mollusca: Gastropoda) to food deprivation and regional productivity. Mar. Ecol. Prog. Ser. 441, 129–139 (2011).
ADS  CAS  Article  Google Scholar 

40.
Ramajo, L. et al. Food supply confers calcifiers resistance to ocean acidification. Sci. Rep. 6, 1–6 (2016).
Article  CAS  Google Scholar 

41.
Thomas, A. C. & Strub, P. T. Interannual variability in phytoplankton pigment distribution during the spring transition along the west-coast of North America. J. Geophys. Res. 94, 18095–18117 (1989).
ADS  CAS  Article  Google Scholar 

42.
Bednaršek, N. & Ohman, M. D. Changes in pteropod vertical distribution, abundance and species richness in the California Current System due to ocean acidification. Mar. Ecol. Prog. Ser. 523, 93–103 (2015).
ADS  Article  CAS  Google Scholar 

43.
Lalli, C. M. & Gilmer, R. W. Pelagic Snails: The Biology of Holoplanktonic Gastropod Mollusks (Stanford University Press, Stanford, 1989).
Google Scholar 

44.
Seibel, B. A., Dymowska, A. & Rosenthal, J. Metabolic temperature compensation and coevolution of locomotory performance in pteropod molluscs. Integr. Comp. Biol. 47, 880–891 (2007).
PubMed  Article  PubMed Central  Google Scholar 

45.
Checa, A. G. Physical and biological determinants of the fabrication of molluscan shell microstructures. Front. Mar. Sci. 5, 535 (2018).
Article  Google Scholar 

46.
Marin, F., Le Roy, N. & Marie, B. The formation and mineralization of mollusk shell. Front. Biosci. 4, 1099–1125 (2012).
Article  Google Scholar 

47.
Kroeker, K. J., Kordas, R. L. & Harley, C. D. G. Embracing interactions in ocean acidification research: Confronting multiple stressor scenarios and context dependence. Biol. Lett. 13, 20160802 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Gruber, N. et al. Rapid progression of ocean acidification in the California Current System. Science 337, 220–223 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Buitenhuis, E. T., Le Quéré, C., Bednaršek, N. & Schiebel, R. Large contribution of pteropods to shallow CaCO3 export. Glob. Biogeochem. Cycles 33, 458–468 (2019).
ADS  CAS  Article  Google Scholar 

50.
Mackas, D. L. & Galbraith, M. D. Pteropod time-series from the NE Pacific. ICES J. Mar. Sci. 69, 448–459 (2012).
Article  Google Scholar 

51.
Lueker, T. J., Dickson, A. G. & Keeling, C. D. Ocean pCO2 calculated from dissolved inorganic carbon, alkalinity, and equations for K1 and K2: Validation based on laboratory measurements of CO2 in gas and seawater at equilibrium. Mar. Chem. 70, 105–119 (2000).
CAS  Article  Google Scholar 

52.
Kerney, M. P. & Cameron, R. A. D. A Field Guide to the Land Snails of Britain and North-West Europe (Collins, London, 1979).
Google Scholar 

53.
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 

54.
Oksanen, J., et al. Vegan: Community Ecology Package. R package version 2.5-2 (2018).

55.
Wall-Palmer, D. et al. Biogeography and genetic diversity of the atlantid heteropods. Progr. Oceanogr. 160, 1–25 (2018).
ADS  Article  Google Scholar 

56.
Excoffier, L. & Lischer, H. E. L. Arlequin suite version 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).
PubMed  Article  Google Scholar 

57.
Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).
CAS  PubMed  Article  Google Scholar 

58.
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–2187 (2016).
CAS  PubMed  Article  Google Scholar 

59.
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar  More

1.
Molinos, J. G. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–87 (2016).
Google Scholar 
2.
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
PubMed  Article  CAS  Google Scholar 

3.
Brook, B. W. & Alroy, J. Pattern, Process, Inference and Prediction in Extinction Biology (Royal Society, 2017).

4.
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).
Article  CAS  Google Scholar 

5.
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).
Google Scholar 

6.
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).
CAS  Article  Google Scholar 

7.
Collins, K. S., Edie, S. M., Hunt, G., Roy, K. & Jablonski, D. Extinction risk in extant marine species integrating palaeontological and biodistributional data. Proc. Biol. Sci. 285, 20181698 (2018).
PubMed  PubMed Central  Google Scholar 

8.
Finnegan, S. et al. Paleontological baselines for evaluating extinction risk in the modern oceans. Science 348, 567–570 (2015).
CAS  PubMed  Article  Google Scholar 

9.
van Woesik, R. et al. Hosts of the Plio-Pleistocene past reflect modern-day coral vulnerability. Proc. R. Soc. B 279, 2448–2456 (2012).
PubMed  Article  Google Scholar 

10.
Harnik, P. G. et al. Extinctions in ancient and modern seas. Trends Ecol. Evol. 27, 608–617 (2012).
PubMed  Article  Google Scholar 

11.
Kiessling, W. & Kocsis, Á. T. Adding fossil occupancy trajectories to the assessment of modern extinction risk. Biol. Lett. 12, 20150813 (2016).
PubMed  PubMed Central  Article  Google Scholar 

12.
Calosi, P., Putnam, H. M., Twitchett, R. J. & Vermandele, F. Marine metazoan modern mass extinction: improving predictions by integrating fossil, modern, and physiological data. Annu. Rev. Mar. Sci. 11, 369–390 (2019).
Article  Google Scholar 

13.
Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).
Article  Google Scholar 

14.
Beaugrand, G. Theoretical basis for predicting climate-induced abrupt shifts in the oceans. Philos. Trans. R. Soc. B 370, 20130264 (2015).
Article  Google Scholar 

15.
Lord, J. P., Barry, J. P. & Graves, D. Impact of climate change on direct and indirect species interactions. Mar. Ecol. Prog. Ser. 571, 1–11 (2017).
Article  Google Scholar 

16.
Bond, D. P. G. & Grasby, S. E. On the causes of mass extinctions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 478, 3–29 (2017).
Article  Google Scholar 

17.
Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Temperature-dependent hypoxia explains biogeography and severity of end-Permian marine mass extinction. Science 362, eaat1327 (2018).
PubMed  Article  CAS  Google Scholar 

18.
Reddin, C. J., Nätscher, P. S., Kocsis, Á. T., Pörtner, H.-O. & Kiessling, W. Marine clade sensitivities to climate change conform across timescales. Nat. Clim. Change 10, 249–253 (2020).
Article  Google Scholar 

19.
Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).
PubMed  PubMed Central  Article  Google Scholar 

20.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach 2nd edn (Springer, 2010).

21.
McKinney, M. L. Extinction vulnerability and selectivity: combining ecological and paleontological views. Annu. Rev. Ecol. Syst. 28, 495–516 (1997).
Article  Google Scholar 

22.
Foote, M., Crampton, J. S., Beu, A. G. & Cooper, R. A. On the bidirectional relationship between geographic range and taxonomic duration. Paleobiology 34, 421–433 (2008).
Article  Google Scholar 

23.
Payne, J. L., Truebe, S., Nützel, A. & Chang, E. T. Local and global abundance associated with extinction risk in late Paleozoic and early Mesozoic gastropods. Paleobiology 37, 616–632 (2011).
Article  Google Scholar 

24.
Harnik, P. G. Direct and indirect effects of biological factors on extinction risk in fossil bivalves. Proc. Natl Acad. Sci. USA 108, 13594–13599 (2011).
CAS  PubMed  Article  Google Scholar 

25.
Harnik, P. G., Simpson, C. & Payne, J. L. Long-term differences in extinction risk among the seven forms of rarity. Proc. R. Soc. B 279, 4969–4976 (2012).
PubMed  Article  Google Scholar 

26.
Hopkins, M. J., Simpson, C. & Kiessling, W. Differential niche dynamics among major marine invertebrate clades. Ecol. Lett. 17, 314–323 (2014).
PubMed  Article  Google Scholar 

27.
Svenning, J. ‐C. & Skov, F. Limited filling of the potential range in European tree species. Ecol. Lett. 7, 565–573 (2004).
Article  Google Scholar 

28.
Normand, S. et al. Postglacial migration supplements climate in determining plant species ranges in Europe. Proc. R. Soc. B 278, 3644–3653 (2011).
PubMed  Article  Google Scholar 

29.
Stigall, A. L. When and how do species achieve niche stability over long time scales? Ecography 37, 1123–1132 (2014).
Google Scholar 

30.
Steinbauer, M. J. et al. Biogeographic ranges do not support niche theory in radiating Canary Island plant clades. Glob. Ecol. Biogeogr. 25, 792–804 (2016).
Article  Google Scholar 

31.
Foster, G. L., Hull, P., Lunt, D. J. & Zachos, J. C. Placing our Current ‘Hyperthermal’ in the Context of Rapid Climate Change in our Geological Past (The Royal Society Publishing, 2018).

32.
Leckie, R. M., Bralower, T. J. & Cashman, R. Oceanic anoxic events and plankton evolution: biotic response to tectonic forcing during the mid‐Cretaceous. Paleoceanography 17, 13-1–13-29 (2002).
Article  Google Scholar 

33.
Coxall, H. K. & Pearson, P. N. in Deep Time Perspectives on Climate Change: Marrying the Signal From Computer Models and Biological Proxies Vol. 2 (eds Williams, M. et al.) 351–387 (Geological Society of London, 2007).

34.
Ritterbush, K. A. & Foote, M. Association between geographic range and initial survival of Mesozoic marine animal genera: circumventing the confounding effects of temporal and taxonomic heterogeneity. Paleobiology 43, 209–223 (2017).
Article  Google Scholar 

35.
Stigall, A. L. Analysing links between biogeography, niche stability and speciation: the impact of complex feedbacks on macroevolutionary patterns. Palaeontology 56, 1225–1238 (2013).
Article  Google Scholar 

36.
BouDagher-Fadel, M. K. Evolution and Geological Significance of Larger Benthic Foraminifera (UCL Press, 2018).

37.
Reddin, C. J., Kocsis, Á. T. & Kiessling, W. Marine invertebrate migrations trace climate change over 450 million years. Glob. Ecol. Biogeogr. 27, 704–713 (2018).
Article  Google Scholar 

38.
Valentine, J. W. Temporal bias in extinctions among taxonomic categories. J. Paleontol. 48, 549–552 (1974).

39.
Kocsis, Á. T., Reddin, C. J., Alroy, J. & Kiessling, W. The r package divDyn for quantifying diversity dynamics using fossil sampling data. Methods Ecol. Evol. 10, 735–743 (2019).
Article  Google Scholar 

40.
Gradstein, F. M., Ogg, J. G., Schmitz, M. & Ogg, G. The Geologic Time Scale 2012 (Elsevier, 2012).

41.
Foote, M. Origination and extinction components of taxonomic diversity: general problems. Paleobiology 26, 74–102 (2000).
Article  Google Scholar 

42.
Veizer, J. & Prokoph, A. Temperatures and oxygen isotopic composition of Phanerozoic oceans. Earth Sci. Rev. 146, 92–104 (2015).
CAS  Article  Google Scholar 

43.
Song, H., Wignall, P. B., Song, H., Dai, X. & Chu, D. Seawater temperature and dissolved oxygen over the past 500 million years. J. Earth Sci. 30, 236–243 (2019).
Article  CAS  Google Scholar 

44.
Grossman, E. L. Applying oxygen isotope paleothermometry in deep time. Paleontol. Soc. Pap. 18, 39–68 (2012).
Article  Google Scholar 

45.
Henkes, G. A. et al. Temperature evolution and the oxygen isotope composition of Phanerozoic oceans from carbonate clumped isotope thermometry. Earth Planet. Sci. Lett. 490, 40–50 (2018).
CAS  Article  Google Scholar 

46.
Ryb, U. & Eiler, J. M. Oxygen isotope composition of the Phanerozoic ocean and a possible solution to the dolomite problem. Proc. Natl Acad. Sci. USA 115, 6602–6607 (2018).
CAS  PubMed  Article  Google Scholar 

47.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

48.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. https://doi.org/10.18637/jss.v067.i01 (2015).

49.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

50.
Quené, H. & van den Bergh, H. Examples of mixed-effects modeling with crossed random effects and with binomial data. J. Mem. Lang. 59, 413–425 (2008).
Article  Google Scholar 

51.
Malik, W. A., Marco-Llorca, C., Berendzen, K. & Piepho, H.-P. Choice of link and variance function for generalized linear mixed models: a case study with binomial response in proteomics. Commun. Stat. Theory Methods 49, 1–20 (2019).

52.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Science & Business Media, 2009).

53.
Fox, G. A., Negrete-Yankelevich, S. & Sosa, V. J. Ecological Statistics: Contemporary Theory and Application (Oxford Univ. Press, 2015).

54.
Lieberman, M. D. & Cunningham, W. A. Type I and Type II error concerns in fMRI research: re-balancing the scale. Soc. Cogn. Affect. Neurosci. 4, 423–428 (2009).
PubMed  PubMed Central  Article  Google Scholar 

55.
Durbin, J. & Watson, G. S. Testing for serial correlation in least squares regression. Biometrika 58, 1–19 (1971).
Google Scholar 

56.
Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).
PubMed  PubMed Central  Article  Google Scholar 

57.
IPCC. Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018). More

This is the first large scale analysis of feline desexing practices in Australia using outcomes documented in the patient medical record. The findings complement those of previous studies that used survey data to analyse the attitudes and opinions of veterinary professionals and owners to desexing31,34,38. The prevalence of desexing among cats in Australia, found to be 83.6%, confirms that desexing rates in Australia are among the highest reported internationally. Survey-based studies have reported that approximately 90% of cats in Australia are desexed, compared with 80% in the USA, and 43% in Italy39,40,41,42,43,44,45. A recent EPR-based study conducted in the UK reported the prevalence of feline desexing as 77%46. While population-wide analyses of desexing status provide a useful snapshot of practices in a region, most do not consider reproductive history.
A clear shift over time towards desexing cats at a younger age was evident here. EAD was 1.76 times more likely to have been carried out among desexed cats born between 2010 to 2017, than in those born between 1995 and 2009. This move towards earlier desexing was apparent in all age groups studied. Despite this trend, EAD had been carried out in only 21.5% of desexed females in the recent period. In fact, only 59.8% of females had been desexed by 6 months of age, which is the traditional recommendation and the most common recommendation reported by vets in Australia31,47. Despite a move towards earlier desexing, opportunities to control reproduction by prepubertal desexing are still being lost.
For an individual female cat, desexing at 6 months or later may be of little consequence, since they may not yet have reached puberty or had access to a mate. A recent survey of cat owners in Australia and New Zealand however found that 66% of cats had outdoor access45. From a population control perspective, eliminating the possibility of pregnancy by adopting EAD as standard has merit. The body of scientific evidence generated specifically to address the short-term and long-term safety of EAD overwhelmingly validates this practice4,17,22,23,24,25,26,27,28,29.
The impact that tighter control of reproduction among owned cats would have on shelter and stray populations is not yet clear. Populations of owned cats (completely reliant on humans) feral cats (living independently of humans) and stray cats (intermediate relationship with humans) do not exist in isolation48. Anthropogenic factors, including the provision of food, abandonment, and failure to curb reproduction, influence cat abundance and movement through these populations. Modelling population dynamics in owned, unowned (stray and feral) and shelter-housed cats holds promise to inform cat management strategies in the future49.
In multivariable models, for cats born 2010–2017, sex, breed, state and socioeconomic indices were all significantly associated with both desexing status and age at surgery. Females were less likely than males to be desexed and, among desexed cats, females were less likely than males to have been desexed at ≤ 4 months, supporting future measures to promote EAD in female cats. The reasons for this difference were not investigated but, conceivably, it may be due to higher fees for desexing females at some practices, or a greater awareness of spraying and roaming behaviours in males than pregancies in young female cats.
Not surprisingly purebred cats were less likely to be desexed than mixed breeds. In contrast, the finding that purebred cats were 2.7 times less likely to undergo EAD was unexpected because breeders commonly request EAD so that progeny for the pet market can be sold without delay50. It is plausible that this result reflects a greater demand in Australia for EAD from the charity and shelter sector, where mixed breed cats predominate, than from breeders. In line with this possibility, recent surveys found 70–80% of cats in Australia and New Zealand are of mixed breed and acquired from shelters, veterinary clinics, friends and as strays40,45,51. The higher odds of EAD in males than females was even greater among purebred cats, a result that may have been influenced by the practice of retaining more entire females than males for breeding.
The breeding season in Australia and New Zealand extends year round with peaks of kittening in spring and summer inferred from shelter admissions9,52,53. Cats born in winter had the lowest odds of being desexed in each age group. One explanation for this finding is that promotion of desexing by veterinary practices and welfare groups is less likely in winter because fewer kittens are born. This seasonal difference is certainly seen in the UK, where the RSPCA conducts desexing campaigns in Autumn to prevent the peak of spring litters54.
State or territory influenced both whether a cat was desexed, and the odds of EAD. Compared with cats in New South Wales (NSW), those in Victoria (VIC) and South Australia (SA) were more likely, and those in Queensland (QLD) less likely to be desexed. Again, compared with NSW, the odds of being desexed at ≤ 4 months were 1.45 greater for cats in VIC and 1.5–2.3 times less for those in QLD, SA and ACT. Desexing is handled inconsistently between Australia’s states and territories. Mandatory desexing legislation exists in ACT (by 3 months of age) and in SA, Tasmania (TAS), WA (by 6 months of age), with some exceptions. No legal requirement to desex cats exists at state level in VIC, QLD, NSW or Northern Territory (NT), although desexing is indirectly incentivized in NSW (by 6 months of age) and VIC (by 3 months of age) where registration is mandatory, and reduced registration fees are applied for desexed cats. No consistent relationship between our findings and state legislation related to desexing cats was identified. In fact, in ACT, where desexing of pet cats at 3 months of age has been a legal requirement since 2007, the second lowest odds of EAD were identified. Most veterinarians practicing in ACT (90%), surveyed 10 years after the legislation was introduced, gave recommendations inconsistent with the legislation and 35% were unaware that desexing by 3 months was mandatory in the ACT34. Whether and how legislation might be an appropriate tool to influence reproduction in owned cats and, indirectly, overpopulation should be further investigated.
Socioeconomic conditions influenced both whether a cat was desexed or not, and the age at desexing. Entire cats were more common in remote, low income and disadvantaged areas. This finding is concerning, given that outdoor access was more likely in non-urban than urban areas in a study of households in Australia and New Zealand45, implying more opportunity to find a mate. In addition, stray cat density correlated positively with socioeconomic deprivation in a New Zealand-based study employing geographically weighted regression analyses55. Together, these findings support the promotion of desexing campaigns in non-urban areas.
Economic indicators such as household income influenced whether a cat was desexed; the odds of being desexed were around 1.2 times greater in the highest compared to the lowest income areas. A similar, but more dramatic effect was reported in a study conducted in the USA where the prevalence of desexing increased from 51.4% to 96.2% as household income increased43. Among desexed cats, EAD was least likely in low income areas, but highest in the most socio-economically disadvantaged areas. Although this might seem paradoxical, IRSD is based on broader indicators of disadvantage than income alone. A UK study, similarly, identified that EAD was most likely in the most deprived regions, and that chances of being desexed by 6 months were more likely in higher income areas56. Possible explanations for these observations include the preferential targeting of areas of greatest disadvantage, rather than those with fewer economic resources, by discount desexing programs promoting EAD, or preferential sourcing of kittens in disadvantaged areas from organizations that routinely practice EAD, such as shelters.
There are limitations to our study that should be considered when interpreting the results. Cats that were either not registered with a veterinary practice, or were registered with a practice that did not contribute to VCA during the study periods were not studied. Therefore actual desexing prevalences are almost certainly lower than the estimates reported here. The study population represents cats that are accessible for desexing and is expected to comprise cats kept as pets, for breeding, owned by shelters, semi-owned cats and others. Provenance and lifestyle were not investigated because we chose not to collect data from the examination text field in VCA because of its low positive predictive value57, and because these data are inconsistently recorded. This precluded the analysis of other variables that may have been related to desexing outcomes such access to outdoors and the number and species of pets. Data collection was not uniform across Australia and variations in sample size, for example between states, may have affected our results. Also it is possible that data for the same cat presenting at more than one practice could be counted more than once, although a previous study using VCA found that  More

1.
Chen M, Zeng G, Zhang J, Xu P, Chen A, Lu L. Global landscape of total organic carbon, nitrogen and phosphorus in lake water. Sci Rep. 2015;5:15043.
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Toming K, Kotta J, Uuemaa E, Sobek S, Kutser T, Tranvik LJ. Predicting lake dissolved organic carbon at a global scale. Sci Rep. 2020;10:8471.
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Tranvik LJ, Downing JA, Cotner JB, Loiselle SA, Striegl RG, Ballatore TJ, et al. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol Oceanogr. 2009;54:2298–314.
CAS  Article  Google Scholar 

4.
Tranvik LJ, Cole JJ, Prairie YT. The study of carbon in inland waters-from isolated ecosystems to players in the global carbon cycle. Limnol Oceanogr Lett. 2018;3:41–48.
Article  Google Scholar 

5.
Jaffé R, McKnight D, Maie N, Cory R, McDowell WH, Campbell JL. Spatial and temporal variations in DOM composition in ecosystems: the importance of long-term monitoring of optical properties. J Geophys Res Biogeosci. 2008;113:G04032.
Article  CAS  Google Scholar 

6.
Crump BC, Kling GW, Bahr M, Hobbie JE. Bacterioplankton community shifts in an Arctic Lake correlate with seasonal changes in organic matter source. Appl Environ Microbiol. 2003;69:2253–68.
PubMed  PubMed Central  Article  Google Scholar 

7.
Fasching C, Behounek B, Singer GA, Battin TJ. Microbial degradation of terrigenous dissolved organic matter and potential consequences for carbon cycling in brown-water streams. Sci Rep. 2014;4:4981.
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Yakimovich KM, Emilson EJS, Carson MA, Tanentzap AJ, Basiliko N, Mykytczuk NCS. Plant litter type dictates microbial communities responsible for greenhouse gas production in amended lake sediments. Front Microbiol. 2018;9:2662.
PubMed  PubMed Central  Article  Google Scholar 

9.
Attermeyer K, Hornick T, Kayler ZE, Bahr A, Zwirnmann E, Grossart HP, et al. Enhanced bacterial decomposition with increasing addition of autochthonous to allochthonous carbon without any effect on bacterial community composition. Biogeosciences. 2014;11:1479–89.
Article  Google Scholar 

10.
Fabian J, Zlatanovic S, Mutz M, Premke K. Fungal-bacterial dynamics and their contribution to terrigenous carbon turnover in relation to organic matter quality. ISME J. 2017;11:415–25.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Quigley LNM, Edwards A, Steen AD, Buchan A. Characterization of the interactive effects of labile and recalcitrant organic matter on microbial growth and metabolism. Front Microbiol. 2019;10:493.
PubMed  PubMed Central  Article  Google Scholar 

12.
Tranvik LJ. Degradation of dissolved organic matter in humic waters by bacteria. In: Hessen DOTLJ, editor. Aquatic Humic Substances. Berlin, Heidelberg: Springer; 1998.

13.
Søndergaard M, Borch NH, Riemann B. Dynamics of biodegradable DOC produced by freshwater plankton communities. Aquat Micro Ecol. 2000;23:73–83.
Article  Google Scholar 

14.
Berg B, McClaugherty C. Initial litter chemical composition. Plant Litter. 2014;3:53–83.
Article  Google Scholar 

15.
Bugg TD, Ahmad M, Hardiman EM, Rahmanpour R. Pathways for degradation of lignin in bacteria and fungi. Nat Prod Rep. 2011;28:1883–96.
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Klotzbücher T, Kaiser K, Guggenberger G, Gatzek C, Kalbitz K. A new conceptual model for the fate of lignin in decomposing plant litter. Ecology. 2011;92:1052–62.
PubMed  Article  PubMed Central  Google Scholar 

17.
Grey J, Jones RI, Sleep D. Seasonal changes in the importance of the source of organic matter to the diet of zooplankton in Loch Ness, as indicated by stable isotope analysis. Limnol Oceanogr. 2001;46:505–13.
Article  Google Scholar 

18.
Cole JJ, Carpenter SR, Kitchell JF, Pace ML. Pathways of organic carbon utilization in small lakes: results from a whole-lake 13C addition and coupled model. Limnol Oceanogr. 2002;47:1664–75.
CAS  Article  Google Scholar 

19.
Guenet B, Danger M, Abbadie L, Lacroix G. Priming effect: bridging the gap between terrestrial and aquatic ecology. Ecology. 2010;91:2850–61.
PubMed  Article  PubMed Central  Google Scholar 

20.
Bianchi TS. The role of terrestrially derived organic carbon in the coastal ocean: a changing paradigm and the priming effect. Proc Natl Acad Sci USA. 2011;108:19473–81.
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Bengtsson MM, Attermeyer K, Catalán N. Interactive effects on organic matter processing from soils to the ocean: are priming effects relevant in aquatic ecosystems? Hydrobiologia. 2018;822:1–17.
CAS  Article  Google Scholar 

22.
Kuzyakov Y, Friedel JK, Stahr K. Review of mechanisms and quantification of priming effects. Soil Biol Biochem. 2000;32:1485–98.
CAS  Article  Google Scholar 

23.
Bianchi TS, Ward ND. Editorial: the role of priming in terrestrial and aquatic ecosystems. Front Earth Sci. 2019;7:321.
Article  Google Scholar 

24.
Halvorson HM, Francoeur SN, Findlay RH, Kuehn KA. Algal-mediated priming effects on the ecological stoichiometry of leaf litter decomposition: a meta-analysis. Front Earth Sci. 2019;7:76.
Article  Google Scholar 

25.
Kayler ZE, Premke K, Gessler A, Gessner MO, Griebler C, Hilt S, et al. Integrating aquatic and terrestrial perspectives to improve insights into organic matter cycling at the landscape scale. Front Earth Sci. 2019;7:127.
Article  Google Scholar 

26.
Danger M, Cornut J, Chauvet E, Chavez P, Elger A, Lecerf A. Benthic algae stimulate leaf litter decomposition in detritus-based headwater streams: a case of aquatic priming effect? Ecology. 2013;94:1604–13.
PubMed  Article  PubMed Central  Google Scholar 

27.
Guenet B, Danger M, Harrault L, Allard B, Jauset-Alcala M, Bardoux G, et al. Fast mineralization of land-born C in inland waters: first experimental evidences of aquatic priming effect. Hydrobiologia. 2013;721:35–44.
Article  CAS  Google Scholar 

28.
Ward ND, Sawakuchi HO, Richey JE, Keil RG, Bianchi TS. Enhanced aquatic respiration associated with mixing of clearwater tributary and turbid Amazon river waters. Front Earth Sci. 2019;7:101.
Article  Google Scholar 

29.
Bianchi TS, Thornton DCO, Yvon-Lewis SA, King GM, Eglinton TI, Shields MR, et al. Positive priming of terrestrially derived dissolved organic matter in a freshwater microcosm system. Geophys Res Lett. 2015;42:5460–67.
CAS  Article  Google Scholar 

30.
Tang KW, Gladyshev MI, Dubovskaya OP, Kirillin G, Grossart H-P. Zooplankton carcasses and non-predatory mortality in freshwater and inland sea environments. J Plankton Res. 2014;36:597–612.
CAS  Article  Google Scholar 

31.
Cauchie HM, Jaspar-Versali MF, Hoffmann L, Thomé JP. Analysis of the seasonal variation in biochemical composition of Daphnia magna Straus (Crustacea: Branchiopoda: Anomopoda) from an aerated wastewater stabilisation pond. Ann Limnol. 1999;35:223–31.
Article  Google Scholar 

32.
Smirnov NN. Physiology of the Cladocera, 2nd ed., London, Academic Press: Elsevier; 2017.

33.
Dubovskaya OP, Tang KW, Gladyshev MI, Kirillin G, Buseva Z, Kasprzak P, et al. Estimating in situ zooplankton non-predation mortality in an oligo-mesotrophic lake from sediment trap data: caveats and reality check. PLoS ONE. 2015;10:e0131431.
PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Kirillin G, Grossart H-P, Tang KW. Modeling sinking rate of zooplankton carcasses: effects of stratification and mixing. Limnol Oceanogr. 2012;57:881–94.
Article  Google Scholar 

35.
Tang KW, Hutalle KML, Grossart HP. Microbial abundance, composition and enzymatic activity during decomposition of copepod carcasses. Aquat Micro Ecol. 2006;45:219–27.
Article  Google Scholar 

36.
Tang KW, Bickel SL, Dziallas C, Grossart HP. Microbial activities accompanying decomposition of cladoceran and copepod carcasses under different environmental conditions. Aquat Micro Ecol. 2009;57:89–100.
Article  Google Scholar 

37.
Kolmakova OV, Gladyshev MI, Fonvielle JA, Ganzert L, Hornick T, Grossart HP. Effects of zooplankton carcasses degradation on freshwater bacterial community composition and implications for carbon cycling. Environ Microbiol. 2019;21:34–49.
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Corno G, Salka I, Pohlmann K, Hall AR, Grossart HP. Interspecific interactions drive chitin and cellulose degradation by aquatic microorganisms. Aquat Micro Ecol. 2015;76:27–37.
Article  Google Scholar 

39.
Masigol H, Khodaparast SA, Woodhouse JN, Rojas‐Jimenez K, Fonvielle J, Rezakhani F, et al. The contrasting roles of aquatic fungi and oomycetes in the degradation and transformation of polymeric organic matter. Limnol Oceanogr. 2019;64:2662–78.
CAS  Article  Google Scholar 

40.
Gessner MO, Chauvet E. Importance of stream microfungi in controlling breakdown rates of leaf litter. Ecology. 1994;75:1807–17.
Article  Google Scholar 

41.
Grossart HP, Van den Wyngaert S, Kagami M, Wurzbacher C, Cunliffe M, Rojas-Jimenez K. Fungi in aquatic ecosystems. Nat Rev Microbiol. 2019;17:339–54.
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Osono T. Functional diversity of ligninolytic fungi associated with leaf litter decomposition. Ecol Res. 2019;35:30–43.
Article  CAS  Google Scholar 

43.
Taube R, Ganzert L, Grossart HP, Gleixner G, Premke K. Organic matter quality structures benthic fatty acid patterns and the abundance of fungi and bacteria in temperate lakes. Sci Total Environ. 2018;610-611:469–81.
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Cragg SM, Beckham GT, Bruce NC, Bugg TD, Distel DL, Dupree P, et al. Lignocellulose degradation mechanisms across the Tree of Life. Curr Opin Chem Biol. 2015;29:108–19.
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Wilhelm RC, Singh R, Eltis LD, Mohn WW. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 2019;13:413–29.
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Kuikman PJ, Jansen AG, van Veen JA, Zehnder AJB. Protozoan predation and the turnover of soil organic carbon and nitrogen in the presence of plants. Biol Fertil Soils. 1990;10:22–28.
CAS  Article  Google Scholar 

47.
White DC, Davis WM, Nickels JS, King JD, Bobbie RJ. Determination of the sedimentary microbial biomass by extractible lipid phosphate. Oecologia. 1979;40:51–62.
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Romaní AM, Fischer H, Mille-Lindblom C, Tranvik LJ. Interactions of bacteria and fungi on decomposing litter: differential extracellular enzyme activities. Ecology. 2006;87:2559–69.
PubMed  Article  PubMed Central  Google Scholar 

49.
Hutalle-Schmelzer KM, Zwirnmann E, Kruger A, Grossart HP. Changes in pelagic bacteria communities due to leaf litter addition. Micro Ecol. 2010;60:462–75.
Article  Google Scholar 

50.
Smith EJ, Davison W, Hamilton-Taylor J. Methods for preparing synthetic freshwaters. Water Res. 2002;36:1286–96.
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Attermeyer K, Premke K, Hornick T, Hilt S, Grossart HP. Ecosystem-level studies of terrestrial carbon reveal contrasting bacterial metabolism in different aquatic habitats. Ecology. 2013;94:2754–66.
PubMed  Article  PubMed Central  Google Scholar 

52.
Halbedel S (2015) Protocol for CO2 sampling in waters by the use of the headspaceequilibration technique, based on the simple gas equation; second update. Protoc Exch. https://assets.researchsquare.com/files/nprot-4275/v1/manuscript.pdf

53.
Cheng W. Measurement of rhizosphere respiration and organic matter decomposition using natural 13C. Plant Soil. 1996;183:263–68.
CAS  Article  Google Scholar 

54.
Taube R, Fabian J, Van den Wyngaert S, Agha R, Baschien C, Gerphagnon M, et al. Potentials and limitations of quantification of fungi in freshwater environments based on PLFA profiles. Fungal Ecol. 2019;41:256–68.
Article  Google Scholar 

55.
Zhang Z, Qu Y, Li S, Feng K, Wang S, Cai W, et al. Soil bacterial quantification approaches coupling with relative abundances reflecting the changes of taxa. Sci Rep. 2017;7:4837.
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Mangelsdorf K, Karger C, Zink K-G. Phospholipids as life markers in geological habitats. Hydrocarbons, oils and lipids: diversity, origin, chemistry and fate. 2019. pp. 1–29.

57.
Frostegård Å, Tunlid A, Bååth E. Microbial biomass measured as total lipid phosphate in soils of different organic content. J Microbiol Methods. 1991;14:151–63.
Article  Google Scholar 

58.
Nercessian O, Noyes E, Kalyuzhnaya MG, Lidstrom ME, Chistoserdova L. Bacterial populations active in metabolism of C1 compounds in the sediment of Lake Washington, a freshwater lake. Appl Environ Microbiol. 2005;71:6885–99.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, et al. Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys. 2015;10:1–43.
Article  Google Scholar 

61.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Murali A, Bhargava A, Wright ES. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome. 2018;6:140.
PubMed  PubMed Central  Article  Google Scholar 

63.
De Caceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.
PubMed  Article  PubMed Central  Google Scholar 

64.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559.
Article  CAS  Google Scholar 

65.
Taipale SJ, Kainz MJ, Brett MT. Diet-switching experiments show rapid accumulation and preferential retention of highly unsaturated fatty acids in Daphnia. Oikos. 2011;120:1674–82.
Article  Google Scholar 

66.
Corno G, Jurgens K. Structural and functional patterns of bacterial communities in response to protist predation along an experimental productivity gradient. Environ Microbiol. 2008;10:2857–71.
PubMed  Article  PubMed Central  Google Scholar 

67.
Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17.
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Song HK, Song W, Kim M, Tripathi BM, Kim H, Jablonski P, et al. Bacterial strategies along nutrient and time gradients, revealed by metagenomic analysis of laboratory microcosms. FEMS Microbiol Ecol. 2017;93:fix114.
Article  CAS  Google Scholar 

69.
Bardgett RD, Kandeler E, Tscherko D, Hobbs PJ, Bezemer TM, Jones TH, et al. Below-ground microbial community development in a high temperature world. Oikos. 1999;85:193–203.
Article  Google Scholar 

70.
Hammel KE, Kapich AN, Jensen KA, Ryan ZC. Reactive oxygen species as agents of wood decay by fungi. Enzym Micro Technol. 2002;30:445–53.
CAS  Article  Google Scholar 

71.
Rojas-Jimenez K, Fonvielle JA, Ma H, Grossart H-P. Transformation of humic substances by the freshwater Ascomycete Cladosporium sp. Limnol Oceanogr. 2017;62:1955–62.
CAS  Article  Google Scholar 

72.
Nierman WC, Feldblyum TV, Laub MT, Paulsen IT, Nelson KE, Eisen JA, et al. Complete genome sequence of Caulobacter crescentus. Proc Natl Acad Sci USA. 2001;98:4136–41.
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Zheng W, Lehmann A, Ryo M, Valyi KK, Rillig MC. Growth rate trades off with enzymatic investment in soil filamentous fungi. Sci Rep. 2020;10:11013.
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Bärlocher F, Boddy L. Aquatic fungal ecology—How does it differ from terrestrial? Fungal Ecol. 2016;19:5–13.
Article  Google Scholar 

75.
Lange L, Barrett K, Pilgaard B, Gleason F, Tsang A. Enzymes of early-diverging, zoosporic fungi. Appl Microbiol Biotechnol. 2019;103:6885–902.
CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Janusz G, Pawlik A, Swiderska-Burek U, Polak J, Sulej J, Jarosz-Wilkolazka A, et al. Laccase properties, physiological functions, and evolution. Int J Mol Sci. 2020;21:966.
CAS  PubMed Central  Article  Google Scholar 

77.
Catalán N, Kellerman AM, Peter H, Carmona F, Tranvik LJ. Absence of a priming effect on dissolved organic carbon degradation in lake water. Limnol Oceanogr. 2015;60:159–68.
Article  Google Scholar 

78.
Bengtsson MM, Wagner K, Burns NR, Herberg ER, Wanek W, Kaplan LA, et al. No evidence of aquatic priming effects in hyporheic zone microcosms. Sci Rep. 2014;4:5187.
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Tanentzap AJ, Fitch A, Orland C, Emilson EJS, Yakimovich KM, Osterholz H, et al. Chemical and microbial diversity covary in fresh water to influence ecosystem functioning. Proc Natl Acad Sci USA. 2019;116:24689–95.
CAS  PubMed  Article  PubMed Central  Google Scholar 

80.
Orland C, Emilson EJS, Basiliko N, Mykytczuk NCS, Gunn JM, Tanentzap AJ. Microbiome functioning depends on individual and interactive effects of the environment and community structure. ISME J. 2019;13:1–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Winder M, Sommer U. Phytoplankton response to a changing climate. Hydrobiologia. 2012;698:5–16.
Article  Google Scholar 

82.
Pothoven SA, Fahnenstiel GL. Spatial and temporal trends in zooplankton assemblages along a nearshore to offshore transect in southeastern Lake Michigan from 2007 to 2012. J Gt Lakes Res. 2015;41:95–103.
Article  Google Scholar 

83.
Selmeczy GB, Abonyi A, Krienitz L, Kasprzak P, Casper P, Telcs A, et al. Old sins have long shadows: climate change weakens efficiency of trophic coupling of phyto- and zooplankton in a deep oligo-mesotrophic lowland lake (Stechlin, Germany)—a causality analysis. Hydrobiologia. 2018;831:101–17.
Article  CAS  Google Scholar  More

This study comprehensively evaluated the risk associations between cause-specific ambulance services, extreme temperatures, and mass concentrations of PM2.5 and its constituents. The significant cold effects on chest pain and headache/dizziness/vertigo/fainting/syncope and heat effects on coma and unconsciousness and lying at public were observed, while the risk of ambulance services of OHCA was elevated in both extreme heat and cold environments. Ambulance services of respiratory distress, lying at public, and OHCA increased as the PM2.5 concentration increased, and the risk was significant at the PM2.5 concentration of 20–60 60 μg/m3 for ambulance services of lying at public and higher than 60 μg/m3 for respiratory distress. After controlling for effects of daily average temperature and PM2.5 concentration, this study still identified the significant effects of sulfate and EC on ambulance services of lying at public and OC on headache/dizziness/vertigo/fainting/syncope as the concentrations of PM2.5 constituents were at 90th percentile.
Limited studies assessed associations between ambulance calls and ambient environment9,13,19,22,23,24,25,26,27. Studies in Emilia-Romagna in Italy23, Brisbane in Australia26, Taiwan19, and Huainan and Luoyang in China22,24, have indicated the numbers of ambulance calls associated with extreme heat; the risks generally increase as the daily temperature exceeds 27 °C19,23,26. However, no consistent finding for cold threshold was identified19,28. Kaohsiung City has a tropical climate (daily temperature ranging from 13.5 °C to 31.5 °C), but it is cooler than cities located near the equator, e.g., Singapore and Manila. Except for ambulance service of OHCA, we found that the significant risks associated with temperature were only identified in environments with extreme temperatures ( 90th percentiles; Fig. 3).
Fine particulate matter (PM2.5) are characterized with a small diameter ( More