Philippa Kaur

Parra-Lopez, C., Groot, J. C. J., Carmona-Torres, C. & Rossing, W. A. H. Integrating public demands into model-based design for multifunctional agriculture: an application to intensive Dutch dairy landscapes. Ecol. Econ. 67(4), 538–551 (2008).Article 

Google Scholar 
Pinto-Correia, T., Guiomar, N., Guerra, C.A. et al. Assessing the ability of rural (2016)UPA. Desarrollo rural. Oportunidades Desaprovechadas La Tierra 254, 31–33 (2016).
Google Scholar 
Abreu, I., Nunes, J. M. & Mesias, F. J. Can rural development Be measured? Design and application of a synthetic index to Portuguese municipalities. Soc. Indic. Res. 145, 1107–1123 (2019).Article 

Google Scholar 
Doxiadis, C. A. Ekistics: an introduction to the science of human settlements (Oxford University Press, 1968).
Google Scholar 
Algeciras, J. A. R., Coch, H. & Perez, G. D. L. P. Human thermal comfort conditions and urban planning in hot-humid climates-The case of Cuba. Int. J. Biometeorol. 60, 1151–1164 (2016).Article 

Google Scholar 
Zhang, H., Zhang, S. & Liu, Z. Evolution and influencing factors of China’s rural population distribution patterns since 1990. PLoS ONE 15, e0233637 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eurostat. Eurostat Regional Yearbook. 2019 Edition. Publications Office of the European Union, Luxembourg (2019)Overview of CAP Reform 2014–2020. Agricultural Policy Perspectives. Brief, no. 5, December 2013. European Commission.Marsden, T. & Sonnino, R. Rural development and the regional state: denying multifunctional agriculture in the UK. J. Rural Stud. 24(4), 422–431 (2008).Article 

Google Scholar 
Adamowicz, M. Normative Aspects of Rural Development Strategy and Policy in The European Union Normative Aspects of Rural Development Strategy and Policy in The European Union, (2018)Léon, Y. Rural development in Europe: a research frontier for agricultural economists. Eur. Rev. Agric. Econ. 32, 301–317 (2005).Article 

Google Scholar 
Biegańska, J., Środa-Murawska, S., Kruzmetra, Z. & Swiaczny, F. Peri-Urban development as a significant rural development trend. Quaest. Geogr. 37, 125–140 (2018).Article 

Google Scholar 
Lee I.-H. Change of rural development policy in South Korea After Korean War. J. Reg City Plan, 2021.Oh, Y.-Y. et al. The selection of proper resource and change of salinity in Helianthus tuberosus L. cultivated in Saemangeum reclaimed tidal land. Korean J. Environ. Agric. 37, 73–78 (2018).Article 

Google Scholar 
Yoon, J.-Y., Jeong, J.-H. & Choi, S.-K. Validation of reference genes for quantifying changes in physiological gene expression in apple tree under cold stress and virus infection. Radiat. Prot. Dosim. 26, 144–158 (2020).
Google Scholar 
Faradiba, F. & Zet, L. The impact of climate factors, disaster, and social community in rural development. J. Asian Finance Econ. Bus. 7, 707–717 (2020).Article 

Google Scholar 
Kaneko, M., Ohta, R., Vingilis, E. & Mathews, M. Thomas Robert freeman; systematic scoping review of factors and measures of rurality: toward the development of a rurality index for health care research in Japan. BMC Health Services Res. 21, 1–11 (2021).Article 

Google Scholar 
Yokoyama, S. Sustainable activities for rural development, New Frontiers in Regional Science: Asian Perspectives, (2019).Georgios, C., & Barraí, H. Social innovation in rural governance: a comparative case study across the marginalised rural EU. J. Rural Stud. (2021)Michalek, J. & Zarnekow, N. Application of the rural development index to analysis of rural regions in Poland and Slovakia. Soc. Indic. Res. 105, 1–37 (2012).Article 

Google Scholar 
Liu, Y., Wang, G. & Zhang, F. Spatio-temporal dynamic patterns of rural area development in eastern coastal China. Chin. Geogr. Sci. 23, 173–181 (2013).Article 

Google Scholar 
Kim, T.-H. & Yang, S.-R. Construction of the rural development index: the case of Vietnam. J. Rural Dev. 39, 113–142 (2016).
Google Scholar 
Houkai, W. Current top ten frontier issues [J]in the study of agriculture. Rural Areas Rural Econ. China 4, 2–6 (2019).
Google Scholar 
Hu, Y., Fu, R. & Jin, S. Ecological environment concern in the organic link between poverty alleviation and rural revitalization. Reform 10, 141–148 (2019).
Google Scholar 
Zhang H., Gao L., & Yan K. On the theoretical origin, main innovation and realization path of rural revitalization thought rural economy in China, (11), 2–16 (2018).Feiwei, S. & Zegan, X. Construction and empirical analysis of the index system of beautiful villages in Zhejiang Province. J. Huazhong Agric. Univ. (Social Sciences Edition) 02, 45-51+132 (2017).
Google Scholar 
Research Group of Shanghai Rural Revitalization Index. Construction and evaluation of the index system of rural revitalization in Shanghai. Sci. Dev. 9, 56–63 (2020).World Bank, Expanding the Measure of Wealth. Indicators of Environmentally Sustainable Development. World Bank, Washington, D.C. (1997)Hicks, D. A. The inequality-adjusted human development index: a constructive proposal. World Dev. 25, 1283–1298 (1997).ADS 
Article 

Google Scholar 
Zhao, R., Shao, C. & He, R. Spatiotemporal evolution of ecosystem health of China’s Provinces based on SDGs. Int. J Environ. Res. Public Health 18, 10569 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Kageyama, A., Desenvolvimento rural : conceitos e aplicaçao ao caso brasileiro. UFRGS Editora, Porto Alegre (Brasil) (2008).Abreu, I. Construçao de um índice de desenvolvimento rural e sua aplicaçao ao Alto Alentejo. Instituto Polit´ecnico de Portalegre (2014)Castellani, V. & Sala, S. Sustainable performance index for tourism policy development. Tour. Manag. 31, 871–880 (2009).Article 

Google Scholar 
Hashemi, N. The role of ecotourism in sustainable rural development. J. Rural Dev. Stud. 13(3), 173–188 (2010).
Google Scholar 
Alkire, S., Conconi, A., & Seth, S. Multidimensional Poverty Index 2013: Brief methodological note and results. Oxford Poverty and Human Development Initiative (OPHI) (2014b)Alkire, S., Foster, J. & Santos, M. E. Where did identification go?. J. Econ. Inequal. 9, 501–505 (2011).Article 

Google Scholar 
Alkire, S. et al. Multidimensional poverty measurement and analysis (Oxford University Press, Oxford, 2015).MATH 
Book 

Google Scholar 
Alkire, S., & Seth, S. Identifying destitution through linked subsetsof multidimensionally poor: an ordinal approach, OPHI Working Paper 99. University of Oxford (2016)Li, X., Yang, H., Jia, J., Shen, Y. & Liu, J. Index system of sustainable rural development based on the concept of ecological livability. Environ. Impact Assess. Rev. 86, 1–12 (2021).
Google Scholar 
Guo, X. & Hu, Y. Construction of evaluation index system of rural revitalization level. Agric. Econ. Manag. 05, 5–15 (2020).
Google Scholar 
Kong, X. & Xia, J. The value logic relation and synergy path choice between rural revitalization strategy and rural integration development. J. Northwest Univ. (Philosophy and Social Sciences Edition) 49(02), 10–18 (2019).
Google Scholar 
Wen, T. Three hundred years: the context and development of Chinese rural construction. Open Age. (04) (2016)Ren, C. A Study on the basis, constraints and institutional supply of industrial prosperity. Acad. Acad. 07, 15–27 (2018).
Google Scholar 
Ye, X., Cheng, Y., Zhao, J. & Ning, X. Rural revitalization during the 14th Five-year plan: trend judgment, general thinking and safeguard mechanism. Rural Econ. 09, 1–9 (2020).
Google Scholar 
Zhu, Q. The sociological explanation of the prosperity of rural industry-industry in the context of rural revitalization. J. China Agric. Univ. (Social Sciences Edition) 35(03), 89–95 (2018).
Google Scholar 
Bithas, K. A bioeconomic approach to sustainability with ecological thresholds as an operational indicator. Environ. Sustain. Indic. 6, 100027 (2020).Article 

Google Scholar 
Zheng, X. The East Asian experience of Rural Revitalization and its Enlightenment to China—Take Japan and South Korea as an example. Lanzhou J. 11, 200–208 (2019).ADS 

Google Scholar 
Srivastava, P. K., Kulshreshtha, K., Monhanty, C. S., Pushpangadan, P. & Singh, A. Stakeholder-based SWOT analysis for successful municipal solid waste managementin Lucknow, India. Waste Manag 25(5), 531–537 (2005).CAS 
PubMed 
Article 

Google Scholar 
Alcon, F., Tapsuwan, S., Martínez-Paz, J. M., Brouwer, R. & Miguel, M. Forecasting deficit irrigation adoption using a mixed stakeholder assessment methodology. Technol. Forecast. Soc. Change 83, 183–193. https://doi.org/10.1016/j.techfore.2013.07.003 (2014).Article 

Google Scholar 
Chen, Y., Yang, G., Sweeney, S. & Feng, Y. Household biogas use in rural China: a study of opportunities and constraints. Renew. Sust. Energ. Rev. 14(1), 545–549 (2010).Article 

Google Scholar 
Yeh, C. H. A problem-based selection of multi-attribute decision-making methods. Int. Trans. Oper. Res. 9(2), 169–181 (2002).MATH 
Article 

Google Scholar 
Chakraborty, S., & Yeh, C. -H. A simulation comparison of normalization procedures for TOPSIS, In 2009 International Conference on Computers & Industrial Engineering, IEEE, 2009.Yoon, K. & Hwang, C. L. Multiple attribute decision making. Eur. J. Oper. Res. 4(4), 287–288 (1995).
Google Scholar 
Sharma, N., Khan, Z.A., Siddiquee, A.N., Wahid, M.A. Proc. Inst. Mech. Eng. Part C:J. Mech. Eng. Sci. 233 (2019) 1–10.Maniraj, S. & Thanigaivelan, R. Optimization of electrochemical micromachining process parameters for machining of AMCs with different % compositions of GGBS using Taguchi and TOPSIS methods. Trans. Indian Inst. Met. 72, 3057–3066 (2019).CAS 
Article 

Google Scholar 
Martinez-Fernandez, C. et al. Shrinking cities in Australia, Japan, Europe and the USA: From a global process to local policy responses. Prog. Plan. 105, 1–48 (2016).Article 

Google Scholar 
Mo, G. Green poverty reduction: the value orientation and realization path of ecological poverty alleviation in the battle against poverty—a series of studies on the mechanism of improving the performance of precision poverty alleviation. Modern Econ. Discuss. 11, 10–14 (2016).
Google Scholar 
Wu, L. Echanism and path selection of poverty alleviation in deep-poverty areas. Chin. Soft Sci. 7, 63–70 (2018).
Google Scholar 
Shengzu, Gu. et al. Countermeasure reflections on advancing the poverty relief in the 13th five-year plan. China Financial Res 2, 7–16 (2016).
Google Scholar 
Xie, Z. & Xunwu, J. Giving full play to the advantages of financial financing to help lift out of poverty. China Finance 3, 83–84 (2018).
Google Scholar 
Chen, W. A Way to realize the effective linkage between poverty relief and rural revitalization. Guizhou Soc. Sci. 1, 11–14 (2020).
Google Scholar 
Bijker, R. & Haartsen, T. More than counterurbanisation: migration to popular andless-popular rural areas in the Netherlands. Popul. Space Place 18, 643–657 (2012).Article 

Google Scholar  More

Breed, M. F. et al. The potential of genomics for restoring ecosystems and biodiversity. Nat. Rev. Genet. 20, 615–628 (2019).CAS 
PubMed 
Article 

Google Scholar 
Carpenter, S. R., Stanley, E. H. & Vander Zanden, M. J. State of the world’s freshwater ecosystems: Physical, chemical, and biological changes. Annu. Rev. Environ. Resour. 36, 75–99 (2011).Article 

Google Scholar 
Geist, J. Integrative freshwater ecology and biodiversity conservation. Ecol. Indic. 11, 1507–1516 (2011).Article 

Google Scholar 
Jeppesen, E., Søndergaard, M., Meerhoff, M., Lauridsen, T. L. & Jensen, J. P. Shallow lake restoration by nutrient loading reduction–some recent findings and challenges ahead. Hydrobiologia 584, 239–252 (2007).CAS 
Article 

Google Scholar 
Søndergaard, M. & Jeppesen, E. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. J. Appl. Ecol. 44, 1089–1094 (2007).Article 

Google Scholar 
Marburg, A. E., Turner, M. G. & Kratz, T. K. Natural and anthropogenic variation in coarse wood among and within lakes. J. Ecol. 94, 558–568 (2006).Article 

Google Scholar 
Schindler, D. W. Recent advances in the understanding and management of eutrophication. Limnol. Oceanogr. 51, 356–363 (2006).ADS 
Article 

Google Scholar 
Lau, S. S. S. & Lane, S. N. Continuity and change in environmental systems: The case of shallow lake ecosystems. Prog. Phys. Geogr. Earth Environ. 25, 178–202 (2001).Article 

Google Scholar 
Brinkhurst, R. O. Distribution and abundance of Tubificid (Oligochaeta) species in Toronto harbour, Lake Ontario. J. Fish. Res. Board Can. 27, 1961–1969 (1970).Article 

Google Scholar 
Wood, L. W. & Chua, K. E. Glucose flux at the sediment-water interface of Toronto Harbour, Lake Ontario, with reference to pollution stress. Can. J. Microbiol. 19, 413–420 (1973).CAS 
PubMed 
Article 

Google Scholar 
Nriagu, J. O., Wong, H. K. T. & Snodgrass, W. J. Historical records of metal pollution in sediments of Toronto and Hamilton harbours. J. Gt. Lakes Res. 9(3), 365–373 (1983).CAS 
Article 

Google Scholar 
Toronto & Region Remedial Action Plan. Metro Toronto and Region Remedial Action Plan (1989).Dahmer, S. C., Matos, L. & Morley, A. Restoring Toronto’s waters: Progress toward delisting the Toronto and Region area of concern. Aquat. Ecosyst. Health Manag. 21, 229–233 (2018).Article 

Google Scholar 
Munawar, M., Norwood, W., McCarthy, L. & Mayfield, C. In situ bioassessment of dredging and disposal activities in a contaminated ecosystem: Toronto Harbour. Hydrobiologia https://doi.org/10.1007/978-94-009-1896-2_62 (1989).Article 

Google Scholar 
Dahmer, S. C., Matos, L. & Jarvie, S. Assessment of the degradation of aesthetics beneficial use impairment in the Toronto and region area of concern. Aquat. Ecosyst. Health Manag. 21, 276–284 (2018).Article 

Google Scholar 
Metro Toronto and Region Remedial Action Plan. Within Reach: 2015 Toronto an Region Remedial Action Plan Progress Report (2016).Burniston, D. & Waltho, J. Report on Sediment Quality in the Toronto Inner Harbour 2007 (2011).Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8, 1265–1275 (2017).Article 

Google Scholar 
Emilson, C. E. et al. DNA metabarcoding and morphological macroinvertebrate metrics reveal the same changes in boreal watersheds across an environmental gradient. Sci. Rep. 7, 12777 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Aylagas, E., Borja, Á., Muxika, I. & Rodríguez-Ezpeleta, N. Adapting metabarcoding-based benthic biomonitoring into routine marine ecological status assessment networks. Ecol. Indic. 95, 194–202 (2018).Article 

Google Scholar 
Bush, A. et al. Studying ecosystems with DNA metabarcoding: Lessons from biomonitoring of aquatic macroinvertebrates. Front. Ecol. Evol. 7, 434 (2019).Article 

Google Scholar 
Serrana, J. M., Miyake, Y., Gamboa, M. & Watanabe, K. Comparison of DNA metabarcoding and morphological identification for stream macroinvertebrate biodiversity assessment and monitoring. Ecol. Indic. 101, 963–972 (2019).Article 

Google Scholar 
Fernández, S., Rodríguez-Martínez, S., Martínez, J. L., Garcia-Vazquez, E. & Ardura, A. How can eDNA contribute in riverine macroinvertebrate assessment? A metabarcoding approach in the Nalón River (Asturias, Northern Spain). Environ. DNA 1, 385–401 (2019).Article 

Google Scholar 
Hajibabaei, M. et al. Watered-down biodiversity? A comparison of metabarcoding results from DNA extracted from matched water and bulk tissue biomonitoring samples. PLoS ONE 14, e0225409 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).PubMed 
Article 

Google Scholar 
Hajibabaei, M., Baird, D. J., Fahner, N. A., Beiko, R. & Golding, G. B. A new way to contemplate Darwin’s tangled bank: How DNA barcodes are reconnecting biodiversity science and biomonitoring. Philos. Trans. R. Soc. B. Biol. Sci. 371, 20150330 (2016).Article 
CAS 

Google Scholar 
Beermann, A. J., Zizka, V. M. A., Elbrecht, V., Baranov, V. & Leese, F. DNA metabarcoding reveals the complex and hidden responses of chironomids to multiple stressors. Environ. Sci. Eur. 30, 26 (2018).Article 

Google Scholar 
Bush, A. et al. DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness. Proc. Natl. Acad. Sci. 117, 8539–8545 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Compson, Z. G. et al. Chapter Two—Linking DNA Metabarcoding and Text Mining to Create Network-Based Biomonitoring Tools: A Case Study on Boreal Wetland Macroinvertebrate Communities. In Advances in Ecological Research Vol. 59 (eds Bohan, D. A. et al.) 33–74 (Academic Press, 2018).
Google Scholar 
Fernandes, K. et al. DNA metabarcoding—A new approach to fauna monitoring in mine site restoration. Restor. Ecol. 26, 1098–1107 (2018).Article 

Google Scholar 
Fernandes, K. et al. Invertebrate DNA metabarcoding reveals changes in communities across mine site restoration chronosequences. Restor. Ecol. 27, 1177–1186 (2019).Article 

Google Scholar 
Poikane, S. et al. Benthic macroinvertebrates in lake ecological assessment: A review of methods, intercalibration and practical recommendations. Sci. Total Environ. 543, 123–134 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Macher, J.-N. et al. Comparison of environmental DNA and bulk-sample metabarcoding using highly degenerate cytochrome c oxidase I primers. Mol. Ecol. Resour. 18, 1456–1468 (2018).CAS 
PubMed 
Article 

Google Scholar 
Marshall, N. T. & Stepien, C. A. Macroinvertebrate community diversity and habitat quality relationships along a large river from targeted eDNA metabarcode assays. Environ. DNA 2, 572–586 (2020).Article 

Google Scholar 
Metro Toronto and Region Remedial Action Plan. Updates on Actions 2013–2014. (2013).López-López, E. & Sedeño-Díaz, J. E. Biological indicators of water quality: The role of fish and macroinvertebrates as indicators of water quality. In Environmental Indicators (eds Armon, R. H. & Hänninen, O.) 643–661 (Springer Netherlands, 2015). https://doi.org/10.1007/978-94-017-9499-2_37.Chapter 

Google Scholar 
Berry, O. et al. A Comparison of Morphological and DNA Metabarcoding Analysis of Diets in Exploited Marine Fishes (2015).Sweeney, B. W., Battle, J. M., Jackson, J. K. & Dapkey, T. Can DNA barcodes of stream macroinvertebrates improve descriptions of community structure and water quality?. J. N. Am. Benthol. Soc. 30, 195–216 (2011).Article 

Google Scholar 
Banerji, A. et al. Spatial and temporal dynamics of a freshwater eukaryotic plankton community revealed via 18S rRNA gene metabarcoding. Hydrobiologia 818, 71–86 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Porter, T. M. et al. Widespread occurrence and phylogenetic placement of a soil clone group adds a prominent new branch to the fungal tree of life. Mol. Phylogenet. Evol. 46, 635–644 (2008).CAS 
PubMed 
Article 

Google Scholar 
Rosling, A. et al. Archaeorhizomycetes: Unearthing an ancient class of ubiquitous soil fungi. Science 333, 876–879 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Mandaville, S. M. Benthic Macroinvertebrates in Freshwaters—Taxa Tolerance Values, Metrics, and Protocols, vol. 128. http://lakes.chebucto.org/H-1/tolerance.pdf (2002).Trzcinski, M. K. et al. The effects of food web structure on ecosystem function exceeds those of precipitation. J. Anim. Ecol. 85, 1147–1160 (2016).PubMed 
Article 

Google Scholar 
Liu, X. & Wang, H. Contrasting patterns and drivers in taxonomic versus functional diversity, and community assembly of aquatic plants in subtropical lakes. Biodivers. Conserv. 27(12), 3103–3118 (2018).Article 

Google Scholar 
Kovalenko, K. E., Brady, V. J., Ciborowski, J. J. H., Ilyushkin, S. & Johnson, L. B. Functional changes in littoral macroinvertebrate communities in response to watershed-level anthropogenic stress. PLoS ONE 9, e101499 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Luiza-Andrade, A., Montag, L. F. A. & Juen, L. Functional diversity in studies of aquatic macroinvertebrates community. Scientometrics 111, 1643–1656 (2017).Article 

Google Scholar 
MacMillan, G. A., Chételat, J., Heath, J. P., Mickpegak, R. & Amyot, M. Rare earth elements in freshwater, marine, and terrestrial ecosystems in the eastern Canadian Arctic. Environ. Sci. Process. Impacts 19, 1336–1345 (2017).CAS 
PubMed 
Article 

Google Scholar 
Pastorino, P. et al. Macrobenthic invertebrates as tracers of rare earth elements in freshwater watercourses. Sci. Total Environ. 698, 134282 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kulaš, A. et al. Ciliates (Alveolata, Ciliophora) as bioindicators of environmental pressure: A karstic river case. Ecol. Indic. 124, 107430 (2021).Article 

Google Scholar 
Persaud, D., Lomas, T., Boyd, D. & Mathai, S. Historical Development and Quality of the Toronto Waterfront Sediments (1985).Milani, D. & Grapentine, L. Assessment of Sediment Quality in the Bay of Quinte Area Of Concern (2000).Reynoldson, T. B., Bailey, R. C., Day, K. E. & Norris, R. H. Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state. Aust. J. Ecol. 20(1), 198–219 (1995).Article 

Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 
Article 

Google Scholar 
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhan, A. et al. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 4, 558–565 (2013).Article 

Google Scholar 
Gibson, J. et al. Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics. Proc. Natl. Acad. Sci. 111, 8007–8012 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gibson, J. F. et al. Large-scale biomonitoring of remote and threatened ecosystems via high-throughput sequencing. PLoS ONE 10, e0138432 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Porter, T. M. & Hajibabaei, M. METAWORKS: A flexible, scalable bioinformatic pipeline for multi-marker biodiversity assessments. bioRxiv https://doi.org/10.1101/2020.07.14.202960 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).PubMed 
Article 
CAS 

Google Scholar 
Anon. Conda. (2016).Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep. 8, 4226 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Porter, T. M. Eukaryote CO1 Reference set for the RDP Classifier (Zenodo, 2017) https://doi.org/10.5281/zenodo.4741447.Book 

Google Scholar 
Porter, T. M. SILVA 18S Reference Set for the RDP Classifier(Zenodo, 2018) https://doi.org/10.5281/zenodo.4741433.Book 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2020).
Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009). https://doi.org/10.1007/978-0-387-98141-3.Book 
MATH 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package (2020).Komsta, L. & Novomestky, F. moments: Moments, cumulants, skewness, kurtosis and related tests (2015).U.S. Environmental Protection Agency. Freshwater Biological Traits Database (Final Report) EPA/600/R-11/038F. (2012)U.S. Environmental Protection Agency. Freshwater Biological Traits Database (2012).Schmidt-Kloiber, A. & Hering, D. An online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol. Indic. 53, 271–282 (2015).Article 

Google Scholar 
Moog, O. Fauna Aquatica Austriaca – Catalogue for autecological Classification of Austrian Aquatic Organisms (1995).Tachet, H., Bournaud, M., Richoux, P., Usseglio-Polatera, P. Invertébrés d’eau douce – systématique, biologie, écologie (2010).Nally, R. M. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. https://doi.org/10.1023/B:BIOC.0000009515.11717.0b (2004).Article 

Google Scholar  More

Chai et al. Stable water isotope and surface heat flux simulation using ISOLSM: Evaluation against in-situ measurements. J. Hydrol. 523, 67–78 (2015).Article 

Google Scholar 
Brooks et al. Stable isotope estimates of evaporation: Inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnol. Oceanogr. 59, 2150–2165 (2014).Article 

Google Scholar 
Good, S. P., Noone, D. & Bowen, G. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349, 175–177 (2015).CAS 
Article 

Google Scholar 
Gupta, A., Gerber, E. P. & Lauritzen, P. H. Numerical impacts on tracer transport: A proposed intercomparison test of atmospheric general circulation models. Quart. J. Roy. Meteor. Soc. 146, 3937–3964 (2020).Article 

Google Scholar 
Kanner, L. C., Buenning, N. H., Stott, L. D., Timmermann, A. & Noone, D. The role of soil processes in d18O. Global Biogeochem. Cycles 28, 239–252 (2014).CAS 
Article 

Google Scholar 
Remondi, F., Kircher, J. W., Burlando, P. & Fatichi, S. Water flux tracking with a distributed hydrologic model to quantify controls on the spatio-temporal variability of transit time distributions. Water Resour. Res. 54, 3081–3099 (2018).Article 

Google Scholar 
Abbott, B. W. et al. Using multi-tracer inference to move beyond single catchment ecohydrology. Earth-Sci. Rev. 160, 19–42 (2016).Article 

Google Scholar 
Krause, P., Boyle, D. P. & Bäse, F. Comparison of different efficiency criteria for hydrological model assessment. Adv. in Geosci. 5, 89–97 (2005).Article 

Google Scholar 
Bowen, G. J. & Good, S. P. Incorporating water isotopes in hydrological and water resource investigations. Wiley Interdiscip. Rev.: Water 2, 107–119 (2015).Article 

Google Scholar 
McGuire, K. J. & McDonnell, J. J. A review and evaluation of catchment transit time modeling. J. Hydrol. 330, 543–563 (2006).Article 

Google Scholar 
Sprenger, M. et al. The demographics of water: A review of water ages in the critical zone. Rev. Geophys. 57, 800–834 (2019).Article 

Google Scholar 
Turnadge, C. & Smerdon, B. D. A review of methods for modelling environmental tracers in groundwater: Advantages of tracer concentration simulation. J. Hydrol. 519, 3674–3689 (2014).CAS 
Article 

Google Scholar 
Fiorella, R. et al. Calibration Strategies for Detecting Macroscale Patterns in NEON Atmospheric Carbon Isotope Observations. J. Geophys. Res. Biogeosci. 126 (2021).Xiao, W., Wei, Z. & Wen, X. Evapotranspiration partitioning at the ecosystem scale using the stable isotope method—A review. Agric For Meteorol. 263, 346–361 (2018).Article 

Google Scholar 
Wu, Y. et al. Stable isotope measurements show increases in corn water use efficiency under deficit irrigation. Sci Rep 8, 14113 (2018).Article 

Google Scholar 
Al-Oqaili, F., Good, S. P., Frost, K. & Higgins, C. W. Differences in soil evaporation between row and interrow positions in furrowed agricultural fields. Vadose Zone J. 19, e20086 (2020).CAS 
Article 

Google Scholar 
Bowen, G. J., Cai, Z., Fiorella, R. P. & Putman, A. L. Isotopes in the water cycle: Regional- to global-scale patterns and applications. Annu. Rev. Earth Planet. Sci. 47, 453–479 (2019).CAS 
Article 

Google Scholar 
Lu, X. et al. Partitioning of evapotranspiration using a stable isotope technique in an arid and high temperature agricultural production system. Agric. Water Manag. 179, 103–109 (2017).Article 

Google Scholar 
Wieser, G. et al. Stable water use efficiency under climate change of three sympatric conifer species at the alpine treeline. Front. Plant Sci. 7, 799 (2016).Article 

Google Scholar 
Pataki, D. E. et al. The application and interpretation of Keeling plots in terrestrial carbon cycle research. Global Biogeochem. Cycles, 17 (2003).Miller, J. B., & Tans, P. P., Calculating isotopic fractionation from atmospheric measurements at various scales. Tellus, 55 (2003).Finkenbiner, C. E., Good, S. P., Allen, S. T., Fiorella, R. P. & Bowen, G. J. A statistical method for generating temporally downscaled geochemical tracers in precipitation. J. Hydrometeorol. 22 (2021).NEON (National Ecological Observatory Network). Precipitation (DP1.00006.001), RELEASE-2022. https://doi.org/10.48443/6wkc-1p05. Dataset accessed from https://data.neonscience.org on May 12, 2022.Lunch, C. K. & Laney, C. M. NEON (National Ecological Observatory Network). neonUtilities: Utilities for working with NEON data. R package version 1.3.4. https://github.com/NEONScience/NEON-utilities (2020).Lee, R. and S. Weintraub. NEON User Guide to Stable Isotopes in Precipitation (NEON.DPI.00038) Version B. NEON (National Ecological Observatory Network). (2021).IAEA: Global network of isotopes in precipitation. https://www.iaea.org/services/networks/gnip 2020.Allen, S. T., Kirchner, J. W. & Goldsmith, G. R. Predicting spatial patterns in precipitation isotope (δ2H and δ18O) seasonality using sinusoidal isoscapes. Geophys. Res. 45, 4859–4868 (2018).
Google Scholar 
Craig, H. Isotopic variations in meteoric waters. Science 133, 1702–1703 (1961).CAS 
Article 

Google Scholar 
Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).Article 

Google Scholar 
Sklar, A. Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Stat. Univ. Paris. 8, 229–231 (1959).MATH 

Google Scholar 
NEON (National Ecological Observatory Network). Bundled data products – eddy covariance (DP4.00200.001). https://data.neonscience.org (2021).Good, S. P., Soderberg, K., Wang, L., & Caylor, K. K. Uncertainties in the assessment of the isotopic composition of surface fluxes: A direct comparison of techniques using laser‐based water vapor isotope analyzers. J. Geophys. Res. Atmos. 177 (2012).Wutzler, T. et al. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosci. 15, 5015–5030 (2018).CAS 
Article 

Google Scholar 
Zobitz, J. M., Keener, J. P., Schnyder, H. & Bowling, D. R. Sensitivity analysis and quantification of uncertainty for isotopic mixing relationships in carbon cycle research. Agric For Meteorol. 136, 56–75 (2006).Article 

Google Scholar 
Wehr, R. & Saleska, S. R. An improved isotopic method for partitioning net ecosystem-atmosphere CO2 exchange. Agric For Meteorol. 214, 515–531 (2015).Article 

Google Scholar 
Bailey, A., Noone, D., Berkelhammer, M., Steen-Larsen, H. C. & Sato, P. The stability and calibration of water vapor isotope ratio measurements during long-term deployments. Atmos. Meas. Tech. 8, 4521–4538 (2015).CAS 
Article 

Google Scholar 
Rambo, J., Lai, C., Farlin, J., Schroeder, M. & Bible, K. Vapor isotope ratios using off-axis cavity-enhanced absorption spectroscopy. J Atmos. Ocean Technol. 28, 1448–1457 (2011).Article 

Google Scholar 
Finkenbiner, C. The National Ecological Observation Network Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) Dataset, HydroShare, https://doi.org/10.4211/hs.e74edc35d45441579d51286ea01b519f (2022). More

Mathematical theoryWe consider a biological response (e.g. body size, survival, biodiversity) to two environmental drivers (i.e. any abiotic or biotic factor) but the same idea may be applied to a larger number of drivers. The response depends of a set of predictors consisting in the magnitudes (m1 and m2) and time scales of fluctuation of two drivers (i = 1, 2); in addition, the response is quantified at least once after the fluctuations have been experienced (Fig. 1a).Time is defined using two different frames; chronological (= clock) time (measured by clocks) and biological time. For the “clock” time scales of the fluctuations (t1, t2) there are associated biological times (τ1, τ2). Likewise, for the clock time at which the response is quantified (({t}^{^*})) there is an associated biological time (τ({^*})).Biological time is the proportion of (clock) time needed to reach a life history event (e.g. moulting, maturity). Hence, for t1, t2 and t({^*}) we obtain τi = ti/D and τ({^*}) = t({^*})/D, (D = clock time needed to reach such life history event). We express the τi and τ({^*}) in terms of a function L = 1/D. For instance, for t({^*}) we obtain:$${tau }^{^*}={t}^{^*}cdot{L}$$
(1)
where L = L(ω) = D−1(ω) characterises the timing of a life history event (with units as the inverse of clock time units). L depends on the set of predictors ω associated to the fluctuations; an important set of predictors will be defined by thermal fluctuations (the amplitude and time scales), which in ectotherm species have a strong influence on developmental time32,33. We find by differentiation that L provides the transform function between clock and biological time frames; for instance, if L does not depend on any ti we have L = dτ/dti.The response is expressed as a function of the predictors defined above, as R(m1, m2, t1, t2, t({^*})) = r[m1, m2, τ1 (t1), τ2(t2), τ({^*})]. The contribution of each predictor to the response is better understood by the partial derivatives with respect to each predictor; this defines a system of partial differential equations (PDE; Supplementary note 1) which expressed in matrix form give the following matrix equation.$$left[begin{array}{c}frac{dR}{d{m}_{1}}\ frac{dR}{d{m}_{2}}\ frac{dR}{d{t}_{1}}\ frac{dR}{d{t}_{2}}\ frac{dR}{d{t}^{^*}}end{array}right]=left[begin{array}{ccccc}1& frac{d{m}_{2}}{d{m}_{1}}& frac{d{tau }_{1}}{d{m}_{1}}& frac{d{tau }_{2}}{d{m}_{1}}& frac{d{tau }^{^*}}{d{m}_{1}}\ frac{d{m}_{1}}{d{m}_{2}}& 1& frac{d{tau }_{1}}{d{m}_{2}}& frac{d{tau }_{2}}{d{m}_{2}}& frac{d{tau }^{^*}}{d{m}_{2}}\ frac{d{m}_{1}}{d{t}_{1}}& frac{d{m}_{2}}{d{t}_{1}}& frac{d{tau }_{1}}{d{t}_{1}}& frac{d{tau }_{2}}{d{t}_{1}}& 0\ frac{d{m}_{1}}{d{t}_{2}}& frac{d{m}_{2}}{d{t}_{2}}& frac{d{tau }_{1}}{d{t}_{2}}& frac{d{tau }_{2}}{d{t}_{2}}& 0\ 0& 0& 0& 0& frac{d{tau }^{^*}}{d{t}^{^*}}end{array}right]cdot left[begin{array}{c}frac{dr}{d{m}_{1}}\ frac{dr}{d{m}_{2}}\ frac{dr}{d{tau }_{1}}\ frac{dr}{d{tau }_{2}}\ frac{dr}{d{tau }^{^*}}end{array}right]$$
(2)
In the PDE (Eq. 2), the left-hand side is a vector column of the derivatives of the response in clock time (R), with respect to each predictor; the right-hand side is the standard (= inner) product of a matrix (M) by a vector of the derivatives of the response in biological time (r), i.e. R = Mr. The matrix contains the derivatives of the predictors with respect to each other, with time both expressed in clock or biological scales; one can think of M as an object containing coefficients that transform r into R in the same way as a constant (= 1000) would transform kilometres into meters of distance. The large number of terms in M highlights the considerable diversity and the challenges in quantifying responses to multivariate environmental fluctuations. We show below how to use Eq. (2) to quantify the effect of fluctuating environmental drivers on biological responses, as mediated by biological time.First, we note that M contains three groups of terms: (1) Terms accounting for situations where the magnitude of a driver affects the magnitude of the second driver (e.g. temperature drives oxygen concentration in aquatic habitats): these are dmi/dmj for any i, j = 1, 2. (2) Terms accounting for cases where the magnitudes and time scales of stressors are related: dmi/dtj and dmi/dti. (3) Terms where biological time depends on the magnitude or time scale of the environmental fluctuation dτi/dtj and dτi/dmj. The terms of groups (1) and (2) are zero when they are mutually independent, such as in a factorial experiment with orthogonal manipulation. We will set those to zero in the rest of this analysis.Second, we note that for group (3) there are three scenarios: (3a) biological time does not depend on any environmental driver. This is the trivial case where biological time is proportional to clock time, not considered here; M is simplified to a diagonal matrix, i.e. with constants in the diagonal, and zero’s otherwise leading to a single constant term per equation (3b). Biological time depends on the magnitudes of any or both drivers. In such case, τ1 τ2, and τ({^*}) will be driven by the same equation: if τi = ti · L (m1, m2) we obtain dτi/dtj = dτi/dti = L (m1, m2). (3c) Biological time depends on the time scale of the fluctuations: in such case, differentiating Eq. (1) with respect to time, we obtain dτi/dti = L + ti dL/dti.Here, we explore four special cases where the equations are simplified to highlight the importance of biological time in modifying the responses as compared to clock time. We start with the simplest case where there is a single environmental variable and then we consider cases with two variables. We focus on cases representing the most frequent experiments carried out on multiple driver research, i.e. factorial manipulations where terms of the groups 1 and 2 are zero.Case 1: responses to the magnitude of a single variableWe start with the simplest case i.e. where the response is driven by the magnitude of a single driver, e.g. temperature (= m). Examples of this case are laboratory experiments quantifying the effect of temperature on body mass or survival of a given species, or mesocosm experiments quantifying effects of temperature on species richness where thermal treatments are kept constant over time. Here, the response is quantified at different times, both in the clock and biological frames. In such case we have R(m, t({^*})) = r[m, τ({^*})(m, t({^*}))] and the PDEs simplify to.$$frac{dR}{dm}=frac{partial r}{partial m}+frac{partial r}{partial {tau }^{^*}}cdot frac{d{tau }^{^*}}{dm}$$
(3)
From Eq. (3), and because dR/dm ≠ dr/dm, we see that the response to the magnitude of the driver depends on a component quantifying the effect biological time: as long as dτ({^*})/dm ≠ 0 the time reference frame affects the observed effect of m on the response. The simulation illustrated in Fig. 2 shows a case where there are differences between the observed responses at clock vs biological times. In the simulated experiment, there is a strong effect of the magnitude of the driver on the response at clock time, but such effect is much less pronounced at biological time. By contrast, there is no effect when the response is measured in the biological time frame.Figure 2Case 1: Response to the magnitude of a single variable (m). Horizontal line: measurement taken at clock time t({^*}) = t({^*})c; note that, along the line, the response increase with m (it crosses the colour gradient). Curve with yellow circles: measurements taken at a constant biological time (τ({^*})c = 100); along the curve, the response does not vary with m. The equations used were: R = m(0.5t({^*})), τ({^*}) = t. m giving r = 0.5. τ({^*}) not depending on m.Full size imageEquation (3) (details in Supplementary code 1) captures an obvious but important feature of experiments manipulating temperature over the development of ectotherms, for instance, from birth to metamorphosis; namely that there is no consistent definition of a simultaneous event across the different time frames. Experiments are usually stopped at different clock times because organisms must be sampled at the same biological time. All points located in the horizonal line in Fig. 3 represent simultaneous events, as defined in clock time occurring at different temperatures (e.g. whether an animal is dead or alive); however, simultaneous events occurring in biological time are represented by the points on the curve. Hence, Fig. 2 gives a geometric representation of such fact. Temperature as a driver of developmental rates32 is a central candidate to produce responses that differ at clock vs biological time.
We explore further this case with an example where the response is expressed as a function of time and an instantaneous rate μ(m) quantifying for instance mortality, growth or biomass loss. For this example, we obtain R(m, t({^*})) = r[μ(m), τ({^*})(m, t({^*}))]. By differentiating in both sides, we get:$$frac{dR}{dm}=frac{partial r}{partial mu }cdot frac{dmu }{dm}+frac{partial r}{partial {tau }^{^*}}cdot frac{d{tau }^{^*}}{dm}$$
(4)
Equation (4) shows that m affects the response through two components: the instantaneous rate (dμ/dm) and the biological time (dτ({^*})/dm). We call the first component “eco-physiological” and the second component “phenological” (m drives the timing of a biological event, e.g. time to maturation). Those components are not evident if the response is expressed in clock time; otherwise we would obtain dR/dm = ∂R/∂μ · dμ/dm.In order to better understand Eq. (4), consider an example where the response is biomass loss experienced by an organism during the process of migration (e.g. towards a feeding or reproductive ground); when the access to food during migration is very limited the result should be a decrease in body mass reserves through time. Let biomass (B) be modelled as an exponential decaying function of time and an instantaneous rate of biomass loss μ; let μ depend on temperature (= m) such that, μ = μ(m). In such case we obtain:$$B(m,t)={e}^{-mu left(mright)cdot {t}^{^*}}={e}^{-mu left(mright)cdot {tau }^{^*}left(m,{t}^{^*}right)}$$
(5)
By differentiation in both sides of Eq. (5) we get:$$frac{dB}{dm}={-e}^{-mu left(mright)cdot {tau }^{^*}left(m,{t}^{^*}right)}left{{tau }^{^*}cdot frac{dmu }{dm}+mu cdot frac{d{tau }^{^*}}{dm}right}$$
(6)
Equation (6) shows the eco-physiological (dμ/dm) and phenological components (dτ({^*})/dm) within the brackets. If μ responds linearly to temperature, then dμ/dm would be represented by a constant quantifying the thermal sensitivity of biomass loss; the value of such constant would depend on physiological processes associated to use of reserves to sustain movement and the basal metabolic rate. Likewise, if τ({^*}) responds linearly to temperature, the dτ({^*})/dm would be driven by a constant controlling the sensitivity of developmental time to temperature.Because biomass is a trait that is central to fitness, Eq. (6) gives the indirect contribution of phenological and physiological responses to fitness. Assuming that fitness should be maximised, adaptive responses should involve the mitigation of negative effect of m on both components of Eq. (5), represented by the partial derivative of the right-hand term. For instance, organisms with the ability to minimise the eco-physiological effect (through e.g. a compensatory physiological mechanisms) or the phenological effect (e.g. shortening the exposure time) would complete the migration minimal loss of reserves.By generalization, Eqs. (4–6) help us to provide biological meaning to the terms of the matrix M: any term of the form dτ({^*})/dmj, dτi/dmj or dτi/dtj represents the effect of an environmental driver on the timing of a phenological event; hence, they are phenological components. Terms that contain the effect of an environmental variable on an instantaneous rate are eco-physiological components. By substitution we find that the terms of the matrix in Eq. (2) can be classified in two categories according to whether the component is eco-physiological (E) or phenological (P):$$left[begin{array}{ccccc}E& 0& P& P& P\ 0& E& P& P& P\ 0& 0& P& P& 0\ 0& 0& P& P& 0\ 0& 0& 0& 0& Pend{array}right]$$
(7)
Case 2: multiple driver responsesHere we expand the previous case by looking at a response to the magnitude of two different drivers; i.e. keeping the levels of each driver constant over the duration of the experiment. Examples of this case are experiments quantifying the effect of temperature and nutrient load on body mass (e.g. in a rearing containers) or species richness (e.g. in mesocosms). This case is represented by the terms of first two rows of the matrix and the vectors of Eq. (2), with the terms of the remaining rows set to zero. Here, there are different scenarios, but we focus on the one highlighting the importance of biological time.Consider a case where biological time depends on the magnitude of the first driver while the response is explicitly driven by the magnitude of the second driver (Fig. 4). For instance, the response may be the survival rate of a host organism exposed to different temperature and parasitic load. The response in clock time is described as R(mP, t({^*})). The driver controlling the biological time is temperature (mT) while the parasitic load (mP) controls survival. In such case, dτ({^*})/∂mP = 0, dR/dmP ≠ 0 and dR/dmT = 0. Although by definition the response in clock time does not depend on mT , it will do so in biological time. This is because, applying the matrix multiplication in Eq. (2), we obtain:
$$frac{partial R}{partial {m}_{T}}=frac{partial r}{partial {m}_{T}}+frac{partial r}{partial tau *}cdot frac{dtau *}{d{m}_{T}}$$
(8a)
$$0=frac{partial r}{partial {m}_{T}}+frac{partial r}{partial tau *}cdot frac{dtau *}{d{m}_{T}}$$
(8b)
$$frac{partial r}{partial {m}_{T}}=-frac{partial r}{partial tau *}cdot frac{dtau *}{d{m}_{T}}$$
(8c)
The second right-hand term in Eq. (8a) quantifies the effect of temperature on the response mediated by biological time. In order to better understand the responses, consider a simple linear response: R = R0 − mP·t({^*}) and notice that, for a fixed clock time (t({^*})c) the effect of the magnitude of parasitism is constant (dR/dmP = −t({^*})c); hence, the response can be understood, geometrically, as a flat surface with slope not depending on temperature. Now, note that under the specific conditions of our example, r = R0 − mP·τ({^*})/L(mT). Hence, for a fixed biological time (τ({^*})c) we obtain ∂r/∂mP = −τ({^*})c/L(mT); i.e. the importance of the parasitic effect depends now on temperature. In addition, this example is valid for the case of additive effects of any two environmental drivers: assuming R = R0 − (a1·mP + a2· mT)·t({^*}) (a1, a2 are constants), we obtain dR/dmP = −a1t({^*}); however, ∂r/∂mP = −a1τ({^*})c/L(mT). In words, additive effects observed in clock time become interactive in biological time. This is illustrated in the simulation (Supplementary code 2) depicted in Fig. 4: the response in clock time depends on a single driver (parasite load); however, the response in biological time is interactive, i.e. the effect of parasite load depends on temperature.Figure 3Case 2: Multiple driver responses. (A) Modelled responses (colour scale) at a specific clock (t({^*}) = 40) and biological times (τ({^*}) = 1), showing an interactive effect only in the biological time frame. (B) Interaction plots of the responses for specific levels of temperature and a second driver showing that the effect high temperature mitigates the negative effect of the second driver on the response. The response was modelled with as a sigmoidal function R = exp(−t({^*})φ) with φ = 0.1[1 + exp(m2/2)]−1 to produce a strong gradient in the range of m2 = 25–30 units. The biological time was modelled based on the effect of temperature on the development of marine organisms33 as so that t({^*}) = τ({^*}) exp[−22.47 + 0.64/(k(m1 + 273)], i.e., using the Arrhenius equation with k: Boltzmann constant (≈ 8.617 10–5 eV K−1).Full size imageCase 3: role of clock and biological time scale of fluctuationPrevious examples did not consider, the time scale of the fluctuations as drivers of the response. Here we explore how a biological variable (= survival rate) responds to different levels of magnitude of a driver (= temperature) and to simultaneously changing the time scale of a fluctuation (from clock to biological time) of a second driver (= food limitation). As model, we use larval stages of a crab because there is sufficient information on the effect of temperature and food levels on survival and the timing of moulting33,34.We performed the so-called point-of-reserve-saturation experiment (PRS35), i.e. exposing groups of recently hatched larvae of the crab Hemigrapsus sanguineus to different initial feeding periods (= our time scale of fluctuation), after which they were starved until they either died or moulted to the second larval stage (Supplementary Fig. 1). H. sanguineus is originated from East Asia but has invaded the Atlantic shores of North America and North Europe36,37. This experiment was carried out at 4 temperature levels (15–21 °C), within the range of thermal tolerance of larvae of this species, i.e. where the magnitude of temperature does not affect survival38,39. In addition, because there is a single level of food limitation (= starvation), the magnitude of food limitation (mF) is not considered as a variable in the example.The response variable was the proportion of first stage larvae surviving the moulting event to the second stage, set to biological time τ({^*}) = 1. In response to different starvation periods (preceded by feeding), the survival shows a sigmoidal pattern35, characterised by a parameter, PRS50. This is the point of development where larval reserves are “saturated”; i.e. enough reserves have been accumulated during the previous feeding period to ensure survival and moulting to the next stage.Under the conditions of the experiment, the survival proportion (= R) is driven only by the time scale of a fluctuation (here t1 = t, τ1 = τ for simplicity), characterised by the starvation period; hence, R = R(t) = r[τ(t)] given that there is a single time of observation fixed to τ({^*}) = 1. Because biological time does not depend t, we get L = dτ/dt and:$$frac{dR}{dt}=frac{partial r}{partial tau }cdot mathcal{L}({m}_{2})$$
(9)
Equation (9) is represented in the PDE by the terms of row 3 and column 4 of M multiplied by the term of row 3 of the column vector r; dτ/dt = L(m), m represents the magnitude of temperature.The relationship between biological time and temperature was best explained by a power function D(T) = aTb (Fig. 4A, Supplementary Table 1, Supplementary Fig. 2), in consistence with previous studies36,40. The interaction between starvation time and temperature was weak (Supplementary Fig. 3); best models retained starvation time only at 21 °C where the percentage of explained variance was still low (R2  More

Harbach, C. J. et al. Delayed senescence in soybean: Terminology, research update, and survey results from growers. Plant Health Progress 17, 76–83 (2016).Article 

Google Scholar 
Hobbs, H. A. et al. Green stem disorder of soybean. Plant Dis. 90, 513–518 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hill, C. B., Hartman, G. L., Esgar, R. & Hobbs, H. A. Field evaluation of green stem disorder in soybean cultivars. Crop Sci. 46, 879–885 (2006).Article 

Google Scholar 
Morita, K. et al. (2006) Effect of green stem on soiled bean index at harvest of soybean by combine harvester. Hokuriku Crop Sci. 41, 107–109 (2006) (in Japanese).
Google Scholar 
Ogiwara, H. Delayed leaf senescence. In: Agriculture, Forestry and Fisheries Research Council of Japan, ed. Soybean-technical development for improving national food self-sufficiency ratio. Annotated bibliography of Agriculture, Forestry, and Fisheries Research, vol. 27, 291–294 (2002). (in Japanese).Crafts-Brandner, S. J. & Egli, D. B. Sink removal and leaf senescence in soybean. Plant Physiol. 85, 662–666 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crafts-Brandner, S. J., Below, F. E., Harper, J. E. & Hageman, R. H. Effects of pod removal on metabolism and senescence of nodulating and nonnodulating soybean isolines. Plant Physiol. 75, 311–317 (1984).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Egli, D. B. & Bruening, W. P. Depodding causes green-stem syndrome in soybean. Crop Manag. 5(1), 1–9. https://doi.org/10.1094/CM-2006-0104-01-RS (2006).Article 

Google Scholar 
Htwe, N. M. P. S. et al. Leaf senescence of soybean at reproductive stage is associated with induction of autophagy-related genes, GmATG8c, GmATG8i and GmATG4. Plant Prod. Sci. 14, 141–147 (2011).CAS 
Article 

Google Scholar 
Leopold, A. C., Niedergang-Kamien, E. & Janick, J. Experimental modification of plant senescence. Plant Physiol. 34, 570–573 (1959).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mondal, M. H., Brun, W. A. & Brenner, M. L. Effects of sink removal on photosynthesis and senescence in leaves of soybean (Glycine max L.) plants. Plant Physiol. 61, 394–397 (1978).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Effect of pod removal on leaf senescence in soybean. Plant Physiol. 70, 1544–1548 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Effect of pod removal on leaf photosynthesis and soluble protein composition of field-grown soybeans. Plant Physiol. 73, 121–124 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Purification and characterization of a soybean leaf storage glycoprotein. Plant Physiol. 73, 125–129 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Staswick, P. E. Developmental regulation and the influence of plant sinks on vegetative storage protein gene expression in soybean leaves. Plant Physiol. 89, 309–315 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sato, J., Shiraiwa, T., Sakashita, M., Tsujimoto, Y. & Yoshida, R. The occurrence of delayed stem senescence in relation to trans-zeatin riboside level in the xylem exudate in soybeans grown under excess-wet and drought soil conditions. Plant Prod. Sci. 10, 460–467 (2007).Article 

Google Scholar 
Takehara, T. et al. Occurrence of delayed leaf senescence of soybean caused by Rhizoctonia aerial blight in Japan. Jpn. Agric. Res. Q. 50, 201–208 (2016).Article 

Google Scholar 
Boethel, D. J. et al. Delayed maturity associated with southern green stink bug (Heteroptera: Pentatomidae) injury at various soybean phenological stages. J. Econ. Entomol. 93, 707–712 (2000).CAS 
PubMed 
Article 

Google Scholar 
Islam, M. M. et al. Nitrogen manipulation affects leaf senescence during late seed filling in soybean. Acta Physiol. Plant. 39, 42 (2017).Article 
CAS 

Google Scholar 
Yamazaki, R., Katsube-Tanaka, T. & Shiraiwa, T. Effect of thinning and shade removal on green stem disorder in soybean. Plant Prod. Sci. 21, 83–92 (2018).CAS 
Article 

Google Scholar 
Yamazaki, R., Katsube-Tanaka, T., Kawasaki, Y., Katayama, K. & Shiraiwa, T. Effect of thinning on cultivar differences of green stem disorder in soybean. Plant Prod. Sci. 22, 311–318 (2019).CAS 
Article 

Google Scholar 
Board, J. E. & Tan, Q. Assimilatory capacity effects on soybean yield components and pod number. Crop Sci. 35, 846–851 (1995).Article 

Google Scholar 
Egli, D. B. Soybean reproductive sink size and short-term reductions in photosynthesis during flowering and pod set. Crop Sci. 50, 1971–1977 (2010).Article 

Google Scholar 
Wells, R., Schulze, L. L., Ashley, D. A., Boerma, H. R. & Brown, R. H. Cultivar differences in canopy apparent photosynthesis and their relationship to seed yield in soybean. Crop Sci. 22, 886–890 (1982).Article 

Google Scholar 
Islam, M. M. et al. Nitrogen redistribution and its relationship with the expression of GmATG8c during seed filling in soybean. J. Plant Physiol. 192, 71–74 (2016).CAS 
PubMed 
Article 

Google Scholar 
Zhao, X., Zheng, S. H. & Arima, S. Influence of nitrogen enrichment during reproductive growth stage on leaf nitrogen accumulation and seed yield in soybean. Plant Prod. Sci. 17, 209–217 (2014).CAS 
Article 

Google Scholar 
Brown, A. W. & Hudson, K. A. Transcriptional profiling of mechanically and genetically sink-limited soybeans. Plant Cell Environ. 40, 2307–2318 (2017).CAS 
PubMed 
Article 

Google Scholar 
Tranbarger, T. J., Franceschi, V. R., Hildebrand, D. F. & Grimes, H. D. The soybean 94-kilodalton vegetative storage protein is a lipoxygenase that is localized in paraveinal mesophyll cell vacuoles. Plant Cell 3, 973–987 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
Melo, B. P. et al. Revisiting the soybean GmNAC superfamily. Front. Plant Sci. 9, 1864 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, H. J. et al. Gene regulatory cascade of senescence-associated NAC transcription factors activated by ETHYLENE-INSENSITIVE2-mediated leaf senescence signaling in Arabidopsis. J. Exp. Bot. 65, 4023–4036 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Tucker, M. L., Burke, A., Murphy, C. A., Thai, V. K. & Ehrenfried, M. L. Gene expression profiles for cell wall-modifying proteins associated with soybean cyst nematode infection, petiole abscission, root tips, flowers, apical buds, and leaves. J. Exp. Bot. 58, 3395–3406 (2007).CAS 
PubMed 
Article 

Google Scholar 
Turner, G. W. et al. Experimental sink removal induces stress responses, including shifts in amino acid and phenylpropanoid metabolism, in soybean leaves. Planta 235, 939–954 (2012).CAS 
PubMed 
Article 

Google Scholar 
Roach, T. & Krieger-Liszkay, A. The role of the PsbS protein in the protection of photosystems I and II against high light in Arabidopsis thaliana. Biochim. Biophys. Acta Bioenerg. 1817, 2158–2165 (2012).CAS 
Article 

Google Scholar 
Horton, P., Ruban, A. V. & Walters, R. G. Regulation of light harvesting in green plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47, 655–684 (1996).CAS 
PubMed 
Article 

Google Scholar 
Hutin, C. et al. Early light-induced proteins protect Arabidopsis from photooxidative stress. Proc. Natl. Acad. Sci. U.S.A. 100, 4921–4926 (2003).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Wang, H. et al. Functional characterization of dihydroflavonol-4-reductase in anthocyanin biosynthesis of purple sweet potato underlies the direct evidence of anthocyanins function against abiotic stresses. PLoS ONE 8, e78484 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Saravitz, D. M. & Siedow, J. N. The differential expression of wound-inducible lipoxygenase genes in soybean leaves. Plant Physiol. 110, 287–299 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pimenta, M. R. et al. The stress-induced soybean NAC transcription factor GmNAC81 plays a positive role in developmentally programmed leaf senescence. Plant Cell Physiol. 57, 1098–1114 (2016).CAS 
PubMed 
Article 

Google Scholar 
Fujimoto, M. et al. Transcriptional switch for programmed cell death in pith parenchyma of sorghum stems. Proc. Natl. Acad. Sci. U.S.A. 115, 8783–8792 (2018).Article 
CAS 

Google Scholar 
Egli, D. B. Variation in leaf starch and sink limitations during seed filling in soybean. Crop Sci. 39, 1361–1368 (1999).CAS 
Article 

Google Scholar 
Board, J. E. & Harville, B. G. Late-planted soybean yield response to reproductive source/sink stress. Crop Sci. 38, 763–771 (1998).Article 

Google Scholar 
Fatichin, Zheng, S. H., Narasaki, K. & Arima, S. Genotypic adaptation of soybean to late sowing in southwestern Japan. Plant Prod. Sci. 16, 123–130 (2013).CAS 
Article 

Google Scholar 
Wakasugi, K. & Fujimori, S. Subsurface Water Level Control System “FOEAS” that promotes the full use of paddy fields. J. Jpn. Soc. Irrig. Drain. Rural Eng. 77, 705–708 (2009) (in Japanese).
Google Scholar 
Fehr, W. R. & Caviness, C. E. Stages of soybean development. Spec. Rep. 80. Iowa Agric. Home Econ. Exp. Stn. Iowa State Univ., Ames. (1977).Furuya, T. & Umezaki, T. Simplified distinction method of degree of delayed stem maturation of soybean plants. Jpn. J. Crop Sci. 62, 126–127 (1993) (in Japanese with English abstract).Article 

Google Scholar 
Kim, D. et al. TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

Current scientific definitions of pondsWe compiled existing scientific definitions of ponds by conducting a backwards and forwards search of papers referenced in or subsequently referencing three seminal pond papers8,17,18 (see “Methods”). We ultimately compiled 54 pond definitions from scientific literature (data available19). The variables most often included in definitions were surface area (91% of definitions), depth (48%), permanence (48%), origin (i.e., natural or human-made; 33%), and standing water (33%; Fig. 2a). When surface area or depth were included in definitions, they were often mentioned qualitatively (e.g., “small” and “shallow”). Of the 61% of definitions that included a maximum pond surface area, the range was 0.1 to 100 ha, the median was 2 ha, and all but two definitions were ≤ 10 ha (Fig. 2b). For depth, only 17% of studies provided a maximum depth cutoff, which ranged 2 to 8 m (Fig. 2c). Of the 26 definitions mentioning permanence, 22 stated that ponds could be temporary or permanent and only three indicated that ponds are exclusively permanent waterbodies. Of the 18 definitions mentioning origin, 17 mentioned that ponds could be natural or human-made with the remaining study indicating ponds can have diverse origins.Figure 2Summary of “pond” definitions from scientific literature including (a) presence of various morphological, biological, and physical characteristics in the definition as blue bars (n = 54 definitions total). Bold black lines indicate the number of definitions with surface area and depth values. Histograms of the upper limits from “pond” definitions for (b) surface area and (c) maximum depth.Full size imageOther important factors included in definitions related to morphometry. For example, 30% of definitions mentioned the potential for plants to colonize the entire basin, which relates to high light penetration (mentioned in 11% of definitions) and/or shallow depths. For example, Wetzel11 defines ponds as having enough light penetration that macrophyte photosynthesis can occur over the entire waterbody. As such, these conditions may be comparable to the littoral region of lakes (11% of definitions). Lastly, 7% of pond definitions mentioned mixing versus stratification, whereby ponds mix more than lakes20 yet less than shallow lakes due to a smaller fetch16.To assess if there was agreement in pond definitions among papers, we examined the number of times each definition was cited. Across the 54 definitions, there were 89 citations of 48 unique papers. Ultimately, most papers (75%) were only cited only once, indicating no consensus in pond definition. The most cited paper was Biggs et al.21, which accounted for 15% of citations. The next two most cited papers were Oertli et al.17 and Sondergaard et al.18, which were seminal papers included in our backwards-forwards search, and each comprised 8% of citations.International definitionsAt an international level, there is no consensus on how to discriminate among ponds, lakes, and wetlands. In North America, wetlands are generally considered to be shallow:  More

Genome’s collection and phylogenetic analysisThe study examined the genomes of 139 isolates, 61 of environmental samples (ENV) and 78 clinical (CLI) (Supplementary Table 1, Supplementary Fig. 1), with origin in 21 countries: USA (23/139, 17%), UK, Portugal and Spain (each 15/139, 33%), China (14/139, 10%), Germany (13/139, 9%), Thailand (11/139, 8%) and other countries (each  More

Haque, M. N. Dietary manipulation: A sustainable way to mitigate methane emissions from ruminants. J. Anim. Sci. Technol. 60, 1–10. https://doi.org/10.1186/s40781-018-0175-7(2018) (2018).Article 

Google Scholar 
IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press (in press).Lauder, A. R. et al. Offsetting methane emissions—An alternative to emission equivalence metrics. Int. J. Greenh. 12, 419–429. https://doi.org/10.1016/j.ijggc.2012.11.028 (2013).CAS 
Article 

Google Scholar 
Hill, J., McSweeney, C., Wright, A. G., Bishop-Hurley, G. & Kalantar-Zadeh, K. Measuring methane production from ruminants. Trends Biotechnol. 34, 26–35. https://doi.org/10.1016/j.tibtech.2015.10.004 (2016).CAS 
Article 
PubMed 

Google Scholar 
Van Zanten, H. H. E. et al. Defining a land boundary for sustainable livestock consumption. Glob Change Biol. 24, 4185–4194. https://doi.org/10.1111/gcb.14321 (2018).ADS 
Article 

Google Scholar 
Naumann, H. D., Tedeschi, L. O., Zeller, W. E. & Huntley, N. F. The role of condensed tannins in ruminant animal production: Advances, limitations and future directions. Rev. Bras. de Zootec. 46, 929–949. https://doi.org/10.1590/S1806-92902017001200009 (2017).Article 

Google Scholar 
Mueller-Harvey, I. Unravelling the conundrum of tannins in animal nutrition and health. J. Sci. Food Agric. 86, 2010–2037. https://doi.org/10.1002/jsfa.2577 (2006).CAS 
Article 

Google Scholar 
Burggraaf, V. T. et al. Morphology and agronomic performance of white clover with increased flowering and condensed tannin concentration. N. Z. J. Agric. Res. 49, 147–155. https://doi.org/10.1080/00288233.2006.9513704 (2006).CAS 
Article 

Google Scholar 
Einarsson, R. et al. Crop production and nitrogen use in European cropland and grassland 1961–2019. Sci. Data 8, 288. https://doi.org/10.1038/s41597-021-01061-z (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Salminen, J.-P. & Karonen, M. Chemical ecology of tannins and other phenolics: we need a change in approach. Funct. Ecol. 25, 325–338. https://doi.org/10.1111/j.1365-2435.2010.01826.x (2011).Article 

Google Scholar 
Zeller, W. E. Activity, purification, and analysis of condensed tannins: current state of affairs and future endeavors. Crop Sci. 59, 886–904. https://doi.org/10.2135/cropsci2018.05.0323 (2019).CAS 
Article 

Google Scholar 
Barbehenn, R. V. & Peter Constabel, C. Tannins in plant–herbivore interactions. Phytochemistry 72, 1551–1565. https://doi.org/10.1016/j.phytochem.2011.01.040 (2011).CAS 
Article 
PubMed 

Google Scholar 
Chung, Y. H. et al. Enteric methane emission, diet digestibility, and nitrogen excretion from beef heifers fed sainfoin or alfalfa1. J. Anim. Sci. 91, 4861–4874. https://doi.org/10.2527/jas.2013-6498 (2013).CAS 
Article 
PubMed 

Google Scholar 
Christensen, R. G. et al. Effects of feeding birdsfoot trefoil hay on neutral detergent fiber digestion, nitrogen utilization efficiency, and lactational performance by dairy cows1. J. Dairy Sci. 98, 7982–7992. https://doi.org/10.3168/jds.2015-9348 (2015).CAS 
Article 
PubMed 

Google Scholar 
Jonker, A. & Yu, P. The occurrence, biosynthesis, and molecular structure of proanthocyanidins and their effects on legume forage protein precipitation, digestion and absorption in the ruminant digestive tract. Int. J. Mol. Sci. 18, 1105. https://doi.org/10.3390/ijms18051105 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Barry, T. N. & McNabb, W. C. The implications of condensed tannins on the nutritive value of temperate forages fed to ruminants. Br. J. Nutr. 81, 263–272. https://doi.org/10.1017/S0007114599000501 (1999).CAS 
Article 
PubMed 

Google Scholar 
Verma, S., Taube, F. & Malisch, C. S. Examining the variables leading to apparent incongruity between antimethanogenic potential of tannins and their observed effects in ruminants—A review. Sustainability 13, 2743. https://doi.org/10.3390/su13052743 (2021).CAS 
Article 

Google Scholar 
Malisch, C. S. et al. Large variability of proanthocyanidin content and composition in Sainfoin (Onobrychis viciifolia). J. Agric. Food Chem. 63, 10234–10242. https://doi.org/10.1021/acs.jafc.5b04946 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Verma, S., Salminen, J.-P., Taube, F. & Malisch, C. S. Large inter- and intraspecies variability of polyphenols and proanthocyanidins in eight temperate forage species indicates potential for their exploitation as nutraceuticals. J. Agric. Food Chem. 69, 12445–12455. https://doi.org/10.1021/acs.jafc.1c03898 (2021).CAS 
Article 
PubMed 

Google Scholar 
Lorenz, H., Reinsch, T., Kluß, C., Taube, F. & Loges, R. Does the admixture of forage herbs affect the yield performance, yield stability and forage quality of a grass clover ley?. Sustainability 12, 5842. https://doi.org/10.3390/su12145842 (2020).Article 

Google Scholar 
Hofer, D. et al. Yield of temperate forage grassland species is either largely resistant or resilient to experimental summer drought. J. Appl. Ecol. 53, 1023–1034. https://doi.org/10.1111/1365-2664.12694 (2016).Article 

Google Scholar 
Mueller-Harvey, I. et al. Benefits of condensed tannins in forage legumes fed to ruminants : Importance of structure, concentration and diet compsition. Crop Sci. 59, 861–885. https://doi.org/10.2135/cropsci2017.06.0369 (2017).CAS 
Article 

Google Scholar 
Loza, C. et al. Assessing the potential of diverse forage mixtures to reduce enteric methane emissions in vitro. Animals 11, 1126. https://doi.org/10.3390/ani11041126 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Min, B. R. et al. Dietary mitigation of enteric methane emissions from ruminants: A review of plant tannin mitigation options. Anim. Nutr. 6, 231–236. https://doi.org/10.1016/j.aninu.2020.05.002 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
van Gastelen, S., Dijkstra, J. & Bannink, A. Are dietary strategies to mitigate enteric methane emission equally effective across dairy cattle, beef cattle, and sheep?. J. Dairy Sci. 102, 6109–6130. https://doi.org/10.3168/jds.2018-15785 (2019).CAS 
Article 
PubMed 

Google Scholar 
Hatew, B. et al. Relationship between in vitro and in vivo methane production measured simultaneously with different dietary starch sources and starch levels in dairy cattle. Anim. Feed Sci. Technol. 202, 20–31. https://doi.org/10.1016/j.anifeedsci.2015.01.012 (2015).CAS 
Article 

Google Scholar 
Storm, I. M. L. D., Hellwing, A. L. F., Nielsen, N. I. & Madsen, J. Methods for measuring and estimating methane emission from ruminants. Animals 2, 160–183. https://doi.org/10.3390/ani2020160 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Dewhurst, R. J., Delaby, L., Moloney, A., Boland, T. & Lewis, E. Nutritive value of forage legumes used for grazing and silage. Irish J. Agric. Food Res. 48, 167–187 (2009).CAS 

Google Scholar 
Hakl, J., Fuksa, P., Konečná, J. & Šantrůček, J. Differences in the crude protein fractions of lucerne leaves and stems under different stand structures. Grass Forage Sci. 71, 413–423. https://doi.org/10.1111/gfs.12192 (2016).CAS 
Article 

Google Scholar 
Jayanegara, A., Makkar, H. & Becker, K. The use of principal component analysis in identifying and integrating variables related to forage quality and methane production. J. Indones. Trop. Anim. 34, 241–247. https://doi.org/10.14710/jitaa.34.4.241-247 (2009).Article 

Google Scholar 
Maccarana, L. et al. Methodological factors affecting gas and methane production during in vitro rumen fermentation evaluated by meta-analysis approach. J. Anim. Sci. Biotechnol. 7, 35–35. https://doi.org/10.1186/s40104-016-0094-8 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Baruah, L., Malik, P. K., Kolte, A. P., Dhali, A. & Bhatta, R. Methane mitigation potential of phyto-sources from Northeast India and their effect on rumen fermentation characteristics and protozoa in vitro. Vet. World 11, 809–818. https://doi.org/10.14202/vetworld.2018.809-818 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hassanat, F. & Benchaar, C. Assessment of the effect of condensed (acacia and quebracho) and hydrolysable (chestnut and valonea) tannins on rumen fermentation and methane production in vitro. J. Sci. Food Agric. 93, 332–339. https://doi.org/10.1002/jsfa.5763 (2013).CAS 
Article 
PubMed 

Google Scholar 
Naumann, H. et al. Relationships between structures of condensed tannins from texas legumes and methane production during in vitro rumen digestion. Molecules 23, 2123. https://doi.org/10.3390/molecules23092123 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Jayanegara, A., Makkar, H. P. S. & Becker, K. Addition of purified tannin sources and polyethylene glycol treatment on methane emission and rumen fermentation in vitro. Media Peternakan 38, 57–63. https://doi.org/10.5398/medpet.2015.38.1.57 (2015).Article 

Google Scholar 
Jayanegara, A., Goel, G., Makkar, H. P. S. & Becker, K. Divergence between purified hydrolysable and condensed tannin effects on methane emission, rumen fermentation and microbial population in vitro. Anim. Feed Sci. Technol. 209, 60–68. https://doi.org/10.1016/j.anifeedsci.2015.08.002 (2015).CAS 
Article 

Google Scholar 
Hatew, B. et al. Diversity of condensed tannin structures affects rumen in vitro methane production in sainfoin (Onobrychis viciifolia) accessions. Grass Forage Sci. 70, 474–490. https://doi.org/10.1111/gfs.12125 (2015).CAS 
Article 

Google Scholar 
Huyen, N. T. et al. Structural features of condensed tannins affect in vitro ruminal methane production and fermentation characteristics. J. Agric. Sci. 154, 1474–1487. https://doi.org/10.1017/S0021859616000393 (2016).CAS 
Article 

Google Scholar 
Salami, S. A. et al. Characterisation of the ruminal fermentation and microbiome in lambs supplemented with hydrolysable and condensed tannins. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiy061 (2018).Article 
PubMed 

Google Scholar 
Salminen, J. P., Karonen, M. & Sinkkonen, J. Chemical ecology of tannins: Recent developments in tannin chemistry reveal new structures and structure-activity patterns. Chem.-Eur. J. 17, 2806–2816. https://doi.org/10.1002/chem.201002662 (2011).CAS 
Article 
PubMed 

Google Scholar 
Bezabih, M., Pellikaan, W. F., Tolera, A., Khan, N. A. & Hendriks, W. Chemical composition and in vitro total gas and methane production of forage species from the Mid Rift Valley grasslands of Ethiopia. Grass Forage Sci. 69, 635–643. https://doi.org/10.1111/gfs.12091 (2013).CAS 
Article 

Google Scholar 
Navarrete, S., Kemp, P. D., Pain, S. J. & Back, P. J. Bioactive compounds, aucubin and acteoside, in plantain (Plantago lanceolata L.) and their effect on in vitro rumen fermentation. Anim. Feed Sci. Technol. 222, 158–167. https://doi.org/10.1016/j.anifeedsci.2016.10.008 (2016).CAS 
Article 

Google Scholar 
Basha, N. A., Scogings, P. F. & Nsahlai, I. V. Effects of season, browse species and polyethylene glycol addition on gas production kinetics of forages in the subhumid subtropical savannah, South Africa. J. Sci. Food Agric. 93, 1338–1348. https://doi.org/10.1002/jsfa.5895 (2013).CAS 
Article 
PubMed 

Google Scholar 
O’Donovan, L. & Brooker, J. D. Effect of hydrolysable and condensed tannins on growth, morphology and metabolism of Streptococcus gallolyticus (S. caprinus) and Streptococcus bovis. Microbiology 147, 1025–1033. https://doi.org/10.1099/00221287-147-4-1025 (2001).CAS 
Article 
PubMed 

Google Scholar 
Bhatta, R. et al. Difference in the nature of tannins on in vitro ruminal methane and volatile fatty acid production and on methanogenic archaea and protozoal populations. J. Dairy Sci. 92, 5512–5522. https://doi.org/10.3168/jds.2008-1441 (2009).CAS 
Article 
PubMed 

Google Scholar 
Naumann, H. D. et al. Effect of molecular weight and concentration of legume condensed tannins on in vitro larval migration inhibition of Haemonchus contortus. Vet. Parasitol. 199, 93–98. https://doi.org/10.1016/j.vetpar.2013.09.025 (2014).CAS 
Article 
PubMed 

Google Scholar 
Jayanegara, A., Goel, G., Makkar, H.P.S., & Becker, K. Reduction in
methane emissions from ruminants by plant secondary metabolites: Effects of polyphenols and saponins. Food and Agriculture Organization of the United Nations (FAO) Rome, Italy, 151–157. ISBN 978-92-5-106697-3 (2010).Hatew, B. et al. Impact of variation in structure of condensed tannins from sainfoin (Onobrychis viciifolia) on in vitro ruminal methane production and fermentation characteristics. J. Anim. Physiol. Anim. Nutr. 100, 348–360. https://doi.org/10.1111/jpn.12336 (2016).CAS 
Article 

Google Scholar 
Waghorn, G. C., Douglas, G. B., Niezen, J. H., McNabb, W. C. & Foote, A. G. Forages with condensed tannins-their management and nutritive value for ruminants. Proc. N. Z. Grassl. Assoc., 60, 89−98 (1998).Woodward, S. L., Waghorn, G. C. & Lassey, K. Early indications that feeding Lotus will reduce methane emissions from ruminants. Proc. N. Z. Soc. Anim. Prod. 61, 23–26 (2001).
Google Scholar 
Molle, G. et al. Responses to condensed tannins of flowering sulla (Hedysarum coronarium L.) grazed by dairy sheep: Part 1: Effects on feeding behaviour, intake, diet digestibility and performance. Livest. Sci. 123, 138–146. https://doi.org/10.1016/j.livsci.2008.11.018 (2009).Article 

Google Scholar 
Orlandi, T., Kozloski, G. V., Alves, T. P., Mesquita, F. R. & Ávila, S. C. Digestibility, ruminal fermentation and duodenal flux of amino acids in steers fed grass forage plus concentrate containing increasing levels of Acacia mearnsii tannin extract. Anim. Feed Sci. Technol. 210, 37–45. https://doi.org/10.1016/j.anifeedsci.2015.09.012 (2015).CAS 
Article 

Google Scholar 
Patra, A. K. & Yu, Z. Effects of adaptation of in vitro rumen culture to garlic oil, nitrate, and saponin and their combinations on methanogenesis, fermentation, and abundances and diversity of microbial populations. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01434 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Niderkorn, V. et al. Effect of increasing the proportion of chicory in forage-based diets on intake and digestion by sheep. Animal 13, 718–726. https://doi.org/10.1017/S1751731118002185 (2019).CAS 
Article 
PubMed 

Google Scholar 
Lee, J., Hemmingson, N., Minneé, E. & Clark, C. Management strategies for chicory (Cichorium intybus) and plantain (Plantago lanceolata): Impact on dry matter yield, nutritive characteristics, and plant density. Crop Pasture Sci. 66, 168. https://doi.org/10.1071/CP14181 (2015).CAS 
Article 

Google Scholar 
Cong, W.-F., Jing, J., Rasmussen, J., Søegaard, K. & Eriksen, J. Forbs enhance productivity of unfertilised grass-clover leys and support low-carbon bioenergy. Sci. Rep. 7, 1422. https://doi.org/10.1038/s41598-017-01632-4 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sanderson, M. A., Labreveux, M., Hall, M. H. & Elwinger, G. F. Nutritive value of chicory and English plantain forage. Crop Sci. 43, 1797. https://doi.org/10.2135/cropsci2003.1797 (2003).CAS 
Article 

Google Scholar 
Van Soest, P. J., Robertson, J. B. & Lewis, B. A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2 (1991).Article 
PubMed 

Google Scholar 
Engström, M. T. et al. Rapid qualitative and quantitative analyses of proanthocyanidin oligomers and polymers by UPLC-MS/MS. J. Agric. Food Chem. 62, 3390–3399. https://doi.org/10.1021/jf500745y (2014).CAS 
Article 
PubMed 

Google Scholar 
Menke, K. & Steingass, H. Estimation of the energetic feed value obtained from chemical analysis and in vitro gas production using rumen fluid. Anim. Res. Dev. 28, 7–55 (1988).
Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Venables, B. & Ripley, B. Generalised linear models. In Modern Applied Statistics With S.(4th edition) 183–208 (Springer, 2013). More