Ecology

Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
Roberto Danovaro, Emanuela Fanelli, Laura Carugati & Antonio Dell’Anno

Stazione Zoologica Anton Dohrn, Naples, Italy
Roberto Danovaro, Emanuela Fanelli & Jacopo Aguzzi

Instituto de Ciencias del Mar (ICM-CSIC), Barcelona, Spain
Jacopo Aguzzi

National Oceanography Centre, Southampton, UK
David Billett & Henry A. Ruhl

Department of Sciences and Engineering of Materials, Environment and Urban Planning (SIMAU), Polytechnic University of Marche, Ancona, Italy
Cinzia Corinaldesi

IUCN Global Marine and Polar Programme, Cambridge, MA, USA
Kristina Gjerde

School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
Alan J. Jamieson

The Biodiversity Research Group, The School of Biological Sciences, Centre for Biodiversity and Conservation Science, The University of Queensland, Brisbane, Queensland, Australia
Salit Kark

Louisiana Universities Marine Consortium, Chauvin, LA, USA
Craig McClain

Center for Marine Biodiversity and Conservation and Integrative Oceanography Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
Lisa A. Levin

Department of Geography, The Hebrew University of Jerusalem, Jerusalem, Israel
Noam Levin

Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
Noam Levin

Norwegian Institute for Water Research, Oslo, Norway
Eva Ramirez-Llodra

Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA
Henry A. Ruhl

Department of Oceanography, University of Hawaii at Mano’a, Honolulu, HI, USA
Craig R. Smith

Departments of Ocean Sciences and Biology, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada
Paul V. R. Snelgrove

Jacobs University, Bremen, Germany
Laurenz Thomsen

Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University, Durham, NC, USA
Cindy L. Van Dover

School of Biological Sciences and Swire Institute of Marine Science, The University of Hong Kong, Hong Kong SAR, China
Moriaki Yasuhara

State Key Laboratory of Marine Pollution, City University of Hong Kong, Hong Kong SAR, China
Moriaki Yasuhara

R.D., E.F., J.A., D.B., L.C., C.C., A.D., K.G., A.J.J., S.K., C.M., L.A.L., N.L., E.R.-L., H.A.R., C.R.S., P.V.R.S., L.T., C.L.V.D. and M.Y. equally contributed to the work, critically revised the final version and gave approval for publication. More

1.
Zahn G, Amend AS. Foliar fungi alter reproductive timing and allocation in Arabidopsis under normal and water-stressed conditions. Fungal Ecol. 2019;41:101–6.
Article  Google Scholar 
2.
Arnold AE, Engelbrecht BMJ. Fungal endophytes nearly double minimum leaf conductance in seedlings of a neotropical tree species. J Trop Ecol. 2007;23:369–72.
Article  Google Scholar 

3.
Taghavi S, Garafola C, Monchy S, Newman L, Hoffman A, Weyens N, et al. Genome survey and characterization of endophytic bacteria exhibiting a beneficial effect on growth and development of poplar trees. Appl Environ Microbiol. 2009;75:748–57.
CAS  PubMed  Article  Google Scholar 

4.
Berg G, Köberl M, Rybakova D, Müller H, Grosch R, Smalla K. Plant microbial diversity is suggested as the key to future biocontrol and health trends. FEMS Microbiol Ecol. 2017;93:fix050. https://doi.org/10.1093/femsec/fix050.

5.
Choudoir MJ, Barberan A, Menninger HL, Dunn RR, Fierer N. Variation in range size and dispersal capabilities of microbial taxa. Ecology. 2017;99:322–34.
Article  Google Scholar 

6.
Klironomos JN. Feedback with soil biota contributes to plant rarity and invasiveness in communities. Nature. 2002;417:67–70.
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Dini-Andreote F, Raaijmakers JM. Embracing community ecology in plant microbiome research. Trends Plant Sci. 2018;23:467–9.
CAS  PubMed  Article  Google Scholar 

8.
Beilsmith K, Thoen MPM, Brachi B, Gloss AD, Khan MH, Bergelson J. Genome-wide association studies on the phyllosphere microbiome: embracing complexity in host-microbe interactions. Plant J Cell Mol Biol. 2019;97:164–81.
CAS  Article  Google Scholar 

9.
Peay KG, Kennedy PG, Talbot JM. Dimensions of biodiversity in the Earth mycobiome. Nat Rev Microbiol. 2016;14:434–47.
CAS  PubMed  Article  Google Scholar 

10.
Wang J, Soininen J, He J, Shen J. Phylogenetic clustering increases with elevation for microbes. Environ Microbiol Rep. 2012;4:217–26.
PubMed  Article  Google Scholar 

11.
Zimmerman NB, Vitousek PM. Fungal endophyte communities reflect environmental structuring across a Hawaiian landscape. Proc Natl Acad Sci USA. 2012;109:13022–7.
CAS  PubMed  Article  Google Scholar 

12.
Yang Y, Gao Y, Wang S, Xu D, Yu H, Wu L, et al. The microbial gene diversity along an elevation gradient of the Tibetan grassland. ISME J. 2014;8:430–40.
CAS  PubMed  Article  Google Scholar 

13.
Shen C, Ni Y, Liang W, Wang J, Chu H. Distinct soil bacterial communities along a small-scale elevational gradient in alpine tundra. Front Microbiol. 2015;6:582.

14.
Yao F, Yang S, Wang Z, Wang X, Ye J, Wang X, et al. Microbial taxa distribution is associated with ecological trophic cascades along an elevation gradient. Front Microbiol. 2017;8:2071.

15.
Na X, Xu TT, Li M, Ma F, Kardol P. Bacterial diversity in the rhizosphere of two phylogenetically closely related plant species across environmental gradients. J Soils Sediment. 2017;17:122–32.
CAS  Article  Google Scholar 

16.
Jangid K, Williams MA, Franzluebbers AJ, Schmidt TM, Coleman DC, Whitman WB. Land-use history has a stronger impact on soil microbial community composition than aboveground vegetation and soil properties. Soil Biol Biochem. 2011;43:2184–93.
CAS  Article  Google Scholar 

17.
Talbot JM, Bruns TD, Taylor JW, Smith DP, Branco S, Glassman SI, et al. Endemism and functional convergence across the North American soil mycobiome. Proc Natl Acad Sci USA. 2014;111:6341–6.
CAS  PubMed  Article  Google Scholar 

18.
Oono R, Rasmussen A, Lefèvre E. Distance decay relationships in foliar fungal endophytes are driven by rare taxa: distance decay in fungal endophytes. Environ Microbiol. 2017;19:2794–805.
CAS  PubMed  Article  Google Scholar 

19.
Amend AS, Cobian GM, Laruson AJ, Remple K, Tucker SJ, Poff KE, et al. Phytobiomes are compositionally nested from the ground up. PeerJ. 2019;7:e6609.
PubMed  PubMed Central  Article  Google Scholar 

20.
Fitzpatrick CR, Copeland J, Wang PW, Guttman DS, Kotanen PM, Johnson MTJ. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc Natl Acad Sci USA. 2018;115:E1157–65.
CAS  PubMed  Article  Google Scholar 

21.
Bálint M, Tiffin P, Hallström B, O’Hara RB, Olson MS, Fankhauser JD, et al. Host genotype shapes the foliar fungal microbiome of balsam poplar (Populus balsamifera). PLoS ONE. 2013;8:e53987.
PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Bálint M, Bartha L, O’Hara RB, Olson MS, Otte J, Pfenninger M, et al. Relocation, high-latitude warming and host genetic identity shape the foliar fungal microbiome of poplars. Mol Ecol. 2015;24:235–48.
PubMed  Article  CAS  Google Scholar 

23.
Coleman‐Derr D, Desgarennes D, Fonseca‐Garcia C, Gross S, Clingenpeel S, Woyke T, et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. N. Phytol. 2016;209:798–811.
Article  CAS  Google Scholar 

24.
Lindow SE, Brandl MT. Microbiology of the phyllosphere. Appl Environ Microbiol. 2003;69:1875–83.
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Redford AJ, Bowers RM, Knight R, Linhart Y, Fierer N. The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves. Environ Microbiol. 2010;12:2885–93.
PubMed  PubMed Central  Article  Google Scholar 

26.
Leff JW, Del Tredici P, Friedman WE, Fierer N. Spatial structuring of bacterial communities within individual Ginkgo biloba trees. Environ Microbiol. 2015;17:2352–61.
PubMed  Article  Google Scholar 

27.
Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.
CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
de Souza RSC, Okura VK, Armanhi JSL, Jorrín B, Lozano N, da Silva MJ, et al. Unlocking the bacterial and fungal communities assemblages of sugarcane microbiome. Sci Rep. 2016;6:28774.
PubMed  PubMed Central  Article  CAS  Google Scholar 

29.
Wearn JA, Sutton BC, Morley NJ, Gange AC. Species and organ specificity of fungal endophytes in herbaceous grassland plants. J Ecol. 2012;100:1085–92.
Article  Google Scholar 

30.
Beckers B, Op De Beeck M, Weyens N, Boerjan W, Vangronsveld J. Structural variability and niche differentiation in the rhizosphere and endosphere bacterial microbiome of field-grown poplar trees. Microbiome. 2017;5:25.
PubMed  PubMed Central  Article  Google Scholar 

31.
Desgarennes D, Garrido E, Torres-Gomez MJ, Peña-Cabriales JJ, Partida-Martinez LP. Diazotrophic potential among bacterial communities associated with wild and cultivated Agave species. FEMS Microbiol Ecol. 2014;90:844–57.
CAS  PubMed  Article  Google Scholar 

32.
Fonseca-García C, Coleman-Derr D, Garrido E, Visel A, Tringe SG, Partida-Martínez LP. The cacti microbiome: interplay between habitat-filtering and host-specificity. Front Microbiol. 2016;7:150.
PubMed  PubMed Central  Article  Google Scholar 

33.
Lindström ES, Langenheder S. Local and regional factors influencing bacterial community assembly. Environ Microbiol Rep. 2012;2012:1–9.
Article  Google Scholar 

34.
Kembel SW, O’Connor TK, Arnold HK, Hubbell SP, Wright SJ, Green JL. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proc Natl Acad Sci USA. 2014;111:13715–20.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Frank AC, Saldierna Guzmán JP, Shay JE. Transmission of bacterial endophytes. Microorganisms. 2017;5:70.
PubMed Central  Article  CAS  Google Scholar 

36.
Wu Z, Raven PH, Hong D. Hibiscus tiliaceus. eFloras. St. Louis, MO: Missouri Botanical Garden & Cambridge, MA: Harvard University Herbaria; 2019. p. 287–8.

37.
Motooka P, Castro L, Nelson D, Nagai G, Ching L. Weeds of Hawaiʻi’s pastures and natural areas: an identification and management guide. Honolulu: University of Hawaiʻi Press; 2014.
Google Scholar 

38.
Quesada T, Hughes J, Smith K, Shin K, James P, Smith J. A low-cost spore trap allows collection and real-time PCR quantification of airborne Fusarium circinatum spores. Forests. 2018;9:586.
Article  Google Scholar 

39.
Smith DP, Peay KG. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS ONE. 2014;9:e90234.
PubMed  PubMed Central  Article  CAS  Google Scholar 

40.
Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems. 2016;1:e00009–15.
PubMed  Article  Google Scholar 

41.
Bellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P, Kauserud H. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol. 2010;10:189.
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Rivers AR, Weber KC, Gardner TG, Liu S, Armstrong SD. ITSxpress: software to rapidly trim internally transcribed spacer sequences with quality scores for marker gene analysis. F1000Research. 2018;7:1418.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Hannon GJ. FASTX-Toolkit: FASTQ/A short-reads pre-processing tools. 2010. http://hannonlab.cshl.edu/fastx_toolkit/.

44.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.
PubMed  PubMed Central  Article  Google Scholar 

45.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.
PubMed  PubMed Central  Article  Google Scholar 

47.
Gdanetz K, Benucci GMN, Vande Pol N, Bonito G. CONSTAX: a tool for improved taxonomic resolution of environmental fungal ITS sequences. BMC Bioinforma. 2017;18:538.
Article  Google Scholar 

48.
Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun. 2017;8:1188.
PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226.
PubMed  PubMed Central  Article  Google Scholar 

51.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Giambelluca TW, Shuai X, Barnes ML, Alliss RJ, Longman RJ, Miura T, et al. Evapotranspiration of Hawai’i. 2014. Final report submitted to the U.S. Army Corps of Engineers—Honolulu District, and the Commission on Water Resource Management, State of Hawai’i.

53.
Hijmans RJ. raster: geographic data analysis and modeling. R package v 2.9-5. 2019. https://CRAN.R-project.org/package=raster.

54.
Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 

55.
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R package v 2.5-5. 2019. https://CRAN.R-project.org/package=vegan.

56.
Guillot G, Rousset F. Dismantling the Mantel tests. Methods Ecol Evol. 2013;4:336–44.
Article  Google Scholar 

57.
Dormann CF, Gruber B, Fruend J. Introducing the bipartite package: analysing ecological networks. R N. 2008;8:8–11.
Google Scholar 

58.
Dormann CF, Fründ J, Blüthgen N, Gruber B. Indices, graphs and null models: analyzing bipartite ecological networks. Open Ecol J. 2009;2:7–24.

59.
Atmar W, Patterson BD. The measure of order and disorder in the distribution of species in fragmented habitat. Oecologia. 1993;96:373–82.
PubMed  Article  Google Scholar 

60.
Almeida‐Neto M, Guimarães P, Guimarães PR, Loyola RD, Ulrich W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos. 2008;117:1227–39.
Article  Google Scholar 

61.
Zarraonaindia I, Owens SM, Weisenhorn P, West K, Hampton-Marcell J, Lax S, et al. The soil microbiome influences grapevine-associated microbiota. mBio. 2015;6:e02527–14. https://mbio.asm.org/content/6/2/e02527-14.

62.
Zinger L, Amaral-Zettler LA, Fuhrman JA, Horner-Devine MC, Huse SM, Welch DBM, et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE. 2011;6:e24570.
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Matthew W, Fraser DBG, Pauline F, Grierson BL, Kendrick GaryA. Metagenomic evidence of microbial community responsiveness to phosphorus and salinity gradients in seagrass sediments. Front Microbiol. 2018;9:1703.
Article  Google Scholar 

64.
Peay KG, Schubert MG, Nguyen NH, Bruns TD. Measuring ectomycorrhizal fungal dispersal: macroecological patterns driven by microscopic propagules. Mol Ecol. 2012;21:4122–36.
PubMed  Article  Google Scholar 

65.
Massoni J, Bortfeld-Miller M, Jardillier L, Salazar G, Sunagawa S, Vorholt JA. Consistent host and organ occupancy of phyllosphere bacteria in a community of wild herbaceous plant species. ISME J. 2020;14:245–58.
CAS  PubMed  Article  Google Scholar 

66.
Treseder KK, Maltz MR, Hawkins BA, Fierer N, Stajich JE, McGuire KL. Evolutionary histories of soil fungi are reflected in their large-scale biogeography. Ecol Lett. 2014;17:1086–93.
PubMed  Article  Google Scholar 

67.
Nguyen HDT, Chabot D, Hirooka Y, Roberson RW, Seifert KA. Basidioascus undulatus: genome, origins, and sexuality. IMA Fungus. 2015;6:215–31.
PubMed  PubMed Central  Article  Google Scholar 

68.
Becraft ED, Woyke T, Jarett J, Ivanova N, Godoy-Vitorino F, Poulton N, et al. Rokubacteria: genomic giants among the uncultured bacterial phyla. Front Microbiol. 2017;8:2264.

69.
Meiser A, Bálint M, Schmitt I. Meta-analysis of deep-sequenced fungal communities indicates limited taxon sharing between studies and the presence of biogeographic patterns. N Phytol. 2014;201:623–35.
CAS  Article  Google Scholar 

70.
Crowther TW, Maynard DS, Crowther TR, Peccia J, Smith JR, Bradford MA. Untangling the fungal niche: the trait-based approach. Front Microbiol. 2014;5:579.
PubMed  PubMed Central  Article  Google Scholar 

71.
Shakya M, Gottel N, Castro H, Yang ZK, Gunter L, Labbé J, et al. A multifactor analysis of fungal and bacterial community structure in the root microbiome of mature Populus deltoides trees. PLoS ONE. 2013;8:e76382.
CAS  PubMed  PubMed Central  Article  Google Scholar  More

1.
Andersen, H. C. Den grimme ælling. In Nye Eventyr. Første Bind. Første Samling (C.A. Reitzels Forlag, 1843).
2.
Brøndegaard, V. J. Folk og flora: dansk etnobotanik (Rosenkilde og Bagger, 1987).

3.
Giles, M. et al. Butterbur. J. Herb. Pharmacother. 5, 119–143 (2009).
Google Scholar 

4.
Aydin, A. A., Zerbes, V., Parlar, H. & Letzel, T. The medical plant butterbur (Petasites): analytical and physiological (re)view. J. Pharm. Biomed. Anal. 75, 220–229 (2013).
CAS  Article  PubMed  Google Scholar 

5.
Giversen, I., Brimer, L., & Kristiansen, B. Danmarks Vilde Lægeplanter (Gyldendal A/S, 2014).

6.
Asen, A. Plants of possible monastic origin, growing in the past or present, at medieval monastery grounds in Norway. In Plants and Culture: Seeds of the Cultural Heritage of Europe (ed. Morel, J.P.) 227–238 (Edipuglia, 2009).

7.
Solberg, S. O. More than just weeds. NordGen’s work with Cultural Relict Plants and Bernt Løjtnant’s inventories from Denmark (Nordic Genetic Resource Center, 2014).

8.
Thomet, O. A., Wiesmann, U. N., Blaser, K. & Simon, H. U. Differential inhibition of inflammatory effector functions by petasin, isopetasin and neopetasin in human eosinophils. Clin. Exp. Allergy 31, 1310–1320 (2001).
CAS  Article  PubMed  Google Scholar 

9.
Sutherland, A. & Sweet, B. V. Butterbur: an alternative therapy for migraine prevention. Am. J. Health Syst. Pharm. 67, 705–711 (2010).
CAS  Article  PubMed  Google Scholar 

10.
Benemei, S., De Logu, F., Li, Puma, S., Marone, I.M., Coppi, E., Ugolini, F., Liedtke, W., Pollastro, F., Appendino, G., Geppetti, P., Materazzi, S., Nassini, R. The anti-migraine component of butterbur extracts, isopetasin, desensitizes peptidergic nociceptors by acting on TRPA1 cation channel. Br J Pharmacol. 174, 2897–2911 (2017).

11.
Anderson, N. & Borlak, J. Hepatobiliary events in migraine therapy with herbs: the case of petadolex, a petasites hybridus extract. J. Clin. Med. 8, 652 (2019).
CAS  Article  PubMed Central  Google Scholar 

12.
Kozlov, V., Lavrenova, G., Savlevich, E. & Bazarkina, K. Evidence-based phytotherapy in allergicrhinitis rhinitis. Clin. Phytosci. 4, 1–8 (2018).
Article  CAS  Google Scholar 

13.
Avula, B., Wang, Y. H., Wang, M., Smillie, T. J. & Khan, I. A. Simultaneous determination of sesquiterpenes and pyrrolizidine alkaloids from the rhizomes of Petasites hybridus (L.) GM et Sch and dietary supplements using UPLC-UV and HPLC-TOF-MS methods. J. Pharm. Biomed. Anal. 70, 53–63 (2012).
CAS  Article  PubMed  Google Scholar 

14.
Kalin, P. The common butterbur (Petasites hybridus)—portrait of a medicinal herb. Forsch Komplementarmed Klass Naturheilkd 10, 41–44 (2003).
PubMed  PubMed Central  Google Scholar 

15.
Roberts, M. F. & Wink, M. Alkaloids: Biochemistry, Ecology, and Medicinal Applications (Springer, Berlin, 1998).
Google Scholar 

16.
Aniszewski, T. Alkaloids: Chemistry, Biology, Ecology, and Applications 2nd edn. (Elsevier, Hoboken, 2015).
Google Scholar 

17.
International Agency for Research on Cancer (IARC). IARC Monographs on the Evaluation of Carcinogenic Risks to Humans; Some Traditional Herbal Medicines, Somemycotoxins, Naphthalene and Styrene (IARC Press, Lyon, 2002).
Google Scholar 

18.
Xia, Q., He, X., Ma, L., Chen, S. & Fu, P. P. Pyrrolizidine alkaloid secondary pyrrolic metabolites construct multiple activation pathways leading to DNA adduct formation and potential liver tumor initiation. Chem. Res. Toxicol. 31, 619–628 (2018).
CAS  Article  PubMed  Google Scholar 

19.
Yang, L. & Stockigt, J. Trends for diverse production strategies of plant medicinal alkaloids. Nat. Prod. Rep. 27, 1469–1479 (2010).
CAS  Article  PubMed  Google Scholar 

20.
Wang, T. et al. Pyrrolizidine alkaloids in honey: quantification with and without standards. Food Control 98, 227–237 (2019).
CAS  Article  Google Scholar 

21.
Scholtz, S., MacMorris, L., Krogmann, F. & Auffarth, G. U. Poisons, drugs and medicine: on the use of atropine and scopolamine in medicine and ophthalmology: an historical review of their applications. J. Eye Stud. Treat. 1, 51–58 (2019).
Google Scholar 

22.
Matolcsy, G., Nadasy, M. & Andriska, V. Pesticide Chemistry 32nd edn. (Elsevier, Hoboken, 1989).
Google Scholar 

23.
Dembitsky, V. M., Gloriozova, T. A. & Poroikov, V. V. Naturally occurring plant isoquinoline N-oxide alkaloids: their pharmacological and SAR activities. Phytomedicine 22, 183–202 (2015).
CAS  Article  PubMed  Google Scholar 

24.
Europen Food Safety Authority. Risks for human health related to the presence of pyrrolizidine alkaloids in honey, tea, herbal infusions and food supplements. EFSA Journal. https://www.efsa.europa.eu/en/efsajournal/pub/4908 (2017).

25.
European Medicines Agency, Committee on Herbal Medicinal Products. Public Statement on Contamination of Herbal Medicinal Products/Traditional Herbal Medicinal Products with Pyrrolizidine Alkaloids—Transitional Recommendations for Risk Management and Quality Control. https://www.ema.europa.eu/en/documents/public-statement/public-statement-contamination-herbal-medicinal-products/traditional-herbal-medicinal-products-pyrrolizidine-alkaloids_en.pdf (2016).

26.
López-Pacheco, I. Y. et al. Anthropogenic contaminants of high concern: existence in water resources and their adverse effects. Sci. Total Environ. 690, 1068–1088 (2019).
ADS  Article  CAS  PubMed  Google Scholar 

27.
Angeles, L. F. & Aga, D. S. Catching the elusive persistent and mobile organic compounds: novel sample preparation and advanced analytical techniques. Trends Environ. Anal. Chem. 25, e00078 (2020).
CAS  Article  Google Scholar 

28.
Reemtsma, T. et al. Mind the gap: persistent and mobile organic compounds—water contaminants that slip through. Environ. Sci. Technol. 50, 10308–10315 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

29.
Furlong, E. T. et al. Nationwide reconnaissance of contaminants of emerging concern in source and treated drinking waters of the United States: pharmaceuticals. Sci. Total Environ. 579, 1629–1642 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

30.
Boxall, A. B. A. The environmental side effects of medication. EMBO Rep. 5, 1110–1116 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Gao, J. et al. Stability of alcohol and tobacco consumption biomarkers in a real rising main sewer. Water Res. 138, 19–26 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

32.
Lian, L., Yan, S., Yao, B., Chan, S. & Song, W. Photochemical transformation of nicotine in wastewater effluent. Environ. Sci. Technol. 51, 11718–11730 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

33.
Stuart, M., Lapworth, D., Crane, E. & Hart, A. Review of risk from potential emerging contaminants in UK groundwater. Sci. Total Environ. 416, 1–21 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

34.
Turner, R. D. R., Warne, M. S., Dawes, L. A., Thompson, K. & Will, G. D. Greywater irrigation as a source of organic micro-pollutants to shallow groundwater and nearby surface water. Sci. Total Environ. 669, 570–578 (2019).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

35.
Robertson, J. & Stevens, K. Pyrrolizidine alkaloids. Nat. Prod. Rep. 31, 1721–1788 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

36.
Rosemann, G. M., Botha, C. J. & Eloff, J. N. Distinguishing between toxic and non-toxic pyrrolizidine alkaloids and quantification by liquid chromatography–mass spectrometry. Phytochem. Lett. 8, 126–131 (2014).
CAS  Google Scholar 

37.
European Medicines Agency, Committee on Herbal Medicinal Products. Public Statement on the Use of Herbal Medicinal Products Containing Toxic, Unsaturated Pyrrolizidine Alkaloids (PAs). https://www.ema.europa.eu/en/use-herbal-medicinal-products-containing-toxic-unsaturated-pyrrolizidine-alkaloids-pas (2014).

38.
Prakash, A. S., Pereira, T. N., Reilly, P. E. & Seawright, A. A. Pyrrolizidine alkaloids in human diet. Mutat. Res. 433, 53–67 (1999).
Google Scholar 

39.
Bolechova, M., Caslavsky, J., Pospichalova, M. & Kosubova, P. UPLC-MS/MS method for determination of selected pyrrolizidine alkaloids in feed. Food Chem. 170, 265–270 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

40.
van Egmond, H. P. Natural toxins: risks, regulations and the analytical situation in Europe. Anal. Bioanal. Chem. 378, 1152–1160 (2004).
Article  CAS  PubMed  Google Scholar 

41.
Hoogenboom, L. A. P. et al. Carry-over of pyrrolizidine alkaloids from feed to milk in dairy cows. Food Addit. Contam. A 28, 359–372 (2011).
CAS  Article  Google Scholar 

42.
Mattocks, A.R. Chemistry and toxicology of pyrrolizidine alkaloids. Academic Pr. (1986).

43.
Molyneux, R. J., Gardner, D. L., Colegate, S. M. & Edgar, J. A. Pyrrolizidine alkaloid toxicity in livestock: a paradigm for human poisoning?. Food Addit. Contam. A 28, 293–307 (2011).
CAS  Article  Google Scholar 

44.
Ebmeyer, J. et al. Human CYP3A4-mediated toxification of the pyrrolizidine alkaloid lasiocarpine. Food Chem. Toxicol. 130, 79–88 (2019).
CAS  Article  PubMed  Google Scholar 

45.
He, X. et al. Primary and secondary pyrrolic metabolites of pyrrolizidine alkaloids form DNA adducts in human A549 cells. Toxicol. In Vitro 54, 286–294 (2019).
CAS  Article  PubMed  Google Scholar 

46.
Chen, T., Mei, N. & Fu, P. P. Genotoxicity of pyrrolizidine alkaloids. J. Appl. Toxicol. 30, 183–196 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

47.
Huxtable, R. J. Activation and pulmonary toxicity of pyrrolizidine alkaloids. Pharmacol. Therapeut. 47, 371–389 (1990).
CAS  Article  Google Scholar 

48.
McLean, E. K. The toxic actions of pyrrolizidine senecio-D alkaloids. Pharmacol. Ther. 47, 371–389 (1990).
Google Scholar 

49.
Mori, H. et al. Some toxic properties of a carcinogenic pyrrolizidine alkaloid, petasitenine. J. Toxicol. Sci. 9, 143–149 (1984).
CAS  Article  PubMed  Google Scholar 

50.
European Food Safety Authority. Scientific opinion on pyrrolizidine alkaloids in food and feed. EFSA J. 9, 2406 (2011).
Article  CAS  Google Scholar 

51.
Wang, Y. P., Yan, J., Fu, P. P. & Chou, M. W. Human liver microsomal reduction of pyrrolizidine alkaloid N-oxides to form the corresponding carcinogenic parent alkaloid. Toxicol. Lett. 155, 411–420 (2005).
CAS  Article  PubMed  Google Scholar 

52.
Yang, M. et al. Intestinal and hepatic biotransformation of pyrrolizidine alkaloid N-oxides to toxic pyrrolizidine alkaloids. Arch. Toxicol. 93, 2197–2209 (2019).
CAS  Article  PubMed  Google Scholar 

53.
Chou, M. W. et al. Riddelliine N-oxide is a phytochemical and mammalian metabolite with genotoxic activity that is comparable to the parent pyrrolizidine alkaloid riddelliine. Toxicol. Lett. 145, 239–247 (2003).
CAS  Article  PubMed  Google Scholar 

54.
Xia, Q., Chou, M. W., Kadlubar, F. F., Chan, P. C. & Fu, P. P. Human liver microsomal metabolism and DNA adduct formation of the tumorigenic pyrrolizidine alkaloid, riddelliine. Chem. Res. Toxicol. 16, 66–73 (2003).
CAS  Article  PubMed  Google Scholar 

55.
Moreira, R., Pereira, D. M., Valentao, P. & Andrade, P. Pyrrolizidine alkaloids: chemistry, pharmacology, toxicology and food safety. Int. J. Mol. Sci. 19, 1668 (2018).
Article  CAS  PubMed Central  Google Scholar 

56.
Edgar, J. A., Colegate, S. M., Boppre, M. & Molyneux, R. J. Pyrrolizidine alkaloids in food: a spectrum of potential health consequences. Food Addit. Contam. A 28, 308–324 (2011).
CAS  Article  Google Scholar 

57.
International Programme on Chemical Safety (WHO). Pyrrolizidine Alkaloids. Environmental Health Criteria 80 (1988).

58.
European Medicines Agency. Committee on Herbal Medicinal Products (HMPC), EMA/HMPC/893108/2011 Rev. 1. Public statement on the use of herbal medicinal products containing toxic, unsaturated pyrrolizidine alkaloids (PAs) including recommendations regarding contamination of herbal medicinal products with pyrrolizidine alkaloids (2020).

59.
Committee on Toxicity of Chemicals in Food, Consumer Products and the Environment (United Kingdom). COT Statement on Pyrrolizidine Alkaloids in Food (2008).

60.
Chen, L. H. et al. Simultaneous determination and risk assessment of pyrrolizidine alkaloids in Artemisia capillaris Thunb by UPLC-MS/MS together with chemometrics. Molecules 24, 1077 (2019).
CAS  Article  PubMed Central  Google Scholar 

61.
The German Federal Institute for Risk Assessment (BfR) (in German: Bundesinstitut für Risikobewertung). Analytik und Toxizität von Pyrrolizidinalkaloiden sowie eine Einschätzung des gesundheitlichen Risikos durch deren Vorkommen in Honig (2013).

62.
The German Federal Institute for Risk Assessment (in German: Bundesinstitut für Risikobewertung). Updated risk assessment on levels of 1,2-unsaturated pyrrolizidine alkaloids (2020).

63.
The German Federal Institute for Risk Assessment (in German: Bundesinstitut für Risikobewertung). Frequently asked questions on pyrrolizidine alkaloids in foods (2020).

64.
Kopp, T., Abdel-Tawab, M. & Mizaikoff, B. Extracting and analyzing pyrrolizidine alkaloids in medicinal plants: a review. Toxins. 12, 320 (2020).
CAS  Article  PubMed Central  Google Scholar 

65.
Smith, L. W. & Culvenor, C. C. Plant sources of hepatotoxic pyrrolizidine alkaloids. J. Nat. Prod. 44, 129–152 (1981).
CAS  Article  PubMed  Google Scholar 

66.
Edgar, J. A., Roeder, E. & Molyneux, R. J. Honey from plants containing pyrrolizidine alkaloids: a potential threat to health. J. Agric. Food Chem. 50, 2719–2730 (2002).
CAS  Article  PubMed  Google Scholar 

67.
Kempf, M., Reinhard, A. & Beuerle, T. Pyrrolizidine alkaloids (PAs) in honey and pollen-legal regulation of PA levels in food and animal feed required. Mol. Nutr. Food Res. 54, 158–168 (2010).
CAS  Article  PubMed  Google Scholar 

68.
Dubecke, A., Beckh, G. & Lullmann, C. Pyrrolizidine alkaloids in honey and bee pollen. Food Addit. Contam. A 28, 348–358 (2011).
CAS  Article  Google Scholar 

69.
Gottschalk, C. et al. Spread of Jacobaea vulgaris and occurrence of pyrrolizidine alkaloids in regionally produced honeys from Northern Germany: inter- and intra-site variations and risk assessment for special consumer groups. Toxins 12, 1–19 (2020).
Article  CAS  Google Scholar 

70.
Hama, J. R. & Strobel, B. W. Pyrrolizidine alkaloids quantified in soil and water using UPLC-MS/MS. RSC Adv. 9, 30350–30357 (2019).
CAS  Article  Google Scholar 

71.
Selmar, D. et al. Horizontal natural product transfer: intriguing insights into a newly discovered phenomenon. J. Agric. Food Chem. 67, 8740–8745 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

72.
Gunthardt, B. F. et al. “Is there anybody else out there?”—First insights from a suspect screening for phytotoxins in surface water. Chimia 74, 129–135 (2020).
PubMed  PubMed Central  Google Scholar 

73.
Schonsee, C. D. & Bucheli, T. D. Experimental Determination of octanol−water partition coefficients of selected natural toxins. J. Chem. Eng. 65, 1946–1953 (2020).
CAS  Google Scholar 

74.
Ellegaard-Jensen, L., Horemans, B., Raes, B., Aamand, J. & Hansen, L. H. Groundwater contamination with 2,6-dichlorobenzamide (BAM) and perspectives for its microbial removal. Appl. Microbiol. Biotechnol. 101, 5235–5245 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

75.
Stuart, M., Lapwortha, D., Cranea, E. & Hart, A. Review of risk from potential emerging contaminants in UK groundwater. Sci. Total Environ. 416, 1–21 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

76.
Lapworth, D. J., Baran, N., Stuart, M. E. & Ward, R. S. Emerging organic contaminants in groundwater: a review of sources, fate and occurrence. Environ. Pollut. 163, 287–303 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration.

78.
Bucheli, T. D. Phytotoxins: environmental micropollutants of concern?. Environ. Sci. Technol. 48, 13027–13033 (2014).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

79.
Wink, M. & Hartmann, T. Sites of enzymatic synthesis of quinolizidine alkaloids and their accumulation in Lupinus polyphyllus. Zeitschrift für Pflanzenphysiologie 102, 337–344 (1981).
CAS  Google Scholar 

80.
Hama, J. R. & Strobel, B. W. Natural alkaloids from narrow-leaf and yellow lupins transfer to soil and soil solution in agricultural fields. Environ. Sci. Eur. 32, 126 (2020).
CAS  Article  Google Scholar 

81.
Anonymous. Solanine poisoning. Br. Med. J. 2, 1458–1459. https://doi.org/10.1136/bmj.2.6203.1458-a (1979).
Article  Google Scholar 

82.
Aichinger, G., Pantazi, F. & Marko, D. Combinatory estrogenic effects of bisphenol A in mixtures with alternariol and zearalenone in human endometrial cells. Toxicol. Lett. 319, 242–249 (2020).
CAS  Article  PubMed  Google Scholar 

83.
Gunthardt, B. F., Hollender, J., Hungerbuhler, K., Scheringer, M. & Bucheli, T. D. Comprehensive toxic plants–phytotoxins database and its application in assessing aquatic micropollution potential. J. Agric. Food Chem. 66, 7577–7588 (2018).
CAS  Article  PubMed  Google Scholar 

84.
Global Biodiversity Information Facility. https://www.gbif.org/species/9490132. Accessed 6th February 2020.

85.
Hama, J.R. & Strobel, B.W. Occurrence of pyrrolizidine alkaloids in ragwort plants, soils and surface waters at the field scale in Grassland. Sci. Total Environ (Article reference:STOTEN_142822) (Accepted). Available online 16 October 2020, 142822 (2020). More

1.
Karl, D. M. Solar energy capture and transformation in the sea. Elementa Sci. Anthrop. 2, 000021 (2013).
Article  Google Scholar 
2.
Kirk, J. T. O. The vertical attenuation of irradiance as a function of the optical properties of the water. Limnol. Oceanogr. 48, 9–17 (2003).
ADS  Article  Google Scholar 

3.
Mladenov, N. et al. Dust inputs and bacteria influence dissolved organic matter in clear alpine lakes. Nat. Commun. 2, 405 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Brahney, J., Mahowald, N., Ward, D. S., Ballantyne, A. P. & Neff, J. C. Is atmospheric phosphorus pollution altering global alpine Lake stoichiometry?. Glob. Biogeochem. Cycles 29, GB5137 (2015).
Article  CAS  Google Scholar 

5.
Goudie, A. Human Impact on the Natural Environment: Past, Present and Future 8th edn. (Wiley, New York, 2019).
Google Scholar 

6.
Stockwell, J. D. et al. Storm impacts on phytoplankton community dynamics in lakes. Glob. Change Biol. 26, 2756–2784 (2020).
ADS  Article  Google Scholar 

7.
Beardall, J., Sobrino, C. & Stojkovic, S. Interactions between the impacts of ultraviolet radiation, elevated CO2, and nutrient limitation on marine primary producers. Photochem. Photobiol. Sci. 8, 1257–1265 (2009).
CAS  Article  PubMed  Google Scholar 

8.
Gao, K., Zhang, Y. & Häder, D.-P. Individual and interactive effects of ocean acidification, global warming, and UV radiation on phytoplankton. J. Appl. Phycol. 30, 743–759 (2018).
CAS  Article  Google Scholar 

9.
Brennan, G. & Collins, S. Growth responses of a green alga to multiple environmental drivers. Nat. Clim. Change 5, 892–897 (2015).
ADS  Article  Google Scholar 

10.
Jackson, M. C., Loewen, C. J. G., Vinebrooke, R. D. & Chimimba, C. T. Net effects of multiple stressors in freshwater ecosystems: A meta-analysis. Glob. Change Biol. 22, 180–189 (2016).
ADS  Article  Google Scholar 

11.
Van de Waal, D. B. & Litchman, E. Multiple global change stressor effects on phytoplankton nutrient acquisition in a future ocean. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190706 (2020).
Article  CAS  PubMed  PubMed Central  Google Scholar 

12.
Winston, B., Scott, J. T. & Pollock, E. The synergistic effect of elevated CO2 and phosphorus on reservoir eutrophication. Lake Reservoir Manag. 32, 373–385 (2016).
CAS  Article  Google Scholar 

13.
Villar-Argaiz, M. et al. Growth impacts of Saharan dust, mineral nutrients, and CO2 on a planktonic herbivore in southern Mediterranean lakes. Sci. Total Environ. 639, 118–128 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

14.
Carrillo, P., Delgado-Molina, J. A., Medina-Sánchez, J. M., Bullejos, F. J. & Villar-Argaiz, M. Phosphorus inputs unmask negative effects of ultraviolet radiation on algae in a high mountain lake. Glob. Change Biol. 14, 423–439 (2008).
ADS  Article  Google Scholar 

15.
Edwards, K. F., Thomas, M. K., Klausmeier, C. A. & Litchman, E. Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Limnol. Oceanogr. 61, 1232–1244 (2016).
ADS  Article  Google Scholar 

16.
Belarde, T. A. & Railsback, S. F. New predictions from old theory: Emergent effects of multiple stressors in a model of piscivorous fish. Ecol. Model. 326, 54–62 (2016).
Article  Google Scholar 

17.
Carrillo, P. et al. Vulnerability of mixotrophic algae to nutrient pulses and UVR in an oligotrophic Southern and Northern Hemisphere lake. Sci. Rep. 7, 6333 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

18.
Helbling, E. W. et al. Interactive effects of vertical mixing, nutrients and ultraviolet radiation: In situ photosynthetic responses of phytoplankton from high mountain lakes in Southern Europe. Biogeosciences 10, 1037–1050 (2013).
ADS  Article  Google Scholar 

19.
Verpoorter, C. A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41, 6396–6402 (2014).
ADS  Article  Google Scholar 

20.
Downing, J. Emerging global role of small lakes and ponds: Little things mean a lot. Limnetica 29, 9–24 (2010).
Google Scholar 

21.
Mendonça, R. et al. Organic carbon burial in global lakes and reservoirs. Nat. Commun. 8, 1694 (2018).
ADS  Article  CAS  Google Scholar 

22.
Hilt, S., Brothers, S., Jeppesen, E., Veraart, A. J. & Kosten, S. Translating regime shifts in shallow lakes into changes in ecosystem functions and services. Bioscience 67, 928–936 (2017).
Article  Google Scholar 

23.
Helbling, E. W., Banaszak, A. T. & Villafañe, V. E. Global change feed-back inhibits cyanobacterial photosynthesis. Sci. Rep. 5, 14514 (2015).
ADS  Article  CAS  Google Scholar 

24.
Villafañe, V. E. et al. Dual role of DOM in a scenario of global change on photosynthesis and structure of coastal phytoplankton from the South Atlantic Ocean. Sci. Total Environ. 634, 1352–1361 (2018).
ADS  Article  CAS  PubMed  Google Scholar 

25.
Williamson, C. E. et al. The interactive effects of stratospheric ozone depletion, UV radiation, and climate change on aquatic ecosystems. Photochem. Photobiol. Sci. 18, 717–746 (2019).
CAS  Article  PubMed  Google Scholar 

26.
Sanders, R. W. et al. Shifts in microbial food web structure and productivity after additions of naturally occurring dissolved organic matter: Results from large-scale lacustrine mesocosms. Limnol. Oceanogr. 60, 2130–2144 (2015).
ADS  CAS  Article  Google Scholar 

27.
Williamson, C. E. et al. Solar ultraviolet radiation in a changing climate. Nat. Clim. Change 4, 434–441 (2014).
ADS  Article  Google Scholar 

28.
Ayoub, L. M., Hallock, P., Coble, P. G. & Bell, S. S. MAA-like absorbing substances in Florida Keys phytoplankton vary with distance from shore and CDOM: Implications for coral reefs. J. Exp. Mar. Biol. Ecol. 420–421, 91–98 (2012).
Article  CAS  Google Scholar 

29.
Häder, D. P., Villafañe, V. E. & Helbling, E. W. Productivity of aquatic primary producers under global climate change. Photochem. Photobiol. Sci. 13, 1370–1392 (2014).
Article  CAS  PubMed  Google Scholar 

30.
IPCC. Climate Change. The Physical Science Basis 1–1535 (Cambridge University Press, New York, 2013).
Google Scholar 

31.
Llewellyn, C. A. & Airs, R. L. Distribution and abundance of MAAs in 33 species of microalgae across 13 classes. Mar. Drugs 8, 1273–1291 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Buma, A. G. J. et al. Wavelength-dependent xanthophyll cycle activity in marine microalgae exposed to natural ultraviolet radiation. Eur. J. Phycol. 44, 515–524 (2009).
CAS  Article  Google Scholar 

33.
Graham, P. J., Nguyen, B., Burdyny, T. & Sinton, D. A penalty on photosynthetic growth in fluctuating light. Sci. Rep. 7, 12513. https://doi.org/10.1038/s41598-017-12923-1 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Vialet-Chabrand, S. R. M., Matthews, J. S. A., Simjin, A., Raines, C. A. & Lawson, T. Importance of fluctuations in light on plant photosynthetic acclimation. Plant Physiol. 173, 2163–2179 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Behrenfeld, M. J., Halsey, K. H. & Milligan, A. J. Evolved physiological responses of phytoplankton to their integrated growth environment. Philos. Trans. R. Soc. Lond B Biol. Sci. 363, 2687–2703 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Bamstedt, U. Productivity related to ambient photon flux for phytoplankton communities under different turbid conditions. Hydrobiologia 837, 109–115 (2019).
Article  Google Scholar 

37.
Vinebrooke, R. D. et al. Impacts of multiple stressors on biodiversity and ecosystem functioning: The role of species co-tolerance. Oikos 104, 451–457 (2004).
Article  Google Scholar 

38.
Lin, H. et al. Phytoplankton: The fate of photons absorbed by phytoplankton in the global ocean. Science 351, 264–267 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

39.
Falkowski, P. G., Lin, H. & Gorbunov, M. Y. What limits photosynthetic energy conversion efficiency in nature? Lessons from the oceans. Phylos. Trans. R. Soc. B Biol. Sci. 372, 20160376 (2017).
Article  CAS  Google Scholar 

40.
Sinistro, R. et al. Responses of phytoplankton and related microbial communities to changes in the limnological conditions of shallow lakes: A short-term cross-transplant experiment. Hydrobiologia 752, 139–153 (2015).
CAS  Article  Google Scholar 

41.
González-Olalla, J. M., Medina-Sánchez, J. M., Lozano, I. L., Villar-Argaiz, M. & Carrillo, P. Climate-driven shifts in algal-bacterial interaction of highmountain lakes in two years spanning a decade. Sci. Rep. 8, 10278 (2018).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

42.
Rojo, C. et al. Are the small-sized plankton communities of oligotrophic ecosystems resilient to UVR and P pulses?. Freshw. Sci. 36, 760–773 (2017).
Article  Google Scholar 

43.
Medina-Sánchez, J. M., Delgado-Molina, J. A., Bratbak, G., Bullejos, F. J. & Carrillo, P. Maximum in the middle: Nonlinear response of microbial plankton to ultraviolet radiation and phosphorus. PLoS ONE 8, e60223 (2013).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

44.
Cabrerizo, M. J., Medina-Sánchez, J. M., Dorado-García, I., Villar-Argaiz, M. & Carrillo, P. Rising nutrient-pulse frequency and high UVR strengthen microbial interactions. Sci. Rep. 7, 43615 (2017).
ADS  Article  PubMed  PubMed Central  Google Scholar 

45.
Carrillo, P. et al. Synergistic effects of UVR and simulated stratification on commensalistic algal-bacterial relationship in two optically contrasting oligotrophic Mediterranean lakes. Biogeosciences 12, 697–712 (2015).
ADS  Article  Google Scholar 

46.
Durán, C., Medina-Sánchez, J. M., Herrera, G. & Carrillo, P. Changes in the phytoplankton-bacteria coupling triggered by joint action of UVR, nutrients, and warming in Mediterranean high-mountain lakes. Limnol. Oceanogr. 61, 413–429 (2016).
ADS  Article  CAS  Google Scholar 

47.
Durán-Romero, C., Medina-Sánchez, J. M. & Carrillo, P. Uncoupled phytoplankton-bacterioplankton relationship by multiple drivers interacting at different temporal scales in a high-mountain Mediterranean lake. Sci. Rep. 10, 350 (2020).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

48.
APHA. Standard Methods for the Examination of Water and Wastewater (American Public Health Association, Washington, 2017).
Google Scholar 

49.
Villafañe, V. E., Gao, K., Li, P. & Helbling, E. W. Vertical mixing within the epilimnion modulates UVR-induced photoinhibition in tropical freshwater phytoplankton from southern China. Freshw. Biol. 52, 1260–1270 (2007).
Article  CAS  Google Scholar 

50.
Morales-Baquero, R., Pulido-Villena, E. & Reche, I. Atmospheric inputs of phosphorus and nitrogen to the southwest Mediterranean region: Biogeochemical responses of high mountain lakes. Limnol. Oceanogr. 51, 830–837 (2006).
ADS  CAS  Article  Google Scholar 

51.
Benner, R. & Strom, M. A critical evaluation of the analytical blank associated with DOC measurements by high-temperature catalytic oxidation. Mar. Chem. 41, 153–160 (1993).
CAS  Article  Google Scholar 

52.
Utermöhl, H. Zur Vervollkommnung der quantitativen Phytoplankton-Methodik. Internationale Vereinigung fur Theoretische und Angewandte Limnologie 9, 1–38 (1958).
Google Scholar 

53.
Steemann Nielsen, E. The use of radio-active carbon (C14) for measuring organic production in the sea. ICES J. Mar. Sci. 18, 117–140 (1952).
Article  Google Scholar 

54.
Harvey, B. P., Gwynn-Jones, D. & Moore, P. J. Meta-analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming. Ecol. Evol. 3, 1016–1030 (2013).
Article  PubMed  PubMed Central  Google Scholar  More

1.
White, T. C. R. The role of food, weather and climate in limiting the abundance of animals. Biol. Rev. 83, 227–248. https://doi.org/10.1111/j.1469-185X.2008.00041.x (2008).
CAS  Article  Google Scholar 
2.
Moreno, J. & Møller, A. P. Extreme climatic events in relation to global change and their impact on life histories. Curr. Zool. 57, 375–389. https://doi.org/10.1093/czoolo/57.3.375 (2011).
Article  Google Scholar 

3.
Briedis, M., Hahn, S. & Adamík, P. Cold spell en route delays spring arrival and decreases apparent survival in a long-distance migratory songbird. BMC Ecol. 17, 11. https://doi.org/10.1186/s12898-017-0121-4 (2017).
Article  PubMed  PubMed Central  Google Scholar 

4.
Welbergen, J. A., Klose, S. M., Markus, N. & Eby, P. Climate change and the effects of temperature extremes on Australian flying-foxes. Proc. R. Soc. B Biol. Sci. 275, 419–425. https://doi.org/10.1098/rspb.2007.1385 (2008).
Article  Google Scholar 

5.
Griebel, I. & Dawson, R. D. Predictors of nestling survival during harsh weather events in an aerial insectivore, the tree swallow (Tachycineta bicolor). Can. J. Zool. 97, 81–90. https://doi.org/10.1139/cjz-2018-0070 (2019).
Article  Google Scholar 

6.
Intergovernmental Pannel on Climate Change (IPCC). Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change. (eds. Masson-Delmotte, V. et al.) (World Meteorological Organization, 2018).

7.
Intergovernmental Pannel on Climate Change (IPCC). Climate change 2007: Impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. (eds. Parry, M. L., O. F. Canziani, J. P. Palutikof, P. J. van der Linden and Hanson, C.E.) 976 (Cambridge University Press, Cambridge, 2007).

8.
Bailey, L. D. & van de Pol, M. Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events. J. Anim. Ecol. 85, 85–96. https://doi.org/10.1111/1365-2656.12451 (2016).
Article  Google Scholar 

9.
Clutton-Brock, T. H. Reproductive Success: Studies of Individual Variation in Contrasting Breeding Systems (The University of Chicago Press, Chicago, 1988).
Google Scholar 

10.
Newton, I. Lifetime Reproductive Success in Birds (Academic Press, Cambridge, 1989).
Google Scholar 

11.
Weegman, M. D., Arnold, T. W., Dawson, R. D., Winkler, D. W. & Clark, R. G. Integrated population models reveal local weather conditions are the key drivers of population dynamics in an aerial insectivore. Oecologia 185, 119–130. https://doi.org/10.1007/s00442-017-3890-8 (2017).
ADS  Article  PubMed  PubMed Central  Google Scholar 

12.
Cam, E., Link, W. A., Cooch, E. G., Monnat, J.-Y. & Danchin, E. Individual covariation in life-history traits: seeing the trees despite the forest. Am. Nat. 159, 96–105. https://doi.org/10.1086/324126 (2002).
Article  PubMed  PubMed Central  Google Scholar 

13.
Chambert, T., Rotella, J. J., Higgs, M. D. & Garrott, R. A. Individual heterogeneity in reproductive rates and cost of reproduction in a long-lived vertebrate. Ecol. Evol. 3, 2047–2060. https://doi.org/10.1002/ece3.615 (2013).
Article  PubMed  PubMed Central  Google Scholar 

14.
Cam, E. et al. Looking for a needle in a haystack: inference about individual fitness components in a heterogeneous population. Oikos 122, 739–753. https://doi.org/10.1111/j.1600-0706.2012.20532.x (2013).
Article  Google Scholar 

15.
Jenouvrier, S., Péron, C. & Weimerskirch, H. Extreme climate events and individual heterogeneity shape life-history traits and population dynamics. Ecol. Monogr. 85, 605–624 (2015).
Article  Google Scholar 

16.
Jensen, H. et al. Lifetime reproductive success in relation to morphology in the house sparrow Passer domesticus. J. Anim. Ecol. 73, 599–611. https://doi.org/10.1111/j.0021-8790.2004.00837.x (2004).
Article  Google Scholar 

17.
Saino, N. et al. Longevity and lifetime reproductive success of barn swallow offspring are predicted by their hatching date and phenotypic quality. J. Anim. Ecol. 81, 1004–1012. https://doi.org/10.1111/j.1365-2656.2012.01989.x (2012).
Article  PubMed  PubMed Central  Google Scholar 

18.
Winkler, D. W. et al. Full lifetime perspectives on the costs and benefits of lay date variation in tree swallows. Ecology 101, e03109. https://doi.org/10.1002/ecy.3109 (2020).

19.
Krüger, O. Age at first breeding and fitness in goshawk Accipiter gentilis. J. Anim. Ecol. 74, 266–273. https://doi.org/10.1111/j.1365-2656.2005.00920.x (2005).
Article  Google Scholar 

20.
Blums, P. & Clark, R. G. Correlates of lifetime reproductive success in three species of European ducks. Oecologia 140, 61–67. https://doi.org/10.1007/s00442-004-1573-8 (2004).
ADS  Article  PubMed  Google Scholar 

21.
Le Boeuf, B., Condit, R. & Reiter, J. Lifetime reproductive success of northern elephant seals (Mirounga angustirostris). Can. J. Zool. 97, 1203–1217. https://doi.org/10.1139/cjz-2019-0104 (2019).
Article  Google Scholar 

22.
Tuljapurkar, S., Steiner, U. K. & Orzack, S. H. Dynamic heterogeneity in life histories. Ecol. Lett. 12, 93–106. https://doi.org/10.1111/j.1461-0248.2008.01262.x (2009).
Article  PubMed  Google Scholar 

23.
Snyder, R. E. & Ellner, S. P. Pluck or luck: does trait variation or chance drive variation in lifetime reproductive success?. Am. Nat. 191, E90–E107. https://doi.org/10.1086/696125 (2018).
Article  PubMed  Google Scholar 

24.
Cam, E., Aubry, L. M. & Authier, M. The conundrum of heterogeneities in life history studies. Trends Ecol. Evol. 31, 872–886. https://doi.org/10.1016/j.tree.2016.08.002 (2016).
Article  PubMed  Google Scholar 

25.
Jenouvrier, S. et al. When the going gets tough, the tough get going: effect of extreme climate on an Antarctic seabird’s life history. bioRxiv https://doi.org/10.1101/791855 (2019).
Article  Google Scholar 

26.
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368. https://doi.org/10.1890/08-1034.1 (2009).
Article  PubMed  Google Scholar 

27.
Grant, P. R. & Grant, B. R. Non-random fitness variation in two populations of Darwin’s finches. Proc. R. Soc. B. Biol. Sci. 267, 131–138. https://doi.org/10.1098/rspb.2000.0977 (2000).
CAS  Article  Google Scholar 

28.
Johnson, W. C., Boettcher, S. E., Poiani, K. A. & Guntenspergen, G. Influence of weather extremes on the water levels of glaciated prairie wetlands. Wetlands 24, 385–398. https://doi.org/10.1672/0277-5212(2004)024[0385:IOWEOT]2.0.CO;2 (2004).
Article  Google Scholar 

29.
Dawson, R. D. Timing of breeding and environmental factors as determinants of reproductive performance of tree swallows. Can. J. Zool. 86, 843–850. https://doi.org/10.1139/Z08-065 (2008).
Article  Google Scholar 

30.
Winkler, D. W. et al. Tree swallow (Tachycineta bicolor). In The Birds of North America Online (ed. Poole, A.) (Cornell Lab of Ornithology, Cornell, 2011).
Google Scholar 

31.
Winkler, D. W., Luo, M. K. & Rakhimberdiev, E. Temperature effects on food supply and chick mortality in tree swallows (Tachycineta bicolor). Oecologia 173, 129–138. https://doi.org/10.1007/s00442-013-2605-z (2013).
ADS  Article  PubMed  PubMed Central  Google Scholar 

32.
Fast, M. Climate Variability, Timing of Nesting and Breeding Success of Tree Swallows (Tachycineta bicolor) (University of Saskatchewan, Saskatchewan, 2007).
Google Scholar 

33.
Harriman, V. B. Seasonal Variation in Quality and Survival of Nestlings Tree Swallows (Tachycineta bicolor): Tests of Alternative Hypotheses (University of Saskatchewan, Saskatchewan, 2014).
Google Scholar 

34.
Martínez-Padilla, J., Vergara, P. & Fargallo, J. A. Increased lifetime reproductive success of first-hatched siblings in common kestrels Falco tinnunculus. Ibis 159, 803–811. https://doi.org/10.1111/ibi.12494 (2017).
Article  Google Scholar 

35.
Weatherhead, P. J. & Dufour, K. W. Fledging success as an index of recruitment in red-winged blackbirds. Auk 117, 627–633. https://doi.org/10.1093/auk/117.3.627 (2000).
Article  Google Scholar 

36.
Williams, G. C. Natural selection, the costs of reproduction, and a refinement of Lack’s principle. Am. Nat. 100, 687–690 (1966).
Article  Google Scholar 

37.
Murphy, M. T. Lifetime reproductive success of female eastern kingbirds (Tyrannus tyrannus): influence of lifespan, nest predation, and body size. Auk 124, 1010–1022. https://doi.org/10.1093/auk/124.3.1010 (2007).
Article  Google Scholar 

38.
Tarwater, C. E. & Arcese, P. Young females pay higher costs of reproduction in a short-lived bird. Behav. Ecol. Sociobiol. 71, 84. https://doi.org/10.1007/s00265-017-2309-1 (2017).
Article  Google Scholar 

39.
Vedder, O. & Bouwhuis, S. Heterogeneity in individual quality in birds: overall patterns and insights from a study on common terns. Oikos 127, 719–727. https://doi.org/10.1111/oik.04273 (2018).
Article  Google Scholar 

40.
Murphy, M. T., Armbrecth, B., Vlamis, E. & Pierce, A. Is reproduction by tree swallows cost free?. Auk 117, 902–912. https://doi.org/10.1093/auk/117.4.902 (2000).
Article  Google Scholar 

41.
Wheelwright, N. T., Leary, J. & Fitzgerald, C. The costs of reproduction in tree swallows (Tachycineta bicolor). Can. J. Zool. 69, 2540–2547. https://doi.org/10.1139/z91-358 (1991).
Article  Google Scholar 

42.
Shutler, D., Clark, R. G., Fehr, C. & Diamond, A. W. Time and recruitment costs as currencies in manipulation studies on the costs of reproduction. Ecology 87, 2938–2946. https://doi.org/10.1890/0012-9658(2006)87[2938:TARCAC]2.0.CO;2 (2006).
Article  Google Scholar 

43.
Verhulst, S. & Nilsson, J. Å. The timing of birds’ breeding seasons: a review of experiments that manipulated timing of breeding. Philos. Trans. R. Soc. B. 363, 399–410. https://doi.org/10.1098/rstb.2007.2146 (2008).
Article  Google Scholar 

44.
Grüebler, M. U. & Naef-Daenzer, B. Fitness consequences of timing of breeding in birds: date effects in the course of a reproductive episode. J. Avian Biol. 41, 282–291. https://doi.org/10.1111/j.1600-048X.2009.04865.x (2010).
Article  Google Scholar 

45.
Plard, F. et al. The influence of birth date via body mass on individual fitness in a long-lived mammal. Ecology 96, 1516–1528. https://doi.org/10.1890/14-0106.1 (2015).
Article  Google Scholar 

46.
Raja-aho, S., Eeva, T., Suorsa, P., Valkama, J. & Lehikoinen, E. Juvenile barn swallows Hirundo rustica L. from late broods start autumn migration younger, fuel less effectively and show lower return rates than juveniles from early broods. Ibis 159, 892–901. https://doi.org/10.1111/ibi.12492 (2018).
Article  Google Scholar 

47.
Svensson, E. Natural selection on avian breeding time: causality, fecundity-dependent, and fecundity-independent selection. Evolution 51, 1276–1283. https://doi.org/10.1111/j.1558-5646.1997.tb03974.x (1997).
Article  Google Scholar 

48.
Rioux Paquette, S., Pelletier, F., Garant, D. & Bélisle, M. Severe recent decrease of adult body mass in a declining insectivorous bird population. Proc. R. Soc. B. Biol. Sci. 281, 20140649. https://doi.org/10.1098/rspb.2014.0649 (2014).
Article  Google Scholar 

49.
Winkler, D. W. & Allen, P. E. The seasonal decline in tree swallow clutch size: physiological constraint or strategic adjustment?. Ecology 77, 922–932 (1996).
Article  Google Scholar 

50.
Harriman, V. B., Dawson, R. D., Bortolotti, L. E. & Clark, R. G. Seasonal patterns in reproductive success of temperate-breeding birds: experimental tests of the date and quality hypotheses. Ecol. Evol. 7, 2122–2132. https://doi.org/10.1002/ece3.2815 (2017).
Article  PubMed  PubMed Central  Google Scholar 

51.
Verhulst, S., van Balen, J. H. & Tinbergen, J. M. Seasonal decline in reproductive success of the great tit: variation in time or quality?. Ecology 76, 2392–2403 (1995).
Article  Google Scholar 

52.
Blums, P., Clark, R. G. & Mednis, A. Patterns of reproductive effort and success in birds: path analyses of long-term data from European ducks. J. Anim. Ecol. 71, 280–295. https://doi.org/10.1046/j.1365-2656.2002.00598.x (2002).
Article  Google Scholar 

53.
Pärt, T., Knape, J., Low, M., Öberg, M. & Arlt, D. Disentangling the effects of date, individual, and territory quality on the seasonal decline in fitness. Ecology 98, 2102–2110. https://doi.org/10.1002/ecy.1891 (2017).
Article  PubMed  PubMed Central  Google Scholar 

54.
Brinkhof, M. W. G. & Cavé, A. J. Food supply and seasonal variation in breeding success: an experiment in the European coot. Proc. R. Soc. B Biol. Sci. 264, 291–296. https://doi.org/10.1098/rspb.1997.0041 (1997).
ADS  Article  Google Scholar 

55.
Rossmanith, E., Höntsch, K., Blaum, N. & Jeltsch, F. Reproductive success and nestling diet in the lesser spotted woodpecker (Picoides minor): the early bird gets the caterpillar. J. Ornithol. 148, 323–332. https://doi.org/10.1007/s10336-007-0134-4 (2007).
Article  Google Scholar 

56.
Kim, S. Y., Velando, A., Torres, R. & Drummond, H. Effects of recruiting age on senescence, lifespan and lifetime reproductive success in a long-lived seabird. Oecologia 166, 615–626. https://doi.org/10.1007/s00442-011-1914-3 (2011).
ADS  Article  PubMed  PubMed Central  Google Scholar 

57.
Mourocq, E. et al. Life span and reproductive cost explain interspecific variation in the optimal onset of reproduction. Evolution 70, 296–313. https://doi.org/10.1111/evo.12853 (2016).
Article  PubMed  PubMed Central  Google Scholar 

58.
Hoset, K. S., Villers, A., Wistbacka, R. & Selonen, V. Pulsed food resources, but not forest cover, determine lifetime reproductive success in a forest-dwelling rodent. J. Anim. Ecol. 86, 1235–1245. https://doi.org/10.1111/1365-2656.12715 (2017).
Article  PubMed  PubMed Central  Google Scholar 

59.
Teplitsky, C., Mills, J. A., Yarrall, J. W. & Merilä, J. Heritability of fitness components in a wild bird population. Evolution 63, 716–726. https://doi.org/10.1111/j.1558-5646.2008.00581.x (2009).
Article  PubMed  PubMed Central  Google Scholar 

60.
McCleery, R. H. et al. Components of variance underlying fitness in a natural population of the great tit Parus major. Am. Nat. 164, E62–E72. https://doi.org/10.1086/422660 (2004).
CAS  Article  PubMed  Google Scholar 

61.
Salles, O. C. et al. Strong habitat and weak genetic effects shape the lifetime reproductive success in a wild clownfish population. Ecol. Lett. 23, 265–273. https://doi.org/10.1111/ele.13428 (2020).
Article  PubMed  Google Scholar 

62.
McCleery, R. H. & Perrins, C. M. Lifetime reproductive success of the great tit, Parus major in Reproductive success 136–153 (The University of Chicago Press, Chicago, 1988).

63.
Twining, C. W., Shipley, J. R. & Winkler, D. W. Aquatic insects rich in omega-3 fatty acids drive breeding success in a widespread bird. Ecol. Lett. 21, 1812–1820. https://doi.org/10.1111/ele.13156 (2018).
Article  PubMed  Google Scholar 

64.
Clark, R. G. et al. Geographic variation and environmental correlates of apparent survival rates in adult tree swallows Tachycineta bicolor. J. Avian Biol. 49, 012514. https://doi.org/10.1111/jav.01659 (2018).
Article  Google Scholar 

65.
Cox, A. R., Robertson, R. J., Rendell, W. B. & Bonier, F. Population decline in tree swallows (Tachycineta bicolor) linked to climate change and inclement weather on the breeding ground. Oecologia 192, 713–722. https://doi.org/10.1007/s00442-020-04618-8 (2020).
ADS  Article  PubMed  Google Scholar 

66.
Cox, A. R., Robertson, R. J., Fedy, B. C., Rendell, W. B. & Bonier, F. Demographic drivers of local population decline in tree swallows (Tachycineta bicolor) in Ontario, Canada. Condor 120, 842–851. https://doi.org/10.1650/CONDOR-18-42.1 (2018).
Article  Google Scholar 

67.
Shutler, D. et al. Spatiotemporal patterns in nest box occupancy by tree swallows across North America. Avian Conserv. Ecol. https://doi.org/10.5751/ACE-00517-070103 (2012).
Article  Google Scholar 

68.
Winkler, D. W. et al. Breeding dispersal and philopatry in the tree swallow. Condor 106, 768–776. https://doi.org/10.1093/condor/106.4.768 (2004).
Article  Google Scholar 

69.
Lambrechts, M. M. et al. Will estimates of lifetime recruitment of breeding offspring on small-scale study plots help us to quantify processes underlying adaptation?. Oikos 86, 147–151. https://doi.org/10.2307/3546579 (1999).
Article  Google Scholar 

70.
Mantyka-Pringle, C. et al. Antagonistic, synergistic and direct effects of land use and climate on Prairie wetland ecosystems: ghosts of the past or present?. Divers. Distrib. 25, 1924–1940. https://doi.org/10.1111/ddi.12990 (2019).
Article  Google Scholar 

71.
Johnson, W. C. et al. Prairie wetland complexes as landscape functional units in a changing climate. Bioscience 60, 128–140. https://doi.org/10.1525/bio.2010.60.2.7 (2010).
Article  Google Scholar 

72.
Zhao, Q., Silverman, E., Fleming, K. & Boomer, G. S. Forecasting waterfowl population dynamics under climate change—does the spatial variation of density dependence and environmental effects matter?. Biol. Conserv. 194, 80–88. https://doi.org/10.1016/j.biocon.2015.12.006 (2016).
Article  Google Scholar 

73.
British Columbia Ministry of Environment. Indicators of climate change for British Columbia update. (Province of British Columbia, 2016).

74.
Cox, A. R., Robertson, R. J., Lendvai, Á. Z., Everitt, K. & Bonier, F. Rainy springs linked to poor nestling growth in a declining avian aerial insectivore (Tachycineta bicolor). Proc. R. Soc. B Biol. Sci. 286, 20190018. https://doi.org/10.1098/rspb.2019.0018 (2019).
Article  Google Scholar 

75.
O’Brien, E. L. & Dawson, R. D. Context-dependent genetic benefits of extra-pair mate choice in a socially monogamous passerine. Behav. Ecol. Sociobiol. 61, 775–782. https://doi.org/10.1007/s00265-006-0308-8 (2007).
Article  Google Scholar 

76.
Whittingham, L. A. & Dunn, P. O. Female responses to intraspecific brood parasitism in the tree swallow. Condor 103, 166–170. https://doi.org/10.1093/condor/103.1.166 (2001).
Article  Google Scholar 

77.
Shutler, D. & Clark, R. G. Causes and consequences of tree swallow (Tachycineta bicolor) dispersal in Saskatchewan. Auk 120, 619–631. https://doi.org/10.1093/auk/120.3.619 (2003).
Article  Google Scholar 

78.
Leffelaar, D. & Robertson, R. J. Nest usurpation and female competition for breeding opportunities by tree swallows. Wilson Bull. 97, 221–224 (1985).
Google Scholar 

79.
Stutchbury, B. J. & Robertson, R. J. Floating populations of female tree swallows. Auk 102, 651–654 (1985).
Article  Google Scholar 

80.
Clark, R. G. & Shutler, D. Avian habitat selection: pattern from process in nest-site use by ducks?. Ecology 80, 272–287. https://doi.org/10.1890/0012-9658(1999)080[0272:AHSPFP]2.0.CO;2 (1999).
Article  Google Scholar 

81.
O’Brien, E. L. & Dawson, R. D. Perceived risk of ectoparasitism reduces primary reproductive investment in tree swallows Tachycineta bicolor. J. Avian Biol. 36, 269–275. https://doi.org/10.1111/j.0908-8857.2005.03562.x (2005).
Article  Google Scholar 

82.
Dawson, R. D., Lawrie, C. C. & O’Brien, E. L. The importance of microclimate variation in determining size, growth and survival of avian offspring: experimental evidence from a cavity nesting passerine. Oecologia 144, 499–507. https://doi.org/10.1007/s00442-005-0075-7 (2005).
ADS  Article  Google Scholar 

83.
Bitton, P.-P. & Dawson, R. D. Age-related differences in plumage characteristics of male tree swallows Tachycineta bicolor: hue and brightness signal different aspects of individual quality. J. Avian Biol. 39, 446–452. https://doi.org/10.1111/j.0908-8857.2008.04283.x (2008).
Article  Google Scholar 

84.
Hussell, D. J. T. Age and plumage color in female tree swallows. J. F. Ornithol. 54, 312–318 (1983).
Google Scholar 

85.
Gómez, J. et al. Effects of geolocators on reproductive performance and annual return rates of a migratory songbird. J. Ornithol. 155, 37–44. https://doi.org/10.1007/s10336-013-0984-x (2014).
Article  Google Scholar 

86.
Gustafsson, L. & Pärt, T. Acceleration of senescence in the collared flycatcher Ficedula albicollis by reproductive costs. Nature 347, 279–281. https://doi.org/10.1038/347279a0 (1990).
ADS  Article  Google Scholar 

87.
Nooker, J. K., Dunn, P. O. & Whittingham, L. A. Effects of food abundance, weather, and female condition on reproduction in tree swallows (Tachycineta bicolor). Auk 122, 1225–1238. https://doi.org/10.1093/auk/122.4.1225 (2005).
Article  Google Scholar 

88.
Ardia, D. R., Wasson, M. F. & Winkler, D. W. Individual quality and food availability determine yolk and egg mass and egg composition in tree swallows Tachycineta bicolor. J. Avian Biol. 37, 252–259. https://doi.org/10.1111/l.2006.0908.8857.03624.x (2006).
Article  Google Scholar 

89.
Clark, R. G., Pöysä, H., Runko, P. & Paasivaara, A. Spring phenology and timing of breeding in short-distance migrant birds: phenotypic responses and offspring recruitment patterns in common goldeneyes. J. Avian Biol. 45, 457–465. https://doi.org/10.1111/jav.00290 (2014).
Article  Google Scholar 

90.
Robertson, R. J. & Rendell, W. B. A long-term study of reproductive performance in tree swallows: the influence of age and senescence on output. J. Anim. Ecol. 70, 1014–1031. https://doi.org/10.1046/j.0021-8790.2001.00555.x (2001).
Article  Google Scholar 

91.
Ardia, D. R. Individual quality mediates trade-offs between reproductive effort and immune function in tree swallows. J. Anim. Ecol. 74, 517–524. https://doi.org/10.1111/j.1365-2656.2005.00950.c (2005).
Article  Google Scholar 

92.
Dunn, P. O., Winkler, D. W., Whittingham, L. A., Hannon, S. J. & Robertson, R. J. A test of the mismatch hypothesis: how is timing of reproduction related to food abundance in an aerial insectivore?. Ecology 92, 450–461. https://doi.org/10.1890/10-0478.1 (2011).
Article  PubMed  Google Scholar 

93.
Lefcheck, J. S. PiecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579. https://doi.org/10.1111/2041-210X.12512 (2016).
Article  Google Scholar 

94.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2019).

95.
SAS Institute Incorporated. SAS (Data Analysis Software System), Version 9.4 (SAS Institute Incorporated, 2016). More

1.
Ruisi, P. et al. Long-term effects of no tillage treatment on soil N availability, N uptake, and 15N-fertilizer recovery of durum wheat differ in relation to crop sequence. Field Crop. Res. 189, 51–58 (2016).
Article  Google Scholar 
2.
Díaz-Zorita, M., Duarte, G. A. & Grove, J. H. A review of no-till systems and soil management for sustainable crop production in the subhumid and semiarid Pampas of Argentina. Soil Tillage Res. 65, 1–18 (2002).
Article  Google Scholar 

3.
Díaz-Zorita, M. Cambios en el uso de pesticidas y fertilizantes. Cienc. hoy 15, 28–29 (2005).
Google Scholar 

4.
Iturri, L. A. & Buschiazzo, D. E. Light acidification in N-fertilized loess soils along a climosequence affected chemical and mineralogical properties in the short-term. CATENA 139, 92–98 (2016).
CAS  Article  Google Scholar 

5.
Finck, A. Fertilizer and Fertilization. Basics and Instructions for Fertilizing Crops (FAO, Rome, 1979).
Google Scholar 

6.
Smil, V. Nitrogen and food production: proteins for human diets. AMBIO A J. Hum. Environ. 31, 126–131 (2002).
Article  Google Scholar 

7.
Rice, K. C. & Herman, J. S. Acidification of Earth: an assessment across mechanisms and scales. Appl. Geochem. 27, 1–14 (2012).
CAS  Article  Google Scholar 

8.
Zhalnina, K. et al. Soil pH determines microbial diversity and composition in the park grass experiment. Microb. Ecol. 69, 395–406 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Kemmitt, S. J., Wright, D. & Jones, D. L. Soil acidification used as a management strategy to reduce nitrate losses from agricultural land. Soil Biol. Biochem. 37, 867–875 (2005).
CAS  Article  Google Scholar 

10.
Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).
CAS  Article  Google Scholar 

11.
Tian, D. & Niu, S. A global analysis of soil acidification caused by nitrogen addition. Environ. Res. Lett. 10, 24019 (2015).
Article  CAS  Google Scholar 

12.
Binkley, D. & Richter, D. Nutrient cycles and H+ budgets of forest ecosystems. In Advances in Ecological Research16 (eds MacFayden, A. & Ford, E. D.) 1–51 (Elsevier, Amsterdam, 1987).
Google Scholar 

13.
Bowman, W. D., Cleveland, C. C., Halada, Ĺ, Hreško, J. & Baron, J. S. Negative impact of nitrogen deposition on soil buffering capacity. Nat. Geosci. 1, 767 (2008).
ADS  CAS  Article  Google Scholar 

14.
Chadwick, O. A. & Chorover, J. The chemistry of pedogenic thresholds. Geoderma 100, 767–770 (2001).
Article  Google Scholar 

15.
Dubiková, M., Cambier, P., Šucha, V. & Čaplovic̆ová, M. Experimental soil acidification. Appl. Geochem. 17, 245–257 (2002).
Article  Google Scholar 

16.
Watmough, S. A., Eimers, M. C. & Dillon, P. J. Manganese cycling in central Ontario forests: response to soil acidification. Appl. Geochem. 22, 1241–1247 (2007).
CAS  Article  Google Scholar 

17.
Neves, N. R. et al. Photosynthesis and oxidative stress in the restinga plant species Eugenia uniflora L. exposed to simulated acid rain and iron ore dust deposition: potential use in environmental risk assessment. Sci. Total Environ. 407, 3740–3745 (2009).
ADS  CAS  Article  PubMed  Google Scholar 

18.
Moir, J., Jordan, P., Moot, D. & Lucas, R. Phosphorus response and optimum pH ranges of twelve pasture legumes grown in an acid upland New Zealand soil under glasshouse conditions. J. Soil Sci. Plant Nutr. 16, 438–460 (2016).
CAS  Google Scholar 

19.
Huang, Z. et al. Long-term nitrogen deposition linked to reduced water use efficiency in forests with low phosphorus availability. New Phytol. 210, 431–442 (2016).
CAS  Article  PubMed  Google Scholar 

20.
Tang, X. et al. Effects of inorganic and organic amendments on the uptake of lead and trace elements by Brassica chinensis grown in an acidic red soil. Chemosphere 119, 177–183 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

21.
Redel, Y. et al. Assessment of phosphorus status influenced by Al and Fe compounds in volcanic grassland soils. J. Soil Sci. Plant Nutr. 16, 490–506 (2016).
CAS  Google Scholar 

22.
Mitran, T. & Mani, P. K. Effect of organic amendments on rice yield trend, phosphorus use efficiency, uptake, and apparent balance in soil under long-term rice-wheat rotation. J. Plant Nutr. 40, 1312–1322 (2017).
CAS  Article  Google Scholar 

23.
Xin, X. et al. Yield, phosphorus use efficiency and balance response to substituting long-term chemical fertilizer use with organic manure in a wheat-maize system. Field Crop. Res. 208, 27–33 (2017).
Article  Google Scholar 

24.
Qaswar, M. et al. Partial substitution of chemical fertilizers with organic amendments increased rice yield by changing phosphorus fractions and improving phosphatase activities in fluvo-aquic soil. J. Soils Sediments 20, 1–12 (2019).
Google Scholar 

25.
Ahmed, W. et al. Changes in phosphorus fractions associated with soil chemical properties under long-term organic and inorganic fertilization in paddy soils of southern China. PLoS ONE 14, e0216881 (2019).
Article  PubMed  PubMed Central  Google Scholar 

26.
Simonsson, M. et al. Pools and solubility of soil phosphorus as affected by liming in long-term agricultural field experiments. Geoderma 315, 208–219 (2018).
ADS  CAS  Article  Google Scholar 

27.
Holland, J. E., White, P. J., Glendining, M. J., Goulding, K. W. T. & McGrath, S. P. Yield responses of arable crops to liming—an evaluation of relationships between yields and soil pH from a long-term liming experiment. Eur. J. Agron. 105, 176–188 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

28.
Weng, L., Vega, F. A. & Van Riemsdijk, W. H. Competitive and synergistic effects in pH dependent phosphate adsorption in soils: LCD modeling. Environ. Sci. Technol. 45, 8420–8428 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

29.
Eriksson, A. K., Hesterberg, D., Klysubun, W. & Gustafsson, J. P. Phosphorus dynamics in Swedish agricultural soils as influenced by fertilization and mineralogical properties: insights gained from batch experiments and XANES spectroscopy. Sci. Total Environ. 566, 1410–1419 (2016).
ADS  Article  CAS  PubMed  Google Scholar 

30.
Murrmann, R. P. & Peech, M. Effect of pH on labile and soluble phosphate in soils 1. Soil Sci. Soc. Am. J. 33, 205–210 (1969).
ADS  CAS  Article  Google Scholar 

31.
Veith, J. A. & Sposito, G. Reactions of aluminosilicates, aluminum hydrous oxides, and aluminum oxide with o-phosphate: the formation of X-ray amorphous analogs of variscite and montebrasite 1. Soil Sci. Soc. Am. J. 41, 870–876 (1977).
ADS  CAS  Article  Google Scholar 

32.
Stevens, R. L. & Bayard, E. Clay mineralogy of agricultural soils (Ap horizon) in Västergötland, SW Sweden. GFF 116, 87–91 (1994).
Article  Google Scholar 

33.
Cabrera, F., Madrid, L. & De Arambarri, P. Adsorption of phosphate by various oxides: theoretical treatment of the adsorption envelope. J. Soil Sci. 28, 306–313 (1977).
CAS  Article  Google Scholar 

34.
Zhang, H., Bo-ren, W., Ming-gang, X. U. & Ting-lu, F. A. N. Crop yield and soil responses to long-term fertilization on a red soil in Southern China. Pedosph. Int. J. 19, 199–207 (2009).
CAS  Article  Google Scholar 

35.
Cai, Z. et al. Intensified soil acidification from chemical N fertilization and prevention by manure in an 18-year field experiment in the red soil of southern China. J. Soils Sediments 15, 260–270 (2015).
CAS  Article  Google Scholar 

36.
Qaswar, M. et al. Yield sustainability, soil organic carbon sequestration and nutrients balance under long-term combined application of manure and inorganic fertilizers in acidic paddy soil. Soil Tillage Res. 198, 104569 (2020).
Article  Google Scholar 

37.
Fang, Y. et al. Nitrogen deposition and forest nitrogen cycling along an urban–rural transect in southern China. Glob. Change Biol. 17, 872–885 (2011).
ADS  Article  Google Scholar 

38.
Liu, L., Zhang, X., Wang, S., Zhang, W. & Lu, X. Bulk sulfur (S) deposition in China. Atmos. Environ. 135, 41–49 (2016).
ADS  CAS  Article  Google Scholar 

39.
Jia, Y. et al. Spatial and decadal variations in inorganic nitrogen wet deposition in China induced by human activity. Sci. Rep. 4, 3763 (2014).
Article  CAS  PubMed  PubMed Central  Google Scholar 

40.
FAO. World Reference Base for Soil Resources 2014: International soil classification systems for naming soils and creating legends for soil maps (Updated 2015). World Soil Resources Reports No. 106 (2014).

41.
Baxter, S. World reference base for soil resources. Exp. Agric. 43, 264 (2007).
Article  Google Scholar 

42.
Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211 (1984).
Article  Google Scholar 

43.
Guo, Y. et al. Long-term grazing affects relationships between nitrogen form uptake and biomass of alpine meadow plants. Plant Ecol. 218, 1035–1045 (2017).
Article  Google Scholar 

44.
Zhou, H. et al. Stability of alpine meadow ecosystem on the Qinghai-Tibetan Plateau. Chin. Sci. Bull. 51, 320–327 (2006).
Article  Google Scholar 

45.
Nelson, D. W. & Sommers, L. Total carbon, organic carbon, and organic matter. Methods Soil Anal. Part 2 Chem. Microbiol. Prop. 9, 539–579 (1982).
Google Scholar 

46.
Pages, A. L., Miller, R. H. & Dennis, R. K. Methods of Soil Analysis. Part 2 Chemical Methods (Soil Science Society of America Inc., Madison, 1982).
Google Scholar 

47.
Black, C. A. Methods of Soil Analysis Part II. Chemical and Microbiological Properties (American Society of Agriculture, St. Joseph, 1965).
Google Scholar 

48.
Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1964).
Article  Google Scholar 

49.
Knudsen, D., Peterson, G. A. & Pratt, P. F. Lithium, sodium, and potassium. In Methods of Soil Analysis. Part 2. Chemical and microbiological properties (ed. Norman, A. G.) 225–246 (American Society of Agronomy Soil Science Society of America, Madison, 1982).
Google Scholar 

50.
Lu, R. K. Analytical Methods of Soil Agricultural Chemistry (China Agricultural Science and Technology Press, Beijing, 2000).
Google Scholar 

51.
Pavinato, P. S., Rodrigues, M., Soltangheisi, A., Sartor, L. R. & Withers, P. J. A. Effects of cover crops and phosphorus sources on maize yield, phosphorus uptake, and phosphorus use efficiency. Agron. J. 109, 1039–1047 (2017).
CAS  Article  Google Scholar 

52.
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
CAS  Article  PubMed  Google Scholar 

53.
Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).
ADS  CAS  Article  PubMed  Google Scholar 

54.
Schroder, J. L. et al. Soil acidification from long-term use of nitrogen fertilizers on winter wheat. Soil Sci. Soc. Am. J. 75, 957 (2011).
ADS  CAS  Article  Google Scholar 

55.
Chen, D., Lan, Z., Hu, S. & Bai, Y. Effects of nitrogen enrichment on belowground communities in grassland: relative role of soil nitrogen availability vs. soil acidification. Soil Biol. Biochem. 89, 99–108 (2015).
CAS  Article  Google Scholar 

56.
Han, T. et al. The links between potassium availability and soil exchangeable calcium, magnesium, and aluminum are mediated by lime in acidic soil. J. Soils Sediments https://doi.org/10.1007/s11368-018-2145-6 (2018).
Article  Google Scholar 

57.
Bouwman, A. F., Van Vuuren, D. P., Derwent, R. G. & Posch, M. A global analysis of acidification and eutrophication of terrestrial ecosystems. Water. Air Soil Pollut. 141, 349–382 (2002).
ADS  CAS  Article  Google Scholar 

58.
Tang, C. et al. Biological amelioration of subsoil acidity through managing nitrate uptake by wheat crops. Plant Soil 338, 383–397 (2011).
CAS  Article  Google Scholar 

59.
Stevens, C. J., Dise, N. B. & Gowing, D. J. Regional trends in soil acidification and exchangeable metal concentrations in relation to acid deposition rates. Environ. Pollut. 157, 313–319 (2009).
CAS  Article  PubMed  Google Scholar 

60.
Haynes, R. J. Effects of liming on phosphate availability in acid soils. Plant Soil 68, 289–308 (1982).
CAS  Article  Google Scholar 

61.
Nierop, K. G. J. J., Jansen, B. & Verstraten, J. M. Dissolved organic matter, aluminium and iron interactions: precipitation induced by metal/carbon ratio, pH and competition. Sci. Total Environ. 300, 201–211 (2002).
ADS  CAS  Article  PubMed  Google Scholar 

62.
Li, H. et al. Past, present, and future use of phosphorus in Chinese agriculture and its influence on phosphorus losses. Ambio 44, 274–285 (2015).
CAS  Article  PubMed Central  Google Scholar 

63.
Smyth, T. J. & Sanchez, P. A. Effects of lime, silicate, and phosphorus applications to an oxisol on phosphorus sorption and ion retention. Soil Sci. Soc. Am. J. 44, 500–505 (1980).
ADS  CAS  Article  Google Scholar 

64.
Curtin, D. & Syers, J. K. Lime-induced changes in indices of soil phosphate availability. Soil Sci. Soc. Am. J. 65, 147–152 (2001).
ADS  CAS  Article  Google Scholar 

65.
Barth, V. P. et al. Stratification of soil chemical and microbial properties under no-till after liming. Appl. Soil Ecol. 130, 169–177 (2018).
Article  Google Scholar 

66.
Abdi, D. et al. Residual effects of paper mill biosolids and liming materials on soil microbial biomass and community structure. Can. J. Soil Sci. 97, 188–199 (2016).
Google Scholar 

67.
Park, J.-S. & Ro, H.-M. Early-stage changes in chemical phosphorus speciation induced by liming deforested soils. J. Soil Sci. Plant Nutr. 2, 435–447 (2018).
Google Scholar 

68.
Malhi, S. S., Nyborg, M. & Harapiak, J. T. Effects of long-term N fertilizer-induced acidification and liming on micronutrients in soil and in bromegrass hay. Soil Tillage Res. 48, 91–101 (1998).
Article  Google Scholar 

69.
Kostic, L. et al. Liming of anthropogenically acidified soil promotes phosphorus acquisition in the rhizosphere of wheat. Biol. Fertil. Soils 51, 289–298 (2015).
CAS  Article  Google Scholar 

70.
Shahin, M., Esitken, A. & Pirlak, L. The effects of lime does on some morphological and fruit characteristics of some strawberry (Fragaria Xananssa Duch.) cultivars. In IX International Scientific Agriculture Symposium” AGROSYM 2018″, Jahorina, Bosnia and Herzegovina, 4–7 October 2018. Book of Proceedings 575–582 (University of East Sarajevo, Faculty of Agriculture, 2018).

71.
Rheinheimer, D. S., Tiecher, T., Gonzatto, R., Zafar, M. & Brunetto, G. Residual effect of surface-applied lime on soil acidity properties in a long-term experiment under no-till in a Southern Brazilian sandy Ultisol. Geoderma 313, 7–16 (2018).
ADS  CAS  Article  Google Scholar 

72.
Goulding, K. W. T. Soil acidification and the importance of liming agricultural soils with particular reference to the United Kingdom. Soil Use Manag. 32, 390–399 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

73.
Liu, X. Y., Rashti, M. R., Esfandbod, M., Powell, B. & Chen, C. R. Liming improves soil microbial growth, but trash blanket placement increases labile carbon and nitrogen availability in a sugarcane soil of subtropical Australia. Soil Res. 56, 235–243 (2018).
Article  Google Scholar 

74.
Yadvinder-Singh, B.-S. & Timsina, J. Crop residue management for nutrient cycling and improving soil productivity in rice-based cropping systems in the tropics. Adv. Agron. 85, 269–407 (2005).
Article  Google Scholar  More

Research scope and experimental design
The study was conducted on rice production in Tar Pat Village, Maubin, Myanmar (16.617° N, 95.680° E) in the wet season (WS) 2014 and the dry seasons (DS) of 2015 and 2016. Sin Thukha variety with a growing time of 135 days was used for all 3 seasons. Best practices were identified based on the indicators of energy balance, cost-benefits, and GHGE for a functional unit (FU) of 1 ha of rice production. The last factor (GHGE) was estimated using the attributional LCA27,28,29 approach following LCA ISO standard ISO1404:44. Figure 1 shows the system boundary covering all processes of rice production from preharvest (cultivation) to postharvest (until milling). The primary data were collected in harvest and postharvest processes while the secondary data of pre-harvest processes were used in the system analysis. The conversion factors for energy and GHGE of the agronomic inputs, fuel and power consumption, and related transportations were interpreted from the ECOINVENT 3 database (version 3.3)19.
Figure 1

Inputs and outputs of the research system.

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Table 1 shows the research treatments with their major features and applied practices in the different seasons. For the WS2014, a comparative analysis was conducted for two farmer practices (FPs) and one improved practice (IPR). The two farmer practices corresponded to the scenarios of stacking rice plants in the field for 1 week (FP1w) and for 4 weeks (FP4w) after manual cutting. The IPR scenario involved threshing within 12 h after harvest. In addition, the IPR included use of a flatbed dryer for drying the rice, and hermetic bags for storage, instead of sun drying and farmer-granary bags for storage under FP.
Table 1 Scenarios and post-harvest operations covered in the study in Maubin, Ayeyarwady delta, Myanmar during three rice cropping seasons.
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In the dry season of 2015 and 2016, the analysis was conducted for two scenarios, which were farmer practice (FP) and improved post-harvest operations with a combine harvester, flatbed dryer, and hermetic storage (IPRc). Neither of the scenarios had delays or stacking of the rice plants, because farmers were able to thresh the rice immediately after it was manually harvested. The practices involved in FP were manual operations such as cutting of the mature rice plants and sun-drying. The scenarios of DS2015 and DS2016 differed from the WS2014 because the farmers did not stack rice in the DS prior to threshing.
The experiment was set up in fields of farmers on 4110 m2 for WS2014 and 5850 m2 for both DS2015 and DS2016. Each scenario was replicated 5 times in the different plots and were distributed using a completely randomized design (CRD). For the WS2014 experiment, there were 15 plots for three scenarios with each plot 270 m2. The paddy was harvested and processed based on the respective IPR and FP scenarios. The FP scenario included a thresher locally fabricated by the farmers based on the axial threshing principle that was powered by a two-wheel tractor with a 15 HP diesel engine (Fig. 2a), sun drying, and granary bags containing approximately 50 kg of paddy each. The IPR scenario involved a TC-800 axial flow thresher with a 7.5 HP engine (Fig. 2b)30. Compared to the farmer thresher, the imported unit was manufactured and marketed by a branded company in the Philippines and tested by IRRI to ensure good performance. For DS2015 and DS2016, there were 10 plots for two scenarios with each plot 390 m2. Rice was harvested using a Kubota-DC-70G combine harvester with 70 HP (Fig. 2c). A flatbed dryer and hermetic bags for storage of dried grain were used for IPR in all three seasons. The flatbed dryer with 4 t batch−1 capacity (Fig. 2d) was locally made based on published designs6, and was used for the IPR in both the wet and dry seasons. Hermetic bags for storage, also called “Super bags”31 hold 50 kg of paddy per bag. Milling operations were in-situ measured at the local rice mill (two-stage milling system) with 1 t h−1 capacity, located at Maubin, and were applied for both FP and IPR scenarios.
Figure 2

(a) Farmer thresher. (b) Imported thresher, TC-800. (c) Combine harvester Kubota-DC-70G. (d) Flatbed dryer 4 ton batch−1.

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Measurement and quantification of harvest and post-harvest losses
Shattering loss during cutting, stacking, and combine harvesting was determined through sampling of 5 plots using 1 m2 quadrants for each scenario. Shattering losses were calculated based on the ratio between shattered grains and yield at an adjusted moisture content of 14% wet basis (MC). In threshing, the grain losses were quantified in the stacked rice, at the separation process, in the cleaning process, and under the machine during the threshing operation. The sum of these losses comprised the threshing loss. The design did not quantify losses caused in drying and storage during handling, and grain lost to birds and rodents. See Htwe et al.9 for estimate of losses caused by rodents at this study site. The losses associated with discoloration, milling recovery (MR), and head rice recovery (HRR) caused by in-field stacking, delay of drying and storage methods were measured after milling.
MR and HRR were measured on milled rice. Three subsamples of 500 g of paddy were taken randomly from the grain harvested in each plot. The samples were cleaned using a Seedburo Paddy Blower, then 250 g of filled grain were passed twice in a RISE 10″ Rubber Roll Husker, then through a SATAKE Abrasive Whitener, and finally, through a SATAKE laboratory rice grader. MR and HRR were calculated using Eqs. (1) and (2), respectively.

$$MR; (%)=frac{Weight;of;milled ;rice ;left(include ;broken; grainsright)}{Weight; of ;paddy ;samples} times100$$
(1)

$$HRR ;(%) =frac{Weight; of ;whole; grains}{Weight ;of; paddy ;samples} times 100$$
(2)

Discoloration of grain was caused by fungi, bacteria, and environmental conditions such as high humidity and temperature. Milled rice kernels having more than 0.5% grains with a color other than white (usually yellow) or with a spotted surface were considered discolored32. To measure discoloration, three 25 g samples of the product were collected randomly. Discolored grains with spots, streaks, or having more than 0.5% differently colored surface were separated and weighed to calculate the percentage of discoloration based on Eq. (3).

$$Discoloration ;(%)= frac{weight ;of ;discolored ;grains (text{g})}{weight ;of ;sample; (25 ;text{g})} times 100$$
(3)

Energy efficiency and GHG emissions
This study investigated net energy value (NEV) and net energy ratio (NER) which are commonly used to quantify energy efficiency of a production systems33,34,35. NEV accounted for the inputs and outputs of the systems per the FU (ha of rice production) (Eq. 4) while the NER was the ratio between the output and input energy values (Eq. 5).

$$NEV left(frac{GJ}{ha}right)=E{V}_{outputs}-E{V}_{inputs}$$
(4)

where the EVoutputs accounted for rice grain products and by-products such as broken and discolored grains, bran, husks, and straw. The EVinputs includes all the energy consumption of rice production from cultivation to milling; this includes agronomic inputs, machine production, fuel and power consumption, and labor use.

$$NER=frac{E{V}_{outputs}}{E{V}_{inputs}}$$
(5)

Table 2 shows energy values embed in the whole milled rice, broken and discolored rice, bran, husk, and straw. Energy value (EV) of rice product36 is 15.2 MJ kg−1 while that of rice bran, broken rice, and discolored rice is 9.6 MJ kg−1 (Econivent 3 database19) with an assumption that these by-products are used for cattle feed and have a similar economic value. EV of rice husk is 8.7 MJ kg−1 (Ecoinvent 3 database19) and straw is 3.5 MJ kg−120 based on an asumption that partially harvested straw were collected for mushroom production. The collected amount of rice straw was about 50% of the grain yield at harvest34. The EV per kg was then translated to the FU based on the grain yield and post-harvest losses measured in the experiments. In particular, rice husk and bran were assumed to be 20 and 10% of the milled rice produced, respectively. EV of the cultivation (excluding harvesting and transportation) was about 12 and 16 GJ ha−1 for the small-farm irrigated rice production in the WS and DS, respectively, as reported in research in the same region (Ayeyarwaddy delta of Myanmar)37. EV of machine production was accounted for via a depreciation of 5 years. Fuel and power consumption of harvest and post-harvest operations were measured and translated to EV using the coversion factors. The energy of manual labor was calculated based on the metabolic equivalent of tasks (MET) (Table 2). Ainsworth et al.38 described the MET as the ratio of the human metabolic rate when performing an activity to the metabolic rate at rest. This ratio is converted to an energy value as MJ per hour working using the method described by Quilty et al.39 with the assumption of a mean Asian human body weight of 55 kg. For paddy transportation, the tractor-hauled trailor option was used for all scenarios with a distance of 15 km from the field to the station of drying, storage, and milling.
Table 2 Conversion factors for energy and GHGE.
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The GHGE were accounted for the whole production from cultivation to milling. The yield and grain losses were taken into account through a rice product recovery ratio as shown in Eq. (6).

$$GHGE ;(text{kg} ,{text{ha}}^{-1})=frac{text{G}H{G}_{cultivation}+ GH{G}_{harvest} + GH{G}_{postharvest} }{Product; recovery; ratio}$$
(6)

where GHGcultivation for the irrigated rice cultivation in Myanmar was about 2000 and 1200 kgCO2-eq ha−1 in WS and DS, repectively, as reported in recent research at the same site40. GHGE of harvest and post-harvest operations were calculated based on emissions generated during production of harvest and post-harvest equipment, input materials, and fuel consumption during operations. The unit of in-field emissions was per ha while that of off-field emissions was per kg of rice grains. The off-field emission values were therefore translated to attribute for the FU (ha) based on rice yield (kg ha−1). Furthermore, the associated unit (kg of rice grains) was considered as the product at the end of the life-cycle boundary, its carbon footprint therefore accounted for the post-harvest losses or product recovery. The recovered rice product (whole grains) was calculated from the grain yield with a consideration of harvest and postharvest losses. The postharvest losses were brokenness (HRR) and discoloration at harvest, drying, storage, milling, and handling. The rice product recovery ratio was calculated based on Eq. (7).

$$Product ;recovery; ratio =left(1-Los{s}_{harvesting}right)* HRR*(1-Discoloration)$$
(7)

The conversion factors for GHGE of related fuel and power consumption, machine production, and transportation are shown in Table 2. In particular, GHGE from the electric power consumption for drying and milling was translated from Ecoinvent 3 data (version 3.3) for the “rest of the world (ROW)”.
Cost–benefit analysis
Similar to the energy efficiency analysis, cost-benefits were quantified through the net income value (NIV) (Eq. 8) and net income ratio (NIR) (Eq. 9). NIV accounted for the cost of production and income value (IV) of products and co-products while the NIR was the ratio between NIV and the input cost.

$$NIV left(frac{$US}{text{ha}}right)=I{V}_{(whole; rice + broken; rice + discolored; rice+bran+husk+ straw)}-(Cos{t}_{cultivation}+{ Cost}_{left(harvest; and ;post {text{-}}harvestright)}),$$
(8)

$$NIR=frac{NIV}{(Cos{t}_{cultivation}+{ Cost}_{left(harvest ;and ;post{text{-}}harvestright)})}$$
(9)

The price of rice product was $US 400 per t1. Price of discolored rice was assumed to be the same as bran price, which is $US 140 per t1. Cost of the cultivation (excluding harvesting and transportation) was about 650 $US ha−1 for small-farm irrigated rice production in the Ayeyarwaddy delta of Myanmar37. Costs of the post-harvest operations were calculated based on the corresponding depreciation, maintenance, interest, energy consumption, and labor of all related equipment used in the operations from harvesting to milling. The component costs of input materials, labor, and energy included in the analysis were collected based on assessments in Myanmar in 2018 (Table 3).
Table 3 Cost and life span of different component costs of input materials, labor, and energy based on assessments conducted in the Ayeyarwady Delta region of Myanmar in 2018.
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Harvesting loss was used to conduct a sensitivity analysis for NIV and NIR for both the wet and dry seasons. This analysis only applied for the improved post-harvest operations with the flatbed dryer and hermetic storage.
Statistical analysis and software
Analysis of Variance (ANOVA) Single Factor and Two-Factor with replication and F-Test Two-Sample for Variances tools incorporated in Excel were used to evaluate the effects of the contrasting post-harvest management scenarios on the measured post-harvest losses, energy, and GHGE. The ECOINVENT-3 database (version 3.3)19 in association with Cumulative Energy Demand 1.09 method41 and the Global Warming Potential—100 years (GWP100a) presented in IPCC 201342, were used to interpret the conversion factors of energy (MJ) embedded and GHGE (CO2-eq) from the agronomic inputs and fuel consumption. All these databases and methods (Ecoinvent, Cumulatiive Energy Demand, and IPCC) are incorporated in SIMAPRO version 8.5.0.041. More