Ecology

1.Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Woolhouse, M. E. et al. Temporal trends in the discovery of human viruses. Proc. R. Soc. B 275, 2111–2115 (2008).PubMed 
PubMed Central 

Google Scholar 
3.Smith, K. F. et al. Global rise in human infectious disease outbreaks. J. R. Soc. Interface 11, 20140950 (2014).PubMed 
PubMed Central 

Google Scholar 
4.Carlson, C. J. et al. Climate change will drive novel cross-species viral transmission. Preprint at bioRxiv https://doi.org/10.1101/2020.01.24.918755 (2020).5.Swei, A., Couper, L. I., Coffey, L. L., Kapan, D. & Bennett, S. Patterns, drivers, and challenges of vector-borne disease emergence. Vector Borne Zoonotic Dis. 20, 159–170 (2020).PubMed 
PubMed Central 

Google Scholar 
6.Belay, E. D. et al. Zoonotic disease programs for enhancing global health security. Emerg. Infect. Dis. 23, S65 (2017).PubMed Central 

Google Scholar 
7.Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).PubMed 
PubMed Central 

Google Scholar 
8.Carroll, D. et al. The global virome project. Science 359, 872–874 (2018).CAS 
PubMed 

Google Scholar 
9.Carlson, C. J., Zipfel, C. M., Garnier, R. & Bansal, S. Global estimates of mammalian viral diversity accounting for host sharing. Nat. Ecol. Evol. 3, 1070–1075 (2019).PubMed 

Google Scholar 
10.Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science 362, 577–580 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Han, B. A. et al. Undiscovered bat hosts of filoviruses. PLoS Negl. Trop. Dis. 10, e0004815 (2016).PubMed 
PubMed Central 

Google Scholar 
12.Schmidt, J. P. et al. Spatiotemporal fluctuations and triggers of Ebola virus spillover. Emerg. Infect. Dis. 23, 415 (2017).PubMed 
PubMed Central 

Google Scholar 
13.Guth, S., Visher, E., Boots, M. & Brook, C. E. Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal–human interface. Phil. Trans. R. Soc. Biol. Sci. 374, 20190296 (2019).
Google Scholar 
14.Glennon, E. E. et al. Syndromic detectability of haemorrhagic fever outbreaks. Preprint at medRxiv https://doi.org/10.1101/2020.03.28.20019463 (2020).15.Pigott, D. M. et al. Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis. Lancet 390, 2662–2672 (2017).PubMed 
PubMed Central 

Google Scholar 
16.Palmer, S., Brown, D. & Morgan, D. Early qualitative risk assessment of the emerging zoonotic potential of animal diseases. BMJ 331, 1256–1260 (2005).PubMed 
PubMed Central 

Google Scholar 
17.Grange, Z. L. et al. Ranking the risk of animal-to-human spillover for newly discovered viruses. Proc. Natl Acad. Sci. USA 118, e2002324118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Carlson, C. J. From PREDICT to prevention, one pandemic later. Lancet Microbe 1, e6–e7 (2020).PubMed 
PubMed Central 

Google Scholar 
19.Holmes, E., Rambaut, A. & Andersen, K. Pandemics: spend on surveillance, not prediction. Nature 558, 180–182 (2018).CAS 
PubMed 

Google Scholar 
20.Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231 (2001).
Google Scholar 
21.Mouquet, N. et al. Predictive ecology in a changing world. J. Appl. Ecol. 52, 1293–1310 (2015).
Google Scholar 
22.Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Stephens, P. R. et al. Global mammal parasite database version 2.0. Ecology 98, 1476 (2017).PubMed 

Google Scholar 
24.Wardeh, M., Risley, C., McIntyre, M. K., Setzkorn, C. & Baylis, M. Database of host–pathogen and related species interactions, and their global distribution. Sci. Data 2, 150049 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Shaw, L. P. et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol. Ecol. 29, 3361–3379 (2020).PubMed 

Google Scholar 
26.Gibb, R. et al. Data proliferation, reconciliation, and synthesis in viral ecology. BioScience https://doi.org/10.1093/biosci/biab080 (2021).27.Dallas, T., Park, A. W. & Drake, J. M. Predicting cryptic links in host–parasite networks. PLoS Comput. Biol. 13, e1005557 (2017).PubMed 
PubMed Central 

Google Scholar 
28.Poisot, T. et al. Imputing the mammalian virome with linear filtering and singular value decomposition. Preprint at https://arxiv.org/abs/2105.14973 (2021).29.Carlson, C. J. et al. The Global Virome in One Network (VIRION): an atlas of vertebrate–virus associations. Preprint at bioRxiv https://doi.org/10.1101/2021.08.06.455442 (2021).30.Albery, G. F., Eskew, E. A., Ross, N. & Olival, K. J. Predicting the global mammalian viral sharing network using phylogeography. Nat. Commun. 11, 2260 (2020).31.Davies, T. J. & Pedersen, A. B. Phylogeny and geography predict pathogen community similarity in wild primates and humans. Proc. R. Soc. B Biol. Sci. 275, 1695–1701 (2008).
Google Scholar 
32.Guy, C., Thiagavel, J., Mideo, N. & Ratcliffe, J. M. Phylogeny matters: revisiting ‘a comparison of bats and rodents as reservoirs of zoonotic viruses’. R. Soc. Open Sci. 6, 181182 (2019).PubMed 
PubMed Central 

Google Scholar 
33.Washburne, A. D. et al. Taxonomic patterns in the zoonotic potential of mammalian viruses. PeerJ 6, e5979 (2018).PubMed 
PubMed Central 

Google Scholar 
34.Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Stephens, P. R. et al. The macroecology of infectious diseases: a new perspective on global-scale drivers of pathogen distributions and impacts. Ecol. Lett. 19, 1159–1171 (2016).PubMed 

Google Scholar 
36.Longdon, B., Brockhurst, M. A., Russell, C. A., Welch, J. J. & Jiggins, F. M. The evolution and genetics of virus host shifts. PLoS Pathog. 10, e1004395 (2014).PubMed 
PubMed Central 

Google Scholar 
37.Farrell, M. J., Elmasri, M., Stephens, D. A. & Davies, T. J. Predicting missing links in global host–parasite networks. bioRxiv https://doi.org/10.1101/2020.02.25.965046 (2020).38.Gilbert, A. T. et al. Deciphering serology to understand the ecology of infectious diseases in wildlife. EcoHealth 10, 298–313 (2013).PubMed 

Google Scholar 
39.Becker, D. J., Seifert, S. N. & Carlson, C. J. Beyond infection: integrating competence into reservoir host prediction. Trends Ecol. Evol. 35, 1062–1065 (2020).PubMed 
PubMed Central 

Google Scholar 
40.Walsh, M. G., Mor, S. M., Maity, H. & Hossain, S. A preliminary ecological profile of Kyasanur Forest disease virus hosts among the mammalian wildlife of the Western Ghats, India. Ticks Tick Borne Dis. 11, 101419 (2020).PubMed 

Google Scholar 
41.Plowright, R. K. et al. Prioritizing surveillance of Nipah virus in India. PLoS Negl. Trop. Dis. 13, e0007393 (2019).PubMed 
PubMed Central 

Google Scholar 
42.Schmidt, J. P. et al. Ecological indicators of mammal exposure to Ebolavirus. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180337 (2019).
Google Scholar 
43.Worsley-Tonks, K. E. et al. Using host traits to predict reservoir host species of rabies virus. PLoS Negl. Trop. Dis. 14, e0008940 (2020).PubMed 
PubMed Central 

Google Scholar 
44.Woolhouse, M. E. & Gowtage-Sequeria, S. Host range and emerging and reemerging pathogens. Emerg. Infect. Dis. 11, 1842 (2005).PubMed 
PubMed Central 

Google Scholar 
45.Johnson, C. K. et al. Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci. Rep. 5, 14830 (2015).
Google Scholar 
46.Elena, S. F. & Sanjuán, R. Adaptive value of high mutation rates of RNA viruses: separating causes from consequences. J. Virol. 79, 11555–11558 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Duffy, S. Why are RNA virus mutation rates so damn high? PLoS Biol. 16, e3000003 (2018).PubMed 
PubMed Central 

Google Scholar 
48.Grewelle, R. E. Larger viral genome size facilitates emergence of zoonotic diseases. Preprint at bioRxiv https://doi.org/10.1101/2020.03.10.986109 (2020).49.Mollentze, N. & Streicker, D. G. Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts. Proc. Natl Acad. Sci. USA 117, 9423–9430 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Walker, J. W., Han, B. A., Ott, I. M. & Drake, J. M. Transmissibility of emerging viral zoonoses. PLoS ONE 13, e0206926 (2018).PubMed 
PubMed Central 

Google Scholar 
51.Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2010146117 (2020).52.Zhang, Z. et al. Rapid identification of human-infecting viruses. Transbound. Emerg. Dis. 66, 2517–2522 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Eng, C. L., Tong, J. C. & Tan, T. W. Predicting zoonotic risk of influenza A viruses from host tropism protein signature using random forest. Int. J. Mol. Sci. 18, 1135 (2017).PubMed Central 

Google Scholar 
54.Li, J. et al. Machine learning methods for predicting human-adaptive influenza A viruses based on viral nucleotide compositions. Mol. Biol. Evol. 37, 1224–1236 (2020).CAS 
PubMed 

Google Scholar 
55.Kim, B., Niu, X., Hunter, D. R. & Cao, X. A dynamic additive and multiplicative effects model with application to the United Nations voting behaviors. Preprint at https://arxiv.org/abs/1803.06711 (2018).56.Becker, D. et al. Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs. Lancet Microbe (in the press).57.Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc. Natl Acad. Sci. USA 112, 7039–7044 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Plourde, B. T. et al. Are disease reservoirs special? Taxonomic and life history characteristics. PLoS ONE 12, e0180716 (2017).PubMed 
PubMed Central 

Google Scholar 
59.Keesing, F. et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468, 647–652 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Albery, G. F. & Becker, D. J. Fast-lived hosts and zoonotic risk. Trends Parasitol. 37, 117–129 (2021).CAS 
PubMed 

Google Scholar 
61.Young, C. C. & Olival, K. J. Optimizing viral discovery in bats. PLoS ONE 11, e0149237 (2016).PubMed 
PubMed Central 

Google Scholar 
62.Albery, G. F. et al. Urban-adapted mammal species have more known pathogens. Preprint at bioRxiv https://doi.org/10.1101/2021.01.02.425084 (2021).63.Wille, M., Geoghegan, J. L. & Holmes, E. C. How accurately can we assess zoonotic risk? PLoS Biol. 19, e3001135 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Gibb, R. et al. Mammal virus diversity estimates are unstable due to accelerating discovery effort. Preprint at bioRxiv https://doi.org/10.1101/2021.08.10.455791 (2021).65.Xu, G. J. et al. Comprehensive serological profiling of human populations using a synthetic human virome. Science 348, aaa0698 (2015).PubMed 
PubMed Central 

Google Scholar 
66.Geoghegan, J. L. & Holmes, E. C. Predicting virus emergence amid evolutionary noise. Open Biol. 7, 170189 (2017).PubMed 
PubMed Central 

Google Scholar 
67.Fischhoff, I. R., Castellanos, A. A., Rodrigues, J. P., Varsani, A. & Han, B. A. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2021.1651 (2021).68.Hou, Y. et al. Angiotensin-converting enzyme 2 (ACE2) proteins of different bat species confer variable susceptibility to SARS-CoV entry. Arch. Virol. 155, 1563–1569 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Thompson, A. J., de Vries, R. P. & Paulson, J. C. Virus recognition of glycan receptors. Curr. Opin. Virol. 34, 117–129 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Kocher, J. F. et al. Bat caliciviruses and human noroviruses are antigenically similar and have overlapping histo-blood group antigen binding profiles. Mbio 9, e00869-18 (2018).PubMed 
PubMed Central 

Google Scholar 
71.Chiramel, A. I. et al. TRIM5α restricts flavivirus replication by targeting the viral protease for proteasomal degradation. Cell Rep. 27, 3269–3283 (2019).CAS 
PubMed 

Google Scholar 
72.Young, F., Rogers, S. & Robertson, D. L. Predicting host taxonomic information from viral genomes: a comparison of feature representations. PLoS Comput. Biol. 16, e1007894 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).CAS 
PubMed 

Google Scholar 
74.Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Truong, P., Garcia-Vallve, S. & Puigbo, P. An unsupervised algorithm for host identification in flaviviruses. Life https://doi.org/10.3390/life11050442 (2021).76.Mollentze, N., Babayan, S. & Streicker, D. Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biol. 19, e3001390 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Wang, W. et al. A network-based integrated framework for predicting virus–prokaryote interactions. NAR Genom. Bioinform. 2, lqaa044 (2020).PubMed 
PubMed Central 

Google Scholar 
78.Bartoszewicz, J. M., Seidel, A. & Renard, B. Y. Interpretable detection of novel human viruses from genome sequencing data. NAR Genom. Bioinform. 3, lqab004 (2021).PubMed 
PubMed Central 

Google Scholar 
79.He, X. et al. Neural collaborative filtering. In Proc. 26th International Conference on World Wide Web 26, 173–182 (Republic and Canton of Geneva, Switzerland, 2017).80.Fout, A., Byrd, J., Shariat, B. & Ben-Hur, A. Protein interface prediction using graph convolutional networks. NIPS’17: Proc. 31st International Conference on Neural Information Processing Systems 31, 6533–6542 (2017).
Google Scholar 
81.Hamilton, W. L., Ying, R. & Leskovec, J. Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40, 52–74 (2017).
Google Scholar 
82.Bergner, L. M. et al. Characterizing and evaluating the zoonotic potential of novel viruses discovered in vampire bats. Viruses 13, 252 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Dietze, M. C. et al. Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc. Natl Acad. Sci. USA 115, 1424–1432 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Schulz, J. E. et al. Serological evidence for henipa-like and filo-like viruses in Trinidad bats. J. Infect. Dis. 221, S375–S382 (2020).PubMed 
PubMed Central 

Google Scholar 
85.Brook, C. E. et al. Disentangling serology to elucidate henipa- and filovirus transmission in Madagascar fruit bats. J. Anim. Ecol. 88, 1001–1016 (2019).PubMed 
PubMed Central 

Google Scholar 
86.Seifert, S. N. et al. Rousettus aegyptiacus bats do not support productive Nipah virus replication. J. Infect. Dis. 221, S407–S413 (2020).CAS 
PubMed 

Google Scholar 
87.Carlson, C. J. et al. The future of zoonotic risk prediction. Phil. Trans. R. Soc. B Biol. Sci. 376, 20200358 (2021).
Google Scholar 
88.Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Menachery, V. D. et al. A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nat. Med. 21, 1508–1513 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Guan, Y. et al. Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science 302, 276–278 (2003).CAS 
PubMed 

Google Scholar 
91.Woo, P. C. Y. et al. Characterization and complete genome sequence of a novel coronavirus, coronavirus HKU1, from patients with pneumonia. J. Virol. 79, 884–895 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 310, 676–679 (2005).CAS 
PubMed 

Google Scholar 
93.Wang, M. et al. SARS-CoV infection in a restaurant from palm civet. Emerg. Infect. Dis. 11, 1860–1865 (2005).PubMed 
PubMed Central 

Google Scholar 
94.Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, e1006698 (2017).PubMed 
PubMed Central 

Google Scholar 
95.Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Xiao, K. et al. Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins. Nature 583, 286–289 (2020).CAS 
PubMed 

Google Scholar 
97.Lam, T.-Y. et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).CAS 
PubMed 

Google Scholar 
98.Wacharapluesadee, S. et al. Evidence for SARS-CoV-2 related coronaviruses circulating in bats and pangolins in Southeast Asia. Nat. Commun. 12, 972 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
99.Holmes, E. C. et al. The origins of SARS-CoV-2: a critical review. Cell 184, 4848–4856 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
100.Oude Munnink, B. B. et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 371, 172–177 (2021).CAS 
PubMed 

Google Scholar 
101.Chandler, J. C. et al. SARS-CoV-2 exposure in wild white-tailed deer (Odocoileus virginianus). Proc. Natl Acad. Sci. USA 118, e2114828118 (2021).PubMed 

Google Scholar 
102.Jia, P., Dai, S., Wu, T. & Yang, S. New approaches to anticipate the risk of reverse zoonosis. Trends Ecol. Evol. 36, 580–590 (2021).PubMed 
PubMed Central 

Google Scholar 
103.Lednicky, J. A. et al. Isolation of a novel recombinant canine coronavirus from a visitor to Haiti: further evidence of transmission of coronaviruses of zoonotic origin to humans. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab924 (2021).104.Vlasova, A. N. et al. Novel canine coronavirus isolated from a hospitalized pneumonia patient, East Malaysia. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab456 (2021).105.Lednicky, J. A. et al. Emergence of porcine delta-coronavirus pathogenic infections among children in Haiti through independent zoonoses and convergent evolution. Preprint at medRxiv https://doi.org/10.1101/2021.03.19.21253391 (2021).106.Hay, A. J. & McCauley, J. W. The WHO global influenza surveillance and response system (GISRS)—a future perspective. Influenza Other Respir. Viruses 12, 551–557 (2018).PubMed 
PubMed Central 

Google Scholar 
107.Subbarao, K. et al. Characterization of an avian influenza A (H5N1) virus isolated from a child with a fatal respiratory illness. Science 279, 393–396 (1998).CAS 
PubMed 

Google Scholar 
108.Kandeel, A. et al. Zoonotic transmission of avian influenza virus (H5N1), Egypt, 2006–2009. Emerg. Infect. Dis. 16, 1101 (2010).PubMed 
PubMed Central 

Google Scholar 
109.Ke, C. et al. Human infection with highly pathogenic avian influenza A (H7N9) virus, China. Emerg. Infect. Dis. 23, 1332 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
110.Gaidet, N. et al. Evidence of infection by H5N2 highly pathogenic avian influenza viruses in healthy wild waterfowl. PLoS Pathog. 4, e1000127 (2008).PubMed 
PubMed Central 

Google Scholar 
111.Webster, R. G., Bean, W. J., Gorman, O. T., Chambers, T. M. & Kawaoka, Y. Evolution and ecology of influenza A viruses. Microbiol. Mol. Biol. Rev. 56, 152–179 (1992).CAS 

Google Scholar 
112.Pawar, S. D. et al. Avian influenza surveillance reveals presence of low pathogenic avian influenza viruses in poultry during 2009–2011 in the West Bengal State, India. Virol. J. 9, 151 (2012).PubMed 
PubMed Central 

Google Scholar 
113.Parry, R., Wille, M., Turnbull, O. M., Geoghegan, J. L. & Holmes, E. C. Divergent influenza-like viruses of amphibians and fish support an ancient evolutionary association. Viruses 12, 1042 (2020).CAS 
PubMed Central 

Google Scholar 
114.Campbell, P. J. et al. The M segment of the 2009 pandemic influenza virus confers increased neuraminidase activity, filamentous morphology, and efficient contact transmissibility to A/Puerto Rico/8/1934-based reassortant viruses. J. Virol. 88, 3802–3814 (2014).PubMed 
PubMed Central 

Google Scholar 
115.Carlson, C. Evolutionary surprise, artificial intelligence, and H5N8. The Verena Blog https://www.viralemergence.org/blog/evolutionary-surprise-artificial-intelligence-and-h5n8 (2021).116.Wardeh, M., Baylis, M. & Blagrove, M. S. Predicting mammalian hosts in which novel coronaviruses can be generated. Nat. Commun. 12, 780 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
117.Crossman, L. C. Leveraging deep learning to simulate coronavirus spike proteins has the potential to predict future zoonotic sequences. Preprint at bioRxiv https://doi.org/10.1101/2020.04.20.046920 (2020). More

1.Colwell, R. K. Spatial scale and the synchrony of ecological disruption. Nature https://doi.org/10.1038/s41586-021-03760-4 (2021).2.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).ADS 
CAS 
Article 

Google Scholar 
3.Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).Article 

Google Scholar 
4.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
CAS 
Article 

Google Scholar 
5.Ricklefs, R. E. Disintegration of the ecological community. Am. Nat. 172, 741–750 (2008).Article 

Google Scholar 
6.Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).ADS 
CAS 
Article 

Google Scholar 
7.Nadeau, C. P., Urban, M. C. & Bridle, J. R. Coarse climate change projections for species living in a fine-scaled world. Glob. Change Biol. 23, 12–24 (2017).ADS 
Article 

Google Scholar 
8.Stewart, S. B. et al. Climate extreme variables generated using monthly time‐series data improve predicted distributions of plant species. Ecography 44, 626–639 (2021).Article 

Google Scholar 
9.Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).ADS 
Article 

Google Scholar 
10.McKechnie, A. E. & Wolf, B. O. The Physiology of Heat Tolerance in Small Endotherms. Physiology 34, 302–313 (2019).CAS 
Article 

Google Scholar 
11.Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

Google Scholar 
12.Mahony, C. R. & Cannon, A. J. Wetter summers can intensify departures from natural variability in a warming climate. Nat. Commun. 9, 783 (2018).ADS 
Article 

Google Scholar 
13.Molnár, P. K., Derocher, A. E., Thiemann, G. W. & Lewis, M. A. Predicting survival, reproduction and abundance of polar bears under climate change. Biol. Conserv. 143, 1612–1622 (2010).Article 

Google Scholar 
14.Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).CAS 
Article 

Google Scholar 
15.Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl Acad. Sci. USA 113, 13791–13796 (2016).Article 

Google Scholar 
16.Genin, A., Levy, L., Sharon, G., Raitsos, D. E. & Diamant, A. Rapid onsets of warming events trigger mass mortality of coral reef fish. Proc. Natl Acad. Sci. USA 117, 25378–25385 (2020).ADS 
CAS 
Article 

Google Scholar 
17.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).ADS 
Article 

Google Scholar 
18.Ruthrof, K. X. et al. Subcontinental heat wave triggers terrestrial and marine, multi-taxa responses. Sci. Rep. 8, 13094 (2018).ADS 
Article 

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

Google Scholar 
20.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
CAS 
Article 

Google Scholar 
21.Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).ADS 
Article 

Google Scholar 
22.Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).Article 

Google Scholar 
23.Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).ADS 
CAS 
Article 

Google Scholar 
24.Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).ADS 
CAS 
Article 

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

Google Scholar 
26.NOAA National Geophysical Data Center. 2009: ETOPO1 1 Arc-Minute Global Relief Model. NOAA National Centers for Environmental Information. Accessed 10.05.2021. More

1.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).ADS 
CAS 
Article 

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

Google Scholar 
3.Sih, T. L., Cappo, M. & Kingsford, M. Deep-reef fish assemblages of the Great Barrier Reef shelf-break (Australia). Sci. Rep. 7, 10886 (2017).ADS 
Article 

Google Scholar 
4.Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).ADS 
CAS 
Article 

Google Scholar 
5.Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).ADS 
CAS 
Article 

Google Scholar 
6.Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. Proc. Natl Acad. Sci. USA 107, 9707–9711 (2010).ADS 
CAS 
Article 

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

Google Scholar 
8.Stroud, J. T. et al. Is a community still a community? Reviewing definitions of key terms in community ecology. Ecol. Evol. 5, 4757–4765 (2015).Article 

Google Scholar 
9.Lima, F. P. et al. Loss of thermal refugia near equatorial range limits. Glob. Change Biol. 22, 254–263 (2016).ADS 
Article 

Google Scholar 
10.Lenoir, J. & Svenning, J. C. Climate‐related range shifts–a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2014).Article 

Google Scholar 
11.Bell, G. Evolutionary rescue. Annu. Rev. Ecol. Evol. Syst. 48, 605–627 (2017).Article 

Google Scholar 
12.Ackerly, D. D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 164, S165–S184 (2003).Article 

Google Scholar 
13.Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

Google Scholar 
14.Rangel, T. F. et al. Modeling the ecology and evolution of biodiversity: biogeographical cradles, museums, and graves. Science 361, eaar5452 (2018). CAS 
Article 

Google Scholar 
15.Desbruyères, D., McDonagh, E. L., King, B. A. & Thierry, V. Global and full-depth ocean temperature trends during the early twenty-first century from Argo and repeat hydrography. J. Clim. 30, 1985–1997 (2017).ADS 
Article 

Google Scholar  More

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Many seabirds form long-term pairings, but do not necessarily mate for life — and are more likely to ‘break up’ in years when environmental conditions are unfavourable, researchers reveal.

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References1.Ventura, F., Granadeiro, J. P., Lukacs, P. M., Kuepfer, A. & Catry, P. Proc. R. Soc. B https://doi.org/10.1098/rspb.2021.2112 (2021).Article 

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1.Loisel, J. et al. A database and synthesis of northern peatland soil properties and Holocene carbon and nitrogen accumulation. Holocene 24, 1028–1042 (2014).
Google Scholar 
2.Loisel, J. et al. Insights and issues with estimating northern peatland carbon stocks and fluxes since the Last Glacial Maximum. Earth Sci. Rev. 165, 59–80 (2017).CAS 

Google Scholar 
3.Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, L13402 (2010).4.Scharlemann, J. P., Tanner, E. V., Hiederer, R. & Kapos, V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag. 5, 81–91 (2014).CAS 

Google Scholar 
5.Chambers, F. M., Barber, K. E., Maddy, D. & Brew, J. A 5500-year proxy-climate and vegetation record from blanket mire at Talla Moss, Borders, Scotland. The Holocene 7, 391–399 (1997).
Google Scholar 
6.Charman, D. J., Blundell, A., Chiverrell, R. C., Hendon, D. & Langdon, P. G. Compilation of non-annually resolved Holocene proxy climate records: stacked Holocene peatland palaeo-water table reconstructions from northern Britain. Quat. Sci. Rev. 25, 336–350 (2006).
Google Scholar 
7.Swindles, G. T. et al. Widespread drying of European peatlands in recent centuries. Nat. Geosci. 12, 922–928 (2019).CAS 

Google Scholar 
8.van der Linden, M. & van Geel, B. Late Holocene climate change and human impact recorded in a south Swedish ombrotrophic peat bog. Palaeogeogr. Palaeoclimatol. Palaeoecol. 240, 649–667 (2006).
Google Scholar 
9.Clymo, R. S. The limits to peat bog growth. Philos. Trans. R. Soc. B Biol. Sci. 303, 605–654 (1984).
Google Scholar 
10.Hessen, D. O., Ågren, G. I., Anderson, T. R., Elser, J. J. & de Ruiter, P. C. Carbon sequestration in ecosystems: the role of stoichiometry. Ecology 85, 1179–1192 (2004).
Google Scholar 
11.Damman, A. W. H. Distribution and movement of elements in ombrotrophic peat bogs. Oikos 30, 480–495 (1978).CAS 

Google Scholar 
12.Malmer, N. Patterns in the growth and the accumulation of inorganic constituents in the Sphagnum cover on ombrotrophic bogs in Scandinavia. Oikos 53, 105–120 (1988).CAS 

Google Scholar 
13.Wang, R. et al. Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100. Glob. Change Biol. 23, 4854–4872 (2017).
Google Scholar 
14.Du, E. et al. Imbalanced phosphorus and nitrogen deposition in China’s forests. Atmos. Chem. Phys. 16, 8571–8579 (2016).CAS 

Google Scholar 
15.Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).CAS 

Google Scholar 
16.Bragazza, L. et al. Atmospheric nitrogen deposition promotes carbon loss from peat bogs. Proc. Natl. Acad. Sci. USA 103, 19386–19389 (2006).CAS 

Google Scholar 
17.Bragazza, L. et al. High nitrogen deposition alters the decomposition of bog plant litter and reduces carbon accumulation. Glob. Change Biol. 18, 1163–1172 (2012).
Google Scholar 
18.Aerts, R., Wallén, B. & Malmer, N. Growth-limiting nutrients in Sphagnum-dominated bogs subject to low and high atmospheric nitrogen supply. J. Ecol. 80, 131–140 (1992).
Google Scholar 
19.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, 1369–1383 (2015).20.Charman, D. J. et al. Drivers of Holocene peatland carbon accumulation across a climate gradient in northeastern North America. Quat. Sci. Rev. 121, 110–119 (2015).
Google Scholar 
21.Charman, D. J. et al. Climate-related changes in peatland carbon accumulation during the last millennium. Biogeosciences 10, 929–944 (2013).
Google Scholar 
22.Beilman, D. W., MacDonald, G. M., Smith, L. C. & Reimer, P. J. Carbon accumulation in peatlands of West Siberia over the last 2000 years. Glob. Biogeochem. Cycles 23, GB1012 (2009).23.Wang, M., Moore, T. R., Talbot, J. & Richard, P. J. H. The cascade of C:N:P stoichiometry in an ombrotrophic peatland: from plants to peat. Environ. Res. Lett. 9, 024003 (2014).CAS 

Google Scholar 
24.Wang, M., Moore, T. R., Talbot, J. & Riley, J. L. The stoichiometry of carbon and nutrients in peat formation. Glob. Biogeochem. Cycles 29, 113–121 (2015).
Google Scholar 
25.Gorham, E. & Janssens, J. A. The distribution and accumulation of chemical elements in five peat cores from the mid-continent to the eastern coast of North America. Wetlands 25, 259–278 (2005).
Google Scholar 
26.Ratcliffe, J. L. et al. Rapid carbon accumulation in a peatland following Late Holocene tephra deposition, New Zealand. Quat. Sci. Rev. 246, 106505 (2020).
Google Scholar 
27.Kylander, M. E. et al. Mineral dust as a driver of carbon accumulation in northern latitudes. Sci. Rep. 8, 6876 (2018).28.Hughes, P. D. M. et al. The impact of high tephra loading on late-Holocene carbon accumulation and vegetation succession in peatland communities. Quat. Sci. Rev. 67, 160–175 (2013).
Google Scholar 
29.Limpens, J., Berendse, F. & Klees, H. How phosphorus availability affects the impact of nitrogen deposition on Sphagnum and vascular plants in bogs. Ecosystems 7, 793–804 (2004).CAS 

Google Scholar 
30.Fritz, C. et al. Nutrient additions in pristine Patagonian Sphagnum bog vegetation: can phosphorus addition alleviate (the effects of) increased nitrogen loads. Plant Biol. 14, 491–499 (2012).CAS 

Google Scholar 
31.White, J. R. & Reddy, K. R. Influence of phosphorus loading on organic nitrogen mineralization of everglades soils. Soil Sci. Soc. Am. J. 64, 1525 (2000).CAS 

Google Scholar 
32.Bledsoe, R. B., Goodwillie, C. & Peralta, A. L. Long-term nutrient enrichment of an oligotroph-dominated wetland increases bacterial diversity in bulk soils and plant rhizospheres. mSphere 5, e00035-20 (2020).
Google Scholar 
33.Lin, X. et al. Microbial community stratification linked to utilization of carbohydrates and phosphorus limitation in a boreal peatland at Marcell Experimental Forest, Minnesota, USA. Appl. Environ. Microbiol. 80, 3518–3530 (2014).
Google Scholar 
34.Sjögersten, S., Cheesman, A. W., Lopez, O. & Turner, B. L. Biogeochemical processes along a nutrient gradient in a tropical ombrotrophic peatland. Biogeochemistry 104, 147–163 (2011).
Google Scholar 
35.Cheesman, A. W., Turner, B. L. & Ramesh Reddy, K. Soil phosphorus forms along a strong nutrient gradient in a tropical ombrotrophic wetland. Soil Sci. Soc. Am. J. 76, 1496–1506 (2012).CAS 

Google Scholar 
36.Kivimäki, S. K., Sheppard, L. J., Leith, I. D. & Grace, J. Long-term enhanced nitrogen deposition increases ecosystem respiration and carbon loss from a Sphagnum bog in the Scottish Borders. Environ. Exp. Bot. 90, 53–61 (2013).
Google Scholar 
37.Moore, T. R., Knorr, K.-H., Thompson, L., Roy, C. & Bubier, J. L. The effect of long-term fertilization on peat in an ombrotrophic bog. Geoderma 343, 176–186 (2019).CAS 

Google Scholar 
38.Hill, B. H. et al. Ecoenzymatic stoichiometry and microbial processing of organic matter in northern bogs and fens reveals a common P-limitation between peatland types. Biogeochemistry 120, 203–224 (2014).CAS 

Google Scholar 
39.Vitousek, P. M. et al. Towards an ecological understanding of biological nitrogen fixation. In The Nitrogen Cycle at Regional to Global Scales (eds. Boyer, E. W. & Howarth, R. W.) 1–45 (Springer Netherlands, 2002).40.Larmola, T. et al. Methanotrophy induces nitrogen fixation during peatland development. Proc. Natl. Acad. Sci. USA 111, 734–739 (2014).CAS 

Google Scholar 
41.van den Elzen, E. et al. Symbiosis revisited: phosphorus and acid buffering stimulate N2 fixation but not Sphagnum growth. Biogeosciences 14, 1111–1122 (2017).
Google Scholar 
42.van den Elzen, E., Bengtsson, F., Fritz, C., Rydin, H. & Lamers, L. P. M. Variation in symbiotic N2 fixation rates among Sphagnum mosses. PLoS ONE 15, e0228383 (2020).
Google Scholar 
43.Toberman, H. et al. Dependence of ombrotrophic peat nitrogen on phosphorus and climate. Biogeochemistry 125, 11–20 (2015).CAS 

Google Scholar 
44.Basilier, K., Granhall, U., Stenström, T.-A. & Stenstrom, T.-A. Nitrogen fixation in wet minerotrophic moss communities of a subarctic mire. Oikos 31, 236 (1978).CAS 

Google Scholar 
45.Lin, X. et al. Microbial metabolic potential for carbon degradation and nutrient (nitrogen and phosphorus) acquisition in an ombrotrophic peatland. Appl. Environ. Microbiol. 80, 3531–3540 (2014).
Google Scholar 
46.Kox, M. A. R. et al. Effects of nitrogen fertilization on diazotrophic activity of microorganisms associated with Sphagnum magellanicum. Plant Soil 406, 83–100 (2016).CAS 

Google Scholar 
47.Bubier, J. L., Moore, T. R. & Bledzki, L. A. Effects of nutrient addition on vegetation and carbon cycling in an ombrotrophic bog. Glob. Change Biol. 13, 1168–1186 (2007).
Google Scholar 
48.Fritz, C., Lamers, L. P. M., Riaz, M., van den Berg, L. J. L. & Elzenga, T. J. T. M. Sphagnum mosses – masters of efficient N-uptake while avoiding intoxication. PLoS ONE 9, e79991 (2014).
Google Scholar 
49.Morris, P. J. et al. Global peatland initiation driven by regionally asynchronous warming. Proc. Natl. Acad. Sci. USA 115, 4851–4856 (2018).CAS 

Google Scholar 
50.Schillereff, D. N. et al. Long-term macronutrient stoichiometry of UK ombrotrophic peatlands. Sci. Total Environ. 572, 1561–1572 (2016).CAS 

Google Scholar 
51.Sjöström, J. K. et al. Paleodust deposition and peat accumulation rates – bog size matters. Chem. Geol. 554, 119795 (2020).
Google Scholar 
52.Kylander, M. E. et al. Potentials and problems of building detailed dust records using peat archives: an example from Store Mosse (the “Great Bog”), Sweden. Geochim. Cosmochim. Acta 190, 156–174 (2016).CAS 

Google Scholar 
53.Mahowald, N. et al. Global distribution of atmospheric phosphorus sources, concentrations and deposition rates, and anthropogenic impacts. Glob. Biogeochem. Cycles 22, 1–19 (2008).
Google Scholar 
54.Tipping, E. et al. Atmospheric deposition of phosphorus to land and freshwater. Environ. Sci.: Processes Impacts 16, 1608–1617 (2014).CAS 

Google Scholar 
55.Wang, R. et al. Significant contribution of combustion-related emissions to the atmospheric phosphorus budget. Nat. Geosci. 8, 48–54 (2015).CAS 

Google Scholar 
56.Newman, E. I. Phosphorus inputs to terrestrial ecosystems. J. Ecol. 83, 713–726 (1995).
Google Scholar 
57.Worrall, F., Moody, C. S., Clay, G. D., Burt, T. P. & Rose, R. The total phosphorus budget of a peat-covered catchment. J. Geophys. Res. Biogeosci. 121, 1814–1828 (2016).CAS 

Google Scholar 
58.Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).
Google Scholar 
59.Bedford, B. L., Walbridge, M. R. & Aldous, A. Patterns in nutrient availability and plant diversity of temperate North American Wetlands. Ecology 80, 2151–2169 (1999).
Google Scholar 
60.Güsewell, S. N: P ratios in terrestrial plants: variation and functional significance: Tansley review. New Phytol. 164, 243–266 (2004).
Google Scholar 
61.Yan, J. et al. Preliminary investigation of phosphorus adsorption onto two types of iron oxide-organic matter complexes. J. Environ. Sci. 42, 152–162 (2016).CAS 

Google Scholar 
62.Barrow, N. J. Comparing two theories about the nature of soil phosphate. Eur. J. Soil Sci. 72, 679–685 (2021).CAS 

Google Scholar 
63.Bridgham, S. D., Pastor, J., Janssens, J. A., Chapin, C. & Malterer, T. J. Multiple limiting gradients in peatlands: a call for a new paradigm. Wetlands 16, 45–65 (1996).
Google Scholar 
64.Kuhry, P. & Vitt, D. H. Fossil carbon/nitrogen ratios as a measure of peat decomposition. Ecology 77, 271–275 (1996).
Google Scholar 
65.Kuhry, P., Halsey, L. A., Bayley, S. E. & Vitt, D. H. Peatland development in relation to Holocene climatic change in Manitoba and Saskatchewan (Canada). Can. J. Earth Sci. 29, 1070–1090 (1992).CAS 

Google Scholar 
66.Malmer, N. & Wallén, B. Input rates, decay losses and accumulation rates of carbon in bogs during the last millennium: internal processes and environmental changes. The Holocene 14, 111–117 (2004).
Google Scholar 
67.Malmer, N. & Holm, E. Variation in the C/N-quotient of peat in relation to decomposition rate and age determination with 210 Pb. Oikos 43, 171–182 (1984).CAS 

Google Scholar 
68.Larsson, A., Segerstrom, U., Laudon, H. & Nilsson, M. Holocene carbon and nitrogen accumulation rates and contemporary carbon export in discharge: a study from a boreal fen catchment. Holocene 27, 48 (2016), https://doi.org/10.1177/0959683616675936.69.Berendse, F. et al. Raised atmospheric CO2 levels and increased N deposition cause shifts in plant species composition and production in Sphagnum bogs. Glob. Change Biol. 7, 591–598 (2001).
Google Scholar 
70.Juutinen, S., Bubier, J. L. & Moore, T. R. Responses of vegetation and ecosystem CO2 exchange to 9 years of nutrient addition at Mer Bleue bog. Ecosystems 13, 874–887 (2010).CAS 

Google Scholar 
71.Lequy, É., Legout, A., Conil, S. & Turpault, M. P. Aeolian dust deposition rates in Northern French forests and inputs to their biogeochemical cycles. Atmos. Environ. 80, 281–289 (2013).CAS 

Google Scholar 
72.Harrison, J. A., Caraco, N. & Seitzinger, S. P. Global patterns and sources of dissolved organic matter export to the coastal zone: results from a spatially explicit, global model. Glob. Biogeochem. Cycles 19, GB4S04 (2005).73.Yu, Z. Holocene carbon flux histories of the world’s peatlands: global carbon-cycle implications. The Holocene 21, 761–774 (2011).
Google Scholar 
74.Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry. (Elsevier, Amsterdam, 2013).
Google Scholar 
75.Peñuelas, J. et al. Human-induced nitrogen–phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun. 4, 2934 (2013).76.Larmola, T. et al. Vegetation feedbacks of nutrient addition lead to a weaker carbon sink in an ombrotrophic bog. Glob. Change Biol. 19, 3729–3739 (2013).
Google Scholar 
77.Li, F. et al. Organic carbon linkage with soil colloidal phosphorus at regional and field scales: insights from size fractionation of fine particles. Environ. Sci. Technol. 55, 5815–5825 (2021).CAS 

Google Scholar 
78.Spohn, M. Increasing the organic carbon stocks in mineral soils sequesters large amounts of phosphorus. Glob. Change Biol. 26, 4169–4177 (2020).
Google Scholar 
79.Sjöström, J. Mid-Holocene Mineral Dust Deposition in Raised Bogs in Southern Sweden: Processes and Links. PhD thesis, Stockholm Univ. (2021).80.Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Change 8, 907–913 (2018).CAS 

Google Scholar 
81.Wilson, R. M. et al. Stability of peatland carbon to rising temperatures. Nat. Commun. 7, 13723 (2016).CAS 

Google Scholar 
82.Dorrepaal, E. et al. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature 460, 616–619 (2009).CAS 

Google Scholar 
83.Clymo, R. S. & Bryant, C. L. Diffusion and mass flow of dissolved carbon dioxide, methane, and dissolved organic carbon in a 7-m deep raised peat bog. Geochim. Cosmochim. Acta 72, 2048–2066 (2008).CAS 

Google Scholar 
84.Morris, P. J., Waddington, J. M., Benscoter, B. W. & Turetsky, M. R. Conceptual frameworks in peatland ecohydrology: looking beyond the two-layered (acrotelm-catotelm) model. Ecohydrology 4, 1–11 (2011).
Google Scholar 
85.Rydin, H. & Jeglum, J. The Biology of Peatlands (Oxford University Press, 2013).86.Limpens, J., Heijmans, M. M. P. D. & Berendse, F. The nitrogen cycle in boreal peatlands. Boreal Peatl. Ecosyst. 188, 195–230 (2006).CAS 

Google Scholar 
87.Biester, H., Knorr, K.-H., Schellekens, J., Basler, A. & Hermanns, Y.-M. Comparison of different methods to determine the degree of peat decomposition in peat bogs. Biogeosciences 11, 2691–2707 (2014).CAS 

Google Scholar 
88.Zaccone, C., Plaza, C., Ciavatta, C., Miano, T. M. & Shotyk, W. Advances in the determination of humification degree in peat since: Applications in geochemical and paleoenvironmental studies. Earth-Sci. Rev. 185, 163–178 (2018).CAS 

Google Scholar 
89.Alboukadel Kassambara. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr (CRAN, 2020).90.Legendre, P. & Oksanen, J. lmodel2: Model II Regression. R package version 1.7–3. https://CRAN.R-project.org/package=lmodel2 (CRAN, 2018).91.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2016).
Google Scholar 
92.Tipping, E. et al. Long-term increases in soil carbon due to ecosystem fertilization by atmospheric nitrogen deposition demonstrated by regional-scale modelling and observations. Sci. Rep. 7, 1890 (2017).CAS 

Google Scholar 
93.Bragazza, L. & Limpens, J. Dissolved organic nitrogen dominates in European bogs under increasing atmospheric N deposition. Glob. Biogeochem. Cycles 18, GB4018 (2004).94.Turunen, J., Roulet, N. T., Moore, T. R. & Richard, P. J. H. Nitrogen deposition and increased carbon accumulation in ombrotrophic peatlands in eastern Canada. Glob. Biogeochem. Cycles 18, 1–12 (2004).
Google Scholar 
95.Lund, M., Christensen, T. R., Mastepanov, M., Lindroth, A. & Ström, L. Effects of N and P fertilization on the greenhouse gas exchange in two northern peatlands with contrasting N deposition rates. Biogeosciences 6, 2135–2144 (2009).CAS 

Google Scholar 
96.Xu, J., Morris, P. J., Liu, J. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134–140 (2018).
Google Scholar  More

Study regionsWe conducted the field survey in the Kyushu of southern Japan (Fig. 2a,b). The rice-transplanting schedule in Kyushu, is generally late (in late June24) because it is necessary to delay rice-transplanting timing after the harvest of wheat as a back crop22,23. However, since the technique of early transplanting was established around 1960, early transplanting (in early April24) has been practiced instead of a back crop of wheat in some areas in Kyushu25,26, so the rice-transplanting schedules differ at the regional scale. We established two study regions with early-transplanting schedules (Karatsu in northern Kyushu; N33.4, E129.9 and Amakusa in central Kyushu; N32.5, E130.1), and two study regions with late-transplanting schedules (Itoshima in northern Kyushu; N33.6, E130.2 and Uki in central Kyushu; N32.6, E130.8) (Fig. 2c–f). Each study region is 10–20 km2, and the distances between the regions are 20–140 km. Climatic conditions between the regions are similar (Supplementary Table S3). The size of each region is much larger than the size of the typical territory of breeding buzzards (approximately 500 m radius from the nest18).Figure 2Map of (a) Japan and (b) the entire study regions, (c) Karatsu, (d) Amakusa, (e) Itoshima and (f) Uki. Squares represent study blocks. Light and dark grey areas indicate waterbodies and residential areas, respectively. Background map source is the black map of Japan (http://www.craftmap.box-i.net/japan/line.php) and the topographic data of Fundamental Geospatial Data developed by the Geospatial Information Authority of Japan (https://www.gsi.go.jp/kiban/).Full size imageBuzzards surveyWe examined the distribution of buzzards in 2019. Buzzards are migratory birds that breed in Japan, northeastern China, and the Russian Far East in summer, and overwinter in the Ryukyu Islands, Southeast Asia, and southern China27. In our study regions, buzzards start breeding in April, soon after returning from their wintering area. Buzzards incubate their eggs from late April until late May when they hatch. Once the eggs hatch, buzzards feed nestlings. Then nestlings start to fledge in late June, but the adults continue feeding their fledglings for several weeks. Buzzards migrate to their wintering grounds around October. The breeding season of buzzards thus overlaps largely with the rice production season, but there is a slight but significant differences in seasonality, i.e., paddies are already planted and flooded before hatching in early rice-transplanting schedules, while not yet flooded in late rice-transplanting schedules.Because breeding buzzards were thought to prefer mosaic landscapes of farmland and forest17,19, we established 62 study blocks that included edges between farmland and forest in each study region (Fig. 2c–f; northern-early: 17 central-early: 11 northern-late: 17 central-late: 17). The study blocks were 400 m square and located at least 700 m apart from each other, a distance determined from the knowledge that buzzards intensively use an area of 200 m from their nests18. To examine the presence/absence of breeding buzzards in each block, we conducted 2 days of 30-min observations during the breeding season (April to July). Using a pilot study, we determined that this observation time was enough to minimize the possibility of missing buzzards. We identified breeding individuals based on displays, feeding behaviors, and territorial behaviors.Land use surveyDuring the brood-rearing period (late June to early July) in 2019, we recorded the land use (forest, grassland, flooded paddies, non-flooded paddies) in each block. We then used these data to create a land use map in each block using QGIS3.16.228 and overlaying it on Google Earth aerial photographs in 2017.Prey species surveyWe surveyed the distribution of prey species in paddies and grasslands in the study regions in 2019 and 2020. Based on the previous studies on the feeding habits of buzzards17,18,19,20,21, we surveyed the distribution of frogs and orthopterans larger than 3 cm as prey of this size is considered their main prey. We established survey transects in paddies and grasslands in our study blocks. We conducted surveys twice each year during the brood-rearing period (late June to early July), when breeding buzzards need a large amount of prey. We walked along the transects and counted the number of prey species observed within 0.5-m of both sides. This survey method is suitable to assess prey availability29 because buzzards visually search for prey (e.g.20,30). A total of 148 20 m-transects were placed in paddies in 34 blocks and 157 15-m transects were placed in grasslands in 37 blocks, and each transect was surveyed in both or either 2019 and 2020. In the paddy transects, we recorded the height and coverage of vegetation, the ditch characteristics (none, concrete ditch, earthen ditch), the surrounding land use (10 m width from the transect: flooded paddy, non-flooded paddy, grassland, forest, stone wall, and road), and flooding or non-flooding in the paddy field adjacent to transects. In grassland transects, we recorded the height and coverage of vegetation and the grassland types on which the transect was located (abandoned land, orchard, farmland, bank, forest edge).Statistical analysisBuzzard modelTo investigate the habitat selection of buzzards, we used a generalized linear model with a binomial error distribution. We used the edge length between the landscape elements and forest as independent variables, because the edge length, rather than the area of the landscape elements, is known to be an important determinant for buzzard distribution19. We prepared a land use map and calculated the edge length between the landscape elements and forest by using the field calculator of QGIS, and values were standardized (mean = 0 and SD = 1).To explore the variation in habitat selection across regions with different transplanting schedules, we first used a model that included the interaction term of transplanting schedules and landscape elements as independent variables. The length of paddy-forest edges, grassland-forest edges, the transplanting schedules, the interaction term of the edge length and the transplanting schedules, and the study regions were included as independent variables (details of independent variables: Supplementary Table S4). The presence/absence of breeding buzzards was included as a dependent variable. We analyzed the full model and all sub-models containing different combinations of all independent variables, including the null model. We regarded models that had ΔAIC values (the difference between the AIC value of the focal model and that of the best-fit model) of  0.6) to avoid serious multicollinearity.We performed all analyses in R 4.0.333, using the glmmTMB packages34 for model fitting, the MuMIn package35 for model selection and averaging, and the ggplot236 for graphic illustration or results. More

1.Revelle, R. & Suess, H. E. Carbon dioxide exchange between atmosphere and ocean and the question of an increase of atmospheric CO2 during the past decades. Tellus 9, 18–27 (1957).ADS 
CAS 

Google Scholar 
2.Frankignoulle, M. A complete set of buffer factors for acid/base CO2 system in seawater. J. Mar. Syst. 5, 111–118 (1994).
Google Scholar 
3.Egleston, E. S., Sabine, C. L. & Morel, F. M. M. Revelle revisited: Buffer factors that quantify the response of ocean chemistry to changes in DIC and alkalinity. Glob. Biogeochem. Cycles 24, GB1002 (2010).ADS 

Google Scholar 
4.Bates, N. et al. A time-series view of changing surface ocean chemistry due to ocean uptake of anthropogenic CO2 and ocean acidification. Oceanography 27(1), 126–141 (2014).MathSciNet 

Google Scholar 
5.Lauvset, S., Gruber, N., Landschützer, P., Olsen, A. & Tjiputra, J. Trends and drivers in global surface ocean pH over the past 3 decades. Biogeosciences 12(5), 1285–1298 (2015).ADS 

Google Scholar 
6.Ríos, A. F. et al. Decadal acidification in the Atlantic. Proc. Natl. Acad. Sci. 112(32), 9950–9955 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
7.Schulz, K. G. & Riebesell, U. Diurnal changes in seawater carbonate chemistry speciation at increasing atmospheric carbon dioxide. Mar. Biol. 160, 1889–1899 (2013).CAS 
PubMed 

Google Scholar 
8.Provoost, P., van Heuven, S., Soetaert, K., Laane, R. W. P. M. & Middelburg, J. J. Seasonal and long-term changes in pH in the Dutch coastal zone. Biogeosciences 7, 3869–3878 (2010).ADS 
CAS 

Google Scholar 
9.Hofmann, G. E. et al. High-frequency dynamics of ocean pH: A multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Borges, A. V. & Gypens, N. Carbonate chemistry in the coastal zone responds more strongly to eutrophication than ocean acidification. Limnol. Oceanogr. 55, 346–353 (2010).ADS 
CAS 

Google Scholar 
11.Cai, W.-J. et al. Acidification of subsurface coastal waters enhanced by eutrophication. Nat. Geosci. 4, 766–770 (2011).ADS 
CAS 

Google Scholar 
12.Sunda, W. G. & Cai, W.-J. Eutrophication induced CO2-acidification of subsurface coastal waters: Interactive effects of temperature, salinity, and atmospheric pCO2. Environ. Sci. Technol. 46, 10651–10659 (2012).ADS 
CAS 
PubMed 

Google Scholar 
13.Jury, C. P., Thomas, F. I. M., Atkinson, M. J. & Toonen, R. J. Buffer capacity, ecosystem feedbacks, and seawater chemistry under global change. Water 5, 1303–1325 (2013).CAS 

Google Scholar 
14.Hagens, M. et al. Biogeochemical processes and buffering capacity concurrently affect acidification in a seasonally hypoxic coastal marine basin. Biogeosciences 12, 1561–1583 (2015).ADS 

Google Scholar 
15.Santschi, P., Höhener, P., Benoit, G. & Buchholtz-ten, B. M. Chemical processes at the sediment–water interface. Mar. Chem. 30, 269–315 (1990).CAS 

Google Scholar 
16.Pawlik, J. R. Chemical ecology of the settlement of benthic marine invertebrates. Oceangr. Mar. Biol. Annu. Rev. 30, 273–335 (1992).
Google Scholar 
17.Marinelli, R. L. & Woodin, S. A. Experimental evidence for linkages between infaunal recruitment, disturbance, and sediment surface chemistry. Limnol. Oceanogr. 47(1), 221–229 (2002).ADS 
CAS 

Google Scholar 
18.Clements, J. C. & Hunt, H. L. Marine animal behaviour in a high CO2 ocean. Mar. Ecol. Prog. Ser. 536, 259–279 (2015).ADS 
CAS 

Google Scholar 
19.Vopel, K., Laverock, B., Cary, C. & Pilditch, C. A. Effects of warming and CO2 enrichment on O2 consumption, porewater oxygenation and pH of subtidal silt sediment. Aquat. Sci. 83, 8 (2021).CAS 

Google Scholar 
20.Green, M. A., Jones, M. E., Boudreau, C. L., Moore, R. L. & Westman, B. A. Dissolution mortality of juvenile bivalves in coastal marine deposits. Limnol. Oceanogr. 49(3), 727–734 (2004).ADS 

Google Scholar 
21.Green, M. A., Waldbusser, G., Reilly, S., Emerson, K. & O’Donnell, S. Death by dissolution: Sediment saturation state as a mortality factor for juvenile bivalves. Limnol. Oceanogr. 54(4), 1037–1047 (2009).ADS 
CAS 

Google Scholar 
22.Green, M. A., Waldbusser, G. G., Hubazc, L., Cathcart, E. & Hall, J. Carbonate mineral saturation state as the recruitment cue for settling bivalves in marine muds. Estuaries Coasts 36, 18–27 (2013).CAS 

Google Scholar 
23.Clements, J. C., Woodard, K. D. & Hunt, H. L. Porewater acidification alters the burrowing behavior and post-settlement dispersal of juvenile soft-shell clams (Mya arenaria). J. Exp. Mar. Biol. Ecol. 477, 103–111 (2016).
Google Scholar 
24.Ries, J. B., Ghazaleh, M. N., Connolly, B., Westfield, I. & Castillo, K. D. Impacts of seawater saturation state (ΩA = 0.4–4.6) and temperature (10, 25 °C) on the dissolution kinetics of whole-shell biogenic carbonates. Geochim. Cosmochim. Acta 192, 318–337 (2016).ADS 
CAS 

Google Scholar 
25.Nimer, N. A., Brownlee, C. & Merrett, M. J. Extracellular carbonic anhydrase facilitates carbon dioxide availability for photosynthesis in the marine dinoflagellate Prorocentrum micans. Plant Physiol. 120, 105–112 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Hopkinson, B. M., Meile, C. & Shen, C. Quantification of extracellular carbonic anhydrase activity in two marine diatoms and investigation of its role. Plant Physiol. 162, 1142–1152 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Tachibana, M. et al. Localization of putative carbonic anhydrase in two marine diatoms, Phaeodactylum tricornutum and Thalassiosira pseudonana. Photosynth. Res. 109, 205–221 (2011).CAS 
PubMed 

Google Scholar 
28.Samukawa, M., Shen, C., Hopkinson, B. M. & Matsuda, Y. Localization of putative carbonic anhydrases in the marine diatom, Thalassiosira pseudonana. Photosynth. Res. 121, 235–249 (2014).CAS 
PubMed 

Google Scholar 
29.Matsuda, Y., Hopkinson, B. M., Nakajima, K., Dupont, C. L. & Tsuji, Y. Mechanisms of carbon dioxide acquisition and CO2 sensing in marine diatoms: A gateway to carbon metabolism. Philos. Trans. R. Soc. B 372, 20160403 (2017).
Google Scholar 
30.Milligan, A. J. & Morel, F. M. M. A proton buffering role for silica in diatoms. Science 297, 1848–1850 (2002).ADS 
CAS 
PubMed 

Google Scholar 
31.Subhas, A. V. et al. Catalysis and chemical mechanisms of calcite dissolution in seawater. Proc. Natl. Acad. Sci. 114, 8175–8180 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Middelburg, J. J., Soetaert, K. & Hagens, M. Ocean alkalinity, buffering and biogeochemical processes. Rev. Geophys. 58, e2019RG000681 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
33.Soetaert, K., Hofmann, A. F., Middelburg, J. J., Meysman, F. J. R. & Greenwood, J. The effect of biogeochemical processes on pH. Mar. Chem. 105, 30–51 (2007).CAS 

Google Scholar 
34.Zhu, Q., Aller, R. C. & Fan, Y. Two-dimensional pH distributions and dynamics in bioturbated marine sediments. Geochim. Cosmochim. Acta 70, 4933–4949 (2006).ADS 
CAS 

Google Scholar 
35.Vopel, K., Del-Río, C. & Pilditch, C. A. Effects of CO2 enrichment on benthic primary production and inorganic nitrogen fluxes in two coastal sediments. Sci. Rep. 8, 1035 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
36.Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanzen 167, 191–194 (1975).CAS 

Google Scholar 
37.Dickson, A. G., Sabine, C. L. & Christian, J. R. Guide to best practices for ocean CO2 measurements: PICES Special Publication 3. http://cdiac.ornl.gov/oceans/Handbook_2007.html (2007).38.Lewis, E. & Wallace, D. W. R. Program Developed for CO2 System Calculations. ORNL/CDIAC-105 (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, 1998).
Google Scholar 
39.Dickson, A. G. Standard potential of the reaction: AgCl(s) + 12H2(g) = Ag(s) + HCL(aq), and the standard acidity constant of the ion HSO4− in synthetic sea water from 273.15 to 318.15 K. J. Chem. Thermodyn. 22, 113–127 (1990).CAS 

Google Scholar 
40.Mehrbach, C., Culberson, C. H., Hawley, J. E. & Pytkowicz, R. N. Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18, 897–907 (1973).ADS 
CAS 

Google Scholar 
41.Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissolution of carbonic acid in seawater media. Deep Sea Res. 34(10), 1733–1743 (1987).ADS 
CAS 

Google Scholar 
42.Berg, P. N., Risgaard-Petersen, N. & Rysgaard, S. Interpretation of measured concentration profiles in sediment pore water. Limnol. Oceanogr. 43, 1500–1510 (1998).ADS 
CAS 

Google Scholar 
43.Revsbech, N. P., Nielsen, L. P. & Ramsing, N. B. A novel microsensor for determination of apparent diffusivity in sediments. Limnol. Oceanogr. 43, 986–992 (1998).ADS 
CAS 

Google Scholar 
44.Vopel, K., Pilditch, C. A., Wilson, P. & Ellwood, M. J. Oxidation of surface sediment: Effects of disturbance depth and seawater flow speed. Mar. Ecol. Prog. Ser. 392, 43–55 (2009).ADS 
CAS 

Google Scholar 
45.Broecker, W. S. & Peng, T.-H. Gas exchange rates between air and sea. Tellus 26(1–2), 21–35 (1974).ADS 
CAS 

Google Scholar 
46.Cussler, E. L. Diffusion: Mass Transfer in Fluid Systems (Cambridge University Press, 2009).
Google Scholar 
47.Li, Y.-H. & Gregory, S. Diffusion of ions in sea water and in deep-sea sediments. Geochim. Cosmochim. Acta 38(5), 703–714 (1974).ADS 
CAS 

Google Scholar 
48.Ullman, W. J. & Aller, R. C. Diffusion coefficients in nearshore marine sediments. Limnol. Oceanogr. 27(3), 552–556 (1982).ADS 
CAS 

Google Scholar 
49.Jørgensen, B. B. & Revsbech, N. P. Diffusive boundary layers and the oxygen uptake of sediments and detritus. Limnol. Oceanogr. 30(1), 111–122 (1985).ADS 

Google Scholar 
50.Rasmussen, H. & Jørgensen, B. B. Microelectrode studies of seasonal oxygen uptake in a coastal sediment: Role of molecular diffusion. Mar. Ecol. Prog. Ser. 81, 289–303 (1992).ADS 
CAS 

Google Scholar 
51.Nordstrom, D. K., Jenne, E. A. & Ball, J. W. Redox equilibria of iron in acid mine waters. In Chemical Modeling in Aqueous Systems. American Chemical Society Symposium Series Vol. 93 (ed. Jenne, E. A.) 57–79 (American Chemical Society, 1979).
Google Scholar 
52.Dushoff, J., Kain, M. P. & Bolker, B. M. I can see clearly now: Reinterpreting statistical significance. Methods Ecol. Evol. 10, 756–759 (2019).
Google Scholar  More

Case studyThe Zayandeh-Rud basin (Fig. 1), a arid region of Iran, was selected to evaluate the SPIs. The Zayandeh-Rud basin is located in the central part of Iran. It has an area of 26,972 km2 area, where there are multiple water stakeholders such as agriculture, industry, urban and the environment sectors, with agriculture being the main user of the basin. Water resources in the basin are divided into surface water and groundwater. Approximately 100,000 ha among 113,000 ha of the agricultural area is irrigated by Zayandeh-Rud dam, and 3100 mm3 of water resources are used in the agricultural sector. The main surface water source in the basin, Zayandeh-Rud River originates in the Zagros Mountains and is about 350 km long in a west to east direction passing by the city of Isfahan. The Zayandeh-Rud River is an important water source for the agricultural, industrial, health, and urban sectors in Central Iran and the Chaharmahal-Bakhtiari and Isfahan provinces.Figure 1The location of the Zayandeh-Rud basin in Iran.Full size imageMulti-criteria decision makingMulti-criteria decision making includes two categories of multi-objective decision making and multi-criteria decision making, which are implemented to select the best decision among several alternatives or to evaluate decisions. This work applies decision making as a multi-criteria decision to achieve a goal. Each decision includes objectives, alternatives, and criteria. A problem’s goal is first defined. Alternatives are different options for wastewater management in this instance that are assigned weights based on their contribution to achieving the goal. Criteria are also factors that are measured by the purpose of the alternatives23. The AHP method helps achieve a defined goal after completing the steps outlined below.The AHP methodThe Analytical Hierarchy Process (AHP), developed by Saaty24, is a multi-criteria decision-making method for solving complex problems. It combines objective and quantitative evaluation in an integrated manner based on multi-level comparisons, and helps organize the essential aspects of a problem into a hierarchical format. It regularly organizes tangible and intangible factors and offers a structured and a relatively simple solution to decision problems. The AHP method ranks alternatives propose to tackle a decision-making problem. The ranking is based through a sequence of pairwise comparisons of evaluation criteria and sub-criteria.The AHP structureIn a hierarchical structure the communication flow is top-down. First, indicators and evaluation criteria are defined from experts who are asked for their expert opinions. The criteria serve the purpose of determining the relative worth of alternatives entertained to solve a multi-criteria decision-making problem. Thereafter, the problem is divided into criteria and sub-criteria for the evaluation of alternatives. Figure 2 depicts a generic AHP structure depicting a goal to be met with (n) = 4 evaluation criteria, and (m=3) alternatives to cope with a problem (in our case SIPs).Figure 2Goal, criteria, and alternatives in a generic hierarchical structure.Full size imageThe pairwise comparison matrixThe pairwise comparison matrix ((A)), called the Saaty Hierarchy Matrix, measures the importance of each criterion (or sub-criterion) relative to other criteria based on a numeric scale ranging from 1 to 9. Criteria that are extremely preferred, very strongly preferred, strongly preferred, moderately preferred, and equally preferred are assigned the values 9, 7, 5, 3, and 1, respectively, in the scale of preference; intermediate values are assigned to adjacent scales of preference. Thus, the values 8, 6, 4, and 2 are assigned respectively to the adjacent scales (9,7), (7,5), (5,3), and (3,1)24. These numerical assignment of values is made based on the opinion of experts25. The pairwise comparison matrix ((A)), therefore, represents a set of relative weights assigned to the criteria23. The general form of a pairwise comparison matrix when there are (n) evaluation criteria is written in Eq. (1):$$A=left[{a}_{ij}right]=left[begin{array}{cccc}{1=w}_{1}/{w}_{1}& {w}_{1}/{w}_{2}& dots & {w}_{1}/{w}_{n}\ {w}_{2}/{w}_{1}& 1={w}_{2}/{w}_{2}& dots & {w}_{2}/{w}_{n}\ .& .& dots & .\ .& .& dots & .\ .& .& dots & .\ {w}_{n}/{w}_{1}& {w}_{n}/{w}_{2}& …& 1={w}_{n}/{w}_{n}end{array}right]$$
(1)

where ({w}_{i}/{w}_{j}) denotes the weight assigned to the (i)-th criterion relative to the (j)-th criterion24. Clearly, ({a}_{ji}=1/{a}_{ij}), with ({a}_{ji}={a}_{ij}=1) when (i=j).The ratio matrixThe ratio matrix ((R)) has elements ({r}_{ij}) is calculated by Eq. (2):$$R=left[{r}_{ij}right]=left[begin{array}{cccc}1& {a}_{12}& dots & {a}_{1n}\ 1/{a}_{12}& 1& dots & {a}_{2n}\ .& .& .& .\ .& .& .& .\ .& .& .& .\ 1/{a}_{1n}& 1/{a}_{2n}& dots & 1end{array}right]$$
(2)

clearly, ({r}_{ij}={a}_{ij}) when (jge i), and ({r}_{ij}=1/{a}_{ji}) when (j More