Ecology

1.Zhang, X. et al. Plant defense resistance in natural enemies of a specialist insect herbivore. Proc. Natl. Acad. Sci. U. S. A. 116, 23174–23181. https://doi.org/10.1073/pnas.1912599116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
2.Wittstock, J. & Gershenzon, U. Constitutive plant toxins and their role in defense against herbivores and pathogens. Curr. Opin. Plant Biol. 5, 300–307. https://doi.org/10.1016/s1369-5266(02)00264-9 (2002).CAS 
Article 
PubMed 

Google Scholar 
3.Stahl, E., Hilfiker, O. & Reymond, P. Plant-arthropod interactions: Who is the winner?. Plant J. 93, 703–728. https://doi.org/10.1111/tpj.13773 (2018).CAS 
Article 
PubMed 

Google Scholar 
4.de Bruijn, W. J. C., Gruppen, H. & Vincken, J. P. Structure and biosynthesis of benzoxazinoids: Plant defence metabolites with potential as antimicrobial scaffolds. Phytochemistry 155, 233–243. https://doi.org/10.1016/j.phytochem.2018.07.005 (2018).CAS 
Article 
PubMed 

Google Scholar 
5.Arbona, V. & Gomez-Cadenas, A. Metabolomics of disease resistance in crops. Curr. Issues Mol. Biol. 19, 13–30 (2016).PubMed 

Google Scholar 
6.Ben-Abu, Y., Beiles, A., Flom, D. & Nevo, E. Adaptive evolution of benzoxazinoids in wild emmer wheat, Triticum dicoccoides, at “Evolution Canyon”, Mount Carmel, Israel. PLoS ONE 13(2), e0190424. https://doi.org/10.1371/journal.pone.0190424 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Frey, M., Schullehner, K., Dick, R., Fiesselmann, A. & Gierl, A. Benzoxazinoid biosynthesis, a model for evolution of secondary metabolic pathways in plants. Phytochemistry 70(15–16), 1645–1651. https://doi.org/10.1016/j.phytochem.2009.05.012 (2009).CAS 
Article 
PubMed 

Google Scholar 
8.Zdero, C., Bohlmann, F. & Niemeyer, H. M. Isocedrene and guaiane derivatives from Pleocarphus revolutus. J. Nat. Prod. 51, 509–512. https://doi.org/10.1021/np50057a009 (1988).CAS 
Article 
PubMed 

Google Scholar 
9.Carlsen, S. C. et al. Allelochemicals in rye (Secale cereale L.): Cultivar and tissue differences in the production of benzoxazinoids and phenolic acids. Nat. Prod. Commun. 4, 199–208 (2009).CAS 
PubMed 

Google Scholar 
10.Martos, A., Givovich, A. & Niemeyer, H. M. Effect of DIMBOA, an aphid resistance factor in wheat, on the aphid predator Eriopis connexa Germar (Coleoptera: Coccinellidae). J. Chem. Ecol. 18, 469–479. https://doi.org/10.1007/BF00994245 (1992).CAS 
Article 
PubMed 

Google Scholar 
11.Perez, F. J. Allelopathic effect of hydroxamic acids from cereals on Avena sativa and A. fatua Francisco. Phytochemistry 29, 773–776. https://doi.org/10.1016/0031-9422(90)80016-A (1990).CAS 
Article 

Google Scholar 
12.Dutartre, L., Hilliou, F. & Feyereisen, R. Phylogenomics of the benzoxazinoid biosynthetic pathway of Poaceae: Gene duplications and origin of the Bx cluste. BMC Evol. Biol. 12, 64. https://doi.org/10.1186/1471-2148-12-64 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Meredith, A., Wilkes, D. R. M. & Copeland, L. Hydroxamic acids in cereal roots inhibit the growth of take-all. Soil Biol. Biochem. 31, 1831–1836. https://doi.org/10.1016/S0038-0717(99)00104-2 (1999).Article 

Google Scholar 
14.Macias, F. A., Valerin, M. D., Oliveros-Bastidas, A., Castellano, D. & Simonet, A. M. Structure-activity relationships (SAR) studies of benzoxazinones, their degradation products and analogues. phytotoxicity on standard target species (STS). J. Agric. Food Chem. 53, 538–548. https://doi.org/10.1021/jf0484071 (2005).CAS 
Article 
PubMed 

Google Scholar 
15.Nakagawa, E., Amano, T., Hirai, N. & Iwamura, H. Partial purification and characterisation of a 2,4,5-trichlorophenol detoxifying O-glucosyltransferase from wheat. Phytochemistry 38, 1349–1354. https://doi.org/10.1016/s0031-9422(03)00191-2 (2003).Article 

Google Scholar 
16.Levy, A. A. & Feldman, M. Intra-population and inter-population variations in grain protein percentage in wild tetraploid wheat, Triticum-turgidum var dicoccoides. Euphytica 42(3), 251–258. https://doi.org/10.1007/BF00034461 (1989).Article 

Google Scholar 
17.Święcicka, M. et al. Changes in benzoxazinoid contents and the expression of the associated genes in rye (Secale cereale L.) due to brown rust and the inoculation procedure. PLoS ONE https://doi.org/10.1371/journal.pone.0233807 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Levy, A. A., Galili, G. & Feldman, M. Polymorphism and genetic-control of high molecular-weight glutenin subunits in wild tetraploid wheat Triticum-turgidum var dicoccoides. Heredity 61, 63–72. https://doi.org/10.1007/BF00034461 (1988).CAS 
Article 

Google Scholar 
19.Abu-Zaitoun, S. et al. Unlocking the genetic diversity within a Middle-East panel of durum wheat landraces for adaptation to semi-arid climate. Agronomy 8, 233–245 (2018).Article 

Google Scholar 
20.Avivi, L. High grain protein content in wild wheat. Can J. Genet. Cytol. 19, 569–570. https://doi.org/10.1139/g77-062 (1977).Article 

Google Scholar 
21.Ozkan, H., Levy, A. A. & Feldman, M. Allopolyploidy-induced rapid genome evolution in the wheat (Aegilops-Triticum) group. Plant Cell 8, 1735–1747. https://doi.org/10.1105/tpc.010082 (2001).Article 

Google Scholar 
22.Yang, M. et al. Plant-plant-microbe mechanisms involved in soil-borne disease suppression on a maize and pepper intercropping system. PLoS ONE https://doi.org/10.1371/journal.pone.0115052 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Hanhineva, K. et al. Non-targeted analysis of spatial metabolite composition in strawberry (Fragariaxananassa) flowers. Phytochemistry 69(13), 2463–2481. https://doi.org/10.1016/j.phytochem.2008.07.009 (2008).CAS 
Article 
PubMed 

Google Scholar 
24.Haas, M., Schreiber, M. & Mascher, M. Domestication and crop evolution of wheat and barley: Genes, genomics, and future directions. J. Integr. Plant Biol. 61(3), 204–225. https://doi.org/10.1111/jipb.12737 (2019).Article 
PubMed 

Google Scholar 
25.Beleggia, R. et al. Evolutionary metabolomics reveals domestication-associated changes in tetraploid wheat kernels. Mol. Biol. Evol. 33(7), 1740–1753. https://doi.org/10.1093/molbev/msw050 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Ugine, T. A., Krasnoff, S. B., Grebenok, R. J., Behmer, S. T. & Losey, E. Prey nutrient content creates omnivores out of predators. Ecol. Lett. 22, 275–283. https://doi.org/10.1111/ele.13186 (2019).Article 
PubMed 

Google Scholar 
27.Coll, M. & Guershon, M. Omnivory in terrestrial arthropods: Mixing plant and prey diets. Annu. Rev. Entomol. 47, 267–297. https://doi.org/10.1146/annurev.ento.47.091201.145209 (2002).CAS 
Article 
PubMed 

Google Scholar 
28.Calvert, W. H., Hedrick, L. E. & Brower, L. P. Mortality of the monarch butterfly (Danaus plexippus L.): Avian predation at five overwintering sites in Mexico. Science 204, 847–851. https://doi.org/10.1126/science.204.4395.847 (1979).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Skelhorn, J. & Rowe, C. Avian predators taste-reject aposematic prey on the basis of their chemical defence. Biol. Lett. 2, 348–350. https://doi.org/10.1098/rsbl.2006.0483 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Kumar, P., Pandit, S. S., Steppuhn, A. & Baldwin, L. T. Natural history-driven, plant-mediated RNAi-based study reveals CYP6B46’s role in a nicotine-mediated antipredator herbivore defense. Proc. Natl. Acad. Sci. U.S.A. 111, 1245–1252. https://doi.org/10.1073/pnas.1314848111 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
31.Matthews, S. B. et al. Metabolite profiling of a diverse collection of wheat lines using ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry. PLoS ONE 7(8), e44179. https://doi.org/10.1371/journal.pone.0044179 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Parween, T., Jan, S., Mahmooduzzafar, S., Fatma, T. & Siddiqui, Z. H. Selective effect of pesticides on plant. Crit. Rev. Food Sci. Nutr. 56(1), 160–179. https://doi.org/10.1080/10408398.2013.787969 (2016).CAS 
Article 
PubMed 

Google Scholar 
33.Masisi, K., Beta, T. & Moghadasian, M. H. Antioxidant properties of diverse cereal grains: A review on in vitro and in vivo studies. Food Chem. 96, 90–97. https://doi.org/10.1016/j.foodchem.2015.09.021 (2016).CAS 
Article 

Google Scholar 
34.Hostetler, G. L., Ralston, R. A. & Schwartz, S. J. Flavones: Food sources, bioavailability, metabolism, and bioactivity. Adv. Nutr. 8(3), 423–435. https://doi.org/10.3945/an.116.012948 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Perez-Vizcaino, F. & Fraga, C. G. Research trends in flavonoids and health. Arch. Biochem. Biophys. 646, 107–112. https://doi.org/10.1016/j.abb.2018.03.022 (2018).CAS 
Article 
PubMed 

Google Scholar 
36.Nevo, E. “Evolution Canyon,” a potential microscale monitor of global warming across life. Proc. Natl. Acad. Sci. U. S. A. 109(8), 2960–2965. https://doi.org/10.1073/pnas.1120633109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Nevo, E. et al. Evolution of wild cereals during 28 years of global warming in Israel. Proc. Natl. Acad. Sci. U. S. A. 109(9), 3412–3415. https://doi.org/10.1073/pnas.1121411109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Hebelstrup, K. H. Differences in nutritional quality between wild and domesticated forms of barley and emmer wheat. Plant Sci. 256, 1–4. https://doi.org/10.1016/j.plantsci.2016.12.006 (2017).CAS 
Article 
PubMed 

Google Scholar 
39.Avni, R. et al. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science 357(6346), 93–97. https://doi.org/10.1126/science.aan0032 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
40.Salamini, F., Ozkan, H., Brandolini, A., Schäfer-Pregl, R. & Martin, W. Genetics and geography of wild cereal domestication in the near east. Nat. Rev. Genet. 3(6), 429–441. https://doi.org/10.1038/nrg817 (2002).CAS 
Article 
PubMed 

Google Scholar 
41.Zörb, C., Langenkämper, G., Betsche, T., Niehaus, K. & Barsch, A. Metabolite profiling of wheat grains (Triticum aestivum L.) from organic and conventional agriculture. J. Agric. Food Chem. 54(21), 8301–8306. https://doi.org/10.1016/j.phytochem.2007.06.020 (2006).CAS 
Article 
PubMed 

Google Scholar 
42.Zörb, C., Niehaus, K., Barsch, A., Betsche, T. & Langenkämper, G. Levels of compounds and metabolites in wheat ears and grains in organic and conventional agriculture. J. Agric. Food Chem. 57(20), 9555–9562. https://doi.org/10.1021/jf9019739 (2009).CAS 
Article 
PubMed 

Google Scholar 
43.Zörb, C., Betsche, T. & Langenkämper, G. Search for diagnostic proteins to prove authenticity of organic wheat grains (Triticum aestivum L.). J. Agric. Food Chem. 57(7), 2932–2937. https://doi.org/10.1021/jf802923r (2009).CAS 
Article 
PubMed 

Google Scholar 
44.Hanhineva, K. et al. Qualitative characterization of benzoxazinoid derivatives in whole grain rye and wheat by LC-MS metabolite profiling. J. Agric. Food Chem. 59(3), 921–927. https://doi.org/10.1021/jf103612u (2011).CAS 
Article 
PubMed 

Google Scholar 
45.Brodsky, L., Moussaieff, A., Shahaf, N., Aharoni, A. & Rogachev, I. Evaluation of peak picking quality in LC–MS metabolomics data. Anal. Chem. 82(22), 9177–9187. https://doi.org/10.1021/ac101216e (2010).CAS 
Article 
PubMed 

Google Scholar 
46.Ben-Abu, Y. et al. Durum wheat evolution—A genomic analysis. Proc. Int. Symp. Genet. Breed. Durum Wheat 110, 29–44 (2014).
Google Scholar 
47.Iannucci, A., Fragasso, M., Beleggia, R., Nigro, F. & Papa, R. Evolution of the crop rhizosphere: Impact of domestication on root exudates in tetraploid wheat (Triticum turgidum L.). Front. Plant Sci. 8, 2124. https://doi.org/10.3389/fpls.2017.02124 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Okada, K., Abe, H. & Arimura, G. Jasmonates induce both defense responses and communication in monocotyledonous and dicotyledonous plants. Plant Cell Physiol. 56(1), 16–27. https://doi.org/10.1093/pcp/pcu158 (2015).CAS 
Article 
PubMed 

Google Scholar 
49.Givovich, A., Morse, S., Cerda, H., Niemeyer, H. M. & Wratten, S. D. Hydroxamic acid glucosides in honeydew of aphids feeding on wheat. J. Chem. Ecol. 18, 841–846. https://doi.org/10.1007/BF00988324 (1992).CAS 
Article 
PubMed 

Google Scholar 
50.Shavit, R., Batyrshina, Z. S., Dotan, N. & Tzin, V. Cereal aphids differently affect benzoxazinoid levels in durum wheat. PLoS ONE https://doi.org/10.1371/journal.pone.0208103 (2018).Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Abdel-Aal, H. K., Aggour, M. A. & Fahim, M. A. Petroleum and Gas Field Processing. Petroleum and Gas Field Processing (Marcel Dekker, 2015). doi:https://doi.org/10.1201/9780429021350.2.Vikram, A., Lipus, D. & Bibby, K. Produced water exposure alters bacterial response to biocides. Environ. Sci. Technol. 48, 13001–13009 (2014).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
3.Cluff, M. A., Hartsock, A., MacRae, J. D., Carter, K. & Mouser, P. J. Temporal changes in microbial ecology and geochemistry in produced water from hydraulically fractured marcellus shale gas wells. Environ. Sci. Technol. 48, 6508–6517 (2014).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
4.Gregory, K. & Mohan, A. M. Current perspective on produced water management challenges during hydraulic fracturing for oil and gas recovery. Environ. Chem. 12, 261 (2015).CAS 
Article 

Google Scholar 
5.da Silva Almeida, F. B. P., Esquerre, K. P. S. O. R., Soletti, J. I. & De Farias Silva, C. E. Coalescence process to treat produced water: an updated overview and environmental outlook. Environ. Sci. Pollut. Res. 26, 28668–28688 (2019).6.Kabyl, A., Yang, M., Abbassi, R. & Li, S. A risk-based approach to produced water management in offshore oil and gas operations. Process Saf. Environ. Prot. 139, 341–361 (2020).CAS 
Article 

Google Scholar 
7.Danforth, C. et al. An integrative method for identification and prioritization of constituents of concern in produced water from onshore oil and gas extraction. Environ. Int. 134, 105280 (2020).8.Bachmann, R. T., Johnson, A. C. & Edyvean, R. G. J. Biotechnology in the petroleum industry: An overview. Int. Biodeterior. Biodegrad. 86, 225–237 (2014).CAS 
Article 

Google Scholar 
9.Rellegadla, S., Jain, S. & Agrawal, A. Oil reservoir simulating bioreactors: tools for understanding petroleum microbiology. Appl. Microbiol. Biotechnol. 104, 1035–1053 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Weschenfelder, S. E. et al. Evaluation of ceramic membranes for oilfield produced water treatment aiming reinjection in offshore units. J. Pet. Sci. Eng. 131, 51–57 (2015).CAS 
Article 

Google Scholar 
11.Benka-Coker, M. O., Metseagharun, W. & Ekundayo, J. A. Abundance of sulphate-reducing bacteria in Niger Delta oilfield waters. Bioresour. Technol. 54, 151–154 (1995).CAS 
Article 

Google Scholar 
12.Magot, M., Ollivier, B. & Patel, B. K. C. Microbiology of petroleum reservoirs. Antonie van Leeuwenhoek, Int. J. Gen. Mol. Microbiol. 77, 103–116 (2000).13.Santillan, E. F. U., Choi, W., Bennett, P. C. & Diouma Leyris, J. The effects of biocide use on the microbiology and geochemistry of produced water in the Eagle Ford formation, Texas, U.S.A. J. Pet. Sci. Eng. 135, 1–9 (2015).14.Liu, H. et al. The effect of magneticfield on biomineralization and corrosion behavior of carbon steel induced by iron-oxidizing bacteria. Corros. Sci. 102, 93–102 (2016).CAS 
Article 
ADS 

Google Scholar 
15.Katebian, L. et al. Inhibiting quorum sensing pathways to mitigate seawater desalination RO membrane biofouling. Desalination 393, 135–143 (2016).CAS 
Article 

Google Scholar 
16.Tang, K., Baskaran, V. & Nemati, M. Bacteria of the sulphur cycle: An overview of microbiology, biokinetics and their role in petroleum and mining industries. Biochem. Eng. J. 44, 73–94 (2009).CAS 
Article 

Google Scholar 
17.Kahrilas, G. A., Blotevogel, J., Stewart, P. S. & Borch, T. Biocides in Hydraulic Fracturing Fluids: A Critical Review of Their Usage, Mobility, Degradation, and Toxicity. Environ. Sci. Technol. 49, 16–32 (2015).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
18.Tanji, Y., Toyama, K., Hasegawa, R. & Miyanaga, K. Biological souring of crude oil under anaerobic conditions. Biochem. Eng. J. 90, 114–120 (2014).CAS 
Article 

Google Scholar 
19.Vikram, A., Bomberger, J. M. & Bibby, K. J. Efflux as a Glutaraldehyde Resistance Mechanism in Pseudomonas fluorescens and Pseudomonas aeruginosa Biofilms. Antimicrob. Agents Chemother. 59, 3433–3440 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Struchtemeyer, C. G., Morrison, M. D. & Elshahed, M. S. A critical assessment of the efficacy of biocides used during the hydraulic fracturing process in shale natural gas wells. Int. Biodeterior. Biodegrad. 71, 15–21 (2012).CAS 
Article 

Google Scholar 
21.Turkiewicz, A., Brzeszcz, J. & Kapusta, P. The application of biocides in the oil and gas industry. Nafta-Gaz R. 69, nr, 103–111 (2013).22.Videla, H. A. & Herrera, L. K. Microbiologically influenced corrosion: looking to the future. Int. Microbiol. 8, 169–180 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Xu, D., Li, Y. & Gu, T. Mechanistic modeling of biocorrosion caused by biofilms of sulfate reducing bacteria and acid producing bacteria. Bioelectrochemistry 110, 52–58 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Sachan, R. & Singh, A. K. Comparison of microbial influenced corrosion in presence of iron oxidizing bacteria (strains DASEWM1 and DASEWM2). Constr. Build. Mater. 256, 119438 (2020).25.Hino, S., Kazuya, W. & Takahashi, N. Isolation and Characterization of Slime-Producing Bacteria Capable of Utilizing Petroleum Hydrocarbons as a Sole Carbon Source. J. Ferment. Bioeng. 84, 528–531 (1997).CAS 
Article 

Google Scholar 
26.Kim, H. S., Wright, K., Piccioni, J., Cho, D. J. & Cho, Y. I. Inactivation of bacteria by the application of spark plasma in produced water. Sep. Purif. Technol. 156, 544–552 (2015).CAS 
Article 

Google Scholar 
27.Nazina, T. N. et al. Functional and phylogenetic microbial diversity in formation waters of a low-temperature carbonate petroleum reservoir. Int. Biodeterior. Biodegrad. 81, 71–81 (2013).CAS 
Article 

Google Scholar 
28.Stipanicev, M. et al. Corrosion of carbon steel by bacteria from North Sea offshore seawater injection systems: Laboratory investigation. Bioelectrochemistry 97, 76–88 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Li, X. X. et al. Microbiota and their affiliation with physiochemical characteristics of different subsurface petroleum reservoirs. Int. Biodeterior. Biodegrad. 120, 170–185 (2017).CAS 
Article 

Google Scholar 
30.Jurelevicius, D. et al. Bacterial community response to petroleum hydrocarbon amendments in freshwater, marine, and hypersaline water-containing microcosms. Appl. Environ. Microbiol. 79, 5927–5935 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
31.Silva, T. R., Verde, L. C. L., Santos Neto, E. V. & Oliveira, V. M. Diversity analyses of microbial communities in petroleum samples from Brazilian oil fields. Int. Biodeterior. Biodegrad. 81, 57–70 (2013).32.Beech, I. B. & Gaylarde, C. C. Recent advances in the study of biocorrosion: an overview. Rev. Microbiol. 30, 117–190 (1999).Article 

Google Scholar 
33.Korenblum, E., Valoni, É., Penna, M. & Seldin, L. Bacterial diversity in water injection systems of Brazilian offshore oil platforms. Appl. Microbiol. Biotechnol. 85, 791–800 (2010).CAS 
PubMed 
Article 

Google Scholar 
34.Vasconcellos, S. P. et al. The potential for hydrocarbon biodegradation and production of extracellular polymeric substances by aerobic bacteria isolated from a Brazilian petroleum reservoir. World J. Microbiol. Biotechnol. 27, 1513–1518 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Postgate, J. R. The sulphate-reducing bacteria, 2nd edn. (Cambridge University Press, 1984).36.Baars, J. K. Over Sulphatreductie door Bacterien. (Dissertation, 1930).37.Whiteley, A. S. & Bailey, M. J. Bacterial community structure and physiological state within an industrial phenol bioremediation system. Appl. Environ. Microbiol. 66, 2400–2407 (2000).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
38.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
39.Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
40.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 
CAS 

Google Scholar 
41.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.R Core Team. R: A Language and Environment for Statistical Computing. (2021).43.McMurdie, P. J. & Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One 8, e61217 (2013).44.Lahti, L., Shetty, S., Blake, T. & Salojarvi, J. Tools for microbiome analysis in R. Version 1(5), 28 (2017).
Google Scholar 
45.Muturi, E. J., Njoroge, T. M., Dunlap, C. & Cáceres, C. E. Blood meal source and mixed blood-feeding influence gut bacterial community composition in Aedes aegypti. Parasit. Vectors 14, 83 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
48.Oksanen, J. et al. vegan: Community Ecology Package. (2020).49.Zare, N. et al. Using enriched water and soil-based indigenous halophilic consortia of an oilfield for the biological removal of organic pollutants in hypersaline produced water generated in the same oilfield. Process Saf. Environ. Prot. 127, 151–161 (2019).CAS 
Article 

Google Scholar 
50.Belgini, D. R. B. et al. Integrated diversity analysis of the microbial community in a reverse osmosis system from a Brazilian oil refinery. Syst. Appl. Microbiol. 41, 473–486 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.de Sousa Pires, A. et al. Molecular diversity and abundance of the microbial community associated to an offshore oil field on the southeast of Brazil. Int. Biodeterior. Biodegradation 160, 105215 (2021).52.Tüccar, T., Ilhan-Sungur, E. & Muyzer, G. Bacterial Community Composition in Produced Water of Diyarbakır Oil Fields in Turkey. Johnson Matthey Technol. Rev. https://doi.org/10.1595/205651320X15911723486216 (2020).Article 

Google Scholar 
53.Aurepatipan, N., Champreda, V., Kanokratana, P., Chitov, T. & Bovonsombut, S. Assessment of bacterial communities and activities of thermotolerant enzymes produced by bacteria indigenous to oil-bearing sandstone cores for potential application in Enhanced Oil Recovery. J. Pet. Sci. Eng. 163, 295–302 (2018).CAS 
Article 

Google Scholar 
54.Li, X. X. et al. Dominance of Desulfotignum in sulfate-reducing community in high sulfate production-water of high temperature and corrosive petroleum reservoirs. Int. Biodeterior. Biodegrad. 114, 45–56 (2016).CAS 
Article 

Google Scholar 
55.Lan, G. Enrichment and diversity analysis of the thermophilic microbes in a high temperature petroleum reservoir. African J. Microbiol. Res. 5, 1850–1857 (2011).ADS 

Google Scholar 
56.Nguyen, T. T., Cochrane, S. K. J. & Landfald, B. Perturbation of seafloor bacterial community structure by drilling waste discharge. Mar. Pollut. Bull. 129, 615–622 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Ganesh Kumar, A., Nivedha Rajan, N., Kirubagaran, R. & Dharani, G. Biodegradation of crude oil using self-immobilized hydrocarbonoclastic deep sea bacterial consortium. Mar. Pollut. Bull. 146, 741–750 (2019).58.Birkeland, N. K. Sulfate-Reducing Bacteria and Archaea. in Petroleum Microbiology (eds. Ollivier, B. & Magot) 35–54 (American Society of Microbiology, 2005). doi:https://doi.org/10.1128/9781555817589.ch3.59.Gam, Z. B. A. et al. Desulfovibrio tunisiensis sp. nov., a novel weakly halotolerant, sulfate-reducing bacterium isolated from exhaust water of a Tunisian oil refinery. Int. J. Syst. Evol. Microbiol. 59, 1059–1063 (2009).60.Christman, G. D., León-Zayas, R. I., Zhao, R., Summers, Z. M. & Biddle, J. F. Novel clostridial lineages recovered from metagenomes of a hot oil reservoir. Sci. Rep. 10, 8048 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
61.Li, X.-X. et al. Diversity and Composition of Sulfate-Reducing Microbial Communities Based on Genomic DNA and RNA Transcription in Production Water of High Temperature and Corrosive Oil Reservoir. Front. Microbiol. 8, (2017).62.Popoola, L., Grema, A., Latinwo, G., Gutti, B. & Balogun, A. Corrosion problems during oil and gas production and its mitigation. Int. J. Ind. Chem. 4, 35 (2013).Article 

Google Scholar 
63.Street, C. N. & Gibbs, A. Eradication of the corrosion-causing bacterial strains Desulfovibrio vulgaris and Desulfovibrio desulfuricans in planktonic and biofilm form using photodisinfection. Corros. Sci. 52, 1447–1452 (2010).CAS 
Article 

Google Scholar 
64.Varjani, S. J. & Gnansounou, E. Microbial dynamics in petroleum oilfields and their relationship with physiological properties of petroleum oil reservoirs. Bioresour. Technol. 245, 1258–1265 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.da Rosa, J. P., Tibúrcio, S. R. G., Marques, J. M., Seldin, L. & Coelho, R. R. R. Streptomyces lunalinharesii 235 prevents the formation of a sulfate-reducing bacterial biofilm. Brazilian J. Microbiol. 47, 603–609 (2016).Article 
CAS 

Google Scholar 
66.Xie, C. H. & Yokota, A. Pleomorphomonas oryzae gen. nov., sp. nov., a nitrogen-fixing bacterium isolated from paddy soil of Oryza sativa. Int. J. Syst. Evol. Microbiol. 55, 1233–1237 (2005).67.Madhaiyan, M. et al. Pleomorphomonas diazotrophica sp. nov., an endophytic N-fixing bacterium isolated from root tissue of Jatropha curcas L. Int. J. Syst. Evol. Microbiol. 63, 2477–2483 (2013).68.Im, W.-T. Pleomorphomonas koreensis sp. nov., a nitrogen-fixing species in the order Rhizobiales. Int. J. Syst. Evol. Microbiol. 56, 1663–1666 (2006).69.Sung, H. R., Yoon, J. H. & Ghim, S. Y. Shewanella dokdonensis sp. nov., isolated from seawater. Int. J. Syst. Evol. Microbiol. 62, 1636–1643 (2012).70.Torri, A. et al. Shewanella algae infection in Italy: report of 3 years’ evaluation along the coast of the northern Adriatic Sea. New Microbes New Infect. 23, 39–43 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Semple, K. M. & Westlake, D. W. S. Characterization of iron-reducing Alteromonas putrefaciens strains from oil field fluids. Can. J. Microbiol. 33, 366–371 (1987).CAS 
Article 

Google Scholar 
72.Kim, D. D. et al. Microbial community analyses of produced waters from high-temperature oil reservoirs reveal unexpected similarity between geographically distant oil reservoirs. Microb. Biotechnol. 11, 788–796 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Angelova, A. G., Ellis, G. A., Wijesekera, H. W. & Vora, G. J. Microbial Composition and Variability of Natural Marine Planktonic and Biofouling Communities From the Bay of Bengal. Front. Microbiol. 10, (2019).74.Salgar-Chaparro, S. J., Lepkova, K., Pojtanabuntoeng, T., Darwin, A. & Machuca, L. L. Microbiologically influenced corrosion as a function of environmental conditions: A laboratory study using oilfield multispecies biofilms. Corros. Sci. 169, 108595 (2020).75.Michas, A. et al. More than 2500 years of oil exposure shape sediment microbiomes with the potential for syntrophic degradation of hydrocarbons linked to methanogenesis. Microbiome 5, 118 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
76.Tinker, K. et al. Geochemistry and Microbiology Predict Environmental Niches With Conditions Favoring Potential Microbial Activity in the Bakken Shale. Front. Microbiol. 11, (2020).77.Zhong, C., Nesbø, C. L., Goss, G. G., Lanoil, B. D. & Alessi, D. S. Response of aquatic microbial communities and bioindicator modelling of hydraulic fracturing flowback and produced water. FEMS Microbiol. Ecol. 96, (2020).78.Xiao, M. et al. Bacterial community diversity in a low-permeability oil reservoir and its potential for enhancing oil recovery. Bioresour. Technol. 147, 110–116 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Gulliver, D., Lipus, D., Tinker, K., Ross, D. & Sarkar, P. The Geochemistry and Microbial Ecology of Produced Waters from Three Different Unconventional Oil and Gas Regions. in Proceedings of the 8th Unconventional Resources Technology Conference (American Association of Petroleum Geologists, 2020). doi:https://doi.org/10.15530/urtec-2020-2979.80.Davis, J. P., Struchtemeyer, C. G. & Elshahed, M. S. Bacterial Communities Associated with Production Facilities of Two Newly Drilled Thermogenic Natural Gas Wells in the Barnett Shale (Texas, USA). Microb. Ecol. 64, 942–954 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Daly, R. A. et al. Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales. Nat. Microbiol. 1, 16146 (2016).82.Lipus, D. et al. Predominance and Metabolic Potential of Halanaerobium spp. in Produced Water from Hydraulically Fractured Marcellus Shale Wells. Appl. Environ. Microbiol. 83, (2017).83.Zdanowski, M. K. et al. Enrichment of Cryoconite Hole Anaerobes: Implications for the Subglacial Microbiome. Microb. Ecol. 73, 532–538 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Guo, H., Chen, C., Lee, D.-J., Wang, A. & Ren, N. Sulfur–nitrogen–carbon removal of Pseudomonas sp. C27 under sulfide stress. Enzyme Microb. Technol. 53, 6–12 (2013).85.Brahmacharimayum, B. & Ghosh, P. K. Effects of different environmental and operating conditions on sulfate bioreduction in shake flasks by mixed bacterial culture predominantly Pseudomonas aeruginosa. Desalin. Water Treat. 57, 17911–17921 (2016).CAS 
Article 

Google Scholar 
86.Gao, P., Wang, H., Li, G. & Ma, T. Low-Abundance Dietzia Inhabiting a Water-Flooding Oil Reservoir and the Application Potential for Oil Recovery. Biomed Res. Int. 2019, 1–11 (2019).CAS 

Google Scholar 
87.Gao, P., Li, Y., Tan, L., Guo, F. & Ma, T. Composition of Bacterial and Archaeal Communities in an Alkali-Surfactant-Polyacrylamide-Flooded Oil Reservoir and the Responses of Microcosms to Nutrients. Front. Microbiol. 10, (2019).88.Liu, Y.-F. et al. Metabolic capability and in situ activity of microorganisms in an oil reservoir. Microbiome 6, 5 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
89.Li, D. et al. Microbial biodiversity in a Malaysian oil field and a systematic comparison with oil reservoirs worldwide. Arch. Microbiol. 194, 513–523 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Sierra-Garcia, I. N. et al. Microbial diversity in degraded and non-degraded petroleum samples and comparison across oil reservoirs at local and global scales. Extremophiles 21, 211–229 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Xingbiao, W., Yanfen, X., Sanqing, Y., Zhiyong, H. & Yanhe, M. Influences of microbial community structures and diversity changes by nutrients injection in Shengli oilfield, China. J. Pet. Sci. Eng. 133, 421–430 (2015).Article 
CAS 

Google Scholar 
92.Li, G. et al. The relative abundance of alkane-degrading bacteria oscillated similarly to a sinusoidal curve in an artificial ecosystem model from oil-well products. Environ. Microbiol. 20, 3772–3783 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Cui, K. et al. Stimulation of indigenous microbes by optimizing the water cut in low permeability reservoirs for green and enhanced oil recovery. Sci. Rep. 9, 15772 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
94.Cleary, A. et al. Bioremediation of strontium and technetium contaminated groundwater using glycerol phosphate. Chem. Geol. 509, 213–222 (2019).CAS 
Article 
ADS 

Google Scholar 
95.Xia, X. et al. Changes in groundwater bacterial community during cyclic groundwater‐table variations. Hydrol. Process. hyp.13917 (2020) doi:https://doi.org/10.1002/hyp.13917.96.Braun, K. & Gibson, D. T. Anaerobic degradation of 2-aminobenzoate (anthranilic acid) by denitrifying bacteria. Appl. Environ. Microbiol. 48, 102–107 (1984).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
97.Cai, M. et al. Crude oil as a microbial seed bank with unexpected functional potentials. Sci. Rep. 5, 16057 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
98.Arai, H. Regulation and Function of Versatile Aerobic and Anaerobic Respiratory Metabolism in Pseudomonas aeruginosa. Front. Microbiol. 2, (2011).99.Hilgarth, M., Lehner, E. M., Behr, J. & Vogel, R. F. Diversity and anaerobic growth of Pseudomonas spp. isolated from modified atmosphere packaged minced beef. J. Appl. Microbiol. 127, 159–174 (2019). More

Type-I errorIn the case of two identical unimodal von Mises distributions, seven tests did not maintain Type-I error near the nominal 5% level, at least when sample sizes were small. These tests were the Kuiper two-sample test, the non equal concentration parameters approach ANOVA, the P-test, the Watson’s large-sample nonparametric test, the Watson–Williams test and the Rao dispersion test (Fig. 2). The Type-I error results were similar for the unimodal wrapped skew-normal distribution, except that the Wallraff test and Fisher’s method also showed Type-I error inflation (Fig. S1). No other methods showed evidence of failure to control Type-I error rate across different testing situations (Figs. S2–S5), except for the Log-likelihood ratio ANOVA in the case of two identical asymmetrical bimodal distributions (Fig. S3). In summary, only eight out of 18 tests reliably controlled the Type-I error rate near the nominal 5% level across all the situations investigated. These included five tests for identical distribution, the Watson’s U2 test, the Large-sample Mardia–Watson–Wheeler test, the Watson-Wheeler test, the embedding approach ANOVA, the MANOVA approach, the Rao polar test for differences in mean direction, and two tests for differences in concentration, the Levene’s test and the concentration test. We focus only on these tests in our explorations of statistical power.Figure 2Type-I error of all tests using von Mises distributions for different sample sizes: 10 and 10 (A), 20 and 20 (B), 50 and 50 (C), 20 and 30 (D) and 10 and 50 (E). Concentration (κ, kappa) increases for both distributions from 0 to 8. Tests are grouped according to their null hypotheses.Full size imagePower to detect differences in concentrationThe most powerful test to detect concentration differences between two von Mises distributions was the MANOVA approach, which offered superior power especially at lower sample sizes (Fig. 3). The Watson’s U2 test was also very powerful, followed by the Watson–Wheeler and the Large-sample Mardia–Watson–Wheeler tests with only marginally lower power. The embedding approach ANOVA had lower power, but, notably, was still more powerful than the Concentration test and Levene’s test, both specifically designed to detect differences in concentration. As expected, the Rao polar test was not sensitive to differences in concentration. The general results for two unimodal wrapped skew-normal distributions were comparable to the results for unimodal von Mises distributions, with the only exception of superior performance of Levene’s test in situations with highly asymmetric samples sizes (Fig. S6).Figure 3Power of all included tests when comparing von Mises distributions of differing concentrations using different sample sizes: 10 and 10 (A), 20 and 20 (B), 50 and 50 (C), 20 and 30 (D) and 10 and 50 (E). The first distribution is fixed at κ = 0, the second increases from 0 towards 8.Full size imageWhen comparing axial von Mises distributions, only the Watson’s U2 test offered acceptable power (Fig. S7). For the symmetrical trimodal distributions, overall power was very low, and again, only the Watson’s U2 providing some power (Fig. S8). The asymmetrical bimodal (Fig. S9) situation showed acceptable power of the MANOVA approach and Watson’s U2, however, for the asymmetrical trimodal distribution power was low with the Watson’s U2 providing the best results (Fig. S10).Power to detect differences in the mean/medianThe power to detect angular differences between two von Mises distributions was highest for the MANOVA approach at small sample sizes (n = 10), followed by the Watson’s U2, Watson-Wheeler test and the Large-sample Mardia-Watson-Wheeler test (Fig. 4). Notably, the Levene’s test also showed acceptable power levels, clearly failing to detect specifically concentration differences (to which it was less sensitive, see Fig. 4). The concentration test was not sensitive to the differences in mean direction. Special cases were the embedding approach ANOVA and the Rao polar test. The ANOVA approach showed, with the exception of very unequal sample sizes (n = 10/50), a unimodal response, with increasing power levels from 0° to 90° difference, but then rapidly decreasing power towards 180° difference. The Rao polar test showed an even stranger pattern, with, at higher sample sizes, very good power when the difference was either around 45° or 135°, but with power levels dropping to 0.05 in between these two peaks (at 90°). The results were similar for the wrapped skew-normal distribution, with the exceptions that the Rao polar test showed strongly reduced power and switched from a bimodal to a unimodal power curve with a peak around 60°, and the Levene’s test completely lost its power (Fig. S11).Figure 4Power of all included tests when comparing von Mises distributions (kappa for both = 2) of differing directions using different sample sizes: 10 and 10 (A), 20 and 20 (B), 50 and 50 (C), 20 and 30 (D) and 10 and 50 (E). The first distribution is fixed at 0°, the second increases from 0° towards 180.Full size imageFor axial distributions, only the Watson’s U2 test offered acceptable power levels, although large sample sizes (~ n = 100) were required for the power to reach over 50% (Fig. S12). All other tests failed to detect the difference in mean direction between two axial distributions. For symmetric trimodal distributions none of the tests used was sensitive to differences in mean direction (Fig. S13).When comparing asymmetrical bimodal distributions, the general trends were similar to the unimodal case. However, over all sample sizes the MANOVA approach offered the best power. The Watson–Wheeler test was considerably less powerful in this situation, as were the Watson’s U2 test and the Large-sample Mardia–Watson–Wheeler test (Fig. S14). The Levene’s test showed a unimodal-shaped power curve. The asymmetrical trimodal situation was, again, similar to the asymmetrical bimodal situation (Fig. S15), with the exception of the Levene’s test, which showed steady power increase with angular difference (instead of the hump-shaped curve).Power to detect differences in distribution typeWhen comparing a unimodal and an axial bimodal distribution, which increased similarly in concentration, we found that the MANOVA approach again offered the best power in particular at low samples sizes, followed by the Watson’s U2 test, the Large-sample Mardia–Watson–Wheeler test and Watson–Wheeler test (Fig. 5). While the embedding approach ANOVA and the Levene’s test had varying but usable power levels, the concentration test was only sensitive to such differences at low concentration values. The Rao polar test was not sensitive to such differences.Figure 5Power of all included tests when comparing von Mises distributions of differing number of modes (unimodal and axially bimodal) using different sample sizes: 10 and 20 (A), 20 and 40 (B), 50 and 100 (C), 20 and 60 (D) and 10 and 100 (E). The concentration (κ) of both increases from 0 to 8.Full size imageThe picture was only marginally different when comparing a von Mises with a wrapped skew-normal distribution (Fig. S16). For low sample sizes (n = 10) the MANOVA approach offered great power, followed by the embedding approach ANOVA. The latter offered good power throughout the range of sample sizes tested, followed by the Watson’s U2 test, the Large-sample Mardia–Watson–Wheeler test and Levene’s test. Also, the Rao polar test showed lower, but acceptable sensitivity to distribution type. The concentration test only showed very low power, that (as expected) increased with increasing concentrations of the respective distributions.We summarize the results obtained in the power analysis in Table 2. In all situations, either the Watson’s U2 test or the MANOVA approach offered the best power.Table 2 Ranking of tests based on the power comparisons for the main scenarios encountered in potential data sets (using different distributions: unimodal, axial, asymmetrical bimodal, symmetrical trimodal, asymmetrical trimodal), in cases were only one test performed acceptable the others ranks were left blank (see Table 1 for abbreviations).Full size tableReal data examplesTesting the performance of the robust tests on real data sets revealed, predominantly, the expected test behavior. In the example of homing pigeons where a difference in concentration was expected, all tests, with the exception of the Rao polar test and, notably, the concentration test, showed a significant difference between the distributions (Fig. 6A). Therefore, we can conclude, in accordance with the respective publication19, that sectioning of the olfactory nerve disrupted the homing behavior of pigeons.Figure 6Results from example data. Shown are results of pigeon (A), ant (B) and bat orientations (C). Control groups are on the left panels and experimental groups on the right. The tests are abbreviated according to Table 1, significant test results are indicated in red with asterisk and non-significant in blue. For each circular plot directional data is shown as dots on the circle (each dot is one individual), the arrows represent the mean direction and the dashed line the 95% confidence interval.Full size imageIn the ant example, where no difference between the groups was expected, there was no significant difference between the distributions detected by most of the tests (Fig. 6B). Only the concentration test showed a significant difference. Based on the other tests we would conclude that there was no biological meaningful difference between the two distributions. Therefore, ants appear to be able to transfer visual information from one eye to the other.In the bat example, where a difference in mean direction was expected, the Watson’s U2, the Mardia–Watson–Wheeler, Watson-Wheeler test and the MANOVA approach showed a significant difference (Fig. 6C). Notably, the Rao polar, Levene’s, and concentration tests and the embedding approach ANOVA failed to show a significant difference. At least for the Rao polar test, one would have expected a significant difference, as the two distributions are clearly 180° apart. This outcome concurs with our simulation results where the Rao polar test failed to distinguish distributions on the same and orthogonal axes (Fig. 4). As the results of the tests where quite mixed this example highlights the need for choosing a test with appropriate power to detect the expected differences. Based on the results of the most powerful tests, we conclude that the bats showed a mirrored orientation, as expected in the experimental design. More

Data acquisitionOur analysis was based on open access crop production data from the United Nation’s Food and Agricultural Organization (FAO) spanning from 1961 to 201718. We extracted data on area harvested (in ha) for 339 FAO-defined crop groups being grown in all UN-recognized countries. Since our research centred on understanding, quantifying, and mapping changes in crop diversity in current agricultural lands, countries that cease to exist (e.g., Yugoslavia) were not included in our analysis, resulting in data for 201 countries (Table S1). Prior to analyses, we adjusted certain crop group listings following our previous analyses of global changes in crop diversity8. Specifically, “Cottonlint” and “Cottonseed” were duplicated in our dataset and were therefore compiled as “Seedcotton”, while “Palmkernels” were renamed as “Oilpalmfruit.” Additionally, “Fruitpomenes”, “Fruitstonenes”, and “Grainmixed” were removed from analysis since these crop groupings are not associated with any specific crop species in the FAO database18. Finally, “Mushroomsandtruffles” were removed since it relates to non-plant species, and “Coir” was removed because it is a plant by-product.Changes in crop richness over timeAll statistical analyses were performed using R version 3.3.3 statistical software (R Foundation for Statistical Computing, Vienna, Austria). The initial step in our analysis was to calculate both crop richness and evenness for each country, at each individual year, using the vegan R package38. Based on these datasets, we then used the analytical framework developed by8 to evaluate how crop species richness and evenness have changed in each individual country across its entire data range.Specifically, in their analysis Martin et al.8 found that piecewise linear regression models provided the strongest descriptions of crop species richness change over time, across 21 of 22 FAO-defined regions globally. We therefore followed this approach by fitting a piecewise linear regression model for each country individually, that predicts changes in species richness over time. Piecewise model fitting was a two-step process, whereby for each country we first fit a linear regression model of the form:$$S = a + left( {b times {text{year}}} right)$$
(1)
where a is the intercept and b represents the rate of change in crop group richness (S) through time. This linear model (Eq. 1) was then used as the basis of a piecewise linear regression model, which was fitted in order to estimate breakpoints in the relationship between S and year. Specifically, piecewise models were fit using the segmented function in the segmented R package39, and were of the form:$$S = a + bleft( {{text{year}}} right) + left( {left( {c({text{year}} -uppsi _{1} } right) times Ileft( {{text{year}} >uppsi _{1} } right)} right) + left( {dleft( {{text{year}} -uppsi _{2} } right) times Ileft( {{text{year}} >uppsi _{2} } right)} right)$$
(2)
where a is as in Eq. (1), and b represents the slope of the S-year relationship prior to the first breakpoint (ψ1). Here, c represents the difference in the slope of the S-year relationship between the first and second piecewise model segments; the c parameter therefore applies only when the first conditional indicator function (denoted by “I”) is true. Similarly, d represents the difference in slopes for the S-year relationship between the first, second, and third segments, which only applies when the second conditional indicator function is true. In sum, the slope of the relationship between S and year is equal to b prior to the ψ1, is equal to b + c between ψ1and ψ2, and is equal to b + c + d after ψ2. Piecewise models were fit with initial starting parameters of 1975 and 2000 for ψ1 and ψ2, respectively. The ψ1 and ψ2 parameters were tuned manually for 29 countries with a shortened data range, following visual inspection of data (see Tables S1 and S2).Based on this piecewise regression model procedure, we then used parameters from Eq. (2) to determine three key indicator points of crop diversity change through time for each country (displayed visually in Fig. 1). Indicator 1 reflects the onset of diversification in each country, and was calculated as Breakpoint 1 (ψ1) in Eq. (2); this indicator therefore corresponds to the year in which notable changes in species richness began. Indicator 2 reflects the duration of the crop diversification period in each country, and was calculated as the difference between breakpoints 2 and 1 (i.e., ψ2-ψ1 from Eq. 2); this indicator therefore represents the duration of the period when crop prominent changes in crop diversity occurred. Finally, Indicator 3 reflects the rate at which crop diversity changed throughout the diversification period in each country; this indicator was calculated as the rate of crop diversity change (between ψ1 and ψ2), which in our models corresponded to the sum of the slopes (1) prior to the first breakpoint, and (2) between the first and second breakpoints (i.e., corresponding to b + c in Eq. 2). For each indicator we then calculated summary statistics as either mean ± standard deviations or median ± median absolute deviations (m.a.d.), where data was normally or log-normally distributed, respectively. Country values for each indicator were mapped using the mapCountryData function in the rworldmap R package40.Changes in crop evenness over timeEvaluations of temporal changes in crop evenness at national scales followed this same analytical approach as above. First, for each country-by-year combination we calculated Pielou’s evenness index (J′)—which ranges from 0 to 1, with values closer to 0 indicating less evenness or greater abundance of a few dominant crop groups, and values closer to 1 representing more equitable abundances of crop groups—as:$$J^{prime} = frac{H^prime }{{ln left( S right)}}$$
(3)
where S is again crop richness, and H′ is the Shannon–Weiner diversity index calculated as:$$H^prime = – mathop sum limits_{i = 1}^{S} p_{i} ln p_{i}$$
(4)
where pi represents the relative proportion of the ith crop group for a given country-by-year combination. In these evenness calculations, all values of pi were estimated as the relative proportion of agricultural area (measured in ha) occupied by a given crop commodity group, within a country at a given year; this analytical approach was employed by Martin et al.8 when assessing crop group composition at supra-national scales. We then evaluated how J′ values changed in each country through time by replicating our stepwise modelling analyses above, substituting J′ for S in Eqs. (1) and (2), and extracting the same model indicators (Fig. 1). Finally, we calculated summary statistics and mapped each of these indicators, as described above.Changes in crop composition across countries and over timeWe used multivariate analyses to evaluate how temporal changes in S and J′ influenced crop composition across countries and over time. To do so, we created a community composition matrix whereby national-level crop assemblages were estimated for each of the country-by-year combinations. In this matrix, area harvested was taken as an approximation of the abundance of each crop group within each country-by-year combination (again following Martin et al.8). Since these abundances (or area harvested) across country-by-year combinations varied over orders of magnitude, we used non-metric multidimensional scaling (NMDS) to analyze and visualize spatial (country) and temporal (year) differences in crop diversity. Specifically, we used the vegan R package38 to calculate all 58,899,231 Bray–Curtis dissimilarities among all 10,854 data points (i.e., crop group composition in every country-by-year data point), as:$$BC_{jk} = frac{{sum i left| {x_{ij} – x_{ik} } right|}}{{sum i left( {x_{ij} + x_{ik} } right)}}$$
(5)
where BCjk represents the dissimilarity between the jth and kth community, xij represents the abundance (i.e., area harvested) of crop group i in sample j, and xik represents the abundance of crop group i in sample k. We then used a multivariate analysis of variance (i.e., an Adonis test), to test for significant differences in Bray–Curtis distances as a function of country, year, and a country-by-year interaction. Significance was assessed using a permutation test, with 99 permutations used.Latitudinal gradients in crop richnessTo test our hypotheses surrounding the presence of, and temporal changes in, latitudinal gradients in crop group diversity, we focused on 164 countries for which crop group diversity was available in both 1961 and 2017. For each of these two datasets, we fit a separate linear regression model that predicts crop group richness as a function of latitude (expressed as an absolute value) and a 2nd-order polynomial term for the ‘latitude2’ variable. From both of these models, we extracted and compared latitude value at which crop group richness was estimated/ modelled to peak.Predictors of change in crop diversity and compositionWe tested if Human Development Index (HDI) was correlated with patterns of change in crop diversity and composition. Briefly, the HDI is a composite index of four metrics related to socio-economic status, including life expectancy at birth, expected years of schooling for children at a school-centring age, mean years of schooling for adults ≥ 25 years of age, and log-transformed gross national income per capita. These values are then aggregated on a per country basis, into an HDI index that ranges from 0–1 with higher scores denoting higher performance in these indicators. We employed 2017 HDI values in our analysis here, in order to include the most countries possible in each analysis (since earlier HDI scores are less readily available)41.We then used linear mixed effects models to test if patterns of change in crop diversity and evenness varied systematically with HDI values. This entailed fitting six linear mixed models, where each of our six indicators (i.e., Indicators 1–3 for both S and J′) were predicted as a function of HDI; these models also accounted for potential spatial autocorrelation in Indicator values by including the FAO-defined continent identity and FAO-defined region identity of each country, as a nested random variable. Models were fit using the lme function in the nlme R package41. We then estimated the proportion of variation in each indicator that is explained by HDI, continent identity, and region identity, using the varcomp function in the ape R package42—which partitioned explained variation across continents and regions—as well as the sem.model.fits function in the piecewiseSEM R package43—which partitioned explained variation across the fixed (i.e., model intercept and HDI) vs. random (i.e., continent and region) effects. Due to differences in HDI data availability and in the number of piecewise models that converged, n = 152 countries for all models of S indicators and n = 139 countries for all models of J′ indicators. Log-transformed values of Indicators were used in these analyses where they better approximated a log-normal distribution, as determined using the fitdistrplus function in the fitdistrplus R package44. More

1.Forney, K. A. & Wade, P. R. Worldwide distribution and abundance of killer whales. In Whales, Whaling and Ocean Ecosystems (eds Estes, J. A. et al.) 145–162 (University of California Press, 2006).
Google Scholar 
2.Hoelzel, A. R., Dahlheim, M. & Stern, S. J. Low genetic variation among killer whales (Orcinus orca) in the eastern north Pacific and genetic differentiation between foraging specialists. J. Hered. 89, 121–128. https://doi.org/10.1093/jhered/89.2.121 (1998).CAS 
Article 
PubMed 

Google Scholar 
3.Matkin, C. O., Ellis, G. M., Saulitis, E. L., Barrett-Lennard, L. G. & Matkin, D. Killer Whales of Southern Alaska (North Gulf Oceanic Society, 1999).
Google Scholar 
4.Barrett-Lennard, L. G. Population structure and mating patterns of Killer Whales (Orcinus orca) as revealed by DNA analysis. PhD thesis, University of British Columbia (2000). https://open.library.ubc.ca/collections/ubctheses/831/items/1.0099652.5.Ford, J. K. B., Ellis, G. M. & Balcomb, K. C. Killer Whales: The Natural History and Genealogy of Orcinus orca in British Columbia and Washington (UBC Press, 2000).
Google Scholar 
6.Bigg, M. A., Olesiuk, P. F., Ellis, G. M., Ford, J. K. B. & Balcomb, K. C. Social organization and genealogy of resident killer whales (Orcinus orca) in the coastal waters of British Columbia and Washington State. Rep. Int. Whal. Comm. 12, 383–405 (1990).
Google Scholar 
7.Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).Article 

Google Scholar 
8.Matkin, C. O., Ellis, G. M., Olesiuk, P. & Saulitis, E. L. Association patterns and genealogies of resident killer whales (Orcinus orca) in Prince William Sound, Alaska. Fish. Bull. 97, 900–919 (1999).
Google Scholar 
9.Saulitis, E. L., Matkin, C. O., Barrett-Lennard, L., Heise, K. & Ellis, G. M. Foraging strategies of sympatric killer whale (Orcinus orca) populations in Prince William Sound, Alaska. Mar. Mamm. Sci. 16, 94–109. https://doi.org/10.1111/j.1748-7692.2000.tb00906.x (2000).Article 

Google Scholar 
10.Ford, J. K. B. & Ellis, G. M. Selective foraging by fish-eating killer whales Orcinus orca in British Columbia. Mar. Ecol. Prog. Ser. 316, 185–199. https://doi.org/10.3354/meps316185 (2006).Article 
ADS 

Google Scholar 
11.Ivkovich, T. V., Filatova, O. A., Burdin, A. M., Sato, H. & Hoyt, E. The social organization of resident-type killer whales (Orcinus orca) in Avacha Gulf, Northwest Pacific, as revealed through association patterns and acoustic similarity. Mamm. Biol. 75, 198–210. https://doi.org/10.1016/j.mambio.2009.03.006 (2010).Article 

Google Scholar 
12.Baird, R. W. & Dill, L. M. Occurrence and behaviour of transient killer whales: Seasonal and pod-specific variability, foraging behaviour, and prey handling. Can. J. Zool. 73, 1300–1311 (1995).Article 

Google Scholar 
13.Baird, R. W. & Dill, L. M. Ecological and social determinants of group size in transient killer whales. Behav. Ecol. 7(4), 408–416. https://doi.org/10.1093/beheco/7.4.408 (1996).Article 

Google Scholar 
14.Dahlheim, M. et al. Eastern temperate North Pacific offshore killer whales (Orcinus orca): Occurrence, movements, and insights into feeding ecology. Mar. Mamm. Sci. 24(3), 719–729. https://doi.org/10.1111/j.1748-7692.2008.00206.x (2008).Article 

Google Scholar 
15.Ford, J. K. B. et al. Shark predation and tooth wear in a population of northeastern Pacific killer whales. Aquat. Biol. 11, 213–224. https://doi.org/10.3354/ab00307 (2011).Article 

Google Scholar 
16.Ford, J. K. B., Stredulinsky, E. H., Ellis, G. M., Durban, J. W. & Pilkington, J. F. Offshore Killer Whales in Canadian Pacific Waters: Distribution, Seasonality, Foraging Ecology, Population Status and Potential for Recovery. DFO Canadian Science Advisory Secretariat, Doc. 2014/088 (2014). https://www.dfo-mpo.gc.ca/csas-sccs/publications/resdocs-docrech/2014/2014_088-eng.html.17.Morin, P. et al. Complete mitochondrial genome phylogeographic analysis of killer whales (Orcinus orca) indicates multiple species. Genome Res. 858, 908–915. https://doi.org/10.1101/gr.102954.109 (2010).CAS 
Article 

Google Scholar 
18.Morin, P. A. et al. Geographic and temporal dynamics of a global radiation and diversification in the killer whale. Mol. Ecol. 24(15), 3964–3979. https://doi.org/10.1111/mec.13284 (2015).Article 
PubMed 

Google Scholar 
19.Foote, A. D. et al. Genome-culture coevolution promotes rapid divergence of killer whale ecotypes. Nat. Commun. 7, 11693. https://doi.org/10.1038/ncomms11693 (2016).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
20.Schevill, W. E. & Watkins, W. A. Sound structure and directionality in Orcinus (killer whale). Zoologica 51, 71–78 (1966).
Google Scholar 
21.Diercks, K. J., Trochta, R. T., Greenlaw, C. F. & Evans, W. E. Recording and analysis of dolphin echolocation signals. J. Acoust. Soc. Am. 49, 1729–1932 (1971).Article 
ADS 

Google Scholar 
22.Diercks, K. J., Trochta, R. T. & Evans, W. E. Delphinid sonar: Measurement and analysis. J. Acoust. Soc. Am. 54, 200–204 (1973).Article 
ADS 

Google Scholar 
23.Steiner, W. W., Hain, J. H., Winn, H. E. & Perkins, P. J. Vocalizations and feeding behavior of the killer whale. J. Mammal. 60, 823–827 (1979).Article 

Google Scholar 
24.Awbrey, F. T., Thomas, J. A., Evans, W. E. & Leatherwood, S. Ross sea killer whale vocalizations: Preliminary description and comparison with those of some Northern Hemisphere killer whales. Rep. Int. Whal. Comm. 32, 667–670 (1982).
Google Scholar 
25.Ford, J. K. B. Acoustic behaviour of resident killer whales (Orcinus orca) off Vancouver Island, British Columbia. Can. J. Zool. 67(3), 727–745 (1989).Article 

Google Scholar 
26.Barrett-Lennard, L. G., Ford, J. K. B. & Heise, K. A. The mixed blessing of echolocation: Differences in sonar used by fish-eating and mammal-eating killer whales. Anim. Behav. 51, 553–565 (1996).Article 

Google Scholar 
27.Ford, J. K. B. Vocal traditions among resident killer whales (Orcinus orca) in coastal waters of British Columbia. Can. J. Zool. 69, 1454–1483 (1991).Article 

Google Scholar 
28.Miller, P. J. O. Mixed-directionality of killer whale stereotyped calls: A direction of movement cue?. Behav. Ecol. Sociobiol. 52, 262–270. https://doi.org/10.1007/s00265-002-0508-9 (2002).Article 

Google Scholar 
29.Miller, P. J. O., Shapiro, A. D., Tyack, P. L. & Solow, A. R. Call-type matching in vocal exchanges of free-ranging resident killer whales, Orcinus orca. Anim. Behav. 67, 1099–1107. https://doi.org/10.1016/j.anbehav.2003.06.017 (2004).Article 

Google Scholar 
30.Filatova, O. A. Independent acoustic variation of the higher- and lower-frequency components of biphonic calls can facilitate call recognition and social affiliation in killer whales. PLoS ONE 15(7), e0236749. https://doi.org/10.1371/journal.pone.0236749 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Thomsen, F., Franck, D. & Ford, J. K. B. On the communicative significance of whistles in wild killer whales (Orcinus orca). Sci. Nat. 89, 404–407. https://doi.org/10.1007/s00114-002-0351-x (2002).CAS 
Article 

Google Scholar 
32.Riesch, R. & Deecke, V. B. Whistle communication in mammal-eating killer whales (Orcinus orca): Further evidence for acoustic divergence between ecotypes. Behav. Ecol. Sociobiol. 65, 1377–1387. https://doi.org/10.1007/s00265-011-1148-8 (2011).Article 

Google Scholar 
33.Deecke, V. B., Ford, J. K. B. & Slater, P. J. B. The vocal behaviour of mammal-eating killer whales: Communicating with costly calls. Anim. Behav. 69, 395–405. https://doi.org/10.1016/j.anbehav.2004.04.014 (2005).Article 

Google Scholar 
34.Saulitis, E. L., Matkin, C. O. & Fay, F. H. Vocal repertoire and acoustic behavior of the isolated AT1 killer whale subpopulation in southern Alaska. Can. J. Zool. 83, 1015–1029. https://doi.org/10.1139/Z05-089 (2005).Article 

Google Scholar 
35.Deecke, V., Slater, P. & Ford, J. Selective habituation shapes acoustic predator recognition in harbour seals. Nature 420, 171–173. https://doi.org/10.1038/nature01030 (2002).CAS 
Article 
PubMed 
ADS 

Google Scholar 
36.Foote, A. & Nystuen, J. Variation in call pitch among killer whale ecotypes. J. Acoust. Soc. Am. 123(3), 1747. https://doi.org/10.1121/1.2836752 (2008).Article 
PubMed 
ADS 

Google Scholar 
37.Yurk, H., Barrett-Lennard, L. G., Ford, J. K. B. & Matkin, C. O. Cultural transmission within maternal lineages: Vocal clans in resident killer whales in southern Alaska. Anim. Behav. 63(6), 1103–1119. https://doi.org/10.1006/anbe.2002.3012 (2002).Article 

Google Scholar 
38.Filatova, O., Fedutin, I. D., Burdin, A. & Hoyt, E. The structure of the discrete call repertoire of killer whales (Orcinus orca) from southeast Kamchatka. Bioacoustics 16, 261–280. https://doi.org/10.1080/09524622.2007.9753581 (2007).Article 

Google Scholar 
39.Deecke, V. B., Ford, J. K. B. & Spong, P. Quantifying complex patterns of bioacoustic variation: Use of a neural network to compare killer whale (Orcinus orca) dialects. J. Acoust. Soc. Am. 105, 2499. https://doi.org/10.1121/1.426853 (1999).CAS 
Article 
PubMed 
ADS 

Google Scholar 
40.Matkin, C., Barrett-Lennard, L., Yurk, H., Ellifrit, D. & Trites, A. Ecotypic variation and predatory behaviour among killer whales (Orcinus orca) off the eastern Aleutian Islands, Alaska. Fish. Bull. 105, 74–87 (2007).
Google Scholar 
41.Filatova, O. A. et al. Call diversity in the North Pacific killer whale populations: Implications for dialect evolution and population history. Anim. Behav. 83(3), 595–603. https://doi.org/10.1016/j.anbehav.2011.12.013 (2012).Article 

Google Scholar 
42.Danishevskaya, A. Y. et al. Crowd intelligence can discern between repertoires of killer whale ecotypes. Bioacoustics 29(3), 1–13. https://doi.org/10.1080/09524622.2018.1538902 (2018).Article 

Google Scholar 
43.Yurk, H. Vocal culture and social stability in resident killer whales (Orcinus orca) of the northeastern Pacific. PhD Thesis, University of British Columbia (2005).44.Matkin, C. O., Testa, J., Ellis, G. & Saulitis, E. Life history and population dynamics of southern Alaska resident killer whales. Mar. Mamm. Sci. 30(2), 460–469. https://doi.org/10.1111/mms.12049 (2014).Article 

Google Scholar 
45.Matkin, C. O., Olsen, D., Ellis, G., Ylitalo, G. & Andrews, R. Long-Term Killer Whale Monitoring in Prince William Sound/Kenai Fjords. Exxon Valdez Oil Spill Trustee Council Project 16120114-M Final Report (2018). https://www.arlis.org/docs/vol1/EVOS/2018/16120114-M.pdf.46.Matkin, C. O., Matkin, D. R., Ellis, G. M., Saulitis, E. & McSweeney, D. Movements of resident killer whales in southeastern Alaska and Prince William Sound, Alaska. Mar. Mamm. Sci. 13(3), 469–475. https://doi.org/10.1111/j.1748-7692.1997.tb00653.x (1997).Article 

Google Scholar 
47.Parsons, K. M. et al. Geographic patterns of genetic differentiation among killer whales in the northern North Pacific. J. Hered. 104(6), 737–754. https://doi.org/10.1093/jhered/est037 (2013).Article 
PubMed 

Google Scholar 
48.Olsen, D. W., Matkin, C. O., Andrews, R. D. & Atkinson, S. Seasonal and pod-specific differences in core use areas by resident killer whales in the Northern Gulf of Alaska. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 196–202. https://doi.org/10.1016/j.dsr2.2017.10.009 (2018).Article 
ADS 

Google Scholar 
49.Matkin, C. O. et al. Contrasting abundance and residency patterns of two sympatric populations of transient killer whales (Orcinus orca) in the northern Gulf of Alaska. Fish. Bull. 110(2), 143–155 (2012).
Google Scholar 
50.Matkin, C., Saulitis, E., Ellis, G., Olesiuk, P. & Rice, S. Ongoing population-level impacts on killer whales (Orcinus orca) following the Exxon Valdez oil spill in Prince William Sound, Alaska. Mar. Ecol. Prog. Ser. 356(1983), 269–281. https://doi.org/10.3354/meps07273 (2008).Article 
ADS 

Google Scholar 
51.Scheel, D., Matkin, C. O. & Saulitis, E. Distribution of killer whale pods in Prince William Sound, Alaska 1984–1996. Mar. Mamm. Sci. 17(3), 555–569. https://doi.org/10.1111/j.1748-7692.2001.tb01004.x (2001).Article 

Google Scholar 
52.Matkin, C. O. et al. Monitoring, tagging, feeding habits, and restoration of killer whales in Prince William Sound/Kenai Fjords 2010–2012. Exxon Valdez Oil Spill Trustee Council Project 16120114-M Final Report (2013). https://evostc.state.ak.us/media/2520/2010-10100742-final.pdf.53.Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333(6040), 301–306. https://doi.org/10.1126/science.1205106 (2011).CAS 
Article 
PubMed 
ADS 

Google Scholar 
54.Albouy, C. et al. Global vulnerability of marine mammals to global warming. Sci. Rep. 10, 548. https://doi.org/10.1038/s41598-019-57280-3 (2020).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
55.Suryan, R. M. et al. Ecosystem response persists after a prolonged marine heatwave. Sci. Rep. 11, 6235. https://doi.org/10.1038/s41598-021-83818-5 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Williams, R., Lusseau, D. & Hammond, P. S. The role of social aggregations and protected areas in killer whale conservation: The mixed blessing of critical habitat. Biol. Conserv. 142(4), 709–719. https://doi.org/10.1016/j.biocon.2008.12.004 (2009).Article 

Google Scholar 
57.Davis, G. E. et al. Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014. Sci. Rep. 7, 13460. https://doi.org/10.1038/s41598-017-13359-3 (2017).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
58.Romagosa, M. et al. Baleen whale acoustic presence and behaviour at a Mid-Atlantic migratory habitat, the Azores Archipelago. Sci. Rep. 10, 4766. https://doi.org/10.1038/s41598-020-61849-8 (2020).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
59.Frasier, K. E. et al. Cetacean distribution models based on visual and passive acoustic data. Sci. Rep. 11, 8240. https://doi.org/10.1038/s41598-021-87577-1 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Burham, R. E., Palm, R. S., Duffus, D. A., Mouy, X. & Riera, A. The combined use of visual and acoustic data collection techniques for winter killer whale (Orcinus orca) observations. Glob. Ecol. Conserv. 8, 24–30. https://doi.org/10.1016/j.gecco.2016.08.001 (2016).Article 

Google Scholar 
61.Rice, A. et al. Spatial and temporal occurrence of killer whale ecotypes off the outer coast of Washington State, USA. Mar. Ecol. Prog. Ser. 572, 255–268. https://doi.org/10.3354/meps12158 (2017).Article 
ADS 

Google Scholar 
62.Riera, A., Pilkington, J. F., Ford, J. K. B., Stredulinsky, E. H. & Chapman, N. R. Passive acoustic monitoring off Vancouver Island reveals extensive use by at-risk Resident killer whale (Orcinus orca) populations, Endanger. Species Res. 39, 221–234. https://doi.org/10.3354/esr00966 (2019).Article 

Google Scholar 
63.Rice, A. et al. Cetacean occurrence in the Gulf of Alaska from long-term passive acoustic monitoring. Mar. Biol. 168, 72. https://doi.org/10.1007/s00227-021-03884-1 (2021).Article 

Google Scholar 
64.Dahlheim, M. E. & Matkin, C. O. Assessment of injuries to Prince William Sound killer whales. In Marine mammals and the ‘Exxon Valdez’ (ed. Loughlin, T. R.) 163–172 (Academic Press, 1994).Chapter 

Google Scholar 
65.Ford, M. J. et al. Estimation of a killer whale (Orcinus orca) population’s diet using sequencing analysis of DNA from feces. PLoS ONE 11(1), e0144956. https://doi.org/10.1371/journal.pone.0144956 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Hanson, M. B. et al. Endangered predators and endangered prey: Seasonal diet of Southern Resident killer whales. PLoS ONE 16(3), e0247031. https://doi.org/10.1371/journal.pone.0247031 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Bishop, M. A. & Eiler, J. H. Migration patterns of post-spawning Pacific herring in a subarctic sound. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 108–115. https://doi.org/10.1016/j.dsr2.2017.04.016 (2018).Article 
ADS 

Google Scholar 
68.Larson, W. A. et al. Single-nucleotide polymorphisms reveal distribution and migration of Chinook salmon (Oncorhynchus tshawytscha) in the Bering Sea and North Pacific Ocean. Can. J. Fish. Aquat. Sci. 70(1), 128–141. https://doi.org/10.1139/cjfas-2012-0233 (2013).CAS 
Article 

Google Scholar 
69.Wright, B. M. et al. Fine-scale foraging movements by fish-eating killer whales (Orcinus orca) relate to the vertical distributions and escape responses of salmonid prey (Oncorhynchus spp.). Mov. Ecol. 5, 3. https://doi.org/10.1186/s40462-017-0094-0 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Olsen, D. W., Matkin, C. O., Mueter, F. J. & Atkinson, S. Social behavior increases in multipod aggregations of southern Alaska resident killer whales (Orcinus orca). Mar. Mamm. Sci. 36, 1150–1159. https://doi.org/10.1111/mms.12715 (2020).CAS 
Article 

Google Scholar 
71.McKinstry, C. A. E. & Campbell, R. W. Seasonal variation of zooplankton abundance and community structure in Prince William Sound, Alaska, 2009–2016. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 69–78. https://doi.org/10.1016/j.dsr2.2017.08.016 (2018).Article 
ADS 

Google Scholar 
72.Thorne, R. E. Trends in adult and juvenile herring distribution and abundance in Prince William Sound. Exxon Valdez Oil Spill Restoration Project 070830 Final Report (2010).73.Hanson, M. B. et al. Species and stock identification of prey consumed by endangered southern resident killer whales in their summer range. End. Spec. Res. 11(1), 69–82. https://doi.org/10.3354/esr00263 (2010).MathSciNet 
Article 
ADS 

Google Scholar 
74.Filatova, O. A. et al. The function of multi-pod aggregations of fish-eating killer whales (Orcinus orca) in Kamchatka, Far East Russia. J. Ethol. 27, 333. https://doi.org/10.1007/s10164-008-0124-x (2009).Article 

Google Scholar 
75.Yurk, H., Filatova, O., Matkin, C. O., Barrett-Lennard, L. G. & Brittain, M. Sequential habitat use by two resident killer whale (Orcinus orca) clans in Resurrection Bay, Alaska, as determined by remote acoustic monitoring. Aquat. Mamm. 36(1), 67–78. https://doi.org/10.1578/AM.36.1.2010.67 (2010).Article 

Google Scholar 
76.Maniscalco, J. M., Matkin, C. O., Maldini, D., Calkins, D. G. & Atkinson, S. Assessing killer whale predation on Steller sea lions from field observations in Kenai Fjords, Alaska. Mar. Mamm. Sci. 23(2), 306–321. https://doi.org/10.1111/j.1748-7692.2007.00103.x (2007).Article 

Google Scholar 
77.Brown, E. D., Wang, J., Vaughan, S. L., & Norcross, B. L. Identifying seasonal spatial scale for the ecological analysis of herring and other forage fish in Prince William Sound, Alaska. Ecosystem Approaches for Fisheries Management, Alaska Sea Grant College Program AK-SG-99-01, 499–510 (1999). https://seagrant.uaf.edu/lib/aksg/9901/AK-SG-99-01-g.pdf.78.Moran, J. R., O’Dell, M. B., Arimitsu, M. L., Straley, J. M. & Dickson, D. M. S. Seasonal distribution of Dall’s porpoise in Prince William Sound, Alaska. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 164–172. https://doi.org/10.1016/j.dsr2.2017.11.002 (2018).Article 
ADS 

Google Scholar 
79.Frost, K. J., Lowry, L. F. & Ver Hoef, J. M. Monitoring the trend of harbor seals in Prince William Sound, Alaska, after the Exxon Valdez Oil Spill. Mar. Mamm. Sci. 15(2), 494–506. https://doi.org/10.1111/j.1748-7692.1999.tb00815.x (1999).Article 

Google Scholar 
80.Lowry, L. F., Frost, K. J., Ver Hoef, J. M. & Delong, R. A. Movements of satellite-tagged subadult and adult harbor seals in Prince William Sound, Alaska. Mar. Mamm. Sci. 17(4), 835–861. https://doi.org/10.1111/j.1748-7692.2001.tb01301.x (2001).Article 

Google Scholar 
81.Riera, A., Ford, J. K. & Ross Chapman, N. Effects of different analysis techniques and recording duty cycles on passive acoustic monitoring of killer whales. J. Acoust. Soc. Am. 134(3), 2393–2404. https://doi.org/10.1121/1.4816552 (2013).Article 
PubMed 
ADS 

Google Scholar 
82.Miller, P. J. O. Diversity in sound pressure levels and estimated active space of resident killer whale vocalizations. J. Comp. Physiol. 192, 449. https://doi.org/10.1007/s00359-005-0085-2 (2006).Article 

Google Scholar 
83.Holt, M. M., Noren, D., Veirs, V., Emmons, C. & Veirs, S. R. Speaking up: Killer whales (Orcinus orca) increase their call amplitude in response to vessel noise. J. Acoust. Soc. Am. 125, 27–32. https://doi.org/10.1121/1.3040028 (2009).Article 
ADS 

Google Scholar 
84.Veirs, S., Veirs, V. & Wood, J. D. Ship noise extends to frequencies used for echolocation by endangered killer whales. PeerJ 4, e1657. https://doi.org/10.7717/peerj.1657 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
85.Joy, R. et al. Potential benefits of vessel slowdowns on endangered southern resident killer whales. Front. Mar. Sci. 6, 344. https://doi.org/10.3389/fmars.2019.00344 (2019).Article 

Google Scholar 
86.Williams, R., Veirs, S., Veirs, V., Ashe, E. & Mastick, N. Approaches to reduce noise from ships operating in important killer whale habitats. Mar. Pollut. Bull. 139, 459–469. https://doi.org/10.1016/j.marpolbul.2018.05.015 (2019).CAS 
Article 
PubMed 

Google Scholar 
87.Esler, D. et al. Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 36–42. https://doi.org/10.1016/j.dsr2.2017.04.007 (2018).Article 
ADS 

Google Scholar 
88.Buckman, A. H. et al. PCB-associated changes in mRNA expression in killer whales (Orcinus orca) from the NE Pacific Ocean. Environ. Sci. Technol. 45(23), 10194–10202. https://doi.org/10.1021/es201541j (2011).CAS 
Article 
PubMed 
ADS 

Google Scholar 
89.Lawson, T. M. et al. Concentrations and profiles of organochlorine contaminants in North Pacific resident and transient killer whale (Orcinus orca) populations. Sci. Total. Environ. 722, 137776. https://doi.org/10.1016/j.scitotenv.2020.137776 (2020).CAS 
Article 
PubMed 
ADS 

Google Scholar 
90.DeMarban, A. Oily water leaks into Port Valdez from pipeline terminal, Alyeska says. Anchorage Daily News, Apr. 14, 2020. https://www.adn.com/business-economy/energy/2020/04/14/oily-water-spills-into-port-valdez-from-pipeline-terminal-alyeska-says/.91.Gillespie, D. et al. PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localisation of cetaceans. Proc. Inst. Acoust. 30, 67–75 (2008).
Google Scholar 
92.Zhong, M. et al. Detecting, classifying, and counting blue whale calls with Siamese neural networks. J. Acoust. Soc. Am. 149, 3086. https://doi.org/10.1121/10.0004828 (2021).Article 
PubMed 
ADS 

Google Scholar 
93.Audacity Team. Audacity®: Free Audio Editor and Recorder [Computer application]. Version 2.3.2 (2019). https://audacityteam.org/.94.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
Google Scholar 
95.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
96.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).97.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.4-10. (2021). https://CRAN.R-project.org/package=raster98.GEBCO Compilation Group. GEBCO 2020 Grid. (2020). https://doi.org/10.5285/a29c5465-b138-234d-e053-6c86abc040b9. More

AffiliationsEarth, Atmospheric, and Planetary Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USAGabriela Serrato MarksSchool of Science, Technology, Accessibility, Mathematics and Public Health, Gallaudet University, Washington DC, USACaroline SolomonScience, Technology & Society Department, Rochester Institute of Technology, Rochester, NY, USAKaitlin Stack WhitneyAuthorsGabriela Serrato MarksCaroline SolomonKaitlin Stack WhitneyCorresponding authorsCorrespondence to
Gabriela Serrato Marks, Caroline Solomon or Kaitlin Stack Whitney. More

Our approach provides data on species richness across independent gradients of land-use intensity and climate. Furthermore, by combining Malaise traps and DNA-metabarcoding, our work is not limited to single factors such as biomass measurements or assessment of single taxa to reveal drivers of insect communities. We found the lowest species richness in arable fields embedded in agricultural landscapes, and the lowest biomass in settlements embedded in urban landscapes. The effects of land-use type were independent of those of local temperatures and climate. Biomass and richness measures differed according to land-use intensity. Our study recorded a difference in insect biomass of 42% from semi-natural to urban environments, but no difference from semi-natural to agricultural environments. This appears to be in contrast with the results documented in a similar analysis6, which showed a temporal decline in insect biomass of >75% in small, protected areas surrounded by an agricultural landscape. Interestingly, in Hallmann et al.6, the few plots in semi-natural landscapes also showed a similar temporal decline as those in agricultural landscapes (Supplementary Fig. 3b). On the other hand, the variation in total BIN richness matched the magnitude of the temporal decline (~35%) determined over a decade in grasslands and forests by Seibold et al.13The hump-shaped seasonal pattern of biomass and associated daily biomass values were in accordance with the time series of Hallmann et al.6, thus demonstrating the comparability of our space-for-time approach with approaches based on time series (Supplementary Fig. 3). However, the contrasting phenological patterns of biomass and total BIN richness after controlling for temperature are evidence that both facets of biodiversity might respond differently, with biomass more strongly driven by pure season, e.g. via plant phenology or day-length, and BIN richness more dependent on local temperature. Divergent responses of biomass variation and species richness have already been described in temporal studies of insects in freshwater systems27 and nocturnal moths in the United Kingdom19,28,29, but not in studies of terrestrial arthropods, including those recorded in comprehensive datasets of hyper-diverse orders such as Diptera and the Hymenoptera.The positive relationships between local temperature and biomass variation and BIN richness were consistent with earlier results6,20 and can be explained (1) by the higher activity of species at higher temperatures, which increases the likelihood of trapping30 and (2) by the fact that insects are ectothermic organisms, i.e., their metabolism is enhanced by increasing temperatures, which in turn can lead to higher reproduction and survival rates and thus to larger populations31. Our additional analyses on the negative effects occurring at the highest temperatures did not provide any such indications for our three measures. Moreover, insects, and in particular many endangered insect species in Central Europe, are thermophilic32, which would explain the observed response of total BIN richness, and especially the very steep response of the richness of red-listed species, to local temperature.Despite the positive or neutral effect of macroclimate and the consistently positive effect of local temperature on insect biomass and BIN richness, global warming can cause shifts in insect communities that threaten biodiversity in specific biomes or elevations7,8,33, by a mismatch between host plant and insect phenology34,35 or by the trait-specific responses of species to climate variations, as shown for butterflies in California33. Nevertheless, the responses of insect populations and insect diversity to climate change are poorly understood, such that clear patterns, with distinct winners and losers, can still not be discerned33. In addition, insect responses to climate change are geographically variable and likely to be disproportionally higher at higher latitudes and elevations or in hot tropical or Mediterranean areas33. However, it is precisely the large topographic variation of mountains that may offer climate pockets that act as refugia, thus allowing insects to survive during periods of extreme climatic conditions or climate variation33,36. Our study supports this possibility, by showing that the responses of total insect richness, the richness of red-listed species, and biomass to higher local temperatures in a cultivated landscape in Central Europe (mean annual temperature of ~5 to 10 °C and annual precipitation between 550 and 2000 mm) are consistently positive. A further rise in temperature, as expected in the near future, poses a high risk of pushing more insect species in our study area to their thermal limits and even to extinction37.The clear biomass patterns which we show indicate a continuous change of biomass from forests to arable fields and further to settlements, of total BIN richness from forests to arable fields, and of red-listed species richness from forests to meadows and arable fields. This underlines the importance of forests as a backbone of insect diversity in cultivated landscapes, and particularly of forest gaps, which are rich in species within forests13,38. Our study is the first to our knowledge to directly compare forests (and forest gaps) with agricultural and urban habitats. Comparable studies using standardized insect sampling across a broad range of land-use types are rare, but data on the biomass of moths obtained by light trapping in different habitats over many decades19 are consistent with our findings and indicate a general pattern that is independent of the sampling method. At the landscape scale, we found biomass was highest in agricultural landscapes and lowest in urban landscapes, whereas red-listed richness was highest in semi-natural landscapes, followed by urban landscapes and lowest in agricultural landscapes. Although we could not confirm the negative effects of agricultural landscapes on biomass, as described by Hallmann et al.6, our results are in line with those of Seibold et al.13, who reported negative effects of surrounding arable fields on the temporal trends in grasslands in terms of species richness but not insect biomass.The contrasting pure seasonal patterns of biomass variation and BIN richness, as well as their different responses to land use, may have methodological or biological causes. A possible methodological reason for the low partial effects of season on BIN richness during summer but high partial effects on total biomass is that high insect biomass occurs particularly during periods of high temperatures, which would have increased evaporation of the ethanol used for preservation, accelerating the degradation of DNA. Similar effects were shown for samples stored over long periods39 of time. However, in our study, the collection bottles contained sufficient amounts of ethanol such that a methodological effect due to ethanol evaporation was unlikely. Moreover, high temperatures and not the pure seasonal effect better explained the higher BIN richness in this study. A second methodological reason for the lower BIN richness is that small species are often “overlooked” in biomass-rich samples40,41,42. To avoid this problem, we divided each sample into two fractions (small and large species) and sequenced them separately. With the exclusion of these methodological reasons, the most likely explanation for our findings is a biological one related to the composition of the samples. An increase in large species in certain habitats or at a certain time of year could influence biomass but not necessarily the total number of species. However, our additional models of total biomass using the BIN richness of the most important taxonomic orders as predictors provided an important clue. Across all habitats, biomass variation was best explained by the increase in BIN richness of three species groups, Orthoptera, Lepidoptera, and Diptera. Of the diverse taxa Coleoptera, Hymenoptera, and Diptera, only the BIN richness of the latter positively affected total biomass, and it was principally the richness of the two groups with many large species (Orthoptera and Lepidoptera) driving the pure seasonal effect. This can be explained by the fact that Lepidoptera abundance peaks in July43, thus coinciding with the higher abundances of most species of hemimetabolous Orthoptera during the summer44, and therefore accounting for the purely seasonal peak of insect biomass in summer.The contrasting responses of biomass variation and BIN richness point to differences in the respective mechanisms. Insect biomass is positively related to productivity and is thus highest in agricultural landscapes and in forests habitats embedded in agricultural landscapes managed to maximize plant productivity and continuous plant biomass45,46. Insect biomass is lowest in urban environments, where productivity is limited due to a high percentage of sealed areas without vegetation. However, insect biomass along gradients of urbanization has been poorly investigated47 such that large differences in the negative effects of urbanization on the abundances of different taxonomic groups cannot be ruled out48. Moreover, urban areas include additional potential stressors, such as light pollution, that might also negatively affect insect biomass49. In contrast to biomass, the richness of all taxa and of threatened species was relatively high in urban habitats. This was especially the case for urban habitats embedded in semi-natural landscapes, although a similar species richness may occur through the interplay of semi-natural habitats with green spaces characterized by a highly variable design and management50 as well as with the natural but also anthropogenically enhanced plant diversity of urban areas47,51,52.The lowest BIN richness generally observed in our study, in arable fields embedded in agricultural landscapes, is consistent with the results of a recent meta-analysis of insect time series9. In that study, the temporal declines in insect populations of terrestrial invertebrates were largest in regions with generally high agricultural land-use intensity, such as Central Europe and the American Midwest. Our direct comparison of different land-use types independent of gradients of macro- and microclimate suggests that the strong declines in insect richness reported for several taxa5 are indeed driven by intensive agriculture and the associated homogenization of the landscape53, not by urban environments. To assess the significance of our two main results on biomass and species richness, however, it is necessary to consider the proportions of the land-use types in question. In our study, agricultural land comprised 48% of the area whereas settlements accounted for ~12%. Since habitat amount is a fundamental parameter for insect populations, it must also be taken into account in a country-wide strategy11.Our finding of a lack of significant interactions between the highly significant local temperature and land use contrasts in part with the previously reported strong effects resulting from the interaction between land use and climate along the elevational gradient of the Kilimanjaro. That finding implied that land-use effects are mediated by climate, especially at high elevations17. Interaction effects between land use and climate may thus occur mainly within more extreme climates54 rather than within the temperate climate exemplified by our study region. By considering macroclimate and the directly measured local temperature and humidity as well as land use, we were able to show that pure land-use effects, when evaluated as habitat effects controlled for local temperature and humidity, strongly influence insect populations. However, despite the increasing awareness among scientists and urban planners that land use at local and landscape scales impacts not only insects but also local climate, the implications have mostly been ignored in international climate negotiations55. Trees, with the reduced local temperatures offered by their canopy layer56 and their hosting of a high species richness of insects, as shown in our study, are thus of particular importance as refuges for insect diversity in temperate zones.By covering the full range of land-use intensities along the climate gradient of a typical cultivated region and measuring both insect biomass and total insect richness, our study’s methodology provided mechanistic insights into the changes of insect populations in areas where a meta-analysis identified the most severe population declines9. Nevertheless, additional studies should focus on biomes other than the cultivated landscapes of the temperate zone, such as cold boreal, dry Mediterranean, or hot tropical areas. Here, the different characteristics of the biome may result in land-use intensification being of less importance than climate change. In addition, the use of metabarcoding to identify all insects within a sample broadens the range for similar space-for-time studies. In contrast to well replicated, standardized time-series data that may require decades to generate the information needed to guide conservation actions, space-for-time approaches covering full gradients of land use and climate are a viable option to identify the drivers of insect decline and thus provide timely information for decision-makers; however, replications from several years should be included to take into account the effects of extreme events.The weak effect of climate variables on insect biomass but the consistently positive effect of local temperature on biomass variation and BIN richness suggests that, at least within the climate range of our temperate study region, the recent warming that has led to higher local temperatures should promote insect biomass and species richness. However, further warming, extreme heat, and drought events may negatively affect biodiversity, although non-linear responses can be expected in other climates or across longer gradients. Moreover, the strong dependency of local temperature on land use indicates that changes in land use impact local climate conditions, such as by accelerating temperature increases in agricultural and urban regions. The contrasting responses of biomass variation and BIN richness to local and landscape-scale land use point to differential effects of shifts in land use on insect populations, with ongoing urbanization leading to a decline in biomass, and conversion to agriculture to a decline in species richness. Based on our results, we recommend that actions aimed at preventing further insect decline should focus on (1) increasing insect biomass, for example by improving “green” habitats in urban environments57 and reducing the extent of vegetation-free sealed surfaces and (2) stopping the ongoing loss of species, by adapting agri-environmental schemes and promoting habitats dominated by trees, even in urban environments. More

Assume n countries ((nge 2)) are located in a transboundary river basin and they are the players of an evolutionary game in which the countries’ strategies concerning water sharing in the basin evolve over time. Each country can choose between a cooperative strategy or a non-cooperative strategy. The game’s interactions and players’ payoffs vary with the number n and the location of the countries within the river basin, specifically, in relation to whether they are upstream-located or downstream-located countries within the river basin. The probability of country i choosing a cooperative strategy is herein denoted by ({x}_{1}^{(i)}), (i=1, 2, dots , n), and there are ({2}^{n}) payoff sets for all the combinations of the countries’ strategies. This paper assesses the interactions between three countries sharing a transboundary river basin.Problem descriptionLet 1, 2, and 3 denote three countries sharing a transboundary river basin. Country 1 is upstream and countries 2 and 3 are located downstream. Country 1 can use maximum amount of the water of the river and choose not to share it with the downstream countries. This strategy, however, may trigger conflict with the two other countries of political, social, economic, security, and environmental natures. Instead, Country 1 can release excess water to be shared by Countries 2 and 3. Countries 2 and 3 are inclined to cooperate with Country 1 unless other benefits emerge by being non-cooperative with Country 1.There are two types of benefits and one type of cost in the payoff matrix of the assumed problem that are economic in nature. The first is a water benefit earned by a country from receiving the water from the transboundary river. The set of benefits related to water use includes economic benefits earned from agricultural, urban, and industrial development benefits. It should be noted that the water benefit for Country 1 means the economic benefit of consuming more water than its water right from the river. So, water benefits of Country 2 and 3 are the economic benefit of consuming excess water of upstream which is released by Country 1.The second is a potential benefit earned from the cooperative strategy of a country. Cooperation benefits stem from sustainability conditions like social interests, environmental benefits and political conjunctures such as international alliances and harmony from amicable interactions with neighboring countries. The parameters F and E (water benefit and potential benefit, respectively) encompass a number of benefit parameters; nevertheless, parameters were simplified to two benefit parameters to simplify the complexity of the water-sharing problem. Costs forced on other countries from non-cooperation by a country involves commercial, security, political, diplomatic, military, and environmental costs. Figure 1 displays the locations of three countries and their shifting interactions in a transboundary river basin.Figure 1Schematic of the transboundary river and riparian countries with their shifting interactions.Full size imageBasic assumptionsThe evolutionary game model of interactions between riparian countries in the transboundary river basin rests on the following assumptions:
Assumption 1

There are three countries (i.e., players) in the game of transboundary water sharing, each seeking to maximize its payoff from the game.

Assumption 2

Country 1 has two possible strategies. One is for Country 1 to release a specified amount of water to the downstream countries (this would be Country 1’s cooperative strategy). The cooperative strategy by Country 1 would produce benefits F2 and F3 to Countries 2 and 3, respectively. By being cooperative Country 1 would attain a benefit E1 called the potential benefit from cooperative responses from the downstream countries. The other strategy is for Country 1 to deny water to the downstream countries (this would be Country 1’s non-cooperative strategy), in which case Country 1 would earn the water benefit F1 from using water that would otherwise be released, but would forego the potential benefit E1. Moreover, by pursuing a non-cooperative strategy Country 1 would inflict a cost C1m to the downstream countries.

Assumption 3

There are two possible strategies for Country 2. One is for Country 2 to accept the behavior of Country 1 (this would be Country 2’s cooperative strategy), which would cause earning a potential benefit E2 to Country 2. Recall that if Country 2 acquiesces to Country 1’s cooperative behavior it would receive a benefit F2. Or, Country 2 may disagree with Country 1 (this would be Country 2’s non-cooperative strategy), in which case, Country 2 would lose benefit E2, and it would inflict a cost C2m to the other countries.

Assumption 4

Similar to Country 2, Country 3 has two possible strategies. One is for Country 3 to agree Country 1’s behavior (this would be Country 3’s cooperative strategy) attaining a potential benefit E3. Recall that if Country 3 agrees with Country 1’s cooperative behavior it would gain a benefit F3. Another strategy for Country 3 is to oppose Country 1 (this would be Country 3’s non-cooperative strategy) missing the benefit E3 and forcing a cost C3m to the other countries.
Table 1 defines the benefits and costs that enter in the transboundary water-sharing game described in this work. The payoff to country (i=mathrm{1,2},3) depends on its own strategy and on the strategies of the other countries, and each country may choose to be cooperative or non-cooperative. The strategies of country (i) are denoted by 1 (cooperation) and 2 (non-cooperation). The probabilities of country (i)’s strategies are denoted by ({x}_{1}^{(i)}) and by ({x}_{2}^{(i)}), in which the former represents cooperation and the latter represents non-cooperation. Clearly, ({x}_{1}^{(i)})+ ({x}_{2}^{(i)}) = 1. The payoff to country (i=1, 2, 3) when the strategies of Countries 1, 2, 3 are (j, k,l), respectively, where (j, k,l) may take the value 1 (cooperation) or 2 (non-cooperation) is denoted by ({U}_{jkl}^{left(iright)}). Thus, for instance, the payoff to country (i=2) is represented by ({U}_{212}^{(2)}) when Countries 1 and 3 are non-cooperative and Country 2’s strategy is cooperative. Evidently, there are 23 payoffs to each country given there are three countries involved and each can be cooperative or non-cooperative. Table 2 shows the symbols for the payoffs that accrue to each country under the probable strategies.Table 1 Benefits and costs.Full size tableTable 2 Payoff matrix under cooperation or non-cooperation.Full size tableFormulation of the transboundary water-sharing strategies as an evolutionary gameThe expected payoff to country (i) is expressed by the following equation:$${U}^{(i)}=sumlimits_{j = 1}^2 {sumlimits_{k = 1}^2 {sumlimits_{l = 1}^2} } {x}_{j}^{(1)}{x}_{k}^{(2)}{x}_{l}^{(3)} {U}_{jkl}^{(i)} quad i=1, 2, 3$$
(1)
The following describe the expected payoffs of Country 1 when it acts cooperatively (({U}_{1}^{(1)})) or non-cooperatively (({U}_{2}^{(1)})):$${U}_{1}^{(1)}={x}_{1}^{(2)}{x}_{1}^{(3)}{U}_{111}^{left(1right)}+{x}_{1}^{(2)}{x}_{2}^{(3)}{U}_{112}^{left(1right)}+{x}_{2}^{(2)}{x}_{1}^{(3)}{U}_{121}^{(1)}+{x}_{2}^{(2)}{x}_{2}^{(3)}{U}_{122}^{(1)}$$
(2)
$${U}_{2}^{(1)}={x}_{1}^{(2)}{x}_{1}^{(3)}{U}_{211}^{left(1right)}+{x}_{1}^{(2)}{x}_{2}^{(3)}{U}_{212}^{left(1right)}+{x}_{2}^{(2)}{x}_{1}^{(3)}{U}_{221}^{left(1right)}+{x}_{2}^{(2)}{x}_{2}^{(3)}{U}_{222}^{left(1right)}$$
(3)
Therefore, the expected payoff of Country 1 is ({U}^{(1)}) which is equal to:$${U}^{(1)}={x}_{1}^{(1)}{U}_{1}^{(1)}+{x}_{2}^{(1)}{U}_{2}^{(1)}= sumlimits_{j = 1}^2 {sumlimits_{k = 1}^2 {sumlimits_{l = 1}^2 } }{x}_{j}^{(1)}{x}_{k}^{(2)}{x}_{l}^{(3)} {U}_{jkl}^{(1)}$$
(4)
The expected payoffs of Countries 2 and 3 can be similarly obtained as done for Country 1. The cooperative and non-cooperative expected payoffs of all countries can be expressed in terms of the payoffs listed in Table 1. The results are found in Appendix A.Replication dynamics equationsThe replication dynamics equations describe the time change of the probabilities of a player’s strategies. The replication dynamics equation of Countries (i) is denoted by ({G}^{(i)}left({x}_{1}^{(i)}right)) which is as follow22:$${G}^{(i)}left({x}_{1}^{(i)}right)=frac{d{x}_{1}^{(i)}}{dt}={x}_{1}^{(i)}left({U}_{1}^{(i)}-{U}^{(i)}right)$$
(5)
The replication dynamics equations of Countries 1, 2 and 3 are presented in Appendix B according to the benefits and costs showed in Table 1.Stability analysis of a country’s strategiesUnder the assumption of bounded rationality each country does not know which strategies may lead to the optimal solution in the game. Therefore, the countries’ strategies change over time until a stable (i.e., time-independent) solution named evolutionary stable strategy (ESS) is attained. The evolutionary stable theorem for replication dynamics equation states that a stable probability of cooperation ({x}_{1}^{(i)}) for country (i) occurs if the following conditions hold25: (1) ({G}^{(i)}left({x}_{1}^{(i)}right)=0), and (2) (d{G}^{(i)}left({x}_{1}^{(i)}right)/d{x}_{1}^{(i)} More