Philippa Kaur

1.
Legras, J. L., Merdinoglu, D., Cornuet, J. M. & Karst, F. Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history. Mol. Ecol. 16, 2091–2102 (2007).
CAS  Article  Google Scholar 
2.
McGovern, P. E. et al. Fermented beverages of pre- and proto-historic China. Proc. Natl. Acad. Sci. U.S.A. 101, 17593–17598. https://doi.org/10.1073/pnas.0407921102 (2004).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

3.
Cavalieri, D., McGovern, P., Hartl, D., Mortimer, R. & Polsinelli, M. Evidence for S. cerevisiae fermentation in ancient wine. J. Mol. Evol. 57, S226–S232 (2003).
ADS  CAS  Article  Google Scholar 

4.
McGovern, P., Hartung, U., Badler, V., Glusker, D. & Exner, L. The beginnings of winemaking and viniculture in the ancient Near East and Egypt. Expedition 39, 3–21 (1997).
Google Scholar 

5.
Dudley, R. Ethanol, fruit ripening, and the historical origins of human alcoholism in primate frugivory. Integr. Comp. Biol. 44, 315–323 (2004).
CAS  Article  Google Scholar 

6.
Dudley, R. Fermenting fruit and the historical ecology of ethanol ingestion: is alcoholism in modern humans an evolutionary hangover?. Addiction 97, 381–388. https://doi.org/10.1046/j.1360-0443.2002.00002.x (2002).
Article  PubMed  Google Scholar 

7.
Carrigan, M. A. et al. Hominids adapted to metabolize ethanol long before human-directed fermentation. Proc. Natl. Acad. Sci. U.S.A. 112, 458–463. https://doi.org/10.1073/pnas.1404167111 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

8.
Alba-Lois, L. & Segal-Kischinevzky, C. Yeast fermentation and the making of beer and wine https://www.nature.com/scitable/topicpage/yeast-fermentation-and-the-making-of-beer-14372813 (2010).

9.
Malacarne, M., Martuzzi, F., Summer, A. & Mariani, P. Protein and fat composition of mare’s milk: some nutritional remarks with reference to human and cow’s milk. Int. Dairy J. 12, 869–877. https://doi.org/10.1016/S0958-6946(02)00120-6 (2002).
CAS  Article  Google Scholar 

10.
Brady, M. First Taste. How Indigenous Australians Learned About Grog (Alcohol Education and Rehabilitation Foundation Ltd, Canberra, 2008).
Google Scholar 

11.
Brady, M. & McGrath, V. Making Tuba in the Torres Strait islands: the cultural diffusion and geographic mobility of an alcoholic drink. J. Pac. Hist. 45, 315–330. https://doi.org/10.1080/00223344.2010.530811 (2010).
Article  PubMed  Google Scholar 

12.
Varela, C. The impact of non-Saccharomyces yeasts in the production of alcoholic beverages. Appl. Microbiol. Biotechnol. 100, 9861–9874. https://doi.org/10.1007/s00253-016-7941-6 (2016).
CAS  Article  PubMed  Google Scholar 

13.
Jolly, N. P., Varela, C. & Pretorius, I. S. Not your ordinary yeast: non-Saccharomyces yeasts in wine production uncovered. FEMS Yeast Res. 14, 215–237. https://doi.org/10.1111/1567-1364.12111 (2014).
CAS  Article  PubMed  Google Scholar 

14.
Steinkraus, K. H. Handbook of Indigenous Fermented Foods, Second Edition, Revised and Expanded (Marcel Dekker, New York, 1995).
Google Scholar 

15.
Tamang, J. P., Watanabe, K. & Holzapfel, W. H. Review: diversity of microorganisms in global fermented foods and beverages. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.00377 (2016).
Article  PubMed  PubMed Central  Google Scholar 

16.
Bahiru, B., Mehari, T. & Ashenafi, M. Yeast and lactic acid flora of tej, an indigenous Ethiopian honey wine: variations within and between production units. Food Microbiol. 23, 277–282. https://doi.org/10.1016/j.fm.2005.05.007 (2006).
CAS  Article  PubMed  Google Scholar 

17.
Vallejo, J. A. et al. Atypical yeasts identified as Saccharomyces cerevisiae by MALDI-TOF MS and gene sequencing are the main responsible of fermentation of chicha, a traditional beverage from Peru. Syst. Appl. Microbiol. 36, 560–564. https://doi.org/10.1016/j.syapm.2013.09.002 (2013).
CAS  Article  PubMed  Google Scholar 

18.
Puerari, C., Magalhães-Guedes, K. T. & Schwan, R. F. Physicochemical and microbiological characterization of chicha, a rice-based fermented beverage produced by Umutina Brazilian Amerindians. Food Microbiol. 46, 210–217. https://doi.org/10.1016/j.fm.2014.08.009 (2015).
CAS  Article  PubMed  Google Scholar 

19.
Escalante, A. et al. Characterization of bacterial diversity in Pulque, a traditional Mexican alcoholic fermented beverage, as determined by 16S rDNA analysis. FEMS Microbiol. Lett. 235, 273–279. https://doi.org/10.1016/j.femsle.2004.04.045 (2004).
CAS  Article  PubMed  Google Scholar 

20.
Lappe-Oliveras, P. et al. Yeasts associated with the production of Mexican alcoholic nondistilled and distilled Agave beverages. FEMS Yeast Res. 8, 1037–1052. https://doi.org/10.1111/j.1567-1364.2008.00430.x (2008).
CAS  Article  PubMed  Google Scholar 

21.
Jung, M. J., Nam, Y. D., Roh, S. W. & Bae, J. W. Unexpected convergence of fungal and bacterial communities during fermentation of traditional Korean alcoholic beverages inoculated with various natural starters. Food Microbiol. 30, 112–123. https://doi.org/10.1016/j.fm.2011.09.008 (2012).
Article  PubMed  Google Scholar 

22.
Greppi, A. et al. Determination of yeast diversity in ogi, mawe, gowe and tchoukoutou by using culture-dependent and -independent methods. Int. J. Food Microbiol. 165, 84–88. https://doi.org/10.1016/j.ijfoodmicro.2013.05.005 (2013).
CAS  Article  PubMed  Google Scholar 

23.
Spitaels, F. et al. The microbial diversity of traditional spontaneously fermented lambic beer. PLoS ONE https://doi.org/10.1371/journal.pone.0095384 (2014).
Article  PubMed  PubMed Central  Google Scholar 

24.
Tapsoba, F., Legras, J. L., Savadogo, A., Dequin, S. & Traore, A. S. Diversity of Saccharomyces cerevisiae strains isolated from Borassus akeassii palm wines from Burkina Faso in comparison to other African beverages. Int. J. Food Microbiol. 211, 128–133. https://doi.org/10.1016/j.ijfoodmicro.2015.07.010 (2015).
Article  PubMed  Google Scholar 

25.
Bokulich, N. A., Bamforth, C. W. & Mills, D. A. Brewhouse-resident microbiota are responsible for multi-stage fermentation of American coolship ale. PLoS ONE 7, e35507. https://doi.org/10.1371/journal.pone.0035507 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Bokulich, N. A., Thorngate, J. H., Richardson, P. M. & Mills, D. A. Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate. Proc. Natl. Acad. Sci. U.S.A. 111, E139–E148. https://doi.org/10.1073/pnas.1317377110 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

27.
Siren, K. et al. Taxonomic and functional characterization of the microbial community during spontaneous in vitro fermentation of Riesling must. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.00697 (2019).
Article  PubMed  PubMed Central  Google Scholar 

28.
Morgan, H. H., du Toit, M. & Setati, M. E. The grapevine and wine microbiome: insights from high-throughput amplicon sequencing. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.00820 (2017).
Article  PubMed  PubMed Central  Google Scholar 

29.
Williams, K. J. & Potts, B. M. The natural distribution of Eucalyptus species in Tasmania. Tasforests 8, 39–165 (1996).
Google Scholar 

30.
Calder, J. A. & Kirkpatrick, J. B. Climate change and other factors influencing the decline of the Tasmanian cider gum (Eucalyptus gunnii). Aust. J. Bot. 56, 684–692. https://doi.org/10.1071/BT08105 (2008).
Article  Google Scholar 

31.
Sanger, J. C., Davidson, N. J., O’Grady, A. P. & Close, D. C. Are the patterns of regeneration in the endangered Eucalyptus gunnii ssp. divaricata shifting in response to climate?. Austral. Ecol. 36, 612–620. https://doi.org/10.1111/j.1442-9993.2010.02194.x (2011).
Article  Google Scholar 

32.
Lahti, L. & Shetty, S. Tools for microbiome analysis in R. Version 1.9.1 https://microbiome.github.com/microbiome (2017).

33.
Morrison-Whittle, P. & Goddard, M. R. Quantifying the relative roles of selective and neutral processes in defining eukaryotic microbial communities. ISME J. 9, 2003–2011. https://doi.org/10.1038/ismej.2015.18 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Morrison-Whittle, P. & Goddard, M. R. From vineyard to winery: a source map of microbial diversity driving wine fermentation. Environ. Microbiol. 20, 75–84. https://doi.org/10.1111/1462-2920.13960 (2018).
Article  PubMed  Google Scholar 

35.
Brooker, M. I. H. A Key to Eucalypts in Britain and Ireland. (Forestry Commission Booklet 50: The Stationery Office, 1983).

36.
Forrest, M. & Moore, T. Eucalyptus gunnii: a possible source of bioenergy?. Biomass Bioenerg. 32, 978–980. https://doi.org/10.1016/j.biombioe.2008.01.010 (2008).
CAS  Article  Google Scholar 

37.
Guimarães, R. et al. Aromatic plants as a source of important phytochemicals: vitamins, sugars and fatty acids in Cistus ladanifer, Cupressus lusitanica and Eucalyptus gunnii leaves. Ind. Crop Prod. 30, 427–430. https://doi.org/10.1016/j.indcrop.2009.08.002 (2009).
CAS  Article  Google Scholar 

38.
Bugarin, D. et al. Essential oil of Eucalyptus gunnii hook. As a novel source of antioxidant, antimutagenic and antibacterial agents. Molecules 19, 19007–19020. https://doi.org/10.3390/molecules191119007 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Leborgne, N. et al. Introduction of specific carbohydrates into Eucalyptus gunnii cells increases their freezing tolerance. Eur. J. Biochem. 229, 710–717. https://doi.org/10.1111/j.1432-1033.1995.0710j.x (1995).
CAS  Article  PubMed  Google Scholar 

40.
Stuckel, J. G. & Low, N. H. The chemical composition of 80 pure maple syrup samples produced in North America. Food Res. Int. 29, 373–379. https://doi.org/10.1016/0963-9969(96)00000-2 (1996).
CAS  Article  Google Scholar 

41.
Taylor, M. W., Tsai, P., Anfang, N., Ross, H. A. & Goddard, M. R. Pyrosequencing reveals regional differences in fruit-associated fungal communities. Environ. Microbiol. 16, 2848–2858 (2014).
CAS  Article  Google Scholar 

42.
Pinto, C. et al. Wine fermentation microbiome: a landscape from different Portuguese wine appellations. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00905 (2015).
Article  PubMed  PubMed Central  Google Scholar 

43.
Miura, T., Sanchez, R., Castaneda, L. E., Godoy, K. & Barbosa, O. Is microbial terroir related to geographic distance between vineyards?. Environ. Microbiol. Rep. 9, 742–749. https://doi.org/10.1111/1758-2229.12589 (2017).
CAS  Article  PubMed  Google Scholar 

44.
Knight, S. J., Karon, O. & Goddard, M. R. Small scale fungal community differentiation in a vineyard system. Food Microbiol. https://doi.org/10.1016/j.fm.2019.103358 (2019).
Article  PubMed  Google Scholar 

45.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688. https://doi.org/10.1126/science.1256688 (2014).
CAS  Article  PubMed  Google Scholar 

46.
Lin, Y. T., Whitman, W. B., Coleman, D. C. & Chiu, C. Y. Effects of reforestation on the structure and diversity of bacterial communities in subtropical low mountain forest soils. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01968 (2018).
Article  PubMed  PubMed Central  Google Scholar 

47.
Grangeteau, C. et al. Wine microbiology is driven by vineyard and winery anthropogenic factors. Microb. Biotechnol. 10, 354–370. https://doi.org/10.1111/1751-7915.12428 (2017).
CAS  Article  PubMed  Google Scholar 

48.
Burns, K. N. et al. Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: differentiation by geographic features. Soil Biol. Biochem. 91, 232–247. https://doi.org/10.1016/j.soilbio.2015.09.002 (2015).
CAS  Article  Google Scholar 

49.
Portillo, M. D. C., Franquès, J., Araque, I., Reguant, C. & Bordons, A. Bacterial diversity of Grenache and Carignan grape surface from different vineyards at Priorat wine region (Catalonia, Spain). Int. J. Food Microbiol. 219, 56–63. https://doi.org/10.1016/j.ijfoodmicro.2015.12.002 (2016).
Article  Google Scholar 

50.
Castaneda, L. E. & Barbosa, O. Metagenomic analysis exploring taxonomic and functional diversity of soil microbial communities in Chilean vineyards and surrounding native forests. PeerJ 5, e3098. https://doi.org/10.7717/peerj.3098 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Setati, M. E., Jacobson, D. & Bauer, F. F. Sequence-based analysis of the Vitis vinifera L. cv Cabernet Sauvignon grape must Mycobiome in three South African vineyards employing distinct agronomic systems. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01358 (2015).
Article  PubMed  PubMed Central  Google Scholar 

52.
Miura, T. et al. Shifts in the composition and potential functions of soil microbial communities responding to a no-tillage practice and bagasse mulching on a sugarcane plantation. Biol. Fertil. Soils 52, 307–322. https://doi.org/10.1007/s00374-015-1077-1 (2016).
CAS  Article  Google Scholar 

53.
Miura, T., Sanchez, R., Castaneda, L. E., Godoy, K. & Barbosa, O. Shared and unique features of bacterial communities in native forest and vineyard phyllosphere. Ecol. Evol. 9, 3295–3305. https://doi.org/10.1002/ece3.4949 (2019).
Article  PubMed  PubMed Central  Google Scholar 

54.
Hendgen, M. et al. Effects of different management regimes on microbial biodiversity in vineyard soils. Sci. Rep. https://doi.org/10.1038/s41598-018-27743-0 (2018).
Article  PubMed  PubMed Central  Google Scholar 

55.
Montecchia, M. S. et al. Pyrosequencing reveals changes in soil bacterial communities after conversion of Yungas forests to agriculture. PLoS ONE 10, 18. https://doi.org/10.1371/journal.pone.0119426 (2015).
CAS  Article  Google Scholar 

56.
Gleeson, D., Mathes, F., Farrell, M. & Leopold, M. Environmental drivers of soil microbial community structure and function at the Avon River Critical Zone Observatory. Sci. Total Environ. 571, 1407–1418. https://doi.org/10.1016/j.scitotenv.2016.05.185 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

57.
Kemler, M. et al. Ion Torrent PGM as tool for fungal community analysis: a case study of Endophytes in Eucalyptus grandis reveals high taxonomic diversity. PLoS ONE https://doi.org/10.1371/journal.pone.0081718 (2013).
Article  PubMed  PubMed Central  Google Scholar 

58.
Piškur, J., Rozpędowska, E., Polakova, S., Merico, A. & Compagno, C. How did Saccharomyces evolve to become a good brewer?. Trends Genet. 22, 183–186. https://doi.org/10.1016/j.tig.2006.02.002 (2006).
CAS  Article  PubMed  Google Scholar 

59.
Lloyd, K. G., Steen, A. D., Ladau, J., Yin, J. Q. & Crosby, L. Phylogenetically novel uncultured microbial cells dominate Earth microbiomes. Msystems https://doi.org/10.1128/mSystems.00055-18 (2018).
Article  PubMed  PubMed Central  Google Scholar 

60.
Steen, A. D. et al. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 13, 3126–3130. https://doi.org/10.1038/s41396-019-0484-y (2019).
Article  PubMed  PubMed Central  Google Scholar 

61.
Thrash, J. C. Culturing the uncultured: Risk versus reward. Msystems https://doi.org/10.1128/mSystems.00130-19 (2019).
Article  PubMed  PubMed Central  Google Scholar 

62.
Varela, C., Pizarro, F. & Agosin, E. Biomass content governs fermentation rate in nitrogen-deficient wine musts. Appl. Environ. Microbiol. 70, 3392–3400. https://doi.org/10.1128/Aem.70.6.3392-3400.2004 (2004).
CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Parker, M. et al. Factors contributing to interindividual variation in retronasal odor perception from aroma glycosides: The tole of odorant sensory detection threshold, oral microbiota, and hydrolysis in saliva. J. Agric. Food Chem. https://doi.org/10.1021/acs.jafc.9b05450 (2019).
Article  PubMed  Google Scholar 

64.
Bokulich, N. A. & Mills, D. A. Improved selection of internal transcribed spacer-specific primers enables quantitative, ultra-high-throughput profiling of fungal communities. Appl. Environ. Microbiol. 79, 2519–2526. https://doi.org/10.1128/AEM.03870-12 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

65.
Sternes, P. R., Lee, D., Kutyna, D. R. & Borneman, A. R. A combined meta-barcoding and shotgun metagenomic analysis of spontaneous wine fermentation. bioRxiv https://doi.org/10.1101/098061 (2017).
Article  Google Scholar 

66.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

67.
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. https://doi.org/10.14806/ej.17.1.200 (2011).
Article  Google Scholar 

68.
Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. https://doi.org/10.1093/bioinformatics/btr507 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

69.
Mahe, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ 2, e593. https://doi.org/10.7717/peerj.593 (2014).
Article  PubMed  PubMed Central  Google Scholar 

70.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

71.
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

72.
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5.4 https://CRAN.R-project.org/package=vegan (2019).

73.
Li, C., Yu, G. & Zhu, C. microbiomeViz—an R package for visualizing microbiome data https://github.com/lch14forever/microbiomeViz (2018).

74.
Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R J. 5, 144–161 (2013).
Article  Google Scholar 

75.
Kassambara, A. ggpubr: ‘ggplot2’ based publication eady plots. R package version 0.2 https://CRAN.R-project.org/package=ggpubr (2018).

76.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (SpringerVerlag, New York, 2009).
Google Scholar 

77.
Team, R. C. R: a language and environment for statistical computing https://www.R-project.org/ (2017). More

1.
IPCC Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (WMO and UNEP, 2019).
2.
Erb, K. H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).
CAS  Article  Google Scholar 

3.
Searchinger, T. D., Wirsenius, S., Beringer, T. & Dumas, P. Assessing the efficiency of changes in land use for mitigating climate change. Nature 564, 249–253 (2018).
CAS  Article  Google Scholar 

4.
West, P. C. et al. Trading carbon for food: global comparison of carbon stocks vs. crop yields on agricultural land. Proc. Natl Acad. Sci. USA 107, 19645–19648 (2010).
CAS  Article  Google Scholar 

5.
Shepon, A., Eshel, G., Noor, E. & Milo, R. The opportunity cost of animal based diets exceeds all food losses. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1713820115 (2018).

6.
Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 992, 987–992 (2018).
Article  Google Scholar 

7.
Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).
CAS  Article  Google Scholar 

8.
Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).
Article  Google Scholar 

9.
Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change 6, 452–461 (2016).
Article  Google Scholar 

10.
Batchelor, J. L., Ripple, W. J., Wilson, T. M. & Painter, L. E. Restoration of riparian areas following the removal of cattle in the northwestern great basin. Environ. Manage. 55, 930–942 (2014).
Article  Google Scholar 

11.
Sitters, J., Kimuyu, D. M., Young, T. P., Claeys, P. & Olde Venterink, H. Negative effects of cattle on soil carbon and nutrient pools reversed by megaherbivores. Nat. Sustain. 3, 360–366 (2020).
Article  Google Scholar 

12.
Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision (FAO, 2012).

13.
Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 6736, 3–49 (2019).
Google Scholar 

14.
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

15.
Fry, J. P., Mailloux, N. A., Love, D. C., Milli, M. C. & Cao, L. Feed conversion efficiency in aquaculture: do we measure it correctly? Environ. Res. Lett. 13, 024017 (2018).
Article  Google Scholar 

16.
Van Zanten, H. H. E. et al. Defining a land boundary for sustainable livestock consumption. Glob. Change Biol. https://doi.org/10.1111/gcb.14321 (2018).

17.
Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).
CAS  Article  Google Scholar 

18.
Randerson, J. T. et al. Multicentury changes in ocean and land contributions to the climate–carbon feedback. Glob. Biogeochem. Cycles 29, 744–759 (2015).
CAS  Article  Google Scholar 

19.
Smith, P. et al. How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Glob. Change Biol. 19, 2285–2302 (2013).
Article  Google Scholar 

20.
Schmidinger, K. & Stehfest, E. Including CO2 implications of land occupation in LCAs-method and example for livestock products. Int. J. Life Cycle Assess. 17, 962–972 (2012).
CAS  Article  Google Scholar 

21.
Stehfest, E. et al. Climate benefits of changing diet. Clim. Change 95, 83–102 (2009).
CAS  Article  Google Scholar 

22.
Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 22, GB1003 (2008).
Article  Google Scholar 

23.
Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).
Article  Google Scholar 

24.
Cassidy, E. S., West, P. C., Gerber, J. S. & Foley, J. A. Redefining agricultural yields: from tonnes to people nourished per hectare. Environ. Res. Lett. 8, 034015 (2013).
Article  Google Scholar 

25.
Bouwman, A. F., Van der Hoek, K. W., Eickhout, B. & Soenario, I. Exploring changes in world ruminant production systems. Agric. Syst. 84, 121–153 (2005).
Article  Google Scholar 

26.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
CAS  Article  Google Scholar 

27.
Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).
CAS  Article  Google Scholar 

28.
Erb, K. H. et al. Biomass turnover time in terrestrial ecosystems halved by land use. Nat. Geosci. 9, 674–678 (2016).
CAS  Article  Google Scholar 

29.
Fetzel, T. et al. Quantification of uncertainties in global grazing systems assessment. Glob. Biogeochem. Cycles 31, 1089–1102 (2017).
CAS  Article  Google Scholar  More

1.
Plastics Europe. Plastics—the Facts 2019; 2019. https://www.plasticseurope.org/application/files/9715/7129/9584/FINAL_web_version_Plastics_the_facts2019_14102019.pdf.
2.
Ritchie H, Roser M. Plastic pollution. Our World in Data; 2018. https://ourworldindata.org/plastic-pollution.

3.
UNEP. Single-use plastics: a roadmap for sustainability; 2018. https://wedocs.unep.org/bitstream/handle/20.500.11822/25496/singleUsePlastic_sustainability.pdf?sequence=1&isAllowed=y.

4.
Geyer R, Jambeck JR, Law KL. Production, use, and fate of all plastics ever made. Sci Adv. 2017;3:e1700782. https://doi.org/10.1126/sciadv.1700782.
CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Jambeck JR, Geyer R, Wilcox C, Siegler TR, Perryman M, Andrady A, et al. Plastic waste inputs from land into the ocean. Science. 2015;347:768–71. https://doi.org/10.1126/science.1260352.
CAS  Article  PubMed  Google Scholar 

6.
Derraik JGB. The pollution of the marine environment by plastic debris: a review. Mar Pollut Bull. 2002;44:842–52.
CAS  PubMed  Google Scholar 

7.
World Economic Forum. Ellen MacArthur Foundation. The new plastics economy: rethinking the future of plastics; 2016. www3.weforum.org/docs/WEF_The_New_Plastics_Economy.pdf.

8.
Stelfox M, Hudgins J, Sweet M. A review of ghost gear entanglement amongst marine mammals, reptiles and elasmobranchs. Mar Pollut Bull. 2016;11:6–17.
Google Scholar 

9.
Rios LM, Moore C, Jones PR. Persistent organic pollutants carried by synthetic polymers in the ocean environment. Mar Pollut Bull. 2007;54:1230–7.
CAS  PubMed  Google Scholar 

10.
Rochman CM, Hoh E, Kurobe T, Teh SJ. Ingested plastic transfers hazardous chemicals to fish and induces hepatic stress. Sci Rep. 2013;3:32632. https://doi.org/10.1038/srep03263.
Article  Google Scholar 

11.
Setala O, Fleming-Lehtinen V, Lehtiniemi M. Ingestion and transfer of microplastics in the planktonic food web. Environ Pollut. 2014;185:77–83.
CAS  PubMed  Google Scholar 

12.
Lucas N, Bienaime C, Belloy C, Queneudec M, Silvestre F, Nava-Saucedo JE. Polymer biodegradation: mechanisms and estimation techniques. Chemosphere. 2008;73:429–42.
CAS  PubMed  Google Scholar 

13.
Harrison JP, Boardman C, O’Callaghan K, Delort AM, Song J. Biodegradability standards for carrier bags and plastic films in aquatic environments: a critical review. R Soc Open Sci. 2018;5:171792. https://doi.org/10.1098/rsos.171792.
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Sudesh K, Abe H, Doi Y. Synthesis, structure and properties of polyhydroxyalkanoates: biological polyesters. Prog Polym Sci. 2000;25:1503–55.
CAS  Google Scholar 

15.
Labet M, Thielemans W. Synthesis of polycaprolactone: a review. Chem Soc Rev. 2009;38:3484–504.
CAS  PubMed  Google Scholar 

16.
Tserki V, Matzinos P, Pavlidou E, Vachliotis D, Panayiotou C. Biodegradable aliphatic polyesters. Part I. Properties and biodegradation of poly(butylene succinate-co-butylene adipate). Polym Degrad Stabil. 2006;91:367–76.
CAS  Google Scholar 

17.
Yamamoto M, Witt U, Skupin G, Beimborn D, Müller J. Biodegradable aliphatic-aromatic polyesters: “Ecoflex®”. Biopolymers Online; 2005.

18.
Nampoothiri KM, Nair NR, John RP. An overview of the recent developments in polylactide (PLA) research. Bioresour Technol. 2010;101:8493–501.
Google Scholar 

19.
Ku H, Wang H, Pattarachaiyakoop N, Trada M. A review on the tensile properties of natural fiber reinforced polymer composites. Compos B Eng. 2011;42:856–73.
Google Scholar 

20.
Jendrossek D, Schirmer A, Schlegel HG. Biodegradation polyhydroxyalkanoic acids. Appl Microbiol Biot. 1996;46:451–63.
CAS  Google Scholar 

21.
Kasuya K, Ohura T, Masuda K, Doi Y. Substrate and binding specificities of bacterial polyhydroxybutyrate depolymerases. Int J Biol Macromol. 1999;24:329–36.
CAS  PubMed  Google Scholar 

22.
Tokiwa Y, Suzuki T. Hydrolysis of polyesters by lipases. Nature. 1977;270:76–78.
CAS  PubMed  Google Scholar 

23.
Jaeger KE, Steinbuchel A, Jendrossek D. Substrate specificities of bacterial polyhydroxyalkanoate depolymerases and lipases: bacterial lipases hydrolyze poly(omega-hydroxyalkanoates). Appl Environ Microbiol. 1995;61:3113–8.
CAS  PubMed  PubMed Central  Google Scholar 

24.
Murphy CA, Cameron JA, Huang SJ, Vinopal RT. Fusarium polycaprolactone depolymerase is cutinase. Appl Environ Microbiol. 1996;62:456–60.
CAS  PubMed  PubMed Central  Google Scholar 

25.
Maeda H, Yamagata Y, Abe K, Hasegawa F, Machida M, Ishioka R, et al. Purification and characterization of a biodegradable plastic-degrading enzyme from Aspergillus oryzae. Appl Microbiol Biotechnol. 2005;67:778–88.
CAS  PubMed  Google Scholar 

26.
Murphy CA, Cameron JA, Huang SJ, Vinopal RT. A second polycaprolactone depolymerase from Fusarium, a lipase distinct from cutinase. Appl Microbiol Biotechnol. 1998;50:692–6.
CAS  Google Scholar 

27.
Ando Y, Yoshikawa K, Yoshikawa T, Nishioka M, Ishioka R, Yakabe Y. Biodegradability of poly(tetramethylene succinate-co-tetramethylene adipate): I. Enzymatic hydrolysis. Polym Degrad Stabil. 1998;61:129–37.
CAS  Google Scholar 

28.
Shinozaki Y, Morita T, Cao X, Yoshida S, Koitabashi M, Watanabe T, et al. Biodegradable plastic-degrading enzyme from Pseudozyma antarctica: cloning, sequencing, and characterization. Appl Microbiol Biotechnol. 2013;97:2951–9.
CAS  PubMed  Google Scholar 

29.
Muroi F, Tachibana Y, Soulenthone P, Yamamoto K, Mizuno T, Sakurai T, et al. Characterization of a poly(butylene adipate-co-terephthalate) hydrolase from the aerobic mesophilic bacterium Bacillus pumilus. Polym Degrad Stabil. 2017;137:11–22.
CAS  Google Scholar 

30.
Tokiwa Y, Calabia BP. Biodegradability and biodegradation of poly(lactide). Appl Microbiol Biotechnol. 2006;72:244–51.
CAS  PubMed  Google Scholar 

31.
Williams DF. Enzymic hydrolysis of polylactic acid. Eng Med. 1981;10:5–7.
Google Scholar 

32.
Nakayama A, Yamano N, Kawasaki N. Biodegradation in seawater of aliphatic polyesters. Polym Degrad Stabil. 2019;166:290–9.
CAS  Google Scholar 

33.
Luzier WD. Materials derived from biomass biodegradable materials. Proc Natl Acad Sci USA. 1992;89:839–42.
CAS  PubMed  Google Scholar 

34.
Kasuya K, Takagi K, Ishiwatari S, Yoshida Y, Doi Y. Biodegradabilities of various aliphatic polyesters in natural waters. Polym Degrad Stabil. 1998;59:327–32.
CAS  Google Scholar 

35.
Teramoto N, Urata K, Ozawa K, Shibata M. Biodegradation of aliphatic polyester composites reinforced by abaca fiber. Polym Degrad Stabil. 2004;86:401–9.
CAS  Google Scholar 

36.
Sekiguchi T, Saika A, Nomura K, Watanabe T, Fujimoto Y, Enoki M, et al. Biodegradation of aliphatic polyesters soaked in deep seawaters and isolation of poly(ε-caprolactone)-degrading bacteria. Polym Degrad Stabil. 2011;96:1397–403.
CAS  Google Scholar 

37.
Fujimaki T. Processability and properties of aliphatic polyesters, ‘BIONOLLE’, synthesized by polycondensation reaction. Polym Degrad Stabil. 1998;59:209–14.
CAS  Google Scholar 

38.
Witt U, Müller RJ, Deckwer WD. New biodegradable polyester-copolymers from commodity chemicals with favorable use properties. J Environ Polym Degrad. 1995;3:215–23.
CAS  Google Scholar 

39.
Bagheri AR, Laforsch C, Greiner A, Agarwal S. Fate of so‐called biodegradable polymers in seawater and freshwater. Glob Chall. 2017;1:1700048. https://doi.org/10.1002/gch2.201700048.
Article  PubMed  PubMed Central  Google Scholar 

40.
Zambrano MC, Pawlak JJ, Daystar J, Ankeny M, Goller CC, Venditti RA. Aerobic biodegradation in freshwater and marine environments of textile microfibers generated in clothes laundering: effects of cellulose and for polyester-based microfibers on the microbiome. Mar Pollut Bull. 2020;151:110826. https://doi.org/10.1016/j.marpolbul.2019.110826.
CAS  Article  PubMed  Google Scholar 

41.
Taylor LE, Henrissat B, Coutinho PM, Ekborg NA, Hutcheson SW, Weiner RA. Complete cellulase system in the marine bacterium Saccharophagus degradans strain 2-40T. J Bacteriol. 2006;188:3849–61.
CAS  PubMed  PubMed Central  Google Scholar 

42.
Yang JC, Madupu R, Durkin SA, Ekborg NA, Pedamallu CS, Hostetler JB, et al. The Complete genome of Teredinibacter turnerae T7901: an intracellular endosymbiont of marine wood-boring bivalves (shipworms). PLoS ONE. 2009;4:e6085. https://doi.org/10.1371/journal.pone.0006085.
CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Sawant SS, Salunke BK, Taylor LE, Kim BS. Enhanced agarose and xylan degradation for production of polyhydroxyalkanoates by co-culture of marine bacterium, Saccharophagus degradans and its contaminant, Bacillus cereus. Appl Sci. 2017;7. https://doi.org/10.3390/app7030225.

44.
Liu G, Wu SM, Jin WH, Sun CM. Amy63, a novel type of marine bacterial multifunctional enzyme possessing amylase, agarase and carrageenase activities. Sci Rep. 2016;6:18726. https://doi.org/10.1038/srep18726.
CAS  Article  PubMed  PubMed Central  Google Scholar 

45.
Ramesh S, Mathivanan N. Screening of marine actinomycetes isolated from the Bay of Bengal, India for antimicrobial activity and industrial enzymes. World J Microb Biotechnol. 2009;25:2103–11.
CAS  Google Scholar 

46.
Nogi Y, Yoshizumi M, Miyazaki M. Thalassospira povalilytica sp. nov., a polyvinyl-alcohol-degrading marine bacterium. Int J Syst Evol Microbiol. 2014;64:1149–53.
CAS  PubMed  Google Scholar 

47.
Sedlacek P, Slaninova E, Enev V, Koller M, Nebesarova J, Marova I, et al. What keeps polyhydroxyalkanoates in bacterial cells amorphous? A derivation from stress exposure experiments. Appl Microbiol Biot. 2019;103:1905–17.
CAS  Google Scholar 

48.
Steinbüchel A, Valentin HE. Diversity of bacterial polyhydroxyalkanoic acids. FEMS Microbiol Lett. 1995;128:219–28.
Google Scholar 

49.
Madison LL, Huisman GW. Metabolic engineering of poly(3-hydroxyalkanoates): From DNA to plastic. Microbiol Mol Biol Rev 1999;63:21–53.
CAS  PubMed  PubMed Central  Google Scholar 

50.
Kunioka M, Tamaki A, Doi Y. Crystalline and thermal properties of bacterial copolyesters: poly(3-hydroxybutyrate-co-3-hydroxyvalerate) and poly(3-hydroxybutyrate-co-4-hydroxybutyrate). Macromolecules. 1989;22:694–7.
CAS  Google Scholar 

51.
Doi Y, Kitamura S, Abe H. Microbial synthesis and characterization of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate). Macromolecules. 1995;28:4822–8.
CAS  Google Scholar 

52.
Abe H, Doi Y. Side-chain effect of second monomer units on crystalline morphology, thermal properties, and enzymatic degradability for random copolyesters of (R)-3-hydroxybutyric acid with (R)-3-hydroxyalkanoic acids. Biomacromolecules. 2002;3:133–8.
CAS  PubMed  Google Scholar 

53.
Shimamura E, Kasuya K, Kobayashi G, Shiotani T, Shima Y, Doi Y. Physical-properties and biodegradability of microbial poly(3-hydroxybutyrate-co-3-hydroxyhexanoate). Macromolecules. 1994;27:878–80.
CAS  Google Scholar 

54.
Suriyamongkol P, Weselake R, Narine S, Moloney M, Shah S. Biotechnological approaches for the production of polyhydroxyalkanoates in microorganisms and plants—a review. Biotechnol Adv. 2007;25:148–75.
CAS  PubMed  Google Scholar 

55.
Rehm BHA, Kruger N, Steinbüchel A. A new metabolic link between fatty acid de novo synthesis and polyhydroxyalkanoic acid synthesis—the phaG gene from Pseudomonas putida KT2440 encodes a 3-hydroxyacyl-acyl carrier protein coenzyme A transferase. J Biol Chem. 1998;273:24044–51.
CAS  PubMed  Google Scholar 

56.
Rehm BHA, Mitsky TA, Steinbüchel A. Role of fatty acid de novo biosynthesis in polyhydroxyalkanoic acid (PHA) and rhamnolipid synthesis by pseudomonads: establishment of the transacylase (PhaG)-mediated pathway for PHA biosynthesis in Escherichia coli. Appl Environ Microbiol. 2001;67:3102–9.
CAS  PubMed  PubMed Central  Google Scholar 

57.
Fukui T, Shiomi N, Doi Y. Expression and characterization of (R)-specific enoyl coenzyme A hydratase involved in polyhydroxyalkanoate biosynthesis by Aeromonas caviae. J Bacteriol. 1998;180:667–73.
CAS  PubMed  PubMed Central  Google Scholar 

58.
Chek MF, Kim SY, Mori T, Arsad H, Samian MR, Sudesh K, et al. Structure of polyhydroxyalkanoate (PHA) synthase PhaC from Chromobacterium sp. USM2, producing biodegradable plastics. Sci Rep. 2017;7:5312. https://doi.org/10.1038/s41598-017-05509-4.
CAS  Article  PubMed  PubMed Central  Google Scholar 

59.
Mezzolla V, D’Urso OF, Poltronieri P. Role of PhaC type I and type II enzymes during PHA biosynthesis. Polymer. 2018;10:910. https://doi.org/10.3390/polym10080910.
CAS  Article  Google Scholar 

60.
Rehm BHA. Polyester synthases: natural catalysts for plastics. Biochem J. 2003;376:15–33.
CAS  PubMed  PubMed Central  Google Scholar 

61.
Tsuge T, Hyakutake M, Mizuno K. Class IV polyhydroxyalkanoate (PHA) synthases and PHA-producing Bacillus. Appl Microbiol Biotechnol. 2015;99:6231–40.
CAS  PubMed  Google Scholar 

62.
Lenz RW, Marchessault RH. Bacterial polyesters: biosynthesis, biodegradable plastics and biotechnology. Biomacromolecules. 2005;6:1–8.
CAS  PubMed  Google Scholar 

63.
Higuchi-Takeuchi M, Morisaki K, Numata K. A Screening method for the isolation of polyhydroxyalkanoate-producing purple non-sulfur photosynthetic bacteria from natural seawater. Front Microbiol. 2016;7:1509. https://doi.org/10.3389/fmicb.2016.01509.
Article  PubMed  PubMed Central  Google Scholar 

64.
Sabirova JS, Ferrer M, Regenhardt D, Timmis KN, Golyshin PN. Proteomic insights into metabolic adaptations in Alcanivorax borkumensis induced by alkane utilization. J Bacteriol. 2006;188:3763–73.
CAS  PubMed  PubMed Central  Google Scholar 

65.
Shi XL, Wu YH, Jin XB, Wang CS, Xu XW. Alteromonas lipolytica sp. nov., a poly-beta-hydroxybutyrateproducing bacterium isolated from surface seawater. Int J Syst Evol Microbiol. 2017;67:237–42.
CAS  PubMed  Google Scholar 

66.
Mohandas SP, Balan L, Jayanath G, Anoop BS, Philip R, Cubelio SS, et al. Biosynthesis and characterization of polyhydroxyalkanoate from marine Bacillus cereus MCCB 281 utilizing glycerol as carbon source. Int J Biol Macromol. 2018;119:380–92.
CAS  PubMed  Google Scholar 

67.
Sathiyanarayanan G, Saibaba G, Kiran GS, Selvin J. Process optimization and production of polyhydroxybutyrate using palm jaggery as economical carbon source by marine sponge-associated Bacillus licheniformis MSBN12. Bioproc. Biosyst Eng. 2013;36:1817–27.
CAS  Google Scholar 

68.
Lopez-Cortes A, Lanz-Landazuri A, Garcia-Maldonado JQ. Screening and isolation of PHB-producing bacteria in a polluted marine microbial mat. Microbiol Ecol. 2008;56:112–20.
CAS  Google Scholar 

69.
Prabhu NN, Santimano MC, Mavinkurve S, Bhosle SN, Garg S. Native granule associated short chain length polyhydroxyalkanoate synthase from a marine derived Bacillus sp. NQ-11/A2. Anton Leeuw. 2010;97:41–50.
CAS  Google Scholar 

70.
Hong JW, Song HS, Moon YM, Hong YG, Bhatia SK, Jung HR, et al. Polyhydroxybutyrate production in halophilic marine bacteria Vibrio proteolyticus isolated from the Korean peninsula. Bioprocess Biosyst Eng. 2019;42:603–10.
CAS  PubMed  Google Scholar 

71.
Kiran GS, Lipton AN, Priyadharshini S, Anitha K, Suarez LEC, Arasu MV, et al. Antiadhesive activity of poly-hydroxy butyrate biopolymer from a marine Brevibacterium casei MSI04 against shrimp pathogenic vibrios. Microb Cell Fact. 2014;13:114. https://doi.org/10.1186/s12934-014-0114-3.
CAS  Article  PubMed  PubMed Central  Google Scholar 

72.
Yamada M, Yukita A, Hanazumi Y, Yamahata Y, Moriya H, Miyazaki M, et al. Poly(3-hydroxybutyrate) production using mannitol as a sole carbon source by Burkholderia sp. AIU M5M02 isolated from a marine environment. Fish Sci. 2018;84:405–12.
CAS  Google Scholar 

73.
Methe BA, Nelson KE, Deming JW, Momen B, Melamud E, Zhang XJ, et al. The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses. Proc Natl Acid Sci USA. 2005;102:10913–8.
CAS  Google Scholar 

74.
Numata K, Morisaki K, Tomizawa S, Ohtani M, Demura T, Miyazaki M. Synthesis of poly- and oligo(hydroxyalkanoate)s by deep-sea bacteria, Colwellia spp., Moritella spp., and Shewanella spp. Polym J. 2013;45:1094–100.
CAS  Google Scholar 

75.
Hai T, Lange D, Rabus R, Steinbüchel A. Polyhydroxyalkanoate (PHA) accumulation in sulfate-reducing bacteria and identification of a class III PHA synthase (PhaEC) in Desulfococcus multivorans. Appl Environ Microbiol. 2004;70:4440–8.
CAS  PubMed  PubMed Central  Google Scholar 

76.
Wang H, Tomasch J, Jarek M, Wagner-Dobler I. A dual-species co-cultivation system to study the interactions between Roseobacters and dinoflagellates. Front Microbiol. 2014;5:311. https://doi.org/10.3389/fmicb.2014.00311.
Article  PubMed  PubMed Central  Google Scholar 

77.
Xiao N, Jiao NZ. Formation of polyhydroxyalkanoate in aerobic anoxygenic phototrophic bacteria and its relationship to carbon source and light availability. Appl Environ Microbiol. 2011;77:7445–50.
CAS  PubMed  PubMed Central  Google Scholar 

78.
Shrivastav A, Mishra SK, Shethia B, Pancha I, Jain D, Mishra S. Isolation of promising bacterial strains from soil and marine environment for polyhydroxyalkanoates (PHAs) production utilizing Jatropha biodiesel byproduct. Int J Biol Macromol. 2010;47:283–7.
CAS  PubMed  Google Scholar 

79.
Simon-Colin C, Raguénès G, Cozien J, Guezennec JG. Halomonas profundus sp. nov., a new PHA-producing bacterium isolated from a deep-sea hydrothermal vent shrimp. J Appl Microbiol. 2008;104:1425–32.
CAS  PubMed  Google Scholar 

80.
Lemechko P, Le Fellic M, Bruzaud S. Production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) using agro-industrial effluents with tunable proportion of 3-hydroxyvalerate monomer units. Int J Biol Macromol. 2019;128:429–34.
CAS  PubMed  Google Scholar 

81.
Han X, Satoh Y, Kuriki Y, Seino T, Fujita S, Suda T, et al. Polyhydroxyalkanoate production by a novel bacterium Massilia sp. UMI-21 isolated from seaweed, and molecular cloning of its polyhydroxyalkanoate synthase gene. J Biosci Bioeng. 2014;118:514–9.
CAS  PubMed  Google Scholar 

82.
Doronina NV, Trotsenko YA, Tourova TP. Methylarcula marina gen. nov., sp. nov. and Methylarcula terricola sp. nov.: novel aerobic, moderately halophilic, facultatively methylotrophic bacteria from coastal saline environments. Int J Syst Evol Microbiol. 2000;50:1849–59.
CAS  PubMed  Google Scholar 

83.
Liu XJ, Zhang J, Hong PH, Li ZJ. Microbial production and characterization of poly-3-hydroxybutyrate by Neptunomonas antarctica. Peerj. 2016;4:e2291. https://doi.org/10.7717/peerj.2291.
CAS  Article  PubMed  PubMed Central  Google Scholar 

84.
Cho JC, Giovannoni SJ. Oceanicola granulosus gen. nov., sp. nov. and Oceanicola batsensis sp. nov., poly-β-hydroxybutyrate-producing marine bacteria in the order ‘Rhodobacterales’. Int J Syst Evol Microbiol. 2004;54:1129–36.
CAS  PubMed  Google Scholar 

85.
Numata K, Morisaki K. Screening of marine bacteria to synthesize polyhydroxyalkanoate from lignin: contribution of lignin derivatives to biosynthesis by Oceanimonas doudoroffii. ACS Sustain Chem Eng. 2015;3:569–73.
CAS  Google Scholar 

86.
Boyandin AN, Kalacheva GS, Rodicheva EK, Volova TG. Synthesis of reserve polyhydroxyalkanoates by luminescent bacteria. Microbiology. 2008;77:318–23.
CAS  Google Scholar 

87.
Wang Q, Zhang HX, Chen Q, Chen XL, Zhang YZ, Qi QS. A marine bacterium accumulates polyhydroxyalkanoate consisting of mainly 3-hydroxydodecanoate and 3-hydroxydecanoate. World J Microbiol Biotechnol. 2010;26:1149–53.
Google Scholar 

88.
Simon-Colin C, Alain K, Colin S, Cozien J, Costa B, Guezennec JG, et al. A novel mcl PHA-producing bacterium, Pseudomonas guezennei sp. nov., isolated from a ‘kopara’ mat located in Rangiroa, an atoll of French Polynesia. J Appl Microbiol. 2008;104:581–6.
CAS  PubMed  Google Scholar 

89.
Jamil N, Ahmed N, Edwards DH. Characterization of biopolymer produced by Pseudomonas sp. CMG607w of marine origin. J Gen Appl Microbiol. 2007;53:105–9.
CAS  PubMed  Google Scholar 

90.
Higuchi-Takeuchi M, Numata K. Acetate-inducing metabolic states enhance polyhydroxyalkanoate production in marine purple non-sulfur bacteria under aerobic conditions. Front Bioeng Biotechnol. 2019;7:118. https://doi.org/10.3389/fbioe.2019.00118.
Article  PubMed  PubMed Central  Google Scholar 

91.
Higuchi-Takeuchi M, Morisaki K, Toyooka K, Numata K. Synthesis of high-molecular-weight polyhydroxyalkanoates by marine photosynthetic purple bacteria. PLoS ONE. 2016;11. https://doi.org/10.1371/journal.pone.0160981.

92.
Gonzalez-Garcia Y, Nungaray J, Cordova J, Gonzalez-Reynoso O, Koller M, Atlic A, et al. Biosynthesis and characterization of polyhydroxyalkanoates in the polysaccharide-degrading marine bacterium Saccharophagus degradans ATCC 43961. J Ind Microbiol Biotechnol. 2008;35:629–33.
CAS  PubMed  Google Scholar 

93.
Ting L, Williams TJ, Cowley MJ, Lauro FM, Guilhaus M, Raftery MJ, et al. Cold adaptation in the marine bacterium, Sphingopyxis alaskensis, assessed using quantitative proteomics. Environ Microbiol. 2010;12:2658–76.
CAS  PubMed  Google Scholar 

94.
Shrivastav A, Mishra SK, Mishra S. Polyhydroxyalkanoate (PHA) synthesis by Spirulina subsalsa from Gujarat coast of India. Int J Biol Macromol. 2010;46:255–60.
CAS  PubMed  Google Scholar 

95.
Sasidharan RS, Bhat SG, Chandrasekaran M. Biocompatible polyhydroxybutyrate (PHB) production by marine Vibrio azureus BTKB33 under submerged fermentation. Ann Microbiol. 2015;65:455–65.
CAS  Google Scholar 

96.
Mohandas SP, Balan L, Lekshmi N, Cubelio SS, Philip R, Bright SIS. Production and characterization of polyhydroxybutyrate from Vibrio harveyi MCCB 284 utilizing glycerol as carbon source. J Appl Microbiol. 2017;122:698–707.
CAS  PubMed  Google Scholar 

97.
Wei YH, Chen WC, Wu HS, Janarthanan OM. Biodegradable and biocompatible biomaterial, polyhydroxybutyrate, produced by an indigenous Vibrio sp. BM-1 isolated from marine environment. Mar Drugs. 2011;9:615–24.
CAS  PubMed  PubMed Central  Google Scholar 

98.
Numata K, Doi Y. Biosynthesis of Polyhydroxyalkanaotes by a novel facultatively anaerobic Vibrio sp. under marine conditions. Mar Biotechnol. 2012;14:323–31.
CAS  PubMed  Google Scholar 

99.
Stabili L, Cardone F, Alifano P, Tredici SM, Piraino S, Corriero G, et al. Epidemic Mortality of the sponge Ircinia variabilis (Schmidt, 1862) associated to proliferation of a Vibrio Bacterium. Microb Ecol. 2012;64:802–13.
PubMed  Google Scholar 

100.
Foong CP, Lau NS, Deguchi S, Toyofuku T, Taylor TD, Sudesh K, et al. Whole genome amplification approach reveals novel polyhydroxyalkanoate synthases (PhaCs) from Japan Trench and Nankai Trough seawater. BMC Microbiol. 2014;14:318. https://doi.org/10.1186/s12866-014-0318-z.
CAS  Article  PubMed  PubMed Central  Google Scholar 

101.
Doi Y, Kanesawa Y, Tanahashi N, Kumagai Y. Biodegradation of microbial polyesters in the marine-environment. Polym Degrad Stabil. 1992;36:173–7.
CAS  Google Scholar 

102.
Tsuji H, Suzuyoshi K. Environmental degradation of biodegradable polyesters 2. Poly(ε-caprolactone), poly[(R)-3-hydroxybutyrate], and poly(L-lactide) films in natural dynamic seawater. Polym Degrad Stabil. 2002;75:357–65.
CAS  Google Scholar 

103.
Tsuji H, Suzuyoshi K. Environmental degradation of biodegradable polyesters 1. Poly(ε-caprolactone), poly[(R)-3-hydroxybutyrate], and poly(L-lactide) films in controlled static seawater. Polym Degrad Stabil. 2002;75:347–55.
CAS  Google Scholar 

104.
Rutkowska M, Krasowska K, Heimowska A, Adamus G, Sobota M, Musiol M, et al. Environmental degradation of blends of atactic poly (R,S)-3-hydroxybutyrate with natural PHBV in Baltic sea water and compost with activated sludge. J Polym Environ. 2008;16:183–91.
CAS  Google Scholar 

105.
Volova TG, Boyandin AN, Vasil’ev AD, Karpov VA, Kozhevnikov IV, Prudnikova SV, et al. Biodegradation of polyhydroxyalkanoates (PHAs) in the South China Sea and identification of PHA-degrading bacteria. Microbiology. 2011;80:252. https://doi.org/10.1134/S0026261711020184.
CAS  Article  Google Scholar 

106.
Thellen C, Coyne M, Froio D, Auerbach M, Wirsen C, Ratto JA. A processing, characterization and marine biodegradation study of melt-extruded polyhydroxyalkanoate (PHA) films. J Polym Environ. 2008;16:1–11.
CAS  Google Scholar 

107.
Deroine M, Cesar G, Le Duigou A, Davies P, Bruzaud S. Natural degradation and biodegradation of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) in liquid and solid marine environments. J Polym Environ. 2015;23:493–505.
CAS  Google Scholar 

108.
Imam SH, Gordon SH, Shogren RL, Tosteson TR, Govind NS, Greene RV. Degradation of starch-poly(beta-hydroxybutyrate-co-beta-hydroxyvalerate) bioplastic in tropical coastal waters. Appl Environ Microbiol. 1999;65:431–7.
CAS  PubMed  PubMed Central  Google Scholar 

109.
Suzuki M, Tachibana Y, Kazahaya J, Takizawa R, Muroi F, Kasuya K. Difference in environmental degradability between poly(ethylene succinate) and poly(3-hydroxybutyrate). J Polym Res. 2017;24:217. https://doi.org/10.1007/s10965-017-1383-4.
CAS  Article  Google Scholar 

110.
Dilkes-Hoffman LS, Lant PA, Laycock B, Pratt S. The rate of biodegradation of PHA bioplastics in the marine environment: a meta-study. Mar Pollut Bull. 2019;142:15–24.
CAS  PubMed  Google Scholar 

111.
Zettler ER, Mincer TJ, Amaral-Zettler LA. Life in the “Plastisphere”: microbial communities on plastic marine debris. Environ Sci Technol. 2013;47:7137–46.
CAS  PubMed  Google Scholar 

112.
Morohoshi T, Ogata K, Okura T, Sato S. Molecular characterization of the bacterial community in biofilms for degradation of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) films in seawater. Microbes Environ. 2018;33:19–25.
PubMed  PubMed Central  Google Scholar 

113.
Pinnell LJ, Turner JW. Shotgun metagenomics reveals the benthic microbial community response to plastic and bioplastic in a coastal marine environment. Front Microbiol. 2019;10:1252. https://doi.org/10.3389/fmicb.2019.01252.
Article  PubMed  PubMed Central  Google Scholar 

114.
Zadjelovic V, Chhun A, Quareshy M, Silvano E, Hernandez-Fernaud JR, Aguilo-Ferretjans MM, et al. Beyond oil degradation: enzymatic potential of Alcanivorax to degrade natural and synthetic polyesters. Environ Microbiol. 2020;22:1356–69.
CAS  PubMed  PubMed Central  Google Scholar 

115.
Kita K, Ishimaru K, Teraoka M, Yanase H, Kato N. Properties of poly(3-hydroxybutyrate) depolymerase from a marine bacterium, Alcaligenes faecalis AE122. Appl Environ Microbiol. 1995;61:1727–30.
CAS  PubMed  PubMed Central  Google Scholar 

116.
Mergaert J, Wouters A, Anderson C, Swings J. In-situ biodegradation of poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) in natural-waters. Can J Microbiol. 1995;41:154–9.
CAS  PubMed  Google Scholar 

117.
Kato C, Honma A, Sato S, Okura T, Fukuda R, Nogi Y. Poly 3-hydroxybutyrate-co-3-hydroxyhexanoate films can be degraded by the deep-sea microbes at high pressure and low temperature conditions. High Press Res. 2019;39:248–57.
CAS  Google Scholar 

118.
Volova TG, Boyandin AN, Vasiliev AD, Karpov VA, Prudnikova SV, Mishukova OV, et al. Biodegradation of polyhydroxyalkanoates (PHAs) in tropical coastal waters and identification of PHA-degrading bacteria. Polym Degrad Stabil. 2010;95:2350–9.
CAS  Google Scholar 

119.
Ma WT, Lin JH, Chen HJ, Chen SY, Shaw GC. Identification and characterization of a novel class of extracellular poly(3-hydroxybutyrate) depolymerase from Bacillus sp Strain NRRL B-14911. Appl Environ Microbiol. 2011;77:7924–32.
CAS  PubMed  PubMed Central  Google Scholar 

120.
Kasuya K, Mitomo H, Nakahara M, Akiba A, Kudo T, Doi Y. Identification of a marine benthic P(3HB)-degrading bacterium isolate and characterization of its P(3HB) depolymerase. Biomacromolecules. 2000;1:194–201.
CAS  PubMed  Google Scholar 

121.
Ghanem NB, Mabrouk MES, Sabry SA, El-Badan DES. Degradation of polyesters by a novel marine Nocardiopsis aegyptia sp. nov.: Application of Plackett-Burman experimental design for the improvement of PHB depolymerase activity. J Gen Appl Microbiol. 2005;51:151–8.
CAS  PubMed  Google Scholar 

122.
Leathers TD, Govind NS, Greene RV. Biodegradation of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) by a tropical marine bacterium, Pseudoalteromonas sp. NRRL b-30083. J Polym Environ. 2000;8:119–24.
CAS  Google Scholar 

123.
Uefuji M, Kasuya K, Doi Y. Enzymatic degradation of poly (R)-3-hydroxybutyrate: secretion and properties of PHB depolymerase from Pseudomonas stutzeri. Polym Degrad Stabil. 1997;58:275–81.
CAS  Google Scholar 

124.
Sung CC, Tachibana Y, Suzuki M, Hsieh WC, Kasuya K. Identification of a poly(3-hydroxybutyrate)-degrading bacterium isolated from coastal seawater in Japan as Shewanella sp. Polym Degrad Stabil. 2016;129:268–74.
CAS  Google Scholar 

125.
Mabrouk MM, Sabry SA. Degradation of poly (3-hydroxybutyrate) and its copolymer poly (3-hydroxybutyrate-co-3-hydroxyvalerate) by a marine Streptomyces sp. SNG9. Microbiol Res. 2001;156:323–35.
CAS  PubMed  Google Scholar 

126.
Kita K, Mashiba S, Nagita M, Ishimaru K, Okamoto K, Yanase H, et al. Cloning of poly(3-hydroxybutyrate) depolymerase from a marine bacterium, Alcaligenes faecalis AE122, and characterization of its gene product. BBA-Gene Struct Expr. 1997;1352:113–22.
CAS  Google Scholar 

127.
Kasuya K, Takano T, Tezuka Y, Hsieh WC, Mitomo H, Doi Y. Cloning, expression and characterization of a poly(3-hydroxybutyrate) depolymerase from Marinobacter sp. NK-1. Int J Biol Macromol. 2003;33:221–6.
CAS  PubMed  Google Scholar 

128.
Ohura T, Kasuya K, Doi Y. Cloning and characterization of the polyhydroxybutyrate depolymerase gene of Pseudomonas stutzeri and analysis of the function of substrate-binding domains. Appl Environ Microbiol. 1999;65:189–97.
CAS  PubMed  PubMed Central  Google Scholar 

129.
Hisano T, Kasuya K, Tezuka Y, Ishii N, Kobayashi T, Shiraki M, et al. The crystal structure of polyhydroxybutyrate depolymerase from Penicillium funiculosum provides insights into the recognition and degradation of biopolyesters. J Mol Biol. 2006;356:993–1004.
CAS  PubMed  Google Scholar 

130.
Kasuya K, Inoue Y, Tanaka T, Akehata T, Iwata T, Fukui T, et al. Biochemical and molecular characterization of the polyhydroxybutyrate depolymerase of Comamonas acidovorans YM1609, isolated from freshwater. Appl Environ Microbiol. 1997;63:4844–52.
CAS  PubMed  PubMed Central  Google Scholar 

131.
Sung CC, Tachibana Y, Kasuya K. Characterization of a thermolabile poly(3-hydroxybutyrate) depolymerase from the marine bacterium Shewanella sp. JKCM-AJ-6,1α. Polym Degrad Stabil. 2016;129:212–21.
CAS  Google Scholar 

132.
NOAA. Sea surface temperature (sst) contour charts. 2020. https://www.ospo.noaa.gov/Products/ocean/sst/contour/.

133.
Kroeker KJ, Kordas RL, Crim R, Hendriks IE, Ramajo L, Singh GS, et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob Change Biol. 2013;19:1884–96.
Google Scholar 

134.
Xue DW, Zhang XQ, Lu XL, Chen G, Chen ZH. Molecular and evolutionary mechanisms of cuticular wax for plant drought tolerance. Front Plant Sci. 2017;8:621. https://doi.org/10.3389/fpls.2017.00621.
Article  PubMed  PubMed Central  Google Scholar 

135.
Yeats TH, Rose JKC. The formation and function of plant cuticles. Plant Physiol. 2013;163:5–20.
CAS  PubMed  PubMed Central  Google Scholar 

136.
Philippe G, Sørensen I, Jiao C, Sun X, Fei Z, Domozych DS, et al. Cutin and suberin: assembly and origins of specialized lipidic cell wall scaffolds. Curr Opin Plant Biol. 2020;55:11–20.
CAS  PubMed  Google Scholar 

137.
Broder L, Tesi T, Salvado JA, Semiletov IP, Dudarev OV, Gustafsson O. Fate of terrigenous organic matter across the Laptev Sea from the mouth of the Lena River to the deep sea of the Arctic interior. Biogeosciences. 2016;13:5003–19.
Google Scholar 

138.
Broder L, Tesi T, Andersson A, Eglinton TI, Semiletov IP, Dudarev OV, et al. Historical records of organic matter supply and degradation status in the East Siberian Sea. Org Geochem. 2016;91:16–30.
Google Scholar 

139.
Goni MA, Yunker MB, Macdonald RW, Eglinton TI. Distribution and sources of organic biomarkers in arctic sediments from the Mackenzie River and Beaufort Shelf. Mar Chem. 2000;71:23–51.
CAS  Google Scholar 

140.
Prahl FG, Ertel JR, Goni MA, Sparrow MA, Eversmeyer B. Terrestrial organic-carbon contributions to sediments on the Washington margin. Geochim Cosmochim Acta. 1994;58:3035–48.
CAS  Google Scholar 

141.
Kaal J, Serrano O, Cortizas AM, Baldock JA, Lavery PS. Millennial-scale changes in the molecular composition of Posidonia australis seagrass deposits: Implications for Blue Carbon sequestration. Org Geochem. 2019;137:103898. https://doi.org/10.1016/j.orggeochem.2019.07.007.
CAS  Article  Google Scholar 

142.
Moran KL, Bjorndal KA. Simulated green turtle grazing affects nutrient composition of the seagrass Thalassia testudinum. Mar Biol. 2007;150:1083–92.
CAS  Google Scholar 

143.
Olsen JL, Rouze P, Verhelst B, Lin YC, Bayer T, Collen J, et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature. 2016;530:331–5.
CAS  PubMed  Google Scholar 

144.
Sugiura H, Kawasaki Y, Suzuki T, Maegawa M. The structural and histochemical analyses and chemical characters of the cuticle and epidermal walls of cotyledon in ungerminated seeds of Zostera marina L. Fish Sci. 2009;75:369–77.
CAS  Google Scholar 

145.
Lu B, Wang GX, Huang D, Ren ZL, Wang XW, Zhen ZC, et al. Comparison of PCL degradation in different aquatic environments: effects of bacteria and inorganic salts. Polym Degrad Stabil. 2018;150:133–9.
CAS  Google Scholar 

146.
Molitor R, Bollinger A, Kubicki S, Loeschcke A, Jaeger KE, Thies S. Agar plate-based screening methods for the identification of polyester hydrolysis by Pseudomonas species. Microb Biotechnol. 2020;13:274–84.
CAS  PubMed  Google Scholar 

147.
Suzuki M, Tachibana Y, Oba K, Takizawa R, Kasuya K. Microbial degradation of poly(ε-caprolactone) in a coastal environment. Polym Degrad Stabil. 2018;149:1–8.
CAS  Google Scholar 

148.
Almeida EL, AFC Rincon, Jackson SA, ADW Dobson. In silico screening and heterologous expression of a polyethylene terephthalate hydrolase (PETase)-like enzyme (SM14est) with polycaprolactone (PCL)-degrading activity, from the marine sponge-derived strain Streptomyces sp. SM14. Front Microbiol. 2019;10:2187. https://doi.org/10.3389/fmicb.2019.02187.
Article  PubMed  Google Scholar 

149.
Sekiguchi T, Sato T, Enoki M, Kanehiro H, Uematsu K, Kato C. Isolation and characterization of biodegradable plastic degrading bacteria from deep-sea environments. JAMSTEC Rep. Res Dev. 2011;11:33–41.
Google Scholar 

150.
Kovacic F, Babic N, Krauss U, Jaeger K. Classification of lipolytic enzymes from bacteria. In: Handbook of hydrocarbon and lipid microbiology. Cham: Springer; 2019;24. https://doi.org/10.1007/978-3-319-39782-5_39-1.

151.
Arpigny JL, Jaeger KE. Bacterial lipolytic enzymes: classification and properties. Biochem J. 1999;343:177–83.
CAS  PubMed  PubMed Central  Google Scholar 

152.
Nikolaivits E, Kanelli M, Dimarogona M, Topakas E. A middle-aged enzyme still in its prime: recent advances in the field of cutinases. Catalysts. 2018;8:612. https://doi.org/10.3390/catal8120612.
CAS  Article  Google Scholar 

153.
Chen S, Su LQ, Chen J, Wu J. Cutinase: characteristics, preparation, and application. Biotechnol Adv. 2013;31:1754–67.
CAS  PubMed  Google Scholar 

154.
Chen S, Tong X, Woodard RW, Du GC, Wu J, Chen J. Identification and characterization of bacterial cutinase. J Biol Chem. 2008;283:25854–62.
CAS  PubMed  PubMed Central  Google Scholar 

155.
Reis P, Holmberg K, Watzke H, Leser ME, Miller R. Lipases at interfaces: a review. Adv Colloid Interfac. 2009;147:237–50.
Google Scholar 

156.
Zumstein MT, Rechsteiner D, Roduner N, Perz V, Ribitsch D, Guebitz GM, et al. Enzymatic hydrolysis of polyester thin films at the nanoscale: effects of polyester structure and enzyme active-site accessibility. Environ Sci Technol. 2017;51:7476–85.
CAS  PubMed  Google Scholar 

157.
Shi K, Jing J, Song L, Su TT, Wang ZY. Enzymatic hydrolysis of polyester: Degradation of poly(ε-caprolactone) by Candida antarctica lipase and Fusarium solani cutinase. Int J Biol Macromol. 2020;144:183–9.
CAS  PubMed  Google Scholar 

158.
Trodler P, Pleiss J. Modeling structure and flexibility of Candida antarctica lipase B in organic solvents. BMC Struct Biol. 2008;8:9. https://doi.org/10.1186/1472-6807-8-9.
CAS  Article  PubMed  PubMed Central  Google Scholar 

159.
Hajighasemi M, Nocek BP, Tchigvintsev A, Brown G, Flick R, Xu XH, et al. Biochemical and structural insights into enzymatic depolymerization of polylactic acid and other polyesters by microbial carboxylesterases. Biomacromolecules. 2016;17:2027–39.
CAS  PubMed  PubMed Central  Google Scholar 

160.
Danso D, Schmeisser C, Chow J, Zimmermann W, Wei R, Leggewie C, et al. New insights into the function and global distribution of polyethylene terephthalate (PET)-degrading bacteria and enzymes in marine and terrestrial metagenomes. Appl Environ Microbiol. 2018;84:e02773–17. https://doi.org/10.1128/AEM.02773-17.
CAS  PubMed  PubMed Central  Google Scholar 

161.
Bollinger A, Thies S, Knieps-Grunhagen E, Gertzen C, Kobus S, Hoppner A, et al. A novel polyester hydrolase from the marine bacterium Pseudomonas aestusnigri – Structural and functional insights. Front Microbiol. 2020;11:114. https://doi.org/110.3389/fmicb.2020.00114.
PubMed  PubMed Central  Google Scholar 

162.
Shinomiya M, Iwata T, Kasuya K, Doi Y. Cloning of the gene for poly(3-hydroxybutyric acid) depolymerase of Comamonas testosteroni and functional analysis of its substrate-binding domain. FEMS Microbiol Lett. 1997;154:89–94.
CAS  PubMed  Google Scholar  More

Plant material
In vitro-grown plantlets of ten different Ipomoea batatas cultivars; Camote Mata Serrano (CAM; Cip-420530); Cinitavo (CIN,Cip-440669); CMR 1112 (CMR; Cip-440145); Espelma (ESP, Cip-421028); Ibarreno (IBA; Cip-400989); Jewel (JEW; Cip-440031); Manchester Hawk (MAN; Cip-400040); Tanzania (TAN; Cip-440166); Tis 87/0029 (TIS; Cip-442764); and Trujillano (TRUJ; Cip-420665) were supplied by CIP (Lima, Peru). These ten cultivars were chosen to represent the diversity of the cultivars present at the sweet potato genebank of CIP as they originate from a broad range of countries over 4 different continents.
Plant multiplication
In vitro plantlets were propagated on “CIP medium” and plain MS medium50). The CIP medium contains half strength MS salts (Duchefa Biochemie, M0221) supplemented with 30 g/L sucrose, 2.8 g/L gelrite, 2 mg/L calciumpanthotenate, 100 mg/L calciumnitrate, 200 mg/L ascorbic acid and 10 mg/L gibberellic acid, pH was set to 6.12 before autoclavation (b.a.). The MS medium contains MS salts and vitamins (Duchefa Biochemie, M0222) supplemented with 25 g/L sucrose and 2.8 g/L gelrite, pH was set to 6.12 b.a.. Nodal fragments from the in vitro plantlets were excised in a sterile laminar flow bench. This was done by removing the leaves and roots of the plantlet, after which 1 cm stem fragments were cut, each containing one axillary meristem in the middle. Three fragments were transferred to each culture tube and grown in a 24 °C growth room on a 16/8 h light/dark regime with the light being provided with 36 W (cool white)/ 840 Lumilux fluorescent lights. The material was subcultured every 5–6 weeks.
Six weeks after initiation, the number of new nodes was counted. This experiment was initially executed on the following 4 cultivars: TIS, IBA, JEW and TAN. Further propagation of all 10 cultivars was done with the medium that proved to be the most productive.
Meristem excision
Two meristem types were excised using a binocular microscope; apical and axillary meristems. The apical meristems were excised by trimming the top leaves until the apical meristem is visible. Then a cut is made in the stem underneath the apical meristem, leaving the apical dome with 2 to 4 leaf primordia (Fig. 7). The axillary meristems were excised by first removing the leaves completely, including the petiole. From this a small cube of 1mm3 was excised containing the axillary meristem on one of the sides (Fig. 7) A movie demonstrating this process is added as supplementary information in the digital version of this paper (see Online Supplementary Resource 1). The exact position of the axillary meristem on the stem was not taken into account in this research, since it was proven that this had no significant impact on the survival rate after cryopreservation39.
Figure 7

Excised apical (left) and axillary meristem (right) from a CIN and ESP cultivar plantlet respectively, displayed on millimetre paper. The black bar represents 1 mm.

Full size image

Preculture
In the “no preculture method”, the excised meristems are transferred on top of a sterile filter paper placed on a MS plate (MS, 30 g/L sucrose and 3 g/L gelrite, pH set to 6.12 b.a.). As soon as sufficient meristems for that specific experiment are excised, they are subjected on the same day to the cryopreservation procedure. In the “preculture method”, the excised meristems are transferred on top of a sterile filter paper placed on a 0.3 M MS plate (MS, 102.7 g/L sucrose; 3 g/L gelrite, pH set to 6.12 b.a.) and kept on this medium in the dark for14 to 16 h before cryopreservation takes place.
The difference between precultured and non-precultured meristems was tested by comparing the post-thaw survival and regeneration rate of 950 of both axillary and apical meristems originating from 3 different cultivars (IBA, CIN, and CMR). These were cryopreserved via the droplet cryopreservation protocol using the following parameters: Loading solution (LS) 20 min; Plant Vitrification Solution (PVS2) 30 min; Recovery Solution (RS) 15 min and 2.22 µM BA regeneration medium.
Droplet cryopreservation
Precultured or non-precultured meristems were transferred to a sterile 30 ml plastic tube containing 15 ml of LS ( MS supplemented with 2 M glycerol and 0.4 M sucrose; pH 5.8), where they remain at room temperature for 20 min. Then the LS was removed from the tube with a sterile plastic boll pipette, taking care not to remove or damage the meristems.
The empty tube was then filled with 15 ml chilled PVS251 with the Murashige- Tucker medium replaced by MS ( MS supplemented with 30% glycerol, 15% ethylene glycol, 15% DMSO and 0.4 M sucrose, pH 5.8) and subsequently placed in an ice bath for 30 min21,23.
Of each sample of 10 meristems, 3 were directly transferred from the PVS2 to the RS (MS supplemented with 1.2 M sucrose, pH 5.8) at room temperature to act as a control. The remaining 7 meristems were transferred with a plastic boll pipet to a sterile aluminium foil strip (4 × 15 mm). From this strip, the remaining PVS2 fluid was removed until only a thin layer of PVS2 surrounds each meristem. Subsequently the aluminium strip was plunged into liquid nitrogen (LN). When the LN surrounding the aluminium strip stopped boiling, the strip containing the meristems was transferred to a 2 mL cryotube filled with liquid nitrogen where the meristems remained for at least 30 min. To warm the meristems, the aluminium strip with meristems was removed from the cryotube with liquid nitrogen and directly plunged in the RS at room temperature.
Both control and cryopreserved meristems were exposed to the RS for 15 min. Following this, they were placed one by one with a plastic boll pipette onto a filter paper placed on a MS plate containing 0.3 M sucrose. The plates were then sealed with parafilm and stored overnight in darkness at a temperature of 24 °C. The next day, the meristems were transferred on to the regeneration medium in an upright position, with the meristematic domes not fully submerged in the medium. The meristems were left in the dark for 7 days, where after they were moved into the light.
After 1 month they were moved from the regeneration medium to new MS plates (Fig. 8).
Figure 8

Step-by-step cryopreservation process of sweet potato.

Full size image

Effect of the age of the in vitro plantlet
The effect of plant age was tested by comparing the post-thaw regeneration rates of 215 meristems, for both apical and axillary each, from 3, 6 and 9 weeks old plantlets from the JEW, IBA, MAN and CMR cultivars, using the following parameters: preculture (0.3 M sucrose); 20 min treatment with LS; 30 min with PVS2; 15 min with RS and regeneration on 0.3 M sucrose medium (1 day) followed by MS with 2.22 µM 6-Benzyladenine (BA) regeneration medium.
Effect of toxicity of the loading solution
For this experiment 24 apical and 72 axillary meristems of the cultivars TIS, TAN, IBA and JEW were subjected to 3 different LS treatment times (20, 180 and 360 min). After the LS treatment the meristems were transferred to a MS plate containing 0.3 M sucrose for one day plate after which they were transferred to an MS plate. Thereafter, the survival and regeneration rates were compared.
Effect of composition of the regeneration media
Three different media were tested: MS (MS supplemented with 25 g/L sucrose and 2.8 g/L gelrite), Hirai* (MS supplemented with 30 g/L sucrose, 1 g/L casein hydrolysate, gibberellic acid 0.5 mg/L and 2 g/L gelrite; which is a slightly altered medium of Hirai and Sakai18, and 2.22 µM BA (MS supplemented with 25 g/L sucrose; 2.8 g/L gelrite and 2.22 µM BA). The pH of the media were set to 6.12 b.a. These media were already previously reported to have been used successfully for cryopreservation of sweet potato meristems. The survival and regeneration rate of these media after cryopreservation were compared for 939 apical and 939 axillary meristems from 4 cultivars (TIS, JEW, IBA and TAN). The cryopreservation parameters were: No preculture; LS 20 min; PVS2 15 min; RS 15 min.
Effect of axillary versus apical meristem
This was tested by comparing the post-thaw regeneration rate of 475 apical and 475 axillary meristems originating from 3 different cultivars (IBA, CIN, and CMR). These were cryopreserved using the following parameters: No preculture; LS 20 min; PVS2 30 min; RS 15 min and 2.22 µM BA regeneration medium.
Post-cryopreservation regrowth
Observations were executed one and two months after cryopreservation using a binocular microscope.
To express the results of the regrowth(survival) and regeneration, 7 categories of post thaw reactions are distinguished. In case of doubt, the lower growth category is taken in order to avoid false positives. The categories are summarized below and a visual representation of a typical meristem in each of the 7 categories is shown in Fig. 5.
Full regeneration (F) are those meristems that have grown multiple leaves, each containing a new meristem in the axil, and that are growing visible roots. These plantlets are able to regenerate into a new plant that can be subcultured and transferred to the soil. A Hyperhydricity (H) score is given to meristems which do form new leaves and meristems, but are growing abnormally. These plantlets have narrow leaves with a thick stem and roots that grow upwards. These are not categorized as regeneration as subculturing these plants will not lead to plantlets that can be transferred to the field. Shoot growth (S) is linked to meristems that produced a limited number of leaves, around 3, and then stop growing. They remain rootless. Tip growth (T) means that the meristem is visibly growing but shows no unfolded leafs. A callus (C) score is given when there is no visible growth other than callus. Black (B) or White (W) is given to meristems that have died either after or before/during cryopreservation. In many cases, callus growth is associated with one of the above categories.
To calculate the post thaw regeneration rate of a plate, the Full regeneration (F) meristems were counted and divided by the total number of meristems on the plate. The survival rate was calculated by counting all meristems with living tissue (F, H, S, T and C).
Statistical analysis
The comparison of the number of nodes after 6 weeks on the 2 propagation media was executed using a one-sided tail, student t-test (homoscedastic) with a P-value  More

1.
Gibbs, J. P. Importance of small wetlands for the persistence of local populations of wetland-associated animals. Wetlands 13(1), 25–31 (1993).
Google Scholar 
2.
Muneepeerakul, R. et al. Neutral metacommunity models predict fish diversity patterns in Mississippi–Missouri basin. Nature 453(7192), 220–222 (2008).
ADS  CAS  PubMed  Google Scholar 

3.
Kinlan, B. P. & Gaines, S. D. Propagule dispersal in marine and terrestrial environments: A community perspective. Ecology 84(8), 2007–2020 (2003).
Google Scholar 

4.
Niebuhr, B. B. et al. Survival in patchy landscapes: The interplay between dispersal, habitat loss and fragmentation. Sci. Rep. 5, 11898 (2015).
ADS  PubMed  PubMed Central  Google Scholar 

5.
Deal, E., Braun, J. & Botter, G. Understanding the role of rainfall and hydrology in determining fluvial erosion efficiency. J. Geophys. Res. Earth Surf. 123(4), 744–778 (2018).
ADS  Google Scholar 

6.
Kadoya, T. Assessing functional connectivity using empirical data. Popul. Ecol. 51(1), 5–15 (2009).
Google Scholar 

7.
Hanski, I. & Gilpin, M. Metapopulation dynamics: Brief history and conceptual domain. Biol. J. Lin. Soc. 42(1–2), 3–16 (1991).
Google Scholar 

8.
Cadotte, M. W. Dispersal and species diversity: A meta-analysis. Am. Nat. 167(6), 913–924 (2006).
PubMed  Google Scholar 

9.
Koelle, K. & Vandermeer, J. Dispersal-induced desynchronization: From metapopulations to metacommunities. Ecol. Lett. 8(2), 167–175 (2005).
Google Scholar 

10.
Foltête, J. C., Clauzel, C., Vuidel, G. & Tournant, P. Integrating graph-based connectivity metrics into species distribution models. Landsc. Ecol. 27(4), 557–569 (2012).
Google Scholar 

11.
Tournant, P., Afonso, E., Roué, S., Giraudoux, P. & Foltête, J. C. Evaluating the effect of habitat connectivity on the distribution of lesser horseshoe bat maternity roosts using landscape graphs. Biol. Cons. 164, 39–49 (2013).
Google Scholar 

12.
Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across a freshwater habitat gradient. Annu. Rev. Ecol. Syst. 27(1), 337–363 (1996).
Google Scholar 

13.
Creed, I. F. et al. Enhancing protection for vulnerable waters. Nat. Geosci. 10(11), 809–815 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

14.
Rains, M. C. et al. Geographically isolated wetlands are part of the hydrological landscape. Hydrol. Process. 30(1), 153–160 (2016).
ADS  Google Scholar 

15.
Levins, R. Some demographic and genetic consequences of environmental heterogeneity for biological control. Am. Entomol. 15(3), 237–240 (1969).
Google Scholar 

16.
Gilpin, M. (ed.) Metapopulation Dynamics: Empirical and Theoretical Investigations (Academic Press, New York, 2012).
Google Scholar 

17.
Gibbs, J. P. Wetland loss and biodiversity conservation. Conserv. Biol. 14(1), 314–317 (2000).
Google Scholar 

18.
Boughton, E. H., Quintana-Ascencio, P. F., Bohlen, P. J., Jenkins, D. G. & Pickert, R. Land-use and isolation interact to affect wetland plant assemblages. Ecography 33(3), 461–470 (2010).
Google Scholar 

19.
Smith, L. L. et al. Biological connectivity of seasonally ponded wetlands across spatial and temporal scales. JAWRA J. Am. Water Resour. Assoc. 55(2), 334–353 (2019).
ADS  Google Scholar 

20.
Le, P. V. & Kumar, P. Power law scaling of topographic depressions and their hydrologic connectivity. Geophys. Res. Lett. 41(5), 1553–1559 (2014).
ADS  Google Scholar 

21.
Bertassello, L. E. et al. Wetlandscape fractal topography. Geophys. Res. Lett. 45(14), 6983–6991 (2018).
ADS  Google Scholar 

22.
Hurst, H. E. (1965). Long term storage. An experimental study.

23.
Mandelbrot, B. B. (1975). Les objets fractals: forme, hasard et dimension.

24.
Keitt, T. H. Spectral representation of neutral landscapes. Landsc. Ecol. 15(5), 479–494 (2000).
Google Scholar 

25.
Park, J., Botter, G., Jawitz, J. W. & Rao, P. S. C. Stochastic modeling of hydrologic variability of geographically isolated wetlands: Effects of hydro-climatic forcing and wetland bathymetry. Adv. Water Resour. 69, 38–48 (2014).
ADS  Google Scholar 

26.
Bertassello, L. E., Rao, P. S. C., Jawitz, J. W., Aubeneau, A. F. & Botter, G. Wetlandscape hydrologic dynamics driven by shallow groundwater and landscape topography. Hydrol. Process. 2, 2 (2019).
Google Scholar 

27.
Wu, Q. et al. Efficient delineation of nested depression hierarchy in digital elevation models for hydrological analysis using level-set method. JAWRA J. Am. Water Resour. Assoc. 55(2), 354–368 (2019).
ADS  Google Scholar 

28.
Saura, S. & Pascual-Hortal, L. A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landsc. Urban Plan. 83(2–3), 91–103 (2007).
Google Scholar 

29.
Bunn, A. G., Urban, D. L. & Keitt, T. H. Landscape connectivity: A conservation application of graph theory. J. Environ. Manag. 59(4), 265–278 (2000).
Google Scholar 

30.
Urban, D. & Keitt, T. Landscape connectivity: A graph-theoretic perspective. Ecology 82(5), 1205–1218 (2001).
Google Scholar 

31.
Fortuna, M. A., Gómez-Rodríguez, C. & Bascompte, J. Spatial network structure and amphibian persistence in stochastic environments. Proc. R. Soc. B Biol. Sci. 273(1592), 1429–1434 (2006).
Google Scholar 

32.
Hayashi, M. & Van der Kamp, G. Simple equations to represent the volume–area–depth relations of shallow wetlands in small topographic depressions. J. Hydrol. 237(1–2), 74–85 (2000).
ADS  Google Scholar 

33.
Rittenhouse, T. A. & Semlitsch, R. D. Distribution of amphibians in terrestrial habitat surrounding wetlands. Wetlands 27(1), 153–161 (2007).
Google Scholar 

34.
Osher, S. & Fedkiw, R. P. Level set methods: an overview and some recent results. J. Comput. Phys. 169(2), 463–502 (2001).
ADS  MathSciNet  CAS  MATH  Google Scholar 

35.
National Map Viewer. Available online: https://viewer.nationalmap.gov (accessed on July 2018).

36.
Gallant, J. C., Moore, I. D., Hutchinson, M. F. & Gessler, P. Estimating fractal dimension of profiles: A comparison of methods. Math. Geol. 26(4), 455–481 (1994).
Google Scholar 

37.
Voss, R. F. Fractals in nature: from characterization to simulation. In The Science of Fractal Images 21–70 (Springer, New York, 1988).
Google Scholar 

38.
Russ, J. C. Fractal Surfaces (Plenum, New York, 1994).
Google Scholar 

39.
Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M. & Turlure, C. Individual dispersal, landscape connectivity and ecological networks. Biol. Rev. 88(2), 310–326 (2013).
PubMed  Google Scholar 

40.
Zamberletti, P., Zaffaroni, M., Accatino, F., Creed, I. F. & De Michele, C. Connectivity among wetlands matters for vulnerable amphibian populations in wetlandscapes. Ecol. Model. 384, 119–127 (2018).
Google Scholar 

41.
Barabási, A. L. & Albert, R. Emergence of scaling in random networks. Science 286(5439), 509–512 (1999).
ADS  MathSciNet  PubMed  MATH  Google Scholar 

42.
Cox, D. & Lewis, P. The statistical analysis of series of events. Popul. Sci https://doi.org/10.1007/978-94-011-7801-3 (1966).
Article  MATH  Google Scholar 

43.
Diggle, P. J. Statistical methods for spatial point patterns in ecology. Spat. Tempor. Anal. Ecol. 2, 99–150 (1979).
Google Scholar 

44.
Diggle, P. J. Statistical analysis of spatial and spatio-temporal point patterns (CRC Press, Boca Raton, 2013).
Google Scholar 

45.
Cohen, M. J. et al. Do geographically isolated wetlands influence landscape functions?. Proc. Natl. Acad. Sci. 113, 1978–1986. https://doi.org/10.1073/pnas.1512650113 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

46.
Galpern, P., Manseau, M. & Fall, A. Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biol. Cons. 144(1), 44–55 (2011).
Google Scholar 

47.
Gustafson, E. J. How has the state of art for quantification of landscape patterns advanced in the twenty first century?. Landscape Ecol. 34, 2065–2202 (2019).
Google Scholar 

48.
Shreevastava, A., Bhalachandran, S., McGrath, G. S., Huber, M. & Rao, P. S. C. Paradoxical impact of sprawling intra-Urban Heat Islets: Reducing mean surface temperatures while enhancing local extremes. Sci. Rep. 9(1), 1–10 (2019).
ADS  Google Scholar 

49.
Werner, E. E., Skelly, D. K., Relyea, R. A. & Yurewicz, K. L. Amphibian species richness across environmental gradients. Oikos 116, 1697–1712. https://doi.org/10.1111/j.0030-1299 (2007).
Article  Google Scholar 

50.
Kantrud, H. A. & Stewart, R. E. Use of natural basin wetlands by breeding waterfowl in North Dakota. J. Wildlife Manag. 2, 243–253 (1977).
Google Scholar 

51.
Euliss, N. H. et al. The wetland continuum: A conceptual framework for interpreting biological studies. Wetlands 24, 448–458. https://doi.org/10.1672/0277-5212(2004)024 (2004).
Article  Google Scholar 

52.
Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7(7), 601–613 (2004).
Google Scholar 

53.
Kuefler, D., Hudgens, B., Haddad, N. M., Morris, W. F. & Thurgate, N. The conflicting role of matrix habitats as conduits and barriers for dispersal. Ecology 91(4), 944–950 (2010).
PubMed  Google Scholar 

54.
Winter, T. C. Relation of streams, lakes, and wetlands to groundwater flow systems. Hydrogeol. J. 7(1), 28–45. https://doi.org/10.1007/s100400050178 (1999).
ADS  Article  Google Scholar  More

1.
FAO. The State of World Fisheries and Aquaculture 2018-Meeting the sustainable development goals. Rome. Licence: CC BY-NC-SA 3.0 IGO. (2018). https://www.fao.org/documents/card/en/c/I9540EN/.
2.
Cheung, W. W. L. et al. Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Glob. Change Biol. 16, 24–35. https://doi.org/10.1111/j.1365-2486.2009.01995.x (2010).
ADS  Article  Google Scholar 

3.
Myers, R. A. & Worm, B. Rapid worldwide depletion of predatory fish communities. Nature 423, 280–283. https://doi.org/10.1038/nature01610 (2003).
ADS  Article  PubMed  CAS  Google Scholar 

4.
Worm, B. et al. Rebuilding global fisheries. Science 325, 578–585. https://doi.org/10.1126/science.1173146 (2009).
ADS  Article  PubMed  CAS  Google Scholar 

5.
van Gemert, R. & Andersen, K. H. Challenges to fisheries advice and management due to stock recovery. ICES J. Mar. Sci. 75, 1864–1870. https://doi.org/10.1093/icesjms/fsy084 (2018).
Article  Google Scholar 

6.
Cadrin, S. X., Kerr, L. A. & Mariani, S. Stock identification methods: an overview, in Stock Identification Methods: Applications in Fishery Science (eds S.X. Cadrin, L.A. Kerr, & S. Mariani) 535–552 (Academic Press, 2014).

7.
Kerr, L. A. et al. Lessons learned from practical approaches to reconcile mismatches between biological population structure and stock units of marine fish. ICES J. Mar. Sci. 74, 1708–1722. https://doi.org/10.1093/icesjms/fsw188 (2017).
Article  Google Scholar 

8.
Ovenden, J. R., Berry, O., Welch, D. J., Buckworth, R. C. & Dichmont, C. M. Ocean’s eleven: A critical evaluation of the role of population, evolutionary and molecular genetics in the management of wild fisheries. Fish. Fish. 16, 125–159. https://doi.org/10.1111/faf.12052 (2015).
Article  Google Scholar 

9.
Conover, D. O., Clarke, L. M., Munch, S. B. & Wagner, G. N. Spatial and temporal scales of adaptive divergence in marine fishes and the implications for conservation. J. Fish Biol. 69, 21–47. https://doi.org/10.1111/j.1095-8649.2006.01274.x (2006).
Article  Google Scholar 

10.
Stockwell, C. A., Hendry, A. P. & Kinnison, M. T. Contemporary evolution meets conservation biology. Trends Ecol. Evol. 18, 94–101. https://doi.org/10.1016/s0169-5347(02)00044-7 (2003).
Article  Google Scholar 

11.
Begg, G. A. & Waldman, J. R. An holistic approach to fish stock identification. Fish. Res. 43, 35–44. https://doi.org/10.1016/S0165-7836(99)00065-X (1999).
Article  Google Scholar 

12.
Hastings, A. & Botsford, L. W. Persistence of spatial populations depends on returning home. Proc. Natl. Acad. Sci. USA 103, 6067–6072. https://doi.org/10.1073/pnas.0506651103 (2006).
ADS  Article  PubMed  CAS  Google Scholar 

13.
Harma, C., Brophy, D., Minto, C. & Clarke, M. The rise and fall of autumn-spawning herring (Clupea harengus L.) in the Celtic Sea between 1959 and 2009: Temporal trends in spawning component diversity. Fish. Res. 121–122, 31–42. https://doi.org/10.1016/j.fishres.2012.01.005 (2012).
Article  Google Scholar 

14.
Brophy, D. & King, P. A. Larval otolith growth histories show evidence of stock structure in north east Atlantic blue whiting (Micromesistius poutassou). ICES J. Mar. Sci. 64, 1136–1144. https://doi.org/10.1093/icesjms/fsm080 (2007).
Article  Google Scholar 

15.
Secor, D. H. The unit stock concept: bounded fish and fisheries in Stock Identification Methods 2nd edn (eds CadrinLisa, S.X. & Mariani, A.K.) 7–28 (Academic Press, 2014).

16.
Marengo, M. et al. Combining microsatellite, otolith shape and parasites community analyses as a holistic approach to assess population structure of Dentex dentex. J. Sea Res. 128, 1–14. https://doi.org/10.1016/j.seares.2017.07.003 (2017).
ADS  Article  Google Scholar 

17.
Taillebois, L. et al. Strong population structure deduced from genetics, otolith chemistry and parasite abundances explains vulnerability to localized fishery collapse in a large Sciaenid fish, Protonibea diacanthus. Evol. Appl. 10, 978–993. https://doi.org/10.1111/eva.12499 (2017).
Article  PubMed  PubMed Central  Google Scholar 

18.
Welch, D. J. et al. Integrating different approaches in the definition of biological stocks: A northern Australian multi-jurisdictional fisheries example using grey mackerel, Scomberomorus semifasciatus. Mar. Policy 55, 73–80. https://doi.org/10.1016/j.marpol.2015.01.010 (2015).
Article  Google Scholar 

19.
Reis-Santos, P. et al. Reconciling differences in natural tags to infer demographic and genetic connectivity in marine fish populations. Sci. Rep. 8, 10343. https://doi.org/10.1038/s41598-018-28701-6 (2018).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

20.
Kritzer, J. P. & Liu, O. R. Fishery management strategies for addressing complex spatial structure in marine fish stocks in Stock Identification Methods 2nd edn (eds Cadrin, S.X., Kerr, L.A., & Mariani, S.) 29–57 (Academic Press, 2014).

21.
Abaunza, P. et al. Stock identity of horse mackerel (Trachurus trachurus) in the Northeast Atlantic and Mediterranean Sea: Integrating the results from different stock identification approaches. Fish. Res. 89, 196–209. https://doi.org/10.1016/j.fishres.2007.09.022 (2008).
Article  Google Scholar 

22.
Sala-Bozano, M., Ketmaier, V. & Mariani, S. Contrasting signals from multiple markers illuminate population connectivity in a marine fish. Mol. Ecol. 18, 4811–4826. https://doi.org/10.1111/j.1365-294X.2009.04404.x (2009).
Article  PubMed  CAS  Google Scholar 

23.
Fromentin, J. M. & Powers, J. E. Atlantic bluefin tuna: Population dynamics, ecology, fisheries and management. Fish Fish. 6, 281–306. https://doi.org/10.1111/j.1467-2979.2005.00197.x (2005).
Article  Google Scholar 

24.
Collette, B. B. et al. High value and long life-double jeopardy for tunas and billfishes. Science 333, 291–292. https://doi.org/10.1126/science.1208730 (2011).
ADS  Article  PubMed  CAS  Google Scholar 

25.
Collette, B. et al. Thunnus thynnus. The IUCN Red List of Threatened Species 2011: e.T21860A9331546. https://doi.org/10.2305/IUCN.UK.2011-2.RLTS.T21860A9331546.en. (2011) (Downloaded on 25 June 2020).

26.
Fromentin, J.-M., Bonhommeau, S., Arrizabalaga, H. & Kell, L. T. The spectre of uncertainty in management of exploited fish stocks: The illustrative case of Atlantic bluefin tuna. Mar. Policy 47, 8–14. https://doi.org/10.1016/j.marpol.2014.01.018 (2014).
Article  Google Scholar 

27.
ICCAT. Report of the standing committee on research and statistics (SCRS). Madrid, Spain, 30 September–4 October 2019. https://www.iccat.int/Documents/Meetings/Docs/2019/REPORTS/2019_SCRS_ENG.pdf (2019).

28.
ICCAT. Report of the 2017 ICCAT bluefin stock assessment meeting. Madrid, Spain 20–28 July, 2017. https://www.iccat.int/Documents/SCRS/DetRep/BFT_SA_ENG.pdf (2017).

29.
Rooker, J. R. et al. Crossing the line: Migratory and homing behaviors of Atlantic bluefin tuna. Mar. Ecol. Prog. Ser. 504, 265–276. https://doi.org/10.3354/meps10781 (2014).
ADS  Article  CAS  Google Scholar 

30.
Arregui, I. et al. Movements and geographic distribution of juvenile bluefin tuna in the Northeast Atlantic, described through internal and satellite archival tags. ICES J. Mar. Sci. 75, 1560–1572. https://doi.org/10.1093/icesjms/fsy056 (2018).
Article  Google Scholar 

31.
Lutcavage, M. E., Brill, R. W., Skomal, G. B., Chase, B. C. & Howey, P. W. Results of pop-up satellite tagging of spawning size class fish in the Gulf of Maine: Do North Atlantic bluefin tuna spawn in the mid-Atlantic?. Can. J. Fish. Aquat. Sci. 56, 173–177. https://doi.org/10.1139/cjfas-56-2-173 (1999).
Article  Google Scholar 

32.
Block, B. A. et al. Electronic tagging and population structure of Atlantic bluefin tuna. Nature 434, 1121–1127. https://doi.org/10.1038/nature03463 (2005).
ADS  Article  PubMed  CAS  Google Scholar 

33.
Boustany, A. M., Reeb, C. A. & Block, B. A. Mitochondrial DNA and electronic tracking reveal population structure of Atlantic bluefin tuna (Thunnus thynnus). Mar. Biol. 156, 13–24. https://doi.org/10.1007/s00227-008-1058-0 (2008).
Article  CAS  Google Scholar 

34.
Rodríguez-Ezpeleta, N. et al. Determining natal origin for improved management of Atlantic bluefin tuna. Front. Ecol. Environ. 17, 439–444. https://doi.org/10.1002/fee.2090 (2019).
Article  Google Scholar 

35.
Rooker, J. R. et al. Natal homing and connectivity in Atlantic bluefin tuna populations. Science 322, 742–744. https://doi.org/10.1126/science.1161473 (2008).
ADS  Article  PubMed  CAS  Google Scholar 

36.
Block, B. A. et al. Migratory movements, depth preferences, and thermal biology of Atlantic bluefin tuna. Science 293, 1310–1314. https://doi.org/10.1126/science.1061197 (2001).
ADS  Article  PubMed  CAS  Google Scholar 

37.
Galuardi, B. et al. Complex migration routes of Atlantic bluefin tuna (Thunnus thynnus) question current population structure paradigm. Can. J. Fish. Aquat. Sci. 67, 966–976. https://doi.org/10.1139/f10-033 (2010).
Article  Google Scholar 

38.
McGowan, M. F. & Richards, W. J. Bluefin tuna, Thunnus thynnus, larvae in the Gulf Stream off the southeastern United States—satellite and shipboard observations of their environment. Fish. Bull. 87, 615–631. https://spo.nmfs.noaa.gov/sites/default/files/pdf-content/1989/873/mcgowan.pdf (1989).

39.
Richardson, D. E. et al. Discovery of a spawning ground reveals diverse migration strategies in Atlantic bluefin tuna (Thunnus thynnus). Proc. Natl. Acad. Sci. USA 113, 3299–3304. https://doi.org/10.1073/pnas.1525636113 (2016).
ADS  Article  PubMed  CAS  Google Scholar 

40.
Muhling, B. A. et al. Collection of larval bluefin tuna (Thunnus thynnus) outside documented western Atlantic spawning grounds. Bull. Mar. Sci. 87, 687–694. https://doi.org/10.5343/bms.2010.1101 (2011).
Article  Google Scholar 

41.
Leach, A. W., Levontin, P., Holt, J., Kell, L. T. & Mumford, J. D. Identification and prioritization of uncertainties for management of Eastern Atlantic bluefin tuna (Thunnus thynnus). Mar. Policy 48, 84–92. https://doi.org/10.1016/j.marpol.2014.03.010 (2014).
Article  Google Scholar 

42.
Carruthers, T. Evaluating management strategies for Atlantic bluefin tuna, Report 5: Completion and release of the first comprehensive ABFT MSE package for use by stakeholders in MP testing. Short-term contract for modelling approaches: support to BFT assessment (GBYP 06/2017) of the Atlantic-wide research programme on bluefin tuna (ICCAT-GBYP-Phase 7). https://www.iccat.int/GBYP/Docs/Modelling_Phase_7_MSE_Framework.pdf (2018).

43.
Albaina, A. et al. Single nucleotide polymorphism discovery in albacore and Atlantic bluefin tuna provides insights into worldwide population structure. Anim. Genet. 44, 678–692. https://doi.org/10.1111/age.12051 (2013).
Article  PubMed  CAS  Google Scholar 

44.
Carlsson, J., McDowell, J. R., Carlsson, J. E. L. & Graves, J. E. Genetic identity of YOY bluefin tuna from the eastern and western Atlantic spawning areas. J. Hered. 98, 23–28. https://doi.org/10.1093/jhered/esl046 (2007).
Article  PubMed  CAS  Google Scholar 

45.
Puncher, G. N. et al. Spatial dynamics and mixing of bluefin tuna in the Atlantic Ocean and Mediterranean Sea revealed using next-generation sequencing. Mol. Ecol. Resour. 18, 620–638. https://doi.org/10.1111/1755-0998.12764 (2018).
Article  PubMed  CAS  Google Scholar 

46.
Brophy, D. et al. Otolith shape variation in bluefin tuna (Thunnus thynnus) from different regions of the North Atlantic: A potential marker of stock origin. Mar. Freshw. Res. 67, 1023–1036. https://doi.org/10.1071/MF15086 (2016).
Article  Google Scholar 

47.
Dickhut, R. M. et al. Atlantic bluefin tuna (Thunnus thynnus) population dynamics delineated by organochlorine tracers. Environ. Sci. Technol. 43, 8522–8527. https://doi.org/10.1021/es901810e (2009).
ADS  Article  PubMed  CAS  Google Scholar 

48.
Fraile, I., Arrizabalaga, H. & Rooker, J. R. Origin of Atlantic bluefin tuna (Thunnus thynnus) in the Bay of Biscay. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsu156 (2014).
Article  Google Scholar 

49.
Rooker, J. R. et al. Wide-ranging temporal variation in transoceanic movement and population mixing of bluefin tuna in the North Atlantic Ocean. Front. Mar. Sci. 6, 398. https://doi.org/10.3389/fmars.2019.00398 (2019).
Article  Google Scholar 

50.
Corriero, A. et al. Size and age at sexual maturity of female bluefin tuna (Thunnus thynnus L. 1758) from the Mediterranean Sea. J. Appl. Ichthyol. 21, 483–486. https://doi.org/10.1111/j.1439-0426.2005.00700.x (2005).
Article  Google Scholar 

51.
Heinisch, G., Rosenfeld, H., Knapp, J. M., Gordin, H. & Lutcavage, M. E. Sexual maturity in western Atlantic bluefin tuna. Sci. Rep. 4, 7205–7205. https://doi.org/10.1038/srep07205 (2014).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

52.
Muhling, B. et al. Reproduction and larval biology in tunas, and the importance of restricted area spawning grounds. Rev. Fish Biol. Fish. https://doi.org/10.1007/s11160-017-9471-4 (2017).
Article  Google Scholar 

53.
Aranda, G., Abascal, F. J., Varela, J. L. & Medina, A. Spawning behaviour and post-spawning migration patterns of atlantic bluefin tuna (Thunnus thynnus) ascertained from satellite archival tags. PLoS ONE 8, e76445. https://doi.org/10.1371/journal.pone.0076445 (2013).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

54.
Rooker, J. R. et al. Evidence of trans-Atlantic movement and natal homing of bluefin tuna from stable isotopes in otoliths. Mar. Ecol. Prog. Ser. 368, 231–239. https://doi.org/10.3354/meps07602 (2008).
ADS  Article  Google Scholar 

55.
Liaw, A. & Wiener, M. Classification and Regression by randomForest. R News 2, 18–22. https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf (2002).

56.
R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2018).

57.
Mercier, L. et al. Selecting statistical models and variable combinations for optimal classification using otolith microchemistry. Ecol. Appl. 21, 1352–1364. https://doi.org/10.1890/09-1887.1 (2011).
Article  PubMed  Google Scholar 

58.
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).
Article  MATH  Google Scholar 

59.
Strobl, C., Malley, J. & Tutz, G. An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14, 323–348. https://doi.org/10.1037/a0016973 (2009).
Article  PubMed  PubMed Central  Google Scholar 

60.
Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. VSURF: An R package for variable selection using random forests. R J. https://doi.org/10.32614/RJ-2015-018 (2015).
Article  Google Scholar 

61.
Molnar, C., Bischl, B. & Casalicchio, G. iml: An R package for interpretable machine learning. J. Open Source Softw. 3(26), 786. https://doi.org/10.21105/joss.00786 (2018).
ADS  Article  Google Scholar 

62.
Ruckdeschel, R., Kohl, K., Stabla, T. & Camphausen, F. S4 Classes for Distributions. R News 6 (2), 2–6. https://CRAN.R-project.org/doc/Rnews/ (2006).

63.
Trueman, C. N., MacKenzie, K. M. & Palmer, M. R. Identifying migrations in marine fishes through stable-isotope analysis. J. Fish. Biol. 81, 826–847. https://doi.org/10.1111/j.1095-8649.2012.03361.x (2012).
Article  PubMed  CAS  Google Scholar 

64.
Labelle, M., Hoch, T., Liorzou, B. & Bigot, J.-L. Indices of bluefin tuna (Thunnus thynnus thynnus) abundance derived from sale records of French purse seine catches in the Mediterranean Sea. Aquat. Living Resour. 10, 329–342. https://doi.org/10.1051/alr:1997036 (1997).
Article  Google Scholar 

65.
LeGrande, A. N. & Schmidt, G. A. Global gridded data set of the oxygen isotopic composition in seawater. Geophys. Res. Lett. https://doi.org/10.1029/2006GL026011 (2006).
Article  Google Scholar 

66.
Ishii, M., Shouji, A., Sugimoto, S. & Matsumoto, T. Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection. Int. J. Climatol. 25, 865–879. https://doi.org/10.1002/joc.1169 (2005).
Article  Google Scholar 

67.
Kitagawa, T. et al. Otolith δ18O of Pacific bluefin tuna Thunnus orientalis as an indicator of ambient water temperature. Mar. Ecol. Prog. Ser. 481, 199–209. https://doi.org/10.3354/meps10202 (2013).
ADS  Article  CAS  Google Scholar 

68.
Rooker, J. R. et al. Life history and stock structure of Atlantic bluefin tuna (Thunnus thynnus). Rev. Fish. Sci. 15, 265–310. https://doi.org/10.1080/10641260701484135 (2007).
Article  Google Scholar 

69.
Friedman, J. H. & Popescu, B. E. Predictive learning via rule ensembles. Ann. Appl. Stat. 2, 916–954. https://doi.org/10.1214/07-AOAS148 (2008).
MathSciNet  Article  MATH  Google Scholar 

70.
Gruber, N. et al. Spatiotemporal patterns of carbon-13 in the global surface oceans and the oceanic suess effect. Global Biogeochem. Cycles 13, 307–335. https://doi.org/10.1029/1999GB900019 (1999).
ADS  Article  CAS  Google Scholar 

71.
Martino, J. C., Doubleday, Z. A. & Gillanders, B. M. Metabolic effects on carbon isotope biomarkers in fish. Ecol. Indic. 97, 10–16. https://doi.org/10.1016/j.ecolind.2018.10.010 (2019).
Article  CAS  Google Scholar 

72.
Cort, J. L., Arregui, I., Estruch, V. D. & Deguara, S. Validation of the growth equation applicable to the eastern Atlantic bluefin tuna, Thunnus thynnus (L.), using Lmax, tag-recapture, and first dorsal spine analysis. Rev. Fish. Sci. Aquac. 22, 239–255. https://doi.org/10.1080/23308249.2014.931173 (2014).
Article  Google Scholar 

73.
Fromentin, J.-M. Descriptive analysis of the ICCAT bluefin tuna tagging database. Collect. Vol. Sci. Pap. ICCAT 54, 353–362 (2002).
Google Scholar 

74.
Arrizabalaga, H. et al. Life history and migrations of Mediterranean bluefin tuna in The Future of Bluefin Tunas (ed B.A. Block) (John Hopkins University Press, 2019).

75.
Quílez-Badia, G. et al. The WWF/GBYP multi-annual bluefin tuna electronic tagging program (2008–2013): Repercussions for management. Collect. Vol. Sci. Pap. ICCAT 71(4), 1789–1802 (2015).
Google Scholar 

76.
Di Natale, A., Tensek, S. & García, A. P. Preliminary information about the ICCAT GBYP tagging activities in Phase 5. Collect. Vol. Sci. Pap. ICCAT 72(6), 1589–1613 (2016).
Google Scholar 

77.
Kerr, L. A., Cadrin, S. X., Secor, D. H. & Taylor, N. G. Modeling the implications of stock mixing and life history uncertainty of Atlantic bluefin tuna. Can. J. Fish. Aquat. Sci. 74, 1990–2004. https://doi.org/10.1139/cjfas-2016-0067 (2016).
Article  Google Scholar 

78.
Kerr, L. A. et al. Mixed stock origin of Atlantic bluefin tuna in the US rod and reel fishery (Gulf of Maine) and implications for fisheries management. Fish. Res. 224, 105461. https://doi.org/10.1016/j.fishres.2019.105461 (2020).
Article  Google Scholar 

79.
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag New York, 2016). More

1.
Robert Feldmeth, C., Stone, E. A. & Brown, J. H. An increased scope for thermal tolerance upon acclimating pupfish (Cyprinodon) to cycling temperatures. J. Comp. Physiol. 89, 39–44 (1974).
Google Scholar 
2.
Hertz, P. E. Adaptation to altitude in two West Indian anoles. Animals 195, 25–37 (1981).
Google Scholar 

3.
Hertz, P. E. & Huey, R. B. Compensation for altitudinal changes in the thermal environment by some anolis lizards on Hispaniola. Ecology 62, 515–521 (1981).
Google Scholar 

4.
Hertz, P. E., Huey, R. B. & Nevo, E. Fight versus flight: body temperature influences defensive responses of lizards. Anim. Behav. 30, 676–679 (1982).
Google Scholar 

5.
Stillman, J. H. Acclimation capacity underlies susceptibility to climate change. Science 301, 65 (2003).
CAS  PubMed  Google Scholar 

6.
Chown, S. L., Gaston, K. J. & Robinson, D. Macrophysiology: large-scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).
Google Scholar 

7.
Chown, S. L. et al. Adapting to climate change: a perspective from evolutionary physiology. Clim. Res. 43, 3–15 (2010).
Google Scholar 

8.
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).
PubMed  Google Scholar 

9.
Bowler, K. & Terblanche, J. S. Insect thermal tolerance: what is the role of ontogeny, ageing and senescence?. Biol. Rev. 83, 339–355 (2008).
PubMed  Google Scholar 

10.
Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. R. Soc. B 276, 1939–1948 (2009).
PubMed  Google Scholar 

11.
Waldschmidt, S. & Tracy, C. R. Interactions between a lizard and its thermal environment: implications for sprint performance and space utilization in the lizard Uta stansburiana. Ecology 64, 476–484 (1983).
Google Scholar 

12.
Huey, R. B. & Bennett, A. F. Phylogenetic studies of coadaptation: preferred temperatures versus optimal performance temperature of lizards. Evolution 41, 1098–1115 (1987).
PubMed  Google Scholar 

13.
Huey, R. B. & Stevenson, R. D. Intergrating thermal physiology and ecology of ecotherms: a discussion of approaches. Am. Zool. 19, 357–366 (1979).
Google Scholar 

14.
Huey, R. B. & Kingsolver, J. G. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4, 131–135 (1989).
CAS  PubMed  Google Scholar 

15.
Huey, R. B. & Kingsolver, J. G. Evolution of resistance to high temperature in ectotherms. Am. Nat. 142, 21–46 (1993).
Google Scholar 

16.
Angilletta, M. J., Wilson, R. S., Navas, C. A. & James, R. S. Tradeoffs and the evolution of thermal reaction norms. Trends Ecol. Evol. 18, 234–240 (2003).
Google Scholar 

17.
Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, Oxford, 2009).
Google Scholar 

18.
Kingsolver, J. G. et al. Complex life cycles and the responses of insects to climate change. Integr. Comp. Biol. 51, 719–732 (2011).
PubMed  Google Scholar 

19.
Logan, M. L., Cox, R. M. & Calsbeek, R. Natural selection on thermal performance in a novel thermal environment. Proc. Natl. Acad. Sci. 111, 14165–14169 (2014).
ADS  CAS  PubMed  Google Scholar 

20.
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures?. Ecol. Lett. 19, 1372–1385 (2016).
PubMed  Google Scholar 

21.
Izem, R. & Kingsolver, J. G. Variation in continuous reaction norms: quantifying directions of biological interest. Am. Nat. 166, 277–289 (2005).
PubMed  Google Scholar 

22.
Frazier, M. R., Huey, R. B. & Berrigan, D. Thermodynamics constrains the evolution of insect population growth rates: “warmer is better”. Am. Nat. 168, 512–520 (2006).
CAS  PubMed  Google Scholar 

23.
Kingsolver, J. G. The well-temperatured biologist. Am. Nat. 174, 755–768 (2009).
PubMed  Google Scholar 

24.
Bennett, A. F. Thermal dependence of locomotor capacity. Am. J. Physiol. Regul. Integr. Comp. Physiol. 259, 253–258 (1990).
Google Scholar 

25.
van Damme, R., Bauwens, D. & Verheyen, R. F. Evolutionary rigidity of thermal physiology: the case of the cool temperate lizard Lacerta vivipara. Oikos 57, 61 (1990).
Google Scholar 

26.
Swoap, S. J., Johnson, T. P., Josephson, R. K. & Bennett, A. F. Temperature, muscle power output and limitations on burst locomotor performance of the lizard Dipsosaurus dorsalis. J. Exp. Biol. 174, 199–213 (1993).
Google Scholar 

27.
Vicenzi, N., Corbalán, V., Miles, D., Sinervo, B. & Ibargüengoytía, N. Range increment or range detriment? Predicting potential changes in distribution caused by climate change for the endemic high-Andean lizard Phymaturus palluma. Biol. Conserv. 206, 151–160 (2017).
Google Scholar 

28.
Vicenzi, N., Kubisch, E., Ibargüengoytía, N. & Corbalán, V. Thermal sensitivity of performance of Phymaturus palluma (Liolaemidae) in the highlands of Aconcagua : vulnerability to global warming in the Andes. Amphibia-Reptilia 01, 1–12 (2018).
Google Scholar 

29.
Brattstrom, B. H. Thermal acclimation in anuran amphibians as a function of latitude and altitude. Comp. Biochem. Physiol. 24, 93–111 (1968).
CAS  PubMed  Google Scholar 

30.
Bauwens, D., Castilla, A. M., Van Damme, R. & Verheyen, R. F. Field body temperatures and thermoregulatory behavior of the high altitude lizard, Lacerta bedriagae. J. Herpetol. 24, 88–91 (1990).
Google Scholar 

31.
Adolph, S. C. & Porter, W. P. Temperature, activity, and lizard life histories. Am. Nat. 142, 273–295 (1993).
CAS  PubMed  Google Scholar 

32.
Díaz, J. A. & Cabezas-Díaz, S. Seasonal variation in the contribution of different behavioural mechanisms to lizard thermoregulation. Funct. Ecol. 18, 867–875 (2004).
Google Scholar 

33.
Munoz, M. M. et al. Evolutionary stasis and lability in thermal physiology in a group of tropical lizards. Proc. R. Soc. B 281, 20132433–20132433 (2014).
PubMed  Google Scholar 

34.
Cei, J. M. Reptiles del centro, centro-oeste y sur de la Argentina. Herpetofauna de las zonas áridas y semiáridas. Mitt. zool. Mus. vol. 64 (Torino: Museo Regionale di Scienze Naturali, 1988).

35.
Scolaro, J. A. Reptiles Patagónicos Sur: Una Guía de Campo (Universidad Nacional de la Patagonia, Comodoro Rivadavia, 2005).
Google Scholar 

36.
Cecchetto, N. R., Medina, S. M., Taussig, S. & Ibargüengoytía, N. R. The lizard abides: cold hardiness and winter refuges of Liolaemus pictus argentinus in Patagonia, Argentina. Can. J. Zool. 782, 773–782 (2019).
Google Scholar 

37.
Ibargüengoytía, N. R. et al. Thermal biology of the southernmost lizards in the world: Liolaemus sarmientoi and Liolaemus magellanicus from Patagonia, Argentina. J. Therm. Biol. 35, 21–27 (2010).
Google Scholar 

38.
Fernández, J., Smith, J., Scolaro, A. & Ibargüengoytía, N. R. Performance and thermal sensitivity of the southernmost lizards in the world, Liolaemus sarmientoi and Liolaemus magellanicus. J. Therm. Biol. 36, 15–22 (2011).
Google Scholar 

39.
Piantoni, C., Ibargüengoytía, N. R. & Cussac, V. E. Age and growth of the Patagonian lizard Phymaturus patagonicus. Amphibia-Reptilia 27, 385–392 (2006).
Google Scholar 

40.
Boretto, J. M. & Ibargüengoytía, N. R. Phymaturus of Patagonia, Argentina: reproductive biology of Phymaturus zapalensis (Liolaemidae) and a comparison of sexual dimorphism within the genus. J. Herpetol. 43, 96–104 (2009).
Google Scholar 

41.
Gutiérrez, J. A., Piantoni, C. & Ibargüengoytía, N. R. Altitudinal effects on life history parameters in populations of Liolaemus pictus argentinus (Sauria:Liolaemidae). Acta Herpetol. 8, 9–17 (2013).
Google Scholar 

42.
Pianka, E. R. Comparative autecology of the lizard Cnemidophorus tigris in different parts of its georgraphic range. Ecology 51, 703–720 (1970).
Google Scholar 

43.
James, C. & Shine, R. Life-history strategies of australian lizards: a comparison between the tropics and the temperate zone. Oecologia 75, 307–316 (1988).
ADS  PubMed  Google Scholar 

44.
Piantoni, C., Navas, C. A. & Ibargüengoytía, N. R. A real tale of Godzilla: impact of climate warming on the growth of a lizard. Biol. J. Linn. Soc. 126, 768–782 (2019).
Google Scholar 

45.
Gutiérrez, J. A., Krenz, J. D. & Ibargüengoytía, N. R. Effect of altitude on thermal responses of Liolaemus pictus argentinus in Argentina. J. Therm. Biol. 35, 332–337 (2010).
Google Scholar 

46.
Medina, M. et al. Thermal biology of genus Liolaemus: a phylogenetic approach reveals advantages of the genus to survive climate change. J. Therm. Biol. 37, 579–586 (2012).
Google Scholar 

47.
Huey, R. B. Physiological consequences of habitat selection. Am. Nat. 137, 91–115 (1991).
Google Scholar 

48.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).
PubMed  Google Scholar 

49.
Irschick, D. J. & Meyers, J. J. An analysis of the relative roles of plasticity and natural selection in the morphology and performance of a lizard (Urosaurus ornatus). Oecologia 153, 489–499 (2007).
ADS  PubMed  Google Scholar 

50.
Strobbe, F., McPeek, M. A., De Block, M., De Meester, L. & Stoks, R. Survival selection on escape performance and its underlying phenotypic traits: a case of many-to-one mapping. J. Evol. Biol. 22, 1172–1182 (2009).
CAS  PubMed  Google Scholar 

51.
Lima, S. L. Putting predators back into behavioral predator–prey interactions. Trends Ecol. Evol. 17, 70–75 (2002).
Google Scholar 

52.
Herczeg, G. et al. Experimental support for the cost–benefit model of lizard thermoregulation: the effects of predation risk and food supply. Oecologia 155, 1–10 (2008).
ADS  PubMed  Google Scholar 

53.
Lopez-Darias, M., Schoener, T. W., Spiller, D. A. & Losos, J. B. Predators determine how weather affects the spatial niche of lizard prey: exploring niche dynamics at a fine scale. Ecology 93, 2512–2518 (2012).
PubMed  Google Scholar 

54.
Bakken, G. S. & Angilletta, M. J. How to avoid errors when quantifying thermal environments. Funct. Ecol. 28, 96–107 (2014).
Google Scholar 

55.
Zagar, A., Carretero, M. A., Marguc, D., Simcic, T. & Vrezec, A. A metabolic syndrome in terrestrial ectotherms with different elevational and distribution patterns. Ecography 41, 1728–1739 (2018).
Google Scholar 

56.
Bartheld, J. L., Artacho, P. & Bacigalupe, L. Thermal performance curves under daily thermal fluctuation: a study in helmeted water toad tadpoles. J. Therm. Biol. 70, 80–85 (2017).
PubMed  Google Scholar 

57.
Kingsolver, J. G. & Huey, R. B. Introduction: the evolution of morphology, performance, and fitness. Integr. Comp. Biol. 43, 361–366 (2006).
Google Scholar 

58.
Bonino, M. F. et al. Running in cold weather: Morphology, thermal biology, and performance in the southernmost lizard clade in the world (Liolaemus lineomaculatus section: Liolaemini: Iguania). J. Exp. Zool. A. 315, 495–503 (2011).
Google Scholar 

59.
Kubisch, E. L., Fernández, J. & Ibargüengoytía, N. R. Is locomotor performance optimised at preferred body temperature? A study of Liolaemus pictus argentinus from northern Patagonia, Argentina. J. Therm. Biol. 36, 328–333 (2011).
Google Scholar 

60.
Angilletta, M. J. Jr. Thermal and physiological constraints on energy assimilation in a widespread lizard (Sceloporus undulatus). Ecology 82, 3044–3056 (2001).
Google Scholar 

61.
Buckley, L. B., Ehrenberger, J. C. & Angilletta, M. J. Jr. Thermoregulatory behaviour limits local adaptation of thermal niches and confers sensitivity to climate change. Funct. Ecol. 29, 1038–1047 (2015).
Google Scholar 

62.
Taucare-Rios, A., Veloso, C. & Bustamante, R. O. Thermal niche conservatism in an environmental gradient in the spider Sicarius thomisoides (Araneae: Sicariidae): implications for microhabitat selection. J. Therm. Biol. 78, 298–303 (2018).
CAS  PubMed  Google Scholar 

63.
Medina, M., Scolaro, J. A., Méndez-de la Cruz, F., Sinervo, B. & Ibargüengoytía, N. R. Thermal relationships between body temperature and environment conditions set upper distributional limits on oviparous species. J. Therm. Biol. 36, 527–534 (2011).
Google Scholar 

64.
Ibargüengoytía, N. R., Renner, M. L. & Boretto, J. M. Thermal effects on locomotion in the nocturnal gecko Homonota darwini (Gekkonidae). Amphibia-Reptilia 28, 235–246 (2007).
Google Scholar 

65.
Medina, S. M. & Ibargüengoytía, N. R. How do viviparous and oviparous lizards reproduce in Patagonia? A comparative study of three species of Liolaemus. J. Arid Environ. 74, 1024–1032 (2010).
ADS  Google Scholar 

66.
Boretto, J. M., Cabezas-Cartes, F. & Ibargüengoytía, N. R. Slow life histories in lizards living in the highlands of the Andes Mountains. J. Comp. Physiol. B 188, 491–503 (2018).
PubMed  Google Scholar 

67.
Nussey, D. H., Wilson, A. J. & Brommer, J. E. The evolutionary ecology of individual phenotypic plasticity in wild populations. J. Evol. Biol. 20, 831–844 (2007).
CAS  PubMed  Google Scholar 

68.
Artacho, P., Jouanneau, I. & Le Galliard, J.-F. Interindividual variation in thermal sensitivity of maximal sprint speed, thermal behavior, and resting metabolic rate in a lizard. Physiol. Biochem. Zool. 86, 458–469 (2013).
PubMed  Google Scholar 

69.
Bonino, M. F., Moreno Azócar, D. L., Schulte, J. A. & Cruz, F. B. Climate change and lizards: changing species’ geographic ranges in Patagonia. Reg. Environ. Chang. 15, 1121–1132 (2015).
Google Scholar 

70.
Gvozdík, L. & Castilla, A. M. A comparative study of preferred body temperatures and critical thermal tolerance limits among populations of Zootoca vivipara (Squamata: Lacertidae) along an altitudinal gradient. J. Herpetol. 35, 486–492 (2001).
Google Scholar 

71.
Angilletta, M. J. Estimating and comparing thermal performance curves. J. Therm. Biol. 31, 541–545 (2006).
Google Scholar 

72.
Hertz, P. E., Huey, R. B. & Nevo, E. Homage to Santa Anita: thermal sensitivity of sprint speed in agamid lizards. Evolution 37, 1075–1084 (1983).
PubMed  Google Scholar 

73.
Zamora-Camacho, F. J., Rubiño-Hispán, M. V., Reguera, S. & Moreno-Rueda, G. Thermal dependence of sprint performance in the lizard Psammodromus algirus along a 2200-meter elevational gradient: Cold-habitat lizards do not perform better at low temperatures. J. Therm. Biol. 52, 90–96 (2015).
PubMed  Google Scholar 

74.
Huey, R. B. & Slatkin, M. Cost and benefits of lizard thermoregulation. Q. Rev. Biol. 51, 363–384 (1976).
CAS  PubMed  Google Scholar 

75.
Logan, M. L., Fernandez, S. G. & Calsbeek, R. Abiotic constraints on the activity of tropical lizards. Funct. Ecol. 29, 694–700 (2015).
Google Scholar 

76.
Sears, M. W. & Angilletta, M. J. Costs and benefits of thermoregulation revisited: both the heterogeneity and spatial structure of temperature drive energetic costs. Am. Nat. 185, E94–E102 (2015).
PubMed  Google Scholar 

77.
Basson, C. H., Levy, O., Angilletta, M. J. & Clusella-Trullas, S. Lizards paid a greater opportunity cost to thermoregulate in a less heterogeneous environment. Funct. Ecol. 31, 856–865 (2017).
Google Scholar 

78.
Stankowich, T. & Blumstein, D. T. Fear in animals: a meta-analysis and review of risk assessment. Proc. R. Soc. B 272, 2627–2634 (2005).
PubMed  Google Scholar 

79.
Stephens, D. W. & Charnov, E. L. Optimal foraging: some simple stochastic models. Behav. Ecol. Sociobiol. 10, 251–263 (1982).
Google Scholar 

80.
Kacelnik, A. & Bateson, M. Risky theories: the effects of variance on foraging decisions. Am. Zool. 36, 402–434 (1996).
Google Scholar 

81.
Lister, B. C. & Aguayo, A. G. Seasonality, predation, and the behaviour of a tropical mainland anole. J. Anim. Ecol. 61, 717–733 (1992).
Google Scholar 

82.
Broeckhoven, C. & Nortier, F. Some like it hot: camera traps unravel the effects of weather conditions and predator presence on the activity levels of two lizards. PLoS ONE 10, 1–15 (2015).
Google Scholar 

83.
Angilletta, M. J., Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27, 249–268 (2002).
Google Scholar 

84.
Ibargüengoytía, N. R. et al. Volcanic ash from Puyehue-Cordon Caulle eruptions affects running performance and body condition of Phymaturus lizards in Patagonia, Argentina. Biol. J. Linn. Soc. 118, 842–851 (2016).
Google Scholar 

85.
Geng, J., Dong, W., Wu, Q. & Lu, H.-L. Thermal tolerance for two cohorts of a native and an invasive freshwater turtle species. Acta Herpetol. 13, 83–88 (2018).
Google Scholar 

86.
Thompson, M. E., Halstead, B. J. & Donnelly, M. A. Thermal quality influences habitat use of two Anolis species. J. Therm. Biol. 18, 54–61 (2018).
Google Scholar 

87.
Bakken, G. S. Measurement and application of operative and standard operative temperatures in ecology. Am. Zool. 32, 194–216 (1992)

88.
Medina, S. M. Adaptaciones morfológicas y fisiológicas ligadas a la transición oviparidad-viviparidad en lagartos de climas fríos: reproducción y fisiología térmica. (PhD thesis, Universidad Nacional del Comahue, 2010).

89.
Lindsey, A. A. & Newman, J. E. Use of official wather data in spring time: temperature analysis of an Indiana phenological record. Ecology 37, 812–823 (1956).
Google Scholar 

90.
Guisan, A. & Hofer, U. Predicting reptile distributions at the mesoscale: relation to climate and topography. J. Biogeogr. 30, 1233–1243 (2003).
Google Scholar 

91.
Schwanz, L. E. & Janzen, F. J. Climate change and temperature-dependent sex determination: can individual plasticity in nesting phenology prevent extreme sex ratios?. Physiol. Biochem. Zool. 81, 826–834 (2008).
PubMed  Google Scholar 

92.
Murphy, M. A., Evans, J. S. & Storfer, A. Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91, 252–261 (2010).
PubMed  Google Scholar 

93.
Boyero, L. et al. A global experiment suggests climate warming will not accelerate litter decomposition in streams but might reduce carbon sequestration. Ecol. Lett. 14, 289–294 (2011).
PubMed  Google Scholar 

94.
Graae, B. J. et al. On the use of weather data in ecological studies along altitudinal and latitudinal gradients. Oikos 121, 3–19 (2012).
Google Scholar 

95.
Mitchell, N. et al. Linking eco-energetics and eco-hydrology to select sites for the assisted colonization of Australia’s rarest reptile. Biology 2, 1–25 (2012).
PubMed  PubMed Central  Google Scholar 

96.
Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).
Google Scholar 

97.
Legendre, P. lmodel2: Model II Regression. (2014).

98.
R Core Team. R: A Language and Environment for Statistical Computing. (R Core Team, Vienna, 2019).

99.
Wood, S. & Wood, M. S. Package ‘mgcv’. R Packag. version 1–7 (2015).

100.
Hastie, T. & Tibshirani, R. Generalized additive models: some applications. J. Am. Stat. Assoc. 82, 371–386 (1987).
MATH  Google Scholar  More

1.
Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl Acad. Sci. USA 114, 10678–10683 (2017).
CAS  PubMed  Google Scholar 
2.
Panetta, A. M., Stanton, M. L. & Harte, J. Climate warming drives local extinction: evidence from observation and experimentation. Sci. Adv. 4, eaaq1819 (2018).

3.
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261 (2017).
ADS  CAS  PubMed  Google Scholar 

4.
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270 (2018).
ADS  PubMed  Google Scholar 

5.
Rees, S. E., Foster, N. L., Langmead, O., Pittman, S. & Johnson, D. E. Defining the qualitative elements of Aichi Biodiversity Target 11 with regard to the marine and coastal environment in order to strengthen global efforts for marine biodiversity conservation outlined in the United Nations Sustainable Development Goal 14. Mar. Policy 93, 241–250 (2018).
Google Scholar 

6.
Gray C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).

7.
Cinner, J. E. et al. Gravity of human impacts mediates coral reef conservation gains. Proc. Natl Acad. Sci. USA 115, E6116 (2018).
CAS  PubMed  Google Scholar 

8.
Rasolofoson, R. A. et al. Impacts of community forest management on human economic well-being across Madagascar. Conserv. Lett. 10, 346–353 (2017).
Google Scholar 

9.
Ban, N. C. et al. Well-being outcomes of marine protected areas. Nat. Sustain. 2, 524–532 (2019).
Google Scholar 

10.
Oldekop, J. A., Holmes, G., Harris, W. E. & Evans, K. L. A global assessment of the social and conservation outcomes of protected areas. Conserv. Biol. 30, 133–141 (2016).
CAS  PubMed  Google Scholar 

11.
Roberts, C. M. et al. Marine reserves can mitigate and promote adaptation to climate change. Proc. Natl Acad. Sci. USA 114, 6167–6175 (2017).
ADS  CAS  PubMed  Google Scholar 

12.
Gurney, G. G. et al. Poverty and protected areas: an evaluation of a marine integrated conservation and development project in Indonesia. Glob. Environ. Change 26, 98–107 (2014).
Google Scholar 

13.
Andam, K. S., Ferraro, P. J., Sims, K. R. E., Healy, A. & Holland, M. B. Protected areas reduced poverty in Costa Rica and Thailand. Proc. Natl Acad. Sci. USA 107, 9996 (2010).
ADS  CAS  PubMed  Google Scholar 

14.
Keppel, G., Morrison, C., Watling, D., Tuiwawa Marika, V. & Rounds Isaac, A. Conservation in tropical Pacific Island countries: why most current approaches are failing. Conserv. Lett. 5, 256–265 (2012).
Google Scholar 

15.
Gill, D. A. et al. Social synergies, tradeoffs, and equity in marine conservation impacts. Annu. Rev. Environ. Resour. 44, 347–372 (2019).
Google Scholar 

16.
Dureuil, M., Boerder, K., Burnett, K. A., Froese, R. & Worm, B. Elevated trawling inside protected areas undermines conservation outcomes in a global fishing hot spot. Science 362, 1403–1407 (2018).
ADS  CAS  PubMed  Google Scholar 

17.
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).
CAS  PubMed  Google Scholar 

18.
Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti–Mara ecosystem. Science 363, 1424–1428 (2019).
ADS  CAS  PubMed  Google Scholar 

19.
Ewers, R. M. & Rodrigues, A. S. L. Estimates of reserve effectiveness are confounded by leakage. Trends Ecol. Evol. 23, 113–116 (2008).
PubMed  Google Scholar 

20.
Lewison, R. L. et al. Accounting for unintended consequences of resource policy: connecting research that addresses displacement of environmental impacts. Conserv. Lett. 12, e12628 (2019).
PubMed  PubMed Central  Google Scholar 

21.
Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).
ADS  CAS  PubMed  Google Scholar 

22.
Grorud-Colvert, K. et al. High-profile international commitments for ocean protection: empty promises or meaningful progress? Mar. Policy 105, 52–66 (2019).
Google Scholar 

23.
Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

24.
Chertow, M., Fugate, E. & Ashton W. In Long Term Socio-Ecological Research. Studies in Society-Nature Interactions across Spatial and Temporal Scales (eds Sing, S. J. et al.) Vol. 2, 315–338 (Springer, Dordrecht, 2013).

25.
Brodie, G., Pikacha, P. & Tuiwawa, M. In Conservation Biology: Voices from the Tropics, Ch. 21 (eds Navjot, S., Luke, G. & Peter, R.), 181–187 (Wiley, 2013).

26.
Coulthard, S. et al. Exploring ‘islandness’ and the impacts of nature conservation through the lens of wellbeing. Environ. Conserv. 44, 298–309 (2017).
Google Scholar 

27.
Reenberg, A., Birch-Thomsen, T., Mertz, O., Fog, B. & Christiansen, S. Adaptation of human coping strategies in a small island society in the SW Pacific—50 years of change in the coupled human–environment system on Bellona, Solomon Islands. Hum. Ecol. 36, 807–819 (2008).
Google Scholar 

28.
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
PubMed  Google Scholar 

29.
Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl Acad. Sci. USA 113, 11261 (2016).
CAS  PubMed  Google Scholar 

30.
Cooper, W. E., Pyron, R. A. & Garland, T. Island tameness: living on islands reduces flight initiation distance. Proc. R. Soc. Ser. B 281, 20133019 (2014).
Google Scholar 

31.
Tershy, B. R., Shen, K. W., Newton, K. M., Holmes, N. D. & Croll, D. A. The importance of islands for the protection of biological and linguistic diversity. Bioscience 65, 592–597 (2015).
Google Scholar 

32.
Spatz, D. R. et al. Globally threatened vertebrates on islands with invasive species. Sci. Adv. 3, e1603080 (2017).

33.
O’Leary, B. C. et al. Addressing criticisms of large-scale marine protected areas. Bioscience https://doi.org/10.1093/biosci/biy021 (2018).

34.
Visconti, P. et al. Protected area targets post-2020. Science eaav6886 (2019).

35.
Sala, E. et al. Assessing real progress towards effective ocean protection. Mar. Policy 91, 11–13 (2018).
Google Scholar 

36.
Noss, R. F. et al. Bolder thinking for conservation. Conserv. Biol. 26, 1–4 (2012).
PubMed  Google Scholar 

37.
Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front. Ecol. Environ. 17, 259–264 (2019).
Google Scholar 

38.
Watson, J. E. M. et al. Catastrophic declines in wilderness areas undermine global environment targets. Curr. Biol. 26, 2929–2934 (2016).
CAS  PubMed  Google Scholar 

39.
Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904 (2018).
ADS  CAS  PubMed  Google Scholar 

40.
Pimm, S. L., Jenkins, C. N. & Li, B. V. How to protect half of Earth to ensure it protects sufficient biodiversity. Sci. Adv. 4, eaat2616 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

41.
Schleicher, J., Peres, C. A. & Leader-Williams, N. Conservation performance of tropical protected areas: how important is management? Conserv. Lett. 12, e12650 (2019).
Google Scholar 

42.
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209 (2019).
ADS  CAS  PubMed  Google Scholar 

43.
Fox, H. E. et al. Explaining global patterns and trends in marine protected area (MPA) development. Mar. Policy 36, 1131–1138 (2012).
Google Scholar 

44.
Marinesque, S., Kaplan, D. M. & Rodwell, L. D. Global implementation of marine protected areas: is the developing world being left behind? Mar. Policy 36, 727–737 (2012).
Google Scholar 

45.
Joppa, L. N. & Pfaff, A. Global protected area impacts. Proc. R. Soc. Ser. B 278, 1633–1638 (2011).
Google Scholar 

46.
Devillers, R. et al. Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection? Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 480–504 (2015).
Google Scholar 

47.
Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Naturalist 163, 192–211 (2004).
Google Scholar 

48.
Kinlock Nicole, L. et al. Explaining global variation in the latitudinal diversity gradient: meta‐analysis confirms known patterns and uncovers new ones. Glob. Ecol. Biogeogr. 27, 125–141 (2017).
Google Scholar 

49.
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl Acad. Sci. USA 106, 9322–9327 (2009).
ADS  CAS  PubMed  Google Scholar 

50.
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, 2602–2610 (2013).
ADS  Google Scholar 

51.
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
ADS  PubMed  PubMed Central  Google Scholar 

52.
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141 (2017).
ADS  CAS  PubMed  Google Scholar 

53.
Grenié, M. et al. Functional rarity of coral reef fishes at the global scale: hotspots and challenges for conservation. Biol. Conserv. 226, 288–299 (2018).
Google Scholar 

54.
Luck, G. W. A review of the relationships between human population density and biodiversity. Biol. Rev. 82, 607–645 (2007).
PubMed  Google Scholar 

55.
Maffi, L. Linguistic, cultural, and biological diversity. Annu. Rev. Anthropol. 34, 599–617 (2005).
Google Scholar 

56.
Gorenflo, L. J., Romaine, S., Mittermeier, R. A. & Walker-Painemilla, K. Co-occurrence of linguistic and biological diversity in biodiversity hotspots and high biodiversity wilderness areas. Proc. Natl Acad. Sci. USA 109, 8032 (2012).
ADS  CAS  PubMed  Google Scholar 

57.
Hua, X., Greenhill, S. J., Cardillo, M., Schneemann, H. & Bromham, L. The ecological drivers of variation in global language diversity. Nat. Commun. 10, 2047 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

58.
Butchart, S. H. M. et al. Protecting important sites for biodiversity contributes to meeting global conservation targets. PLoS ONE 7, e32529 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

59.
O’Hara, C. C., Villaseñor-Derbez, J. C., Ralph, G. M. & Halpern, B. S. Mapping status and conservation of global at-risk marine biodiversity. Conserv. Lett. e12651 (2019).

60.
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).
ADS  CAS  PubMed  Google Scholar 

61.
Cámara-Leret, R., Fortuna, M. A. & Bascompte, J. Indigenous knowledge networks in the face of global change. Proc. Natl Acad. Sci. USA 116, 9913 (2019).
PubMed  Google Scholar 

62.
Wang, C. & Steiner, B. Can ethno-linguistic diversity explain cross-country differences in social capital?: a global perspective. Econ. Rec. 91, 338–366 (2015).
Google Scholar 

63.
Page, S. E. How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton University Press, 2008).

64.
Weeks, R. & Adams, V. M. Research priorities for conservation and natural resource management in Oceania’s small‐island developing states. Conserv. Biol. 32, 72–83 (2017).
PubMed  Google Scholar 

65.
Benitez-Capistros, F., Camperio, G., Hugé, J., Dahdouh-Guebas, F. & Koedam, N. Emergent conservation conflicts in the Galapagos Islands: human-giant tortoise interactions in the rural area of Santa Cruz Island. PLoS ONE 13, e0202268 (2018).
PubMed  PubMed Central  Google Scholar 

66.
McCrea-Strub, A. et al. Understanding the cost of establishing marine protected areas. Mar. Policy 35, 1–9 (2011).
Google Scholar 

67.
Balmford, A., Gaston, K. J., Blyth, S., James, A. & Kapos, V. Global variation in terrestrial conservation costs, conservation benefits, and unmet conservation needs. Proc. Natl Acad. Sci. USA 100, 1046 (2003).
ADS  CAS  PubMed  Google Scholar 

68.
Cetas, E. R. & Yasué, M. A systematic review of motivational values and conservation success in and around protected areas. Conserv. Biol. 31, 203–212 (2017).
PubMed  Google Scholar 

69.
Kerwath, S. E., Winker, H., Götz, A. & Attwood, C. G. Marine protected area improves yield without disadvantaging fishers. Nat. Commun. 4, 1–6 (2013).

70.
Leclerc, C., Courchamp, F. & Bellard, C. Insular threat associations within taxa worldwide. Sci. Rep. 8, 6393 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

71.
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).

72.
Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).
Google Scholar 

73.
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).
ADS  CAS  PubMed  Google Scholar 

74.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 

75.
Broxton, P. D., Zeng, X., Sulla-Menashe, D. & Troch, P. A. A global land cover climatology using MODIS data. J. Appl. Meteorol. Climatol. 53, 1593–1605 (2014).
ADS  Google Scholar 

76.
Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World 20th edn (SIL International, 2017).

77.
Burnham, K. P. & Anderson D. R. Model Selection and Inference. A Practical Information-Theoretic Approach (Springer, 1998).

78.
Estrella, A. A new measure of fit for equations with dichotomous dependent variables. J. Bus. Econ. Stat. 16, 198–205 (1998).
Google Scholar 

79.
Azen, R. & Traxel, N. Using dominance analysis to determine predictor importance in logistic regression. J. Educ. Behav. Stat. 34, 319–347 (2009).
Google Scholar 

80.
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285 (1988).
ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

81.
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).
Google Scholar 

82.
Romdal, T. S., Araújo, M. B. & Rahbek, C. Life on a tropical planet: niche conservatism and the global diversity gradient. Glob. Ecol. Biogeogr. 22, 344–350 (2013).
Google Scholar 

83.
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323 (2019).
ADS  Google Scholar 

84.
Pina, T. E. N., Carvalho, W. D., Rosalino, L. M. C. & Hilario. R. R. Drivers of mammal richness, diversity and occurrence in heterogeneous landscapes composed by plantation forests and natural environments. For. Ecol. Manag. 449, 117467 (2019).

85.
Bradie, J. & Leung, B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44, 1344–1361 (2017).
Google Scholar 

86.
Kreft, H., Jetz, W., Mutke, J., Kier, G. & Barthlott, W. Global diversity of island floras from a macroecological perspective. Ecol. Lett. 11, 116–127 (2008).
PubMed  Google Scholar 

87.
Bohm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).
Google Scholar 

88.
Farneda, F. Z. et al. Predicting biodiversity loss in island and countryside ecosystems through the lens of taxonomic and functional biogeography. Ecography 43, 97–106 (2020).
Google Scholar 

89.
MacArthur, R. H. & Wilson, E. O. Equilibrium-theory of insular zoogeography. Evolution 17, 373-& (1963).
Google Scholar 

90.
Stratford, E. Island Geographies: Essays and Conversations (Routledge, London, 2017).

91.
Steinbauer, M. J., Otto, R., Naranjo-Cigala, A., Beierkuhnlein, C. & Fernández-Palacios, J.-M. Increase of island endemism with altitude – speciation processes on oceanic islands. Ecography 35, 23–32 (2012).
Google Scholar 

92.
Haddaway, N. R., Styles, D. & Pullin, A. S. Environmental impacts of farm land abandonment in high altitude/mountain regions: a systematic map of the evidence. Environ. Evid. 2, 18 (2013).
Google Scholar 

93.
Johnson, M. P., Frost, N. J., Mosley, M. W. J., Roberts, M. F. & Hawkins, S. J. The area-independent effects of habitat complexity on biodiversity vary between regions. Ecol. Lett. 6, 126–132 (2003).
Google Scholar 

94.
Darling, E. S. et al. Social-environmental drivers inform strategic management of coral reefs in the Anthropocene. Nat. Ecol. Evol. 3, 1341–1350 (2019).
PubMed  Google Scholar  More