Ecology

1.
Le Mire, G. et al. Implementing plant biostimulants and biocontrol strategies in the agroecological management of cultivated ecosystems. Biotechnol. Agron. Soc. Environ. 20, 299–313. https://doi.org/10.25518/1780-4507.12717 (2016).
Article  Google Scholar 
2.
Altieri, M. Á. Agroecology: A new research and development paradigm for world agriculture. Agric. Ecosyst. Environ. 27, 37–46. https://doi.org/10.1016/0167-8809(89)90070-4 (1989).
Article  Google Scholar 

3.
Posmyk, M. M. & Szafrańska, K. Biostimulators: A new trend towards solving an old problem. Front. Plant Sci. 7, 48. https://doi.org/10.3389/fpls.2016.00748 (2016).
Article  Google Scholar 

4.
Szparaga, A. & Kocira, S. Generalized logistic functions in modelling emergence of Brassica napus L.. PLoS ONE 13, e0201980. https://doi.org/10.1371/journal.pone.0201980 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Koo, A. J. Metabolism of the plant hormone jasmonate: A sentinel for tissue damage and master regulator of stress response. Phytochem. Rev. 17, 51–80. https://doi.org/10.1007/s11101-017-9510-8 (2018).
CAS  Article  Google Scholar 

6.
Trevisan, S., Manoli, A., Ravazzolo, L., Franceschi, C. & Quaggiotti, S. mRNA-sequencing analysis reveals transcriptional changes in root of maize seedlings treated with two increasing concentrations of a new biostimulant. J. Agric. Food Chem. 65, 9956–9969. https://doi.org/10.1021/acs.jafc.7b03069 (2017).
CAS  Article  PubMed  Google Scholar 

7.
Szparaga, A. et al. Modification of growth, yield, and the nutraceutical and antioxidative potential of soybean through the use of synthetic biostimulants. Front. Plant Sci. 9, 1401. https://doi.org/10.3389/fpls.2018.01401 (2018).
Article  PubMed  PubMed Central  Google Scholar 

8.
Cocetta, G. & Ferrante, A. Nutritional and Nutraceutical Value of Vegetable Crops as Affected by Biostimulants Application. In: eLS. (Wiley, Chichester, 2020). https://doi.org/10.1002/9780470015902.a0028906.

9.
Kocira, S. Effect of applying a biostimulant containing seaweed and amino acids on the content of fiber fractions in three soybean cultivars. Legume Res. 42, 341–347. https://doi.org/10.18805/LR-412 (2019).
Article  Google Scholar 

10.
Regulation (EU) 2019/1009 of the European Parliament and of the Council of 5 June 2019. https://eur-lex.europa.eu/eli/reg/2019/1009/oj (2019).

11.
Chehade, A., Chami, A., Angelica, S., Pascali, D. & Paolo, F. Biostimulants from food processing by-products: Agronomic, quality and metabolic impacts on organic tomato (Solanum lycopersicum L.). J. Sci. Food Agric. 98, 1426–1436. https://doi.org/10.1002/jsfa.8610 (2018).
CAS  Article  PubMed  Google Scholar 

12.
Stirk, W. A., Tarkowská, D., Turečová, V., Strnad, M. & van Staden, J. Abscisic acid, gibberellins and brassinosteroids in Kelpak®, a commercial seaweed extract made from Ecklonia maxima. J. Appl. Phycol. 26, 561–567. https://doi.org/10.1007/s10811-013-0062-z (2014).
CAS  Article  Google Scholar 

13.
Szczepanek, M., Siwik-Ziomek, A. & Wilczewski, E. Effect of biostimulant on accumulation of Mg in winter oilseed rape under different mineral fertilization rates. J. Elementol. 22, 1375–1385. https://doi.org/10.5601/jelem.2017.22.1.1317 (2017).
Article  Google Scholar 

14.
Kocira, S. et al. Effect of an amino acids-containing biostimulator on common bean crop. Przem. Chem. 94(10), 1732–1736. https://doi.org/10.15199/62.2015.10.16 (2015).
CAS  Article  Google Scholar 

15.
Calvo, P., Nelson, L. & Kloepper, J. W. Agricultural uses of plant biostimulants. Plant Soil. 383, 3–41. https://doi.org/10.1007/s11104-014-2131-8 (2014).
CAS  Article  Google Scholar 

16.
Colla, G. et al. Protein hydrolysates as biostimulants in horticulture. Sci. Hortic. 196, 28–38. https://doi.org/10.1016/j.scienta.2015.08.037 (2015).
CAS  Article  Google Scholar 

17.
Sharma, H. S. S., Fleming, C., Selby, C., Rao, J. R. & Martin, T. Plant biostimulants: A review on the processing of macroalgae and use of extracts for crop management to reduce abiotic and biotic stresses. J. Appl. Phycol. 26, 465–490. https://doi.org/10.1007/s10811-013-0101-9 (2014).
CAS  Article  Google Scholar 

18.
Ertani, A., Pizzeghello, D., Francioso, O., Tinti, A. & Nardi, S. Biological activity of vegetal extracts containing phenols on plant metabolism. Molecules 21, 205–219. https://doi.org/10.3390/molecules21020205 (2016).
CAS  Article  PubMed Central  Google Scholar 

19.
Michałek, W., Kocira, A., Findura, P., Szparaga, A. & Kocira, S. The influence of biostimulant Asahi SL on the photosynthetic activity of selected cultivars of Phaseolus vulgaris L.. Rocz. Ochr. Sr. 20, 1286–1301 (2018).
Google Scholar 

20.
Hara, P., Szparaga, A. & Czerwińska, E. Ecological methods used to control fungi that cause diseases of the crop plant. Rocz. Ochr. Sr. 20, 1764–1775 (2018).
Google Scholar 

21.
Mejía-Teniente, L. et al. Use of elicitors as an approach for sustainable agriculture. Afr. J. Biotechnol. 9, 9155–9162 (2010).
Google Scholar 

22.
Chandler, D. et al. The development, regulation and use of biopesticides for integrated pest management. Philos. Trans. R. Soc. B. 366, 1987–1998. https://doi.org/10.1098/rstb.2010.0390 (2011).
Article  Google Scholar 

23.
Wezel, A. et al. Agroecological practices for sustainable agriculture. A review. Agron. Sustain. Dev. 34, 1–20. https://doi.org/10.1007/s13593-013-0180-7 (2014).
Article  Google Scholar 

24.
Brown, P. & Saa, S. Biostimulants in agriculture. Front. Plant Sci. 6, 671. https://doi.org/10.3389/fpls.2015.00671 (2015).
Article  PubMed  PubMed Central  Google Scholar 

25.
Van Oosten, M. J., Pepe, O., De Pascale, S., Silletti, S. & Maggio, A. The role of biostimulants and bioeffectors as alleviators of abiotic stress in crop plants. Technol. Agric. 4, 5. https://doi.org/10.1186/s40538-017-0089-5 (2017).
CAS  Article  Google Scholar 

26.
Grabowska, A., Kunicki, E., Sekara, A., Kalisz, A. & Wojciechowska, R. The effect of cultivar and biostimulant treatment on the carrot yield and its quality. Veg. Crops Res. Bull. 77, 37–48. https://doi.org/10.2478/v10032-012-0014-1 (2012).
Article  Google Scholar 

27.
Kolomaznik, K., Pecha, J., Friebrova, V., Janacova, D. & Vasek, V. Diffusion of biostimulators into plant tissues. Heat Mass Transf. 48, 1505–1512. https://doi.org/10.1007/s00231-012-0998-6 (2012).
ADS  CAS  Article  Google Scholar 

28.
Gozzo, F. & Faoro, F. Systemic acquired resistance (50 years after discovery): Moving from the lab to the field. J. Agric. Food Chem. 61, 12473–12491. https://doi.org/10.1021/jf404156x (2013).
CAS  Article  PubMed  Google Scholar 

29.
Bashan, Y., de Bashan, L. E., Prabhu, S. R. & Hernandez, J.-P. Advances in plant growth-promoting bacterial inoculant technology: Formulations and practical perspectives (1998–2013). Plant Soil. 378(1–2), 1–33. https://doi.org/10.1007/s11104-013-1956-x (2014).
CAS  Article  Google Scholar 

30.
Cox, M. & Wong, B. Biological crop chemistry primer: Green shoots through green products, Piper Jaffray industry note. Web site 2013 [cited 4 May 2020]. https://files.ctctcdn.com/f569d87b001/8445a3b3-dcf8-4654-8d3b-bd079e55022d.pdf.

31.
Arora, N. K., Khare, E. & Maheshwari, D. K. Plant growth promoting rhizobacteria: constraints in bioformulation, commercialization, and future strategies. In Plant Growth and Health Promoting Bacteria (ed Maheshwari, D.K.) 97–116 (Springer, Dordrecht, 2010). https://doi.org/10.1007/978-3-642-13612-2_5.

32.
Walters, D. R., Ratsep, J. & Havis, N. D. Controlling crop diseases using induced resistance: Challenges for the future. J. Exp. Bot. 64(5), 1263–1280. https://doi.org/10.1093/jxb/ert026 (2013).
CAS  Article  PubMed  Google Scholar 

33.
Rodriguez-Saona, C., Kaplan, I., Braasch, J., Chinnasamy, D. & Williams, L. Field responses of predaceous arthropods to methyl salicylate: A meta-analysis and case study in cranberries. Biol. Control. 59(2), 294–303. https://doi.org/10.1016/j.biocontrol.2011.06.017 (2011).
CAS  Article  Google Scholar 

34.
Łączyński, A. et al. Wyniki produkcji roślinnej w 2017 r. (Główny Urząd Statystyczny Warszawa, 2018).

35.
Szparaga, A. et al. Towards sustainable agriculture—agronomic and economic effects of biostimulant use in common bean cultivation. Sustainability. 11, 4575. https://doi.org/10.3390/su11174575 (2019).
CAS  Article  Google Scholar 

36.
Kocira, S. et al. Effects of seaweed extract on yield and protein content of two common bean (Phaseolus vulgaris L.) cultivars. Legume Res. 41, 589–593 (2018).
Google Scholar 

37.
Kocira, A., Świeca, M., Kocira, S., Złotek, U. & Jakubczyk, A. Enhancement of yield, nutritional and nutraceutical properties of two common bean cultivars following the application of seaweed extract (Ecklonia maxima). Saudi J. Biol. Sci. 25, 563–571. https://doi.org/10.1016/j.sjbs.2016.01.039 (2018).
CAS  Article  PubMed  Google Scholar 

38.
Kocira, S. Effect of amino acid biostimulant on the yield and nutraceutical potential of soybean. Chil. J. Agric. Res. 79, 17–25. https://doi.org/10.4067/S0718-58392019000100017 (2019).
Article  Google Scholar 

39.
Kocira, A. et al. Changes in biochemistry and yield in response to biostimulants applied in bean (Phaseolus vulgaris L.). Agronomy 10, 189. https://doi.org/10.3390/agronomy10020189 (2020).
CAS  Article  Google Scholar 

40.
Rouphael, Y., Cardarelli, M., Bonini, P. & Colla, G. Synergistic action of a microbial-based biostimulant and a plant derived-protein hydrolysate enhances lettuce tolerance to alkalinity and salinity. Front. Plant Sci. 8, 131. https://doi.org/10.3389/fpls.2017.00131 (2017).
Article  PubMed  PubMed Central  Google Scholar 

41.
Shahabivand, S., Padash, A., Aghaee, A., Nasiri, Y. & Rezaei, P. F. Plant biostimulants (Funneliformis mosseae and humic substances) rather than chemical fertilizer improved biochemical responses in peppermint. Iran. J. Plant Physiol. 8, 2333–2344. https://doi.org/10.22034/ijpp.2018.539109 (2018).
Article  Google Scholar 

42.
Fujita, Y., Fujita, M., Shinozaki, K. & Yamaguchi-Shinozaki, K. ABA-mediated transcriptional regulation in response to osmotic stress in plants. J. Plant Res. 124, 509–525. https://doi.org/10.1007/s10265-011-0412-3 (2011).
CAS  Article  PubMed  Google Scholar 

43.
Xiong, H. et al. Overexpression of OsMYB48–1, a novel MYB-related transcription factor, enhances drought and salinity tolerance in rice. PLoS ONE 9, e92913. https://doi.org/10.1371/journal.pone0092913 (2014).
ADS  Article  PubMed  PubMed Central  Google Scholar 

44.
Trivellini, A. et al. Survive or die? A molecular insight into salt-dependant signaling network. Environ. Exp. Bot. 132, 140–153. https://doi.org/10.1016/j.envexpbot.2016.07.007 (2016).
CAS  Article  Google Scholar 

45.
Hare, P. D. & Cress, W. A. Metabolic implications of stress-induced proline accumulation in plants. Plant Growth Regul. 21, 79–102. https://doi.org/10.1023/A:1005703923347 (1997).
CAS  Article  Google Scholar 

46.
Mattioli, R., Costantino, P. & Trovato, M. Proline accumulation in plants. Plant Signal. Behav. 4, 1016–1018. https://doi.org/10.4161/psb.4.11.9797 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Cheynier, V., Comte, G., Davies, K. M. & Lattanzio, V. Plant phenolics: Recent advances on their biosynthesis, genetics, and ecophysiology. Plant Physiol. Biochem. 72, 1–20. https://doi.org/10.1016/j.plaphy.2013.05.009 (2013).
CAS  Article  PubMed  Google Scholar 

48.
Bulgari, R., Trivellini, A. & Ferrante, A. Effects of two doses of organic extract-based biostimulant on greenhouse lettuce grown under increasing NaCl concentrations. Front. Plant Sci. 9, 1870. https://doi.org/10.3389/fpls.2018.01870 (2019).
Article  PubMed  PubMed Central  Google Scholar 

49.
Rouphael, Y. et al. Effect of Ecklonia maxima seaweed extract on yield, mineral composition, gas exchange and leaf anatomy of zucchini squash grown under saline conditions. J. Appl. Phychol. 29, 459–470. https://doi.org/10.1007/s10811-016-0937-x (2017).
CAS  Article  Google Scholar 

50.
Vanacker, H., Carver, T. L. W. & Foyer, C. H. Pathogen-induced changes in the antioxidant status of the apoplast in barley leaves. Plant Physiol. 117, 1103–1114. https://doi.org/10.1104/pp.117.3.1103 (1998).
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Lawlor, D. W. & Tezara, W. Causes of decreased photosynthetic rate and metabolic capacity in water-deficient leaf cells: A critical evaluation of mechanisms and integration of processes. Ann. Bot. 103, 561–579. https://doi.org/10.1093/aob/mcn244 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Ertani, A., Schiavon, M., Altissimo, A., Franceschi, A. & Nardi, S. Phenol-containing organic substances stimulate phenylpropanoid metabolism in Zea mays. J. Plant Nutr. Soil Sci. 174, 496–503. https://doi.org/10.1002/jpln.201000075 (2011).
CAS  Article  Google Scholar 

53.
Bettoni, M. M. et al. Nutritional quality and yield of onion as affected by different application methods and doses of humic substances. J. Food Comp. Anal. 51, 37–44. https://doi.org/10.1016/j.jfca.2016.06.008 (2016).
CAS  Article  Google Scholar 

54.
Ertani, A., Schiavon, M., Muscolo, A. & Nardi, S. Alfalfa plant-derived biostimulant stimulates short-term growth of salt stressed Zea mays L. plants. Plant Soil. 364, 145–158. https://doi.org/10.1007/s11104-012-1335-z (2013).
CAS  Article  Google Scholar 

55.
Ertani, A. et al. The use of organic biostimulants in hot pepper plants to help low input sustainable agriculture. Chem. Biol. Technol. Agric. 2, 11. https://doi.org/10.1186/s40538-015-0039-z (2015).
CAS  Article  Google Scholar 

56.
Oboh, G. & Ademosun, A. O. Characterization of the antioxidant properties of phenolic extracts from some citrus peels. J. Food Sci. Technol. 49, 729–736. https://doi.org/10.1007/s13197-010-0222-y (2012).
CAS  Article  PubMed  Google Scholar 

57.
Serrano, M. et al. Antioxidant and nutritive constituents during sweet pepper development and ripening are enhanced by nitrophenolate treatments. Food Chem. 118, 497–503. https://doi.org/10.1016/j.foodchem.2009.05.006 (2010).
CAS  Article  Google Scholar 

58.
Krasensky, J., Carmody, M., Sierla, M. & Kangasjärvi, J. Ozone and reactive oxygen species. Wiley Online Library Web side. 2017 March 20 [cited 4 May 2020]. https://doi.org/10.1002/9780470015902.a0001299.pub3.

59.
Ciarmiello, L. F., Woodrow, P., Fuggi, A., Pontecorvo, G. & Carillo, P. Plant genes for abiotic stress. In Abiotic Stress in Plants—Mechanisms and Adaptations (eds Shanker, A. & Venkateswarlu, B.) 283–308 (InTech, Croatia, 2011).
Google Scholar 

60.
Woziak, E., Blaszczak, A., Wiatrak, P. & Canady, M. Biostimulant mode of action: Impact of biostimulant on whole-plant. In The Chemical Biology of Plant Biostimulants (eds Geelen, D. & Xu, L.) 207–227 (Wiley, Hoboken, 2020).
Google Scholar 

61.
Woziak, E., Blaszczak A., Wiatrak, P. & Canady M. Biostimulant mode of action: Impact of biostimulant on cellular level. In The Chemical Biology of Plant Biostimulants (eds. Geelen, D. & Xu, L.) 229–243 (Wiley, Hoboken, 2020).

62.
Upadhyay, S. & Dixit, M. Role of polyphenols and other phytochemicals on molecular signaling. Oxid. Med. Cell. Longev. https://doi.org/10.1155/2015/504253 (2015).
Article  PubMed  PubMed Central  Google Scholar 

63.
Martindale, J. L. & Holbrook, N. J. Cellular response to oxidative stress: Signaling for suicide and survival. J. Cell. Physiol. 192, 1–15. https://doi.org/10.1002/jcp.10119 (2002).
CAS  Article  PubMed  Google Scholar 

64.
Los, F. G. B., Zielinski, A. A. F., Wojeicchowski, J. P., Nogueira, A. & Demiate, I. M. Beans (Phaseolus vulgaris L.): Whole seeds with complex chemical composition. Curr. Opin. Food Sci. 19, 63–71. https://doi.org/10.1016/j.cofs.2018.01.010 (2018).
Article  Google Scholar 

65.
Abbas, S. M. The influence of biostimulants on the growth and on the biochemical composition of viciafaba CV. Giza 3 beans. Rom. Biotechnol. Lett. 18, 8061–8068 (2013).
ADS  CAS  Google Scholar 

66.
Aloni, R., Langhans, M., Aloni, E. & Ullrich, C. I. Role of cytokinin in the regulation of root gravitropism. Planta 220, 177–182. https://doi.org/10.1007/s00425-004-1381-8 (2004).
CAS  Article  PubMed  Google Scholar 

67.
Aloni, R., Aloni, E., Langhans, M. & Ullrich, C. I. Role of cytokinin and auxin in shaping root architecture: Regulating vascular differentiation, lateral root initiation, root apical dominance and root gravitropism. Ann. Bot. 97, 883–893. https://doi.org/10.1093/aob/mcl027 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

68.
Aloni, R., Tollier, M. T. & Monties, B. The role of auxin and gibberellin in controlling lignin formation in primary phloem fibers and in xylem of Coleus-blumei stems. Plant Physiol. 94, 1743–1747. https://doi.org/10.1104/pp.94.4.1743 (1990).
CAS  Article  PubMed  PubMed Central  Google Scholar 

69.
Mauriat, M. & Moritz, T. Analyses of GA20ox- and GID1-over-expressing aspen suggest that gibberellins play two distinct roles in wood formation. Plant J. 58, 989–1003. https://doi.org/10.1111/j.1365-313X.2009.03836.x (2009).
CAS  Article  PubMed  Google Scholar 

70.
Dayan, J., Schwarzkopf, M., Avni, A. & Aloni, R. Enhancing plant growth and fiber production by silencing GA 2-oxidase. Plant Biotechnol. J. 8, 425–435. https://doi.org/10.1111/j.1467-7652.2009.00480.x (2010).
CAS  Article  PubMed  Google Scholar 

71.
Dombrowski, J. E. & Martin, R. C. Evaluation of reference genes for quantitative RT-PCR in Lolium temulentum under abiotic stress. Plant Sci. 176, 390–396. https://doi.org/10.1016/j.plantsci.2008.12.005 (2009).
CAS  Article  Google Scholar 

72.
Gómez-Merino, F. C. & Trejo-Téllez, L. I. The role of beneficial elements in triggering adaptive responses to environmental stressors and improving plant performance. In Biotic and Abiotic Stress Tolerance in Plants (ed Vats, S) 137–172 (Springer, Singapore, 2018). https://doi.org/10.1007/978-981-10-9029-5_6.

73.
Nemes, N. Comparatice analysis of organic and non-organic farming systems: A critical assessment of farm profitability. FAO Web side. [cited 4 May 2020]. https://www.fao.org/tempref/docrep/fao/011/ak355e/ak355e00.pdf (2017).

74.
Mariano, R. A. Profitability analysis of irradiated carrageenan as a biostimulant in small-scale rice farming in selected provinces in the Philippines. J. Glob. Bus. Trade. 14, 15–30 (2018).
Article  Google Scholar 

75.
Abad, L. V., Aranilla, C. T., Relleve, L. S. & Dela Rosa, A. M. Emerging applications of radiation-modified carrageenans. Nucl. Instrum. Methods B. 336, 167–172. https://doi.org/10.1016/j.nimb.2014.07.005 (2014).
ADS  CAS  Article  Google Scholar 

76.
Khan, W. et al. Seaweed extracts as biostimulants of plant growth and development. J. Plant Growth Regul. 28, 386–399. https://doi.org/10.1007/s00344-009-9103-x (2009).
CAS  Article  Google Scholar 

77.
Jesus, A. A., Lima, S. F., Vendruscolo, E. P., Alvarez, R. C. F. & Contardi, L. M. Agroeconomic analysis of sweet corn grown with biostimulant applied on seed. Rev. Fac. Agron. 115, 119–127 (2016).
Google Scholar 

78.
Zhang, X. & Schmidt, R. E. Hormone-containing products’ impact on antioxidant status of tall fescue and creeping bentgrass subjected to drought. Crop Sci. 40, 1344–1249. https://doi.org/10.2135/cropsci2000.4051344x (2000).
CAS  Article  Google Scholar 

79.
Crepaldi, S. A. Contabilidade Rural: Uma Abordagem Decisorial 2nd edn. (São Paulo, Atlas, 1998).
Google Scholar 

80.
Kocira, S., Szparaga, A., Kuboń, M., Czerwińska, E. & Piskier, T. Morphological and biochemical responses of Glycine max (L.) Merr. to the use of seaweed extract. Agronomy 9, 93. https://doi.org/10.3390/agronomy9020093 (2019).
CAS  Article  Google Scholar 

81.
Świeca, M., Gawlik-Dziki, U., Kowalczyk, D. & Złotek, U. Impact of germination time and type of illumination on the antioxidant compounds and antioxidant capacity of Lens culinaris sprouts. Sci. Hortic. 140, 87–95. https://doi.org/10.1016/j.scienta.2012.04.005 (2012).
CAS  Article  Google Scholar 

82.
Singleton, V. & Rossi, J. Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. Am. J. Enol. Vitic. 16, 144–158 (1965).
CAS  Google Scholar 

83.
Lamaison, J. L. C. & Carnet, A. Teneurs en principaux flavonoids des fleurs de Crataegeus monogyna Jacq et de Crataegeus laevigata (Poiret D. C) en fonction de la vegetation. Pharm. Acta Helv. 65, 315–320. https://doi.org/10.1016/j.nfs.2018.10.001 (1990).
CAS  Article  Google Scholar 

84.
Fuleki, T. & Francis, F. J. Quantitative methods for anthocyanins. 1. Extraction and determination of total anthocyanin in cranberries. J. Food Sci. 33, 72–77. https://doi.org/10.1111/j.1365-2621.1968.tb00887.x (1968).
CAS  Article  Google Scholar 

85.
Pulido, R., Bravo, L. & Saura-Calixto, F. Antioxidant activity of dietary polyphenols as determined by a modified ferric reducing/antioxidant power assay. J. Agric. Food Chem. 48, 3396–3402. https://doi.org/10.1021/jf9913458 (2000).
CAS  Article  PubMed  Google Scholar 

86.
Jimenez-Alvarez, D. et al. High-throughput methods to assess lipophilic and hydrophilic antioxidant capacity of food extracts in vitro. J. Agric. Food Chem. 56(10), 3470–3477. https://doi.org/10.1021/jf703723ss (2008).
CAS  Article  PubMed  Google Scholar 

87.
Sancho, R. A. S., Pavan, V. & Pastore, G. M. Effect of in vitro digestion on bioactive compounds and antioxidant activity of common bean seed coats. Food Res. Int. 76, 74–78. https://doi.org/10.1016/j.foodres.2014.11.042 (2015).
CAS  Article  Google Scholar 

88.
Carillo, P. & Gibon, Y. Protocol: extraction and determination of proline. [cited 4 January 2020]. https://prometheuswiki.publish.csiro.au/tiki.

89.
Redmile-Gordon, M. A., Armenise, E., White, R. P., Hirsch, P. R. & Goulding, K. W. T. A comparison of two colorimetric assays, based upon Lowry and Bradford techniques, to estimate total protein in soil extracts. Soil Biol. Biochem. 67, 166–173. https://doi.org/10.1016/j.soilbio.2013.08.017 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

90.
Goñi, I., Garcia-Alonso, A. & Saura-Calixto, F. A starch hydrolysis procedure to estimate glycemic index. Nutr. Res. 17(3), 427–437. https://doi.org/10.1016/S0271-5317(97)00010-9 (1997).
Article  Google Scholar 

91.
AOCS Approved Procedure Ba 6a-05. [cited 2 September 2020]. https://www.ssco.com.tw/Ankom/PDF_file/Crude%20Fiber%20Method%20A200.pdf.

92.
Szparaga, A. Wybrane Właściwości Fizyczne, Mechaniczne, Chemiczne i Plon Nasion Fasoli Zwykłej (Phaseolus Vulgaris L.) w Zależności od Metody Aplikacji Biostymulatorów. (Polskie Towarzystwo Inżynierii Rolniczej, 2019).

93.
Szparaga, A. et al. Survivability of probiotic bacteria in model systems of non-fermented and fermented coconut and hemp milks. Sustainability. 11, 6093. https://doi.org/10.3390/su11216093 (2019).
CAS  Article  Google Scholar  More

DeepHL system architecture
The DeepHL system consists of three server computers. The first one is a web server that receives a trajectory data file from a user and provides analysis results to the user (Intel Xeon E5-2620 v4, 16 cores, 32 GB RAM, Ubuntu 14.04). The second one is a storage server that stores data files and analysis results. The third one is a GPU server that analyzes data provided by the user (Intel Xeon E5-2620 v4, 32 cores, 512 GB RAM, four NVIDIA Quadro P6000, Ubuntu 14.04). Supplementary Information, Algorithm, provides a complete description of the DeepHL method. DeepHL is accessible on the Internet through http://www-mmde.ist.osaka-u.ac.jp/maekawa/deephl/. Supplementary Information, User guide to DeepHL, provides a user guide to DeepHL. In addition, Supplementary Information, Usage of Python-based Software, and Supplementary Software 1 present the Python code of DeepHL.
Preprocessing
An input trajectory is a series of timestamps and X/Y coordinates associated with a class label. To perform position- and rotation-independent analysis, we convert the series into time series of speed and relative angular speed and then standardize them (Supplementary Information, Algorithm). Note that the absolute coordinates of wild animals, which can relate to the distance from a nest or feeding location, for example, are important in understanding behavior of the animals. Hence, DeepHL allows the original coordinates to be input to DeepHL-Net along with the speed and relative angular speed. In addition, other biological time-series sensor data measured by the user can be fed into DeepHL-Net when these time-series data are included in a data file uploaded by the user. For example, a time series of the heading direction of animals obtained from digital compasses can be useful for behavior understanding. Moreover, primitive features usually used in trajectory analysis can be easily fed into DeepHL-Net. DeepHL automatically computes the travel distance from the initial position, the straight-line distance from the initial position, and the angle from the initial position (Supplementary Table 1) as primitive features. Using the web interface of DeepHL, the user can easily select primitive features and other sensor data to be fed into DeepHL-Net (Supplementary Information, User guide to DeepHL). See Supplementary Information, Effect of input features, for effects of input features on classification accuracy. Normally, the inputs of DeepHL-Net are two-dimensional time series, that is, speed and relative angular speed. When we input an additional time series (such as the original coordinates) into DeepHL-Net, the additional time series are added as additional dimensions of the inputs.
Multi-scale layer-wise attention model (DeepHL-Net)
Here, we explain DeepHL-Net shown in Fig. 2f in detail. The input of the model is a time series of primitive features, that is, an lMAX × Nf matrix, where lMAX is the maximum length of the input trajectories and Nf is the dimensionality of the time series, that is, the number of the primitive features. Because the lengths of observed trajectories are not identical to each other in many cases, we fill in missing elements in the matrix with  −1.0 and mask them when we train DeepHL-Net. In each 1D convolutional layer of the convolutional stacks, we extract features by convolving input features through the time dimension using a filter with a width (kernel size) of Ft. We use different filter widths in the four convolutional stacks (3%, 6%, 9%, and 12% of lMAX) to extract features at different levels of scale. We use a stride (step size) of one sample in terms of the time axis. We also use padding to allow the outputs of a layer to have the same length as the layer inputs. In addition, to reduce an overfitting, we employ a dropout, which is a simple regularization technique in which randomly selected neurons are dropped during training44. The dropout rate used in this study is 0.5.
In each LSTM layer of the LSTM stacks, we extract features considering the long-term dependencies of the input features. LSTM is a recurrent neural network architecture with memory cells, and it permits us to learn temporal relationships over a long time scale. LSTM learns long-term dependencies by employing memory cells that hold past information, updating the cell state using write, read, and reset operations with input, output, and forget gates (see Supplementary Information, Algorithm). In addition, we employ dropout to reduce overfitting. The attention information of each layer is computed by using Eq. (1), and then it is multiplied by the layer output. Here, the softmax and tanh functions in Eq. (1) are defined as follows:

$$,{text{softmax}},({x}_{j})=frac{exp ({x}_{j})}{{sum }_{i}exp ({x}_{i})},$$
(2)

$$tanh ({x}_{j})=frac{exp ({x}_{j})-exp (-{x}_{j})}{exp ({x}_{j})+exp (-{x}_{j})}.$$
(3)

Note that parameters in Eq. (1) for each layer, that is, Wa and ba, as well as parameters in the convolutional and LSTM layers are estimated during the network training phase. Here, we introduced the tanh activation function into Eq. (1) to smooth out the output attention values. When an outlying large value is included in WaZT + ba at time t, attention values other than time t become extremely small without using the tanh function. When we visualize a trajectory using such attention values, only a single data point is colored in red, making it difficult for a user to identify important segments.
Training and testing of DeepHL-Net
The DeepHL user can select the parameters of DeepHL-Net used in the analysis, that is, the number of convolutional/LSTM layers and the number of neurons in each layer (default: four layers with 16 neurons). Then, DeepHL-Net is trained on 80% of randomly selected trajectories to minimize the binary classification error of the training data, employing backpropagation based on Adam45 (Supplementary Information, Algorithm). (Note that each trajectory has a class label for binary classification.) Then, the trained DeepHL-Net is tested using the remaining 20% of trajectories to compute the classification accuracy, providing an indication of the degree of difference between the two classes.
Computing the score of each layer
To screen the layers in DeepHL-Net, we compute a score for each layer according to Eq. (4)

$$s({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})={s}_{mathrm{fc}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})+{s}_{mathrm{it}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}}).$$
(4)

Here, ({A}_{i,{C}_{mathrm{A}}}) is a set of attention vectors calculated from trajectories belonging to class A using the ith layer. In addition, ({A}_{i,{C}_{mathrm{B}}}) is a set of attention vectors calculated from trajectories belonging to class B using the ith layer. As mentioned in the main text, an attention vector from a discriminator layer should have large values within limited segments. Therefore, ({s}_{mathrm{fc}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})) in Eq. (4) calculates the averaged variance of the attention values normalized by the average length of the trajectories, as described in Eq. (5). When the layer focuses on a part of a trajectory, the variance increases

$${s}_{mathrm{fc}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})=sqrt{frac{1}{| {A}_{i,{C}_{mathrm{A}}}cup {A}_{i,{C}_{mathrm{B}}}| cdot l({A}_{i,{C}_{mathrm{A}}}cup {A}_{i,{C}_{mathrm{B}}})}sum _{{bf{a}}in {A}_{i,{C}_{mathrm{A}}}cup {A}_{i,{C}_{mathrm{B}}}}V({bf{a}})}.$$
(5)

Note that V(⋅) calculates the variance and l(⋅) calculates the average length of the trajectories. We take the square root of the average variance to derive the average standard deviation. Using (l({A}_{i,{C}_{mathrm{A}}}cup {A}_{i,{C}_{mathrm{B}}})), which calculates the average length of ({A}_{i,{C}_{mathrm{A}}}cup {A}_{i,{C}_{mathrm{B}}}), we normalize the computed variance. Because the softmax function in Eq. (1) ensures that all values sum to 1, resulting in a larger variance for longer trajectories, we normalize the average variance using the average length.
In addition, as mentioned in the main text, the distribution of attention values by the layer for one class should be different from that for another class. Therefore, ({s}_{mathrm{it}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})) calculates the difference between the distributions of the attention values of classes A and B as follows:

$${s}_{mathrm{it}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})=(1-,{mathrm{Intersect}},(h(A_{{i,{C}}_{mathrm{A}}}),h({{A}}_{{i,{C}}_{mathrm{B}}}))).$$
(6)

Here, h(⋅) calculates a normalized histogram of attention with 200 bins, and Intersect(⋅ , ⋅) calculates the area overlap between two histograms, and is described as follows:

$${mathrm{Intersect}},(H_{1},H_{2})=mathop{sum}limits_{i}min (H_{1}(i),H_{2}(i)),$$
(7)

where H1(i) shows the normalized frequency of the ith bin of histogram H1. As described in Eq. (4), the final score is calculated as the sum of the two scores of ({s}_{mathrm{fc}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})) and ({s}_{mathrm{it}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})).
Here, ({s}_{mathrm{fc}}({A}_{i,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}})) in Eq. (4) is used to find a layer that focuses only on a portion of a trajectory. Owing to the term, only a small important portion of trajectories is highlighted in many cases, as shown in Figs. 3, 5, and 6, especially for the trajectories of beetles. However, substantial portions of several trajectories of the normal mice are highlighted, as shown in Fig. 4d. Because the characteristics of the normal mouse trajectories are the distance from the initial position, the segments in the trajectories far from the initial position are highlighted.
Computing the correlation between attention values and handcrafted features
To help the user understand the meaning of the highlights, DeepHL automatically computes the Pearson correlation coefficients between the attention values of each layer and handcrafted features computed by DeepHL, as shown in Supplementary Table 1. In addition, the correlation coefficients with sensor data and handcrafted features included in a trajectory data file are automatically computed. Computing the correlation with environmental sensor data can reveal the relationship between a behavior and environmental conditions. If a specific behavior is exhibited only when the temperature is high, for example, we can infer that the behavior relates to the high temperature condition. Furthermore, DeepHL automatically computes the moving average, moving variance, and derivative of each of the above features/sensor data, and then computes the correlation coefficients with the attention values, which are presented to the user (Supplementary Fig. 1).
Computing the difference between distributions of each handcrafted feature for the two classes within highlighted segments
To help the user understand the meaning of the highlights, DeepHL automatically computes the difference between distributions of each handcrafted feature for two classes within highlighted segments. The difference is computed as follows:

$${mathrm{diff}}({A}_{i,{C}_{mathrm{A}}},{F}_{j,{C}_{mathrm{A}}},{A}_{i,{C}_{mathrm{B}}},{F}_{j,{C}_{mathrm{B}}})=1-,{mathrm{Intersect}},(h(m({{A}}_{{i,{C}}_{mathrm{A}}},{{F}}_{{j,{C}}_{mathrm{A}}})),h(m({{A}}_{{i,{C}}_{mathrm{B}}},{{F}}_{{j,{C}}_{mathrm{B}}}))).$$
(8)

Here, ({F}_{j,{C}_{mathrm{A}}}) is a set of time series of the jth handcrafted feature calculated from trajectories belonging to class A. In addition, m(⋅ , ⋅) is a masking function that extracts feature values within highlighted segments. Because the softmax function in each attention layer ensures that all attention values in a sum of 1, we consider an attention value larger than c/(# time slices) as a potential attended value (c = 1.2 in our implementation).
Data acquisition of worms
Data acquisition was performed according to Yamazoe-Umemoto et al.22. In brief, several worms were placed in the center of an agar plate in a 9-cm Petri dish, 30% 2-nonanone (v/v, EtOH) was spotted on the left side of the plate, which was covered by a lid and placed on the bench upside down. Then, the images of the plate were captured with a high-resolution USB camera for 12 min at 1 Hz. Because the worms do not exhibit odor avoidance behavior during the first 2 min because of the rapid increase in odor concentration46, the data for the following 10 min (i.e., 600 s) was used. From the images, individual worms were identified and the position of the centroid was recorded by an image processing software Move-tr/2D (v. 8.31; Library Inc., Japan). The number of recorded trajectories is 325 (Supplementary Table 2). The comparison was between the naive worms (control class) and the worms after preexposure to the odor (preexposed class).
DeepHL analysis of worms
A multivariate time series of movement speed, relative angular speed, distances from the initial position, and angle from the initial position extracted from the time series of trajectories was fed into DeepHL-Net, yielding a binary classification accuracy of 93.9%, where 20% of the data are used as test data. The discriminator layer used in this investigation has the highest score of all layers. As shown in Fig. 3d, which was calculated from the moving variance of the speed within highlighted segments, we can state that the changes in the speed of preexposed worms is larger than those of control worms. Figure 3e shows spectrograms of the speed calculated from entire trajectories (Fig. 3c) with a 128-s wide sliding window shifted in 1-sample intervals. In addition, Fig. 3f shows histograms of the dominant frequency of speed calculated from entire trajectories using the 128-s wide sliding window shifted in 1-sample intervals. These results also indicate the difference in the frequency of speed between the preexposed and control worms. Our investigation revealed that the dominant frequency of speed significantly differs between the preexposed and control worms using GLMM with Gaussian distributions (t = −6.60; d.f. = 322.8; p = 1.68 × 10−10, effect size(r2) = 0.232). The p value is two sided. Individual factors were treated as random effects. The number of data points for the control class is n = 76, 784 and that for the preexposed class is n = 75, 750. We used GLMM with Gaussian distributions because the objective variable has a continuous value and we used the lmerTest package (v. 2.0–36) of R (v. 3.4.3) for the analysis.
Data acquisition of mice
We collected 52 trajectories of normal mice and unilateral 6-hydroxydopamine (OHDA) lesion mouse models of PD while they freely moved for 10 min in an open field (60 × 55 cm2, wall height = 20 cm; normal: 22, PD: 30). The trajectories were detected by the animal’s head position, which was captured by an overhead digital video camera (60 fps). Two sets of small red and green light-emitting diodes were mounted above the animal’s head so that it could be located in each frame. Custom softwares based on Matlab (R2018b, Mathworks, MA, USA) and LabVIEW (Labview 2018, National Instruments, TX, USA) were used for tracking. We then created 30-s segments by splitting each trajectory because training a DNN requires a number of trajectories. We used 966 segments in total (normal: 374, PD: 592) collected from nine C57BL/6J mice (normal: 5, PD: 4). Note that we excluded 30-s segments that contain no movements of a mouse.
DeepHL analysis of mice
Movement speed, relative angular speed, travel distances, straight-line and travel distances from the initial position, and angle from the initial position were fed into our model. The accuracy for the binary classification of normal and 6-OHDA model mice was 74.7%, where 20% of the data are used as test data. The score of the discriminator layer was the highest of all LSTM layers and the sixth highest of all layers. Our investigation revealed that the behavior of visiting locations far away from the initial position can be characteristic of normal mice.
To evaluate PD symptoms from animal behaviors, previous studies have exclusively focused on the movement speed of animals in the open-field tests (frequency and bout duration of ambulation as well as immobility or fine movement) because typical symptoms in the animal model of PD are thought to be slowness of movement and a paucity of spontaneous movements. As shown in Fig. 4e–g, we found significant differences in average movement speed during ambulation periods, average movement speed during fine movement periods, and average maximum distance within a ±60-s window in a session. These differences were derived from the findings of DeepHL using the two-sided Wilcoxon rank-sum test (W = 544, p = 3.486 × 10−5, effect size (Cliff’s delta) = −0.648; W = 511, p = 5.869 × 10−4, effect size (Cliff’s delta) = −0.548; W = 521, p = 2.666 × 10−4, effect size (Cliff’s delta) = −0.579). The 95% confidence intervals are [1.222, 3.481], [0.139, 0.468], and [13.726, 43.175], respectively. We used the exactRankTests package (v. 0.8–29) of R (v. 3.2.3). Note that these behavioral features are extracted from original 10-min trajectories.
The maximum distance, which was derived from a finding of DeepHL, is more useful for evaluating the PD symptoms than conventional measures based on the movement speed. Note that the new feature is designed based on an insight drawn from an analysis by deep learning. These results suggest that DeepHL helps find a novel measure not directly linked to the movement speed, that is, a straight-line distance within a certain time window. When the aim of an animal is to visit all locations in an area, the travel distance over a short duration commonly becomes longer. Besides, it is well known that rodents, including mice and rats, spontaneously prefer to explore an environment, particularly in novel places. Thus, DeepHL may capture the fact that the abnormal behavior of the 6-OHDA lesion model of PD hinders such spontaneous behavioral traits of normal mice. Indeed, the 6-OHDA lesion mouse model appears to remain in the same place. Although this hypothesis should be verified based on the causality between behavioral traits and neural activity patterns underlying PD symptoms using neuronal recording together with its optogenetic manipulation in the basal ganglia and motor cortex23, it is beyond the scope of this study.
Behavioral features of mice
According to Kravitz et al.23, ambulation was defined as periods when the velocity of the animal’s center point averaged >2 cm/s for at least 0.5 s. Immobility was defined as continuous periods of time during which the average change of the trajectory was More

1.
Ljungqvist, F. C. A new reconstruction of temperature variability in the extra‐tropical northern hemisphere during the last two millennia. Geogr. Ann. Ser. A, Phys. Geogr. 92, 339–351 (2010).
2.
Wang, Y. et al. The holocene asian monsoon: links to solar changes and north atlantic climate. Science 80(308), 854–857 (2005).
ADS  Article  CAS  Google Scholar 

3.
Zhang, P. et al. A test of climate, sun, and culture relationships from an 1810-year Chinese cave record. Science 80(322), 940–942 (2008).
ADS  Article  CAS  Google Scholar 

4.
Wang, X. et al. Climate, desertification, and the rise and collapse of China’s historical dynasties. Hum. Ecol. 38, 157–172 (2010).
Article  Google Scholar 

5.
Paulsen, D. E., Li, H.-C. & Ku, T.-L. Climate variability in central China over the last 1270 years revealed by high-resolution stalagmite records. Quat. Sci. Rev. 22, 691–701 (2003).
ADS  Article  Google Scholar 

6.
Lee, H. & Zhang, D. Space-time integration in geography and GIScience. Space-time integration in geography and giscience: research frontiers in the US and China (Springer Netherlands, 2015). https://doi.org/10.1007/978-94-017-9205-9.

7.
Jia, D., Li, Y. & Fang, X. Complexity of factors influencing the spatiotemporal distribution of archaeological settlements in northeast China over the past millennium. Quat. Res. 89, 413–424 (2018).
Article  Google Scholar 

8.
Lee, U. The comparative historical study on the weather characteristics in the second half of the 15th century. Korean Stud. 21, 389–415 (2012).
Google Scholar 

9.
Jo, K. et al. 1000-Year quasi-periodicity of weak monsoon events in temperate northeast Asia since the mid-Holocene. Sci. Rep. 7, 15196 (2017).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

10.
Jo, K. et al. Mid-latitude interhemispheric hydrologic seesaw over the past 550,000 years. Nature 508, 378–382 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

11.
Lee, E. et al. Multi-proxy records of Holocene hydroclimatic and environmental changes on the southern coast of South Korea. Palaeogeogr. Palaeoclimatol. Palaeoecol. 545, 109642 (2020).
Article  Google Scholar 

12.
Park, J. Solar and tropical ocean forcing of late-Holocene climate change in coastal East Asia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 469, 74–83 (2017).
Article  Google Scholar 

13.
Constantine, M., Kim, M. & Park, J. Mid- to late Holocene cooling events in the Korean Peninsula and their possible impact on ancient societies. Quat. Res. 92, 98–108 (2019).
CAS  Article  Google Scholar 

14.
Lim, J. et al. Holocene coastal environmental change and ENSO-driven hydroclimatic variability in East Asia. Quat. Sci. Rev. 220, 75–86 (2019).
ADS  Article  Google Scholar 

15.
Yum, J. G., Takemura, K., Tokuoka, T. & Yu, K. M. Holocene environmental changes of the Hwajinpo Lagoon on the eastern coast of Korea. J. Paleolimnol. 29, 155–166 (2003).
Article  Google Scholar 

16.
Cheung, R. C. W. et al. Decadal- to centennial-scale East Asian summer monsoon variability over the past millennium: An oceanic perspective. Geophys. Res. Lett. 45, 7711–7718 (2018).
ADS  Article  Google Scholar 

17.
Fujiki, T. & Yasuda, Y. Vegetation history during the Holocene from Lake Hyangho, northeastern Korea. Quat. Int. 123–125, 63–69 (2004).
Article  Google Scholar 

18.
Song, B. et al. Pollen record of the mid- to late-Holocene centennial climate change on the East coast of South Korea and its influential factors. J. Asian Earth Sci. 151, 240–249 (2018).
ADS  Article  Google Scholar 

19.
Hwang, S., Kim, J.-Y. & Kim, S. Environmental changes and embankment addition of Reservoir Gonggeomji, Sangju City between Late Silla- and Early Goryeo dynasty. J. Korean Geomorphol. Assoc. 21, 165–180 (2014).
Google Scholar 

20.
Jhun, J. & Moon, B. Restorations and analyses of rainfall amount observed by Chukwookee. Asia-Pacific J. Atmos. Sci. 33, 691–707 (1997).
Google Scholar 

21.
Yoo, C., Park, M., Kim, H. J. & Jun, C. Comparison of annual maximum rainfall events of modern rain gauge data (1961–2010) and Chukwooki data (1777–1910) in Seoul Korea. J. Water Clim. Chang. 9, 58–73 (2018).
Article  Google Scholar 

22.
Lim, J., Lee, J.-Y., Hong, S.-S. & Kim, J.-Y. Late Holocene flooding records from the floodplain deposits of the Yugu River South Korea. Geomorphology 180–181, 109–119 (2013).
ADS  Article  Google Scholar 

23.
Li, J. et al. Quantitative Holocene climatic reconstructions for the lower Yangtze region of China. Clim. Dyn. 50, 1101–1113 (2018).
Article  Google Scholar 

24.
Sun, J. et al. Quantitative precipitation reconstruction in the east-central monsoonal China since the late glacial period. Quat. Int. 521, 175–184 (2019).
Article  Google Scholar 

25.
Stebich, M. et al. Holocene vegetation and climate dynamics of NE China based on the pollen record from Sihailongwan Maar Lake. Quat. Sci. Rev. 124, 275–289 (2015).
ADS  Article  Google Scholar 

26.
Li, J. et al. East Asian summer monsoon precipitation variations in China over the last 9500 years: A comparison of pollen-based reconstructions and model simulations. The Holocene 26, 592–602 (2016).
ADS  Article  Google Scholar 

27.
Cao, X. et al. Impacts of the spatial extent of pollen-climate calibration-set on the absolute values, range and trends of reconstructed Holocene precipitation. Quat. Sci. Rev. 178, 37–53 (2017).
ADS  Article  Google Scholar 

28.
Wu, D. et al. Decoupled early Holocene summer temperature and monsoon precipitation in southwest China. Quat. Sci. Rev. 193, 54–67 (2018).
ADS  Article  Google Scholar 

29.
Park, J. A modern pollen–temperature calibration data set from Korea and quantitative temperature reconstructions for the Holocene. The Holocene 21, 1125–1135 (2011).
ADS  Article  Google Scholar 

30.
Tian, F. et al. Pollen-climate relationships in time (9 ka, 6 ka, 0 ka) and space (upland vs. lowland) in eastern continental Asia. Quat. Sci. Rev. 156, 1–11 (2017).
ADS  Article  Google Scholar 

31.
Herzschuh, U. et al. Position and orientation of the westerly jet determined Holocene rainfall patterns in China. Nat. Commun. 10, 2376 (2019).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

32.
Li, C., Wu, Y. & Hou, X. Holocene vegetation and climate in Northeast China revealed from Jingbo Lake sediment. Quat. Int. 229, 67–73 (2011).
Article  Google Scholar 

33.
Hu, C. et al. Quantification of Holocene Asian monsoon rainfall from spatially separated cave records. Earth Planet. Sci. Lett. 266, 221–232 (2008).
ADS  CAS  Article  Google Scholar 

34.
Wen, R. et al. Holocene precipitation and temperature variations in the East Asian monsoonal margin from pollen data from Hulun Lake in northeastern Inner Mongolia China. Boreas 39, 262–272 (2010).
Article  Google Scholar 

35.
Berger, A. & Loutre, M. F. Insolation values for the climate of the last 10 million years. Quat. Sci. Rev. 10, 297–317 (1991).
ADS  Article  Google Scholar 

36.
Zhao, K. et al. Contribution of ENSO variability to the East Asian summer monsoon in the late Holocene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 449, 510–519 (2016).
Article  Google Scholar 

37.
Giry, C. et al. Mid- to late Holocene changes in tropical Atlantic temperature seasonality and interannual to multidecadal variability documented in southern Caribbean corals. Earth Planet. Sci. Lett. 331–332, 187–200 (2012).
ADS  Article  CAS  Google Scholar 

38.
Viles, H. Interannual, decadal and multidecadal scale climatic variability and geomorphology. Earth-Science Rev. 61, 105–131 (2003).
ADS  Article  Google Scholar 

39.
Lim, J. & Fujiki, T. Vegetation and climate variability in East Asia driven by low-latitude oceanic forcing during the middle to late Holocene. Quat. Sci. Rev. 30, 2487–2497 (2011).
ADS  Article  Google Scholar 

40.
Williams, J. W., Post*, D. M., Cwynar, L. C., Lotter, A. F. & Levesque, A. J. Rapid and widespread vegetation responses to past climate change in the North Atlantic region. Geology 30, 971 (2002).
ADS  CAS  Article  Google Scholar 

41.
Yu, Z. Late quaternary dynamics of tundra and forest vegetation in the southern niagara escarpment Canada. New Phytol. 157, 365–390 (2003).
Article  Google Scholar 

42.
Yu, Z. Rapid response of forested vegetation to multiple climatic oscillations during the last deglaciation in the northeastern United States. Quat. Res. 67, 297–303 (2007).
Article  Google Scholar 

43.
Richter, H. & Kituta, S. Ecophysiology of long-distance water transport in trees. in Trees in a Changing Environment- Ecophysiology, Adaptation, and Future Survival (eds. Tausz, M. & Grulke, N.) (Springer Nature, 2014).

44.
Johnson, M. T. & Agrawal, A. A. The ecological play of predator–prey dynamics in an evolutionary theatre. Trends Ecol. Evol. 18, 549–551 (2003).
Article  Google Scholar 

45.
Tobolski, K. & Ammann, B. Macrofossils as records of plant responses to rapid Late Glacial climatic changes at three sites in the Swiss Alps. Palaeogeogr. Palaeoclimatol. Palaeoecol. 159, 251–259 (2000).
Article  Google Scholar 

46.
Ammann, B. Biotic responses to rapid climatic changes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 159, 191–201 (2000).
Article  Google Scholar 

47.
Walther, G. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).
ADS  CAS  Article  PubMed  Google Scholar 

48.
Lischke, H., Lotter, A. F. & Fischlin, A. Untangling a Holocene pollen record with forest model simulations and independent climate data. Ecol. Modell. 150, 1–21 (2002).
Article  Google Scholar 

49.
Steinhilber, F., Beer, J. & Fröhlich, C. Total solar irradiance during the Holocene. Geophys. Res. Lett. 36, L19704 (2009).
ADS  Article  Google Scholar 

50.
Solanki, S. K., Usoskin, I. G., Kromer, B., Schüssler, M. & Beer, J. Unusual activity of the Sun during recent decades compared to the previous 11,000 years. Nature 431, 1084–1087 (2004).
ADS  CAS  Article  PubMed  Google Scholar 

51.
Yi, S. & Kim, J.-Y. Pollen analysis at Paju Unjeong, South Korea: Implications of land-use changes since the late Neolithic. The Holocene 22, 227–234 (2012).
ADS  Article  Google Scholar 

52.
Yi, S., Yang, D.-Y. & Jia, H. Pollen record of agricultural cultivation in the west–central Korean Peninsula since the Neolithic Age. Quat. Int. 254, 49–57 (2012).
Article  Google Scholar 

53.
Yi, S., Saito, Y., Zhao, Q. & Wang, P. Vegetation and climate changes in the Changjiang (Yangtze River) Delta, China, during the past 13,000 years inferred from pollen records. Quat. Sci. Rev. 22, 1501–1519 (2003).
ADS  Article  Google Scholar 

54.
Kim, C. & Cheong, K. Research Report of Antiquities Vol. 204: Gonggeomji. Gyeongsangbukdo Institute of Cultural Properties, 230 p (2013) (in Korean).

55.
Ammann, B. et al. Quantification of biotic responses to rapid climatic changes around the Younger Dryas- a synthesis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 159, 313–347 (2000).
Article  Google Scholar 

56.
Jo, K., Woo, K. S., Hong, G. H., Kim, S. H. & Suk, B. C. Rainfall and hydrological controls on speleothem geochemistry during climatic events (droughts and typhoons): an example from Seopdong Cave, Republic of Korea. Earth Planet. Sci. Lett. 295, 441–450 (2010).
ADS  CAS  Article  Google Scholar 

57.
Danzeglocke, U., Joris, O. & Weninger, B. CalPal-2007online. Available at: https://www.calpal-online.de.

58.
Blaauw, M. Methods and code for ‘classical’ age-modelling of radiocarbon sequences. Quat. Geochronol. 5, 512–518 (2010).
Article  Google Scholar 

59.
Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).
CAS  Article  Google Scholar 

60.
Moore, P. D., Webb, J. A. & Collison, M. E. Pollen Analysis 2nd edn. (Blackwell Scientific Publications, Oxford, 1991).
Google Scholar 

61.
Stockmarr, J. Tablets with spores used in absolute pollen analysis. Pollen Spores 13, 615–621 (1971).
Google Scholar 

62.
Grimm, E. Tilia 1.7.16 Software. Illinois State Museum, Research and Collection Center, Springfield, II. (2011).

63.
Chevalier, M., Cheddadi, R. & Chase, B. M. CREST (Climate REconstruction SofTware): a probability density function (PDF)-based quantitative climate reconstruction method. Clim. Past 10, 2081–2098 (2014).
Article  Google Scholar 

64.
Chevalier, M. Enabling possibilities to quantify past climate from fossil assemblages at a global scale. Glob. Planet. Change 175, 27–35 (2019).
ADS  Article  Google Scholar 

65.
Lim, J., Yi, S., Nahm, W.-H. & Kim, J.-Y. Holocene millennial-scale vegetation changes in the Yugu floodplain, Kongju area, central South Korea. Quat. Int. 254, 92–98 (2012).
Article  Google Scholar 

66.
Jung, S.-K. & McDonald, K. Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization. BMC Bioinformatics 12, 340 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

67.
Singal developer. Signal: Signal processing. (2013).

68.
Polanco-Martinez, Josue, M., Medina-Elizalde, Martin, A., Goni, Maria, Fernanda, S. & Mudelsee, M. BINCOR: An R package for Estimating the Correlation between Two Unevenly Spaced Time Series. R J. 11, 170 (2019).

69.
Yim, T. & Kira, T. Distribution forest vegetation and climate in the Korea Peninsula. I. Distribution of some indices of thermal climate. Japanese J. Ecol. 25, 77–88 (1975).

70.
Steinhilber, F. et al. 9,400 years of cosmic radiation and solar activity from ice cores and tree rings. Proc. Natl. Acad. Sci. 109, 5967–5971 (2012).
ADS  CAS  Article  PubMed  Google Scholar 

71.
Stott, L. et al. Decline of surface temperature and salinity in the western tropical Pacific Ocean in the Holocene epoch. Nature 431, 56–59 (2004).
ADS  CAS  Article  PubMed  Google Scholar 

72.
Moy, C. M., Seltzer, G. O., Rodbell, D. T. & Anderson, D. M. Variability of El Niño/Southern Oscillation activity at millennial timescales during the Holocene epoch. Nature 420, 162–165 (2002).
ADS  CAS  Article  PubMed  Google Scholar  More

1.
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
CAS  Article  Google Scholar 
2.
Smeraldo, S. et al. Modelling risks posed by wind turbines and power lines to soaring birds: the black stork (Ciconia nigra) in Italy as a case study. Biodivers. Conserv. 29, 1959–1976 (2020).
Article  Google Scholar 

3.
Gutierrez, B. L. et al. An island of wildlife in a human-dominated landscape: the last fragment of primary forest on the Osa Peninsula’s Golfo Dulce coastline Costa Rica. PLoS ONE 14, e0214390 (2019).
CAS  PubMed Central  Article  PubMed  Google Scholar 

4.
Padalia, H. et al. Assessment of historical forest cover loss and fragmentation in Asian elephant ranges in India. Environ. Monit. Assess. 191, 802 (2019).
Article  Google Scholar 

5.
Sodhi, N. S., Lee, T. M., Koh, L. P. & Brook, B. W. A meta-analysis of the impact of anthropogenic forest disturbance on Southeast Asia’s biotas. Biotropica 41, 103–109 (2009).
Article  Google Scholar 

6.
Beier, P. Determining minimum habitat areas and habitat corridors for cougars. Conserv. Biol. 7, 94–108 (1993).
Article  Google Scholar 

7.
MacNally, R. & Bennett, A. F. Species-specific prediction of the impact of habitat fragmentation: local extinction of birds in the box-ironbark forests of central Victoria Australia. Biol. Conserv. 82, 147–155 (1997).
Article  Google Scholar 

8.
Hanski, I. Habitat connectivity, habitat continuity, and metapopulations in dynamic landscapes. Oikos 8, 209–219 (1999).
Article  Google Scholar 

9.
Weaver, J. L., Paquet, P. C. & Ruggerio, L. F. Resilience and conservation of large carnivores in the Rocky Mountains. Conserv. Biol. 10, 964–976 (1996).
Article  Google Scholar 

10.
Smith, J. B., Nielsen, C. K. & Hellgren, E. C. Suitable habitat for recolonizing large carnivores in the midwestern USA. Oryx 50, 555–564 (2016).
Article  Google Scholar 

11.
Morehouse, A. T., Hughes, C., Manners, N., Bectell, J. & Bruder, T. Carnivores and communities: a case study of human-carnivore conflict mitigation in southwestern Alberta. Front. Ecol. Evol. 8, 2 (2020).
Article  Google Scholar 

12.
Pelton, M. R. et al. American black bear conservation action plan in Bears (ed. Servheen, C., Herrero, S., & Peyton, B.) 144–146. Status survey and conservation action plan. (IUCN/SSC Bear and Polar Bear Specialist Groups, 1999).

13.
Williamson, D. F. In the Black: Status, Management, and Trade of the American Black Bear (Ursus americanus) in North America (TRAFFIC North America. World Wildlife Fund, Washington, DC, 2002).
Google Scholar 

14.
Hristienko, H. & McDonald, J. E. Jr. Going into the 21st century: a perspective on trends and controversies in the management of the American black bear. Ursus 18, 72–88 (2007).
Article  Google Scholar 

15.
Scheick, B. K. & McCown, W. Geographic distribution of American black bears in North America. Ursus 25, 24–33 (2014).
Article  Google Scholar 

16.
Wright, S. Evolution and the genetics of populations (The University of Chicago Press, Chicago, 1984).
Google Scholar 

17.
Wooding, J. B. & Hardisky, T. S. Home range, habitat use, and mortality of black bears in north-central Florida. Int. Conf. Bear Res. Manag. 9, 349–356 (1994).
Google Scholar 

18.
Florida Game and Fresh Water Fish Commission. Management of the Black Bear in Florida: A Staff Report to the Commissioners (Florida Game and Fresh Water Fish Commission, Tallahassee, 1993).
Google Scholar 

19.
Florida Fish and Wildlife Conservation Commission. Florida Black Bear Management Plan (Florida Game and Fresh Water Fish Commission, Tallahassee, 2019).
Google Scholar 

20.
Dixon, J. D. Genetic consequences of habitat fragmentation and loss: the case of the Florida black bear (Ursus americanus floridanus). Conserv. Genet. 8, 455–464 (2007).
Article  Google Scholar 

21.
Brown, J. H. Challenges in Estimating Size and Conservation of Black Bear in West-Central Florida. Thesis, University of Kentucky (2004)

22.
Humm, J. M., McCown, J. W., Scheick, B. K. & Clark, J. D. Spatially explicit population estimates for black bears based on cluster sampling. J. Wildl. Manag. 81, 1187–1201 (2017).
Article  Google Scholar 

23.
Florida Fish and Wildlife Conservation Commission. Florida Black Bear Management Plan (Florida Game and Fresh Water Fish Commission, Tallahassee, 2012).
Google Scholar 

24.
Carr, M. H. & Zwick, P. D. Technical Report Florida 2070: Mapping Florida’s Future—Alternative Patterns of Development in 2070 (Geoplan Center at the University of Florida, Gainesville, 2016).
Google Scholar 

25.
Noss, R. E., Quiqley, H. B., Hornocker, M. G., Merrill, T. & Paquet, P. C. Conservation biology and carnivore conservation in the Rocky Mountains. Conserv. Biol. 10, 94–96 (1996).
Google Scholar 

26.
Breitenmoser, U. Large predators in the Alps: the fall and rise of man’s competitors. Biol. Conserv. 83, 279–289 (1998).
Article  Google Scholar 

27.
Waser, P. M. Patterns and consequences of dispersal in gregarious carnivores. In Carnivore Behavior, Ecology, and Evolution (ed. Gittleman, J. L.) 267–295 (Cornell University Press, Ithaca, 1996).
Google Scholar 

28.
Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
Article  Google Scholar 

29.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Article  Google Scholar 

30.
Yackulic, C. B. et al. Presence-only modelling using MAXENT: When can we trust the inferences?. Methods Ecol. Evol. 4, 236–243 (2013).
Article  Google Scholar 

31.
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).
Article  Google Scholar 

32.
De Oliveira Moreira, D. et al. The distributional ecology of the maned sloth: Environmental influences on its distribution and gaps in knowledge. PLoS ONE. 9, 1–12 (2014).
Google Scholar 

33.
Martin, J. et al. Brown bear habitat suitability in the Pyrenees: transferability across sites and linking scales to make the most of scarce data. J. Appl. Ecol. 49, 621–631 (2012).
Article  Google Scholar 

34.
Khosravi, R., Hemami, R. K. M. & Cushman, S. A. Multi-scale niche modeling of three sympatric felids of conservation importance in central Iran. Landsc. Ecol. 34, 2451–2467 (2019).
Article  Google Scholar 

35.
Maehr, D. S., McCown, J. W., Land, E. D. & Roof, J. C. Southwest Florida Black Bear Habitat Use, Distribution, Movements, and Conservation Strategy (Florida Game and Fresh Water Fish Commission, Gainesville, 1992).
Google Scholar 

36.
McCown, W., Kublis, P., Eason, T. & Scheick, B. Black Bear Movements and Habitat Use Relative to Roads in Ocala National Forest (Florida Fish and Wildlife Commission, Gainesville, 2004).
Google Scholar 

37.
Dobey, S. Ecology of Florida black bears in the Okefenokee-Osceola ecosystem. Wildl. Monogr. 158, 1–41 (2005).
Google Scholar 

38.
Ulrey, W. A. Home Range, Habitat Use, and Food Habits of the Black Bear in South-Central Florida. Thesis, University of Kentucky (2008)

39.
Karelus, D. L., McCown, J. W., Scheick, B. K., van de Kerk, M. & Oli, M. K. Home ranges and habitat selection by black bears in a newly colonized population in Florida. Southeast Nat. 15, 346–364 (2016).
Article  Google Scholar 

40.
Karelus, D. L., McCown, J. W., Scheick, B. K. & Oli, M. K. Microhabitat features influencing habitat use by Florida black bears. Glob. Ecol. Conserv. 13, e00367 (2018).
Article  Google Scholar 

41.
Olson, D. M. & Dinerstein, E. The Global 200: Priority ecoregions for global conservation. Ann. MO Bot. Gard. 89, 125–126 (2002).
Article  Google Scholar 

42.
U.S. Census Bureau. Population and housing unite estimates vintage 2018. Washington, DC (2018).

43.
Burby, R. & May, P. Making Governments Plan (John Hopkins University Press, Baltimore, 1997).
Google Scholar 

44.
Boarnet, M. G., McLaughlin, R. B. & Carruthers, J. I. Does state growth management change the pattern of urban growth? Evidence from Florida. Reg. Sci. Urban Econ. 41, 236–252 (2011).
Article  Google Scholar 

45.
Seibert, S. G. Status and Management of Black Bears in Apalachicola National Forest (Florida Game and Fresh Water Fish Commission, Gainesville, 2013).
Google Scholar 

46.
Land, E. D. Southwest Florida Black Bear Habitat Use, Distribution, Movements, and Conservation Strategy (Florida Game and Fresh Water Fish Commission, Tallahassee, 1994).
Google Scholar 

47.
McCown, W., Eason, T. H. & Cunningham, M. W. Black Bear Movements and Habitat Use Relative to Roads in Ocala National Forest (Florida Fish and Wildlife Conservation Commission, Gainesville, 2001).
Google Scholar 

48.
Stratman, M. R., Alden, C. D., Pelton, M. R. & Sunquist, M. E. Habitat use by American black bears in the sandhills of Florida. Ursus 12, 109–114 (2001).
Google Scholar 

49.
Maehr, D. W. et al. Spatial characteristics of an isolated Florida black bear population. Southeast Nat. 2, 433–446 (2003).
Article  Google Scholar 

50.
Orlando, M. A. The Ecology and Behavior of an Isolated Black Bear Population in West Central Florida. Thesis, University of Kentucky (2003)

51.
Annis, K. M. The Impact of Translocation on Nuisance Florida Black Bears. Thesis, University of Florida (2007).

52.
Neils, A. M. Florida Black Bear (Ursus americanus floridanus) at the Urban-Wildlife Interface: Are They Different? Thesis, University of Florida (2011).

53.
Guthrie, J. M. Modeling Movement Behavior and Road Crossing the Black Bear of South Central Florida. Thesis, University of Kentucky (2012).

54.
Baruch-Mordo, S. et al. Stochasticity in natural forage production affects use of urban areas by black bears: implications to management of human-bear conflicts. PLoS ONE 9, e85122 (2014).
ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

55.
Lewis, J. S., Rachlow, J. L., Garton, E. O. & Vierling, L. A. Effects of habitat on GPS collar performance: using data screening to reduce location error. J. Appl. Ecol. 44, 663–671 (2007).
Article  Google Scholar 

56.
Clark, J. D., Laufenberg, J. S., Davidson, M. & Murrow, J. L. Connectivity among subpopulations of Louisiana black bears as estimated by a step selection function. J. Wildl. Manage. 79, 1347–1360 (2015).
Article  Google Scholar 

57.
Beumer, L. T., van Beest, F. M., Stelvig, M. & Schmidt, N. M. Spatiotemproal dynamics in habitat suitability of a large Arctic herbivore: environmental heterogeneity is key to a sedentary lifestyle. Glob. Ecol. Conserv. 18, e00647 (2019).
Article  Google Scholar 

58.
Hinton, J. W. et al. Space use and habitat selection by resident and transient red wolves (Canis rufus). PLoS ONE 11, e0167603 (2016).
PubMed Central  Article  CAS  PubMed  Google Scholar 

59.
Fourcade, Y., Engler, J. O., Rodder, D. & Secondi, J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9, e97122 (2014).
ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

60.
Pellerin, M., Said, S. & Gaillard, J.-M. Roe deer Capreolus capreolus home-range sizes estimated from VHF and GPS data. Wildl. Biol. 14, 101–110 (2009).
Article  Google Scholar 

61.
Signer, J., Fieberg, J. & Avgar, T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9, 880–890 (2019).
PubMed Central  Article  PubMed  Google Scholar 

62.
Maehr, D. S. & Brady, J. R. Food habits of Florida black bears. J. Wildl. Manag. 48, 230–235 (1984).
Article  Google Scholar 

63.
Hellgren, E. C., Vaughan, M. R. & Stauffer, D. F. Macrohabitat use by black bears in a southeastern wetland. J Wildl. Manag. 55, 442–448 (1991).
Article  Google Scholar 

64.
Karelus, D. L. et al. Effects of environmental factors and landscape features on movement patterns of Florida black bears. J. Mammal. 98, 1463–1478 (2017).
Article  Google Scholar 

65.
Florida Natural Areas Inventory. Florida Forever Board of Trustees Projects (2018).

66.
McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. University of Massachusetts, Amherst, MA. https://www.umass.edu/landeco/research/fragstats/fragstats.html. (2012).

67.
Riley, S. J., DeGloria, S. D. & Elliot, R. A terrain ruggedness index that quantifies topographic heterogeneity. Intermt. J. Sci. 5, 1–4 (1999).
Google Scholar 

68.
Clark, J. D., Dunn, J. E. & Smith, K. G. A multivariate model of female black bear habitat use for a geographic information system. J. Wildl. Manag. 57, 519–526 (1993).
Article  Google Scholar 

69.
U.S. Geological Survey. National Elevation Dataset. Washington, DC (2016).

70.
Ditmer, M. A., Noyce, K. V., Fieberg, J. R. & Garshelisa, D. L Delineating the ecological and geographic edge of an opportunist: The American black bear exploiting an agricultural landscape. Ecol. Model. 387, 205–219 (2018).
Article  Google Scholar 

71.
U.S. Department of Agriculture National Agriculture Statistics Service. Census of Agriculture, Ag Census Web Maps. Washington, DC (2016).

72.
Hostetler, J. A. et al. Demographic consequences of anthropogenic influences: Florida black bears in north-central Florida. Biol. Conserv. 142, 2456–2463 (2009).
Article  Google Scholar 

73.
Center for International Earth Science Information Network – CIESIN – Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY (2016).

74.
Brody, A. J. & Pelton, M. R. Effects of roads on black bears in western North Carolina. Wildl. Soc. B 17, 5–10 (1989).
Google Scholar 

75.
U.S. Census Bureau. TIGER/Line Shapefiles (machine readable data files). Washington DC (2016).

76.
U.S. Geological Survey. National Hydrology Dataset. Washington, DC (2018).

77.
U.S. Fish & Wildlife Service. National Wetlands Inventory Data. St Petersburg, FL (2018).

78.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).

79.
Esri. ArcGIS Desktop: Release 10.4. Redlands, CA: Environmental Systems Research Institute (2015).

80.
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Article  Google Scholar 

81.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Article  Google Scholar 

82.
Calenge, C. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2016).
Article  Google Scholar 

83.
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Article  Google Scholar 

84.
Phillips, S. J., Dudik, M., & Schapire, R. E. A maximum entropy approach to species distribution modeling in Proceedings of the twenty-first international conference on machine learning (technical coordinators Greiner, R. & Schuurmans, D.) 655–662 (ACM Press, 2004).

85.
Hernandez, P. A. et al. Predicting species distributions in poorly-studied landscapes. Biodivers. Conserv. 17, 1353–1366 (2008).
Article  Google Scholar 

86.
Poor, E. E., Loucks, C., Jakes, A. & Urban, D. L. Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations. PLoS ONE 7, e49390 (2012).
ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

87.
Duan, R.-Y., Kong, X.-Q., Huang, M.-Y., Fan, W.-Y. & Wang, Z.-G. The predictive performance and stability of six species distribution models. PLoS ONE 9, e112764 (2014).
ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

88.
Zhang, J. et al. MaxEnt modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park China. Ecol. Evol. 9, 6643–6654 (2019).
PubMed Central  Article  PubMed  Google Scholar 

89.
Bertolino, S. et al. Spatially-explicit models as tools for implementing effective management strategies for invasive alien mammals. Mammal. Rev. 50, 87–199 (2020).
Google Scholar 

90.
Alsamadisi, A. G., Tran, L. T. & Papes, M. Employing inferences across scales: integrating spatial data with different resolutions to enhance Maxent models. Ecol. Model. 415, 108857 (2020).
Article  Google Scholar 

91.
Peralvo, M. F., Cuesta, F. & van Manen, F. Delineating priority habitat areas for the conservation of Andean bears in northern Ecuador. Ursus 16, 222–233 (2005).
Article  Google Scholar 

92.
Mahalanobis, P. C. On the generalized distance in statistics. Proc. Natl. Aacd. Sci. India 2, 49–55 (1936).
MATH  Google Scholar 

93.
Browning, D. M., Beaupre, S. J. & Duncan, L. Using partitioned Mahalanobis D2 (K) to formulate a GIS-based model of timber rattlesnake hibernacula. J. Wildl. Manag. 69, 33–44 (2005).
Article  Google Scholar 

94.
Griffin, S. C., Taper, M. L., Hoffman, R. & Mills, L. S. Ranking Mahalanobis Distance models for predictions of occupancy from presence-only data. J. Wildl. Manag. 74, 1112–1121 (2010).
Article  Google Scholar 

95.
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
Article  Google Scholar 

96.
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
Article  Google Scholar 

97.
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).
Article  Google Scholar 

98.
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199, 142–152 (2006).
Article  Google Scholar 

99.
Murrow, J. L. & Clark, J. D. Effects of hurricanes Katrina and Rita on Louisiana black bear habitat. Ursus 23, 192–205 (2012).
Article  Google Scholar 

100.
Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 7881 (2005).
Article  CAS  Google Scholar 

101.
Broennimann, B. & Di Cola, V. A. ecospat: Spatial Ecology Miscellaneous Methods. R package version 3.0 (2018).

102.
Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).
Article  Google Scholar 

103.
Hellgren, E. C., Bales, S. L., Gregory, M. S., Leslie, D. M. Jr. & Clark, J. D. Testing a Mahalanobis Distance model of black bear habitat use in the Ouichita Mountains of Oklahoma. J. Wildl. Manag. 71, 924–928 (2007).
Article  Google Scholar 

104.
Murrow, J. L., Thatcher, C. A., van Manen, F. T. & Clark, J. A data-based conservation planning tool for Florida Panthers. Environ. Model. Assess. 18, 159–170 (2013).
Article  Google Scholar 

105.
NOAA Office for Coastal Management. Detailed method for mapping sea level rise inundation. (NOAA, 2017).

106.
Pelton, M. R. 2003. Black bear. In Wild Mammals of North America: Biology, Management, and Conservation (eds Feldhamer, J. A. et al.) 547–555 (Johns Hopkins University, Baltimore, 2003).
Google Scholar 

107.
Thuiller, W., Brotons, L., Araujo, M. B. & Lavorel, S. Effects of restricting environmental range of data to project current and future species distributions. Ecography 27, 165–172 (2004).
Article  Google Scholar 

108.
Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393 (2005).
Article  Google Scholar 

109.
Kopp, R. E. et al. Probalistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth’s Future 2, 383–406 (2014).
ADS  Article  Google Scholar 

110.
Xiao, H. & Tang, Y. Assess the “superposed” effects of storm surge from a Category 3 hurricane. and continuous sea-level rise on saltwater intrusion into the surficial aquifer in coastal east-central Florida (USA). Environ. Sci. Pollut Res. 26, 21882–21889 (2019).
CAS  Article  Google Scholar 

111.
Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–234 (2014).
ADS  CAS  Article  Google Scholar 

112.
Mukul, S. A. et al. Combined effects of climate change and sea-level rise project dramatic habitat loss of the globally endangered Bengal tiger in the Bangladesh Sundarbans. Sci. Total Environ. 663, 830–840 (2019).
ADS  CAS  Article  Google Scholar 

113.
Poor, E. E., Shao, Y. & Kelly, M. J. Mapping and predicting forest loss in a Sumatran tiger landscape from 2002 to 2050. J. Environ. Manag. 231, 397–404 (2019).
Article  Google Scholar 

114.
Durner, G. M. et al. Predicting 21st-century polar bear habitat distribution from global climate models. Ecol. Monogr. 79, 25–58 (2009).

115.
Yovovich, V., Allen, M. L., Macaulay, L. T. & Wilmers, C. C. Using spatial characteristics of apex carnivore communication and reproductive behaviors to predict responses to future human development. Biodivers. Conserv. 29, 2589–2603 (2020).
Article  Google Scholar 

116.
Muhly, T. B. et al. Functional response of wolves to human development across boreal North America. Ecol. Evol. 9, 10801–10815 (2019).
PubMed Central  Article  PubMed  Google Scholar 

117.
Zeller, K. A., Wattles, D. W., Conlee, L. & Destefano, S. Response of female black bears to a high-density road network and identification of long-term road mitigation sites. Anim. Conserv. https://doi.org/10.1111/acv.12621 (2020).
Article  Google Scholar 

118.
Morales-González, A., Ruiz-Villar, H., Ordiz, A. & Penteriani, V. Large carnivores living alongside humans: Brown bears in human-modified landscapes. Glob. Ecol. Conserv. 22, 1–13 (2020).
Google Scholar 

119.
Maletzke, B. et al. Cougar response to a gradient of human development. Ecosphere 8, 1–14 (2017).
Article  Google Scholar 

120.
Barrington-Leigh, C. & Millard-Ball, A. A century of sprawl in the United States. PNAS https://doi.org/10.1073/pnas.1504033112 (2015).
Article  Google Scholar  More

1.
Owen, M. A. et al. An experimental investigation of chemical communication in the polar bear. J. Zool. 295, 36–43. https://doi.org/10.1111/jzo.12181 (2015).
Article  Google Scholar 
2.
Yasui, T., Tsukise, A. & Meyer, W. Histochemical analysis of glycoconjugates in the eccrine glands of the raccoon digital pads. Eur. J. Histochem. 48, 393–402 (2009).
Google Scholar 

3.
Meyer, W. & Bartels, T. Histochemical study on the eccrine glands in the foot pad of the cat. Basic Appl. Histochem. 33, 219–238 (1989).
CAS  PubMed  Google Scholar 

4.
Meyer, W. & Tsukise, A. Lectin histochemistry of snout skin and foot pads in the wolf and the domesticated dog (Mammalia: Canidae). Ann. Anat. Anatomischer Anzeiger 177, 39–49. https://doi.org/10.1016/S0940-9602(11)80129-9 (1995).
CAS  Article  PubMed  Google Scholar 

5.
Parillo, F. & Diverio, S. Glycocomposition of the apocrine interdigital gland secretions in the fallow deer (Dama dama). Res. Vet. Sci. 86, 194–199. https://doi.org/10.1016/j.rvsc.2008.08.004 (2009).
CAS  Article  PubMed  Google Scholar 

6.
Müller-Schwarze, D., Källquist, L., Mossing, T., Brundin, A. & Andersson, G. Responses of reindeer to interdigital secretions of conspecifics. J. Chem. Ecol. 4, 325–335. https://doi.org/10.1007/bf00989341 (1978).
Article  Google Scholar 

7.
Sergiel, A. et al. Histological, chemical and behavioural evidence of pedal communication in brown bears. Sci. Rep. 7, 1052. https://doi.org/10.1038/s41598-017-01136-1 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

8.
Kruuk, H. Otters: Ecology, Behaviour and Conservation (Oxford University Press, Oxford, 2006).
Google Scholar 

9.
Gorman, M. L. & Trowbridge, B. J. in Carnivore Behavior, Ecology, and Evolution (ed John L. Gittleman) 57–88 (Springer, New York, 1989).

10.
Spotte, S. Societies of Wolves and Free-Ranging Dogs (Cambridge University Press, Cambridge, 2012).
Google Scholar 

11.
Sillero-Zubiri, C. & Macdonald, D. W. Scent-marking and territorial behaviour of Ethiopian wolves Canis simensis. J. Zool. 245, 351–361. https://doi.org/10.1111/j.1469-7998.1998.tb00110.x (1998).
Article  Google Scholar 

12.
Cassidy, K. A., Mech, L. D., MacNulty, D. R., Stahler, D. R. & Smith, D. W. Sexually dimorphic aggression indicates male gray wolves specialize in pack defense against conspecific groups. Behav. Proc. 136, 64–72. https://doi.org/10.1016/j.beproc.2017.01.011 (2017).
Article  Google Scholar 

13.
Rothman, R. J. & Mech, L. D. Scent-marking in lone wolves and newly formed pairs. Anim. Behav. 27, 750–760. https://doi.org/10.1016/0003-3472(79)90010-1 (1979).
Article  Google Scholar 

14.
Udell, M. A. R., Dorey, N. R. & Wynne, C. D. L. What did domestication do to dogs? A new account of dogs’ sensitivity to human actions. Biol. Rev. 85, 327–345. https://doi.org/10.1111/j.1469-185X.2009.00104.x (2010).
Article  PubMed  Google Scholar 

15.
Miklósi, Á. Dog Behaviour, Evolution, and Cognition 2nd edn. (Oxford University Press, Oxford, 2015).
Google Scholar 

16.
Rosell, F. Secrets of the Snout: The Dog’s Incredible Nose (University of Chicago Press, Chicago, 2018).
Google Scholar 

17.
Dunbar, I. F. Olfactory preferences in dogs: the response of male and female beagles to conspecific odors. Behav. Biol. 20, 471–481 (1977).
CAS  Article  PubMed  Google Scholar 

18.
Lisberg, A. E. & Snowdon, C. T. Effects of sex, social status and gonadectomy on countermarking by domestic dogs, Canis familiaris. Anim. Behav. 81, 757–764. https://doi.org/10.1016/j.anbehav.2011.01.006 (2011).
Article  Google Scholar 

19.
Ranson, E. & Beach, F. A. Effects of testosterone on ontogeny of urinary behavior in male and female dogs. Horm. Behav. 19, 36–51. https://doi.org/10.1016/0018-506X(85)90004-2 (1985).
CAS  Article  PubMed  Google Scholar 

20.
Natynczuk, S., Bradshaw, J. W. S. & McDonald, D. W. Chemical constituents of the anal sacs of domestic dogs. Biochem. Syst. Ecol. 17, 83–87. https://doi.org/10.1016/0305-1978(89)90047-1 (1989).
CAS  Article  Google Scholar 

21.
Sherman, C. K., Reisner, I. R., Taliaferro, L. A. & Houpt, K. A. Characteristics, treatment, and outcome of 99 cases of aggression between dogs. Appl. Anim. Behav. Sci. 47, 91–108. https://doi.org/10.1016/0168-1591(95)01013-0 (1996).
Article  Google Scholar 

22.
Pal, S. K., Ghosh, B. & Roy, S. Agonistic behaviour of free-ranging dogs (Canis familiaris) in relation to season, sex and age. Appl. Anim. Behav. Sci. 59, 331–348. https://doi.org/10.1016/S0168-1591(98)00108-7 (1998).
Article  Google Scholar 

23.
Trisko, R. K., Sandel, A. A. & Smuts, B. Affiliation, dominance and friendship among companion dogs. Behaviour 153, 693–725. https://doi.org/10.1163/1568539X-00003352 (2016).
Article  Google Scholar 

24.
Rosvall, K. A. Intrasexual competition in females: evidence for sexual selection?. Behav. Ecol. 22, 1131–1140. https://doi.org/10.1093/beheco/arr106 (2011).
Article  PubMed  PubMed Central  Google Scholar 

25.
Beach, F. A. Coital behaviour in dogs. VIII. Social affinity, dominance and sexual preference in the bitch. Behaviour 36, 131. https://doi.org/10.1163/156853970X00088 (1970).
Article  Google Scholar 

26.
Pageat, P. & Gaultier, E. Current research in canine and feline pheromones. Vet. Clin. Small Anim. Pract. 33, 187–211. https://doi.org/10.1016/s0195-5616(02)00128-6 (2003).
Article  Google Scholar 

27.
Bekoff, M. Ground scratching by male domestic dogs: a composite signal. J. Mammal. 60, 847–848. https://doi.org/10.2307/1380206 (1979).
Article  Google Scholar 

28.
Hepper, P. & Wells, D. in Handbook of Olfaction and Gustation (ed Richard Doty) 591–604 (Wiley-Blackwell, 2015).

29.
Nicolaides, N. Skin lipids: their biochemical uniqueness. Science 186, 19–26 (1974).
ADS  CAS  Article  PubMed  Google Scholar 

30.
Fierer, N., Hamady, M., Lauber, C. L. & Knight, R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc. Natl. Acad. Sci. 105, 17994–17999. https://doi.org/10.1073/pnas.0807920105 (2008).
ADS  Article  PubMed  Google Scholar 

31.
Craig, A. Forebrain emotional asymmetry: a neuroanatomical basis?. Trends Cognit. Sci. 9, 566–571. https://doi.org/10.1016/j.tics.2005.10.005 (2005).
Article  Google Scholar 

32.
Royet, J.-P. & Plailly, J. Lateralization of olfactory processes. Chem. Senses 29, 731–745. https://doi.org/10.1093/chemse/bjh067 (2004).
Article  PubMed  Google Scholar 

33.
Siniscalchi, M. et al. Sniffing with the right nostril: lateralization of response to odour stimuli by dogs. Anim. Behav. 82, 399–404. https://doi.org/10.1016/j.anbehav.2011.05.020 (2011).
Article  Google Scholar 

34.
Lisberg, A. E. & Snowdon, C. T. The effects of sex, gonadectomy and status on investigation patterns of unfamiliar conspecific urine in domestic dogs, Canis familiaris. Anim. Behav. 77, 1147–1154. https://doi.org/10.1016/j.anbehav.2008.12.033 (2009).
Article  Google Scholar 

35.
Fanjul, M. S., Zenuto, R. R. & Busch, C. Use of olfaction for sexual recognition in the subterranean rodent Ctenomys talarum. Acta theriologica 48, 35–46 (2003).
Article  Google Scholar 

36.
Hart, B. L. Environmental and hormonal influences on urine marking behavior in the adult male dog. Behav. Biol. 11, 167–176. https://doi.org/10.1016/S0091-6773(74)90321-6 (1974).
CAS  Article  PubMed  Google Scholar 

37.
Johnston, R. E., Derzie, A., Chiang, G., Jernigan, P. & Lee, H.-C. Individual scent signatures in golden hamsters: evidence for specialization of function. Anim. Behav. 45, 1061–1070. https://doi.org/10.1006/anbe.1993.1132 (1993).
Article  Google Scholar 

38.
Gilfillan, G. D., Vitale, J. D., McNutt, J. W. & McComb, K. Spontaneous discrimination of urine odours in wild African lions, Panthera leo. Anim. Behav. 126, 177–185. https://doi.org/10.1016/j.anbehav.2017.02.003 (2017).
Article  Google Scholar 

39.
Rostain, R. R., Ben-David, M., Groves, P. & Randall, J. A. Why do river otters scent-mark? An experimental test of several hypotheses. Anim. Behav. 68, 703–711. https://doi.org/10.1016/j.anbehav.2003.10.027 (2004).
Article  Google Scholar 

40.
Blundell, G. M., Ben-David, M. & Bowyer, R. T. Sociality in river otters: cooperative foraging or reproductive strategies?. Behav. Ecol. 13, 134–141. https://doi.org/10.1093/beheco/13.1.134 (2002).
Article  Google Scholar 

41.
Mills, M. Behavioural mechanisms in territory and group maintenance of the brown hyaena, Hyaena brunnea, in the southern Kalahari. Anim. Behav. 31, 503–510. https://doi.org/10.1016/s0003-3472(83)80072-4 (1983).
Article  Google Scholar 

42.
Boydston, E. E., Morelli, T. L. & Holekamp, K. E. Sex differences in territorial behavior exhibited by the spotted hyena (Hyaenidae, Crocuta crocuta). Ethology 107, 369–385. https://doi.org/10.1046/j.1439-0310.2001.00672.x (2001).
Article  Google Scholar 

43.
Bamberger, M. & Houpt, K. A. Signalment factors, comorbidity, and trends in behavior diagnoses in dogs: 1,644 cases (1991–2001). J. Am. Vet. Med. Assoc. 229, 1591–1601. https://doi.org/10.2460/javma.229.10.1591 (2006).
Article  PubMed  Google Scholar 

44.
Starling, M. J., Branson, N., Thomson, P. C. & McGreevy, P. D. Age, sex and reproductive status affect boldness in dogs. Vet. J. 197, 868–872. https://doi.org/10.1016/j.tvjl.2013.05.019 (2013).
Article  PubMed  Google Scholar 

45.
Bodnariu, A. L. I. N. A. Indicators of stress and stress assessment in dogs. Lucr. Stiint. Med. Vet. 41, 20–26 (2008).
Google Scholar 

46.
Pal, S. K. Factors influencing intergroup agonistic behaviour in free-ranging domestic dogs (Canis familiaris). Acta Ethol. 18, 209–220. https://doi.org/10.1007/s10211-014-0208-2 (2015).
Article  Google Scholar 

47.
Derix, R. et al. Male and female mating competition in wolves: female suppression vs. male intervention. Behaviour 127(1–2), 141–174 (1993).
Article  Google Scholar 

48.
Udell, M. A. R. & Wynne, C. D. L. A review of domestic dogs’ (Canis familiaris) human-like behaviors: or why behavior analysts should stop worrying and love their dogs. J. Exp. Anal. Behav. 89, 247–261. https://doi.org/10.1901/jeab.2008.89-247 (2008).
Article  PubMed  PubMed Central  Google Scholar 

49.
Kubinyi, E., Turcsán, B. & Miklósi, Á. Dog and owner demographic characteristics and dog personality trait associations. Behav. Proc. 81, 392–401. https://doi.org/10.1016/j.beproc.2009.04.004 (2009).
Article  Google Scholar 

50.
Siniscalchi, M., d’Ingeo, S. & Quaranta, A. The dog nose “KNOWS” fear: asymmetric nostril use during sniffing at canine and human emotional stimuli. Behav. Brain Res. 304, 34–41. https://doi.org/10.1016/j.bbr.2016.02.011 (2016).
Article  PubMed  Google Scholar 

51.
Peters, R. & Mech, L. D. in Wolf and Man (eds Roberta L. Hall & Henry S. Sharp) 133–147 (Academic Press, 1978).

52.
Thoß, M. et al. Regulation of volatile and non-volatile pheromone attractants depends upon male social status. Sci. Rep. 9, 489. https://doi.org/10.1038/s41598-018-36887-y (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Samuel, L. et al. Fears from the past? The innate ability of dogs to detect predator scents. Anim. Cognit. 23, 1–9 (2020).
Article  Google Scholar 

54.
Thomsett, L. R. Structure of canine skin. Br. Vet. J. 142(2), 116–123 (1986).
CAS  Article  PubMed  Google Scholar 

55.
Traniello, J. F. & Bakker, T. C. Minimizing observer bias in behavioral research: blinded methods reporting requirements for behavioral ecology and sociobiology. Behav. Ecol. 69, 1573–1574. https://doi.org/10.1007/s00265-015-2001-2 (2015).
Article  Google Scholar 

56.
Fugazza, C. & Miklósi, Á. Domestic dog cognition and behavior 177–200 (Springer, Berlin, 2014).
Google Scholar 

57.
Siniscalchi, M., Bertino, D. & Quaranta, A. Laterality and performance of agility-trained dogs. Later. Asymmetries Body Br. Cognit. 19, 219–234. https://doi.org/10.1080/1357650X.2013.794815 (2014).
Article  Google Scholar 

58.
McKinley, J. & Sambrook, T. D. Use of human-given cues by domestic dogs (Canis familiaris) and horses (Equus caballus). Anim. Cogn. 3, 13–22. https://doi.org/10.1007/s100710050046 (2000).
Article  Google Scholar 

59.
Johnen, D., Heuwieser, W. & Fischer-Tenhagen, C. An approach to identify bias in scent detection dog testing. Appl. Anim. Behav. Sci. 189, 1–12 (2017).
Article  Google Scholar 

60.
Mulholland, M. M., Olivas, V. & Caine, N. G. The nose may not know: dogs’ reactions to rattlesnake odours. Appl. Anim. Behav. Sci. 204, 108–112. https://doi.org/10.1016/j.applanim.2018.04.001 (2018).
Article  Google Scholar 

61.
Greer, K. A., Canterberry, S. C. & Murphy, K. E. Statistical analysis regarding the effects of height and weight on life span of the domestic dog. Res. Vet. Sci. 82, 208–214. https://doi.org/10.1016/j.rvsc.2006.06.005 (2007).
Article  PubMed  Google Scholar 

62.
Gardner, M. & McVety, D. Treatment and Care of the Geriatric Veterinary Patient (Wiley, Hoboken, 2017).
Google Scholar 

63.
Crowley, J. & Adelman, B. The Complete Dog Book: Official Publication of the American Kennel Club (Howell House, New York, 1998).
Google Scholar 

64.
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794. https://doi.org/10.7717/peerj.4794 (2018).
Article  PubMed  PubMed Central  Google Scholar 

65.
MuMIn: Multi-Model Inference (2018).

66.
Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35. https://doi.org/10.1007/s00265-010-1029-6 (2011).
Article  Google Scholar 

67.
Anderson, D. R. Model Based Inference in the Life Sciences: A Primer on Evidence (Springer, Berlin, 2007).
Google Scholar 

68.
Cumming, G. Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis (Routledge, London, 2013).
Google Scholar 

69.
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biomet. J. 50, 346–363. https://doi.org/10.1002/bimj.200810425 (2008).
MathSciNet  Article  MATH  Google Scholar  More

1.
Vatanparast, M. et al. First molecular phylogeny of the pantropical genus Dalbergia: implications for infrageneric circumscription and biogeography. S. Afr. J. Bot. 89, 143–149 (2013).
CAS  Article  Google Scholar 
2.
Saha, S. et al. Ethnomedicinal, phytochemical, and pharmacological profile of the genus Dalbergia L. (Fabaceae). Phytopharmacology 4, 291–346 (2013).
Google Scholar 

3.
Sprent, J. I. Legume Nodulation: A Global Perspective (Wiley, Hoboken, 2009).
Google Scholar 

4.
Bhagwat, R. M., Dholakia, B. B., Kadoo, N. Y., Balasundaran, M. & Gupta, V. S. Two new potential barcodes to discriminate Dalbergia species. PLoS ONE 10, 1–18 (2015).
Article  CAS  Google Scholar 

5.
EIA. Routes of Extinction: The Corruption and Violence Destroying SIAMESE Rosewood in the Mekong (Environmental Investigation Agency, London, 2014).
Google Scholar 

6.
EIA. The Hongmu Challenge: A Briefing for the 66th Meeting of the CITES Standing Committee, January 2016 (2016).

7.
Winfield, K., Scott, M. & Graysn, C. Global status of Dalbergia and Pterocarpus rosewood producing species in trade. in Convention on International Trade in Endangered Species 17th Conference of Parties – Johannesburg (2016).

8.
Bentham, G. Synopsis of Dalbergieae, a Tribe of Leguminosae. J. Proc. Linn. Soc. Lond. Bot. 4, 1–128 (1860).
MathSciNet  Article  Google Scholar 

9.
Lavin, M. et al. The dalbergioid legumes (Fabaceae): delimitation of a pantropical monophyletic clade. Am. J. Bot. 88, 503 (2001).
CAS  Article  PubMed  Google Scholar 

10.
Hartvig, I. et al. Population genetic structure of the endemic rosewoods Dalbergia cochinchinensis and D. oliveri at a regional scale reflects the Indochinese landscape and life-history traits. Ecol. Evol. 8, 530–545 (2018).
Article  PubMed  Google Scholar 

11.
Hartvig, I., Czako, M., Kjær, E. D., Nielsen, L. R. & Theilade, I. The use of DNA barcoding in identification and conservation of rosewood (Dalbergia spp.). PLoS ONE 10, e0138231 (2015).
Article  CAS  PubMed  PubMed Central  Google Scholar 

12.
Wattoo, J. I., Saleem, M. Z., Shahzad, M. S., Arif, A. & Hameed, A. DNA barcoding: amplification and sequence analysis of rbcl and matK genome regions in three divergent plant species. Adv. Life Sci. 4, 03–07 (2016).
CAS  Google Scholar 

13.
Phong, D. T., Tang, D. V., Hien, V. T. T., Ton, N. D. & Van, H. N. Nucleotide diversity of a nuclear and four chloroplast DNA regions in rare tropical wood species of dalbergia in Vietnam: a DNA barcode identifying utility. Asian J. Appl. Sci. 02, 116–125 (2014).
Google Scholar 

14.
Resende, L. C., Ribeiro, R. A. & Lovato, M. B. Diversity and genetic connectivity among populations of a threatened tree (Dalbergia nigra) in a recently fragmented landscape of the Brazilian Atlantic Forest. Genetica 139, 1159–1168 (2011).
Article  PubMed  Google Scholar 

15.
Buzatti, R. S. O., Ribeiro, R. A., Filho, J. P. L. & Lovato, M. B. Fine-scale spatial genetic structure of Dalbergia nigra (Fabaceae), a threatened and endemic tree of the Brazilian Atlantic Forest. Genet. Mol. Biol. 35, 838–846 (2012).
Article  Google Scholar 

16.
Liu, F.-M. et al. De novo transcriptome analysis of Dalbergia odorifera and transferability of SSR markers developed from the transcriptome. Forests 10, 98 (2019).
Article  Google Scholar 

17.
Xu, D.-P., Xu, S.-S., Zhang, N.-N., Yang, Z.-J. & Hong, Z. Chloroplast genome of Dalbergia cochinchinensis (Fabaceae), a rare and Endangered rosewood species in Southeast Asia. Mitochondrial DNA B 4, 1144–1145 (2019).
Article  Google Scholar 

18.
Wariss, H. M., Yi, T.-S., Wang, H. & Zhang, R. Characterization of the complete chloroplast genome of Dalbergia odorifera (Leguminosae), a rare and critically endangered legume endemic to China. Conserv. Genet. Resour. https://doi.org/10.1007/s12686-017-0866-2 (2017).
Article  Google Scholar 

19.
Liu, Y., Huang, P., Li, C.-H., Zang, F.-Q. & Zheng, Y.-Q. Characterization of the complete chloroplast genome of Dalbergia cultrata (Leguminosae). Mitochondrial DNA B 4, 2369–2370 (2019).
Article  Google Scholar 

20.
Deng, C., Xin, G., Zhang, J. & Zhao, D. Characterization of the complete chloroplast genome of Dalbergia hainanensis (Leguminosae), a vulnerably endangered legume endemic to China. Conserv. Genet. Resour. 1, 105–108 (2018).
Google Scholar 

21.
Song, Y., Zhang, Y., Xu, J., Li, W. & Li, M. F. Characterization of the complete chloroplast genome sequence of Dalbergia species and its phylogenetic implications. Sci. Rep. 9, 1–10 (2019).
ADS  Article  CAS  Google Scholar 

22.
Lateef, A., Prabhudas, S. K. & Natarajan, P. RNA sequencing and de novo assembly of Solanum trilobatum leaf transcriptome to identify putative transcripts for major metabolic pathways. Sci. Rep. 8, 15375 (2018).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

23.
Keilwagen, J., Hartung, F., Paulini, M., Twardziok, S. O. & Grau, J. Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi. BMC Bioinform. 19, 189 (2018).
Article  CAS  Google Scholar 

24.
Wang, B., Kumar, V., Olson, A. & Ware, D. Reviving the transcriptome studies: an insight into the emergence of single-molecule transcriptome sequencing. Front. Genet. 10, 384 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Lamble, S. et al. Improved workflows for high throughput library preparation using the transposome-based nextera system. BMC Biotechnol. 13, 104 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Buffalo, V. Scythe—a Bayesian adapter trimmer (version 0.994 BETA) [Software] (2011). https://github.com/vsbuffalo/scythe.

28.
Joshi, N. A. & Fass, J. N. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software] (2011). https://github.com/najoshi/sickle.

29.
Carruthers, M. et al. De novo transcriptome assembly, annotation and comparison of four ecological and evolutionary model salmonid fish species. BMC Genomics 19, 32 (2018).
Article  CAS  PubMed  PubMed Central  Google Scholar 

30.
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

31.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
CAS  Article  Google Scholar 

33.
Haas, B. J. TransDecoder (2018). https://github.com/TransDecoder/TransDecoder.

34.
Kriventseva, E. V. et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucl. Acids Res. 47, D807–D811 (2019).
CAS  Article  PubMed  Google Scholar 

35.
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543 (2017).
Article  CAS  PubMed Central  Google Scholar 

36.
Bertioli, D. J. et al. The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat. Genet. 48, 438–446 (2016).
CAS  Article  Google Scholar 

37.
Smith-Unna, R., Boursnell, C., Patro, R., Hibberd, J. M. & Kelly, S. TransRate: reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 26, 1134–1144 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
UniProt: a worldwide hub of protein knowledge. Nucl. Acids Res.47, D506–D515 (2019).

39.
Cheng, C.-Y. et al. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 89, 789–804 (2017).
CAS  Article  PubMed  Google Scholar 

40.
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucl. Acids Res. 47, D427–D432 (2019).
CAS  Article  PubMed  Google Scholar 

41.
Almagro Armenteros, J. J. et al. SignalP 50 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).
CAS  Article  PubMed  Google Scholar 

42.
Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).
CAS  Article  PubMed  Google Scholar 

43.
Emms, D. M. & Kelly, S. OrthoFinder2: fast and accurate phylogenomic orthology analysis from gene sequences. BioRxiv https://doi.org/10.1101/466201 (2018).
Article  Google Scholar 

44.
Guo, L. et al. The opium poppy genome and morphinan production. Science 362, 343–347 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

45.
Nakamura, T., Yamada, K. D., Tomii, K. & Katoh, K. Parallelization of MAFFT for large-scale multiple sequence alignments. Bioinformatics 34, 2490–2492 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

46.
Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucl. Acids Res. 34, W609–W612 (2006).
CAS  Article  PubMed  Google Scholar 

47.
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and high-performance computing. Nat. Methods 9, 772 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

48.
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Bouckaert, R. et al. BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Brea, M., Zamuner, A. B., Matheos, S. D., Iglesias, A. & Zucol, A. F. Fossil wood of the Mimosoideae from the early Paleocene of Patagonia, Argentina. Alcheringa An Australas. J. Palaeontol. 32, 427–441 (2008).
Article  Google Scholar 

51.
Hane, J. K. et al. A comprehensive draft genome sequence for lupin (Lupinus angustifolius), an emerging health food: insights into plant-microbe interactions and legume evolution. Plant Biotechnol. J. 15, 318–330 (2017).
CAS  Article  PubMed  Google Scholar 

52.
Lavin, M., Herendeen, P. S. & Wojciechowski, M. F. Evolutionary rates analysis of leguminosae implicates a rapid diversification of lineages during the tertiary. Syst. Biol. 54, 575–594 (2005).
Article  PubMed  Google Scholar 

53.
Moretzsohn, M. C. et al. A study of the relationships of cultivated peanut (Arachis hypogaea) and its most closely related wild species using intron sequences and microsatellite markers. Ann. Bot. 111, 113–126 (2013).
CAS  Article  PubMed  Google Scholar 

54.
Ye, J. et al. WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update. Nucl. Acids Res. 46, W71 (2018).
CAS  Article  PubMed  Google Scholar 

55.
De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269–1271 (2006).
Article  CAS  PubMed  Google Scholar 

56.
Mi, H., Muruganujan, A. & Thomas, P. D. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucl. Acids Res. 41, D377–D386 (2013).
CAS  Article  PubMed  Google Scholar 

57.
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
CAS  Article  PubMed  Google Scholar 

58.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57(1), 289–300 (1995).
MathSciNet  MATH  Google Scholar 

59.
Sun, J. et al. Adaptation to deep-sea chemosynthetic environments as revealed by mussel genomes. Nat. Ecol. Evol. 1, 0121 (2017).
Article  Google Scholar 

60.
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. ClusterProfiler: an R package for comparing biological themes among gene clusters. Omi. A J. Integr. Biol. 16, 284–287 (2012).
CAS  Article  Google Scholar 

61.
Soltis, D. E., Soltis, P. S., Bennett, M. D. & Leitch, I. J. Evolution of genome size in the angiosperms. Am. J. Bot. 90, 1596–1603 (2003).
Article  PubMed  Google Scholar 

62.
Hiremath, S. C. & Nagasampige, M. H. Genome size variation and evolution in some species of Dalbergia Linn.f. (Fabaceae). Caryologia 57, 367–372 (2004).
Article  Google Scholar 

63.
Lawrence, G. H. M. Taxonomy of Vascular Plants (IBH Publishing Co., Oxford, 1973).
Google Scholar 

64.
Lombello, R. A. & Forni-Martins, E. R. Chromosome studies and evolution in Sapindaceae. Caryologia 51, 89–93 (1998).
Article  Google Scholar 

65.
Sheremet’ev, S. N. & Gamalei, Y. V. Towards angiosperms genome evolution in time. arXiv (2013).

66.
Carlquist, S. Anatomy of vine and liana stems: a review and synthesis. In The Biology of Vines (eds Putz, F. E. & Mooney, H. A.) 53–72 (University of Cambridge Press, Cambridge, 1991).
Google Scholar 

67.
Li, Q. et al. The phylogenetic analysis of Dalbergia (Fabaceae: Papilionaceae) based on different DNA barcodes. Holzforschung 71, 939–949 (2017).
CAS  Article  Google Scholar 

68.
Lavin, M. et al. Metacommunity process rather than continental tectonic history better explains geographically structured phylogenies in legumes. Philos. Trans. R. Soc. B Biol. Sci. 359, 1509–1522 (2004).
CAS  Article  Google Scholar 

69.
Kučerová, J. Miocénna flóra z lokalít Kalonda a Mučín. Acta Geol. Slovaca 1, 65–70 (2009).
Google Scholar 

70.
Gao, S.-X. & Zhou, Z.-K. The megafossil legumes from China. In Advances in Legume Systematics (eds Herendeen, P. S. & Dilcher, D. L.) (The Royal Botanic Gardens, Kew, 1992).
Google Scholar 

71.
de Saporta, G. Dalbergia phleboptera Saporta. Muséum national d’Histoire naturelle (2015). https://science.mnhn.fr/institution/mnhn/collection/f/item/14084.?lang=en_US.

72.
De Bruyn, M. et al. Borneo and Indochina are major evolutionary hotspots for southeast Asian biodiversity. Syst. Biol. 63, 879–901 (2014).
Article  PubMed  Google Scholar 

73.
Koenen, E. J. M. et al. The origin and early evolution of the legumes are a complex paleopolyploid phylogenomic tangle closely associated with the cretaceous-paleogene (K-Pg) boundary. biorxiv https://doi.org/10.1101/577957 (2019).
Article  Google Scholar 

74.
Lespinet, O., Wolf, Y. I., Koonin, E. V. & Aravind, L. The role of lineage-specific gene family expansion in the evolution of eukaryotes. Genome Res. 12, 1048–1059 (2002).
CAS  Article  PubMed  PubMed Central  Google Scholar 

75.
Ming, Y. et al. Molecular footprints of inshore aquatic adaptation in Indo-Pacific humpback dolphin (Sousa chinensis). Genomics https://doi.org/10.1016/j.ygeno.2018.07.015 (2018).
Article  PubMed  Google Scholar 

76.
Force, A. et al. Preservation of duplicate genes by complementary, degenerative mutations. Genetics 151, 1531–1545 (1999).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Luengo, T. M., Mayer, M. P. & Rüdiger, S. G. The Hsp70–Hsp90 chaperone cascade in protein folding. Trends Cell Biol. 29(2), 164–177. https://doi.org/10.1016/j.tcb.2018.10.004 (2019).
CAS  Article  Google Scholar 

78.
Jacob, P., Hirt, H. & Bendahmane, A. The heat-shock protein/chaperone network and multiple stress resistance. Plant Biotechnol. J. 15, 405–414 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

79.
Yamada, K. et al. Cytosolic HSP90 regulates the heat shock response that is responsible for heat acclimation in Arabidopsis thaliana. J. Biol. Chem. 282, 37794–37804 (2007).
CAS  Article  PubMed  Google Scholar 

80.
Clément, M. et al. The cytosolic/nuclear HSC70 and HSP90 molecular chaperones are important for stomatal closure and modulate abscisic acid-dependent physiological responses in arabidopsis. Plant Physiol. 156, 1481–1492 (2011).
Article  CAS  PubMed  PubMed Central  Google Scholar 

81.
Hou, Q. & Bartels, D. Comparative study of the aldehyde dehydrogenase (ALDH) gene superfamily in the glycophyte Arabidopsis thaliana and Eutrema halophytes. Ann. Bot. 115, 465–479 (2015).
CAS  Article  PubMed  Google Scholar 

82.
Missihoun, T. D. & Kotchoni, S. O. Aldehyde dehydrogenases and the hypothesis of a glycolaldehyde shunt pathway of photorespiration. Plant Signal. Behav. 13, e1449544 (2018).
Article  CAS  PubMed  PubMed Central  Google Scholar 

83.
Estioko, L. P. et al. Differences in responses to flooding by germinating seeds of two contrasting rice cultivars and two species of economically important grass weeds. AoB Plants 6, plu064 (2014).
Article  CAS  PubMed  PubMed Central  Google Scholar 

84.
Brocker, C. et al. Aldehyde dehydrogenase (ALDH) superfamily in plants: Gene nomenclature and comparative genomics. Planta 237, 189–210 (2013).
CAS  Article  PubMed  Google Scholar 

85.
Sharma, B., Joshi, D., Yadav, P. K., Gupta, A. K. & Bhatt, T. K. Role of ubiquitin-mediated degradation system in plant biology. Front. Plant Sci. 7, 806 (2016).
PubMed  PubMed Central  Google Scholar 

86.
Walters, K. J., Goh, A. M., Wang, Q., Wagner, G. & Howley, P. M. Ubiquitin family proteins and their relationship to the proteasome: a structural perspective. Biochimica et Biophysica Acta Mol. Cell Res. 1695, 73–87 (2004).
CAS  Article  Google Scholar 

87.
Liu, Z.-B. et al. A novel membrane-bound E3 ubiquitin ligase enhances the thermal resistance in plants. Plant Biotechnol. J. 12, 93–104 (2014).
Article  CAS  PubMed  Google Scholar 

88.
Macho, A. P. & Zipfel, C. Plant PRRs and the activation of innate immune signaling. Mol. Cell 54, 263–272 (2014).
CAS  Article  PubMed  Google Scholar 

89.
Martin, G. B., Bogdanove, A. J. & Sessa, G. Understanding the functions of plant disease resistance proteins. Annu. Rev. Plant Biol. 54, 23–61 (2003).
CAS  Article  PubMed  Google Scholar 

90.
Cohn, J., Sessa, G. & Martin, G. B. Innate immunity in plants. Curr. Opin. Immunol. 13, 55–62 (2001).
CAS  Article  PubMed  Google Scholar 

91.
Lehmann, P. Structure and evolution of plant disease resistance genes. J. Appl. Genet. 43, 403–414 (2002).
ADS  PubMed  Google Scholar 

92.
Jeffares, D. C., Tomiczek, B., Sojo, V. & dos Reis, M. A beginners guide to estimating the non-synonymous to synonymous rate ratio of all protein-coding genes in a genome. in Parasite Genomics Protocols: Second Edition 65–90 (Springer Fachmedien, 2014). https://doi.org/10.1007/978-1-4939-1438-8_4.

93.
Andersen, E. J., Ali, S., Byamukama, E., Yen, Y. & Nepal, M. P. Disease resistance mechanisms in plants. Genes 9(7), 339 (2018).
Article  CAS  PubMed Central  Google Scholar 

94.
IUCN. The IUCN Red List of Threatened Species. Veresion 2019–2 (2019). https://www.iucnredlist.org.

95.
Federhen, S. The NCBI taxonomy database. Nucl. Acids Res. 40(D1), D136–D143 (2012).
CAS  Article  PubMed  Google Scholar 

96.
Brandies, P., Peel, E., Hogg, C. J. & Belov, K. The value of reference genomes in the conservation of threatened species. Genes 10, 846 (2019).
CAS  Article  PubMed Central  Google Scholar 

97.
Supple, M. A. & Shapiro, B. Conservation of biodiversity in the genomics era. Genome Biol. 19(1), 1–12 (2018).
Article  Google Scholar 

98.
Fuentes-Pardo, A. P. & Ruzzante, D. E. Whole-genome sequencing approaches for conservation biology: advantages, limitations and practical recommendations. Mol. Ecol. 26, 5369–5406 (2017).
CAS  Article  PubMed  Google Scholar 

99.
Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

100.
Bragg, J. G., Potter, S., Bi, K. & Moritz, C. Exon capture phylogenomics: efficacy across scales of divergence. Mol. Ecol. Resour. 16, 1059–1068 (2016).
CAS  Article  PubMed  Google Scholar 

101.
İpek, A., İpek, M., Ercişli, S. & Tangu, N. A. Transcriptome-based SNP discovery by GBS and the construction of a genetic map for olive. Funct. Integr. Genomics 17, 493–501 (2017).
Article  CAS  PubMed  Google Scholar 

102.
Vatanparast, M., Powell, A., Doyle, J. J. & Egan, A. N. Targeting legume loci: a comparison of three methods for target enrichment bait design in Leguminosae phylogenomics. Appl. Plant Sci. 6, e1036 (2018).
Article  PubMed  PubMed Central  Google Scholar 

103.
Ouborg, N. J. Integrating population genetics and conservation biology in the era of genomics. Biol. Lett. 6, 3–6 (2010).
Article  PubMed  Google Scholar 

104.
CITES. Consideration of Proposals for Amendment of Appendices I and II. Convention on International Trade in Endangered Species of Wild Fauna and Flora. (Convention on International Trade in Endangered Species of Wild Fauna and Flora, 2017).

105.
Asian Regional Workshop (Conservation & Sustainable Management of Trees Viet Nam). Dalbergia cochinchinensis. The IUCN Red List of Threatened Species. e.T32625A9719096 (1998). https://doi.org/10.2305/IUCN.UK.1998.RLTS.T32625A9719096.en.

106.
Bernal, R., Gradstein, S. & Celis, M. Catálogo de plantas y líquenes de Colombia (Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, 2015).
Google Scholar 

107.
World Conservation Monitoring Centre. Dalbergia melanoxylon. The IUCN Red List of Threatened Species 1998. e.T32504A9710439 (1998). https://doi.org/10.2305/IUCN.UK.1998.RLTS.T32504A9710439.en.

108.
ILDIS. International Legume Database and Information Service V10.39 (2011).

109.
Nghia, N. H. Dalbergia oliveri. The IUCN Red List of Threatened Species 1998. e.T32306A9693932 (1998). https://doi.org/10.2305/IUCN.UK.1998.RLTS.T32306A9693932.en.

110.
Orwa, C., Mutua, A., Kindt, R., Jamnadass, R. & Anthony, S. Agroforestree Database: A Tree Reference and Selection Guide Version 4.0. (2009). https://www.worldagroforestry.org/sites/treedbs/treedatabases.asp. More

The role of CCA as reef consolidators
We found a significant correlation between the proportion of reefs that contain CCA as secondary reef builders and the proportion of true reefs over the last 150 million years. Coral reefs can benefit from CCA in various ways. Relating to the reef ridge, the stony pavement made up by the algae protects the ridge from onrushing waves and also consolidates the reef flats behind the ridges11. With reference to the whole reef, CCA reinforce the structure created by corals, fill cracks, bind together much of the sand, dead corals and debris, and thereby create a stable substrate and reduce reef erosion22. Larval settlement, metamorphosis, and recruitment of several coral species is strictly determined by chemosensory recognition of specific signal molecules uniquely available in specific CCA23.
However, it has to be considered that there are modern reefs that cope with wavy, high-energy environments without the aid of CCA, as for example the Alacran reef in Mexico12. CCA are not the only possibility to add rigidity to a reef. Submarine lithification can be more important than CCA in creating calcite precipitates, especially when environmental and ecological conditions are unfavourable for the growth of CCA, e.g. because of the lack of light. Submarine lithification in the form of Mg-calcite precipitates exists in many forms, including cemented micritic crusts and infillings of cracks. Additionally, their respective carbonate sources may be abiotic24 or originate from a great variety of organisms, including reef fish25. Therefore, they do also play an important role for the structural integrity of coral reefs24. CCA abundance may benefit from reef growth in terms of ecological niches provided, additionally increasing the positive correlation. We thus suggest that the significant correlation between the proportion of reefs reinforced by CCA as secondary reef builders and the proportion of true reefs can be interpreted as a mutual benefit. On the one hand, the presence of CCA can add stability to coral reefs, especially when the reef ridge is exposed to heavy wave action. On the other hand, sufficient reef growth can be a prerequisite for a larger abundance of CCA. A shift towards one side in this mutual dependence is subject to the particular features of each reef, as for example if CCA rather benefit from the shelter of crevices in reefs with high grazing pressure or if corals rather benefit from the presence of CCA at sites of intense wave exposure.
The physicochemical parameters ocean temperature, sea level, and RCO2
CCA occur worldwide from the tropics10 to polar latitudes26 and temperature is one of the primary determinants in their geographical distribution, and the boundaries of their biogeographical regions are associated with isotherms27. Therefore, the identification of ocean temperature as an important driver of CCA reefs is reasonable. Aguirre, et al.28 reported that throughout the history of CCA, species richness broadly correlates with global mean palaeotemperature. However, only the diversity of the order Sporolithales varies positively with temperature, whereas the diversity of the order Corallinales varies negatively with temperature. Accordingly, the warm-water Sporolithales were most species-rich during the warm Cretaceous, but they declined and were rapidly replaced by the Corallinales as Cenozoic temperatures declined. In recent environments, members of the Sporolithales are confined to greater water depths while in euphotic reefs, they do not play a role as reef stabilizers28 and occupy only cryptic habitats sensu Kobluk29, i.e. cavities that serve as well-protected habitats and are not subject to the full spectrum of environmental and biotic controls that exist on the reef surface28. The wave-pounded intertidal algal ridges are built predominantly by Porolithon onkodes (Heydrich) Foslie 1909, P. gardineri (Foslie) Foslie 1909, P. craspedium (Foslie) Foslie 1909, and Lithophyllum kotschyanum Unger 1858 in the Indo-Pacific. In the Atlantic, the main reef reinforcers are Porolithon onkodes (Heydrich) Foslie 1909 and Lithophyllum congestum (Foslie) Foslie 1900. All these species belong to the ‘cool’-water adapted Corallinales. Thus, the increasing capacity of CCA to stabilize coral reefs is in line with the general trend of decreasing ocean temperatures.
A change in sea level does not impact the capacity of CCA to reinforce coral reefs, likely because sea level changes measured on the level of geological stages have no effect on reef formation5. On shorter time scales, sea level is expected to influence the formation of coral reefs, but probably not the CCA’s reef enforcing capacity. We conclude this because the environmental tolerances of CCA in terms of sea level fluctuation are much wider than those of reef corals. Most CCA species appear uniquely tolerant of aerial exposure10. Additionally, many CCA are very well adapted to changes in salinity and especially to low photon irradiances30. The environmental tolerances of reef corals are narrower31,32.
Considering our assumption that there is a mutual relationship between the presence of CCA and the growth of true reefs, another reason might be that one of the most important genera in modern coral reefs, Acropora Oken, 1815, is well adapted to cope with rapid sea-level changes. First observed as an important reef builder in the Oligocene33, Acropora has become a dominant reef builder from the Pleistocene until today, when sea-level fluctuations increased in rate and magnitude34. Indeed, there is a temporal overlap between the first decline in the fraction of CCA reefs—between the Turonian and the Campanian—and a maximum in sea level. Despite this, sea level is not selected as a relevant explanatory variable for the fraction of CCA reefs by the GLM because the relationship between the fraction of CCA reefs and sea level varies inconsistently throughout entire time series of the analysed 150 million years. While the decline in the fraction of CCA reefs may additionally be linked to an increase in temperature before and a significant drop in CCA diversity during the period with a low fraction of CCA reefs, data of the analysis are not suitable to conclusively identify the driver for this particular CCA crisis.
For the entire time series, RCO2, was identified by the model as a minor driver, which may be explained by the fact that an increase of atmospheric pCO2 has only little to no impact on mean ocean surface pH on timescales exceeding 10,000 years35. A plausible reason is that slow rates of CO2 release lead to a different balance of carbonate chemistry changes and a smaller seawater CaCO3 saturation response. This is because the alkalinity released by rock weathering on land must ultimately be balanced by the preservation and burial of CaCO3 in marine sediments. The burial is controlled by the CaCO3 saturation state of the ocean and therefore, the saturation is ultimately regulated by weathering on long time scales, and not by atmospheric pCO2. The effect of weathering on atmospheric pCO2 is much weaker than the effect of weathering on ocean pH. The much stronger effect of weathering on ocean pH allows pH and CaCO3 saturation to be almost decoupled for slowly increasing atmospheric pCO235.
The influence of CCA species diversity
The quantification of CCA species diversity in the geological past is associated to a number of challenges. While for recent CCA the extensive use of molecular phylogenetic methods resolved the four orders (Corallinales, Hapalidiales, Sporolithales, and Rhodogorgonales) currently recognized in the subclass Corallinophycidae as monophyletic lineages36,37, we have to rely on morphological characters since molecular methods are not available for the identification of fossil CCA. Because CCA show a pronounced phenotypic plasticity depending on environmental factors, their taxonomic identification depends on morphological characters like conceptacles (i.e. spore chambers) and the arrangement of cells in different areas of the thallus, features often not adequately preserved in fossil CCA. This has led to a great number of fossil CCA taxa that have been described on the basis of only a few anatomical characters of doubtful taxonomic value38. The inclusion of such taxa precludes fully reliable diversity estimations. To circumvent such problems, we used rarefied species data reviewed by experts on fossil CCA taxonomy28.
Our results show that high CCA diversity is linked to a higher abundance of CCA in true coral reefs. This might seem to contrast with the fact that in modern reefs, the wave-pounded intertidal algal ridges are built predominantly by only a few species while the ones making up the majority of diversity have a cryptic, hidden mode of life protected from full or direct exposure to major physical environmental factors and therefore do not contribute significantly to reef stabilization. However, if several CCA species were contributing to the same ecosystem function, a higher species diversity may have buffered reef systems from losing all species associated with the key function of supporting reef development39. As discussed in detail in the next section, the abundance of CCA in true reefs was transiently reduced four times since the Early Cretaceous. Except for the earliest crisis, this was likely caused by the origin and diversification of echinoids and parrot fish, prominent groups of bioeroding organisms that denude CCA. However, the CCA-coral reef system successfully recovered all times. We argue that this was supported by functional redundancy of CCA, because a diverse group of abundant species with a wider range of responses can help absorb disturbances39. This redundancy of responses to events among species within a functional group—the reef cementers—is an important component of resilience and the maintenance of ecosystem services. The amount of CCA biomass is critical in terms of the cementing capacity. Multi-species community models40 have shown that with consecutive native species’ extinctions at high diversity levels, species extinction usually only leads to a slight decrease in the total biomass of the native community. However, when starting from a lower initial diversity, a few consecutive species extinctions cause a relatively large biomass loss that ultimately leads to collapse. It should also be stressed that sometimes single species are responsible for the functioning of an ecosystem (i.e., keystone species), even if the ecosystem features a generally high biodiversity. Therefore, such ecosystems will decline if this key species is removed41.
Experiments with plants in rangelands42 showed that functional diversity maintains ecosystem functioning. At heavily grazed sites, some species dominant in the ungrazed communities were lost or substantially reduced. In four out of five cases, the minor species that replaced these lost ones were their functional analogues. Accordingly, we suggest that formerly less dominant but functionally analogous grazing-tolerant species increased in abundance and contributed to the maintenance of ecosystem functions. CCA species removed or reduced in biomass by grazing pressure can be replaced in terms of their ecosystem service, i.e. reef cementation, by other CCA that are better adapted to grazing.
This implies that in recent coral reef environments, areas with high CCA diversity—potentially including species occupying cryptic habitats—are more resilient against disturbance. Because the skeletal mineralogies of CCA vary considerably among species43, this resilience possibly applies also to future ocean acidification.
The evolution of herbivory and transient reef crises
The data reveal four crises in the abundance of CCA within true reefs, during the Cretaceous (Turonian–Campanian), the Paleocene (Selandian–Thanetian), the Miocene (Serravallian), and the Pliocene (Zanclean–Piacenzian). The reason that the timing of the Paleocene crisis differs from the known Paleocene–Eocene crisis20 might be that our study focuses on the number of true reefs, while the Paleocene-Eocene crisis is expressed by a change in cumulative metazoan reef volume. Except for the first one, all crises observed here occurred synchronous with pronounced evolutionary events in clades of grazing organisms. Cementing and binding is the main function of CCA in the facilitation of true coral reefs. The decline in CCA abundance during the Selandian–Thanetian corresponds with a marked increase in the rate of morphological evolution in echinoids (Fig. 2). This includes major shifts in lifestyle and the evolution of new subclades in this group44, with a net trend towards improved mobility and feeding ability also on CCA16. Regarding the Serravallian and Zanclean–Piacenzian crises, echinoids appear to play a very minor role as their evolutionary rates constantly decreased over time44. However, another important clade of coralline grazers, the parrot fishes (Scarinae Rafinesque, 1810) may have become major players45. Although reef-grazing fish have existed for nearly 400 Ma, specialized detritivores feeding on macroalgae have only been known since the Miocene46. This is also in line with the radiation of acroporid corals since the mid Miocene47, whose branched morphologies create interstitial niches for parrot fish but also for cryptic CCA species. The parrot fishes (Scarinae) first appeared in the Serravallian45, which may have caused the third crisis in CCA reef cementing capacity. The lineage diversification of Scarinae was most pronounced during the Zanclean-Piacenzian, which we deem responsible for the third crisis.
The abundance of CCA in true coral reefs recovered relatively fast after all crises probably due to morphological adaptations developed within the CCA. Experiments have shown that echinoids are able to graze tissues to depths averaging 88 µm16, which is critical for CCA with thin crust morphologies. The resulting decline of thin crust morphologies led to the occupation of niches by branching CCA16. The twig-like morphologies of branching CCA prevent echinoids from denuding CCA thallus and confine this process to the tips of the branches. CCA are able to transfer nutrients within their thallus16. Therefore, these superficial grazing wounds can be rapidly healed if sufficient nutrient reservoirs are present in other, ungrazed parts of the algae. Meristems and conceptacles engulfed in the thallus may be another adaptation pertinent to the relatively low impact of echinoid grazing, as this is a plausible strategy to protect the reproductive and growth structures of the CCA. The more intense grazing pressure exerted by the parrot fishes, which bite CCA to an average depth of 288 µm16 and are able to eat the tips of branched CCA48 may have resulted in a greater abundance of CCA with very thick crusts. Thick-crust CCA possess larger nutrient reservoirs making them capable to recover also from grazing exerted by parrot fishes. All these adaptations and their development are congruent with the origination and diversification of the grazer clades as already outlined in other studies16,49,50. Today and potentially already during the geological history, CCA did not only successfully adapt to various grazer clades but even required the grazing pressure to stay free of epiphytes49. Here we show for the first time that the process of grazer evolution may also have affected the potential capacity of the CCA to reinforce coral reefs for three times during the geological past.
Future implications for the capacity of CCA to reinforce coral reefs
As it concerns some of the most important biodiversity hot spots on our planet2, the potential future impact of the ongoing global change on the capacity of CCA to reinforce coral reefs should become a focal point of reef research. Despite the implementation of numerous mesocosm and aquaria experiments51,52,53, long-term data in the magnitude of months on CCA responses to modified environmental parameters are still sparse. Also, the change from ambient to modified parameters (e.g. pCO2, temperature) happens much faster than at natural rates.
The impact of elevated pCO2 on CCA depends on the rate of change. While fast rates are critical, slow pCO2 increase may even result in increased net calcification at moderately elevated pCO2 levels54. However, this comes at the cost of structural integrity of the CCA skeleton which, in turn, makes the CCA likely more susceptible to bioerosion. Bioerosion by echinoids and parrot fishes is beneficial to CCA at the present state, as it removes fast growing fleshy algae and other epiphytes49, but nothing is known about the future of this interaction when the integrity of the CCA skeletons is altered. Additionally, it has been shown that elevated pCO2 levels accelerate sponge reef bioerosion55,56,57. Therefore, a combination of increased bioerosion rates affecting corals and CCA might lead to strongly deteriorated conditions for coral reef formation. As outlined above, a greater CCA diversity might also increase their resilience against ocean acidification because of the great variety in skeletal mineralogies.
Regarding elevated temperatures, the outcome for CCA is unpredictable. Depending on the examined species, elevated temperatures affect CCA primary production in different ways: some species show no or negligible response30, some change their skeletal chemistry in terms of dolomite concentration58, and others respond with strongly impaired germination success59 or declining skeletal densities60. Due to the positive influence of cooler temperatures on CCA’s abundance in true reefs detected in our study, elevated temperatures will likely have a negative outcome but also here, the rate of change might be similarly important as the magnitude.
To estimate the future of CCA’s potential to facilitate coral reef growth in the face of global change, we encourage long term experiments—preferably in near-natural mesocosm studies—including the main reef stabilizing CCA species. More

Plants
Wheat (variety Galaxie) was sown on John Innes No. 2 compost in 24-cell trays with 2–5 plants per cell, and maintained on a 16:8 h light:dark cycle at 18 °C (day) and 15 °C(night), with 80% RH in a MLR-352H-PE climate chamber (Sanyo). All plants were used for experiments at 2 weeks old.
Bacteria and fungi
Pseudomonas syringae pv. syringae strains reported to exhibit ice nucleation activity (281, 3010) or not reported to exhibit ice nucleation activity (1902, 3012) were purchased from the National Collection of Plant Pathogenic Bacteria (FERA, UK). Strains selected were not originally isolated from wheat (281, 1902—isolated from Syringa vulgaris; 3010, 3012—isolated from Malus sylvestris) and are not known wheat pathogens. Ice-nucleation activity was confirmed by floating smooth foil squares on the surface of a water/methanol ice bath, cooled to − 10, − 8, − 6, − 4 or − 2 °C. 100 μL droplets of sterile, distilled water were pipetted onto the foil squares and ice crystals formed spontaneously, within 1 min, only at − 10 °C. Two microliters of overnight bacterial culture were added to water droplets and time taken for ice formation recorded. Strain 281 showed the strongest ice nucleation activity and 1902 showed no detectable effect on ice formation; 3010 and 3012 were intermediate (Supplementary Table S1). Bacteria were maintained in 50% glycerol at − 80 °C and were grown on LB agar at 28 °C for all applications. For all experiments involving Z. tritici, the model isolate IPO3237 was used.
Bacterial pathogenicity tests
Bacteria were streaked onto LB agar and grown for 3 days before resuspension in 10 mM MgCl2 at 107 cfu/mL. Bacterial suspensions were sprayed onto wheat plants using a hand held atomiser at a rate of approximately 0.5 mL per cell of 2–5 two week old plants, giving visible misting of both leaf surfaces, on all leaves, without runoff of inoculum. Six cells of plants were inoculated with each strain. Plants were then returned to growth chambers and monitored for symptom development for 28 days. Only strain 3010 induced clear symptoms within this timeframe, although some plants inoculated with strain 3012 also showed mild chlorosis of leaf tips after day 14 (Supplementary Table S2). In further experiments, only strains 281 (INA+) and 1902 (INA−) were used.
Ion leakage measurements
10 cm lengths of 6–9 treated leaves were excised and placed in 10 mL ddH2O for 12 h. Conductivity of the ddH2O was measured using a conductivity meter and then leaves were boiled for 1 h and measurement repeated. ddH2O controls, without leaves, were treated in the same fashion. Ion leakage was reported as a percentage of total conductivity after boiling; control values were subtracted.
Propidium iodide staining for cell death
1 cm leaf sections were immersed for 1 h, in the dark, in 0.05% (w/v) propidium iodide (PI), mounted in 0.1% (v/v) phosphate buffered saline (PBS, pH 7) and viewed using a Leica SP8 confocal microscope using argon laser emission at 500 nm with detection at 600–630 nm. Five leaf sections were viewed for each treatment and 3 fields of view visualised in each leaf section. Cell death was scored as number of cells showing internal (cytoplasmic or nuclear) red fluorescence / total number of cells in field of view. No cell death was recorded.
Wheat inoculation
14 day-old wheat plants were inoculated with either INA+ or INA− bacteria suspended in 10 mM MgCl2 at 107 cfu/mL by spraying with a handheld atomiser until leaves were visibly beaded with moisture on both surfaces, avoiding runoff. Controls were sprayed with 10 mM MgCl2 only.
For assays involving co-inoculation of plants with bacteria and Z. tritici, plants were allowed to dry for an hour before inoculation with Z. tritici blastospores. Blastospores were suspended in sterile distilled water at 105 cfu/mL, a low inoculum density preventing saturation of infection28. Inoculated plants were returned to growth chambers and kept under plastic cloches for 72 h, then maintained as usual.
Analysis of STB speed and severity
Plants were observed at 7, 10, 12, 14, 16, 18, 21, 24 and 28 days post inoculation (dpi) and the most severe symptom on each leaf recorded. At 28 dpi, all inoculated leaves were harvested, rehydrated for 1 h, then scanned at high resolution. Pycnidia were enumerated and leaf area measured in scanned images using ImageJ28.
Antibiotic and competitor application
INA+ bacterial inoculation was carried out as before. Plants were allowed to dry for 1 h at room temperature, then spray inoculated with INA− bacteria suspended in 10 mM MgCl2 at 107 cfu/mL, or sprayed with ampicillin solution (50 μg/mL). Following this second treatment, plants were returned to growth chambers for 24 h. Freezing treatment and subsequent Z. tritici inoculation was then carried out as above.
Estimation of bacterial populations
Two methods were used to estimate bacterial populations on leaves. Firstly, 1 cm leaf samples were harvested from 3 randomly selected leaves in each treatment. These samples were mounted on glass slides in phosphate buffered saline, to which BacLight Green bacterial stain and propidium iodide counterstain were added at 0.05% (w/v) each. After 10 min, leaf samples were imaged at 20× magnification using a Leica SP8 confocal microscope. 5 μM z-stacks were collected at three randomly selected fields of view for each leaf sample and maximum projections created from these. Laser power, gain, and other parameters were held constant between fields of view and samples. The number of green pixels in each projection was then counted using ImageJ software and summed across the three fields of view. The percentage of pixels in the three fields of view which were green was then calculated and used as a proxy for percentage leaf area covered by bacteria. Secondly, 1 cm leaf samples were harvested from 3 more randomly selected leaves in each treatment and homogenised in 10 mM MgCl2. Homogenate was diluted 1/10, 1/100 and 1/1000 in MgCl2 and five 10 μL samples of each dilution spotted onto King’s B29 agar with Pseudomonas selective supplement CFC (Oxoid). Colonies were counted after 24 h incubation at 28 °C. Both procedures were carried out at 1, 4, 7, 10 and 14 dpi.
Experimental design and statistical methods
Specific details of experimental design and analyses are presented in the figure legends alongside the relevant results. Some general principles were applied. Randomisation: where a set of pots of plants was divided between treatments, pots were numbered and randomly generated numbers used to select those assigned to each treatment. For selection of microscope fields of view within a leaf, the slide was placed so that the ‘bottom left’ part of the leaf was in view and a random distance (in mm, bounded by length and width of the leaf) moved in the x and y dimensions to select each field of view, returning to the 0,0 position before each selection.
Replication: technical replication was used in all experiments, with the number of such replicates given in each figure legend. Three complete repeat experiments were carried out in most cases, with figure legends stating where replication was different (min. 2 repeats) and all data presented represent the mean of such replicates, with error bars showing standard errors. Statistical analyses: data were analysed using ANOVA unless otherwise stated, with appropriate checks for homoscedasticity and other assumptions. More