Ecology

1.
Caro, T. M. Antipredator Defenses in Birds and Mammals (University of Chicago Press, 2005).
2.
Emlen, D. J. The evolution of animal weapons. Annu. Rev. Ecol. Evol. Syst. 39, 387–413 (2008).
Article  Google Scholar 

3.
Toledo, L. F., Sazima, I. & Haddad, C. F. Behavioural defences of anurans: An overview. Ethol. Ecol. Evol. 23, 1–25 (2011).
Article  Google Scholar 

4.
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).
Article  Google Scholar 

5.
Pettorelli, N., Coulson, T., Durant, S. M. & Gaillard, J. Predation, individual variability and vertebrate population dynamics. Oecologia 167, 305–314 (2011).
ADS  PubMed  Article  Google Scholar 

6.
Stankowich, T. Armed and dangerous: predicting the presence and function of defensive weaponry in mammals. Adapt. Behav. 20, 32–43 (2011).
Article  Google Scholar 

7.
Longson, C. G. & Joss, J. M. P. Optimal toxicity in animals: Predicting the optimal level of chemical defences. Funct. Ecol. 20, 731–735 (2006).
Article  Google Scholar 

8.
Relyea, R. A. Predators come and predators go: The reversibility of predator-induced traits. Ecology 84, 1840–1848 (2003).
Article  Google Scholar 

9.
Tollrian, R. & Harvell, D. The Ecology and Evolution of Inducible Defenses (Princeton University Press, 1999).

10.
Daly, D., Higginson, A. D., Chen, D., Ruxton, G. D. & Speed, M. P. Density-dependent investment in costly anti-predator defenses: An explanation for the weak survival benefit of group living. Ecol. Lett. 15, 576–583 (2012).
PubMed  Article  Google Scholar 

11.
Kosmala, G., Brown, G. P. & Shine, R. Thin-skinned invaders: Geographic variation in the structure of the skin among populations of cane toads (Rhinella marina). Biol. J. Linn. Soc. 131, 611–621 (2020).
Article  Google Scholar 

12.
Duellman, W. E. & Trueb, L. Biology of Amphibians (McGraw-Hill, 1994).

13.
Wells, K. The Ecology and Behavior of Amphibians (University of Chicago Press, 2007).

14.
König, E., Bininda-Emonds, O. R. P. & Shaw, C. The diversity and evolution of anuran skin peptides. Peptides 63, 96–117 (2014).
PubMed  Article  CAS  Google Scholar 

15.
Hettyey, A., Tóth, Z. & Van Buskirk, J. Inducible chemical defences in animals. Oikos 123, 1025–1028 (2014).
Article  Google Scholar 

16.
Blennerhasset, R., Bell-Anderson, K., Shine, R. & Brown, G. P. The cost of chemical defence: The impact of toxin depletion on growth and behaviour of cane toads (Rhinella marina). Proc. R. Soc. B. 286, 20190867 (2019).
Article  CAS  Google Scholar 

17.
Chen, W., Hudson, C. M., DeVore, J. L. & Shine, R. Sex and weaponry: The distribution of toxin-storage glands on the bodies of male and female cane toads (Rhinella marina). Ecol. Evol. 7, 8950–8957 (2017).
PubMed  PubMed Central  Article  Google Scholar 

18.
O’Donohoe, M. A. et al. Diversity and evolution of the parotoid macrogland in true toads (Anura: Bufonidae). Zool. J. Linn. Soc. 187, 453–478 (2019).
Article  Google Scholar 

19.
Shine, R. The ecological impact of invasive cane toads (Bufo marinus) in Australia. Q. Rev. Biol. 85, 253–291 (2010).
PubMed  Article  Google Scholar 

20.
Ujvari, B. et al. Isolation breeds naivety: island living robs Australian varanid lizards of toad-toxin immunity via four-base-pair mutation. Evolution 67, 289–294 (2013).
PubMed  Article  Google Scholar 

21.
Pearcy, A. Selective feeding in Keelback snakes Tropidonophis mairii in an Australian wetland. Aust. Zool. 35, 843–845 (2011).
Article  Google Scholar 

22.
Llewelyn, J. et al. Behavioural responses of an Australian colubrid snake (Dendrelaphis punctulatus) to a novel toxic prey item (the Cane Toad Rhinella marina). Biol. Invasions 20, 2507–2516 (2018).
Article  Google Scholar 

23.
van Bocxlaer, I. et al. Gradual adaptation toward a range-expansion phenotype initiated the global radiation of toads. Science 327, 679–682 (2010).
ADS  PubMed  Article  CAS  Google Scholar 

24.
Hudson, C. M., Vidal-García, M., Murray, T. G. & Shine, R. The accelerating anuran: evolution of locomotor performance in cane toads (Rhinella marina, Bufonidae) at an invasion front. Proc. R. Soc. B 287, 20201964 (2020).
PubMed  Article  Google Scholar 

25.
Ward-Fear, G., Greenlees, M. J. & Shine, R. Toads on lava: spatial ecology and habitat use of invasive cane toads (Rhinella marina) in Hawai’i. PLoS ONE 11, e0151700 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Ward-Fear, G., Pearson, D. J., Brown, G. P. & Shine, R. Ecological immunization: in situ training of free-ranging predatory lizards reduces their vulnerability to invasive toxic prey. Biol. Lett. 12, 20150863 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Crossland, M. R., Brown, G. P., Anstis, M., Shilton, C. & Shine, R. Mass mortality of native anuran tadpoles in tropical Australia due to the invasive cane toad (Bufo marinus). Biol. Conserv. 141, 2387–2394 (2008).
Article  Google Scholar 

28.
Hayes, R. A., Crossland, M. R., Hagman, M., Capon, R. J. & Shine, R. Ontogenetic variation in the chemical defences of cane toads (Bufo marinus): Toxin profiles and effects on predators. J. Chem. Ecol. 35, 391–399 (2009).
CAS  PubMed  Article  Google Scholar 

29.
Hagman, M., Hayes, R. A., Capon, R. J. & Shine, R. Alarm cues experienced by cane toad tadpoles affect post-metamorphic morphology and chemical defences. Funct. Ecol. 23, 126–132 (2009).
Article  Google Scholar 

30.
Üveges, B. et al. Age-and environment-dependent changes in chemical defences of larval and post-metamorphic toads. BMC Evol. Biol. 17, 137 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Üveges, B. et al. Chemical defense of toad tadpoles under risk by four predator species. Ecol. Evol. 9, 6287–6299 (2019).
PubMed  PubMed Central  Article  Google Scholar 

32.
Bókony, V., Üveges, B., Verebélyi, V., Ujhegyi, N. & Móricz, Á. M. Toads phenotypically adjust their chemical defences to anthropogenic habitat change. Sci. Rep. 9, 3163 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Hettyey, A. et al. Predator-induced changes in the chemical defence of a vertebrate. J. Anim. Ecol. 88, 1925–1935 (2019).
PubMed  Article  Google Scholar 

34.
Hudson, C. M, Brown, G. P., Stuart, K. & Shine, R. Sexual and geographic divergence in head widths of invasive cane toads, Rhinella marina (Anura: Bufonidae) is driven by both rapid evolution and plasticity. Biol. J. Linn. Soc. 124, 188–199 (2018).

35.
Phillips, B. L., Brown, G. P., Webb, J. K. & Shine, R. Invasion and the evolution of speed in toads. Nature 439, 803 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Hudson, C. M., McCurry, M. R., Lundgren, P., McHenry, C. R. & Shine, R. Constructing an invasion machine: The rapid evolution of a dispersal-enhancing phenotype during the cane toad invasion of Australia. PLoS ONE 11, e0156950 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Hudson, C. M., Brown, G. P. & Shine, R. It is lonely at the front: Contrasting evolutionary trajectories in male and female invaders. R. Soc. Open Sci. 3, 160687 (2016).
ADS  PubMed  PubMed Central  Article  Google Scholar 

38.
Brown, G., Kelehear, C. & Shine, R. The early toad gets the worm: Cane toads at an invasion front benefit from higher prey availability. J. Anim. Ecol. 82, 854–862 (2013).
PubMed  Article  Google Scholar 

39.
Shine, R., Brown, G. P. & Phillips, B. L. An evolutionary process that assembles phenotypes through space rather than time. Proc. Natl Acad. Sci. USA 108, 5708–5711 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

40.
Phillips, B. & Shine, R. The morphology, and hence impact, of an invasive species (the cane toad, Bufo marinus) changes with time since colonization. Anim. Conserv. 8, 407–413 (2005).
Article  Google Scholar 

41.
Roff, D. A. Comparing sire and dam estimates of heritability: Jackknife and likelihood approaches. Heredity 100, 32–38 (2008).
CAS  PubMed  Article  Google Scholar 

42.
Kliber, A. & Eckert, C. G. Interaction between founder effect and selection during biological invasion in an aquatic plant. Evolution 59, 1900–1913 (2005).
CAS  PubMed  Google Scholar 

43.
Shine, R. Cane Toad Wars (University of California Press, 2018).

44.
Toledo, R. C. & Jared, C. Cutaneous adaptations to water balance in amphibians. Comp. Biochem. Physiol. A 105, 593–608 (1993).
Article  Google Scholar 

45.
Kosmala, G., Brown, G. P., Shine, R. & Christian, K. Skin resistance to water gain and loss has changed in cane toads (Rhinella marina) during their Australian invasion. Ecol. Evol. 10, 13071–13079 (2020).
PubMed  PubMed Central  Article  Google Scholar 

46.
Crossland, M. R. & Shine, R. Cues for cannibalism: Cane toad tadpoles use chemical signals to locate and consume conspecific eggs. Oikos 120, 327–332 (2011).
Article  Google Scholar 

47.
DeVore, J. L., Crossland, M. & Shine, R. Tradeoffs affect the adaptive value of plasticity: Stronger cannibal-induced defenses incur greater costs in toad larvae. Ecol. Monogr. https://doi.org/10.1002/ecm.1426 (2020).
Article  Google Scholar 

48.
Greenlees, M. J. & Shine, R. Impacts of eggs and tadpoles of the invasive cane toad (Bufo marinus) on aquatic predators in tropical Australia. Austral Ecol. 36, 53–58 (2011).
Article  Google Scholar 

49.
Somaweera, R., Crossland, M. R. & Shine, R. Assessing the potential impact of invasive cane toads on a commercial freshwater fishery in tropical Australia. Wildl. Res. 38, 380–385 (2011).
Article  Google Scholar 

50.
Cao, Y., Cui, K., Pan, H., Wu, J. & Wang, L. The impact of multiple climatic and geographic factors on the chemical defences of Asian toads (Bufo gargarizans Cantor). Sci. Rep. 9, 17236 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Hague, M. T. J., Stokes, A. N., Feldman, C. R., Brodie, E. D. Jr. & Brodie, E. D. III. The geographic mosaic of arms race coevolution is closely matched to prey population structure. Evol. Lett. 4, 317–332 (2020).

52.
Jared, C. et al. Parotoid macroglands in toad (Rhinella jimi): Their structure and functioning in passive defence. Toxicon 54, 197–207 (2009).
CAS  PubMed  Article  Google Scholar 

53.
Toledo, R. C. & Jared, C. Cutaneous granular glands and amphibian venoms. Comp. Biochem. Physiol. A 111, 1–29 (1995).
ADS  Article  Google Scholar 

54.
Maciel, N. M. et al. Composition of indolealkylamines of Bufo rubescens cutaneous secretions compared to six other Brazilian bufonids with phylogenetic implications. Comp. Biochem. Physiol. B 134, 641–649 (2003).
PubMed  Article  CAS  Google Scholar 

55.
Sciani, J. M., Angeli, C. B., Antoniazzi, M. M., Jared, C. & Pimenta, D. C. Differences and similarities among parotoid macrogland secretions in South American toads: A preliminary biochemical delineation. Sci. World J. 2013, 937407 (2013).
Article  CAS  Google Scholar 

56.
Habermehl, G. Venomous Animals and Their Toxins (Springer-Verlag, 1981).

57.
Garrett, C. M. & Boyer, D. M. Bufo marinus (cane toad) predation. Herpetol. Rev. 24, 148 (1993).
Google Scholar 

58.
Pineau, X. & Romanoff, C. Envenomation of domestic carnivores. Rec. Méd. Vét. 171, 182–192 (1995).
Google Scholar 

59.
Sakate, M. & Lucas de Oliveira, P. C. Toad envenoming in dogs: effects and treatment. J. Venom. Anim. Toxins 6, 52–62 (2000).

60.
Slade, R. W. & Moritz, C. Phylogeography of Bufo marinus from its natural and introduced ranges. Proc. R. Soc. B 265, 769–777 (1998).
CAS  PubMed  Article  Google Scholar 

61.
Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. The cane toad’s (Chaunus [Bufo] marinus) increasing ability to invade Australia is revealed by a dynamically updated range model. Proc. R. Soc. B 274, 1413–1419 (2007).
PubMed  Article  Google Scholar 

62.
Urban, M., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, 134–148 (2008).
Article  Google Scholar 

63.
Nullet, D., Juvik, J. O. & Wall, A. A Hawaiian mountain climate cross-section. Clim. Res. 5, 131–137 (1995).
Article  Google Scholar 

64.
Kelehear, C. & Shine, R. Non-reproductive male cane toads (Rhinella marina) withhold sex-identifying information from their rivals. Biol. Lett. 15, 2019046 (2019).
Article  Google Scholar 

65.
Shine, R., Everitt, C., Woods, D. & Pearson, D. J. An evaluation of methods used to cull invasive cane toads in tropical Australia. J. Pest Sci. 91, 1081–1091 (2018).
Article  Google Scholar 

66.
Phillips, B. L. et al. Parasites and pathogens lag behind their host during periods of host range-advance. Ecology 91, 872–881 (2010).
PubMed  Article  Google Scholar 

67.
Hudson, C. M., Brown, G. P. & Shine, R. Effects of toe-clipping on growth, body condition, and locomotion of cane toads (Rhinella marina). Copeia 105, 257–260 (2017).
Article  Google Scholar 

68.
Wilson, A. J. et al. An ecologist’s guide to the animal model. J. Anim. Ecol. 79, 13–26 (2010).
PubMed  Article  Google Scholar  More

1.
Millennium Ecosystem Assessment. Ecosystems and human well-being: Biodiversity synthesis (World Resources Institute, Washington, DC, 2005). http://www.millenniumassessment.org/documents/document.354.aspx.pdf (accessed 22 April 2020).
2.
Willis, K. & Birks, H. What is natural? The need for a long-term perspective. Science 314(5803), 1261–1266. https://doi.org/10.1126/science.1122667 (2006).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

3.
Birks, H. J. B. et al. Does pollen-assemblage richness reflect floristic richness? A review of recent developments and future challenges. Rev. Palaeobot. Palynol. 228, 1–25. https://doi.org/10.1016/j.revpalbo.2015.12.011 (2016).
Article  Google Scholar 

4.
Li, K., Liao, M., Ni, J., Liu, X. & Wang, Y. Treeline composition and biodiversity change on the southeastern Tibetan Plateau during the past millennium, inferred from a high-resolution alpine pollen record. Quat. Sci. Rev. 206, 44–55. https://doi.org/10.1016/j.quascirev.2018.12.029 (2019).
ADS  Article  Google Scholar 

5.
Bálint, M. et al. Environmental DNA time series in ecology. Trends Ecol. Evol. 33, 945–957. https://doi.org/10.1016/j.tree.2018.09.003 (2018).
Article  PubMed  Google Scholar 

6.
Garlapati, D., Charankumar, B., Ramu, K., Madeswaran, P. & Ramana Murthy, M. V. A review on the applications and recent advances in environmental DNA (eDNA) metagenomics. Rev. Environ. Sci. Biotechnol. 18, 389–411. https://doi.org/10.1007/s11157-019-09501-4 (2019).
CAS  Article  Google Scholar 

7.
Hebert, P. D. N., Cywinska, A., Ball, S. L. & DeWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. B Biol. Sci. 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).
CAS  Article  Google Scholar 

8.
Kress, W. J. & Erickson, D. L. DNA barcodes: Genes, genomics, and bioinformatics. Proc. Natl. Acad. Sci. USA 105, 2761–2762. https://doi.org/10.1073/pnas.0800476105 (2008).
ADS  Article  PubMed  Google Scholar 

9.
CBOL Plant Working Group. A DNA barcode for land plants. Proc. Natl. Acad. Sci. USA 106, 12794–12797. https://doi.org/10.1073/pnas.0905845106 (2009).
Article  Google Scholar 

10.
China Plant BOL Group. Comparative analysis of a large dataset indicates that internal transcribed spacer (ITS) should be incorporated into the core barcode for seed plants. Proc. Natl. Acad. Sci. USA 108, 19641–19646. https://doi.org/10.1073/pnas.1104551108 (2011).
ADS  Article  Google Scholar 

11.
Li, X. W. et al. Plant DNA barcoding: From gene to genome. Biol. Rev. Camb. Philos. 90, 157–166. https://doi.org/10.1111/brv.12104 (2015).
Article  Google Scholar 

12.
Fior, S. et al. Spatiotemporal reconstruction of the Aquilegia rapid radiation through next-generation sequencing of rapidly evolving cpDNA regions. New Phytol. 198, 579–592. https://doi.org/10.1111/nph.12163 (2013).
Article  PubMed  Google Scholar 

13.
Staats, M. et al. Advances in DNA metabarcoding for food and wildlife forensic species identification. Anal. Bioanal. Chem. 408, 4615–4630. https://doi.org/10.1007/s00216-016-9595-8 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Taberlet, P. et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 35, e14. https://doi.org/10.1093/nar/gkl938 (2007).
CAS  Article  Google Scholar 

15.
Kraaijeveld, K. et al. Efficient and sensitive identification and quantification of airborne pollen using next-generation DNA sequencing. Mol. Ecol. Resour. 15, 8–16. https://doi.org/10.1111/1755-0998.12288 (2015).
CAS  Article  PubMed  Google Scholar 

16.
Leontidou, K. et al. DNA metabarcoding of airborne pollen: New protocols for improved taxonomic identification of environmental samples. Aerobiologia 34, 63–74. https://doi.org/10.1007/s10453-017-9497-z (2018).
Article  Google Scholar 

17.
Parducci, L. et al. Ancient plant DNA in lake sediments. New Phytol. 214, 924–942 (2017).
CAS  Article  Google Scholar 

18.
Giguet-Covex, C. et al. New insights on lake sediment DNA from the catchment: Importance of taphonomic and analytical issues on the record quality. Sci. Rep. 9, 1–21 (2019).
CAS  Article  Google Scholar 

19.
Bovo, S. et al. Shotgun metagenomics of honey DNA: Evaluation of a methodological approach to describe a multi-kingdom honey bee derived environmental DNA signature. PLoS ONE 13, 1–19. https://doi.org/10.1371/journal.pone.0205575 (2018).
CAS  Article  Google Scholar 

20.
Yoccoz, N. G. et al. DNA from soil mirrors plant taxonomic and growth form diversity. Mol. Ecol. 21, 3647–3655 (2012).
CAS  Article  Google Scholar 

21.
Parducci, L. et al. Shotgun environmental DNA, pollen, and macrofossil analysis of lateglacial lake sediments from southern Sweden. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00189 (2019).
Article  Google Scholar 

22.
Alsos, I. G. et al. Plant DNA metabarcoding of lake sediments: How does it represent the contemporary vegetation. PLoS ONE 13, 1–23. https://doi.org/10.1371/journal.pone.0195403 (2018).
CAS  Article  Google Scholar 

23.
Willerslev, E. et al. Ancient biomolecules from deep ice cores reveal a forested southern Greenland. Science 317, 111–114. https://doi.org/10.1126/science.1141758 (2007).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

24.
Willerslev, E. et al. Diverse plant and animal genetic records from holocene and pleistocene sediments. Science 300, 791–795 (2003).
ADS  CAS  Article  Google Scholar 

25.
Willerslev, E. et al. Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506, 47–51. https://doi.org/10.1038/nature12921 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

26.
Zimmermann, H. et al. Sedimentary ancient DNA and pollen reveal the composition of plant organic matter in Late Quaternary permafrost sediments of the Buor Khaya Peninsula (north-eastern Siberia). Biogeosciences 14, 575–596. https://doi.org/10.5194/bg-14-575-2017 (2017).
ADS  CAS  Article  Google Scholar 

27.
Alaeddini, R. Forensic implications of PCR inhibition—A review. Forensic Sci. Int. Genet. 6, 297–305. https://doi.org/10.1016/j.fsigen.2011.08.006 (2012).
CAS  Article  PubMed  Google Scholar 

28.
Haeberli, W. & Alean, J. Temperature and accumulation of high altitude firn in the alps. Ann. Glaciol. 6, 161–163. https://doi.org/10.3189/1985AoG6-1-161-163 (1985).
ADS  Article  Google Scholar 

29.
Bennett, K. D. & Buck, C. E. Interpretation of lake sediment accumulation rates. Holocene 26, 1092–1102. https://doi.org/10.1177/0959683616632880 (2016).
ADS  Article  Google Scholar 

30.
Festi, D. et al. A novel pollen-based method to detect seasonality in ice cores: A case study from the Ortles glacier, South Tyrol, Italy. J. Glaciol. 61, 815–824. https://doi.org/10.3189/2015JoG14J236 (2015).
ADS  Article  Google Scholar 

31.
Nakazawa, F. Application of pollen analysis to dating of ice cores from lower-latitude glaciers. J. Geophys. Res. 109, 168–170. https://doi.org/10.1029/2004JF000125 (2004).
Article  Google Scholar 

32.
Nakazawa, F. et al. Dating of seasonal snow/firn accumulation layers using pollen analysis. J. Glaciol. 51, 483–490. https://doi.org/10.3189/172756505781829179 (2005).
ADS  Article  Google Scholar 

33.
Nakazawa, F. et al. Establishing the timing of chemical deposition events on Belukha Glacier, Altai Mountains, Russia, using Pollen analysis. Arctic Antarct. Alp. Res. 43, 66–72. https://doi.org/10.1657/1938-4246-43.1.66 (2011).
Article  Google Scholar 

34.
Nakazawa, F., Konya, K., Kadota, T. & Ohata, T. Reconstruction of the depositional environment upstream of Potanin Glacier, Mongolian Altai, from pollen analysis. Environ. Res. Lett. 7, 035402. https://doi.org/10.1088/1748-9326/7/3/035402 (2012).
ADS  Article  Google Scholar 

35.
Santibañez, P. et al. Glacier mass balance interpreted from biological analysis of firn cores in the Chilean lake district. J. Glaciol. 54, 452–462. https://doi.org/10.3189/002214308785837101 (2008).
ADS  Article  Google Scholar 

36.
Uetake, J. et al. Biological ice-core analysis of Sofiyskiy glacier in the Russian Altai. Ann. Glaciol. 43, 70–78. https://doi.org/10.3189/172756406781811925 (2006).
ADS  CAS  Article  Google Scholar 

37.
Andreev, A. A., Nikolaev, V. I., Boi’sheiyanov, D. Y. & Petrov, V. N. Pollen and isotope investigations of an ice core from Vavilov ice cap, October revolution island, Severnaya Zemlya archipelago, Russia. Geogr. Phys. Quat. 51, 379–389. https://doi.org/10.7202/033137ar (1997).
Article  Google Scholar 

38.
Liu, K. B., Reese, C. A. & Thompson, L. G. A potential pollen proxy for ENSO derived from the Sajama ice core. Geophys. Res. Lett. 34, 1–5. https://doi.org/10.1029/2006GL029018 (2007).
Article  Google Scholar 

39.
Reese, C. A., Liu, K. B. & Thompson, L. G. An ice-core pollen record showing vegetation response to Late-glacial and Holocene climate changes at Nevado Sajama, Bolivia. Ann. Glaciol. 54, 183–190. https://doi.org/10.3189/2013AoG63A375 (2013).
ADS  CAS  Article  Google Scholar 

40.
Papina, T. et al. Biological proxies recorded in a Belukha ice core, Russian Altai. Clim. Past 9, 2399–2411. https://doi.org/10.5194/cp-9-2399-2013 (2013).
Article  Google Scholar 

41.
Winkler, S. et al. An introduction to mountain glaciers as climate indicators with spatial and temporal diversity. Erdkunde 64, 97–118. https://doi.org/10.3112/erdkunde.2010.02.01 (2010).
Article  Google Scholar 

42.
Citterio, M. et al. The fluctuations of Italian glaciers during the last century: A contribution to knowledge about alpine glacier changes. Geogr. Ann. Ser. A Phys. Geogr. 89, 167–184. https://doi.org/10.1111/j.1468-0459.2007.00316.x (2007).
Article  Google Scholar 

43.
Knoll, C. & Kerschner, H. A glacier inventory for South Tyrol, Italy, based on airborne laser-scanner data. Ann. Glaciol. 50, 46–52. https://doi.org/10.3189/172756410790595903 (2009).
ADS  Article  Google Scholar 

44.
Diolaiuti, G., Bocchiola, D., D’agata, C. & Smiraglia, C. Evidence of climate change impact upon glaciers’ recession within the Italian Alps: The case of Lombardy glaciers. Theor. Appl. Climatol. 109, 429–445. https://doi.org/10.1007/s00704-012-0589-y (2012).
ADS  Article  Google Scholar 

45.
IPCC. Climate Change 2014: Synthesis Report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Core Writing Team, R.K. Pachauri and L.A. Meyer) 151 (IPCC, Geneva, 2014).

46.
Maggi, V. et al. Variability of anthropogenic and natural compounds in high altitude-high accumulation alpine glaciers. Hydrobiologia 562, 43–56. https://doi.org/10.1007/s10750-005-1804-y (2006).
CAS  Article  Google Scholar 

47.
Gabrielli, P. et al. Age of the Mt. Ortles ice cores, the Tyrolean Iceman and glaciation of the highest summit of South Tyrol since the Northern Hemisphere Climatic Optimum. Cryosphere 10, 2779–2797. https://doi.org/10.5194/tc-10-2779-2016 (2016).
ADS  Article  Google Scholar 

48.
Bohleber, P. et al. Temperature and mineral dust variability recorded in two low-accumulation Alpine ice cores over the last millennium. Clim. Past 14, 21–37. https://doi.org/10.5194/cp-14-21-2018 (2018).
Article  Google Scholar 

49.
Rizzi, C., Finizio, A., Maggi, V. & Villa, S. Spatial–temporal analysis and risk characterisation of pesticides in Alpine glacial streams. Environ. Pollut. 248, 659–666. https://doi.org/10.1016/j.envpol.2019.02.067 (2019).
CAS  Article  PubMed  Google Scholar 

50.
Garzonio, R. et al. Mapping the suitability for ice-core drilling of glaciers in the European Alps and the Asian High Mountains. J. Glaciol. 64, 12–26. https://doi.org/10.1017/jog.2017.75 (2018).
ADS  Article  Google Scholar 

51.
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59. https://doi.org/10.1038/nmeth.2276 (2013).
CAS  Article  PubMed  Google Scholar 

52.
Olds, B. P. et al. Estimating species richness using environmental DNA. Ecol. Evol. 6, 4214–4226. https://doi.org/10.1002/ece3.2186 (2016).
Article  PubMed  PubMed Central  Google Scholar 

53.
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895. https://doi.org/10.1111/mec.14350 (2017).
Article  PubMed  Google Scholar 

54.
Soons, M. B. & Ozinga, W. A. How important is long-distance seed dispersal for the regional survival of plant species?. Divers. Distrib. 11, 165–172. https://doi.org/10.1111/j.1366-9516.2005.00148.x (2005).
Article  Google Scholar 

55.
Lyscov, V. N. & Moshkovsky, Y. S. DNA cryolysis. Biochim. Biophys. Acta 190, 101–110 (1969).
CAS  Article  Google Scholar 

56.
Pietramellara, G. et al. Extracellular DNA in soil and sediment: Fate and ecological relevance. Biol. Fertil. Soils 45, 219–235 (2009).
CAS  Article  Google Scholar 

57.
Lindahl, T. & Nyberg, B. Rate of depurination of native deoxyribonucleic acid. Biochemistry 11, 3610–3618 (1972).
CAS  Article  Google Scholar 

58.
Strickler, K. M., Fremier, A. K. & Goldberg, C. S. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol. Conserv. 183, 85–92 (2015).
Article  Google Scholar 

59.
Bortenschlager, S. Aspects of pollen morphology in the Cupressaceae. Grana 29, 129–137 (1990).
Article  Google Scholar 

60.
Kurmann, M. H. Pollen morphology and ultrastructure in the Cupressaceae. Acta Bot. Gall. 141, 141–147 (1994).
Article  Google Scholar 

61.
Chichiriccò, G. & Pacini, E. Cupressus arizonica pollen wall zonation and in vitro hydration. Plant Syst. Evol. 270, 231–242 (2008).
Article  Google Scholar 

62.
Moran, T., Marshall, S. J. & Sharp, M. J. Isotope thermometry in melt-affected ice cores. J. Geophys. Res. Earth Surf. 116, 1–10. https://doi.org/10.1029/2010JF001738 (2011).
CAS  Article  Google Scholar 

63.
Baroni, C., Armiraglio, S., Gentili, R. & Carton, A. Landform-vegetation units for investigating the dynamics and geomorphologic evolution of alpine composite debris cones (Valle dell’Avio, Adamello Group, Italy). Geomorphology 84, 59–79 (2007).
ADS  Article  Google Scholar 

64.
Coissac, E., Riaz, T. & Puillandre, N. Bioinformatic challenges for DNA metabarcoding of plants and animals. Mol. Ecol. 21, 1834–1847. https://doi.org/10.1111/j.1365-294X.2012.05550.x (2012).
CAS  Article  PubMed  Google Scholar 

65.
Celesti-Grapow, L. et al. (eds) Flora vascolare alloctona e invasiva delle regioni d’Italia (Casa Editrice Università La Sapienza, Roma, 2010).
Google Scholar 

66.
Wu, P.-C., Su, H.-J., Lung, S.-C.C., Chen, M.-J. & Lin, W.-P. Pollen of Broussonetia papyrifera: An emerging aeroallergen associated with allergic illness in Taiwan. Sci. Total Environ. 657, 804–810. https://doi.org/10.1016/j.scitotenv.2018.11.324 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

67.
Kelly, R. P. et al. Genetic and manual survey methods yield different and complementary views of an ecosystem. Front. Mar. Sci. 3, 1–11. https://doi.org/10.3389/fmars.2016.00283 (2017).
Article  Google Scholar 

68.
Baksay, S. et al. Experimental quantification of pollen with DNA metabarcoding using ITS1 and trnL. Sci. Rep. 10, 4202. https://doi.org/10.1038/s41598-020-61198-6 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

69.
Picotti, S., Francese, R., Giorgi, M., Pettenati, F. & Carcione, J. M. Estimation of glacier thicknesses and basal properties using the horizontal-to-vertical component spectral ratio (HVSR) technique from passive seismic data. J. Glaciol. 63, 229–248. https://doi.org/10.1017/jog.2016.135 (2017).
ADS  Article  Google Scholar 

70.
Smiraglia, C. et al. The evolution of the Italian glaciers from the previous data base to the new Italian inventory. Preliminary considerations and results. Geogr. Fis. e Din. Quat. 38, 79–87. https://doi.org/10.4461/GFDQ.2015.38.08 (2015).
Article  Google Scholar 

71.
Comitato Glaciologico Italiano & Consiglio Nazionale delle Ricerche. Catasto dei ghiacciai italiani. Anno geofisico 1957–1958. Volume III—Ghiacciai della Lombardia e dell’Ortles-Cevedale. (Comitato Glaciologico Italiano, Torino, 1961).

72.
Marson, L. Sui ghiacciai dell’Adamello – Presanella (alto bacino del Sarca – Mincio). Boll. Soc. Geogr. It. 7, 546–568 (1906).
Google Scholar 

73.
Servizio Glaciologico Lombardo. Ghiacciai in Lombardia (Edizioni Bolis, Bergamo, 1992).
Google Scholar 

74.
Payer, J. Originalkarte der Adamello-Presanella Alpen, scala di 1:56.000. In Pajer J. – Die Adamello-Presanella Alpen nach den Forschungen und Aufnahmen, Petermanns Geogr. Mitt. Erganzungs-Hefte, 11 (17) (Gotha, 1865).

75.
Bombarda, R. Il cuore Bianco. Guida ai ghiacciai del Trentino (Edizioni Arca, 1996).

76.
Baroni, C., Carton, A. & Casarotto, C. I ghiacciai dell’Adamello. In: Itinerari Glaciologici sulle montagne italiane (ed. Comitato Glaciologico Italiano) Vol. 3 (Società Geologica Italiana, Roma, 2017).

77.
Bertoni, E. & Casarotto, C. Estensione dei ghiacciai trentini dalla fine della Piccola Età glaciale a oggi. Rilevamento sul terreno, digitalizzazione GIS e analisi. (2015). Progetto finanziato dal Servizio sviluppo sostenibile e aree protette della PAT (rif. prot. n. P001/0640691/29-2014-16 dd. 2/12/2014) (accessed on 27 April 2020). http://www.climatrentino.it/binary/pat_climaticamente/osservatorio_trentino_clima/2014_Estensione_dei_ghiacciai_dalla_fine_della_Piccola_Et_Glaciale_a_oggi_MUSE_.1462456788.pdf.

78.
Abeni, F. et al. Hydrogen and oxygen stable isotope fractionation in body fluid compartments of dairy cattle according to season, farm, breed, and reproductive stage. PLoS ONE 10(5), e0127391. https://doi.org/10.1371/journal.pone.0127391 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

79.
Bocchiola, D., Bombelli, G. M., Camin, F. & Ossi, P. M. Field study of mass balance, and hydrology of the West Khangri Nup Glacier (Khumbu, Everest). Water 12(2), 433. https://doi.org/10.3390/w12020433 (2020).
Article  Google Scholar 

80.
Erdtman, G. The acetolysis method, A revised description. Svensk Bot. Tidskr. 54, 561–569 (1960).
Google Scholar 

81.
Faegri, K. & Iversen, J. Textbook of Pollen Analysis (Wiley, London, 1989).
Google Scholar 

82.
Bucher, E., Kofler, V., Vorwohl, G. & Zieger, E. Lo spettro pollinico dei mieli dell’Alto Adige (Laboratorio Biologico, Agenzia Provinciale per l’Ambiente, Laives, Bolzano. 2004).

83.
Albanese, D. et al. MICCA: Aa complete and accurate software for taxonomic profiling of metagenomic data. Sci. Rep. 5, 9743 (2015).
CAS  Article  Google Scholar  More

1.
di Castri, F. History of biological invasions with emphasis on the Old World. In Biological invasions: a global perspective, (ed. Drake J., di Castri, F., Groves, R., Kruger, F., Mooney, H. A., Rejmanek, M., Williamson, M.) 1–30 (Wiley, New York, 1989).
2.
Reeve, E. Domestication of Plants in the Old World: The origin and spread of cultivated plants in West Asia, Europe, and the Nile Valley (ed. Zohary, D. & Hopf, M.) (Clarendon Press, Oxford, 1994).

3.
Mack, R. N. et al. Biotic invasions: causes, epidemiology, global consequences, and control. Ecol. Appl. 10, 689–710 (2000).
Article  Google Scholar 

4.
Worner, S. P. & Gevrey, M. Modelling global insect pest species assemblages to determine risk of invasion. J. Appl. Ecol. 43, 858–867 (2006).
Article  Google Scholar 

5.
Desneux, N., Luna, M. G., Guillemaud, T. & Urbaneja, A. The invasive South American tomato pinworm, Tuta absoluta, continues to spread in Afro-Eurasia and beyond: the new threat to tomato world production. J. Pest Sci. 84, 403–408 (2011).
Article  Google Scholar 

6.
Desneux, N. et al. Biological invasion of European tomato crops by Tuta absoluta: ecology, geographic expansion and prospects for biological control. J. Pest Sci. 83, 197–215 (2010).
Article  Google Scholar 

7.
Sridhar, V., Chakravarthy, A. K. & Asokan, R. New record of the invasive South American tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) in India. Pest. Manag. Hort. 20, 148–154 (2014).
Google Scholar 

8.
Galdino, T. V. S. et al. Is the performance of a specialist herbivore affected by female choices and the adaptability of the offspring?. PLoS ONE 10, 1–18 (2015).
Article  CAS  Google Scholar 

9.
Campos, M. R., Biondi, A., Adiga, A., Guedes, R. N. C. & Desneux, N. From the Western Palaearctic region to beyond: Tuta absoluta 10 years after invading Europe. J. Pest Sci. 90, 787–796 (2017).
Article  Google Scholar 

10.
Cherif, A. & François, V. A review of Tuta absoluta (Lepidoptera: Gelechiidae) host plants and their impact on management strategies. Biotechnol. Agron. Soc. Environ. 23 (4) (2019).

11.
Picanço, M. C., Bacci, L., Crespo, A. L. B., Miranda, M. M. M. & Martins, J. C. Effect of integrated pest management practices on tomato production and conservation of natural enemies. Agric. For. Entomol. 9, 327–335 (2007).
Article  Google Scholar 

12.
Biondi, A., Guedes, R. N. C., Wan, F. H. & Desneux, N. Ecology, worldwide spread, and Management of the Invasive South American Tomato Pinworm, Tuta absoluta: past, present, and future. Annu. Rev. Entomol. 63, 239–258 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Guedes, R. N. C. et al. Insecticide resistance in the tomato pinworm Tuta absoluta: patterns, spread, mechanisms, management and outlook. J. Pest Sci. https://doi.org/10.1007/s10340-019-01086-9 (2019).
Article  Google Scholar 

14.
Santana, P. A., Kumar, L., Da Silva, R. S. & Picanço, M. C. Global geographic distribution of Tuta absoluta as affected by climate change. J. Pest Sci. 92, 1373–1385 (2019).
Article  Google Scholar 

15.
Luna, M. G. et al. Biological control of Tuta absoluta in Argentina and Italy: evaluation of indigenous insects as natural enemies. EPPO Bulletin 42, 260–267 (2012).
ADS  Article  Google Scholar 

16.
Bacci, L. et al. Natural mortality factors influencing the tomato leafminer Tuta absoluta in open-field tomato crops in South America. Pest Manage. Sci. https://doi.org/10.1002/ps.5173 (2019).
Article  Google Scholar 

17.
Megido, R. C., Brostaux, Y., Haubruge, E. & Verheggen, F. J. Propensity of the tomato leafminer, Tuta absoluta (Lepidoptera: Gelechiidae), to develop on four potato plant varieties. Am. Potato J. 90, 255–260 (2013).
Article  Google Scholar 

18.
Sylla, S., Brévault, T., Monticelli, L. S., Diarra, K. & Desneux, N. Geographic variation of host preference by the invasive tomato leaf miner Tuta absoluta: implications for host range expansion. J. Pest Sci. https://doi.org/10.1007/s10340-019-01094-9 (2019).
Article  Google Scholar 

19.
Vargas, H. C. Observaciones sobre la biologıa y enemigos naturales de la polilla del tomate, Gnorimoschema absoluta (Meyrick) (Lepidoptera: Gelechiidae). Idesia 1, 75–110 (1970).
Google Scholar 

20.
Gilardón, E., Pocovi, M., Hernández, C., Collavino, G. & Olsen, A. Papel da 2-tridecanona e dos tricomas glandulares tipo VI na resistência do tomateiro a Tuta absoluta. Pesqui. Agropecu. Bras. 36, 929–933 (2001).
Article  Google Scholar 

21.
Pereyra, P. C. & Sánchez, N. E. Effect of two solanaceous plants on developmental and population parameters of the tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Neotrop. Entomol. 35, 671–676 (2006).
PubMed  Article  PubMed Central  Google Scholar 

22.
Moreira, L. A. et al. Antibiosis of eight Lycopersicon genotypes to Tuta absoluta (Lepidoptera: Gelechiidae). Rer. Ceres 56, 283–287 (2009).
Google Scholar 

23.
Negi, S., Sharma, P. L., Sharma, K. C. & Verma, S. C. Effect of host plants on developmental and population parameters of invasive leafminer, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Phytoparasitica 46, 213–221 (2018).
Article  Google Scholar 

24.
Satishchandra, K. N., Chakravarthy, A. K., Özgökçe, M. S. & Atlihan, R. Population growth potential of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) on tomato, potato, and eggplant. J. Appl. Entomol. 143, 518–526 (2019).
Article  Google Scholar 

25.
Younes, A. A., Zohdy, N. Z. M., Abulfadl, H. A. & Fathy, R. Life table parameters of the tomato leafminer, Tuta absoluta (Lepidoptera: Gelechiidae), on three solanaceous host plants. Afr. Entomol. 27, 461–467 (2019).
Article  Google Scholar 

26.
Cherif, A., Attia-Barhoumi, S., Mansour, R., Zappalà, L. & Grissa-Lebdi, K. Elucidating key biological parameters of Tuta absoluta on different host plants and under various temperature and relative humidity regimes. Entomol. Gen. https://doi.org/10.1127/entomologia/2019/0685 (2019).
Article  Google Scholar 

27.
Knapp, S. Tobacco to tomatoes: a phylogenetic perspective on fruit diversity in the Solanaceae. J. Exp. Bot. 53, 2001–2022 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Silva, G. A. et al. Control failure likelihood and spatial dependence of insecticide resistance in the tomato pinworm, Tuta absoluta. Pest Manage. Sci. 67, 913–920 (2011).
ADS  CAS  Article  Google Scholar 

29.
Southwood, T. R. E. & Hendersen, P. A. Ecological Methods (ed. Southwood, T. R. E. & Hendersen, P. A.) (Blackwell Science, 2000).

30.
Maia, H. N., Luiz, A. A. J. & Campanhola, C. Statistical inference on associated fertility life table parameters using jackknife technique: computational aspects. J. Econ. Entomol. 93, 511–518 (2000).
Article  Google Scholar 

31.
Paré, P. W. & Tumlinson, J. H. Plant volatiles as a defense against insect herbivores. Plant Physiol. 121, 325–332 (1999).
PubMed  PubMed Central  Article  Google Scholar 

32.
Feeny, P., Stadler, E., Ahman, I. & Carter, M. Effects of plant odor on oviposition by the black swallowtail butterfly, Papilio polyxenes (Lepidoptera: Papilionidae). J. Insect. Behav. 2, 803–827 (1989).
Article  Google Scholar 

33.
Proffit, M. et al. Attraction and oviposition of Tuta absoluta females in response to tomato leaf volatiles. J. Chem. Ecol. 37, 565–574 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Friedman, M. Analysis of biologically active compounds in potatoes (Solanum tuberosum), tomatoes (Lycopersicon esculentum), and jimson weed (Datura stramonium) seeds. J. Chromatogr. A 1054, 143–155 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Antônio, A. D. C., Silva, D. J. H. D., Picanço, M. C., Santos, N. T. & Fernandes, M. E. S. Tomato plant inheritance of antixenotic resistance to tomato leafminer. Pesqui. Agropecu. Bras. 46, 74–80 (2011).
Article  Google Scholar 

36.
Miranda, M. M. M., Picanço, M., Zanuncio, J. C. & Guedes, R. N. C. Ecological life table of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Biocontrol Sci. Technol. 8, 597–606 (1998).
Article  Google Scholar 

37.
Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval lepidoptera. Annu. Rev. Entomol. 47, 361–393 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Krainacker, D. A., Carey, J. R. & Vargas, R. I. Effect of larval host on life history traits on the Mediterranean fruit fly Ceratitis capitata. Oecologia 73, 583–590 (1987).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Razmjou, J., Naseri, B. & Hemati, S. A. Comparative performance of the cotton bollworm, Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) on various host plants. J. Pest. Sci. 87, 29–37 (2014).
Article  Google Scholar 

40.
Calvo, D. & Molina, J. M. Fecundity-body size relationship and other reproductive aspects of Streblote panda (Lepidoptera: Lasiocampidae). Ann. Entomol. Soc. Am. 98, 191–196 (2005).
Article  Google Scholar 

41.
Awmack, C. S. & Leather, S. R. Host plant quality and fecundity in herbivorous insects. Annu. Rev. Entomol. 47, 817–844 (2000).
Article  Google Scholar 

42.
Boggs, C. L. & Freeman, K. D. Larval food limitation in butterflies: effects on adult resource allocation and fitness. Oecologia 144, 353–361 (2005).
ADS  PubMed  Article  Google Scholar 

43.
Tammaru, T., Esperk, T. & Castellanos, I. No evidence for costs of being large in females of Orgyia spp. (Lepidoptera, Lymantriidae): larger is always better. Oecologia 133, 430–438 (2002).
ADS  PubMed  Article  Google Scholar 

44.
Calvo, D. & Molina, J. M. Utilization of blueberry by the lappet moth Streblote panda Hübner (Lepidoptera: Lasiocampidae): survival, development and larval performance. J. Econ. Entomol. 97, 957–963 (2004).
CAS  PubMed  Article  Google Scholar 

45.
Kanle Satishchandra, N., Chakravarthy, A. K., Özgökçe, M. S. & Atlihan, R. Population growth potential of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) on tomato, potato, and eggplant. J. Appl. Entomol. 143, 518–526 (2019).
Article  Google Scholar 

46.
Gotelli, N. J. A primer of ecology (ed. Gotelli, N. J.) (Sinauer Associates Incorporated Press, 1995).

47.
Birch, L. The intrinsic rate of natural increase of an insect population. J. Econ. Entomol. 17, 15–26 (1948).
Google Scholar 

48.
Lage, J., Skovmand, B. & Andersen, S. B. Characterization of greenbug (Homoptera: Aphididae) resistance in synthetic hexaploid wheats. J. Econ. Entomol. 96, 1922–1928 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Smith, C. M. Plant Resistance to Arthropods: Molecular and Conventional Approaches (ed. Smith, C. M.) (Springer, Berlin, 2005).

50.
Fathi, S. A. A., Bozorg-Amirkalaee, M. & Sarfaraz, R. M. Preference and performance of Plutella xylostella (L.) (Lepidoptera: Plutellidae) on canola cultivars. J. Pest Sci. 84, 41–47 (2011).
Article  Google Scholar 

51.
Thomazini, A. P. B. W., Vendramim, J. D., Brunherotto, R. & Lopes, M. T. Efeito de genótipos de tomateiro sobre a biologia e oviposição de Tuta absoluta (Meyrick) (Lep.: Gelechiidae). Neotrop. Entomol. 30, 283–288 (2001).
Article  Google Scholar 

52.
Boiça Junior, A. L., Bottega, D. B., Lourenção, A. L. & Rodrigues, N. E. L. Não preferência para oviposição e alimentação por Tuta absoluta (Meyrick) em genótipos de tomateiro. Arq. Inst. Biol. 79, 541–548 (2012).
Article  Google Scholar 

53.
Erdogan, P. & Babaroglu, N. E. Life table of the tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). J. Agric. Facul. Gaziosmanpasa Univ. 31, 80–89 (2014).
Google Scholar 

54.
Malakar, R. & Tingey, W. M. Glandular trichomes of Solanum berthaultii and its hybrids with potato deter oviposition and impair growth of potato tuber moth. Entomol. Exp. Appl. 94, 249–257 (2000).
Article  Google Scholar 

55.
Horgan, F. G., Quiring, D. T., Lagnaoui, A., Salas, A. R. & Pelletier, Y. Periderm and cortex based resistance to tuber feeding Phthorimaea operculella in two wild potato species. Entomol. Exp. Appl. 125, 249–258 (2007).
Article  Google Scholar 

56.
Mackauer, M. Genetic problems in the production of biological control agents. Annu. Rev. Entomol. 21, 369–385 (1976).
Article  Google Scholar 

57.
Spielman, D. & Brook, B. W. Frankham R (2004) Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. USA 101, 15261–15264 (2000).
ADS  Article  CAS  Google Scholar 

58.
Urbaneja, A., Vercher, R., Navarro, V., García Marí, F. & Porcuna, J. L. La polilla del tomate, Tuta absoluta. Phytoma 194, 16–23 (2007).
Google Scholar 

59.
Campos, R. G. Control químico del “minador de hojas y tallos de la papa” (Scrobipalpula absoluta Meyrick) en el valle del Cañete. Rev. Peru Entomol. 19, 102–106 (1976).
Google Scholar 

60.
Garzia, T. G. Physalis peruviana L. (Solanaceae), a host plant of Tuta absoluta in Italy. IOBC/WPRS Bull 49, 231–232 (2009).

61.
Bawin, T., Dujeu, D., De Backer, L., Francis, F. & Verheggen, F. J. Ability of Tuta absoluta (Lepidoptera: Gelechiidae) to develop on alternative host plant species. Can. Entomol. 148, 434–442 (2016).
Article  Google Scholar  More

Ethics statement
The study complied with the existing rules and guidelines outlined by the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), Government of India, the Institutional Animal Ethics Committee’s (IAEC) and guidelines of Indian Institute of Science Education and Research (IISER) Kolkata. All experimental protocols followed here have been approved by the Institutional Animal Ethics Committee’s (IAEC) and guidelines of Indian Institute of Science Education and Research (IISER) Kolkata, Government of India. No animals were euthanized or sacrificed during any part of the study, and behavioral observations were conducted without any chemical treatment on the individuals. At the end of the experiments, all fish were returned to stock tanks and continued to be maintained in the laboratory.
Procuring subject animals and maintenance
We used wild-caught zebrafish (from Howrah district, West Bengal, India), bought from a commercial supplier. The fish were maintained in the laboratory in mixed-sex groups of approximately 60 individuals in well-aerated holding tanks (60 × 30 × 30 cm) filled with filtered water. The lighting in the laboratory was maintained at 14 hL:10 hD to mimic the natural LD cycle in zebrafish. They were fed commercially purchased freeze-dried blood worms once a day alternating with brine shrimp Artemia. The holding tanks were provided with standard corner filters for circulation. They were maintained in the laboratory for six months before experiments were conducted to ensure they were all adults and were reproductively mature. Holding room temperature was maintained between 23 and 25 °C.
Experimental setup
The experiments were conducted in a square glass arena (83 × 83 cm), with a half-diagonal of the square from the center that approximated ten fish standard body lengths (i.e. 40 cm, assuming one body length of adult zebrafish to be about 4 cm) (Fig. 1). Each corner of the arena was provided with a square chamber (of sides 10 cm) built from transparent mesh (using synthetic fish nets) for housing the females. This design allowed for the stimuli females to be localized in the patches and not escape into the arena while simultaneously ensuring that the test males can have visuo-chemical communication with the females. The center of the arena was provided with a removable chamber (with holes) for acclimation of the males prior to the trial.
Figure 1

Diagrammatic representation of the arena for the density experimental set-up. The central chamber (indicated by a circle) represents the area where the test males were released and the corner square chamber (separated by transparent mesh) contained females of varying density. The distance of each patch from the central chamber was 40 cm.

Full size image

Three sets of experiments were performed to test their association preferences under (1) only varying female densities (2) increasing female and vegetation densities and (3) increasing female densities with decreasing vegetation.
Association preference experiment with varying female densities
For this experiment, each small chamber within the arena housed two (low number), four (medium number), eight (high number) or no (blank) females. These chambers represented patches of varying female numbers. The position of the female-containing chambers, as well as the composition of females within each patch, was randomized between trials. A total of 20 males were tested for their association preferences. Details on the data collected are provided in Supplementary File S1.
Association preference experiment with vegetation
For this experiment, the female-housing chambers (patches) were provided with vegetation (using artificial plants) of varying density (Fig. 2). Each subject fish was tested under two experimental settings. In E1, the number of females was proportional to the density of associated vegetation cover. We used four different densities of females, each associated with different densities of plants
1.
one female + no plants (no vegetation—N)

2.
two females + two plants (low vegetation—L)

3.
four females + three plants (moderate vegetation—M) and

4.
eight females + five plants (high vegetation—H).

Figure 2

Diagrammatic representation of the arena the vegetation experimental set-up. The central chamber (indicated by a circle) represents the area where the test males were released and the corner square chambers (separated by transparent mesh) contained females of varying density and each patch was associated with variable number of plastic plants representing vegetation cover.

Full size image

For E2, we interchanged in the vegetation cover for the two and eight female patches. The patch composition in E2 set were as follows
1.
one female + no plants (no vegetation—N)

2.
two females + eight plants (high vegetation—H)

3.
four females + three plants (moderate vegetation—M) and

4.
eight females + two plants (low vegetation—L).

All test males were tested in E1 and E2 on consecutive days in no particular order. Details on the data collected are provided in Supplementary Files S2 and S3.
Experimental protocol
For the experiment involving association preferences with only varying female numbers a total of 20 males were tested, while 24 males were tested for experiments on the association preferences in varying female numbers combined with vegetation density gradients (E1 and E2 experiments). The experiments were performed two months’ apart to ensure the fish do not retain any memory from the first experiment, and thus they could be treated as two independent sets. We isolated subject males of comparable sizes and kept them in individual isolation in 500 ml jars for four days prior to experiments as that allowed us to keep track of individual fish and also stimulated mate-seeking behavior21,22. They were fed freeze-dried blood worms every day at constantly maintained feeding times. The gravid females that were used for the experiment as stimuli for association were isolated (about 22 females) in a small holding tank (30 × 20 × 20 cm) with a feeding regimen similar to the test males. Before the start of each trial, we introduced the females into each chamber (patch) randomly (according to the experimental setup described above) and left them there for 15 min. for acclimation. A single male individual was then gently introduced into the central cylindrical chamber (with a hand-net), open at both ends (made of transparent plastic and provided with holes). After a five-minute acclimation period, the chamber was slowly removed to allow the male to swim freely in the arena and video recording was commenced. Video recordings were done using a camera (Sony DCR-PJ5, Sony DCR-SX22) placed perpendicularly above the arena. The test fish (males and females) were fed only after the end of experimental trials, on each day of experiments. At the end of the trials, the fish were returned to their holding tanks. No subject male fish were tested more than once per experimental setup and trial. The females used for the patches, were housed together (but separate from their male counterparts) in a smaller tank. Before the trials the females were picked randomly and assigned into each patch. During the experiment, the position of females being used was randomized between trials from patch to patch, to avoid the possibility of bias among the subject males for any particular females in the patches.
We recorded the behavior of each test fish for 10 min. All videos were analyzed using the software BORIS23. A single visit to any of the patch was denoted when the male approaches within 6 cm (1.5 times their average body length) of the patch. We collected data on three parameters: total number of visits to each patch, the total amount of time spent in each patch and the mean time spent per visit within each patch. The same overall protocol was followed for all sets of experiments.
Statistical analyses
We noted the total number of visits to each patch, the total duration of time spent in each patch and mean time spent per visit per patch for the entire ten minutes duration of video recording for each test male. We calculated preference index (I) the total number of visits (I_visit) and total time spent (I_time) for each patch as proportion of the total visits made to all four patches24.

I_visit for patch A = No. of visit to patch A/(visit to patch A + visit to patch B + visit to patch C + visit to patch D).

I_time for patch A = time spent in patch A/(time spent in patch A + time spent in patch B + time spent in patch C + time spent in patch D).

All statistical analyses were performed in R studio (version 1.1.463)25. We developed generalized linear mixed models (GLMMs) using package glmmTMB (version 0.2.3)26 with ‘fish’ as the random factor and ‘Patches’ as the fixed factor, with four levels representing the four choices for the test (male) fish. Preference for total number of visits (I_visit) as well as total time spent (I_time) were found to fit beta distribution with values ranging between 0 and 1. For data fitting, we added 0.0001 to every value, to remove zeroes. Relevelled models were used to compare the parameters between the four patches. Link = logit was used under beta family to construct the GLMM models.
For analyzing the data for the second and third experiments involving varying female densities along with vegetation densities (E1 and E2), we followed a similar procedure of constructing a GLMM followed by post hoc tests. GLMM models were constructed with a single independent variable, “patch”, that had four levels, designated as H (high vegetation density), M (moderate vegetation density), L (low vegetation density) and N (no vegetation). More

Connectivity confers resilience
We observed inherently higher grazing performance in better-connected, unperturbed systems (Fig. 2a,f). The two better-connected systems (connectivity = 14.5; 21.6) had significantly higher grazing performance than the least-connected system (connectivity = 9.6), demonstrating a positive, non-linear, relationship between connectivity and ecosystem function in the controls (Fig. 2a,f; S1, S2). Physical disturbances, however, did not appear to dramatically alter this relationship at the system level (Fig. 2b,c), despite clear impacts on performance in the affected patch (Fig. 2g,h).
Figure 2

Grazing performance in response to ten experimental disturbance treatments at three connectivity levels. Grazing performance is algal consumption as a proportion of total algae available (mean ± SE). In d: both stressors were applied to the same patch (same). In e: stressors were applied to different patches (diff).

Full size image

Each disturbance regime resulted in a significant decrease in grazing performance within the affected patch (Fig. 2f:j; S2), but not always across the entire system (e.g. Fig. 2a:e; S1). Impacts within affected areas were sometimes offset at the whole system level by increases within unstressed patches because the stressors themselves triggered animal aggregations in unstressed regions. For example, the animals typically left heat-stressed areas, sometimes finding refuge in the unstressed patch. This meant that unstressed protected areas sometimes returned higher grazing performance than in the control scenarios, offsetting losses in the affected patch and resulting in no significant change in performance across the whole system (Fig. 2b,c).
Multiple stressor scenarios
Multiple stressors had variable effects on the shape of the connectivity-resilience relationship (Fig. 2; Extended data Fig. 2). When heat-stress and harvesting were applied to the same patch, grazing performance was heavily reduced under all connectivity scenarios, but the shape of the connectivity-resilience relationship remained positive (Fig. 2d,i). However, when applied to different patches, these effects were strongly antagonistic. Heat-stress offset the effect of harvesting, creating a slight negative relationship between connectivity and resilience. There was a loss in grazing performance at higher connectivity levels and, by contrast, a slight gain at the lowest connectivity (Fig. 2e,j). Heat-stress likely encouraged animals to move away from the hotter patch, into the harvesting patch. These patches were in closer proximity in better-connected systems, making animal congregations in the harvesting patch more likely, thus increasing the relative impact of harvesting and changing the shape of the connectivity-resilience relationship.
Disease interactions
We applied a 50% consumption penalty for infected individuals in disease simulations, representing a realistic simulation of real-world diseases that can hinder animal performance. White-spot disease, for example, affects shrimp consumption rates before causing mortality, allowing the disease to spread while also suppressing consumption rates27. Some diseases may have different magnitudes of effect on individuals. Thus, we also simulated stronger disease effects by reducing grazing rates for infected crabs by 100%. In general, the simulated disease infected more crabs at higher connectivity levels (Extended data Fig. 3), leading to higher consumption penalties with increasing connectivity in most scenarios (Fig. 3a,b). The 50% disease effect level did not negate the inherent benefits of connectivity observed in unperturbed systems (Fig. 2), but at a more strongly negative relationship between connectivity and grazing performance was observed under a 100% penalty (Fig. 3b). Thus, disease had a higher impact on ecosystem function in better-connected systems (Fig. 3; Extended data Fig. 4).
Figure 3

Box and violin plots of effect size for each of the (a) 50% and (b) 100% disease effect scenarios and (c) standard error plot of variation across all spatial scales and connectivity levels. Linear model (dashed line) in (a) and (b) provided as visual guide of direction of trend. Data in (c) are the grand-averages of within-treatment standard errors.

Full size image

There was also less variability in grazing performance with higher connectivity, but not significantly (Fig. 3c). The within-treatment standard error in grazing performance was empirically lowest in the best-connected systems across three spatial assessment levels (Fig. 3c), suggesting that grazing responses were more predictable in better-connected systems (Fig. 3c).
Conservation implications
Given the setting of reserves in complex spatial mosaics, with multiple stressors, it is necessary to have a better understanding of how connectivity can change the way ecosystems respond to stressors. We show that these relationships are complex, even in simplified, controlled systems. Despite the microcosm scale of our experiments, our results support real-world phenomena that have been linked with the benefits of connectivity and/or protected areas. Thus, we suggest these findings contribute valuable information to support the future design of research and management strategies for natural systems. For example, Marine Protected Areas (MPAs) can be considered analogous refuges to the unaffected patches in our harvesting scenarios. MPAs provide an offsetting service in natural ecosystems where, by excluding harvesting, they provide a refuge and source of animal resupply28 that supports fisheries and acts to maintain ecosystem function e.g.22. Our systems responded similarly to these real-world examples in that the number of animals available for harvest was highest at the edge of the simulated MPA, as observed in the best-connected systems (Extended data Fig. 5). This phenomenon was strongest in the multiple-stressor scenario that applied heat-stress to the patch that was protected from harvesting pressure. Heat-stressed animals vacated the hotter protected area, exposing them to possible capture.
Additionally, connectivity may provide a stabilising effect on ecosystem function, a phenomena that may partially contribute to previous findings that connectivity strengthens ecosystem resilience (e.g.29). Thus, when connectivity is low, ecosystems may experience greater variability in the performance of key ecosystem functions, potentially limiting capacity to resist or recover from disturbance.
We tested the role that connectivity plays in shaping animal functional responses to single and multiple disturbance events of different types. To do so, we quantified the effect that different combinations of stressors had on the grazing performance of a widespread mesograzer, the yellow-footed hermit crab (Clibanarius virescens), in purpose-built arenas at three levels of connectivity. Connectivity can be measured in many ways, with effects being difficult to quantify between systems with different numbers of redundant or complimentary routes, motifs such as triangular or circular clusters, or ‘hubs’ that connect multiple patches to one central node30. To minimise unintended effects of altering the number or place of connections, we altered system connectivity by varying the location of important patches (containing food) within a standard 4 × 3 node grid of approx. 42 × 32 cm (Fig. 1), rather than by adding or removing connections. We selected system configurations (habitat patch placements) that were symmetrical along both the x and y axes, minimising the risk of introducing unintentional confounding effects. This created a base system with 12 nodes and 14 edges in all cases.
Connectivity within each system was calculated using a modified measure of closeness31, as in Eq. (1):

$$text{Connectivity }= frac{1}{T+P+D}times 100$$
(1)

where; T = average shortest path length from food node 1 to all other nodes; P = average shortest path length from food node 2 to all other nodes; and D = shortest path length between both food nodes. All path lengths were counted as integer steps between nodes.
Standard experimental procedure for all treatments
We tested ten treatments at each connectivity level, resulting in N = 236 replications; between 73 and 82 at each connectivity level.
Each replication involved slowly warming crabs and arenas to the desired temperature (defined per treatment below) over a 4-h period, approximately mimicking daily warming cycles. One crab was then added to each patch (12 total in the system), and six 1 mg algal pellets were added to each of the two food patches (coloured patches in Fig. 1). Every 20 min, the number of pellets remaining was counted, and an additional six pellets were added to each patch. Each experimental replication lasted 1 h (3 × 20-min intervals), starting when crabs were first added to the arena. Thus, in all cases, 18 pellets were added to each patch; 36 total to the system. This was determined as the control level because an individual crab is expected to consume approximately three pellets per hour at an optimal temperature 29.5 °C25. Hence, by adding pellets equal to the mean consumption rate (one per crab per 20-min period), we simulated a stable system in which consumption was approximately equal to algal production in the absence of stressors. Any reduction in consumption (driven by stressors) below optimal rates implies that algal mass would increase over time, suggesting that the system is trending towards a phase shift. Thus, by our definition, lower consumption makes the system less resilient.
Treatment specifics
Control
The control treatments were run as per the standard experimental procedure described above with no stressor applied.
Heat-stress
For the temperature stressor treatment, the experiment was run as per the standard experimental procedure, but with a temperature stress applied to the half of the arena incorporating an affected patch ‘Zone B’). Water was heated using a combination of sous vide precision cookers and aquarium heaters, arranged in a way that ensured the stability of target temperatures for the duration of the experiment. The stressed half of the arena was set to 33.5 °C, and the unstressed half was set to optimal temperature (29.5 °C; as per24). The 4 °C increase in temperature is expected to decrease consumption rates by approx. 15–20%24, with an additional effect on movement expected to amplify this effect. This is intended to simulate a system with connected heat-stressed and refuge areas.
Harvesting pressure
A harvesting stressor was applied that simulated a fishery management scenario with a fixed bag limit. We designated one food patch as a protected area and resupply point (blue in Fig. 1), and the other as a harvesting area (red in Fig. 1). Because our systems included two distinct habitat patches, separated by different amounts of featureless habitat at different connectivity levels, we equate these to reef or vegetated habitat where fauna are likely to congregate near resources. Similarly, fishers are likely to congregate in the same areas, unless excluded. Thus, by restricting harvesting in one of the patches, we have simulated the broad dynamics of an enclosed bay that contains both protected and high harvesting pressure habitat areas, and some unprotected, but featureless areas in-between that would be expected to experience low harvesting pressure, which we did not attempt to simulate.
In this scenario, we set a bag-limit that allowed up to three crabs (or as many as were present under three) to be harvested from the affected patch at the end of each 20 min interval, and then the same number of crabs were added to the protected area, simulating a maximum sustainable yield management (MSY) scenario. Under the harvesting only stressor, all other experimental procedures were as per the standard scenario, with temperature set to optimal (29.5 °C) across the entire arena.
Heat-stress and harvesting
To investigate how multiple stressors interact with the connectivity-resilience relationship, we also applied both heat-stress and harvesting to the same arenas simultaneously in two ways. First, by applying both stressors to the same patch (same), and second by applying the heat-stress to the protected area and the harvesting pressure to the other (diff).
Disease
We applied a simulated effect of disease by recording interactions between individuals and applying a 50% and 100% (separately) consumption penalty for ‘infected’ crabs. Reduced consumption is a known effect observed in individuals infected by numerous diseases, including some that are known to affect crustacean mesograzers (e.g. white-spot disease27). We recorded all experimental treatments on video (GoPro models) and then extracted data on the movement and interactions between crabs from each video. Unique colour markers were used to track individual crabs, enabling data to be recorded for all occasions during which contact was made between crabs (including the total duration of each interaction). We also recorded the time that each crab entered and/or exited one of the designated habitat patches.
Disease spread scenarios were then modelled from interaction data, whereby we designated an individual crab as being the infection vector (starting with a disease) and then quantified how the disease spread through the system through crab-to-crab interactions. For each treatment replication, 12 sub-replications were assessed simulating each of the 12 different crabs starting with the disease. See Extended data Fig. 1 for example of infection pathways taken for each ‘starting crab’ during one physical replicate. Interaction and movement data were extracted from videos manually. Disease spread was then simulated from interaction data in R using customised code.
For each disease spread scenario we applied the stressor as a 50% reduction in consumption for diseased crabs as our primary test level, also testing the effect at a 100% reduction level to observe a worst-case scenario. The effect was calculated based on the total consumption within a period and time that crabs (both infected and uninfected) spent in close proximity to food using the below equations:
1.
Consumption after disease = 
Disease effect (as percentage) × observed consumption rate × cumulative infection time.

2.
Observed consumption rate = (frac{total ; consumption}{sum_{n=1}^{12} crab ; n ; total ; time ; in ; food ; patch})

3.
Cumulative infection time = (sum_{n=1}^{12}crab ; n ; time ; in ; food ; patch ; while ; infected)

See worked example in Extended data Table 1.
Statistical analyses
We assessed the effect of treatment (connectivity level and stressor(s)) with generalised linear models (glm). Effect was assessed at two levels: both at the whole system level, as total consumption across both habitat patches combined and, within the affected patch only (affected), tested in separate analyses. Treatments (as factors) were: Control; heat-stress, harvesting; heat-stress and harvesting (same); heat-stress and harvesting (diff). Noting that in the heat-stress and harvesting (diff) treatment there was no designated ‘unaffected’ patch because both patches were affected by at least one stressor. For consistency, we included the patch affected by harvesting in all affected patch analyses. Connectivity level was the system’s connectivity metric (9.6; 14.5; 21.6), also set as a factor.
To identify the best model, we started with the most complex model that included all possible interaction terms, and used a leave-one-out technique, exclude the most complex interaction term until a significant interaction was identified using Analysis of Deviance (ANOVA) with chi-squared test (detailed outputs in S1; S2). The final glm model was selected as the most complex equation (i.e. largest interaction term) that returned a significant interaction in this test, resulting in a final model for both system level and affected patch level of:
Consumption ~ Harvesting + Heat-stress + Connectivity + Disease + Harvesting:heat-stress + Harvesting:Connectivity + Heat-stress:Connectivity + Harvesting:Disease + Heat-stress:Disease + Connectivity:Disease + Harvesting:Heat-stress:Connectivity.
Mean ± SE plots presented were derived from outputs for this glm equation. Alpha was set to 0.05.
Treatment differences were assessed using model outputs, with significant differences defined as non-overlapping treatment values for model fit (mean) ± standard error.
Disease effect size was calculated as:

$$Effect;size , = , left( {diseased;consumption , {-} , replication;consumption} right)/replication;consumption$$ More

1.
Aslam, M., Ahmad, K., Arslan, A. M. & Amir, M. M. Salinity stress in crop plants: Effects of stress, tolerance mechanisms and breeding strategies for improvement. J. Agric. Basic Sci. 2(1), 2518–4210 (2017).
Google Scholar 
2.
Kirsten, B., Abbey, F. W., Thomas, D., Amitava, C. & Jason, H. Soil salinity: A threat to global food security. Agron. J. 108(6), 2189–2200 (2016).
Article  CAS  Google Scholar 

3.
Shakeel, A. A. et al. Drought induced changes in growth, osmolyte accumulation and antioxidant metabolism of three maize hybrids. Front. Plant Sci. 8(69), 1–12 (2017).
Google Scholar 

4.
Abd-ElBaki, G. K. et al. Nitrate reductase in Zea mays L. under salinity. Plant Cell Environ. 23, 515–521 (2000).
CAS  Article  Google Scholar 

5.
Flores, P., Botella, M. Á., Martínez, V. & Cerdá, A. C. Ionic and osmotic effects of nitrate reductase activity in tomato seedlings. J. Plant Physiol. 156, 552–557 (2000).
CAS  Article  Google Scholar 

6.
Petronia, C., Gabriella, M., Francesco, N. & Amodio, F. Nitrate reductase in durum wheat seedlings as affected by nitrate nutrition and salinity. Funct. Plant Biol. 32(3), 209–219 (2005).
Article  Google Scholar 

7.
Flowers, T. J. et al. Salt sensitivity in chickpea. Plant Cell Environ. 3(4), 490–509 (2010).
MathSciNet  Article  CAS  Google Scholar 

8.
Husen, A., Iqbal, M., Sohrab, S. S. & Ansari, M. K. A. Salicylic acid alleviates salinity-caused damage to foliar functions, plant growth and antioxidant system in Ethiopian mustard (Brassica carinata A. Br.). Agric. Food Secur. 7(1), 44 (2018).
Article  Google Scholar 

9.
Farhangi-Abriz, S. & Torabian, S. Biochar improved nodulation and nitrogen metabolism of soybean under salt stress. Symbiosis. 74(3), 215–223 (2018).
CAS  Article  Google Scholar 

10.
Gupta, B. & Huan, B. Mechanism of salinity tolerance in plants: Physiological, biochemical, and molecular characterization. Int. J. Genomics. 1, 701596. https://doi.org/10.1155/2014/701596 (2014).
CAS  Article  Google Scholar 

11.
Zhang, W. J. et al. Silicon promotes growth and root yield of Glycyrrhiza uralensis, under salt and drought stresses through enhancing osmotic adjustment and regulating antioxidant metabolism. Crop Prot. 107, 1–11 (2018).
Article  CAS  Google Scholar 

12.
Saqib, M., Zörb, C. & Schubert, S. Salt resistant and salt-sensitive wheat genotypes show similar biochemical reaction at protein level in the first phase of salt stress. J. Plant Nutr. Soil Sci. 169(4), 542–548 (2006).
CAS  Article  Google Scholar 

13.
Turan, M. A., Katkat, V. & Taban, S. Salinity-induced stomatal resistance, proline, chlorophyll and ion concentrations of bean. Int. J. Agric. Res. 2(5), 483–488 (2007).
CAS  Article  Google Scholar 

14.
Memon, S. A., Hou, X. L. & Wang, L. J. Morphological analysis of salt stress response of pak Choi. Electron. J. Environ. Agric. Food Chem. 9(1), 248–254 (2010).
CAS  Google Scholar 

15.
Keyvan, A. & Setsuko, K. Crop and medicinal plants proteomics in response to salt stress. Front. Plant Sci. 4(8), 8 (2013).
Google Scholar 

16.
Dadkhah, A. R. Effect of salt stress on growth and essential oil of Matricaria chamomilla. Planta Med. 5(10), 643–646 (2010).
Google Scholar 

17.
Aziz, E. E., Al-Amier, H. & Craker, L. E. Influence of salt stress on growth and essential oil production in peppermint, pennyroyal, and apple mint. J. Herbs Spices Med. Plants. 14(1–2), 77–87 (2008).
CAS  Article  Google Scholar 

18.
Leithy, S., Gaballah, M. S. & Gomaa, A. M. Associative impact of bio-and organic fertilizers on geranium plants grown under saline conditions. Electron. J. Environ. Agric. Food Chem. 1(3), 617–626 (2009).
Google Scholar 

19.
Najafian, S., Khoshkhui, M. & Tavallali, V. Effect of salicylic acid and salinity in rosemary (Rosmarinus officinalis L): Investigation on changes in gas exchange, water relations, and membrane stabilization. Aust. J. Basic. Appl. Sci. 3(3), 322–328 (2009).
CAS  Google Scholar 

20.
Taarit, M. B. et al. Plant growth, essential oil yield and composition of sage (Salvia officinalis L.) fruits cultivated under salt stress conditions. Ind. Crops Prod. 30(3), 333–337 (2009).
Article  CAS  Google Scholar 

21.
Queslati, S. et al. Physiological and antioxidant responses of Mentha pulegium (Pennyroyal) to salt stress. Acta Physiol. Plant. 32(2), 289–296 (2010).
Article  CAS  Google Scholar 

22.
Seyed, M. Z., Faezeh, M., Saadat, S. & Mohsen, P. Selenium and silica nanostructure-based recovery of strawberry plants subjected to drought stress. Sci. Rep. 10, 17672. https://doi.org/10.1038/s41598-020-74273-9 (2020).
CAS  Article  Google Scholar 

23.
Yan, et al. Silicon improves rice salinity resistance by alleviating ionic toxicity and osmotic constraint in an organ-specific pattern. Front. Plant Sci. 11, 260. https://doi.org/10.3389/fpls.2020.00260 (2020).
ADS  Article  PubMed  PubMed Central  Google Scholar 

24.
Mateos-Naranjo, E., Andrades-Moreno, L. & Davy, A. J. Silicon alleviates deleterious effects of high salinity on the halophytic grass Spartina densiflora. Plant Physiol. Biochem. 63, 115–121 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Chen, D. Q., Yin, L., Deng, X. P. & Wang, S. W. Silicon increases salt tolerance by influencing the two-phase growth response to salinity in wheat (Triticum aestivum L). Acta Physiol. Plant. 36(9), 2531–2535 (2014).
CAS  Article  Google Scholar 

26.
Khattab, H. I., Emam, M. A., Emam, M. M., Helal, N. M. & Mohamed, R. M. Effect of selenium and silicon on transcription factors NAC5 and DREB2A involved in drought-responsive gene expression in rice. Biol. Plant. 58(2), 265–273 (2014).
CAS  Article  Google Scholar 

27.
Zhu, Y. X. & Gong, H. G. Beneficial effects of silicon on salt and drought tolerance in plants. Agron. Sustain. Dev. 34(2), 455–472 (2013).
Article  CAS  Google Scholar 

28.
Zhang, X. H. et al. Effect of silicon on seed germination and the physiological characteristics of Glycyrrhiza uralensis under different levels of salinity. J. Hortic. Sci. Biotechnol. 90(4), 439–443 (2015).
CAS  Article  Google Scholar 

29.
Marcin, R. N. & Maria, S. The relationship between carbon and nitrogen metabolism in cucumber leaves acclimated to salt stress. Peer J. 6(3), e6043 (2018).
Google Scholar 

30.
Zhang, D. D. et al. Enhanced of α-ketoglutarate production in Torulopsis glabrata: Redistribution of carbon flux from pyruvate to α-ketoglutarate. Biotechnol. Bioprocess Eng. 14(2), 134–139 (2009).
ADS  CAS  Article  Google Scholar 

31.
Nunes-Nesi, A., Fernie, A. R. & Stitt, M. Metabolic and signaling aspects underpinning the regulation of plant carbon nitrogen interactions. Mol. Plant. 3(6), 973–996 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Miller, A. J., Fan, X. R., Shen, Q. R. & Smith, S. J. Amino acids and nitrate as signals for the regulation of nitrogen acquisition. J. Exp. Bot. 59(1), 111–119 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Reynolds, M. P. Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance. J. Exp. Bot. 62(2), 469–486 (2010).
PubMed  PubMed Central  Google Scholar 

34.
Yan, B. B. et al. The effects of endogenous hormones on the flowering and fruiting of Glycyrrhiza uralensis. Plants Basel. 8(11), 519 (2019).
CAS  PubMed Central  Article  Google Scholar 

35.
Mochida, K. et al. Draft genome assembly and annotation of Glycyrrhiza uralensis, a medicinal legume. Plant J. 89(2), 181–194 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

36.
An, C.-G. et al. Effect of KCl or K2SO4 supplement to nutrient solution on yield and fruit quality in sweet peppers (Capsicum annuum “Special” and ’Fiesta’). Hortic. Sci. Technol. 24(2), 181–189 (2006).
Google Scholar 

37.
Lang, D. Y., Yu, X. X., Jia, X. X., Li, Z. X. & Zhang, X. H. Methyl jasmonate improves metabolism and growth of NaCl-stressed Glycyrrhiza uralensis seedlings. Sci. Hortic. 266, 109287. https://doi.org/10.1016/j.scienta (2020).
CAS  Article  Google Scholar 

38.
Verma, A. K., Upadhyay, S. K., Verma, P. C., Solomon, S. & Singh, S. B. Functional analysis of sucrose phosphate synthase (SPS) and sucrose synthase (SS) in sugarcane (Saccharum) cultivars. Plant Biol. 13(2), 325–332 (2010).
PubMed  Article  CAS  PubMed Central  Google Scholar 

39.
Orathai, W., Lih, S. K. & Liang, Y. S. The changes in physical, bio-chemical, physiological characteristics and enzyme activities of mango cv. Jinhwang during fruit growth and development. NJAS-Wagen. J. Life Sc. 72–73, 7–12 (2015).
Google Scholar 

40.
Charles, J. B., Christine, H. F., Janice, T., Stephen, A. R. & Quick, W. P. Elevated sucrose-phosphate synthase activity in transgenic tobacco sustains photosynthesis in older leaves and alters development. J. Exp. Bot. 54(389), 1813–1820 (2003).
Article  Google Scholar 

41.
Wang, X. W. et al. In vitro evaluation of the hypoglycemic properties of lactic acid bacteria and its fermentation adaptability in apple juice. LWT-Food Sci. Technol. 136, 110363. https://doi.org/10.1016/j.lwt.2020.110363 (2020).
CAS  Article  Google Scholar 

42.
Ali, A., Jha, P., Sandhu, K. S. & Raghuram, N. Spirulina nitrate-assimilating enzymes (NR, NiR, GS) have higher specific activities and are more stable than those of rice. Physiol. Mol. Biol. Plant. 14(3), 179–182 (2008).
CAS  Article  Google Scholar 

43.
Patel, J. G., Kumar, N. J. I., Kumar, R. N. & Khan, S. R. Evaluation of nitrogen fixing enzyme activities in response to pyrene bioremediation efficacy by defined artificial microalgal-bacterial consortium of Gujarat, India. Polycycl. Aromat. Compd. 38(3), 282–293 (2018).
CAS  Article  Google Scholar 

44.
Liu, C. G. et al. Carbon and nitrogen metabolism in leaves and roots of dwarf bamboo (Fargesia denudata Yi) subjected to drought for two consecutive years during sprouting period. J. Plant Growth Regul. 33, 243–255 (2014).
CAS  Article  Google Scholar 

45.
Magomya, A. M., Kubmarawa, D., Ndahi, J. A. & Yebpella, G. G. Determination of plant proteins via the Kjeldahl method and amino acid analysis: A comparative study. Int. J. Sci. Technol. Res. 3(4), 68–72 (2014).
Google Scholar 

46.
Yang, H. L. et al. Molybdenum blue photometry method for the determination of colloidal silica and soluble silica in leaching solution. Anal. Methods. https://doi.org/10.1039/C5AY01306B (2015).
Article  Google Scholar 

47.
Marino, D., González, E. M. & Arrese-Igor, C. Drought effects on carbon and nitrogen metabolism of pea nodules can be mimicked by paraquat: Evidence for the occurrence of two regulation pathways under oxidative stresses. J. Exp. Bot. 57(3), 665–673 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Shao, Q. S. et al. Effects of NaCl stress on nitrogen metabolism of cucumber seedlings. Russ. J. Plant Physiol. 62(5), 595–603 (2015).
CAS  Article  Google Scholar 

49.
Irani, S. & Todd, C. D. Ureide metabolism under abiotic stress in Arabidopsis thaliana. J. Plant Physiol. 199, 87–95 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Ahmad, P. et al. Silicon (Si) supplementation alleviates NaCl toxicity in Mung Bean [Vigna radiata, (L.) Wilczek] through the modifications of physio-biochemical attributes and key antioxidant enzymes. J. Plant Growth Regul. 38, 70–82 (2018).
Article  CAS  Google Scholar 

51.
Liang, Y. C., Chen, Q., Liu, Q., Zhang, W. H. & Ding, R. X. Exogenous silicon (Si) increases antioxidant enzyme activity and reduces lipid peroxidation in roots of salt-stressed barley (Hordeum vulgare L.). J. Plant Physiol. 160(10), 1157–1164 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Kim, Y. H. et al. Silicon application to rice root zone influenced the phytohormonal and antioxidant responses under salinity stress. J. Plant Growth Regul. 33(2), 137–149 (2013).
Article  CAS  Google Scholar 

53.
Haghighi, M. & Pessarakli, M. Influence of silicon and nano-silicon on salinity tolerance of cherry tomatoes (Solanum lycopersicum L.) at early growth stage. Sci. Hortic. 161(24), 111–117 (2013).
CAS  Article  Google Scholar 

54.
Zhu, Y. X. et al. Silicon improves salt tolerance by increasing root water uptake in Cucumis sativus, L. Plant Cell Rep. 34(9), 1629–1646 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Fernandes, F. M., Arrabaca, M. C. & Carvalho, L. M. M. Sucrose metabolism in Lupinus albus L. under salt stress. Biol. Plant. 48(2), 317–319 (2004).
CAS  Article  Google Scholar 

56.
Miyako, K. et al. Cytosolic GLUTAMINE SYNTHETASE1;1 modulates metabolism and chloroplast development in roots. Plant Physiol. 182(4), 1894–1909 (2020).
Article  CAS  Google Scholar 

57.
Joaquim, A. G. S. et al. Proline accumulation and glutamine synthetase activity are increased by salt-induced proteolysis in cashew leaves. J. Plant Physiol. 160(2), 115–123 (2003).
Article  Google Scholar 

58.
Dresler, S., Wójcik, M., Bednarek, W., Hanaka, A. & Tukiendorf, A. The effect of silicon on maize growth under cadmium stress. Russ. J. Plant Physiol. 62(1), 86–92 (2015).
CAS  Article  Google Scholar 

59.
Muneer, S. & Jeong, B. R. Proteomic analysis of salt-stress responsive proteins in roots of tomato (Lycopersicon esculentum L.) plants towards silicon efficiency. Plant Growth Regul. 77(2), 133–146 (2015).
ADS  CAS  Article  Google Scholar 

60.
Dorairaj, D., Ismail, M. R., Sinniah, U. R. & Ban, T. K. Influence of silicon on growth, yield, and lodging resistance of MR219, a lowland rice of Malaysia. J. Plant Nutr. 40(8), 1111–1124 (2017).
CAS  Article  Google Scholar 

61.
Garg, N. & Singh, S. Arbuscular mycorrhiza Rhizophagus irregularis and silicon modulate growth, proline biosynthesis and yield in Cajanus cajan L. Millsp. (pigeonpea) genotypes under cadmium and zinc stress. J. Plant Growth Regul. 37(6), 46–63 (2018).
CAS  Article  Google Scholar  More

1.
Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. https://doi.org/10.1016/S0169-5347(02)00045-9 (2003).
Article  Google Scholar 
2.
Bronstein, J. L. The costs of mutualism. Am. Zool. 839, 825–839 (2001).
Google Scholar 

3.
Bronstein, J. L. The evolution of facilitation and mutualism. J. Ecol. 97, 1160–1170 (2009).
Article  Google Scholar 

4.
Boucher, D. H., James, S. & Keeler, K. H. The ecology of mutualism. Annu. Rev. Ecol. Syst. https://doi.org/10.1146/annurev.es.13.110182.001531 (1982).
Article  Google Scholar 

5.
Stachowicz, J. J. The structure of ecological communities. Bioscience 51, 235–246 (2001).
Article  Google Scholar 

6.
Bulleri, F., Bruno, J. F., Silliman, B. R. & Stachowicz, J. J. Facilitation and the niche: Implications for coexistence, range shifts and ecosystem functioning. Funct. Ecol. https://doi.org/10.1111/1365-2435.12528 (2016).
Article  Google Scholar 

7.
Callaway, R. M. Positive interactions among plants. Bot. Rev. 61, 306–337 (1995).
Article  Google Scholar 

8.
Brooker, R. W. et al. Facilitation in plant communities: the past, the present, and the future. J. Ecol. 96, 18–34 (2008).
MathSciNet  Article  Google Scholar 

9.
Kotler, B. P., Blaustein, L. & Brown, J. S. Predator facilitation: the combined effect of snakes and owls on the foraging behavior of gerbils. Ann. Zool. Fennici 29, 199–206 (1992).
Google Scholar 

10.
Nummi, P. & Hahtola, A. The beaver as an ecosystem engineer. Ecography (Cop.) 31, 519–524 (2008).
Article  Google Scholar 

11.
Odadi, W. O., Jain, M., Van Wieren, S. E., Prins, H. H. T. & Rubenstein, D. I. Facilitation between bovids and equids on an African savanna. Evol. Ecol. Res. 13, 237–252 (2011).
Google Scholar 

12.
Harvey, J. A., Ode, P. J., Malcicka, M. & Gols, R. Short-term seasonal habitat facilitation mediated by an insect herbivore. Basic Appl. Ecol. 17, 447–454 (2016).
Article  Google Scholar 

13.
Tirado, R. & Pugnaire, F. I. Community structure and positive interactions in constraining environments. Oikos 111, 437–444 (2005).
Article  Google Scholar 

14.
Oliveras de Ita, A. & Rojas-Soto, O. R. Ant presence in Acacias: An association that maximizes nesting success in birds? Wilson J. Ornithol. 118, 563–566 (2006).

15.
Quinn, J. L. & Ueta, M. Protective nesting associations in birds. Ibis (Lond. 1859). 150, 146–167 (2008).

16.
Haemig, P. D. Symbiotic nesting of birds with formidable animals: A review with applications to biodiversity conservation. Biodivers. Conserv. 10, 527–540 (2001).
Article  Google Scholar 

17.
Burtner, B. F. & Frederick, P. C. Attraction of Nesting Wading Birds to Alligators (Alligator mississippiensis). Testing the ‘Nest Protector’ Hypothesis. Wetlands 37, 697–704 (2017).

18.
Hay, M. E. Associational plant defenses and the maintenance of species diversity: Turning competitors into accomplices. Am. Soc. Nat. 128, 617–641 (1986).
Google Scholar 

19.
Atsatt, P. R. & O’Dowd, D. J. Plant Defense Guilds. Science (80-. ). 193, 24–29 (1976).

20.
Barbosa, P. et al. Associational resistance and associational susceptibility: Having right or wrong neighbors. Annu. Rev. Ecol. Evol. Syst. 40, 1–20 (2009).
Article  Google Scholar 

21.
Myers, J. G. The nesting-together of birds, wasps and ants. (1929).

22.
Moreau, R. E. Bird-Insect nesting associations. Ibis (Lond. 1859). 460–471 (1936).

23.
Durango, S. The nesting associations of birds with social insects and with birds of different species. Ibis (Lond. 1859). 91, 140–143 (1949).

24.
Grimes, L. G. The breeding of Heuglin’s masked weaver and its nesting association with the red weaver ant. Ostrich 44, 170–175 (1973).
Article  Google Scholar 

25.
Uchida, H. Passerine birds nesting close to nests of birds of prey. Japanese J. Ornithol. 35, 25–31 (1986).
Article  Google Scholar 

26.
Pius, S. M. & Leberg, P. L. The protector species hypothesis: Do black skimmers find refuge from predators in gull-billed tern colonies?. Ethology 104, 273–284 (1998).
Article  Google Scholar 

27.
Richardson, D. S. & Bolen, G. M. A nesting association between semi-colonial Bullock’s orioles and yellow-billed magpies: Evidence for the predator protection hypothesis. Behav. Ecol. Sociobiol. 46, 373–380 (1999).
Article  Google Scholar 

28.
Nell, L. A., Frederick, P. C., Mazzotti, F. J., Vliet, K. A. & Brandt, L. A. Presence of breeding birds improves body condition for a crocodilian nest protector. PLoS ONE 11, 1–16 (2016).
Article  CAS  Google Scholar 

29.
Freestone, A. L. Facilitation drives local abundance and regional distribution of a rare plant in a harsh environment. Ecology 87, 2728–2735 (2006).
PubMed  Article  Google Scholar 

30.
Frederick, P. C. & Collopy, M. W. The role of predation in determining reproductive success of colonially nesting wading birds in the Florida everglades. Condor 91, 860–867 (1989).
Article  Google Scholar 

31.
Rodgers, J. A. Jr. On the antipredator advantages of coloniality: A word of caution. Wilson Bull. 99, 269–271 (1987).
Google Scholar 

32.
Hoogland, J. L. & Sherman, P. W. Advantages and Disadvantages of Bank Swallow (Riparia riparia) Coloniality. Source Ecol. Monogr. 46, 33–58 (1976).
Article  Google Scholar 

33.
Møller, A. P. Advantages and disadvantages of coloniality in the swallow Hirundo rustica. Anim. Behav. 35, 819–832 (1987).
Article  Google Scholar 

34.
White, C. L., Frederick, P. C., Main, M. B. & Rodgers Jr., J. A. Nesting island creation for water birds. Univ. Florida IFAS Ext. 7 (2005).

35.
Jungwirth, A., Josi, D., Walker, J. & Taborsky, M. Benefits of coloniality: Communal defence saves anti-predator effort in cooperative breeders. Funct. Ecol. 29, 1218–1224 (2015).
Article  Google Scholar 

36.
Deneubourg, J. L. & Goss, S. Collective patterns and decision-making. Ethol. Ecol. Evol. 1, 295–311 (1989).
Article  Google Scholar 

37.
Couzin, I. D. Collective cognition in animal groups. Trends Cogn. Sci. 13, 36–43 (2009).
PubMed  Article  Google Scholar 

38.
Fasola, M. & Alieri, R. Conservation of heronry Ardeidae sites in North Italian agricultural landscapes. Biol. Conserv. 62, 219–228 (1992).
Article  Google Scholar 

39.
Van Eerden, M. R., Koffijberg, K. & Platteeuw, M. Riding on the crest of the wave: Possibilities and limitations for a thiriving population of migratoy Great Cormorant (Phalacrocorax carbo). in Man-Dominated wetlands 338 (Nederlandse Ornithologische Unie (NOU), 1995).

40.
Burger, J. A model for the evolution of mixed-species colonies of Ciconiiformes. Q. Rev. Biol. 56, 143–167 (1981).
Article  Google Scholar 

41.
Hafner, H. Heron nest site conservation. in Heron Conservation (eds. Kushlan, J. A. & Hafner, H.) 201–217 (Academic Press, 2000).

42.
Ogden, J. C. Nesting by wood storks in Natural. Altered, and Artificial Wetlands in Central and Northern Florida. 14, 39–45 (1991).
Google Scholar 

43.
Parsons, K. C., Schmidt, S. R. & Matz, A. C. Regional patterns of wading bird productivity in Northeastern. Int. J. Waterbird Biol. 24, 323–330 (2001).
Google Scholar 

44.
Parsons, K. C. Reproductive success of wading birds using phragmites marsh and upland nesting habitats. Source Estuaries Part B 26, 596–601 (2003).
Google Scholar 

45.
Paton, P. W. C., Harris, R. J. & Trocki, C. L. Distribution and abundance of breeding birds in Boston Harbor. Northeast. Nat. 12, 145–168 (2005).
Article  Google Scholar 

46.
Robinson, S. K. Coloniality in the yellow-rumped cacique as a defense against nest predators. Auk 102, 506–519 (1985).
Article  Google Scholar 

47.
Post, W. Nest survival in a large ibis-heron colony during a three-year decline to extinction. Waterbird Soc. 13, 50–61 (1990).
Article  Google Scholar 

48.
Strong, A. M., Sawicki, R. J. & Thomasbancroft ’, G. Effects of predator presence on the nesting distribution of White-Crowned Pigeons in Florida Bay. Wilson Bull 103, 415–425 (1991).

49.
Kelly, J. P., Pratt, H. M. & Greene, P. L. The distribution, reproductive success, and habitat characteristics of heron and egret breeding colonies in the San Francisco Bay Area. Colon. Waterbirds 16, 18–27 (1993).
Article  Google Scholar 

50.
Erwin, R. M., Hatfield, J. S. & Wilmers, T. J. The value and vulnerability of small estuarine islands for conserving metapopulations of breeding waterbirds. Biol. Conserv. 71, 187–191 (1995).
Article  Google Scholar 

51.
Tsai, J. S., Reichert, B. E., Frederick, P. C. & Meyer, K. D. Breeding site longevity and site characteristics have intrinsic value for predicting persistence of colonies of an Endangered Bird. Wetlands 36, 639–647 (2016).
Article  Google Scholar 

52.
Frederick, P. C. & Collopy, M. W. Nesting success of five ciconiiform species in relation to water conditions in the Florida Everglades. Auk 106, 625–634 (1989).
Google Scholar 

53.
Post, W. & Seals, C. A. Breeding biology of the common moorhen in an impounded Cattail Marsh. J. F. Ornithol. 71, 437–442 (1991).
Article  Google Scholar 

54.
Hoover, J. P. Water depth influences nest predation for a wetland-dependent bird in fragmented bottomland forests. Biol. Conserv. 127, 37–45 (2006).
Article  Google Scholar 

55.
Coulter, M. C. & Bryan, L. A. Factors affecting reproductive success of wood storks (Mycteria americana) in East- Central Georgia. Auk 112, 237–243 (1995).
Article  Google Scholar 

56.
Dusi, J. L. & Dusi, R. T. Ecological factors contributing to nesting failure in a Heron Colony. Source Wilson Bull. 80, 458–466 (1968).
Google Scholar 

57.
Jenni, D. A. A study of the ecology of four species of herons during the breeding season at Lake Alice, Alachua County Florida. Ecol. Monogr. 39, 245–270 (1969).
Article  Google Scholar 

58.
Nell, L. A. & Frederick, P. C. Fallen nestlings and regurgitant as mechanisms of nutrient transfer from nesting wading birds to crocodilians. Wetlands 35, 723–732 (2015).
Article  Google Scholar 

59.
Gabel, W., Frederick, P. & Zabala, J. Nestling carcasses from colonially breeding wading birds: Patterns of access and energetic relevance for a vertebrate scavenger community. Sci. Rep. 9, 14512 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

60.
Tiner, R. W. Wetlands of the United States: current status and recent trends (U.S. Fish and Wildlife Service, Habitat Resources, 1984).
Google Scholar 

61.
Gardner, B., Garner, L. A., Cobb, D. T. & Moorman, C. E. Factors affecting occupancy and abundance of American alligators at the northern extent of their range. J. Herpetol. 50, 541–547 (2016).
Article  Google Scholar 

62.
Elsey, R. M. & Woodward, A. R. Alligator mississippiensis (American Alligator). in Crocodiles: Status Survey and Conservation Action Plan. (eds. Manolis, S. C. & Stevenson, C.) 1–4 (Crocodile Specialist Group, 2010).

63.
Parlin, A., Dinkelacker, S. & Mccall, A. Do habitat characteristics influence American alligator occupancy of barrier islands in North Carolina?. Southeast. Nat. 14, 33–40 (2015).
Article  Google Scholar 

64.
Dunham, K., Dinkelacker, S. & Miller, J. A stage-based population model for American alligators in northern latitudes. J. Wildl. Manage. 78, 440–447 (2014).
Article  Google Scholar 

65.
O’Brien, T. G. & Doerr, P. D. Night count surveys for alligators in coastal counties of North Carolina. J. Herpetol. 20, 444–448 (1986).
Article  Google Scholar 

66.
Bent, A. C. Life Histories of North American Marsh Birds. (Dover Publications, Inc., 1963).

67.
Beaver, D. L., Osborn, R. G. & Custer, T. W. Nest-site and colony characteristics of wading birds in selected Atlantic coast colonies. Wilson Bull. 92, 200–220 (1980).
Google Scholar 

68.
Custer, T. W. & Osborn, R. G. Wading birds as biological indicators: 1975 colony survey. US Fish Wildl. Serv. Spec. Sci. Rep. Wildl. 206, 1–28 (1977).

69.
Schweitzer, S. H. et al. Status and Distribution of Colonial Waterbirds during the 2017 Nesting Season in Coastal North Carolina. (2017).

70.
Annual Performance Report, Inland Colonial Waterbird Survey. http://repository.upi.edu/1360/1/s_d5451_0604180_chapter1.pdf (1996).

71.
Soots Jr., R. F. & Parnell, J. F. Inland Heronries of North Carolina. N.C. Sea Grant Publ. UNC-SG-78–10, Raleigh NC. 10–16 (1979).

72.
Parnell, J. F. & McCrimmon, D. A. 1983 supplement to Atlas of Colonial Waterbirds of North Carolina estuaries. UNC Sea Grant Publication UNC-SG-84–07 (1984).

73.
iNaturalist.org. iNaturalist Research-Grade Observations. Cooccurence dataset https://doi.org/https://doi.org/10.15468/ab3s5x (2019).

74.
Lauren, D. J. Effect of salt stress on electrolyte balance, corticosterone titer, and nitrogen metabolism in the American alligator, Alligator mississippiensis (Daudin, 1802). 81, 1–141 (1982).

75.
Brisbin, L. I. Jr., Standora, E. A. & Vargo, M. J. Body temperatures and behavior of American Alligators during cold winter weather. Am. Midl. Nat. 107, 209–218 (1982).
Article  Google Scholar 

76.
Dunson, W. & Mazzotti, F. Salinity as a limiting factor in the distribution of reptiles in Florida Bay: A theory for the estuarine origin of marine snakes and turtles. Bull. Mar. Sci. 44, 229–244 (1989).
Google Scholar 

77.
Birkhead, W. S. & Bennett, C. R. Observations of a small population of estuarine-inhabiting alligators near Southport North Carolina. Brimleyana 6, 111–117 (1981).
Google Scholar 

78.
Seebacher, F. A review of thermoregulation and physiological performance in reptiles: What is the role of phenotypic flexibility? J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 175, 453–461 (2005).

79.
Fujisaki, I. et al. Home range and movements of American alligators (Alligator mississippiensis) in an estuary habitat. Anim. Biotelemetry 2, 1–10 (2014).
Article  Google Scholar 

80.
McIlhenny, E. A. Alligator’s life history (Christopher Publishing House, USA, 1935).
Google Scholar 

81.
Joanen, T. & Mcnease, L. L. Ecology and physiology of nesting and early development of the American Alligator. Am. Zool. 29, 987–998 (1989).
Article  Google Scholar 

82.
Rosenblatt, A. E. & Heithaus, M. R. Does variation in movement tactics and trophic interactions among American alligators create habitat linkages?. J. Anim. Ecol. 80, 786–798 (2011).
PubMed  Article  Google Scholar 

83.
ESRI. ArcGIS Desktop: Release 10.6. Redlands (2018). http://www.esri.com/

84.
Schneider, D. G., Mech, L. D. & Tester, J. R. Movements of female raccoons and their young as determined by radio-tracking. Anim. Behav. Monogr. 4, 1–43 (1971).
Article  Google Scholar 

85.
Lotze, J. & Anderson, S. American Society of Mammalogists Procyon lotor. Mamm. Species 1–8 (1979).

86.
Pedlar, J. H., Fahring, L. & Merriam, G. Raccoon habitat use at 2 spatial scales. J. Wildl. Manage. 61, 102–112 (1997).
Article  Google Scholar 

87.
Porter, J. H., Dueser, R. D. & Moncrief, N. D. Cost-distance analysis of mesopredators as a tool for avian habitat restoration on a naturally fragmented landscape. J. Wildl. Manage. 79, 220–234 (2015).
Article  Google Scholar 

88.
Page, K. L. et al. Backyard raccoon latrines and risk for Baylisascaris procyonis transmission to humans reemergence of strongyloidiasis. Emerg. Infect. Dis. 15, 60–61 (2009).
Article  Google Scholar 

89.
Prange, S., Gehrt, S. D. & Wiggers, E. P. Demographic factors contributing to high raccoon densities in urban landscapes. Wildl. Manag. 67, 324–333 (2003).
Article  Google Scholar 

90.
Gibbs, J. P. & Kinkel, L. K. Determinants of the size and location of great blue heron colonies. Colon. Waterbirds 20, 1–7 (1997).
Article  Google Scholar 

91.
Bancroft, T. G., Strong, A. M., Sawicki, R. J., Hoffman, W. & Jewell, S. D. Relationships among wading bird foraging patters, colony locations, and hydrology in the Everglades. in Everglades: the ecosystem and its restoration (eds. Davis, S. & Ogden, J. C.) 615–657 (St. Lucie Press, 1994).

92.
Stolen, E. D., Collazo, J. A. & Percival, H. F. Scale-dependent habitat selection of nesting great egrets and snowy egrets. Waterbirds 30, 384–393 (2007).
Article  Google Scholar 

93.
Custer, T. W. & Osborn, R. G. Feeding habitat use by colonially-breeding herons, egrets, and ibises in North Carolina. Auk 95, 733–743 (1978).
Google Scholar 

94.
Thompson, D. H. Feeding areas of great blue herons and great egrets nesting within the floodplain of the Upper Mississippi River. Proc. Colon. Waterbird Gr. 2, 202–213 (1979).
Article  Google Scholar 

95.
Custer, C. M. & Galli, J. Feeding habitat selection by great blue herons and great egrets nesting in East Central Minnesota. Waterbirds 25, 115–124 (2006).
Article  Google Scholar 

96.
Siderelis, K. A Standard Classification System for the Mapping of Land Use and Land Cover. (1994).

97.
Best, L. B. & Stauffer, D. F. Factors affecting nesting success in riparian bird communities. Condor 82, 149–158 (1980).
Article  Google Scholar 

98.
Nilsson, S. G. The evolution of nest-site selection among hole-nesting birds: The importance of nest predation and competition. 15, 167–175 (1984).
Google Scholar 

99.
Greer, R. C., Cordes, C. & Keller, C. Analysis of colonial wading habitat in Louisiana. in Proceedings 4th Marsh and Estuary Management Symposium (eds. Bryan, C., Zwank, P. & Chabrek, R.) 143–151 (Louisiana Coop. Fish and Wildlife Research Unit, 1985).

100.
Eason, P., Rabea, B. & Attum, O. Island shape, size, and isolation affect nest-site selection by Little Terns. J. F. Ornithol. 83, 372–380 (2012).
Article  Google Scholar 

101.
Google Earth Pro. (2018). https://www.google.com/earth/versions/

102.
Tukey, J. W. Exploratory data analysis. in Exploratory Data Analysis (Addison-Wesley Publishing Company, 1977). doi:https://doi.org/10.1007/978-1-4419-7976-6.

103.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).
Article  Google Scholar 

104.
Crawley, M. J. The R Book. The R Book https://doi.org/10.1002/9780470515075 (2007).
Article  MATH  Google Scholar 

105.
Fleming, D. M., Palimisano, A. W. & Joanen, T. Food habits of coastal marsh raccoons with observation of alligator nest predation. Proc. Annu. Conf. Southeast. Assoc. Fish Wildl. Agencies 30, 348–357 (1976).

106.
Hunt, H. R. & Ogden, J. J. Selected aspects of nesting ecology of American Alliagtors in the Okefenokee Swamp. J. Herpetol. 25, 448–453 (1991).
Article  Google Scholar 

107.
Ruckdeschel, C. & Shoop, C. R. Aspects of wood stork nesting ecology on Cumberland Island Georgia, USA. Oriole 52, 21–27 (1987).
Google Scholar 

108.
Erwin, R. M., Spendelow, J. A., Geissler, P. H. & Williams, K. B. Relationships between nesting populations of wading birds and habitat features along the Atlantic Coast. in Waterfowl and Wetlands Symposium: Proceedings of a Symposium on Waterfowl and Wetlands Management in the Coastal Zone of the Atlantic Flyway (eds. Whitman, W. R. & Meredith, W. H.) 55–69 (Delaware Coastal Management Program, Delaware Department of Natural Resources and Environmental Control, 1986).

109.
Hartman, L. H. & Eastman, D. S. Distribution of introduced raccoons Procyon lotor on the Queen Charlotte Islands: Implications for burrow-nesting seabirds. Biol. Conserv. 88, 1–13 (1999).
Article  Google Scholar 

110.
Gibbs, J. P., Woodward, S., Hunter, M. L. & Hutchinson, A. E. Determinants of great blue heron colony distribution in coastal Maine. Auk 104, 38–47 (1987).
Article  Google Scholar 

111.
Cox, W. A. et al. Nest site selection by reddish egrets in Florida. J. Wildl. Manage. 83, 184–191 (2019).
Article  Google Scholar 

112.
Palmer, M. L. & Mazzotti, F. J. Structure of Everglades alligator holes. Southeast. Nat. 9, 487–496 (2004).
Google Scholar 

113.
Craighead, F. C. S. The role of the alligator in shaping plant communities and maintaining wildlife in the Southern Everglades. Florida Nat. 41, 2–94 (1968).
Google Scholar 

114.
Loftus, W. F. & Eklund, A. M. Long-term dynamics of an Everglades small-fish assemblage. in Everglades: the ecosystem and its restoration (eds. Davis, S. M. & Ogden, J. C.) 461–83 (St. Lucie Press, 1994).

115.
Diez, D. C. & Jackson, D. R. Use of American alligator nests by nesting turtles. J. Herpetol. 13, 510–512 (1979).
Article  Google Scholar 

116.
Kushlan, J. A. & Kushlan, M. S. Function of nest attendance in the American alligator. Herpetologica 36, 27–32 (1980).
Google Scholar 

117.
Kushlan, J. An ecological study of an alligator pond in the Big Cypress swamp of Southern Florida (University of Miami, Coral Gables, FL, 1972).
Google Scholar 

118.
Gawlik, D. E. & Rocque, D. A. Avian communities in bayheads, willowheads, and sawgrass marshes of the central everglades. Wilson Bull. 110, 45–55 (1998).
Google Scholar 

119.
Campbell, M. R. & Mazzotti, F. J. Characterization of natural and artificial alligator holes. Southeast. Nat. 3, 583–594 (2004).
Article  Google Scholar 

120.
Stachowicz, J. J. Niche expansion by positive interactions: realizing the fundamentals. A comment on Rodriguez-Cabal et al. Ideas Ecol. Evol. 5, 42–43 (2012). More

1.
Liang, X., Liu, Y. Z., Chen, K., Li, Y. F. & Yang, J. L. Identification of MyD88-4 in Mytilus coruscus and expression changes in response to Vibrio chagasii challenge (in Chinese with English abstract). J. Fish. China. 43, 2347–2358 (2019).
Google Scholar 
2.
Li, T. W. Marine Biology (in Chinese) (China Ocean Press, Beijing, 2013).
Google Scholar 

3.
Liang, X. et al. Effects of dynamic succession of Vibrio biofilms on settlement of the mussel Mytilus coruscus (in Chinese with English abstract). J. Fish. China. 44, 118–129 (2020).
Google Scholar 

4.
Whalan, S. & Webster, N. S. Sponge larval settlement cues: the role of microbial biofilms in a warming ocean. Sci. Rep. 4, 4072 (2014).
ADS  CAS  Article  Google Scholar 

5.
Satuito, C. G., Natoyama, K., Yamazaki, M. & Fusetani, N. Induction of attachment and metamorphosis of laboratory cultured mussel Mytilus edulis galloprovincialis larvae by microbial film. Fish. Sci. 61, 223–227 (1995).
CAS  Article  Google Scholar 

6.
Zhao, B., Zhang, S. & Qian, P. Y. Larval settlement of the silver-or goldlip pearl oyster Pinctada maxima (Jameson) in response to natural biofilms and chemical cues. Aquaculture 220, 883–901 (2003).
Article  Google Scholar 

7.
Rahim, S. A. K. A., Li, J. Y. & Kitamura, H. Larval metamorphosis of the sea urchins, Pseudocentrotus depressus and Anthocidaris crassispina in response to microbial film. Mar. Biol. 144, 71–78 (2004).
Article  Google Scholar 

8.
Bao, W. Y., Satuito, C. G., Yang, J. L. & Kitamura, H. Larval settlement and metamorphosis of the mussel Mytilus galloprovincialis in response to biofilms. Mar. Biol. 150, 565–574 (2007).
Article  Google Scholar 

9.
Huang, Y., Callahan, S. & Hadfield, M. G. Recruitment in the sea: bacterial genes required for inducing larval settlement in a polychaete worm. Sci. Rep. 2, 228 (2012).
ADS  Article  Google Scholar 

10.
Wang, C. et al. Larval settlement and metamorphosis of the mussel Mytilus coruscus in response to natural biofilms. Biofouling 28, 249–256 (2012).
Article  Google Scholar 

11.
Yang, J. L. et al. Larval settlement and metamorphosis of the mussel Mytilus coruscus in response to monospecific bacterial biofilms. Biofouling 29, 247–259 (2013).
CAS  Article  Google Scholar 

12.
Liang, X. et al. The flagellar gene regulates biofilm formation and mussel larval settlement and metamorphosis. Int. J. Mol. Sci. 21, 710 (2020).
CAS  Article  Google Scholar 

13.
Peng, L. H., Liang, X., Xu, J. K., Dobretsov, S. & Yang, J. L. Monospecific biofilms of Pseudoalteromonas promote larval settlement and metamorphosis of Mytilus coruscus. Sci. Rep. 10, 2577 (2020).
ADS  CAS  Article  Google Scholar 

14.
Schippers, A. et al. Prokaryotic cells of the deep sub-seafloor biosphere identified as living bacteria. Nature 433, 861–864 (2005).
ADS  CAS  Article  Google Scholar 

15.
Orcutt, B. N., Sylvan, J. B., Knab, N. J. & Edwards, K. J. Microbial ecology of the dark ocean above, at, and below the seafloor. Microbiol. Mol. Biol. Rev. 75, 361–422 (2011).
CAS  Article  Google Scholar 

16.
Woodall, L. C. et al. Deep-sea anthropogenic macrodebris harbours rich and diverse communities of bacteria and archaea. PLoS ONE 13, e0206220 (2018).
Article  Google Scholar 

17.
Wieczorek, S. K. & Todd, C. D. Inhibition and facilitation of settlement of epifaunal marine invertebrate larvae by microbial biofilm cues. Biofouling 12, 81–118 (1998).
Article  Google Scholar 

18.
Qian, P. Y., Lau, S. C. K., Dahms, H. U., Dobretsov, S. & Harder, T. Marine biofilms as mediators of colonization by marine macroorganisms: implications for antifouling and aquaculture. Mar. Biotechnol. 9, 399–410 (2007).
CAS  Article  Google Scholar 

19.
Dobretsov, S. in Marine and Industrial Biofouling (eds Flemming, H. C. et al.) 293–313 (Springer, 2009).

20.
Huang, S. & Hadfield, M. G. Composition and density of bacterial biofilms determine larval settlement of the polychaete Hydroides elegans. Mar. Ecol. Prog. Ser. 260, 161–172 (2003).
ADS  CAS  Article  Google Scholar 

21.
Tran, C. & Hadfield, M. G. Larvae of Pocillopora damicornis (Anthozoa) settle and metamorphose in response to surface-biofilm bacteria. Mar. Ecol. Prog. Ser. 433, 85–96 (2011).
ADS  Article  Google Scholar 

22.
Dahms, H. U., Dobretsov, S. & Qian, P. Y. The effect of bacterial and diatom biofilms on the settlement of the bryozoan Bugula neritina. J. Exp. Mar. Biol. Ecol. 313, 191–209 (2004).
Article  Google Scholar 

23.
Lau, S. C. K., Thiyagarajan, V. & Qian, P. Y. The bioactivity of bacterial isolates in Hong Kong waters for the inhibition of barnacle (Balanus amphitrite Darwin) settlement. J. Exp. Mar. Biol. Ecol. 282, 43–60 (2003).
Article  Google Scholar 

24.
Lau, S. C. K. & Qian, P. Y. Larval settlement in the serpulid polychaete Hydroides elegans in response to bacterial films: an investigation of the nature of putative larval settlement cue. Mar. Biol. 138, 321–328 (2001).
Article  Google Scholar 

25.
Bao, W. Y., Yang, J. L., Satuito, C. G. & Kitamura, H. Larval metamorphosis of the mussel Mytilus galloprovincialis in response to Alteromonas sp. 1: evidence for two chemical cues?. Mar. Biol. 152, 657–666 (2007).
Article  Google Scholar 

26.
Unabia, C. R. C. & Hadfield, M. G. Role of bacteria in larval settlement and metamorphosis of the polychaete Hydroides elegans. Mar. Biol. 133, 55–64 (1999).
Article  Google Scholar 

27.
Hadfield, M. G. Biofilms and marine invertebrate larvae: what bacteria produce that larvae use to choose settlement sites. Annu. Rev. Mar. Sci. 3, 453–470 (2011).
ADS  Article  Google Scholar 

28.
Flemming, H. C. & Wingender, J. The biofilm matrix. Nat. Rev. Microbiol. 8, 623–633 (2010).
CAS  Article  Google Scholar 

29.
Flemming, H. C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).
CAS  Article  Google Scholar 

30.
Karygianni, L., Ren, Z., Koo, H. & Thurnheer, T. Biofilm matrixome: extracellular components in structured microbial communities. Trends Microbiol. 28, 668–681 (2020).
CAS  Article  Google Scholar 

31.
Fulaz, S., Vitale, S., Quinn, L. & Casey, E. Nanoparticle–biofilm interactions: the role of the EPS matrix. Trends Microbiol. 27, 915–926 (2019).
CAS  Article  Google Scholar 

32.
Dragoš, A. & Kovács, Á. T. The peculiar functions of the bacterial extracellular matrix. Trends Microbiol. 25, 257–266 (2017).
Article  Google Scholar 

33.
Mayer, C. et al. The role of intermolecular interactions: studies on model systems for bacterial biofilms. Int. J. Biol. Macromol. 26, 3–16 (1999).
CAS  Article  Google Scholar 

34.
Liang, X. et al. Effects of biofilms of deep-sea bacteria under varying temperatures on larval metamorphosis of Mytilus coruscus (in Chinese with English abstract). J. Fish. China. 44, 131–144 (2020).
Google Scholar 

35.
Huggett, M. J., Williamson, J. E., de Nys, R., Kjelleberg, S. & Steinberg, P. D. Larval settlement of the common Australian sea urchin Heliocidaris erythrogramma in response to bacteria from the surface of coralline algae. Oecologia 149, 604–619 (2006).
ADS  Article  Google Scholar 

36.
Yang, J. L., Satuito, C. G., Bao, W. Y. & Kitamura, H. Induction of metamorphosis of pediveliger larvae of the mussel Mytilus galloprovincialis Lamarck, 1819 using neuroactive compounds, KCl, NH4Cl and organic solvents. Biofouling 24, 461–470 (2008).
CAS  Article  Google Scholar 

37.
Yang, J. L., Li, Y. F., Bao, W. Y., Satuito, C. G. & Kitamura, H. Larval metamorphosis of the mussel Mytilus galloprovincialis Lamarck, 1819 in response to neurotransmitter blockers and tetraethylammonium. Biofouling 27, 193–199 (2011).
CAS  Article  Google Scholar 

38.
Yang, J. L., Satuito, C. G., Bao, W. Y. & Kitamura, H. Larval settlement and metamorphosis of the mussel Mytilus galloprovincialis on different macroalgae. Mar. Biol. 152, 1121–1132 (2007).
Article  Google Scholar 

39.
Bao, W. Y., Lee, O. O., Chung, H. C., Li, M. & Qian, P. Y. Copper affects biofilm inductiveness to larval settlement of the serpulid polychaete Hydroides elegans (Haswell). Biofouling 26, 119–128 (2009).
Article  Google Scholar 

40.
Peng, L. H. et al. A bacterial polysaccharide biosynthesis-related gene inversely regulates larval settlement and metamorphosis of Mytilus coruscus. Biofouling 36, 753–765 (2020).
CAS  Article  Google Scholar  More