Ecology

1.
Burgess, S. D., Bowring, S. & Shen, S.-Z. High-precision timeline for Earth’s most severe extinction. Proc. Natl Acad. Sci. USA 111, 3316–3321 (2014).
Article  Google Scholar 
2.
Svensen, H. et al. Siberian gas venting and the end-Permian environmental crisis. Earth Planet. Sci. Lett. 277, 490–500 (2009).
Article  Google Scholar 

3.
Burgess, S. D. & Bowring, S. A. High-precision geochronology confirms voluminous magmatism before, during, and after Earth’s most severe extinction. Sci. Adv. 1, e1500470 (2015).
Article  Google Scholar 

4.
Saunders, A. D. Two LIPs and two Earth-system crises: the impact of the North Atlantic igneous province and the Siberian Traps on the Earth-surface carbon cycle. Geol. Mag. 153, 201–222 (2016).
Article  Google Scholar 

5.
Cui, Y. & Kump, L. R. Global warming and the end-Permian extinction event: proxy and modeling perspectives. Earth Sci. Rev. 149, 5–22 (2015).
Article  Google Scholar 

6.
Sun, Y. et al. Lethally hot temperatures during the early Triassic greenhouse. Science 338, 366–370 (2012).
Article  Google Scholar 

7.
Brand, U. et al. The end-Permian mass extinction: a rapid volcanic CO2 and CH4-climatic catastrophe. Chem. Geol. 323, 121–144 (2012).
Article  Google Scholar 

8.
Winguth, A. M. E., Shields, C. A. & Winguth, C. Transition into a hothouse world at the Permian–Triassic boundary—a model study. Palaeogeogr. Palaeoclimatol. Palaeoecol. 440, 316–327 (2015).
Article  Google Scholar 

9.
Garbelli, C. et al. Neotethys seawater chemistry and temperature at the dawn of the end-Permian mass extinction. Gondwana Res. 35, 272–272 (2016).
Article  Google Scholar 

10.
Wang, W. et al. A high-resolution middle to late Permian paleotemperature curve reconstructed using oxygen isotopes of well-preserved brachiopod shells. Earth Planet. Sci. Lett. 540, 116245 (2020).
Article  Google Scholar 

11.
Lau, K. V. et al. Marine anoxia and delayed Earth system recovery after the end-Permian extinction. Proc. Natl Acad. Sci. USA 113, 2360–2365 (2016).
Article  Google Scholar 

12.
Elrick, M. et al. Global-ocean redox variation during the middle-late Permian through Early Triassic based on uranium isotope and Th/U trends of marine carbonates. Geology 45, 163–166 (2017).
Article  Google Scholar 

13.
Zhang, F. F. et al. Congruent Permian–Triassic δ238U records at Panthalassic and Tethyan sites: confirmation of global-oceanic anoxia and validation of the U-isotope paleoredox proxy. Geology 46, 327–330 (2018).
Article  Google Scholar 

14.
Korte, C. et al. Carbon, sulfur, oxygen and strontium isotope records, organic geochemistry and biostratigraphy across the Permian/Triassic boundary in Abadeh, Iran. Int. J. Earth Sci. 93, 565–581 (2004).
Google Scholar 

15.
Grice, K. et al. Photic zone euxinia during the Permian–Triassic superanoxic event. Science 307, 706–709 (2005).
Article  Google Scholar 

16.
Payne, J. L. et al. Calcium isotope constraints on the end-Permian mass extinction. Proc. Natl Acad. Sci. USA 107, 8543–8548 (2010).
Article  Google Scholar 

17.
Clarkson, M. O. et al. Ocean acidification and the Permo-Triassic mass extinction. Science 348, 229–232 (2015).
Article  Google Scholar 

18.
Garbelli, C., Angiolini, L. & Shen, S.-Z. Biomineralization and global change: a new perspective for understanding the end-Permian extinction. Geology 45, 19–22 (2017).
Article  Google Scholar 

19.
Hönisch, B., Hemming, N. G., Archer, D., Siddall, M. & McManus, J. F. Atmospheric carbon dioxide concentration across the mid-Pleistocene transition. Science 324, 1551–1554 (2009).
Article  Google Scholar 

20.
Rae, J. W. B. et al. CO2 storage and release in the deep Southern Ocean on millennial to centennial timescales. Nature 562, 569–573 (2018).
Article  Google Scholar 

21.
Gutjahr, M. et al. Very larger release of mostly volcanic carbon during the Palaeocene–Eocene thermal maximum. Nature 548, 573–577 (2017).
Article  Google Scholar 

22.
Henehan, M. et al. Rapid ocean acidification and protracted Earth system recovery followed the end-Cretaceous Chicxulub impact. Proc. Natl Acad. Sci. USA 116, 22500–22504 (2019).
Article  Google Scholar 

23.
Müller, T. et al. Ocean acidification during the early Toarcian extinction event: Evidence from boron isotopes in brachiopods. Geology https://doi.org/10.1130/G47781.1 (2020).

24.
Posenato, R. Survival patterns of microbenthic marine assemblages during the end-Permian mass extinction in the western Tethys (Dolomites, Italy). Palaeogeogr. Palaeoclimatol. Palaeoecol. 280, 150–167 (2019).
Article  Google Scholar 

25.
Wallmann, K. et al. Periodic changes in the Cretaceous ocean and climate caused by marine redox see-saw. Nat. Geosci. 12, 456–462 (2019).
Article  Google Scholar 

26.
Retallack, G. J. A 300-million-year record of atmospheric carbon dioxide from fossil plant cuticles. Nature 411, 287–290 (2001).
Article  Google Scholar 

27.
Goddéris, Y. et al. Causal or casual link between the rise of nannoplankton calcification and a tectonically-driven massive decrease in Late Triassic atmospheric CO2? Earth Planet. Sci. Lett. 267, 247–255 (2008).
Article  Google Scholar 

28.
McElwain, J. C., Wagner, P. J. & Hesselbo, S. P. Fossil plant relative abundances indicate sudden loss of Late Triassic biodiversity in East Greenland. Science 324, 1554–1556 (2009).
Article  Google Scholar 

29.
Witkowski, C. R., Weijers, J. W. H., Blais, B., Schouten, S. & Damste, J. S. S. Molecular fossils from phytoplankton reveal secular (p_{{rm{CO}}_2}) trend over the Phanerozoic. Sci. Adv. 4, eaat4556 (2018).

30.
Joachimski, M. M. et al. Climate warming in the latest Permian and the Permian–Triassic mass extinction. Geology 40, 195–198 (2012).
Article  Google Scholar 

31.
Schobben, M., Joachimski, M. M., Korn, D., Leda, L. & Korte, C. Palaeotethys seawater temperature rise and an intensified hydrological cycle following the end-Permian mass extinction. Gondwana Res. 26, 675–683 (2014).
Article  Google Scholar 

32.
Algeo, T. J. et al. Plankton and productivity during the Permian–Triassic boundary crisis: an analysis of organic carbon fluxes. Glob. Planet. Change 105, 52–67 (2013).
Article  Google Scholar 

33.
Saitoh, M. et al. Nitrogen isotope chemostratigraphy across the Permian–Triassic boundary at Chaotian, Sichuan, South China. J. Asian Earth Sci. 93, 113–128 (2014).
Article  Google Scholar 

34.
Sun, Y. D. et al. Ammonium ocean following the end-Permian mass extinction. Earth Planet. Sci. Lett. 518, 211–222 (2019).
Article  Google Scholar 

35.
Shen, J. et al. Marine productivity changes during the end-Permian crisis and Early Triassic recovery. Earth Sci. Rev. 149, 136–162 (2015).
Article  Google Scholar 

36.
Canfield, D. E. Models of oxic respiration, denitrification and sulfate reduction in zones of coastal upwelling. Geochim. Cosmochim. Acta 70, 5753–5765 (2006).
Article  Google Scholar 

37.
Anderson, R. F. et al. Deep-sea oxygen depletion and ocean carbon sequestration during the last ice age. Glob. Biogeochem. Cycles 33, 301–317 (2019).
Article  Google Scholar 

38.
Zeebe, R. & Westbroek, P. A simple model for the CaCO3 saturation state of the ocean: The ‘Strangelove’, the ‘Neritan’, and the ‘Cretan’ Ocean. Geochem. Geophys. Geosyst. 4, 1104 (2003).
Article  Google Scholar 

39.
Vollstaedt, H. et al. The Phanerozoic δ88/86Sr record of seawater: new constraints on past changes in oceanic carbonate fluxes. Geochim. Cosmochim. Acta 128, 249–265 (2014).
Article  Google Scholar 

40.
Silva-Tamayo, J. C. et al. Global perturbation of the marine calcium cycle during the Permian–Triassic transition. Geol. Soc. Am. Bull. 130, 1323–1338 (2018).
Article  Google Scholar 

41.
Woods, A. Assessing Early Triassic paleoceanographic conditions via unusual sedimentary fabrics and features. Earth Sci. Rev. 137, 6–18 (2014).
Article  Google Scholar 

42.
Burgess, S. D., Muirhead, J. D. & Bowring, S. A. Initial pulse of Siberian Traps sills as the trigger of the end-Permian mass extinction. Nat. Commun. 8, 164 (2017).
Article  Google Scholar 

43.
Song, H. J., Wignall, P. B., Tong, J. N. & Yin, H. F. Two pulses of extinction during the Permian–Triassic crisis. Nat. Geosci. 6, 52–56 (2013).
Article  Google Scholar 

44.
Brayard, A. et al. Transient metazoan reefs in the aftermath of the end-Permian mass extinction. Nat. Geosci. 4, 693–697 (2011).
Article  Google Scholar 

45.
Martindale, R. C., Foster, W. J. & Velledits, F. The survival, recovery, and diversification of Metazoan reef ecosystems following the end-Permian mass extinction event. Palaeogeogr. Palaeoclimatol. Palaeoecol. 513, 100–115 (2019).
Article  Google Scholar 

46.
Reynard, S. et al. Interacting effects of CO2 partial pressure and temperature on photosynthesis and calcification in a scleractinian coral. Glob. Change Biol. 9, 1660–1668 (2003).
Article  Google Scholar 

47.
Beatty, T. W., Zonneveld, J.-P. & Henderson, C. M. Anomalously diverse Early Triassic ichnofossil assemblages in northwest Pangea: a case for shallow-marine habitable zone. Geology 36, 771–774 (2008).
Article  Google Scholar 

48.
Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).
Article  Google Scholar 

49.
Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681–686 (2005).
Article  Google Scholar 

50.
Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oxygen content during the past five decades. Nature 542, 335–339 (2017).
Article  Google Scholar 

51.
Posenato, R. Marine biotic events in the Lopingian succession and latest Permian extinction in the Southern Alps (Italy). Geol. J. 45, 195–215 (2010).
Article  Google Scholar 

52.
Broglio Loriga, C., Neri, C., Pasini, M. & Posenato, R. in Permian and Permian–Triassic Boundary in the South-Alpine segment of the western Tethys, and Additional Reports (ed. Cassinis, G.) 5–44 (Societa Geologica Italiana, 1988).

53.
Posenato, R. The athyridoids of the transitional beds between Bellerophon and Werfen formations (uppermost Permian, Southern Alps, Italy). Riv. Ital. Paleontol. Soc. 1071, 197–226 (2001).
Google Scholar 

54.
Kearsey, T., Twichett, R. J., Price, G. D. & Grimes, S. T. Isotope excursion and palaeotemperature estimates from the Permian/Triassic boundary in the Southern Alps (Italy). Palaeogeogr. Palaeoclimatol. Palaeoecol. 279, 29–40 (2009).
Article  Google Scholar 

55.
Muttoni, G. et al. Opening of the neo-Tethys ocean and the Pangea B to Pangea A transformation during the Permian. GeoArabia 14, 17–48 (2009).
Google Scholar 

56.
Reichow, M. K. et al. The timing and extent of the eruption of the Siberian Traps large igneous province: implications for the end-Permian environmental crisis. Earth Planet. Sci. Lett. 277, 9–20 (2009).
Article  Google Scholar 

57.
Brand, U., Logan, A., Hiller, N. & Richardson, J. Geochemistry of modern brachiopods: applications and implications for oceanography and paleoceanography. Chem. Geol. 198, 305–334 (2003).
Article  Google Scholar 

58.
Rollion-Bard, C. et al. Assessing the biomineralisation processes in the shell layers of modern brachiopods from oxygen isotopic composition and elemental ratios: implications for their use as paleoenvironmental proxies. Chem. Geol. 524, 49–66 (2019).
Article  Google Scholar 

59.
Jurikova, H. et al. Boron isotope systematics of cultured brachiopods: response to acidification, vital effects and implications for palaeo-pH reconstruction. Geochim. Cosmochim. Acta 248, 370–386 (2019).
Article  Google Scholar 

60.
Jurikova, H. et al. Incorporation of minor and trace elements into cultured brachiopods: implications for proxy application with new insights from a biomineralisation model. Geochim. Cosmochim. Acta 286, 418–440 (2020).
Article  Google Scholar 

61.
Jurikova, H. et al. Boron isotope composition of the cold-water coral Lophelia pertusa along the Norwegian margin: zooming into a potential pH-proxy by combining bulk and high-resolution approaches. Chem. Geol. 513, 143–152 (2019).
Article  Google Scholar 

62.
Kasemann, S. A., Schmidt, D. N., Bijima, J. & Foster, G. L. In situ boron isotope analyses in marine carbonates and its application for foraminifera and palaeo-pH. Chem. Geol. 260, 138–147 (2009).
Article  Google Scholar 

63.
Lemarchand, D., Gaillardet, J., Lewin, É. & Allègre, C. J. The influence of rivers on marine boron isotopes and implications for reconstructing past ocean pH. Nature 408, 951–954 (2000).
Article  Google Scholar 

64.
Joachimski, M. M., Simon, L., van Geldern, R. & Lécuyer, C. Boron isotope geochemistry of Paleozoic brachiopod calcite: implications for a secular change in the boron isotope geochemistry of seawater over the Phanerozoic. Geochim. Cosmochim. Acta 69, 4035–4044 (2005).
Article  Google Scholar 

65.
Klochko, K., Kaufman, A. J., Wengsheng, Y., Byrne, R. H. & Tossell, J. A. Experimental measurement of boron isotope fractionation in seawater. Earth Planet. Sci. Lett. 248, 276–285 (2006).
Article  Google Scholar 

66.
Lécuyer, C., Grandjean, P., Reynard, B., Albarède, F. & Telouk, P. 11B/10B analysis of geological materials by ICP-MS Plasma 54: application to the boron fractionation between brachiopod calcite and seawater. Chem. Geol. 186, 45–55 (2002).
Article  Google Scholar 

67.
Ridgwell, A. A mid Mesozoic revolution in the regulation of ocean chemistry. Mar. Chem. 217, 339–357 (2005).
Google Scholar 

68.
Penman, D. E., Hönisch, B., Rasbury, E. T., Hemming, N. G. & Spero, H. J. Boron, carbon, and oxygen isotopic composition of brachiopod shells: intra-shell variability, controls, and potential as a paleo-pH recorder. Chem. Geol. 340, 32–39 (2013).
Article  Google Scholar 

69.
Garbelli, C., Angiolini, L., Brand, U. & Jadoul, F. Brachiopod fabric, classes and biogeochemistry: implications for the reconstruction and interpretation of seawater carbon-isotope curves and records. Chem. Geol. 371, 60–67 (2014).
Article  Google Scholar 

70.
Khiel, J. T. & Shields, C. A. Climate simulations of the latest Permian: implications for mass extinction. Geology 33, 757–760 (2005).
Article  Google Scholar 

71.
Lowenstein, T. K., Timofeeff, M. N., Brennan, S. T., Hardie, L. A. & Demicco, R. V. Oscillations in Phanerozoic seawater chemistry: evidence from fluid inclusions. Science 294, 1086–1088 (2001).
Article  Google Scholar 

72.
Berner, R. A. & Kothavala, Z. GEOCARB III: A revised model of atmospheric CO2 over Phanerozoic time. Am. J. Sci. 301, 182–204 (2001).
Article  Google Scholar 

73.
Royer, D. L., Donnadieu, Y., Park, J., Kowalczyk, J. & Godderis, Y. Error analysis of CO2 and O2 estimates from the long-term geochemical model GEOCARBSULF. Am. J. Sci. 314, 1259–1283 (2014).
Article  Google Scholar 

74.
Wallmann, K., Schneider, B. & Sarnthein, M. Effects of eustatic sea-level change, ocean dynamics, and nutrient utilization on atmospheric (p_{{rm{CO}}_2}) and seawater composition over the last 130,000 years. Clim. Past 12, 339–375 (2016).
Article  Google Scholar 

75.
Berner, R. A. GEOCARB II: a revised model of atmospheric CO2 over Phanerozoic time. Am. J. Sci. 294, 56–91 (1994).
Article  Google Scholar  More

1.
Bradshaw, C. J. & Brook, B. W. Human population reduction is not a quick fix for environmental problems. Proc. Natl. Acad. Sci. 111, 16610–16615 (2014).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Xu, W. et al. Strengthening protected areas for biodiversity and ecosystem services in China. Proc. Natl. Acad. Sci. USA 114, 1601–1606. https://doi.org/10.1073/pnas.1620503114 (2017).
CAS  Article  PubMed  Google Scholar 

3.
Kong, L. et al. Habitat conservation redlines for the giant pandas in China. Biol. Cons. 210, 83–88. https://doi.org/10.1016/j.biocon.2016.03.028 (2017).
Article  Google Scholar 

4.
4Ji, D. in South China Morning Post(2017).

5.
5Johnson, I. in New York Times(2015).

6.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).
ADS  CAS  Article  Google Scholar 

8.
Pimm, S. L. & Raven, P. Biodiversity: extinction by numbers. Nature 403, 843–845 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

9.
DeFries, R., Hansen, A., Turner, B., Reid, R. & Liu, J. Land use change around protected areas: management to balance human needs and ecological function. Ecol. Appl. 17, 1031–1038 (2007).
PubMed  Article  Google Scholar 

10.
Juffe-Bignoli, D. et al. Protected planet report 2014 (UNEP-WCMC, Cambridge, 2014).
Google Scholar 

11.
Schulze, K. et al. An assessment of threats to terrestrial protected areas. Conserv. Lett. 11, e12435 (2018).
Article  Google Scholar 

12.
Achiso, Z. Biodiversity and human livelihoods in protected areas: worldwide perspective—a review. SSR Inst. Int. J. Life Sci. 6, 2565–2578 (2020).
Article  Google Scholar 

13.
Ma, Z. et al. Changes in area and number of nature reserves in China. Conserv. Biol. 33, 1066–1075 (2019).
PubMed  PubMed Central  Article  Google Scholar 

14.
State Forestry Administration. Wildlife Conservation and Nature Reserve Construction Project [In Chinese]. http://www.forestry.gov.cn/portal/main/s/438/content-32569.html (2001).

15.
Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. Nat. Clim. Change 8, 499 (2018).
ADS  Article  Google Scholar 

16.
Daskin, J. H. & Pringle, R. M. Warfare and wildlife declines in Africa’s protected areas. Nature 553, 328 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

17.
Riggio, J., Jacobson, A. P., Hijmans, R. J. & Caro, T. How effective are the protected areas of East Africa?. Glob. Ecol. Conserv. 17, e00573 (2019).
Article  Google Scholar 

18.
Wang, W., Ren, G., He, Y. & Zhu, J. Habitat degradation and conservation status assessment of gallinaceous birds in the Trans-Himalayas, China. J. Wildl. Manag. 72, 1335–1341 (2008).
Article  Google Scholar 

19.
Lei, F. & Lu, T. China Endemic Birds 516–522 (Science Press, Beijing, 2006).
Google Scholar 

20.
20IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org (2020).

21.
21CITES. Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendices I, II and III. https://cites.org/eng/app/appendices.php (2017).

22.
Zhang, Z. W., Ding, C. Q., Ding, P. & Zheng, G. M. The current statts and a conservation strategy for species of Gralliformes in China. Chin. Biodivers. 11, 414–421 (2003).
Google Scholar 

23.
Zheng, G. M. & Wang, Q. S. China Red Data Book of Endangered Animals (Aves) (Science Press, Beijing, 1998).
Google Scholar 

24.
Zhang, Z. et al. Distribution and Population Status of Brown-Eared Pheasant in China 91–96 (World Pheasant Association, Hexham, 2002).
Google Scholar 

25.
25Zhang, Z. W., Zhang, G. & Song, J. Population status and conservation strategies of Brown Eared pheasant. In: Proceedings of the 4th Annual Symposium on Birds Across the two side of Taiwan Strait (2000).

26.
26Liu, Y. N. Studies on population status and habitat suitablity evalution of Brown Eared pheasants (Crossoptilon mantchuricum) in China. Master degree dissertation thesis, Beijing Normal University (2017).

27.
Li, X. & Liu, R. The Brown Eared Pheasant (International Academic Publishers, Bern, 1993).
Google Scholar 

28.
Wang, X. H. & An, C. L. Study of Brown Eared-pheasant population distribution in Xiaowutaishan Nature Reserve. Wildlife 28, 14–16 (2007) [In Chinese].
Google Scholar 

29.
Zhang, G. et al. Scale-dependent wintering habitat selection by brown eared pheasant in Luyashan Nature Reserve of Shanxi, China. Acta Ecol. Sin. 25, 952–957 (2005).
Google Scholar 

30.
Brown, D. et al. Land Use and Land Cover Change (Pacific Northwest National Laboratory (PNNL), Richland, WA, 2014).
Google Scholar 

31.
Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).
ADS  CAS  Article  Google Scholar 

34.
Warren, M. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

35.
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).
ADS  CAS  Article  Google Scholar 

36.
Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Zhou, C., Zhao, Y., Connelly, J. W., Li, J. & Xu, J. Current nature reserve management in China and effective conservation of threatened pheasant species. Wildl. Biol. 1, wlb.00258. https://doi.org/10.2981/wlb.00258 (2017).
Article  Google Scholar 

39.
Zhou, C., Xu, J. & Zhang, Z. Dramatic decline of the vulnerable Reeves’s pheasant Syrmaticus reevesii, endemic to central China. Oryx 49, 529–534 (2015).
Article  Google Scholar 

40.
Dudley, N., Groves, C., Redford, K. H. & Stolton, S. Where now for protected areas? Setting the stage for the 2014 World Parks Congress. Oryx 48, 496–503 (2014).
Article  Google Scholar 

41.
Ervin, J. The three new R’s for protected areas: repurpose, reposition and reinvest’. Parks 19, 75 (2013).
Article  Google Scholar 

42.
Ervin, J. et al. In: CBD Technical Series Vol. 44 5 (Convention on Biological Diversity, 2010).

43.
Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
Article  Google Scholar 

44.
Viña, A. & Liu, J. Hidden roles of protected areas in the conservation of biodiversity and ecosystem services. Ecosphere 8, e01864 (2017).
Article  Google Scholar 

45.
Li, D. et al. Habitat selection of breeding brown eared-pheasants (Crossoptilon mantchuricum) in Xiaowutaishan National Nature Reserve, Hebei Province, China. Front. Biol. China 4, 102–110. https://doi.org/10.1007/s11515-008-0090-2 (2008).
Article  Google Scholar 

46.
Song, K. et al. Modeling habitat factors and suitability for the Brown Eared Pheasant (Crossoption mantchuricum) in Baihuashan National Nature Reserve, Beijing. Chin. J. Zool. 51, 363–372 (2016).
Google Scholar 

47.
Li, H. Q., Lian, Z. M. & Chen, C. G. Winter foraging habitat selection of brown-eared pheasant (Crossoptilon mantchuricum) and the common pheasant (Phasianus colchicus) in Huanglong Mountains, Shaanxi Province. Acta Ecol. Sin. 29, 335–340 (2009).
CAS  Article  Google Scholar 

48.
Zhang, G., Zhang, Z., Yang, F. & Li, S. Habitat selection of brown-eared pheasant at the Wulushan National Nature Reserve of Shanxi, China. Scientia Silvae Sinicae 46, 100–103 (2010).
Google Scholar 

49.
Mi, C. R., Huettmann, F. & Guo, Y. M. Obtaining the best possible predictions of habitat selection for wintering Great Bustards in Cangzhou, Hebei Province with rapid machine learning analysis. Chin. Sci. Bull. 59, 4323–4331 (2014).
Article  Google Scholar 

50.
Lu, N., Jing, Y., Lloyd, H. & Sun, Y.-H. Assessing the distributions and potential risks from climate change for the Sichuan Jay (Perisoreus internigrans). The Condor 114, 365–376 (2012).
Article  Google Scholar 

51.
Razgour, O., Hanmer, J. & Jones, G. Using multi-scale modelling to predict habitat suitability for species of conservation concern: the grey long-eared bat as a case study. Biol. Cons. 144, 2922–2930 (2011).
Article  Google Scholar 

52.
Phillips, S. J., Dudík, M. & Schapire, S. E. Maxent software for modeling species niches and distributions (Version 3.4. 1). Tillgänglig från. http://biodiversityinformatics.amnh.org/open_source/maxent (2017).

53.
Lu, N., Jia, C.-X., Lloyd, H. & Sun, Y.-H. Species-specific habitat fragmentation assessment, considering the ecological niche requirements and dispersal capability. Biol. Conserv. 152, 102–109 (2012).
Article  Google Scholar 

54.
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
Article  Google Scholar 

55.
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
Article  Google Scholar 

56.
Fouquet, A., Ficetola, G. F., Haigh, A. & Gemmell, N. Using ecological niche modelling to infer past, present and future environmental suitability for Leiopelma hochstetteri, an endangered New Zealand native frog. Biol. Cons. 143, 1375–1384 (2010).
Article  Google Scholar 

57.
Hu, J., Hu, H. & Jiang, Z. The impacts of climate change on the wintering distribution of an endangered migratory bird. Oecologia 164, 555–565 (2010).
ADS  PubMed  Article  PubMed Central  Google Scholar 

58.
Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393 (2005).
Article  Google Scholar 

59.
59Scott, J. M. et al. Gap analysis: a geographic approach to protection of biological diversity. Wildl. Monogr. 3–41 (1993).

60.
Riitters, K., Wickham, J., O’Neill, R., Jones, K. B. & Smith, E. Global-scale patterns of forest fragmentation. Conservation Ecology 4(2) (2000). More

1.
IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).
2.
Miller-Rushing, A. J. & Primack, R. B. Global warming and flowering times in Thoreau’s Concord: a community perspective. Ecology 89, 332—341 (2008).
Article  Google Scholar 

3.
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Article  Google Scholar 

4.
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).
Article  Google Scholar 

5.
Wolkovich, E. M. et al. Warming experiments underpredict plant phenological responses to climate change. Nature 485, 494–497 (2012).
CAS  Article  Google Scholar 

6.
Rutishauser, T., Luterbacher, J., Defila, C., Frank, D. & Wanner, H. Swiss spring plant phenology 2007: extremes, a multi-century perspective, and changes in temperature sensitivity. Geophys. Res. Lett. 35, L05703 (2008).
Article  Google Scholar 

7.
Yu, H. Y., Luedeling, E. & Xu, J. C. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).
CAS  Article  Google Scholar 

8.
Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).
Article  CAS  Google Scholar 

9.
Fu, Y. S. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).
CAS  Article  Google Scholar 

10.
Chuine, I. et al. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break. Glob. Change Biol. 22, 3444–3460 (2016).
Article  Google Scholar 

11.
Harrington, C. A. & Gould, P. J. Tradeoffs between chilling and forcing in satisfying dormancy requirements for Pacific Northwest tree species. Front. Plant Sci. 6, 120 (2015).
Article  Google Scholar 

12.
Zohner, C. M., Benito, B. M., Svenning, J. C. & Renner, S. S. Day length unlikely to constrain climate-driven shifts in leaf-out times of northern woody plants. Nat. Clim. Change 6, 1120–1123 (2016).
Article  Google Scholar 

13.
Basler, D. & Körner, C. Photoperiod and temperature responses of bud swelling and bud burst in four temperate forest tree species. Tree Physiol. 34, 377–388 (2014).
Article  Google Scholar 

14.
Caffarra, A., Donnelly, A., Chuine, I. & Jones, M. B. Modelling the timing of Betula pubescens bud-burst. I. Temperature and photoperiod: a conceptual model. Clim. Res. 46, 147–157 (2011).
Article  Google Scholar 

15.
Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).
CAS  Article  Google Scholar 

16.
Caffarra, A., Donnelly, A. & Chuine, I. Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Clim. Res. 46, 159–170 (2011).
Article  Google Scholar 

17.
Fraga, H., Pinto, J. G. & Santos, J. A. Climate Change projections for chilling and heat forcing conditions in European vineyards and olive orchards: a multi-model assessment. Climatic Change 152, 179–193 (2019).
Article  Google Scholar 

18.
Heide, O. Daylength and thermal time responses of budburst during dormancy release in some northern deciduous trees. Physiol. Plant. 88, 531–540 (1993).
CAS  Article  Google Scholar 

19.
Singh, R. K., Svystun, T., AlDahmash, B., Jönsson, A. M. & Bhalerao, R. P. Photoperiod- and temperature-mediated control of phenology in trees—a molecular perspective. New Phytol. 213, 511–524 (2017).
CAS  Article  Google Scholar 

20.
Vitasse, Y. & Basler, D. What role for photoperiod in the bud burst phenology of European beech. Eur. J. For. Res. 132, 1–8 (2013).
Article  Google Scholar 

21.
Vitasse, Y. & Basler, D. Is the use of cuttings a good proxy to explore phenological responses of temperate forests in warming and photoperiod experiments? Tree Physiol. 34, 174–183 (2014).
Article  Google Scholar 

22.
Laube, J. et al. Chilling outweighs photoperiod in preventing precocious spring development. Glob. Change Biol. 20, 170–182 (2014).
Article  Google Scholar 

23.
Basler, D. & Körner, C. Photoperiod sensitivity of bud burst in 14 temperate forest tree species. Agric. For. Meteorol. 165, 73–81 (2012).
Article  Google Scholar 

24.
Caffarra, A. & Donnelly, A. The ecological significance of phenology in four different tree species: effects of light and temperature on bud burst. Int. J. Biometeorol. 55, 711–721 (2011).
Article  Google Scholar 

25.
Ohlemüller, R., Gritti, E. S., Sykes, M. T. & Thomas, C. D. Towards European climate risk surfaces: the extent and distribution of analogous and non-analogous climates 1931–2100. Glob. Ecol. Biogeogr. 15, 395–405 (2006).
Article  Google Scholar 

26.
Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).
Article  Google Scholar 

27.
Williams, J. W., Jackson, S. T. & Kutzbacht, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl Acad. Sci. USA 104, 5738–5742 (2007).
CAS  Article  Google Scholar 

28.
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

29.
Xu, Y., Ramanathan, V. & Victor, D. G. Global warming will happen faster than we think. Nature 564, 30–32 (2018).

30.
Wolkovich, E. M. et al. Observed Spring Phenology Responses in Experimental Environments (OSPREE) (Knowledge Network for Biocomplexity, 2019); https://doi.org/10.5063/F1CZ35KB

31.
Richardson, E. A model for estimating the completion of rest for ‘Redhaven’ and ’Elberta’ peach trees. HortScience 9, 331–332 (1974).
Google Scholar 

32.
Dennis, F. Problems in standardizing methods for evaluating the chilling requirements for the breaking of dormancy in buds of woody plants. HortScience 38, 347–350 (2003).
Article  Google Scholar 

33.
Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2006).

34.
Fu, Y. H. et al. Short photoperiod reduces the temperature sensitivity of leaf-out in saplings of Fagus sylvatica but not in horse chestnut. Glob. Change Biol. 25, 1696–1703 (2019).
Article  Google Scholar 

35.
Bradley, N. L., Leopold, A. C., Ross, J. & Huffaker, W. Phenological changes reflect climate change in Wisconsin. Proc. Natl Acad. Sci. USA 96, 9701–9704 (1999).
CAS  Article  Google Scholar 

36.
Gauzere, J., Lucas, C., Ronce, O., Davi, H. & Chuine, I. Sensitivity analysis of tree phenology models reveals increasing sensitivity of their predictions to winter chilling temperature and photoperiod with warming climate. Ecol. Model. 441, 108805 (2019).
Article  Google Scholar 

37.
Heide, O. & Prestrud, A. Low temperature, but not photoperiod, controls growth cessation and dormancy induction and release in apple and pear. Tree Physiol. 25, 109–114 (2005).
CAS  Article  Google Scholar 

38.
van der Schoot, C., Paul, L. K. & Rinne, P. L. H. The embryonic shoot: a lifeline through winter. J. Exp. Bot. 65, 1699–1712 (2014).
Article  CAS  Google Scholar 

39.
Fishman, S., Erez, A. & Couvillon, G. The temperature dependence of dormancy breaking in plants: mathematical analysis of a two-step model involving a cooperative transition. J. Theor. Biol. 124, 473–483 (1987).
Article  Google Scholar 

40.
Weinberger, J. H. et al. Chilling requirements of peach varieties. Proc. J. Am. Soc. Hort. Sci. 56, 122–128 (1950).

41.
Polgar, C. A., Primack, R. B., Williams, E. H., Stichter, S. & Hitchcock, C. Climate effects on the flight period of Lycaenid butterflies in Massachusetts. Biol. Conserv. 160, 25–31 (2013).
Article  Google Scholar 

42.
Vitasse, Y. Ontogenic changes rather than difference in temperature cause understory trees to leaf out earlier. New Phytol. 198, 149–155 (2013).
Article  Google Scholar 

43.
Laube, J., Sparks, T. H., Estrella, N. & Menzel, A. Does humidity trigger tree phenology? Proposal for an air humidity based framework for bud development in spring. New Phytol. 202, 350–355 (2014).
Article  Google Scholar 

44.
Li, C., Stevens, B. & Marotzke, J. Eurasian winter cooling in the warming hiatus of 1998–2012. Geophys. Res. Lett. 42, 8131–8139 (2015).
Article  Google Scholar 

45.
Balling, R. C. J., Michaels, P. J. & Knappenberger, P. C. Analysis of winter and summer warming rates in gridded temperature time series. Clim. Res. 9, 175–181 (1998).
Article  Google Scholar 

46.
Hänninen, H. Effects of climatic change on trees from cool and temperate regions: an ecophysiological approach to modelling of bud burst phenology. Can. J. Bot. 73, 183–199 (1995).
Article  Google Scholar 

47.
Güsewell, S., Furrer, R., Gehrig, R. & Pietragalla, B. Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason. Glob. Change Biol.23, 5189–5202 (2017).
Article  Google Scholar 

48.
Roberts, A. M., Tansey, C., Smithers, R. J. & Phillimore, A. B. Predicting a change in the order of spring phenology in temperate forests. Glob. Change Biol.21, 2603–2611 (2015).
Article  Google Scholar 

49.
Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151, 264–269 (2009).
Article  Google Scholar 

50.
Kicinski, M. Publication bias in recent meta-analyses. PLoS ONE 8, e81823 (2013).

51.
Gurevitch, J., Morrow, L. L., Wallace, A. & Walsh, J. S. A meta-analysis of competition in field experiments. Am. Nat. 140, 539–572 (1992).
Article  Google Scholar 

52.
Gurevitch, J. & Hedges, L. V. Statistical issues in ecological meta-analyses. Ecology 80, 1142–1149 (1999).
Article  Google Scholar 

53.
Lin, L. F. & Chu, H. T. Quantifying publication bias in meta-analysis. Biometrics 74, 785–794 (2018).
Article  Google Scholar 

54.
Luedeling, E. & Brown, P. H. A global analysis of the comparability of winter chill models for fruit and nut trees. Int. J. Biometeorol. 55, 411–421 (2011).
Article  Google Scholar 

55.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

56.
Luedeling, E. chillR: statistical methods for phenology analysis in temperate fruit trees. R package version 0.70.17 (2019).

57.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. & Jones, P. D. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
Article  Google Scholar 

58.
Livneh, B.et al. A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013. Sci. Data 2, 150042 (2015).

59.
Harrington, C. A., Gould, P. J. & St Clair, J. B. Modeling the effects of winter environment on dormancy release of Douglas-fir. For. Ecol. Manag. 259, 798–808 (2010).
Article  Google Scholar 

60.
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. https://doi.org/10.18637/jss.v076.i01(2017).

61.
Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3 (2018).

62.
Gelman, A. et al. Bayesian Data Analysis (CRC Press, 2014).

63.
Gauzere, J. et al. Integrating interactive effects of chilling and photoperiod in phenological process-based models. A case study with two European tree species: Fagus sylvatica and Quercus petraea. Agric. For. Meteorol. 244, 9–20 (2017).
Article  Google Scholar 

64.
Saikkonen, K. et al. Climate change-driven species’ range shifts filtered by photoperiodism. Nat. Clim. Change 2, 239 (2012).
Article  Google Scholar 

65.
Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ. 38, 1725–1736 (2015).
Article  Google Scholar 

66.
Chuine, I., Garcia de Cortazar Atauri, I., Hanninen, H. & Kramer, K. in Phenology: An Integrative Environmental Science (ed. Schwartz M.) 275–293 (Springer, 2013).

67.
Stan Development Team Stan User’s Guide v.2.19 (Stan, 2019). More

Experimental design and sample collection
This study was carried out in accordance with the provisions in the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes (7th edition, 2004) and all protocols were approved by the Animal Ethics Committee of La Trobe University. Twelve Angus × Friesian cattle foetuses at 5, 6 and 7 months gestation (n = 4 per age) were collected from Radford Warragul Abattoir, Victoria, Australia. Approximately 35–45 min after cows were slaughtered by abattoir staff, the intact uterus (containing the placenta and foetus) was removed. All sampling was conducted at the abattoir using sterile equipment and procedures. The outside surface of the amniotic sac was rinsed three times with sterilised phosphate-buffered saline (PBS; pH 7.0) to remove excess blood. The amnion was cut using sterile scalpels and amniotic fluid was sampled. The amniotic fluid was suctioned using sterile 50-mL syringes with tubing and the amniotic fluid was transferred immediately into 50-mL tubes. Then, the amniotic sac was opened further, the umbilical cord was cut, and the foetus was removed. The abdomen of the foetus was opened using sterilised equipment and the rostral and caudal ends of each GIT compartment were tied with sterile surgical thread to avoid mixing of the contents. The compartments were then separated between the ties. Each compartment was longitudinally incised along the dorsal line. Tissue samples (~ 2 cm2) of the rumen were taken from the dorsal area and caecal tissue samples were taken from the region 5 cm after the ileocaecal valve. Meconium pellets (~ 100 g) were taken by severing the rectum 5 cm from the anus. The fluid, tissue and meconium samples were collected into sterile 15-mL or 50-mL polypropylene centrifuge tubes. All samples were immediately placed into dry ice for transport. All samples were processed within 6 h of collection to extract gDNA.
DNA extraction
Genomic DNA was extracted from 250 mg of ruminal tissue, ruminal fluid, caecal tissue, caecal fluid and meconium. An 8-mL aliquot of amniotic fluid was centrifuged (11,000g, 5 min) to produce sufficient material in the pellet for extraction. DNA was extracted using an Isolate II Genomic DNA kit following the manufacturer’s instructions. Final DNA concentrations and purity were estimated using a P330-Class NanoPhotometer (Implen, München, Germany). All samples were stored at − 80 °C for later analysis.
16S rRNA library preparation and sequencing
Libraries were prepared for sequencing on an Illumina MiSeq following the protocol ‘16S Metagenomic Sequencing Library Preparation’ (Part # 15044223 Rev. B; Illumina, San Diego, CA, USA). The locus-specific primers were the universal 16S rRNA primer pairs S-D-Bact-0341-b-S-17 (5′-CCTACGGGNGGCWGCAG-3′) and S-D-Bact-0785-a-A-21 (5′-GACTACHVGGGTATCTAATCC-3′), Archaea349F (5′-GYGCASCAGKCGMGAAW-3′), and Archaea806R (5′-GGACTACVSGGGTATCTAAT-3′), which target the V3–V4 region of the bacterial and archaeal 16S rRNA genes, respectively. Primers had forward (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3′) and reverse (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3′) Illumina overhang adaptors merged to the 5′ ends.
PCR was performed in 25-µL reactions using 5 µL of each forward and reverse primer (10 µM), 12.5 µL 2 × KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Boston, MA, USA) and 2.5 µL of genomic DNA template (5 µL/ng). PCR cycle settings for the amplification of the bacterial and archaeal V3–V4 region were as follows: denaturation at 95 °C for 3 min, followed by 28 (bacterial) or 30 (archaeal) cycles of 30 s at 95 °C, 30 s at 55 °C and 30 s at 72 °C, followed by an extension step at 72 °C for 5 min. To normalise libraries prior to pooling, the DNA content of PCR reactions was quantified using an Agilent D1000 ScreenTape System (Agilent Technologies, CA, USA). Samples were adjusted to the same molarity (4 nM), pooled, and paired-end sequenced (2 × 300 bp) on an Illumina MiSeq platform. The MiSeq run was performed at La Trobe University Genomics Platform (Melbourne, Australia).
Analysis of sequence data
Raw, de-multiplexed, fastq files were re-barcoded, joined and quality filtered using the UPARSE clustering pipeline (USEARCH version 9.2.64; https://drive5.com/uparse)46. Paired-end reads were merged such that alignments with > 20 bp difference (i.e. approximately more than 10–14% mismatched) were discarded, and merged reads less than 300 bp in length were discarded. Reads that could not be assembled were discarded. Merged reads were quality filtered by discarding reads with total expected errors > 1.0. ESVs were generated with the “unoise3” command47. Taxonomic assignments were performed using the UTAX algorithm. Reference databases were created using the RDP_trainset_15 dataset, available from the UTAX downloads page (https://drive5.com/usearch/manual/utax_downloads.html). A minimum percentage identity of 90% was required for an ESV to be considered a database match hit. ESVs identified as chloroplasts and mitochondrial DNA were removed from the data. After filtering, the average read number (± SD) for each git compartment was: 4342 ± 1594 reads for the AM , 17,276 ± 17,376 reads for the CF, 6182 ± 3918 reads for the CT, 5732 ± 3262 reads for the Mec, 5630 ± 2234 reads for the RF and 5847 ± 4052 reads for the RT. The rarefying threshold of 1000 reads was chosen to maximise the amount of reads included in the analysis whilst minimize the number of samples excluded from the analysis. A total of three bacterial samples resulted in reads below the rarefication threshold (1000 reads) and were excluded from downstream alpha- and beta-diversity analyses. The samples were: Month_5_Mec-2, Month_6_CF-3 and Month_6_Mec-1. DNA extraction or library preparation was unsuccessful for the following samples: n = 0 (5 months cecum fluid), n = 1 (6 and 7 months rumen tissue, 7 months amniotic fluid), n = 2 (5 months rumen tissue, 6 months amniotic fluid), n = 3 (5 and 6 months cecum tissue, 5 months meconium), the remaining GIT compartments and months were n = 4. Raw fastq files for this project and metadata have been deposited with the NCBI SRA database and can be accessed using Bioproject ID: PRJNA421384 or SRA study ID: SRP126299.
Bacterial culture from ruminal fluid samples and identification of bacterial isolates
A 50-mL sample of ruminal fluid was taken from each foetus and maintained under anaerobic conditions (Oxoid AnaeroJar with an AnaeroGen™). A 1-mL aliquot of the ruminal fluid was transferred to anaerobic solid medium and cultured at 37 °C for 48–72 h. The anaerobic solid medium had the following composition (per litre of distilled water): 15 g agar (Oxoid), 10 g peptone (Oxoid), 10 g yeast extract, 8.8 g Oxoid Lab-Lemco beef extract powder, 10 g proteose peptone (Oxoid), 12 g dextrose, 10 g KH2PO4, 12 g NaCl, 20 g soluble starch, 1.2 g l-cysteine hydrochloride and 0.3 g sodium thioglycollate with a pH (at 25 °C) of 7.3 ± 0.1. Colonies were isolated and subcultured 5 times onto new agar media plates, except for the control plates (n = 3) which showed no microbial growth. Colonies were subcultured on fresh media and DNA extracted. The extracts for 5, 6 and 7 months were combined prior to next-generation sequencing of the 16S rRNA genes to characterise the taxonomic structure.
Quantitative PCR
Quantitative PCR (qPCR) was used to enumerate total bacterial and archaeal DNA copy number in each sample type (GIT component and amniotic fluid) as an indicator of abundance. The primer pairs bacF (5′-CCATTGTAGCACGTGTGTAGCC-3′) and bacR (5′-CGGCAACGAGCGCAACCC-3′) were used to amplify bacterial 16S rRNA, and Archaea364F (5′-CCTACGGGRBGCAGCAGG-3′) and Archaea1386R (5′-GCGGTGTGTGCAAGGAGC-3′) were used to amplify archaeal 16S rRNA. PCR reactions were run in triplicate on a CFX Connect Real-Time PCR Detection System (Bio-Rad, CA, USA). The total volume of each reaction mix was 20 μL, comprising 10 μL of SensiFAST SYBR Green Master Mix (Bioline), 0.4 μL of each forward and reverse primer (10 µM), sterile DNA-free water, and 7 ng of DNA. Triplicate control samples (no-DNA templates) were included to verify that no contaminating nucleic acid was introduced into the master mix or into samples. Positive controls contained gDNA extracted from laboratory cultured bacteria (E. coli strain DH5α) and archaea (Methanobrevibacter smithii), respectively. Thermocycling conditions were as follows: initial denaturation for 3 min at 94 °C, followed by 40 cycles of 10 s at 94 °C, 30 s at 60 °C. This was followed by a dissociation protocol (increasing 1 °C every 30 s from 60 °C to 98 °C).
A standard curve was constructed using serial tenfold dilutions from 10−1 to 10−11 of DNA from the bacterium E. coli strain DH5α (Stratagene, CA, USA) or the archaeon M. smithii. Real-time PCR efficiency ranged from 97 to 102%. Copy numbers for each standard curve were calculated based on the following equation: (NA × A × 10−9)/(660 × n), where NA is the Avogadro constant (6.02 × 1023 mol−1), A is the molecular weight of DNA molecules (ng/mol) and n is the length of amplicon (bp).
Control procedures for sample contamination
Potential airborne bacteria were passively sampled to determine if there was a detectable contribution of environmental bacteria contaminating the foetal samples. Sampling tubes containing aerobic or anaerobic medium were opened and exposed to the dissection area in the abattoir for the duration of sampling from each foetus. The exposed media were incubated at 37 °C and samples taken at 72 h and at 2, 3 and 4 weeks. DNA was extracted using an Isolate II Genomic DNA kit. Final DNA concentrations and purity were estimated using a P330-Class NanoPhotometer.
The dissection table and external surfaces of the amniotic sac and intestinal compartments were swabbed to determine if there was a detectable contribution of bacteria contaminating the foetal samples. For each foetus, 9 swabs were taken at six locations using sterile Fisherbrand synthetic-tipped applicator swabs (Thermo Fisher Scientific, MA, USA). The surface of the dissection table (first location) was washed with 70% ethanol and then swabbed prior to dissecting each foetus. The external surface of the amniotic sac (second) was rinsed three times with sterilised PBS to remove excess blood and then swabbed prior to opening the sac. The skin of the foetal abdomen (third) was rinsed three times with sterilised PBS to remove amniotic fluid and then swabbed prior to opening. The external (mesenteric) surfaces of the rumen (fourth), caecum (fifth) and rectum (sixth location) were separately swabbed prior to opening. The 9 swabs for each foetus were tested for the presence of microorganisms, using three swabs for each of the three methods: qPCR, anaerobic culture and aerobic culture. DNA was extracted from three of the swabs using an Isolate II Genomic DNA kit. Final DNA concentrations and purity were estimated using a P330-Class NanoPhotometer. Quantitative PCR was used to more accurately quantify the presence of DNA.
Anaerobic and aerobic liquid mediums were inoculated from three swabs each. Three swabs were placed into a 2.5-L anaerobic Oxoid AnaeroJar with an AnaeroGen sachet (Thermo Fisher Scientific) and three swabs were placed into aerobic Oxoid Nutrient Broth (Thermo Fisher Scientific). All mediums were incubated at 37 °C for 48–72 h. Monitoring for growth during storage at 4 °C was continued for up to one month. The anaerobic liquid medium had the following composition (per litre of distilled water): 10 g peptone (Oxoid), 10 g yeast extract, 8.8 g Oxoid Lab-Lemco beef extract powder, 10 g proteose peptone (Oxoid), 12 g dextrose, 10 g KH2PO4, 12 g NaCl, 20 g soluble starch, 1.2 g L-cysteine hydrochloride and 0.3 g sodium thioglycollate with a pH (at 25 °C) of 7.3 ± 0.1. The aerobic nutrient broth had the following composition (per litre of distilled water): 10 g Lab-Lemco powder, 10 g peptone and 5 g NaCl, with a pH (at 25 °C) of 7.5 ± 0.2.
Controls for kit contamination
Two blank DNA spin columns from two different Isolate II Genomic DNA kits were used as no-template controls to determine if the kits were a source of contamination during DNA extraction and library preparation of samples. The no-template controls were tested using qPCR and Illumina MiSeq next-generation sequencing.
Statistical analysis of the total community
The adequacy of the sampling effort to capture the microbial community richness was examined by generating species rarefaction curves and species accumulation plots using the ‘rarecurve’ and ‘specaccum’ functions in the R library vegan (v. 2.3-4)48 using R 3.2.049. Alpha- and beta-diversity analyses were performed on archaeal and bacterial OTU-matrices rarefied to depths of 2000 and 1000 reads, respectively, using the ‘phyloseq’ (v. 1.16-2)50 and ‘vegan’ (v. 2.4-0)48,51 packages in the R programming language (v. 3.3.1)49. Normality and variance homogeneity of the data were tested using the ‘shapiro.test’ and ‘bartlett.test’ functions. As normality and homogeneity of variance assumptions were not met, Kruskal–Wallis tests were carried out using the ‘kruskal.test’ function with Dunn’s test of multiple comparisons used for post hoc testing. The NMDS ordinations were used to visualise differences between communities from different sample types (GIT components and amniotic fluid). Dissimilarity matrices were generated using the weighted and unweighted UniFrac metrics52. Analysis of similarity (ANOSIM) procedures were implemented to test for significant differences in the mean group centroids53. Differential abundance testing of ESVs was performed using the DESeq2 extension available within the ‘phyloseq’ package50,54. Tests were performed by applying model-fitting normalisation to unrarefied ESV tables as recommended by McMurdie and Holmes for each taxonomic rank50. For differential abundance tests only ESVs with high ( > 90%) confidence values for phylum-level taxonomic assignments were considered. All low confidence taxonomic assignment we re-classified as ‘unknown’. Differences were considered significant if Benjamin-Hochberg adjusted p  More

1.
Hansson, L. A. & Akesson, S. Animal Movement Across Scales (Oxford University Press, Oxford, 2014).
Google Scholar 
2.
Dingle, H. & Drake, V. A. What is migration?. Bioscience 57, 113–121 (2007).
Article  Google Scholar 

3.
Chapman, B. B., Brönmark, C., Nilsson, J. Å. & Hansson, L. A. The ecology and evolution of partial migration. Oikos 120, 1764–1775 (2011).
Article  Google Scholar 

4.
Newton, I. The Migration Ecology of Birds (Elservier, New York, 2010).
Google Scholar 

5.
Cadahía, L. et al. Advancement of spring arrival in a long-term study of a passerine bird: Sex, age and environmental effects. Oecologia 184, 917–929 (2017).
ADS  PubMed  Article  PubMed Central  Google Scholar 

6.
Ogonowski, M. S. & Conway, C. J. Migratory decisions in birds: Extent of genetic versus environmental control. Oecologia 161, 199–207 (2009).
ADS  PubMed  Article  PubMed Central  Google Scholar 

7.
Berthold, P., Helbig, A. J., Mohr, G. & Querner, U. Rapid microevolution of migratory behaviour in a wild bird species. Nature 360, 668–670 (1992).
ADS  Article  Google Scholar 

8.
Studds, C. E., Kyser, T. K. & Marra, P. P. Natal dispersal driven by environmental conditions interacting across the annual cycle of a migratory songbird. Proc. Natl. Acad. Sci. 105, 2929–2933 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Dale, C. A. & Leonard, M. L. Reproductive consequences of migration decisions by Ipswich Sparrows (Passerculus sandwichensis princeps). Can. J. Zool. 89, 100–108 (2011).
Article  Google Scholar 

10.
Gilroy, J. J., Gill, J. A., Butchart, S. H. M., Jones, V. R. & Franco, A. M. A. Migratory diversity predicts population declines in birds. Ecol. Lett. 19, 308–317 (2016).
PubMed  Article  Google Scholar 

11.
Teitelbaum, C. S. et al. Experience drives innovation of new migration patterns of whooping cranes in response to global change. Nat. Commun. 7, 12793. https://doi.org/10.1038/ncomms12793 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
Rubolini, D., Møller, A. P., Rainio, K. & Lehikoinen, E. Intraspecific consistency and geographic variability in temporal trends of spring migration phenology among european bird species. Clim. Res. 35, 135–146 (2007).
Article  Google Scholar 

13.
Greig, E. I., Wood, E. M. & Bonter, D. N. Winter range expansion of a hummingbird is associated with urbanization and supplementary feeding. Proc. R. Soc. B Biol. Sci. 248, 20170256. https://doi.org/10.1098/rspb.2017.0256 (2017).
Article  Google Scholar 

14.
Gill, J. A. et al. Why is timing of bird migration advancing when individuals are not?. Proc. R. Soc. B Biol. Sci. 281, 20132161. https://doi.org/10.1098/rspb.2013.2161 (2013).
Article  Google Scholar 

15.
Oro, D., Genovart, M., Tavecchia, G., Fowler, M. S. & Martínez-Abraín, A. Ecological and evolutionary implications of food subsidies from humans. Ecol. Lett. 16, 1501–1514 (2013).
PubMed  Article  Google Scholar 

16.
Gilbert, N. I. et al. Are white storks addicted to junk food? Impacts of landfill use on the movement and behaviour of resident white storks (Ciconia ciconia) from a partially migratory population. Mov. Ecol. 4, 7. https://doi.org/10.1186/s40462-016-0070-0 (2016).
Article  PubMed  PubMed Central  Google Scholar 

17.
Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science 344, 6179. https://doi.org/10.1126/science.1242552 (2014).
CAS  Article  Google Scholar 

18.
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

19.
Wilmers, C. C. et al. The golden age of bio-logging: How animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753 (2015).
PubMed  Article  Google Scholar 

20.
Weimerskirch, H., Delord, K., Guitteaud, A., Phillips, R. A. & Pinet, P. Extreme variation in migration strategies between and within wandering albatross populations during their sabbatical year, and their fitness consequences. Sci. Rep. 5, 1–7. https://doi.org/10.1038/srep08853 (2015).
CAS  Article  Google Scholar 

21.
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, 6240. https://doi.org/10.1126/science.aaa2478 (2015).
CAS  Article  Google Scholar 

22.
Signer, J., Fieberg, J. & Avgar, T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9, 880–890 (2019).
PubMed  PubMed Central  Article  Google Scholar 

23.
Edelhoff, H., Signer, J. & Balkenhol, N. Path segmentation for beginners: An overview of current methods for detecting changes in animal movement patterns. Mov. Ecol. 4, 21. https://doi.org/10.1186/s40462-016-0086-5 (2016).
Article  PubMed  PubMed Central  Google Scholar 

24.
Marzluff, J. M., Millspaugh, J. J., Hurvitz, P. & Handcock, M. S. Relating resources to a probabilistic measure of space use: Forest fragments and Steller’s Jays. Ecology 85, 1411–1427 (2004).
Article  Google Scholar 

25.
Börger, L., Dalziel, B. D. & Fryxell, J. M. Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecol. Lett. 11, 637–650 (2008).
PubMed  Article  Google Scholar 

26.
López-López, P., García-Ripollés, C. & Urios, V. Food predictability determines space use of endangered vultures: Implications for management of supplementary feeding. Ecol. Appl. 24, 938–949 (2014).
PubMed  Article  Google Scholar 

27.
van Beest, F. M., Mysterud, A., Loe, L. E. & Milner, J. M. Forage quantity, quality and depletion as scaledependent mechanisms driving habitat selection of a large browsing herbivore. J. Anim. Ecol. 79, 910–922 (2010).
PubMed  Google Scholar 

28.
Edwards, M. A., Nagy, J. A. & Derocher, A. E. Low site fidelity and home range drift in a wide-ranging, large Arctic omnivore. Anim. Behav. 77, 23–28 (2009).
Article  Google Scholar 

29.
Cagnacci, F. et al. Partial migration in roe deer: Migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos 120, 1790–1802 (2011).
Article  Google Scholar 

30.
Monsarrat, S. et al. How predictability of feeding patches affects home range and foraging habitat selection in AVIAN social scavengers?. PLoS One 8, e53077. https://doi.org/10.1371/journal.pone.0053077 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

31.
van Overveld, T. et al. Food predictability and social status drive individual resource specializations in a territorial vulture. Sci. Rep. 8, 1–13. https://doi.org/10.1038/s41598-018-33564-y (2018).
CAS  Article  Google Scholar 

32.
López-López, P., Benavent-Corai, J., García-Ripollés, C. & Urios, V. Scavengers on the move: Behavioural changes in foraging search patterns during the annual cycle. PLoS One 8, e54352. https://doi.org/10.1371/journal.pone.0054352 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Devault, T. L., Reinhart, B. D., Brisbin, I. L. & Rhodes, O. E. Flight behavior of Black and Turkey vultures: Implications for reducing bird–aircraft collisions. J. Wildl. Man. 69, 610–608 (2005).
Article  Google Scholar 

34.
Alarcón, P. A. E. & Lambertucci, S. A. A three-decade review of telemetry studies on vultures and condors. Mov. Ecol. 6, 13. https://doi.org/10.1186/s40462-018-0133-5 (2018).
Article  PubMed  PubMed Central  Google Scholar 

35.
BirdLife International. IUCN Red List for birds. https://www.birdlife.org (2016).

36.
Del Moral, J. C. El alimoche común en España. Población reproductora en 2008 y método de censo.(2009).

37.
BirdLife International. European Red List of Birds. Office for Official Publications of the European Countries (2015).

38.
Phipps, W. L. et al. Spatial and temporal variability in migration of a soaring raptor across three continents. Front. Ecol. Evol. 7, 323. https://doi.org/10.3389/fevo.2019.00323 (2019).
Article  Google Scholar 

39.
Oppel, S. et al. High juvenile mortality during migration in a declining population of a long-distance migratory raptor. Ibis 157, 545–557 (2015).
Article  Google Scholar 

40.
García-Ripollés, C., López-López, P. & Urios, V. First description of migration and wintering of adult egyptian vultures neophron percnopterus tracked by GPS satellite telemetry. Bird Study 57, 261–265 (2010).
Article  Google Scholar 

41.
SEO/BirdLife. Atlas de las aves en invierno en España 2007–2010. Atlas de las aves en invierno en España 2007–2010 (2012).

42.
Di Vittorio, M. et al. Wintering of Egyptian vultures (Neophron percnopterus) in Sicily: New data. Arx. Misc. Zool. 1, 114–116 (2016).
Article  Google Scholar 

43.
Buechley, E. R. & Şekercioğlu, Ç. H. The avian scavenger crisis: Looming extinctions, trophic cascades, and loss of critical ecosystem functions. Biol. Conserv. 198, 220–228 (2016).
Article  Google Scholar 

44.
Sanz-Aguilar, A., De Pablo, F. & Donázar, J. A. Age-dependent survival of island vs. mainland populations of two avian scavengers: Delving into migration costs. Oecologia 179, 405–414 (2015).
ADS  PubMed  Article  Google Scholar 

45.
Mateo-Tomás, P. & Olea, P. P. Diagnosing the causes of territory abandonment by the Endangered Egyptian vulture Neophron percnopterus: The importance of traditional pastoralism and regional conservation. Oryx. 44, 424–433 (2010).
Article  Google Scholar 

46.
Donázar, J. A. Los Buitres Ibéricos: Biología y Conservación. (Reyero, 1993).

47.
Felicísimo Pérez, Á. M. Elaboración del atlas climático de Extremadura mediante un Sistema de Información Geográfica. GeoFocus 1, 17–23 (2001).
Google Scholar 

48.
López-López, P., Maiorano, L., Falcucci, A., Barba, E. & Boitani, L. Hotspots of species richness, threat and endemism for terrestrial vertebrates in SW Europe. Acta Oecol. 37, 399–412 (2011).
Article  Google Scholar 

49.
Traba, J., García De La Morena, E. L., Morales, M. B. & Suárez, F. Determining high value areas for steppe birds in Spain: Hot spots, complementarity and the efficiency of protected areas. Biodivers. Conserv. 16, 3255–3275 (2007).
Article  Google Scholar 

50.
Arrondo, E. et al. Invisible barriers: Differential sanitary regulations constrain vulture movements across country borders. Biol. Conserv. 219, 46–52 (2018).
Article  Google Scholar 

51.
Sergio, F. et al. No effect of satellite tagging on survival, recruitment, longevity, productivity and social dominance of a raptor, and the provisioning and condition of its offspring. J. App. Ecol. 52, 1665–1675 (2015).
Article  Google Scholar 

52.
Finlayson, C. Birds of the Strait of Gibraltar (T. & A. D Poyser, London, 1992).
Google Scholar 

53.
Panuccio, M., Martín, B., Morganti, M., Onrubia, A. & Ferrer, M. Long-term changes in autumn migration dates at the Strait of Gibraltar reflect population trends of soaring birds. Ibis 159, 55–65 (2017).
Article  Google Scholar 

54.
Onrubia, A. Spatial and Temporal Patterns of Soaring Birds Migration Through the Strait of Gibraltar (University of León, Spain, 2015).
Google Scholar 

55.
Zuberogoitia, I., Zabala, J., Martínez, J. A., Martínez, J. E. & Azkona, A. Effect of human activities on Egyptian vulture breeding success. Anim. Conserv. 11, 313–320 (2008).
Article  Google Scholar 

56.
Signer, J. & Balkenhol, N. Reproducible home ranges (rhr): A new, user-friendly R package for analyses of wildlife telemetry data. Wildl. Soc. B. 39, 358–363 (2015).
Article  Google Scholar 

57.
QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://qgis.osgeo.org. Qgisorg (2014).

58.
Hooten, M. B., Hanks, E. M., Johnson, D. S. & Alldredge, M. W. Reconciling resource utilization and resource selection functions. J. Anim. Ecol. 86, 1146–1154 (2013).
Article  Google Scholar 

59.
Boyce, M. S. Scale for resource selection functions. Divers. Distrib. 12, 269–276 (2006).
Article  Google Scholar 

60.
R Development Core Team. R: A Language and Environment for Statistical Computing. (2018).

61.
Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour?. J. Anim. Ecol. 75, 1182–1189 (2006).
PubMed  Article  Google Scholar 

62.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48 (2015).
Article  Google Scholar 

63.
Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 73–79 (2016).
Article  Google Scholar 

64.
Fox, J. et al. car: Companion to Applied Regression. In: R Package Version 2.0-21 (2018).

65.
Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Emmeans: Estimated Marginal Means, aka Least-Squares Means. R Package Version 1.15-15.https://doi.org/10.1080/00031305.1980.10483031 (2019).

66.
Donovan, T. M. et al. Quantifying home range habitat requirements for bobcats (Lynx rufus) in Vermont, USA. Biol. Conserv. 144, 2799–2809 (2011).
Article  Google Scholar 

67.
Eggeman, S. L., Hebblewhite, M., Bohm, H., Whittington, J. & Merrill, E. H. Behavioural flexibility in migratory behaviour in a long-lived large herbivore. J. Anim. Ecol. 85, 785–797 (2016).
PubMed  Article  Google Scholar 

68.
Blanco, G. & Tella, J. L. Temporal, spatial and social segregation of red-billed choughs between two types of communal roost: A role for mating and territory acquisition. Anim. Behav. 59, 1219–1227 (1999).
Article  Google Scholar 

69.
Lambertucci, S. A. & Ruggiero, A. Cliffs used as communal roosts by andean condors protect the birds from weather and predators. PLoS One 8, e67304. https://doi.org/10.1371/journal.pone.0067304 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

70.
Bijleveld, A. I., Egas, M., van Gils, J. A. & Piersma, T. Beyond the information centre hypothesis: Communal roosting for information on food, predators, travel companions and mates?. Oikos 119, 277–285 (2010).
Article  Google Scholar 

71.
Powell, R. A. & Mitchell, M. S. What is a home range?. J. Mammal. 93, 248–258 (2012).
Google Scholar 

72.
Sanz-Aguilar, A., Jovani, R., Melián, C. J., Pradel, R. & Tella, J. L. Multi-event capture–recapture analysis reveals individual foraging specialization in a generalist species. Ecology 96, 1650–1660 (2015).
Article  Google Scholar 

73.
Margalida, A., Donázar, J. A., Carrete, M. & Sánchez-Zapata, J. A. Sanitary versus environmental policies: Fitting together two pieces of the puzzle of European vulture conservation. J. Appl. Ecol. 47, 931–935 (2010).
Article  Google Scholar 

74.
Negro, J. J. et al. An unusual source of essential carotenoids. Nature 416, 807–808 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Rey Benayas, J. M. & De La Montaña, E. Identifying areas of high-value vertebrate diversity for strengthening conservation. Biol. Conserv. 114, 357–370 (2003).
Article  Google Scholar 

76.
Botha, A. J. et al.Multi-species action plan to conserve African-Eurasian vultures (vulture MsAP). Raptors MOU Technical Publication (2017).

77.
Santangeli, A., Girardello, M., Buechley, E. R., Eklund, J. & Phipps, W. L. Navigating spaces for implementing raptor research and conservation under varying levels of violence and governance in the Global South. Biol. Conserv. 239, 108212. https://doi.org/10.1016/j.biocon.2019.108212 (2019).
Article  Google Scholar 

78.
Sanz-Aguilar, A. et al. Action on multiple fronts, illegal poisoning and wind farm planning, is required to reverse the decline of the Egyptian vulture in southern Spain. Biol. Conserv. 187, 10–18 (2015).
Article  Google Scholar 

79.
Blanco, G., Cortés-Avizanda, A., Frías, Ó., Arrondo, E. & Donázar, J. A. Livestock farming practices modulate vulture diet-disease interactions. Global Ecol. Conserv. 17, e00518. https://doi.org/10.1016/j.gecco.2018.e00518 (2019).
Article  Google Scholar 

80.
Duriez, O. et al. Vultures attacking livestock: A problem of vulture behavioural change or farmers’ perception?. Bird Conserv. Int. 29, 437–453 (2019).
Article  Google Scholar  More

Within-generation HGM
After matings of compatible hyphal tips grown from spores, haploid dikaryotic nuclei (n + n) of A. gallica fuse to produce diploid monokaryons (2n). As monokaryons are persistent in vegetative stages and often possess two distinct molecular-marker alleles, the model of vegetative heterozygous diploidy is widely accepted. But since other studies show vegetative stages can possess recombinant, haploid nuclei, an alternative hypothesis has been advanced. This hypothesis proposes a life cycle in which a vegetative-stage haploidization produces HGM6,7,17,18. Our analyses confirm that vegetative-stage hyphae can be haploid (Fig. 1, Supplementary Table S1), while still possessing two different molecular-marker alleles (Supplementary Table S2).
Although RFLP data are consistent with both heterozygous diploid and haploid genetic mosaic models, DNA content data and EF1α sequence data both argue against the heterozygous diploid model. Since EF1α is a single-copy gene, multiple cloned sequences isolated from a single hyphal filament should have only 1 haplotype if the filament is a diploid homozygote or 2 haplotypes if it is a diploid heterozygote; but it could have 1, 2, 3 or more haplotypes if it is a haploid genetic mosaic. The upper limit on the number of haplotypes detected in a hyphal filament is set by the number of hyphal compartments recovered during cell-line isolation. We estimate that, on average, six contiguous compartments were harvested each time we isolated a hyphal filament line; and there were 26 instances in which 3 or more clones were successfully sequenced from within a single hyphal filament line. In these 26 lines, we detected 1 or 2 haplotypes 11 times and 3 or 4 haplotypes 15 times (Table 1, Supplementary Table S3a–c). The 11 instances in which 1 or 2 haplotypes were detected are compatible with either model; but the 15 instances in which 3 or 4 haplotypes were detected are compatible with only the haploid genetic mosaic model. In conjunction with the finding of haploidy in vegetative stages, this finding argues against the heterozygous diploid model and supports the haploid genetic mosaic model. We define a haploid genetic mosaic as a mycelium with haplotypes that vary within and among hyphae. As an example, Fig. 6 depicts two haploid genetic mosaic rhizomorph hyphal filament lines that were isolated from the Raynham genet.
Figure 6

Haploid Genetic Mosaicism is exemplified in two rhizomorph hyphal filament lines (09r27 and 09r50) isolated from the Raynham genet. The mycelium containing hyphae with these haplotypes exhibits both within-line and among-line nuclear heterogeneity.

Full size image

Haplotype designations hap 1, hap 3…hap 13 refer to EF1α haplotypes listed in rows 1, 3, 5, 6, 8, 12, and 13 of Table 1. Note that (1) haplotype 13 is the only haplotype shared by both filament lines; (2) the order of the nuclei in the filaments is not known, so it is arbitrarily shown as numerical; (3) the spacers are hypothetical, as usually a maximum of 6 nuclei were included in an isolate.
We are not the first to propose HGM in Armillaria. Ullrich and Anderson19 considered stable diploidy as the most likely explanation for prototrophy in mated auxotrophs of Armillaria mellea. However, they also presented an alternative hypothesis that they considered a less likely but possible explanation for their results: “Alternatively, it is possible that an unusual (unprecedented) type of heterokaryon is present, i.e., one that is vegetatively stable in a filamentous fungus with uninucleate cells and intact septa.” Our results appear to be an example of Ullrich and Anderson’s alternative model.
Because hyphal extension requires mitosis, contiguous compartments within growing hyphal tips should contain a series of identical nuclei. How then, in rhizomorphs capable of undergoing mitosis for decades, can within-hyphal filament HGM persist? Korhonen20 was the first to document nuclear migration through cytoplasmic bridges in Armillaria. We found cytoplasmic bridges to be common in monokaryotic rhizomorph hyphae collected in nature (Fig. 3) and hyphae grown in culture (Fig. 4). Because nuclei were frequently found in or near bridges, we propose nuclear exchange through bridges as a mechanism that maintains within-line and among-line HGM (Fig. 7).
Figure 7

In this model, Haploid Genetic Mosaicism is maintained by nuclear exchange across cytoplasmic bridges connecting rhizomorph hyphal filament tips.

Full size image

Growth
Gallic acid growth experiments revealed significant line effects, treatment effects, and line × treatment effects for all 4 sets of Raynham and Bridgewater cell-lines (ANOVA P  More

Psyllids or jumping plant-lice are a group of small, generally host-specific plant-sap sucking insects with around 4000 described species1. A few species are major pests on fruits or vegetables, mostly by transmitting plant pathogens. Others damage forest plantations or ornamental plants by removal of plant-sap, stunting new growth, inducing galls or secreting honeydew and wax, an ideal substrate for sooty mould which reduces photosynthesis2. Modern psyllids, defined by the enlarged and immobile metacoxae in adults allowing them to jump, display a wide range of morphological diversity regarding the head, antennae, legs, forewings, terminalia, etc. in adults and body shape, antennal structure and the type of setae or wax pores in immatures. Modern psyllids are documented in the fossil record since the Eocene (Lutetian)3 (Fig. 1). The stem-group of modern psyllids constitutes, according to Burckhardt & Poinar, 20194, the paraphyletic Liadopsyllidae Martynov, 19265 with 17 species and six genera (Liadopsylla Handlirsch, 19256, Gracilinervia Becker-Migdisova, 19857, Malmopsylla Becker-Migdisova, 19857, Mirala Burckhardt & Poinar, 20194, Neopsylloides Becker-Migdisova, 19857 and Pauropsylloides Becker-Migdisova, 19857) from early Jurassic to late Cretaceous4,8. Shcherbakov9 added three species from the Lower Cretaceous for one of which he erected the genus Stigmapsylla and for the other two the subgenus Liadopsylla (Basicella). He also transferred two previously described species from Liadopsylla to Cretapsylla Shcherbakov9. Further he resurrected the Malmopsyllidae Becker-Migdisova, 19857 splitting it into Malmopsyllinae (for Gracilinervia, Malmopsylla, Neopsylloides and Pauropsylloides) and Miralinae Shcherbakov9 (for Mirala). Apart from three species described from amber fossils, all Mesozoic psyllids are poorly preserved impression fossils of which usually only the forewing is preserved. The current classification of Mesozoic psyllids (Liadopsyllidae and Malmopsyllidae) is based almost exclusively upon forewing characters7,9, despite that several phylogenetically significant characters from other body parts have been described from amber inclusions4,8. Judging from the impression fossils, Liadopsyllidae and Malmopsyllidae appear morphologically quite homogeneous but this may be a result of the surprisingly scarce fossil record of psyllids compared to other insect groups. The discoveries of Cretaceous amber fossils radically alter this picture, e.g. the recently described Mirala burmanica Burckhardt & Poinar, 2019 from Myanmar amber4.
Figure 1

Relationships and stratigraphic distribution of Liadopsyllidae and its subunits within Sternorrhyncha according to Drohojowska & Szwedo10, Hakim et al.11 and Drohojowska et al.12, modified. Numbers denote described taxa of fossil Liadopsyllidae—1: Liadopsylla geinitzi Handlirsch, 1925—Lower Jurassic, Mecklenburg, Germany, 2: Liadopsylla obtusa Ansorge, 1996—Lower Jurassic, Mecklenburg-Vorpommern, Germany, 3: Liadopsylla asiatica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 4: Liadopsylla brevifurcata Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 5: Liadopsylla grandis Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 6. Liadopsylla karatavica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 7. Liadopsylla longiforceps Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 8. Liadopsylla tenuicornis Martynov, 1926—Upper Jurassic, Karatau, Kazakhstan, 9. Liadopsylla turkestanica Becker-Migdisova, 1949—Upper Jurassic, Karatau, Kazakhstan, 10. Gracilinervia mastimatoides Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 11. Malmopsylla karatavica Becker- Migdisova, 1985 – Upper Jurassic, Karatau, Kazakhstan, 12. Neopsylloides turutanovae Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 13. Pauropsylloides jurassica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 14. Liadopsylla mongolica Shcherbakov, 1988—Lower Cretaceous, Bon Tsagaan, Mongolia 15. Liadopsylla apedetica Ouvrard, Burckhardt et Azar, 2010—Lower Cretaceous, Lebanon, 16. Liadopsylla lautereri (Shcherbakov, 2020)—Lower Cretaceous, Buryatia, Russia 17. Liadopsylla loginovae (Shcherbakov, 2020)—Lower Cretaceous, Buryatia, Russia 18. Stigmapsylla klimaszewskii Shcherbakov, 2020—Lower Cretaceous, Buryatia, Russia 19. Mirala burmanica Burckhardt et Poinar, 2019—mid-Cretaceous, Kachin amber, 20. Amecephala pusilla gen. et sp. nov.—mid-Cretaceous, Kachin amber, 21. Liadopsylla hesperia Ouvrard et Burckhardt, 2010—Upper Cretaceous, Raritan amber, U.S.A.

Full size image

Here we describe a second taxon of Mesozoic psyllids from Kachin amber, Amecephala pusilla gen. et sp. nov., possessing a series of characters unique within Mesozoic psyllids, discuss the phylogenetic relationships within the group, and provide an updated key to genera as well a checklist of recognised species (Table 1).
To satisfy a requirement by Article 8.5.3 of the International Code of Zoological Nomenclature this publication has been registered in ZooBank with the LSID: urn:lsid:zoobank.org:act:D3AF7597-47BF-4D6C-9020-982F4C20315E.
Table 1 Annotated checklist of known species of Liadopsyllidae Martynov, 19265.
Full size table

Systematic palaeontology
Order Hemiptera Linnaeus, 175817
Suborder Sternorrhyncha Amyot et Audinet-Serville, 184318
Superfamily Psylloidea Latreille, 180719
Family Liadopsyllidae Martynov, 19265
Genus †Amecephala gen. nov
urn:lsid:zoobank.org:act:9DABC236-FFB9-4305-82EC-4E293212849B
Type species
† Amecephala pusilla sp. nov., by present designation and monotypy.
Etymology From ancient Greek ἡ άμε [ē áme] = shovel and ἡ κεφαλή [ē kefalé] = head for its shovel-shaped head. Gender: feminine.
Diagnosis
Vertex rectangular; coronal suture developed in apical half; median ocellus on ventral side of head, situated at the apex of frons which is large, triangular; genae not produced into processes; toruli oval, medium sized, situated in front of eyes below vertex. Eyes hemispheric, relatively small (Fig. 2a,b,e,g). Antenna with pedicel about as long as flagellar segments 1 and 8, longer than remainder of segments. Pronotum ribbon-shaped, relatively long, laterally of equal length as medially. Forewing (Fig. 2a,b,f,g) elongate, widest in the middle, narrowly rounded at apex; pterostigma short and broad, triangular, not delimited at base by a vein thus vein R1 not developed; veins R and M + Cu subequal in length; vein Rs relatively short, slightly curved towards fore margin; vein M shorter than its branches which are of subequal length; cell cu1 low and very long. Female terminalia short, cuneate.
Figure 2

(a‒i) Amecephala pusilla gen. et sp. nov. imago. Drawing of body in dorsal view (a), Body in dorsal view (b), Metatarsus (c), Drawing of hind leg (d), Head in dorsal view (e), Forewing (f), Body in ventral view (g), Basal part of claval suture (h), Distal part of claval suture (i); Scale bars: 0.5 mm (a,b); 0.2 mm (f,g); 0.1 mm (c,d,e,h,i).

Full size image

Description
Head weakly inclined from longitudinal body axis; about as wide as pronotum and mesoscutum, dorso-ventrally compressed. Vertex rectangular; anterior margin weakly curved, indented in the middle; posterior margin slightly concavely curved; coronal suture developed in apical half, basal half not visible; lateral ocelli near posterior angles of vertex, hardly raised; median ocellus on ventral side of head, situated at the apex of frons which is large, triangular; genae not produced into processes; preocular sclerites lacking; toruli oval, medium sized, situated in front of eyes below vertex; clypeus partly covered by gas bubble, appearing flattened, pear-shaped. Eyes hemispheric, relatively small (Fig. 2a,b,e,g). Antenna 10-segmented, filiform, moderately long, flagellum 1.6 times as long as head width; pedicel very long, about as long as flagellar segments 1 and 8; rhinaria not visible (Fig. 2a,b). Thorax (ventrally not visible) with pronotum wider than mesopraescutum as wide as mesoscutum, laterally of the same length as medially. Mesothorax large; mesopraescutum triangular, with arcuate anterior margin, almost twice wider than long in the middle; mesopraescutum slightly longer than pronotum in the middle; mesoscutum subtrapezoid with slightly arched anterior margin, about 3.0 times wider than long in the middle; delimitation between mesoscutum and mesoscutellum clearly visible. Metascutellum trapezoid, narrower than mesoscutellum with a submedian longitudinal low ridge on either side. Parapterum and tegula forming small oval structures of about the same size; the former slightly in front of the latter. Forewing (Fig. 2a,b,f) membranous, elongate, narrow at base, widest in the middle, narrowly rounded at apex which lies in cell m1 near the apex of vein M3+4; vein C + Sc narrow; cell c + sc long, widening toward apex; costal break not visible, perhaps absent; pterostigma short and broad, triangular, not delimited at base by a vein thus vein R1 not developed; vein R + M + Cu relatively short; veins R and M + Cu subequal in length; vein R2 relatively short and straight; vein Rs relatively short, slightly curved towards fore margin; vein M shorter than its branches which are of subequal length; vein Cu short, splitting into very long Cu1a and short Cu1b, hence cell cu1 low and very long; claval suture visible (Fig. 2h,i); anal break near to apex of vein Cu1b (Fig. 2f,i). Hindwing (Fig. 2a) shorter than forewing, more than twice as long as wide, membranous; venation indistinct. Legs similar in shape and size, long, slender (Fig. 2c,d,g); femora slightly enlarged distally, tibiae long and slightly enlarged distally; metatibia lacking genual spine and apical sclerotized spurs, but bearing several apical bristles and, in distal quarter, a row of short bristles (Fig. 2d); tarsi two-segmented, tubular of similar length though basal segment slightly thicker than apical one, claws large, one-segmented, pulvilli absent (Fig. 2c–d). Abdomen appearing flattened, tergites and sternites not clearly visible. Female terminalia short, slightly shorter than head width, cuneate (Fig. 2a,b,g).
Revised key to Mesozoic psylloid genera (after Burckhardt & Poinar4 , modified)
1.
Forewing lacking pterostigma………………………………………………………………………………………………………………Liadopsylla Handlirsch, 1921 (= Cretapsylla Shcherbakov, 2020 syn. nov.; = Basicella Shcherbakov, 2020 syn. nov.)
-Forewing bearing pterostigma………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….2

2.
Vein Rs in forewing straight, veins Rs and M subparallel; vein M not branched; vein R shorter than M + Cu; vein Cu1b almost straight, directed toward wing base………………………………………………….Mirala Burckhardt et Poinar, 2020
-Combination of characters different. Vein Rs in forewing concavely curved towards fore margin (not visible in Stigmapsylla), veins Rs and M from base to apex first converging then diverging; vein M branched; vein Cu1b straight or curved, directed toward hind margin or apex of wing………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………3

3.
Vein R of forewing distinctly shorter than M + Cu……………………………………………………………………………………………………………………………………………………………………………………………………………….Stigmapsylla Shcherbakov, 2020
-Vein R of forewing distinctly longer than M + Cu, or veins R and M + Cu subequal in length…………………………………………………………………………………………………………………………………………………………………………………………….4

4.
Vein R of forewing distinctly longer than M + Cu; vein Cu1a almost straight…………………………………………………………………………………………………………………………………………………………………..Malmopsylla Becker-Migdisova, 1985
-Veins R and M + Cu of forewing subequal in length; vein Cu1a distinctly curved……………………………………………………………………………………………………………………………………………………………………………………………………………..5

5.
Forewing with cell cu1 low and very long, around 6.0 times as long high……………………………………………………………………………………………………………………………………………………………………………………………..Amecephala gen. nov.
-Forewing with cell cu1 higher and shorter, less than 2.5 times as long high…………………………………………………………………………………………………………………………………………………………………………………………………………………….6

6.
Forewing with long pterostigma, vein R2 straight……………………………………………………………………………………………………………………………………………………………………………………………………..Neopsylloides Becker-Migdisova, 1985
-Forewing with short pterostigma, vein R2 curved……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….7

7.
Vein R + M + Cu of forewing ending at basal quarter of wing……………………………………………………………………………………………………………………………………………………………………………………..Gracilinervia Becker-Migdisova, 1985
-Vein R + M + Cu of forewing ending at basal third of wing…………………………………………………………………………………………………………………………………………………………………………………..Pauropsylloides Becker-Migdisova, 1985

†Amecephala pusilla sp. nov
urn:lsid:zoobank.org:act:6B20A4F4-57DB-4F06-A43C-5DE3653D76E3 (Fig. 2a–i)
Etymology
From Latin pusillus = tiny, very small—for its small body size.
Holotype
Female, specimen number MAIG 6686; deposited in the Museum of Amber Inclusion, University of Gdańsk, Gdańsk, Poland. Complete and well-preserved (Fig. 2b,g), probably slightly compressed dorso-ventrally; the wings appear slightly detached from thorax and have been probably forced away from the thorax by the compression. Several gas bubbles on the ventral body side obscure parts of the head, thorax, abdomen, legs and the right forewing (Fig. 2g). Syniclusions: Aleyrodidae (part; second part in broken piece).
Locality and stratum
Myanmar, Kachin State, Hukawng Valley, SW of Maingkhwan, former Noije Bum 2001 Summit Site amber mine (closed). Lowermost Cenomanian, Upper Cretaceous.
Species diagnosis
As for the genus.
Description
Female; male unknown. Body minute, 1.20 mm long including forewing when folded over body. Head (ventrally partly covered by gas bubble) 0.28 mm wide, 0.10 mm long; vertex width 0.20 mm wide, 0.09 mm long; microsculpture or setae not visible. Antenna (Fig. 2a,b) with globular scape and cylindrical pedicel, thinner and longer than scape; flagellum 0.40 mm long; 1.6 times as long as head width; flagellar segments slightly more slender than pedicel, relative lengths as 1.0:0.7:0.6:0.6:0.6:0.6:0.7:1.0; flagellar segment 8 bearing two subequal terminal setae shorter that the segment. Clypeus and rostrum not visible, covered by gas bubble. Forewing (Fig. 2a,b,f,g) 0.90 mm long, 0.30 mm wide, 3.0 times as long as wide; membrane transparent, colourless, veins pale; anterior margin curved basally, posterior margin almost straight; vein R + M + Cu ending in basal fifth of wing; vein R slightly shorter that M + Cu; bifurcation of vein R proximal to middle of wing; cell r1 relatively narrow; vein R2 distinctly shorter than Rs; vein Rs relatively short, strongly curved towards fore margin; vein M slightly longer than veins R and M + Cu; M branching proximal to Rs–Cu1a line; cell m1 value more than 2.6, cell cu1 value more than 6.0; surface spinules not visible. Hindwing (Fig. 2b,f) membranous, transparent and colourless. Female terminalia (Fig. 2a,b,g) with apically pointed proctiger; circumanal ring irregularly oval, about half as long as proctiger. More

Characteristics of oasis change in the Hexi Corridor
Oasis area variation at the river basin and county scales
The distribution of stable oasis in three river basins and seventeen administration regions is shown in Fig. 1a. The total oasis area in the Hexi Corridor has increased from 10,707.7 km2 in 1986 to 14,950.1 km2 in 2015 (Fig. 1b), with an increase factor of 1.4 from the start to the end years and an average annual increase of 140 km2. At the river basin scale, the HHRB has the largest oasis area with 47% of the total oasis area, followed by SYRB with 40%. The SLRB charactered by drier environments has the least oasis area of 13%. The oasis change types in the Hexi Corridor over the last 30 years are mainly “expansion”, which is supplemented by “retreating” (Fig. 1b). The oasis area variation of administration regions during the past thirty years is shown in Fig. 1c. It is observed that the variation tendency of the oasis area at administration regions scale was the same as that on the river basin scale. The oasis areas in Liangzhou District, Ganzhou District, Minqin County, Yongchang County, Suzhou District, and Shandan Country were more than 1000 km2 in most time. Conversely, the oasis area in Jiayuguan District and Sunan County was less than 200 km2.
Figure 1

Variation of oases area in three river basins of the Hexi Corridor during the past thirty years. (a) was generated using ArcGIS 10.3, www.esri.com.

Full size image

The stable oasis and maximum oasis distribution
The stable oasis was extracted from the area where the oasis exists in all seven periods, and the maximum oasis area was depicted from the area where the oasis existed once in the past thirty years. It can be seen that the stable oasis area is 9062 km2, while the maximum oasis area reaches 16,374 km2, which is almost two times larger than that of the stable oasis.
The stable oases distribute in alluvial and pluvial fans, the river plains in middle reaches, and the catchment area in the lower reaches (Fig. 2). The maximum oases extended from the stable oases, which mainly located at the edges of the alluvial–proluvial fans, low-lying areas next to rivers and ditches, and the oases-deserts ecotone.
Figure 2

The distribution of stable oases and maximum oases in the Hexi Corridor. The map was generated using ArcGIS 10.3, www.esri.com.

Full size image

The constraints of oasis development
Geomorphological characteristics of oasis distribution
The geomorphological conditions, formed in the geological history period, is critical for the process of oases development. To investigate the possible relationship between limiting factors and oasis distribution, the distribution frequency, which is the ratio of number in specific condition among all oasis raster number, was introduced and the scatter plots and normal distribution fitting curves were plotted. Figure 3a shows the altitude of the oasis is mainly between 1000 m that is near the lowest value in the study area to 2500 m. The elevation of stable (maximum) oasis peaks in 1500 (1450) m, and accounted for 3.5% (4.5%), which suggests that when oases expand, they tend to occupy the lower elevation. The oases are mainly located in the plains along rivers or irrigation canal systems where slopes flatter than 5° (Fig. 3b), most of them are located in the level ground with a slope flatter than 3°. The area of the stable and the maximum oases located in flat place (slope = 0) account 64% and 76%, respectively, which indicated that the oasis expansion mainly occurs on flat ground. The analysis of the oasis on eight slopes shows that the majority of the slope oasis is concentrated in the north slope and the northeast slope, accounting for about 60%, while the east slope and the northwest slope also have a part, accounting for 30% (Fig. 3c). The aspect of slope oasis expansion mainly takes place in sunny slope (Northwest, West, Southwest, south, southeast), which due to that almost all of the shady slope has been covered by oasis. On the contrary, there are many deserts in sunny slope, as long as the necessary moisture conditions will be occupied by the oasis. The different aspects result in varying amounts of solar radiation, which affects evapotranspiration and consequently water balance in the soil. More specifically shady aspects have more moisture for vegetation growth due to less evapotranspiration, on the other hand, sunny aspects experience potentially higher rates of evapotranspiration, supporting less moisture for vegetation growth26. The fitted normal distribution formula of DEM frequency for stable and maximum oases are given in Fig. 3a, the fits reached a significant level (p  total population  > AWD  > Primary industry  > GDP  > tertiary industry  > secondary industry (Table 1).
Table 1 The relative degree of incidence between urban expansion and driving factors based on panel data in the Hexi Corridor (1986–2015).
Full size table

The GRD of Population, especially for the rural laborer, is the highest. The increase of the nonagricultural population directly stimulated urban residential, commercial, industrial, transportation, and other related industry development. Consequently, urban land expanded in this area. The population growth was a major factor in oasis variation29. During the past 30 years, the population increased from 1.06 to 5.07 million (378% increase), while the oasis area increased from 10,707 to 14,950 km2 (39.6% increase) in the Hexi Corridor. The rise in population will unavoidably lead to an increase in arable land for survival.
Secondly, the GRD between oasis expansion and AWD is pervasively high with the value around 0.9. The water resource including the precipitation and runoff play an important role in the spatial expansion of oasis. The Hexi Corridor located in a typical arid region, where the most vital limiting factor for both vegetation growth and economic development is the limited water resource. Concerning the shortage of water resources, it is difficult to irrigate many newly reclaimed agricultural oases in the Hexi Corridor. That is to say, water resources cannot afford continuous growth. Thus, AWD of oases is significantly positively correlated with the area of oases.
Thirdly, the GRD between economic factors, including GDP, Primary industry, Secondary industry and Tertiary industry, is about 0.6, which is relatively low comparing to that of population, water resource. The GRD of primary industry is highest with a value of 0.7 among the economic factors. Separately, for the type of agriculture oases contains most of the administration regions in the study area, the GRD of the primary industry was considerably higher than that of secondary and tertiary industry, the agriculture was their first driving force. For the resource-based cities and towns, like Jinchuan District, Jiayuguan City, the GRD of secondary and tertiary industry is essentially equal to that of primary industry, the secondary industry and tertiary industry played a vital role in oasis development. More