Ecology

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

1.
Bouvier T, del Giorgio PA. Key role of selective viral-induced mortality in determining marine bacterial community composition. Environ Microbiol. 2007;9:287–97.
CAS  PubMed  Article  Google Scholar 
2.
Brown MR, Baptista JC, Lunn M, Swan DL, Smith SJ, Davenport RJ, et al. Coupled virus – bacteria interactions and ecosystem function in an engineered microbial system. Water Res. 2019;152:264–73.
CAS  PubMed  Article  Google Scholar 

3.
Shapiro OH, Kushmaro A, Brenner A. Bacteriophage predation regulates microbial abundance and diversity in a full-scale bioreactor treating industrial wastewater. ISME J. 2010;4:327–36.
PubMed  Article  Google Scholar 

4.
Buckling A, Rainey PB. Antagonistic coevolution between a bacterium and a bacteriophage. Proc R Soc B Biol Sci. 2002;269:931–6.
Article  Google Scholar 

5.
Stern A, Sorek R. The phage-host arms race: shaping the evolution of microbes. BioEssays. 2011;33:43–51.
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Rostøl JT, Marraffini L. (Ph)ighting phages: how bacteria resist their parasites. Cell Host Microbe. 2019;25:184–94.
PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Paez-Espino D, Sharon I, Morovic W, Stahl B, Thomas BC, Barrangou R, et al. CRISPR immunity drives rapid phage genome evolution in streptococcus thermophilus. MBio. 2015;6:1–9.
CAS  Article  Google Scholar 

8.
Common J, Morley D, Westra ER, Van Houte S. CRISPR-Cas immunity leads to a coevolutionary arms race between Streptococcus thermophilus and lytic phage. Philos Trans R Soc B Biol Sci. 2019;374:20180098.
CAS  Article  Google Scholar 

9.
Flores CO, Valverde S, Weitz JS. Multi-scale structure and geographic drivers of cross-infection within marine bacteria and phages. ISME J. 2013;7:520–32.
PubMed  Article  Google Scholar 

10.
Breitbart M, Rohwer F. Here a virus, there a virus, everywhere the same virus? Trends Microbiol. 2005;13:278–84.
CAS  PubMed  Article  Google Scholar 

11.
Heilmann S, Sneppen K, Krishna S. Coexistence of phage and bacteria on the boundary of self-organized refuges. Proc Natl Acad Sci USA. 2012;109:12828–33.
CAS  PubMed  Article  Google Scholar 

12.
Patterson AG, Jackson SA, Taylor C, Evans GB, Salmond GPC, Przybilski R, et al. Quorum sensing controls adaptive immunity through the regulation of multiple CRISPR-Cas systems. Mol Cell. 2016;64:1102–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Alseth EO, Pursey E, Luján AM, McLeod I, Rollie C, Westra ER. Bacterial biodiversity drives the evolution of CRISPR-based phage resistance. Nature. 2019;574:549–52.
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Sousa JAM, de, Rocha EPC. Environmental structure drives resistance to phages and antibiotics during phage therapy and to invading lysogens during colonisation. Sci Rep. 2019;9:1–13.
Article  CAS  Google Scholar 

15.
Scanlan PD. Bacteria–bacteriophage coevolution in the human gut: implications for microbial diversity and functionality. Trends Microbiol. 2017;25:614–23.
CAS  PubMed  Article  Google Scholar 

16.
Dang VT, Sullivan MB. Emerging methods to study bacteriophage infection at the single-cell level. Front Microbiol. 2014;5:724.
PubMed  PubMed Central  Article  Google Scholar 

17.
Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol Rev. 2016;40:258–72.
CAS  PubMed  Article  Google Scholar 

18.
Barrangou R, Fremaux C, Deveau H, Richards M, Boyaval P, Moineau S, et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science. 2007;315:1709–12.
CAS  PubMed  Article  Google Scholar 

19.
McGinn J, Marraffini LA. Molecular mechanisms of CRISPR–Cas spacer acquisition. Nat Rev Microbiol. 2019;17:7–12.
CAS  PubMed  Article  Google Scholar 

20.
Andersson AF, Banfield JF. Virus population dynamics and acquired virus resistance in natural microbial communities. Science. 2008;320:1047–50.
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Sun CL, Thomas BC, Barrangou R, Banfield JF. Metagenomic reconstructions of bacterial CRISPR loci constrain population histories. ISME J. 2016;10:858–70.
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Stern A, Mick E, Tirosh I, Sagy O, Sorek R. CRISPR targeting reveals a reservoir of common phages associated with the human gut microbiome. Genome Res. 2012;22:1985–94.
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Emerson JB, Andrade K, Thomas BC, Norman A, Allen EE, Heidelberg KB, et al. Virus-host and CRISPR dynamics in archaea-dominated hypersaline Lake Tyrrell, Victoria, Australia. Archaea. 2013;2013:370871.
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Laanto E, Hoikkala V, Ravantti J, Sundberg LR. Long-term genomic coevolution of host-parasite interaction in the natural environment. Nat Commun. 2017;8:111.
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Held NL, Herrera A, Quiroz HC, Whitaker RJ. CRISPR associated diversity within a population of Sulfolobus islandicus. PLoS ONE. 2010;5:e12988.
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Tomida J, Morita Y, Shibayama K, Kikuchi K, Sawa T, Akaike T, et al. Diversity and microevolution of CRISPR loci in Helicobacter cinaedi. PLoS ONE. 2017;12:e0186241.
PubMed  PubMed Central  Article  CAS  Google Scholar 

27.
Pauly MD, Bautista MA, Black JA, Whitaker RJ. Diversified local CRISPR-Cas immunity to viruses of Sulfolobus islandicus. Philos Trans R Soc B Biol Sci. 2019;374:20180093.
CAS  Article  Google Scholar 

28.
Richter C, Dy RL, McKenzie RE, Watson BNJ, Taylor C, Chang JT, et al. Priming in the Type I-F CRISPR-Cas system triggers strand-independent spacer acquisition, bi-directionally from the primed protospacer. Nucleic Acids Res. 2014;42:8516–26.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Watson BNJ, Vercoe RB, Salmond GPC, Westra ER, Staals RHJ, Fineran PC. Type I-F CRISPR-Cas resistance against virulent phages results in abortive infection and provides population-level immunity. Nat Commun. 2019;10:1–8.
CAS  Article  Google Scholar 

30.
Childs LM, England WE, Young MJ, Weitz JS, Whitaker RJ. CRISPR-induced distributed immunity in microbial populations. PLoS ONE. 2014;9:e101710.
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Common J, Walker-Sünderhauf D, van Houte S, Westra ER. Diversity in CRISPR-based immunity protects susceptible genotypes by restricting phage spread and evolution. J Evol Biol. 2020;33:1097–108.
CAS  Article  Google Scholar 

32.
Payne P, Geyrhofer L, Barton NH, Bollback JP. CRISPR-based herd immunity can limit phage epidemics in bacterial populations. Elife. 2018;7:e32035.
PubMed  PubMed Central  Article  Google Scholar 

33.
Van Houte S, Ekroth AKE, Broniewski JM, Chabas H, Ashby B, Bondy-Denomy J, et al. The diversity-generating benefits of a prokaryotic adaptive immune system. Nature. 2016;532:385–8.
PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Chabas H, Lion S, Nicot A, Meaden S, van Houte S, Moineau S, et al. Evolutionary emergence of infectious diseases in heterogeneous host populations. PLoS Biol. 2018;16:e2006738.
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Morley D, Broniewski JM, Westra ER, Buckling A, van Houte S. Host diversity limits the evolution of parasite local adaptation. Mol Ecol. 2017;26:1756–63.
PubMed  Article  PubMed Central  Google Scholar 

36.
Westra ER, Van Houte S, Gandon S, Whitaker R. The ecology and evolution of microbial CRISPR-Cas adaptive immune systems. Philos Trans R Soc B Biol Sci. 2019;374:20190101.
CAS  Article  Google Scholar 

37.
Fernández L, Rodríguez A, García P. Phage or foe: an insight into the impact of viral predation on microbial communities. ISME J. 2018;12:1171–9.
PubMed  PubMed Central  Article  CAS  Google Scholar 

38.
Pérez MV, Guerrero LD, Orellana E, Figuerola EL, Erijman L. Time Series Genome-Centric Analysis Unveils Bacterial Response to Operational Disturbance in Activated Sludge. mSystems. 2019;4:e00169–19.
PubMed  PubMed Central  Article  Google Scholar 

39.
Goodfellow M, Kumar Y, Maldonado LA. Bergey’s manual of systematics of archaea and bacteria. Hoboken, New Jersey: John Wiley & Sons, Inc; 2015.
Google Scholar 

40.
Drzyzga O. The strengths and weaknesses of Gordonia: a review of an emerging genus with increasing biotechnological potential. Crit Rev Microbiol. 2012;38:300–16.
CAS  PubMed  Article  Google Scholar 

41.
Arenskötter M, Bröker D, Steinbüchel A. Biology of the metabolically diverse genus Gordonia. Appl Environ Microbiol. 2004;70:3195–204.
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
de los Reyes MF, de los Reyes FL, Hernandez M, Raskin L. Quantification of Gordona amarae strains in foaming activated sludge and anaerobic digester systems with oligonucleotide hybridization probes. Appl Environ Microbiol. 1998;64:2503–12.
Article  Google Scholar 

43.
Kragelund C, Remesova Z, Nielsen JL, Thomsen TR, Eales K, Seviour R, et al. Ecophysiology of mycolic acid-containing Actinobacteria (Mycolata) in activated sludge foams. FEMS Microbiol Ecol. 2007;61:174–84.
CAS  PubMed  Article  Google Scholar 

44.
Russell DA, Hatfull GF. PhagesDB: The actinobacteriophage database. Bioinformatics. 2017;33:784–6.
CAS  PubMed  Article  Google Scholar 

45.
Pope WH, Mavrich TN, Garlena RA, Guerrero-Bustamante CA, Jacobs-Sera D, Montgomery MT, et al. Bacteriophages of Gordonia spp. Display a spectrum of diversity and genetic relationships. mBio. 2017;8:e01069–17.
PubMed  PubMed Central  Article  Google Scholar 

46.
Montgomery MT, Guerrero Bustamante CA, Dedrick RM, Jacobs-Sera D, Hatfull GF. Yet more evidence of collusion: a new viral defense system encoded by Gordonia phage CarolAnn. mBio. 2019;10:e02417–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Rodriguez-R LM, Konstantinidis KT. Bypassing cultivation to identify bacterial species, culture-independent genomic approaches identify credibly distinct clusters, avoid cultivation bias, and provide true insights into microbial species. Microbe. 2014;9:111–7.
Google Scholar 

48.
Quince C, Delmont TO, Raguideau S, Alneberg J, Darling AE, Collins G, et al. DESMAN: a new tool for de novo extraction of strains from metagenomes. Genome Biol. 2017;18:181.
PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Néron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018;46:W246–51.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Biswas A, Staals RHJ, Morales SE, Fineran PC, Brown CM. CRISPRDetect: a flexible algorithm to define CRISPR arrays. BMC Genomics. 2016;17:356.
PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Pope WH, Jacobs-Sera D. Annotation of bacteriophage genome sequences using DNA master: an overview. Methods Mol Biol. 2018;1681:217–29.
CAS  PubMed  Article  Google Scholar 

56.
Crooks GE, Hon G, Chandonia JM, Brenner SE. WebLogo: a sequence logo generator. Genome Res. 2004;14:1188–90.
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Skennerton CT, Imelfort M, Tyson GW. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 2013;41:e105.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Rho M, Wu YW, Tang H, Doak TG, Ye Y. Diverse CRISPRs evolving in human microbiomes. PLoS Genet. 2012;8:e1002441.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27:2987–93.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Keck F, Rimet F, Bouchez A, Franc A. Phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol Evol. 2016;6:2774–80.
PubMed  PubMed Central  Article  Google Scholar 

61.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Lei J, Sun Y. Assemble CRISPRs from metagenomic sequencing data. Bioinformatics. 2016;32:i520–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Lam TJ, Ye Y. Long reads reveal the diversification and dynamics of CRISPR reservoir in microbiomes. BMC Genomics. 2019;20:567.
PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Leenay RT, Maksimchuk KR, Slotkowski RA, Agrawal RN, Gomaa AA, Briner AE, et al. Identifying and visualizing functional PAM diversity across CRISPR-Cas systems. Mol Cell. 2016;62:137–47.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Jackson SA, McKenzie RE, Fagerlund RD, Kieper SN, Fineran PC, Brouns SJJ. CRISPR-Cas: adapting to change. Science. 2017;356:eaal5056.
PubMed  Article  CAS  PubMed Central  Google Scholar 

67.
Levy A, Goren MG, Yosef I, Auster O, Manor M, Amitai G, et al. CRISPR adaptation biases explain preference for acquisition of foreign DNA. Nature. 2015;520:505–10.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Ellington MJ, Heinz E, Wailan AM, Dorman MJ, de Goffau M, Cain AK, et al. Contrasting patterns of longitudinal population dynamics and antimicrobial resistance mechanisms in two priority bacterial pathogens over 7 years in a single center. Genome Biol. 2019;20:184.
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Jackson SA, Birkholz N, Malone LM, Fineran PC. Imprecise spacer acquisition generates CRISPR-Cas immune diversity through primed adaptation. Cell Host Microbe. 2019;25:250–60.
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Deveau H, Barrangou R, Garneau JE, Labonté J, Fremaux C, Boyaval P, et al. Phage response to CRISPR-encoded resistance in Streptococcus thermophilus. J Bacteriol. 2008;190:1390–400.
CAS  PubMed  Article  Google Scholar 

71.
Semenova E, Jore MM, Datsenko KA, Semenova A, Westra ER, Wanner B, et al. Interference by clustered regularly interspaced short palindromic repeat (CRISPR) RNA is governed by a seed sequence. Proc Natl Acad Sci USA. 2011;108:10098–103.
CAS  PubMed  Article  Google Scholar 

72.
Iranzo J, Lobkovsky AE, Wolf YI, Koonin EV. Evolutionary dynamics of the prokaryotic adaptive immunity system CRISPR-Cas in an explicit ecological context. J Bacteriol. 2013;195:3834–44.
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Lenski RE. Coevolution of bacteria and phage: Are there endless cycles of bacterial defenses and phage counterdefenses? J Theor Biol. 1984;108:319–25.
CAS  PubMed  Article  Google Scholar 

74.
Bull JJ, Christensen KA, Scott C, Jack BR, Crandall CJ, Krone SM. Phage-bacterial dynamics with spatial structure: Self organization around phage sinks can promote increased cell densities. Antibiotics. 2018;7:8.
PubMed Central  Article  CAS  PubMed  Google Scholar 

75.
Hall AR, Scanlan PD, Morgan AD, Buckling A. Host-parasite coevolutionary arms races give way to fluctuating selection. Ecol Lett. 2011;14:635–42.
PubMed  Article  PubMed Central  Google Scholar 

76.
Best A, Ashby B, White A, Bowers R, Buckling A, Koskella B, et al. Host-parasite fluctuating selection in the absence of specificity. Proc R Soc B Biol Sci. 2017;284:20171615.
Article  Google Scholar 

77.
Gómez P, Buckling A. Bacteria-phage antagonistic coevolution in soil. Science. 2011;332:106–9.
PubMed  Article  CAS  PubMed Central  Google Scholar 

78.
Ignacio-Espinoza JC, Ahlgren NA, Fuhrman JA. Long-term stability and Red Queen-like strain dynamics in marine viruses. Nat Microbiol. 2020;5:265–71.
CAS  PubMed  Article  PubMed Central  Google Scholar 

79.
Lopez Pascua L, Hall AR, Best A, Morgan AD, Boots M, Buckling A. Higher resources decrease fluctuating selection during host-parasite coevolution. Ecol Lett. 2014;17:1380–8.
PubMed  PubMed Central  Article  Google Scholar 

80.
Hampton HG, Watson BNJ, Fineran PC. The arms race between bacteria and their phage foes. Nature. 2020;577:327–36.
CAS  PubMed  Article  Google Scholar 

81.
Brockhurst MA, Chapman T, King KC, Mank JE, Paterson S, Hurst GDD. Running with the Red Queen: The role of biotic conflicts in evolution. Proc R Soc B Biol Sci. 2014;281:20141382.
Article  Google Scholar 

82.
Vuono DC, Benecke J, Henkel J, Navidi WC, Cath TY, Munakata-Marr J, et al. Disturbance and temporal partitioning of the activated sludge metacommunity. ISME J. 2015;9:425–35.
CAS  PubMed  Article  Google Scholar 

83.
Kuai L, Verstraete W. Ammonium removal by the oxygen-limited autotrophic nitrification- denitrification system. Appl Environ Microbiol. 1998;64:4500–6.
CAS  PubMed  PubMed Central  Article  Google Scholar 

84.
Hosseinidoust Z, Tufenkji N, van de Ven TGM. Formation of biofilms under phage predation: considerations concerning a biofilm increase. Biofouling. 2013;29:457–68.
CAS  PubMed  Article  Google Scholar 

85.
Doron S, Melamed S, Ofir G, Leavitt A, Lopatina A, Keren M, et al. Systematic discovery of antiphage defense systems in the microbial pangenome. Science. 2018;359:eaar4120.
PubMed  PubMed Central  Article  CAS  Google Scholar 

86.
Winter C, Bouvier T, Weinbauer MG, Thingstad TF. Trade-Offs between competition and defense specialists among unicellular planktonic organisms: the ‘Killing the Winner’ hypothesis revisited. Microbiol Mol Biol Rev. 2010;74:42–57.
CAS  PubMed  PubMed Central  Article  Google Scholar 

87.
Tyson GW, Banfield JF. Rapidly evolving CRISPRs implicated in acquired resistance of microorganisms to viruses. Environ Microbiol. 2008;10:200–7.
CAS  PubMed  Google Scholar 

88.
Weinberger AD, Wolf YI, Lobkovsky AE, Gilmore MS, Koonin EV. Viral diversity threshold for adaptive immunity in prokaryotes. mBio. 2012;3:e00456–12.
CAS  PubMed  PubMed Central  Article  Google Scholar 

89.
Cohan FM, Perry EB. A systematics for discovering the fundamental units of bacterial diversity. Curr Biol. 2007;17:R373–86.
CAS  PubMed  Article  Google Scholar 

90.
Bendall ML, Stevens SLR, Chan LK, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.
PubMed  PubMed Central  Article  Google Scholar 

91.
Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ Microbiol. 2006;8:732–40.
PubMed  Article  Google Scholar 

92.
Ayarza JM, Erijman L. Balance of neutral and deterministic components in the dynamics of activated sludge floc assembly. Micro Ecol. 2011;61:486–95.
Article  Google Scholar 

93.
de los Reyes FL. Foam in wastewater treatment facilities. In: Handbook of hydrocarbon and lipid microbiology. Berlin Heidelberg: Springer; 2010. pp. 2401–11.

94.
Petrovski S, Seviour RJ, Tillett D. Prevention of Gordonia and Nocardia stabilized foam formation by using bacteriophage GTE7. Appl Environ Microbiol. 2011;77:7864–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

95.
Liu M, Gill JJ, Young R, Summer EJ. Bacteriophages of wastewater foaming-associated filamentous Gordonia reduce host levels in raw activated sludge. Sci Rep. 2015;5:13754.
PubMed  PubMed Central  Article  Google Scholar 

96.
Curtis TP, Head IM, Graham DW. Peer reviewed: theoretical ecology for engineering biology. Environ Sci Technol. 2003;37:64A–70A.
PubMed  Article  Google Scholar 

97.
Daims H, Taylor MW, Wagner M. Wastewater treatment: a model system for microbial ecology. Trends Biotechnol. 2006;24:483–9.
CAS  PubMed  Article  Google Scholar  More

1.
Sun, F., Roderick, M. L. & Farquhar, G. D. Rainfall statistics, stationarity, and climate change. Proc. Natl Acad. Sci. USA 115, 2305–2310 (2018).
CAS  Article  Google Scholar 
2.
Polade, S. D., Pierce, D. W., Cayan, D. R., Gershunov, A. & Dettinger, M. D. The key role of dry days in changing regional climate and precipitation regimes. Sci. Rep. 4, 1–8 (2014).
Google Scholar 

3.
Pascale, S., Lucarini, V., Feng, X., Porporato, A. & ul Hasson, S. Projected changes of rainfall seasonality and dry spells in a high greenhouse gas emissions scenario. Clim. Dyn. 46, 1331–1350 (2016).
Article  Google Scholar 

4.
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 1–9 (2017).
CAS  Article  Google Scholar 

5.
Sun, F., Roderick, M. L. & Farquhar, G. D. Changes in the variability of global land precipitation. Geophys. Res. Lett. 39, 1–6 (2012).
CAS  Article  Google Scholar 

6.
Feng, X., Porporato, A. & Rodriguez-Iturbe, I. Changes in rainfall seasonality in the tropics. Nat. Clim. Change 3, 811–815 (2013).
Article  Google Scholar 

7.
Rajah, K. et al. Changes to the temporal distribution of daily precipitation. Geophys. Res. Lett. 41, 8887–8894 (2014).
Article  Google Scholar 

8.
Sloat, L. L. et al. Increasing importance of precipitation variability on global livestock grazing lands. Nat. Clim. Change 8, 214–218 (2018).
Article  Google Scholar 

9.
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).
CAS  Article  Google Scholar 

10.
Medvigy, D., Wofsy, S. C., Munger, J. W. & Moorcroft, P. R. Responses of terrestrial ecosystems and carbon budgets to current and future environmental variability. Proc. Natl Acad. Sci. USA 107, 8275–8280 (2010).
CAS  Article  Google Scholar 

11.
Knapp, A. K. et al. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 298, 2202–2205 (2002).
CAS  Article  Google Scholar 

12.
Guan, K. et al. Continental-scale impacts of intra-seasonal rainfall variability on simulated ecosystem responses in Africa. Biogeosciences 11, 6939–6954 (2014).
Article  Google Scholar 

13.
Ross, I. et al. How do variations in the temporal distribution of rainfall events affect ecosystem fluxes in seasonally water-limited Northern Hemisphere shrublands and forests? Biogeosciences 9, 1007–1024 (2012).
Article  Google Scholar 

14.
Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 1–9 (2015).
Article  CAS  Google Scholar 

15.
Dawson, T. E. & Goldsmith, G. R. The value of wet leaves. New Phytol. 219, 1156–1169 (2018).
Article  Google Scholar 

16.
Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).
CAS  Article  Google Scholar 

17.
Zeppel, M. J. B., Wilks, J. V. & Lewis, J. D. Impacts of extreme precipitation and seasonal changes in precipitation on plants. Biogeosciences 11, 3083–3093 (2014).
Article  Google Scholar 

18.
Wilcox, K. R. et al. Asymmetric responses of primary productivity to precipitation extremes: a synthesis of grassland precipitation manipulation experiments. Glob. Change Biol. 23, 4376–4385 (2017).
Article  Google Scholar 

19.
Zhang, Y. et al. Extreme precipitation patterns and reductions of terrestrial ecosystem production across biomes. J. Geophys. Res. Biogeosci. 118, 148–157 (2013).
Article  Google Scholar 

20.
Guo, Q. et al. Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution. Glob. Change Biol 18, 3624–3631 (2012).
Article  Google Scholar 

21.
Gherardi, L. A. & Sala, O. E. Effect of interannual precipitation variability on dryland productivity: a global synthesis. Glob. Change Biol 25, 269–276 (2019).
Article  Google Scholar 

22.
Ciemer, C. et al. Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall. Nat. Geosci. 12, 174–179 (2019).
CAS  Article  Google Scholar 

23.
Knapp, A. K. et al. Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58, 811–821 (2008).
Article  Google Scholar 

24.
Newman, E. A., Kennedy, M. C., Falk, D. A. & McKenzie, D. Scaling and complexity in landscape ecology. Front. Ecol. Evol. 7, 293 (2019).
Article  Google Scholar 

25.
Beguería, S., Vicente-Serrano, S. M., Tomás-Burguera, M. & Maneta, M. Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability. Int. J. Climatol. 36, 3413–3422 (2016).
Article  Google Scholar 

26.
Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1–38 (2018).
Article  Google Scholar 

27.
Jung, M. et al. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences 17, 1343–1365 (2020).
Article  Google Scholar 

28.
Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Sci. Data 4, 165–170 (2017).
Google Scholar 

29.
D’Onofrio, D., Sweeney, L., von Hardenberg, J. & Baudena, M. Grass and tree cover responses to intra-seasonal rainfall variability vary along a rainfall gradient in African tropical grassy biomes. Sci. Rep. 9, 1–10 (2019).
Article  CAS  Google Scholar 

30.
Zhou, W. et al. Plant waterlogging/flooding stress responses: from seed germination to maturation. Plant Physiol. Biochem. 148, 228–236 (2020).
CAS  Article  Google Scholar 

31.
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).
Article  Google Scholar 

32.
Tolk, J. A., Howell, T. A., Steiner, J. L., Krieg, D. R. & Schneider, A. D. Role of transpiration suppression by evaporation of intercepted water in improving irrigation efficiency. Irrig. Sci. 16, 89–95 (1995).
Article  Google Scholar 

33.
Berry, Z. C., Emery, N. C., Gotsch, S. G. & Goldsmith, G. R. Foliar water uptake: processes, pathways, and integration into plant water budgets. Plant Cell Environ. 42, 410–423 (2019).
CAS  Article  Google Scholar 

34.
Munne-Bosch, S., Nogues, S. & Alegre, L. Diurnal variations of photosynthesis and dew absorption by leaves in two evergreen shrubs growing in Mediterranean field conditions. New Phytol. 144, 109–119 (1999).
Article  Google Scholar 

35.
Martin, C. E. & Von Willert, D. J. Leaf epidermal hydathodes and the ecophysiological consequences of foliar water uptake in species of Crassula from the Namib Desert in southern Africa. Plant Biol. 2, 229–242 (2000).
Article  Google Scholar 

36.
Breshears, D. D. et al. Foliar absorption of intercepted rainfall improves woody plant water status most during drought. Ecology 89, 41–47 (2008).
Article  Google Scholar 

37.
Ritter, F., Berkelhammer, M. & Beysens, D. Dew frequency across the US from a network of in situ radiometers. Hydrol. Earth Syst. Sci. 23, 1179–1197 (2019).
Article  Google Scholar 

38.
Marschner, B. & Kalbitz, K. Controls of bioavailability and biodegradability of dissolved organic matter in soils. Geoderma 113, 211–235 (2003).
CAS  Article  Google Scholar 

39.
Yuan, Z. Y. et al. Experimental and observational studies find contrasting responses of soil nutrients to climate change. Elife 6, 1–19 (2017).
Google Scholar 

40.
Trujillo, E., Molotch, N. P., Goulden, M. L., Kelly, A. E. & Bales, R. C. Elevation-dependent influence of snow accumulation on forest greening. Nat. Geosci. 5, 705–709 (2012).
CAS  Article  Google Scholar 

41.
Fatichi, S., Ivanov, V. Y. & Caporali, E. Investigating interannual variability of precipitation at the global scale: Is there a connection with seasonality? J. Clim. 25, 5512–5523 (2012).
Article  Google Scholar 

42.
Knapp, A. K., Ciais, P. & Smith, M. D. Reconciling inconsistencies in precipitation–productivity relationships: implications for climate change. New Phytol. 214, 41–47 (2017).
Article  Google Scholar 

43.
Moreno-Jiménez, E. et al. Aridity and reduced soil micronutrient availability in global drylands. Nat. Sustain. 2, 371–377 (2019).
Article  Google Scholar 

44.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An overview of the global historical climatology network-daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).
Article  Google Scholar 

45.
Fan, Y. & van den Dool, H. A global monthly land surface air temperature analysis for 1948-present. J. Geophys. Res. Atmos. 113, 1–18 (2008).
Article  CAS  Google Scholar 

46.
Monti, A. & Venturi, G. A simple method to improve the estimation of the relationship between rainfall and crop yield. Agron. Sustain. Dev. 27, 255–260 (2007).
Article  Google Scholar 

47.
Gu, L., Pallardy, S. G., Hosman, K. P. & Sun, Y. Impacts of precipitation variability on plant species and community water stress in a temperate deciduous forest in the central US. Agric. For. Meteorol. 217, 120–136 (2016).
Article  Google Scholar  More