Ecology

1.
Grigg, G. & Kirshner, D. Biology and Evolution of Crocodylians (CSIRO Publishing, 2015). https://doi.org/10.1071/9781486300679.
Google Scholar 
2.
Singh, L. A. K. Ecological studies on the Indian gharial Gavialis gangeticus (Gmelin) (Reptilia, Crocodilia). PhD Thesis, Utkal University, Odisha (1978).

3.
Whitaker, R. The management of crocodilians in India. In Wildlife Management; Crocodiles and Alligators (eds Webb, G. J. W. et al.) 63–72 (Surrey Beatty and Sons, 1987).
Google Scholar 

4.
Hussain, S. A. Reproductive success, hatchling survival and rate of increase of gharial Gavialis gangeticus in National Chambal Sanctuary, India. Biol. Conserv. 87, 261–268 (1999).
Article  Google Scholar 

5.
Bustard, H. R. A future for the Gharial. Cheetal 17, 3–8 (1975).
Google Scholar 

6.
Hussain, S. A. Basking site and water depth selection by gharial Gavialis gangeticus Gmelin 1789 (Crocodylia, Reptilia) in National Chambal Sanctuary, India and its implication for river conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 19, 127–133 (2009).
Article  Google Scholar 

7.
Lang, J. W., Chowfin, S. & Ross, J. P. Gavialis gangeticus (errata version published in 2019). IUCN Red List Threat. Species 2019 (2019).

8.
Basu, D. Saving the gharial. Indian Wildlifer 1, 7–15 (1981).
Google Scholar 

9.
Singh, V. B. The status of the gharial (Gavialis gangeticus) in U.P. and its rehabilitation. J. Bombay Nat. Hist. Soc. 75, 668–683 (1978).
Google Scholar 

10.
Stevenson, C. & Whitaker, R. Indian Gharial Gavialis gangeticus. In Crocodiles. Status Survey and Conservation Action Plan (eds Manolis, S. C. & Stevenson, C.) 139–143 (Crocodile Specialist Group, 2010).
Google Scholar 

11.
Whitaker, R. & Basu, D. The gharial (Gavialis gangeticus) a review. J. Bombay Nat. Hist. Soc. 79, 531–548 (1982).
Google Scholar 

12.
Whitaker, R. The gharial: Going extinct again. Iguana 14, 25–33 (2007).
Google Scholar 

13.
Lang, J. W., Jailabdeen, A. & Kumar, P. Gharial ecology project—Update 2018–2019. IUCN-SSC Crocodile Spec. Gr. Newsl. 37, 15–17 (2018).
Google Scholar 

14.
IUCN/SSC. Guidelines for Reintroductions and Other Conservation Translocations IUCN. Version 1.0. Gland, Switzerland: IUCN Species Survival Commission viiii + 57 pp. (2013).

15.
Schwartz, M. K. Guidelines on the use of molecular genetics in reintroduction programs. EU LIFE-Nature Proj. to Guidel. reintroduction Threat. species 51–58 (2005).

16.
White, L. C., Moseby, K. E., Thomson, V. A., Donnellan, S. C. & Austin, J. J. Long-term genetic consequences of mammal reintroductions into an Australian conservation reserve. Biol. Conserv. 219, 1–11 (2018).
Article  Google Scholar 

17.
Weeks, A. R. et al. Assessing the benefits and risks of translocations in changing environments: A genetic perspective. Evol. Appl. 4, 709–725 (2011).
PubMed  PubMed Central  Article  Google Scholar 

18.
Hughes, A. R., Inouye, B. D., Johnson, M. T. J., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).
PubMed  Article  Google Scholar 

19.
Katdare, S. et al. Gharial (Gavialis gangeticus) populations and human influences on habitat on the River Chambal, India. Aquat. Conserv. Mar. Freshw. Ecosyst. 21, 364–371 (2011).
Article  Google Scholar 

20.
Nair, T., Thorbjarnarson, J. B., Aust, P. & Krishnaswamy, J. Rigorous gharial population estimation in the Chambal: Implications for conservation and management of a globally threatened crocodilian. J. Appl. Ecol. 49, 1046–1054 (2012).
Article  Google Scholar 

21.
Hussain, S. A. Ecology of gharial (Gavialis gangeticus) in National Chambal Sanctuary. MPhil Thesis, Aligarh Muslim University, Uttar Pradesh (1991).

22.
Sharma, S. P. et al. Mitochondrial DNA analysis reveals extremely low genetic diversity in a managed population of the Critically Endangered Gharial (Gavialis gangeticus, Gmelin 1789). Herpetol. J. 30, 202–206 (2020).
Article  Google Scholar 

23.
Jogayya, K. N., Meganathan, P. R., Dubey, B. & Haque, I. Novel microsatellite DNA markers for Indian Gharial (Gavialis gangeticus). Conserv. Genet. Resour. 5, 787–790 (2013).
Article  Google Scholar 

24.
Zhu, H., Wu, X., Xue, H., Wei, L. & Hu, Y. Isolation of polymorphic microsatellite loci from the Chinease alligator (Alligator sinensis). Mol. Ecol. Resour. 9, 892–894 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Glenn, T. C. et al. Characterization of microsatellite DNA loci in American alligators. Copeia 3, 591–601 (1998).
Article  Google Scholar 

26.
Ojeda, G. N., Amavet, P. S., Rueda, E. C., Siroski, P. A. & Larriera, A. Mating system of Caiman yacare (Reptilia: Alligatoridae) described from microsatellite genotypes. J. Hered. 108, 135–141 (2017).
PubMed  PubMed Central  Google Scholar 

27.
Yu, D. et al. Analysis of genetic variation and bottleneck in a captive population of Siamese crocodile using novel microsatellite loci. Conserv. Genet. Resour. 3, 217–220 (2011).
Article  Google Scholar 

28.
Hinlo, M. R. P. et al. Population genetics implications for the conservation of the Philippine Crocodile Crocodylus mindorensis Schmidt, 1935 (Crocodylia: Crocodylidae). J. Threat. Taxa 6, 5513–5533 (2014).
Article  Google Scholar 

29.
Mcvay, J. D. et al. Evidence of multiple paternity in Morelet’s Crocodile (Crocodylus moreletii) in Belize, CA, inferred from microsatellite markers. J. Exp. Zool. Part A Ecol. Genet. Physiol. 309, 643–648 (2008).
Article  Google Scholar 

30.
Dever, J. A., Strauss, R. E., Rainwater, T. R., McMurry, S. T. & Densmore, I. L. D. Genetic diversity, population subdivision, and gene flow in Morelet’s crocodile (Crocodylus moreletii) from Belize, Central America. Copeia 4, 1078–1091 (2002).
Article  Google Scholar 

31.
Aggarwal, R. K., Lalremruata, A. & Dubey, B. Development of fourteen novel microsatellite markers of Crocodylus palustris, the Indian mugger, and their cross-species transferability in ten other crocodilians. Conserv. Genet. Resour. 7, 197–200 (2014).
Article  Google Scholar 

32.
Campos, J. C., Mobaraki, A., Abtin, E., Godinho, R. & Brito, J. C. Preliminary assessment of genetic diversity and population connectivity of the Mugger Crocodile in Iran. Amphib. Reptil. 39, 126–131 (2018).
Article  Google Scholar 

33.
Garner, A., Rachlow, J. L. & Hicks, J. F. Patterns of genetic diversity and its loss in mammalian populations. Conserv. Biol. 19, 1215–1221 (2005).
Article  Google Scholar 

34.
Rossi, N. A. et al. High levels of population genetic differentiation in the American crocodile (Crocodylus acutus). PLoS ONE 15, e0235288 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
van Asch, B. et al. Phylogeography, genetic diversity, and population structure of Nile crocodile populations at the fringes of the southern African distribution. PLoS ONE 14, 1–20 (2019).
Google Scholar 

36.
Luck, N. L. et al. Mitochondrial DNA analyses of the saltwater crocodile (Crocodylus porosus) from the Northern Territory of Australia. Aust. J. Zool. 60, 18–25 (2012).
Article  Google Scholar 

37.
Russello, M. A., Brazaitis, P., Gratten, J., Watkins-Colwell, G. J. & Caccone, A. Molecular assessment of the genetic integrity, distinctiveness and phylogeographic context of the Saltwater crocodile (Crocodylus porosus) on Palau. Conserv. Genet. 8, 777–787 (2007).
CAS  Article  Google Scholar 

38.
Ray, D. A. et al. Low levels of nucleotide diversity in Crocodylus moreletiiand evidence of hybridization with C. acutus. Conserv. Genet. 5, 449–462 (2004).
CAS  Article  Google Scholar 

39.
Eckert, C. G., Samis, K. E. & Lougheed, S. C. Genetic variation across species’ geographical ranges: The central-marginal hypothesis and beyond. Mol. Ecol. 17, 1170–1188 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).
CAS  PubMed  Article  Google Scholar 

41.
Romiguier, J. et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515, 261–263 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

42.
Allendorf, F. W. & Luikart, G. Conservation and the Genetics of Populations (Blackwell Publishing, 2007).
Google Scholar 

43.
Guries, R. P. & Ledig, F. T. Genetic structure of populations and differentiation in forest trees. in Conkle, MT (tech. coord.) Proceedings of the symposium on isozymes of North American forest trees and forest insects. USDA For. Serv. Gen. Tech. Rep. PSW-48 42–47 (1979).

44.
Biebach, I. & Keller, L. F. Inbreeding in reintroduced populations: The effects of early reintroduction history and contemporary processes. Conserv. Genet. 11, 527–538 (2010).
Article  Google Scholar 

45.
Wang, J. Estimating pairwise relatedness in a small sample of individuals. Heredity (Edinb). 119, 302–313 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Degiorgio, M. & Rosenberg, N. A. An unbiased estimator of gene diversity in samples containing related individuals p. Mol. Biol. Evol. 26, 501–512 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).
PubMed  PubMed Central  Article  Google Scholar 

48.
Girod, C., Vitalis, R., Leblois, R. & Fréville, H. Inferring population decline and expansion from microsatellite data: A simulation-based evaluation of the msvar method. Genetics 188, 165–179 (2011).
PubMed  PubMed Central  Article  Google Scholar 

49.
Luikart, G. & Cornuet, J. M. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv. Biol. 12, 228–237 (1998).
Article  Google Scholar 

50.
Keller, L. F. et al. Immigration and the ephemerality of a natural population bottleneck: Evidence from molecular markers. Proc. R Soc. London. Ser. B Biol. Sci. 268, 1387–1394 (2001).
CAS  Article  Google Scholar 

51.
Cristescu, R., Sherwin, W. B., Handasyde, K., Cahill, V. & Cooper, D. W. Detecting bottlenecks using BOTTLENECK 1.2.02 in wild populations: The importance of the microsatellite structure. Conserv. Genet. 11, 1043–1049 (2010).
Article  Google Scholar 

52.
Peery, M. Z. et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21, 3403–3418 (2012).
PubMed  Article  PubMed Central  Google Scholar 

53.
Hoban, S. M., Gaggiotti, O. E. & Bertorelle, G. The number of markers and samples needed for detecting bottlenecks under realistic scenarios, with and without recovery: A simulation-based study. Mol. Ecol. 22, 3444–3450 (2013).
PubMed  Article  PubMed Central  Google Scholar 

54.
Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Sambrook, J., Fritsch, E. F. & Maniatis, T. Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 1989).
Google Scholar 

56.
Miquel, C. et al. Quality indexes to assess the reliability of genotypes in studies using noninvasive sampling and multiple-tube approach. Mol. Ecol. Notes 6, 985–988 (2006).
Article  Google Scholar 

57.
Oaks, J. R. A time-calibrated species tree of Crocodylia reveals a recent radiation of the true crocodiles. Evolution (N.Y.) 65, 3285–3297 (2011).
Google Scholar 

58.
Broquet, T. & Petit, E. Quantifying genotyping errors in noninvasive population genetics. Mol. Ecol. 13, 3601–3608 (2004).
CAS  PubMed  Article  Google Scholar 

59.
Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).
CAS  PubMed  Article  Google Scholar 

60.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Article  CAS  Google Scholar 

61.
Valière, N. GIMLET: A computer program for analysing genetic individual identification data. Mol. Ecol. Notes 2, 377–379 (2002).
Google Scholar 

62.
Peakall, R. & Smouse, P. E. GenALEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program cervus accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
PubMed  Article  PubMed Central  Google Scholar 

64.
Kalinowski, S. T. HP-RARE 1.0—A computer program for performing rarefaction on measures of allelic richness.pdf. Mol. Ecol. Notes 5, 187–189 (2005).
CAS  Article  Google Scholar 

65.
Weir, B. S. & Cockerham, C. Estimating F-statistics for the analysis of population structure. Evolution (N. Y.). 38, 1358–1370 (1984).
CAS  Google Scholar 

66.
Hedrick, P. W. A standardized genetic differentiation measure. Evolution (N. Y.). 59, 1633–1638 (2005).
CAS  Google Scholar 

67.
Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).
PubMed  Article  PubMed Central  Google Scholar 

68.
Archer, F. I., Adams, P. E. & Schneiders, B. B. stratag: An r package for manipulating, summarizing and analysing population genetic data. Mol. Ecol. Resour. 17, 5–11 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).
PubMed  PubMed Central  Article  Google Scholar 

71.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  Article  Google Scholar 

72.
Earl, D. A. & VonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Article  Google Scholar 

73.
Rosenberg, N. A. DISTRUCT: A program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138 (2004).
Article  Google Scholar 

74.
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).
PubMed  PubMed Central  Google Scholar 

75.
Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinforma. Online. 1, 47–50 (2005).
CAS  Google Scholar 

76.
Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).
Article  Google Scholar 

77.
Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318 (2001).
CAS  PubMed  Article  Google Scholar 

78.
Di Rienzo, A. et al. Mutational processes of simple-sequence repeat loci in human populations. Proc. Natl. Acad. Sci. USA 91, 3166–3170 (1994).
ADS  PubMed  Article  Google Scholar 

79.
Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acid Symp. Ser. 41, 95–98 (1999).
CAS  Google Scholar 

81.
Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
CAS  Article  Google Scholar  More

1.
D’Odorico, P. & Porporato, A. Dryland Ecohydrology (Springer, 2019).
2.
Smith, W. K. et al. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sens. Environ. 233, 111401 (2019).
Article  Google Scholar 

3.
Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007).
CAS  Article  Google Scholar 

4.
Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).
Article  CAS  Google Scholar 

5.
Middleton, N. & Thomas, D. S. G. World Atlas of Desertification 2nd edn (Wiley, 1997).

6.
Budyko, M. I. & Miller, D. H. International Geophysics Series: Climate and Life Vol. 18 (Academic Press, 1974).

7.
Feng, S. & Fu, Q. Expansion of global drylands under a warming climate. Atmos. Chem. Phys. 13, 10081–10094 (2013).
CAS  Article  Google Scholar 

8.
Fu, Q. & Feng, S. Responses of terrestrial aridity to global warming. J. Geophys. Res. Atmos. 119, 7863–7875 (2014).
Article  Google Scholar 

9.
Scheff, J. & Frierson, D. M. W. Terrestrial aridity and its response to greenhouse warming across CMIP5 climate models. J. Clim. 28, 5583–5600 (2015).
Article  Google Scholar 

10.
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).
Google Scholar 

11.
Huang, J., Yu, H., Dai, A., Wei, Y. & Kang, L. Drylands face potential threat under 2 °C global warming target. Nat. Clim. Change 7, 417–422 (2017).
Article  Google Scholar 

12.
Park, C.-E. et al. Keeping global warming within 1.5 °C constrains emergence of aridification. Nat. Clim. Change 8, 70–74 (2018).
Article  Google Scholar 

13.
Koutroulis, A. G. Dryland changes under different levels of global warming. Sci. Total Environ. 655, 482–511 (2019).
CAS  Article  Google Scholar 

14.
Park, C. E. et al. Inequal responses of drylands to radiative forcing geoengineering methods. Geophys. Res. Lett. 46, 14011–14020 (2019).
Article  Google Scholar 

15.
Wei, Y. et al. Drylands climate response to transient and stabilized 2 °C and 1.5 °C global warming targets. Clim. Dyn. 53, 2375–2389 (2019).
Article  Google Scholar 

16.
Yao, J. et al. Accelerated dryland expansion regulates future variability in dryland gross primary production. Nat. Commun. 11, 1665 (2020).
CAS  Article  Google Scholar 

17.
Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).
CAS  Article  Google Scholar 

18.
Rajaud, A. & de Noblet-Ducoudré, N. Tropical semi-arid regions expanding over temperate latitudes under climate change. Climatic Change 144, 703–719 (2017).
Article  Google Scholar 

19.
Yang, Y. et al. Disconnection between trends of atmospheric drying and continental runoff. Water Resour. Res. 54, 4700–4713 (2018).
Article  Google Scholar 

20.
Greve, P., Roderick, M. L., Ukkola, A. M. & Wada, Y. The aridity index under global warming. Environ. Res. Lett. 14, 124006 (2019).
CAS  Article  Google Scholar 

21.
Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).
Article  Google Scholar 

22.
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).
Article  CAS  Google Scholar 

23.
Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42, 181–203 (2011).
Article  Google Scholar 

24.
Berg, A. & Sheffield, J. Soil moisture–evapotranspiration coupling in CMIP5 models: relationship with simulated climate and projections. J. Clim. 31, 4865–4878 (2018).
Article  Google Scholar 

25.
Mahowald, N. et al. Projections of leaf area index in Earth system models. Earth Syst. Dyn. 7, 211–229 (2016).
Article  Google Scholar 

26.
Sherwood, S. & Fu, Q. A drier future? Science 343, 737–739 (2014).
CAS  Article  Google Scholar 

27.
Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).
Article  Google Scholar 

28.
Berg, A. & Sheffield, J. Climate change and drought: the soil moisture perspective. Curr. Clim. Change Rep. 4, 180–191 (2018).
Article  Google Scholar 

29.
Lavergne, A. et al. Observed and modelled historical trends in the water‐use efficiency of plants and ecosystems. Glob. Change Biol. 25, 2242–2257 (2019).
Article  Google Scholar 

30.
Friedlingstein, P. Carbon cycle feedbacks and future climate change. Phil. Trans. R. Soc. A 373, 20140421 (2015).
Article  CAS  Google Scholar 

31.
Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).
CAS  Article  Google Scholar 

32.
Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc. Natl Acad. Sci. USA 115, 4093–4098 (2018).
CAS  Article  Google Scholar 

33.
Berg, A. & Sheffield, J. Evapotranspiration partitioning in CMIP5 models: uncertainties and future projections. J. Clim. 32, 2653–2671 (2019).
Article  Google Scholar 

34.
Cao, L., Bala, G., Caldeira, K., Nemani, R. & Ban-Weiss, G. Importance of carbon dioxide physiological forcing to future climate change. Proc. Natl Acad. Sci. USA 107, 9513–9518 (2010).
CAS  Article  Google Scholar 

35.
Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).
Article  CAS  Google Scholar 

36.
Kooperman, G. J. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8, 434–440 (2018).
Article  Google Scholar 

37.
Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10, 4321–4345 (2017).
Article  Google Scholar 

38.
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
CAS  Article  Google Scholar 

39.
He, B., Wang, S., Guo, L. & Wu, X. Aridity change and its correlation with greening over drylands. Agric. For. Meteorol. 278, 107663 (2019).
Article  Google Scholar 

40.
Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 0081 (2017).
Article  Google Scholar 

41.
Burrell, A. L., Evans, J. P. & De Kauwe, M. G. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nat. Commun. 11, 3853 (2020).
CAS  Article  Google Scholar 

42.
Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 236–244 (2017).
Article  Google Scholar 

43.
Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).
CAS  Article  Google Scholar 

44.
Liu, Y. et al. Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization. Nat. Geosci. 12, 809–814 (2019).
CAS  Article  Google Scholar 

45.
Zeng, Z. et al. Responses of land evapotranspiration to Earth’s greening in CMIP5 Earth System Models. Environ. Res. Lett. 11, 104006 (2016).
Article  Google Scholar 

46.
Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).
Article  Google Scholar 

47.
Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).
CAS  Article  Google Scholar 

48.
Scheff, J., Seager, R., Liu, H. & Coats, S. Are glacials dry? Consequences for paleoclimatology and for greenhouse warming. J. Clim. 30, 6593–6609 (2017).
Article  Google Scholar 

49.
Ault, T. R. On the essentials of drought in a changing climate. Science 368, 256–260 (2020).
CAS  Article  Google Scholar 

50.
Berg, A. & Sheffield, J. Historic and projected changes in coupling between soil moisture and evapotranspiration (ET) confounded by the role of different ET components. J. Geophys. Res. Atmos. 124, 5791–5806 (2019).
Google Scholar 

51.
Berg, A. & McColl, K. R code for ‘No global drylands expansion under greenhouse warming’. Zenodo https://doi.org/10.5281/zenodo.4490414 (2021). More

1.
Dixon, T. & Pretorius, I. S. Drawing on the past to shape the future of synthetic yeast research. Int. J. Mol. Sci. 21, 7156 (2020).
CAS  PubMed Central  Article  PubMed  Google Scholar 
2.
Dixon, T., Curach, N. & Pretorius, I. S. Bio-informational futures: the convergence of artificial intelligence and synthetic biology. EMBO Rep. 21, e50036 (2020a). 1–5.
CAS  Article  Google Scholar 

3.
Dixon, T., Williams, T. C. & Pretorius, I. S. Sensing the future of bio-informational engineering. Nat. Commun. 12, 388 (2021).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Layeghifard, M., Hwang, D. W. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25, 217–228 (2017).
CAS  PubMed  Article  Google Scholar 

5.
Pretorius, I. S. Tasting the terroir of wine yeast innovation. FEMS Yeast Res. 20, foz084 (2020).
CAS  PubMed  Article  Google Scholar 

6.
Gibson, D. G. et al. Creation of a bacterial cell controlled by a chemically synthesized genome. Science 329, 52–56 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Pretorius, I. S. & Boeke, J. D. Yeast 2.0 − Connecting the dots in the construction of the world’s first functional synthetic eukaryotic genome. FEMS Yeast Res. 18, foy032 (2018).
PubMed Central  Article  CAS  PubMed  Google Scholar 

8.
Richardson, S. M. et al. Design of a synthetic yeast genome. Science 355, 1040–1044 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

9.
Muller, E. E. L. et al. Using metabolic networks to resolve ecological properties of microbiomes. Curr. Opin. Syst. Biol. 8, 73–80 (2018).
Article  Google Scholar 

10.
Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).
CAS  Article  Google Scholar 

11.
Roume, H. et al. Comparative integrated omics: identification of key functionalities in microbial community-wide metabolic networks. npj Biofilms Microbiomes 1, 15007 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

13.
McCarty, N. S. & Ledesma-Amaro, R. Synthetic Biology tools to engineer microbial communities for Biotechnology. Trends Biotechnol. 37, 181–197 (2018).
PubMed  Article  CAS  Google Scholar 

14.
Peris, D. et al. Synthetic hybrids of six yeast species. Nat. Commun. 11, 2085 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Yi, X. & Dean, A. M. Adaptive landscapes in the age of synthetic biology. Mol. Biol. Evol. 36, 890–907 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Goel, A., Wortel, M. T., Molenaar, D. & Teusink, B. Metabolic shifts: a fitness perspective for microbial cell factories. Biotechnol. Lett. 34, 2147–2160 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
De Vrieze, J. & Verstraete, W. Perspectives for microbial community composition in anaerobic digestion: from abundance and activity to connectivity. Environ. Microbiol. 18, 2797–2809 (2016).
PubMed  Article  CAS  Google Scholar 

18.
Wolfe, B. E. & Dutton, R. J. Fermented foods as experimentally tractable microbial ecosystems. Cell 161, 49–55 (2015).
CAS  PubMed  Article  Google Scholar 

19.
Lurgi, M., Thomas, T., Wemheuer, B., Webster, N. S. & Montoya, J. M. Modularity and predicted functions of the global sponge-microbiome network. Nat. Commun. 10, 992 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

20.
Cao, H., Gibson, T., Bashan, A. & Liu, Y. Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons. BioEssays 39, 1600188 (2016).
Article  Google Scholar 

21.
Liu, Z. et al. Network analyses in microbiome based on high-throughput multi-omics data. Brief. Bioinform. 00, 1–17 (2020).
Google Scholar 

22.
Danczak, R. E. et al. Using metacommunity ecology to understand environmental metabolomes. Nat. Commun. 11, 6369 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Dini-Andreote, F. et al. Dynamics of bacterial community succession in a saltmarsh chronosequence: evidences for temporal niche partitioning. ISME J. 8, 1989–2001 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Heintz-Buschart, A. et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat. Microbiol. 2, 16180 (2016).
CAS  PubMed  Article  Google Scholar 

25.
Toju, H. et al. Scoring species for synthetic community design: Network analyses of functional core microbiomes. Front. Microbiol. 11, 1361 (2020).
PubMed  PubMed Central  Article  Google Scholar 

26.
Medlock, G. L. et al. Inferring metabolic mechanisms of interaction within a defined gut microbiota. Cell Syst. 7, 245–257 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Gibson, D. G. Programming biological operating systems: genome design, assembly and activation. Nat. Meth 11, 521–526 (2014).
CAS  Article  Google Scholar 

28.
Hillson, N. et al. Building a global alliance of biofoundries. Nat. Commun. 10, 2040 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

29.
Palluk, S. et al. De novo DNA synthesis using polymerase-nucleotide conjugates. Nat. Biotechnol. 36, 645–650 (2018).
CAS  PubMed  Article  Google Scholar 

30.
Coradini, A. L. V., Hull, C. B. & Ehrenreich, I. M. Building genomes to understand biology. Nat. Commun. 11, 6177 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Bartley, B. A. et al. Organizing genome engineering for the gigabase scale. Nat. Commun. 11, 689 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Lau, Y. H. et al. Large-scale recoding of a bacterial genome by iterative recombineering of synthetic DNA. Nucleic Acids Res. 45, 6971–6980 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Kouprina, N. & Larionov, V. Transformation-associated recombination (TAR) cloning for genomics studies and synthetic biology. Chromosoma 125, 621–632 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Benders, G. A. et al. Cloning whole bacterial genomes in yeast. Nucleic Acids Res. 38, 2558–2569 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Mortimer, R. K. Radiobiological and genetic studies on a polyploid series (haploid to hexaploid) of Saccharomyces cerevisiae. Radiat. Res. 9, 312–326 (1958).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Shao, Y. et al. Creating a functional single-chromosome yeast. Nature 560, 331–335 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Hossain, A. et al. Automated design of thousands of nonrepetitive parts for engineering stable genetic systems. Nat. Biotechnol. 38, 1466–1475 (2020).
PubMed  Article  CAS  Google Scholar 

38.
Decoene, T., Peters, G., De Maeseneire, S. L. & De Mey, M. Toward predictable 5′UTRs in Saccharomyces cerevisiae: Development of a yUTR calculator. ACS Synth. Biol. 7, 622–634 (2018).
CAS  PubMed  Article  Google Scholar 

39.
Weenink, T., van der Hilst, J., McKiernan, R. M. & Ellis, T. Design of RNA hairpin modules that predictably tune translation in yeast. Synth. Biol. 3, ysy019 (2018).
CAS  Article  Google Scholar 

40.
Kotopka, B. J. & Smolke, C. D. Model-driven generation of artificial yeast promoters. Nat. Commun. 11, 2113 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Curran, K. A. et al. Short synthetic terminators for improved heterologous gene expression in yeast. ACS Synth. Biol. 4, 824–832 (2015).
CAS  PubMed  Article  Google Scholar 

42.
MacPherson, M. & Saka, Y. Short synthetic terminators for assembly of transcription units in vitro and stable chromosomal integration in yeast S. cerevisiae. ACS Synth. Biol. 6, 130–138 (2017).
CAS  PubMed  Article  Google Scholar 

43.
Morse, N. J., Gopal, M. R., Wagner, J. M. & Alper, H. S. Yeast terminator function can be modulated and designed on the basis of predictions of nucleosome occupancy. ACS Synth. Biol. 6, 2086–2095 (2017).
CAS  PubMed  Article  Google Scholar 

44.
Gräslund, S. et al. Structural Genomics Consortium: Protein production and purification. Nat. Methods 5, 135–146 (2008).
PubMed  Article  Google Scholar 

45.
Lin, Y., Zou, X., Zheng, Y., Cai, Y. & Dai, J. Improving chromosome synthesis with a semiquantitative phenotypic assay and refined assembly strategy. ACS Synth. Biol. 8, 2203–2211 (2019).
CAS  PubMed  Article  Google Scholar 

46.
Mitchell, L. A. et al. Synthesis, debugging, and effects of synthetic chromosome consolidation: synVI and beyond. Science 355, eaaf4831 (2017).
PubMed  Article  CAS  Google Scholar 

47.
Wu, Y. et al. Bug mapping and fitness testing of chemically synthesized chromosome X. Science 355, eaaf4706 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Salinas, F. et al. Fungal light-oxygen-voltage domains for optogenetic control of gene expression and flocculation in yeast. mBio 9, e00626–18 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Shen, Y. et al. SCRaMbLE generates designed combinatorial stochastic diversity in synthetic chromosomes. Genome Res. 26, 36–49 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Jin, J., Jia, B. & Yuan, Y. J. Yeast chromosomal engineering to improve industrially-relevant phenotypes. Curr. Opin. Biotechnol. 66, 165–170 (2020).
CAS  PubMed  Article  Google Scholar 

51.
Dymond, J. & Boeke, J. The Saccharomyces cerevisiae SCRaMbLE system and genome minimization. Bioeng. Bugs 3, 168–171 (2012).
PubMed  PubMed Central  Google Scholar 

52.
Lee, D., Lloyd, N. D. R., Pretorius, I. S. & Borneman, A. R. Heterologous production of raspberry ketone in the wine yeast Saccharomyces cerevisiae via pathway engineering and synthetic enzyme fusion. Micro. Cell Fact. 15, 49 (2016).
Article  CAS  Google Scholar 

53.
Williams, T. C., Pretorius, I. S. & Paulsen, I. T. Synthetic evolution of metabolic productivity using biosensors. Trends Biotechnol. 34, 371–381 (2016).
CAS  PubMed  Article  Google Scholar 

54.
Williams, T. C., Xu, X., Ostrowski, M., Pretorius, I. S. & Paulsen, I. T. Positive-feedback, ratiometric biosensor expression improves high-throughput metabolite-producer screening efficiency in yeast. Synth. Biol. 2, ysw002 (2017).
CAS  Article  Google Scholar 

55.
Zhou, K., Qiao, K., Edgar, S. & Stephanopoulos, G. Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33, 377–383 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Belda, I. et al. Unraveling the enzymatic basis of wine ‘flavorome’: a phylo-functional study of wine related yeast species. Front. Microbiol. 7, 1–13 (2016).
Article  Google Scholar 

57.
Belda, I. et al. Microbial contribution to wine aroma and its intended use for wine quality improvement. Molecules 22, 1–29 (2017).
Article  CAS  Google Scholar 

58.
Bokulich, N. A. et al. Associations among wine grape microbiome, metabolome, and fermentation behaviour suggest contribution to regional wine characteristics. mBio 7, 1–12 (2016).
Article  Google Scholar 

59.
Liu, D., Chen, Q., Zhang, P., Chen, D. & Howell, K. S. The fungal microbiome is an important component of vineyard ecosystems and correlates with regional distinctiveness of wine. mSphere 5, e00534–20 (2020).
CAS  PubMed  PubMed Central  Google Scholar  More

1.
Estrada, B., Aroca, R., Maathuis, F. J., Barea, J. M. & Ruiz-Lozano, J. M. Arbuscular mycorrhizal fungi native from a mediterranean saline area enhance maize tolerance to salinity through improved ion homeostasis. Plant Cell Environ. 36, 1771–1782 (2013).
CAS  PubMed  Article  Google Scholar 
2.
Uva, R. H. & Whitlow, T. H. Beach plum (Prunus maritima Marsh.): Small farm sustainability through crop diversification and value added products. HortScience 38, 793 (2003).
Google Scholar 

3.
Yan, D. L., Wang, G., Fang, K., Zai, X. M. & Qin, P. Introduction, cultivation and utilization of salt-tolerance beach plum. China For. Sci. Technol. 20, 67–69 (2006).
Google Scholar 

4.
Zhang, H. S., Wu, X. H. & Li, G. Interactions between arbuscular mycorrhizal fungi and phosphate solubilizing fungus (Mortierella sp.) and their effects on Kostelelzkya virginica growth and enzyme activities of rhizosphere and bulk soils at different salinities. Biol. Fert. Soils 47, 543–554 (2011).
CAS  Article  Google Scholar 

5.
Ait-El-Mokhtar, M. et al. Alleviation of detrimental effects of salt stress on date palm (Phoenix dactylifera L.) by the application of arbuscular mycorrhizal fungi and/or compost. Front. Sustain. Food Syst. 4, 131 (2020).
Article  Google Scholar 

6.
Porcel, R., Redondo-Gómez, S. & Mateos-Naranjo, E. Arbuscular mycorrhizal symbiosis ameliorates the optimum quantum yield of photosystem II and reduces non-photochemical quenching in rice plants subjected to salt stress. J. Plant Physiol. 185, 75–83 (2015).
CAS  PubMed  Article  Google Scholar 

7.
Sheng, M., Tang, M. & Chen, H. Influence of arbuscular mycorrhizae on photosynthesis and water status of maize plants under salt stress. Mycorrhiza 18, 287–296 (2008).
CAS  PubMed  Article  Google Scholar 

8.
Harbinson, J. Improving the accuracy of chlorophyll fluorescence measurements. Plant Cell Environ. 36, 1751–1754 (2013).
PubMed  Article  Google Scholar 

9.
Zhu, X. C., Song, F. B., Liu, S. Q. & Liu, T. D. Arbuscular mycorrhizae improves photosynthesis and water status of Zea mays L. under drought stress. Plant Soil Environ. 58, 186–191 (2012).
CAS  Article  Google Scholar 

10.
Wang, F., Sun, Y. & Shi, Z. Arbuscular mycorrhiza enhances biomass production and salt tolerance of sweet sorghum. Microorganisms 7, 289 (2019).
CAS  PubMed Central  Article  PubMed  Google Scholar 

11.
Qiu, Y. J. et al. Mediation of arbuscular mycorrhizal fungi on growth and biochemical parameters of Ligustrum vicaryi in response to salinity. Physiol. Mol. Plant Pathol. 112, 101522 (2020).
CAS  Article  Google Scholar 

12.
Zhang, H. S., Qin, P. & Zhang, W. M. Effects of inoculation of arbuscular mycorrhizal fungus and Apophysomyces spartina on P-uptake of castor oil plant (Ricinus communis L.) and rhizosphere soil enzyme activities under salt stress. Agri. Sci. Technol. 15, 659 (2014).
CAS  Google Scholar 

13.
Ghorchiani, M., Etesami, H. & Alikhani, H. A. Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agric. Ecosyst. Environ. 258, 59–70 (2018).
CAS  Article  Google Scholar 

14.
Augé, R. M., Toler, H. D., Sams, C. E. & Nasim, G. Hydraulic conductance and water potential gradients in squash leaves showing mycorrhiza-induced increases in stomatal conductance. Mycorrhiza 18, 115–121 (2008).
PubMed  Article  Google Scholar 

15.
Sharma, S., Compant, S., Ballhausen, M. B., Ruppel, S. & Franken, P. The interaction between Rhizoglomus irregulare and hyphae attached phosphate solubilizing bacteria increases plant biomass of Solanum lycopersicum. Microbiol. Res. 240, 126556 (2020).
CAS  PubMed  Article  Google Scholar 

16.
Vassilev, N., Eichler-Löbermann, B. & Vassileva, M. Stress-tolerant P-solubilizing microorganisms. Appl. Microbiol. Biot. 95, 851–859 (2012).
CAS  Article  Google Scholar 

17.
Ait-El-Mokhtar, M. et al. Use of mycorrhizal fungi in improving tolerance of the date palm (Phoenix dactylifera L.) seedlings to salt stress. Sci. Hort. 253, 429–438 (2019).
Article  Google Scholar 

18.
Zai, X. M., Zhu, S. N., Qin, P., Che, L. & Luo, F. X. Effect of Glomus mosseae on chlorophyll content, chlorophyll fluorescence parameters, and chloroplast ultrastructure of beach plum (Prunus maritima) under NaCl stress. Photosynthetica 50, 323–328 (2012).
CAS  Article  Google Scholar 

19.
Navarro, J. M., Pérez-Tornero, O. & Morte, A. Alleviation of salt stress in citrus seedlings inoculated with arbuscular mycorrhizal fungi depends on the rootstock salt tolerance. J. Plant Physiol. 171, 76–85 (2014).
CAS  PubMed  Article  Google Scholar 

20.
Toro, M., Azcon, R. & Herrera, R. Effects on yield and nutrition of mycorrhizal and nodulated Pueraria phaseolides exerted by P-solubilizing rhizobacteria. Biol. Fertil. Soils 21, 23–29 (1996).
Article  Google Scholar 

21.
Singh, S. & Kapoor, K. K. Inoculation with phosphate-solubilizing microorganisms and a vesicular-arbuscular mycorrhizal fungus improves dry matter yield and nutrient uptake by wheat grown in a sandy soil. Biol. Fertil. Soils 28, 139–144 (1999).
CAS  Article  Google Scholar 

22.
Osorio, N. W. & Habte, M. Synergistic influence of an arbuscular mycorrhizal fungus and a P solubilizing fungus on growth and P uptake of Leucaena leucocephala in an Oxisol. Arid Land Res. Manag. 15, 263–274 (2001).
CAS  Article  Google Scholar 

23.
Khan, M. S., Zaidi, A. & Wani, P. A. Role of phosphate-solubilizing microorganisms in sustainable agriculture—A review. Agron. Sustain Dev. 27, 29–43 (2007).
Article  Google Scholar 

24.
Saxena, J., Saini, A., Ravi, I., Chandra, S. & Garg, V. Consortium of phosphate-solubilizing bacteria and fungi for promotion of growth and yield of chickpea (Cicer arietinum). J. Crop Improv. 29, 353–369 (2015).
CAS  Article  Google Scholar 

25.
Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis (Academic Press, 2008).
Google Scholar 

26.
Ben-Laouane, R., Baslam, M., Ait-El-Mokhtar, M., Anli, M. & Meddich, A. Potential of native arbuscular mycorrhizal fungi, rhizobia, and/or green compost as alfalfa (Medicago sativa) enhancers under salinity. Microorganisms 8, 1695 (2020).
CAS  PubMed Central  Article  PubMed  Google Scholar 

27.
Hodge, A., Campbell, C. D. & Fitter, A. H. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature 413, 297–299 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

28.
Johansen, A., Finlay, R. D. & Olsson, P. A. Nitrogen metabolism of external hyphae of the arbuscular mycorrhizal fungus Glomus intraradices. New Phytol. 133, 705–712 (1996).
CAS  Article  Google Scholar 

29.
Vicente-Sánchez, J. et al. Arbuscular mycorrhizal symbiosis alleviates detrimental effects of saline reclaimed water in lettuce plants. Mycorrhiza 24, 339–348 (2014).
PubMed  Article  CAS  Google Scholar 

30.
Abdel-Fattah, G. M. & Asrar, A. W. A. Arbuscular mycorrhizal fungal application to improve growth and tolerance of wheat (Triticum aestivum L.) plants grown in saline soil. Acta Physiol. Plant. 34, 267–277 (2012).
CAS  Article  Google Scholar 

31.
Marschner, P. Rhizosphere biology. In Marschner’s Mineral Nutrition of Higher Plants 3rd edn (ed. Marschner, P.) 369–388 (Academic Press, 2012).
Google Scholar 

32.
Abd-Allah, E. F. & Egamberdieva, D. Arbuscular mycorrhizal fungi enhance basil tolerance to salt stress through improved physiological and nutritional status. Pak. J. Bot. 48, 37–45 (2016).
Google Scholar 

33.
Van den Driessche, R. Effects of nutrients on stock performance in the forest. In Mineral Nutrition of Conifer Seedlings (ed. van den Driessche, R.) 229–260 (CRC Press, 1991).
Google Scholar 

34.
Ebel, R. C., Duan, X., Still, D. W. & Augé, R. M. Xylem sap abscisic acid concentration and stomatal conductance of mycorrhizal Vigna unguiculata in drying soil. New Phytol. 135, 755–761 (1997).
CAS  Article  Google Scholar 

35.
Ruiz-Lozano, J. M. & Aroca, R. Host response to osmotic stresses: Stomatal behaviour and water use efficiency of arbuscular mycorrhizal plants. In Arbuscular Mycorrhizas: Physiology and Function 239–256 (Springer, 2010).
Google Scholar 

36.
Birhane, E., Sterck, F. J., Fetene, M., Bongers, F. & Kuyper, T. W. Arbuscular mycorrhizal fungi enhance photosynthesis, water use efficiency, and growth of frankincense seedlings under pulsed water availability conditions. Oecologia 169, 895–904 (2012).
ADS  PubMed  PubMed Central  Article  Google Scholar 

37.
Evelin, H., Giri, B. & Kapoor, R. Ultrastructural evidence for AMF mediated salt stress mitigation in Trigonellafoenum graecum. Mycorrhiza 23, 71–86 (2012).
PubMed  Article  CAS  Google Scholar 

38.
Aroca, R. et al. Arbuscular mycorrhizal symbiosis influences strigolactone production under salinity and alleviates salt stress in lettuce plants. J. Plant Physiol. 170, 47–55 (2013).
CAS  PubMed  Article  Google Scholar 

39.
Jungklang, J. Physiological and biochemical mechanisms of salt tolerance in Sesbania rostrata Berm and Obem. PhD Thesis (Agric Univ Teckuba, 2005).

40.
Baker, N. R. & Rosenqvist, E. Applications of chlorophyll fluorescence can improve crop production strategies: Examination of future possibilities. J. Exp. Bot. 55, 1607–1621 (2004).
CAS  PubMed  Article  Google Scholar 

41.
Nwugo, C. C. & Huerta, A. J. Effects of silicon nutrition on cadmium uptake, growth and photosynthesis of rice plants exposed to low-level cadmium. Plant Soil 311, 73–86 (2008).
CAS  Article  Google Scholar 

42.
Henriques, F. S. Leaf chlorophyll fluorescence: Background and fundamentals for plant biologist. Bot. Rev. 75, 249–270 (2009).
Article  Google Scholar 

43.
Gong, M. G., Tang, M., Chen, H., Zhang, Q. & Feng, X. Effects of two Glomus species on the growth and physiological performance of Sophor davidii seedlings under water stress. New For. 44, 399–408 (2013).
Article  Google Scholar 

44.
Kaschuk, G., Kuyper, T. W., Leffelaar, P. A., Hungria, M. & Giller, K. E. Are the rate of photosynthesis stimulated by the carbon sink strength of rhizobial and arbuscular mycorrhizal symbioses. Soil Biol. Biochem. 41, 1233–1244 (2009).
CAS  Article  Google Scholar 

45.
Hoagland, D. R. & Arnon, D. I. The water-culture method for growing plants without soil. Univ. Calif. Agric. Res. Stn. Circ. 347, 1–39 (1950).
Google Scholar 

46.
Bradstreet, R. B. The kjeldahl method of organic nitrogen. Anal. Chem. 26, 185–187 (1965).
Article  Google Scholar 

47.
Li, Z. G., Luo, Y. M. & Teng, Y. Research Methods of Soil and Environmental Microorganisms 64–83 (Science Press, 2008).
Google Scholar 

48.
Mcgonigle, T. P., Miller, M. H., Evans, D. G., Fairchild, G. L. & Swan, J. A. A new method which gives an objective measure of colonization of roots by vesicular-arbuscular mycorrhizal fungi. New Phytol. 115, 495–501 (1990).
Article  Google Scholar 

49.
Xie, Z., Song, F., Xu, H., Shao, H. & Song, R. Effects of silicon on photosynthetic characteristics of maize (Zea mays L.) on alluvial soil. Sci. World J. 2014, 1–6 (2014).
Google Scholar 

50.
Chen, X. L., Li, S. Q., Ren, X. L. & Li, S. X. Effect of atmospheric NH3 and hydroponic solution nitrogen levels on chlorophyll fluorescence of corn genotypes with different nitrogen use efficiencies. Acta Ecol. Sin. 28, 1026–1032 (2008).
CAS  Google Scholar  More

1.
Millennium Ecosystem Assessment (Program). Ecosystems and human well-being: our human planet: summary for decision-makers. The Millennium Ecosystem Assessment series. https://doi.org/10.1196/annals.1439.003 (2005).
2.
Mulder, C. et al. 10 Years later: revisiting priorities for science and society a decade after the millennium ecosystem assessment. Adv. Ecol. Res. 53, 1–53 (2015).

3.
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270 (2018).

4.
Díaz, S. et al. The IPBES Conceptual Framework—connecting nature and people. Curr. Opin. Environ. Sustainab. 14, 1–16 (2015).

5.
Hungate, B. A. et al. Linking biodiversity and ecosystem services: current uncertainties and the necessary next steps. Bioscience 64, 49–57 (2014).
Article  Google Scholar 

6.
Díaz, S., Fargione, J., Chapin, F. S. & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol. 4, e277 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).
PubMed  Article  Google Scholar 

8.
Harvey, E., Gounand, I., Ward, C. L. & Altermatt, F. Bridging ecology and conservation: from ecological networks to ecosystem function. J. Appl. Ecol. 54, 371–379 (2017).
Article  Google Scholar 

9.
Jacob, U. et al. Valuing biodiversity and ecosystem services in a complex marine ecosystem. (eds Belgrano, A., Woodward, G. & Jacob U.) in Aquatic Functional Biodiversity. 189–207 (Academic Press, 2015).

10.
Dee, L. E. et al. Operationalizing network theory for ecosystem service assessments. Trends Ecol. Evol. 32, 118–130 (2017).
PubMed  Article  Google Scholar 

11.
Bohan, D. et al. Networking our way to better ecosystem service provision. Trends Ecol. Evol. 31, 105–115 (2016).
Article  Google Scholar 

12.
Dunne, A. J. et al. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Article  Google Scholar 

13.
Binzer, A. et al. The susceptibility of species to extinctions in model communities. Basic Appl. Ecol. 12, 590–599 (2011).
Article  Google Scholar 

14.
Eklöf, A., Tang, S. & Allesina, S. Secondary extinctions in food webs: a Bayesian network approach. Methods Ecol. Evol. 4, 760–770 (2013).
Article  Google Scholar 

15.
Dunne, J. A. & Williams, R. J. Cascading extinctions and community collapse in model food webs. Philos. Trans. R. Soc. B Biol. Sci. 364, 1711–1723 (2009).
Article  Google Scholar 

16.
Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

17.
Dobson, A. Food-web structure and ecosystem services: insights from the Serengeti. Philos. Trans. R. Soc. B Biol. Sci. 364, 1665–1682 (2009).
Article  Google Scholar 

18.
Estrada, E. Food webs robustness to biodiversity loss: the roles of connectance, expansibility and degree distribution. J. Theor. Biol. 244, 296–307 (2007).
MathSciNet  PubMed  MATH  Article  PubMed Central  Google Scholar 

19.
Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).

20.
Curtsdotter, A. et al. Robustness to secondary extinctions: comparing trait-based sequential deletions in static and dynamic food webs. Basic Appl. Ecol. 12, 571–580 (2011).
Article  Google Scholar 

21.
Srinivasan, U. T., Dunne, J. A., Harte, J. & Martinez, N. D. Response of complex food webs to realistic extinction sequences. Ecology 88, 671–682 (2007).

22.
Larsen, T. H., Williams, N. M. & Kremen, C. Extinction order and altered community structure rapidly disrupt ecosystem functioning. Ecol. Lett. 8, 538–547 (2005).

23.
Dee, L. E., De Lara, M., Costello, C. & Gaines, S. D. To what extent can ecosystem services motivate protecting biodiversity? Ecol. Lett. 20, 935–946 (2017).
PubMed  Article  Google Scholar 

24.
Dunne, J., Williams Richard, J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Article  Google Scholar 

25.
Allesina, S., Bodini, A. & Pascual, M. Functional links and robustness in food webs. Philos. Trans. R. Soc. B Biol. Sci. 364, 1701–1709 (2009).
Article  Google Scholar 

26.
Staniczenko, P. P. A., Lewis, O. T., Jones, N. S. & Reed-Tsochas, F. Structural dynamics and robustness of food webs. Ecol. Lett. 13, 891–899 (2010).
PubMed  Article  Google Scholar 

27.
Kremen, C. Managing ecosystem services: what do we need to know about their ecology? Ecol. Lett. 8, 468–479 (2005).
PubMed  Article  Google Scholar 

28.
Allesina, S. et al. The robustness and restoration of a network of ecological networks. Science 5, 1–8 (2013).
Google Scholar 

29.
Bane, M. S., Pocock, M. J. O. & James, R. Effects of model choice, network structure, and interaction strengths on knockout extinction models of ecological robustness. Ecol. Evol. 8, 10794–10804 (2018).
PubMed  PubMed Central  Article  Google Scholar 

30.
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and robustness of marine food webs. Mar. Ecol. Prog. Ser. 273, 291–302 (2004).
ADS  Article  Google Scholar 

31.
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Müller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).
PubMed  Article  Google Scholar 

32.
Thellmann, K. et al. Tipping points in the supply of ecosystem services of a mountainous watershed in Southeast Asia. Sustain 10, 1–15 (2018).
Article  Google Scholar 

33.
Cardinale, B. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–68 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

34.
Eklöf, A. & Ebenman, B. Species loss and secondary extinctions in simple and complex model communities. J. Anim. Ecol. 75, 239–246 (2006).
PubMed  Article  Google Scholar 

35.
Vieira, M. C. & Almeida-Neto, M. A simple stochastic model for complex coextinctions in mutualistic networks: robustness decreases with connectance. Ecol. Lett. 18, 144–152 (2015).
PubMed  Article  Google Scholar 

36.
Wilmers, C. C., Estes, J. A., Edwards, M., Laidre, K. L. & Konar, B. Do trophic cascades affect the storage and flux of atmospheric carbon? An analysis of sea otters and kelp forests. Front. Ecol. Environ. 10, 409–415 (2012).
Article  Google Scholar 

37.
Estes, J. A. et al. Trophic downgrading of planet earth. Science https://doi.org/10.1126/science.1205106 (2011).

38.
He, Q. & Silliman, B. R. Consumer control as a common driver of coastal vegetation worldwide. Ecol. Monogr. 86, 278–294 (2016).

39.
Ives, A. R. & Cardinale, B. J. Food-web interactions govern the resistance of communities after non-random extinctions. Nature 429, 174–177 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Brose, U. Complex food webs prevent competitive exclusion among producer species. Proc. R. Soc. B Biol. Sci. 275, 2507–2514 (2008).

41.
Rudolf, V. H. W. & Lafferty, K. D. Stage structure alters how complexity affects stability of ecological networks. Ecol. Lett. 14, 75–79 (2011).
CAS  PubMed  Article  Google Scholar 

42.
De Visser, S. N., Freymann, B. P. & Olff, H. The Serengeti food web: empirical quantification and analysis of topological changes under increasing human impact. J. Anim. Ecol. 80, 484–494 (2011).
PubMed  Article  Google Scholar 

43.
Perry, G. L. W., Moloney, K. A. & Etherington, T. R. Using network connectivity to prioritise sites for the control of invasive species. J. Appl. Ecol. 54, 1238–1250 (2017).
Article  Google Scholar 

44.
Gross, K. & Cardinale, B. J. The functional consequences of random vs. ordered species extinctions. Ecol. Lett. 8, 409–418 (2005).
Article  Google Scholar 

45.
Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).

46.
Gaston, K. J. et al. Population abundance and ecosystem service provision: the case of birds. Bioscience 68, 264–272 (2018).
PubMed  PubMed Central  Article  Google Scholar 

47.
Davies, T. W. et al. Dominance, biomass and extinction resistance determine the consequences of biodiversity loss for multiple coastal ecosystem processes. PLoS ONE 6, e28362 (2011).

48.
Balvanera, P., Kremen, C. & Martínez-Ramos, M. Applying community structure analysis to ecosystem function: examples from pollination and carbon storage. Ecol. Appl. 15, 360–375 (2005).

49.
Xiao, H. et al. Win-wins for biodiversity and ecosystem service conservation depend on the trophic levels of the species providing services. J. Appl. Ecol. 55, 2160–2170 (2018).
Article  Google Scholar 

50.
Dobson, A., Allesina, S., Lafferty, K. & Pascual, M. The assembly, collapse and restoration of food webs. Philos. Trans. R. Soc. B Biol. Sci. 364, 1803–1806 (2009).
Article  Google Scholar 

51.
McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 1–8 (2016).
Article  CAS  Google Scholar 

52.
Hechinger, R. F. et al. Food webs including parasites, biomass, body sizes, and life stages for three California/Baja California estuaries. Ecology 92, 791 (2011).
Article  Google Scholar 

53.
California Department of Fish and Wildlife. 2018-2019 California Saltwater Sport Fishing Regulations. p. 12–14 (2018).

54.
eBird. eBird: an online database of bird distribution and abundance. https://ebird.org. (2012).

55.
Dee, L. E. et al. When do ecosystem services depend on rare species? Trends Ecol. Evol. xx, 1–13 (2019).
Google Scholar 

56.
Strimas-Mackey, M., Miller, E. & Hochachka, W. Cornell Lab of Ornithology. eBird Data Extraction and Processing in R [R package auk version 0.3.2]. (Comprehensive R Archive Network (CRAN), 2019).

57.
Secretaria de Medio Ambiente Y Recursos Naturales. Secretaria de Medio Ambiente Y Recursos Naturales. Subsecretaria de gestion para la proteccion ambiental. (2018).

58.
Kones, J. K., Soetaert, K., van Oevelen, D. & Owino, J. O. Are network indices robust indicators of food web functioning? A Monte Carlo approach. Ecol. Modell. 220, 370–382 (2009).

59.
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Syst. 1695, 426 (2006).
Google Scholar 

60.
Booth, J. E., Gaston, K. J., Evans, K. L. & Armsworth, P. R. The value of species rarity in biodiversity recreation: a birdwatching example. Biol. Conserv. 144, 2728–2732 (2011).
Article  Google Scholar 

61.
Wang, H., Zhewei, W., Junhao, G., Wang, S. & Huang, Z. Personalized PageRank to a Target Node (Cornell University, 2020).

62.
Bryan, K. & Leise, T. The linear algebra behind Google. SIAM Rev. 3, 13 (2009).
MATH  Google Scholar 

63.
Allesina, S. & Pascual, M. Googling food webs: can an eigenvector measure species’ importance for coextinctions? PLoS Comput. Biol. 5, e1000494 (2009).

64.
Rabinwitz, D. Seven forms of rarity. (ed Sygne, H.) in The Biological Aspects of Rare Plant Conservation. 205–217 (John Wiley & Sons Ltd., 1981).

65.
Pimm, S. L., Jones, H. L. & Diamond, J. On the risk of extinction. Am. Nat. 132, 757–785 (1988).
Article  Google Scholar 

66.
Lyons, K. G., Brigham, C. A., Traut, B. H. & Schwartz, M. W. Rare species and ecosystem functioning. Conserv. Biol. 19, 1019–1024 (2005).
Article  Google Scholar 

67.
Smith, M. D. & Knapp, A. K. Dominant species maintain ecosystem function with non-random species loss. Ecol. Lett. 6, 509–517 (2003).

68.
Jacob, U. et al. The role of body size in complex food webs. A cold case. Adv. Ecol. Res. 45, 181–223 (2011).

69.
Lafferty, K. D. et al. Parasites in food webs: the ultimate missing links. Ecol. Lett. 11, 533–546 (2008).
PubMed  PubMed Central  Article  Google Scholar  More

1.
ICES. Final report of the Working Group on Nephrops Surveys (WGNEPS), 2–3 November 2017. (2017).
2.
EU. Council Regulation (EU) 2019/124 of 30 January 2019 fixing for 2019 the fishing opportunities for certain fish stocks and groups of fish stocks, applicable in Union waters and, for Union fishing vessels, in certain non-Union waters. L29, 1–166 (2019).

3.
EUROSTAT. The collection and compilation of fish catch and landing statistics in member coutries of the European economic area. (2020).

4.
Aguzzi, J., Bozzano, A. & Sardà, F. First observations on Nephrops norvegicus (L.) burrow densites on the continental shelf off the Catalan coast (western Mediterranean). Crustaceana 77, 299–310 (2004).
Article  Google Scholar 

5.
Maynou, F. X., Sardà, F. & Conan, G. Y. Assessment of the spatial structure and biomass evaluation of Nephrops norvegicus (L.) populations in the northwestern Mediterranean by geostatistics. ICES J. Mar. Sci. 55, 102–120 (1998).

6.
Sala, A. Influence of tow duration on catch performance of trawl survey in the Mediterranean Sea. PLoS ONE 13, (2018).

7.
Farmer, A. S. D. Synopsis of the biological data on the Norway lobster Nephrops norvegicus (Linnaeus, 1758). FAO Fish. Synopsis 112, 1–97 (1975).
Google Scholar 

8.
Atkinson, R. J. A. & Eastman, L. B. Burrow dwelling in Crustacea. Nat. Hist. Crustac. 2, 78–117 (2015).
Google Scholar 

9.
Sbragaglia, V. et al. Fighting over burrows: the emergence of dominance hierarchies in the Norway lobster (Nephrops norvegicus ). J. Exp. Biol. 220, 4624–4633 (2017).
Article  Google Scholar 

10.
Aguzzi, J. & Sardà, F. A history of recent advancements on Nephrops norvegicus behavioral and physiological rhythms. Rev. Fish Biol. Fish. 18, 235–248 (2008).
Article  Google Scholar 

11.
Aguzzi, J., Sardà, F., Abelló, P., Company, J. B. & Rotllant, G. Diel and seasonal patterns of Nephrops norvegicus (Decapoda: Nephropidae) catchability in the western Mediterranean. Mar. Ecol. Prog. Ser. 258, 201–211 (2003).
ADS  Article  Google Scholar 

12.
Bell, M. C., Redant, F. & Tuck, I. Nephrops species. In Lobsters: Biology (ed. Phillips, B.) 412–461 (Oxford Blackwell Publishing, 2006).
Google Scholar 

13.
Sbragaglia, V. et al. Dusk but not dawn burrow emergence rhythms of Nephrops norvegicus (Crustacea: Decapoda). Sci. Mar. 77, 641–647 (2014).
Article  Google Scholar 

14.
Chapman, C. J., Johnstone, A. D. F. & Rice, A. L. The Behaviour and Ecology of the Norway Lobster, _Nephrops norvegicus_ (L). Barnes H Proc. 9th Eur. Mar. Biol. Symp. Aberdeen Univ. Press. Aberdeen 59–74 (1975).

15.
Aguzzi, J., Company, J. B. & Sardà, F. The activity rhythm of berried and unberried females of Nephrops norvegicus (Decapoda, Nephropidae). Crustaceana 80, 1121–1134 (2007).
Article  Google Scholar 

16.
Sbragaglia, V., García, J. A., Chiesa, J. J. & Aguzzi, J. Effect of simulated tidal currents on the burrow emergence rhythms of the Norway lobster (Nephrops norvegicus). Mar. Biol. 162, 2007–2016 (2015).
Article  Google Scholar 

17.
Tuck, I. D., Parsons, D. M., Hartill, B. W. & Chiswell, S. M. Scampi (Metanephrops challengeri) emergence patterns and catchability. ICES J. Mar. Sci. 72, i199–i210 (2015).
Article  Google Scholar 

18.
Aguzzi, J., Company, J. B. & Sardà, F. Feeding activity rhythm of Nephrops norvegicus of the western Mediterranean shelf and slope grounds. Mar. Biol. 144, 463–472 (2004).
Article  Google Scholar 

19.
Hemmi, J. M. Predator avoidance in fiddler crabs: 1. Escape decisions in relation to the risk of predation. Anim. Behav. 69, 603–614 (2005).

20.
Leocadio, A., Weetman, A. & Wieland, K. Using UWTV surveys to assess and advise on Nephrops stocks. ICES Cooperative Research Report No. 340. 49 (2018).

21.
Morello, E. B., Antolini, B., Gramitto, M. E., Atkinson, R. J. A. & Froglia, C. The fishery for Nephrops norvegicus (Linnaeus, 1758) in the central Adriatic Sea (Italy): preliminary observations comparing bottom trawl and baited creels. Fish. Res. 95, 325–331 (2009).
Article  Google Scholar 

22.
ICES. Report of the Working Group on Nephrops Surveys (WGNEPS) 6–8 November 2018. (2018).

23.
ICES. Working Group on Nephrops Surveys (WGNEPS; outputs from 2019). ICES Scientific Reports. 2:16. https://doi.org/10.17895/ices.pub.5968 (2020).

24.
Campbell, N., Dobby, H. & Bailey, N. Investigating and mitigating uncertainties in the assessment of Scottish Nephrops norvegicus populations using simulated underwater television data. ICES J. Mar. Sci. 66, 646–655 (2009).
Article  Google Scholar 

25.
Martinelli, M. et al. Towed underwater television towards the quantification of Norway lobster, squat lobsters and sea pens in the Adriatic Sea. Acta Adriat. 54, 3–12 (2013).
Google Scholar 

26.
ICES. Report of the Workshop and training course on Nephrops Burrow Identification (WKNEPHBID). (2008).

27.
ICES. Report on the Workshop on Nephrops Burrow Counting (WKNEPS) 9–11 November 2016. (2016).

28.
ICES. Report of the Study Group on Nephrops (WKNEPH), 28 February–1 March 2009. (2009).

29.
ICES. Report of the Benchmark Workshop on Nephrops (WKNEPH), 2–6 March 2009. (2009).

30.
Sardà, F. & Aguzzi, J. A review of burrow counting as an alternative to other typical methods of assessment of Norway lobster populations. Rev. Fish Biol. Fish. 22, 409–422 (2012).
Article  Google Scholar 

31.
Rice, A. L. & Chapman, C. J. Observations on the burrows and burrowing behaviour of two mud-dwelling decapod crustaceans, Nephrops norvegicus and Goneplax rhomboides. Mar. Biol. Int. J. Life Ocean. Coast. Waters 10, 330–342 (1971).

32.
Chapman, C. J. & Rice, A. L. Some direct observations on the ecology and behaviour of the Norway lobster Nephrops norvegicus. Mar. Biol. Int. J. Life Ocean. Coast. Waters 10, 321–329 (1971).

33.
Cobb, J. S. & Wang, D. Fisheries biology of lobsters and crayfishes. Provenzano A.D. Biol. Crustac. 10, 167–247 (1985).

34.
Maynou, F. & Sardà, F. Nephrops norvegicus population and morphometrical characteristics in relation to substrate heterogeneity. Fish. Res. 30, 139–149 (1997).
Article  Google Scholar 

35.
Tuck, I. D., Atkinson, R. J. A. & Chapman, C. J. The structure and seasonal variability in the spatial distribution of nephrops norvegicus burrows. Ophelia 40, (1994).

36.
Tuck, I. D., Chapman, C. J. & Atkinson, R. J. A. Population biology of the Norway lobster, Nephrops norvegicus (L.) in the Firth of Clyde, Scotland – I: Growth and density. ICES J. Mar. Sci. 54, 125–135 (1997).

37.
ICES. Report of the Study Group on Nephrops Surveys (SGNEPS), 6–8 March 2012. (2012).

38.
Gerritsen, H. & Lordan, C. Integrating vessel monitoring systems (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution. ICES J. Mar. Sci. 68, 245–252 (2011).
Article  Google Scholar 

39.
Ligas, A., Sartor, P. & Colloca, F. Trends in population dynamics and fishery of Parapenaeus longirostris and Nephrops norvegicus in the Tyrrhenian Sea (NW Mediterranean): the relative importance of fishery and environmental variables. Mar. Ecol. 32, 25–35 (2011).
ADS  Article  Google Scholar 

40.
Morello, E. B., Froglia, C. & Atkinson, R. J. A. Underwater television as a fishery-independent method for stock assessment of Norway lobster (Nephrops norvegicus) in the central Adriatic Sea (Italy). ICES J. Mar. Sci. 64, 1116–1123 (2007).
Article  Google Scholar 

41.
Atkinson, R. J. A. & Naylor, E. An endogenous activity rhythm and the rhythmicity of catches of Nephrops norvegicus (L). J. Exp. Mar. Bio. Ecol. 25, 95–108 (1976).
Article  Google Scholar 

42.
Hammond, R. D. & Naylor, E. Effects of dusk and dawn on locomotor activity rhythms in the Norway lobster Nephrops norvegicus. Mar. Biol. 39, 253–260 (1977).
Article  Google Scholar 

43.
Katoh, E., Sbragaglia, V., Aguzzi, J. & Breithaupt, T. Sensory biology and behaviour of Nephrops norvegicus. Adv. Mar. Biol. 64, 35–106 (2013).
Google Scholar 

44.
Aguzzi, J., Allué, R. & Sardà, F. Characterisation of seasonal and diel variations in Nephrops norvegicus (Decapoda: Nephropidae) landings off the Catalan Coasts. Fish Res. 69, 293–300 (2004).

45.
Powell, A. & Eriksson, S. P. Reproduction: life cycle, larvae and larviculture. In Advances in Marine Biology 64, 201–245 (Elsevier, 2013).

46.
Refinetti, R. Circadian Physiology. Fr. Taylor, New York. https://doi.org/10.1201/b19527 (2006).

47.
Chiesa, J. J., Aguzzi, J., García, J. A., Sardà, F. & De La Iglesia, H. O. Light intensity determines temporal niche switching of behavioral activity in deep-water Nephrops norvegicus (Crustacea: Decapoda). J. Biol. Rhythms 25, 277–287 (2010).
Article  Google Scholar 

48.
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 73, 3–36 (2011).

49.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org (2020).

50.
Wood, S. N., Pya, N. & Säfken, B. Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).
MathSciNet  CAS  Article  Google Scholar 

51.
Aguzzi, J. & Company, J. B. Chronobiology of deep-water decapod crustaceans on continental margins. In Advances in Marine Biology 58, 155–225 (Elsevier, 2010).

52.
Gibson, R. N., Atkinson, R. J. A. & Gordon, J. D. M. Challenges to the assessment of benthic populations and biodiversity as a result of rhythmic behaviour: video solutions from cabled observatories. Oceanogr. Mar. Biol. An Annu. Rev. 50, 233–284 (2012).
Google Scholar 

53.
Catchpole, T. L. & Revill, A. S. Gear technology in Nephrops trawl fisheries. Rev. Fish Biol. Fish. 18, 17–31 (2008).
Article  Google Scholar 

54.
Main, J. & Sangster, G. I. The Behaviour of the Norway Lobster, Nephrops norvegicus (L.), During Trawling. Scottish Fish. Res. Rep. 34, 1–23 (1985).

55.
Ungfors, A. et al. Nephrops fisheries in European waters. Adv. Mar. Biol. 64, 247–314 (2013).
Article  Google Scholar 

56.
Jerlov, N. G. Optical Oceanography 194 (Elsevier, 1968).

57.
Herring, P. The biology of the deep ocean. J. Hered. 93, (2002).

58.
Laidre, M. E. Evolutionary ecology of burrow construction. In The Natural History of the Crustacea: Life Histories (eds. Thiel, M. & Wellborn, G.) 5, 279–302 (Oxford University Press, 2018).

59.
Trenkel, V. M., Rochet, M. & Mahevas, S. Interactions between fishing strategies of Nephrops trawlers in the Bay of Biscay and Norway lobster diel activity patterns. Fish. Manag. Ecol. 15, 11–18 (2008).
Google Scholar 

60.
Aguzzi, J. et al. Monochromatic blue light entrains diel activity cycles in the Norway lobster, Nephrops norvegicus (L.) as measured by automated video-image analysis. Sci. Mar. 73, 773–783 (2009).

61.
Colloca, F., Scarcella, G. & Libralato, S. Recent trends and impacts of fisheries exploitation on Mediterranean stocks and ecosystems. Front. Mar. Sci. 4, (2017).

62.
Marine Institute. The Stock Book 2019: Annual Review of Fish Stocks in 2019 with Management Advice for 2020. Mar. Institute, Galway, Irel. (2019).

63.
Aguzzi, J. et al. New high-tech flexible networks for the monitoring of deep-sea ecosystems. Environ. Sci. Technol. 53, 6616–6631 (2019).
ADS  CAS  Article  Google Scholar 

64.
Chatzievangelou, D., Aguzzi, J., Ogston, A., Suárez, A. & Thomsen, L. Visual monitoring of key deep-sea megafauna with an Internet operated crawler as a tool for ecological status assessment. Prog. Oceanogr. 102321 (2020).

65.
Rountree, R. A. et al. Towards an optimal design for ecosystem-level ocean observatories. Front. Mar. Sci. 1–69 (2019).

66.
Masmitja, I. et al. Mobile robotic platforms for the acoustic tracking of deep water demersal fishery resources. Sci. Robot. 5, eabc3701 (2020).

67.
Aguzzi, et al. Fish-stock assessment using video imagery from worldwide cabled observatory networks. ICES J. Mar. Sci. 77, 2396–2410 (2020).
Article  Google Scholar  More

1.
Griffiths, H. J., Barnes, D. K. & Linse, K. Towards a generalized biogeography of the Southern Ocean benthos. J. Biogeogr. 36(1), 162–177 (2009).
Article  Google Scholar 
2.
Halanych, K. M. & Mahon, A. R. Challenging dogma concerning biogeographic patterns of Antarctica and the Southern Ocean. Annu. Rev. Ecol. Evol. Syst. 49, 355–378 (2018).
Article  Google Scholar 

3.
Broyer, C. & Koubbi, P. Chapter 1.1. Phylogeography and population genetics. In: De Broyer C., Koubbi P., Griffiths H.J., Raymond B., Udekem d’Acoz C. d’, et al. (eds.) Biogeographic Atlas of the Southern Ocean (Scientific Committee on Antarctic Research, Cambridge, 2014) pp. 2–5.

4.
Allcock, A. L. & Strugnell, J. M. Southern Ocean diversity: new paradigms from molecular ecology. Trends Ecol. Evol. 27(9), 520–528 (2012).
PubMed  Article  Google Scholar 

5.
Holder, K., Montgomerie, R. & Friesen, V. L. A test of the glacial refugium hypothesis using patterns of mitochondrial and nuclear DNA sequence variation in rock ptarmigan (Lagopus mutus). Evolution 53(6), 1936–1950 (1999).
CAS  PubMed  Google Scholar 

6.
Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refuges revisited: individualistic responses of species in space and time. Proc. R. Soc. Lond. B Biol. Sci. 277(1682), 661–671 (2010).
Google Scholar 

7.
Hewitt, G. M. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Lin. Soc. 58(3), 247–276 (1996).
Article  Google Scholar 

8.
Hewitt, G. M. The structure of biodiversity–insights from molecular phylogeography. Front. Zool. 1(1), 4 (2004).
PubMed  PubMed Central  Article  Google Scholar 

9.
Fedorov, V. B. & Stenseth, N. C. Multiple glacial refugia in the North American Arctic: inference from phylogeography of the collared lemming (Dicrostonyx groenlandicus). Proc. R. Soc. Lond. B Biol. Sci. 269(1505), 2071–2077 (2002).
Article  Google Scholar 

10.
Dalén, L. et al. Ancient DNA reveals lack of postglacial habitat tracking in the arctic fox. Proc. Natl. Acad. Sci. 104(16), 6726–6729 (2007).
ADS  PubMed  Article  Google Scholar 

11.
Thatje, S., Hillenbrand, C. D., Mackensen, A. & Larter, R. Life hung by a thread: endurance of Antarctic fauna in glacial periods. Ecology 89(3), 682–692 (2008).
PubMed  Article  Google Scholar 

12.
Weihe, E. & Abele, D. Differences in the physiological response of inter-and subtidal Antarctic limpets Nacella concinna to aerial exposure. Aquat. Biol. 4(2), 155–166 (2008).
Article  Google Scholar 

13.
Abele, D. et al. Pelagic and benthic communities of the Antarctic ecosystem of Potter Cove: genomics and ecological implications. Mar. Genom. 33, 1–11 (2017).
CAS  Article  Google Scholar 

14.
Provan, J. & Bennett, K. D. Phylogeographic insights into cryptic glacial refuges. Trends Ecol. Evol. 23(10), 564–571 (2008).
PubMed  Article  Google Scholar 

15.
Thatje, S., Hillenbrand, C. D. & Larter, R. On the origin of Antarctic marine benthic community structure. Trends Ecol. Evol. 20(10), 534–540 (2005).
PubMed  Article  Google Scholar 

16.
Díaz, A. et al. Genetic structure and demographic inference of the regular sea urchin Sterechinus neumayeri (Meissner, 1900) in the Southern Ocean: the role of the last glaciation. PLoS ONE 13(6), e0197611 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

17.
González-Wevar, C. A., Saucède, T., Morley, S. A., Chown, S. L. & Poulin, E. Extinction and recolonization of maritime Antarctica in the limpet Nacella concinna (Strebel, 1908) during the last glacial cycle: toward a model of Quaternary biogeography in shallow Antarctic invertebrates. Mol. Ecol. 22(20), 5221–5236 (2013).
PubMed  Article  Google Scholar 

18.
Fraser, C. I., Terauds, A., Smellie, J., Convey, P. & Chown, S. L. Geothermal activity helps life survive glacial cycles. Proc. Natl. Acad. Sci. 111(15), 5634–5639 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

19.
Maggs, C. A. et al. Evaluating signatures of glacial refugia for North Atlantic benthic marine taxa. Ecology 89(sp11), S108–S122 (2008).
PubMed  Article  Google Scholar 

20.
Lee, J. R. et al. Climate change drives expansion of Antarctic ice-free habitat. Nature 547(7661), 49 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

21.
Mcgaughran, A. et al. Contrasting phylogeographical patterns for springtails reflect different evolutionary histories between the Antarctic Peninsula and Continental Antarctica. J. Biogeogr. 37, 103–119 (2010).
Article  Google Scholar 

22.
Mcgaughran, A., Stevens, M. I., Hogg, I. D. & Carapelli, A. Extreme glacial legacies: a synthesis of the Antarctic springtail phylogeographic record. Insects 2, 62–82 (2011).
PubMed  PubMed Central  Article  Google Scholar 

23.
Fraser, C. I., Nikula, R., Ruzzante, D. E. & Waters, J. M. Poleward bound: biological impacts of Southern Hemisphere glaciation. Trends Ecol. Evol. 27(8), 462–471 (2012).
PubMed  Article  Google Scholar 

24.
Moore, J. M., Carvajal, J. I., Rouse, G. W. & Wilson, N. G. The Antarctic circumpolar current isolates and connects: structured circumpolarity in the sea star Glabraster Antarctica. Ecol. Evol. 8(21), 10621–10633 (2018).
PubMed  PubMed Central  Article  Google Scholar 

25.
Beu, A. G. Before the ice: biogeography of Antarctic Paleogene molluscan faunas. Palaeogeogr. Palaeoclimatol. Palaeoecol. 284(3–4), 191–226 (2009).
Article  Google Scholar 

26.
Linse, K. Chapter 5.11. Bivalvia. In: De Broyer C., Koubbi P., Griffiths H.J., Raymond B., Udekem d’Acoz C. d’, et al. (eds.) Biogeographic Atlas of the Southern Ocean (Scientific Committee on Antarctic Research, Cambridge, 2014), pp. 126–127.

27.
Linse, K., Cope, T., Lörz, A. N. & Sands, C. Is the Scotia Sea a centre of Antarctic marine diversification? Some evidence of cryptic speciation in the circum-Antarctic bivalve Lissarca notorcadensis (Arcoidea: Philobryidae). Polar Biol. 30(8), 1059–1068 (2007).
Article  Google Scholar 

28.
Arntz, W. E., Gutt, J. & Klages, M. (1997). Antarctic marine biodiversity: an overview. In Antarctic Communities: Species, Structure and Survival 3–14 (Cambridge University Press, Cambridge, 1997).

29.
Brandt, A., Linse, K. & Mühlenhardt-Siegel, U. Biogeography of Crustacea and Mollusca of the SubAntarctic and Antarctic regions. Sci. Mar. 63(S1), 383–389 (1999).
Article  Google Scholar 

30.
Brandt, A., Linse, K. & Schüller, M. Bathymetric distribution patterns of Southern Ocean macrofaunal taxa: Bivalvia, Gastropoda, Isopoda and Polychaeta. Deep Sea Res. Part I 56(11), 2013–2025 (2009).
Article  Google Scholar 

31.
Canapa, A. et al. A molecular analysis of the systematics of three Antarctic bivalves. Ital. J. Zool. 67(S1), 127–132 (2000).
CAS  Article  Google Scholar 

32.
Page, T. J. & Linse, K. More evidence of speciation and dispersal across the Antarctic Polar Front through molecular systematics of Southern Ocean Limatula (Bivalvia: Limidae). Polar Biol. 25(11), 818–826 (2002).
Article  Google Scholar 

33.
Passos, F. D. & Magalhães, F. T. A comparative study of the Bivalvia (Mollusca) from the continental shelves of Antarctica and Brazil. Biota. Neotrop. 11(1), 143–155 (2011).
Article  Google Scholar 

34.
Jackson, J. A., Linse, K., Whittle, R. & Griffiths, H. J. The evolutionary origins of the Southern Ocean philobryid bivalves: hidden biodiversity, ancient persistence. PLoS ONE 10(4), e0121198 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
González-Wevar, C. A. et al. Cryptic speciation in Southern Ocean Aequiyoldia eightsii (Jay, 1839): Mio-Pliocene trans-Drake Passage separation and diversification. Prog. Oceanogr. 174, 44–54 (2019).
ADS  Article  Google Scholar 

36.
Powell, D. K., Tyler, P. A. & Peck, L. S. Effect of sperm concentration and sperm ageing on fertilisation success in the Antarctic soft-shelled clam Laternula elliptica and the Antarctic limpet Nacella concinna. Mar. Ecol. Prog. Ser. 215, 191–200 (2001).
ADS  Article  Google Scholar 

37.
Taylor, J. D. et al. Left in the cold? Evolutionary origin of Laternula elliptica, a keystone bivalve species of Antarctic benthos. Biol. J. Lin. Soc. 123(2), 360–376 (2018).
Article  Google Scholar 

38.
Lau, S. C., Grange, L. J., Peck, L. S. & Reed, A. J. The reproductive ecology of the Antarctic bivalve Aequiyoldia eightsii (Protobranchia: Sareptidae) follows neither Antarctic nor taxonomic patterns. Pol. Biol., 1–14 (2018).

39.
Helmuth, B., Veit, R. R. & Holberton, R. Long-distance dispersal of a subAntarctic brooding bivalve (Gaimardia trapesina) by kelp-rafting. Mar. Biol. 120(3), 421–426 (1994).
Article  Google Scholar 

40.
Hunter, R. L. & Halanych, K. M. Evaluating connectivity in the brooding brittle star Astrotoma agassizii across the Drake Passage in the Southern Ocean. J. Hered. 99(2), 137–148 (2008).
CAS  PubMed  Article  Google Scholar 

41.
Sherman, C. D. H., Hunt, A. & Ayre, D. J. Is life history a barrier to dispersal? Contrasting patterns of genetic differentiation along an oceanographically complex coast. Biol. J. Linn. Soc. 95, 106–116 (2008).
Article  Google Scholar 

42.
Thornhill, D. J., Mahon, A. R., Norenburg, J. L. & Halanych, K. M. Open-ocean barriers to dispersal: a test case with the Antarctic Polar Front and the ribbon worm Parborlasia corrugatus (Nemertea: Lineidae). Mol. Ecol. 17(23), 5104–5117 (2008).
CAS  PubMed  Article  Google Scholar 

43.
Janosik, A. M., Mahon, A. R. & Halanych, K. M. Evolutionary history of Southern Ocean Odontaster sea star species (Odontasteridae; Asteroidea). Polar Biol. 34(4), 575–586 (2011).
Article  Google Scholar 

44.
Thatje, S. Effects of capability for dispersal on the evolution of diversity in Antarctic benthos. Integr. Comp. Biol. ics105 (2012).

45.
Hüne, M. et al. Low level of genetic divergence between Harpagifer fish species (Perciformes: Notothenioidei) suggests a Quaternary colonization of Patagonia from the Antarctic Peninsula. Polar Biol. 38(5), 607–617 (2015).
Article  Google Scholar 

46.
Pelseneer, P. Mollusques (Amphineures, Gastropodes et Lamellibranches) Expédition Antartique Belge: Résultats Voyage du S. Y. Belgica en 1897–1898–1899 7 85, pp., pls. 1–9 (1903).

47.
Shabica, S. V. Reproductive biology of the brooding Antarctic lamellibranch Kidderia Subquadrata Pelseneer 1974. MSc Thesis. School of Oceanography, Oregon.

48.
Linse, K., Griffiths, H. J., Barnes, D. K. & Clarke, A. Biodiversity and biogeography of Antarctic and sub-Antarctic mollusca. Deep Sea Res. Part II 53(8), 985–1008 (2006).
ADS  Article  Google Scholar 

49.
Baird, H. P., Miller, K. J. & Stark, J. S. Genetic population structure in the Antarctic benthos: insights from the widespread amphipod Orchomenella franklini. PloS one 7(3), e34363 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Havermans, C. et al. Genetic and morphological divergences in the cosmopolitan deep-sea amphipod Eurythenes gryllus reveal a diverse abyss and a bipolar species. PLoS ONE 8(9), e74218 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Hoffman, J. I., Clarke, A., Linse, K. & Peck, L. S. Effects of brooding and broadcasting reproductive modes on the population genetic structure of two Antarctic gastropod molluscs. Mar. Biol. 158(2), 287–296 (2011).
Article  Google Scholar 

52.
González-Wevar, C. A., Nakano, T., Cañete, J. I. & Poulin, E. Molecular phylogeny and historical biogeography of Nacella (Patellogastropoda: Nacellidae) in the Southern Ocean. Mol. Phylogenet. Evol. 56(1), 115–124 (2010).
PubMed  Article  Google Scholar 

53.
Moreau, C. et al. Is reproductive strategy a key factor in understanding the evolutionary history of Southern Ocean Asteroidea (Echinodermata)?. Ecol. Evol. 9(15), 8465–8478 (2019).
PubMed  PubMed Central  Article  Google Scholar 

54.
Thompson, A. F., Heywood, K. J., Thorpe, S. E., Renner, A. H. & Trasviña, A. Surface circulation at the tip of the Antarctic Peninsula from drifters. J. Phys. Oceanogr. 39(1), 3–26 (2009).
ADS  Article  Google Scholar 

55.
Du, G., Zhang, Z., Zhou, M., Zhu, Y. & Zhong, Y. The upper 1000-m slope currents North of the South Shetland Islands and Elephant Island based on ship cruise observations. J. Ocean Univ. China 17(2), 420–432 (2018).
ADS  Article  Google Scholar 

56.
Hofmann, E. E., Klinck, J. M., Lascara, C. M. & Smith, D. A. Water mass distribution and circulation west of the Antarctic Peninsula and including Bransfield Strait. Found. Ecol. Res. West Antarctic Peninsula 70, 61–80 (1996).
Article  Google Scholar 

57.
Leiva, C., Riesgo, A., Avila, C., Rouse, G. W. & Taboada, S. Population structure and phylogenetic relationships of a new shallow-water Antarctic phyllodocid annelid. Zoolog. Scr. 47(6), 714–726 (2018).
Article  Google Scholar 

58.
Savidge, D. K. & Amft, J. A. Circulation on the West Antarctic Peninsula derived from 6 years of shipboard ADCP transects. Deep Sea Res. Part I 56(10), 1633–1655 (2009).
Article  Google Scholar 

59.
Veit-Köhler, G. et al. Oceanographic and topographic conditions structure benthic meiofauna communities in the Weddell Sea, Bransfield Strait and Drake Passage (Antarctic). Prog. Oceanogr. 162, 240–256 (2018).
ADS  Article  Google Scholar 

60.
Barlett, E. M. R., Tosonotto, G. V., Piola, A. R., Sierra, M. E. & Mata, M. M. On the temporal variability of intermediate and deep waters in the Western Basin of the Bransfield Strait. Deep Sea Res. Part II 149, 31–46 (2018).
Article  Google Scholar 

61.
Riesgo, A., Taboada, S. & Avila, C. Evolutionary patterns in Antarctic marine invertebrates: an update on molecular studies. Mar. Genom. 23, 1–13 (2015).
Article  Google Scholar 

62.
Sugden, D. E. & Clapperton, C. M. The maximum ice extent on island groups in the Scotia Sea Antarctica. Quatern. Res. 7(2), 268–282 (1977).
ADS  Article  Google Scholar 

63.
Gersonde, R., Crosta, X., Abelmann, A. & Armand, L. Sea-surface temperature and sea ice distribution of the Southern Ocean at the EPILOG Last Glacial Maximum-a circum-Antarctic view based on siliceous microfossil records. Quatern. Sci. Rev. 24(7–9), 869–896 (2005).
ADS  Article  Google Scholar 

64.
Ingólfsson, Ó. & Hjort, C. Glacial history of the Antarctic Peninsula since the Last Glacial Maximum—a synthesis. Polar Res. 21(2), 227–234 (2002).
Google Scholar 

65.
Simms, A. R., Milliken, K. T., Anderson, J. B. & Wellner, J. S. The marine record of deglaciation of the South Shetland Islands, Antarctica since the Last Glacial Maximum. Quatern. Sci. Rev. 30(13–14), 1583–1601 (2011).
ADS  Article  Google Scholar 

66.
Herron, M. J. & Anderson, J. B. Late quaternary glacial history of the South Orkney Plateau Antarctica. Quatern. Res. 33(3), 265–275 (1990).
ADS  Article  Google Scholar 

67.
Anderson, J. B., Shipp, S. S., Lowe, A. L., Wellner, J. S. & Mosola, A. B. The Antarctic ice sheet during the last glacial maximum and its subsequent retreat history: a review. Quatern. Sci. Rev. 21(1–3), 49–70 (2002).
ADS  Article  Google Scholar 

68.
Nikula, R., Fraser, C. I., Spencer, H. G. & Waters, J. M. Circumpolar dispersal by rafting in two subantarctic kelp-dwelling crustaceans. Mar. Ecol. Prog. Ser. 405, 221–230 (2010).
ADS  CAS  Article  Google Scholar 

69.
Fraser, C. I., Morrison, A. & Rojas, P. O. (2020). Biogeographic Processes Influencing Antarctic and sub-Antarctic Seaweeds. In Antarctic Seaweeds 43–57 (Springer, Cham, 2020).

70.
Ávila, C. et al. Invasive marine species discovered on non-native kelp rafts in the warmest Antarctic island. Sci. Rep. 10(1), 1–9 (2020).
Article  CAS  Google Scholar 

71.
Takahata, N. & Slatkin, M. Private alleles in a partially isolated population II: distribution of persistence time and probability of emigration. Theor. Popul. Biol. 30(2), 180–193 (1986).
MathSciNet  CAS  PubMed  MATH  Article  Google Scholar 

72.
Stevens, M. I. & Hogg, I. D. Long-term isolation and recent range expansion from glacial refugia revealed for the endemic springtail Gomphiocephalus hodgsoni from Victoria Land Antarctica. Mol. Ecol. 12(9), 2357–2369 (2003).
CAS  PubMed  Article  Google Scholar 

73.
Rogers, A. D. Evolution and biodiversity of Antarctic organisms: a molecular perspective. Philos. Trans. R. Soc. B Biol. Sci. 362(1488), 2191–2214 (2007).
CAS  Article  Google Scholar 

74.
Dahlgren, T. G., Weinberg, J. R. & Halanych, K. M. Phylogeography of the ocean quahog (Arctica islandica): influences of paleoclimate on genetic diversity and species range. Mar. Biol. 137(3), 487–495 (2000).
Article  Google Scholar 

75.
Thorson, G. Reproductive and larval ecology of marine bottom invertebrates. Biol. Rev. 25(1), 1–45 (1950).
CAS  PubMed  Article  Google Scholar 

76.
Peck, L. S. & Bullough, L. W. Growth and population structure in the infaunal bivalve Yoldia eightsi in relation to iceberg activity at Signy Island Antarctica. Mar. Biol. 117(2), 235–241 (1993).
Article  Google Scholar 

77.
Waller, C. L., Barnes, D. K. & Convey, P. Ecological contrasts across an Antarctic land–sea interface. Austral Ecol. 31(5), 656–666 (2006).
Article  Google Scholar 

78.
Barnes, D. K. & Conlan, K. E. Disturbance, colonization and development of Antarctic benthic communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362(1477), 11–38 (2007).
PubMed  Article  Google Scholar 

79.
Hebert, P. D., Ratnasingham, S. & de Waard, J. R. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. Lond. B Biol. Sci. 270(Suppl 1), S96–S99 (2003).
CAS  Google Scholar 

80.
Cook, L. G., Edwards, R. D., Crisp, M. D. & Hardy, N. B. Need morphology always be required for new species descriptions?. Invertebr. Syst. 24(3), 322–326 (2010).
Article  Google Scholar 

81.
Chown, S. L. et al. The changing form of Antarctic biodiversity. Nature 522(7557), 431–438 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

82.
Wilson, N. G., Schrodl, M. & Halanych, K. M. Ocean barriers and glaciation: evidence for explosive radiation of mitochondrial lineages in the Antarctic sea slug Doris kerguelenensis (Mollusca, Nudibranchia). Mol. Ecol. 18, 965–984 (2009).
PubMed  Article  Google Scholar 

83.
Allcock, A. L. et al. Cryptic speciation and the circumpolarity debate: a case study on endemic Southern Ocean octopuses using the COI barcode of life. Deep Sea Res. II 58, 242–249 (2011).
ADS  Article  Google Scholar 

84.
Verheye, M. L., Backeljau, T. & d’Acoz, C. D. U. Looking beneath the tip of the iceberg: diversification of the genus Epimeria on the Antarctic shelf (Crustacea, Amphipoda). Polar Biol 39, 925–945 (2016).
Article  Google Scholar 

85.
Durand, J. D., Blel, H., Shen, K. N., Koutrakis, E. T. & Guinand, B. Population genetic structure of Mugil cephalus in the Mediterranean and Black Seas: a single mitochondrial clade and many nuclear barriers. Mar. Ecol. Prog. Ser. 474, 243–261 (2013).
ADS  Article  Google Scholar 

86.
Bonnet, T., Leblois, R., Rousset, F. & Crochet, P. A. A reassessment of explanations for discordant introgressions of mitochondrial and nuclear genomes. Evolution 71(9), 2140–2158 (2017).
PubMed  Article  Google Scholar 

87.
Kearse, M. et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28(12), 1647–1649 (2012).
PubMed  PubMed Central  Article  Google Scholar 

88.
Librado, P. & Rozas, J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25(11), 1451–1452 (2009).
CAS  PubMed  Article  Google Scholar 

89.
Excoffier, L. & Lischer, H. E. Arlequin suite ver 35: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10(3), 564–567 (2010).
PubMed  Article  Google Scholar 

90.
Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).
CAS  PubMed  PubMed Central  Article  Google Scholar 

91.
Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).
CAS  Google Scholar 

92.
Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph Stat. 5, 299–314 (1996).
Google Scholar 

93.
Dupanloup, I., Schneider, S. & Excoffier, L. A simulated annealing approach to define the genetic structure of populations. Mol. Ecol. 11(12), 2571–2581 (2002).
CAS  PubMed  Article  Google Scholar 

94.
Guillot, G., Mortier, F. & Estoup, A. GENELAND: a computer package for landscape genetics. Mol. Ecol. Notes 5(3), 712–715 (2005).
CAS  Article  Google Scholar 

95.
Salzburger, W., Ewing, G. B. & Von Haeseler, A. The performance of phylogenetic algorithms in estimating haplotype genealogies with migration. Mol. Ecol. 20(9), 1952–1963 (2011).
PubMed  Article  Google Scholar 

96.
Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123(3), 585–595 (1989).
CAS  PubMed  PubMed Central  Article  Google Scholar 

97.
Fu, Y. X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147(2), 915–925 (1997).
CAS  PubMed  PubMed Central  Article  Google Scholar 

98.
Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10(4), e1003537 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

99.
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9(8), 772 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

100.
Marko, P. B. & Moran, A. L. Out of sight, out of mind: high cryptic diversity obscures the identities and histories of geminate species in the marine bivalve subgenus Acar. J. Biogeogr. 36(10), 1861–1880 (2009).
Article  Google Scholar 

101.
Wilke, T., Schultheiß, R. & Albrecht, C. As time goes by: a simple fool’s guide to molecular clock approaches in invertebrates. Am. Malacol. Bull. 27(1/2), 25–45 (2009).
Article  Google Scholar 

102.
Ho, S. Y., Phillips, M. J., Cooper, A. & Drummond, A. J. Time dependency of molecular rate estimates and systematic overestimation of recent divergence times. Mol. Biol. Evol. 22(7), 1561–1568 (2005).
CAS  PubMed  Article  Google Scholar 

103.
Hoareau, T. B. Late glacial demographic expansion motivates a clock overhaul for population genetics. Syst. Biol. 65(3), 449–464 (2015).
PubMed  Article  Google Scholar 

104.
Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v1 6, 2014 (2016).

105.
Cornuet, J. M. et al. DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).
CAS  PubMed  Article  Google Scholar 

106.
Cabrera, A. A. & Palsbøll, P. J. Inferring past demographi changes from contemporary genetic data: a simulation-based evaluation of the ABC methods implemented in DIYABC. Mol. Ecol. Resour. 17(6), e94–e110 (2017).
CAS  PubMed  Article  Google Scholar  More

Zoologist Alice Hughes leads the landscape-ecology research group at Xishuangbanna Tropical Botanical Garden in Menglun, Yunnan province, China.Credit: Michael C. Orr

British zoologist Alice Hughes has been working at the Xishuangbanna Tropical Botanical Garden in Menglun, Yunnan province, in southern China, for nearly eight years. She reveals what she has learnt about the country’s approach to ecological conservation ahead of its first United Nations biodiversity conference in Kunming, Yunnan, in May.
What is your current role?
I lead the landscape-ecology research group at one of China’s most diverse botanical gardens. My team aims to better understand the lives of animals and how they interact with their environments. This helps us to create more effective methods of conserving a biodiverse environment.
The 18-person team, which is part of the Chinese Academy of Sciences (CAS), does everything from mapping biodiversity to researching the illegal and legal trade in different species, to find out where and why our natural world is changing. We then develop actionable measures to help stem the worst effects of those changes.
For example, many members of my team are working on the various species of Rhinolophus bats. Our genetic research suggests that around 70% of the Rhinolophus bat species haven’t been described in scientific literature. If you can’t describe a species, then you can’t conserve it.
How did you come to work in China, and what’s it like?
In 2011, I moved to Thailand from the United Kingdom as part of my postdoctoral research, before heading to Australia and finally taking a position in China in 2013.
At first, I was naive about how different the culture might be in Asian countries and it’s definitely been a steep learning curve. Adaptability is important. I think that many people in the West are much too ready to disbelieve or find fault with actions from China, and Chinese scientists. As a result, there is sensitivity in China’s research community, especially around things that have frequently been an issue, such as the regulation of the trade of exotic wildlife. As a foreigner, it is a challenging balance to provide advice without it being seen as overly critical. I can participate in these discussions at a high level because I have worked here long enough: people know I will listen and provide my perspectives based on fact, rather than prejudice.

I’ve worked on some difficult and potentially sensitive topics, such as endangered species, wildlife trade and the Belt and Road Initiative, which aims to link global trade routes to China through international infrastructure development, for example. I focus on the possible impacts these might have on biodiversity and how to minimize them. China is wary of being accused of driving extensive biodiversity loss, especially as it is investing in scientific research to avert it.
I’ve been invited to join a variety of both central and regional government working groups. It’s a privilege to be in those groups and work with some of the country’s top scientists, especially when it comes to international or UN meetings.
Working for CAS is the equivalent to being an employee of the government. Many people outside China still find it surprising that foreigners work in scientific institutes here, even though the number is growing.
I’m also unusual because I’m a foreign woman. In the time I have worked here I don’t think I have met any other European women with full-time faculty positions in China. In my institute there are more than ten foreign men with such positions. It’s not easy for Chinese women either. At the institute, we have 43 research groups; only 3 are led by women.
It’s important for all conservation scientists to be open minded and willing to find out what’s going to work in any country and culture to help tackle the global problem of biodiversity loss, and develop solutions that work in that societal context. A good example of that came last year, when some specialists called for a global ban on wild-meat consumption, amid fears over new diseases that originate in wild animals and cause outbreaks in humans. Now that might sound like a great idea, but in many parts of Africa there is not enough water to raise livestock, and people depend on wild meat for food.
This means that rather than recommending a blanket ban, a better solution might be a system that monitors what is traded, and provides recommendations as to which species can be eaten safely and sustainably.

Xishuanganna tropical botanic garden in southwest China’s Yunnan Province.Credit: Xinhua/eyevine

Do foreign scientists need to speak Chinese to work in China?
For most of my team, neither English nor Chinese is their first language. We have around 12 different nationalities, so discussions take place predominantly in English, as a default.
I work closely with my Chinese colleagues to make sure that our research work is properly communicated when it’s published in Chinese. In meetings with Chinese colleagues, someone will translate pertinent points to me, or I’ll translate my slides into Chinese and present in English. I also have my reports and briefs translated, and with advances in translation software, we can get what is needed done.
It’s easy to have misunderstandings when you’re translating ideas between different languages, so we’re careful to look for any linguistic nuances that might change the perceived meaning.
How is China balancing urbanization with conservation?
It’s an ongoing challenge. The concept of an ecological civilization — the government’s vision for environmentally sustainable growth in China — was written into the country’s constitution in 2018 after it was made a national priority in 2012, which is a huge commitment to sustainability.
A principal policy is the ecological conservation red-line plan, an idea that has been developed over the past decade. Across China, large areas of land are now being protected from industrial and urban development, in part to ensure that crucial ecosystems, such as wetlands that limit floods, can continue to function effectively. Multimillion-dollar developments have been torn down during its roll-out. China is one of few countries to have enacted such a science-based, top-down vision of how to balance human need with the maintenance of ecological services and preservation of biodiversity.

It’s not all perfect, though: I know that on paper, these ecological red lines now exist and in certain biodiversity hotspots they have been enforced. But not every region is the same. Areas have high levels of autonomy and in Yunnan, where I live, there have been more challenges for the local government to work with provincial governments for many practical and political reasons. The saying goes, ‘The mountains are high, and the emperor is far away’: places that are far away from Beijing feel less pressure to enact centralized policies because there is less supervision.
The south of China has seen lots of deforestation, which is hugely damaging to biodiversity. Natural forests have been replaced with for-profit tree plantations, usually planted with rubber or eucalyptus, which have had a hugely negative impact on biodiversity. Sustainable forestry is a real issue across Asia.
China is leading an important biodiversity conference in May. What are your expectations?
It’s the first time that China will host the UN biodiversity conference and this puts the spotlight on what they are doing to help the situation.
I know there are a number of senior Chinese officials who would like to see China take on more of an international leadership role, in addition to making efforts to preserve biodiversity domestically.
The current set of UN goals for global biodiversity expired last year and the next set, which is planned to be agreed at the convention, must encourage countries to plant diverse, native species. Currently, there is no pressure coming from politicians to do that, even though we know we suffer biodiversity loss as a result: we’re often hung up on targets, even if those targets are virtue signalling, rather than real change.
Also, governments tend to try to meet their targets in the easiest and most economically beneficial way. So they meet their tree-planting targets by planting by just a few, non-diverse species that are often not even native to the country.
We still need to include more practical goals in policy documents, such as enabling sustainable supply chains, to focus on the mechanisms behind biodiversity loss.
What strikes you as unique about the Chinese ecological-research environment?
A Chinese ecologist needs to be fast to act. The time frame to submit an application for a grant can be very quick. Often you have less than 24 hours to respond. Also, most initiatives are tied to the government’s five-year plans, so our priorities need to adapt to reflect those five-year cycles.
In the past two years, there has been a complete inventory of all China’s marine and terrestrial protected areas so they can be accurately mapped and future targets can be based on them. That really is an unparalleled effort.
This involved mapping 400 marine protected areas, and 13,600 terrestrial ones. I haven’t heard of anything equivalent to this scale and speed in any other country.
The most positive thing for me is that science matters here. The annual budget for scientific research is increasing and the findings from our applied research inform national policy. That is something the West would do well to remember. More