Ecology

1.
Wall, D. H. et al. Soil Ecology And Ecosystem Services. p. 406 (Oxford University Press, 2012).
2.
Blouin, M. et al. A review of earthworm impact on soil function and ecosystem services. Eur. J. Soil Sci. 64, 161–182 (2013).
Google Scholar 

3.
Baveye, P. C., Baveye, J. & Gowdy, J. Soil ‘Ecosystem’ services and natural capital: critical appraisal of research on uncertain ground. Front. Environ. Sci. Eng. China 4, 1–49 (2016).
Google Scholar 

4.
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).
ADS  CAS  PubMed  Google Scholar 

5.
Heemsbergen & Hal, V. Biodiversity effects on soil processes explained by interspecific functional dissimilarity biodiversity effects on soil processes explained by interspecific. Science 306, 8–10 (2004).
Google Scholar 

6.
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
ADS  CAS  PubMed  Google Scholar 

7.
Schuldt, A. et al. Biodiversity across trophic levels drives multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

8.
Risch, A. C. et al. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. Nat. Commun. 9, 3684 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

9.
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
ADS  CAS  PubMed  Google Scholar 

10.
Maestre, F. T. et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl Acad. Sci. USA 112, 201516684 (2015).
Google Scholar 

11.
Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).
PubMed  Google Scholar 

12.
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

13.
Song, D. et al. Large-scale patterns of distribution and diversity of terrestrial nematodes. Appl. Soil Ecol. 114, 161–169 (2017).
Google Scholar 

14.
Delgado-Baquerizo, M. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat. Ecol. Evol. 1, 1339–1347 (2017).
PubMed  Google Scholar 

15.
Pärtel, M., Bennett, J. A. & Zobel, M. Macroecology of biodiversity: disentangling local and regional effects. New Phytol. 211, 404–410 (2016).
PubMed  Google Scholar 

16.
Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).
ADS  PubMed  PubMed Central  Google Scholar 

17.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
ADS  CAS  PubMed  Google Scholar 

18.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

19.
Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).
ADS  CAS  PubMed  Google Scholar 

20.
Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).

21.
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
ADS  CAS  PubMed  Google Scholar 

22.
van der Plas, F. et al. Jack-of-all-trades effects drive biodiversity-ecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109 (2016).
ADS  PubMed  PubMed Central  Google Scholar 

23.
van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol. Lett. 21, 31–42 (2018).
PubMed  Google Scholar 

24.
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

25.
Cameron, E. K. et al. Global gaps in soil biodiversity data. Nat. Ecol. Evol. 2, 1042–1043 (2018).
PubMed  PubMed Central  Google Scholar 

26.
Wetzel, F. T. et al. Unlocking biodiversity data: prioritization and filling the gaps in biodiversity observation data in Europe. Biol. Conserv. 221, 78–85 (2018).
Google Scholar 

27.
Amano, T. & Sutherland, W. J. Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proc. Biol. Sci. 280, 20122649 (2013).
PubMed  PubMed Central  Google Scholar 

28.
Eisenhauer, N., Bonn, A. & Guerra, C. A. Recognizing the quiet extinction of invertebrates. Nat. Commun. 10, 50 (2019).

29.
Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).
Google Scholar 

30.
Paleari, S. Is the European Union protecting soil? A critical analysis of Community environmental policy and law. Land Use Policy 64, 163–173 (2017).
Google Scholar 

31.
Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006 (2016).
PubMed  Google Scholar 

32.
Costello, M. J., Michener, W. K., Gahegan, M., Zhang, Z.-Q. & Bourne, P. E. Biodiversity data should be published, cited, and peer reviewed. Trends Ecol. Evol. 28, 454–461 (2013).
PubMed  Google Scholar 

33.
Bingham, H. C., Doudin, M. & Weatherdon, L. V. The biodiversity informatics landscape: elements, connections and opportunities. 3, e14059 (2017).

34.
Gibb, H. et al. A global database of ant species abundances. Ecology 98, 883–884 (2017).
PubMed  Google Scholar 

35.
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

36.
Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 71, 711–730 (2017).
CAS  PubMed  Google Scholar 

37.
de Groot, R. S., Alkemade, R., Braat, L., Hein, L. & Willemen, L. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272 (2010).
Google Scholar 

38.
Stewart, G. Meta-analysis in applied ecology. Biol. Lett. 6, 78–81 (2010).
PubMed  Google Scholar 

39.
Rillig, M. C. et al. Biodiversity research: data without theory—theory without data. Front. Ecol. Evol. 3, 20 (2015).
ADS  Google Scholar 

40.
Coleman, D. C., Callaham, M. A. & Crossley, D. A., Jr. Fundamentals of Soil Ecology. (Academic Press, 2017).

41.
Lavelle, P. & Spain, A. Soil Ecology. (Springer Science & Business Media, 2001).

42.
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).
Google Scholar 

43.
Phillips, H. R. P. et al. 2019. Global distribution of earthworm diversity. Science https://doi.org/10.1126/science.aax4851 (2019).

44.
van den Hoogen J., et al. Soil nematode abundance and functional group composition at a global scale. Nature https://doi.org/10.1038/s41586-019-1418-6 (2019).

45.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
PubMed  Google Scholar 

46.
Orgiazzi, A. et al. Global Soil Biodiversity Atlas (JRC and the Global Soil Biodiversity Initiative, 2016).

47.
Guenard, B., Weiser, M. D. & Gomez, K. The Global Ant Biodiversity Informatics (GABI) database: synthesizing data on the geographic distribution of ant species (Hymenoptera: Formicidae). Myrmecol. News 24, 83–89 (2017).

48.
Nielsen, U. N. et al. The enigma of soil animal species diversity revisited: the role of small-scale heterogeneity. PLoS ONE 5, e11567 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

49.
Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Biol. 42, S3–S15 (2006).

50.
Evans, T. A., Dawes, T. Z., Ward, P. R. & Lo, N. Ants and termites increase crop yield in a dry climate. Nat. Commun. 2, 262–267 (2011).
ADS  PubMed  PubMed Central  Google Scholar 

51.
Eisenhauer, N., Bowker, M. A., Grace, J. B. & Powell, J. R. From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology. Pedobiologia 58, 65–72 (2015).
Google Scholar 

52.
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
ADS  CAS  PubMed  Google Scholar 

53.
Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
PubMed  Google Scholar 

54.
Fraser, L. H. et al. Plant ecology. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science 349, 302–305 (2015).
ADS  CAS  PubMed  Google Scholar 

55.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

56.
Borgman, C. L., Wallis, J. C. & Enyedy, N. Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries. Int. J. Digit. Lib. 7, 17–30 (2007).
Google Scholar 

57.
Hampton, S. E. et al. Big data and the future of ecology. Front. Ecol. Environ. 11, 156–162 (2013).
Google Scholar 

58.
Pey, B. et al. Current use of and future needs for soil invertebrate functional traits in community ecology. Basic Appl. Ecol. 15, 194–206 (2014).
Google Scholar 

59.
Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Chang. Biol. 21, 973–985 (2015).
ADS  PubMed  Google Scholar 

60.
Blankinship, J. C., Niklaus, P. A. & Hungate, B. A. A meta-analysis of responses of soil biota to global change. Oecologia 165, 553–565 (2011).
ADS  PubMed  Google Scholar 

61.
Wheeler, Q. D., Raven, P. H. & Wilson, E. O. Taxonomy: impediment or expedient? Science 303, 285 (2004).
CAS  PubMed  Google Scholar 

62.
Delgado-Baquerizo, M. & Eldridge, D. J. Cross-biome drivers of soil bacterial alpha diversity on a worldwide scale. Ecosystems 22, 1–12 (2019).
Google Scholar 

63.
Hursh, A. et al. The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale. Glob. Chang. Biol. 23, 2090–2103 (2017).
ADS  PubMed  Google Scholar 

64.
Wang, Q., Liu, S. & Tian, P. Carbon quality and soil microbial property control the latitudinal pattern in temperature sensitivity of soil microbial respiration across Chinese forest ecosystems. Glob. Chang. Biol. 24, 2841–2849 (2018).
ADS  PubMed  Google Scholar 

65.
Prăvălie, R. Drylands extent and environmental issues. A global approach. Earth-Sci. Rev. 161, 259–278 (2016).
ADS  Google Scholar 

66.
Delgado-baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 325, 320–325 (2018).
ADS  Google Scholar 

67.
Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. (2018).

68.
Cowan, D. A. et al. Microbiomics of Namib Desert habitats. Extremophiles 24, 17–29 (2020).
CAS  PubMed  Google Scholar 

69.
Rutgers, M. et al. Mapping earthworm communities in Europe. Appl. Soil Ecol. 97, 98–111 (2016).
Google Scholar 

70.
Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology 99, 583–596 (2018).
PubMed  Google Scholar 

71.
Chen, S., Zou, J., Hu, Z., Chen, H. & Lu, Y. Global annual soil respiration in relation to climate, soil properties and vegetation characteristics: summary of available data. Agric. For. Meteorol. 198–199, 335–346 (2014).
ADS  Google Scholar 

72.
Zhang, D., Hui, D., Luo, Y. & Zhou, G. Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors. J. Plant Ecol. 1, 85–93 (2008).
Google Scholar 

73.
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. USA 104, 5925–5930 (2007).
ADS  CAS  PubMed  Google Scholar 

74.
Cameron, E. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019)

75.
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).
ADS  CAS  PubMed  Google Scholar 

76.
Eisenhauer, N. et al. Plant diversity effects on soil food webs are stronger than those of elevated CO2 and N deposition in a long-term grassland experiment. Proc. Natl Acad. Sci. USA 110, 6889–6894 (2013).
ADS  CAS  PubMed  Google Scholar 

77.
Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).
ADS  CAS  PubMed  Google Scholar 

78.
Menegotto, A. & Rangel, T. F. Mapping knowledge gaps in marine diversity reveals a latitudinal gradient of missing species richness. Nat. Commun. 9, 4713 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

79.
Eisenhauer, N. et al. Priorities for research in soil ecology. Pedobiologia 63, 1–7 (2017).
PubMed  PubMed Central  Google Scholar 

80.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
PubMed  PubMed Central  Google Scholar 

81.
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
ADS  CAS  Google Scholar 

82.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 489, 326–326 (2012).
ADS  CAS  Google Scholar 

83.
Titeux, N. et al. Biodiversity scenarios neglect future land-use changes. Glob. Chang. Biol. 22, 2505–2515 (2016).
ADS  PubMed  Google Scholar 

84.
Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).
Google Scholar 

85.
Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 3, 52 (2012).
ADS  Google Scholar 

86.
Kharin, V. V., Zwiers, F. W., Zhang, X. & Hegerl, G. C. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Clim. 20, 1419–1444 (2007).
ADS  Google Scholar 

87.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

88.
Bronselaer, B. et al. Change in future climate due to Antarctic meltwater. Nature 564, 53–58 (2018).
ADS  CAS  PubMed  Google Scholar 

89.
Collins, M., Knutti, R., Arblaster, J., Dufresne, J. L. & Fichefet, T. Long-term climate change: projections, commitments and irreversibility. Chapter 12 (eds T. Stocker et al) 1029–1136 (Cambridge University Press, 2013).

90.
Delgado-Baquerizo, M., Eldridge, D. J., Hamonts, K. & Singh, B. K. Ant colonies promote the diversity of soil microbial communities. ISME J. https://doi.org/10.1038/s41396-018-0335-2 (2019).

91.
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).
ADS  CAS  PubMed  Google Scholar 

92.
Thomson, S. A. et al. Taxonomy based on science is necessary for global conservation. PLoS Biol. 16, e2005075 (2018).
PubMed  PubMed Central  Google Scholar 

93.
Drew, L. W. Are we losing the science of taxonomy? Bioscience 61, 942–946 (2011).
Google Scholar 

94.
Paknia, O., Sh., H. R. & Koch, A. Lack of well-maintained natural history collections and taxonomists in megadiverse developing countries hampers global biodiversity exploration. Organ. Divers. Evol. 15, 619–629 (2015).
Google Scholar 

95.
Prathapan, K. D. et al. When the cure kills-CBD limits biodiversity research. Science 360, 1405–1406 (2018).
ADS  CAS  PubMed  Google Scholar 

96.
Neumann, D. et al. Global biodiversity research tied up by juridical interpretations of access and benefit sharing. Org. Divers. Evol. 18, 1–12 (2017).
Google Scholar 

97.
Leimu, R. & Koricheva, J. What determines the citation frequency of ecological papers? Trends Ecol. Evol. 20, 28–32 (2005).
PubMed  Google Scholar 

98.
Hugerth, L. W. & Andersson, A. F. Analysing microbial community composition through amplicon sequencing: from sampling to hypothesis testing. Front. Microbiol. 8, 1561 (2017).
PubMed  PubMed Central  Google Scholar 

99.
Terrat, S. et al. Meta-barcoded evaluation of the ISO standard 11063 DNA extraction procedure to characterize soil bacterial and fungal community diversity and composition. Microb. Biotechnol. 8, 131–142 (2015).
CAS  PubMed  Google Scholar 

100.
Kõljalg, U., Larsson, K. H. & Abarenkov, K. UNITE: a database providing web‐based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166, 1063–1068 (2005).

101.
Mathieu, J., Caro, G. & Dupont, L. Methods for studying earthworm dispersal. Appl. Soil Ecol. 123, 339–344 (2018).
Google Scholar 

102.
Pauchard, N. Access and benefit sharing under the convention on biological diversity and its protocol: what can some numbers tell us about the effectiveness of the regulatory regime? Resources 6, 11 (2017).
Google Scholar 

103.
Saha, S., Saha, S. & Saha, S. K. Barriers in Bangladesh. Elife 7, e41926 (2018).

104.
Prathapan, K. D. & Rajan, P. D. Biodiversity access and benefit-sharing: weaving a rope of sand. Curr. Sci. 100, 290–293 (2011).
Google Scholar 

105.
van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).
ADS  PubMed  Google Scholar 

106.
Terrat, S. et al. Mapping and predictive variations of soil bacterial richness across France. PLoS ONE 12, e0186766 (2017).
PubMed  PubMed Central  Google Scholar 

107.
Makiola, A., et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).

108.
Maestre, F. T. & Eisenhauer, N. Recommendations for establishing global collaborative networks in soil ecology. Soil Organ. 91, 73–85 (2019).
Google Scholar 

109.
Phillips, H. R. P. et al. Red list of a black box. Nat. Ecol. Evol. 1, 0103 (2017).
Google Scholar 

110.
Davison, J. et al. Microbial island biogeography: isolation shapes the life history characteristics but not diversity of root-symbiotic fungal communities. ISME J. https://doi.org/10.1038/s41396-018-0196-8 (2018).

111.
Overmann, J. Significance and future role of microbial resource centers. Syst. Appl. Microbiol. 38, 258–265 (2015).
PubMed  Google Scholar 

112.
Overmann, J. & Scholz, A. H. Microbiological research under the nagoya protocol: facts and fiction. Trends Microbiol. 25, 85–88 (2017).
CAS  PubMed  Google Scholar 

113.
Bockmann, F. A. et al. Brazil’s government attacks biodiversity. Science 360, 865 (2018).
ADS  CAS  PubMed  Google Scholar 

114.
Scbd-Unep. Nagoya Declaration on Biodiversity in Development Cooperation. 2 (UNEP, 2010).

115.
Perrings, C. et al. Ecosystem services for 2020. Science 330, 323–324 (2010).
ADS  CAS  PubMed  Google Scholar 

116.
Bond-Lamberty, B. & Thomson, A. A global database of soil respiration data. Biogeosci. Discuss. 7, 1321–1344 (2010).
ADS  Google Scholar 

117.
Bamforth, S. S. Interpreting soil ciliate biodiversity. Plant Soil 170, 159–164 (1995).
CAS  Google Scholar 

118.
Mathieu, J. EGrowth: a global database on intraspecific body growth variability in earthworm. Soil Biol. Biochem. 122, 71–80 (2018).
CAS  Google Scholar 

119.
Fierer, N., Strickland, M. S., Liptzin, D., Bradford, M. A. & Cleveland, C. C. Global patterns in belowground communities. Ecol. Lett. 12, 1238–1249 (2009).
PubMed  Google Scholar 

120.
Nelson, M. B., Martiny, A. C. & Martiny, J. B. H. Global biogeography of microbial nitrogen-cycling traits in soil. Proc. Natl Acad. Sci. USA 113, 8033–8040 (2016).
CAS  PubMed  Google Scholar 

121.
Chen, J., Yang, S. T., Li, H. W., Zhang, B. & Lv, J. R. Research on geographical environment unit division based on the method of natural breaks (Jenks). ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XL-4/W3, pp. 47–50 (2013).

122.
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).
PubMed  PubMed Central  Google Scholar 

123.
Rousseeuw, P. J. & van Zomeren, B. C. Unmasking multivariate outliers and leverage points. J. Am. Stat. Assoc. 85, 633–639 (1990).
Google Scholar 

124.
Jackson, D. A. & Chen, Y. Robust principal component analysis and outlier detection with ecological data. Environmetrics 15, 129–139 (2004).
Google Scholar 

125.
Mallavan B. P., Minasny B., McBratney A. B., in Digital Soil Mapping. pp. 137–150 (Springer, Dordrecht, 2010).

126.
Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).
PubMed  Google Scholar 

127.
Hengl, T. et al. SoilGrids1km–global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

128.
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric. Ecosyst. Environ. 126, 81–97 (2008).
Google Scholar 

129.
Karger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. Sci. Data 4, 1–19 (2017).
Google Scholar 

130.
Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) | The Long Term Archive. Available at: https://lta.cr.usgs.gov/GMTED2010. Accessed on 6 December 2018.

131.
European Space Agency. ESA – Land Cover CCI – Product User Guide Version 2.0. (2017).

132.
Frostegård, Å., Tunlid, A. & Bååth, E. Microbial biomass measured as total lipid phosphate in soils of different organic content. J. Microbiol. Methods 14, 151–163 (1991).
Google Scholar 

133.
Campbell, C. D., Chapman, S. J., Cameron, C. M., Davidson, M. S. & Potts, J. M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

134.
Eppo, P. M. Nematode extraction. EPPO Bull. 43, 471–495 (2013).
Google Scholar 

135.
ISO/FDIS. Soil Quality – Sampling Of Soil Invertebrates – Part 1: Hand-sorting And Extraction Of Earthworms. (ISO, 2018).

136.
ISO. Soil quality – Sampling Of Soil Invertebrates – Part 4: Sampling, Extraction And Identification Of Soil-Inhabiting Nematodes. (ISO, 09-2011).

137.
Hunter, P. A. DEAL for open access: The negotiations between the German DEAL project and publishers have global implications for academic publishing beyond just Germany. EMBO Rep. 19, e46317 (2018).

138.
Knapp, A. K. et al. Past, present, and future roles of long-term experiments in the LTER network. Bioscience 62, 377–389 (2012).

139.
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
ADS  CAS  PubMed  Google Scholar 

140.
Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 1–8 (2017).
Google Scholar 

141.
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).
ADS  CAS  PubMed  Google Scholar 

142.
Gilbert, J. A., Jansson, J. K. & Knight, R. The earth microbiome project: successes and aspirations. BMC Biol. 12, 69 (2014).
PubMed  PubMed Central  Google Scholar 

143.
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).
ADS  CAS  PubMed  Google Scholar 

144.
Darcy, J. L., Lynch, R. C., King, A. J., Robeson, M. S. & Schmidt, S. K. Global distribution of Polaromonas phylotypes–evidence for a highly successful dispersal capacity. PLoS ONE 6, e23742 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

145.
Hendershot, J. N., Read, Q. D., Henning, J. A., Sanders, N. J. & Classen, A. T. Consistently inconsistent drivers of microbial diversity and abundance at macroecological scales. Ecology 98, 1757–1763 (2017).
PubMed  Google Scholar 

146.
Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).
ADS  CAS  PubMed  Google Scholar 

147.
Lozupone, C. A. & Knight, R. Global patterns in bacterial diversity. Proc. Natl Acad. Sci. USA 104, 11436–11440 (2007).
ADS  CAS  PubMed  Google Scholar 

148.
Neal, A. L. et al. Phylogenetic distribution, biogeography and the effects of land management upon bacterial non-specific Acid phosphatase Gene diversity and abundance. Plant Soil 427, 175–189 (2018).
CAS  PubMed  Google Scholar 

149.
Shoemaker, W. R., Locey, K. J. & Lennon, J. T. A macroecological theory of microbial biodiversity. Nat. Ecol. Evol. 1, 107 (2017).
PubMed  Google Scholar 

150.
Bates, S. T. et al. Examining the global distribution of dominant archaeal populations in soil. ISME J. 5, 908–917 (2011).
MathSciNet  CAS  PubMed  Google Scholar 

151.
Davison, J. et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science 349, 970–973 (2015).
ADS  CAS  PubMed  Google Scholar 

152.
Kivlin, S. N., Hawkes, C. V. & Treseder, K. K. Global diversity and distribution of arbuscular mycorrhizal fungi. Soil Biol. Biochem. 43, 2294–2303 (2011).
CAS  Google Scholar 

153.
Pärtel, M. et al. Historical biome distribution and recent human disturbance shape the diversity of arbuscular mycorrhizal fungi. New Phytol. 216, 227–238 (2017).
PubMed  Google Scholar 

154.
Põlme, S. et al. Biogeography of ectomycorrhizal fungi associated with alders (Alnus spp.) in relation to biotic and abiotic variables at the global scale. New Phytol. 198, 1239–1249 (2013).
PubMed  Google Scholar 

155.
Sharrock, R. A. et al. A global assessment using PCR techniques of mycorrhizal fungal populations colonising Tithonia diversifolia. Mycorrhiza 14, 103–109 (2004).
CAS  PubMed  Google Scholar 

156.
Tedersoo, L. et al. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Mol. Ecol. 21, 4160–4170 (2012).
PubMed  Google Scholar 

157.
Öpik, M., Moora, M., Liira, J. & Zobel, M. Composition of root-colonizing arbuscular mycorrhizal fungal communities in different ecosystems around the globe: arbuscular mycorrhizal fungal communities around the globe. J. Ecol. 94, 778–790 (2006).
Google Scholar 

158.
Öpik, M. et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). New Phytol. 188, 223–241 (2010).
PubMed  Google Scholar 

159.
Stürmer, S. L., Bever, J. D. & Morton, J. B. Biogeography of arbuscular mycorrhizal fungi (Glomeromycota): a phylogenetic perspective on species distribution patterns. Mycorrhiza 28, 587–603 (2018).
PubMed  Google Scholar 

160.
Bates, S. T. et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 7, 652–659 (2013).
CAS  PubMed  Google Scholar 

161.
Lara, E., Roussel‐Delif, L. & Fournier, B. Soil microorganisms behave like macroscopic organisms: patterns in the global distribution of soil euglyphid testate amoebae. J. Biogeogr. 43, 520–532 (2016).

162.
Finlay, B. J., Esteban, G. F., Clarke, K. J. & Olmo, J. L. Biodiversity of terrestrial protozoa appears homogeneous across local and global spatial scales. Protist 152, 355–366 (2001).
CAS  PubMed  Google Scholar 

163.
Chao, A., C. Li, P., Agatha, S. & Foissner, W. A statistical approach to estimate soil ciliate diversity and distribution based on data from five continents. Oikos 114, 479–493 (2006).

164.
Foissner, W. Global soil ciliate (Protozoa, Ciliophora) diversity: a probability-based approach using large sample collections from Africa, Australia and Antarctica. Biodivers. Conserv. 6, 1627–1638 (1997).
Google Scholar 

165.
Nielsen, U. N. et al. Global-scale patterns of assemblage structure of soil nematodes in relation to climate and ecosystem properties: Global-scale patterns of soil nematode assemblage structure. Glob. Ecol. Biogeogr. 23, 968–978 (2014).
Google Scholar 

166.
Wu, T., Ayres, E., Bardgett, R. D., Wall, D. H. & Garey, J. R. Molecular study of worldwide distribution and diversity of soil animals. Proc. Natl Acad. Sci. USA 108, 17720–17725 (2011).
ADS  CAS  PubMed  Google Scholar 

167.
Robeson, M. S. et al. Soil rotifer communities are extremely diverse globally but spatially autocorrelated locally. Proc. Natl Acad. Sci. USA 108, 4406–4410 (2011).
ADS  CAS  PubMed  Google Scholar 

168.
Wall, D. H. et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob. Chang. Biol. 14, 2661–2677 (2008).

169.
Pachl, P. et al. The tropics as an ancient cradle of oribatid mite diversity. Acarologia 57, 309–322 (2016).
Google Scholar 

170.
Dahlsjö, C. A. L. et al. First comparison of quantitative estimates of termite biomass and abundance reveals strong intercontinental differences. J. Trop. Ecol. 30, 143–152 (2014).
Google Scholar 

171.
Briones, M. J. I., Ineson, P. & Heinemeyer, A. Predicting potential impacts of climate change on the geographical distribution of enchytraeids: a meta‐analysis approach. Glob. Chang. Biol. 13, 2252–2269 (2007).

172.
Silver, W. L. & Miya, R. K. Global patterns in root decomposition: comparisons of climate and litter quality effects. Oecologia 129, 407–419 (2001).
ADS  PubMed  Google Scholar 

173.
Zhang, T. ’an, Chen, H. Y. H. & Ruan, H. Global negative effects of nitrogen deposition on soil microbes. ISME J. 12, 1817–1825 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

174.
Sinsabaugh, R. L., Turner, B. L. & Talbot, J. M. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).

175.
Xu, M. & Shang, H. Contribution of soil respiration to the global carbon equation. J. Plant Physiol. 203, 16–28 (2016).
CAS  PubMed  Google Scholar 

176.
Raich, J. W. & Tufekciogul, A. Vegetation and soil respiration: correlations and controls. Biogeochemistry 48, 71–90 (2000).
CAS  Google Scholar 

177.
Wang, J., Chadwick, D. R., Cheng, Y. & Yan, X. Global analysis of agricultural soil denitrification in response to fertilizer nitrogen. Sci. Total Environ. 616-617, 908–917 (2018).
ADS  CAS  PubMed  Google Scholar 

178.
Rahmati, M. et al. Development and analysis of the Soil Water Infiltration Global database. Earth Syst. Sci. Data 10, 1237–1263 (2017).
ADS  Google Scholar 

179.
Serna-Chavez, H. M., Fierer, N. & van Bodegom, P. M. Global drivers and patterns of microbial abundance in soil: Global patterns of soil microbial biomass. Glob. Ecol. Biogeogr. 22, 1162–1172 (2013).
Google Scholar 

180.
Howison, R. A., Olff, H., Koppel, J. & Smit, C. Biotically driven vegetation mosaics in grazing ecosystems: the battle between bioturbation and biocompaction. Ecol. Monogr. 87, 363–378 (2017).

181.
Lehmann, A., Zheng, W. & Rillig, M. C. Soil biota contributions to soil aggregation. Nat. Ecol. Evol.1, 1–9 (2017).
Google Scholar 

182.
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).
Google Scholar 

183.
van Straaten Oliver, Z. R. T. A. & Bossio, D. Carbon, Land And Water: A Global Analysis Of The Hydrologic Dimensions Of Climate Change Mitigation Through Afforestation/reforestation. (IWMI, 2006). More

1.
van Valen, L. M. Resetting the Phanerozoic community evolution. Nature 307, 93–106 (1984).
Google Scholar 
2.
Alroy, J. Dynamics of origination and extinction in the marine fossil record. Proc. Natl Acad. Sci. USA 105, 11536–11542 (2008).
CAS  PubMed  Google Scholar 

3.
Foote, M. Origination and extinction through the Phanerozoic: a new approach. J. Geol. 111, 125–148 (2003).
Google Scholar 

4.
Gilinsky, N. L. Volatility and the Phanerozoic decline of background extinction intensity. Paleobiology 20, 445–458 (1994).
Google Scholar 

5.
Lieberman, B. S. & Melott, A. L. Declining volatility, a general property of disparate systems: from fossils, to stocks, to the stars. Palaeontology 56, 1297–1304 (2013).
Google Scholar 

6.
Knope, M. L., Bush, A. M., Frishkoff, L. O., Heim, N. A. & Payne, J. L. Ecologically diverse clades dominate the oceans via extinction resistance. Science 367, 1035–1038 (2020).
CAS  PubMed  Google Scholar 

7.
Janzen, D. H. On ecological fitting. Oikos 45, 308–310 (1985).
Google Scholar 

8.
Agosta, S. J. & Klemens, J. A. Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution. Ecol. Lett. 11, 1123–1134 (2008).
PubMed  Google Scholar 

9.
Nielsen, S. N. & Müller, F. in Handbook of Ecosystem Theories and Management (eds Jørgensen, S. E. & Müller, F.) 195–216 (CRC Press, 2000).

10.
Hui, C. et al. Defining invasiveness and invasibility in ecological networks. Biol. Invasions 18, 971–983 (2016).
Google Scholar 

11.
Foote, M. et al. Rise and fall of species occupancy in Cenozoic fossil mollusks. Science 318, 1131–1134 (2007).
CAS  PubMed  Google Scholar 

12.
Foote, M. Symmetric waxing and waning of marine invertebrate genera. Paleobiology 33, 517–529 (2007).
Google Scholar 

13.
Liow, L. H. & Stenseth, N. C. The rise and fall of species: implications for macroevolutionary and macroecological studies. Proc. R. Soc. Lond. B 274, 2745–2752 (2007).
Google Scholar 

14.
Zliobaite, I., Fortelius, M. & Stenseth, N. C. Reconciling taxon senescence with the Red Queen’s hypothesis. Nature 552, 92–95 (2017).
CAS  PubMed  Google Scholar 

15.
Gillespie, R. G. et al. Comparing adaptive radiations across space, time, and taxa. J. Hered. 111, 1–20 (2020).
PubMed  Google Scholar 

16.
Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism: American Society of Naturalists EO Wilson Award address. Am. Nat. 175, 623–639 (2010).
PubMed  Google Scholar 

17.
Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596 (2010).
CAS  PubMed  Google Scholar 

18.
Erwin, D. H. Novelty and innovation in the history of life. Curr. Biol. 25, R930–R940 (2015).
CAS  PubMed  Google Scholar 

19.
Gould, S. J. & Vrba, E. S. Exaptation—a missing term in the science of form. Paleobiology 8, 4–15 (1982).
Google Scholar 

20.
Cooper, A. & Fortey, R. Evolutionary explosions and the phylogenetic fuse. Trends Ecol. Evol. 13, 151–156 (1998).
CAS  PubMed  Google Scholar 

21.
Jablonski, D. & Bottjer, D. J. in Major Evolutionary Radiations (eds Taylor, P. D. & Larwood, G. P.) 17–57 (Systematics Association, 1990).

22.
Jablonski, D. Approaches to macroevolution: 1. general concepts and origin of variation. Evol. Biol. 44, 427–450 (2017).
PubMed  PubMed Central  Google Scholar 

23.
Uyeda, J. C., Hansen, T. F., Arnold, S. J. & Pienaar, J. The million-year wait for macroevolutionary bursts. Proc. Natl Acad. Sci. USA 108, 15908–15913 (2011).
CAS  PubMed  Google Scholar 

24.
Kröger, B., Desrochers, A. & Ernst, A. The reengineering of reef habitats during the Great Ordovician Biodiversification Event. PALAIOS 32, 584–599 (2017).
Google Scholar 

25.
Robeck, H. E., Maley, C. C. & Donoghue, M. J. Taxonomy and temporal diversity patterns. Paleobiology 26, 171–187 (2000).
Google Scholar 

26.
Hendricks, J. R., Saupe, E. E., Myers, C. E., Hermsen, E. J. & Allmon, W. D. The generification of the fossil record. Paleobiology 40, 511–528 (2014).
Google Scholar 

27.
Wagner, P. J., Aberhan, M., Hendy, A. & Kiessling, W. The effects of taxonomic standardization on sampling-standardized estimates of historical diversity. Proc. R. Soc. B 274, 439–444 (2007).
PubMed  Google Scholar 

28.
Plotnick, R. E. & Wagner, P. J. Roundup of the usual suspects: common genera in the fossil record and the nature of the wastebasket taxa. Paleobiology 32, 126–146 (2006).
Google Scholar 

29.
Bambach, R. K., Bush, M. A. & Erwin, D. H. Autecology and the filling of ecospace: key metazoan radiations. Palaeontology 50, 1–22 (2007).
Google Scholar 

30.
Knope, M. L., Heim, N. A., Frishkoff, L. O. & Payne, J. L. Limited role of functional differentiation in early diversification of animals. Nat. Commun. 6, 6455 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Sepkoski, J. J. A kinetic model of Phanerozoic taxonomic diversity. III Post-Paleozoic families and mass extinctions. Paleobiology 10, 246–267 (1984).
Google Scholar 

32.
Alroy, J. The shifting balance of diversity among major marine animal groups. Science 329, 1191–1194 (2010).
CAS  PubMed  Google Scholar 

33.
Liow, L. H. & Nichols, J. D. in The Paleontological Society Short Course, October 30th 2010 (eds Alroy, J. & Hunt, G.) 81–94 (Cambridge Univ. Press, 2010).

34.
Kiessling, W. & Kocsis, Á. T. Adding fossil occupancy trajectories to the assessment of modern extinction risk. Biol. Lett. 12, 20150813 (2016).
PubMed  PubMed Central  Google Scholar 

35.
Fridley, J. D., Vandermast, D. B., Kuppinger, D. M., Manthey, M. L. & Peet, R. K. Co-occurrence based assessment of habitat generalists and specialists: a new approach for the measurement of niche width. J. Ecol. 95, 707–722 (2007).
Google Scholar 

36.
Hofmann, R., Tietje, M. & Aberhan, M. Diversity partitioning in Phanerozoic benthic marine communities. Proc. Natl Acad. Sci. USA 116, 79–83 (2019).
PubMed  Google Scholar 

37.
Bottjer, D. J., Hagadorn, J. W. & Dornbos, S. Q. The Cambrian substrate revolution. GSA Today 10, 1–7 (2000).
Google Scholar 

38.
Knoll, A. H. & Follows, M. J. A bottom-up perspective on ecosystem change in Mesozoic oceans. Proc. R. Soc. B 283, 20161755 (2016).
PubMed  Google Scholar 

39.
Bambach, R. K. Seafood through time: changes in biomass, energetics, and productivity in the marine ecosystem. Paleobiology 19, 372–397 (1993).
Google Scholar 

40.
Westrop, S. R. The life habits of the Ordovician illaenine trilobite Bumastoides. Lethaia 16, 15–24 (1983).
Google Scholar 

41.
O’Dea, A. & Jackson, J. Environmental change drove macroevolution in cupuladriid bryozoans. Proc. R. Soc. B 276, 3629–3634 (2009).
PubMed  Google Scholar 

42.
Rasmussen, C. M. Ø., Kröger, B., Nielsen, M. L. & Colmenar, J. Cascading trend of Early Paleozoic marine radiations paused by Late Ordovician extinctions. Proc. Natl Acad. Sci. USA 116, 7207 (2019).
PubMed  Google Scholar 

43.
Bush, A. M. & Bambach, R. K. Sustained Mesozoic–Cenozoic diversification of marine Metazoa: a consistent signal from the fossil record. Geology 43, 979–982 (2015).
Google Scholar 

44.
Leibold, M. A. & McPeek, M. A. Coexistence of the niche and neutral perspectives in community ecology. Ecology 87, 1399–1410 (2006).
PubMed  Google Scholar 

45.
McPeek, M. A. The ecological dynamics of clade diversification and community assembly. Am. Nat. 172, E270–E284 (2008).
PubMed  Google Scholar 

46.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Google Scholar 

47.
Wagner, P. J., Aberhan, M., Hendy, A. & W, K. The effects of taxonomic standardization on occurrence-based estimates of diversity. Proc. R. Soc. Lond. B 274, 439–444 (2007).
Google Scholar 

48.
Cohen, K. M., Harper, D. A. T. & Gibbard, P. L. ICS International Chronostratigraphic Chart 2018/08 (International Commission on Stratigraphy, IUGS, 2018); www.stratigraphy.org

49.
Nichols, J. D. & Pollock, K. H. Estimating taxonomic diversity, extinction rates, and speciation rates from fossil data using capture–recapture models. Paleobiology 9, 150–163 (1983).
Google Scholar 

50.
Connolly, S. R. & Miller, A. I. Joint estimation of sampling and turnover rates from fossil databases: capture–mark–recapture methods revisited. Paleobiology 27, 767–751 (2001).
Google Scholar 

51.
Schwarz, C. J. & Arnason, A. N. A general methodology for the analysis of capture–recapture experiments in open populations. Biometrics 52, 860–873 (1996).
Google Scholar 

52.
Pradel, R. Utilization of capture–mark–recapture for the study of recruitment and population growth rate. Biometrics 52, 703–709 (1996).
Google Scholar 

53.
Liow, L. H., Reitan, T. & Harnik, P. G. Ecological interactions on macroevolutionary time scales: clams and brachiopods are more than ships that pass in the night. Ecol. Lett. 18, 1030–1039 (2015).
PubMed  Google Scholar 

54.
Alroy, J. A more precise speciation and extinction rate estimator. Paleobiology 41, 633–639 (2015).
Google Scholar 

55.
Kocsis, Á. T., Reddin, C. J., Alroy, J. & Kiessling, W. The r package divDyn for quantifying diversity dynamics using fossil sampling data. Methods Ecol. Evol. 10, 735–743 (2019).
Google Scholar 

56.
Cornette, J. L. & Lieberman, B. S. Random walks in the history of life. Proc. Natl Acad. Sci. USA 101, 187–191 (2004).
CAS  PubMed  Google Scholar 

57.
Fuller, W. A. Introduction to Statistical Time Series (Wiley, 2009).

58.
Manthey, M. & Fridley, J. D. Beta diversity metrics and the estimation of niche width via species co-occurrence data: reply to Zeleny. J. Ecol. 97, 18–22 (2009).
Google Scholar  More

1.
Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett.7, 1225–1241 (2004).
Google Scholar 
2.
Cushman, S. A. et al. Editorial: the least cost path from landscape genetics to landscape genomics: challenges and opportunities to explore NGS data in a spatially explicit context. Front. Genet.9, 215 (2018).
PubMed  PubMed Central  Google Scholar 

3.
Pereira, A. Plant abiotic stress challenges from the changing environment. Front. Plant Sci.7, 1123 (2016).
PubMed  PubMed Central  Google Scholar 

4.
Rellstab, C. et al. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol.24, 4348–4370 (2015).
PubMed  Google Scholar 

5.
Zhu, J. K. Abiotic stress signaling and responses in plants. Cell167, 313–324 (2016).
PubMed  PubMed Central  CAS  Google Scholar 

6.
Allendorf, F. W., Hohenlohe, P. A. & Luikart, G. Genomics and the future of conservation genetics. Nat. Rev. Genet.11, 697–709 (2010).
PubMed  CAS  Google Scholar 

7.
Radwan, J. & Babik, W. The genomics of adaptation. Proc. Biol. Sci.279, 5024–5028 (2012).
PubMed  PubMed Central  Google Scholar 

8.
Li, Y. et al. Ten years of landscape genomics: challenges and opportunities. Front. Plant Sci.8, 2136 (2017).
PubMed  PubMed Central  Google Scholar 

9.
Cushman, S. A. Grand challenges in evolutionary and population genetics: the importance of integrating epigenetics, genomics, modeling, and experimentation. Front. Genet.5, 197 (2014).
PubMed  PubMed Central  Google Scholar 

10.
Guggisberg, A. et al. The genomic basis of adaptation to calcareous and siliceous soils in Arabidopsis lyrata. Mol. Ecol.27, 5088–5103 (2018).
PubMed  Google Scholar 

11.
Brennan, R. S. et al. Integrative population and physiological genomics reveals mechanisms of adaptation in killifish. Mol. Biol. Evol.35, 2639–2653 (2018).
PubMed  CAS  Google Scholar 

12.
Chen, C. et al. Population genomics provide insights into the evolution and adaptation of the eastern honey bee (Apis cerana). Mol. Biol. Evol.35, 2260–2271 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

13.
Dittberner, H. et al. Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana. Mol. Ecol.27, 4052–4065 (2018).
PubMed  CAS  Google Scholar 

14.
Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol.35, 792–806 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

15.
Ahrens, C. W., Byrne, M. & Rymer, P. D. Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species. Mol. Ecol.28, 2502–2516 (2019).
PubMed  CAS  Google Scholar 

16.
Wright, S. Evolution in Mendelian populations. Genetics16, 97–159 (1931).
PubMed  PubMed Central  CAS  Google Scholar 

17.
Miao, C. Y. et al. Landscape genomics reveal that ecological character determines adaptation: a case study in smoke tree (Cotinus coggygria Scop.). BMC Evol. Biol.17, 202 (2017).
PubMed  PubMed Central  Google Scholar 

18.
Li, J. X. et al. Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics. BMC Plant Biol.18, 306 (2018).
PubMed  PubMed Central  Google Scholar 

19.
Arciero, E. et al. Demographic history and genetic adaptation in the Himalayan region inferred from genome-wide SNP genotypes of 49 populations. Mol. Biol. Evol.35, 1916–1933 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

20.
Friis, G. et al. Genome-wide signals of drift and local adaptation during rapid lineage divergence in a songbird. Mol. Ecol.27, 746–760 (2018).
Google Scholar 

21.
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett.18, 1–16 (2015).
PubMed  Google Scholar 

22.
Guerrero, J. et al. Soil environment is a key driver of adaptation in Medicago truncatula: new insights from landscape genomics. N. Phytol.219, 378–390 (2018).
CAS  Google Scholar 

23.
Keller, S. R. et al. Local adaptation in the flowering-time gene network of balsam poplar, Populus balsamifera L. Mol. Biol. Evol.29, 3143–3152 (2012).
PubMed  CAS  Google Scholar 

24.
Manel, S. et al. Genome assemblies, genomic resources and their influence on the detection of the signal of positive selection in genome scans. Mol. Ecol.25, 170–184 (2016).
PubMed  CAS  Google Scholar 

25.
Fu, Z. Z. et al. Molecular data and ecological niche modeling reveal population dynamics of widespread shrub Forsythia suspensa (Oleaceae) in China’s warm-temperate zone in response to climate change during the Pleistocene. BMC Evol. Biol.14, 114 (2014).
PubMed  PubMed Central  Google Scholar 

26.
Hamrick, J. L. & Godt, M. J. Plant Population Genetics, Breeding, and Genetic Resources (Sinauer, Sunderland, 1990).

27.
Hewitt, G. M. Genetic consequences of climatic oscillations in the quaternary. Philos. Trans. R. Soc. Lond. B Biol. Sci.359, 183–195 (2004).
PubMed  PubMed Central  CAS  Google Scholar 

28.
Manel, S. & Holderegger, R. Ten years of landscape genetics. Trends Ecol. Evol.28, 614–621 (2013).
PubMed  Google Scholar 

29.
Balkenhol, N. et al. Current status, future opportunities, and remaining challenges in landscape genetics. In (eds Balkenhol, N. C., et al.). Landscape Genetics: Concepts, Methods, Applications (Wiley, Hoboken, 2015).

30.
Yang, J. et al. Landscape population genomics of forsythia (Forsythia suspensa) reveal that ecological habitats determine the adaptive evolution of species. Front. Plant Sci.8, 481 (2017).
PubMed  PubMed Central  Google Scholar 

31.
Sun, X. et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PLoS ONE8, e58700 (2013).
PubMed  PubMed Central  CAS  Google Scholar 

32.
Sollars, E. S. A. et al. Genome sequence and genetic diversity of European ash trees. Nature541, 212–216 (2017).
PubMed  CAS  Google Scholar 

33.
Unver, T. et al. Genome of wild olive and the evolution of oil biosynthesis. Proc. Natl Acad. Sci. USA114, E9413–E9422 (2017).
PubMed  CAS  Google Scholar 

34.
Yang, X. et al. The chromosome-level quality genome provides insights into the evolution of the biosynthesis genes for aroma compounds of Osmanthus fragrans. Hortic. Res.5, 72 (2018).
PubMed  PubMed Central  Google Scholar 

35.
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res.19, 1655–1664 (2009).
PubMed  PubMed Central  CAS  Google Scholar 

36.
Wallander, E. & Albert, V. A. Phylogeny and classification of Oleaceae based on rps16 and trnL-F sequence data. Am. J. Bot.87, 1827–1841 (2000).
PubMed  CAS  Google Scholar 

37.
Wang, T. Q. et al. TCM treatment of anemopyretic cold rule analysis. J. Tianjin Univ. Tradit. Chin. Med.37, 113–117 (2018).
Google Scholar 

38.
Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet.48, 134–143 (2016).
PubMed  CAS  Google Scholar 

39.
Najafi, S., Sorkheh, K. & Nasernakhaei, F. Characterization of the APETALA2/Ethylene-responsive factor (AP2/ERF) transcription factor family in sunflower. Sci. Rep.8, 11576 (2018).
PubMed  PubMed Central  Google Scholar 

40.
Xie, Z. et al. AP2/ERF transcription factor regulatory networks in hormone and abiotic stress responses in Arabidopsis. Front. Plant Sci.10, 228 (2019).
PubMed  PubMed Central  Google Scholar 

41.
Chakraborty, U. & Pradhan, B. Drought stress-induced oxidative stress and antioxidative responses in four wheat (Triticum aestivum L.) varieties. Arch. Agron. Soil Sci.58, 617–630 (2012).
CAS  Google Scholar 

42.
Noureddine, Y. Changes of peroxidase activities under cold stress in annuals populations of medicago. Mol. Plant Breed.6, 5 (2015).
Google Scholar 

43.
Gong, L. et al. Transcriptome profiling of the potato (Solanum tuberosum L.) plant under drought stress and water-stimulus conditions. PLoS ONE10, e0128041 (2015).
PubMed  PubMed Central  Google Scholar 

44.
Wang, M. et al. Comparative transcriptome analysis to elucidate the enhanced thermotolerance of tea plants (Camellia sinensis) treated with exogenous calcium. Planta249, 775–786 (2019).
PubMed  CAS  Google Scholar 

45.
Schöttler, M. A. et al. Photosynthetic complex stoichiometry dynamics in higher plants: biogenesis, function, and turnover of ATP synthase and the cytochrome b6f complex. J. Exp. Bot.66, 2373–2400 (2015).
PubMed  Google Scholar 

46.
Collakova, E. & DellaPenna, D. The role of homogentisate phytyltransferase and other tocopherol pathway enzymes in the regulation of tocopherol synthesis during abiotic stress. Plant Physiol.133, 930–940 (2003).
PubMed  PubMed Central  CAS  Google Scholar 

47.
Gavalas, N. A. & Clark, H. E. On the role of manganese in photosynthesis: kinetics of photoinhibition in manganese-deficent and 3-(4-chlorophenyl)-1, 1-dimethylurea-inhibited Euglena gracilis. Plant Physiol.47, 139–143 (1971).
PubMed  PubMed Central  CAS  Google Scholar 

48.
Chen, C. Y. et al. Structural basis of jasmonate-amido synthetase FIN219 in complex with glutathione S-transferase FIP1 during the JA signal regulation. Proc. Natl Acad. Sci. USA114, E1815–E1824 (2017).
PubMed  CAS  Google Scholar 

49.
Nisar, N. et al. Carotenoid metabolism in plant. Mol. Plant8, 68–82 (2015).
PubMed  CAS  Google Scholar 

50.
Landguth, E. L. et al. Modeling multilocus selection in an individual-based, spatially-explicit landscape genetics framework. Mol. Ecol. Resour.20, 605–615 (2020).
PubMed  Google Scholar 

51.
Ram, S. Role of alcohol dehydrogenase, malate dehydrogenase and malic enzyme in flooding tolerance in Brachiaria Species. J. Plant Biochem. Biot.9, 45–47 (2000).
CAS  Google Scholar 

52.
Butsayawarapat, P. et al. Comparative transcriptome analysis of waterlogging-sensitive and tolerant zombi pea (Vigna vexillata) reveals energy conservation and root plasticity controlling waterlogging tolerance. Plants8, 264 (2019).
PubMed Central  CAS  Google Scholar 

53.
Ohsawa, T. & Ide, Y. Global patterns of genetic variation in plant species along vertical and horizontal gradients on mountains. Glob. Ecol. Biogeogr.17, 152–163 (2008).
Google Scholar 

54.
Yang, J. et al. Landscape genomics analysis of Achyranthes bidentata reveal adaptive genetic variations are driven by environmental variations relating to ecological habit. Popul. Ecol.59, 355–362 (2017).
Google Scholar 

55.
Fu, Z. Z. et al. Population genetics of the widespread shrub Forsythia suspensa (Oleaceae) in warm-temperate China using microsatellite loci: implication for conservation. Plant Syst. Evol.302, 1–9 (2016).
Google Scholar 

56.
Marcais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics27, 764–770 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

57.
Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol.36, 338–345 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

58.
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive κ-mer weighting and repeat separation. Genome Res.27, 722–736 (2017).
PubMed  PubMed Central  CAS  Google Scholar 

59.
Chakraborty, M. et al. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Res.44, e147 (2016).
PubMed  PubMed Central  Google Scholar 

60.
Vaser, R. et al. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res.27, 737–746 (2017).
PubMed  PubMed Central  CAS  Google Scholar 

61.
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE9, e112963 (2014).
PubMed  PubMed Central  Google Scholar 

62.
Simão, F. A. et al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics31, 3210 (2015).
PubMed  Google Scholar 

63.
Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics23, 1061–1067 (2007).
PubMed  CAS  Google Scholar 

64.
Zhang, J. et al. High-density genetic map construction and identification of a locus controlling weeping trait in an ornamental woody plant (Prunus mume Sieb. et Zucc). DNA Res.22, 1–9 (2015).
Google Scholar 

65.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics25, 1754–1760 (2009).
PubMed  PubMed Central  CAS  Google Scholar 

66.
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res.20, 1297–1303 (2010).
PubMed  PubMed Central  CAS  Google Scholar 

67.
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics25, 2078–2079 (2009).
PubMed  PubMed Central  Google Scholar 

68.
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet.38, 904–909 (2006).
PubMed  CAS  Google Scholar 

69.
Kumar, S. et al. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol. Biol. Evol.35, 1547–1549 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

70.
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour.10, 564–567 (2010).
PubMed  Google Scholar 

71.
Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet.8, e1002967 (2012).
PubMed  PubMed Central  CAS  Google Scholar 

72.
Foll, M. & Gaggiotti, O. E. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics180, 977–993 (2008).
PubMed  PubMed Central  Google Scholar 

73.
Hijmans, R. J. et al. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Resour. Newsl.127, 15–19 (2001).
Google Scholar 

74.
Frichot, E. et al. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol.30, 1687–1699 (2013).
PubMed  PubMed Central  CAS  Google Scholar 

75.
Joost, S. et al. A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol. Ecol.16, 3955–3969 (2007).
PubMed  CAS  Google Scholar 

76.
Stucki, S. et al. High performance computation of landscape genomic models including local indicators of spatial association. Mol. Ecol. Resour.17, 1072–1089 (2017).
PubMed  CAS  Google Scholar 

77.
Oksanen, J. et al. Vegan: Community Ecology Package. R. Package Version 2.4-5 (2017).

78.
Altschul, S. F. et al. Basic local alignment search tool. J. Mol. Biol.215, 403–410 (1990).
CAS  Google Scholar  More

Here we tested the capacity of VHR imagery to provide estimates useful for monitoring whale distribution and densities, using a direct comparison with a ship-based line transect survey to gauge the relative sighting rates obtained by the satellite platform in comparison to that of the ship. Our results show that density estimates derived from satellite imagery (0.13 whales per km2, CV = 0.38—taken from calm waters) are approximately 0.39 of those estimated from the ship-based survey (0.33 whales per km2, CV = 0.09); an encouraging result suggesting that data from satellite imagery has potential to detect whales at similar levels to a traditional survey method. These results match our expectation that image derived densities would be lower than that of the ship-survey, with the instantaneous nature of the image acquisition on the satellite platform likely a strong driver of these differences, in addition to limitations in image resolution and the potential for random fluctuations in local whale densities during the time between acquisition of satellite images and the vessel-based survey. However they also demonstrate that satellite surveys have sufficient whale detection capacity that they can provide a complementary approach to monitoring whale presence in remote regions where regular surveys are difficult.
In setting up this study, we chose an area that (1) is of specific scientific interest in terms of whales; (2) is remote and relatively difficult to access, but has had some whale survey effort; (3) where the environmental conditions are changing; and (4) where whale density and habitat use patterns are required to understand population recovery from exploitation and spatial overlap with the regional fishery for Antarctic krill. We focused on an area where one whale species very strongly predominates (humpback whales) in order that our results have potential use for inference about the density patterns of this species, and as there is a smaller likelihood that species mis-identification would introduce bias. We also chose a sea channel which is relatively sheltered, reducing the likelihood of turbulent sea conditions (particularly wind on sea), which can make satellite images useless for survey. Our site selection considerations highlight the limitations still facing development of VHR as a platform, and we consider these limitations and next steps to address them in the following sections. We propose that this method can be used to investigate spatial and temporal patterns of whale distribution and densities, supplementing existing methods, providing that the limitations of this new method are carefully considered during design and implementation.
Weather conditions, specifically the sea state, impact detectability of whales at sea. Sea state is known to influence the ability of observers to detect animals, with worsening conditions reducing the detection probability. Consequently, effort is typically halted when conditions exceed a predefined limit. In all at-sea surveys, sea state increases the likelihood that the assumption of perfect detection on the track line will be violated. If detection off the track line is impacted by environmental conditions, inclusion of covariates in the detection function can take account of this bias44 (up to a cut off, normally 5). However, if poor sighting conditions impact detection on the track line, alternative methods such as a double-observer/platform study or a mark recapture approach can be implemented to account for and quantify this bias. For an image-based survey, poorer weather conditions will also reduce the ability of the observer to differentiate FOIs from background noise (i.e. breaking waves, wind lines, etc.)30. This results in fewer features being identified, and lower reported densities. Poor sea state, and associated wind conditions, typically ground aerial surveys, whether manned or UAS-based, or force them to be aborted inflight. Here we show that worsening sea states in the south of the study area on the day that the image was taken (Fig. 2), correspond to lower perceived and estimated densities in these regions. Compared to the northern area, the surface conditions of the southern image were less conducive to the visual detection of FOIs, showing an increased frequency of white-caps and wind lines, possibly because this region is prone to katabatic winds sweeping into the channel. Densities in the south of the survey area, where the sea state was poorer, were 0.4 of those from calmer regions (0.05 versus 0.13 whales per km2, CV = 0.58 and 0.38, respectively, Table 2). To address this effect in the future, an adapted version of a Mark-Recapture Distance Sampling (MRDS) analysis, such as45 using multiple observers to review images33, could be applied to assess variations in detectability as a function of covariates (i.e. sea state), and investigate the impact of perception bias on whale detection. However, to accurately parameterise a multi-covariate model, several tens, if not hundreds of whale detections would be needed. Another approach could be to collect multiple images of the same area very close in time (within several seconds to a minute of each other), to quantify the variation in whale detections according to sea state when variation in true whale density is likely to be negligible. In the present study, density comparisons were made using data from the northern (calmer) portion of the imagery only (0.13 whales per km2, CV = 0.38, Table 2).
When planning satellite imagery analysis, species composition of the focal area needs to be carefully considered, because at present this approach has very limited capacity to differentiate between species when compared to in situ surveys, due to the resolution of the images (~ 30 cm in this study). Our density estimates most likely reflect the density of humpback whales using the area of the Gerlache Strait in summer, because these are the most commonly sighted species in this region, both in terms of previous surveys, where they comprise  > 80% of sightings15,16, and during the present ship-based survey ( > 95% of the groups were identified as humpback whales). During summer periods, other larger baleen whale species tend to be seen further offshore, exhibiting affinity for the more open waters of the Bransfield Strait15. Smaller cetacean species (e.g. Antarctic minke whales, Balaenoptera bonarensis and both Type A and B killer whales46,47,48, Orcinus orca), co-occur with humpback whales in the Gerlache Strait but are unlikely to be misidentified as humpback whales, either by ship or imagery surveys, because of their differing size, surface behaviours and morphology. Southern right whales Eubalaena australis are occasionally sighted in this region too16. However, head callosities are normally visible in overhead imagery of this species, and offer a clear means of differentiation30,31. Since other species likely reflect at best a very small fraction of the image-survey detections, they are unlikely to comprise a significant component of the overall density estimates.
Obtaining reliable whale density estimates require adjustments for biases. In addition to perception bias, as mentioned above, another key bias is availability bias45. Availability bias is the underestimation of density that occurs as a result of a proportion of animals being underwater, or too deep in the water for detection by the survey platform as it passes a point in the ocean. In the present study, we applied an estimate of surface availability49 (where availability is 1-availability bias), which was derived by taking dive-recording suction cup tag data from humpback whales in the same region and time, to estimate the proportion of time a whale spends at the surface, versus its dive. Applying this correction, density was initially estimated as 0.12 whales per km2 (CV = 0.38) over the whole region surveyed, and as 0.13 whales per km2 (CV = 0.38) in calmer waters. However, we note that when tag data are processed, the analyst determines the threshold at which the animal transitions from being present at the surface, to when it dives50. Typically, for baleen whales, dives are classified as such when the whale is  > 4–5 m for  > 20 s. However, with such a threshold, shallow dives of  More

1.
Chang, P. et al. Oceanic link between abrupt changes in the North Atlantic Ocean and the African monsoon. Nat. Geosci. 1, 444 (2008).
ADS  CAS  Google Scholar 
2.
Kushnir, Y., Robinson, W. A., Chang, P. & Robertson, A. W. The physical basis for predicting Atlantic sector seasonal-to-interannual climate variability. J. Clim. 19, 5949–5970 (2006).
ADS  Google Scholar 

3.
Brandt, P. et al. Equatorial upper-ocean dynamics and their interaction with the West African monsoon. Atmos. Sci. Lett. 12, 24–30 (2011).
ADS  Google Scholar 

4.
Sultan, B. & Janicot, S. Abrupt shift of the ITCZ over West Africa and intra-seasonal variability. Geophys. Res. Lett. 27, 3353–3356 (2000).
ADS  Google Scholar 

5.
Liebmann, B. & Mechoso, C. R. The South American Monsoon System. In The Global Monsoon System: Research and Forecast 2nd edn, (eds Chang, C.‐P. et al.) Ch. 8, pp. 137–157, (World Scientific Publishing, 2010).

6.
Nobre, P. & Shukla, J. Variations of sea surface temperature, wind stress, and rainfall over the tropical Atlantic and South America. J. Clim. 9, 2464–2479 (1996).
ADS  Google Scholar 

7.
Foltz, G. R. et al. The Tropical Atlantic Observing System. Front. Mar. Sci. 6, 206 (2019).
ADS  Google Scholar 

8.
Jochum, M. et al. The impact of oceanic near-inertial waves on climate. J. Clim. 26, 2833–2844 (2013).
ADS  Google Scholar 

9.
Johns, W. E., Brandt, P. & Chang, P. Tropical Atlantic variability and coupled model climate biases: results from the Tropical Atlantic Climate Experiment (TACE). Clim. Dyn. 43, 2887–2887 (2014).
Google Scholar 

10.
Toniazzo, T. & Woolnough, S. Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts. Clim. Dyn. 43, 2889–2913 (2014).
Google Scholar 

11.
Zuidema, P. et al. Challenges and prospects for reducing coupled climate model SST biases in the eastern tropical Atlantic and Pacific Oceans: the U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group. Bull. Am. Meteorol. Soc. 97, 2305–2328 (2016).
ADS  Google Scholar 

12.
Dutkiewicz, S., Hickman, A. E. & Jahn, O. Modelling ocean-colour-derived chlorophyll a. Biogeosciences 15, 613–630 (2018).
ADS  CAS  Google Scholar 

13.
Siegel, D. A. et al. Solar radiation, phytoplankton pigments and the radiant heating of the equatorial Pacific warm pool. J. Geophys. Res. 100, 4885–4891 (1995).
ADS  Google Scholar 

14.
Strutton, P. G. & Chavez, F. P. Biological heating in the equatorial Pacific: observed variability and potential for real-time calculation. J. Clim. 17, 1097–1109 (2004).
ADS  Google Scholar 

15.
Bourlès, B. et al. PIRATA: a sustained observing system for tropical Atlantic climate research and forecasting. Earth Space Sci. 6, 577–616 (2019).
ADS  Google Scholar 

16.
Foltz, G. R., Grodsky, S. A., Carton, J. A. & McPhaden, M. J. Seasonal mixed layer heat budget of the tropical Atlantic Ocean. J. Geophys. Res. 108, 3146 (2003).
ADS  Google Scholar 

17.
Foltz, G. R., Schmid, C. & Lumpkin, R. Seasonal cycle of the mixed layer heat budget in the northeastern tropical Atlantic ocean. J. Clim. 26, 8169–8188 (2013).
ADS  Google Scholar 

18.
Foltz, G. R., Schmid, C. & Lumpkin, R. An enhanced PIRATA dataset for tropical Atlantic ocean–atmosphere research. J. Clim. 31, 1499–1524 (2018).
ADS  Google Scholar 

19.
Hummels, R., Dengler, M. & Bourles, B. Seasonal and regional variability of upper ocean diapycnal heat flux in the Atlantic cold tongue. Prog. Oceanogr. 111, 52–74 (2013).
ADS  Google Scholar 

20.
Hummels, R., Dengler, M., Brandt, P. & Schlundt, M. Diapycnal heat flux and mixed layer heat budget within the Atlantic Cold Tongue. Clim. Dyn. 43, 3179–3199 (2014).
Google Scholar 

21.
Wade, M. et al. A one-dimensional modeling study of the diurnal cycle in the equatorial Atlantic at the PIRATA buoys during the EGEE-3 campaign. Ocean Dyn. 61, 1–20 (2011).
ADS  Google Scholar 

22.
Wade, M., Caniaux, G. & Du Penhoat, Y. Variability of the mixed layer heat budget in the eastern equatorial Atlantic during 2005–2007 as inferred using Argo floats. J. Geophys. Res. 116, 17 (2011).
Google Scholar 

23.
McPhaden, M. J., Cronin, M. F. & McClurg, D. C. Meridional structure of the seasonally varying mixed layer temperature balance in the eastern Tropical Pacific. J. Clim. 21, 3240–3260 (2008).
ADS  Google Scholar 

24.
Moum, J. N., Perlin, A., Nash, J. D. & McPhaden, M. J. Seasonal sea surface cooling in the equatorial Pacific cold tongue controlled by ocean mixing. Nature 500, 64 (2013).
ADS  CAS  PubMed  Google Scholar 

25.
Schlundt, M. et al. Mixed layer heat and salinity budgets during the onset of the 2011 Atlantic cold tongue. J. Geophys. Res. 119, 7882–7910 (2014).
ADS  Google Scholar 

26.
Smyth, W. D., Moum, J. N., Li, L. & Thorpe, S. A. Diurnal shear instability, the descent of the surface shear layer, and the deep cycle of equatorial turbulence. J. Phys. Oceanogr. 43, 2432–2455 (2013).
ADS  Google Scholar 

27.
Giordani, H., Caniaux, G. & Voldoire, A. Intraseasonal mixed-layer heat budget in the equatorial Atlantic during the cold tongue development in 2006. J. Geophys. Res. 118, 650–671 (2013).
ADS  Google Scholar 

28.
Huang, B., Xue, Y., Zhang, D., Kumar, A. & McPhaden, M. J. The NCEP GODAS ocean analysis of the tropical Pacific mixed layer heat budget on seasonal to interannual time scales. J. Clim. 23, 4901–4925 (2010).
ADS  Google Scholar 

29.
Jouanno, J., Marin, F., du Penhoat, Y., Sheinbaum, J. & Molines, J.-M. Seasonal heat balance in the upper 100 m of the equatorial Atlantic Ocean. J. Geophys. Res. 116, C09003 (2011).
ADS  Google Scholar 

30.
Pham, H. T., Sarkar, S. & Winters, K. B. Large-eddy simulation of deep-cycle turbulence in an equatorial undercurrent model. J. Phys. Oceanogr. 43, 2490–2502 (2013).
ADS  Google Scholar 

31.
Pham, H. T., Smyth, W. D., Sarkar, S. & Moum, J. N. Seasonality of deep cycle turbulence in the eastern equatorial Pacific. J. Phys. Oceanogr. 47, 2189–2209 (2017).
ADS  Google Scholar 

32.
Brandt, P. et al. On the role of circulation and mixing in the ventilation of oxygen minimum zones with a focus on the eastern tropical North Atlantic. Biogeosciences 12, 489–512 (2015).
ADS  Google Scholar 

33.
MacKinnon, J. A. et al. Climate process team on internal wave–driven ocean mixing. Bull. Am. Meteorol. Soc. 98, 2429–2454 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

34.
Alford, M. H., MacKinnon, J. A., Simmons, H. L. & Nash, J. D. Near-inertial internal gravity waves in the ocean. Annu. Rev. Mar. Sci. 8, 95–123 (2016).
ADS  Google Scholar 

35.
Pollard, R. T. & Millard, R. C. Comparison between observed and simulated wind-generated inertial oscillations. Deep Sea Res. Oceanogr. Abstr. 17, 813–821 (1970).
ADS  Google Scholar 

36.
Falkowski, P. G., Barber, R. T. & Smetacek, V. Biogeochemical controls and feedbacks on ocean primary production. Science 281, 200 (1998).
CAS  PubMed  Google Scholar 

37.
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701 (2013).
ADS  CAS  Google Scholar 

38.
Schafstall, J., Dengler, M., Brandt, P. & Bange, H. Tidal-induced mixing and diapycnal nutrient fluxes in the Mauritanian upwelling region. J. Geophys. Res. 115, C10014 (2010).
ADS  Google Scholar 

39.
Peters, H., Gregg, M. C. & Toole, J. M. On the parameterization of equatorial turbulence. J. Geophys. Res. 93, 1199–1218 (1988).
ADS  Google Scholar 

40.
Leaman, K. D. & Sanford, T. B. Vertical energy propagation of inertial waves: a vector spectral analysis of velocity profiles. J. Geophys. Res. (1896–1977) 80, 1975–1978 (1975).
ADS  Google Scholar 

41.
Thorpe, S. A. Turbulence and mixing in a Scottish Loch. Philos. Trans. R. Soc. Lond. Ser. A 286, 125–181 (1977).
ADS  Google Scholar 

42.
Fischer, T. Microstructure Measurements during METEOR Cruise M119, PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.920592 (2020).

43.
Ozmidov, R. V. On the turbulent exchange in a stably stratified ocean. Izv. Acad. Sci. USSR Atmos. Ocean Phys., 1, 861–871 (1965).
Google Scholar 

44.
Smyth, W. D. & Moum, J. N. Ocean mixing by Kelvin–Helmholtz instability. Oceanography 25, 140–149 (2012).
Google Scholar 

45.
Kraus, E. B. & Turner, J. S. A one-dimensional model of the seasonal thermocline II. The general theory and its consequences. Tellus 19, 98–106 (1967).
ADS  Google Scholar 

46.
Plueddemann, A. J. & Farrar, J. T. Observations and models of the energy flux from the wind to mixed-layer inertial currents. Deep Sea Res. Part II 53, 5–30 (2006).
ADS  Google Scholar 

47.
Large, W. G. & Pond, S. Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr. 11, 324–336 (1981).
ADS  Google Scholar 

48.
Donelan, M. A. et al. On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett. 31, L18306 (2004).
ADS  Google Scholar 

49.
D’Asaro, E. A. The energy flux from the wind to near-inertial motions in the surface mixed layer. J. Phys. Oceanogr. 15, 1043–1059 (1985).
ADS  Google Scholar 

50.
Atlas, R. et al. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Am. Meteorol. Soc. 92, 157–174 (2011).
ADS  Google Scholar 

51.
Wentz, F. et al. Remote Sensing Systems Cross-calibrated Multi-platform (CCMP) 6-hourly Ocean Vector Wind Analysis Product on 0.25 deg Grid, Version 2.0 (Remote Sensing Systems, Santa Rosa, CA, Google Scholar, 2015).

52.
Moisan, J. R. & Niiler, P. P. The seasonal heat budget of the North Pacific: net heat flux and heat storage rates (1950–1990). J. Phys. Oceanogr. 28, 401–421 (1998).
ADS  Google Scholar 

53.
Stevenson, J. W. & Niiler, P. P. Upper ocean heat budget during the Hawaii-to-Tahiti shuttle experiment. J. Phys. Oceanogr. 13, 1894–1907 (1983).
ADS  Google Scholar 

54.
Belanger, J. I., Jelinek, M. T. & Curry, J. A. A climatology of easterly waves in the tropical Western Hemisphere. Geosci. Data J. 3, 40–49 (2016).
ADS  Google Scholar 

55.
Burpee, R. W. The origin and structure of easterly waves in the lower troposphere of North Africa. J. Atmos. Sci. 29, 77–90 (1972).
ADS  Google Scholar 

56.
Carlson, T. N. Synoptic histories of three African disturbances that developed into Atlantic hurricanes. Mon. Weather Rev. 97, 256–276 (1969).
ADS  Google Scholar 

57.
Pytharoulis, I. & Thorncroft, C. The low-level structure of African easterly waves in 1995. Mon. Weather Rev. 127, 2266–2280 (1999).
ADS  Google Scholar 

58.
Kiladis, G. N., Thorncroft, C. D. & Hall, N. M. J. Three-dimensional structure and dynamics of African easterly waves. Part I: Observations. J. Atmos. Sci. 63, 2212–2230 (2006).
ADS  Google Scholar 

59.
Thorncroft, C. & Hodges, K. African easterly wave variability and its relationship to Atlantic tropical cyclone activity. J. Clim. 14, 1166–1179 (2001).
ADS  Google Scholar 

60.
Mickett, J. B., Serra, Y. L., Cronin, M. F. & Alford, M. H. Resonant forcing of mixed layer inertial motions by atmospheric easterly waves in the northeast tropical Pacific. J. Phys. Oceanogr. 40, 401–416 (2010).
ADS  Google Scholar 

61.
Wu, M.-L. C., Reale, O., Schubert, S. D., Suarez, M. J. & Thorncroft, C. D. African easterly jet: barotropic instability, waves, and cyclogenesis. J. Clim. 25, 1489–1510 (2012).
ADS  Google Scholar 

62.
Wu, M.-L. C., Reale, O. & Schubert, S. D. A characterization of African easterly waves on 2.5–6-day and 6–9-day time scales. J. Clim. 26, 6750–6774 (2013).
ADS  Google Scholar 

63.
Large, W. G., McWilliams, J. C. & Doney, S. C. Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev. Geophys. 32, 363–403 (1994).
ADS  Google Scholar 

64.
Foltz, G. R., Evan, A. T., Freitag, H. P., Brown, S. & McPhaden, M. J. Dust accumulation biases in PIRATA shortwave radiation records. J. Atmos. Ocean. Technol. 30, 1414–1432 (2013).
ADS  Google Scholar 

65.
Balmaseda, M. A., Mogensen, K. & Weaver, A. T. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q. J. R. Meteorol. Soc. 139, 1132–1161 (2013).
ADS  Google Scholar 

66.
Morel, A. & Antoine, D. Heating rate within the upper ocean in relation to its bio-optical state. J. Phys. Oceanogr. 24, 1652–1665 (1994).
ADS  Google Scholar 

67.
Sweeney, C. et al. Impacts of shortwave penetration depth on large-scale ocean circulation and heat transport. J. Phys. Oceanogr. 35, 1103–1119 (2005).
ADS  Google Scholar 

68.
Foltz, G. R. & McPhaden, M. J. Impact of barrier layer thickness on SST in the central tropical North Atlantic. J. Clim. 22, 285–299 (2009).
ADS  Google Scholar 

69.
Fischer, J., Brandt, P., Dengler, M., Müller, M. & Symonds, D. Surveying the upper ocean with the ocean surveyor: a new phased array Doppler current profiler. J. Atmos. Ocean. Technol. 20, 742–751 (2003).
ADS  Google Scholar 

70.
Wolk, F., Yamazaki, H., Seuront, L. & Lueck, R. G. A new free-fall profiler for measuring biophysical microstructure. J. Atmos. Ocean. Technol. 19, 780–793 (2002).
ADS  Google Scholar 

71.
Merckelbach, L., Berger, A., Krahmann, G., Dengler, M. & Carpenter, J. R. A dynamic flight model for slocum gliders and implications for turbulence microstructure measurements. J. Atmos. Ocean. Technol. 36, 281–296 (2019).
ADS  Google Scholar 

72.
Osborn, T. R. Estimates of the local rate of vertical diffusion from dissipation measurements. J. Phys. Oceanogr. 10, 83–89 (1980).
ADS  Google Scholar 

73.
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes: The Art of Scientific Computing (Cambridge University Press, 2007).

74.
Hoyer, S. et al. pydata/xarray v0.10.8. Version v0.10.8 (Zenodo, 2018).

75.
Hoyer, S. & Hammam, J. xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. 5, 10 (2017).
Google Scholar 

76.
Rocklin, M. Dask: Parallel Computation with Blocked algorithms and Task Scheduling, in Proceedings of the 14th Python in Science Conference. (eds  Huff, K. & Bergstra, J.) (2015).

77.
Oliphant T. E. A Guide to NumPy (Trelgol Publishing, 2006).

78.
Lam, S. K., Pitrou, A. & Seibert, S. Numba: a LLVM-based Python JIT compiler. In Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC (Association for Computing Machinery, 2015).

79.
Mertens, C. & Schott, F. Interannual variability of deep-water formation in the Northwestern Mediterranean. J. Phys. Oceanogr. 28, 1410–1424 (1998).
ADS  Google Scholar 

80.
Lascaratos, A., Williams, R. G. & Tragou, E. A mixed-layer study of the formation of Levantine intermediate water. J. Geophys. Res. 98, 14739–14749 (1993).
ADS  Google Scholar 

81.
Schmidtko, S., Johnson, G. C. & Lyman, J. M. MIMOC: a global monthly isopycnal upper-ocean climatology with mixed layers. J. Geophys. Res. 118, 1658–1672 (2013).
ADS  Google Scholar  More

Physical and mechanical properties of wood
The AD (0.62 to 0.68 g/cm3) and OD (0.58 to 0.65 g/cm3) values obtained this study were similar with respect to basic density33 in the same sample trees (Table 2), whereas our results for mean AD values were relatively higher than those for L. sibirica reported by Ishiguri et al.27 and lower than those reported by Koizumi et al.5. Radial variations of AD and OD showed similar patterns to those reported by other researchers of L. sibirica5 and L. kaempferi16. Meanwhile, Cáceres et al.3 reported an influence of extractives on density in L. kaempferi. They found that the hot-water extractive content of L. kaempferi varied between 2.9 to 6.9% among 20 provenances, suggesting that actual wood density might be about 5% lower than AD. As shown in Table 3, cold-water extractive content ranged from 7.3 to 16.1%, and the mean values of EOD (0.54 g/cm3, Table 2) were about 10% lower values compared to OD (0.61 g/cm3, Table 2). These results indicated that the effect of cold- or hot-water extractives on wood density might be greater in L. sibirica compared to other Larix species.
Ishiguri et al.27 reported that radial shrinkage at 1% moisture content change showed almost constant values from pith to bark, whereas tangential shrinkage increased up to 4 cm from pith and then became constant at around 0.3%. The mean values and radial variation patterns examined in this study for shrinkage in both the radial and tangential directions in L. sibirica were similar to those of L. sibirica examined by Ishiguri et al.27.
Although the tree ages varied, the mean values of MOE, MOR, and CS of the L. sibirica trees in the present study (Table 4) were similar to those found in a previous study for L. sibirica that grow naturally in Mongolia27 but lower than those for L. sibirica that grow naturally in Russia5 and higher than those for L. kaempferi planted in Japan22,24. The mean SS was higher than that of L. sibirica planted in Finland9 and L. kaempferi planted in Japan24. In the radial variation, similar radial trends were found in L. sibirica that grow naturally in Mongolia27 and in L. kaempferi planted in Japan22.
Based on the obtained results, the mean values of the physical and mechanical properties of L. sibirica collected from five different provenances in Mongolia are similar to those of L. sibirica and other Larix species found in other countries. Thus, wood resources from L. sibirica harvested in Mongolia can be used for similar purposes to other Larix species, such as construction materials.
Juvenile and mature wood
The boundary between juvenile and mature wood ranged from the 17th to 24th annual rings from the pith (Table 6). The results were similar to those reported for L. kaempferi trees17,22,42. However, Ishiguri et al.27 showed that juvenile wood might exist within 4 cm from the pith in L. sibirica. In the present study, the boundary was within 2 to 5 cm from the pith among the provenances, suggesting that juvenile wood formation in L. sibirica trees that grow naturally in Mongolia is not only affected by tree age but also by growing conditions.
We previously reported that mean values of annual ring width were 1.55, 2.47, 0.49, 1.86, and 1.74 for Khentii, Arkhangai, Zavkhan, Khuvsgul, and Selenge, respectively33. This result indicates that the radial growth rate was extremely slow in Zavkhan compared to other four provenances. Shiokura and Watanabe28 reported that suppressed radial growth in the initial stage of tree growth resulted in prolonging the juvenile wood formation period in Picea jezoensis and Abies sachalinensis. Although significant differences among provenances were also found in annual ring number from the pith in the boundary between juvenile and mature wood (Table 6), the difference in the earliest (17th) and the latest (24th) annual ring number from the pith in the boundary was only 7 years. Thus, the radial growth rate in L. sibirica does not have a strong effect on the cambial age at which the production of mature wood cells begins. However, further research is needed to clarify the relationship between the radial growth rate and annual ring number from the pith in the boundary between juvenile and mature wood in this species.
As shown in Table 7, significant differences between juvenile and mature wood were found in the mean values of physical properties, tracheid length, and mechanical properties, except for SS: the values of the physical and mechanical properties of juvenile wood were lower than those of mature wood. These lower values can be explained by shorter tracheid length and lower wood density. Similar results were obtained by several researchers of softwood species17,22,24,28,29. For example, Koizumi et al.24 found that, in L. kaempferi, the mean MOE, MOR, CS, and SS values were 8.2 GPa, 93.3 MPa, 54.0 MPa, and 11.5 MPa in juvenile wood and 9.5 GPa, 97.2 MPa, 55.1 MPa, and 11.4 MPa in mature wood, respectively. Bao et al.25 reported that the mechanical properties of juvenile wood were significantly lower than those of mature wood in Larix olgenis and L. kaempferi. We also found lower mechanical properties, basic density, and shorter latewood tracheid length of juvenile wood in 67-year-old L. kaempferi22. Thus, the presence of juvenile wood should be considered when utilizing wood resources of this species as construction materials requiring higher strength properties.
Correlation among physical and mechanical properties of wood
Figure 4 shows the correlation coefficients of the physical and mechanical properties of three different wood types (all types of wood, juvenile wood, and mature wood). In general, wood density is positively related to shrinkage in the radial and tangential directions44. The results of this study showed significant correlations between radial shrinkage at 1% moisture content and EOD in mature wood and all wood, suggesting that EOD can predict shrinkage in the radial direction in this species. Wood density is also positively correlated with many types of mechanical properties of wood45,46. CS was positively correlated with all types of wood densities measured in this study. The MOE and MOR in mature wood and all wood only exhibited a significant positive correlation with EOD. These results indicate that MOE and MOR values were correlated with wood substances without extractives, and these values in juvenile wood might be related to other properties, such as microfibril angle. Luostarinen and Heräjärvi10 reported that water-soluble arabinogalactan contents were weakly correlated with SS in L. sibirica. SS was significantly correlated with AD, but not with EOD, suggesting that cold water-soluble extractives, such as arabinogalactan, might be affected on the SS in this species.
Based on these results, strength properties (e.g., bending properties and compressive strength) can be estimated with each other and predicted by EOD. In addition, SS might be influenced by the presence of cold water-soluble extractives, such as arabinogalactan.
Among-provenance variations
Cáceres et al.3 reported that significant among-provenance differences were not found in basic and oven-dry densities, whereas hot-water extractive content was significantly affected by provenances in L. kaempferi. We also previously demonstrated that no significant differences among provenances were found in the basic density of L. sibirica naturally grown in Mongolia33. Although the cold-water extractive content significantly differed among provenances in this study, all examined densities, such as AD, OD, and EOD, showed no significant differences among the five provenances (Tables 2 and 3), indicating that wood density might not vary greatly among provenances. Thus, it can be concluded that genetic variations in relation to wood density might be small in L. sibirica trees naturally grown in Mongolia.
In half-sib families of P. jezoensis, F-values obtained by an ANOVA test for AD, MOE, and MOR among families gradually decreased from juvenile to mature wood47. In addition, Kumar et al.48 reported that estimates of narrow-sense heritability for MOE were generally higher in the corewood than in the outer wood in Pinus radiata. For Larix species, significant differences in wood density, CS, and SS but not in MOE and MOR were found in outer wood among 23 provenances for 31-year-old L. kaempferi24. Thus, genetic variations in the physical and mechanical properties of juvenile wood were higher than in mature wood in many softwood species. Significant differences were also found in most of the mechanical properties among provenances, except for CS (Table 4). In addition, significant differences were found in all examined physical and mechanical properties except for CS in mature wood among the five provenances, while no differences were found in juvenile wood for many properties (Table 7). Similar results were obtained in estimated MOE and MOR values at the 10th and 30th annual rings from the pith: no significant among-provenance variations were found in MOE and MOR at the 10th annual ring from the pith, but significant differences were found in the 30th annual ring from the pith (Table 5). Although the environmental conditions in the five provenances were not the same, the genetic variations in physical and mechanical properties among provenances were large in mature wood compared to juvenile wood for L. sibirica grown naturally in Mongolia. Further research is needed to clarify the genetic factors of the physical and mechanical properties of wood in L. sibirica.
Based on the results, there are significant among-provenance differences in the physical and mechanical properties of wood, especially in mature wood, in L. sibirica grown naturally in Mongolia. The physical and mechanical properties of wood in this species, especially in mature wood, can be improved by establishing tree breeding programs: families or clones with higher mechanical properties can be produced to achieve sustainable forestry in Mongolia. More

One of the major challenges in the field of contemporary ecology is the documentation of ecosystem change over time. Among coastal marine biota, coral reefs are home to a unique hotspot of biodiversity. In the last decades, coral reefs are undergoing a severe decline worldwide1,2,3 due to a combination of ocean acidification4,5, and seawater warming6,7, their adverse impacts intensified by anthropogenic eutrophication and pollution8. These bring about both the decline in live reef cover and a decrease in coral species diversity1. Hence it is of paramount importance to monitor and document the rates of reef decline and identify the relative importance of stressors in each reef. An additional benefit of automated analysis of reef images is its potential as a tool to evaluate the long-term success of bioremediation projects of damaged coral reefs9 and reef protection measures.
The use of artificial intelligence (AI) to solve the time-consuming, tedious manual classification of coral species and determination of their abundance in real-time, is a Herculean task by itself due to the immense numbers of necessary images and their examination. Automated Deep learning (DL), a branch of AI, has the potential of solving this problem efficiently, and by far exceeds in terms of reliability and accuracy human reef documentation and monitoring. Like the human brain, the more data the computer learns under the DL mechanism, the better it performs at distinguishing among classes of coral species in the present application.
The highly-diverse coral reefs of the Gulf of Eilat (Aqaba) are of special scientific interest not only for being the most Northern reefs, but for benefiting the economy of neighboring communities in both Israel and Jordan. Although highly-diverse10, these reefs have suffered from a sequence of disasters, including a rare low tide in 197011, the recovery from which was impeded by repeated oil pollution following the closure of the Suez Canal between 1967 and 197512. The subsequent recovery of the gulf’s reefs was slowed down again by the episode of the cooling of the Gulf’s waters due to the eruption of Mount Pinatubo in 1991. That event caused erosion of the Gulf’s thermocline and led to deep mixing of its waters, enriching surface waters with nutrients. The resulting proliferation and subsequent decomposition of seaweeds smothered some 25% of the juvenile corals13. The anthropogenic eutrophication of the Gulf due to the increase in fish farming until the farm closing14, also reduced the transparency of its waters by increasing the concentration of phytoplankton15. These events, as well as the forthcoming Red-Dead Canal, call for frequent, detailed monitoring of any changes in the situation of the coral reefs of the Gulf, as are evident in the reduction of live coral cover and species biodiversity.
Since its early development, DL has been used in human facial discrimination16, handwriting recognition17, and forensic applications, such as fingerprint identification18 and voice analysis19.
DL has also been applied to various coral reef studies, in which it was used to discriminate among benthos types: sand, urchins, and three types of branched corals: brain coral, massive favids, and dead coral20, as well as to distinguish between healthy and bleached corals (see, e.g.,21,22). Shihavuddin et al.20 demonstrated the capability of DL to identify five coral genera from large assemblages of underwater images. In a recent study identification among branched and brain corals, was reported23.
In their research, Gómez Ríos et al.23 also distinguished among favids, brain coral, and three branched coral types: I, II, and III (an urchin, dead corals, and pavements based on an image mosaic).
Among DL studies at species level discrimination the following datasets are noteworthy:
The first dataset is the Pacific Labelled Corals (PLC) dataset, which contains 5,090 images from four locations: 1. Mo’orea (French Polynesia), 2. Northern Line Islands, 3. Nanwan Bay (Taiwan), and 4. Heron Reef (Australia). The PLC dataset contains: 251,988 annotations from these four locations made by a coral reef expert using a random point tool. In addition, six experts cross-annotated 200 images from each location21.
The second dataset is the Mo’orea Labeled Corals (MLC) dataset that includes five coral classes: Acropora, Pavona, Montipora, Pocillopora, and Porites, and four non-coral classes: crustose coralline algae, turf algae, macroalgae and sand. The MLC dataset contains over 400,000 human expert annotations of 2055 Mo’orea island survey images (https://vision.ucsd.edu/datasetsAll).
To apply DL to a coral reef dataset, there is no need to sample small fragments of corals for the subsequent tedious identification in the laboratory. DL enables direct classification of a large amount of photographs, in minimum time.
The novelty of the present study is the application of a fully-automated DL-based methodology to an imaging dataset, for the classification of 11 common coral types from the set of species reported in the Gulf of Eilat11. We applied DL to over 5,000 underwater images taken specifically by us from a shallow reef in the Gulf of Eilat, with the aim of documenting the distribution of the test types in the sample reef.
We demonstrated the power of DL, using shallow coral reefs in the Gulf of Eilat for comparison with its efficiency in other reef sites. That site allowed us to compare our DL data with the detailed manual coral surveys previously conducted on these reefs.
Corals and reefs
The nature and global importance of corals and the rapid destructive impact of Global Climate Change call for extensive and fast indexing and monitoring. Coral reefs cover less than 1% of the total area of the oceans and seas, yet they are the main repository of oceanic biodiversity (25% of all marine species)24. Extant hermatypic (reef-building) coral species are estimated at 3,23524, of which 100 were recorded in Eilat, Gulf of Aqaba, Northern Red Sea10. Hexacorals, based on six fold symmetry, or scleractinian corals are the most important hermatypic organisms25.
The decline of reefs leads to the collapse of their entire complex ecosystem depending on the calcium carbonate skeletons of the corals intricate reef structures, for food and shelter. Hermatypic corals are home to symbiotic algae living within their cells in specialized organelles, the symbiosomes. Called zooxanthellae by their first reporter, Brandt26, these greenish microalgae limited to sunlit shallow waters (~ 0–120 m), provide the corals with energy through their photosynthesis27,28, which also stimulates calcification29.
In most corals, the tentacles are retracted by day and spread out at night30 to catch plankton and other small organisms, while avoiding diurnal coral-feeding predators. This behaviour also optimizes the supply of oxygen for nocturnal respiration.
Unlike in shallow water, corals satisfy their energy needs in the deep water and dim light by zooplankton consumption, as an energy supplement to the algal light-limited photosynthetic products (see review by Dubinsky and Iluz28).
The photosynthetic activity of the zooxanthellae, raises the internal pH of the coral facilitating the skeletal calcification by “light enhanced calcification”31,32, a paradigm recently challenged by Cohen et al.33. Conversely, ocean acidification makes coral calcification more difficult.
The future of coral reefs
Coral reefs are exposed to many dangers because of global climate-change effects34,35, blast and cyanide fishing36, coral collection by the marine coral aquarium trade37, sunscreen use38, and light pollution interference with lunar cycle reproduction timing39. SCUBA diving pressure40. Anthropogenic eutrophication, acts synergistically with all the above listed detrimental factors, stimulating fast seaweed growth, that easily outcompete the slowly growing corals. The ensuing algal blooms, smother the coral colonies and prevent the settlement of juveniles41. Kaneohe Bay, a coral reef ecosystem at Oahu, Hawaii, illustrates the sensitivity of coral reefs to nutrient enrichment resulting from treated sewage disposal, leading to the reversible proliferation of seaweeds42. Fish cage farming released nutrients that affected the coral reefs in Eilat by causing deterioration in water quality due to eutrophication and by promoting seaweed growth and phytoplankton proliferation reducing the Gulf’s water transparency, thus reducing light necessary for symbiont photosynthesis, interfering with reproduction, increasing bio-erosion and epizootic infestation14.
Coral species differ in their tolerance to climate change and coral bleaching43. Corals experience bleaching as water temperature increases and causes loss of the zooxanthellae, and subsequently of live coral tissue, resulting in wide spread coral mortality followed by reef destruction. Unless the algal population recovers within weeks, the bleaching results in widespread reef mortality44. The ongoing increase in atmospheric carbon dioxide since the industrial revolution leads to ocean acidification or lowering of ocean pH, and affects corals negatively by shifting the balance from skeletal aragonite deposition toward its dissolution4. In addition, light pollution by artificial light, even at the weakest intensities45,46, can cause the disruption of coral reproduction that is controlled by lunar periodicity47,48. The planned Red Sea–Dead Sea Conveyance49 will cause a change in the regime of the Gulf currents50. Such a change could reduce the supply of larvae of corals and other reef organisms, and have a far-reaching deleterious impact on reef systems.
The real-time characteristics of DL tools are crucial for the rapid detection of reef damage allowing implementation of bioremediation measures. The DL characteristics are valuable tools assuring the health and long-term survival of the coral reefs in the Gulf of Eilat and worldwide.
Deep learning
The efficiency of the methodology of DL-based classification of coral species consists of efficient algorithms that reveal and extract common-patterns and features from large image datasets. Two popular schemes applied to coral reef data are the convolutional neural network (CNN)21 and deep belief net (DBN)21. A generic structure of CNN is a multi-layer, feed-forward, supervised neural network that recognizes objects from spatial-based images with little or no pre-processing. It consists of: (1) feature extraction (convolution layer); (2) distortion invariance (sub-sampling layer); and (3) classification (output layer). A DBN, consists of probabilistic models composed of multiple layers of random variables51.
Any coral-reef classification should consist of five main steps:
1.
Taking sufficient high quality underwater images.

2.
Detecting the chosen coral and cropping its image.

3.
Downscaling the cropped images to 200 × 200 pixels.

4.
Preprocessing the images to compensate for different imperfections (e.g., blurring, colour change, sunlight wave patterns, sky colour, nekton scattering effects etc.).

5.
Labelling each one of the coral species is labelled.

In case of an automatic model, Steps 3 and 4 may not be required.
Traditional machine learning methods need extensive domain expertise, human intervention, and are only capable of what they were originally designed for.
Additional works on growth modelling and quantification of morphological variation in coral types are due to, Kruszyński et al.52 and Chindapol et al.53. The former focused on the analysis of three-dimensional (3D) coral images scanned by X-ray tomography, and the latter, modelled the effects of flow on colony growth and shape, using analyzed advection–diffusion equations.
The increased interest in DL has also been recently reflected in the analysis of previously published coral datasets. Specifically, recent work22 has demonstrated the efficiency of neural networks and DL in distinguishing among various marine benthos components such as bare ground, seagrass meadows, algal cover, sponges, and identified some coral species. Additional recent work has shown the capability of neural networks and DL to distinguish among coral species and live corals from bleached colonies (see, e.g.,21,22).
Mahmood et al.22 combined CNN representations with manually obtained colony parameters. Their algorithms, based on image information, extract CNN images obtained from the deep VGGnet network with a 2-layer multilayer perceptron (MLP) classifier (trained on the MLC dataset). They achieved 77.9% accuracy.
Mahmood et al.54, reviewed the power of DL for machine monitoring of coral reefs.
Mahmood et al. (2016a)55 reported a decrease trend in coral density and species numbers in the reefs of Abrolhos Islands. Their analysis was based on CNN images obtained from VGGnet. They proved the reliability of their classifier on unlabelled coral image mosaics.
Mahmood et al. (2018)56 used CNN-based features and ResFeats to annotate corals and demonstrated the temporal changes in their association. They applied generic features from VGGnet and ResNet to classify corals and non-corals. They analysed unlabeled coral mosaics of three Abrolhos Island sites generating maps for the aforementioned mosaics.
Mahmood et al. (2020)57 applied computerized DL characterization of annotated kelp species. They presented an automatic hierarchical classification method to classify kelps in collected images. Their study summarises the considerable advantages of using deep residual networks (ResNets) over traditional, manual classifications of the same reefs. They showed that the sibling hierarchical training approach outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs. 57.6% and 77.2% vs. 59.0%) on Benthoz15 and Rottnest datasets, respectively. They used an application to study the changes in kelp cover over time for annually repeated AUV surveys.
Mahmood et al. (2020)58 evaluated how well features extracted from deep neural networks transfer to underwater image classification. They investigated the effectiveness of transfer learning of the ResFeats. They proposed applying new image features (called ResFeats) extracted from the different convolutional layers of a deep residual network pre-trained on ImageNet to the MLC, Benthoz15, EILAT and RSMAS datasets.
Gómez-Ríos et al.23 included more corals than previous studies by applying three CNNs: Inception v359, ResNet60, and DenseNet61 (see Supplementary Table 1).
Two datasets were analysed: Both the EILAT and RSMAS were analysed. These datasets comprise patches of coral images discriminating branched and massive colonies. The EILAT dataset contains 1123 images of eight classes (sand, urchin, branched type I, II, and III corals, brain coral, favid coral, and dead coral) and the RSMAS dataset contains 776 images of 14 classes, including 9 classes of the following scleractinian coral species: Acropora cervicornis, Acropora palmata, Diploria strigosa, Montastraea cavernosa, Meandrina meandrites, Montipora spp., Siderastrea sidereal, Colpophyllia natans (a boulder brain coral), and the colonial fire coral Millepora alcicornis (a species of hydrozoa with a calcareous skeleton). The other five classes included in the RAMAS dataset are non-coral species: Diadema antillarum is a sea urchin, Gorgonians are a genus of soft corals in the family Gorgoniidae, Palythoas palythoa is a genus of anthozoans in the order Zoantharia, and sponge fungus and tunicates are marine invertebrates of the subphylum Tunicata.
CoralNet (https://coralnet.ucsd/edu), conceived by Beijbom et al.9,62, uses deep neural networks for fully- or semi-automated image annotation. It also serves as a convenient, user-friendly collaboration platform. In early 2019, Williams et al.63 in a large study showed that the automated annotations for CoralNet Beta, produced benthic cover estimates comparable to controls gathered by human annotation.
Hoegh-Guldberg states that “CoralNet will allow the world’s scientists to quickly assess the health of endangered coral reefs at scales never dreamed of before”, in (https://blogs.nvidia.com/blog/2016/06/22/deep-learning-save-coral-reefs/).
The BenthoBox image labelling system for ecologists allows storing images of the dataset.
The software uses learning algorithms to recognise ‘tagged’ seabed features such as sand, algae, sponges and corals.
History of coral classification in the Gulf of Eilat
Traditional methods have been used in Gulf of Eilat research studies for coral classification since the pioneering work by Loya and Slobodkin10. Some 100 coral species were listed in their study.
Whenever confronted with doubt concerning the species of a certain coral underlying a transect, a small piece was sampled and manually identified by a taxonomist11, a tedious and destructive practice based on limited sample size.
These surveys were based on colour photographs taken by a camera with a flash attachment. Close-ups were taken by a Rolleiflex camera. A measuring tape was spread over the reef, and the divers recorded the projected length of all the organisms and substrates underneath the line transect to a resolution of 1 cm. Photographs were taken at 1 m intervals along the transect. This study was based on permanent transects photographed over a period of 20 months that yielded about 3,000 photographs of corals belonging to Loya’s11 list of approximately 100 species. However, the author noted that many cryptic species do not show up in the photographs.
Similar additional surveys were conducted following various disturbances that affected the coral reefs of the Gulf: the 1970 low tide64, the repeated oil spills65, the Pinatubo eruption of 199166, and the fish farming episode of 1995–200814.
Diver-based methods for classifying corals are almost impossible underwater, and require time-consuming expertise. Furthermore, coral pigmentation and morphology are plastic changes in response to environmental forcing functions such as light and current, eliciting wide phenotypic variability28,67. Ever since the National Monitoring Program (https://www.iuieilat.ac.il/Research/NMPmeteodata.aspx) of the Eilat reefs was initiated (2003), annual surveys by divers have been conducted.
The images are taken at a fixed area at six reef sites, namely the North Beach, the Dekel Beach, the Eilat Ashkelon Pipeline Co. Ltd. (EAPC), the coral reserve, the Interuniversity Institute for Marine Sciences in Eilat (IUI) marine laboratory, and Taba. Each site has fixed camera brackets for five cameras, and each of these takes four images. In this way, 20 pictures are taken at each site, and 120 pictures are taken for quantitative analysis of the changes at the various sites. Monitoring is done once a year in early summer. Corals were identified as far as possible at the species level, and were also classified according to functional groups. The results are presented graphically following statistical processing. Due the disintegration of the rock to which the cameras were attached, some new sites had to be added68.
Automated DL seeks to avoid these difficulties, profiting from the latest advances in computerized handling of large quantities of visual images9. Indeed, these novel developments have been increasingly applied to the survey and analysis of coral reefs in the studies listed in Supplementary Table 2. Since all previous surveys, as well as those of the current monitoring program of the Gulf of Eilat reefs, were based on the manual and visual analysis of large numbers of photographs, we present here a first example at using automated machine-based analysis for the red sea coral reef. More