Ecology

1.
Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Guimberteau, M. et al. Impacts of future deforestation and climate change on the hydrology of the Amazon Basin: a multi-model analysis with a new set of land-cover change scenarios. Hydrol. Earth Syst. Sci. 21, 1455–1475 (2017).
ADS  Article  Google Scholar 

3.
Marengo, J. A. & Espinoza, J. C. Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int. J. Climatol. 36, 1033–1050 (2016).
Article  Google Scholar 

4.
Jimenez, J. C. et al. Spatio-temporal patterns of thermal anomalies and drought over tropical forests driven by recent extreme climatic anomalies. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170300 (2018).
Article  Google Scholar 

5.
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

6.
Kumar, J., Hoffman, F. M., Hargrove, W. W. & Collier, N. Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements. Earth Syst. Sci. Data Discuss. 1–25 (2016). https://doi.org/10.5194/essd-2016-36

7.
Baldocchi, D. et al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434 (2001).
ADS  Article  Google Scholar 

8.
Girardin, C. A. J. et al. Seasonal trends of Amazonian rainforest phenology, net primary productivity, and carbon allocation. Glob. Biogeochem. Cycles 30, 700–715 (2016).
ADS  CAS  Article  Google Scholar 

9.
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).
Article  Google Scholar 

10.
Malhi, Y. & Wright, J. Spatial patterns and recent trends in the climate of tropical rainforest regions. Philos. Trans. R. Soc. B Biol. Sci. 359, 311–329 (2004).
Article  Google Scholar 

11.
Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405 (2006).
ADS  Article  Google Scholar 

12.
Morton, D. C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Myneni, R. B. et al. Large seasonal swings in leaf area of Amazon rainforests. Proc. Natl Acad. Sci. USA 104, 4820–4823 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Morton, D. C. et al. Morton et al. reply. Nature 531, E6–E6 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Saleska, S. R., Didan, K., Huete, A. R. & Da Rocha, H. R. Amazon forests green-up during 2005 drought. Science 318, 612 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Samanta, A. et al. Amazon forests did not green-up during the 2005 drought. Geophys. Res. Lett. 37, LG05401 (2010).
ADS  Article  Google Scholar 

18.
Samanta, A. et al. Comment on ‘Drought-induced reduction in global terrestrial net primary production from 2000 through 2009’. Science 333, 1093 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Xu, L. et al. Widespread decline in greenness of Amazonian vegetation due to the 2010 drought. Geophys. Res. Lett. 38, L07402 (2011).
ADS  Article  Google Scholar 

20.
Atkinson, P. M., Dash, J. & Jeganathan, C. Amazon vegetation greenness as measured by satellite sensors over the last decade. Geophys. Res. Lett. 38, L19105 (2011).
ADS  Article  Google Scholar 

21.
Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Samanta, A., Ganguly, S., Vermote, E., Nemani, R. R. & Myneni, R. B. Why is remote sensing of Amazon forest greenness so challenging? Earth Interact. 16, 1–14 (2012).
Article  Google Scholar 

23.
Lyapustin, A., Wang, Y., Laszlo, I. & Korkin, S. Improved cloud and snow screening in MAIAC aerosol retrievals using spectral and spatial analysis. Atmos. Meas. Tech. 5, 843–850 (2012).
Article  Google Scholar 

24.
Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Schmit, T. J. et al. A closer look at the ABI on the GOES-R series. Bull. Am. Meteorol. Soc. 98, 681–698 (2017).
ADS  Article  Google Scholar 

26.
Wu, J. et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 351, 972–976 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Chave, J. et al. Regional and seasonal patterns of litterfall in tropical South America. Biogeosciences 7, 43–55 (2010).
ADS  Article  Google Scholar 

28.
Samanta, A. et al. Seasonal changes in leaf area of Amazon forests from leaf flushing and abscission. J. Geophys. Res. Biogeosci. 117, G01015 (2012).
ADS  Article  Google Scholar 

29.
Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107, 14685–14690 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Myneni, R. B., Nemani, R. R. & Running, S. W. Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans. Geosci. Remote Sens. 35, 1380–1393 (1997).
ADS  Article  Google Scholar 

31.
Hilker, T. et al. On the measurability of change in Amazon vegetation from MODIS. Remote Sens. Environ. 166, 233–242 (2015).
ADS  Article  Google Scholar 

32.
Araújo, A. C. et al. Comparative measurements of carbon dioxide fluxes from two nearby towers in a central Amazonian rainforest: The Manaus LBA site. J. Geophys. Res. 107, 8090 (2002).
Article  Google Scholar 

33.
Holben, B. N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417–1434 (1986).
ADS  Article  Google Scholar 

34.
Galvão, L. S., Ponzoni, F. J., Epiphanio, J. C. N., Rudorff, B. F. T. & Formaggio, A. R. Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data. Int. J. Remote Sens. 25, 1861–1879 (2004).
ADS  Article  Google Scholar 

35.
Fensholt, R., Huber, S., Proud, S. R. & Mbow, C. Detecting canopy water status using shortwave infrared reflectance data from polar orbiting and geostationary platforms. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens 3, 271–285 (2010).
ADS  Article  Google Scholar 

36.
Gao, F., Jin, Y., Li, X., Schaaf, C. B. & Strahler, A. H. Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy. IEEE Trans. Geosci. Remote Sens. 40, 1269–1278 (2002).
ADS  Article  Google Scholar 

37.
Kruijt, B. et al. The robustness of eddy correlation fluxes for Amazon rain forest conditions. Ecol. Appl. 14, 101–113 (2004).
Article  Google Scholar 

38.
Galvão, L. S. et al. On intra-annual EVI variability in the dry season of tropical forest: A case study with MODIS and hyperspectral data. Remote Sens. Environ. 115, 2350–2359 (2011).
ADS  Article  Google Scholar 

39.
NOAA National Centers for Environmental Information. State of the Climate: Global Climate Report for Annual 2018. (2019). Available at: https://www.ncdc.noaa.gov/sotc/global/201813. (Accessed: 18th June 2019)

40.
Andreae, M. O. et al. The Amazon Tall Tower Observatory (ATTO): Overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols. Atmos. Chem. Phys. 15, 10723–10776 (2015).
ADS  CAS  Article  Google Scholar 

41.
Kobayashi, H. & Dye, D. G. Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index. Remote Sens. Environ. 97, 519–525 (2005).
ADS  Article  Google Scholar 

42.
Xu, L. et al. Satellite observation of tropical forest seasonality: spatial patterns of carbon exchange in Amazonia. Environ. Res. Lett. 10, 084005 (2015).
ADS  Article  CAS  Google Scholar 

43.
Doughty, R. et al. TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest. Proc. Natl Acad. Sci. USA 116, 22393–22398 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Bi, J. et al. Sunlight mediated seasonality in canopy structure and photosynthetic activity of Amazonian rainforests. Environ. Res. Lett. 10, 064014 (2015).
ADS  Article  Google Scholar 

45.
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Wu, J. et al. Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. N. Phytol. 217, 1507–1520 (2018).
Article  Google Scholar 

47.
Tang, H. & Dubayah, R. Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. Proc. Natl Acad. Sci. USA 114, 2640–2644 (2017).
CAS  PubMed  Article  Google Scholar 

48.
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).
ADS  Article  Google Scholar 

49.
Justice, C. O., Townshend, J. R. G., Holben, A. N. & Tucker, C. J. Analysis of the phenology of global vegetation using meteorological satellite data. Int. J. Remote Sens. 6, 1271–1318 (1985).
ADS  Article  Google Scholar 

50.
Badgley, G., Anderegg, L. D., Berry, J. A. & Field, C. B. Terrestrial gross primary production: Using NIRv to scale from site to globe. Glob. Chang. Biol. 25, 3731–3740 (2019).
ADS  PubMed  Article  PubMed Central  Google Scholar 

51.
Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 1–7 (2014).
Article  CAS  Google Scholar 

52.
Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Remote Sens. 33, 481–486 (2019).
ADS  Article  Google Scholar 

53.
Sellers, P. J. Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens 6, 1335–1372 (1985).
ADS  Article  Google Scholar 

54.
Smith, M. N. et al. Seasonal and drought‐related changes in leaf area profiles depend on height and light environment in an Amazon forest. N. Phytol. 222, 1284–1297 (2019).
Article  Google Scholar 

55.
Goward, S. N. & Huemmrich, K. F. Vegetation canopy PAR absorptance and the normalized difference vegetation index: An assessment using the SAIL model. Remote Sens. Environ. 39, 119–140 (1992).
ADS  Article  Google Scholar 

56.
Miura, T., Nagai, S., Takeuchi, M., Ichii, K. & Yoshioka, H. Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data. Sci. Rep. 9, 1–12 (2019).
Article  CAS  Google Scholar 

57.
Da Rocha, H. R. et al. Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil. J. Geophys. Res. Biogeosci. 114, G00B12 (2009).
Article  Google Scholar 

58.
Wang, W. et al. An introduction to the Geostationary-NASA Earth Exchange (GeoNEX) Products: 1. Top-of-atmosphere reflectance and brightness temperature. Remote Sens. 12, 1267 (2020).
ADS  Article  Google Scholar 

59.
Lyapustin, A., Martonchik, J., Wang, Y., Laszlo, I. & Korkin, S. Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables. J. Geophys. Res. 116, D03210 (2011).
ADS  Google Scholar 

60.
de Moura, Y. M. et al. Spectral analysis of Amazon canopy phenology during the dry season using a tower hyperspectral camera and MODIS observations. ISPRS J. Photogramm. Remote Sens. 131, 52–64 (2017).
ADS  Article  Google Scholar 

61.
Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
ADS  Article  Google Scholar 

62.
Sorooshian, S. et al. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Am. Meteorol. Soc. 81, 2035–2046 (2000).
ADS  Article  Google Scholar 

63.
Sinyuk, A. et al. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2. Atmos. Meas. Tech. 13, 3375–3411 (2020).
CAS  Article  Google Scholar 

64.
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar  More

Inferring leading-following networks
Defining leading-following events
Unlike studies on collective motion where group movement is tracked continuously5,15, our datasets contain only discrete records of bat appearances at experimental boxes. Quantifying individual influence is, thus, contingent on a rigorous method for inferring leading-following events from discrete recordings of animal occurrences. To denote the information that individuals possess about the location of experimental boxes, we refine the nomenclature used by Kerth and Reckardt3. An individual bat is said to be naïve at time ({{{mathbf {t}}}}_{{{mathbf {1}}}}) regarding a given box, if it has not been recorded by the reading device in that box for all times ({{mathbf {t}}} More

1.
Phillips, H. R. P. et al. Global distribution of earthworm diversity. Science 366, 480–485 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Darwin, C. The Formation of Vegetable Mould Through the Action of Worms. (Cambridge Univ. Press, 1881).

3.
Vila, M. B. C. & Pysek, P. How well do we understand the impacts of alien species on ecosystem services? A pan‐European, cross‐taxa assessment. Front. Ecol. Environ. 8, 135–144 (2010).
Article  Google Scholar 

4.
Callaham, M. A. Pandora’s box contained bait: the global problem of introduced earthworms. Annu. Rev. Ecol. Evol. Syst. 39, 593–613 (2008).
Article  Google Scholar 

5.
Blouin, M. et al. A review of earthworm impact on soil function and ecosystem services. Eur. J. Soil Sci. 64, 161–182 (2013).
Article  Google Scholar 

6.
Qiu, J. & Turner, M. G. Effects of non-native Asian earthworm invasion on temperate forest and prairie soils in the Midwestern US. Biol. Invasions 19, 73–88 (2017).
Article  Google Scholar 

7.
Pejchar, L. & Mooney, H. A. Invasive species, ecosystem services and human well-being. Trends Ecol. Evol. 24, 497–504 (2009).
PubMed  Article  Google Scholar 

8.
Viktorov, A. G. Diversity of polyploid races in the family Lumbricidae. Soil Biol. Biochem. 29, 217–221 (1997).
Article  Google Scholar 

9.
Terhivuo, J. & Saura, A. Dispersal and clonal diversity of North-European parthenogenetic earthworms. Biol. Invasions 8, 1205–1218 (2006).
Article  Google Scholar 

10.
Garbar, A. V. & Vlasenko, R. P. Karyotypes of three species of the genus Aporrectodea Örley (Oligochaeta: Lumbricidae) from the Ukraine. Comp. Cytogenet. 1, 59–62 (2007).
Google Scholar 

11.
Bakhtadze, N. G., Bakhtadze, G. I. & Kvavadze, E. S. The chromosome numbers of Georgian earthworms (Oligochaeta: Lumbricidae). Comp. Cytogenet. 2, 79–83 (2008).
Google Scholar 

12.
Hegarty, M. J. & Hiscock, S. J. Genomic clues to the evolutionary success of polyploid plants. Curr. Biol. 18, R435–R444 (2008).
CAS  PubMed  Article  Google Scholar 

13.
Finigan, P., Tanurdzic, M. & Martienssen, R. A. in Polyploidy and Genome Evolution (Springer, 2012).

14.
Sailer, C., Schmid, B. & Grossniklaus, U. Apomixis allows the transgenerational fixation of phenotypes in hybrid plants. Curr. Biol. 26, 331–337 (2016).
CAS  PubMed  Article  Google Scholar 

15.
Novo, M. et al. Multiple introductions and environmental factors affecting the establishment of invasive species on a volcanic island. Soil Biol. Biochem. 85, 89–100 (2015).
CAS  Article  Google Scholar 

16.
Kang, M. M. Earthworm genome assembly protocol. Zenodo https://doi.org/10.5281/zenodo.4288562 (2020).
Article  Google Scholar 

17.
Lim, J. Y., Yoon, J. & Hovde, C. J. A brief overview of Escherichia coli O157:H7 and its plasmid O157. J. Microbiol. Biotechnol. 20, 5–14 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
van Elsas, J. D., Semenov, A. V., Costa, R. & Trevors, J. T. Survival of Escherichia coli in the environment: fundamental and public health aspects. ISME J. 5, 173–183 (2011).
PubMed  Article  Google Scholar 

19.
Lassegues, M., Milochau, A., Doignon, F., Du Pasquier, L. & Valembois, P. Sequence and expression of an Eisenia-fetida-derived cDNA clone that encodes the 40-kDa fetidin antibacterial protein. Eur. J. Biochem. 246, 756–762 (1997).
CAS  PubMed  Article  Google Scholar 

20.
Rorat, A., Vandenbulcke, F., Galuszka, A., Klimek, B. & Plytycz, B. Protective role of metallothionein during regeneration in Eisenia andrei exposed to cadmium. Comp. Biochem Physiol. 203, 39–50 (2017).
CAS  Google Scholar 

21.
Bilej, M. et al. Distinct carbohydrate recognition domains of an invertebrate defense molecule recognize Gram-negative and Gram-positive bacteria. J. Biol. Chem. 276, 45840–45847 (2001).
CAS  PubMed  Article  Google Scholar 

22.
Cho, J. H., Park, C. B., Yoon, Y. G., Kim, S. C. & Lumbricin, I. A novel proline-rich antimicrobial peptide from the earthworm: purification, cDNA cloning and molecular characterization. Biochim. Biophys. Acta 1408, 67–76 (1998).
CAS  PubMed  Article  Google Scholar 

23.
Skanta, F., Prochazkova, P., Roubalova, R., Dvorak, J. & Bilej, M. LBP/BPI homologue in Eisenia andrei earthworms. Dev. Comp. Immunol. 54, 1–6 (2016).
CAS  PubMed  Article  Google Scholar 

24.
Joskova, R., Silerova, M., Prochazkova, P. & Bilej, M. Identification and cloning of an invertebrate-type lysozyme from Eisenia andrei. Dev. Comp. Immunol. 33, 932–938 (2009).
CAS  PubMed  Article  Google Scholar 

25.
Prochazkova, P. et al. Developmental and immune role of a novel multiple cysteine cluster TLR from Eisenia andrei earthworms. Front. Immunol. 10, 1277 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Skanta, F., Roubalova, R., Dvorak, J., Prochazkova, P. & Bilej, M. Molecular cloning and expression of TLR in the Eisenia andrei earthworm. Dev. Comp. Immunol. 41, 694–702 (2013).
CAS  PubMed  Article  Google Scholar 

27.
Wang, J. et al. Transcriptional responses of earthworm (Eisenia fetida) exposed to naphthenic acids in soil. Environ. Pollut. 204, 264–270 (2015).
CAS  PubMed  Article  Google Scholar 

28.
Silerova, M. et al. Characterization, molecular cloning and localization of calreticulin in Eisenia fetida earthworms. Gene 397, 169–177 (2007).
CAS  PubMed  Article  Google Scholar 

29.
Li, Y., Zhao, C., Lu, X., Ai, X. & Qiu, J. Identification of a cytochrome P450 gene in the earthworm Eisenia fetida and its mRNA expression under enrofloxacin stress. Ecotoxicol. Environ. Saf. 150, 70–75 (2018).
CAS  PubMed  Article  Google Scholar 

30.
Roubalova, R. et al. The effect of dibenzo-p-dioxin- and dibenzofuran-contaminated soil on the earthworm Eisenia andrei. Environ. Pollut. 193, 22–28 (2014).
CAS  PubMed  Article  Google Scholar 

31.
Weiss, C. L., Pais, M., Cano, L. M., Kamoun, S. & Burbano, H. A. nQuire: a statistical framework for ploidy estimation using next generation sequencing. BMC Bioinform. 19, 122 (2018).
Article  CAS  Google Scholar 

32.
Pendleton, M. et al. Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nat. Methods 12, 780–786 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Kokot, M., Dlugosz, M. & Deorowicz, S. KMC 3: counting and manipulating k-mer statistics. Bioinformatics 33, 2759–2761 (2017).
CAS  PubMed  Article  Google Scholar 

34.
Zwarycz, A. S., Nossa, C. W., Putnam, N. H. & Ryan, J. F. Timing and scope of genomic expansion within Annelida: evidence from homeoboxes in the genome of the earthworm Eisenia fetida. Genome Biol. Evol. 8, 271–281 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Simakov, O. et al. Insights into bilaterian evolution from three spiralian genomes. Nature 493, 526–531 (2013).
CAS  PubMed  Article  Google Scholar 

36.
Horn, K. M. et al. Na(+) /K(+) -ATPase gene duplications in clitellate annelids are associated with freshwater colonization. J. Evol. Biol. 32, 580–591 (2019).
CAS  PubMed  Article  Google Scholar 

37.
Horn, K. M. & Anderson, F. E. Spiralian genomes reveal gene family expansions associated with adaptation to freshwater. J. Mol. Evol. 88, 463–472 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Schreiber, F., Patricio, M., Muffato, M., Pignatelli, M. & Bateman, A. TreeFam v9: a new website, more species and orthology-on-the-fly. Nucleic Acids Res. 42, D922–D925 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Li, H. et al. TreeFam: a curated database of phylogenetic trees of animal gene families. Nucleic Acids Res. 34, D572–D580 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Ruan, J. et al. TreeFam: 2008 update. Nucleic Acids Res. 36, D735–D740 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Han, M. V., Thomas, G. W. C., Lugo-Martinez, J. & Hahn, M. W. Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3. Mol. Biol. Evol. 30, 1987–1997 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Hahn, M. W., De Bie, T., Stajich, J. E., Nguyen, C. & Cristianini, N. Estimating the tempo and mode of gene family evolution from comparative genomic data. Genome Res. 15, 1153–1160 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
The Gene Ontology, C. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).
Article  CAS  Google Scholar 

45.
Klopfenstein, D. V. et al. GOATOOLS: a Python library for gene ontology analyses. Sci. Rep. 8, 10872 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Shao, Y. et al. Genome and single-cell RNA-sequencing of the earthworm Eisenia andrei identifies cellular mechanisms underlying regeneration. Nat. Commun. 11, 2656 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Liu, X., Sun, Z., Chong, W., Sun, Z. & He, C. Growth and stress responses of the earthworm Eisenia fetida to Escherichia coli O157:H7 in an artificial soil. Micro. Pathog. 46, 266–272 (2009).
Article  CAS  Google Scholar 

48.
Wang, X., Chang, L. & Sun, Z. Differential expression of genes in the earthworm Eisenia fetida following exposure to Escherichia coli O157:H7. Dev. Comp. Immunol. 35, 525–529 (2011).
PubMed  Article  CAS  Google Scholar 

49.
Wang, X., Chang, L., Sun, Z. & Zhang, Y. Comparative proteomic analysis of differentially expressed proteins in the earthworm Eisenia fetida during Escherichia coli O157:H7 stress. J. Proteome Res. 9, 6547–6560 (2010).
CAS  PubMed  Article  Google Scholar 

50.
Wang, X., Li, X. & Sun, Z. iTRAQ-based quantitative proteomic analysis of the earthworm Eisenia fetida response to Escherichia coli O157:H7. Ecotoxicol. Environ. Saf. 160, 60–66 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

51.
Zhang, Y. et al. PCR-DGGE analysis of earthworm gut bacteria diversity in stress of Escherichia coli O157:H7. Adv. Biosci. Biotechnol. 4, 437–441 (2013).
Article  CAS  Google Scholar 

52.
Fischer, D. S., Theis, F. J. & Yosef, N. Impulse model-based differential expression analysis of time course sequencing data. Nucleic Acids Res. 46, e119 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

53.
Sander, J., Schultze, J. L. & Yosef, N. ImpulseDE: detection of differentially expressed genes in time series data using impulse models. Bioinformatics 33, 757–759 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

54.
Cooper, E. L. Earthworm immunity. Prog. Mol. Subcell. Biol. 15, 10–45 (1996).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Bilej, M., Prochazkova, P., Silerova, M. & Joskova, R. Earthworm immunity. Adv. Exp. Med Biol. 708, 66–79 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).
Article  CAS  Google Scholar 

59.
Sapountzis, P. et al. The enterobacterium Trabulsiella odontotermitis presents novel adaptations related to its association with fungus-growing termites. Appl. Environ. Microbiol. 81, 6577–6588 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Kotak, M. et al. Complete genome sequence of the Opitutaceae bacterium strain TAV5, a potential facultative methylotroph of the wood-feeding termite Reticulitermes flavipes. Genome Announc. https://doi.org/10.1128/genomeA.00060-15 (2015).

61.
Vezina, C., Kudelski, A. & Sehgal, S. N. Rapamycin (AY-22,989), a new antifungal antibiotic. I. Taxonomy of the producing streptomycete and isolation of the active principle. J. Antibiot. 28, 721–726 (1975).
CAS  Article  Google Scholar 

62.
Jeske, O., Jogler, M., Petersen, J., Sikorski, J. & Jogler, C. From genome mining to phenotypic microarrays: planctomycetes as source for novel bioactive molecules. Antonie Van. Leeuwenhoek 104, 551–567 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Jeske, O. et al. Developing techniques for the utilization of planctomycetes as producers of bioactive molecules. Front. Microbiol. 7, 1242 (2016).
PubMed  PubMed Central  Article  Google Scholar 

64.
Kolton, M. et al. Draft genome sequence of Flavobacterium sp. strain F52, isolated from the rhizosphere of bell pepper (Capsicum annuum L. cv. Maccabi). J. Bacteriol. 194, 5462–5463 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Kolton, M. et al. Impact of biochar application to soil on the root-associated bacterial community structure of fully developed greenhouse pepper plants. Appl. Environ. Microbiol. 77, 4924–4930 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Sang, M. K. & Kim, K. D. The volatile-producing Flavobacterium johnsoniae strain GSE09 shows biocontrol activity against Phytophthora capsici in pepper. J. Appl. Microbiol. 113, 383–398 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Youssef, N. H., Blainey, P. C., Quake, S. R. & Elshahed, M. S. Partial genome assembly for a candidate division OP11 single cell from an anoxic spring (Zodletone Spring, Oklahoma). Appl. Environ. Microbiol. 77, 7804–7814 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Havarstein, L. S., Diep, D. B. & Nes, I. F. A family of bacteriocin ABC transporters carry out proteolytic processing of their substrates concomitant with export. Mol. Microbiol. 16, 229–240 (1995).
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Weon, H. Y. et al. Rubellimicrobium aerolatum sp. nov., isolated from an air sample in Korea. Int. J. Syst. Evol. Microbiol. 59, 406–410 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Saha, P. & Chakrabarti, T. Aeromonas sharmana sp. nov., isolated from a warm spring. Int. J. Syst. Evol. Microbiol. 56, 1905–1909 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

71.
Corby-Harris, V. et al. Origin and effect of Alpha 2.2 Acetobacteraceae in honey bee larvae and description of Parasaccharibacter apium gen. nov., sp. nov. Appl. Environ. Microbiol. 80, 7460–7472 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Ryu, J. H. et al. Innate immune homeostasis by the homeobox gene caudal and commensal-gut mutualism in Drosophila. Science 319, 777–782 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Cui, H. et al. Bacterial community shaped by heavy metals and contributing to health risks in cornfields. Ecotoxicol. Environ. Saf. 166, 259–269 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Han, J. I. et al. Complete genome sequence of the metabolically versatile plant growth-promoting endophyte Variovorax paradoxus S110. J. Bacteriol. 193, 1183–1190 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Belimov, A. A. et al. Rhizosphere bacteria containing 1-aminocyclopropane-1-carboxylate deaminase increase yield of plants grown in drying soil via both local and systemic hormone signalling. N. Phytol. 181, 413–423 (2009).
CAS  Article  Google Scholar 

76.
Schmalenberger, A. et al. The role of Variovorax and other Comamonadaceae in sulfur transformations by microbial wheat rhizosphere communities exposed to different sulfur fertilization regimes. Environ. Microbiol. 10, 1486–1500 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Yurgel, S. N., Douglas, G. M., Dusault, A., Percival, D. & Langille, M. G. I. Dissecting community structure in wild blueberry root and soil microbiome. Front. Microbiol. 9, 1187 (2018).
PubMed  PubMed Central  Article  Google Scholar 

78.
Zadel, U. et al. Changes induced by heavy metals in the plant-associated microbiome of Miscanthus x giganteus. Sci. Total Environ. 711, 134433 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

79.
Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Sturzenbaum, S. R., Andre, J., Kille, P. & Morgan, A. J. Earthworm genomes, genes and proteins: the (re)discovery of Darwin’s worms. Proc. Biol. Sci. 276, 789–797 (2009).
CAS  PubMed  Google Scholar 

81.
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

82.
Marcais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

84.
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

85.
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, e112963 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

86.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

87.
Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

88.
English, A. C. et al. Mind the gap: upgrading genomes with Pacific Biosciences RS long-read sequencing technology. PLoS One 7, e47768 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

89.
Chaisson, M. J. & Tesler, G. Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory. BMC Bioinform. 13, 238 (2012).
CAS  Article  Google Scholar 

90.
Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

91.
Nishimura, O., Hara, Y. & Kuraku, S. gVolante for standardizing completeness assessment of genome and transcriptome assemblies. Bioinformatics 33, 3635–3637 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

92.
Liu, B. et al. Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects. arXiv 1308, 2012v1 (2019).
Google Scholar 

93.
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

94.
Rio, D. C., Ares, M., Hannon, G. J. & Nilsen, T. W. Purification of RNA using TRIzol (TRI reagent). Cold Spring Harb. Protoc. 2010, t5439 (2010).
Article  Google Scholar 

95.
Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinform. Chapter 4, Unit 4 10, (2004).

96.
Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).
PubMed  PubMed Central  Article  Google Scholar 

97.
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

98.
Kalvari, I. et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res. 46, D335–D342 (2018).
CAS  PubMed  Article  Google Scholar 

99.
Kalvari, I. et al. Non-coding RNA analysis using the Rfam database. Curr. Protoc. Bioinform. 62, e51 (2018).
Article  CAS  Google Scholar 

100.
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

101.
Apweiler, R. et al. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 32, D115–D119 (2004).
CAS  PubMed  PubMed Central  Article  Google Scholar 

102.
Slater, G. S. & Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinform. 6, 31 (2005).
Article  CAS  Google Scholar 

103.
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

104.
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

105.
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

106.
UniProt, C. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).
Article  CAS  Google Scholar 

107.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
CAS  PubMed  PubMed Central  Article  Google Scholar 

108.
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

109.
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182–W185 (2007).
PubMed  PubMed Central  Article  Google Scholar 

110.
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

111.
Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

112.
Howe, K. L., Bolt, B. J., Shafie, M., Kersey, P. & Berriman, M. WormBase ParaSite – a comprehensive resource for helminth genomics. Mol. Biochem. Parasitol. 215, 2–10 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

113.
Barrett, T. et al. BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata. Nucleic Acids Res. 40, D57–D63 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

114.
Howe, K. L. et al. WormBase 2016: expanding to enable helminth genomic research. Nucleic Acids Res. 44, D774–D780 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

115.
Eddy, S. R. Multiple alignment using hidden Markov models. Proc. Int. Conf. Intell. Syst. Mol. Biol. 3, 114–120 (1995).

116.
Etherington, G. J., Ramirez-Gonzalez, R. H. & MacLean, D. bio-samtools 2: a package for analysis and visualization of sequence and alignment data with SAMtools in Ruby. Bioinformatics 31, 2565–2567 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

117.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

118.
Kuck, P. & Meusemann, K. FASconCAT: convenient handling of data matrices. Mol. Phylogenet. Evol. 56, 1115–1118 (2010).
PubMed  Article  CAS  PubMed Central  Google Scholar 

119.
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

120.
Sanderson, M. J. r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19, 301–302 (2003).
CAS  PubMed  Article  Google Scholar 

121.
Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).
CAS  Article  Google Scholar 

122.
De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269–1271 (2006).
Article  CAS  Google Scholar 

123.
Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).
CAS  PubMed  Article  Google Scholar 

124.
Pedersen, T. L. MSGFplus: an interface between R and MS-GF+. R package version 1.18.0 (2019).

125.
Gatto, L. & Christoforou, A. Using R and Bioconductor for proteomics data analysis. Biochim. et. Biophys. Acta 1844, 42–51 (2014).
CAS  Article  Google Scholar 

126.
Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

127.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

128.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
PubMed  PubMed Central  Article  Google Scholar 

129.
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
PubMed  PubMed Central  Article  Google Scholar 

130.
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

131.
National Genomics Data Center, M. & Partners. Database resources of the National Genomics Data Center in 2020. Nucleic Acids Res. 48, D24–D33 (2020). More

Study area
Water consumption of P. juliflora (hereafter Prosopis) was measured in the Amibara District (Fig. 1) of the Afar Region, Ethiopia (9.16° to 9.21° N and 40.08° to 40.12° E at 740 m a.s.l.). The study area is located in the Awash River Basin and includes both the floodplains of the Awash River and the adjacent dryland. Although relatively water scarce, the Awash River Basin is the most developed and utilized river basin in Ethiopia27. The human and livestock population in this basin are estimated at 18.6 and 34.4 million, respectively, and nearly 70% of large irrigation schemes in Ethiopia are located in the Awash Basin28. The mean annual river flow/discharge of the basin at the terminal Lake Abe is estimated at about 4.6 billion m3 of water although exposed to evaporation27. Water scarcity, particularly in the lower parts of the river, is the major limiting factor for irrigation development, particularly during the low flow season27.
The Afar Region has a mean annual rainfall of about 560 mm29. The region is the hottest part of Ethiopia, with a mean annual temperature of 31 °C. The mean maximum temperature reaches up to 41 °C in June, and the mean minimum temperature ranges from 21 to 22 °C between November and December30. The biome can be described as semi-arid to semi-desert. The natural vegetation consists of scattered dry shrubs, woodland comprising different Vachellia (Acacia) species, bushland, grassland and wooded grassland18. The area has different soil types, including silt fertile soils, sandy soils, heavy clays and rocky outcrops, and a wide range of altitudes ranging from 175 m below sea level to 2,992 m a.s.l. Shiferaw et al.30 found that Prosopis has primarily invaded areas ranging from rangelands to farmland. Main sources of livelihood are pastoralism and some agro-pastoralism around small rural towns30. The main crops grown in the floodplains of Awash River are cotton and sugarcane.
Experimental design
To investigate the temporal and spatial variation in water use by Prosopis across the landscape, data collection was done in the two most heavily invaded habitat types in the Afar Region. These include the floodplains of Awash River and the adjacent non-riparian drylands. Sap flow monitoring stations were established at two sites in the floodplains of the Awash River and at two sites in the dryland area where soil moisture levels were low (Fig. 1). The study areas were representative of other parts of the Afar Region that are invaded by Prosopis.
Site 1 was located near Worer Agricultural Research Center some 200 m away from Awash River and about 30 m from a nearby irrigation canal, and was considered a floodplain site. The area was used for crop production until 2012 after which it was abandoned due to shallow water inducing soil salinity problems. Soon after abandonment, Prosopis invaded the area and established dense stands with 100% canopy cover in most places comprising trees up to 6.5 m height. Soil moisture was relatively high at Site 1 due to proximity to the river. The area invaded by Prosopis was about 6 ha and the soils were temporarily flooded loam and clay soils. Except for annual grasses in some open spaces, there was no undergrowth vegetation, probably due to the dense Prosopis cover.
Site 2 was in the floodplains of a tributary of Awash River and located near Berta locality (Fig. 1). There is a continuous flow of water from a pumped well to less than 10 m from the site where sap flow was measured. The soil is a sandy loam formation, which maintains relatively high moisture content. The Prosopis stand was about 3 ha in size, had a closed canopy and comprised trees of more than 5 m height.
Site 3 was on dryland in the former rangeland of Hallaideghe locality (Fig. 1), characterized by sandy loam soil formation. There was no surface water source except from rainfall and seasonal flooding from the West Harerghe highlands. This area is now invaded by Prosopis with a closed canopy and tree heights of close to 5 m. The size of the invaded area was about 100 ha.
Site 4 was in the drylands of Berta locality (Fig. 1). The soil is sandy with a high rocky outcrop. The dominant indigenous vegetation around this site consisted of a few Senegalia senegal (L.) Britton (or Acacia senegal (L.) Willd) and other small shrubs and grasses. The area is now dominated by Prosopis stands with a closed canopy with trees reaching up to 4 m height. At this site, a meteorological station was installed next to the sap flow monitoring equipment.
The sap flow data in this study therefore provide insight into how tree water use varied across the landscape including (a) floodplains, (b) dryland areas, and (c) across these two most heavily invaded habitats in the Afar Region. All experimental sites were fenced and protected from animal and human interference. Moreover, safety boxes were also made to protect the equipment from weather and other damages.
Tree and stand water use measurements
The amount of water used by individual Prosopis trees was determined using the heat ratio method (HRM) of monitoring tree sap flow31. This technique was selected because it is suitable for measuring low and reverse sap flows which are likely present in desert-adapted species such as Prosopis14,32. In total, four sap flow stations were established (Sites 1 to 4) with three trees instrumented per station. Trees with different stem diameters were selected to capture the variation in transpiration rates in the study region. Stem diameter of the instrumented trees was measured just below the branching at about 60 cm above the ground at all sites. Each sap flow station comprised a CR1000 data logger and an AM16/32B multiplexer, as specified by Campbell Scientific, Inc., Logan UT, USA. Each system was powered by a 70 Ah (12 V) rechargeable battery using 50 W solar panels. Four sets of heaters applied heat to each tree for 0.5 s every hour through a custom-made relay control module. Moreover, a pair of equally placed (0.5 cm) T-Type thermocouples was installed on either side of the heater to measure the sapwood temperature before and after pulsing the heat. With a precision drilling rig, two 2.0 mm diameter holes were carefully made for the thermocouples to minimize errors due to probe misalignments. Heater holes were about 1.8 mm diameter to ensure a tight fit to facilitate heat transfer to the wood during pulsing.
In this sap flow monitoring technique, the heat pulse velocity (Vh, cm/h) is logarithmically related to the ratio of temperature increases upstream and downstream from a heater (v1/v2) as shown in Eq. (1), Burgess et al31:

$$Vh=left(frac{k}{x}right)(mathrm{ln}left(frac{v1}{v2}right))times 3600$$
(1)

where Vh is heat velocity cm/hour, k is the thermal diffusivity which was assigned a nominal value of 2.5 × 10–3 cm2/s for wood, x is the distance (cm) between the heater and either temperature probe (~ 0.5 cm), and v1 and v2 are increases in temperature before and after pulsing31.
The thermocouples were installed in the sapwood at depths ranging from 0.8 to 1.1 cm under the bark to capture the radial changes in sap velocity. Wounding corrections were applied according to the method described by Swanson and Whitfield57. The depth of the sapwood was determined visually as it was possible to distinguish between the sap wood and heartwood boundaries from the changes in the color of the wood. The individual tree sap flow volume in liters per hour were converted to stand level transpiration (in mm per hour) using the approach described by Dzikiti et al14 in which the instrumented trees were assigned to a particular stem size class. The stand level transpiration was then calculated as a weighted sum of the transpiration rates by the trees in each stem size class with the proportion of trees in each size class as the weights. The volumetric soil water content in the root-zone of the trees were measured at each site using a single soil water content reflectometer probe (Model CS616: Campbell Scientific, Inc., Logan UT, USA) installed horizontally at a depth of 50 cm. Sap flow and soil moisture data were measured for 15 months (from November 2016 to January 2018) while evapotranspiration was measured for 11 months (from January to November 2017).
To study the dynamics of total actual evapotranspiration (ETa) from Prosopis stands, an open path eddy covariance (EC) system was installed on Site 1 and data were collected for 11 months from January to November 2017 as it was not possible to continue measuring for more periods due to equipment limitations. The EC was borrowed from Addis Ababa University only for one year so ETa from the other sites could not be measured. The EC system was the IRGASON system which comprised a sonic anemometer (Model: CSAT3A Campbell Scientific Inc., Logan UT, USA) that measured the wind speed in 3-D at 10 Hz frequency. The H2O/CO2 concentrations of the atmosphere were measured using an Infrared Gas Analyzers (Model: EC150, Campbell Scientific, Inc., Logan UT, USA). The collected data was stored by a data logger (Model: CR3000: Campbell Scientific, Inc., Logan UT, USA) on a Compact Flash card module (NL115 or CFM100). To quantify the changes in the energy balance of the study site, two other components of the surface energy balance were measured. These include the net radiation, which was measured using a single component net radiometer (Model: NR-LITE2: Manufacturer: Kipp & Zonnen, Delft, The Netherlands) that was mounted at the top of the tower (~ 7.5 m above the ground). The IRGASON sensor was installed outside the surface roughness layer of the canopy at an average height of about one meter above the Prosopis tree canopy. This ensured a uniform fetch around the tower with a flux foot print of about 100 m radius.
Air temperature and humidity were measured at high frequency using a temperature and humidity Probe (Model HMP155A-L, Campbell Scientific, 2013). The high frequency data were further corrected for 1) lack of sensor levelness (coordinate rotation), 2) sensor time lags, and 3) fluctuations in the air density using the EddyPro version 6.0 software (Li-COR, Nebraska, USA). Sensor separation corrections were not necessary as the IRGA and sonic are a single unit.
Allometric characteristics are one of the major biological factors affecting the eco-physiology of plant species. For example, sapwood area is usually correlated with stem diameter14. The sapwood area estimated from the stem diameter measurements was used to calculate the sap flow volumes from the sap velocity measured by the HRM system.
Weather and soil water dynamics
To measure solar irradiance, precipitation, air temperature, relative humidity and air pressure, an automatic weather station was set up at Site 4, which was located within 7 km from the other three sites. The solar radiation sensor was installed on a horizontal leveling fixture mounted on a south facing cross bar to avoid self-shading errors. A wind sentry was used to measure the wind speed and direction (Model 03,001, R.M. Young; Campbell Scientific, Inc., Logan UT, USA). Rainfall was monitored using a tipping bucket rain gauge (Model TE525-L, Campbell Scientific, Inc., Logan UT, USA). The weather station comprised an Em50 (a 5-channel data logger) and ECH2O utility software from Decagon, USA.
Wind speed was obtained from the weather station located at Worer Agricultural Research Center, which was about 500 m away from Site 1. The weather station had a temperature and humidity probe (Model CS500, Vaisala, Finland) installed at a height of about 2.0 m above ground and the station also measured wind speed using a cup or rotational anemometer installed at 2 m high.
The energy transferred into and out of the ground was measured using clusters of soil heat flux plates (Model: HFP01SC-L, Delft, The Netherlands), while soil temperature was recorded using soil averaging thermocouples (Model: TCAV-L: Campbell Scientific, Inc., Logan UT, USA). The soil heat flux plates were installed at 8 cm depth and the soil averaging and soil moisture data measured with the soil water content reflectometers (Model: CS616-L: Campbell Scientific, Inc., Logan UT, USA) were used to correct the soil heat flux for the energy stored by the soil layer above them. At all sites, the sensors were connected to a data logger (Model CR1000, Campbell Scientific, Inc., Logan UT, USA) programmed with a scan interval of 90 s, and data were stored at hourly intervals over the 11 months study period. All data were downloaded every 21 days from data loggers.
Drivers of water use by the invasive Prosopis
To identify the main drivers of water use by Prosopis invasions in the Awash River basin of the Afar Region correlations were sought between the various water use variables (transpiration and evapotranspiration) as dependent variables and microclimate factors, i.e. solar radiation, wind speed, vapor pressure deficit of the air (VPD), soil moisture, and ET0 as explanatory variables.
Upscaling Prosopis water use moderation to the Afar Regional level
To upscale the Prosopis transpiration and ET from the individual study sites to the regional scale, a regression equation was developed using the fractional vegetation cover information mapped by Shiferaw et al26. This mapping quantified the Prosopis distribution and cover in the Afar Region at a 15 × 15 m spatial resolution based on explanatory variables including Landsat panchromatic images, other biophysical parameters and field observations. The fractional cover map was generated using a robust modelling approach, Random Forest Algorithm, with a large amount of field observations ( > 3000 plots) and seventeen explanatory variables33. Then, we estimated the amount of water used by Prosopis stands at each of the four sites in mm/day per pixel with 100% cover. This was extrapolated to all fractional cover levels per pixel indicated in the fractional cover map for Prosopis in the study area. Moreover, canopy cover from the experimental plots of Prosopis trees was estimated. Then, the relationship between sap flow and fractional cover as well as between ET and fractional cover (Fci) over the invaded area was developed as shown in Eq. (2):

$${text{f }}left( {text{x}} right) = sum ({text{Fci}}({text{Wi}})S)$$
(2)

where f(x) is either total water use (sap flow) or total ET in mm/day over the whole study area; Fci is fractional cover at pixel level i, Wi is water use either from sap flow or stand ET at 100% canopy cover, and S is a pixel size of 225 m2.
Finally, we estimated the financial costs incurred from the loss of water through Prosopis transpiration and ET. This was done by taking the water charge of payment for ecosystems services by investors to Awash Basin Organization which was set at US$ 0.00015 per m3 according to Ayana et al34. Also, we estimated the market price and the net benefits of cotton35,58 and sugarcane59, which are major crops grown in the study area, which could be grown with the amount of water used by Prosopis.
Data reduction and statistical analyses
LoggerNet 4.1 (Campbell Scientific, Inc, Logan UT, USA) was used for downloading sap flow data from data loggers to the laptop and for converting the data to 30 min interval values. EddyPro 6.0 (Licor Nebraska, Lincoln, USA) was employed for processing the high frequency EC data used for calculating ET. Sap flow rates were calculated following Burgess et al31. The FAO Penman–Monteith Eq. (3) was used to calculate the hourly and daily reference evapotranspiration (ETo) using the weather data. Multiple linear regressions were carried out using either sap flow or ET as response variable and solar radiation, soil moisture, wind speed, vapor pressure deficit and potential ET as explanatory variables at a time in an open source R software version 3.3.360. Maps were made using an open source Quantum GIS (QGIS3.8.3) software61. More

1.
Harrison, P. L. et al. Mass spawning in tropical reef corals. Science 223, 1186–1189 (1984).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Willis, B. L., Babcock, R. C., Harrison, P. L. & Oliver, J. K. Patterns in the mass spawning of corals on the Great Barrier Reef from 1981 to 1984. Proc 5th Int Coral Reef Symp 4, 343–348 (1985).
Google Scholar 

3.
Baird, A. H., Guest, J. R. & Willis, B. L. Systematic and biogeographical patterns in the reproductive biology of scleractinian corals. Annu. Rev. Ecol., Evol. Syst. 40, 551–571 (2009).
Article  Google Scholar 

4.
Baird, A. H. et al. Coral reproduction on the world’s southernmost reef at Lord Howe Island, Australia. Aquat. Biol. 23, 275–284 (2015).
Article  Google Scholar 

5.
Harrison, P. L. in Coral Reefs: An Ecosystem in Transition (eds Z. Dubinsky & N. Stambler) 59-85 (Springer Science, 2011).

6.
Keith, S. A. et al. Coral mass spawning predicted by rapid seasonal rise in ocean temperature. Proc. R. Soc. Lond., Ser. B: Biol. Sci. 283, 20160011 (2016).
Google Scholar 

7.
Wolstenholme, J. K. Temporal reproductive isolation and gametic compatibility are evolutionary mechanisms in the Acropora humilis species group (Cnidaria; Scleractinia). Mar. Biol. 144, 567–582 (2004).
Article  Google Scholar 

8.
Morita, M. et al. Reproductive strategies in the intercrossing corals Acropora donei and A. tenuis to prevent hybridization. Coral Reefs 38, 1211–1223 (2019).
ADS  Article  Google Scholar 

9.
Randall, C. J. et al. Sexual production of corals for reef restoration in the Anthropocene. Mar. Ecol. Prog. Ser. 635, 203–232 (2020).
ADS  Article  Google Scholar 

10.
Bouwmeester, J. et al. Multi-species spawning synchrony within scleractinian coral assemblages in the Red Sea. Coral Reefs 34, 65–77 (2015).
ADS  Article  Google Scholar 

11.
Baird, A. H., Blakeway, D. R., Hurley, T. J. & Stoddart, J. A. Seasonality of coral reproduction in the Dampier Archipelago, northern Western Australia. Mar. Biol. 158, 275–285 (2011).
Article  Google Scholar 

12.
Styan, C. A. & Rosser, N. L. Is monitoring for mass spawning events in coral assemblages in north Western Australia likely to detect spawning? Mar. Pollut. Bull. 64, 2523–2527 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Visser, M. E. & Both, C. Shifts in phenology due to global climate change: the need for a yardstick. Proc. R. Soc. Lond., Ser. B: Biol. Sci. 272, 2561–2569 (2005).
Google Scholar 

14.
Hock, K., Doropoulos, C., Gorton, R., Condie, S. A. & Mumby, P. J. Split spawning increases robustness of coral larval supply and inter-reef connectivity. Nat. Comm. 10 (2019).

15.
Sakai, Y. et al. Environmental factors explain spawning day deviation from full moon in the scleractinian coral Acropora. Biol. Lett. 16, 20190760 (2020).
PubMed  PubMed Central  Article  Google Scholar 

16.
Madin, J. S. et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Scientific Data 3, 160017 (2016).
ADS  PubMed  PubMed Central  Article  Google Scholar 

17.
Veron, J., Stafford-Smith, M., DeVantier, L. & Turak, E. Overview of distribution patterns of zooxanthellate Scleractinia. Front. Mar. Sci. 1 (2015).

18.
Veron, J. E. N. Corals of the world. (AIMS, 2000).

19.
Hoeksema, B. W. & Cairns, S. D. World List of Scleractinia. Accessed through: World Register of Marine Species at: http://www.marinespecies.org/aphia.php?p=taxdetails&id=1363 (2020).

20.
Fukami, H. et al. Mitochondrial and nuclear genes suggest that stony corals are monophyletic but most families of stony corals are not (order Scleractinia, class Anthozoa, phylum Cnidaria). PLoS ONE 3 (2008).

21.
Arrigoni, R., Terraneo, T. I., Galli, P. & Benzoni, F. Lobophylliidae (Cnidaria, Scleractinia) reshuffled: Pervasive non-monophyly at genus level. Mol. Phylogen. Evol. 73, 60–64 (2014).
Article  Google Scholar 

22.
Huang, D. et al. Towards a phylogenetic classification of reef corals: the Indo-Pacific genera Merulina, Goniastrea and Scapophyllia (Scleractinia, Merulinidae). Zool. Scr. 43, 531–548 (2014).
Article  Google Scholar 

23.
Babcock, R. C. et al. Synchronous spawnings of 105 scleractinian coral species on the Great Barrier Reef. Mar. Biol. 90, 379–394 (1986).
Article  Google Scholar 

24.
Veron, J. E. N. Corals of Australia and the Indo-Pacific. (Angus & Robertson, 1986).

25.
Bengtson, P. Open Nomenclature. Palaeontology 31, 223–227 (1988).
Google Scholar 

26.
Sigovini, M., Keppel, E. & Tagliapietra, D. Open Nomenclature in the biodiversity era. Methods Ecol. Evol. 7, 1217–1225 (2016).
Article  Google Scholar 

27.
Baird, A. H. et al. Coral Spawning Database. Newcastle University, https://doi.org/10.25405/data.ncl.13082333 (2020).

28.
Babcock, R., Mundy, C., Keesing, J. & Oliver, J. Predictable and unpredictable spawning events: in situ behavioural data from free-spawning coral reef invertebrates. Invertebr. Reprod. Dev. 22, 213–227 (1992).
Article  Google Scholar 

29.
Babcock, R. C. Reproduction and distribution of two species of Goniastrea (Scleractinia) from the Great Barrier Reef Province. Coral Reefs 2, 187–195 (1984).
ADS  Google Scholar 

30.
Babcock, R. C., Willis, B. L. & Simpson, C. J. Mass spawning of corals on a high-latitude coral-reef. Coral Reefs 13, 161–169 (1994).
ADS  Article  Google Scholar 

31.
Boch, C. A., Ananthasubramaniam, B., Sweeney, A. M., Francis, J. D. III & Morse, D. E. Effects of Light Dynamics on Coral Spawning Synchrony. Biol. Bull. 220, 161–173 (2011).
PubMed  Article  PubMed Central  Google Scholar 

32.
Boch, C. A. & Morse, A. N. C. Testing the effectiveness of direct propagation techniques for coral restoration of Acropora spp. Ecol. Eng. 40, 11–17 (2012).
Article  Google Scholar 

33.
Bouwmeester, J., Gatins, R., Giles, E. C., Sinclair-Taylor, T. H. & Berumen, M. L. Spawning of coral reef invertebrates and a second spawning season for scleractinian corals in the central Red Sea. Invertebr. Biol. 135, 273–284 (2016).
Article  Google Scholar 

34.
Bronstein, O. & Loya, Y. Daytime spawning of Porites rus on the coral reefs of Chumbe Island in Zanzibar, Western Indian Ocean (WIO). Coral Reefs 30, 441–441 (2011).
ADS  Article  Google Scholar 

35.
Carroll, A., Harrison, P. L. & Adjeroud, M. Sexual reproduction of Acropora reef corals at Moorea, French Polynesia. Coral Reefs 25, 93–97 (2006).
ADS  Article  Google Scholar 

36.
Chelliah, A. et al. First record of multi-species synchronous coral spawning from Malaysia. Peerj 3, e777 (2015).
PubMed  PubMed Central  Article  Google Scholar 

37.
Chua, C. M., Leggat, W., Moya, A. & Baird, A. H. Temperature affects the early life history stages of corals more than near future ocean acidification. Mar. Ecol. Prog. Ser. 475, 85–92 (2013).
ADS  Article  Google Scholar 

38.
Chua, C. M., Leggat, W., Moya, A. & Baird, A. H. Near-future reductions in pH will have no consistent ecological effects on the early life-history stages of reef corals. Mar. Ecol. Prog. Ser. 486, 143–151 (2013).
ADS  Article  Google Scholar 

39.
Dai, C. F., Soong, K. & Fan, T. Y. Sexual reproduction of corals in northern and southern Taiwan. Proceeding of the 7th International Coral Reef Symposium 1, 448–455 (1992).
Google Scholar 

40.
Doropoulos, C. & Diaz-Pulido, G. High CO2 reduces the settlement of a spawning coral on three common species of crustose coralline algae. Mar. Ecol. Prog. Ser. 475, 93–99 (2013).
ADS  Article  Google Scholar 

41.
Doropoulos, C. et al. Testing industrial-scale coral restoration techniques: Harvesting and culturing wild coral-spawn slicks. Front. Mar. Sci. 6 (2019).

42.
Doropoulos, C., Ward, S., Diaz-Pulido, G., Hoegh-Guldberg, O. & Mumby, P. J. Ocean acidification reduces coral recruitment by disrupting intimate larval-algal settlement interactions. Ecol. Lett., no-no, (2012).

43.
Doropoulos, C., Ward, S., Marshell, A., Diaz-Pulido, G. & Mumby, P. J. Interactions among chronic and acute impacts on coral recruits: the importance of size-escape thresholds. Ecology 93, 2131–2138 (2012).
PubMed  Article  PubMed Central  Google Scholar 

44.
Eyal-Shaham, L. et al. Repetitive sex change in the stony coral Herpolitha limax across a wide geographic range. Scientific Reports 9, 2936 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Eyre, B. D., Glud, R. N. & Patten, N. Mass coral spawning: A natural large-scale nutrient addition experiment. Limnol. Oceanogr. 53, 997–1013 (2008).
ADS  CAS  Article  Google Scholar 

46.
Fadlallah, Y. H. Synchronous spawning of Acropora clathrata coral colonies from the western Arabian Gulf (Saudi Arabia). Bull. Mar. Sci. 59, 209–216 (1996).
Google Scholar 

47.
Field, S. in Reproduction in Reef Corals (eds E. F. Cox, D. A. Krupp, & P. L. Jokiel) 111–119 (Hawaii Institute of Marine Biology, 1998).

48.
Fiebig, S. M. & Vuki, V. C. Mass spawning of scleractinian corals on Fijian reefs and in particular, Suva Reef. The South Pacific Journal of Natural Sciences 15 (1997).

49.
Fiene-Severns, P. in Reproduction in Reef Corals (eds E. F. Cox, D. A. Krupp, & P. L. Jokiel) 22–24 (Hawaii Institute of Marine Biology, 1998).

50.
Fujiwara, S., Kezuka, D., Ishimizu, H., Tabata, S. & Nojima, S. Condition for mass spawning of scleractinian coral Acropora in the Sekisei Lagoon, Ryukyu Islands. Bull. Jap. Soc. Fish. Oceanogr. 79, 130–140 (2015).
Google Scholar 

51.
Fukami, H., Omori, M., Shimoike, K., Hayashibara, T. & Hatta, M. Ecological and genetic aspects of reproductive isolation by different spawning times in Acropora corals. Mar. Biol. 142, 679–684 (2003).
Article  Google Scholar 

52.
Gilmour, J. Experimental investigation into the effects of suspended sediment on fertilisation, larval survival and settlement in a scleractinian coral. Mar. Biol. 135, 451–462 (1999).
Article  Google Scholar 

53.
Gilmour, J. P., Smith, L. D. & Brinkman, R. M. Biannual spawning, rapid larval development and evidence of self-seeding for scleractinian corals at an isolated system of reefs. Mar. Biol. 156, 1297–1309 (2009).
Article  Google Scholar 

54.
Glynn, P. W. et al. Reef coral reproduction in the eastern Pacific: Costa Rica, Panama, and Galapagos Islands (Ecuador). 3. Agariciidae (Pavona gigantea and Gardineroseris planulata). Mar. Biol. 125, 579–601 (1996).
Google Scholar 

55.
Glynn, P. W. et al. Reef coral reproduction in the eastern Pacific: Costa Rica, Panamá, and the Galápagos Islands (Ecuador). VI. Agariciidae, Pavona clavus. Mar. Biol. 158, 1601–1617 (2011).
Article  Google Scholar 

56.
Glynn, P. W. et al. Reproductive ecology of the azooxanthellate coral Tubastraea coccinea in the equatorial eastern pacific: Part V. Dendrophylliidae. Mar. Biol. 153, 529–544 (2008).
Article  Google Scholar 

57.
Glynn, P. W., Colley, S. B., Ting, J. H., Mate, J. L. & Guzman, H. M. Reef coral reproduction in the eastern Pacific: Costa Rica, Panama, and Galapagos Islands (Ecuador). 4. Agariciidae, recruitment and recovery of Pavona varians and Pavona sp. A. Mar. Biol. 136, 785–805 (2000).
Article  Google Scholar 

58.
Gomez, E. J. et al. Gametogenesis and reproductive pattern of the reef-building coral Acropora millepora in northwestern Philippines. Invertebr. Reprod. Dev. 62, 202–208 (2018).
CAS  Article  Google Scholar 

59.
Graham, E. M., Baird, A. H. & Connolly, S. R. Survival dynamics of scleractinian coral larvae and implications for dispersal. Coral Reefs 27, 529–539 (2008).
ADS  Article  Google Scholar 

60.
Gress, E. & Paige, N. & Bollard, S. Observations of Acropora spawning in the Mozambique Channel. West. Indian Ocean J. Mar. Sci. 13, 107 (2014).
Google Scholar 

61.
Guest, J. R., Baird, A. H., Goh, B. P. L. & Chou, L. M. Reproductive seasonality in an equatorial assemblage of scleractinian corals. Coral Reefs 24, 112–116 (2005).
Article  Google Scholar 

62.
Hayashibara, T. & Shimoike, K. Cryptic species of Acropora digitifera. Coral Reefs 21, 224–225 (2002).
Article  Google Scholar 

63.
Hayashibara, T. et al. Patterns of coral spawning at Akajima Island, Okinawa, Japan. Mar. Ecol. Prog. Ser. 101, 253–262 (1993).
ADS  Article  Google Scholar 

64.
Heyward, A., Yamazato, K., Yeemin, T. & Minei, M. Sexual reproduction of coral in Okinawa. Galaxea 6, 331–343 (1987).
Google Scholar 

65.
Heyward, A. J. in Coral Reef Population Biology (eds P. L. Jokiel, R. H. Richmond, & R. A. Rogers) 170–178 (Sea Grant Coop, 1986).

66.
Heyward, A. J. & Babcock, R. C. Self- and cross-fertilization in scleractinian corals. Mar. Biol. 90, 191–195 (1986).
Article  Google Scholar 

67.
Heyward, A. J. & Negri, A. P. Natural inducers for coral larval metamorphosis. Coral Reefs 18, 273–279 (1999).
Article  Google Scholar 

68.
Hirose, M. & Hidaka, M. Early development of zooxanthella-containing eggs of the corals Porites cylindrica and Montipora digitata: The endodermal localization of zooxanthellae. Zool. Sci. 23, 873–881 (2006).
Article  Google Scholar 

69.
Hirose, M., Kinzie, R. A. & Hidaka, M. Timing and process of entry of zooxanthellae into oocytes of hermatypic corals. Coral Reefs 20, 273–280 (2001).
Article  Google Scholar 

70.
Hodgson, G. Potential gamete wastage in synchronously spawning corals due to hybrid inviability. Proceedings of the 6th International Coral Reef Symposium 2, 707–714 (1988).
Google Scholar 

71.
Howells, E. J., Abrego, D., Vaughan, G. O. & Burt, J. A. Coral spawning in the Gulf of Oman and relationship to latitudinal variation in spawning season in the northwest Indian Ocean. Scientific Reports 4, 7484 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Howells, E. J., Berkelmans, R., van Oppen, M. J. H., Willis, B. L. & Bay, L. K. Historical thermal regimes define limits to coral acclimatization. Ecology 94, 1078–1088 (2013).
PubMed  Article  PubMed Central  Google Scholar 

73.
Hunter, C. L. Environmental cues controlling spawning in two Hawaiian corals, Montipora verrucosa and M. dilitata. Proceedings of the 6th International Coral Reef Symposium 2, 727–732 (1988).
Google Scholar 

74.
Itano, D. & Buckley, T. Observations of the mass spawning of corals and palolo (Eunice viridis) in American Samoa. (Department of Marine and Wildlife Resources, American Samoa, American Samoa, 1988).

75.
Jamodiong, E. A. et al. Coral spawning and spawn-slick observation in the Philippines. Mar. Biodivers. 48, 2187–2192 (2018).
Article  Google Scholar 

76.
Kenyon, J. C. Hybridization and polyploidy in the coral genus. Acropora. Pac. Sci. 48, 203–204 (1994).
Google Scholar 

77.
Kenyon, J. C. Latitudinal differences between Palau and Yap in coral reproductive synchrony. Pac. Sci. 49, 156–164 (1995).
Google Scholar 

78.
Kinzie, R. A. Spawning in the reef corals Pocillopora verrucosa and P. eydouxi at Sesoko Island, Okinawa. Galaxea 11, 93–105 (1993).
Google Scholar 

79.
Kojis, B. L. & Quinn, N. J. Aspects of sexual reproduction and larval development in the shallow water hermatypic coral, Goniastrea australensis (Edwards and Haime, 1857). Bull. Mar. Sci. 31, 558–573 (1981).
Google Scholar 

80.
Kojis, B. L. & Quinn, N. J. Reproductive ecology of two faviid corals (Coelenterata: Scleractinia). Mar. Ecol. Prog. Ser. 8, 251–255 (1982).
ADS  Article  Google Scholar 

81.
Kojis, B. L. & Quinn, N. J. Reproductive strategies in four species of Porites (Scleractinia). Proceedings of the 4th International Coral Reef Symposium 2, 145–151 (1982).
Google Scholar 

82.
Kongjandtre, N., Ridgway, T., Ward, S. & Hoegh-Guldberg, O. Broadcast spawning patterns of Favia species on the inshore reefs of Thailand. Coral Reefs 29, 227–234 (2010).
ADS  Article  Google Scholar 

83.
Krupp, D. A. Sexual reproduction and early development of the solitary coral Fungia scutaria (Anthozoa: Scleractinia). Coral Reefs 2, 159–164 (1983).
ADS  Article  Google Scholar 

84.
Ligson, C. A., Tabalanza, T. D., Villanueva, R. D. & Cabaitan, P. C. Feasibility of early outplanting of sexually propagated Acropora verweyi for coral reef restoration demonstrated in the Philippines. Restor. Ecol. 28, 244–251 (2020).
Article  Google Scholar 

85.
Lin, C. H. & Nozawa, Y. Variability of spawning time (lunar day) in Acropora versus merulinid corals: a 7-yr record of in situ coral spawning in Taiwan. Coral Reefs 36, 1269–1278 (2017).
ADS  Article  Google Scholar 

86.
Loya, Y., Heyward, A. & Sakai, K. Reproductive patterns of fungiid corals in Okinawa, Japan. Galaxea 11, 119–129 (2009).
Article  Google Scholar 

87.
Loya, Y. & Sakai, K. Bidirectional sex change in mushroom stony corals. Proceedings of the Royal Society B: Biological Sciences 275, 2335–2343 (2008).
PubMed  Article  PubMed Central  Google Scholar 

88.
Maboloc, E. A., Jamodiong, E. A. & Villanueva, R. D. Reproductive biology and larval development of the scleractinian corals Favites colemani and F. abdita (Faviidae) in northwestern Philippines. Invertebr. Reprod. Dev. 60, 1–11 (2016).
Article  Google Scholar 

89.
Mangubhai, S. Reproductive ecology of the scleractinian corals Echinopora gemmacea and Leptoria phrygia (Faviidae) on equatorial reefs in Kenya. Invertebr. Reprod. Dev. 53, 67–79 (2009).
Article  Google Scholar 

90.
Mangubhai, S., Harris, A. & Graham, N. A. J. Synchronous daytime spawning of the solitary coral Fungia danai (Fungiidae) in the Chagos Archipelago, central Indian Ocean. Coral Reefs 26, 15–15 (2007).
Article  Google Scholar 

91.
Markey, K. L., Baird, A. H., Humphrey, C. & Negri, A. Insecticides and a fungicide affect multiple coral life stages. Mar. Ecol. Prog. Ser. 330, 127–137 (2007).
ADS  CAS  Article  Google Scholar 

92.
Mate, J. F. in Reproduction in Reef Corals (eds E. F. Cox, D. A. Krupp, & P. L. Jokiel) 25-37 (Hawaii Institute of Marine Biology, 1998).

93.
Mate, J. F., Wilson, J., Field, S. & Neves, E. G. in Reproduction in Reef Corals (eds E. F. Cox, D. A. Krupp, & P. L. Jokiel) 25–37 (Hawaii Institute of Marine Biology, 1998).

94.
Mezaki, T. et al. Spawning patterns of high latitude scleractinian corals from 2002 to 2006 at Nishidomari, Otsuki, Kochi, Japan. Kuroshio Biosphere 3, 33–47 (2007).
Google Scholar 

95.
Mohamed, A. R. et al. The transcriptomic response of the coral Acropora digitifera to a competent Symbiodinium strain: the symbiosome as an arrested early phagosome. Mol. Ecol. 25, 3127–3141 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

96.
Mundy, C. N. & Green, A. Spawning observations of corals and other invertebrates in American Samoa. (Department of Marine and Wildlife Resources, American Samoa Governement, Samoa, 1999).

97.
Nitschke, M. R., Davy, S. K. & Ward, S. Horizontal transmission of Symbiodinium cells between adult and juvenile corals is aided by benthic sediment. Coral Reefs 35, 335–344 (2016).
ADS  Article  Google Scholar 

98.
Nozawa, Y. & Harrison, P. L. Temporal settlement patterns of larvae of the broadcast spawning reef coral Favites chinensis and the broadcast spawning and brooding reef coral Goniastrea aspera from Okinawa, Japan. Coral Reefs 24, 274–282 (2005).
Article  Google Scholar 

99.
Nozawa, Y., Tokeshi, M. & Nojima, S. Reproduction and recruitment of scleractinian corals in a high-latitude coral community, Amakusa, southwestern Japan. Mar. Biol. 149, 1047–1058 (2006).
Article  Google Scholar 

100.
Okamoto, M., Nojima, S., Furushima, Y. & Phoel, W. C. A basic experiment of coral culture using sexual reproduction in the open sea. Fish. Sci. 71, 263–270 (2005).
CAS  Article  Google Scholar 

101.
Omori, M., Fukami, H., Kobinata, H. & Hatta, M. Significant drop of fertilization of Acropora corals in 1999. An after-effect of heavy coral bleaching? Limnol. Oceanogr. 46, 704–706 (2001).
ADS  Article  Google Scholar 

102.
Penland, L., Kloulechad, J., Idip, D. & van Woesik, R. Coral spawning in the western Pacific Ocean is related to solar insolation: evidence of multiple spawning events in Palau. Coral Reefs 23, 133–140 (2004).
Article  Google Scholar 

103.
Plathong, S. et al. Daytime gamete release from the reef-building coral, Pavona sp., in the Gulf of Thailand. Coral Reefs 25, 72–72 (2006).
ADS  Article  Google Scholar 

104.
Rapuano, H. et al. Reproductive strategies of the coral Turbinaria reniformis in the northern Gulf of Aqaba (Red Sea). Scientific Reports 7, 42670 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

105.
Raj, K. D. & Edward, J. K. P. Observations on the reproduction of Acropora corals along the Tuticorin coast of the Gulf of Mannar, Southeastern India. Indian. J. Mar. Sci. 39, 219–226 (2010).
Google Scholar 

106.
Richmond, R. H. Competency and dispersal potential of planula larvae of a spawning versus a brooding coral. Proceedings of the 6th International Coral Reef Symposium 2, 827–831 (1988).
Google Scholar 

107.
Sakai, K. Gametogenesis, spawning, and planula brooding by the reef coral Goniastrea aspera (Scleractinia) in Okinawa, Japan. Mar. Ecol. Prog. Ser. 151, 67–72 (1997).
ADS  Article  Google Scholar 

108.
Schmidt-Roach, S., Miller, K. J., Woolsey, E., Gerlach, G. & Baird, A. H. Broadcast spawning by Pocillopora species on the Great Barrier Reef. PLoS ONE 7, e50847 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

109.
Shlesinger, T., Grinblat, M., Rapuano, H., Amit, T. & Loya, Y. Can mesophotic reefs replenish shallow reefs? Reduced coral reproductive performance casts a doubt. Ecology 99, 421–437 (2018).
PubMed  Article  PubMed Central  Google Scholar 

110.
Shlesinger, T. & Loya, Y. Breakdown in spawning synchrony: A silent threat to coral persistence. Science 365, 1002–1007 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

111.
Shlesinger, Y. & Loya, Y. Coral community reproductive patterns: Red Sea versus the Great Barrier Reef. Science 228, 1333–1335 (1985).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

112.
Siboni, N. et al. Using bacterial extract along with differential gene expression in Acropora millepora larvae to decouple the processes of attachment and metamorphosis. PLoS ONE 7, e37774–e37774 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

113.
Simpson, C. J. Mass spawning of scleractinian corals in the Dampier Archipelago and the implications for management of coral reefs in Western Australia. Report No. 244, (Dept. Conservation and Environment Western Australia Bulletin, Perth, 1985).

114.
Stanton, F. G. Spatio-temporal patterns of spawning in the coral, Montipora verrucosa in Hawaii. Proceeding of the 7th International Coral Reef Symposium 1, 489–493 (1992).
Google Scholar 

115.
Tan, C. H. et al. Multispecific synchronous coral spawning on Pulau Bidong, Malaysia, South China Sea. Bull. Mar. Sci. 96, 193–194 (2020).
Article  Google Scholar 

116.
Tomascik, T., Mah, A. J., Nontij, A. & Moosa, M. K. The Ecology of the Indonesian Seas. Vol. One (Periplus, 1997).

117.
Twan, W. H., Hwang, J. S. & Chang, C. F. Sex steroids in scleractinian coral, Euphyllia ancora: Implication in mass spawning. Biol. Reprod. 68, 2255–2260 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

118.
Van Oppen, M. J. H., Willis, B. L., Van Rheede, T. & Miller, D. J. Spawning times, reproductive compatibilities and genetic structuring in the Acropora aspera group: evidence for natural hybridization and semi-permeable species boundaries in corals. Mol. Ecol. 11, 1363–1376 (2002).
PubMed  Article  PubMed Central  Google Scholar 

119.
van Woesik, R. Coral communities at high latitude are not pseudopopulations: evidence of spawning at 32°N, Japan. Coral Reefs 14, 119–120 (1995).
ADS  Article  Google Scholar 

120.
Wallace, C. C. Reproduction, recruitment and fragmentation in nine sympatric species of the coral genus. Acropora. Mar. Biol. 88, 217–233 (1985).
Article  Google Scholar 

121.
Wei, N. W. V. et al. Reproductive Isolation among Acropora species (Scleractinia: Acroporidae) in a marginal coral assemblage. Zool. Stud. 51, 85–92 (2012).
Google Scholar 

122.
Wild, C., Tollrian, R. & Huettel, M. Rapid recycling of coral mass-spawning products in permeable reef sediments. Mar. Ecol. Prog. Ser. 271, 159–166 (2004).
ADS  Article  Google Scholar 

123.
Wilson, J. R. & Harrison, P. L. Sexual reproduction in high latitude coral communities at the Solitary Islands, eastern Australia. Proceedings of the 8th International Coral Reef Symposium, 533–540 (1997).

124.
Wilson, J. R. & Harrison, P. L. Spawning patterns of scleractinian corals at the Solitary Islands – a high latitude coral community in eastern Australia. Mar. Ecol. Prog. Ser. 260, 115–123 (2003).
ADS  Article  Google Scholar 

125.
Wolstenholme, J., Nozawa, Y., Byrne, M. & Burke, W. Timing of mass spawning in corals: potential influence of the coincidence of lunar factors and associated changes in atmospheric pressure from northern and southern hemisphere case studies. Invertebr. Reprod. Dev. 62, 98–108 (2018).
Article  Google Scholar 

126.
Woolsey, E. S., Byrne, M. & Baird, A. H. The effects of temperature on embryonic development and larval survival in two scleractinian corals. Mar. Ecol. Prog. Ser. 493, 179–184 (2013).
ADS  Article  Google Scholar 

127.
Woolsey, E. S., Keith, S. A., Byrne, M., Schmidt-Roach, S. & Baird, A. H. Latitudinal variation in thermal tolerance thresholds of early life stages of corals. Coral Reefs 34, 471–478 (2015).
ADS  Article  Google Scholar 

128.
Yeemin, T. Ecological studies of scleractinian coral communities above the northern limit of coral reef development in the western Pacific PhD thesis, Kyushu University, (1991). More