Philippa Kaur

Almodóvar A, Nicola GG, Ayllón D, Elvira B (2012) Global warming threatens the persistence of Mediterranean brown trout. Glob Chang Biol 18:1549–1560Article 

Google Scholar 
Arscott DB, Tockner K, Ward JV (2001) Thermal heterogeneity along a braided floodplain river (Tagliamento River, northeastern Italy). Can J Fish Aquat Sci 58:2359–2373Article 

Google Scholar 
Baker DJ, Garnett ST, O’Connor J, Ehmke G, Clarke RH, Woinarski JCZ et al. (2019) Conserving the abundance of nonthreatened species. Conserv Biol 33:319–328PubMed 
Article 

Google Scholar 
Blanchet S, Prunier JG, Paz-Vinas I, Saint-Pe K, Rey O, Raffard A et al. (2020) A river runs through it: the causes, consequences, and management of intraspecific diversity in river networks. Evol Appl 13:1195–1213PubMed 
PubMed Central 
Article 

Google Scholar 
Blondel L, Paterson IG, Bentzen P, Hendry AP (2021) Resistance and resilience of genetic and phenotypic diversity to “black swan” flood events: a retrospective analysis with historical samples of guppies. Mol Ecol 30:1017–1028PubMed 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brown LE, Milner AM, Hannah DM (2007) Groundwater influence on alpine stream ecosystems. Freshw Biol 52:878–890Article 

Google Scholar 
Caissie D (2006) The thermal regime of rivers: a review. Freshw Biol 51:1389–1406Article 

Google Scholar 
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
PubMed Central 
Article 

Google Scholar 
Dennenmoser S, Rogers SM, Vamosi SM (2014) Genetic population structure in prickly sculpin (Cottus asper) reflects isolation-by-environment between two life-history ecotypes. Biol J Linn Soc 113:943–957Article 

Google Scholar 
Diniz-Filho JAF, De Campos Telles MP (2002) Spatial autocorrelation analysis and the identification of operational units for conservation in continuous populations. Conserv Biol 16:924–935Article 

Google Scholar 
Diniz-Filho JAF, Soares TN, Lima JS, Dobrovolski R, Landeiro VL, Telles MP, de C et al. (2013) Mantel test in population genetics. Genet Mol Biol 36:475–485PubMed 
PubMed Central 
Article 

Google Scholar 
Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guenard G et al. (2020) adespatial: Multivariate Multiscale Spatial Analysis. R package version 0.3-8. https://CRAN.R-project.org/package=adespatialEarl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361Article 

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

Google Scholar 
Gilbert B, Bennett JR (2010) Partitioning variation in ecological communities: do the numbers add up? J Appl Ecol 47:1071–1082Article 

Google Scholar 
Gilbert B, Lechowicz MJ (2004) Neutrality, niches, and dispersal in a temperate forest understory. Proc Natl Acad Sci USA 101:7651–7656CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Goslee S, Urban D (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22:1–19Article 

Google Scholar 
Goto A (1998) Life-history variations in the fluvial sculpin, Cottus nozawae (Cottidae), along the course of a small mountain stream. Environ Biol Fishes 52:203–212Article 

Google Scholar 
Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 86:485–486Article 

Google Scholar 
Griffith DA, Peres-Neto PR (2006) Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87:2603–2613PubMed 
Article 
PubMed Central 

Google Scholar 
Guillot G, Rousset F (2013) Dismantling the Mantel tests. Methods Ecol Evol 4:336–344Article 

Google Scholar 
Hänfling B, Weetman D (2006) Concordant genetic estimators of migration reveal anthropogenically enhanced source-sink population structure in the river sculpin, Cottus gobio. Genetics 173:1487–1501PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Harmon LJ, Glor RE (2010) Poor statistical performance of the mantel test in phylogenetic comparative analyses. Evolution 64:2173–2178PubMed 
PubMed Central 

Google Scholar 
Heino J, Grönroos M, Ilmonen J, Karhu T, Niva M, Paasivirta L (2013) Environmental heterogeneity and β diversity of stream macroinvertebrate communities at intermediate spatial scales. Freshw Sci 32:142–154Article 

Google Scholar 
Hohenlohe PA, Funk WC, Rajora OP (2021) Population genomics for wildlife conservation and management. Mol Ecol 30:62–82PubMed 
Article 
PubMed Central 

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

Google Scholar 
Iles DT, Williams NM, Crone EE (2018) Source-sink dynamics of bumblebees in rapidly changing landscapes. J Appl Ecol 55:2802–2811Article 

Google Scholar 
Ito N, Gotoh RO, Shirakuma T, Araki Y, Hanzawa N (2018) Genetic structure of glacial-relict populations of a freshwater sculpin, Cottus nozawae, in Yamagata area of the Tohoku district. Biogeography 20:96–102
Google Scholar 
Junker J, Peter A, Wagner CE, Mwaiko S, Germann B, Seehausen O et al. (2012) River fragmentation increases localized population genetic structure and enhances asymmetry of dispersal in bullhead (Cottus gobio). Conserv Genet 13:545–556Article 

Google Scholar 
Kanno Y, Vokoun JC, Letcher BH (2011) Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks. Mol Ecol 20:3711–3729PubMed 
Article 

Google Scholar 
Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. Ecol Lett 7:1225–1241Article 

Google Scholar 
Koizumi I (2011) Integration of ecology, demography and genetics to reveal population structure and persistence: a mini review and case study of stream-dwelling Dolly Varden. Ecol Freshw Fish 20:352–363Article 

Google Scholar 
Koizumi I, Maekawa K (2004) Metapopulation structure of stream-dwelling Dolly Varden charr inferred from patterns of occurrence in the Sorachi River basin, Hokkaido, Japan. Freshw Biol 49:973–981Article 

Google Scholar 
Koizumi I, Yamamoto S, Maekawa K (2006) Decomposed pairwise regression analysis of genetic and geographic distances reveals a metapopulation structure of stream-dwelling Dolly Varden charr. Mol Ecol 15:3175–3189CAS 
PubMed 
Article 

Google Scholar 
Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across. K Mol Ecol Resour 15:1179–1191CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Lamphere BA, Blum MJ (2012) Genetic estimates of population structure and dispersal in a benthic stream fish. Ecol Freshw Fish 21:75–86Article 

Google Scholar 
Legendre P, Fortin MJ, Borcard D (2015) Should the Mantel test be used in spatial analysis? Methods Ecol Evol 6:1239–1247Article 

Google Scholar 
Legendre P, Legendre L (2012) Numerical Ecology, 3rd edn. Elsevier, Amsterdam
Google Scholar 
Lichstein JW (2007) Multiple regression on distance matrices: a multivariate spatial analysis tool. Plant Ecol 188:117–131Article 

Google Scholar 
Lucek K, Keller I, Nolte AW, Seehausen O (2018) Distinct colonization waves underlie the diversification of the freshwater sculpin (Cottus gobio) in the Central European Alpine region. J Evol Biol 31:1254–1267PubMed 
Article 

Google Scholar 
Meirmans PG (2012) The trouble with isolation by distance. Mol Ecol 21:2839–2846PubMed 
Article 

Google Scholar 
Meirmans PG (2014) Nonconvergence in Bayesian estimation of migration rates. Mol Ecol Resour 14:726–733PubMed 
Article 

Google Scholar 
Meirmans PG (2015) Seven common mistakes in population genetics and how to avoid them. Mol Ecol 24:3223–3231PubMed 
Article 

Google Scholar 
Middaugh CR, Kessinger B, Magoulick DD (2018) Climate-induced seasonal changes in smallmouth bass growth rate potential at the southern range extent. Ecol Freshw Fish 27:19–29Article 

Google Scholar 
Ministry of the Environment Government of Japan (2020) Red List of Japan. http://www.env.go.jp/press/107905.html. (In Japanese)Mussmann SM, Douglas MR, Chafin TK, Douglas ME (2019) BA3-SNPs: contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol Evol 10:1808–1813Article 

Google Scholar 
Myers EA, Xue AT, Gehara M, Cox CL, Davis Rabosky AR, Lemos-Espinal J et al. (2019) Environmental heterogeneity and not vicariant biogeographic barriers generate community-wide population structure in desert-adapted snakes. Mol Ecol 28:4535–4548PubMed 
Article 

Google Scholar 
Nagasaka A, Sugiyama S (2010) Factors affecting the summer maximum stream temperature of small streams in northern Japan. Bull Hokkaido Res Inst 47:35–43. (In Japanese with English abstract)
Google Scholar 
Nakajima S, Hirota SK, Matsuo A, Suyama Y, Nakamura F (2020) Genetic structure and population demography of white-spotted charr in the upstream watershed of a large dam. Water 12:2406CAS 
Article 

Google Scholar 
Nakamura F, Yamada H (2005) Effects of pasture development on the ecological functions of riparian forests in Hokkaido in northern Japan. Ecol Eng 24:539–550Article 

Google Scholar 
Natsumeda T (2003) Effects of a severe flood on the movements of Japanese fluvial sculpin. Environ Biol Fishes 68:417–424Article 

Google Scholar 
Nosil P, Vines TH, Funk DJ (2005) Perspective: reproductive isolation caused by natural selection against immigrants from divergent habitats. Evolution 59:705–719PubMed 

Google Scholar 
Oksanen JF, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D et al. (2019) vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=veganOkumura N, Goto A (1996) Genetic variation and differentiation of the two river sculpins, Cottus nozawae and C. amblystomopsis, deduced from allozyme and restriction enzyme-digested mtDNA fragment length polymorphism analyses. Ichthyol Res 43:399–416Article 

Google Scholar 
Olden JD, Naiman RJ (2010) Incorporating thermal regimes into environmental flows assessments: modifying dam operations to restore freshwater ecosystem integrity. Freshw Biol 55:86–107Article 

Google Scholar 
Orsini L, Vanoverbeke J, Swillen I, Mergeay J, De Meester L (2013) Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol Ecol 22:5983–5999PubMed 
Article 

Google Scholar 
Paris JR, Stevens JR, Catchen JM (2017) Lost in parameter space: a road map for STACKS. Methods Ecol Evol 8:1360–1373Article 

Google Scholar 
Peacock MM, Gustin MS, Kirchoff VS, Robinson ML, Hekkala E, Pizzarro-Barraza C et al. (2016) Native fishes in the Truckee River: are in-stream structures and patterns of population genetic structure related? Sci Total Environ 563–564:221–236PubMed 
Article 
CAS 

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

Google Scholar 
Poff NL, Richter BD, Arthington AH, Bunn SE, Naiman RJ, Kendy E et al. (2010) The ecological limits of hydrologic alteration (ELOHA): A new framework for developing regional environmental flow standards. Freshw Biol 55:147–170Article 

Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raufaste N, Francois R (2001) Are partial Mantel tests adequate? Evolution 55:1703–1705CAS 
PubMed 
Article 

Google Scholar 
Richardson JL, Brady SP, Wang IJ, Spear SF (2016) Navigating the pitfalls and promise of landscape genetics. Mol Ecol 25:849–864PubMed 
Article 
PubMed Central 

Google Scholar 
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Rousset F (2002) Partial Mantel tests: reply to Castellano and Balletto. Evolution 56:1874–1875Article 

Google Scholar 
Ruppert JLW, James PMA, Taylor EB, Rudolfsen T, Veillard M, Davis CS et al. (2017) Riverscape genetic structure of a threatened and dispersal limited freshwater species, the Rocky Mountain Sculpin (Cottus sp.). Conserv Genet 18:925–937CAS 
Article 

Google Scholar 
Sexton JP, Hangartner SB, Hoffmann AA (2014) Genetic isolation by environment or distance: Which pattern of gene flow is most common? Evolution 68:1–15CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Sexton JP, Hufford MB, Bateman AC, Lowry DB, Meimberg H, Strauss SY et al. (2016) Climate structures genetic variation across a species’ elevation range: a test of range limits hypotheses. Mol Ecol 25:911–928PubMed 
Article 
PubMed Central 

Google Scholar 
Suyama Y, Matsuki Y (2015) MIG-seq: an effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Sci Rep. 5:1–12Article 
CAS 

Google Scholar 
Suzuki K, Ishiyama N, Koizumi I, Nakamura F (2021) Combined effects of summer water temperature and current velocity on the distribution of a cold-water-adapted scupin (Cottus nozawae). Water 13:975Article 

Google Scholar 
Tague CL, Farrell M, Grant G, Lewis S, Rey S (2007) Hydrogeologic controls on summer stream temperatures in the McKenzie River basin, Oregon. Hydrol Process 21:3288–3300Article 

Google Scholar 
Thomaz AT, Christie MR, Knowles LL (2016) The architecture of river networks can drive the evolutionary dynamics of aquatic populations. Evolution 70:731–739PubMed 
Article 

Google Scholar 
Thorpe RS, Surget-Groba Y, Johansson H (2008) The relative importance of ecology and geographic isolation for speciation in anoles. Philos Trans R Soc B Biol Sci 363:3071–3081Article 

Google Scholar 
Tsuda Y, Nakao K, Ide Y, Tsumura Y (2015) The population demography of Betula maximowicziana, a cool-temperate tree species in Japan, in relation to the last glacial period: Its admixture-like genetic structure is the result of simple population splitting not admixing. Mol Ecol 24:1403–1418CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Uno H (2016) Stream thermal heterogeneity prolongs aquatic-terrestrial subsidy and enhances riparian spider growth. Ecology 97:2547–2553PubMed 
Article 
PubMed Central 

Google Scholar 
Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23:5649–5662PubMed 
Article 

Google Scholar 
Wang IJ, Summers K (2010) Genetic structure is correlated with phenotypic divergence rather than geographic isolation in the highly polymorphic strawberry poison-dart frog. Mol Ecol 19:447–458PubMed 
Article 

Google Scholar 
Watz J, Otsuki Y, Nagatsuka K, Hasegawa K, Koizumi I (2019) Temperature-dependent competition between juvenile salmonids in small streams. Freshw Biol 64:1534–1541Article 

Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
PubMed 
PubMed Central 

Google Scholar 
White SL, Hanks EM, Wagner T (2020) A novel quantitative framework for riverscape genetics. Ecol Appl 30:e02147PubMed 

Google Scholar 
Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMed 
PubMed Central 
Article 

Google Scholar 
Wright S (1943) Isolation by Distance. Genetics 28:114–138CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yagami T, Goto A (2000) Patchy distribution of a fluvial sculpin, Cottus nozawae, in the Gakko River system at the southern margin of its native range. Ichthyol Res 47:277–286Article 

Google Scholar 
Zeileis A, Hothorn T (2002) Diagnostic checking in regression relationships. R News 2:7–10
Google Scholar 
Zeller KA, Creech TG, Millette KL, Crowhurst RS, Long RA, Wagner HH et al. (2016) Using simulations to evaluate Mantel-based methods for assessing landscape resistance to gene flow. Ecol Evol 6:4115–4128PubMed 
PubMed Central 
Article 

Google Scholar  More

Ethics statementField research, including sediment collection, was approved by the Harbormaster of Maizuru Bay (Permission Number 31 issued on July 1, 2016). All the experiments were performed in accordance with the guidelines on the Regulation on Animal Experimentation of Kyoto University (https://www.kyoto-u.ac.jp/en/research/research-compliance-ethics/animal-experiments), the Kyoto Prefecture Fishery Management Rules (http://www.pref.kyoto.jp/reiki/reiki_honbun/a300RG00000634.html), and the ARRIVE guidelines (https://arriveguidelines.org). Fish (15 individuals of jack mackerel juveniles) were anesthetized prior to length and weight measurements using 0.05% of 2-phenoxyethanol, and all individuals recovered from the anesthesia. No fish were sacrificed or injured in the present study. The research plan was approved by the institutional review boards at the Maizuru Fisheries Research Station (MFRS) of Kyoto University.Detection of eDNA in the water and sediment in experimental tanksOn July 6, 2016, natural marine sediment was collected at a depth of 47 m off the shore of Kyoto in the Sea of Japan (35.5544° N, 135.3210° E), using a Smith–McIntyre bottom sampler. Approximately 100 L of sediment was collected from 13 casts and was preserved in four large, covered containers at room temperature until the experiment began. Four sub-samples, 3 g from each container, were used for eDNA extraction and detection of jack mackerel using the method described below, and we confirmed that none of them contained the DNA of this species. The median particle diameter of the sediment was 47.7 μm, and mud content was 61.9% based on analyses using a laser diffraction particle size analyzer (SALD-2200, Shimadzu, Kyoto, Japan). Jack mackerel juveniles were collected by hook-and-line fishing from the pier of the MFRS. This species is the most abundant fish in this area and is typically found in waters 14 °C or warmer43.Four 200 L polycarbonate tanks (66 cm in bottom diameter) were set in the rearing facility of MFRS; three were used as test tanks, and the fourth was used as a control (blank) tank. The marine sediment (24 L, 7 cm in thick layer) was placed in each tank. Fine-filtered seawater was provided 2 d after the sediment had settled. The seawater used was pumped from 6 m depth offshore from the MFRS and filtered by passing through coarse polyvinyl fabric and fine sand of ca. 0.6 mm in diameter (5G-ST, Nikkiso Eiko, Japan; www.nikkiso-eiko.co.jp). Water was supplied at the rate of 490 mL min−1 (four cycles of circulation per day) and was drained from the center of each tank, filtered through a 2 mm mesh net. Aeration was performed at a rate of 600 mL min−1. This flow-through system was maintained throughout the experimental period.Five individual jack mackerel (70.7 ± 4.4 mm in total length and 3.26 ± 0.67 g in wet mass, mean ± SD) were introduced in each of the three test tanks on August 8, 7 days after the sediment was introduced. Defrosted krill Euphausia pacifica (5 g per tank) was fed to the fish between 16:00 and 17:00 every day. Fish were removed from the tanks 14 days after introduction using two hand nets, taking care not to disturb the sediment. Water temperature was recorded using a digital thermometer at 10:00 every day while the fish were kept in the tanks, for the following 14 days after their removal, and once a week thereafter. The water temperature ranged from 24.9 to 29.5 °C (mean = 27.9 °C) during the first four weeks of the experiment and from 9.4 to 27.9 °C (mean = 17.8 °C) during the following months. These conditions are similar to the natural condition that would be undergone by eDNA in sediment; for the last 19 years, the recorded bottom water temperature in the area from which the jack mackerel had been collected ranged from 8.5 to 29.6 °C, with a mean of 18.2 °C (Masuda 200843 with updated data). All rearing equipment was either newly purchased or bleached with 0.1% sodium hypochlorite and rinsed well with tap water before use.Water and sediment samples were collected immediately before the introduction of fish (day 0) and on days 1, 2, 4, 7, and 14 after their introduction. Sampling was also conducted on days 0, 1, 2, 4, 7, and 14, as well as in months 1, 2, 4, 8, and 12 after the removal of fish. Three 1 L water samples and three sediment samples (3 g) were collected from each tank on each sampling day. Water was collected from the drainage outlet in plastic bottles, and sediment was collected in petri dishes (inner diameter of 58 mm and depth of 21 mm). The sediment was collected by pushing the open end of a petri dish onto the surface of the sediment and securing the cover from underneath. Sediment sampling was conducted with a pair of prebleached long-sleeved gloves. One sample was obtained from the central tank area, and another two from near the peripheral tank area. Repetitive collection from the same location was avoided by marking each sampling location with a piece of PVC pipe (similar in diameter to the petri dishes and 3 cm in height).Water was filtered using glass fiber filters (0.7 μm mesh; GF/F 47 mm, GE Healthcare Japan, Tokyo, Japan). This mesh size, along with 0.45 μm, are two of the most commonly used filters in macroorganism eDNA studies44. The amount of eDNA detected using a 0.7 μm mesh is equivalent to that by a 0.45 μm mesh30. Contamination was evaluated by filtering 1 L of reverse osmosis water at the end of each sampling day. The filtered paper was wrapped in aluminum foil and preserved at − 20 °C.Sediment core samplingSediment core samples were collected at four locations (St. 1–4) in and around Nishi-Moune Bay, Kesennuma, Miyagi, Japan (38.8919–38.8932°N, 141.6235–141.6262°E; Fig. 1) on May 20, 2017. St. 1 was in the inner part of the bay where the tsunami impact was assumed to be the highest, with a run-up height of 15 m. St. 2 was located along a shallow rocky shore where the tsunami impact was limited. St. 3 was located at the mouth of the bay, and St. 4 was outside the bay. Average depths of the seafloor where cores were collected were 8.1, 9.6, 23.0, and 14.0 m at stations 1, 2, 3, and 4, respectively. Seafloor temperatures ranged from 9.9 to 11.5 °C. An acrylic pipe (inner diameter of 54 mm, length of 50 cm, and thickness of 3 mm) was pushed into the bottom sediment by a scuba diver. A silicon cap (59 and 52 mm in upper and lower diameter, respectively, and 45 mm in height) was placed on the top of the pipe, and the diver slowly pulled the pipe up and put another cap on the bottom. Three cores were collected from each location and transferred to a boat at the sea surface. Sediment core samples were kept vertical to avoid disturbing the layers and protected from direct sunlight. Cores were immediately transferred to the laboratory within 10 min, and were prepared for the cutting process.The core samples (1 cm thickness) were cut by layers as follows: after removing the bottom cap, a core sample pipe was placed on a stage that pushed the sediment inside. Seawater in the upper part of the pipe was discarded until the top of the sediment appeared on the surface. A thin acrylic plate was used to cut the core, and the cut specimen was placed in a small vinyl bag and preserved at -20 °C. All 12 collected cores were used for eDNA analysis, and one at St. 1 (inner bay) and all three at St. 3 (bay mouth) were used for the analysis of PAHs.DNA extractionDNA extraction from the glass fiber filter was performed following the method described in Yamamoto et al.6 using a DNeasy Blood and Tissue Kit (Qiagen, Hilde, Germany) and a Salivette tube (Sarstedt, Nümbrecht, Germany). Total eDNA was eluted in 100 μL AE buffer and preserved at − 20 °C.DNA extraction from sediment was conducted using a combination of alkaline DNA extraction45 and ethanol precipitation, using a commercial soil DNA extraction kit (Power Soil DNA Isolation Kit, QIAGEN, Hilden, Germany), as described in Sakata et al.26. Wet sediment (ca. 3 g) was placed in a 15 mL tube. Triplicate samples were obtained from each petri dish in the tank experiment, and a single sample was obtained from each layer in the sediment cores. We added 6 mL of 0.33 M NaOH and 3 mL of 10 mM TE buffer (pH = 6.7) to the tube and mixed well using Voltex. The samples were incubated at 94 °C for 50 min, and during this time, they were inverted at 15 and 30 min of incubation. After the incubation, the samples were cooled for several minutes and then centrifuged at 5,000 × g for 30 s. Supernatants (7.5 mL) were collected in 50 mL tubes and 7.5 mL of 1 M Tris HCL buffer (pH = 9.0 in the tank experiment and pH = 6.7 in the core samples), 1.5 mL of 3 M sodium acetate (pH = 5.2), and 30 mL of 99.5% ethanol were added, and mixed well by inversion. Ethanol precipitation was achieved by incubating the mixture for 1 h at − 20 °C. As a negative control of extraction, 3 mL of pure water was treated in the same manner. Sediment in the tank experiment was processed up to the ethanol precipitation on the same day as sampling, whereas core samples were defrosted at room temperature prior to analysis and then preserved as precipitate.The ethanol-precipitated sediment sample was centrifuged at 5,350 × g for 20 min, after which the supernatant was discarded. The precipitate was moved to the PowerBead Tube of the Power Soil Isolation Kit using a microspatula. The debris left in the centrifuged tube was also transferred by dissolving it in 100 μL of pure water. The following procedure was performed according to the protocol of Power Soil. The total eluted DNA (100 μL) was stored at − 20 °C. All the spatulas were bleached prior to use, and brand-new centrifugation tubes were used for the procedure.Quantitative PCRDNA was quantified using real-time TaqMan PCR with a LightCycler 96 Real-Time PCR System (Roche, Basel, Switzerland). Species-specific sets of primers and probes were used to quantify the eDNA of jack mackerel, moon jellyfish, and sea nettle (Supplementary Table S5). For the specimens in the tank experiment, each reaction contained 2 μL of extracted eDNA solution, a final concentration of 900 nM of forward and reverse primers, and 125 nM of TaqMan probe in 1 × PCR master mix (FastStart Essential DNA Master; Roche, Basel, Switzerland). PCR was performed under the following conditions: 10 min at 95 °C, 50 cycles of 10 s at 95 °C, and 1 min at 60 °C. For the core samples, each reaction contained 5 μL of extracted eDNA solution, a final concentration of 900 nM of forward and reverse primers, and 125 nM of TaqMan probe in 1 × TaqMan Environmental Master Mix 2.0 (Thermo Fisher Scientific, Massachusetts, USA). PCR was performed under the following conditions: 2 min at 50 °C, 10 min at 95 °C, 60 cycles of 15 s at 95 °C, and 1 min at 60 °C. PCR was performed in triplicates for each extracted DNA sample. Triplicates of pure water instead of the eDNA solution were used for each PCR performance as a PCR negative control. All PCR negative controls were below the detection level.As a standard for quantification, we used a linearized plasmid containing synthesized artificial DNA fragments of the cytochrome b (CytB) gene sequence of jack mackerel or cytochrome C oxidase subunit I (COI) gene sequences of moon jellyfish and Pacific sea nettle, including target regions. The dilution series of 3.0 × 101–3.0 × 104 was run in PCR in triplicate to obtain quantification curves. Quantification was accepted only when the fitted R2 value was above 0.99 on the quantification curve. The average of the PCR replicates was used to represent the eDNA concentration in each sample. eDNA concentrations were expressed as the number of copies per gram of samples in both water and sediment. As contamination precautions, water filtration, DNA extraction, and PCR reactions were performed in separate rooms, and persons entering one of the above three rooms were not permitted to enter the other rooms.Evaluation of PCR inhibitorsSediment often contains chemicals that inhibit the PCR process. An analysis using an internal positive control (IPC) was conducted to confirm that the eDNA extraction kit successfully removed such inhibitive chemicals. DNA of lambda phage that was not present in the environment was used as the IPC46. Water and sediment samples (n = 12 for each) in the experimental tanks on day 14 after the introduction of fish were used for this experiment. We placed 300 copies of lambda phage DNA in the extracted eDNA with the primer–probe set in the test group, whereas pure water (instead of extracted eDNA) was placed in the control group (n = 3). PCR amplification of the test and control groups was compared, defining delta Ct as the difference in the number of threshold cycles (Ct values) in the PCR between samples with and without extracted eDNA. Delta Ct in the water samples ranged from − 0.49 to + 2.93 cycles, and from − 0.39 to + 0.28 cycles in the sediment samples (Supplementary Table S6). These values were less than + 3 cycles, previously proposed as criteria of inhibition12, and thus the inhibition was negligible in the present method.Analysis of polycyclic aromatic hydrocarbons (PAHs) for detecting tsunami signatureThe sampled sediment cores (one at St. 1 and three at St. 3) were analyzed to quantify PAHs as a tsunami signature. Specimens from every two layers were used for the analysis.Five hundred microliters of mixed acetone solution containing 5 μg mL−1 each of naphthalene-d8, acenaphthene-d10, fluorene-d10, anthracene-d10, fluoranthene-d10, pyrene-d10, and chrysene-d12 as surrogate standards was added to a centrifuge tube containing 1 g of sediment. The analytes were extracted twice by shaking for 10 min with acetone (10 mL). Supernatants mixed with 60 mL of saturated NaCl solution were transferred to a separatory funnel. The analytes were extracted twice with 10 mL hexane, and the organic layer was combined. This layer was then dried over anhydrous Na2SO4 and concentrated to trace level using a rotary evaporator. The solution was concentrated to 1 mL under a nitrogen atmosphere and cleaned using a Florisil Sep-Pak column (Waters Association Co., Ltd.). The Florisil Sep-Pak cartridge for clean-up was washed with 10 mL of hexane. A hexane solution containing the analytes followed by 10 mL of hexane/acetone (99/1) solution were passed through the prewashed cartridge. After the addition of 100 μL of 1 mg L−1 atradine-d5 as an internal standard, the eluate was carefully evaporated with a stream of nitrogen up to 1 mL. The analytes were determined using gas chromatography–mass spectrometry (GC/MS).A Hewlett-Packard 6890 series gas chromatograph equipped with a mass spectrometer (5973 N) was used for PAH analysis. The separation was carried out in a capillary column coated with 5% phenyl methyl silicone (J&W Scientific Co., 30 m length × 0.25 mm i.d., 0.25 μm film thickness). The column temperature was maintained at 50 °C for the first minute and then increased to 290 °C at 20 °C min−1 and to 310 °C at 10 °C min−1. Finally, the column temperature was maintained at 310 °C for 10 min. The interface temperature, ion source temperature, and ion energy were 280 °C, 230 °C, and 70 eV, respectively. Selected ion monitoring was operated under this program. The monitoring ions of 128 (127) for naphthalene, 152 (151) for acenaphthylene, 153 (152) for acenaphthene, 166 (165) for fluorene, 178 (176) for phenanthrene and anthracene, 202 (203) for fluoranthene and pyrene, 228 (229) for benzo[a]anthracene and chrysene, 252 (253) for benzo[b]fluoranthene, 252 (281) for benzo[k]fluoranthene, and benzo[a]pyrene, 276 (207) for dibenzo[a,h]anthracene, indeno[1,2,3-cd]pyrene, and benzo[g,h,i]perylene, were used to quantify the concentrations of PAHs; qualifier ions are indicated in parentheses. One microliter of the sample was injected by splitless injection.Data analysisConcentration of eDNA in the water and sediment of experimental tanks after the introduction of fish was analyzed by repeated-measures (rm) ANOVA; ‘days after the introduction of fish’ was defined as the explanatory variable, ‘concentration of eDNA’ as the response variable, and the ‘triplicates of petri dishes’ as a random factor. Then, eDNA concentrations among days were compared using Tukey’s HSD test. Homoscedasticity in eDNA content was improved by log 10 (x + 1) transformation. The decrease in eDNA in water and sediment samples after the removal of fish was also analyzed by rm ANOVA in both the test and control tanks. A comparison of the eDNA concentrations between the test and control tanks was also conducted by rm ANOVA after the removal of fish. All analyses were performed in R ver. 3.4.2 (using the packages of lmerTest and multcomp)47,48,49.Concentration of eDNA in water and sediment samples after introduction and removal of fish was fitted to eight candidate models as log X (y = a + b * ln(x)), log Y (y = exp(a + b * x)), asymptotic (y = a * x/(1 + b * x)), reciprocal (y = a + b/x), power law (y = a * x ^ b), exponential (y = a * exp(b * x)), and exponential decay (y = a + b * exp(c * x)) using the “nls” function of R. Models with the lowest AIC values were listed, and regression lines were drawn by Kaleida Graph 4.5 (Hulinks, Tokyo, Japan).We tested whether the concentration of jellyfish eDNA was highest in the layers immediately above the signature of the tsunami in the sediment cores collected at St. 3. The depth of peak PAHs was identified in each core, and this was considered to represent the timing of the tsunami. Core samples of eDNA were then divided into the following three parts: (1) upper, including the upper half of the core above the PAH peak, representing recent sedimentation; (2) middle, including the lower half of the core above the PAH peak, representing sedimentation immediately after the tsunami; and (3) lower, including the layers of PAH peak and below, representing sedimentation at the timing of or prior to the tsunami. Concentrations of jellyfish eDNA of each species were compared among these three parts by nested ANOVA (layers nested in triplicate cores) followed by Tukey’s HSD test. More

1.Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 
Article 

Google Scholar 
2.Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science (80-.) 31, 787–790 (2006).ADS 
Article 
CAS 

Google Scholar 
3.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
4.Pandolfi, J. M. et al. Global trajectories of the long-term decline of coral reef ecosystems. Science (80-.) 301, 955–958 (2003).ADS 
CAS 
Article 

Google Scholar 
5.Teixidó, N., Casas, E., Cebrián, E., Linares, C. & Garrabou, J. Impacts on coralligenous outcrop biodiversity of a dramatic coastal storm. PLoS ONE 8, e53742 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Martin, C. S. et al. Coralligenous and maërl habitats: Predictive modelling to identify their spatial distributions across the Mediterranean Sea. Sci. Rep. 4, 1–9 (2014).Article 
CAS 

Google Scholar 
7.Bianchi, C. N. Bioconstruction in marine ecosystems and Italian marine biology. Biol. Mar. Medit. 8, 112–130 (2001).
Google Scholar 
8.Garrabou, J. & Ballesteros, E. Growth of Mesophyllum alternans and Lithophyllum frondosum (Corallinales, Rhodophyta) in the northwestern Mediterranean. Eur. J. Phycol. 35, 1–10 (2000).Article 

Google Scholar 
9.Ballesteros, E., Avançats, E. & Csic, D. B. Mediterannean coralligenous assemblages: A synthesis of present knowledge. Oceanogr. Mar. Biol. Annu. Rev. 44, 123–195 (2006).
Google Scholar 
10.Guidetti, P., Terlizzi, A., Fraschetti, S. & Boero, F. Spatio-temporal variability in fish assemblages associated with coralligenous formations in south eastern Apulia (SE Italy). Ital. J. Zool. 69, 325–331 (2002).Article 

Google Scholar 
11.Casellato, S. & Stefanon, A. Coralligenous habitat in the northern Adriatic Sea: An overview. Mar. Ecol. 29, 321–341 (2008).ADS 
Article 

Google Scholar 
12.Bavestrello, G., Cerrano, C., Zanzi, D. & Cattaneo-Vietti, R. Damage by fishing activities to the Gorgonian coral Paramuricea clavata in the Ligurian Sea. Aquat. Conserv. Mar. Freshw. Ecosyst. 7, 253–262 (1997).Article 

Google Scholar 
13.Piazzi, L., Gennaro, P. & Balata, D. Effects of nutrient enrichment on macroalgal coralligenous assemblages. Mar. Pollut. Bull. 62, 1830–1835 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Sala, E., Garrabou, J. & Zabala, M. Effects of diver frequentation on Mediterranean sublittoral populations of the bryozoan Pentapora fascialis. Mar. Biol. 126, 451–459 (1996).Article 

Google Scholar 
15.García-Rubies, A. & Zabalai Limousin, M. Effects of total fishing prohibition on the Mediterranean), rocky fish assemblages of Medes Islands marine reserve (NW Mediterranean). Sci. Mar. 54, 317–328 (1990).
Google Scholar 
16.Martin, S. & Gattuso, J.-P. Response of Mediterranean coralline algae to ocean acidification and elevated temperature. Glob. Change Biol. 15, 2089–2100 (2009).ADS 
Article 

Google Scholar 
17.Zapata-Ramírez, P. A. et al. Innovative study methods for the Mediterranean coralligenous habitats. Adv. Oceanogr. Limnol. 4, 102–119 (2013).Article 

Google Scholar 
18.Gatti, G., Bianchi, C. N., Morri, C., Montefalcone, M. & Sartoretto, S. Coralligenous reefs state along anthropized coasts: Application and validation of the COARSE index, based on a rapid visual assessment (RVA) approach. Ecol. Indic. 52, 567–576 (2015).Article 

Google Scholar 
19.Kipson, S. et al. Rapid biodiversity assessment and monitoring method for highly diverse benthic communities: A case study of Mediterranean coralligenous outcrops. PLoS ONE 6, e27103 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Sartoretto, S. et al. An integrated method to evaluate and monitor the conservation state of coralligenous habitats: The INDEX-COR approach. Mar. Pollut. Bull. 120, 222–231 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Deter, J., Descamp, P., Ballesta, L., Boissery, P. & Holon, F. A preliminary study toward an index based on coralligenous assemblages for the ecological status assessment of Mediterranean French coastal waters. Ecol. Indic. 20, 345–352 (2012).Article 

Google Scholar 
22.Deter, J., Descamp, P., Boissery, P., Ballesta, L. & Holon, F. A rapid photographic method detects depth gradient in coralligenous assemblages. J. Exp. Mar. Bio. Ecol. 418–419, 75–82 (2012).Article 

Google Scholar 
23.Gibb, R., Browning, E., Glover-Kapfer, P. & Jones, K. E. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol. Evol. 10, 169–185 (2019).Article 

Google Scholar 
24.Mooney, T. A. et al. Listening forward: Approaching marine biodiversity assessments using acoustic methods. R. Soc. Open Sci. 7, (2020).25.Di Iorio, L. et al. ‘Posidonia meadows calling’: A ubiquitous fish sound with monitoring potential. Remote Sens. Ecol. Conserv. 4, 248–263 (2018).Article 

Google Scholar 
26.Parsons, M. J. G., Salgado Kent, C. P., Recalde-Salas, A. & McCauley, R. D. Fish choruses off Port Hedland Western, Australia. Bioacoustics 26, 135–152 (2017).Article 

Google Scholar 
27.Ladich, F. Sound Communication In Fishes (Springer, 2015).Book 

Google Scholar 
28.Amorim, M. C. P. Diversity of sound production in fish. Diversity 1, 71–105 (2006).
Google Scholar 
29.Carriço, R. et al. Temporal dynamics in diversity patterns of fish sound production in the Condor seamount (Azores, NE Atlantic). Deep Sea Res. Part I Oceanogr. Res. Pap. 164, 103357 (2020).Article 

Google Scholar 
30.Desiderà, E. et al. Acoustic fish communities: Sound diversity of rocky habitats reflects fish species diversity and beyond?. Mar. Ecol. Prog. Ser. 608, 183–197 (2019).ADS 
Article 

Google Scholar 
31.Ladich, F. Acoustic communication in fishes: Temperature plays a role. Fish Fish. 19, 598–612 (2018).Article 

Google Scholar 
32.Rabin, L. A. & Greene, C. M. Changes to acoustic communication systems in human-altered environments. J. Comp. Psychol. 116, 137–141 (2002).PubMed 
Article 

Google Scholar 
33.Sueur, J., Krause, B. & Farina, A. Climate change is breaking earth’s beat. Trends Ecol. Evol. 34, 971–973 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Whittaker, R. J. et al. Conservation biogeography: Assessment and prospect. Divers. Distrib. 11, 3–23 (2005).Article 

Google Scholar 
35.Frainer, A. et al. Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc. Natl. Acad. Sci. U.S.A. 114, 12202–12207 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Olden, J. D. et al. Conservation biogeography of freshwater fishes: Recent progress and future challenges. Divers. Distrib. 16, 496–513 (2010).Article 

Google Scholar 
37.Lomolino, M. V., Pijanowski, B. C. & Gasc, A. The silence of biogeography. J. Biogeogr. 42, 1187–1196 (2015).Article 

Google Scholar 
38.Kéver, L., Lejeune, P., Michel, L. N. & Parmentier, E. Passive acoustic recording of Ophidion rochei calling activity in Calvi Bay (France). Mar. Ecol. 37, 1315–1324 (2016).ADS 
Article 

Google Scholar 
39.Picciulin, M. et al. Diagnostics of noctural calls of Sciena umbra (L., fam. Sciaenidae) in a nearshore Mediterranean marine reserve. Bioacoustics 22, 109–120 (2012).Article 

Google Scholar 
40.Bolgan, M., Picciulin, M., Di Iorio, L. & Parmentier, E. Passive acoustic monitoring of fishes in the Mediterranean Sea: from single species to whole communities monitoring. Ecoacoustics Congress, Urbino (Italy) (2021).
Google Scholar 
41.Tricas, T. C. & Boyle, K. S. Acoustic behaviors in Hawaiian coral reef fish communities. Mar. Ecol. Prog. Ser. 511, 1–16 (2014).ADS 
Article 

Google Scholar 
42.Bertucci, F. et al. Local sonic activity reveals potential partitioning in a coral reef fish community. Oecologia 193, 125–134 (2020).ADS 
PubMed 
Article 

Google Scholar 
43.Virgilio, M. & Airoldi, Æ. L. Spatial and temporal variations of assemblages in a Mediterranean coralligenous reef and relationships with surface orientation. Coral Reefs 25, 265–272 (2006).ADS 
Article 

Google Scholar 
44.Doxa, A. et al. Mapping biodiversity in three-dimensions challenges marine conservation strategies: The example of coralligenous assemblages in North-Western Mediterranean Sea. Ecol. Indic. 61, 1042–1054 (2015).
Google Scholar 
45.Casas-Güell, E. et al. Structure and biodiversity of coralligenous assemblages dominated by the precious red coral Corallium rubrum over broad spatial scales. Sci. Rep. 6, (2016).46.Sartoretto, S., Verlaque, M. & Laborel, J. Age of settlement and accumulation rate of submarine ‘coralligène’ (−10 to −60 m) of the northwestern Mediterranean Sea; relation to Holocene rise in sea level. Mar. Geol. 130, 317–331 (1996).ADS 
Article 

Google Scholar 
47.Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management (eds Samson, F. B. & Knopf, F. L.) 130–147 (Springer, 1994).Chapter 

Google Scholar 
48.Rossi, S. & Bramanti, L. Perspectives on the Marine Animal Forests of the World (Springer, 2020).Book 

Google Scholar 
49.Bolgan, M. et al. Fish biophony in a Mediterranean submarine canyon. J. Acoust. Soc. Am. 147, 2466–2477 (2020).ADS 
PubMed 
Article 

Google Scholar 
50.Sebastianutto, L., Picciulin, M., Costantini, M., Rocca, M. & Ferrero, E. A. Four type of sounds for one winner: vocalizations during territorial behavior in the red-mouthed goby Gobius cruentatus (Pisces Gobiidae). Acta Ethol. 11, 115–121 (2008).Article 

Google Scholar 
51.Bertucci, F., Lejeune, P., Payrot, J. & Parmentier, E. Sound production by dusky grouper Epinephelus marginatus at spawning aggregation sites. J. Fish Biol. 87, 400–421 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kéver, L. et al. Sexual dimorphism of sonic apparatus and extreme intersexual variation of sounds in Ophidion rochei (Ophidiidae): First evidence of a tight relationship between morphology and sound characteristics in Ophidiidae. Front. Zool. 9, 1–16 (2012).Article 

Google Scholar 
53.Ladich, F. Ontogenetic Development of Sound Communication in Fishes 127–148 (Springer, 2015).
Google Scholar 
54.Bolgan, M. et al. Sea chordophones make the mysterious /Kwa/ sound: Identification of the emitter of the dominant fish sound in Mediterranean seagrass meadows. J. Exp. Biol. 222, jeb196931 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Picciulin, M., Costantini, M., Hawkins, A. D. & Ferrero, E. A. Sound emissions of Mediterranean damselfish Chromis chromis (Pomacentraidae). Bioacoustics 12, 236–238 (2002).Article 

Google Scholar 
56.Dufossé, M. Recherches sur les bruits et les sons expressifs que font entendre les poissons d’Europe et sur les organes producteurs de ces phénomènes acoustiques ainsi que sur les appareils de l’audition de plusieurs de ces animaux. Ann. Sci. Nat. 20, 1–134 (1874).
Google Scholar 
57.Pardini, R., De Souza, S. M., Braga-Neto, R. & Metzger, J. P. The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape. Biol. Conserv. 124, 253–266 (2005).Article 

Google Scholar 
58.Brokovich, E., Einbinder, S., Shashar, N., Kiflawi, M. & Kark, S. Descending to the twilight-zone: Changes in coral reef fish assemblages along a depth gradient down to 65 m. Mar. Ecol. Prog. Ser. 371, 253–262 (2008).ADS 
Article 

Google Scholar 
59.Jain, M. & Balakrishnan, R. Does acoustic adaptation drive vertical stratification? A test in a tropical cricket assemblage. Behav. Ecol. 23, 343–354 (2012).Article 

Google Scholar 
60.Rodriguez, A. et al. Temporal and spatial variability of animal sound within a neotropical forest. Ecol. Inform. 21, 133–143 (2014).Article 

Google Scholar 
61.Jankowski, M., Graham, N. & Jones, G. Depth gradients in diversity, distribution and habitat specialisation in coral reef fishes: Implications for the depth-refuge hypothesis. Mar. Ecol. Prog. Ser. 540, 203–215 (2015).ADS 
Article 

Google Scholar 
62.Garrabou, J., Ballesteros, E. & Zabala, M. Structure and dynamics of north-western Mediterranean rocky benthic communities along a depth gradient. Estuar. Coast. Shelf Sci. 55, 493–508 (2002).ADS 
Article 

Google Scholar 
63.Gervaise, C., Lossent, J., Di Iorio, L. & Boissery, P. Réseau CALME Caractérisation Acoustique du Littoral Méditerranéen et de ses Ecosystèmes Synthèse des travaux réalisés pour la période [01/01/2015–01/08/2018] 1–109 (Rapp. Sci. Agence l’Eau Rhône, 2019).
Google Scholar 
64.McCauley, R. D. & Cato, D. H. Patterns of fish calling in a nearshore environment in the Great Barrier Reef. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 1289–1293 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Ladich, F. Acoustic communication in fishes: Temperature plays a role. Fish Fish. 19, 598–612 (2018).Article 

Google Scholar 
66.Desiderà, E. Reproductive behaviours of groupers (Epinephelidae) in the Tavolara-Punta Coda Cavallo Marine protected area (NW Mediterranean Sea). (PhD thesis 2019). http://paduaresearch.cab.unipd.it/12786/.67.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).Article 

Google Scholar 
68.Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).Article 

Google Scholar 
69.ter Braak, C. J. F. Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

Google Scholar 
70.Chambers, J. M. Linear models. In Statistical Models in S (eds Chambers, J. M. & Hastie, T. J.) (Wadsworth & Brooks/Cole, 1992).MATH 

Google Scholar 
71.Lepareur F. Evaluation de l’état de conservation des habi,tats naturels marins à l’échelle d’un site Natura 2000 – Guide méthodologique – Version 1. Février 2011. (Rapport SPN 2011 / 3, MNHN, Paris, 2011). http://spn.mnhn.fr/spn_rapports/archivage_rapports/2011/SPN%202011%20-%203%20-%20Rapport_EC_habmar_V1final2.pdf.72.Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). In Wiley StatsRef: Statistics Reference Online (eds Balakrishnan, N. et al.) 1–15 (Wiley, 2017).
Google Scholar  More

1.Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, et al. Ecology: biodiversity and ecosystem functioning: current knowledge and future challenges. Science (80-). 2001;294:804–8.CAS 
Article 

Google Scholar 
2.Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, et al. Biodiversity loss and its impact on humanity. Nature. 2012;486:59–67.CAS 
PubMed 
Article 

Google Scholar 
3.Jochum M, Fischer M, Isbell F, Roscher C, van der Plas F, Boch S, et al. The results of biodiversity-ecosystem functioning experiments are realistic. Nat Ecol Evol. 2020;4:1485–94.PubMed 
Article 

Google Scholar 
4.Balvanera P, Pfisterer AB, Buchmann N, He JS, Nakashizuka T, Raffaelli D, et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol Lett. 2006;9:1146–56.PubMed 
Article 

Google Scholar 
5.Cardinale BJ, Matulich KL, Hooper DU, Byrnes JE, Duffy E, Gamfeldt L, et al. The functional role of producer diversity in ecosystems. Am J Bot. 2011;98:572–92.PubMed 
Article 

Google Scholar 
6.Weißbecker C, Heintz-Buschart A, Bruelheide H, Buscot F, Wubet T. Linking soil fungal generality to tree richness in young subtropical Chinese forests. Microorganisms. 2019; https://doi.org/10.3390/microorganisms7110547.7.Prada-Salcedo LD, Wambsganss J, Bauhus J, Buscot F, Goldmann K. Low root functional dispersion enhances functionality of plant growth by influencing bacterial activities in European forest soils. Env Microbiol. 2020; https://doi.org/10.1111/1462-2920.15244.8.Prada-Salcedo LD, Goldmann K, Heintz-Buschart A, Reitz T, Wambsganss J, Bauhus J, et al. Fungal guilds and soil functionality respond to tree community traits rather than to tree diversity in European forests. Mol Ecol. 2021;30:572–91.CAS 
PubMed 
Article 

Google Scholar 
9.Baldrian P. The known and the unknown in soil microbial ecology. FEMS Microbiol Ecol. 2019; https://doi.org/10.1093/femsec/fiz005.10.van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet. 2014;30:418–26.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Smith SE, Read DJ. Mycorrhizal symbiosis. 2010. Academic press.12.Chen W, Koide RT, Eissenstat DM, Field K. Nutrient foraging by mycorrhizas: from species functional traits to ecosystem processes. Funct Ecol. 2018;32:858–69.Article 

Google Scholar 
13.Bahadur A, Batool A, Nasir F, Jiang S, Mingsen Q, Zhang Q, et al. Mechanistic insights into arbuscular mycorrhizal fungi-mediated drought stress tolerance in plants. Int J Mol Sci. 2019; https://doi.org/10.3390/ijms20174199.14.Pena R, Polle A. Attributing functions to ectomycorrhizal fungal identities in assemblages for nitrogen acquisition under stress. ISME J. 2014;8:321–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.He X-H, Critchley C, Bledsoe C. Nitrogen transfer within and between plants through common mycorrhizal networks (CMNs). CRC Crit Rev Plant Sci. 2003;22:531–67.Article 

Google Scholar 
16.Aerts R. The role of various types of mycorrhizal fungi in nutrient cycling and plant competition. In: Mycorrhizal Ecol. Springer; 2003. p. 117–33.17.Phillips RP, Brzostek E, Midgley MG. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. New Phytol. 2013;199:41–51.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hodge A, Campbell CD, Fitter AH. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature. 2001;413:297–9.CAS 
PubMed 
Article 

Google Scholar 
19.Koide RT, Kabir Z. Extraradical hyphae of the mycorrhizal fungus Glomus intraradices can hydrolyse organic phosphate. New Phytol. 2000;148:511–7.CAS 
PubMed 
Article 

Google Scholar 
20.Martin F, Kohler A, Murat C, Veneault-Fourrey C, Hibbett DS. Unearthing the roots of ectomycorrhizal symbioses. Nat Rev Microbiol. 2016;14:760–73.CAS 
PubMed 
Article 

Google Scholar 
21.Teste FP, Jones MD, Dickie IA. Dual-mycorrhizal plants: their ecology and relevance. New Phytol. 2020;225:1835–51.PubMed 
Article 

Google Scholar 
22.Heklau H, Schindler N, Buscot F, Eisenhauer N, Ferlian O, Prada Salcedo LD, et al. Mixing tree species associated with arbuscular or ectotrophic mycorrhizae reveals dual mycorrhization and interactive effects on the fungal partners. Ecol Evol. 2021.23.Regvar M, Likar M, Piltaver A, Kugonič N, Smith JE. Fungal community structure under goat willows (Salix caprea L.) growing at metal polluted site: the potential of screening in a model phytostabilisation study. Plant Soil. 2010;330:345–56.CAS 
Article 

Google Scholar 
24.Waldrop MP, Zak DR, Blackwood CB, Curtis CD, Tilman D. Resource availability controls fungal diversity across a plant diversity gradient. Ecol Lett. 2006;9:1127–35.PubMed 
Article 

Google Scholar 
25.Hooper DU, Bignell DE, Brown VK, Brussard L, Mark Dangerfield J, Wall DH, et al. Interactions between aboveground and belowground biodiversity in terrestrial ecosystems: patterns, mechanisms, and feedbacks: we assess the evidence for correlation between aboveground and belowground diversity and conclude that a variety of mechanisms co. Bioscience. 2000;50:1049–61.Article 

Google Scholar 
26.Montesinos‐Navarro A, Segarra‐Moragues JG, Valiente‐Banuet A, Verdú M. The network structure of plant–arbuscular mycorrhizal fungi. New Phytol. 2012;194:536–47.PubMed 
Article 

Google Scholar 
27.Bahram M, Harend H, Tedersoo L. Network perspectives of ectomycorrhizal associations. Fungal Ecol. 2014;7:70–7.Article 

Google Scholar 
28.Querejeta J, Egerton-Warburton LM, Allen MF. Topographic position modulates the mycorrhizal response of oak trees to interannual rainfall variability. Ecology. 2009;90:649–62.PubMed 
Article 

Google Scholar 
29.Bergmann J, Weigelt A, van der Plas F, Laughlin DC, Kuyper TW, Guerrero-Ramirez N, et al. The fungal collaboration gradient dominates the root economics space in plants. Sci Adv. 2020;6:eaba3756.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Weißbecker C, Wubet T, Lentendu G, Kühn P, Scholten T, Bruelheide H, et al. Experimental evidence of functional group-dependent effects of tree diversity on soil fungi in subtropical forests. Front Microbiol. 2018;9:2312.PubMed 
PubMed Central 
Article 

Google Scholar 
31.Ferlian O, Cesarz S, Craven D, Hines J, Barry KE, Bruelheide H, et al. Mycorrhiza in tree diversity–ecosystem function relationships: conceptual framework and experimental implementation. Ecosphere. 2018;9:e02226.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Tedersoo L, May TW, Smith ME. Ectomycorrhizal lifestyle in fungi: global diversity, distribution, and evolution of phylogenetic lineages. Mycorrhiza. 2010;20:217–63.PubMed 
Article 

Google Scholar 
33.Kolaříková Z, Kohout P, Krüger C, Janoušková M, Mrnka L, Rydlová J. Root-associated fungal communities along a primary succession on a mine spoil: distinct ecological guilds assemble differently. Soil Biol Biochem. 2017;113:143–52.Article 
CAS 

Google Scholar 
34.Dang P, Vu NH, Shen Z, Liu J, Zhao F, Zhu H, et al. Changes in soil fungal communities and vegetation following afforestation with Pinus tabulaeformis on the Loess Plateau. Ecosphere. 2018;9:e02401.Article 

Google Scholar 
35.Kalucka IL, Jagodzinski AM. Successional traits of ectomycorrhizal fungi in forest reclamation after surface mining and agricultural disturbances: A review. Dendrobiology. 2016;76:91–104.Article 

Google Scholar 
36.Jones MD, Durall DM, Cairney JWG. Ectomycorrhizal fungal communities in young forest stands regenerating after clearcut logging. New Phytol. 2003;157:399–422.PubMed 
Article 

Google Scholar 
37.Rog I, Rosenstock NP, Korner C, Klein T. Share the wealth: trees with greater ectomycorrhizal species overlap share more carbon. Mol Ecol. 2020;29:2321–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Chagnon PL, Bradley RL, Maherali H, Klironomos JN. A trait-based framework to understand life history of mycorrhizal fungi. Trends Plant Sci. 2013;18:484–91.CAS 
PubMed 
Article 

Google Scholar 
39.Ohsowski BM, Zaitsoff PD, Öpik M, Hart MM. Where the wild things are: looking for uncultured Glomeromycota. New Phytol. 2014;204:171–9.PubMed 
Article 
PubMed Central 

Google Scholar 
40.Öpik M, Metsis M, Daniell TJ, Zobel M, Moora M. Large-scale parallel 454 sequencing reveals host ecological group specificity of arbuscular mycorrhizal fungi in a boreonemoral forest. New Phytol. 2009;184:424–37.PubMed 
Article 
CAS 

Google Scholar 
41.Buscot F. Implication of evolution and diversity in arbuscular and ectomycorrhizal symbioses. J Plant Physiol. 2015;172:55–61.CAS 
PubMed 
Article 

Google Scholar 
42.Hiiesalu I, Pärtel M, Davison J, Gerhold P, Metsis M, Moora M, et al. Species richness of arbuscular mycorrhizal fungi: associations with grassland plant richness and biomass. New Phytol. 2014;203:233–44.CAS 
PubMed 
Article 

Google Scholar 
43.Nguyen NH, Williams LJ, Vincent JB, Stefanski A, Cavender-Bares J, Messier C, et al. Ectomycorrhizal fungal diversity and saprotrophic fungal diversity are linked to different tree community attributes in a field‐based tree experiment. Mol Ecol. 2016;25:4032–46.PubMed 
Article 

Google Scholar 
44.Burrows RL, Pfleger FL. Arbuscular mycorrhizal fungi respond to increasing plant diversity. Can J Bot. 2002;80:120–30.Article 

Google Scholar 
45.Eisenhauer N, Lanoue A, Strecker T, Scheu S, Steinauer K, Thakur MP, et al. Root biomass and exudates link plant diversity with soil bacterial and fungal biomass. Sci Rep. 2017;7:44641.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Lange M, Eisenhauer N, Sierra CA, Bessler H, Engels C, Griffiths RI, et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat Commun. 2015;6:1–8.
Google Scholar 
47.Klironomos JN, McCune J, Hart M, Neville J. The influence of arbuscular mycorrhizae on the relationship between plant diversity and productivity. Ecol Lett. 2000;3:137–41.Article 

Google Scholar 
48.Saks Ü, Davison J, Öpik M, Vasar M, Moora M, Zobel M. Root-colonizing and soil-borne communities of arbuscular mycorrhizal fungi in a temperate forest understorey. Botany. 2013;92:277–85.Article 

Google Scholar 
49.Molina R, Horton TR. Mycorrhiza specificity: its role in the development and function of common mycelial networks BT – Mycorrhizal Networks. In: Horton TR, editor. Springer Netherlands, Dordrecht; 2015. p. 1–39.50.van der Linde S, Suz LM, Orme C, Cox F, Andreae H, Asi E, et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature. 2018;558:243–8.PubMed 
Article 
CAS 

Google Scholar 
51.Rasmussen AL, Busby RR, Hoeksema JD. Host preference of ectomycorrhizal fungi in mixed pine–oak woodlands. Can J For Res. 2017;48:153–9.Article 
CAS 

Google Scholar 
52.Soudzilovskaia NA, Vaessen S, van’t Zelfde M, Raes N. Global patterns of mycorrhizal distribution and their environmental drivers. In: Biogeogr. mycorrhizal symbiosis. Springer; 2017. p. 223–35.53.Simard SW, Jones MD, Durall DM. Carbon and nutrient fluxes within and between mycorrhizal plants BT – Mycorrhizal Ecology. In: van der Heijden MGA, Sanders IR, editors. Springer Berlin Heidelberg, Berlin, Heidelberg; 2003. p. 33–74.54.Allen EB, Allen MF, Helm DJ, Trappe JM, Molina R, Rincon E. Patterns and regulation of mycorrhizal plant and fungal diversity. Plant Soil. 1995;170:47–62.CAS 
Article 

Google Scholar 
55.Dickie IA, Koide RT, Fayish AC. Vesicular–arbuscular mycorrhizal infection of Quercus rubra seedlings. New Phytol. 2001;151:257–64.PubMed 
Article 
PubMed Central 

Google Scholar 
56.Davison J, Öpik M, Daniell TJ, Moora M, Zobel M. Arbuscular mycorrhizal fungal communities in plant roots are not random assemblages. FEMS Microbiol Ecol. 2011;78:103–15.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Singavarapu B, Beugnon R, Bruelheide H, Cesarz S, Du J, Eisenhauer N, et al. Tree mycorrhizal type and tree diversity shape the forest soil microbiota. Environ Microbiol. 2021.58.Altermann M, Rinklebe J, Merbach I, Körschens M, Langer U, Hofmann B. Chernozem—soil of the year 2005. J Plant Nutr Soil Sci. 2005;168:725–40.CAS 
Article 

Google Scholar 
59.Wang B, Qiu Y-L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza. 2006;16:299–363.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Vierheilig H, Schweiger P, Brundrett M. An overview of methods for the detection and observation of arbuscular mycorrhizal fungi in roots. Physiol Plant. 2005;125:393–404.CAS 

Google Scholar 
61.Giovannetti M, Mosse B. An evaluation of techniques for measuring vesicular arbuscular mycorrhizal infection in roots. New Phytol. 1980; 489–500.62.White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: PCR Protoc. a Guid. to methods Appl. San Diego; 1990. p. 315–22.63.Wahdan SFM, Reitz T, Heintz-Buschart A, Schädler M, Roscher C, Breitkreuz C, et al. Organic agricultural practice enhances arbuscular mycorrhizal symbiosis in correspondence to soil warming and altered precipitation patterns. Environ Microbiol. 2021. https://doi.org/10.1111/1462-2920.15492.64.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Weißbecker C, Schnabel B, Heintz-Buschart A. Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology. Gigascience. 2020. https://doi.org/10.1093/gigascience/giaa135.66.Opik M, Vanatoa A, Vanatoa E, Moora M, Davison J, Kalwij JM, et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). New Phytol. 2010;188:223–41.CAS 
PubMed 
Article 

Google Scholar 
67.Katoh K, Asimenos G, Toh H. Multiple alignment of DNA sequences with MAFFT. In: Bioinforma. DNA Seq. Anal. Springer; 2009. p. 39–64.68.Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22:2688–90.CAS 
PubMed 
Article 

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

Google Scholar 
70.Nilsson RH, Larsson KH, Taylor A, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018. https://doi.org/10.1093/nar/gky1022.71.Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.Article 

Google Scholar 
72.Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33:2938–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Chytrý M, Tichý L, Holt J, Botta‐Dukát Z. Determination of diagnostic species with statistical fidelity measures. J Veg Sci. 2002;13:79–90.Article 

Google Scholar  More

In total 48 larval specimens of H. tenuicornis were obtained and analysed. We identified 30 larvae of the first instar, 14 of the second instar and 4 of the third instar. Two larvae and one adult specimen of H. tenuicornis were used for molecular phylogenetic placement of the genus within the subfamily Silphinae. The phylogenetic tree was obtained using Bayesian analysis from the concatenated partial 16S (434 bp) and COI (609 bp) sequences (Fig. 1).Figure 1Phylogenetic tree based on Bayesian analysis. Numbers above branches show the posterior probability and bootstrap values (BI)/maximal parsimony (PAUP)/Maximum likelihood (MEGA). Scaphidium quadrimaculatum Olivier, 1790 and Aleochara curtula (Goeze, 1777) (both Staphylinidae) were selected as outgroups.Full size imageSpecies identification based on genetic distancesThe calculated p-distances between concatenated sequences of 16S and COI of larval and adult specimens of H. tenuicornis were between 0.0029 and 0.0078 (the mean calculated p-distance within Heterotemna specimens was 0.01). Conversely, the distance between different species of Silpha was shown to be higher (mean calculated p-distance within the Silpha species was 0.08), thus the larval specimens were confirmed as belonging to the same species as the adult specimen, H. tenuicornis (SM1).Phylogenetic analysesThe Bayesian analysis (posterior probability 99), maximum parsimony bootstrap (84) and maximum likelihood bootstrap (93) strongly supported a clade of the genera Silpha, Heterotemna, Ablattaria and Phosphuga, suggesting close relationships of these genera with Heterotemna inside the genus Silpha, which makes the genus Silpha paraphyletic. The position of H. tenuicornis as a sister lineage to S. tristis Illiger, 1798 was strongly supported by the Bayesian analysis (97) but not strongly supported by the other analyses. The results confirmed the monophyly of the genera Thanatophilus Leach, 1815, Necrodes Leach, 1815, and Oiceoptoma Leach, 1815 within the subfamily Silphinae (Fig. 1).MorphometryThe two commonly used measurements for instar identification, head width and width of protergum , are applicable in the case of H. tenuicornis (Fig. 2c, d) as these two measurements do not overlap between the instars and show significant differences. More specifically, the following measurements were very different between instars; head width (F statistic = 231 on 2, df = 45, p value  More

1.Schipper, J. et al. The status of the world’s land and marine mammals: Diversity, threat, and knowledge. Science 322, 225–230 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Cushman, S. A. Effects of habitat loss and fragmentation on amphibians: A review and prospectus. Biol. Conserv. 128, 231–240 (2006).Article 

Google Scholar 
3.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, 6471 (2019).Article 
CAS 

Google Scholar 
4.Geary, W. L., Nimmo, D. G., Doherty, T. S., Ritchie, E. G. & Tulloch, A. I. T. Threat webs: Reframing the co-occurrence and interactions of threats to biodiversity. J. Appl. Ecol. 56, 1992–1997 (2019).
Google Scholar 
5.Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Di Prisco, G. et al. A mutualistic symbiosis between a parasitic mite and a pathogenic virus undermines honey bee immunity and Health. Proc. Natl. Acad. Sci. USA. 113, 3203–3208 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Grant, E. H. C. et al. Identifying management-relevant research priorities for responding to disease-associated amphibian declines. Glob. Ecol. Conserv. 16, 00441 (2018).
Google Scholar 
9.Schindler, D. W. The cumulative effects of climate warming and other human stresses on Canadian freshwaters in the new millennium. Can. J. Fish. Aquat. Sci. 58, 18–29 (2001).Article 

Google Scholar 
10.Cumulative Effects in Wildlife Management: Impact Mitigation. https://doi.org/10.1017/CBO9781107415324.004 (CRC Press, 2011). 11.Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 11 (2002).
Google Scholar 
12.Rolland, R. M., Hunt, K. E., Kraus, S. D. & Wasser, S. K. Assessing reproductive status of right whales (Eubalaena glacialis) using fecal hormone metabolites. Gen. Comp. Endocrinol. 142, 308–317 (2005).CAS 
PubMed 
Article 

Google Scholar 
13.Sheriff, M. J., Dantzer, B., Delehanty, B., Palme, R. & Boonstra, R. Measuring stress in wildlife: Techniques for quantifying glucocorticoids. Oecologia 166, 869–887 (2011).ADS 
PubMed 
Article 

Google Scholar 
14.Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).PubMed 
Article 

Google Scholar 
15.Beal, A., Rodriguez-Casariego, J., Rivera-Casas, C., Suarez-Ulloa, V. & Eirin-Lopez, J. M. Environmental epigenomics and its applications in marine organisms. in Population Genomics: Marine Organisms (eds. Oleksiak, M. F. & Rajora, O. P.) 325–359. https://doi.org/10.1007/13836_2018_28 (Springer, 2018). 16.Eirin-Lopez, J. M. & Putnam, H. M. Marine environmental epigenetics. Ann. Rev. Mar. Sci. 11, 335–368 (2019).PubMed 
Article 

Google Scholar 
17.Laird, P. W. The power and the promise of DNA methylation markers. Nat. Rev. Cancer 3, 253–266 (2003).CAS 
PubMed 
Article 

Google Scholar 
18.Bird, A. P. CpG-rich islands and the function of DNA methylation. Nature 321, 209–213 (1986).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Cedar, H. & Bergman, Y. Linking DNA methylation and histone modification: patterns and paradigms. Nat. Rev. Genet. 10, 295–304 (2009).CAS 
PubMed 
Article 

Google Scholar 
20.Matosin, N., Cruceanu, C. & Binder, E. B. Preclinical and clinical evidence of DNA methylation changes in response to trauma and chronic stress. Chronic Stress 1, 247054701771076 (2017).Article 

Google Scholar 
21.Radtke, K. M. et al. Transgenerational impact of intimate partner violence on methylation in the promoter of the glucocorticoid receptor. Transl. Psychiatry 1, e21–e26 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Mueller, B. R. & Bale, T. L. Sex-specific programming of offspring emotionality after stress early in pregnancy. J. Neurosci. 28, 9055–9065 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Elliott, E., Ezra-Nevo, G., Regev, L., Neufeld-Cohen, A. & Chen, A. Resilience to social stress coincides with functional DNA methylation of the Crf gene in adult mice. Nat. Neurosci. 13, 1351–1353 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Boersma, G. J. et al. Prenatal stress decreases Bdnf expression and increases methylation of Bdnf exon IV in rats. Epigenetics 9, 437–447 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Turecki, G. & Meaney, M. J. Effects of the social environment and stress on glucocorticoid receptor gene methylation: A systematic review. Biol. Psychiatry 79, 87–96 (2016).CAS 
PubMed 
Article 

Google Scholar 
26.Sterrenburg, L. et al. Chronic stress induces sex-specific alterations in methylation and expression of corticotropin-releasing factor gene in the rat. PLoS ONE 6, 1–14 (2011).Article 
CAS 

Google Scholar 
27.Reeder, D. A. M. & Kramer, K. M. Stress in free-ranging mammals: Integrating physiology, ecology, and natural history. J. Mammal. 86, 225–235 (2005).Article 

Google Scholar 
28.Jeanneteau, F. D. et al. BDNF and glucocorticoids regulate corticotrophin-releasing hormone (CRH) homeostasis in the hypothalamus. Proc. Natl. Acad. Sci. USA. 109, 1305–1310 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Smith, S. M. & Vale, W. W. The role of the hypothalamic-pituitary-adrenal axis in neuroendocrine responses to stress. Dialogues Clin. Neurosci. 8, 383–395 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Turner, J. D. & Muller, C. P. Structure of the glucocorticoid receptor (NR3C1) gene 5′ untranslated region: Identification, and tissue distribution of multiple new human exon 1. J. Mol. Endocrinol. 35, 283–292 (2005).CAS 
PubMed 
Article 

Google Scholar 
31.Bakusic, J., Schaufeli, W., Claes, S. & Godderis, L. Stress, burnout and depression: A systematic review on DNA methylation mechanisms. J. Psychosom. Res. 92, 34–44 (2017).PubMed 
Article 

Google Scholar 
32.Center for Whale Research. Population. https://www.whaleresearch.com. Accessed 11 Jan 2021 (2020).33.Fisheries and Oceans Canada. Recovery Strategy for the Northern and Southern Resident Killer Whales (Orcinus orca) in Canada [Proposed]. Species at Risk Act Recovery Strategy Series, Fisheries & Oceans Canada, Ottawa, x + 84 pp.(2018).34.DFO. Population Status Update for the Northern Resident Killer Whale (Orcinus orca) in 2018. DFO Can. Sci. Advis. Sec. Sci. Resp. 2019/025. (2019).35.Bigg, M. A., Olesiuk, P. F., Ellis, G. M., Ford, J. K. B. & Balcomb, K. C. Social organization and genealogy of resident killer whales (Orcinus orca) in the coastal waters of British Columbia and Washington State. Reports Int. Whal. Comm. 12, 383–405 (1990).
Google Scholar 
36.Ford, J. K. B. & Ellis, G. M. Selective foraging by fish-eating killer whales Orcinus orca in British Columbia. Mar. Ecol. Prog. Ser. 316, 185–199 (2006).ADS 
Article 

Google Scholar 
37.Chen, I.-H. et al. Selection of reference genes for RT-qPCR studies in blood of beluga whales (Delphinapterus leucas). PeerJ 4, e1810 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).CAS 
PubMed 
Article 

Google Scholar 
39.Hoelzel, A. R., Dahlheim, M. E. & Stern, S. J. Low genetic variation among killer whales (Orcinus orca) in the eastern north Pacific and genetic differentiation between foraging specialists. J. Hered. 89, 121–128 (1998).CAS 
PubMed 
Article 

Google Scholar 
40.Yao, M., Stenzel-Poore, M. & Denver, R. J. Structural and functional conservation of vertebrate corticotropin- releasing factor genes: Evidence for a critical role for a conserved cyclic AMP response element. Endocrinology 148, 2518–2531 (2007).CAS 
PubMed 
Article 

Google Scholar 
41.Aguiniga, L. M., Yang, W., Yaggie, R. E., Schaeffer, A. J. & Klumpp, D. J. Acyloxyacyl hydrolase modulates depressive-like behaviors through aryl hydrocarbon receptor. Am. J. Physiol. Regul. Integr. Comp. Physiol. 317, R289–R300 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Hankinson, O. The aryl hydrocarbon receptor complex. Annu. Rev. Pharmacol. Toxicol. 35, 307–340 (1995).CAS 
PubMed 
Article 

Google Scholar 
43.Lundin, J. I. et al. Pre-oil spill baseline profiling for contaminants in Southern Resident killer whale fecal samples indicates possible exposure to vessel exhaust. Mar. Pollut. Bull. 136, 448–453 (2018).CAS 
PubMed 
Article 

Google Scholar 
44.MacDonald, L. H. Evaluating and managing cumulative effects: Process and constraints. Environ. Manag. 26, 299–315 (2000).CAS 
Article 

Google Scholar 
45.National Academies of Sciences Engineering and Medicine. Approaches to Understanding the Cumulative Effects of Stressors on Marine Mammals. https://doi.org/10.17226/23479 (National Academies Press, 2017). 46.Barrett-Lennard, L. G., Smith, T. G. & Ellis, G. M. A cetacean biopsy system using lightweight pneumatic darts, and its effect on the behavior of killer whales. Mar. Mammal Sci. 12, 14–27 (1996).Article 

Google Scholar 
47.Sambrook, J., Fritsch, E. F. & Maniatis, H. Molecular Cloning: A Laboratory Manual (Cold Springs Harbor Laboratory Press, 1989).
Google Scholar 
48.Illumina. 16S Metagenomic Sequencing Library Preparation. Illumina.com 1–28 (2013).49.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.j 17, 10–12 (2011).Article 

Google Scholar 
50.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
52.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 
53.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

The location, diameter, height and circumference of the coral were measured (Table 1, Fig. 2). The Porites was brown to cream in colour and hemispherical in shape (Fig. 2). It was identified as either Porites lutea (Hump or Pore coral) or P. lobata (Lobe coral)14.The primary habitat on the Porites was live coral (70%), followed by sponge, live coral rock and a small amount of macroalgae (Table 2). No recently dead coral, coral rubble or sand was recorded (Table 2). We observed competition between the Porites and other species of coral and invertebrate including encrusting sponge, plating and branching Acropora spp., Montipora, Chlorodesmis, soft coral and zoanthids (Table 2, Figs. 3, 4).Table 2 Reef Health Impact Survey (RHIS) of habitat and species categories on Porites sp.Full size tableFigure 3Detail of the sub-habitats and competitive interactions Porites sp. and boring sponge Cliona viridis (left) and live coral Porites sp. and Montipora sp. (right) along interspecific contact zones.Full size imageFigure 4Detail of Reef Health Impact Survey (RHIS) of Porites.Full size imageThe boring sponge, Cliona viridis, is abundant on the Great Barrier Reef15. It is a common bioeroding species advancing laterally at around 1 cm and to a depth of 1.2 cm per annum15. Abundance of Cliona viridis is often correlated to substrate availability and water energy with the greatest abundance often on the windward side of bommies15. This correlates to our observations as the large proportion of the substrate estimated to cover the bommie (15%) was on the windward side. The sponge’s advances will likely continue to compromise the colony size and health.We recorded marine debris at the base of the Porites. The debris was 2–3 m of rope that appeared to have been wrapped around the base of an adjacent coral. Adjacent to the bommie were three concrete blocks.How big is the Porites coral at Goolboodi compared to other big corals in the GBR, and the world? Potts et al.6 reported a very large, rounded Porites colony, 6.9 m in diameter which is 3.1 m smaller than this study. Lough et al.16 reported coral cores from colonies between 1.6–8.0 m in height with the largest corals of 6.0 m at Havannah, North Molle and Masthead Islands, 7.5 m at Abraham Reef and 8.0 m at Sanctuary Reef. Recognising the limitations of published data, the Porites coral at Goolboodi is the largest diameter coral that has been measured, and the 6th tallest in the GBR. It is unknown if the other corals are still alive or dead.Other comparatively large massive Porites have previously been located throughout the Pacific. These have included multiple bommies measuring more than 10 m4 and one exceptionally large colony observed measuring 17 m × 12 m in American Samoa17. Additionally, large Porites sp. bommies have been observed at Green Island, 30 km east of Taiwan18 as well as an 11 m diameter Porites australiensis at Sesoko Island, Okinawa, Japan19.How old is this massive Porites? In discussions with the Australian Institute of Marine Science (AIMS), there is a robust, linear relationship ( > 80% variance explained) between Porites average linear extension rate and average annual sea surface temperature (SST)20,21 that provides an estimate of colony age from its height. Using average annual SST at 18.5S, 146.5E of 26.12C (from HadiSST data set), the estimated linear extension rate is determined by (2.97 × 26.12) − 65.46 = 1.21 cm/year. Given the colony height of 5.1–5.3 m, this gives an estimated age of 421–438 years. This is well before European exploration and settlement of Australia. AIMS has investigated over 328 colonies of massive Porites corals from 69 reefs along the GBR and has aged them as being from 10–436 years21. AIMS has not investigated this coral (pers. comm Neal Cantin). Based on limitations of published data, the Porites coral at Goolboodi is one of the oldest corals on the GBR.Why is the Porites partially dead on top and living on the side? The proportion of live coral tissue on a colony reflects the cumulative, integrated effect of both beneficial and adverse environmental factors. Substantial portions of coral tissue can die from exposure to sun at low tides or warm water without lethal consequences to the colony as a whole10. Partial mortality of large bommies provides available real estate for opportunistic, fast growing sessile organisms. In this instance, multiple species of tabulate and branching Acropora sp., encrusting Montipora sp. and encrusting sponges are among the benthic organisms to have colonised 30% of the coral bommies’ surface area. Intraspecific competition is also evident from the skeletal barriers created along contact zones22 (Fig. 3). There was no observation of disease or coral bleaching.The Porites is located in a relatively remote, rarely visited and highly protected Marine National Park (green) zone. Its location had not been previously reported and there is no existing database for significant corals in Australia or globally. Cataloguing the location of massive and long-lived corals can have multiple benefits. Scientific benefits include geochemical and isotopic analyses in coral skeletal cores which can help understand century-scale changes in oceanographic events and can be used to verify climate models. Social and economic benefits can include diving tourism, citizen science23 culture and stewardship. Perhaps the Significant Trees Register, which was designed by the National Trust24 to protect and celebrate Australia’s heritage could be considered as a model. There are risks of cataloguing the location of massive corals. It could be damaged by direct and indirect human uses including anchoring, research and pollution.Indigenous languages are an integral part of Indigenous culture, spirituality, and connection to country. We consulted Manbarra Traditional Owners about protocol and an appropriate cultural name for the Porites and they considered: Big (Muga), Home (Wanga), Coral reef (Muugar), Coral (Dhambi), Old (Anki, Gurgu), Old man (Gulula) and Old person (Gurgurbu)25. The recommendation by Manbarra Traditional Owners is that the Porites is named as Muga dhambi (Big coral). The feedback from the process of consultation was very positive with acknowledgement of the respect that the scientists have demonstrated to acknowledge Traditional Owners in this way.The large Porites coral at Goolboodi (Orpheus) Island is unusually rare and resilient. It has survived coral bleaching, invasive species, cyclones, severely low tides and human activities for almost 500 years. In an attempt to contextualise the resilience of these individual Porites we have reviewed major historic disturbances such as coral bleaching which has occurred since at least 1575 and potentially 99 bleaching events in the GBR over the past 400 plus years26. Other indicators such as high-density ‘stress bands’ were recorded from 1877 and are significantly more frequent in the late twentieth and early twenty-first centuries in accordance with rising temperatures from anthropogenic global warming27. In addition there have been an average of 1–2 tropical cyclones per decade (40–80 in total) that have potentially impacted the coral adjacent to Goolboodi Island28,29; 46 tropical cyclones impacted the area between Ingham and Townsville from 1858 to 200830. The cumulative impact of almost 100 bleaching events and up to 80 major cyclones over a period of four centuries, plus declining nearshore water quality contextualise the high resilience of this Porites coral. Looking to the future there is real concern for corals in the GBR due to many impacts including climate change, declining water quality, overfishing and coastal development31,32. This field note provides important geospatial, environmental, and cultural information of a rare coral that can be monitored, appreciated, potentially restored and hopefully inspire future generations to care more for our reefs and culture. More

1.Irwin AJ, Oliver MJ. Are ocean deserts getting larger? Geophys Res Lett. 2009;36:L18609.Article 

Google Scholar 
2.McClain CR, Signorini SR, Christian JR. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Topical Stud Oceanogr. 2004;51:281–301.CAS 
Article 

Google Scholar 
3.Signorini SR, Franz BA, McClain CR. Chlorophyll variability in the oligotrophic gyres: Mechanisms, seasonality and trends. Front Mar Sci. 2015;2:1–11.Article 

Google Scholar 
4.Polovina JJ, Howell EA, Abecassis M. Ocean’s least productive waters are expanding. Geophys Res Lett. 2008;35:L03618.Article 

Google Scholar 
5.Sharma P, Marinov I, Cabre A, Kostadinov T, Singh A. Increasing biomass in the warm oceans: unexpected new insights from SeaWIFS. Geophys Res Lett. 2019;46:3900–10.Article 

Google Scholar 
6.Flombaum P, Wang W-L, Primeau FW, Martiny AC. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat Geosci. 2020;13:116–20.CAS 
Article 

Google Scholar 
7.Carr M-E, Friedrichs MAM, Schmeltz M, Noguchi Aita M, Antoine D, Arrigo KR, et al. A comparison of global estimates of marine primary production from ocean color. Deep Sea Res Part II Topical Stud Oceanogr. 2006;53:741–70.Article 

Google Scholar 
8.Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
PubMed 
Article 

Google Scholar 
9.DeVries T, Primeau F, Deutsch C. The sequestration efficiency of the biological pump. Geophys Res Lett. 2012;39:L13601.Article 
CAS 

Google Scholar 
10.Cabré A, Marinov I, Leung S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 earth system models. Clim Dyn. 2015;45:1253–80.Article 

Google Scholar 
11.Behrenfeld MJ, O’Malley RT, Boss ES, Westberry TK, Graff JR, Halsey KH, et al. Revaluating ocean warming impacts on global phytoplankton. Nat Clim Change. 2015;6:323–30.Article 

Google Scholar 
12.Richardson K, Bendtsen J. Vertical distribution of phytoplankton and primary production in relation to nutricline depth in the open ocean. Mar Ecol Prog Ser. 2019;620:33–46.CAS 
Article 

Google Scholar 
13.Roshan S, DeVries T. Efficient dissolved organic carbon production and export in the oligotrophic ocean. Nat Commun. 2017;8:1–8.CAS 
Article 

Google Scholar 
14.Marañón E, Holligan PM, Barciela R, González N, Mouriño B, Pazó MJ, et al. Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments. Mar Ecol Prog Ser. 2001;216:43–56.Article 

Google Scholar 
15.Pérez V, Fernández E, Marañón E, Morán XAG, Zubkov MV. Vertical distribution of phytoplankton biomass, production and growth in the Atlantic subtropical gyres. Deep Sea Res Part I Oceanographic Res Pap. 2006;53:1616–34.Article 

Google Scholar 
16.Teira E, Mouriño B, Marañón E, Pérez V, Pazó MJ, Serret P, et al. Variability of chlorophyll and primary production in the Eastern North Atlantic subtropical gyre: potential factors affecting phytoplankton activity. Deep Sea Res Part I Oceanographic Res Pap. 2005;52:569–88.CAS 
Article 

Google Scholar 
17.Chisholm SW, Frankel SL, Goericke R, Olson RJ, Palenik B, Waterbury JB, et al. Prochlorococcus marinus nov. Gen. Nov. Sp.: an oxyphototrophic marine prokaryote containing divinyl chlorophyll a and b. Arch Microbiol. 1992;157:297–300.CAS 
Article 

Google Scholar 
18.Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine cyanobacteria Prochlorococcus and Synechococcus. PNAS. 2013;110:9824–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Partensky F, Hess WR, Vaulot D. Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol Mol Biol Rev. 1999;63:106–27.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Li WK. Primary production of prochlorophytes, cyanobacteria, and eucaryotic ultraphytoplankton: Measurements from flow cytometric sorting. Limnol Oceanogr. 1994;39:169–75.CAS 
Article 

Google Scholar 
21.Jardillier L, Zubkov MV, Pearman J, Scanlan DJ. Significant CO2 fixation by small prymnesiophytes in the subtropical and tropical Northeast Atlantic Ocean. ISME J. 2010;4:1180–92.CAS 
PubMed 
Article 

Google Scholar 
22.Irion S, Christaki U, Berthelot H, L’Helguen S, Jardillier L. Small phytoplankton contribute greatly to CO2-fixation after the diatom bloom in the Southern Ocean. ISME J. 2021;15:1–14.Article 
CAS 

Google Scholar 
23.Liu K, Suzuki K, Chen B, Liu H. Are temperature sensitivities of Prochlorococcus and Synechococcus impacted by nutrient availability in the subtropical Northwest Pacific? Limnol Oceanogr. 2020;66:639–51.Article 
CAS 

Google Scholar 
24.D’Hondt S, Spivack AJ, Pockalny R, Ferdelman TG, Fischer JP, Kallmeyer J, et al. Subseafloor sedimentary life in the South Pacific gyre. PNAS. 2009;106:11651–6.PubMed 
PubMed Central 
Article 

Google Scholar 
25.Longhurst A, Sathyendranath S, Platt T, Caverhill C. An estimate of global primary production in the ocean from satellite radiometer data. J Plankton Res. 1995;17:1245–71.Article 

Google Scholar 
26.Morel A, Gentili B, Claustre H, Babin M, Bricaud A, Ras J, et al. Optical properties of the “clearest” natural waters. Limnol Oceanogr. 2007;52:217–29.CAS 
Article 

Google Scholar 
27.Halm H, Lam P, Ferdelman TG, Lavik G, Dittmar T, LaRoche J, et al. Heterotrophic organisms dominate nitrogen fixation in the south pacific gyre. ISME J. 2012;6:1238–49.CAS 
PubMed 
Article 

Google Scholar 
28.Raimbault P, Garcia N. Evidence for efficient regenerated production and dinitrogen fixation in nitrogen-deficient waters of the South Pacific Ocean: impact on new and export production estimates. Biogeosciences. 2008;5:323–38.CAS 
Article 

Google Scholar 
29.Shiozaki T, Bombar D, Riemann L, Sato M, Hashihama F, Kodama T, et al. Linkage between dinitrogen fixation and primary production in the oligotrophic South Pacific Ocean. Glob Biogeochem Cyc. 2018;32:1028–44.CAS 
Article 

Google Scholar 
30.Reintjes G, Tegetmeyer HE, Bürgisser M, Orlić S, Tews I, Zubkov M, et al. On-site analysis of bacterial communities of the ultraoligotrophic South Pacific gyre. Appl Environ Microbiol. 2019;85:e00184–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zielinski O, Henkel R, Voß D, Ferdelman TG. Physical oceanography during Sonne cruise SO245 (Ultrapac). PANGAEA. 2018. https://doi.org/10.1594/PANGAEA.890394.32.Ferdelman TG, Klockgether G, Downes P, Lavik G. Nutrient data from CTD Nisken bottles from Sonne expedition SO-245 “Ultrapac”. PANGAEA. 2019. https://doi.org/10.1594/PANGAEA.899228.33.Arar EJ, Collins GB. Method 445.0: In vitro determination of chlorophyll a and pheophytin a in marine and freshwater algae by fluorescence: U.S. Environmental Protection Agency, Washington, DC; 1997. https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NERL&dirEntryId=309417.34.Welschmeyer N, Naughton S. Improved chlorophyll a analysis: single fluorometric measurement with no acidification. Lake Reserv Manag. 1994;9:123.
Google Scholar 
35.Osterholz H, Kilgour D, Storey DS, Lavik G, Ferdelman T, Niggemann J, et al. Accumulation of DOC in the South Pacific subtropical gyre from a molecular perspective. Mar Chem. 2021;231:103955.CAS 
Article 

Google Scholar 
36.Voß D, Henkel R, Wollschläger J, Zielinski O. Hyperspectral underwater light field measured during the cruise SO245 with R/V Sonne. PANGAEA. 2020. https://doi.org/10.1594/PANGAEA.911558.37.Martínez-Pérez C, Mohr W, Löscher CR, Dekaezemacker J, Littmann S, Yilmaz P, et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat Microbiol. 2016;1:1–7.Article 
CAS 

Google Scholar 
38.Marra J. Net and gross productivity: weighing in with 14C. Aquat Microb Ecol. 2009;56:123–31.Article 

Google Scholar 
39.Ribeiro CG, Marie D, Santos ALD, Brandini FP, Vaulot D. Estimating microbial populations by flow cytometry: comparison between instruments. Limnol Oceanogr Methods. 2016;14:750–8.Article 

Google Scholar 
40.Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.West NJ, Schönhuber WA, Fuller NJ, Amann RI, Rippka R, Post AF, et al. Closely related Prochlorococcus genotypes show remarkably different depth distributions in two oceanic regions as revealed by in situ hybridization using 16 S rRNA-targeted oligonucleotides. Microbiology. 2001;147:1731–44.CAS 
PubMed 
Article 

Google Scholar 
42.Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MMM. Look@NanoSIMS—a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
PubMed 
Article 

Google Scholar 
43.Verity PG, Robertson CY, Tronzo CR, Andrews MG, Nelson JR, Sieracki ME. Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnol Oceanogr. 1992;37:1434–46.CAS 
Article 

Google Scholar 
44.Khachikyan A, Milucka J, Littmann S, Ahmerkamp S, Meador T, Könneke M, et al. Direct cell mass measurements expand the role of small microorganisms in nature. Appl Environ Microbiol. 2019;85:AEM00493–19.Article 

Google Scholar 
45.Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, et al. Improved bacterial 16 S rRNA gene (v4 and v4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. MSystems. 2016;1:e00009–15.PubMed 
Article 

Google Scholar 
46.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rrna primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Comeau AM, Douglas GM, Langille MG. Microbiome helper: a custom and streamlined workflow for microbiome research. MSystems. 2017;2:e00127–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. Qiime allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Haas S, Desai DK, LaRoche J, Pawlowicz R, Wallace DW. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environ Microbiol. 2019;21:3927–52.CAS 
PubMed 
Article 

Google Scholar 
50.Zhang J, Kobert K, Flouri T, Stamatakis A. Pear: a fast and accurate Illumina paired-end read merger. Bioinformatics. 2013;30:614–20.51.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. Vsearch: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
52.Kopylova E, Noé L, Touzet H. Sortmerna: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
PubMed 
Article 

Google Scholar 
53.Mercier C, Boyer F, Bonin A, Coissac E (eds). Sumatra and Sumaclust: fast and exact comparison and clustering of sequences. SeqBio 2013 Workshop 2013: (abstract).54.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16 S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Decelle J, Romac S, Stern RF, Bendif EM, Zingone A, Audic S, et al. PhytoREF: A reference database of the plastidial 16 S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Molec Ecol Res. 2015;15:1435–45.CAS 
Article 

Google Scholar 
57.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The protist ribosomal reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2012;4:D597–604.Article 
CAS 

Google Scholar 
58.Del Campo J, Kolisko M, Boscaro V, Santoferrara LF, Nenarokov S, Massana R, et al. EukRef: phylogenetic curation of ribosomal RNA to enhance understanding of eukaryotic diversity and distribution. PLoS Biol. 2018;16:e2005849.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: A software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Gruber-Vodicka HR, Seah BK, Pruesse E. Phyloflash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. Msystems. 2020;5:e00920.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Farrant GK, Doré H, Cornejo-Castillo FM, Partensky F, Ratin M, Ostrowski M, et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. PNAS. 2016;113:E3365–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Oggerin de Orube M, Fuchs BM. Personal communication: Unpublished shotgun metagenomes collected from in situ pump samples during R/V Sonne expedition SO245. Bremen, Germany. 2021.63.Schlitzer R. Ocean Data View. Bremerhaven, Germany. 2021. https://odv.awi.de.64.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. 2017. https://www.R-project.org/.65.Wickham H. Ggplot2: elegant graphics for data analysis. Springer-Verlag, New York. 2016.66.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package: community ecology package. R package version 2.5–7. 2019. https://CRAN.R-project.org/package=vegan.68.Chaigneau A, Pizarro O. Surface circulation and fronts of the South Pacific Ocean, east of 120°W. Geophys Res Lett. 2005;32:L08605.69.Logares R, Sunagawa S, Salazar G, Cornejo‐Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16 S rRNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ Microbiol. 2014;16:2659–71.CAS 
PubMed 
Article 

Google Scholar 
70.Shi XL, Lepère C, Scanlan DJ, Vaulot D. Plastid 16 s rRNA gene diversity among eukaryotic picophytoplankton sorted by flow cytometry from the South Pacific Ocean. PLOS ONE. 2011;6:e18979.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Fuller NJ, Campbell C, Allen DJ, Pitt FD, Zwirglmaier K, Le Gall F, et al. Analysis of photosynthetic picoeukaryote diversity at open ocean sites in the Arabian Sea using a pcr biased towards marine algal plastids. Aquat Micro Ecol. 2006;43:79–93.Article 

Google Scholar 
72.Raes EJ, Bodrossy L, Kamp JVD, Bissett A, Ostrowski M, Brown MV, et al. Oceanographic boundaries constrain microbial diversity gradients in the South Pacific Ocean. PNAS. 2018;115:E8266–75.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Campbell L, Liu H, Nolla HA, Vaulot D. Annual variability of phytoplankton and bacteria in the subtropical North Pacific Ocean at station ALOHA during the 1991-4 ENSO event. Deep Sea Res Part I Oceanogr Res Pap. 1997;44:167–92.CAS 
Article 

Google Scholar 
74.Viviani DA, Church MJ. Decoupling between bacterial production and primary production over multiple time scales in the North Pacific subtropical gyre. Deep Sea Res Part I Oceanogr Res Pap. 2017;121:132–42.CAS 
Article 

Google Scholar 
75.Rii YM, Duhamel S, Bidigare RR, Karl DM, Repeta DJ, Church MJ. Diversity and productivity of photosynthetic picoeukaryotes in biogeochemically distinct regions of the south east pacific ocean. Limnol Oceanogr. 2016;61:806–24.Article 

Google Scholar 
76.Shi XL, Marie D, Jardillier L, Scanlan DJ, Vaulot D. Groups without cultured representatives dominate eukaryotic picophytoplankton in the oligotrophic South East Pacific Ocean. PLOS ONE. 2009;4:e7657.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Kirkham AR, Lepere C, Jardillier LE, Not F, Bouman H, Mead A, et al. A global perspective on marine photosynthetic picoeukaryote community structure. ISME J. 2013;7:922–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Lepère C, Vaulot D, Scanlan DJ. Photosynthetic picoeukaryote community structure in the South East Pacific Ocean encompassing the most oligotrophic waters on earth. Environ Microbiol. 2009;11:3105–17.PubMed 
Article 
CAS 

Google Scholar 
79.Bender ML, Jönsson B. Is seasonal net community production in the South Pacific subtropical gyre anomalously low? Geophys Res Lett. 2016;43:9757–63.Article 

Google Scholar 
80.Montégut CDB, Madec G, Fischer AS, Lazar A, Iudicone D. Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology. J Geophys Res Oceans. 2004;109:C12003.Article 

Google Scholar 
81.Liu Q, Lu Y. Role of horizontal density advection in seasonal deepening of the mixed layer in the subtropical Southeast Pacific. Adv Atmospher Sci. 2016;33:442–51.Article 

Google Scholar 
82.Sato K, Suga T. Structure and modification of the South Pacific eastern subtropical mode water. J Phys Oceanogr. 2009;39:1700–14.Article 

Google Scholar 
83.Jung J, Furutani H, Uematsu M. Atmospheric inorganic nitrogen in marine aerosol and precipitation and its deposition to the north and south pacific oceans. J Atmospher Chem. 2011;68:157–81.CAS 
Article 

Google Scholar 
84.Pavia FJ, Anderson RF, Winckler G, Fleisher MQ. Atmospheric dust inputs, iron cycling, and biogeochemical connections in the South Pacific Ocean from thorium isotopes. Glob Biogeochem Cycles. 2020;34:e2020GB006562.CAS 

Google Scholar 
85.Bonnet S, Guieu C, Bruyant F, Prášil O, Van Wambeke F, Raimbault P, et al. Nutrient limitation of primary productivity in the Southeast Pacific (Biosope Cruise). Biogeosciences. 2008;5:215–25.CAS 
Article 

Google Scholar 
86.Mahaffey C, Björkman KM, Karl DM. Phytoplankton response to deep seawater nutrient addition in the North Pacific subtropical gyre. Mar Ecol Prog Ser. 2012;460:13–34.CAS 
Article 

Google Scholar 
87.Grob C, Jardillier L, Hartmann M, Ostrowski M, Zubkov MV, Scanlan DJ. Cell-specific CO2 fixation rates of two distinct groups of plastidic protists in the Atlantic Ocean remain unchanged after nutrient addition. Environ Microbiol Rep. 2015;7:211–8.CAS 
PubMed 
Article 

Google Scholar 
88.Vaulot D, Marie D, Olson RJ, Chisholm SW. Growth of Prochlorococcus, a photosynthetic prokaryote, in the equatorial pacific ocean. Science. 1995;268:1480–2.CAS 
PubMed 
Article 

Google Scholar 
89.Grob C, Hartmann M, Zubkov MV, Scanlan DJ. Invariable biomass-specific primary production of taxonomically discrete picoeukaryote groups across the Atlantic Ocean. Environ Microbiol. 2011;13:3266–74.PubMed 
Article 

Google Scholar 
90.Berthelot H, Duhamel S, L’Helguen S, Maguer J-F, Wang S, Cetinić I, et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 2019;13:651.CAS 
PubMed 
Article 

Google Scholar 
91.Zubkov MV, Fuchs BM, Tarran GA, Burkill PH, Amann R. High rate of uptake of organic nitrogen compounds by Prochlorococcus cyanobacteria as a key to their dominance in oligotrophic oceanic waters. Appl Environ Microbiol. 2003;69:1299–304.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Muñoz-Marín MC, Gómez-Baena G, López-Lozano A, Moreno-Cabezuelo JA, Díez J, García-Fernández JM. Mixotrophy in marine picocyanobacteria: use of organic compounds by Prochlorococcus and Synechococcus. ISME J. 2020;14:1065–73.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
93.Timmermans K, Van der Wagt B, Veldhuis M, Maatman A, De Baar H. Physiological responses of three species of marine pico-phytoplankton to ammonium, phosphate, iron and light limitation. J Sea Res. 2005;53:109–20.CAS 
Article 

Google Scholar 
94.Vaulot D, Eikrem W, Viprey M, Moreau H. The diversity of small eukaryotic phytoplankton (≤ 3 μm) in marine ecosystems. FEMS Microbiol Rev. 2008;32:795–820.CAS 
PubMed 
Article 

Google Scholar 
95.Worden AZ, Janouskovec J, McRose D, Engman A, Welsh RM, Malfatti S, et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr Biol. 2012;22:R675–7.CAS 
PubMed 
Article 

Google Scholar 
96.Le Gall F, Rigaut-Jalabert F, Marie D, Garczarek L, Viprey M, Gobet A, et al. Picoplankton diversity in the South-east Pacific Ocean from cultures. Biogeosciences. 2008;5:203–14.Article 

Google Scholar 
97.NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data; Reprocessing. NASA OB.DAAC, Greenbelt, MD, USA. 2018. https://oceancolor.gsfc.nasa.gov/data/10.5067/AQUA/MODIS/L3M/CHL/2018/ Accessed 2019/08/01. More