Ecology

Study areasThe technique of harmonic radar tracking has been applied in nine different localities of Liguria (Italy), in the framework of the control activities developed to contain the spread of V. velutina in this region19,21,30. Four of these study areas (Ameglia, Arcola, Riccò del Golfo in La Spezia district and Finale Ligure in Savona district) were new invasive outbreaks characterised by a low nest density of V. velutina and low predation pressure on honey bee colonies. The other five study areas of Imperia district (Camporosso, Dolceacqua, Ospedaletti, and the two villages of Calvo and Latte in the municipality of Ventimiglia) were located inside the colonised range of the species21, and were characterised by a high nest density and an intensive predation pressure on honey bee colonies (Supplementary Table S1).Harmonic radar trackingThe harmonic radar and the tags that have been used for tracking the flight of V. velutina were designed and developed ad-hoc for following insects in complex environments; their technical and innovative characteristics have been previously described by the authors18. At the beginning of a new tracking session, worker hornets are trapped, usually in apiaries while preying on honey bees, and the transponders are attached on their thorax using an orthodontic glue, without anesthetising the insects. Subsequently, hornets are released from the tagging location and are immediately able to resume their activity, such as flying and preying on honey bees (Fig. 6). The whole tagging procedure requires less than one minute per hornet. Tag weight (15 mg) is approximately 4–7% of the weight of V. velutina workers (mean worker’s weight changes over the season between 189 and 386 mg)26. Moreover, the tag is 3–4 times lighter than the weight of prey’s pellet generally transported to the nest by this species. This information, together with multiple observations of tagged hornets in apiaries and the results achieved by other authors with a radio-tracking experiment (in which it was found that hornets equipped with a tag of weight lesser than 80% of their body weight are considered good flyers)22, suggest that the tags used in this study do not affect the behaviour and the flying abilities of V. velutina.Figure 6Tagged hornets performing their usual predatory behaviour. Tagged individuals of V. velutina hovering in front of honey bee colonies for preying on forager bees (a,b). A tagged hornet that is disjointing a honey bee for gathering the thorax (most energetic part of its prey), that will be brought back to the nest for feeding the brood (c). Two tagged hornets in proximity of the entrance hole of the nest (d).Full size imageThe harmonic radar records independently all the tracks of flying hornets that are inside its detection range. The real-time analysis of the recorded tracks allows understanding the main flying directions. If the nest of V. velutina is located outside of the maximum detection range of the radar (about 500 m in flat terrain)18 or behind physical obstacles, the harmonic radar is moved according to the flying directions of the hornets. The presence of a diffused road network, as in many of our study areas, facilitated the movement of the radar from one position to another. This operation is repeated until the position of the nest is determined. The area where the nest is located is generally highlighted by the presence of several tracks that converge or begin from the same site. The visual inspection of the area permits the exact detection of the position of the nest. In several cases, tagged hornets were visually observed on the surface of the nests (Fig. 6d).The total number of tagged hornets was recorded for each tracking session, together with the radar operation time, the number of radar movements per session, the number of detected nests per session and the minimum distance between the nests and the apiaries where hornets were hunting honey bees (Supplementary Table S2). Hornets were trapped with standard entomological procedures for trapping insects, and experiments were conducted ethically since no hornets were killed, injured, or kept captive after being tagged.Tracking lengths and environmental characteristicsThe main parameter selected for estimating the performance of the harmonic radar in tracking V. velutina in different natural and complex environments is the length of the tracks of tagged insects. To obtain this parameter, fixes (hornets detected by the harmonic radar at each radar’s rotation) were extracted for each tracking session and uploaded on a GIS software32. Afterwards, consecutive fixes of the same track were connected with the shortest line, so to obtain hornet tracks and calculate their length. The advanced radar analyses used for processing the received signals18 allow discriminating the true fixes (position of the hornet) from clutter (reflected signals received from objects in the landscape). However, the presence of obstacles may generate gaps in the received signals (e.g. when a hornet is temporarily flying behind an obstacle such as a house), but these gaps were rare and never occurred for long periods of time. In these cases, if fixes were not clearly recognizable to a track of the same hornet, these were excluded from the analysis. The exclusion of the tracks was performed also in the rare cases during which the presence of multiple tagged hornets did not allow a clear identification of the tracks.The length of the tracks in each fix position (n = 2580) was modelled with a GLMM (see “Data analysis”) to evaluate the effect of environmental features (land cover, elevation above sea level, slope gradient, road density). The land cover layer was obtained through a photo interpretation of satellite images (in a buffer area of 100 m around the minimum convex polygon that encompass all the tracks in each locality) and classification in three macro-levels: open terrains (landscapes predominantly characterised by open areas, such as fields), urban areas (matrices formed by buildings/roads) and woodlands (matrices formed by forests). Elevation above sea level and slope degree were obtained by a digital elevation model (resolution of 20 m).Visual tracking of flying hornetsThe length of the tracks recorded by the harmonic radar was compared with the length of the tracks recorded when adopting a customary technique for tracking insects, such as the visual tracking and triangulation of flying directions20,25. In six of the nine localities where the harmonic radar tracking has been applied (Fig. 4), an operator was waiting near a honey bee colony till one V. velutina worker caught a honey bee. Subsequently, after the hornet disjoined the most energetic parts of its prey (the thorax)33, the operator visually tracked the flight of the hornet when flying back to its nest, using a binocular and by recording with a GPS the position where the hornet disappeared from view. In some cases (n = 4), common flying routes were identified, and we were able to resume the visual tracking with other hornets from the previous disappearance position. Finally, GPS positions were uploaded on a GIS software to calculate the length of the tracks with this technique.In this study, the visual tracking technique has not been implemented systematically for nest detection, therefore the two approaches are compared only by evaluating the recorded length of the tracks. The effectiveness in locating nests, the required time and the associated costs are discussed in the framework of previous studies for tracking V. velutina, taking into account advantages and limits of the different techniques20,22,25.Estimation of V. velutina ground flying speedHarmonic radar tracking allows estimating the ground flying speed of V. velutina, by analysing the distance between each recorded position at consecutive radar rotations. Giving that the time of each radar rotation is fixed (3 s), it is possible to estimate the hornet’s speed between each detection8.The ground flying speed of V. velutina has been estimated in the three localities of La Spezia district, due to the availability of a subsample of clear tracks with consecutive detections per each rotation of the radar and good weather conditions. Furthermore, based on their direction, tracks were classified in homing tracks (H), which belong to hornets flying from the apiary to the nest, and foraging tracks (F), which belong to hornets flying towards the apiary for hunting honey bees. Data on wind speed and direction were obtained from weather stations close to the study areas.Data analysisData analyses were performed with the software R34. Environmental characteristics of the localities were analysed with a Principal Component Analysis (PCA; package factoextra), to understand affinities between study areas and correlations between the considered variables. The length of the tracks between localities recorded with the harmonic radar was compared with the Kruskal–Wallis and the Dunn tests with Bonferroni correction, while the flying speed between foraging and homing hornets was compared with the Wilcoxon rank-sum test (two-tailed).Generalized linear mixed models (GLMM; package lme4) with gamma distribution and log link function were used to assess (1) the influence of environmental variables on the length of the tracks and (2) compare tracking methods between study areas. In the first case, a random slope model has been implemented, by defining the locality and the slope degree as random effects (uncorrelated). In the second case, a standard random intercept model has been implemented, by selecting the locality as random effect. In both cases, continuous variables were standardized, and multi-collinearity of environmental variables was taken into account by calculating the Variance Inflation Factor (VIF). This was 1.5 for elevation and slope degree, and 1.0 for road density. More

1.Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182. https://doi.org/10.1017/S1464793105006950 (2006).Article 
PubMed 

Google Scholar 
2.Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. N. Am. Benthol. Soc. 29, 344–359. https://doi.org/10.1899/08-171.1 (2010).Article 

Google Scholar 
3.Reid, A. J. et al. Emerging threats and persistent challenges for freshwater biodiversity. Biol. Rev. 94, 849–873. https://doi.org/10.1111/brv.12480 (2019).Article 
PubMed 

Google Scholar 
4.Cucherousset, J. & Olden, J. D. Ecological impacts of nonnative freshwater fishes. Fisheries 36, 215–230. https://doi.org/10.1080/03632415.2011.574578 (2011).Article 

Google Scholar 
5.Vander Zanden, M. J., Casselman, J. M. & Rasmussen, J. B. Stable isotope evidence for the food web consequences of species invasions in lakes. Nature 401, 464–467. https://doi.org/10.1038/46762 (1999).ADS 
CAS 
Article 

Google Scholar 
6.Britton, J. R., Davies, G. D. & Harrod, C. Trophic interactions and consequent impacts of the invasive fish Psuedorasbora parva in a native aquatic food web: a field investigation in the UK. Biol. Invasions 12, 1533–1542. https://doi.org/10.1007/s10530-009-9566-5 (2010).Article 

Google Scholar 
7.Cox, J. G. & Lima, S. L. Naiveté and an aquatic-terrestrial dichotomy in the effects of introduced predators. Trends Ecol. Evol. 21, 674–680. https://doi.org/10.1016/j.tree.2006.07.011 (2006).Article 
PubMed 

Google Scholar 
8.Marks, J. C., Haden, G. A., O’Neil, M. & Pace, C. Effects of flow restoration and exotic species removal on recovery of native fish: Lessons from a dam decommissioning. Restor. Ecol. 18, 934–943. https://doi.org/10.1111/j.1526-100X.2009.00574.x (2010).Article 

Google Scholar 
9.Walsworth, T. E., Budy, P. & Thiede, G. P. Longer food chains and crowded niche space: effects of multiple invaders on desert stream food web structure. Ecol. Freshw. Fish 22, 439–452. https://doi.org/10.1111/eff.12038 (2013).Article 

Google Scholar 
10.Rogosch, J. S. & Olden, J. D. Invaders induce coordinated isotopic niche shifts in native fish species. Can. J. Fish. Aquat. Sci. 77, 1348–1358. https://doi.org/10.1139/cjfas-2019-0346 (2020).Article 

Google Scholar 
11.Connell, J. H. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology 42, 710–723. https://doi.org/10.2307/1933500 (1961).Article 

Google Scholar 
12.Zaret, T. M. & Rand, A. S. Competition in tropical stream fishes: Support for the competitive exclusion principle. Ecology 52, 336–342. https://doi.org/10.2307/1934593 (1971).Article 

Google Scholar 
13.Britton, J. R., Ruiz-Navarro, A., Verreycken, H. & Amat-Trigo, F. Trophic consequences of introduced species: comparative impacts of increased interspecific versus intraspecific competitive interactions. Funct. Ecol. 32, 486–495. https://doi.org/10.1111/1365-2435.12978 (2018).Article 
PubMed 

Google Scholar 
14.Connell, J. H. On the prevalence and relative importance of interspecific competition: evidence from field experiments. Am. Nat. 122, 661–696. https://doi.org/10.1086/284165 (1983).Article 

Google Scholar 
15.David, P. et al. Impacts of invasive species on food webs: a review of empirical data. Adv. Ecol. Res. 56, 1–60. https://doi.org/10.1016/bs.aecr.2016.10.001 (2017).Article 

Google Scholar 
16.Vannote, R. L., Wayne Minshall, G., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137. https://doi.org/10.1139/f80-017 (1980).Article 

Google Scholar 
17.Ibañez, C. et al. Convergence of temperate and tropical stream fish assemblages. Ecography 32, 658–670. https://doi.org/10.1111/j.1600-0587.2008.05591.x (2009).Article 

Google Scholar 
18.Winemiller, K. O. et al. Stable isotope analysis reveals food web structure and watershed impacts along the fluvial gradient of a Mesoamerican coastal river. River Res. Appl. 27, 791–803. https://doi.org/10.1002/rra.1396 (2011).Article 

Google Scholar 
19.Ward, J. V. & Stanford, J. A. The serial discontinuity concept: extending the model to floodplain rivers. River Res. Appl. 10, 159–168. https://doi.org/10.1002/rrr.3450100211 (1983).Article 

Google Scholar 
20.Sabo, J. L. et al. Pulsed flows, tributary inputs and food-web structure in a highly regulated river. J. Appl. Ecol. 55, 1884–1895. https://doi.org/10.1111/1365-2664.13109 (2018).Article 

Google Scholar 
21.Sabater, S. Alterations of the global water cycle and their effects on river structure, function and services. Freshw. Rev. 1, 75–89. https://doi.org/10.1608/FRH-1.1.5 (2008).Article 

Google Scholar 
22.Arrantes, C. C., Fitzgerald, D. B., Hoeinghaus, D. J. & Winemiller, K. O. Impacts of hydroelectric dams on fishes and fisheries in tropical rivers through the lens of functional traits. Curr. Opin. Environ. Sustain. 37, 28–40. https://doi.org/10.1016/j.cosust.2019.04.009 (2019).Article 

Google Scholar 
23.Cross, W. F. et al. Ecosystem ecology meets adaptive management: food web response to a controlled flood on the Colorado River, Glen Canyon. Ecol. Appl. 21, 2016–2033. https://doi.org/10.1890/10-1719.1 (2011).Article 
PubMed 

Google Scholar 
24.Cross, W. F. et al. Food web dynamics in a large river discontinuum. Ecol. Monogr. 83, 311–337. https://doi.org/10.1890/12-1727.1 (2013).Article 

Google Scholar 
25.Wellard Kelley, H. A. et al. Macroinvertebrate diets reflect tributary inputs and turbidity-driven changes in food availability in the Colorado River downstream of Glen Canyon Dam. Freshw. Sci. 32, 397–410. https://doi.org/10.1899/12-088.1 (2013).Article 

Google Scholar 
26.Thornton, K. W., Kimmel, B. L. & Payne, F. E. Reservoir Limnology: Ecological Perspectives (John Wiley and Sons, 1990).
Google Scholar 
27.Havel, J. E., Lee, C. E. & Vander Zanden, J. M. Do reservoirs facilitate invasions into landscapes?. Bioscience 55, 518–525. https://doi.org/10.1641/0006-3568(2005)055[0518:DRFIIL]2.0.CO;2 (2005).Article 

Google Scholar 
28.Southwood, T. R. E. Habitat, the templet for ecological strategies?. J. Anim. Ecol. 46, 337–365. https://doi.org/10.2307/3817 (1977).Article 

Google Scholar 
29.Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460. https://doi.org/10.1016/j.tree.2008.03.011 (2008).Article 
PubMed 

Google Scholar 
30.Mercado-Silva, N., Helmus, M. R. & Vander Zanden, M. J. The effects of impoundment and non-native species on a river food web in Mexico’s central plateau. River Res. Appl. 25, 1090–1108. https://doi.org/10.1002/rra.1205 (2009).Article 

Google Scholar 
31.Villéger, S., Blanchet, S., Beauchard, O., Oberdorff, T. & Brosse, S. Homogenization patterns of the world’s freshwater fish faunas. Proc. Natl. Acad. Sci. U. S. A. 108, 18003–18008. https://doi.org/10.1073/pnas.1107614108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Delong, M. D., Thorp, J. H., Thoms, M. C. & McIntosh, L. M. Trophic niche dimensions of fish communities as a function of historical hydrological conditions in a Plains river. River Syst. 19, 177–187. https://doi.org/10.1127/1868-5749/2011/019-0036 (2011).Article 

Google Scholar 
33.Pilger, T. J., Gido, K. B. & Propst, D. L. Diet and trophic niche overlap of native and nonnative fishes in the Gila River, USA: implications for native fish conservation. Ecol. Freshw. Fish 19, 300–321. https://doi.org/10.1111/j.1600-0633.2010.00415.x (2010).Article 

Google Scholar 
34.Mor, J. R. et al. Dam regulation and riverine food-web structure in a Mediterranean river. Sci. Total Environ. 625, 301–310. https://doi.org/10.1016/j.scitotenv.2017.12.296 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Tyus, H. M. & Saunders, J. F. III. Nonnative fish control and endangered fish recovery: lessons from the Colorado River. Fisheries 25, 17–24. https://doi.org/10.1577/1548-8446(2000)025%3c0017:NFCAEF%3e2.0.CO;2 (2000).Article 

Google Scholar 
36.Strayer, D. L. Alien species in fresh waters: ecological effects, interactions with other stressors, and prospects for the future. Freshw. Biol. 55, 152–174. https://doi.org/10.1111/j.1365-2427.2009.02380.x (2010).Article 

Google Scholar 
37.Marks, J. C., Williamson, C. & Hendrickson, D. A. Coupling stable isotope studies with food web manipulations to predict the effects of exotic fish: lessons from Cuatro Ciénegas, Mexico. Aquat. Conserv. 21, 317–323. https://doi.org/10.1002/aqc.1199 (2011).Article 

Google Scholar 
38.Cooke, S. J., Paukert, C. & Hogan, Z. Endangered river fish: factors hindering conservation and restoration. Endanger. Species Res. 17, 179–191. https://doi.org/10.3354/esr00426 (2012).Article 

Google Scholar 
39.Pennock, C. A., Farrington, M. A. & Gido, K. B. Feeding ecology of early life stage Razorback Sucker relative to other sucker species in the San Juan River. Trans. Am. Fish. Soc. 148, 938–951. https://doi.org/10.1002/tafs.10188 (2019).Article 

Google Scholar 
40.Cucherousset, J., Bouletreau, S., Martino, A., Roussel, J. M. & Santoul, F. Using stable isotope analyses to determine the ecological effects of non-native fishes. Fish. Mgmt. Ecol. 19, 111–119. https://doi.org/10.1111/j.1365-2400.2011.00824.x (2012).Article 

Google Scholar 
41.Finlay, J. C. Stable-carbon-isotope ratios of river biota: Implications for energy flow in lotic food webs. Ecology 82, 1052–1064. https://doi.org/10.1890/0012-9658(2001)082[1052:SCIROR]2.0.CO;2 (2001).Article 

Google Scholar 
42.France, R. L. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnol. Oceanogr. 40, 1310–1313. https://doi.org/10.4319/lo.1995.40.7.1310 (1995).ADS 
Article 

Google Scholar 
43.Fry, B. Stable Isotope Ecology (Springer-Verlag, 2006).Book 

Google Scholar 
44.Vander Zanden, M. J., Cabana, G. & Rasmussen, J. B. Comparing trophic position of freshwater fish calculated using stable nitrogen isotope ratios (δ15N) and literature dietary data. Can. J. Fish. Aquat. Sci. 54, 1142–1158. https://doi.org/10.1139/f97-016 (1997).Article 

Google Scholar 
45.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2 (2002).Article 

Google Scholar 
46.Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88, 42–48. https://doi.org/10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2 (2007).Article 
PubMed 

Google Scholar 
47.Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER: stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602. https://doi.org/10.1111/j.1365-2656.2011.01806.x (2011).Article 
PubMed 

Google Scholar 
48.Swanson, H. K. et al. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96, 318–324. https://doi.org/10.1890/14-0235.1 (2015).Article 
PubMed 

Google Scholar 
49.Minckley, W. L. & Deacon, J. E. Battle Against Extinction: Native Fish Management in the American West (The University of Arizona Press, 1991).
Google Scholar 
50.Albrecht, B. A. et al. Use of inflow areas in two Colorado River basin reservoirs by the endangered Razorback Sucker (Xyrauchen texanus). West. N. Am. Nat. 77, 500–514. https://doi.org/10.3398/064.077.0410 (2018).Article 

Google Scholar 
51.Pennock, C. A. et al. Reservoir fish assemblage structure across an aquatic ecotone: Can river-reservoir interfaces provide conservation and management opportunities?. Fish. Manag. Ecol. 28, 1–13. https://doi.org/10.1111/fme.12444 (2021).Article 

Google Scholar 
52.Gido, K. B. & Propst, D. L. Habitat use and association of native and nonnative fishes in the San Juan River, New Mexico and Utah. Copeia 1999, 321–332. https://doi.org/10.2307/1447478 (1999).Article 

Google Scholar 
53.Gido, K. B., Franssen, N. R. & Propst, D. L. Spatial variation in δ15N and δ13C isotopes in the San Juan River, New Mexico and Utah: implications for the conservation of native fishes. Environ. Biol. Fish. 75, 197–207. https://doi.org/10.1007/s10641-006-0009-1 (2006).Article 

Google Scholar 
54.Ryden, D. W. & Ahlm, L. A. Observations on the distribution and movements of Colorado Squawfish, Ptychocheilus lucius, in the San Juan River, New Mexico, Colorado, and Utah. Southwest. Nat. 41, 161–168 (1996).
Google Scholar 
55.Cathcart, C. N. et al. Waterfall formation at a desert river-reservoir delta isolates endangered fishes. River Res. Appl. 34, 948–956. https://doi.org/10.1002/rra.3341 (2018).Article 

Google Scholar 
56.Thomsen, M. S. et al. Impacts of marine invaders on biodiversity depend on trophic position and functional similarity. Mar. Ecol. Prog. Ser. 495, 39–47. https://doi.org/10.3354/meps10566 (2014).ADS 
Article 

Google Scholar 
57.McIntyre, P. B. & Flecker, A. S. Rapid turnover of tissue nitrogen of primary consumers in tropical freshwaters. Oecologia 148, 12–21. https://doi.org/10.1007/s00442-005-0354-3 (2006).ADS 
Article 
PubMed 

Google Scholar 
58.Franssen, N. R., Gilbert, E. I., James, A. P. & Davis, J. E. Isotopic tissue turnover and discrimination factors following a laboratory diet switch in Colorado Pikeminnow (Ptychocheilus lucius). Can. J. Fish. Aq. Sci. 74, 265–272. https://doi.org/10.1139/cjfas-2015-0531 (2017).CAS 
Article 

Google Scholar 
59.Busst, G. M. A. & Britton, J. R. Tissue-specific turnover rates of the nitrogen stable isotope as functions of time and growth in a cyprinid fish. Hydrobiologia 805, 49–60. https://doi.org/10.1007/s10750-017-3276-2 (2018).CAS 
Article 

Google Scholar 
60.Arrington, D. A. & Winemiller, K. O. Preservation effects on stable isotope analysis of fish muscle. Trans. Am. Fish. Soc. 131, 337–342. https://doi.org/10.1577/1548-8659(2002)131%3c0337:PEOSIA%3e2.0.CO;2 (2002).CAS 
Article 

Google Scholar 
61.Hubert, W. A., Pope, K. L. & Dettmers, J. M. Passive capture techniques. In Fisheries Techniques 3rd edn (eds Zale, A. V. et al.) 223–265 (American Fisheries Society, 2012).
Google Scholar 
62.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
63.Fox, J., & Weisberg, S. An {R} Companion to Applied Regression, 2nd edn. (Sage 2011). http://socserv.socci.mcmaster.ca/jfox/Books/Companion64.Lefcheck, S. piecewiseSEM: Piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evo. 7, 573–579. https://doi.org/10.1111/2041-210X.12512 (2016).Article 

Google Scholar 
65.Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213. https://doi.org/10.1098/rsif.2017.0213 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Lysy, M., Stasko, A. D., Swanson, H. K. nicheROVER: (Niche) (R)egion and Niche (Over)lap metrics for multidimensional ecological niches. R package version 1.0 (2014). https://CRAN.R-project.org/package=nicheROVER67.R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2019). Available: https://www.R-project.org/68.Franssen, N. R., Davis, J. E., Ryden, D. W. & Gido, K. B. Fish community responses to mechanical removal of nonnative fishes in a large southwestern river. Fisheries 8, 352–363. https://doi.org/10.1080/03632415.2014.924409 (2014).Article 

Google Scholar 
69.Kelly, D. J. & Jellyman, D. J. Changes in trophic linkages to shortfin eels (Anguilla australis) since the collapse of submerged macrophytes in Lake Ellesmere, New Zealand. Hydrobiologia 579, 161–173. https://doi.org/10.1007/s10750-006-0400-0 (2007).Article 

Google Scholar 
70.Zambrano, L., Valiente, E. & Vander Zanden, M. J. food web overlap among native axolotl (Ambystoma mexicanum) and two exotic fishes: carp (Cyprinus carpio) and tilapia (Oreochromis niloticus) in Xochimilco, Mexico City. Biol. Invasions 12, 3061–3069. https://doi.org/10.1007/s10530-010-9697-8 (2010).Article 

Google Scholar 
71.Córdova-Tapia, F., Contreras, M. & Zambrano, L. Trophic niche overlap between native and non-native fishes. Hydrobiologia 746, 291–301. https://doi.org/10.1007/s10750-014-1944-z (2015).Article 

Google Scholar 
72.Portz, D. E. & Tyus, H. M. Fish humps in two Colorado River fishes: a morphological response to cyprinid predation?. Environ. Biol. Fishes 71, 233–245. https://doi.org/10.1007/s10641-004-0300-y (2004).Article 

Google Scholar 
73.Pennock, C. A. et al. Predicted and observed responses of a nonnative Channel Catfish population following managed removal to aid the recovery of endangered fishes. N. Am. J. Fish. Mgmt. 38, 565–578. https://doi.org/10.1002/nafm.10056 (2018).Article 

Google Scholar 
74.Hedden, S. C. et al. Quantifying consumption of native fishes by nonnative Channel Catfish in a desert river. N. Am. J. Fish. Manag. https://doi.org/10.1002/nafm.10514 (2020).Article 

Google Scholar 
75.Nogueira, M. G., Oliveira, P. C. R. & Britto, Y. T. Zooplankton assemblages (Copepoda and Cladocera) in a cascade of reservoirs of a large tropical river (SE Brazil). Limnetica 27, 151–170 (2008).
Google Scholar 
76.Slaveska-Stamenković, V. et al. Factors affecting distribution pattern of dominant macroinvertebrates in Mantovo Reservoir (Republic of Macedonia). Biologia 67, 1129–1142. https://doi.org/10.2478/s11756-012-0102-1 (2012).Article 

Google Scholar 
77.Behn, K. E. & Baxter, C. V. The trophic ecology of a desert river fish assemblage: influence of season and hydrologic variability. Ecosphere 10, e02583. https://doi.org/10.1002/ecs2.2583 (2019).Article 

Google Scholar 
78.Glenn, E. P., Lee, C., Felger, R. & Zengel, S. Effects of water management on the wetlands of the Colorado River Delta, Mexico. Conserv. Biol. 10, 1175–1186. https://doi.org/10.1046/j.1523-1739.1996.10041175.x (1996).Article 

Google Scholar 
79.Sykes, G. The Colorado River Delta. Publication no. 460. (Carnegie Institution of Washington, D.C. 1937).80.Dalrymple, G. B. & Hamblin, W. K. K-Ar of Pleistocene lava dams in the Grand Canyon in Arizona. Proc. Natl. Acad. Sci. U.S.A. 95, 9744–9749. https://doi.org/10.1073/pnas.95.17.9744 (1998).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
81.Minckley, W. L. Status of the razorback sucker, Xyrauchen texanus (Abbott), in the Lower Colorado River Basin. Southwest. Nat. 28, 165–187. https://doi.org/10.2307/3671385 (1983).Article 

Google Scholar 
82.Doi, H. Spatial patterns of autochthonous and allochthonous resources in aquatic food webs. Popul. Ecol. 51, 57–64. https://doi.org/10.1007/s10144-008-0127-z (2009).Article 

Google Scholar 
83.Thorp, J. H. & Delong, M. D. Dominance of autochthonous autotrophic carbon in food webs of heterotrophic rivers. Oikos 96, 543–550. https://doi.org/10.1034/j.1600-0706.2002.960315.x (2002).Article 

Google Scholar 
84.Rennie, M. D., Sprules, W. G. & Johnson, T. B. Resource switching in fish following a major food web disruption. Oecologia 159, 789–802. https://doi.org/10.1007/s00442-008-1271-z (2009).ADS 
Article 
PubMed 

Google Scholar 
85.Cummings, B. M. & Schindler, D. E. Depth variation in isotopic composition of benthic resources and assessment of sculpin feeding patterns in an oligotrophic Alaskan lake. Aquat. Ecol. 47, 403–414. https://doi.org/10.1007/s10452-013-9453-0 (2013).CAS 
Article 

Google Scholar 
86.Fera, S. A., Rennie, M. D. & Dunlop, E. S. Broad shifts in the resource use of a commercially harvested fish following the invasion of dreissenid mussels. Ecology 98, 1681–1692. https://doi.org/10.1002/ecy.1836 (2017).Article 
PubMed 

Google Scholar 
87.Pennock, C. A., McKinstry, M. C. & Gido, K. B. Razorback Sucker movement strategies across a river-reservoir habitat complex. Trans. Am. Fish. Soc. 149, 620–634. https://doi.org/10.1002/tafs.10262 (2020).Article 

Google Scholar 
88.Vatland, S. & Budy, P. Predicting the invasion success of an introduced omnivore in a large heterogeneous reservoir. Can. J. Fish. Aquat. Sci. 64, 1329–1345. https://doi.org/10.1139/f07-100 (2007).Article 

Google Scholar 
89.Romanuk, T. N., Hayward, A. & Hutchings, J. A. Trophic level scales positively with body size in fishes. Glob. Ecol. Biogeogr. 20, 231–240. https://doi.org/10.1111/j.1466-8238.2010.00579.x (2011).Article 

Google Scholar 
90.Franssen, N. R., Gilbert, E. I., Gido, K. B. & Propst, D. L. Hatchery-reared endangered Colorado pikeminnow (Ptychocheilus lucius) undergo a gradual transition to piscivory after introduction to the wild. Aquat. Conserv. 29, 24–38. https://doi.org/10.1002/aqc.2995 (2019).Article 

Google Scholar 
91.Hoeinghaus, D. J., Winemiller, K. O. & Agostinho, A. A. Hydrogeomorphology and river impoundment affect food-chain length of divers Neotropical food webs. Oikos 117, 984–995. https://doi.org/10.1111/j.2008.0030-1299.16458.x (2008).Article 

Google Scholar 
92.Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221. https://doi.org/10.1038/s41586-019-1111-9 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
93.Pennock, C. A. & Gido, K. B. Spatial and temporal dynamics of fish assemblages in a desert reservoir over 38 years. Hyrdobiologia 848, 1231–1248. https://doi.org/10.1007/s10750-021-04514-z (2021).Article 

Google Scholar 
94.Oliveira, E. F., Minte-Vera, C. V. & Goulart, E. Structure of fish assemblages along spatial gradients in a deep subtropical reservoir (Itaipu Reservoir, Brazil-Paraguay border). Environ. Biol. Fish. 72, 283–304. https://doi.org/10.1007/s10641-004-2582-5 (2005).Article 

Google Scholar 
95.Buckmeier, D. L., Smith, N. G., Fleming, B. P. & Bodine, K. A. Intra-annual variation in river-reservoir interface fish assemblages: implications for fish conservation and management in regulated rivers. River Res. Appl. 30, 780–790. https://doi.org/10.1002/rra.2667 (2014).Article 

Google Scholar 
96.Albrecht, B. A., Holden, P. B., Kegerries, R. B. & Golden, M. E. Razorback sucker recruitment in Lake Mead, Nevada-Arizona, why here?. Lake Reserv. Manage. 26, 336–344. https://doi.org/10.1080/07438141.2010.511966 (2010).Article 

Google Scholar  More

1.Bellwood, D. R., Hughes, T. P., Folke, C. & Nystrom, M. Confronting the coral reef crisis. Nature 429, 827–833. https://doi.org/10.1038/nature02691 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
2.Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742. https://doi.org/10.1126/science.1152509 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
3.Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933 (2003).ADS 
CAS 
Article 

Google Scholar 
4.De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27–year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. 109, 17995. https://doi.org/10.1073/pnas.1208909109 (2012).ADS 
Article 
PubMed 

Google Scholar 
5.Fabricius, K. E. Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis. Mar. Pollut. Bull. 50, 125–146. https://doi.org/10.1016/j.marpolbul.2004.11.028 (2005).CAS 
Article 
PubMed 

Google Scholar 
6.Bruno, J. F., Petes, L. E., Drew Harvell, C. & Hettinger, A. Nutrient enrichment can increase the severity of coral diseases. Ecol. Lett. 6, 1056–1061. https://doi.org/10.1046/j.1461-0248.2003.00544.x (2003).Article 

Google Scholar 
7.Pandolfi, J. M. et al. Are U.S. coral reefs on the slippery slope to slime?. Science 307, 1725–1726. https://doi.org/10.1126/science.1104258 (2005).CAS 
Article 
PubMed 

Google Scholar 
8.Jackson, J. B. C. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637. https://doi.org/10.1126/science.1059199 (2001).CAS 
Article 
PubMed 

Google Scholar 
9.Bahr, K. D., Jokiel, P. L. & Toonen, R. J. The unnatural history of Kāne‘ohe Bay: Coral reef resilience in the face of centuries of anthropogenic impacts. PeerJ 3, e950. https://doi.org/10.7717/peerj.950 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Zaneveld, J. R. et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat. Commun. 7, 11833. https://doi.org/10.1038/ncomms11833 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Courtial, L., Roberty, S., Shick, J. M., Houlbrèque, F. & Ferrier-Pagès, C. Interactive effects of ultraviolet radiation and thermal stress on two reef-building corals. Limnol. Oceanogr. 62, 1000–1013. https://doi.org/10.1002/lno.10481 (2017).ADS 
Article 

Google Scholar 
12.Jokiel, P. L. & York, R. H. Solar Ultraviolet Photobiology of the Reef Coral Pocillopora Damicornis and Symbiotic Zooxanthellae. Bull. Mar. Sci. 32, 301–315 (1982).
Google Scholar 
13.Jokiel, P. L., Lesser, M. P. & Ondrusek, M. E. UV-absorbing compounds in the coral Pocillopora damicornis: Interactive effects of UV radiation, photosynthetically active radiation, and water flow. Limnol. Oceanogr. 42, 1468–1473. https://doi.org/10.4319/lo.1997.42.6.1468 (1997).ADS 
CAS 
Article 

Google Scholar 
14.McKenzie, R. L. et al. Ozone depletion and climate change: impacts on UV radiation. Photochem. Photobiol. Sci. 10, 182–198. https://doi.org/10.1039/C0PP90034F (2011).CAS 
Article 
PubMed 

Google Scholar 
15.Ferrier-Pagès, C. et al. Effects of temperature and UV radiation increases on the photosynthetic efficiency in four scleractinian coral species. Biol. Bull. 213, 76–87. https://doi.org/10.2307/25066620 (2007).Article 
PubMed 

Google Scholar 
16.Ailsa, P. K. & Ross, J. J. Effects of hypo-osmosis on the coral Stylophora pistillata: nature and cause of low-salinity bleaching. Mar. Ecol. Prog. Ser. 253, 145–154 (2003).Article 

Google Scholar 
17.Bessell-Browne, P. et al. Impacts of turbidity on corals: The relative importance of light limitation and suspended sediments. Mar. Pollut. Bull. 117, 161–170. https://doi.org/10.1016/j.marpolbul.2017.01.050 (2017).CAS 
Article 
PubMed 

Google Scholar 
18.Piniak, G. A. Effects of two sediment types on the fluorescence yield of two Hawaiian scleractinian corals. Mar. Environ. Res. 64, 456–468. https://doi.org/10.1016/j.marenvres.2007.04.001 (2007).CAS 
Article 
PubMed 

Google Scholar 
19.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80. https://doi.org/10.1126/science.aan8048 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
20.Jokiel, P. L. & Coles, S. L. Effects of temperature on the mortality and growth of Hawaiian reef corals. Mar. Biol. 43, 201–208. https://doi.org/10.1007/bf00402312 (1977).Article 

Google Scholar 
21.Jokiel, P. L. & Coles, S. L. Response of Hawaiian and other Indo-Pacific reef corals to elevated temperature. Coral Reefs 8, 155–162 (1990).ADS 
Article 

Google Scholar 
22.Glynn, P. W. Coral reef bleaching: facts, hypotheses and implications. Glob. Change Biol. 2, 495–509 (1996).ADS 
Article 

Google Scholar 
23.Edmunds, P., Gates, R. & Gleason, D. The biology of larvae from the reef coral Porites astreoides, and their response to temperature disturbances. Mar. Biol. 139, 981–989. https://doi.org/10.1007/s002270100634 (2001).Article 

Google Scholar 
24.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377. https://doi.org/10.1038/nature21707 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
25.Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. I. Fecundity, fertilization and offspring viability. Coral Reefs 19, 231–239. https://doi.org/10.1007/s003380170003 (2001).Article 

Google Scholar 
26.Ward, S., Harrison, P. & Hoegh-Guldberg, O. Coral bleaching reduces reproduction of scleractinian corals and increases susceptibility to future stress. Proceedings of the Ninth International Coral Reef Symposium, Bali, 23–27 October 2000 2, 1123–1128 (2002).27.Baird, A. H. & Marshall, P. A. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 237, 133–141 (2002).ADS 
Article 

Google Scholar 
28.Paxton, C. W., Baria, M. V. B., Weis, V. M. & Harii, S. Effect of elevated temperature on fecundity and reproductive timing in the coral Acropora digitifera. Zygote 24, 511–516. https://doi.org/10.1017/S0967199415000477 (2016).Article 
PubMed 

Google Scholar 
29.Szmant, A. M. & Gassman, N. J. The effects of prolonged “bleaching” on the tissue biomass and reproduction of the reef coral Montastrea annularis. Coral Reefs 8, 217–224 (1990).ADS 
Article 

Google Scholar 
30.Randall, C. J. & Szmant, A. M. Elevated temperature affects development, survivorship, and settlement of the elkhorn coral, Acropora palmata (Lamarck 1816). Biol. Bull. 217, 269–282 (2009).Article 

Google Scholar 
31.Nozawa, Y. & Harrison, P. L. Effects of elevated temperature on larval settlement and post-settlement survival in scleractinian corals, Acropora solitaryensis and Favites chinensis. Mar. Biol. 152, 1181–1185. https://doi.org/10.1007/s00227-007-0765-2 (2007).Article 

Google Scholar 
32.Cumbo, V. R., Fan, T. Y. & Edmunds, P. J. Effects of exposure duration on the response of Pocillopora damicornis larvae to elevated temperature and high pCO2. J. Exp. Mar. Biol. Ecol. 439, 100–107. https://doi.org/10.1016/j.jembe.2012.10.019 (2013).Article 

Google Scholar 
33.Negri, A. P., Marshall, P. A. & Heyward, A. J. Differing effects of thermal stress on coral fertilization and early embryogenesis in four Indo Pacific species. Coral Reefs 26, 759–763. https://doi.org/10.1007/s00338-007-0258-2 (2007).ADS 
Article 

Google Scholar 
34.Lager, C. V. A., Hagedorn, M. S., Rodgers, K. & Jokiel, P. L. The impact of short-term exposure to near shore stressors on the early life stages of the reef building coral Montipora capitata. PeerJ 8, e9415. https://doi.org/10.7717/peerj.9415 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Nozawa, Y. Annual variation in the timing of coral spawning in a high-latitude environment: Influence of temperature. Biol. Bull. 222, 192–202. https://doi.org/10.1086/BBLv222n3p192 (2012).Article 
PubMed 

Google Scholar 
36.Cox, E. F. Continuation of sexual reproduction in Montipora capitata following bleaching. Coral Reefs 26, 721–724. https://doi.org/10.1007/s00338-007-0251-9 (2007).ADS 
MathSciNet 
Article 

Google Scholar 
37.Armoza-Zvuloni, R., Segal, R., Kramarsky-Winter, E. & Loya, Y. Repeated bleaching events may result in high tolerance and notable gametogenesis in stony corals: Oculina patagonica as a model. Mar. Ecol. Prog. Ser. 426, 149–159 (2011).ADS 
Article 

Google Scholar 
38.Mendes, J. M. & Woodley, J. D. Effect of the 1995–1996 bleaching event on polyp tissue depth, growth, reproduction and skeletal band formation in Montastraea annularis. Mar. Ecol. Prog. Ser. 235, 93–102 (2002).ADS 
Article 

Google Scholar 
39.Levitan, D. R., Boudreau, W., Jara, J. & Knowlton, N. Long-term reduced spawning in Orbicella coral species due to temperature stress. Mar. Ecol. Prog. Ser. 515, 1–10. https://doi.org/10.2307/24894795 (2014).ADS 
Article 

Google Scholar 
40.Edge, S. E., Shearer, T. L., Morgan, M. B. & Snell, T. W. Sub-lethal coral stress: Detecting molecular responses of coral populations to environmental conditions over space and time. Aquat. Toxicol. 128–129, 135–146. https://doi.org/10.1016/j.aquatox.2012.11.014 (2013).CAS 
Article 
PubMed 

Google Scholar 
41.Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338–342. https://doi.org/10.1126/science.aac7125 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
42.Downs, C. A. et al. The use of cellular diagnostics for identifying sub-lethal stress in reef corals. Ecotoxicology 21, 768–782. https://doi.org/10.1007/s10646-011-0837-4 (2012).CAS 
Article 
PubMed 

Google Scholar 
43.Olsen, K., Ritson-Williams, R., Ochrietor, J. D., Paul, V. J. & Ross, C. Detecting hyperthermal stress in larvae of the hermatypic coral Porites astreoides: the suitability of using biomarkers of oxidative stress versus heat-shock protein transcriptional expression. Mar. Biol. 160, 2609–2618. https://doi.org/10.1007/s00227-013-2255-z (2013).CAS 
Article 

Google Scholar 
44.Jones, A. M. & Berkelmans, R. Tradeoffs to thermal acclimation: Energetics and reproduction of a reef coral with heat tolerant Symbiodinium Type-D. J. Mar. Sci. 2011, 185890. https://doi.org/10.1155/2011/185890 (2011).Article 

Google Scholar 
45.Bonesso, J. L., Leggat, W. & Ainsworth, T. D. Exposure to elevated sea-surface temperatures below the bleaching threshold impairs coral recovery and regeneration following injury. PeerJ 5, e3719. https://doi.org/10.7717/peerj.3719 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
46.Bahr, K. D., Jokiel, P. L. & Rodgers, K. S. The 2014 coral bleaching and freshwater flood events in Kāneʻohe Bay, Hawai‘i. PeerJ 3, e1136. https://doi.org/10.7717/peerj.1136 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
47.Bahr, K. D., Rodgers, K. S. & Jokiel, P. L. Impact of Three Bleaching Events on the Reef Resiliency of Kāne‘ohe Bay, Hawai‘i. Front. Mar. Sci. 4, 398 (2017).Article 

Google Scholar 
48.Richards Donà, A. Investigation into the functional role of chromoproteins in the physiology and ecology of the Hawaiian stony coral Montipora flabellata in Kāne‘ohe Bay, O‘ahu. Doctoral Dissertation, University of Hawaiʻi at Mānoa, (2019).49.Hagedorn, M. et al. Potential bleaching effects on coral reproduction. Reprod. Fertil. Dev. https://doi.org/10.1071/rd15526 (2016).Article 

Google Scholar 
50.Jury, C. P. & Toonen, R. J. Adaptive responses and local stressor mitigation drive coral resilience in warmer, more acidic oceans. Proc. Biol. Sci. 286, 20190614. https://doi.org/10.1098/rspb.2019.0614 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Rodgers, K. U. S., Jokiel, P. L., Brown, E. K., Hau, S. & Sparks, R. Over a decade of change in spatial and temporal dynamics of Hawaiian coral reef communities. Pac. Sci. 69, 1–13. https://doi.org/10.2984/69.1.1 (2015).Article 

Google Scholar 
52.Hunter, C. L. & Evans, C. W. Coral reefs in Kaneohe Bay, Hawaii: Two centuries of western influence and two decades of data. Bull. Mar. Sci. 57, 501–515 (1995).
Google Scholar 
53.Heyward, A. J. Sexual reproduction in five species of the coral Montipora. In: Coral Reef Population Biology. Hawaii Institute of Marine Biology Technical Report 37, 170–178 (1985).54.Fenner, D. P. Corals of Hawai’i. A field guide to the hard, black, and soft corals of Hawai’i and the northwest Hawaiian Islands, including Midway. (Mutual Publishing Company, 2005).55.Veron, J. E. N. Corals of the world. Volume 1. (Australia Institute of Marine Science, 2000).56.Forsman, Z. H. et al. Ecomorph or endangered coral? DNA and microstructure reveal hawaiian species complexes: Montipora dilatata/flabellata/turgescens & M. patula/verrilli. PLoS One 5, e15021. https://doi.org/10.1371/journal.pone.0015021 (2010).57.Cunha, R. L. et al. Rare coral under the genomic microscope: timing and relationships among Hawaiian Montipora. BMC Evol. Biol. 19, 153. https://doi.org/10.1186/s12862-019-1476-2 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Padilla-Gamiño, J. L., Weatherby, T. M., Waller, R. G. & Gates, R. D. Formation and structural organization of the egg–sperm bundle of the scleractinian coral Montipora capitata. Coral Reefs 30, 371–380. https://doi.org/10.1007/s00338-010-0700-8 (2011).ADS 
Article 

Google Scholar 
59.Padilla-Gamiño, J. L. et al. Sedimentation and the reproductive biology of the Hawaiian reef-building coral Montipora capitata. Biol. Bull. 226, 8–18 (2014).Article 

Google Scholar 
60.Padilla-Gamiño, J. L. & Gates, R. D. Spawning dynamics in the Hawaiian reef-building coral Montipora capitata. Mar. Ecol. Prog. Ser. 449, 145–160. https://doi.org/10.3354/meps09530 (2012).ADS 
Article 

Google Scholar 
61.Kolinski, S. P. & Cox, E. F. An update on modes and timing of gamete and planula release in Hawaiian scleractinian corals with implications for conservation and management. Pac. Sci. 57, 17–27. https://doi.org/10.1353/psc.2003.0005 (2003).Article 

Google Scholar 
62.Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189. https://doi.org/10.1038/nature04565 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
63.Hunter, C. L. Environmental cues controlling spawning in two Hawaiian corals, Montipora verrucosa and M. dilatata . In Proceedings of the 6th International Coral Reef Symposium Vol. 2 727–732 (1988).64.Binet, M. T., Doyle, C. J., Williamson, J. E. & Schlegel, P. Use of JC-1 to assess mitochondrial membrane potential in sea urchin sperm. J. Exp. Mar. Biol. Ecol. 452, 91–100. https://doi.org/10.1016/j.jembe.2013.12.008 (2014).CAS 
Article 

Google Scholar 
65.Chen, L. B. Mitochondrial membrane potential in living cells. Annu. Rev. Cell Biol. 4, 155–181. https://doi.org/10.1146/annurev.cb.04.110188.001103 (1988).CAS 
Article 
PubMed 

Google Scholar 
66.Schlegel, P., Binet, M. T., Havenhand, J. N., Doyle, C. J. & Williamson, J. E. Ocean acidification impacts on sperm mitochondrial membrane potential bring sperm swimming behaviour near its tipping point. J. Exp. Biol. 218, 1084. https://doi.org/10.1242/jeb.114900 (2015).Article 
PubMed 

Google Scholar 
67.Rodrigues, L. J. & Grottoli, A. G. Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol. Oceanogr. 52, 1874–1882. https://doi.org/10.4319/lo.2007.52.5.1874 (2007).ADS 
Article 

Google Scholar 
68.Parker, G. A. Why are there so many tiny sperm? Sperm competition and the maintenance of two sexes. J. Theor. Biol. 96, 281–294. https://doi.org/10.1016/0022-5193(82)90225-9 (1982).CAS 
Article 
PubMed 

Google Scholar 
69.Hayward, A. & Gillooly, J. F. The cost of sex: Quantifying energetic investment in gamete production by males and females. PLoS ONE 6, e16557. https://doi.org/10.1371/journal.pone.0016557 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.Fisch, J., Drury, C., Towle, E. K., Winter, R. N. & Miller, M. W. Physiological and reproductive repercussions of consecutive summer bleaching events of the threatened Caribbean coral Orbicella faveolata. Coral Reefs 38, 863–876. https://doi.org/10.1007/s00338-019-01817-5 (2019).ADS 
Article 

Google Scholar 
71.Johnston, E. C., Counsell, C. W. W., Sale, T. L., Burgess, S. C. & Toonen, R. J. The legacy of stress: Coral bleaching impacts reproduction years later. Funct. Ecol. 00, 1–11. https://doi.org/10.1111/1365-2435.13653 (2020).Article 

Google Scholar 
72.Omori, M., Fukami, H., Kobinata, H. & Hatta, M. Significant drop of fertilization of Acropora corals in 1999: An after-effect of heavy coral bleaching?. Limnol. Oceanogr. 46, 704–706. https://doi.org/10.4319/lo.2001.46.3.0704 (2001).ADS 
Article 

Google Scholar 
73.Levitan, D. R. & Petersen, C. Sperm limitation in the sea. Trends Ecol. Evol. 10, 228–231. https://doi.org/10.1016/S0169-5347(00)89071-0 (1995).CAS 
Article 
PubMed 

Google Scholar 
74.Yund, P. O. How severe is sperm limitation in natural populations of marine free-spawners?. Trends Ecol. Evol. 15, 10–13. https://doi.org/10.1016/S0169-5347(99)01744-9 (2000).CAS 
Article 
PubMed 

Google Scholar 
75.Benzie, J. A. H. & Dixon, P. The effects of sperm concentration, sperm: Egg ratio, and gamete age on fertilization success in Crown-of-Thorns Starfish (Acanthaster planci) in the Laboratory. Biol. Bull. 186, 139–152. https://doi.org/10.2307/1542048 (1994).CAS 
Article 
PubMed 

Google Scholar 
76.Brazeau, D. A. & Lasker, H. R. Reproductive success in the Caribbean octocoral Briareum asbestinum. Mar. Biol. 114, 157–163. https://doi.org/10.1007/BF00350865 (1992).Article 

Google Scholar 
77.Lasker, H. R. et al. In situ rates of fertilization among broadcast spawning Gorgonian corals. Biol. Bull. 190, 45–55. https://doi.org/10.2307/1542674 (1996).CAS 
Article 
PubMed 

Google Scholar 
78.Coma, R. & Lasker, H. R. Effects of spatial distribution and reproductive biology on in situ fertilization rates of a broadcast-spawning invertebrate. Biol. Bull. 193, 20–29. https://doi.org/10.2307/1542733 (1997).CAS 
Article 
PubMed 

Google Scholar 
79.Oliver, J. & Babcock, R. Aspects of the fertilization ecology of broadcast spawning corals: Sperm dilution effects and in situ measurements of fertilization. Biol. Bull. 183, 409–417 (1992).CAS 
Article 

Google Scholar 
80.Levitan, D. R., Sewell, M. A. & Chia, F.-S. How distribution and abundance influence fertilization success in the Sea Urchin Strongylocentotus franciscanus. Ecology 73, 248–254. https://doi.org/10.2307/1938736 (1992).Article 

Google Scholar 
81.Jamieson, G. S. Marine invertebrate conservation: Evaluation of fisheries over-exploitation Concerns1. Am. Zool. 33, 551–567. https://doi.org/10.1093/icb/33.6.551 (1993).Article 

Google Scholar 
82.Fitt, K., Brown, B. E., Warner, M. E. & Dunne, R. P. Coral bleaching interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs 20, 51–65 (2001).Article 

Google Scholar 
83.Coles, S. L. & Jokiel, P. L. Synergistic effects of temperature, salinity and light on the hermatypic coral Montipora verrucosa. Mar. Biol. 49, 187–195. https://doi.org/10.1007/BF00391130 (1978).Article 

Google Scholar 
84.Torres, J. L., Armstrong, R. A. & Weil, E. Enhanced ultraviolet radiation can terminate sexual reproduction in the broadcasting coral species Acropora cervicornis (Lamarck). J. Exp. Mar. Biol. Ecol. 358, 39–45. https://doi.org/10.1016/j.jembe.2008.01.022 (2008).Article 

Google Scholar 
85.Grunwald, D. J. & Streisinger, G. Induction of mutations in the zebrafish with ultraviolet light. Genet. Res. 59, 93–101. https://doi.org/10.1017/S0016672300030305 (1992).CAS 
Article 
PubMed 

Google Scholar 
86.Lamare, M., Burritt, D. & Lister, K. Chapter Four – Ultraviolet Radiation and Echinoderms: Past, Present and Future Perspectives. Adv. Mar. Biol. 59, 145–187 (Academic Press, 2011).87.Jokiel, P. L. Solar ultraviolet radiation and coral reef Epifauna. Science 207, 1069–1071 (1980).ADS 
CAS 
Article 

Google Scholar 
88.Banaszak, A. T., Barba Santos, M. G., LaJeunesse, T. C. & Lesser, M. P. The distribution of mycosporine-like amino acids (MAAs) and the phylogenetic identity of symbiotic dinoflagellates in cnidarian hosts from the Mexican Caribbean. J. Exp. Mar. Biol. Ecol. 337, 131–146. https://doi.org/10.1016/j.jembe.2006.06.014 (2006).CAS 
Article 

Google Scholar 
89.Leutenegger, A. et al. It’s cheap to be colorful. FEBS J. 274, 2496–2505. https://doi.org/10.1111/j.1742-4658.2007.05785.x (2007).CAS 
Article 
PubMed 

Google Scholar 
90.Rosic, N. N. & Dove, S. Mycosporine-like amino acids from coral dinoflagellates. Appl. Environ. Microbiol. 77, 8478. https://doi.org/10.1128/AEM.05870-11 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
91.Smith, E. G., D’Angelo, C., Salih, A. & Wiedenmann, J. Screening by coral green fluorescent protein (GFP)-like chromoproteins supports a role in photoprotection of zooxanthellae. Coral Reefs 32, 463–474. https://doi.org/10.1007/s00338-012-0994-9 (2013).ADS 
Article 

Google Scholar 
92.Dove, S. Scleractinian corals with photoprotective host pigments are hypersensitive to thermal bleaching. Mar. Ecol. Prog. Ser. 272, 99–116 (2004).ADS 
Article 

Google Scholar 
93.Jokiel, P. L. & Brown, E. Global warming, regional trends and inshore environmental conditions influence coral bleaching in Hawaii. Glob. Change Biol. 10, 1627–1641. https://doi.org/10.1111/j.1365-2486.2004.00836.x (2004).ADS 
Article 

Google Scholar 
94.Pennington, J. T. The ecology of fertilization of Echinoid eggs: The consequences of sperm dilution, adult aggregation, and synchronous spawning. Biol. Bull. 169, 417–430. https://doi.org/10.2307/1541492 (1985).Article 
PubMed 

Google Scholar 
95.Levitan, D. R. & Young, C. M. Reproductive success in large populations: empirical measures and theoretical predictions of fertilization in the sea biscuit Clypeaster rosaceus. J. Exp. Mar. Biol. Ecol. 190, 221–241. https://doi.org/10.1016/0022-0981(95)00039-T (1995).Article 

Google Scholar 
96.Hagedorn, M. et al. Effects of toxic compounds in Montipora capitata on exogenous and endogenous zooxanthellae performance and fertilization success. PLoS ONE 10, e0118364. https://doi.org/10.1371/journal.pone.0118364 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
97.Zuchowicz, N. et al. Assessing coral sperm motility. Sci. Rep. 11, 61. https://doi.org/10.1038/s41598-020-79732-x (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
98.Kolinski, S. P. Sexual reproduction and the early life history of Montipora capitata in Kāne’ohe Bay, O’ahu, Hawai’i. Doctoral Dissertation, University of Hawai’i at Mānoa, (2004).99.Harrington, L., Fabricius, K., De’ath, G. & Negri, A. Recognition and selection of settlement substrata determine post-settlement survival in corals. Ecology 85, 3428–3437. https://doi.org/10.1890/04-0298 (2004).Article 

Google Scholar 
100.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org (R Foundation for Statistical Computing, Vienna, Austria, 2019). More

1.Lanza, B., Pastorelli, C., Laghi, P. & Cimmaruta, R. A review of systematics, taxonomy, genetics, biogeography and natural history of the genus Speleomantes Dubois, 1984 (Amphibia Caudata Plethodontidae). Atti Mus civ stor nat Trieste 52, 5–135 (2006).
Google Scholar 
2.Ficetola, G. F. et al. Differences between microhabitat and broad-scale patterns of niche evolution in terrestrial salamanders. Sci Rep 8, 10575, https://doi.org/10.1038/s41598-018-28796-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Lunghi, E., Manenti, R. & Ficetola, G. F. Seasonal variation in microhabitat of salamanders: environmental variation or shift of habitat selection? PeerJ 3, e1122, https://doi.org/10.7717/peerj.1122 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Ficetola, G. F., Lunghi, E. & Manenti, R. Microhabitat analyses support relationships between niche breadth and range size when spatial autocorrelation is strong. Ecography 43, 1–11, https://doi.org/10.1111/ecog.04798 (2020).Article 

Google Scholar 
5.Culver, D. C. & Pipan, T. The biology of caves and other subterranean habitats 2nd edn (Oxford University Press, 2019).6.Bradley, J. G. & Eason, P. K. Predation risk and microhabitat selection by cave salamanders, Eurycea lucifuga (Rafinesque, 1822). Behaviour 155, 841–859, https://doi.org/10.1163/1568539X-00003505 (2019).Article 

Google Scholar 
7.Salvidio, S., Palumbi, G., Romano, A. & Costa, A. Safe caves and dangerous forests? Predation risk may contribute to salamander colonization of subterranean habitats. The Science of Nature 104, 20, https://doi.org/10.1007/s00114-017-1443-y (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
8.Manenti, R., Melotto, A., Guillaume, O., Ficetola, G. F. & Lunghi, E. Switching from mesopredator to apex predator: how do responses vary in amphibians adapted to cave living? Behavioral Ecology and Sociobiology 74, 126, https://doi.org/10.1007/s00265-020-02909-x (2020).Article 

Google Scholar 
9.Lunghi, E. et al. Field-recorded data on the diet of six species of European Hydromantes cave salamanders. Sci Data 5, 180083, https://doi.org/10.1038/sdata.2018.83 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Lunghi, E. & Bruni, G. Long-term reliability of Visual Implant Elastomers in the Italian cave salamander (Hydromantes italicus). Salamandra 54, 283–286 (2018).
Google Scholar 
11.Mace, G. M. & Lande, R. Assessing extinction threats: towards a reevaluation of IUCN threatened species categories. Conservation Biology 5, 148–157 (1991).Article 

Google Scholar 
12.Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philosophical Transaction of the Royal Society B 367, 1665–1679, https://doi.org/10.1098/rstb.2012.0005 (2012).Article 

Google Scholar 
13.Rondinini, C., Battistoni, A., Peronace, V. & Teofili, C. Lista Rossa IUCN dei Vertebrati Italiani. (Comitato Italiano IUCN e Ministero dell’Ambiente e della Tutela del Territorio e del Mare, 2013).14.European Community. Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Official Journal of the European Union L 206/7, 1–44 (1992).
Google Scholar 
15.Régnier, C. et al. Mass extinction in poorly known taxa. Proc Natl Acad Sci USA 112, 7761–7766, https://doi.org/10.1073/pnas.1502350112 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
16.Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786, https://doi.org/10.1126/science.1103538 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
17.Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol Lett 15, 365–377, https://doi.org/10.1111/j.1461-0248.2011.01736.x (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Connette, G. M., Crawford, J. A. & Peterman, A. E. Climate change and shrinking salamanders: alternative mechanisms for changes in plethodontid salamander body size. Global Change Biology 21, 2834–2843, https://doi.org/10.1111/gcb.12883 (2015).ADS 
Article 
PubMed 

Google Scholar 
19.Heinrichs, J. A., Bender, D. J. & Schumaker, N. H. Habitat degradation and loss as key drivers of regional population extinction. Ecological Modelling 335, 64–73, https://doi.org/10.1016/j.ecolmodel.2016.05.009 (2016).Article 

Google Scholar 
20.Walters, R. J., Blanckenhorn, W. U. & Berger, D. Forecasting extinction risk of ectotherms under climate warming: an evolutionary perspective. Functional Ecology 26, 1324–1338, https://doi.org/10.1111/j.1365-2435.2012.02045.x (2012).Article 

Google Scholar 
21.Zhang, Z. et al. Future climate change will severely reduce habitat suitability of the Critically Endangered Chinese giant salamander. Freshwater Biology 65, 971–980, https://doi.org/10.1111/fwb.13483 (2020).Article 

Google Scholar 
22.Bland, L. M. Global correlates of extinction risk in freshwater crayfish. Animal Conservation 20, 532–542, https://doi.org/10.1111/acv.12350 (2017).Article 

Google Scholar 
23.Lunghi, E. et al. Photographic database of the European cave salamanders, genus Hydromantes. Sci Data 7, 171, https://doi.org/10.1038/s41597-020-0513-8 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
24.Mammola, S. et al. Continental data on cave-dwelling spider communities across Europe (Arachnida: Araneae). Biodivers Data J 7, e38492, https://doi.org/10.3897/BDJ.7.e38492 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
25.MacNeil, R. R. & Brcic, J. Coping with the subterranean environment: a thematic content analysis of the narratives of cave explorers. J Hum Perform Environ 13, Article 6, https://doi.org/10.7771/2327-2937.1089 (2017).Article 

Google Scholar 
26.Zagmajster, M., Culver, D. C., Christman, M. C. & Sket, B. Evaluating the sampling bias in pattern of subterranean species richness: combining approaches. Biodivers Conserv 19, 3035–3048, https://doi.org/10.1007/s10531-010-9873-2 (2010).Article 

Google Scholar 
27.Mammola, S. et al. Collecting eco-evolutionary data in the dark: Impediments to subterranean research and how to overcome them. Ecology and Evolution, https://doi.org/10.1002/ece3.7556 (2021).28.Brown, A. W., Kaiser, K. A. & Allison, D. B. Issues with data and analyses: errors, underlying themes, and potential solutions. Proc Natl Acad Sci USA 115, 2563–2570, https://doi.org/10.1073/pnas.1708279115 (2018).CAS 
Article 
PubMed 

Google Scholar 
29.Crovetto, F., Romano, A. & Salvidio, S. Comparison of two non-lethal methods for dietary studies in terrestrial salamanders. Wildlife Research 39, 266–270, https://doi.org/10.1071/WR11103 (2012).Article 

Google Scholar 
30.Lunghi, E. & Veith, M. Are Visual Implant Alpha tags adequate for individually marking European cave salamanders (genus Hydromantes)? Salamandra 53, 541–544 (2017).
Google Scholar 
31.Swanson, J. E., Bailey, L. L., Muths, E. & Funk, W. C. Factors influencing survival and mark retention in postmetamorphic Boreal chorus frogs. Copeia 2013, 670–675, https://doi.org/10.1643/CH-12-129 (2013).Article 

Google Scholar 
32.Sacchi, R. et al. Photographic identification in reptiles: a matter of scales. Amphibia-Reptilia 31, 489–502 (2010).Article 

Google Scholar 
33.Lunghi, E. et al. On the stability of the dorsal pattern of European cave salamanders (genus Hydromantes). Herpetozoa 32, 249–253, https://doi.org/10.3897/herpetozoa.32.e39030 (2019).Article 

Google Scholar 
34.Lunghi, E. et al. What shapes the trophic niche of European plethodontid salamanders? PLoS ONE 13, e0205672, https://doi.org/10.1371/journal.pone.0205672 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Lunghi, E. et al. The post hoc measurement as a safe and reliable method to age and size plethodontid salamanders. Ecology and Evolution 10, 11111–11116, https://doi.org/10.1002/ece3.6748 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
36.Hedrick, B. P. et al. Digitization and the future of natural history collections. BioScience 70, 243–251, https://doi.org/10.1093/biosci/biz163 (2020).Article 

Google Scholar 
37.Nelson, G. & Ellis, S. The history and impact of digitization and digital data mobilization on biodiversity research. Philosophical Transactions of the Royal Society B 374, 20170391, https://doi.org/10.1098/rstb.2017.0391 (2019).Article 

Google Scholar 
38.Lunghi, E. et al. Interspecific and inter-population variation in individual diet specialization: do environmental factors have a role? Ecology 101, e03088, https://doi.org/10.1002/ecy.3088 (2020).Article 
PubMed 

Google Scholar 
39.Salvidio, S., Romano, A., Oneto, F., Ottonello, D. & Michelon, R. Different season, different strategies: feeding ecology of two syntopic forest-dwelling salamanders. Acta Oecol 43, 42–50 (2012).ADS 
Article 

Google Scholar 
40.Rosenblatt, A. E. et al. Factors affecting individual foraging specialization and temporal diet stability across the range of a large “generalist” apex predator. Oecologia 178, 5–16, https://doi.org/10.1007/s00442-014-3201-6 (2015).ADS 
Article 
PubMed 

Google Scholar 
41.Lunghi, E. et al. Same diet, different strategies: variability of individual feeding habits across three populations of Ambrosi’s cave salamander (Hydromantes ambrosii). Diversity 12, 180, https://doi.org/10.3390/d12050180 (2020).Article 

Google Scholar 
42.Lunghi, E., Manenti, R. & Ficetola, G. F. Do cave features affect underground habitat exploitation by non-troglobite species? Acta Oecol 55, 29–35, https://doi.org/10.1016/j.actao.2013.11.003 (2014).ADS 
Article 

Google Scholar 
43.Lunghi, E. et al. Cave morphology, microclimate and abundance of five cave predators from the Monte Albo (Sardinia, Italy). Biodivers Data J 8, e48623, https://doi.org/10.3897/BDJ.8.e48623 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Carvalho-Rocha, V., Cortês, L. B. & Neckel-Oliveira, S. Interindividual patterns of resource use in three subtropical Atlantic Forest frogs. Austral Ecology 43, 150–158, https://doi.org/10.1111/aec.12552 (2018).Article 

Google Scholar 
45.Lunghi, E. et al. Photos and stomach contents of two mainland Italian Speleomantes salamanders: data from summer 2020. figshare https://doi.org/10.6084/m9.figshare.c.5398368 (2021).46.Martel, A. et al. Batrachochytrium salamandrivorans sp. nov. causes lethal chytridiomycosis in amphibians. Proc Natl Acad Sci USA 110, 15325–15329, https://doi.org/10.1073/pnas.1307356110 (2012).ADS 
Article 

Google Scholar 
47.Treilibs, C. E., Pavey, C. R., Hutchinson, M. N. & Bull, C. M. Photographic identification of individuals of a free-ranging, small terrestrial vertebrate. Ecology and Evolution 6, 800–809, https://doi.org/10.1002/ece3.1883 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Town, C., Marshall, A. & Sethasathien, N. Manta Matcher: automated photographic identification of manta rays using keypoint features. Ecology and Evolution 3, 1902–1914, https://doi.org/10.1002/ece3.587 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
49.MacCoun, R. & Perlmutter, S. Hide results to seek the truth. Nature 526, 187–189, https://doi.org/10.1038/526187a (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
50.Lunghi, E. et al. Thermal equilibrium and temperature differences among body regions in European plethodontid salamanders. J Therm Biol 60, 79–85, https://doi.org/10.1016/j.jtherbio.2016.06.010 (2016).Article 
PubMed 

Google Scholar 
51.Weller, H. I. & Westneat, M. W. Quantitative color profiling of digital images with earth mover’s distance using the R package colordistance. PeerJ 7, e6398, https://doi.org/10.7717/peerj.6398 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
52.Adams, D., Collyer, M. & Kaliontzopoulou, A. geomorph. Geometric Morphometric Analyses of 2D/3D Landmark Data. R package version 3.2.1, https://github.com/geomorphR/geomorph (2020).53.Bendik, N. F., Morrison, T. A., Gluesenkamp, A. G., Sanders, M. S. & O’Donnell, L. J. Computer-assisted photo identification outperforms visible implant elastomers in an endangered salamander, Eurycea tonkawae. PLoS ONE 8, e59424, https://doi.org/10.1371/journal.pone.0059424 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Renet, J., Leprêtre, L., Champagnon, J. & Lambret, P. Monitoring amphibian species with complex chromatophore patterns: a non-invasive approach with an evaluation of software effectiveness and reliability. Herpetological Journal 29, 13–22, https://doi.org/10.33256/hj29.1.1322 (2019).Article 

Google Scholar 
55.Allen-Blevins, C. R., You, X., Hinde, K. & Sela, D. A. Handling stress may confound murine gut microbiota studies. PeerJ 5, e2876, https://doi.org/10.7717/peerj.2876 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Samimi, A. S., Tajik, J., Jarakani, S. & Shojaeepour, S. Evaluation of a five-minute resting period following handling stress on electrocardiogram parameters and cardiac rhythm in sheep. Veterinary Science Development 6, 6481, https://doi.org/10.4081/vsd.2016.6481 (2016).Article 

Google Scholar 
57.Martel, A. et al. Recent introduction of a chytrid fungus endangers Western Palearctic salamanders. Science 346, 630, https://doi.org/10.1126/science.1258268 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Lunghi, E., Corti, C., Manenti, R. & Ficetola, G. F. Consider species specialism when publishing datasets. Nat Ecol Evol 3, 319, https://doi.org/10.1038/s41559-019-0803-8 (2019).Article 
PubMed 

Google Scholar  More

1.Agarwal, V. et al. Enzymatic halogenation and dehalogenation reactions: pervasive and mechanistically diverse. Chem. Rev. 117, 5619–5674 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Atashgahi, S., Haggblom, M. M. & Smidt, H. Organohalide respiration in pristine environments: implications for the natural halogen cycle. Environ. Microbiol. 20, 934–948 (2018).CAS 
PubMed 
Article 

Google Scholar 
3.Gribble G. W. Naturally Occurring Organohalogen Compounds—A Comprehensive Update: (Wien/New York: Springer, 2010).4.Stringer, R. & Johnston, P. Chlorine and the environment: an overview of the chlorine industry. Environ. Sci. Pollut. Res. 8, 146–159 (2001).Article 

Google Scholar 
5.Falandysz, J., Rose, M. & Fernandes, A. R. Mixed poly-brominated/chlorinated biphenyls (pxbs): widespread food and environmental contaminants. Environ. Int. 44, 118–127 (2012).CAS 
PubMed 
Article 

Google Scholar 
6.Zhang, Z. et al. Halogenated organic pollutants in sediments and organisms from mangrove wetlands of the jiulong river estuary, south china. Environ. Res. 171, 145–152 (2019).CAS 
PubMed 
Article 

Google Scholar 
7.Kunze, C. et al. Cobamide-mediated enzymatic reductive dehalogenation via long-range electron transfer. Nat. Commun. 8, 15858 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Wang, S. et al. Electron transport chains in organohalide-respiring bacteria and bioremediation implications. Biotechnol. Adv. 36, 1194–1206 (2018).PubMed 
Article 
CAS 

Google Scholar 
9.Atashgahi S., Lu Y., Smidt H. Overview of known organohalide-respiring bacteria—phylogenetic diversity and environmental distribution. In Adrian L., Löffler F. E., editors. Organohalide-Respiring Bacteria. (Springer, Berlin, 2016).10.Fincker, M. & Spormann, A. M. Biochemistry of catabolic reductive dehalogenation. Annu. Rev. Biochem. 86, 357–386 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Rouzeau-Szynalski, K., Maillard, J. & Holliger, C. Frequent concomitant presence of desulfitobacterium spp. and “dehalococcoides” spp. in chloroethene-dechlorinating microbial communities. Appl. Microbiol. Biotechnol. 90, 361–368 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Wang, S. & He, J. Dechlorination of commercial pcbs and other multiple halogenated compounds by a sediment-free culture containing dehalococcoides and dehalobacter. Environ. Sci. Technol. 47, 10526–10534 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Wang, S. et al. Genomic characterization of three unique dehalococcoides that respire on persistent polychlorinated biphenyls. Proc. Natl. Acad. Sci. U.S.A. 111, 12103–12108 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Bachmann, H. et al. Availability of public goods shapes the evolution of competing metabolic strategies. Proc. Natl. Acad. Sci. U.S.A. 110, 14302–14307 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Yan, J., Ritalahti, K. M., Wagner, D. D. & Loffler, F. E. Unexpected specificity of interspecies cobamide transfer from geobacter spp. To organohalide-respiring dehalococcoides mccartyi strains. Appl. Environ. Microbiol. 78, 6630–6636 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Lawson, C. E. et al. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17, 725–741 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Wang, S., Chen, C., Zhao, S. & He, J. Microbial synergistic interactions for reductive dechlorination of polychlorinated biphenyls. Sci. Total. Environ. 666, 368–376 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Men, Y. et al. Sustainable syntrophic growth of dehalococcoides ethenogenes strain 195 with desulfovibrio vulgaris hildenborough and methanobacterium congolense: Global transcriptomic and proteomic analyses. ISME J. 6, 410–421 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Lu, Q. et al. Dehalococcoides as a potential biomarker evidence for uncharacterized organohalides in environmental samples. Front. Microbiol. 8, 1677 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Xu, G., Lu, Q., Yu, L. & Wang, S. Tetrachloroethene primes reductive dechlorination of polychlorinated biphenyls in a river sediment microcosm. Water Res. 152, 87–95 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Royer, D. L., Osborne, C. P. & Beerling, D. J. Carbon loss by deciduous trees in a co2-rich ancient polar environment. Nature. 424, 60–62 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Wang, S. & He, J. Phylogenetically distinct bacteria involve extensive dechlorination of aroclor 1260 in sediment-free cultures. PLoS One 8, e59178 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Wang, S. et al. Development of an alkaline/acid pre-treatment and anaerobic digestion (apad) process for methane generation from waste activated sludge. Sci. Total Environ. 708, 134564 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Lu, Q. et al. Inhibitory effects of metal ions on reductive dechlorination of polychlorinated biphenyls and perchloroethene in distinct organohalide-respiring bacteria. Environ. Int. 135, 105373 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evolut. 33, 1870–1874 (2016).CAS 
Article 

Google Scholar 
26.Cummings, D. E. et al. Diversity of geobacteraceae species inhabiting metal-polluted freshwater lake sediments ascertained by 16S rDNA analyses. Microb. Ecol. 46, 257–269 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Holmes, V. F., He, J., Lee, P. K. & Alvarez-Cohen, L. Discrimination of multiple dehalococcoides strains in a trichloroethene enrichment by quantification of their reductive dehalogenase genes. Appl. Environ. Microbiol. 72, 5877–5883 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Joshi N. A., Fass J. N. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. https://github.com/najoshi/sickle. (2011).29.Bankevich, A. et al. Spades: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with bowtie 2. Nat. Methods. 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
32.Chen, L. X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Res. 30, 315–333 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. Checkm: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 30, 2068–2069 (2014).CAS 
PubMed 
Article 

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

Google Scholar 
36.Loffler, F. E. et al. Dehalococcoides mccartyi gen. Nov., sp. Nov., obligately organohalide-respiring anaerobic bacteria relevant to halogen cycling and bioremediation, belong to a novel bacterial class, dehalococcoidia classis nov., order dehalococcoidales ord. Nov. And family dehalococcoidaceae fam. Nov., within the phylum chloroflexi. Int. J. Syst. Evol. Microbiol. 63, 625–635 (2013).CAS 
PubMed 
Article 

Google Scholar 
37.Garcia C.A. Subsurface occurrence and potential source areas of chlorinated Ethenes Identified using concentrations and concentration ratios, Air Force Plant 4 and Naval Air Station-Joint Reserve Base Carswell Field, Fort Worth, Texas. U.S. Geological Survey, (2006).38.Nichols H. Use of electrical resistive heating for the remediation of CVOC and petroleum impacts in soil and groundwater, NEWMOA Conference. New York City (2012).39.Maymó-Gatell, X., Chien, Y., Gossett, J. M. & Zinder, S. H. Isolation of a bacterium that reductively dechlorinates tetrachloroethene to ethene. Science. 276, 1568–1571 (1997).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Sung, Y. et al. Geobacter lovleyi sp. Nov. Strain sz, a novel metal-reducing and tetrachloroethene-dechlorinating bacterium. Appl. Environ. Microbiol. 72, 2775–2782 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Amos, B. K., Suchomel, E. J., Pennell, K. D. & Löffler, F. E. Spatial and temporal distributions of Geobacter lovleyi and Dehalococcoides spp. during bioenhanced PCE-NAPL dissolution. Environ. Sci. Technol. 43, 1977–1985 (2009).CAS 
PubMed 
Article 

Google Scholar 
42.Lai, Y. & Becker, J. G. Compounded effects of chlorinated ethene inhibition on ecological interactions and population abundance in a Dehalococcoides–Dehalobacter coculture. Environ. Sci. Technol. 47, 1518–1525 (2013).CAS 
PubMed 

Google Scholar 
43.Lovley, D. R. et al. Geobacter: the microbe electric’s physiology, ecology, and practical applications. Adv. Microb. Physiol. 59, 1–100 (2011).CAS 
PubMed 
Article 

Google Scholar 
44.Reguera, G. & Kashefi, K. The electrifying physiology of geobacter bacteria, 30 years on. Adv. Microb. Physiol. 74, 1–96 (2019).PubMed 
Article 

Google Scholar 
45.Kruse, S., Goris, T., Westermann, M., Adrian, L. & Diekert, G. Hydrogen production by sulfurospirillum species enables syntrophic interactions of epsilonproteobacteria. Nat. Commun. 9, 4872 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Sanford, R. A., Cole, J. R. & Tiedje, J. M. Characterization and description of anaeromyxobacter dehalogenans gen. Nov., sp. Nov., an aryl-halorespiring facultative anaerobic myxobacterium. Appl. Environ. Microbiol. 68, 893–900 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Sung, Y. et al. Characterization of two tetrachloroethene-reducing, acetate-oxidizing anaerobic bacteria and their description as desulfuromonas michiganensis sp. Nov. Appl. Environ. Microbiol. 69, 2964–2974 (2003).CAS 
PubMed 
Article 

Google Scholar 
48.Peng, P. et al. Organohalide-respiring desulfoluna species isolated from marine environments. ISME J. 14, 815–827 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Goris, T. et al. Insights into organohalide respiration and the versatile catabolism of sulfurospirillum multivorans gained from comparative genomics and physiological studies. Environ. Microbiol. 16, 3562–3580 (2014).CAS 
PubMed 
Article 

Google Scholar 
50.Goris, T. et al. Proteomics of the organohalide-respiring epsilonproteobacterium sulfurospirillum multivorans adapted to tetrachloroethene and other energy substrates. Sci. Rep. 5, 13794 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Cross, K. L. et al. Targeted isolation and cultivation of uncultivated bacteria by reverse genomics. Nat. Biotechnol. 37, 1314–1321 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Lewis, W. H. & Ettema, T. J. G. Culturing the uncultured. Nat. Biotechnol. 37, 1278–1279 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Koch, H., van Kessel, M. & Lucker, S. Complete nitrification: insights into the ecophysiology of comammox nitrospira. Appl. Microbiol. Biotechnol. 103, 177–189 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Marcy, Y. et al. Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc. Natl. Acad. Sci. U.S.A. 104, 11889–11894 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Pfeiffer, T., Schuster, S. & Bonhoeffer, S. Cooperation and competition in the evolution of ATP-producing pathways. Science. 292, 504–507 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Roller, B. R. & Schmidt, T. M. The physiology and ecological implications of efficient growth. ISME J. 9, 1481–1487 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
57.Lawson, C. E. & Lucker, S. Complete ammonia oxidation: an important control on nitrification in engineered ecosystems? Curr. Opin. Biotechnol. 50, 158–165 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature. 549, 269–272 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Database constructionCollection of metagenomic dataBetween March 2012 and May 2016, seawater samples were collected from five different locations around Sendai Bay, Ofunato Bay, and A-line. Samples (N = 142) were collected from the surface and the subsurface chlorophyll-a maximum (SCM) layers (Fig. 1). DNA was extracted from microorganisms trapped in filters (pore sizes, 0.2, 0.8, 5, 20, and 100 μm). Shotgun metagenomic sequence data (N = 454) were acquired (Supplementary Information 1), comprising 3.57 × 109 reads and 3.56 × 1011 bases in total (Supplementary Information 2). 16S rRNA gene sequences in the 0.2-μm fraction were subjected to PCR using universal primers to obtain 111 amplicon metagenomic sequences (6.92 × 106 reads, 1.69 × 109 bases) (Supplementary Information 2).Figure 1Location, changes in water temperature, and changes in chlorophyll-a (Chl-a) concentrations at the sampling points. (a) Sampling points along the Pacific coast of northeastern Japan (Sendai Bay, Ofunato Bay, and A-line). (b) Sampling points C5 and C12 in Sendai Bay. The map was generated using Ocean Data View (https://odv.awi.de) with data imported from the NOAA server (accessed on 22 February 2021). (c) Changes in water temperature and chlorophyll concentrations at Sendai Bay and A-line sampling points. Red circles indicate the depth of the sampled water. X-axis: dates from 2012 to 2014. Y-axis: water depth from the surface.Full size imageTime-series analysis of microbial species compositionWe performed microbial taxonomic assignments through analyses of amplicon and shotgun metagenomic sequence data using the SILVA and NCBI NT databases. For C5 and C12 samples from Sendai Bay and A4 and A21 samples from A-line, sufficient time-series data were available to plot changes in microbiota over time (Fig. 2). By clicking the displayed taxon at the website of Ocean Monitoring Database, the microbiota composition of lower taxa is revealed. For example, Fig. 2 shows that the cyanobacteria community increased in abundance during the summer.Figure 2Time-series analysis of microbial communities along the Pacific coast of northeastern Japan. Each sampling point shows the number of ribosomal sequences normalized to 1000 (excluding no hits). Clicking on the graph at the website of Ocean Monitoring Database exhibits the next taxonomic levels. This figure shows an example of the change in Cyanobacteria communities over time from April 2012 to May 2014. SUF: surface layer (1 m), SCM: the subsurface chlorophyll-a maximum layer.Full size imageWe acquired substantial 16S rRNA gene amplicon sequencing data at the C5 fixed point at Sendai Bay between 2012 and 2014. We generated a 3D graph to simultaneously display the date, water depth, and species composition at this site (Fig. 3). By clicking on the taxon shown in the graph at the website of Ocean Monitoring Database, the composition of the microbiota within a lower taxon is displayed. The 3D display is suitable for presenting a bird’s-eye view of the metagenomic data, which is extremely useful for visualizing and understanding the relationships among microbial communities among sampling points. This innovative function was incorporated into the metagenomic database. Figure 3 shows a contour map of chlorophyll concentrations on the x-axis and the proportion of microbial communities on the z-axis. The proportion of flavobacteria may increase following an increase in chlorophyll concentration during a spring bloom. For example, Buchan et al. reported that the proportion of flavobacteria increase late in a spring bloom16; our results show similar patterns (Fig. 3).Figure 3Three-dimensional (3D) display of microbial communities. 3D display of bacterial communities identified using 16S rRNA gene amplicon analysis of the Sendai Bay C5 samples from 2012 to 2014. The x-axis indicates the date, the y-axis indicates the water depth, and the z-axis indicates the percentage abundance of bacterial genera. The contour plot on the xy plane indicates the chlorophyll concentration. The composition of Flavobacteriaceae is shown as an example.Full size imageDigital DNA chip (DDC) databaseA DDC analysis (DDCA) system is useful for visualizing the characteristics of shotgun metagenomic data as a microarray17 of, for example, filter size, water sampling point, water sampling time, temperature, salinity, and nitrate and phosphate concentrations (Fig. 4a). By mapping sequence data against the probe sets described above, which are associated with environmental factors, we predicted that sequence data would be more enriched and inclusive of environmental information. Figure 4b displays the DDCA shotgun metagenomic data of the 0.2–0.8-μm fraction of the C5 sample collected from Sendai Bay on December 1, 2013. The sample contains a bacterial-fraction DNA marker with filter sizes of 0.2–0.8 µm and a specific DNA marker for December in Sendai Bay. Even if there is only NGS data and no environmental information, just by looking at the digital DNA chip, we can assume this sample is extracted from 0.2 to 0.8 µm fraction and is from Sendai Bay (Fig. 4b).Figure 4Visualization of metagenomics data using digital DNA chips. (a) Overview of in silico probes associated with the environmental factors on a digital DNA chip (See Supplementary Information 8 for details). (b) Digital DNA chip of shotgun metagenomics data of a 0.2–0.8-μm fraction of December 1, 2013, Sendai Bay C5. There are 748 probes, and spots that are positive for digital hybridization are shown in red. Negative spots are black. The hybridization positive probes are an indicator of environmental information of the sequence data.Full size imageDevelopment of a shotgun metagenomic databaseWe assembled the shotgun metagenomic sequence data using Megahit version 1.0.218. There were 57.95 M contigs, with an N50 of 995 bp, a maximum length of 307,212 bp, and a total of 12.39 Gbp (Supplementary Information 3). We calculated the abundance pattern of each contig. Those contigs whose appearance pattern matched with a Pearson correlation coefficient of ≥ 0.95 were clustered into a MAG. We next added the annotation of assembled contigs to the results of the BLAST search of the NCBI NT database and using classification by clustering with Pfam (CCP). This novel annotation method is described below. We developed the database showing the abundance pattern of homologous contigs against a queried sequence by BLAST for each sampling point and filter size (Fig. 5). For example, we found novel PolD families using this database.Figure 5Search for homologous contigs to a query sequence and display of temporal variation patterns. Using nucleotide and amino acid sequences as queries, contigs homologous to the query sequence are identified using BLAST, and the temporal variation patterns and taxonomy information of the hit contigs are displayed.Full size imageDevelopment of a new annotation method for metagenome contigsWe annotated the assembled contigs using BLAST to analyze the NCBI NT database. However, we were unable to annotate  > 50% of the contigs (Fig. 6). Therefore, we developed a novel method, i.e., CCP, to annotate contigs according to their species names. Analysis using a single contig generally does not provide sufficient information for assigning an annotation. However, CCP assigns the appropriate annotation to the sequence because it aggregates the Pfam information of all contigs in a MAG. CCP annotates a MAG by comparing the similarity to the reference genome. Comparing the nucleotide sequences of a MAG directly using blastn to analyze the NCBI NT database shows relatively low homology to de novo virus sequences.Figure 6Comparison between BLAST and CCP annotation results at the super-kingdom level. Comparison of classification results using BLAST to annotate contigs and classification by clustering with Pfam (CCP); the percentage of unknowns was 57% for BLAST and 8% for CCP.Full size imageHowever, a Pfam domain search using HMMER, which employs a different principle19 than BLAST, often detects more informative sequences, even those of viruses. For example, phylogenetic trees constructed according to the type and number of Pfam domains of individual bacterial genomes and those of higher eukaryotes such as humans closely approximate those generated using existing phylogenetic trees20. Thus, genomes with a similar Pfam domain may represent phylogenetically closely related species. We, therefore, searched the Pfam domains for reference genomes of viruses, bacteria, archaea, and eukaryotes included in RefSeq (as of August 31, 2015). We next calculated the number of domains for each species and constructed a CCP database. The types and numbers of Pfam domains contained in the contig obtained from the metagenome were summarized in MAG units. We compared the results using the CCP database and annotated the known genomes with the closest correlation coefficient (Fig. 7).Figure 7Overview of CCP. Flowchart of the search of the Pfam domain against known genomes of viruses, bacteria, archaea, and eukaryotes included in RefSeq to create a Pfam hit database. The Pfam domains were searched in metagenome-assembled genome (MAG) units and the known genomes whose type and number of Pfam domains are closest to the MAG.Full size imageBy annotating the top 10,000 contigs with the highest abundance in our database using CCP,  > 90% of the contigs were explained (Fig. 6). In contrast, the BLAST species search (the existing method) returned  15%, indicating that CCP is a robust method, particularly when applied to the identification of virus annotation. We next compared the agreement between CCP and BLAST annotations using contigs annotated using both CCP and BLAST (Supplementary Information 4). The virus-level agreement between CCP and BLAST was 89.6%, and the kingdom-level agreement was 76.9%. It was difficult to determine whether the contig represented a virus using BLAST; however, CCP showed higher accuracy.Shotgun metagenomic analysisPeriodicity of metagenomic dataFor the bacterial fraction (0.2–0.8 µm) of the shotgun metagenomic data from Sendai Bay, we generated a multidimensional scaling (MDS) plot according to the pattern of the abundance of the assembled contigs (Fig. 8). The MDS plot shows similarities among the samples collected during the same month during different years. Furthermore, the plot reveals that the shotgun metagenomic data exhibit an annual seasonal cycle like the 18S rRNA amplicon data10. However, we did not observe the same annual cycle among all contigs. We, therefore, extracted contigs included in the top 20 highly abundant MAGs from the bacterial fractions of Sendai Bay samples collected from March 2012 to April 2014 and plotted the fluctuation patterns. Only one such contig showed a complete 2-year cycle (Fig. 9a), and four contigs showed an incomplete 2-year cycle with peaks in March 2012 and 2013 but not in 2014 (Fig. 9b). Furthermore, 13 MAGs showed a transient pattern (Fig. 9c), and two MAGs showed peaks with irregular patterns (Fig. 9d). These results suggest that marine microbial communities generally undergo an annual cycle.Figure 8Multidimensional scaling (MDS) plot as a function of the abundance of contigs. MDS plots of bacterial fractions (0.2–0.8 µm) of shotgun metagenomic data from 2012 to 2015 acquired from Sendai Bay according to the pattern of abundance of assembled contigs.Full size imageFigure 9Variation patterns of contigs in the top 20 most abundant metagenome-assembled genomes (MAGs).The top 20 MAGs in the bacterial fractions of Sendai Bay C5 and C12 from March 13, 2012, to April 2, 2014, were classified as follows: (a) complete 1-year cycle for 2.5 years, (b) Incomplete 1-year cycle for 2.5 years, (c) transient peaks, and (d) irregular peaks. A peak within 1 month of ≥ 25% relative to the previous year’s peak was considered cyclical.Full size imageIdentification of repeat sequences in the metagenomesDuring the collection and analysis of the DNA sequencing data, we identified a number of repeat sequences in the metagenomes as follows: (TAG)n, (TGA)n, (GAA)n, and (ACA)n microsatellites. We then determined the frequencies and highest numbers of (TAG)n repeats as a function of filter size. We found that the (TAG)n repeats included up to 7.5% of the 5–20-μm fraction (Supplementary Information 5a,b). To investigate whether this was a characteristic feature of the northeastern coastal region of Japan, we analyzed the shotgun metagenomic sequence data of Tara Oceans21,22. As shown in Supplementary Information 5c,d, Tara Oceans data contained up to 1.9% of TAG repeats.To determine whether these (TAG)n repeats represented artifacts of the NGS method, we performed Southern blot and dot-blot hybridization analyses of the DNA samples extracted from seawater (Fig. 10). The dot-blot hybridization experiment analyzed 13 different samples with various content rates (Fig. 10a). We detected signals from the eight samples containing the (TAG)n that were repeated in  > 0.9% of the labeled d54-mer with the (TAG)18 repeat. In contrast, six samples with a low content ( > 0.2%) were negative (Fig. 10b). To determine whether these repeated sequences originated from a single locus or multiple loci, we performed Southern blot analysis (Fig. 10c, Supplementary Information 6) using two samples with high contents of (TAG)n repeats. A (TAG)n representing a single locus is detectable as a discrete band versus the diffuse bands exhibited by two samples with a high content of (TAG)n repeats. The data (Fig. 10c) suggest that the (TAG)n repeats were derived from multiple loci of distinct genomes. Samples with low numbers of (TAG)n repeats were negative.Figure 10Detection of TAG repeats using Southern blot and dot-blot hybridization analyses. (a) Contents of the TAG repeats of the samples according to next-generation sequencing analysis. (b) Dot-blot analyses. The sample numbers and their amounts, (right side) correspond to the signals of each dot in the left panels. The intact pTV119N plasmid without an insert indicates pTV(0). The calculated contents of TAG repeats (%) are indicated in parentheses. (c) Southern blot analysis of EcoRI-digested samples subjected to 0.8% agarose gel electrophoresis. The plasmid pTV (TAG) (0.35 ng and 1 ng) served as a positive control. E. coli genomic DNA served as a negative control. The calculated contents of TAG repeats (%) are indicated on the bottom of each graph. The length (nt) of the TAG-repeated fragment excised from pTV (TAG) is shown on the right.Full size imageThese results reveal for the first time that such repeat sequences are abundant in the genomes of marine microorganisms. However, their species of origin and functional roles were not identified here. The repeat sequences found in Escherichia coli23, subsequently called CRISPR, led to fundamental discoveries that are essential in the field of genetic engineering24. Thus, understanding the biological significance of trinucleotide repeats in marine microorganisms is of particular importance and may reveal a new research frontier. More

Calculation of the indicatorsFigure 1 shows the distribution of the urban greening benefit indicators computed at European scale under the current scenario, while Fig. 2 shows the cumulative distribution of impervious urban areas by increasing value of each indicator, under the current and future scenarios. It should be stressed that, while the indicators of Eqs. (1–4) are computed for every grid cell, the curves of Fig. 2 reflect also the spatial distribution of impervious urban surfaces, and hence they give more prominence to the values of the indicators in the most densely urbanized areas of the continent.Figure 1Maps of benefits per m2 across Europe for ΔTs (a), ΔT (b), RR/P (c) and CB (d), in the present scenario.Full size imageFigure 2Cumulative curves of urban surfaces versus the indicator ΔTs (a), ΔT (b), RR/P (c) and CB (d). The black line represents present conditions, while lines in color stand each for one climatic scenario. The y-axis is the cumulative surface area of the present European urban areas.Full size imageThe reduction of surface temperature ΔTs (Fig. 1a) is highest in the warmer and not excessively dry climates of Central and Southern Europe, reflecting the patterns of actual evapotranspiration. Most European urban areas would achieve temperature reductions of about 3–3.5 °C (Fig. 2a), slightly increasing with the severity of climate heating under the various scenarios, causing a reduction of sensible heat to the atmosphere, a driver of urban heat island effects, between 20 and 40% (see Appendix 1, Supplementary Material for further details). The highest temperature reduction at the roof surface, ΔT, is mostly perceived in the South of Europe (Fig. 1b), consistent with the pattern of potential evapotranspiration, similarly to the production of dry biomass CB (Fig. 1d). The reduction of temperature at the roof is predicted between 15 and 17 °C for most of Europe under the current scenario, and may increase of about 2 °C under the most severe climate scenario (Fig. 2b). Runoff reduction is significantly higher in areas with moderate precipitation, particularly in the plains, compared to rainier areas such as the Atlantic edge of the continent and high mountain ranges (Fig. 1c).The maximum storage volume, Vmax , calculated by Eq. 6, would allow to reuse 92% of the annual runoff, while Vmin and Vavg would allow to store 77% and 86% of the runoff, respectively, as resulting from a daily balance of the storage volume calculated over the 14 year time series. As the storage volume normalized to the annual runoff Rc is 0.24, 0.36 and 0.51 for Vmin, Vavg and Vmax, respectively (Figure 4b), choosing a storage volume equal to Vmin appears to be the most cost-effective solution. Vmin is mapped as shown in Fig. 3a for the case of constant demand, under the current scenario, while in Fig. 3b the volumes are plotted versus the cumulated areas.Figure 3Storage volume Vmin required to store the runoff in the case of constant demand.Full size imageFigure 4Runoff that could be harvested, and normalized storage volume Vmin versus the annual average runoff (Rc) for the case of constant withdrawal, calculated throughout the 14 year time series.Full size imagePhysical and environmental implicationsThese potential effects of green surfaces at European scale correspond to potential benefits. The total benefits extrapolated for the EU are summarized in Table 1. Results are referred to the impervious surfaces corresponding to building roofs, that are assumed to amount to a total of 26,450 km2 as per Bódis et al.40 . This represents 35% of the European impervious surface. Although it is highly unlikely that the majority of the roofs may support a uniform soil cover of 30 cm, they could still bear patches of that thickness over a part of their surface. Moreover, additional surfaces such as sealed ground could be greened. Overall, having in mind these considerations, we pragmatically regard this 35% of impervious urban areas as a maximum extent that could be greened in Europe. All benefits calculated below would obviously scale proportionally for any reduction of the percentage of area subjected to greening. The quantification of Table 1 is explained below.Table 1 Climatic descriptors and quantification of annual benefits at the European scale in the present and future climatic scenarios, assuming to green all roof surfaces, or 35% of the European impervious surfaces.Full size tableThe reduction of land surface temperatures, ΔTs, reduces the thermal irradiation and convective heat flux from urban surfaces (see Appendix 1 of Supplementary Material), which are the drivers of the heat island effect44. As a first order approximation, the reduction of air temperature at 2 m from the surfaces can be expected to be about a half of ΔTs45 as an average value in summer. The reduction of air temperature would generate economic benefits, like the life cycle extension of electronic material and cars, benefits in the health and transport sectors, reduction of social stress and morbidity, and reduction of damages to trees and animals46,47,48.The reduction of the surface temperature ΔT potentially reduces the cooling demand in summer (Eq. 5) by 92 TWh year−1. This energy saving corresponds to 29.9 Mtons of CO2 for the present scenario, considering emissions of 0.325 kg CO2 equivalent kWh−1 for European electricity39. Our estimate is arguably an upper limit of cooling energy savings. In many cases, underroof spaces of buildings are not cooled and effectively work already as an insulation, hence the reduction in the heat transferred from the roofs to underlying inhabited spaces may be lower than we estimate.The yearly produced biomass CB is a benefit in itself whenever the biomass may be used (e.g. crops from urban agriculture). However, more importantly, it may be appraised in terms of carbon and carbon dioxide sequestration. The carbon dioxide sequestered from the atmosphere through biomass growth is 25.9 Mtons year−1 in the present scenario. This must be summed to the reduction of carbon emission following the expected decrease in cooling energy use for a total of 55.8 Mtons, or about 1.2% of the 4500 Mtons CO2 produced in the EU every year37.It should be stressed that carbon dioxide sequestration by the biomass in green roofs is effective only if residues are not significantly degraded. This may be achieved by removing the biomass periodically before it undergoes respiration and mineralization. One could alternatively employ woody plants with a higher carbon accumulation capacity instead of herbaceous vegetation. Although our calculations are referred to a herbaceous annual crop, the results in terms of dry biomass would not be radically different had we considered a tree or shrub crop, as the dry matter potentially produced per unit surface is relatively independent of the plant49. On the other hand, trees and shrubs may be expected to have higher evapotranspiration, thus enhancing the benefits quantified here for a herbaceous crop.If greening is implemented on about 35% of the impervious urban areas, we expect a reduction of runoff in the order of 17.5% compared to the total. Considering that pollutant loads associated to runoff are estimated in the order of about 30 million population equivalents (PE) in terms of biochemical oxygen demand (BOD), about 18 million PE in terms of total nitrogen and about 6 million PE in terms of total phosphorus 6,35, this can be a sizable contribution to the treatment of pollution from European urban areas. Besides the reduction of runoff volume, greened surfaces may also help reduce the frequency of combined sewer overflows because they buffer runoff and release it more slowly than impervious surfaces. This effect is arguably more important for smaller storm events, and tends to disappear as events cause the saturation of green roof storage.It should be stressed that the above analysis considers a soil thickness of 30 cm on greened surfaces. Using the meta-models proposed in30 for the thickness of 10 cm we obtain a ratio between the indicators for thickness of 10 and 30 cm ranging between 80–97% for the reduction of surface temperatures, 55–57% for roof temperatures, 47–57% for biomass, and 84–86% for runoff. Soil thickness affects in particular the roof temperature, due to the associated thermal insulation effect, and the biomass, because a thicker soil can store a larger amount of water and allows a higher evapotranspiration for vegetation growth, while not impeding root growth. A comparison of different climate scenarios sheds light on the sensitivity of our results to the input climatic predictors (P and ET0). From Table 1, it can be calculated that the range (difference between the maximum and minimum value) of precipitation and potential evapotranspiration, as a percentage of the average value, is 20.3%, and 21.4% respectively. The corresponding ranges are 7% of the average for the cooling reduction, 3.7% for the reduced carbon dioxide emission, and 34% for the runoff reduction. The curves in Fig. 2 visualize the relatively small sensitivity of results to the climatic scenario.Economic implicationsMost of the benefits of green roofs are collective. Only a few (e.g. energy saving in summer, and gardening) have an apparent private nature. The costs of greening roofs, on the contrary, are primarily borne by the private owners50. It has been observed that, in the absence of specific incentives, green roof implementation can be economically convenient only for specific commercial and multifamily buildings25. Therefore, private investments should be encouraged through appropriate fiscal and funding policies if the objective is to facilitate a mainstream uptake of this solution. In this section, an indicative cost-benefit analysis is carried out in order to shed light on the possible financing needs at stake, and considering to green the impervious surfaces covered by roofs.The two main benefits that can be easily monetized are the avoided cost of cooling in summer (based on energy prices) and the reduction of carbon dioxide emissions (based on greenhouse gas emissions market prices). By summing the results of Eq. (5) for all gridcells in Europe where the greened surface is assumed to be 35% of the impervious urban area in the gridcell, cooling savings can reach 18.4 billion Є each year for the current scenario. For comparison, the current expenditure for residential cooling in summer can be assumed to be 78 billion Є year−1, based on an electricity use of 391 TWh51. Therefore, the cooling energy saving is 23.5% (18.4 billion Є/78 billion Є), in agreement with the results of Manso et al.15 for the value of 15% estimated for the hot-summer Mediterranean climate.At the present carbon market price of 22.5 Є tons−1 (Ruf and Mazzoni43), the annual benefit related to the estimated reduction of greenhouse gas emissions corresponds to about 1.26 billion Є. It should be stressed how this is apparently an upper limit of this benefit, because not all greened surfaces may correspond to roofs of cooled building volumes, and because the biomass is likely to undergo at least a partial mineralization if not timely removed from the green surfaces. The benefit associated to the reduction of the heat island effect can also be quantified to some extent on the basis of existing literature studies, although their estimation is very complex and would require ad hoc studies. For example, for the city of Phoenix, this benefit was quantified in 80 € for 1 °C decrease per working resident, considering costs of electronic devices, maintenance of cars and performance of cooling47. In another analysis for the Melbourne area, the annual cost was quantified in 18 € per inhabitant, including health, transport, social distress, electric grid faults and damages to animal and trees48. In Malaysia, the annual cost of hazes, related to the urban heat island, was quantified in 12 € per habitant in 1997, including cost of illness, productivity loss, flight cancellation, tourism reduction, decline in fish landings, fire-fighting, cloud seeding and masks46. Therefore, costs can vary significantly among different contexts. Assuming conservatively a yearly benefit of 20 € for each of the ca. 559.5 million European urban inhabitants living in urban areas (75% of the total52), the Net Present Value (NPV) of this benefit over 40 years would be 221 billion € using a discount rate of 4%.The cost of greening the roofs or other impervious surfaces is more difficult to quantify as it depends on several design details and site-specific conditions. For example, in Finland the cost ranges between 70 and 80 Є m−2, in Germany between 13 and 41 Є m−2, while in Switzerland around 20 Є m−253. Assuming an average unit cost of 50 Є m−2, the costs to turn 26,450 km2 of impervious urban areas in Europe into green surfaces amounts to 1323 billion Є. This corresponds to an annual cost (discount rate 4%, 40 years life) of 63 billion euro. This means a cost of 6.3 € m−3 of annual runoff saved (assuming an average annual runoff saving of 10 km3), which is reasonably in line with an estimate of 9.2 € m−3 for the U.S. context, where the annual runoff volume reduction was 12%54 compared to our estimate of 17.5%.Assuming a lifespan of 40 years55 and a discount rate of 4%50, the NPV of the cost saving of summer cooling over 40 years (18.4 billion Є year−1 in Table 1), that is the main private benefit of a green roof installed in a private building, is 364 billion Є (using a discount rate of 4%). The benefits of CO2 reduction, monetized in an emission trading system, would lead to a NPV of 24.85 ≈ 25 billion Є over 40 years (55.8 Mtons year−1). The NPV of the heat island benefit over 40 years would be 221 billion €. Deducting the sum of these benefits (totalling 610 billion €) from the estimated investment of 1323 billion €, yields a net gap of 713 billion Є, corresponding to an annual cost of about 60 € for each of the 559.5 million European citizens living in urban areas. This estimated annual cost is apparently affected by the uncertainty on green roof costs: it could reduce to 4 Є/year per urban citizen if the cost of the green roof is 25 Є m−2, and 129 Є/year per urban citizen if the cost is 80 Є m−2. An annual cost of 60 Є/year per urban citizen may be in many cases compensated by the additional benefits not quantified here. For example, the average increase of property value (rental prices) was estimated to be 8%15. Other benefits can be associated e.g. to leisure and recreation, socialization, amenity of the urban environment, and the creation of habitat or ecological connections in urban areas, besides the abovementioned positive effects in terms of water pollution and floods. Table 2 summarizes the economic results. Table 2 Summary of benefits and costs of urban greening considered in this study for the European context.Full size tableThe harvesting of runoff is a potential additional benefit, but it also entails costs. These can be quantified as a first approximation considering a cost of the storage volume Cs = 50 € m−3, a lifetime of the storage of 100 years, a discount rate of 4% and annual operation and maintenance costs of 3% of the investment. For a unit greened surface, the runoff potentially harvested equals P-RR and can be computed from Eq. 3, while the required storage volume to harvest it is given by Eq. 6. The cost of harvesting one m3 of runoff (marginal harvesting costs) follows from the abovementioned costing parameters. Figure 5 depicts the cumulate value of runoff as a function of the marginal harvesting cost. It can be seen that about 75% of the runoff can be harvested with marginal costs below 0.7 € m−3, a value compatible with urban water prices usually applied in Europe. Cs may be lower than 50 € m−3 , but often it may also be higher. Hence our calculation can be only regarded as a first indication and is accurate not more than within one order of magnitude. The quality of water from green surface runoff harvesting is arguably adequate for non-potable domestic use, but depends on the type of green roof and vegetation13. Figure 5Cumulate runoff versus the cost of storage per unit of runoff, for a storage cost of 50 € m−3. Different climatic scenarios are shown.Full size image More

1.Carpenter, S. R. et al. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8, 559–568 (1998).Article 

Google Scholar 
2.Galloway, J. N. et al. Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. Science 320, 889–892 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.LeBauer, DavidS. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Fay, P. A. et al. Grassland productivity limited by multiple nutrients. Nat. Plants 1, 1–5 (2015).Article 
CAS 

Google Scholar 
5.Yue, K. et al. Stimulation of terrestrial ecosystem carbon storage by nitrogen addition: a meta-analysis. Sci. Rep. 6, 1–10 (2016).Article 
CAS 

Google Scholar 
6.Avolio, M. L. et al. Changes in plant community composition, not diversity, during a decade of nitrogen and phosphorus additions drive above-ground productivity in a tallgrass prairie. J. Ecol. 102, 1649–1660 (2014).CAS 
Article 

Google Scholar 
7.Isbell, F. et al. Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. Proc. Natl Acad. Sci. USA 110, 11911–11916 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Van der Putten, W. H., Bradford, M. A., Pernilla Brinkman, E., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).Article 

Google Scholar 
9.Revillini, D., Gehring, C. A. & Johnson, N. C. The role of locally adapted mycorrhizas and rhizobacteria in plant–soil feedback systems. Funct. Ecol. 30, 1086–1098 (2016).Article 

Google Scholar 
10.Peay, K. G., Kennedy, P. G. & Talbot, J. M. Dimensions of biodiversity in the Earth mycobiome. Nat. Rev. Microbiol. 14, 434–447 (2016).CAS 
PubMed 
Article 

Google Scholar 
11.Semchenko, M. et al. Fungal diversity regulates plant-soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Větrovsky, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 1–9 (2019).Article 
CAS 

Google Scholar 
13.Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis. (Academic Press, 2008).14.Johnson, N. C. Resource stoichiometry elucidates the structure and function of arbuscular mycorrhizas across scales. New Phytol. 185, 631–647 (2010).CAS 
PubMed 
Article 

Google Scholar 
15.Velásquez, A. C., Castroverde, C. D. M. & He, S. Y. Plant–pathogen warfare under changing climate conditions. Curr. Biol. 28, R619–R634 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
17.Reynolds, H. L., Packer, A., Bever, J. D. & Clay, K. Grassroots ecology: plant-microbe-soil interactions as drivers of plant community structure and dynamics. Ecology 84, 2281–2291 (2003).Article 

Google Scholar 
18.Veresoglou, S. D., Barto, E. K., Menexes, G. & Rillig, M. C. Fertilization affects severity of disease caused by fungal plant pathogens. Plant Pathol. 62, 961–969 (2013).Article 

Google Scholar 
19.Walters, D. R. & Bingham, I. J. Influence of nutrition on disease development caused by fungal pathogens: implications for plant disease control. Ann. Appl. Biol. 151, 307–324 (2007).CAS 
Article 

Google Scholar 
20.Knorr, M., Frey, S. D. & Curtis, P. S. Nitrogen additions and litter decomposition: a meta-analysis. Ecology 86, 3252–3257 (2005).Article 

Google Scholar 
21.Chai, Y. et al. Patterns of taxonomic, phylogenetic diversity during a long-term succession of forest on the Loess Plateau, China: insights into assembly process. Sci. Rep. 6, 27087 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Crowther, T. W. et al. Sensitivity of global soil carbon stocks to combined nutrient enrichment. Ecol. Lett. 22, 936–945 (2019).CAS 
PubMed 
Article 

Google Scholar 
23.Fogg, K. The effect of added nitrogen on the rate of decomposition of organic matter. Biol. Rev. 63, 433–462 (1988).Article 

Google Scholar 
24.Bonner, M. T. et al. Why does nitrogen addition to forest soils inhibit decomposition? Soil Biol. Biochem. 137, 107570 (2019).CAS 
Article 

Google Scholar 
25.Zak, D. R. et al. Anthropogenic N deposition, fungal gene expression, and an increasing soil carbon sink in the Northern Hemisphere. Ecology 100, 1–8 (2019).Article 

Google Scholar 
26.Hobbie, S. E. et al. Response of decomposing litter and its microbial community to multiple forms of nitrogen enrichment. Ecol. Monogr. 82, 389–405 (2012).Article 

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

Google Scholar 
28.Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
29.Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol. Evol. 5, 65–73 (2014).Article 

Google Scholar 
30.Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351 (2012).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
31.MacKinnon, D. P., Krull, J. L. & Lockwood, C. M. Equivalence of the mediation, confounding and suppression effect. Prev. Sci. 1, 173–181 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kiers, E. T. et al. Reciprocal rewards stabilize cooperation in the mycorrhizal symbiosis. Science 333, 880–882 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Lekberg, Y. et al. Relative importance of competition and plant–soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Hart, M. M. & Reader, R. J. Taxonomic basis for variation in the colonization strategy of arbuscular mycorrhizal fungi. New Phytol. 153, 335–344 (2002).Article 

Google Scholar 
35.Johnson, N. C. Can fertilization of soil select less mutualistic mycorrhizae? Ecol. Appl. 3, 749–757 (1993).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Weber, S. E. et al. Responses of arbuscular mycorrhizal fungi to multiple coinciding global change drivers. Fungal Ecol. 40, 62–71 (2019).Article 

Google Scholar 
37.Han, Y., Feng, J., Han, M. & Zhu, B. Responses of arbuscular mycorrhizal fungi to nitrogen addition: a meta-analysis. Glob. Change Biol. 26, 7229–7241 (2020).ADS 
Article 

Google Scholar 
38.Treseder, K. K. et al. Arbuscular mycorrhizal fungi as mediators of ecosystem responses to nitrogen deposition: A trait- ­based predictive framework. J. Ecol. 106, 480–489 (2018).39.Sikes, B. A., Cottenie, K. & Klironomos, J. N. Plant and fungal identity determines pathogen protection of plant roots by arbuscular mycorrhizas. J. Ecol. 97, 1274–1280 (2009).Article 

Google Scholar 
40.Maherali, H. & Klironomos, J. N. Influence of phylogeny on fungal community assembly and ecosystem functioning. Science 316, 1746–1748 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
41.Johnson, N. C., Graham, J. H. & Smith, F. A. Functioning of mycorrhizal associations along the mutualism – parasitism continuum. New Phytol. 135, 575–585 (1997).42.Balser, T. C., Treseder, K. K. & Ekenler, M. Using lipid analysis and hyphal length to quantify AM and saprotrophic fungal abundance along a soil chronosequence. Soil Biol. Biochem. 37, 601–604 (2005).CAS 
Article 

Google Scholar 
43.Cappelli, S. L., Pichon, N. A., Kempel, A. & Allan, E. Sick plants in grassland communities: a growth‐defense trade‐off is the main driver of fungal pathogen abundance. Ecol. Lett. 23, 1349–1359 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Grman, E. Plant species differ in their ability to reduce allocation to non-beneficial arbuscular mycorrhizal fungi. Ecology 93, 711–718 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Kardol, P., Martijn, Bezemer, T. & van der Putten, W. H. Temporal variation in plant-soil feedback controls succession. Ecol. Lett. 9, 1080–1088 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Firn, J. et al. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat. Ecol. Evol. 3, 400–406 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Cleland, E. E. et al. Belowground biomass response to nutrient enrichment depends on light limitation across globally distributed grasslands. Ecosystems 22, 1466–1477 (2019).CAS 
Article 

Google Scholar 
48.Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl Acad. Sci. USA 111, 10019–10019 (2014).Article 
CAS 

Google Scholar 
49.Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant-soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1052–1053 (2014).Article 
CAS 

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

Google Scholar 
52.Morriën, E. et al. Soil networks become more connected and take up more carbon as nature restoration progresses. Nat. Commun. 8, 14349 (2017).53.Karimi, B. et al. Microbial diversity and ecological networks as indicators of environmental quality. Environ. Chem. Lett. 15, 265–281 (2017).CAS 
Article 

Google Scholar 
54.Carr, A., Diener, C., Baliga, N. S. & Gibbons, S. M. Use and abuse of correlation analyses in microbial ecology. ISME J. https://doi.org/10.1038/s41396-019-0459-z (2019).55.Prober, S. M. et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol. Lett. 18, 85–95 (2015).PubMed 
Article 

Google Scholar 
56.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
57.Corradi, N. et al. Gene copy number polymorphisms in an arbuscular mycorrhizal fungal population. Appl. Environ. Microbiol. 73, 366–369 (2007).CAS 
PubMed 
Article 

Google Scholar 
58.Tedersoo, L. et al. Response to Comment on “Global diversity and geography of soil fungi. Science 349, 936 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
59.Malcolm, G. M., Kuldau, G. A., Gugino, B. K. & Jiménez-Gasco, M. D. M. Hidden host plant associations of soilborne fungal pathogens: an ecological perspective. Phytopathology 103, 538–544 (2013).CAS 
PubMed 
Article 

Google Scholar 
60.Taylor, D. L. et al. Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for illumina amplicon sequencing. Appl. Environ. Microbiol. 82, 7217–7226 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Zou, K., Thebault, E., Lacroix, G. & Barot, S. Interactions between the green and brown food web determine ecosystem functioning. Funct. Ecol. 30, 1454–1465 (2016).Article 

Google Scholar 
62.Chen, W. et al. Fertility‐related interplay between fungal guilds underlies plant richness–productivity relationships in natural grasslands. New Phytol. 226, 1129–1143 (2020).PubMed 
Article 

Google Scholar 
63.Busby, P. E., Peay, K. G. & Newcombe, G. Common foliar fungi of Populus trichocarpa modify Melampsora rust disease severity. New Phytol. 209, 1681–1692 (2016).CAS 
PubMed 
Article 

Google Scholar 
64.Li, X., Ding, C., Zhang, T. & Wang, X. Fungal pathogen accumulation at the expense of plant-beneficial fungi as a consequence of consecutive peanut monoculturing. Soil Biol. Biochem. 72, 11–18 (2014).Article 
CAS 

Google Scholar 
65.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
66.Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, 1–11 (2012).Article 
CAS 

Google Scholar 
67.Kurtz, Z., Mueller, C., Miraldi, E. & Bonneau, R. SpiecEasi: Sparse Inverse Covariance For Ecological Statistical Inference. R package version 1.0.6 (2019).68.Oksanen, J. et al. vegan: Community Ecology Package. R package (2019).69.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag New York, 2016). More