Ecology

1.McNaughton, S. J., Ruess, R. W. & Seagle, S. W. Large mammals and process dynamics in Aftican ecosystems. Bioscience 38, 794–800 (1988).Article 

Google Scholar 
2.Vanni, M. J. Nutrient cycling by animals in freshwater ecosystems. Annu. Rev. Ecol. Syst. 33, 341–370 (2002).Article 

Google Scholar 
3.Schmitz, O. J. et al. Animating the carbon cycle. Ecosystems 17, 344–359 (2014).CAS 
Article 

Google Scholar 
4.Doughty, C. E. et al. Global nutrient transport in a world of giants. Proc. Natl Acad. Sci. USA 113, 868–873 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Allgeier, J. E., Burkepile, D. E. & Layman, C. A. Animal pee in the sea: consumer-mediated nutrient dynamics in the world’s changing oceans. Glob. Change Biol. 23, 2166–2178 (2017).ADS 
Article 

Google Scholar 
6.Duffy, J. E. Biodiversity and ecosystem function: the consumer connection. Oikos 99, 201–219 (2002).Article 

Google Scholar 
7.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
Article 

Google Scholar 
8.Loreau, M. et al. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
10.McIntyre, P. B., Jones, L. E., Flecker, A. S. & Vanni, M. J. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evolution 4, 230–239 (2020).Article 

Google Scholar 
12.Harvey, P. H. & Pagel, M. D. The Comparative Method in Evolutionary Biology. (Oxford University Press, 1991).13.Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Weeks, B., Claramunt, S. & Cracraft, J. Integrating systematics and biogeography to disentangle the roles of history and ecology in biotic assembly. J. Biogeogr. 43 (2016).15.Reiners, W. A. Complementary models for ecosystems. Am. Nat. 127, 59–73 (1986).Article 

Google Scholar 
16.Schreck, C. B. & Moyle, P. B. Methods for Fish Biology. (American Fisheries Society, 1990).17.Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere. 429 (2002).18.Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 3742 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Gonzalez, A. L. et al. Ecological mechanisms and phylogeny shape invertebrate stoichiometry: a test using detritus-based communities across Central and South America. Funct. Ecol. 32, 2448–2463 (2018).Article 

Google Scholar 
20.Atkinson, C. L., van Ee, B. C. & Pfeiffer, J. M. Evolutionary history drives aspects of stoichiometric niche variation and functional effects within a guild. Ecology 101, e03100 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Schluter, D. The Ecology of Adaptive Radiation. (OUP Oxford, 2000).22.Allgeier, J. E., Wenger, S. & Layman, C. A. Taxonomic identity best explains variation in body nutrient stoichiometry in a diverse marine animal community. Sci. Rep. 10, 13718 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Allgeier, J. E., Wenger, S. J., Schindler, D. E., Rosemond, A. D. & Layman, C. A. Metabolic theory and taxonomic identity predict nutrient cycling in a diverse food web. Proc. Natl Acad. Sci. USA 112, 2640–2647 (2015).Article 
CAS 

Google Scholar 
24.Odum, H. T. & Odum, E. P. Trohic structure and productivity of a windward coral reef community on Eniwetok Atoll. Ecol. Monogr. 25, 291–320 (1955).Article 

Google Scholar 
25.Hatcher, B. G. Coral reef primary productivity—a beggars banquet. Trends Ecol. Evolut. 3, 106–111 (1988).CAS 
Article 

Google Scholar 
26.Deangelis, D. L. Energy-flow, nutrient cycling, and ecosystem resilience. Ecology 61, 764–771 (1980).Article 

Google Scholar 
27.Allgeier, J. E., Valdivia, A., Cox, C. & Layman, C. A. Fishing down nutrients on coral reefs. Nat. Commun. 7, 1–5 (2016).Article 
CAS 

Google Scholar 
28.Allgeier, J. E., Layman, C. A., Mumby, P. J. & Rosemond, A. D. Consistent nutrient storage and supply mediated by diverse fish communities in coral reef ecosystems. Glob. Change Biol. 20, 2459–2472 (2014).ADS 
Article 

Google Scholar 
29.Allgeier, J. E., Layman, C. A., Mumby, P. J. & Rosemond, A. D. Biogeochemical implications of biodiversity loss across regional gradients of coastal marine ecosystems. Ecol. Monogr. 85, 132 (2015).Article 

Google Scholar 
30.Bellwood, D. R. & Wainwright, P. C. CHAPTER 1—The History and Biogeography of Fishes on Coral Reefs. in Coral Reef Fishes (ed Sale, P. F.) 5–32 (Academic Press, 2002). https://doi.org/10.1016/B978-012615185-5/50003-7.31.Littler, M. M., Littler, D. S. & Titlyanov, E. A. Comparisons of N- and P-limited productivity between high granitic islands versus low carbonate atolls in the Seychelles Archipelago: a test of the relative-dominance paradigm. Coral Reefs 10, 199–209 (1991).ADS 
Article 

Google Scholar 
32.Haßler, K. et al. Provenance of nutrients in submarine fresh groundwater discharge on Tahiti and Moorea, French Polynesia. Appl. Geochem. 100, 181–189 (2019).Article 
CAS 

Google Scholar 
33.Carew, J. L. & Mylroie, J. E. Geology of the Bahamas. Geol. Hydrogeol. Carbonate Isl. 54, 91–139 (1997).CAS 
Article 

Google Scholar 
34.Allgeier, J. E., Rosemond, A. D., Mehring, A. S. & Layman, C. A. Synergistic nutrient co-limitation across a gradient of ecosystem fragmentation in subtropical mangrove-dominated wetlands. Limnol. Oceanogr. 55, 2660–2668 (2010).ADS 
CAS 
Article 

Google Scholar 
35.Koch, M. S. & Madden, C. J. Patterns of primary production and nutrient availability in a Bahamas lagoon with fringing mangroves. Mar. Ecol. Prog. Ser. 219, 109–119 (2001).ADS 
CAS 
Article 

Google Scholar 
36.Hendrixson, H. A., Sterner, R. W. & Kay, A. D. Elemental stoichiometry of freshwater fishes in relation to phylogeny, allometry and ecology. J. Fish. Biol. 70, 121–140 (2007).Article 

Google Scholar 
37.Vanni, M. J., Flecker, A. S., Hood, J. M. & Headworth, J. L. Stoichiometry of nutrient recycling by vertebrates in a tropical stream: linking species identity and ecosystem processes. Ecol. Lett. 5, 285–293 (2002).Article 

Google Scholar 
38.Vanni, M. J. & McIntyre, P. B. Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis. Ecology 97, 3460–3471 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Sokal, R. R. The comparative method in evolutionary biology. (eds Paul H. Harvey, Mark D. Pagel) (Oxford University Press, New York, 1991). viii + 239 pp. ISBN 0-19-854640-8. $24.95 (paper). Am. J. Phys. Anthropol. 88, 405–406 (1992).40.Downs, K. N., Hayes, N. M., Rock, A. M., Vanni, M. J. & González, M. J. Light and nutrient supply mediate intraspecific variation in the nutrient stoichiometry of juvenile fish. Ecosphere 7, e01452 (2016).Article 

Google Scholar 
41.Sterner, R. W. & George, N. B. Carbon, nitrogen, and phosphorus stoichiometry of cyprinid fishes. Ecology 81, 127–140 (2000).Article 

Google Scholar 
42.Brown, W. L. Jr & Wilson, E. O. Character displacement. Syst. Biol. 5, 49–64 (1956).
Google Scholar 
43.Losos, J. B. Ecological character displacement and the study of adaptation. Proc. Natl Acad. Sci. USA 97, 5693–5695 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: the next generation. Ecol. Lett. 8, 875–894 (2005).Article 

Google Scholar 
45.Abrams, P. A. Evolution and the consequences of species introductions and deletions. Ecology 77, 1321–1328 (1996).Article 

Google Scholar 
46.Buchan, K. C. The Bahamas. Mar. Pollut. Bull. 41, 94–111 (2000).CAS 
Article 

Google Scholar 
47.Siu, G. et al. Shore fishes of french polynesia. Cybium 41 (2017).48.Miloslavich, P. et al. Marine biodiversity in the Caribbean: regional estimates and distribution patterns. PloS ONE 5, 119–126 (2010).Article 
CAS 

Google Scholar 
49.Schaus, M. H. & Vanni, M. J. Effects of gizzard shad on phytoplankton and nutrient dynamics: role of sediment feeding and fish size. Ecology 81, 1701–1719 (2000).Article 

Google Scholar 
50.Whiles, M. R., Huryn, A. D., Taylor, B. W. & Reeve, J. D. Influence of handling stress and fasting on estimates of ammonium excretion by tadpoles and fish: recommendations for designing excretion experiments. Limnol. Oceanogr. 7, 1–7 (2009).CAS 
Article 

Google Scholar 
51.Taylor, B. W. et al. Improving the fluorometric ammonium method: matrix effects, background fluorescence, and standard additions. J. North Am. Benthol. Soc. 26, 167–177 (2007).Article 

Google Scholar 
52.APHA. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, American Water Works Association, and Water Pollution Control Federation. (1995).53.Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. Proc. Natl Acad. Sci. USA 111, 13757–13762 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Chang, J., Rabosky, D. L., Smith, S. A. & Alfaro, M. E. An r package and online resource for macroevolutionary studies using the ray-finned fish tree of life. Methods Ecol. Evolut. 10, 1118–1124 (2019).Article 

Google Scholar 
56.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evolut. 3, 217–223 (2012).Article 

Google Scholar 
57.Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evolut. Biol. 23, 494–508 (2010).CAS 
Article 

Google Scholar 
58.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R Package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
59.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods Ecol. Evolut. 4, 133–142 (2013).Article 

Google Scholar 
60.Gelman, A. & Hill, J. Data Analysis Using Regression. (Cambridge University Press, 2007).61.Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.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 (2011).PubMed 
Article 
PubMed Central 

Google Scholar  More

The research was carried out on the basis of direct measurements in the surroundings of four selected working coal-fired power plants and four working coking plants. The samples of suspended dust PM10, respirable fraction PM2.5 and submicron particulate matter PM1 were collected in the surroundings of power generation facilities and in the surroundings of coking plants.Location of measurement pointsThe location of the measurement points was selected in southern Poland, around the selected four working coal-fired power plants and four working coking plants. The sampling points in the surroundings of the power plant (P1, P2, P3 and P4) and the coking plant (K1, K2, K3 and K4) were located at the distance of approximately 2 km to the north-east from the respective object (Fig. 1).Figure 1Location of the sampling sites (the map was generated based on data from the BDL18 website).Full size imageThe location of the measurement points was a compromise, taking into account the representativeness of the receptor, the possibility to connect the testing equipment and the consent of the property owners. To eliminate the impact of a heating season, and especially that of low emissions, presented in the studies by19, the measurement sessions were carried out only in the summer season. The samples of particulate matter were collected on a weekly basis, with 4 sessions at one site. The methodology applied in this work is presented in20,21. The location of measurement sites:

point P1: 50° 08′ 37.87″ N; 18° 32′ 15.76″ (Golejów—a suburban district of Rybnik in the Śląskie Voivodeship, in the vicinity of a working power plant with a capacity of 1775 MW; population:

2 300);

point P2: 50° 45′ 35.41″ N; 17° 56′ 20.43″ E (Świerkle—a rural area in the Opolskie Voivodeship (Dobrzeń Wielki commune) near a working power plant with a capacity of 1,492 MW; population: 520);

point P3: 50° 12′ 33.46″ N; 19° 28′ 28.77″ E (Czyżówka—rural area in the Małopolskie Voivodeship (commune of Trzebinia) near a working power plant with a capacity of 786 MW; population: 700);

point P4: 50° 13′ 48.90″ N; 19° 13′ 24.45″ E (suburbs of Jaworzno (Śląskie Voivodeship) in the vicinity of a 1,345 MW power plant; number of inhabitants: 95 500);

K1 point: 50° 10′ 11.36″ N; 18° 40′ 34.35″ E (Czerwionka—Leszczyny in the Śląskie Voivodeship, in the vicinity of a small coking plant; number of inhabitants: 27 300);

K2 point: 50° 3′ 19.76″ N; 18° 30′ 21.69″ E (Popielów—a suburban district of Rybnik in the Śląskie Voivodeship, surrounded by a small working coking plant; population:3 300);

K3 point: 50° 21′ 24.08″ N; 19° 21′ 37.46″ E (Łęka—Dąbrowa Górnicza district, in the Śląskie Voivodeship, surrounded by a large coking plant; number of inhabitants: 700);

K4 point: 50° 21′ 0.47″ N; 18° 53′ 15.44″ E (Bytom—a city in the Śląskie Voivodeship, a small coking plant located on the outskirts of the city; population: 174 700).

The state of air pollution with particulate matter in the area investigated in the study is affected by various local sources of pollution emissions. At the measurement sites P1, P2, P3 and P4, the emissions are mainly from power plant chimneys, but also from auxiliary processes, i.e. coal storage and its transport. In addition, the recorded emissions are also influenced by other industrial plants operating in the vicinity of the measurement sites, domestic and municipal sector and the impact of automotive industry. The measurement sites K1, K2, K3 and K4 involve primarily the emissions accompanying the processes of coal coking as well as auxiliary processes, i.e. coal deposition, its transmission, management of products and post-production wastes. Additionally, they are affected by the emissions from industrial plants and low emission sources operating in this area, as well as the emission from the combustion of solid fuels for domestic or municipal purposes, as well as by the automotive industry.Sampling processThe samples of suspended dust (PM10), respirable fraction (PM2.5) and submicron particulate matter (PM1) were collected using the Dekati PM10 cascade impactor serial No. 6648 by Dekati (Finland) with the air flow rate of (1.8 {mathrm{m}}^{3}/mathrm{h}). The impactor Dekati PM10 guarantees the collection of dust samples for three cutpoint diameters: 10 μm, 2.5 μm and 1 μm. For the sampling at the first, second and third stages of the impactor, polycarbonate filters were used (Nuclepore 800 203, with the diameter of 25 mm, by Whatman International Ltd., Maidstone, UK). At the fourth stage, the dust was collected on a Teflon filter for particles ≤ 1 μm in diameter (Pall Teflo R2PJ047, 47 mm in diameter, by Pall International Ltd., New York, NY, USA). The average volume of air passing through the filters was approximately 300 m3. The impactor’s capture efficiency was characterized by the uncertainty below 2.8%. The mass of dust collected at the individual stages of the impactor was determined by the gravimetric method, and it was referenced to the volume of passed air (left(mathrm{mu g}/{mathrm{m}}^{3}right)) according to the PN-EN1234122. All impactor samples were analysed by inductively coupled plasma mass spectrometry (ICP-MS).The samples were collected at a height of 1.5 m from the ground, i.e. in the breathing zone for people. The respective dust fractions were collected in 7-day cycles from 28 May to 24 September 2014 (16 weeks) in the surroundings of four working coal-fired power plants and from 4 May to 28 August 2015 (16 weeks) in the surroundings of four working coking plants. The measurement campaign comprised four measurement sessions separately for each sampling site. One session comprised dust sampling at each stage of the Dekati PM10 cascade impactor and filters used for reference. The filters were taken back after study period and labeled during the collection process in the field and stored in the plastic containers for safe transportation and storage in laboratory for further analysis.In each measurement session, blind filters were stored at the sampling site, but they were not subjected to exposure. The sample data were corrected from these blanks. The length of the measurement cycles was conditioned by the need to collect an appropriate amount of research material (with the aerodynamic diameter of the dust grains  10 μm). Analogous (7-day) periods of dust sampling were used in the studies by4,23.Polycarbonate and Teflon filters were conditioned before and after dust collection at a temperature of 20 ± 1 °C (relative humidity 50%(pm ) 5%) for 48 h, and then weighed on a microbalance with an accuracy of 1 (mathrm{mu g}) (MXA5/1, by RADWAG, Poland).Taking into account the measurement sessions at four sites in the surroundings of the power plant (P1 (div) P4) and at four sites in the surroundings of the coking plant (K1 (div) K4), the aggregate number of samples exceeded 450.Chemical analysisThe qualitative and quantitative analysis of the obtained solutions was performed by inductively coupled plasma mass spectrometry using an ICP-MS instrument (NexION 300D, PerkinElmer, Inc., Waltham, MA, USA). For all elements determined simultaneously, the same parameters of the instrument were used, which are presented in the publications20,21,24.As standards for the determination of 75As, 111Cd, 59Co, 53Cr, 200Hg, 55Mn, 60Ni, 206Pb, 121Sb and 82Se, we applied the 1000 (mathrm{mu g}/{mathrm{cm}}^{3}) CertPUR ICP multi-element standard solution VI for ICP-MS by Merck, Germany. Ten repetitions were performed for all samples. The determined limits of detection (LOD) were based on 10 independent measurements for blank test. For the results obtained in that way, the mean value and the value of the standard deviation SD were calculated. The values of LOD for individual elements were determined on the basis of the dependence (1):$$mathrm{LOD}= {mathrm{x}}_{mathrm{sr}}+ 3mathrm{SD}$$
(1)

where: xśr—mean concentration value of the element, (mathrm{g}/{mathrm{dm}}^{3}), SD—standard deviation.The determination correctness of the content of the elements was verified with the use of certified reference materials: European Reference Material ERM-CZ120 and Standard Reference Material SRM 1648a (National Institute of Standards and Technology, USA). The recovery with the use of the said certified reference materials was respectively as follows: As (111% for ERM-CZ120 and 96% for SRM 1648a), Cd (97% and 105%), Co (108% and 97%), Cr (103% and 94%), Mn (106% and 100%), Ni (107% and 102%), Pb (107% and 105%) and Sb (99% and 91%). The certified reference materials did not contain Hg or Se. More

1.Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics, 4th edn (Longman, 1996).2.Xiong, W. et al. Nat. Plants https://doi.org/10.1038/s41477-021-00988-w (2021).3.Tadesse, W. et al. Crop Breed Genet Genom. 1, e190005 (2019).
Google Scholar 
4.Li, X., Guo, T., Mu, Q., Li, X. & Yu, J. Proc. Natl Acad. Sci. USA 115, 6679–6684 (2018).CAS 
Article 

Google Scholar 
5.Anderson, D. R. Model Based Inference in the Life Sciences (Springer, 2008).6.Zhao, Y. et al. Sci. Adv. 7, eabf9106 (2021).CAS 
Article 

Google Scholar 
7.Shi, L., Li, B., Kim, C., Kellnhofer, P. & Matusik, W. Nature 591, 234–239 (2021).CAS 
Article 

Google Scholar 
8.Li, J. et al. Mol. Ecol. 28, 3544–3560 (2019).CAS 
Article 

Google Scholar 
9.CGIAR. One CGIAR, https://www.cgiar.org/food-security-impact/one-cgiar/ More

1.Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Schwalm, C. R. et al. Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century. Sci. Rep. 10, 9059 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
4.Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).ADS 
Article 

Google Scholar 
5.Ellsworth, D. S. et al. Elevated CO2 does not increase eucalypt forest productivity on a low-phosphorus soil. Nat. Clim. Change 7, 279–282 (2017).ADS 
CAS 
Article 

Google Scholar 
6.Hararuk, O., Campbell, E. M., Antos, J. A. & Parish, R. Tree rings provide no evidence of a CO2 fertilization effect in old-growth subalpine forests of western Canada. Glob. Change Biol. 25, 1222–1234 (2019).ADS 
Article 

Google Scholar 
7.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 
Article 

Google Scholar 
9.Koven, C. D. et al. Controls on terrestrial carbon feedbacks by productivity versus turnover in the CMIP5 earth system models. Biogeosciences 12, 5211–5228 (2015).ADS 
Article 

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

Google Scholar 
11.Sigurdsson, B. D., Medhurst, J. L., Wallin, G., Eggertsson, O. & Linder, S. Growth of mature boreal Norway spruce was not affected by elevated [CO2] and/or air temperature unless nutrient availability was improved. Tree Physiol. 33, 1192–1205 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. N. Phytol. 229, 2413–2445 (2021).CAS 
Article 

Google Scholar 
13.Gedalof, Z. & Berg, A. A. Tree ring evidence for limited direct CO2 fertilization of forests over the 20th century. Glob. Biogeochem. Cycles 24, (2010).14.van der Sleen, P. et al. No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased. Nat. Geosci. 8, 24–28 (2015).ADS 
Article 
CAS 

Google Scholar 
15.Girardin, M. P. et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl Acad. Sci. USA 113, E8406–E8414 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Giguère-Croteau, C. et al. North America’s oldest boreal trees are more efficient water users due to increased [CO2], but do not grow faster. Proc. Natl Acad. Sci. USA 116, 2749–2754 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Walker, A. P. et al. Decadal biomass increment in early secondary succession woody ecosystems is increased by CO2 enrichment. Nat. Commun. 10, 454 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. https://doi.org/10.1038/s41561-019-0530-4 (2020).19.Schimel, J. P. & Bennett, J. Nitrogen mineralization: challenges of a changing paradigm. Ecology 85, 591–602 (2004).Article 

Google Scholar 
20.Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur? Biogeochemistry 13, 87–115 (1991).Article 

Google Scholar 
21.Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 
Article 

Google Scholar 
22.Näsholm, T., Kielland, K. & Ganeteg, U. Uptake of organic nitrogen by plants. N. Phytol. 182, 31–48 (2009).Article 
CAS 

Google Scholar 
23.Lindahl, B. D. & Tunlid, A. Ectomycorrhizal fungi – potential organic matter decomposers, yet not saprotrophs. N. Phytol. 205, 1443–1447 (2015).CAS 
Article 

Google Scholar 
24.Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Terrer, C. et al. Ecosystem responses to elevated CO2 governed by plant–soil interactions and the cost of nitrogen acquisition. N. Phytol. 217, 507–522 (2018).CAS 
Article 

Google Scholar 
26.Sulman, B. N. et al. Diverse Mycorrhizal associations enhance terrestrial C storage in a global model. Glob. Biogeochem. Cycles 33, 501–523 (2019).ADS 
CAS 
Article 

Google Scholar 
27.Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).ADS 
CAS 
Article 

Google Scholar 
29.Smith, S. E. & Read, D. J. Mycorrhizal symbiosis. (Academic Press, 2010).30.Pellitier, P. T. & Zak, D. R. Ectomycorrhizal fungi and the enzymatic liberation of nitrogen from soil organic matter: why evolutionary history matters. N. Phytol. 217, 68–73 (2018).CAS 
Article 

Google Scholar 
31.Phillips, R. P. et al. Roots and fungi accelerate carbon and nitrogen cycling in forests exposed to elevated CO2. Ecol. Lett. 15, 1042–1049 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).ADS 
CAS 
Article 

Google Scholar 
33.Christian, N. & Bever, J. D. Carbon allocation and competition maintain variation in plant root mutualisms. Ecol. Evol. 8, 5792–5800 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Hortal, S. et al. Role of plant–fungal nutrient trading and host control in determining the competitive success of ectomycorrhizal fungi. ISME J. 11, 2666–2676 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Bogar, L. et al. Plant-mediated partner discrimination in ectomycorrhizal mutualisms. Mycorrhiza 29, 97–111 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Bödeker, I. T. M., Nygren, C. M. R., Taylor, A. F. S., Olson, Å. & Lindahl, B. D. ClassII peroxidase-encoding genes are present in a phylogenetically wide range of ectomycorrhizal fungi. ISME J. 3, 1387–1395 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
37.Hobbie, E. A. & Agerer, R. Nitrogen isotopes in ectomycorrhizal sporocarps correspond to belowground exploration types. Plant Soil 327, 71–83 (2010).CAS 
Article 

Google Scholar 
38.Koide, R. T., Fernandez, C. & Malcolm, G. Determining place and process: functional traits of ectomycorrhizal fungi that affect both community structure and ecosystem function. N. Phytol. 201, 433–439 (2014).Article 

Google Scholar 
39.Lindahl, B. D. et al. A group of ectomycorrhizal fungi restricts organic matter accumulation in boreal forest. Ecol. Lett. 24, 1341–1351 (2021).PubMed 
Article 
PubMed Central 

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

Google Scholar 
41.Read, D. J. & Perez-Moreno, J. Mycorrhizas and nutrient cycling in ecosystems: a journey towards relevance? N. Phytol. 157, 475–492 (2003).CAS 
Article 

Google Scholar 
42.Bödeker, I. T. M. et al. Ectomycorrhizal Cortinarius species participate in enzymatic oxidation of humus in northern forest ecosystems. N. Phytol. 203, 245–256 (2014).Article 
CAS 

Google Scholar 
43.Bogar, L. & Peay, K. Processes maintaining the coexistence of ectomycorrhizal fungi at a fine spatial scale. in Biogeography of Mycorrhizal Symbiosis (ed. Tedersoo, L.) vol. 230 79–105 (Springer, 2017).44.Xu, K. et al. Tree-ring widths are good proxies of annual variation in forest productivity in temperate forests. Sci. Rep. 7, 1945 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.Nehrbass‐Ahles, C. et al. The influence of sampling design on tree-ring-based quantification of forest growth. Glob. Change Biol. 20, 2867–2885 (2014).ADS 
Article 

Google Scholar 
46.Mathias, J. M. & Thomas, R. B. Disentangling the effects of acidic air pollution, atmospheric CO2, and climate change on recent growth of red spruce trees in the Central Appalachian Mountains. Glob. Change Biol. 24, 3938–3953 (2018).ADS 
Article 

Google Scholar 
47.Fierer, N., Barberán, A. & Laughlin, D. C. Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities. Front. Microbiol. 5, 614 (2014).48.Zak, D. R. & Pregitzer, K. S. Spatial and temporal variability of nitrogen cycling in northern lower Michigan. Science 36, 367–380 (1990).
Google Scholar 
49.Zak, D. R., Pregitzer, K. S. & Host, G. E. Landscape variation in nitrogen mineralization and nitrification. Can. J. Res. 16, 1258–1263 (1986).Article 

Google Scholar 
50.Chen, J. & Gupta, A. K. Parametric Statistical Change Point Analysis: With Applications to Genetics, Medicine, and Finance. (Springer Science & Business Media, 2011).51.Thomas, R. Q., Canham, C. D., Weathers, K. C. & Goodale, C. L. Increased tree carbon storage in response to nitrogen deposition in the US. Nat. Geosci. 3, 13–17 (2010).ADS 
Article 
CAS 

Google Scholar 
52.Pellitier, P. T., Zak, D. R., Argiroff, W. A. & Upchurch, R. A. Coupled shifts in ectomycorrhizal communities and plant uptake of organic nitrogen along a soil gradient: an isotopic perspective. Ecosystems (2021).53.Sterkenburg, E., Clemmensen, K. E., Ekblad, A., Finlay, R. D. & Lindahl, B. D. Contrasting effects of ectomycorrhizal fungi on early and late stage decomposition in a boreal forest. ISME J. 12, 2187–2197 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Lilleskov, E. A., Hobbie, E. A. & Fahey, T. J. Ectomycorrhizal fungal taxa differing in response to nitrogen deposition also differ in pure culture organic nitrogen use and natural abundance of nitrogen isotopes. N. Phytol. 154, 219–231 (2002).CAS 
Article 

Google Scholar 
55.Kohler, A. et al. Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. Nat. Genet. 47, 410–415 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Moeller, H. V., Peay, K. G. & Fukami, T. Ectomycorrhizal fungal traits reflect environmental conditions along a coastal California edaphic gradient. FEMS Microbiol. Ecol. 87, 797–806 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Defrenne, C. E. et al. Shifts in Ectomycorrhizal fungal communities and exploration types relate to the environment and fine-root traits across interior douglas-fir forests of Western Canada. Front. Plant Sci. 10, 643 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Fawal, N. et al. PeroxiBase: a database for large-scale evolutionary analysis of peroxidases. Nucleic Acids Res. 41, D441–D444 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Garajova, S. et al. Single-domain flavoenzymes trigger lytic polysaccharide monooxygenases for oxidative degradation of cellulose. Sci. Rep. 6, 28276 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Janusz, G. et al. Lignin degradation: microorganisms, enzymes involved, genomes analysis and evolution. FEMS Microbiol. Rev. 41, 941–962 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 
Article 

Google Scholar 
63.Baldrian, P. Fungal laccases – occurrence and properties. FEMS Microbiol. Rev. 30, 215–242 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Fernandez, C. W. & Kennedy, P. G. Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils? N. Phytol. 209, 1382–1394 (2016).CAS 
Article 

Google Scholar 
65.Reich, P. B. et al. Nitrogen limitation constrains sustainability of ecosystem response to CO2. Nature 440, 922–925 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Oren, R. et al. Soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere. Nature 411, 469–472 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Norby, R. J., Warren, J. M., Iversen, C. M., Medlyn, B. E. & McMurtrie, R. E. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl Acad. Sci. USA 107, 19368–19373 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Andrew, C. & Lilleskov, E. A. Productivity and community structure of ectomycorrhizal fungal sporocarps under increased atmospheric CO2 and O3. Ecol. Lett. 12, 813–822 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Näsholm, T. et al. Are ectomycorrhizal fungi alleviating or aggravating nitrogen limitation of tree growth in boreal forests? N. Phytol. 198, 214–221 (2013).Article 
CAS 

Google Scholar 
70.Finzi, A. C. et al. Increases in nitrogen uptake rather than nitrogen-use efficiency support higher rates of temperate forest productivity under elevated CO2. Proc. Natl Acad. Sci. USA 104, 14014–14019 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Merkel, D. Soil Nutrients in Glaciated Michigan Landscapes: Distribution of Nutrients and Relationships with Stand Productivity. (Doctoral Thesis Submitted to Michigan State University, 1988).72.Host, G. E. & Pregitzer, K. S. Geomorphic influences on ground-flora and overstory composition in upland forests of northwestern lower Michigan. Can. J. Res. 22, 1547–1555 (1992).Article 

Google Scholar 
73.Edwards, I. P. & Zak, D. R. Phylogenetic similarity and structure of Agaricomycotina communities across a forested landscape. Mol. Ecol. 19, 1469–1482 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Change 5, 528–534 (2015).ADS 
Article 

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

Google Scholar 
77.McClaugherty, C. A., Pastor, J., Aber, J. D. & Melillo, J. M. Forest litter decomposition in relation to soil nitrogen dynamics and litter quality. Ecology 66, 266–275 (1985).Article 

Google Scholar 
78.Pastor, J., Aber, J. D., McClaugherty, C. A. & Melillo, J. M. Aboveground production and N and P cycling along a nitrogen mineralization gradient on Blackhawk Island, Wisconsin. Ecology 65, 256–268 (1984).CAS 
Article 

Google Scholar 
79.Serra-Maluquer, X., Mencuccini, M. & Martínez-Vilalta, J. Changes in tree resistance, recovery and resilience across three successive extreme droughts in the northeast Iberian Peninsula. Oecologia 187, 343–354 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Vitousek, P. Nutrient cycling and nutrient use efficiency. Am. Nat. 119, 553–572 (1982).Article 

Google Scholar 
81.Darrouzet-Nardi, A., Ladd, M. P. & Weintraub, M. N. Fluorescent microplate analysis of amino acids and other primary amines in soils. Soil Biol. Biochem. 57, 78–82 (2013).CAS 
Article 

Google Scholar 
82.Ibáñez, I., Zak, D. R., Burton, A. J. & Pregitzer, K. S. Anthropogenic nitrogen deposition ameliorates the decline in tree growth caused by a drier climate. Ecology 99, 411–420 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Lines, E. R., Zavala, M. A., Purves, D. W. & Coomes, D. A. Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Glob. Ecol. Biogeogr. 21, 1017–1028 (2012).Article 

Google Scholar 
84.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).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Konar, A. et al. High-quality genetic mapping with ddRADseq in the non-model tree Quercus rubra. BMC Genomics 18, 417 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
88.Sork, V. L. et al. First draft assembly and annotation of the genome of a California Endemic oak. Genes|Genomes|Genet. 6, 3485–3495 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
90.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
92.Treiber, M. L., Taft, D. H., Korf, I., Mills, D. A. & Lemay, D. G. Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes. BMC Bioinforma. 21, 74 (2020).CAS 
Article 

Google Scholar 
93.Peng, M. et al. Comparative analysis of basidiomycete transcriptomes reveals a core set of expressed genes encoding plant biomass degrading enzymes. Fungal Genet. Biol. 112, 40–46 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Floudas, D. et al. Uncovering the hidden diversity of litter-decomposition mechanisms in mushroom-forming fungi. ISME J. https://doi.org/10.1038/s41396-020-0667-6 (2020).95.Kriventseva, E. V. et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Res. 47, D807–D811 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Quinn, T. P., Erb, I., Richardson, M. F. & Crowley, T. M. Understanding sequencing data as compositions: an outlook and review. Bioinformatics 34, 2870–2878 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).Article 

Google Scholar 
98.Duhamel, M. et al. Plant selection initiates alternative successional trajectories in the soil microbial community after disturbance. Ecol. Monogr. 89, e01367 (2019).Article 

Google Scholar 
99.Qin, C., Zhu, K., Chiariello, N. R., Field, C. B. & Peay, K. G. Fire history and plant community composition outweigh decadal multi-factor global change as drivers of microbial composition in an annual grassland. J. Ecol. 108, 611–625 (2020).CAS 
Article 

Google Scholar 
100.Oksanen, J., et al. Package vegan.101.Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
Article 

Google Scholar 
102.Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Linde, A. V. D. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 583–639 (2002).MathSciNet 
MATH 
Article 

Google Scholar  More

.readcube-buybox { display: none !important;}

After cyclones in the north Atlantic, droves of emaciated, dead seabirds can wash ashore on North American and European beaches. New research probes the cause of these mass-mortality events, called winter wrecks, and suggests that climate change might worsen the pattern1.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
/* style specs end */Subscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-021-02494-7

References1.Clairbaux, M. et al. Curr. Biol. https://doi.org/10.1016/j.cub.2021.06.059 (2021)Article 

Google Scholar 
Download references

Subjects

Ecology

Latest on:

Ecology

Preventing spillover as a key strategy against pandemics
Correspondence 14 SEP 21

Pollination advantage of rare plants unveiled
News & Views 08 SEP 21

Pollinators contribute to the maintenance of flowering plant diversity
Article 08 SEP 21

Jobs

Postdoctoral fellow

Dalhousie University
Halifax, Canada

Postdoctoral Research Fellowship in Pharmacogenomics and Clinical Pharmacology with a Focus on Adverse Drug Reactions

The University of British Columbia (UBC)
Vancouver, Canada

English Speaking Secretary for the Institute Director

Jülich Research Centre (FZJ)
Aachen, Germany

Officer for Strategic Support of Research Grant Applications

German Cancer Research Center in the Helmholtz Association (DKFZ)
Heidelberg, Germany More

1.Lundberg, J. G., Kottelat, M., Smith, G. R., Stiassny, M. L. J. & Gill, A. C. So many fishes, so little time: An overview of recent ichthyological discovery in continental waters. Ann. Mo. Bot. Gard. 87, 26–62 (2000).Article 

Google Scholar 
2.Relyea, R. A. The impact of insecticides and herbicides on the biodiversity and productivity of aquatic communities. Ecol. Appl. 15, 618–627 (2005).Article 

Google Scholar 
3.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Clare, A. I. M. et al. Beyond biodiversity: Can environmental DNA (eDNA) cut it as a population genetics tool?. Genes 10, 192 (2019).Article 
CAS 

Google Scholar 
5.Tsuji, S., Shibata, N., Sawada, H. & Ushio, M. Quantitative evaluation of intraspecific genetic diversity in a natural fish population using environmental DNA. Mol. Ecol. Resour. 20, 1323–1332 (2020).CAS 
PubMed 
Article 

Google Scholar 
6.Miya, M., Gotoh, R. O. & Sado, T. MiFish metabarcoding: A high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples. Fish. Sci. 86, 939–970 (2020).CAS 
Article 

Google Scholar 
7.Dagosta F. C. P. & de Pinna, M. C. C. The fishes of the Amazon: Distribution and biogeographical patterns, with a comprehensive list of species. Bull. Am. Mus. Nat. Hist, 431, 1–163 (2019).8.Jézéquel, C., Tedesco, P. A. & Bigorne, R. A database of freshwater fish species of the Amazon Basin. Sci. Data 7, 96 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Reis, R. E., Kullander, S. O. & Ferraris, C. J. Check List of the Freshwater Fishes of South and Central America. (Edipucrs, 2003).10.Tedesco, P. et al. A global database on freshwater fish species occurrence in drainage basins. Sci. Data 4, 170141 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Brito, P. M., Meunier, F. J. & Leal, M. E. C. Origine et diversification de líchthyofaune Neotropical: Une revue. Cybium 31, 139–153 (2007).
Google Scholar 
12.Lowe-McConnell, R. H. Ecological Studies in Tropical Fish Communities (Cambridge University Press, 1987).Book 

Google Scholar 
13.Bloom, D. D. & Lovejoy, N. R. On the origins of marine derived fishes in South America. J. Biogeogr. 44, 1927–1938 (2017).Article 

Google Scholar 
14.de Santana, C. D. et al. Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator. Nat. Commun. 10, 4000 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Carvalho, L. N., Zuanon, J. & Sazima, I. Natural history of Amazon fishes. In Tropical Biology and Natural Resources Theme (ed. Del-Claro, K.), K. Del-Claro & R. J. Marquis (Session Eds. the Natural History Session), Encyclopedia of Life Support Systems (EOLSS) (Eolss Publishers, 2007).16.Cardoso, Y. P. et al. A continental-wide molecular approach unraveling mtDNA diversity and geographic distribution of the Neotropical genus Hoplias. PLoS ONE 13, e0202024 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Hebert, P. D. N., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, 313–321 (2003).CAS 
Article 

Google Scholar 
18.Baldwin, C. C., Castillo, C. I., Weigt, L. A. & Victor, B. C. Seven new species within western Atlantic Starksia atlantica, S. lepicoelia, and S. sluiteri (Teleostei, Labrisomidae), with comments on congruence of DNA barcodes and species. ZooKeys 79, 21–27 (2011).Article 

Google Scholar 
19.Robertson, D. R. et al. Deep-water bony fishes collected by the B/O Miguel Oliver on the shelf edge of Pacific Central America: An annotated, illustrated and DNA-barcoded checklist. Zootaxa 4348, 1–125 (2017).PubMed 
Article 

Google Scholar 
20.Weigt, L. A. et al. Using DNA barcoding to assess Caribbean reef fish biodiversity: Expanding taxonomic and geographic coverage. PLoS ONE 7, e41059 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Seberg, O. et al. Global genome biodiversity network: Saving a blueprint of the tree of life—a botanical perspective. Ann. Bot. 118, 393–399 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Parenti, L. R. et al. Fishes collected during the 2017 MarineGEO assessment of Kāne‘ohe Bay, O‘ahu, Hawai‘i. J. Mar. Biol. Assoc. UK 100, 607–637 (2020).Article 

Google Scholar 
23.Droege, G. et al. The Global Genome Biodiversity Network (GGBN) Data Standard specification. Database https://doi.org/10.1093/database/baw125 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
24.Marques, V. et al. Blind assessment of vertebrate taxonomic diversity across spatial scales by clustering environmental DNA metabarcoding sequences. Ecography 43, 1779–1790 (2020).Article 

Google Scholar 
25.Leray, M., Knowlton, N., Shien-Lei, H., Nguyen, B. N. & Machida, R. J. GenBank is a reliable resource for 21st biodiversity research. Proc. Natl. Acad. Sci. U.S.A. 116, 22651–22656 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Dillman, C. B. et al. Forensic investigations into a GenBank anomaly: Endangered taxa and the importance of voucher specimens in molecular studies. J. Appl. Ichthyol. 30, 1300–1309 (2014).CAS 
Article 

Google Scholar 
27.Locatelli, N. S., McIntyre, P. B., Therkildsen, N. O. & Baetscher, D. S. GenBank’s reliability is uncertain for biodiversity researchers seeking species-level assignment for eDNA. Proc. Natl. Acad. Sci. U.S.A. 117, 32211–32212 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Jerde, C. L., Wilson, E. A. & Dressler, T. L. Measuring global fish species richness with eDNA metabarcoding. Mol. Ecol. Resour. 19, 19–22 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Nobile, A. B. et al. DNA metabarcoding of Neotropical ichthyoplankton: Enabling high accuracy with lower cost. Metabarcoding Metagenom. 3, 35060 (2019).Article 

Google Scholar 
30.Cilleros, K. et al. Unlocking biodiversity and conservation studies in high diversity environments using environmental DNA (eDNA): A text with Guianese freshwater fishes. Mol. Ecol. Resour. 19, 27–46 (2019).CAS 
PubMed 
Article 

Google Scholar 
31.Sales, N. G., Wangensteen, O. S., Carvalho, D. C. & Mariani, S. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 1, 119–130 (2019).Article 

Google Scholar 
32.Jackman, J. M. C. et al. eDNA in a bottleneck: Obstacles to fish metabarcoding studies in megadiverse freshwater systems. Environ. DNA https://doi.org/10.1002/edna3.191 (2021).Article 

Google Scholar 
33.Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.McElroy, M. E. et al. Calibrating environmental DNA metabarcoding to conventional surveys for measuring fish species richness. Front. Ecol. Evol. 8, 276 (2020).Article 

Google Scholar 
35.Dudgeon, D. Freshwater Biodiversity: Status (Cambridge University Press, 2020).Book 

Google Scholar 
36.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).Article 

Google Scholar 
37.Milan, D. T., Mendes, I. S. & Carvalho, D. C. New 12S metabarcoding primers for enhanced Neotropical freshwater fish biodiversity assessment. Sci. Rep. 10, 17966 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10, 20140562 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).Article 

Google Scholar 
40.Antich, A. et al. To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography. BMC Bioinf. 22, 177 (2021).CAS 
Article 

Google Scholar 
41.Vieira, T. B. et al. A multiple hypothesis approach to explain species richness patterns in neotropical stream-dweller fish communities. PLoS ONE 13, e0204114 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Zuanon, J., Bockmann, F. A. & Sazima, I. A remarkable sand-dwelling fish assemblage from central Amazonia, with comments on the evolution of psammophily in South American freshwater fishes. Neotrop. Ichthyol. 4, 107–118 (2006).Article 

Google Scholar 
43.Sazima, I., Carvalho, L. N., Mendonça, F. P. & Zuanon, J. Fallen leaves on the water-bed: Diurnal camouflage of three night-active fish species in an Amazonian streamlet. Neotrop. Ichthyol. 4, 119–122 (2006).Article 

Google Scholar 
44.Espírito-Santo, H. M. V. & Zuanon, J. Temporary pools provide stability to fish assemblages in Amazon headwater streams. Ecol. Freshw. Fish 26, 475–483 (2017).Article 

Google Scholar 
45.de Pinna, M. C. C., Zuanon, J., Rapp-Py-Daniel, L. R. & Petry, P. A new family of neotropical freshwater fishes from deep fossorial Amazonian habitat, with a reappraisal of morphological characiform phylogeny (Teleostei: Ostariophysi). Zool. J. Linn. Soc. 182, 76–106 (2018).Article 

Google Scholar 
46.López-Rojas, H., Lundberg, J. G. & Marsh, E. Design and operation of a small trawling apparatus for use with dugout canoes. N. Am. J. Fish. Manag. 4, 331–334 (1984).Article 

Google Scholar 
47.Marrero, C. & Taphorn, D. C. Notas sobre la historia natural y la distribution de los peces Gymnotiformes in la cuenca del Rio Apure y otros rios de la Orinoquia. Biollania 8, 123–142 (1991).
Google Scholar 
48.Cox-Fernandes, C., Podos, J. & Lundberg, J. G. Amazonian ecology: Tributaries enhance the diversity of electric fishes. Science 305, 1960–1962 (2004).ADS 
Article 
CAS 

Google Scholar 
49.Peixoto, L. A. W., Dutra, G. M. & Wosiack, W. B. The electric. Glassknife fishes of the Eigenmannia trilineata group (Gymnotiformes: Sternopygidae): Monophyly and description of seven new species. Zool. J. Linn. Soc. 175, 384–414 (2015).Article 

Google Scholar 
50.de Santana, C. D. & Vari, R. P. Electric fishes of the genus Sternarchorhynchus (Teleostei, Ostariophysi, Gymnotiformes); phylogenetic and revisionary studies. Zool. J. Linn. Soc. 159, 223–371 (2010).Article 

Google Scholar 
51.Castro, R. M. C. Evolução da ictiofauna de riachos sul-americanos: Padrões gerais e possíveis processos causais. In Ecologia de peixes de riachos (eds Caramaschi, E. P., Mazzoni, R., & Peres-Neto, P. R.) Série Oecologia Brasiliensis volume VI, PPGE-UFRJ, Rio de Janeiro, 139–155 (1999).52.Mojica, J. I., Castellanos, C. & Lobón-Cerviá, J. High temporal species turnover enhances the complexity of fish assemblages in Amazonian Terra firme streams. Ecol. Freshw. Fish 18, 518–526 (2009).Article 

Google Scholar 
53.de Oliveira, R. R., Rocha, M. M., Anjos, M. B., Zuanon, J. & Rapp Py-Daniel, L. H. Fish fauna of small streams of the Catua-Ipixuna Extractive Reserve, State of Amazonas, Brazil. Check List 5, 154–172 (2009).Article 

Google Scholar 
54.Caramaschi E., Mazzoni, P. R., Bizerril, C. R. S. F. & Peres-Neto, P. R. Ecologia de Peixes de Riachos: Estado Atual e Perspectivas. Oecologia Brasiliensis, v. VI, Rio de Janeiro (1999).55.Anjos, M. B. & Zuanon, J. Sampling effort and fish species richness in small Terra firme forest streams of central Amazonia, Brazil. Neotrop. Ichthyol. 5, 45–52 (2007).Article 

Google Scholar 
56.Mojica, J. I., Lobón-Cerviá, J. & Castellanos, C. Quantifying fish species richness and abundance in Amazonian streams: Assessment of a multiple gear method suitable for Terra firme stream fish assemblages. Fish. Manag. Ecol. 21, 220–233 (2014).Article 

Google Scholar 
57.Barros, D. F. et al. The fish fauna of streams in the Madeira-Purus interfluvial region, Brazilian Amazon. Check List 7, 768–773 (2011).Article 

Google Scholar 
58.Escobar-Camacho, D., Barriga, R. & Ron, S. R. Discovering hidden diversity of characins (Teleostei: Characiformes) in Ecuador’s Yasuní National Park. PLoS ONE 10, e0135569 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Ramirez, J. L. et al. Revealing hidden diversity of the underestimated neotropical ichthyofauna: DNA barcoding in the recently described genus Megaleporinus (Characiformes: Anostomidae). Front. Genet. 8, 149 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Crampton, W. G. R., de Santana, C. D., Waddell, J. C. & Lovejoy, N. R. The Neotropical electric fish genus Brachyhypopomus (Ostariophysi: Gymnotiformes: Hypopomidae): taxonomy and biology, with descriptions of 15 new species. Neotrop. Ichthyol. 14, 639–790 (2016).Article 

Google Scholar 
61.Abel, R. Conservation biology for the biodiversity crisis: A freshwater follow-up. Conserv. Biol. 5, 1435–1437 (2002).Article 

Google Scholar 
62.Dudgeon, D. Prospects for sustaining freshwater biodiversity in the 21st century: Linking ecosystem structure and function. Curr. Opin. Environ. Sustain. 5, 422–430 (2010).Article 

Google Scholar 
63.Jenkins, M. Prospects for biodiversity. Science 302, 1175–1177 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Bunn, S. E. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
65.Albert, J. S. et al. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio 50, 85–94 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Gilbert, M. T. P. et al. The isolation of nucleic acids from fixed, paraffin-embedded tissues–which methods are useful when?. PLoS ONE 2, e537 (2007).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.Campos, P. F. & Gilbert, T. M. DNA extraction from formalin-fixed material. In Ancient DNA 81–85 (Humana Press, 2012).68.Hykin, S. M., Bi, K. & McGuire, J. A. Fixing formalin: A method to recover genomic-scale DNA sequence data from formalin-fixed museum specimens using high-throughput sequencing. PLoS ONE 10, e0141579 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Hagedorn, M. M. et al. Cryopreservation of fish spermatogonial cells: The future of natural history collections. Sci. Rep. 8, 6149 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Albert, J. & Reis, R. E. Historical Biogeography of Neotropical Freshwater Fishes (University of California Press, 2011).Book 

Google Scholar 
71.Sabaj Pérez, M. H. Where the Xingu bends and will soon break. Am. Sci. 103, 395–403 (2015).Article 

Google Scholar 
72.Amigo, I. When will the Amazon hit a tipping point?. Nature 578, 505–507 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
73.Murienne, J. et al. Aquatic DNA for monitoring French Guiana biodiversity. Biodivers. Data J. 7, 37518 (2019).Article 

Google Scholar 
74.McDevitt, A. D. et al. Environmental DNA metabarcoding as an effective and rapid tool for fish monitoring in canals. J. Fish Biol. 95, 679–682 (2019).CAS 
PubMed 
Article 

Google Scholar 
75.Fernandes, G. W. et al. Dismantling Brazil’s science threatens global biodiversity heritage. Perspect. Ecol. Conserv. 15, 239–243 (2017).
Google Scholar 
76.Alves, R. J. V. et al. Brazilian legislation on genetic heritage harms Biodiversity Convention goals and threatens basic biology research and education. An. Acad. Bras. Ciênc. 90, 1279–1284 (2018).PubMed 
Article 

Google Scholar 
77.Overbeck, G. E. et al. Global biodiversity threatened by science budget cuts in Brazil. Bioscience 68, 11–12 (2018).PubMed 
Article 

Google Scholar 
78.Miya, M. et al. Use of a filter cartridge for filtration of water samples and extraction of environmental DNA. J. Vis. Exp. 117, 54741 (2016).
Google Scholar 
79.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
Article 

Google Scholar 
80.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 
Article 

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

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

Google Scholar 
83.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
85.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 

Google Scholar 
86.Miller, M. A. et al. A RESTful API for access to phylogenetic tools via the CIPRES science gateway. Evol. Bioinf. 11, 43–48 (2015).CAS 
Article 

Google Scholar 
87.Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). https://www.Rproject.org/.89.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
90.Oksanen, J., Kindt, R. & O’Hara, B. Package VEGAN. Community Ecology Package, Version 2 (2013).91.Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage, 2019).
Google Scholar 
92.Adler D., Nenadic, O. & Zucchini, W. rgl: 3D visualization device system (OpenGL). R package version 0.93.945. http://CRAN.R-project.org/package=rgl (2013).93.Gu, Z. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Schiettekatte, N. M. D., Brandl, S. J. & Casey, J. M. Fishualize: Color Palettes Based On Fish Species. CRAN version 0.2.0 (2019).95.Chao, A. Estimating population size for sparse data in capture-recapture experiments. Biometrics 45, 427 (1989).MathSciNet 
MATH 
Article 

Google Scholar 
96.Hsieh T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 2.0.20 (2020).97.Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing compositional similarity based on incidence and abundance data. Ecol. Lett. 8, 148–215 (2005).Article 

Google Scholar 
98.Olds, B. P. et al. Estimating species richness using environmental DNA. Ecol. Evol. 6, 4214–4226 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
99.Chao A., Ma, K. H., Hsieh, T. C. & Chiu, C. H. SpadeR (Species-richness Prediction and Diversity Estimation in R): An R package in CRAN. Program and User’s Guide also published at http://chao.stat.nthu.edu.tw/wordpress/software_download/ (2016). More

1.Edwards, S. L. & Marshall, W. S. In Euryhaline Fishes. Fish Physiology Vol. 32 (eds Farrell Stephen, A. P. et al.) 1–44 (Academic Press, 2012).Chapter 

Google Scholar 
2.Evans, D. H., Piermarini, P. M. & Choe, K. P. The multifunctional fish gill: Dominant site of gas exchange, osmoregulation, acid-base regulation, and excretion of nitrogenous waste. Physiol. Rev. 85, 97–177. https://doi.org/10.1152/physrev.00050.2003 (2005).CAS 
Article 
PubMed 

Google Scholar 
3.Kultz, D. Physiological mechanisms used by fish to cope with salinity stress. J. Exp. Biol. 218, 1907–1914. https://doi.org/10.1242/jeb.118695 (2015).Article 
PubMed 

Google Scholar 
4.Schultz, E. T. & McCormick, S. D. In Euryhaline Fishes. Fish Physiology Vol. 32 (eds Farrell, A. P. et al.) 477–533 (Academic Press, 2012).Chapter 

Google Scholar 
5.Scott, G. R., Richards, J. G., Forbush, B., Isenring, P. & Schulte, P. M. Changes in gene expression in gills of the euryhaline killifish Fundulus heteroclitus after abrupt salinity transfer. Am. J. Physiol. Cell Physiol. 287, C300–C309. https://doi.org/10.1152/ajpcell.00054.2004 (2004).CAS 
Article 
PubMed 

Google Scholar 
6.Deane, E. E. & Woo, N. Y. Differential gene expression associated with euryhalinity in sea bream (Sparus sarba). Am. J. Physiol. Regul. Integr. Comp. Physiol. 287, R1054–R1063. https://doi.org/10.1152/ajpregu.00347.2004 (2004).CAS 
Article 
PubMed 

Google Scholar 
7.Scott, G. R., Claiborne, J. B., Edwards, S. L., Schulte, P. M. & Wood, C. M. Gene expression after freshwater transfer in gills and opercular epithelia of killifish: Insight into divergent mechanisms of ion transport. J. Exp. Biol. 208, 2719–2729. https://doi.org/10.1242/jeb.01688 (2005).CAS 
Article 
PubMed 

Google Scholar 
8.Dymowska, A. K., Hwang, P. P. & Goss, G. G. Structure and function of ionocytes in the freshwater fish gill. Respir. Physiol. Neurobiol. 184, 282–292. https://doi.org/10.1016/j.resp.2012.08.025 (2012).CAS 
Article 
PubMed 

Google Scholar 
9.Hiroi, J. & McCormick, S. D. New insights into gill ionocyte and ion transporter function in euryhaline and diadromous fish. Respir. Physiol. Neurobiol. 184, 257–268. https://doi.org/10.1016/j.resp.2012.07.019 (2012).CAS 
Article 
PubMed 

Google Scholar 
10.Hsu, H. H., Lin, L. Y., Tseng, Y. C., Horng, J. L. & Hwang, P. P. A new model for fish ion regulation: Identification of ionocytes in freshwater- and seawater-acclimated medaka (Oryzias latipes). Cell Tissue Res. 357, 225–243. https://doi.org/10.1007/s00441-014-1883-z (2014).CAS 
Article 
PubMed 

Google Scholar 
11.Hwang, P. P. & Lin, L. Y. In The Physiology of Fishes Vol. 4 (eds Evans, D. H. et al.) 205–233 (CRC Press, 2013).
Google Scholar 
12.Evans, T. G. & Somero, G. N. A microarray-based transcriptomic time-course of hyper- and hypo-osmotic stress signaling events in the euryhaline fish Gillichthys mirabilis: Osmosensors to effectors. J. Exp. Biol. 211, 3636–3649. https://doi.org/10.1242/jeb.022160 (2008).CAS 
Article 
PubMed 

Google Scholar 
13.Fiol, D. F. & Kultz, D. Osmotic stress sensing and signaling in fishes. FEBS J. 274, 5790–5798. https://doi.org/10.1111/j.1742-4658.2007.06099.x (2007).CAS 
Article 
PubMed 

Google Scholar 
14.Kultz, D. The combinatorial nature of osmosensing in fishes. Physiology (Bethesda) 27, 259–275. https://doi.org/10.1152/physiol.00014.2012 (2012).CAS 
Article 

Google Scholar 
15.Komoroske, L. M. et al. Sublethal salinity stress contributes to habitat limitation in an endangered estuarine fish. Evol. Appl. 9, 963–981. https://doi.org/10.1111/eva.12385 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
16.Foskett, J. K., Logsdon, C. D., Turner, T., Machen, T. E. & Bern, H. A. Differentiation of the chloride extrusion mechanism during seawater adaptation of a teleost fish, the cichlid Sarotherodon mossambicus. J. Exp. Biol. 93, 209–224 (1981).Article 

Google Scholar 
17.Katoh, F. & Kaneko, T. Short-term transformation and long-term replacement of branchial chloride cells in killifish transferred from seawater to freshwater, revealed by morphofunctional observations and a newly established “time-differential double fluorescent staining” technique. J. Exp. Biol. 206, 4113–4123. https://doi.org/10.1242/jeb.00659 (2003).Article 
PubMed 

Google Scholar 
18.Uchida, K., Kaneko, T., Miyazaki, H., Hasegawa, S. & Hirano, T. Excellent salinity tolerance of mozambique tilapia (Oreochromis mossambicus): Elevated chloride cell activity in the branchial and opercular epithelia of the fish adapted to concentrated seawater. Zool. Sci. 17, 149–160. https://doi.org/10.2108/zsj.17.149 (2000).Article 

Google Scholar 
19.Whitehead, A., Roach, J. L., Zhang, S. & Galvez, F. Salinity- and population-dependent genome regulatory response during osmotic acclimation in the killifish (Fundulus heteroclitus) gill. J. Exp. Biol. 215, 1293–1305. https://doi.org/10.1242/jeb.062075 (2012).Article 
PubMed 

Google Scholar 
20.Mundy, P. C., Jeffries, K. M., Fangue, N. A. & Connon, R. E. Differential regulation of select osmoregulatory genes and Na+/K+-ATPase paralogs may contribute to population differences in salinity tolerance in a semi-anadromous fish. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 240, 110584. https://doi.org/10.1016/j.cbpa.2019.110584 (2020).CAS 
Article 
PubMed 

Google Scholar 
21.Jeffries, K. M. et al. Divergent transcriptomic signatures in response to salinity exposure in two populations of an estuarine fish. Evol. Appl. 12, 1212–1226. https://doi.org/10.1111/eva.12799 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Lam, S. H. et al. Differential transcriptomic analyses revealed genes and signaling pathways involved in iono-osmoregulation and cellular remodeling in the gills of euryhaline Mozambique tilapia, Oreochromis mossambicus. BMC Genomics 15, 921. https://doi.org/10.1186/1471-2164-15-921 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Evans, T. G. & Kultz, D. The cellular stress response in fish exposed to salinity fluctuations. J. Exp. Zool. A Ecol. Integr. Physiol. 333, 421–435. https://doi.org/10.1002/jez.2350 (2020).Article 
PubMed 

Google Scholar 
24.Burg, M. B., Ferraris, J. D. & Dmitrieva, N. I. Cellular response to hyperosmotic stresses. Physiol. Rev. 87, 1441–1474. https://doi.org/10.1152/physrev.00056.2006 (2007).CAS 
Article 
PubMed 

Google Scholar 
25.Tine, M., Bonhomme, F., McKenzie, D. J. & Durand, J. D. Differential expression of the heat shock protein Hsp70 in natural populations of the tilapia, Sarotherodon melanotheron, acclimatised to a range of environmental salinities. BMC Ecol. 10, 11. https://doi.org/10.1186/1472-6785-10-11 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Whitehead, A., Zhang, S., Roach, J. L. & Galvez, F. Common functional targets of adaptive micro- and macro-evolutionary divergence in killifish. Mol. Ecol. 22, 3780–3796. https://doi.org/10.1111/mec.12316 (2013).Article 
PubMed 

Google Scholar 
27.Brennan, R. S., Galvez, F. & Whitehead, A. Reciprocal osmotic challenges reveal mechanisms of divergence in phenotypic plasticity in the killifish Fundulus heteroclitus. J. Exp. Biol. 218, 1212–1222. https://doi.org/10.1242/jeb.110445 (2015).Article 
PubMed 

Google Scholar 
28.Kultz, D. Molecular and evolutionary basis of the cellular stress response. Annu. Rev. Physiol. 67, 225–257. https://doi.org/10.1146/annurev.physiol.67.040403.103635 (2005).CAS 
Article 
PubMed 

Google Scholar 
29.Takei, Y. & Hwang, P.-P. In Biology of Stress in Fish—Fish Physiology Vol. 35 (eds Schreck, C. B. et al.) 207–249 (Academic Press, 2016).Chapter 

Google Scholar 
30.Tseng, Y. C. & Hwang, P. P. Some insights into energy metabolism for osmoregulation in fish. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 148, 419–429. https://doi.org/10.1016/j.cbpc.2008.04.009 (2008).CAS 
Article 
PubMed 

Google Scholar 
31.Chen, X. L., Lui, E. Y., Ip, Y. K. & Lam, S. H. RNA sequencing, de novo assembly and differential analysis of the gill transcriptome of freshwater climbing perch Anabas testudineus after 6 days of seawater exposure. J. Fish Biol. 93, 215–228. https://doi.org/10.1111/jfb.13653 (2018).CAS 
Article 
PubMed 

Google Scholar 
32.Nguyen, T. V., Jung, H., Nguyen, T. M., Hurwood, D. & Mather, P. Evaluation of potential candidate genes involved in salinity tolerance in striped catfish (Pangasianodon hypophthalmus) using an RNA-Seq approach. Mar. Genomics 25, 75–88. https://doi.org/10.1016/j.margen.2015.11.010 (2016).Article 
PubMed 

Google Scholar 
33.Bœuf, G. & Payan, P. How should salinity influence fish growth?. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 130, 411–423. https://doi.org/10.1016/s1532-0456(01)00268-x (2001).Article 
PubMed 

Google Scholar 
34.Makrinos, D. L. & Bowden, T. J. Natural environmental impacts on teleost immune function. Fish Shellfish Immunol. 53, 50–57. https://doi.org/10.1016/j.fsi.2016.03.008 (2016).CAS 
Article 
PubMed 

Google Scholar 
35.Morgan, J. D. & Iwama, G. K. Effects of salinity on growth, metabolism, and ion regulation in juvenile rainbow and steelhead trout (Oncorhynchus mykiss) and fall chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 48, 2083–2094. https://doi.org/10.1139/f91-247 (1991).Article 

Google Scholar 
36.Whitehead, A., Roach, J. L., Zhang, S. & Galvez, F. Genomic mechanisms of evolved physiological plasticity in killifish distributed along an environmental salinity gradient. Proc. Natl. Acad. Sci. U.S.A. 108, 6193–6198. https://doi.org/10.1073/pnas.1017542108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Kozak, G. M., Brennan, R. S., Berdan, E. L., Fuller, R. C. & Whitehead, A. Functional and population genomic divergence within and between two species of killifish adapted to different osmotic niches. Evolution 68, 63–80. https://doi.org/10.1111/evo.12265 (2014).CAS 
Article 
PubMed 

Google Scholar 
38.Hrbek, T. & Meyer, A. Closing of the Tethys Sea and the phylogeny of Eurasian killifishes (Cyprinodontiformes: Cyprinodontidae). J. Evol. Biol. 16, 17–36. https://doi.org/10.1046/j.1420-9101.2003.00475.x (2003).CAS 
Article 
PubMed 

Google Scholar 
39.Schunter, C. et al. Desert fish populations tolerate extreme salinity change to overcome hydrological constraints. bioRxiv. https://doi.org/10.1101/2021.05.14.444120 (2021).Article 

Google Scholar 
40.Marshall, J. C. et al. Go with the flow: The movement behaviour of fish from isolated waterhole refugia during connecting flow events in an intermittent dryland river. Freshw. Biol. 61, 1242–1258. https://doi.org/10.1111/fwb.12707 (2016).Article 

Google Scholar 
41.Kerezsy, A., Balcombe, S. R., Tischler, M. & Arthington, A. H. Fish movement strategies in an ephemeral river in the Simpson Desert, Australia. Austral Ecol. 38, 798–808. https://doi.org/10.1111/aec.12075 (2013).Article 

Google Scholar 
42.Martin, C. H., Crawford, J. E., Turner, B. J. & Simons, L. H. Diabolical survival in Death Valley: Recent pupfish colonization, gene flow and genetic assimilation in the smallest species range on earth. Proc. Biol. Sci. 283, 20152334. https://doi.org/10.1098/rspb.2015.2334 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Mossop, K. D. et al. Dispersal in the desert: Ephemeral water drives connectivity and phylogeography of an arid-adapted fish. J. Biogeogr. 42, 2374–2388. https://doi.org/10.1111/jbi.12596 (2015).Article 

Google Scholar 
44.Collins, J. P., Young, C., Howell, J. & Minckley, W. L. Impact of flooding in a Sonoran desert stream, including elimination of an endangered fish population (Poeciliopsis O. occidentalis, Poeciliidae). Southwest. Nat. 26, 415–423. https://doi.org/10.2307/3671085 (1981).Article 

Google Scholar 
45.Meffe, G. K. Effects of abiotic disturbance on coexistence of predator–prey fish species. Ecology 65, 1525–1534. https://doi.org/10.2307/1939132 (1984).Article 

Google Scholar 
46.Lotan, R. Sodium, chloride and water balance in the euryhaline teleost Aphanius dispar (Rüppell) (Cyprinodontidae). Z. Vgl. Physiol. 65, 455–462. https://doi.org/10.1007/bf00299054 (1969).Article 

Google Scholar 
47.Lotan, R. Osmotic adjustment in the euryhaline teleost Aphanius dispar (Cyprinodontidae). Z. Vgl. Physiol. 75, 383–387. https://doi.org/10.1007/bf00630558 (1971).CAS 
Article 

Google Scholar 
48.Plaut, I. Resting metabolic rate, critical swimming speed, and routine activity of the euryhaline cyprinodontid, Aphanius dispar, acclimated to a wide range of salinities. Physiol. Biochem. Zool. 73, 590–596. https://doi.org/10.1086/317746 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
49.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Andrews, S. FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).51.Song, L. & Florea, L. Rcorrector: Efficient and accurate error correction for Illumina RNA-seq reads. Gigascience 4, 48. https://doi.org/10.1186/s13742-015-0089-y (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29, 644 (2011).CAS 
Article 

Google Scholar 
53.Lafond-Lapalme, J., Duceppe, M. O., Wang, S., Moffett, P. & Mimee, B. A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm. Bioinformatics 33, 1293–1300. https://doi.org/10.1093/bioinformatics/btw793 (2017).CAS 
Article 
PubMed 

Google Scholar 
54.Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512. https://doi.org/10.1038/nprot.2013.084 (2013).CAS 
Article 
PubMed 

Google Scholar 
55.Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: Interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37. https://doi.org/10.1093/nar/gkr367 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421. https://doi.org/10.1186/1471-2105-10-421 (2009).CAS 
Article 

Google Scholar 
57.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359. https://doi.org/10.1038/nmeth.1923 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351 (2015).CAS 
Article 
PubMed 

Google Scholar 
60.BioBam Bioinformatics. OmicsBox – Bioinformatics Made Easy. https://www.biobam.com/omicsbox (2019).61.Huerta-Cepas, J. et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314. https://doi.org/10.1093/nar/gky1085 (2019).CAS 
Article 
PubMed 

Google Scholar 
62.Gotz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36, 3420–3435. https://doi.org/10.1093/nar/gkn176 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419. https://doi.org/10.1038/nmeth.4197 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
65.Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research https://doi.org/10.12688/f1000research.7563.1 (2015).Article 
PubMed 

Google Scholar 
66.Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: Removing the noise and preserving large differences. Bioinformatics 35, 2084–2092. https://doi.org/10.1093/bioinformatics/bty895 (2019).CAS 
Article 
PubMed 

Google Scholar 
67.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).MathSciNet 
Article 
MATH 

Google Scholar 
68.Cui, W. et al. Comparative transcriptomic analysis reveals mechanisms of divergence in osmotic regulation of the turbot Scophthalmus maximus. Fish Physiol. Biochem. 46, 1519–1536. https://doi.org/10.1007/s10695-020-00808-6 (2020).CAS 
Article 
PubMed 

Google Scholar 
69.Lee, S. Y., Lee, H. J. & Kim, Y. K. Comparative transcriptome profiling of selected osmotic regulatory proteins in the gill during seawater acclimation of chum salmon (Oncorhynchus keta) fry. Sci. Rep. 10, 1987. https://doi.org/10.1038/s41598-020-58915-6 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.Su, H., Ma, D., Zhu, H., Liu, Z. & Gao, F. Transcriptomic response to three osmotic stresses in gills of hybrid tilapia (Oreochromis mossambicus female × O. urolepis hornorum male). BMC Genomics 21, 110. https://doi.org/10.1186/s12864-020-6512-5 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.Fischer, D. S., Theis, F. J. & Yosef, N. Impulse model-based differential expression analysis of time course sequencing data. Nucleic Acids Res. 46, e119. https://doi.org/10.1093/nar/gky675 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Hwang, P. P., Lee, T. H. & Lin, L. Y. Ion regulation in fish gills: Recent progress in the cellular and molecular mechanisms. Am. J. Physiol. Regul. Integr. Comp. Physiol. 301, R28–R47. https://doi.org/10.1152/ajpregu.00047.2011 (2011).CAS 
Article 
PubMed 

Google Scholar 
73.Marshall, W. S. Mechanosensitive signalling in fish gill and other ion transporting epithelia. Acta Physiol. (Oxf.) 202, 487–499. https://doi.org/10.1111/j.1748-1716.2010.02189.x (2011).CAS 
Article 

Google Scholar 
74.Lema, S. C., Carvalho, P. G., Egelston, J. N., Kelly, J. T. & McCormick, S. D. Dynamics of gene expression responses for ion transport proteins and aquaporins in the gill of a euryhaline pupfish during freshwater and high-salinity acclimation. Physiol. Biochem. Zool. 91, 1148–1171. https://doi.org/10.1086/700432 (2018).Article 
PubMed 

Google Scholar 
75.Flemmer, A. W. et al. Phosphorylation state of the Na+–K+–Cl− cotransporter (NKCC1) in the gills of Atlantic killifish (Fundulus heteroclitus) during acclimation to water of varying salinity. J. Exp. Biol. 213, 1558–1566. https://doi.org/10.1242/jeb.039644 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
76.Delpire, E. & Gagnon, K. B. SPAK and OSR1: STE20 kinases involved in the regulation of ion homoeostasis and volume control in mammalian cells. Biochem. J. 409, 321–331. https://doi.org/10.1042/BJ20071324 (2008).CAS 
Article 
PubMed 

Google Scholar 
77.Rinehart, J. et al. WNK2 kinase is a novel regulator of essential neuronal cation-chloride cotransporters. J. Biol. Chem. 286, 30171–30180. https://doi.org/10.1074/jbc.M111.222893 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
78.Li, J. et al. Gill transcriptomes reveal expression changes of genes related with immune and ion transport under salinity stress in silvery pomfret (Pampus argenteus). Fish Physiol. Biochem. 46, 1255–1277. https://doi.org/10.1007/s10695-020-00786-9 (2020).CAS 
Article 
PubMed 

Google Scholar 
79.Yancey, P. H., Clark, M. E., Hand, S. C., Bowlus, R. D. & Somero, G. N. Living with water stress: Evolution of osmolyte systems. Science 217, 1214–1222. https://doi.org/10.1126/science.7112124 (1982).ADS 
CAS 
Article 
PubMed 

Google Scholar 
80.Kalujnaia, S., McVee, J., Kasciukovic, T., Stewart, A. J. & Cramb, G. A role for inositol monophosphatase 1 (IMPA1) in salinity adaptation in the euryhaline eel (Anguilla anguilla). FASEB J. 24, 3981–3991. https://doi.org/10.1096/fj.10-161000 (2010).CAS 
Article 
PubMed 

Google Scholar 
81.Cui, W. X. et al. myo-inositol facilitates salinity tolerance by modulating multiple physiological functions in the turbot Scophthalmus maximus. Aquaculture 527, 735451. https://doi.org/10.1016/j.aquaculture.2020.735451 (2020).CAS 
Article 

Google Scholar 
82.Ma, A. et al. Osmoregulation by the myo-inositol biosynthesis pathway in turbot Scophthalmus maximus and its regulation by anabolite and c-Myc. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 242, 110636. https://doi.org/10.1016/j.cbpa.2019.110636 (2020).CAS 
Article 
PubMed 

Google Scholar 
83.Wang, Y. F., Yan, J. J., Tseng, Y. C., Chen, R. D. & Hwang, P. P. Molecular physiology of an extra-renal Cl− uptake mechanism for body fluid Cl− homeostasis. Int. J. Biol. Sci. 11, 1190–1203. https://doi.org/10.7150/ijbs.11737 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
84.Leguen, I., Le Cam, A., Montfort, J., Peron, S. & Fautrel, A. Transcriptomic analysis of trout gill ionocytes in fresh water and sea water using laser capture microdissection combined with microarray analysis. PLoS One 10, e0139938. https://doi.org/10.1371/journal.pone.0139938 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
85.Richards, J. G., Semple, J. W., Bystriansky, J. S. & Schulte, P. M. Na+/K+-ATPase alpha-isoform switching in gills of rainbow trout (Oncorhynchus mykiss) during salinity transfer. J. Exp. Biol. 206, 4475–4486. https://doi.org/10.1242/jeb.00701 (2003).CAS 
Article 
PubMed 

Google Scholar 
86.McCormick, S. D., Regish, A. M. & Christensen, A. K. Distinct freshwater and seawater isoforms of Na+/K+-ATPase in gill chloride cells of Atlantic salmon. J. Exp. Biol. 212, 3994–4001. https://doi.org/10.1242/jeb.037275 (2009).CAS 
Article 
PubMed 

Google Scholar 
87.Bystriansky, J. S., Richards, J. G., Schulte, P. M. & Ballantyne, J. S. Reciprocal expression of gill Na+/K+-ATPase alpha-subunit isoforms alpha1a and alpha1b during seawater acclimation of three salmonid fishes that vary in their salinity tolerance. J. Exp. Biol. 209, 1848–1858. https://doi.org/10.1242/jeb.02188 (2006).CAS 
Article 
PubMed 

Google Scholar 
88.Tipsmark, C. K. et al. Switching of Na+, K+-ATPase isoforms by salinity and prolactin in the gill of a cichlid fish. J. Endocrinol. 209, 237–244. https://doi.org/10.1530/JOE-10-0495 (2011).CAS 
Article 
PubMed 

Google Scholar 
89.Urbina, M. A., Schulte, P. M., Bystriansky, J. S. & Glover, C. N. Differential expression of Na+, K+-ATPase alpha-1 isoforms during seawater acclimation in the amphidromous galaxiid fish Galaxias maculatus. J. Comp. Physiol. B 183, 345–357. https://doi.org/10.1007/s00360-012-0719-y (2013).CAS 
Article 
PubMed 

Google Scholar 
90.Velotta, J. P. et al. Transcriptomic imprints of adaptation to fresh water: Parallel evolution of osmoregulatory gene expression in the Alewife. Mol. Ecol. 26, 831–848. https://doi.org/10.1111/mec.13983 (2017).CAS 
Article 
PubMed 

Google Scholar 
91.Ip, Y. K. et al. Roles of three branchial Na+-K+-ATPase alpha-subunit isoforms in freshwater adaptation, seawater acclimation, and active ammonia excretion in Anabas testudineus. Am. J. Physiol. Regul. Integr. Comp. Physiol. 303, R112–R125. https://doi.org/10.1152/ajpregu.00618.2011 (2012).CAS 
Article 
PubMed 

Google Scholar 
92.Birrer, S. C., Reusch, T. B. & Roth, O. Salinity change impairs pipefish immune defence. Fish Shellfish Immunol. 33, 1238–1248. https://doi.org/10.1016/j.fsi.2012.08.028 (2012).CAS 
Article 
PubMed 

Google Scholar 
93.Delamare-Deboutteville, J., Wood, D. & Barnes, A. C. Response and function of cutaneous mucosal and serum antibodies in barramundi (Lates calcarifer) acclimated in seawater and freshwater. Fish Shellfish Immunol. 21, 92–101. https://doi.org/10.1016/j.fsi.2005.10.005 (2006).CAS 
Article 
PubMed 

Google Scholar 
94.Koppang, E. O., Kvellestad, A. & Fischer, U. In Mucosal Health in Aquaculture (eds Beck, B. H. & Peatman, E.) 93–133 (Academic Press, 2015).Chapter 

Google Scholar 
95.Poulin, R., Blanar, C. A., Thieltges, D. W. & Marcogliese, D. J. The biogeography of parasitism in sticklebacks: Distance, habitat differences and the similarity in parasite occurrence and abundance. Ecography 34, 540–551. https://doi.org/10.1111/j.1600-0587.2010.06826.x (2011).Article 

Google Scholar 
96.Takemura, A. F., Chien, D. M. & Polz, M. F. Associations and dynamics of Vibrionaceae in the environment, from the genus to the population level. Front. Microbiol. 5, 38. https://doi.org/10.3389/fmicb.2014.00038 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
97.Nitzan, S., Shwartsburd, B. & Heller, E. D. The effect of growth medium salinity of Photobacterium damselae subsp. piscicida on the immune response of hybrid bass (Morone saxatilis × M. chrysops). Fish Shellfish Immunol. 16, 107–116. https://doi.org/10.1016/s1050-4648(03)00045-7 (2004).CAS 
Article 
PubMed 

Google Scholar 
98.Zheng, D. H. et al. Effect of temperature and salinity on virulence of Edwardsiella tarda to Japanese flounder, Paralichthys olivaceus (Temminck et Schlegel). Aquac. Res. 35, 494–500. https://doi.org/10.1111/j.1365-2109.2004.01044.x (2004).Article 

Google Scholar 
99.Dominguez, M., Takemura, A., Tsuchiya, M. & Nakamura, S. Impact of different environmental factors on the circulating immunoglobulin levels in the Nile tilapia, Oreochromis niloticus. Aquaculture 241, 491–500. https://doi.org/10.1016/j.aquaculture.2004.06.027 (2004).CAS 
Article 

Google Scholar 
100.Mozanzadeh, M. T. et al. The effect of salinity on growth performance, digestive and antioxidant enzymes, humoral immunity and stress indices in two euryhaline fish species: Yellowfin seabream (Acanthopagrus latus) and Asian seabass (Lates calcarifer). Aquaculture 534, 736329. https://doi.org/10.1016/j.aquaculture.2020.736329 (2021).CAS 
Article 

Google Scholar 
101.Gao, Y., Tang, X., Sheng, X., Xing, J. & Zhan, W. Antigen uptake and expression of antigen presentation-related immune genes in flounder (Paralichthys olivaceus) after vaccination with an inactivated Edwardsiella tarda immersion vaccine, following hyperosmotic treatment. Fish Shellfish Immunol. 55, 274–280. https://doi.org/10.1016/j.fsi.2016.05.042 (2016).CAS 
Article 
PubMed 

Google Scholar 
102.Salinas, I. The mucosal immune system of teleost fish. Biology 4, 525–539. https://doi.org/10.3390/biology4030525 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
103.Reverter, M., Tapissier-Bontemps, N., Lecchini, D., Banaigs, B. & Sasal, P. Biological and ecological roles of external fish mucus: A review. Fishes 3, 41. https://doi.org/10.3390/fishes3040041 (2018).Article 

Google Scholar 
104.Shephard, K. L. Functions for fish mucus. Rev. Fish Biol. Fish. 4, 401–429. https://doi.org/10.1007/Bf00042888 (1994).Article 

Google Scholar 
105.Wong, M. K. S. et al. A sodium binding system alleviates acute salt stress during seawater acclimation in eels. Zool. Lett. 3, 22. https://doi.org/10.1186/s40851-017-0081-8 (2017).Article 

Google Scholar 
106.Malachowicz, M., Wenne, R. & Burzynski, A. De novo assembly of the sea trout (Salmo trutta m. trutta) skin transcriptome to identify putative genes involved in the immune response and epidermal mucus secretion. PLoS One 12, e0172282. https://doi.org/10.1371/journal.pone.0172282 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
107.Roberts, S. D. & Powell, M. D. Comparative ionic flux and gill mucous cell histochemistry: Effects of salinity and disease status in Atlantic salmon (Salmo salar L.). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 134, 525–537. https://doi.org/10.1016/s1095-6433(02)00327-6 (2003).Article 
PubMed 

Google Scholar 
108.Mylonas, C. C. et al. Growth performance and osmoregulation in the shi drum (Umbrina cirrosa) adapted to different environmental salinities. Aquaculture 287, 203–210. https://doi.org/10.1016/j.aquaculture.2008.10.024 (2009).CAS 
Article 

Google Scholar 
109.Roberts, S. D. & Powell, M. D. The viscosity and glycoprotein biochemistry of salmonid mucus varies with species, salinity and the presence of amoebic gill disease. J. Comp. Physiol. B 175, 1–11. https://doi.org/10.1007/s00360-004-0453-1 (2005).CAS 
Article 
PubMed 

Google Scholar 
110.Kalujnaia, S. et al. Transcriptomic approach to the study of osmoregulation in the European eel Anguilla anguilla. Physiol. Genomics 31, 385–401. https://doi.org/10.1152/physiolgenomics.00059.2007 (2007).CAS 
Article 
PubMed 

Google Scholar 
111.Shaw, J. R. et al. The role of SGK and CFTR in acute adaptation to seawater in Fundulus heteroclitus. Cell Physiol. Biochem. 22, 69–78. https://doi.org/10.1159/000149784 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
112.Kammerer, B. D., Sardella, B. A. & Kultz, D. Salinity stress results in rapid cell cycle changes of tilapia (Oreochromis mossambicus) gill epithelial cells. J. Exp. Zool. A Ecol. Genet. Physiol. 311, 80–90. https://doi.org/10.1002/jez.498 (2009).Article 
PubMed 

Google Scholar 
113.Ronkin, D., Seroussi, E., Nitzan, T., Doron-Faigenboim, A. & Cnaani, A. Intestinal transcriptome analysis revealed differential salinity adaptation between two tilapiine species. Comp. Biochem. Physiol. Part D Genomics Proteomics 13, 35–43. https://doi.org/10.1016/j.cbd.2015.01.003 (2015).CAS 
Article 
PubMed 

Google Scholar 
114.Dong, Y. W. et al. Genomic and physiological mechanisms underlying skin plasticity during water to air transition in an amphibious fish. J. Exp. Biol. 224, jeb235515. https://doi.org/10.1242/jeb.235515 (2021).Article 
PubMed 

Google Scholar 
115.Inokuchi, M. & Kaneko, T. Recruitment and degeneration of mitochondrion-rich cells in the gills of Mozambique tilapia Oreochromis mossambicus during adaptation to a hyperosmotic environment. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 162, 245–251. https://doi.org/10.1016/j.cbpa.2012.03.018 (2012).CAS 
Article 
PubMed 

Google Scholar  More

1.Cook, B. I., Wolkovich, E. M. & Parmesan, C. Divergent responses to spring and winter warming drive community level flowering trends. Proc. Natl. Acad. Sci. USA 109, 9000–9005 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science (80-). 333, 1024–1026 (2011).ADS 
CAS 
Article 

Google Scholar 
3.Suggitt, A. J. et al. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8 (2011).Article 

Google Scholar 
4.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).ADS 
Article 

Google Scholar 
5.Suggitt, A. J., et al. Habitatmicroclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8. https://doi.org/10.1111/j.1600-0706.2010.18270.x (2011).Article 

Google Scholar 
6.Kersting, D. K., Bensoussan, N. & Linares, C. Long-term responses of the endemic reef-builder cladocora caespitosa to mediterranean warming. PLoS ONE 8, e70820 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Rodolfo-Metalpa, R. et al. Thermally tolerant corals have limited capacity to acclimatize to future warming. Glob. Change Biol. 20, 3036–3049 (2014).ADS 
Article 

Google Scholar 
8.Fine M., & Loya Y. Coral bleaching in a temperate sea: From colony physiology to population ecology. In: Coral Health and Disease (eds. Rosenberg, E., & Loya, Y). https://doi.org/10.1007/978-3-662-06414-6_6 (Springer, Berlin, Heidelberg 2004).9.Liu, G., Strong, A. E., Skirving, W. J. & Arzayus, L. F. Overview of NOAA Coral Reef Watch Program’s near-real-time satellite global coral bleaching monitoring activities. Proc. 10th Int. Coral Reef Symp. 1793, 1783–1793 (2006).
Google Scholar 
10.Glynn, P. W. Coral reef bleaching: Facts, hypotheses and implications. Glob. Change Biol. 2, 495–509 (1996).ADS 
Article 

Google Scholar 
11.Price, N. N. et al. Global biogeography of coral recruitment: Tropical decline and subtropical increase. Mar. Ecol. Prog. Ser. 621, 1–17 (2019).ADS 
Article 

Google Scholar 
12.Serrano, E. et al. Rapid northward spread of a zooxanthellate coral enhanced by artificial structures and sea warming in the Western Mediterranean. PLoS One 8(1), e52739. https://doi.org/10.1371/journal.pone.0052739 (2013).CAS 
Article 

Google Scholar 
13.Grupstra, C. G. B. et al. Evidence for coral range expansion accompanied by reduced diversity of Symbiodinium genotypes. Coral Reefs 2017 363. 36, 981–985 (2017).
Google Scholar 
14.Serrano, E., Ribes, M. & Coma, R. Demographics of the zooxanthellate coral Oculina patagonica along the Mediterranean Iberian coast in relation to environmental parameters. Sci. Total Environ. 634, 1580–1592 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
15.Leichter, J. J., Helmuth, B. & Fischer, A. M. Variation beneath the surface: Quantifying complex thermal environments on coral reefs in the Caribbean, Bahamas and Florida. J. Mar. Res. 64, 563–588 (2006).Article 

Google Scholar 
16.Gould, K., Bruno, J. F., Ju, R. & Goodbody-Gringley, G. Upper-mesophotic and shallow reef corals exhibit similar thermal tolerance, sensitivity and optima. Coral Reefs https://doi.org/10.1007/s00338-021-02095-w (2021).Article 

Google Scholar 
17.Semmler, R. F., Hoot, W. C. & Reaka, M. L. Are mesophotic coral ecosystems distinct communities and can they serve as refugia for shallow reefs?. Coral Reefs 36, 433–444 (2017).ADS 
Article 

Google Scholar 
18.Shlesinger, T. & Loya, Y. Depth-dependent parental effects create invisible barriers to coral dispersal. Commun. Biol. 4(202), https://doi.org/10.1038/s42003-021-01727-9 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
19.Ledoux, J.-B. et al. Potential for adaptive evolution at species range margins: Contrasting interactions between red coral populations and their environment in a changing ocean. Ecol. Evol. 5, 1178–1192 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Liberman, R., Shlesinger, T., Loya, Y. & Benayahu, Y. Octocoral sexual reproduction: Temporal disparity between mesophotic and shallow-reef populations. Front. Mar. Sci. 5, 1–14 (2018).Article 

Google Scholar 
21.Rapuano, H., Shlesinger, T., Loya, Y., Amit, T. & Grinblat, M. Can mesophotic reefs replenish shallow reefs? Reduced coral reproductive performance casts a doubt. Ecology 99, 421–437 (2017).
Google Scholar 
22.Bongaerts, P., Ridgway, T., Sampayo, E. M. & Hoegh-Guldberg, O. Assessing the ‘deep reef refugia’ hypothesis: Focus on Caribbean reefs. Coral Reefs 29, 1–19 (2010).Article 

Google Scholar 
23.Hoogenboom, M., Rodolfo-Metalpa, R. & Ferrier-Pagès, C. Co-variation between autotrophy and heterotrophy in the Mediterranean coral Cladocora caespitosa. J. Exp. Biol. 213, 2399–2409 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Mass, T. et al. Photoacclimation of Stylophora pistillata to light extremes: Metabolism and calcification. Mar. Ecol. Prog. Ser. 334, 93–102 (2007).ADS 
CAS 
Article 

Google Scholar 
25.Bednarz, V. N., Grover, R., Maguer, J. F., Fine, M. & Ferrier-Pagès, C. The assimilation of diazotroph-derived nitrogen by scleractinian corals depends on their metabolic status. MBio 8, e02058-16 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Tremblay, P. et al. Controlling effects of irradiance and heterotrophy on carbon translocation in the temperate coral Cladocora caespitosa. PLoS ONE 7, e44672 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Aichelman, H. E., Zimmerman, R. C. & Barshis, D. J. Adaptive signatures in thermal performance of the temperate coral Astrangia poculata. J. Exp. Biol. 222, jeb189225 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Shenkar, N., Fine, M. & Loya, Y. Size matters: Bleaching dynamics of the coral Oculina patagonica. Mar. Ecol. Prog. Ser. 294, 181–188 (2005).ADS 
Article 

Google Scholar 
30.Zaquin, T., Zaslansky, P., Pinkas, I. & Mass, T. Simulating bleaching: Long-term adaptation to the dark reveals phenotypic plasticity of the Mediterranean Sea coral Oculina patagonica. Front. Mar. Sci. 6, 662 https://doi.org/10.3389/fmars.2019.00662 (2019).Article 

Google Scholar 
31.De Angelis d’Ossat, G. Altri Zoantari del Terziario della Patagonia. (1908).32.Fine, M. & Loya, Y. The coral Oculina patagonica a new immigrant to the Mediterranean coast of Israel. Isr. J. Zool. 41, 84 (1995).
Google Scholar 
33.Salomidi, M., Katsanevakis, S., Issaris, Y., Tsiamis, K. & Katsiaras, N. Anthropogenic disturbance of coastal habitats promotes the spread of the introduced scleractinian coral Oculina patagonica in the Mediterranean Sea. Biol. Invasions 15, 1961–1971 (2013).Article 

Google Scholar 
34.Fine, M., Zibrowius, H. & Loya, Y. Oculina patagonica: A non-lessepsian scleractinian coral invading the Mediterranean Sea. Mar. Biol. 138, 1195–1203 (2001).Article 

Google Scholar 
35.Sartoretto, S. et al. The alien coral Oculina patagonica De Angelis, 1908 (Cnidaria, Scleractinia) in Algeria and Tunisia. Aquat. Invasions 3, 173–180 (2008).Article 

Google Scholar 
36.Ozer, T., Gertman, I., Kress, N., Silverman, J. & Herut, B. Interannual thermohaline (1979–2014) and nutrient (2002–2014) dynamics in the Levantine surface and intermediate water masses, SE Mediterranean Sea. Glob. Planet. Change 151, 60–67 (2017).ADS 
Article 

Google Scholar 
37.Leydet, K. P. & Hellberg, M. E. The invasive coral Oculina patagonica has not been recently introduced to the Mediterranean from the western Atlantic. BMC Evol. Biol. 15(79), https://doi.org/10.1186/s12862-015-0356-7 (2015).38.Veron, J. E. N. Título: Corals of the world. P.imprenta: Australia. Australian Institute of Marine Science. 463, 31 (2000).39.Einbinder, S. et al. Changes in morphology and diet of the coral Stylophora pistillata along a depth gradient. Mar. Ecol. Prog. Ser. 381, 167–174 (2009).ADS 
Article 

Google Scholar 
40.Goodbody-Gringley, G. & Waletich, J. Morphological plasticity of the depth generalist coral, Montastraea cavernosa, on mesophotic reefs in Bermuda. Ecology 99, 1688–1690 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Malik, A. et al. Molecular and skeletal fingerprints of scleractinian coral biomineralization: From the sea surface to mesophotic depths. Acta Biomater. https://doi.org/10.1016/j.actbio.2020.01.010 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Frade, P. R., Englebert, N., Faria, J., Visser, P. M. & Bak, R. P. M. Distribution and photobiology of Symbiodinium types in different light environments for three colour morphs of the coral Madracis pharensis: Is there more to it than total irradiance?. Coral Reefs 27, 913–925 (2008).ADS 
Article 

Google Scholar 
43.Lesser, M. P. et al. Photoacclimatization by the coral Montastraea cavernosa in the mesophotic zone: Light, food, and genetics. Ecology 91, 990–1003 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Byler, K. A., Carmi-Veal, M., Fine, M. & Goulet, T. L. Multiple symbiont acquisition strategies as an adaptive mechanism in the coral Stylophora pistillata. PLoS ONE 8, 1–7 (2013).Article 
CAS 

Google Scholar 
45.Scucchia, F., Nativ, H., Neder, M., Goodbody-Gringley, G. & Mass, T. Physiological characteristics of Stylophora pistillata larvae across a depth gradient. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00013 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Leydet, K. P. & Hellberg, M. E. Discordant coral–symbiont structuring: factors shaping geographical variation of Symbiodinium communities in a facultative zooxanthellate coral genus, Oculina. Coral Reefs 35, 583–595 (2016).ADS 
Article 

Google Scholar 
47.Rubio-Portillo, E. et al. Eukarya associated with the stony coral Oculina patagonica from the Mediterranean Sea. Mar. Genom. 17, 17–23 (2014).Article 

Google Scholar 
48.Brakel, W. H. Small-scale spatial variation in light available to coral reef benthos: Quantum irradiance measurements from a Jamaican reef. Bull. Mar. Sci. 29, 406–413 (1979).ADS 

Google Scholar 
49.Ben-Zvi, O. et al. Photophysiology of a mesophotic coral 3 years after transplantation to a shallow environment. Coral Reefs 39, 903–913 (2020).Article 

Google Scholar 
50.Wang, C., Arneson, E. M., Gleason, D. F. & Hopkinson, B. M. Resilience of the temperate coral Oculina arbuscula to ocean acidification extends to the physiological level. Coral Reefs 40, 201–214 (2021).Article 

Google Scholar 
51.Hoogenboom, M., Béraud, E. & Ferrier-Pagès, C. Relationship between symbiont density and photosynthetic carbon acquisition in the temperate coral Cladocora caespitosa. Coral Reefs 29, 21–29. https://doi.org/10.1007/s00338-009-0558-9 (2010).ADS 
Article 

Google Scholar 
52.Martinez, S. et al. Effect of different derivatization protocols on the calculation of trophic position using amino acids compound-specific stable isotopes. Front. Mar. Sci. 7, 1–7. https://doi.org/10.3389/fmars.2020.561568 (2020).ADS 
CAS 
Article 

Google Scholar 
53.Wall, C. B., Wallsgrove, N. J., Gates, R. D. & Popp, B. N. Amino acid δ 13C and δ 15N analyses reveal distinct species‐specific patterns of trophic plasticity in a marine symbiosis. Limnol. Oceanogr. 66, 2033–2050. https://doi.org/10.1002/lno.11742 (2021).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Ferrier-Pagès, C. et al. Tracing the trophic plasticity of the coral-dinoflagellate symbiosis using amino acid compound-specific stable isotope analysis. Microorganisms 9, 1–16 (2021).Article 

Google Scholar 
55.Grossowicz, M., Shemesh, E., Martinez, S., Benayahu, Y. & Tchernov, D. New evidence of Melithaea erythraea colonization in the Mediterranean. Estuar. Coast. Shelf Sci. 236, 106652 (2020).Article 

Google Scholar 
56.Leal, M. C. et al. Trophic ecology of the facultative symbiotic coral Oculina arbuscula. Mar. Ecol. Prog. Ser. 504, 171–179 (2014).ADS 
Article 

Google Scholar 
57.Ferrier-Pagès, C. et al. Summer autotrophy and winter heterotrophy in the temperate symbiotic coral Cladocora caespitosa. Limnol. Oceanogr. 56, 1429–1438 (2011).ADS 
Article 

Google Scholar 
58.Ezzat, L., Fine, M., Maguer, J.-F., Grover, R. & Ferrier-Pagès, C. Carbon and nitrogen acquisition in shallow and deep holobionts of the Scleractinian coral S. pistillata. Front. Mar. Sci. 4, 1–12. https://doi.org/10.3389/fmars.2017.00102 (2017).Article 

Google Scholar 
59.Martinez, S. et al. Energy sources of the depth-generalist mixotrophic coral Stylophora pistillata. Front. Mar. Sci. 7, 1–16 (2020).ADS 
CAS 
Article 

Google Scholar 
60.Wall, C. B., Kaluhiokalani, M., Popp, B. N., Donahue, M. J. & Gates, R. D. Divergent symbiont communities determine the physiology and nutrition of a reef coral across a light-availability gradient. ISME J. 14, 945–958 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
61.Fox, M. D., Elliott Smith, E. A., Smith, J. E. & Newsome, S. D. Trophic plasticity in a common reef-building coral: Insights from δ13C analysis of essential amino acids. Funct. Ecol. 33, 2203–2214 (2019).Article 

Google Scholar 
62.Godinot, C., Grover, R., Allemand, D. & Ferrier-Pagès, C. High phosphate uptake requirements of the scleractinian coral Stylophora pistillata. J. Exp. Biol. 214, 2749–2754 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Kersting, D. K. et al. Experimental evidence of the synergistic effects of warming and invasive algae on a temperate reef-builder coral. Sci. Rep. 2015 51 5, 1–8 (2015).
Google Scholar 
64.Suari, Y. et al. A long term physical and biogeochemical database of a hyper-eutrophicated Mediterranean micro-estuary. Data Br. 27, 104809 (2019).Article 

Google Scholar 
65.Liberman, R., Fine, M. & Benayahu, Y. Simulated climate change scenarios impact the reproduction and early life stages of a soft coral. Mar. Environ. Res. 163, 105215. https://doi.org/10.1016/j.marenvres.2020.105215 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
66.Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanz. 167, 191–194 (1975).CAS 
Article 

Google Scholar 
67.Marsh, J. A. Primary productivity of reef-building calcareous red algae. Ecology 51, 255–263 (1970).Article 

Google Scholar 
68.Chisholm, J. R. M. & Gattuso, J.-P. Validation of the alkalinity anomaly technique for investigating calcification of photosynthesis in coral reef communities. Limnol. Oceanogr. 36, 1232–1239 (1991).ADS 
CAS 
Article 

Google Scholar 
69.Schneider, K. & Erez, J. The effect of carbonate chemistry on calcification and photosynthesis in the hermatypic coral Acropora eurystoma. Limnol. Oceanogr. 51, 1284–1293 (2006).ADS 
CAS 
Article 

Google Scholar 
70.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial Cytochrome C oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Arif, C. et al. Assessing Symbiodinium diversity in scleractinian corals via next-generation sequencing-based genotyping of the ITS2 rDNA region. Mol. Ecol. 23, 4418–4433 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Hume, B. C. C. et al. SymPortal: A novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol. Ecol. Resour. 19, 1063–1080 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Grover, R., Maguer, J.-F., Reynaud-Vaganay, S. & Ferrier-Pagès, C. Uptake of ammonium by the scleractinian coral Stylophora pistillata : Effect of feeding, light, and ammonium concentrations. Limnol. Oceanogr. 47, 782–790 (2002).ADS 
Article 

Google Scholar 
75.Tremblay, P., Grover, R., Maguer, J. F., Legendre, L. & Ferrier-Pagès, C. Autotrophic carbon budget in coral tissue: A new 13C-based model of photosynthate translocation. J. Exp. Biol. 215, 1384–1393 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Cowie, G. L. & Hedges, J. I. Improved amino acid quantification in environmental samples: Charge-matched recovery standards and reduced analysis time. Mar. Chem. 37, 223–238 (1992).CAS 
Article 

Google Scholar 
77.Docherty, G., Jones, V. & Evershed, R. P. Practical and theoretical considerations in the gas chromatography/combustion/isotope ratio mass spectrometry δ 13C analysis of small polyfunctional compounds. Rapid Commun. Mass Spectrom. 15, 730–738 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.R Core Team. R: A Language and Environment for Statistical Computing. (2020).79.Fox, J. W., & Weisberg, S. An R companion to applied regression. 3rd ed. (Los Angeles: SAGE Publications, Inc, 2019)
Google Scholar 
80.Villanueva, R. A. M. & Chen, Z. J. ggplot2: Elegant graphics for data analysis (2nd ed.), Measurement: Interdisciplinary Research and Perspectives, 17(3), 160–167. https://doi.org/10.1080/15366367.2019.1565254 (2019).81.Wilke, C. O. cowplot: Streamlined Plot Theme and Plot Annotations for “ggplot2” (R 74 package version 1.1.0) [Computer software]. https://CRAN.Rproject.org/package=cowplot (2020). More